PT	AU	BA	BE	GP	AF	BF	CA	TI	SO	SE	BS	LA	DT	CT	CY	CL	SP	HO	DE	ID	AB	C1	RP	EM	RI	OI	FU	FX	CR	NR	TC	Z9	PU	PI	PA	SN	BN	J9	JI	PD	PY	VL	IS	PN	SU	SI	MA	BP	EP	AR	DI	D2	PG	WC	SC	GA	UT
J	Schuh, AE; Lauvaux, T; West, TO; Denning, AS; Davis, KJ; Miles, N; Richardson, S; Uliasz, M; Lokupitiya, E; Cooley, D; Andrews, A; Ogle, S				Schuh, Andrew E.; Lauvaux, Thomas; West, Tristram O.; Denning, A. Scott; Davis, Kenneth J.; Miles, Natasha; Richardson, Scott; Uliasz, Marek; Lokupitiya, Erandathie; Cooley, Daniel; Andrews, Arlyn; Ogle, Stephen			Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape	GLOBAL CHANGE BIOLOGY			English	Article						agriculture; atmospheric inversions; carbon cycle; CO2 emissions; inventory; Mid-Continent Intensive	CARBON-DIOXIDE EXCHANGE; DATA ASSIMILATION; MESOSCALE INVERSIONS; THEORETICAL ASPECTS; MODELING SYSTEM; UNITED-STATES; TRANSPORT; SURFACE; SINKS; CYCLE	An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 flux over the major croplands of the United States by comparing the results to an inventory of CO2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom-up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion-based mean CO2-C sink estimates were generally slightly larger, 820% for PSU, 1020% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 fluxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region.	[Schuh, Andrew E.] Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA; [Schuh, Andrew E.; Ogle, Stephen] Colorado State Univ, Nat Resources Ecol Lab, Ft Collins, CO 80523 USA; [Lauvaux, Thomas; Davis, Kenneth J.; Miles, Natasha; Richardson, Scott] Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA; [West, Tristram O.] Pacific NW Natl Lab, Joint Global Change Res Inst, College Pk, MD USA; [Denning, A. Scott; Uliasz, Marek] Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USA; [Lokupitiya, Erandathie] Univ Colombo, Dept Zool, Colombo 03, Sri Lanka; [Cooley, Daniel] Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA; [Andrews, Arlyn] NOAA Earth Syst Res Lab, Boulder, CO USA	Schuh, AE (reprint author), Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA.	aschuh@atmos.colostate.edu			Office of Science of the US Department of Energy [DE-AC05-76RL01830]	Andrew Schuh and Thomas Lauvaux performed the biospheric model runs, atmospheric transport runs, and new atmospheric inversions included in this manuscript. Tristram West and Stephen Ogle provided most of the inventory data used for the 'bottom up' portion of the comparison with the exception of fossil fuel inventory data for the MCI region, which was provided by Kevin Gurney (Arizona State) and FIA data provided by Linda Heath and James Smith (US Forest Service). Natasha Miles and Scott Richardson instrumented, maintained and analyzed the PSU 'Ring of towers' CO<INF>2</INF> data for the MCI campaign (and the Missouri Ozarks tower) while Arlyn Andrews did the same for the WBI and WLEF towers within the MCI domain, in addition to the remainder of NOAA's tall tower sites across the US. Marek Uliasz provided assistance with the LPDM model used by both the PSU and CSU inversions. Dan Cooley provided advice and discussion on inversions and state-space modeling. Scott Denning provided the majority of the introduction including the historical context. Ken Davis and Stephen Ogle helped initiate the original campaign, provided many useful comments on manuscript and provided overall guidance to the project. The authors would like to thank a great many people who were involved with this project as well as generous support from the National Aeronautics and Space Administration (NASA #NNX08AK08G), National Oceanic and Atmospheric Administration (NOAA #NA08OAR4320893) and the Department of Energy (DOE #DE-FG02-06ER64317). Thanks to NOAA-ESRL and Andy Jacobson in particular, for many useful discussions on these inversions as well as the CarbonTracker results which were used in this paper. Tower data were graciously provided, and commented on, by Beverly Law and Matthias Goeckede (Oregon State University), NOAA-ESRL (Arlyn Andrews), and Environment Canada (Doug Worthy). This research used the Evergreen computing cluster at the Pacific Northwest National Laboratory. Evergreen is supported by the Office of Science of the US Department of Energy under Contract No. (DE-AC05-76RL01830).	Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Baldocchi D, 2012, EOS T AM GEOPHYS UN, V93, P217; Bishop CH, 2001, MON WEATHER REV, V129, P420, DOI 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2; BOLIN B, 1963, J GEOPHYS RES, V68, P3899; Bolin B, 1959, CHANGES CARBON DIOXI; CarbonTracker, 2009, CT 2009 CO 2 FLUX ES; CarbonTracker, 2011, CARB SYST DOC; Chen JQ, 2008, AGR FOREST METEOROL, V148, P155, DOI 10.1016/j.agrformet.2007.08.016; Cooley D, 2012, ENV ECOLOGICAL STAT, P1; Corbin KD, 2010, TELLUS B, V62, P521, DOI 10.1111/j.1600-0889.2010.00485.x; Crosson ER, 2008, APPL PHYS B-LASERS O, V92, P403, DOI 10.1007/s00340-008-3135-y; Denning AS, 1996, TELLUS B, V48, P543, DOI 10.1034/j.1600-0889.1996.t01-1-00010.x; Denning AS, 1999, TELLUS B, V51, P266, DOI 10.1034/j.1600-0889.1999.00012.x; Denning AS, 2005, SCI IMPLEMENTATION S; Denning AS, 2003, GLOBAL CHANGE BIOL, V9, P1241, DOI 10.1046/j.1365-2486.2003.00613.x; Desai AR, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2011JG001655; Dolman AJ, 2006, B AM METEOROL SOC, V87, P1367, DOI 10.1175/BAMS-87-10-1367; ENTING IG, 1995, TELLUS B, V47, P35, DOI 10.1034/j.1600-0889.47.issue1.5.x; EPA, 2010, INV US GREENH GAS EM; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Filippi D, 2003, GEOPH RES ABSTR; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; FUNG IY, 1987, J GEOPHYS RES-ATMOS, V92, P2999, DOI 10.1029/JD092iD03p02999; Gerbig C, 2003, J GEOPHYS RES, V6, P1; Goeckede M, 2010, J GEOPHYS RES, V115, DOI [DOI 10.1029/2009JD012257, 10.1029/2009JD012257]; Goeckede M, 2010, J GEOPHYS RES, V115, DOI [10.1029/2010JD014443, DOI 10.1029/2010JD014443]; Gourdji SM, 2012, BIOGEOSCIENCES, V9, P457, DOI 10.5194/bg-9-457-2012; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; Gurney KR, 2009, ENVIRON SCI TECHNOL, V43, P5535, DOI 10.1021/es900806c; HEIMANN M, 1986, J GEOPHYS RES-ATMOS, V91, P7765, DOI 10.1029/JD091iD07p07765; King AW, 2007, CCSP 1 STATE CARBON; Krol M, 2005, ATMOS CHEM PHYS, V5, P417; Lauvaux T, 2012, ATMOS CHEM PHYS, V12, P337, DOI 10.5194/acp-12-337-2012; Lauvaux T, 2008, ATMOS CHEM PHYS, V8, P3459; Lauvaux T, 2009, BIOGEOSCIENCES, V6, P1089; Lauvaux T, 2012, TELLUS B, V64, DOI 10.3402/tellusb.v64i0.17980; Law R, 1996, TELLUS B, V48, P613, DOI 10.1034/j.1600-0889.1996.t01-4-00001.x; Lokupitiya E, 2009, BIOGEOSCIENCES, V6, P969; Lokupitiya RS, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009679; Martins DK, 2009, AGR FOREST METEOROL, V149, P1674, DOI 10.1016/j.agrformet.2009.05.005; Meyers TP, 2004, AGR FOREST METEOROL, V125, P105, DOI 10.1016/j.agrformet.2004.03.001; Michalak AM, 2004, J GEOPHYS RES, V109, P1; Miles NL, 2012, J GEOPHYS RES-BIOGEO, V117, DOI 10.1029/2011JG001781; Nicholls ME, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004482; Ogle S, 2006, SCI PLAN MIDCONTINEN; OGLE SM, 2010, GLOBAL CHANGE BIOL, V16, P810, DOI DOI 10.1111/J.1365-2486.2009.01951.X; Olsen SC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003968; PEARMAN GI, 1981, J GEOPHYS RES-OC ATM, V86, P9839, DOI 10.1029/JC086iC10p09839; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Peters W, 2010, GLOBAL CHANGE BIOL, V16, P1317, DOI 10.1111/j.1365-2486.2009.02078.x; PIELKE RA, 1992, METEOROL ATMOS PHYS, V49, P69, DOI 10.1007/BF01025401; Richardson SJ, 2012, J ATMOS OCEAN TECH, V29, P397, DOI 10.1175/JTECH-D-11-00063.1; Schuh AE, 2010, BIOGEOSCIENCES, V7, P1625, DOI 10.5194/bg-7-1625-2010; Schuh AE, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000842; Sellers PJ, 1996, J CLIMATE, V9, P676, DOI 10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2; SELLERS PJ, 1986, J ATMOS SCI, V43, P505, DOI 10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2; Skamarock WC, 2005, DESCRIPTION ADV RES; Solomon S, 2007, IPCC 2007 CLIMATE CH; Stephens BB, 2011, ATMOS MEAS TECH, V4, P2737, DOI 10.5194/amt-4-2737-2011; TANS PP, 1990, SCIENCE, V247, P1431, DOI 10.1126/science.247.4949.1431; Tarantola A., 1987, INVERSE PROBLEM THEO; Tippett MK, 2003, MON WEATHER REV, V131, P1485, DOI 10.1175/1520-0493(2003)131<1485:ESFR>2.0.CO;2; Uliasz M, 1994, ENVIRON MODEL, P71; ULIASZ M, 1993, J APPL METEOROL, V32, P139, DOI 10.1175/1520-0450(1993)032<0139:TAMDMS>2.0.CO;2; Uliasz M, 1991, AIR POLLUTION MODELI, P399; Uliasz M, 1996, ENV MODELING, P145; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; Verma SB, 2005, AGR FOREST METEOROL, V131, P77, DOI 10.1016/j.agrformet.2005.05.003; Wang JW, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007410; West TO, 2011, BIOGEOSCIENCES, V8, P2037, DOI 10.5194/bg-8-2037-2011; Whitaker JS, 2002, MON WEATHER REV, V130, P1913, DOI 10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2; Wofsy SC, 2002, N AM CARBON PROGRAM; Wu L, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD016198; Zupanski D, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008371; Zupanski M, 2005, MON WEATHER REV, V133, P1710, DOI 10.1175/MWR2946.1	76	0	0	WILEY-BLACKWELL	HOBOKEN	111 RIVER ST, HOBOKEN 07030-5774, NJ USA	1354-1013		GLOBAL CHANGE BIOL	Glob. Change Biol.	MAY	2013	19	5					1424	1439		10.1111/gcb.12141		16	Biodiversity Conservation; Ecology; Environmental Sciences	Biodiversity & Conservation; Environmental Sciences & Ecology	121ZN	WOS:000317284700009	
J	Kessomkiat, W; Franssen, HJH; Graf, A; Vereecken, H				Kessomkiat, Wittaya; Franssen, Harrie-Jan Hendricks; Graf, Alexander; Vereecken, Harry			Estimating random errors of eddy covariance data: An extended two-tower approach	AGRICULTURAL AND FOREST METEOROLOGY			English	Article						Random error; Measurement error; Eddy covariance; Energy balance closure; Data assimilation	ENERGY-BALANCE CLOSURE; SEQUENTIAL DATA ASSIMILATION; UNCERTAINTY ASSESSMENT; TURBULENCE STATISTICS; MEASUREMENT SYSTEMS; FLUX MEASUREMENTS; CARBON-DIOXIDE; MODEL; EXCHANGE; FOREST	The incorporation of eddy covariance (EC) data in a land surface model (LSM) with help of data assimilation techniques requires a specification of the uncertainty of EC measurements. EC measurement uncertainty is composed of a systematic and random component. The systematic error is for example related to the energy balance (EB) closure, whereas the random error can be determined on the basis of differences between simultaneous flux measurements from two towers according to Hollinger and Richardson (2005) (Tree Physiol. 25, 873-885, here referred to as classical approach). The two-tower method, however, can be applied only where two towers share very similar environmental conditions. Here, we introduce an extended procedure to estimate the random error from EC data on the basis of the two-tower approach adapted for more heterogeneous environmental conditions. Our extended procedure consists of three main steps: (I) the EB deficit is corrected by distributing the deficit over the latent and sensible heat fluxes according to the evaporative fraction for each tower. This correction is based on the assumption that the EB deficit is due to an underestimation of the turbulent fluxes; (2) heterogeneity (e.g. different soil properties or vegetation characteristics and local variability in precipitation amounts) between two towers can introduce additional systematic flux differences. These differences can be corrected by normalizing turbulent fluxes at each tower according to the averaged evaporative fraction from two towers; (3) the random error can be determined following the two-tower approach using the normalized fluxes for the two towers. EC data from three different sites with different environmental conditions are used to test the classical and our extended approach: (1) three EC towers are placed at the Merken site, Germany and each of the three towers is surrounded by different vegetation types. This allows an evaluation on the basis of three different two-tower pairs. (2) two EC towers are located at the Roccarespampani site, Italy, with the same vegetation type around both towers. However, there are differences in vegetation age and density between these two towers; (3) for the Howland site, Maine, USA also data from two towers are available with very similar environmental conditions around the two towers. The random errors calculated by our extended approach are smaller than random errors from the classical approach, especially for larger net radiation (or large absolute fluxes). In addition, the random errors calculated by our extended approach result also in 9 out of 10 cases in less steep increases of the random error as function of flux magnitude (compared to the classical method). It was also found that atmospheric stability is an interesting alternative explanatory variable for random error of fluxes, which could be of special interest in the context of the extended two-tower approach. We conclude that our extended two-tower approach can be used to determine the random error of EC data for two towers located in more heterogeneous environmental conditions than aimed at by the original approach. (C) 2012 Elsevier B.V. All rights reserved.	[Kessomkiat, Wittaya; Franssen, Harrie-Jan Hendricks; Graf, Alexander; Vereecken, Harry] Forschungszentrum Julich, IBG 3, D-52425 Julich, Germany	Kessomkiat, W (reprint author), Forschungszentrum Julich, IBG 3, Leo Brandt Str, D-52425 Julich, Germany.	w.kessomkiat@fz-juelich.de	Graf, Alexander/D-1963-2009	Graf, Alexander/0000-0003-4870-7622	Helmholtz Climate Initiative REKLIM (Regional Climate Changes); Deutsche Forschungsgemeinschaft (DFG) [GR2687/3-1]	This research is supported by the Helmholtz Climate Initiative REKLIM (Regional Climate Changes). We truly thank the University of Bonn and SFB/Transregio 32 for contributing to the Merken site dataset, CarboEurope, Riccardo Valentini and Dario Papale for providing EC data of the Roccarespampani site, and AmeriFlux, David Hollinger for providing EC data of the Howland site. A. Graf would like to thank the Deutsche Forschungsgemeinschaft (DFG) for funding through the project GR2687/3-1.	Allen R.G., 2005, 56 FAO, P300; Benedict LH, 1996, EXP FLUIDS, V22, P129, DOI 10.1007/s003480050030; Billesbach DP, 2011, AGR FOREST METEOROL, V151, P394, DOI 10.1016/j.agrformet.2010.12.001; Billesbach DP, 1998, J ATMOS OCEAN TECH, V15, P197, DOI 10.1175/1520-0426(1998)015<0197:AIOTTD>2.0.CO;2; Brotzge JA, 2003, J HYDROMETEOROL, V4, P160, DOI 10.1175/1525-7541(2003)4<160:EOTSEB>2.0.CO;2; Culf AD, 2004, GLO CH IGBP, P159; DORAN JC, 1989, J APPL METEOROL, V28, P438, DOI 10.1175/1520-0450(1989)028<0438:MOTHAM>2.0.CO;2; Dragoni D, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008149; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Finkelstein PL, 2001, J GEOPHYS RES-ATMOS, V106, P3503, DOI 10.1029/2000JD900731; Foken T, 2006, ATMOS CHEM PHYS, V6, P4395; FOKEN T, 2011, B AM METEOROLOGICAL, V92, P13; Foken T, 2008, ECOL APPL, V18, P1351, DOI 10.1890/06-0922.1; Franssen HJH, 2010, AGR FOREST METEOROL, V150, P1553, DOI 10.1016/j.agrformet.2010.08.005; FRITSCHEN LJ, 1992, J GEOPHYS RES-ATMOS, V97, P18697; Graf A, 2010, BOUND-LAY METEOROL, V134, P459, DOI 10.1007/s10546-009-9444-9; HALLDIN S, 1992, J ATMOS OCEAN TECH, V9, P762, DOI 10.1175/1520-0426(1992)009<0762:EINRCA>2.0.CO;2; Heusinkveld BG, 2004, AGR FOREST METEOROL, V122, P21, DOI 10.1016/j.agrformet.2003.09.005; Hollinger DY, 1999, GLOB CHANGE BIOL, V5, P891, DOI 10.1046/j.1365-2486.1999.00281.x; Hollinger DY, 2005, TREE PHYSIOL, V25, P873; Hollinger DY, 2004, GLOBAL CHANGE BIOL, V10, P1689, DOI 10.1111/j.1365-2486.2004.00847.x; Hsieh CI, 2000, ADV WATER RESOUR, V23, P765, DOI 10.1016/S0309-1708(99)00042-1; Kormann R, 2001, BOUND-LAY METEOROL, V99, P207, DOI 10.1023/A:1018991015119; Lee XH, 1998, AGR FOREST METEOROL, V91, P39, DOI 10.1016/S0168-1923(98)00071-9; LENSCHOW DH, 1994, J ATMOS OCEAN TECH, V11, P661, DOI 10.1175/1520-0426(1994)011<0661:HLILEW>2.0.CO;2; Liu YQ, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005756; MANN J, 1994, J GEOPHYS RES-ATMOS, V99, P14519, DOI 10.1029/94JD00737; Mauder M, 2013, AGR FOREST METEOROL, V169, P122, DOI 10.1016/j.agrformet.2012.09.006; Meyers TP, 2004, AGR FOREST METEOROL, V125, P105, DOI 10.1016/j.agrformet.2004.03.001; Meyers TP, 1998, J GEOPHYS RES-ATMOS, V103, P22645, DOI 10.1029/98JD01564; Moradkhani H, 2005, WATER RESOUR RES, V41, DOI 10.1029/2004WR003604; Reichle RH, 2002, MON WEATHER REV, V130, P103, DOI 10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2; Richardson A.D., 2012, EDDY COVARIANCE PRAC, P173; Richardson AD, 2006, AGR FOREST METEOROL, V136, P1, DOI 10.1016/j.agrformet.2006.01.007; Tedeschi V, 2006, GLOBAL CHANGE BIOL, V12, P110, DOI 10.1111/j.1365-2486.2005.01081.x; Twine TE, 2000, AGR FOREST METEOROL, V103, P279, DOI 10.1016/S0168-1923(00)00123-4; van de Boer A, 2013, AGR FOREST METEOROL, V169, P1, DOI 10.1016/j.agrformet.2012.09.016; van Dijk A., 2004, 20041 WAG U MET AIR; Vickers D, 2010, TELLUS B, V62, P87, DOI [10.1111/j.1600-0889.2009.00449.x, 10.1111/J.1600-0889.2009.00449.x]; Wilson K, 2002, AGR FOREST METEOROL, V113, P223, DOI 10.1016/S0168-1923(02)00109-0; Zhang JH, 2011, AGR FOREST METEOROL, V151, P70, DOI 10.1016/j.agrformet.2010.09.004	41	0	0	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0168-1923		AGR FOREST METEOROL	Agric. For. Meteorol.	APR 15	2013	171						203	219		10.1016/j.agrformet.2012.11.019		17	Agronomy; Forestry; Meteorology & Atmospheric Sciences	Agriculture; Forestry; Meteorology & Atmospheric Sciences	111HS	WOS:000316513000019	
J	Schadel, C; Luo, YQ; Evans, RD; Fei, SF; Schaeffer, SM				Schaedel, Christina; Luo, Yiqi; Evans, R. David; Fei, Shenfeng; Schaeffer, Sean M.			Separating soil CO2 efflux into C-pool-specific decay rates via inverse analysis of soil incubation data	OECOLOGIA			English	Article						SOC; Labile C; Recalcitrant C; Data assimilation; Parameter estimation	ORGANIC-MATTER DECOMPOSITION; TEMPERATURE SENSITIVITY; ELEVATED CO2; DECONVOLUTION ANALYSIS; CARBON MINERALIZATION; POTENTIAL RESPONSES; MODEL; ECOSYSTEM; DYNAMICS; RESPIRATION	Soil organic matter (SOM) is heterogeneous in structure and has been considered to consist of various pools with different intrinsic turnover rates. Although those pools have been conceptually expressed in models and analyzed according to soil physical and chemical properties, separation of SOM into component pools is still challenging. In this study, we conducted inverse analyses with data from a long-term (385 days) incubation experiment with two types of soil (from plant interspace and from underneath plants) to deconvolute soil carbon (C) efflux into different source pools. We analyzed the two datasets with one-, two- and three-pool models and used probability density functions as a criterion to judge the best model to fit the datasets. Our results indicated that soil C release trajectories over the 385 days of the incubation study were best modeled with a two-pool C model. For both soil types, released C within the first 10 days of the incubation study originated from the labile pool. Decomposition of C in the recalcitrant pool was modeled to contribute to the total CO2 efflux by 9-11 % at the beginning of the incubation. At the end of the experiment, 75-85 % of the initial soil organic carbon (SOC) was modeled to be released over the incubation period. Our modeling analysis also indicated that the labile C-pool in the soil underneath plants was larger than that in soil from interspace. This deconvolution analysis was based on information contained in incubation data to separate carbon pools and can facilitate integration of results from incubation experiments into ecosystem models with improved parameterization.	[Schaedel, Christina] Univ Florida, Dept Biol, Gainesville, FL 32611 USA; [Schaedel, Christina; Luo, Yiqi; Fei, Shenfeng] Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA; [Evans, R. David] Washington State Univ, Sch Biol Sci, Pullman, WA 99164 USA; [Fei, Shenfeng] Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX 77840 USA; [Schaeffer, Sean M.] Univ Tennessee, Dept Biosyst Engn & Soil Sci, Knoxville, TN 37996 USA	Schadel, C (reprint author), Univ Florida, Dept Biol, Gainesville, FL 32611 USA.	cschaedel@ufl.edu			Office of Science (BER), Department of Energy [DE-SC0004601]; US National Science Foundation (NSF) [DEB 0444518, DEB 0743778, DEB 0840964, DBI 0850290, EPS 0919466]; Department of Energy [DE-FG02-03ER63650, DEFG02-03ER63651]; NSF [DEB-98-14358, 02-12819]	We thank Ensheng Weng for help with model development. This study is financially supported by the Office of Science (BER), Department of Energy, under grant DE-SC0004601; US National Science Foundation (NSF) grants DEB 0444518, DEB 0743778, DEB 0840964, DBI 0850290, and EPS 0919466. The authors also gratefully acknowledge grant support from the Department of Energy's Terrestrial Carbon Processes Program (DE-FG02-03ER63650, DEFG02-03ER63651) and the NSF Ecosystem Studies Program (DEB-98-14358 and 02-12819). In addition, we also gratefully acknowledge the DOE's National Nuclear Security Administration for providing utility services and undisturbed land at the Nevada National Security Site (formerly Nevada Test Site) to conduct the FACE experiment.	Amundson R, 2001, ANNU REV EARTH PL SC, V29, P535, DOI 10.1146/annurev.earth.29.1.535; BALESDENT J, 1987, SCI TOTAL ENVIRON, V62, P405, DOI 10.1016/0048-9697(87)90528-6; Billings S. A., 2004, Global Biogeochemical Cycles, V18, pGB1011, DOI 10.1029/2003GB002137; Bond-Lamberty B, 2010, NATURE, V464, P579, DOI 10.1038/nature08930; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Christensen BT, 1992, ADV SOIL SCI, V20, P1; Craine JM, 2010, NAT GEOSCI, V3, P854, DOI [10.1038/ngeo1009, 10.1038/NGEO1009]; Davidson EA, 2006, NATURE, V440, P165, DOI 10.1038/nature04514; Dijkstra FA, 2005, PLANT SOIL, V272, P41, DOI 10.1007/s11104-004-3848-6; Fang C, 2006, BIOGEOSCIENCES, V3, P65; Frank DA, 1998, ECOLOGY, V79, P2229, DOI 10.1890/0012-9658(1998)079[2229:UVLCOS]2.0.CO;2; Haddix ML, 2011, SOIL SCI SOC AM J, V75, P56, DOI 10.2136/sssaj2010.0118; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hayes M. H. B., 1985, Humic substances in soil, sediment and water: geochemistry, isolation and characterization., P329; Holland EA, 2000, GLOBAL BIOGEOCHEM CY, V14, P1137, DOI 10.1029/2000GB001264; HOOK PB, 1991, PLANT SOIL, V138, P247, DOI 10.1007/BF00012252; JENKINSON DS, 1990, PHILOS T ROY SOC B, V329, P361, DOI 10.1098/rstb.1990.0177; Jobbagy EG, 2000, ECOL APPL, V10, P423, DOI 10.2307/2641104; Jordan DN, 1999, GLOB CHANGE BIOL, V5, P659, DOI 10.1046/j.1365-2486.1999.00255.x; Katterer T, 1998, BIOL FERT SOILS, V27, P258, DOI 10.1007/s003740050430; Knorr W, 2005, NATURE, V433, P298, DOI 10.1038/nature03226; Luo Y. Q., 2010, New Phytologist, V188, P10, DOI 10.1111/j.1469-8137.2010.03425.x; Luo YQ, 2001, ECOL MONOGR, V71, P357, DOI 10.1890/0012-9615(2001)071[0357:ECDECP]2.0.CO;2; McLauchlan KK, 2004, SOIL SCI SOC AM J, V68, P1616; Mermut AR, 2000, GLOBAL CLIMATE CHANGE AND PEDOGENIC CARBONATES, P65; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; MOTAVALLI PP, 1994, SOIL BIOL BIOCHEM, V26, P935, DOI 10.1016/0038-0717(94)90106-6; NADELHOFFER KJ, 1990, SOIL SCI SOC AM J, V54, P411; Nunez S, 2001, ACTA OECOL, V22, P269, DOI 10.1016/S1146-609X(01)01122-5; PARTON WJ, 1987, SOIL SCI SOC AM J, V51, P1173; PARTON WJ, 1993, GLOBAL BIOGEOCHEM CY, V7, P785, DOI 10.1029/93GB02042; Paul EA, 2006, SOIL SCI SOC AM J, V70, P1023, DOI 10.2136/sssaj2005.0103; Pendall E, 2007, SOIL BIOL BIOCHEM, V39, P2628, DOI 10.1016/j.soilbio.2007.05.016; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Schaeffer SM, 2003, OECOLOGIA, V134, P547, DOI [10.1007/s00442-002-1130-2, 10.1007/s00442-003-1370-9]; Schaeffer SM, 2007, GLOBAL CHANGE BIOL, V13, P854, DOI 10.1111/j.1365-2486.2007.01324.x; Scharnagl B, 2010, BIOGEOSCIENCES, V7, P763; SCHIMEL DS, 1994, GLOBAL BIOGEOCHEM CY, V8, P279, DOI 10.1029/94GB00993; Schmidt MWI, 2011, NATURE, V478, P49, DOI 10.1038/nature10386; Smith P, 1997, GEODERMA, V81, P153, DOI 10.1016/S0016-7061(97)00087-6; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Su YZ, 2004, ARID LAND RES MANAG, V18, P39, DOI 10.1080/15324980490244997; Townsend AR, 1997, BIOGEOCHEMISTRY, V38, P1, DOI 10.1023/A:1017942918708; Trumbore S, 2000, ECOL APPL, V10, P399, DOI 10.1890/1051-0761(2000)010[0399:AOSOMA]2.0.CO;2; Trumbore SE, 1997, P NATL ACAD SCI USA, V94, P8284, DOI 10.1073/pnas.94.16.8284; VANSOEST PJ, 1967, J ASSOC OFF ANA CHEM, V50, P50; von Lutzow M, 2007, SOIL BIOL BIOCHEM, V39, P2183, DOI 10.1016/j.soilbio.2007.03.007; von Lutzow M, 2009, BIOL FERT SOILS, V46, P1, DOI 10.1007/s00374-009-0413-8; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Wu XW, 2009, J PLANT ECOL-UK, V2, P55, DOI 10.1093/jpe/rtp005; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zhou XH, 2010, NEW PHYTOL, V187, P184, DOI 10.1111/j.1469-8137.2010.03256.x; Zimmermann M, 2007, EUR J SOIL SCI, V58, P658, DOI 10.1111/j.1365-2389.2006.00855.x; Zobitz JM, 2011, OECOLOGIA, V167, P599, DOI 10.1007/s00442-011-2107-9	54	0	0	SPRINGER	NEW YORK	233 SPRING ST, NEW YORK, NY 10013 USA	0029-8549		OECOLOGIA	Oecologia	MAR	2013	171	3			SI		721	732		10.1007/s00442-012-2577-4		12	Ecology	Environmental Sciences & Ecology	109BK	WOS:000316339900011	
J	Chen, ZQ; Shi, RH; Zhang, SP				Chen, Zhuoqi; Shi, Runhe; Zhang, Shupeng			An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data	FRONTIERS OF EARTH SCIENCE			English	Article						AmeriFlux; artificial neural network (ANN); evapotranspiration (ET); remote sensing	CARBON-DIOXIDE EXCHANGE; MODIS VEGETATION INDEXES; EDDY-COVARIANCE; ENERGY-BALANCE; DATA ASSIMILATION; WATER-RESOURCES; UNITED-STATES; SENSED DATA; FLUXES; GRASSLAND	A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale. In this paper, we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index, land surface temperature and surface net radiation) for the period 2002-2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination (R (2)) value for comparison between estimated and measured ET was 0.77, the root-mean-square error was 0.62 mm/d, and the mean residual was - 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.	[Chen, Zhuoqi; Zhang, Shupeng] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China; [Shi, Runhe] E China Normal Univ, Key Lab Geog Informat Sci, Shanghai 200062, Peoples R China	Shi, RH (reprint author), E China Normal Univ, Key Lab Geog Informat Sci, Shanghai 200062, Peoples R China.	shirunhe@gmail.com			Shanghai Science and Technology Committee Program-Special for EXPO [10DZ0581600]; National Basic Research Program of China [2010CB950902]; National Natural Science Foundation of China [41201358]	This work was supported by Shanghai Science and Technology Committee Program-Special for EXPO (No. 10DZ0581600), and the National Basic Research Program of China (No. 2010CB950902), and the National Natural Science Foundation of China (Grant No. 41201358). The flux tower evapotranspiration measurement data were provided by AmeriFlux. We gratefully acknowledge all tower site principle investigators and their teams for providing the evapotranspiration data used in this study. MODIS Vegetation Indexes product and Land Surface Temperature product were obtained from Oak Ridge National Laboratory Distributed Active Archive Center. NASA GEWEX solar radiation data were obtained from the NASA Langley Research Center Atmospheric Science Data Center.	Allen RG, 2007, J IRRIG DRAIN E-ASCE, V133, P395, DOI 10.1061/(ASCE)0733-9437(2007)133:4(395); Allen R.G., 1998, 56 FAO; Bastiaanssen WGM, 2005, J IRRIG DRAIN E-ASCE, V131, P85, DOI 10.1061/(ASCE)0733-9437(2005)131:1(85); Chen JQ, 2004, ECOSYSTEMS, V7, P534, DOI 10.1007/s10021-004-0143-6; Cleugh HA, 2007, REMOTE SENS ENVIRON, V106, P285, DOI 10.1016/j.rse.2006.07.007; Dore S, 2003, OECOLOGIA, V146, P130; Falge E, 2001, AGR FOREST METEOROL, V107, P71, DOI 10.1016/S0168-1923(00)00235-5; Gao X, 2003, IEEE T GEOSCI REMOTE, V41, P2368, DOI 10.1109/TGRS.2003.813840; Gillies RR, 1997, INT J REMOTE SENS, V18, P3145, DOI 10.1080/014311697217026; Haykin S, 1994, NEURAL NETWORKS COMP; Hollinger SE, 2005, AGR FOREST METEOROL, V130, P59, DOI 10.1016/j.agrformet.2005.01.005; Irmak A, 2009, IRRIGATION SCI, V28, P101, DOI 10.1007/s00271-009-0193-9; Jung M, 2010, NATURE, V467, P951, DOI 10.1038/nature09396; Kustas WP, 1996, HYDROLOG SCI J, V41, P495, DOI 10.1080/02626669609491522; Tang QH, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010854; Li ZL, 2009, SENSORS-BASEL, V9, P3801, DOI 10.3390/s90503801; Lu XL, 2010, REMOTE SENS ENVIRON, V114, P1924, DOI 10.1016/j.rse.2010.04.001; Ma SY, 2007, AGR FOREST METEOROL, V147, P157, DOI 10.1016/j.agrformet.2007.07.008; Mackay DS, 2007, WATER RESOUR RES, V43; Misson L, 2007, AGR FOREST METEOROL, V144, P14, DOI 10.1016/j.agrformet.2007.01.006; Monteith J L, 1965, Symp Soc Exp Biol, V19, P205; Mu Q, 2007, REMOTE SENS ENVIRON, V111, P519, DOI 10.1016/j.rse.2007.04.015; Mu QZ, 2011, REMOTE SENS ENVIRON, V115, P1781, DOI 10.1016/j.rse.2011.02.019; Nagler PL, 2005, REMOTE SENS ENVIRON, V97, P337, DOI 10.1016/j.rse.2005.05.011; Nagler PL, 2005, REMOTE SENS ENVIRON, V94, P17, DOI 10.1016/j.rse.2004.08.009; NEMANI RR, 1989, J APPL METEOROL, V28, P276, DOI 10.1175/1520-0450(1989)028<0276:EORSRT>2.0.CO;2; Nishida K, 2003, IEEE T GEOSCI REMOTE, V41, P493, DOI 10.1109/TGRS.2003.811744; Oki T, 2006, SCIENCE, V313, P1068, DOI 10.1126/science.1128845; Olioso A., 2005, Irrigation and Drainage Systems, V19, P377, DOI 10.1007/s10795-005-8143-z; Olioso A, 1999, REMOTE SENS ENVIRON, V68, P341, DOI 10.1016/S0034-4257(98)00121-7; Overgaard J, 2006, BIOGEOSCIENCES, V3, P229; Pan M, 2006, J HYDROMETEOROL, V7, P534, DOI 10.1175/JHM495.1; RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0; Courault D., 2005, Irrigation and Drainage Systems, V19, P223, DOI 10.1007/s10795-005-5186-0; Sims PL, 2001, AGR FOREST METEOROL, V109, P117, DOI 10.1016/S0168-1923(01)00264-7; Su Z, 2002, HYDROL EARTH SYST SC, V6, P85; Twine TE, 2000, AGR FOREST METEOROL, V103, P279, DOI 10.1016/S0168-1923(00)00123-4; Urbanski S, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000293; Verma SB, 2005, AGR FOREST METEOROL, V131, P77, DOI 10.1016/j.agrformet.2005.05.003; Wilson K, 2002, AGR FOREST METEOROL, V113, P223, DOI 10.1016/S0168-1923(02)00109-0; Xu LK, 2004, AGR FOREST METEOROL, V123, P79, DOI 10.1016/j.agrformet.2003.10.004; Yang FH, 2006, IEEE T GEOSCI REMOTE, V44, P3452, DOI 10.1109/TGRS.2006.876297; Yi C, 2000, J GEOPHYS RES-ATMOS, V105, P9991, DOI 10.1029/2000JD900080	43	0	0	SPRINGER	NEW YORK	233 SPRING ST, NEW YORK, NY 10013 USA	2095-0195		FRONT EARTH SCI-PRC	Front. Earth Sci.	MAR	2013	7	1					103	111		10.1007/s11707-012-0346-7		9	Geosciences, Multidisciplinary	Geology	080YD	WOS:000314279400010	
J	Kuppel, S; Chevallier, F; Peylin, P				Kuppel, S.; Chevallier, F.; Peylin, P.			Quantifying the model structural error in carbon cycle data assimilation systems	GEOSCIENTIFIC MODEL DEVELOPMENT			English	Article							CO2 RETRIEVAL ALGORITHM; EDDY-COVARIANCE; DECIDUOUS FOREST; BEECH FOREST; TERRESTRIAL BIOSPHERE; AGGREGATION ERRORS; FLUX MEASUREMENTS; EXCHANGE; VARIABILITY; PREDICTIONS	This study explores the impact of the structural error of biosphere models when assimilating net ecosystem exchange (NEE) measurements or CO2 concentration measurements to optimise uncertain model parameters within carbon cycle data assimilation systems (CCDASs). This error has been proven difficult to identify and is often neglected in the total uncertainty budget. We propose a simple method which is derived from the model-minus-observation mismatch statistics. This diagnosis is applied to a state-of-the-art biogeochemical model using measurements of the net surface CO2 flux at twelve sites located in temperate, deciduous, broadleaf forests. We find that the structural model error in the NEE space has a standard deviation of 1.5 to 1.7 gC m(-2)d(-1), without a significant correlation structure beyond the lag of a few days, and a large spatial structure that can be approximated with an exponential decay of e-folding length of 500 km. In the space of concentrations, its characteristics are commensurate with the transport errors, both for surface air sample measurements and total column measurements. The inferred characteristics are confirmed by complementary optimality diagnostics performed after site-scale parameter optimisations.	[Kuppel, S.; Chevallier, F.; Peylin, P.] CEA CNRS UVSQ, UMR8212, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France	Kuppel, S (reprint author), CEA CNRS UVSQ, UMR8212, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France.	sylvain.kuppel@lsce.ipsl.fr			French Agence Nationale pour la Recherche (project MSDAG); CARBONES project within the EU's 7th Framework Programme for Research and Development; Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA); CNRS-INSU	This work has been supported by the French Agence Nationale pour la Recherche (project MSDAG) and by the CARBONES project within the EU's 7th Framework Programme for Research and Development. The Ph.D. programme of S. Kuppel has been funded by the Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA). The sites PIs of DE-Hai, DK-Sor, FR-Fon, FR-Hes, JP-Tak, UK-Ham, US-Bar, US-Ha1, US-LPH, US-MOz, US-UMB and US-WCr are thanked for making their data available. The authors would like to acknowledge the work of the NASA ACOS project to create the X<INF>CO2</INF> data. This work was performed using HPC resources from DSM-CCRT and [CCRT/CINES/IDRIS] under the allocation 2012-t2012012201 made by GENCI (Grand Equipement National de Calcul Intensif) and by local computer resources of LSCE.The publication of this article is financed by CNRS-INSU.	Aubinet M, 2000, ADV ECOL RES, V30, P113; Baldocchi D, 2008, AUST J BOT, V56, P1, DOI 10.1071/BT07151; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Bocquet M, 2011, Q J ROY METEOR SOC, V137, P1340, DOI 10.1002/qj.837; Chevallier F, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007375; Chevallier F, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL044652; Chevallier F, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL049899; Cook BD, 2004, AGR FOREST METEOROL, V126, P271, DOI 10.1016/j.agrformet.2004.06.008; Crisp D, 2012, ATMOS MEAS TECH, V5, P687, DOI 10.5194/amt-5-687-2012; Desroziers G, 2005, Q J ROY METEOR SOC, V131, P3385, DOI 10.1256/qj.05.108; Gough CM, 2008, BIOSCIENCE, V58, P609, DOI 10.1641/B580708; Granier A, 2008, ANN FOREST SCI, V65, DOI 10.1051/forest:2008052; Gu LH, 2012, AGR FOREST METEOROL, V152, P135, DOI 10.1016/j.agrformet.2011.09.014; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; Hadley JL, 2008, TREE PHYSIOL, V28, P615; Hourdin F, 2006, CLIM DYNAM, V27, P787, DOI 10.1007/s00382-006-0158-0; Houweling S, 2010, ATMOS CHEM PHYS, V10, P9981, DOI 10.5194/acp-10-9981-2010; Ito A, 2006, ECOL RES, V21, P137, DOI 10.1007/s11284-005-0100-7; Jenkins JP, 2007, AGR FOREST METEOROL, V143, P64, DOI 10.1016/j.agrformet.2006.11.008; Kaminski T, 2012, BIOGEOSCIENCES, V9, P3173, DOI 10.5194/bg-9-3173-2012; Kaminski T, 2001, J GEOPHYS RES-ATMOS, V106, P4703, DOI 10.1029/2000JD900581; Kaminski T, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001463; Kato T., 2012, BIOGEOSCIENCES DISCU, V9, P3615, DOI [10.5194/bgd-9-3615-2012, DOI 10.5194/BGD-9-3615-2012]; Knorr W, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001119; Krinner G, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2003GB002199; Kuppel S, 2012, BIOGEOSCIENCES, V9, P3757, DOI 10.5194/bg-9-3757-2012; Lasslop G, 2008, BIOGEOSCIENCES, V5, P1311; Mund M, 2010, TREE PHYSIOL, V30, P689, DOI 10.1093/treephys/tpq027; O'Dell CW, 2012, ATMOS MEAS TECH, V5, P99, DOI 10.5194/amt-5-99-2012; Papale D, 2006, BIOGEOSCIENCES, V3, P571; Pilegaard K, 2001, AGR FOREST METEOROL, V107, P29, DOI 10.1016/S0168-1923(00)00227-6; Prevost-Boure NC, 2010, APPL SOIL ECOL, V46, P26, DOI 10.1016/j.apsoil.2010.06.004; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Rayner PJ, 2011, PHILOS T R SOC A, V369, P1955, DOI 10.1098/rsta.2010.0378; Richardson AD, 2008, AGR FOREST METEOROL, V148, P38, DOI 10.1016/j.agrformet.2007.09.001; Santaren D., 2007, Global Biogeochemical Cycles, V21, pGB2013, DOI 10.1029/2006GB002834; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Talagrand O, 1997, J METEOROL SOC JPN, V75, P191; Tarantola A., 1987, INVERSE PROBLEM THEO; Thompson RL, 2011, ATMOS CHEM PHYS, V11, P3443, DOI 10.5194/acp-11-3443-2011; Urbanski S, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000293; Verbeeck H, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001544; Wang T, 2012, ECOL MODEL, V246, P11, DOI 10.1016/j.ecolmodel.2012.07.017; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Wunch D, 2011, PHILOS T R SOC A, V369, P2087, DOI 10.1098/rsta.2010.0240	45	0	0	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1991-959X		GEOSCI MODEL DEV	Geosci. Model Dev.		2013	6	1					45	55		10.5194/gmd-6-45-2013		11	Geosciences, Multidisciplinary	Geology	118FI	WOS:000317008500004	
J	Kato, T; Knorr, W; Scholze, M; Veenendaal, E; Kaminski, T; Kattge, J; Gobron, N				Kato, T.; Knorr, W.; Scholze, M.; Veenendaal, E.; Kaminski, T.; Kattge, J.; Gobron, N.			Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana	BIOGEOSCIENCES			English	Article							LAND-SURFACE MODEL; ISBA-A-GS; ATMOSPHERIC CO2; SOIL-MOISTURE; EXCHANGE; PHOTOSYNTHESIS; TRANSPIRATION; UNCERTAINTIES; VARIABILITY; BIOSPHERE	Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.	[Kato, T.; Knorr, W.; Scholze, M.] Univ Bristol, Dept Earth Sci, Bristol, Avon, England; [Kato, T.] Japan Agcy Marine Earth Sci & Technol, Res Inst Global Change, Yokohama, Kanagawa, Japan; [Kato, T.] CEA Orme Merisiers, UMR 8212, Lab Sci Climat & Environm, CEA CNRS UVSQ, F-91191 Gif Sur Yvette, France; [Knorr, W.; Scholze, M.] Lund Univ, Dept Phys Geog & Ecosyst Sci, S-22362 Lund, Sweden; [Scholze, M.] Univ Hamburg, Hamburg, Germany; [Veenendaal, E.] Wageningen Univ, Dept Environm Sci, Nat Conservat & Plant Ecol Grp, NL-6700 AP Wageningen, Netherlands; [Kaminski, T.] FastOpt, Hamburg, Germany; [Kattge, J.] Max Planck Inst Biogeochem, D-07745 Jena, Germany; [Gobron, N.] Commiss European Communities, Joint Res Ctr, I-21020 Ispra, Italy	Kato, T (reprint author), Univ Bristol, Dept Earth Sci, Bristol, Avon, England.	tomomichi.kato@lsce.ipsl.fr	Kato, Tomomichi/F-7766-2010	Kato, Tomomichi/0000-0003-3757-3243	Japan Society for the Promotion of Science; Ministry of Education, Science, Culture, Sports and Technology of Japan; QUEST programme; Natural Environment Research Council, UK; European Commission [FP7-283080]; CNRS-INSU	This study was supported by the Postdoctoral Fellowships for Research Abroad, Japan Society for the Promotion of Science, the Ministry of Education, Science, Culture, Sports and Technology of Japan, and the QUEST programme funded by the Natural Environment Research Council, UK funding for this work was partly provided by the European Commission through the project GEOCARBON (FP7-283080).The publication of this article is financed by CNRS-INSU.	Aubinet M, 2000, ADV ECOL RES, V30, P113; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Barbu AL, 2011, BIOGEOSCIENCES, V8, P1971, DOI 10.5194/bg-8-1971-2011; Batjes N. H., 1995, 26 ISRIC; Beerling DJ, 2005, P NATL ACAD SCI USA, V102, P1302, DOI 10.1073/pnas.0408724102; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Calvet JC, 2012, GEOSCI MODEL DEV, V5, P37, DOI 10.5194/gmd-5-37-2012; Campbell G.S., 1998, INTRO ENV BIOPHYSICS; Chen MY, 2002, J HYDROMETEOROL, V3, P249, DOI 10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2; Giering R, 1998, ACM T MATH SOFTWARE, V24, P437, DOI 10.1145/293686.293695; Gobron N, 2000, IEEE T GEOSCI REMOTE, V38, P2489, DOI 10.1109/36.885197; Gobron N, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006511; Heimann M., 1995, 10 DTSCH KLIM; Ichii K, 2009, AGR FOREST METEOROL, V149, P1907, DOI 10.1016/j.agrformet.2009.06.019; Jones PD, 2001, J GEOPHYS RES-ATMOS, V106, P3371, DOI 10.1029/2000JD900564; Kaminski T, 2012, BIOGEOSCIENCES, V9, P3173, DOI 10.5194/bg-9-3173-2012; Kaminski T, 2003, LECT NOTES COMPUT SC, V2668, P95; Kerr YH, 2001, IEEE T GEOSCI REMOTE, V39, P1729, DOI 10.1109/36.942551; Kleidon A., 1998, PHYS CHEM EARTH, V21, P499; Knorr W., 1997, 49 MAX PLANCK I MET; Knorr W, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001119; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P207, DOI 10.1029/1998GB001059; Knorr W, 2000, GLOBAL ECOL BIOGEOGR, V9, P225, DOI 10.1046/j.1365-2699.2000.00159.x; Kolle O., 2002, DOCUMENTATION EDDY S; Lloyd J., 2004, SAFARI 2000 METEOROL, DOI [10.3334/ORNLDAAC/760, DOI 10.3334/0RNLDAAC/760]; Mantlana B. K., 2002, THESIS U NATAL DURBA; MCNAUGHTON KG, 1991, AGR FOREST METEOROL, V54, P279, DOI 10.1016/0168-1923(91)90010-N; Moncrieff JB, 1997, J HYDROL, V189, P589; Nijssen B, 2001, J CLIMATE, V14, P1790, DOI 10.1175/1520-0442(2001)014<1790:GREOSM>2.0.CO;2; Patterson K. A., 1990, THESIS U DELAWARE NE; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Ritchie J. T., 1972, MODEL PREDICTING EVA, P1204; Schenk HJ, 2002, ECOL MONOGR, V72, P311, DOI 10.2307/3100092; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Scholze M., 2003, MODEL STUDIES RESPON; Schuepp P.H., 1990, Boundary-Layer Meteorology, V50, DOI 10.1007/BF00120530; SELLERS PJ, 1985, INT J REMOTE SENS, V6, P1335; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Veenendaal E. M., 2004, GLOBAL CHANGE BIOL, V10, P309; Veenendaal EM, 2008, TREE PHYSIOL, V28, P417; Wang GL, 2005, CLIM DYNAM, V25, P739, DOI 10.1007/s00382-005-0057-9; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x	44	0	0	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1726-4170		BIOGEOSCIENCES	Biogeosciences		2013	10	2					789	802		10.5194/bg-10-789-2013		14	Ecology; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	092BE	WOS:000315093000009	
J	Keenan, TF; Davidson, EA; Munger, JW; Richardson, AD				Keenan, Trevor F.; Davidson, Eric A.; Munger, J. William; Richardson, Andrew D.			Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle	ECOLOGICAL APPLICATIONS			English	Article						biosphere-atmosphere interaction; carbon fluxes; carbon sequestration; climate change research; data assimilation; Harvard Forest, Massachusetts, USA; process-based models	EDDY-COVARIANCE MEASUREMENTS; SUB-ALPINE FOREST; LONG-TERM; DATA FUSION; DATA ASSIMILATION; ESTIMATING PARAMETERS; NONLINEAR INVERSION; MIDLATITUDE FOREST; GLOBAL CHANGE; H2O FLUXES	Primarily driven by concern about rising levels of atmospheric CO2, ecologists and earth system scientists are collecting vast amounts of data related to the carbon cycle. These measurements are generally time consuming and expensive to make, and, unfortunately, we live in an era where research funding is increasingly hard to come by. Thus, important questions are: "Which data streams provide the most valuable information?" and "How much data do we need?" These questions are relevant not only for model developers, who need observational data to improve, constrain, and test their models, but also for experimentalists and those designing ecological observation networks. Here we address these questions using a model-data fusion approach. We constrain a process-oriented, forest ecosystem C cycle model with 17 different data streams from the Harvard Forest (Massachusetts, USA). We iteratively rank each data source according to its contribution to reducing model uncertainty. Results show the importance of some measurements commonly unavailable to carbon-cycle modelers, such as estimates of turnover times from different carbon pools. Surprisingly, many data sources are relatively redundant in the presence of others and do not lead to a significant improvement in model performance. A few select data sources lead to the largest reduction in parameter-based model uncertainty. Projections of future carbon cycling were poorly constrained when only hourly net-ecosystem-exchange measurements were used to inform the model. They were well constrained, however, with only 5 of the 17 data streams, even though many individual parameters are not constrained. The approach taken here should stimulate further cooperation between modelers and measurement teams and may be useful in the context of setting research priorities and allocating research funds.	[Keenan, Trevor F.; Richardson, Andrew D.] Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA; [Davidson, Eric A.] Woods Hole Res Ctr, Falmouth, MA 02540 USA; [Munger, J. William] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA; [Munger, J. William] Harvard Univ, Dept Earth & Planetary Sci, Cambridge, MA 02138 USA	Keenan, TF (reprint author), Harvard Univ, Dept Organism & Evolutionary Biol, 22 Divin Ave, Cambridge, MA 02138 USA.	tkeenan@oeb.harvard.edu			NOAA's Climate Program Office, Global Carbon Cycle Program [NA11OAR4310054]; Northeastern States Research Cooperative; Office of Science (BER), U.S. Department of Energy, through the Northeastern Regional Center of the National Institute for Climatic Change Research; NSF Research Experience for Undergraduates (REU) program; FAS Science Division Research Computing Group at Harvard University	Carbon flux and biometric measurements at HFEMS have been supported by the Office of Science (BER), U.S. Department of Energy (DOE), and the National Science Foundation Long-Term Ecological Research Programs. T. F. Keenan, A. D. Richardson, and J. W. Munger acknowledge support from NOAA's Climate Program Office, Global Carbon Cycle Program, under award NA11OAR4310054. T. F. Keenan and A. D. Richardson acknowledge support from the Northeastern States Research Cooperative, and the Office of Science (BER), U.S. Department of Energy, through the Northeastern Regional Center of the National Institute for Climatic Change Research. We especially thank the many participants who have sustained the long-term data collection, and in particular the summer students engaged in collecting field data, supported by NSF Research Experience for Undergraduates (REU) program, and the Harvard Forest Woods Crew for logistical and maintenance support. The computations in this paper were run on the Odyssey cluster supported by the FAS Science Division Research Computing Group at Harvard University.	BALDOCCHI D, 1994, TREE PHYSIOL, V14, P1069; Ball J., 1987, PROGR PHOTOSYNTHESIS, V4, P221, DOI DOI 10.1007/BF02180320; Barford CC, 2001, SCIENCE, V294, P1688, DOI 10.1126/science.1062962; Barrett DJ, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2002GB001860; Barrett DJ, 2005, AUST J BOT, V53, P689, DOI 10.1071/BT04139; Beer C, 2010, SCIENCE, V329, P834, DOI 10.1126/science.1184984; Beven K, 2006, J HYDROL, V320, P18, DOI 10.1016/j.jhydrol.2005.07.007; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; CAMPBELL GS, 1986, AGR FOREST METEOROL, V36, P317, DOI 10.1016/0168-1923(86)90010-9; dePury DGG, 1997, PLANT CELL ENVIRON, V20, P537, DOI 10.1111/j.1365-3040.1997.00094.x; Delworth TL, 2006, J CLIMATE, V19, P643, DOI 10.1175/JCLI3629.1; Desai A. R., 2010, J GEOPHYS RES, V115, P1; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Franks SW, 1999, HYDROL EARTH SYST SC, V3, P477; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Gaudinski J. B., 2010, GLOBAL BIOGEOCHEMICA, V24, P1; Gaudinski JB, 2009, GLOBAL CHANGE BIOL, V15, P992, DOI 10.1111/j.1365-2486.2008.01736.x; Goulden ML, 1996, GLOB CHANGE BIOL, V2, P169, DOI 10.1111/j.1365-2486.1996.tb00070.x; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hayhoe K, 2008, MITIG ADAPT STRAT GL, V13, P425, DOI 10.1007/s11027-007-9133-2; Heimann M, 2008, NATURE, V451, P289, DOI 10.1038/nature06591; JAYNES ET, 1957, PHYS REV, V106, P620, DOI 10.1103/PhysRev.106.620; Kattge J, 2011, GLOBAL CHANGE BIOL, V17, P2905, DOI 10.1111/j.1365-2486.2011.02451.x; Keenan T, 2011, GLOBAL CHANGE BIOL, V17, P565, DOI 10.1111/j.1365-2486.2010.02254.x; Keenan TF, 2012, GLOBAL CHANGE BIOL, V18, P2555, DOI 10.1111/j.1365-2486.2012.02684.x; Keenan TF, 2012, GLOBAL CHANGE BIOL, V18, P1971, DOI 10.1111/j.1365-2486.2012.02678.x; Keenan TF, 2011, OECOLOGIA, V167, P587, DOI 10.1007/s00442-011-2106-x; Keller M, 2008, FRONT ECOL ENVIRON, V6, P282, DOI 10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kolmogorov A. N., 1968, J COMPUTER MATH, V2, P157; Kramer K, 2002, GLOBAL CHANGE BIOL, V8, P213, DOI 10.1046/j.1365-2486.2002.00471.x; Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689; Leuzinger S, 2011, NEW PHYTOL, V191, P15, DOI 10.1111/j.1469-8137.2011.03778.x; Luo Y., 2003, GLOBAL BIOGEOCHEMICA, V17, P1; Luo Y., 2008, B ECOL SOC AM, V89, P467, DOI DOI 10.1890/0012-9623; Luo YQ, 2011, GLOBAL CHANGE BIOL, V17, P843, DOI 10.1111/j.1365-2486.2010.02265.x; Matamala R, 2003, SCIENCE, V302, P1385, DOI 10.1126/science.1089543; Medlyn BE, 2011, WIRES CLIM CHANGE, V2, P332, DOI 10.1002/wcc.108; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; Metropolis N., 1949, J AM STAT ASSOC, V44, P351; Migliavacca M, 2012, BIOGEOSCIENCES, V9, P2063, DOI 10.5194/bg-9-2063-2012; Moore DJP, 2008, AGR FOREST METEOROL, V148, P1467, DOI 10.1016/j.agrformet.2008.04.013; Morales P, 2005, GLOBAL CHANGE BIOL, V11, P2211, DOI 10.1111/j.1365-2486.2005.01036.x; Pan YD, 2011, SCIENCE, V333, P988, DOI 10.1126/science.1201609; Papale D, 2003, GLOBAL CHANGE BIOL, V9, P525, DOI 10.1046/j.1365-2486.2003.00609.x; Phillips S. C., 2010, J GEOPHYS RES-BIOGEO, V115, P1; Press W. H., 2007, NUMERICAL RECEIPES A; Rayner PJ, 2011, PHILOS T R SOC A, V369, P1955, DOI 10.1098/rsta.2010.0378; Rayner PJ, 2010, CURR OPIN ENV SUST, V2, P289, DOI 10.1016/j.cosust.2010.05.005; Ricciuto D. M., 2011, J GEOPHYS RES, V116, P1; Richardson A. D., NEW PHYTOLO IN PRESS; Richardson AD, 2012, GLOBAL CHANGE BIOL, V18, P566, DOI 10.1111/j.1365-2486.2011.02562.x; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; Richardson AD, 2006, AGR FOREST METEOROL, V136, P1, DOI 10.1016/j.agrformet.2006.01.007; Richardson AD, 2007, GLOBAL CHANGE BIOL, V13, P788, DOI 10.1111/j.1365-2486.2007.01330.x; Riley WJ, 2009, NEW PHYTOL, V184, P387, DOI 10.1111/j.1469-8137.2009.02980.x; Sacks WJ, 2007, OECOLOGIA, V151, P54, DOI 10.1007/s00442-006-0565-2; Savage K, 2009, AGR FOREST METEOROL, V149, P2012, DOI 10.1016/j.agrformet.2009.07.008; Schaefer K. M., J GEOPHYS R IN PRESS; Schwalm CR, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001229; SHANNON CE, 1948, AT&T TECH J, V27, P623; SINCLAIR TR, 1976, J APPL ECOL, V13, P813, DOI 10.2307/2402257; Siqueira MB, 2006, GLOBAL CHANGE BIOL, V12, P1189, DOI 10.1111/j.1365-2486.2006.01158.x; Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Strand AE, 2008, SCIENCE, V319, P456, DOI 10.1126/science.1151382; Templer PH, 2011, NEW PHYTOL, V192, P797, DOI 10.1111/j.1469-8137.2011.03959.x; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Urbanski S, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000293; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Wang YP, 2007, GLOBAL CHANGE BIOL, V13, P652, DOI 10.1111/j.1365-2486.2006.01225.x; Wang YP, 1998, AGR FOREST METEOROL, V91, P89, DOI 10.1016/S0168-1923(98)00061-6; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; Weng ES, 2011, ECOL APPL, V21, P1490, DOI 10.1890/09-1394.1; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; WOFSY SC, 1993, SCIENCE, V260, P1314, DOI 10.1126/science.260.5112.1314; Xiao JF, 2012, J GEOPHYS RES-BIOGEO, V117, DOI 10.1029/2011JG001889; Xu T., 2006, GLOBAL BIOGEOCHEMICA, V20, P1; Zhang L, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001004; Zhou T., 2008, GLOBAL BIOGEOCHEMICA, V22, P1	80	0	0	ECOLOGICAL SOC AMER	WASHINGTON	1990 M STREET NW, STE 700, WASHINGTON, DC 20036 USA	1051-0761		ECOL APPL	Ecol. Appl.	JAN	2013	23	1					273	286				14	Ecology; Environmental Sciences	Environmental Sciences & Ecology	092FM	WOS:000315104800021	
J	Kang, JS; Kalnay, E; Miyoshi, T; Liu, JJ; Fung, I				Kang, Ji-Sun; Kalnay, Eugenia; Miyoshi, Takemasa; Liu, Junjie; Fung, Inez			Estimation of surface carbon fluxes with an advanced data assimilation methodology	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							ENSEMBLE KALMAN FILTER; ADAPTIVE COVARIANCE INFLATION; ATMOSPHERIC CO2; MODEL ERROR; SINKS; INVERSION; DIOXIDE; SYSTEM; PARAMETERS; FORECAST	We perform every 6 h a simultaneous data assimilation of surface CO2 fluxes and atmospheric CO2 concentrations along with meteorological variables using the Local Ensemble Transform Kalman Filter (LETKF) within an Observing System Simulation Experiments framework. In this paper, we focus on the impact of advanced variance inflation methods and vertical localization of column CO2 data on the analysis of CO2. With both additive inflation and adaptive multiplicative inflation, we are able to obtain encouraging multiseasonal analyses of surface CO2 fluxes in addition to atmospheric CO2 and meteorological analyses. Furthermore, we examine strategies for vertical localization in the assimilation of simulated CO2 from GOSAT that has nearly uniform sensitivity from the surface to the upper troposphere. Since atmospheric CO2 is forced by surface fluxes, its short-term variability should be largest near the surface. We take advantage of this by updating observed changes only into the lower tropospheric CO2 rather than into the full column. This results in a more accurate analysis of CO2 in terms of both RMS error and spatial patterns. Assimilating synthetic CO2 ground-based observations and CO2 retrievals from GOSAT and AIRS with the enhanced LETKF, we obtain an accurate estimation of the evolving surface fluxes even in the absence of any a priori information. We also test the system with a longer assimilation window and find that a short window with an efficient treatment for wind uncertainty is beneficial to flux inversion. Since this study assumes a perfect forecast model, future research will explore the impact of model errors. Citation: Kang, J.-S., E. Kalnay, T. Miyoshi, J. Liu, and I. Fung (2012), Estimation of surface carbon fluxes with an advanced data assimilation methodology, J. Geophys. Res., 117, D24101, doi: 10.1029/2012JD018259.	[Kang, Ji-Sun; Kalnay, Eugenia; Miyoshi, Takemasa] Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20707 USA; [Liu, Junjie] CALTECH, Jet Prop Lab, Pasadena, CA USA; [Fung, Inez] Univ Calif Berkeley, Dept Earth & Planetary Sci, Berkeley, CA 94720 USA	Kang, JS (reprint author), Univ Maryland, Dept Atmospher & Ocean Sci, Coll Pk,3437 CSS Bldg, College Pk, MD 20707 USA.	jskang@atmos.umd.edu	Miyoshi, Takemasa/C-2768-2009	Miyoshi, Takemasa/0000-0003-3160-2525	U.S. Department of Energy under DOE [DEFG0207ER64437]; NASA [NNH09ZDA001N-TERRAQUA]	We are grateful to the U.S. Department of Energy for the support of the research project, "Carbon data assimilation with coupled Ensemble Kalman filter," under DOE grant DEFG0207ER64437. Support was also received from NASA NNH09ZDA001N-TERRAQUA. We thank Kayo Ide, Brian Hunt and other members of Weather-Chaos group at the University of Maryland and Fuqing Zhang of PSU for fruitful discussions.	Anderson JL, 2009, TELLUS A, V61, P72, DOI 10.1111/j.1600-0870.2008.00361.x; Anderson JL, 1999, MON WEATHER REV, V127, P2741, DOI 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2; Anderson JL, 2007, TELLUS A, V59, P210, DOI 10.1111/j.1600-0870.2006.00216.x; Andres RJ, 1996, GLOBAL BIOGEOCHEM CY, V10, P419, DOI 10.1029/96GB01523; Baker DF, 2010, ATMOS CHEM PHYS, V10, P4145, DOI 10.5194/acp-10-4145-2010; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Bousquet P, 1999, J GEOPHYS RES-ATMOS, V104, P26161, DOI 10.1029/1999JD900342; Chahine MT, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035022; Chevallier F, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012311; Crisp D, 2004, ADV SPACE RES-SERIES, V34, P700, DOI 10.1016/j.asr.2003.08.062; Danforth CM, 2007, MON WEATHER REV, V135, P281, DOI 10.1175/MWR3289.1; Dee DP, 1999, MON WEATHER REV, V127, P1822, DOI 10.1175/1520-0493(1999)127<1822:MLEOFA>2.0.CO;2; DEE DP, 1995, MON WEATHER REV, V123, P1128, DOI 10.1175/1520-0493(1995)123<1128:OLEOEC>2.0.CO;2; Denman KL, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P499; Desroziers G, 2005, Q J ROY METEOR SOC, V131, P3385, DOI 10.1256/qj.05.108; Enting I. G., 2002, INVERSE PROBLEMS ATM, DOI [10.1017/CBO9780511535741, DOI 10.1017/CB09780511535741]; Feng L, 2009, ATMOS CHEM PHYS, V9, P2619, DOI 10.5194/acp-9-2619-2009; Forster P, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P129; Gurney KR, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002111; Gurney KR, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003082; Houtekamer PL, 2009, MON WEATHER REV, V137, P2126, DOI 10.1175/2008MWR2737.1; Houtekamer PL, 2005, Q J ROY METEOR SOC, V131, P3269, DOI 10.1256/qj.05.135; Hunt BR, 2007, PHYSICA D, V230, P112, DOI 10.1016/j.physd.2006.11.008; Kalnay E, 2007, TELLUS A, V59, P758, DOI 10.1111/J.1600-0870.2007.00261.X; Kang JS, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014673; Kang J.-S., 2009, THESIS U MD COLL PAR; Keeling CD, 1996, NATURE, V382, P146, DOI 10.1038/382146a0; Kort EA, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047612; Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689; Li H, 2009, MON WEATHER REV, V137, P3407, DOI 10.1175/2009MWR2766.1; Li H, 2009, Q J ROY METEOR SOC, V135, P523, DOI 10.1002/qj.371; Liu JJ, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047213; Liu JJ, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016642; Lokupitiya RS, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009679; Michalak AM, 2008, ATMOS CHEM PHYS, V8, P6789; Miyoshi T, 2011, MON WEATHER REV, V139, P1519, DOI 10.1175/2010MWR3570.1; Miyoshi T, 2010, MON WEATHER REV, V138, P2846, DOI 10.1175/2010MWR3209.1; Miyoshi T, 2012, PURE APPL GEOPHYS, V169, P321, DOI 10.1007/s00024-011-0373-4; Molteni F, 2003, CLIM DYNAM, V20, P175, DOI 10.1007/s00382-002-0268-2; Nakajima M., 2010, ISPRS TECHN COMM 8 8, VXXXVIII, P94; Nevison CD, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000408; NOAA Earth System Research Laboratory, 2010, GLOBALVIEW CO2 COOP; Peters W, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006157; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Randerson JT, 1997, GLOBAL BIOGEOCHEM CY, V11, P535, DOI 10.1029/97GB02268; Rayner PJ, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003068; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Sarmiento JL, 2002, PHYS TODAY, V55, P30, DOI 10.1063/1.1510279; Szunyogh I, 2008, TELLUS A, V60, P113, DOI [10.1111/J.1600-0870.2007.00274.x, 10.1111/j.1600-0870.2007.00274.x]; Takahashi T, 2002, DEEP-SEA RES PT II, V49, P1601, DOI 10.1016/S0967-0645(02)00003-6; Wang H, 2009, ATMOS CHEM PHYS, V9, P7313; Whitaker JS, 2008, MON WEATHER REV, V136, P463, DOI 10.1175/2007MWR2018.1; Wofsy SC, 2011, PHILOS T R SOC A, V369, P2073, DOI 10.1098/rsta.2010.0313; Yokota T., 2004, P 24 INT S SPAC TECH, P887; Zeng N, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB0022763; Zupanski D, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008371	56	0	0	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	DEC 19	2012	117								D24101	10.1029/2012JD018259		18	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	061AQ	WOS:000312819900002	
J	Dormann, CF; Schymanski, SJ; Cabral, J; Chuine, I; Graham, C; Hartig, F; Kearney, M; Morin, X; Romermann, C; Schroder, B; Singer, A				Dormann, Carsten F.; Schymanski, Stanislaus J.; Cabral, Juliano; Chuine, Isabelle; Graham, Catherine; Hartig, Florian; Kearney, Michael; Morin, Xavier; Roemermann, Christine; Schroeder, Boris; Singer, Alexander			Correlation and process in species distribution models: bridging a dichotomy	JOURNAL OF BIOGEOGRAPHY			English	Review						Hypothesis generation; mechanistic model; parameterization; process-based model; species distribution model; SDM; uncertainty; validation	INDUCED RANGE SHIFTS; CLIMATE-CHANGE; HABITAT MODELS; PREDICTION UNCERTAINTY; BAYESIAN CALIBRATION; MECHANISTIC MODELS; DATA ASSIMILATION; CARBON-DIOXIDE; FOREST MODEL; DYNAMICS	Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.	[Dormann, Carsten F.] Univ Freiburg, Fac Forest & Environm Sci, D-79106 Freiburg, Germany; [Dormann, Carsten F.] UFZ Helmholtz Ctr Environm Res, Dept Computat Landscape Ecol, D-04318 Leipzig, Germany; [Schymanski, Stanislaus J.] Max Planck Inst Biogeochem, D-07745 Jena, Germany; [Schymanski, Stanislaus J.] Swiss Fed Inst Technol Zurich, CH-8092 Zurich, Switzerland; [Cabral, Juliano] Free Floater Grp Biodivers Macroecol & Conservat, D-37077 Gottingen, Germany; [Chuine, Isabelle] CNRS, Ctr Ecol Fonct & Evolut, Equipe BIOFLUX, F-34293 Montpellier 05, France; [Graham, Catherine; Singer, Alexander] SUNY Stony Brook, Dept Ecol & Evolut, Stony Brook, NY 11794 USA; [Hartig, Florian] UFZ Helmholtz Ctr Environm Res, Dept Ecol Modelling, D-04318 Leipzig, Germany; [Kearney, Michael] Univ Melbourne, Dept Zool, Melbourne, Vic 3010, Australia; [Morin, Xavier] ETH, Inst Terr Okosyst, CH-8092 Zurich, Switzerland; [Roemermann, Christine] Goethe Univ Frankfurt, Inst Phys Geog, D-60438 Frankfurt, Germany; [Roemermann, Christine] Univ Regensburg, Fac Biol & Preclin Med, D-93040 Regensburg, Germany; [Schroeder, Boris] Univ Potsdam, Inst Geoecol, D-14476 Potsdam, Germany; [Schroeder, Boris] Tech Univ Munich, D-85354 Freising Weihenstephan, Germany	Dormann, CF (reprint author), Univ Freiburg, Fac Forest & Environm Sci, Tennenbacherstr 4, D-79106 Freiburg, Germany.	carsten.dormann@biom.uni-freiburg.de	Schroder, Boris/B-7211-2009; Schymanski, Stanislaus/A-5303-2012; Hartig, Florian/G-4510-2010	Schymanski, Stanislaus/0000-0002-0950-2942; Hartig, Florian/0000-0002-6255-9059	LOEWE- BiK-F Biodiversity and the Climate Research Centre Frankfurt; Helmholtz Association [VH-NG 247]; German Research Foundation DFG [DO 686/5-1]; Max Planck Society; DFG [RO 3842/1-1]; Biodiversity and Climate Research Centre (BiK-F), part of the LOEWE programme 'Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz' of Hesse's Ministry of Higher Education, Research and the Arts	We are grateful to the following colleagues whose comments helped us to improve the clarity and focus of this publication: Rampal Etienne, Lee Hannah, Thomas Hickler, Steven I. Higgins, Bob O'Hara, Peter Linder, Greg McInerny, Frank Schurr, Ralf Seppelt and Konstans Wels, as well as Jens-Christian Svenning, Robert Whittaker and two anonymous referees. The work was initiated through a workshop 'The ecological niche as a window to biodiversity', organized by Christine Romermann, Bob O'Hara and Steven Higgins and funded by the LOEWE- BiK-F Biodiversity and the Climate Research Centre Frankfurt. Funding to C. F. D. by the Helmholtz Association (VH-NG 247) and the German Research Foundation DFG (DO 686/5-1), to S.J.S. by the Max Planck Society and to C. R. by the DFG (RO 3842/1-1) is gratefully acknowledged.The papers in this Special Issue arose from two workshops entitled 'The ecological niche as a window to biodiversity' held on 26-30 July 2010 and 24-27 January 2011 in Arnoldshain near Frankfurt, Germany. The workshops combined recent advances in our empirical and theoretical understanding of the niche with advances in statistical modelling, with the aim of developing a more mechanistic theory of the niche. Funding for the workshops was provided by the Biodiversity and Climate Research Centre (BiK-F), which is part of the LOEWE programme 'Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz' of Hesse's Ministry of Higher Education, Research and the Arts.	Aitken SN, 2008, EVOL APPL, V1, P95, DOI 10.1111/j.1752-4571.2007.00013.x; Angert AL, 2005, EVOLUTION, V59, P1671, DOI 10.1554/05-107.1; Applequist WL, 2007, BIODIVERS CONSERV, V16, P1397, DOI 10.1007/s10531-006-6737-x; Araujo MB, 2006, SCIENCE, V313, P1396, DOI 10.1126/science.1131758; Araujo MB, 2005, GLOBAL CHANGE BIOL, V11, P1504, DOI 10.1111/j.1365-2486.2005.001000.x; Arhonditsis GB, 2007, ECOL MODEL, V208, P215, DOI 10.1016/j.ecolmodel.2007.05.020; Austin MP, 2002, ECOL MODEL, V157, P101, DOI 10.1016/S0304-3800(02)00205-3; Bahn V, 2007, GLOBAL ECOL BIOGEOGR, V16, P733, DOI 10.1111/j.1466-8238.2007.00331.x; Barnes N, 2010, NATURE, V467, P753, DOI 10.1038/467753a; Barry S, 2006, J APPL ECOL, V43, P413, DOI 10.1111/j.1365-2664.2006.01136.x; Beale CM, 2008, P NATL ACAD SCI USA, V105, P14908, DOI 10.1073/pnas.0803506105; Beven K, 2001, J HYDROL, V249, P11, DOI 10.1016/S0022-1694(01)00421-8; Bierman SM, 2010, ECOGRAPHY, V33, P451, DOI 10.1111/j.1600-0587.2009.05798.x; Bleloch A. L., 1990, MODEL IT; Bolker BM, 1998, ECOL APPL, V8, P425, DOI 10.2307/2641082; Bonn A, 2001, ECOGRAPHY, V24, P483, DOI 10.1034/j.1600-0587.2001.d01-204.x; Broennimann O, 2007, ECOL LETT, V10, P701, DOI 10.1111/j.1461-0248.2007.01060.x; Buckley LB, 2008, AM NAT, V171, pE1, DOI 10.1086/523949; Bugmann HKM, 1996, ECOLOGY, V77, P2055, DOI 10.2307/2265700; Bugmann HKM, 1995, J BIOGEOGR, V22, P477, DOI 10.2307/2845944; Bugmann HKM, 2000, ECOL APPL, V10, P95, DOI 10.1890/1051-0761(2000)010[0095:EFCABA]2.0.CO;2; Burnham K.P, 2002, MODEL SELECTION MULT; Cabral JS, 2010, GLOBAL ECOL BIOGEOGR, V19, P85, DOI 10.1111/j.1466-8238.2009.00492.x; Cabral JS, 2011, CONSERV BIOL, V25, P73, DOI 10.1111/j.1523-1739.2010.01628.x; Chevin LM, 2010, PLOS BIOL, V8, DOI 10.1371/journal.pbio.1000357; Chuine I, 2001, ECOL LETT, V4, P500, DOI 10.1046/j.1461-0248.2001.00261.x; Chuine I, 2000, J THEOR BIOL, V207, P337, DOI 10.1006/jtbi.2000.2178; Clark JS, 2001, SCIENCE, V293, P657, DOI 10.1126/science.293.5530.657; Cox GM, 2006, ECOL MODEL, V198, P240, DOI 10.1016/j.ecolmodel.2006.04.016; Crout NMJ, 2009, ENVIRON MODELL SOFTW, V24, P1, DOI 10.1016/j.envsoft.2008.06.004; Currie DJ, 2007, J BIOGEOGR, V34, P2009, DOI 10.1111/j.1365-2699.2007.01808.x; Davis AJ, 1998, NATURE, V391, P783, DOI 10.1038/35842; De Marco P, 2008, BIOL LETTERS, V4, P577, DOI 10.1098/rsbl.2008.0210; Dennis RLH, 1999, J INSECT CONSERV, V3, P33, DOI 10.1023/A:1009678422145; Doak DF, 2010, NATURE, V467, P959, DOI 10.1038/nature09439; Dormann CF, 2008, ECOL LETT, V11, P235, DOI 10.1111/j.1461-0248.2007.01142.x; Dormann CF, 2007, ECOGRAPHY, V30, P609, DOI 10.1111/j.2007.0906-7590.05171.x; Dormann CF, 2008, ECOLOGY, V89, P3371, DOI 10.1890/07-1772.1; Draper NR, 1998, APPL REGRESSION ANAL; Eisinger D, 2008, J APPL ECOL, V45, P415, DOI 10.1111/j.1365-2664.2007.01439.x; Elith J, 2006, ECOGRAPHY, V29, P129, DOI 10.1111/j.2006.0906-7590.04596.x; Elith J, 2010, METHODS ECOL EVOL, V1, P330, DOI 10.1111/j.2041-210X.2010.00036.x; Elith J, 2009, ANNU REV ECOL EVOL S, V40, P677, DOI 10.1146/annurev.ecolsys.110308.120159; Farquhar GD, 1997, SCIENCE, V278, P1411, DOI 10.1126/science.278.5342.1411; Giesecke T, 2007, J BIOGEOGR, V34, P118, DOI 10.1111/j.1365-2699.2006.01580.x; Grimm V, 2005, INDIVIDUAL BASED MOD; Guisan A, 2005, ECOL LETT, V8, P993, DOI 10.1111/j.1461-0248.2005.00792.x; Hastie T., 2009, ELEMENTS STAT LEARNI; Hein S, 2007, BASIC APPL ECOL, V8, P310, DOI 10.1016/j.baae.2006.09.012; Heisey DM, 2010, ECOL MONOGR, V80, P221, DOI 10.1890/09-0052.1; Hijmans RJ, 2006, GLOBAL CHANGE BIOL, V12, P2272, DOI 10.1111/j.1365-2486.2006.01256.x; Hodges JS, 2010, ECOLOGY, V91, P3496, DOI 10.1890/10-0052.1; Hothorn T, 2011, ECOL MONOGR, V81, P329, DOI 10.1890/10-0602.1; HUNTLEY B, 1991, ANN BOT-LONDON, V67, P15; Jeltsch F, 2011, AGR ECOSYST ENVIRON, V145, P49, DOI 10.1016/j.agee.2010.12.019; Johnson JB, 2004, TRENDS ECOL EVOL, V19, P101, DOI 10.1016/j.tree.2003.10.013; Judd K, 2003, PHYS REV E, V67, DOI 10.1103/PhysRevE.67.026212; Kearney M, 2009, FUNCT ECOL, V23, P528, DOI 10.1111/j.1365-2435.2008.01538.x; Kearney M, 2009, ECOL LETT, V12, P334, DOI 10.1111/j.1461-0248.2008.01277.x; Kearney M, 2008, ECOGRAPHY, V31, P423, DOI 10.1111/j.0906-7590.2008.05457.x; Kearney M, 2009, P NATL ACAD SCI USA, V106, P3835, DOI 10.1073/pnas.0808913106; Kearney MR, 2010, CONSERV LETT, V3, P203, DOI 10.1111/j.1755-263X.2010.00097.x; Keenan T, 2011, GLOBAL CHANGE BIOL, V17, P565, DOI 10.1111/j.1365-2486.2010.02254.x; Keith DA, 2008, BIOL LETTERS, V4, P560, DOI 10.1098/rsbl.2008.0049; Kirchner J. W., 2006, WATER RESOUR RES, V42; Kleidon A, 2000, GLOB CHANGE BIOL, V6, P507, DOI 10.1046/j.1365-2486.2000.00332.x; Krakauer DC, 2011, J THEOR BIOL, V276, P269, DOI 10.1016/j.jtbi.2011.01.051; Krinner G., 2005, GLOBAL BIOGEOCHEMICA, V19; LaDeau S, 2010, ECOLOGY, V91, P3488, DOI 10.1890/09-2249.1; Latimer AM, 2006, ECOL APPL, V16, P33, DOI 10.1890/04-0609; Lawton JH, 1999, OIKOS, V84, P177, DOI 10.2307/3546712; LEBRETON JD, 1992, ECOL MONOGR, V62, P67, DOI 10.2307/2937171; Lele SR, 2010, ECOLOGY, V91, P3493, DOI 10.1890/10-0099.1; Lele SR, 2010, J AM STAT ASSOC, V105, P1617, DOI 10.1198/jasa.2010.tm09757; Link WA, 2006, ECOLOGY, V87, P2626, DOI 10.1890/0012-9658(2006)87[2626:MWATFO]2.0.CO;2; Loehle C, 1996, ECOL MODEL, V90, P1, DOI 10.1016/0304-3800(96)83709-4; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; Martinez I, 2011, AM NAT, V177, pE136, DOI 10.1086/659623; McInerny GJ, 2011, METHODS ECOL EVOL, V2, P248, DOI 10.1111/j.2041-210X.2010.00077.x; Midgley GF, 2006, DIVERS DISTRIB, V12, P555, DOI 10.1111/j.1366-9516.2006.00273.x; Moilanen A, 2000, J ANIM ECOL, V69, P143, DOI 10.1046/j.1365-2656.2000.00381.x; Mokany K., 2010, DIVERS DISTRIB, V17, P374; Morin X, 2009, ECOLOGY, V90, P1301, DOI 10.1890/08-0134.1; Morin X, 2007, ECOLOGY, V88, P2280, DOI 10.1890/06-1591.1; O'Hara RB, 2009, BAYESIAN ANAL, V4, P85, DOI 10.1214/09-BA403; Ohlemuller R, 2006, GLOBAL ECOL BIOGEOGR, V15, P395, DOI 10.1111/j.1566-822x.2006.00245.x; ORESKES N, 1994, SCIENCE, V263, P641, DOI 10.1126/science.263.5147.641; Pagel J, 2008, ECOL APPL, V18, P2000, DOI 10.1890/07-1305.1; Pagel J, 2012, GLOBAL ECOL BIOGEOGR, V21, P293, DOI 10.1111/j.1466-8238.2011.00663.x; Pautasso M, 2007, CONSERV BIOL, V21, P1333, DOI 10.1111/j.1523-1739.2007.00760.x; Pearman PB, 2008, TRENDS ECOL EVOL, V23, P149, DOI 10.1016/j.tree.2007.11.005; Pearman PB, 2008, ECOL LETT, V11, P357, DOI 10.1111/j.1461-0248.2007.01150.x; Peng CH, 2011, ECOL LETT, V14, P522, DOI 10.1111/j.1461-0248.2011.01603.x; Penteriani V, 2008, ECOL COMPLEX, V5, P66, DOI 10.1016/j.ecocom.2007.06.002; Perry GLW, 2008, PERSPECT PLANT ECOL, V9, P191, DOI 10.1016/j.ppees.2007.07.001; Peterson AT, 1999, SCIENCE, V285, P1265, DOI 10.1126/science.285.5431.1265; Phillips SJ, 2008, ECOGRAPHY, V31, P161, DOI 10.1111/j.0906-7590.2008.5203.x; Press W. H., 2007, NUMERICAL RECIPES AR; R Development Core Team, 2010, R LANG ENV STAT COMP; Randin CF, 2006, J BIOGEOGR, V33, P1689, DOI 10.1111/j.1365-2699.2006.01466.x; Refsgaard JC, 2007, ENVIRON MODELL SOFTW, V22, P1543, DOI [10.1016/j.envsoft.2007.02.004, 10.1016/j.envost.2007.02.004]; Reineking B, 2006, ECOL MODEL, V193, P675, DOI 10.1016/j.ecolmodel.2005.10.003; Reu B, 2011, GLOBAL ECOL BIOGEOGR, V20, P570, DOI 10.1111/j.1466-8238.2010.00621.x; Rickebusch S, 2008, BIOL LETTERS, V4, P556, DOI 10.1098/rsbl.2008.0105; Royle JA, 2005, OIKOS, V110, P353, DOI 10.1111/j.0030-1299.2005.13534.x; Schmolke A, 2010, TRENDS ECOL EVOL, V25, P479, DOI 10.1016/j.tree.2010.05.001; SCHRODER B., 1999, Z OKOLOGIE NATURSCHU, V8, P195; Schulz K, 2001, J CLIMATE, V14, P2551, DOI 10.1175/1520-0442(2001)014<2551:TPUOLS>2.0.CO;2; Schumacher S, 2006, GLOBAL CHANGE BIOL, V12, P1435, DOI 10.1111/j.1365-2486.2006.01188.x; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Smith B, 2001, GLOBAL ECOL BIOGEOGR, V10, P621, DOI 10.1046/j.1466-822X.2001.t01-1-00256.x; Smith GC, 2008, J APPL ECOL, V45, P1675, DOI 10.1111/j.1365-2664.2008.01380.x; SUTHERST RW, 1985, AGR ECOSYST ENVIRON, V13, P281, DOI 10.1016/0167-8809(85)90016-7; Trivedi MR, 2008, GLOBAL CHANGE BIOL, V14, P1089, DOI 10.1111/j.1365-2486.2008.01553.x; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; Vaughan IP, 2003, CONSERV BIOL, V17, P1601, DOI 10.1111/j.1523-1739.2003.00359.x; Wasserberg G, 2009, J APPL ECOL, V46, P457, DOI 10.1111/j.1365-2664.2008.01576.x; Zurell D, 2010, OIKOS, V119, P622, DOI 10.1111/j.1600-0706.2009.18284.x	118	14	14	WILEY-BLACKWELL	HOBOKEN	111 RIVER ST, HOBOKEN 07030-5774, NJ USA	0305-0270		J BIOGEOGR	J. Biogeogr.	DEC	2012	39	12					2119	2131		10.1111/j.1365-2699.2011.02659.x		13	Ecology; Geography, Physical	Environmental Sciences & Ecology; Physical Geography	041FP	WOS:000311384100006	
J	Hartig, F; Dyke, J; Hickler, T; Higgins, SI; O'Hara, RB; Scheiter, S; Huth, A				Hartig, Florian; Dyke, James; Hickler, Thomas; Higgins, Steven I.; O'Hara, Robert B.; Scheiter, Simon; Huth, Andreas			Connecting dynamic vegetation models to data - an inverse perspective	JOURNAL OF BIOGEOGRAPHY			English	Article						Bayesian statistics; calibration; data assimilation; forest models; inverse modelling; model selection; parameterization; plant functional types; predictive uncertainty; process-based models	CLIMATE-CHANGE; SPECIES DISTRIBUTION; FOREST CARBON; ECOSYSTEM STRUCTURE; SIMULATION-MODELS; DATA ASSIMILATION; AFRICAN SAVANNAS; PLANT GEOGRAPHY; RAIN-FORESTS; SELECTION	Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.	[Hartig, Florian; Huth, Andreas] UFZ Helmholtz Ctr Environm Res, D-04318 Leipzig, Germany; [Dyke, James] Univ Southampton, Inst Complex Syst Simulat, Southampton SO17 1BJ, Hants, England; [Hickler, Thomas; O'Hara, Robert B.; Scheiter, Simon] Biodivers & Climate Res Ctr LOEWE BiK F, D-60325 Frankfurt, Germany; [Higgins, Steven I.] Goethe Univ Frankfurt, Inst Phys Geog, D-60438 Frankfurt, Germany	Hartig, F (reprint author), UFZ Helmholtz Ctr Environm Res, Permoserstr 15, D-04318 Leipzig, Germany.	florian.hartig@ufz.de	Higgins, Steven/A-5138-2012; Scheiter, Simon/G-5048-2012; O'Hara, Robert/A-7499-2008; Hartig, Florian/G-4510-2010	Higgins, Steven/0000-0001-5695-9665; O'Hara, Robert/0000-0001-9737-3724; Hartig, Florian/0000-0002-6255-9059	ERC [233066]; Biodiversity and Climate Research Centre (BiK-F), part of the LOEWE programme 'Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz' of Hesse's Ministry of Higher Education, Research and the Arts; Helmholtz Association through the research alliance 'Planetary Evolution and Life'; LOEWE initiative	We would like to thank Christine Romermann, Steve Higgins and Bob O'Hara for organizing the workshop from which this paper originated, as well as the LOEWE initiative (see below) for funding this event. We are grateful for comments by Olga Bykova, Anne Carney, Carsten Dormann, Martin Kazmierczak, Peter Linder, Glenn Marion, Peter Pearman, Christopher Reyer, Stan Schymanski, Robert Whittaker and three anonymous referees, which greatly improved this manuscript. F. H. acknowledges support from ERC advanced grant 233066. Funding for R.B.O'H. and S. S was provided by the Biodiversity and Climate Research Centre (BiK-F), which is part of the LOEWE programme 'Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz' of Hesse's Ministry of Higher Education, Research and the Arts. J.D. acknowledges support from the Helmholtz Association through the research alliance 'Planetary Evolution and Life'.	Allen JRM, 2010, QUATERNARY SCI REV, V29, P2604, DOI 10.1016/j.quascirev.2010.05.031; Andrieu C, 2003, MACH LEARN, V50, P5, DOI 10.1023/A:1020281327116; Araujo MB, 2006, J BIOGEOGR, V33, P1677, DOI 10.1111/j.1365-2699.2006.01584.x; Asner GP, 2009, FRONT ECOL ENVIRON, V7, P269, DOI 10.1890/070152; Asner GP, 2010, P NATL ACAD SCI USA, V107, P16738, DOI 10.1073/pnas.1004875107; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Bohn K, 2011, BIOGEOSCIENCES, V8, P1107, DOI 10.5194/bg-8-1107-2011; Bonan GB, 2008, SCIENCE, V320, P1444, DOI 10.1126/science.1155121; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Bugmann H, 2001, CLIMATIC CHANGE, V51, P259, DOI 10.1023/A:1012525626267; Bugmann HKM, 1996, ECOLOGY, V77, P2055, DOI 10.2307/2265700; Butler A, 2009, ENVIRONMETRICS, V20, P791, DOI 10.1002/env.953; Chambers JQ, 2007, TRENDS ECOL EVOL, V22, P414, DOI 10.1016/j.tree.2007.05.001; Clark JS, 2010, SCIENCE, V327, P1129, DOI 10.1126/science.1183506; Clark JS, 2005, ECOL LETT, V8, P2, DOI 10.1111/j.1461-0248.2004.00702.x; Condit R, 2000, SCIENCE, V288, P1414, DOI 10.1126/science.288.5470.1414; Conti S, 2010, J STAT PLAN INFER, V140, P640, DOI 10.1016/j.jspi.2009.08.006; Cramer W, 2001, GLOBAL CHANGE BIOL, V7, P357, DOI 10.1046/j.1365-2486.2001.00383.x; Diaz S, 2001, TRENDS ECOL EVOL, V16, P646, DOI 10.1016/S0169-5347(01)02283-2; Dislich C, 2009, ERDKUNDE, V63, P347, DOI 10.3112/erdkunde.2009.04.05; Dormann CF, 2012, J BIOGEOGR, V39, P2119, DOI 10.1111/j.1365-2699.2011.02659.x; Dormann CF, 2007, ECOGRAPHY, V30, P609, DOI 10.1111/j.2007.0906-7590.05171.x; Elith J, 2009, ANNU REV ECOL EVOL S, V40, P677, DOI 10.1146/annurev.ecolsys.110308.120159; Fensholt R, 2010, IEEE J-STARS, V3, P271, DOI 10.1109/JSTARS.2010.2048744; Friend AD, 2007, GLOBAL CHANGE BIOL, V13, P610, DOI 10.1111/j.1365-2486.2006.01223.x; Frolking S, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000911; Galbraith D, 2010, NEW PHYTOL, V187, P647, DOI 10.1111/j.1469-8137.2010.03350.x; Gelman A., 2003, BAYESIAN DATA ANAL; Gerten D, 2004, J HYDROL, V286, P249, DOI 10.1016/j.jhydrol.2003.09.029; Grace J, 2004, J ECOL, V92, P189, DOI 10.1111/j.0022-0477.2004.00874.x; Grimm V, 2012, PHILOS T R SOC B, V367, P298, DOI 10.1098/rstb.2011.0180; Gritti ES, 2006, J BIOGEOGR, V33, P145, DOI 10.1111/j.1365-2699.2005.01377.x; Guisan A, 2005, ECOL LETT, V8, P993, DOI 10.1111/j.1461-0248.2005.00792.x; Hanson PJ, 2004, ECOL MONOGR, V74, P443, DOI 10.1890/03-4049; Hartig F, 2011, ECOL LETT, V14, P816, DOI 10.1111/j.1461-0248.2011.01640.x; Hickler T, 2012, GLOBAL ECOL BIOGEOGR, V21, P50, DOI 10.1111/j.1466-8238.2010.00613.x; Higgins SI, 2012, J BIOGEOGR, V39, P2132, DOI 10.1111/j.1365-2699.2012.02752.x; Higgins SI, 2010, ECOLOGY, V91, P1682, DOI 10.1890/08-1368.1; Hubbell SP, 2008, P NATL ACAD SCI USA, V105, P11498, DOI 10.1073/pnas.0801915105; Hurtt GC, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG000937; Huth A, 2000, ECOL MODEL, V134, P1, DOI 10.1016/S0304-3800(00)00328-8; Jeltsch F, 2008, PERSPECT PLANT ECOL, V9, P171, DOI 10.1016/j.ppees.2007.11.004; Jenkins JC, 2001, ECOL APPL, V11, P1174, DOI 10.1890/1051-0761(2001)011[1174:BANEFT]2.0.CO;2; Johnson JB, 2004, TRENDS ECOL EVOL, V19, P101, DOI 10.1016/j.tree.2003.10.013; Kadane JB, 2004, J AM STAT ASSOC, V99, P279, DOI 10.1198/016214504000000269; Kass RE, 1996, J AM STAT ASSOC, V91, P1343, DOI 10.2307/2291752; Kattge J, 2011, GLOBAL CHANGE BIOL, V17, P2905, DOI 10.1111/j.1365-2486.2011.02451.x; Kerr YH, 2007, HYDROGEOL J, V15, P117, DOI 10.1007/s10040-006-0095-3; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kohler P, 2010, BIOGEOSCIENCES, V7, P2531, DOI 10.5194/bg-7-2531-2010; Kohler P, 2000, J TROP ECOL, V16, P591, DOI 10.1017/S0266467400001590; Kramer K, 2010, FOREST ECOL MANAG, V259, P2213, DOI 10.1016/j.foreco.2009.12.023; Krieger G, 2010, P IEEE, V98, P816, DOI 10.1109/JPROC.2009.2038948; Lasslop G, 2008, BIOGEOSCIENCES, V5, P1311; Lefsky MA, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043622; Linder HP, 2012, J BIOGEOGR, V39, P2179, DOI 10.1111/j.1365-2699.2012.02705.x; Lischke H, 2006, ECOL MODEL, V199, P409, DOI 10.1016/j.ecolmodel.2005.11.046; Luo YQ, 2011, ECOL APPL, V21, P1429; Martinez I, 2011, AM NAT, V177, pE136, DOI 10.1086/659623; McMahon SM, 2011, TRENDS ECOL EVOL, V26, P249, DOI 10.1016/j.tree.2011.02.012; Medvigy D, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000812; Miller PA, 2008, J ECOL, V96, P247, DOI 10.1111/j.1365-2745.2007.01342.x; Moorcroft PR, 2001, ECOL MONOGR, V71, P557, DOI 10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2; Pacala SW, 1996, ECOL MONOGR, V66, P1, DOI 10.2307/2963479; Parmesan C, 2006, ANNU REV ECOL EVOL S, V37, P637, DOI 10.1146/annurev.ecolsys.37.091305.110100; Patenaude G, 2008, INT J REMOTE SENS, V29, P1295, DOI [10.1090/01411160701716414, 10.1080/01431160701736414]; Pereira HM, 2010, SCIENCE, V330, P1496, DOI 10.1126/science.1196624; PICKETT S T A, 1989, P110; Prentice IC, 2007, TERRESTRIAL ECOSYSTE, P175, DOI 10.1007/978-3-540-32730-1_15; Prentice IC, 2009, CLIM PAST, V5, P297; Purves DW, 2008, P NATL ACAD SCI USA, V105, P17018, DOI 10.1073/pnas.0807754105; Purves DW, 2007, ECOL MONOGR, V77, P77, DOI 10.1890/05-1923; Roscher C, 2004, BASIC APPL ECOL, V5, P107, DOI 10.1078/1439-1791-00216; Sankaran M, 2005, NATURE, V438, P846, DOI 10.1038/nature04070; Sato H, 2007, ECOL MODEL, V200, P279, DOI 10.1016/j.ecolmodel.2006.09.006; Scheiter S, 2009, GLOBAL CHANGE BIOL, V15, P2224, DOI 10.1111/j.1365-2486.2008.01838.x; Schroder B, 2006, ECOL MODEL, V199, P505, DOI 10.1016/j.ecolmodel.2006.05.036; Schurr FM, 2012, J BIOGEOGR, V39, P2146, DOI 10.1111/j.1365-2699.2012.02737.x; Shugart H.H., 1998, TERRESTRIAL ECOSYSTE; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x; Smith B, 2001, GLOBAL ECOL BIOGEOGR, V10, P621, DOI 10.1046/j.1466-822X.2001.t01-1-00256.x; Stephens PA, 2007, TRENDS ECOL EVOL, V22, P192, DOI 10.1016/j.tree.2006.12.003; Sugita S, 1999, HOLOCENE, V9, P409, DOI 10.1191/095968399666429937; ter Steege H, 2006, NATURE, V443, P444, DOI 10.1038/nature05134; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; Weng ES, 2011, J PLANT ECOL-UK, V4, P178, DOI 10.1093/jpe/rtr018; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Xiaodong Y., 2005, J BIOGEOGR, V32, P1641, DOI 10.1111/j.1365-2699.2005.01293.x; Zhang XY, 2003, REMOTE SENS ENVIRON, V84, P471, DOI 10.1016/S0034-4257(02)00135-9; Zimmermann NE, 2010, ECOGRAPHY, V33, P985, DOI 10.1111/j.1600-0587.2010.06953.x; Zurell D., 2009, OIKOS, V119, P622	92	8	8	WILEY-BLACKWELL	HOBOKEN	111 RIVER ST, HOBOKEN 07030-5774, NJ USA	0305-0270		J BIOGEOGR	J. Biogeogr.	DEC	2012	39	12					2240	2252		10.1111/j.1365-2699.2012.02745.x		13	Ecology; Geography, Physical	Environmental Sciences & Ecology; Physical Geography	041FP	WOS:000311384100015	
J	Goosse, H; Crespin, E; Dubinkina, S; Loutre, MF; Mann, ME; Renssen, H; Sallaz-Damaz, Y; Shindell, D				Goosse, Hugues; Crespin, Elisabeth; Dubinkina, Svetlana; Loutre, Marie-France; Mann, Michael E.; Renssen, Hans; Sallaz-Damaz, Yoann; Shindell, Drew			The role of forcing and internal dynamics in explaining the "Medieval Climate Anomaly"	CLIMATE DYNAMICS			English	Article						Paleoclimate; Last millennium; Medieval Climate Anomaly; Climate modelling; Data assimilation; Atmospheric and ocean dynamics; Radiative forcing	NORTH-ATLANTIC OSCILLATION; LAST MILLENNIUM; PAST MILLENNIUM; CARBON-CYCLE; GULF-STREAM; RADIONUCLIDE RECORDS; SOLAR-ACTIVITY; SYSTEM MODEL; WARM PERIOD; VARIABILITY	Proxy reconstructions suggest that peak global temperature during the past warm interval known as the Medieval Climate Anomaly (MCA, roughly 950-1250 AD) has been exceeded only during the most recent decades. To better understand the origin of this warm period, we use model simulations constrained by data assimilation establishing the spatial pattern of temperature changes that is most consistent with forcing estimates, model physics and the empirical information contained in paleoclimate proxy records. These numerical experiments demonstrate that the reconstructed spatial temperature pattern of the MCA can be explained by a simple thermodynamical response of the climate system to relatively weak changes in radiative forcing combined with a modification of the atmospheric circulation, displaying some similarities with the positive phase of the so-called Arctic Oscillation, and with northward shifts in the position of the Gulf Stream and Kuroshio currents. The mechanisms underlying the MCA are thus quite different from anthropogenic mechanisms responsible for modern global warming.	[Goosse, Hugues; Crespin, Elisabeth; Dubinkina, Svetlana; Loutre, Marie-France; Sallaz-Damaz, Yoann] Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, B-1348 Louvain, Belgium; [Mann, Michael E.] Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA; [Mann, Michael E.] Penn State Univ, Earth & Environm Syst Inst, University Pk, PA 16802 USA; [Renssen, Hans] Vrije Univ Amsterdam, Dept Earth Sci, Sect Climate Change & Landscape Dynam, Amsterdam, Netherlands; [Shindell, Drew] NASA, Goddard Inst Space Studies, New York, NY 10025 USA	Goosse, H (reprint author), Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, Chemin Cyclotron 2, B-1348 Louvain, Belgium.	hugues.goosse@uclouvain.be			FRS-FNRS; Belgian Federal Science Policy Office; EU; NSF [ATM-0902133]; Fond de la Recherche Scientifique de Belgique (FRS-FNRS)	We thank E. Zorita and R. Wilson for comments and all the scientists that collected and analysed the proxy data used in this work. H. G. is Senior Research Associate with the Fonds National de la Recherche Scientifique (FRS-FNRS-Belgium). This work is supported by the FRS-FNRS and by the Belgian Federal Science Policy Office (Research Program on Science for a Sustainable Development) and by EU (project Past4future). M. E. M. acknowledges support from the NSF Paleoclimate program (grant number ATM-0902133). Aurelien Mairesse helped in the design of Fig. 1. Computational resources have been provided by the super-computing facilities of the Universite Catholique de Louvain (CISM/UCL) and the Consortium des Equipements de Calcul Intensif en Federation Wallonie Bruxelles (CECI) funded by the Fond de la Recherche Scientifique de Belgique (FRS-FNRS).	Ammann CM, 2007, P NATL ACAD SCI USA, V104, P3713, DOI 10.1073/pnas.0605064103; Bard E, 2007, QUATERNARY SCI REV, V26, P2301, DOI 10.1016/j.quascirev.2006.07.012; Berger A. L., 1978, J ATMOS SCI, V35, P2363; Brohan P, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006548; Brovkin V, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001662; Burgman R, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2009GL042239; CHARLSON RJ, 1991, TELLUS A, V43, P152, DOI 10.1034/j.1600-0870.1991.00013.x; Corti S, 1999, NATURE, V398, P799; Crowley TJ, 2000, SCIENCE, V289, P270, DOI 10.1126/science.289.5477.270; Crowley TJ, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017801; Dubinkina S, 2011, INT J BIFUR IN PRESS; Esper J, 2009, CLIMATIC CHANGE, V94, P267, DOI [10.1007/s10584-008-9492-z, 10.1007/s10584-008-9492-Z]; Forster P, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P129; Frank DC, 2010, NATURE, V463, P527, DOI 10.1038/nature08769; Frankignoul C, 2001, J PHYS OCEANOGR, V31, P3516, DOI 10.1175/1520-0485(2002)031<3516:GSVAOA>2.0.CO;2; Gonzalez-Rouco F, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL018264; Gonzalez-Rouco F. J., 2011, PAGES (Past Global Changes) News, V19, P7; Goosse H, 1999, J GEOPHYS RES-OCEANS, V104, P23337, DOI 10.1029/1999JC900215; Goosse H, 2005, QUATERNARY SCI REV, V24, P1345, DOI 10.1016/j.quascirev.2004.12.009; Goosse H, 2004, CLIM DYNAM, V23, P153, DOI 10.1007/s00382-004-0424-y; Goosse H, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL022368; Goosse H, 2006, CLIM DYNAM, V27, P165, DOI 10.1007/s00382-006-0128-6; Goosse H, 2010, GEOSCI MODEL DEV, V3, P603, DOI 10.5194/gmd-3-603-2010; Graham NE, 2011, CLIM DYNAM, V37, P1217, DOI 10.1007/s00382-010-0914-z; Hegerl GC, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P663; Hegerl GC, 2000, CLIM DYNAM, V16, P737, DOI 10.1007/s003820000071; HUGHES MK, 1994, CLIMATIC CHANGE, V26, P109, DOI 10.1007/BF01092410; HURRELL JW, 1995, SCIENCE, V269, P676, DOI 10.1126/science.269.5224.676; Jones PD, 2009, HOLOCENE, V19, P3, DOI 10.1177/0959683608098952; Jungclaus JH, 2010, CLIM PAST, V6, P723, DOI 10.5194/cp-6-723-2010; Kalnay E, 2003, ATMOSPHERIC MODELING; Korte M, 2005, EARTH PLANET SC LETT, V236, P348, DOI 10.1016/j.epsl.2004.12.031; Kwon YO, 2010, J CLIMATE, V23, P3249, DOI 10.1175/2010JCLI3343.1; Liu JS, 1998, J AM STAT ASSOC, V93, P1032, DOI 10.2307/2669847; Ljungqvist FC, 2010, GEOGR ANN A, V92A, P339, DOI 10.1111/j.1468-0459.2010.00399.x; Lund DC, 2006, NATURE, V444, P601, DOI 10.1038/nature05277; Mann ME, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008272; Mann ME, 2008, P NATL ACAD SCI USA, V105, P13252, DOI 10.1073/pnas.0805721105; Mann ME, 2009, SCIENCE, V326, P1256, DOI 10.1126/science.1177303; Miller RL, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006323; Muscheler R, 2007, QUATERNARY SCI REV, V26, P82, DOI 10.1016/j.quascirev.2006.07.012; Opsteegh JD, 1998, TELLUS A, V50, P348, DOI 10.1034/j.1600-0870.1998.t01-1-00007.x; Osborn TJ, 2006, CLIM DYNAM, V27, P185, DOI 10.1007/s00382-006-0129-5; Ramankutty N, 1999, GLOBAL BIOGEOCHEM CY, V13, P997, DOI 10.1029/1999GB900046; Sanso B, 2008, BAYESIAN ANAL, V3, P1; Schmidt GA, 2011, GEOSCI MODEL DEV, V4, P33, DOI 10.5194/gmd-4-33-2011; Seager R, 2007, QUATERNARY SCI REV, V26, P2322, DOI 10.1016/j.quascirev.2007.04.020; Servonnat J, 2010, CLIM PAST, V6, P445, DOI 10.5194/cp-6-445-2010; Shapiro AI, 2011, ASTRON ASTROPHYS, V529, DOI 10.1051/0004-6361/201016173; Shindell DT, 2001, SCIENCE, V294, P2149, DOI 10.1126/science.1064363; Shindell DT, 2003, J CLIMATE, V16, P4094, DOI 10.1175/1520-0442(2003)016<4094:VASFOC>2.0.CO;2; Shindell DT, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027468; Swingedouw D, 2011, CLIM DYNAM, V36, P1349, DOI 10.1007/s00382-010-0803-5; Talagrand O, 1997, J METEOROL SOC JPN, V75, P191; Trouet V, 2009, SCIENCE, V324, P78, DOI 10.1126/science.1166349; van Leeuwen PJ, 2009, MON WEATHER REV, V137, P4089, DOI 10.1175/2009MWR2835.1; WALLACE JM, 1995, SCIENCE, V270, P780, DOI 10.1126/science.270.5237.780; Wang YM, 2005, ASTROPHYS J, V625, P522, DOI 10.1086/429689; Wanner H, 2001, SURV GEOPHYS, V22, P321, DOI 10.1023/A:1014217317898; Widmann M, 2010, CLIM PAST, V6, P627, DOI 10.5194/cp-6-627-2010	60	5	5	SPRINGER	NEW YORK	233 SPRING ST, NEW YORK, NY 10013 USA	0930-7575		CLIM DYNAM	Clim. Dyn.	DEC	2012	39	12					2847	2866		10.1007/s00382-012-1297-0		20	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	038VV	WOS:000311203100007	
J	Chatterjee, A; Michalak, AM; Anderson, JL; Mueller, KL; Yadav, V				Chatterjee, Abhishek; Michalak, Anna M.; Anderson, Jeffrey L.; Mueller, Kim L.; Yadav, Vineet			Toward reliable ensemble Kalman filter estimates of CO2 fluxes	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							ATMOSPHERIC DATA ASSIMILATION; ADAPTIVE COVARIANCE INFLATION; PERFECT MODEL EXPERIMENTS; CARBON-DIOXIDE EXCHANGE; TRANSPORT MODELS; ERROR-CORRECTION; SQUARE-ROOT; PART I; INVERSIONS; LOCALIZATION	The use of ensemble filters for estimating sources and sinks of carbon dioxide (CO2) is becoming increasingly common, because they provide a relatively computationally efficient framework for assimilating high-density observations of CO2. Their applicability for estimating fluxes at high-resolutions and the equivalence of their estimates to those from more traditional "batch" inversion methods have not been demonstrated, however. In this study, we introduce a Geostatistical Ensemble Square Root Filter (GEnSRF) as a prototypical filter and examine its performance using a synthetic data study over North America at a high spatial (1 degrees x 1 degrees) and temporal (3-hourly) resolution. The ensemble performance, both in terms of estimates and associated uncertainties, is benchmarked against a batch inverse modeling setup in order to isolate and quantify the degradation in the estimates due to the numerical approximations and parameter choices in the ensemble filter. The examined case studies demonstrate that adopting state-of-the-art covariance inflation and localization schemes is a necessary but not sufficient condition for ensuring good filter performance, as defined by its ability to yield reliable flux estimates and uncertainties across a range of resolutions. Observational density is found to be another critical factor for stabilizing the ensemble performance, which is attributed to the lack of a dynamical model for evolving the ensemble between assimilation times. This and other results point to key differences between the applicability of ensemble approaches to carbon cycle science relative to its use in meteorological applications where these tools were originally developed.	[Chatterjee, Abhishek; Michalak, Anna M.; Yadav, Vineet] Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA; [Chatterjee, Abhishek; Mueller, Kim L.] Univ Michigan, Dept Civil & Environm Engn, Ann Arbor, MI 48109 USA; [Anderson, Jeffrey L.] Natl Ctr Atmospher Res, Boulder, CO 80307 USA	Chatterjee, A (reprint author), Carnegie Inst Sci, Dept Global Ecol, 260 Panama St, Stanford, CA 94305 USA.	abhishch@umich.edu			National Aeronautics and Space Administration (NASA) through an Earth System Science Fellowship [NNX09AO10H, NNX12AB90G]; Sandia National Laboratories, Albuquerque, NM; United States Department of Energy's National Nuclear Security Administration [DE-AC04-94AL85000]	This work was supported by the National Aeronautics and Space Administration (NASA) through an Earth System Science Fellowship for Abhishek Chatterjee, under grant NNX09AO10H. Additional support was provided through NASA grant NNX12AB90G and a contract from Sandia National Laboratories, Albuquerque, NM, funded under a Laboratory Directed Research and Development project. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. The authors acknowledge Ave Arellano, Andy Jacobson and Derek Posselt for fruitful comments and discussions regarding this work. We specially thank Sharon Gourdji for her suggestions and help on the GIM analyses. Finally, we would like to thank the following scientists for sharing their atmospheric measurements that were used in simulating the data-gaps in the observation scenario used in TC1: Arlyn Andrews, Ken Davis, Danilo Dragoni, Marc Fischer, Mathias Goeckede, Ralph Keeling, Bev Law, Natasha Miles, Bill Munger, Matt Parker, Scott Richardson, Britt Stephens, Colm Sweeney, Steven Wofsy and Douglas Worthy.	Anderson JL, 2012, MON WEATHER REV, V140, P2359, DOI 10.1175/MWR-D-11-00013.1; Anderson JL, 2009, TELLUS A, V61, P72, DOI 10.1111/j.1600-0870.2008.00361.x; Anderson JL, 2001, MON WEATHER REV, V129, P2884, DOI 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2; Anderson JL, 1999, MON WEATHER REV, V127, P2741, DOI 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2; Anderson JL, 2007, TELLUS A, V59, P210, DOI 10.1111/j.1600-0870.2006.00216.x; Anderson JL, 2007, PHYSICA D, V230, P99, DOI 10.1016/j.physd.2006.02.011; Baker DF, 2010, ATMOS CHEM PHYS, V10, P4145, DOI 10.5194/acp-10-4145-2010; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Bergemann K, 2010, Q J ROY METEOR SOC, V136, P701, DOI 10.1002/qj.591; Bishop CH, 2011, MON WEATHER REV, V139, P1241, DOI 10.1175/2010MWR3403.1; Bishop CH, 2001, MON WEATHER REV, V129, P420, DOI 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2; Bruhwiler LMP, 2005, ATMOS CHEM PHYS, V5, P2691; Buchwitz M, 2007, ATMOS CHEM PHYS, V7, P4249; Buchwitz M, 2005, ATMOS CHEM PHYS, V5, P941; Burgers G, 1998, MON WEATHER REV, V126, P1719, DOI 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2; Chahine MT, 2006, B AM METEOROL SOC, V87, P911, DOI 10.1175/BAMS-87-7-911; Chevallier F, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD013887; Chevallier F, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007375; Chevallier F, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006390; Chevallier F, 2012, GLOBAL BIOGEOCHEM CY, V26, DOI 10.1029/2010GB003974; Chiles J.-P., 1999, GEOSTATISTICS MODELI, DOI [10.1002/9780470316993, DOI 10.1002/9780470316993]; Ciais P, 2010, CLIMATIC CHANGE, V103, P69, DOI 10.1007/s10584-010-9909-3; Crevoisier C, 2009, ATMOS CHEM PHYS, V9, P4797; Crisp D, 2008, J APPL REMOTE SENS, V2, DOI 10.1117/1.2898457; Dirren S, 2007, MON WEATHER REV, V135, P1455, DOI 10.1175/MWR3358.1; Ehrendorfer M, 2007, METEOROL Z, V16, P795, DOI 10.1127/0941-2948/2007/0256; Engelen RJ, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2002JD002195; Enting I. G., 2002, ATMSOPHERIC SPACE SC, DOI [10.1017/CBO9780511535741, DOI 10.1017/CB09780511535741]; Feng L, 2011, ATMOS CHEM PHYS, V11, P2789, DOI 10.5194/acp-11-2789-2011; Feng L, 2009, ATMOS CHEM PHYS, V9, P2619, DOI 10.5194/acp-9-2619-2009; Furrer R, 2007, J MULTIVARIATE ANAL, V98, P227, DOI 10.1016/j.jmva.2006.08.003; Gaspari G, 1999, Q J ROY METEOR SOC, V125, P723, DOI 10.1256/smsqj.55416; Geels C, 2007, ATMOS CHEM PHYS, V7, P3461; Gerbig C, 2008, ATMOS CHEM PHYS, V8, P591; Gneiting T, 2002, J MULTIVARIATE ANAL, V83, P493, DOI 10.1006/jmva.2001.2056; Gockede M, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012257; Gourdji SM, 2010, ATMOS CHEM PHYS, V10, P6151, DOI 10.5194/acp-10-6151-2010; Gourdji SM, 2012, BIOGEOSCIENCES, V9, P457, DOI 10.5194/bg-9-457-2012; Gourdji SM, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009733; Hamazaki T, 2004, P SOC PHOTO-OPT INS, V5543, P275, DOI 10.1117/12.560589; Hamill TM, 2005, MON WEATHER REV, V133, P3132, DOI 10.1175/MWR3020.1; Hamill TM, 2011, MON WEATHER REV, V139, P117, DOI 10.1175/2010MWR3246.1; Hamill TM, 2001, MON WEATHER REV, V129, P2776, DOI 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2; HORN RA, 1990, SIAM J MATRIX ANAL A, V11, P481, DOI 10.1137/0611034; Houtekamer PL, 2005, MON WEATHER REV, V133, P604, DOI 10.1175/MWR-2864.1; Houtekamer PL, 1998, MON WEATHER REV, V126, P796, DOI 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2; Houtekamer PL, 2009, MON WEATHER REV, V137, P2126, DOI 10.1175/2008MWR2737.1; Houtekamer PL, 2001, MON WEATHER REV, V129, P123, DOI 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2; Houtekamer PL, 2005, Q J ROY METEOR SOC, V131, P3269, DOI 10.1256/qj.05.135; Huntzinger DN, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015048; Jun M, 2011, MON WEATHER REV, V139, P3036, DOI 10.1175/2011MWR3577.1; Kaminski T, 2001, J GEOPHYS RES-ATMOS, V106, P4703, DOI 10.1029/2000JD900581; Kang JS, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014673; Karspeck AR, 2007, J CLIMATE, V20, P4638, DOI 10.1175/JCLI4245.1; Kepert JD, 2009, Q J ROY METEOR SOC, V135, P1157, DOI 10.1002/qj.443; Kepert JD, 2011, COMPUTAT GEOSCI, V15, P239, DOI 10.1007/s10596-010-9188-0; KITANIDIS PK, 1995, WATER RESOUR RES, V31, P2411, DOI 10.1029/95WR01945; Kulawik SS, 2010, ATMOS CHEM PHYS, V10, P5601, DOI 10.5194/acp-10-5601-2010; Lahoz W, 2010, DATA ASSIMILATION: MAKING SENSE OF OBSERVATIONS, P449, DOI 10.1007/978-3-540-74703-1_18; Lauvaux T, 2012, ATMOS CHEM PHYS, V12, P337, DOI 10.5194/acp-12-337-2012; Lawson WG, 2004, MON WEATHER REV, V132, P1966, DOI 10.1175/1520-0493(2004)132<1966:IOSADF>2.0.CO;2; Li H, 2009, Q J ROY METEOR SOC, V135, P523, DOI 10.1002/qj.371; Lin JC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003161; Liu J, 2008, NONLINEAR PROC GEOPH, V15, P645; Lokupitiya RS, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009679; Meng ZY, 2011, MON WEATHER REV, V139, P2025, DOI 10.1175/2011MWR3418.1; Michalak AM, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004422; Michalak AM, 2008, ATMOS CHEM PHYS, V8, P6789; Michalak AM, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005970; Miyazaki K, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015366; Miyoshi T, 2011, MON WEATHER REV, V139, P1519, DOI 10.1175/2010MWR3570.1; National Research Council, 2007, EARTH SCI APPL SPAC; Nerger L, 2005, TELLUS A, V57, P715, DOI 10.1111/j.1600-0870.2005.00141.x; O'Kane TJ, 2008, ENTROPY, V10, P684, DOI 10.3390/e10040684; Oke PR, 2007, OCEAN DYNAM, V57, P32, DOI 10.1007/s10236-006-0088-8; Olsen SC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003968; Olson DM, 2001, BIOSCIENCE, V51, P933, DOI 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2; Ott E, 2004, TELLUS A, V56, P415, DOI 10.1111/j.1600-0870.2004.00076.x; Pena M, 2010, MON WEATHER REV, V138, P1502, DOI 10.1175/2009MWR2854.1; Peters W, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006157; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Peters W, 2010, GLOBAL CHANGE BIOL, V16, P1317, DOI 10.1111/j.1365-2486.2009.02078.x; Pham DT, 2001, MON WEATHER REV, V129, P1194, DOI 10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2; Randerson JT, 1997, GLOBAL BIOGEOCHEM CY, V11, P535, DOI 10.1029/97GB02268; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Rayner PJ, 2001, GEOPHYS RES LETT, V28, P175, DOI 10.1029/2000GL011912; Rayner PJ, 2010, CURR OPIN ENV SUST, V2, P289, DOI 10.1016/j.cosust.2010.05.005; Rodenbeck C., 2005, 6 M PLANCK I BIOG JE; Rood R. B., 2005, SPARC NEWSLETTER, V25, P31; Sacher W, 2008, MON WEATHER REV, V136, P3035, DOI 10.1175/2007MWR2323.1; Schimel D, 2007, P NATL ACAD SCI USA, V104, P18353, DOI 10.1073/pnas.0709331104; Scholes RJ, 2009, TRENDS ECOL EVOL, V24, P427, DOI 10.1016/j.tree.2009.03.006; Schutgens NAJ, 2010, ATMOS CHEM PHYS, V10, P6583, DOI 10.5194/acp-10-6583-2010; SKAMAROCK WC, 2005, 468STR NCAR; Swinbank R, 2010, DATA ASSIMILATION: MAKING SENSE OF OBSERVATIONS, P381, DOI 10.1007/978-3-540-74703-1_15; Szunyogh I, 2005, TELLUS A, V57, P528, DOI 10.1111/j.1600-0870.2005.00136.x; Tippett MK, 2003, MON WEATHER REV, V131, P1485, DOI 10.1175/1520-0493(2003)131<1485:ESFR>2.0.CO;2; Torn RD, 2010, MON WEATHER REV, V138, P4375, DOI 10.1175/2010MWR3361.1; Uzunoglu B, 2007, Q J ROY METEOR SOC, V133, P1281, DOI 10.1002/qj.96; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; Whitaker JS, 2002, MON WEATHER REV, V130, P1913, DOI 10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2; Zhang FQ, 2004, MON WEATHER REV, V132, P1238, DOI 10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2; Zupanski D, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008371	103	0	0	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	NOV 28	2012	117								D22306	10.1029/2012JD018176		17	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	047MH	WOS:000311844500003	
J	Goosse, H; Braida, M; Crosta, X; Mairesse, A; Masson-Delmotte, V; Mathiot, P; Neukom, R; Oerter, H; Philippon, G; Renssen, H; Stenni, B; van Ommen, T; Verleyen, E				Goosse, H.; Braida, M.; Crosta, X.; Mairesse, A.; Masson-Delmotte, V.; Mathiot, P.; Neukom, R.; Oerter, H.; Philippon, G.; Renssen, H.; Stenni, B.; van Ommen, T.; Verleyen, E.			Antarctic temperature changes during the last millennium: evaluation of simulations and reconstructions	QUATERNARY SCIENCE REVIEWS			English	Article						Last millennium; Antarctica; Model-data comparison; Data assimilation; Reconstruction	MEDIEVAL WARM PERIOD; HOLOCENE CLIMATE EVOLUTION; TREE-RING RECORDS; PAST 2 MILLENNIA; SOUTHERN-HEMISPHERE; CARBON-CYCLE; WEST ANTARCTICA; ICE-CORE; PRECIPITATION VARIABILITY; NORTHERN-HEMISPHERE	Temperature changes in Antarctica over the last millennium are investigated using proxy records, a set of simulations driven by natural and anthropogenic forcings and one simulation with data assimilation. Over Antarctica, a long term cooling trend in annual mean is simulated during the period 1000-1850. The main contributor to this cooling trend is the volcanic forcing, astronomical forcing playing a dominant role at seasonal timescale. Since 1850, all the models produce an Antarctic warming in response to the increase in greenhouse gas concentrations. We present a composite of Antarctic temperature, calculated by averaging seven temperature records derived from isotope measurements in ice cores. This simple approach is supported by the coherency displayed between model results at these data grid points and Antarctic mean temperature. The composite shows a weak multi-centennial cooling trend during the pre-industrial period and a warming after 1850 that is broadly consistent with model results. In both data and simulations, large regional variations are superimposed on this common signal, at decadal to centennial timescales. The model results appear spatially more consistent than ice core records. We conclude that more records are needed to resolve the complex spatial distribution of Antarctic temperature variations during the last millennium. (c) 2012 Elsevier Ltd. All rights reserved.	[Goosse, H.; Mairesse, A.; Mathiot, P.; Philippon, G.] Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, B-1348 Louvain, Belgium; [Braida, M.; Stenni, B.] Univ Trieste, Dipartimento Matemat & Geosci, Trieste, Italy; [Crosta, X.] Univ Bordeaux 1, UMR CNRS EPOC, F-33405 Talence, France; [Masson-Delmotte, V.] CEA, CNRS, UVSQ, IPSL,Lab Sci Climat & Environm, F-91198 Gif Sur Yvette, France; [Neukom, R.] Univ Bern, Oeschger Ctr Climate Change Res, CH-3012 Bern, Switzerland; [Neukom, R.] Univ Melbourne, Sch Earth Sci, Melbourne, Vic 3010, Australia; [Oerter, H.] Helmholtz Gemeinschaft, Alfred Wegener Inst Polar & Meeresforsch, Bremerhaven, Germany; [Renssen, H.] Vrije Univ Amsterdam, Dept Earth Sci, Amsterdam, Netherlands; [van Ommen, T.] Australian Antarctic Div, Hobart, Tas, Australia; [van Ommen, T.] Univ Tasmania, Antarctic Climate & Ecosyst CRC, Hobart, Tas, Australia; [Verleyen, E.] Univ Ghent, B-9000 Ghent, Belgium	Goosse, H (reprint author), Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, Pl Louis Pasteur 3, B-1348 Louvain, Belgium.	hugues.goosse@uclouvain.be	Masson-Delmotte, Valerie/G-1995-2011	Masson-Delmotte, Valerie/0000-0001-8296-381X	F.R.S.- FNRS; Belgian Federal Science Policy Office (Research Program on Science for a Sustainable Development); French ANR VANISH; European Union's Seventh Framework programme (FP7) [243908]; Fond de la Recherche Scientifique de Belgique (FRS-FNRS)	We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Online Supplement Table of this paper) for producing and making available their model output, in particular Johann Jungclaus for the MPI model results. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. H.G. is Senior Research Associate with the Fonds National de la Recherche Scientifique (F.R.S.- FNRS-Belgium). This work has been performed in the framework of the ESF HOLOCLIP project (a joint research project of the European Science Foundation Polar-CLIMATE program, which is funded by national contributions from Italy, France, Germany, Spain, Netherlands, Belgium and the United Kingdom) and is also supported by the F.R.S.- FNRS and by the Belgian Federal Science Policy Office (Research Program on Science for a Sustainable Development) and French ANR VANISH. The research leading to these results has received funding from the European Union's Seventh Framework programme (FP7/2007-2013) under grant agreement no 243908, "Past4Future. Climate change - Learning from the past climate". Computational resources have been provided by the supercomputing facilities of the Universite catholique de Louvain (CISM/UCL) and the Consortium des Equipements de Calcul Intensif en Federation Wallonie Bruxelles (CECI) funded by the Fond de la Recherche Scientifique de Belgique (FRS-FNRS). This is HOLOCLIP contribution 13 and Past4-future contribution 31.	Anderson RF, 2009, SCIENCE, V323, P1443, DOI 10.1126/science.1167441; Annan JD, 2012, CLIM PAST, V8, P1141, DOI 10.5194/cp-8-1141-2012; Arblaster JM, 2006, J CLIMATE, V19, P2896, DOI 10.1175/JCLI3774.1; Arzel O, 2006, OCEAN MODEL, V12, P401, DOI 10.1016/j.ocemod.2005.08.002; Barrett BE, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036369; Bertler NAN, 2011, EARTH PLANET SC LETT, V308, P41, DOI 10.1016/j.epsl.2011.05.021; Bitz C.M., 2012, J CLIMATE, V25, P3053; Boer GJ, 2011, CLIM DYNAM, V37, P2253, DOI 10.1007/s00382-011-1112-3; Brook EJ, 2005, QUATERNARY SCI REV, V24, P1333, DOI 10.1016/j.quascirev.2005.02.002; Brovkin V, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001662; Chapman WL, 2007, J CLIMATE, V20, P4096, DOI 10.1175/JCLI4236.1; Connolley WM, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL031648; Cook E.R., 2002, GEOPHYS RES LETT, V29; Cook ER, 2000, CLIM DYNAM, V16, P79, DOI 10.1007/s003820050006; Cook ER, 2006, J QUATERNARY SCI, V21, P689, DOI 10.1002/jqs.1071; Crespin E., HOLOCENE IN PRESS; Crosta X, 2007, GEOCHEM GEOPHY GEOSY, V8, DOI 10.1029/2007GC001718; Denis D, 2010, QUATERNARY SCI REV, V29, P3709, DOI 10.1016/j.quascirev.2010.08.007; Ding Q., 2012, J CLIMATE IN PRESS; Ding QH, 2011, NAT GEOSCI, V4, P398, DOI [10.1038/NGEO1129, 10.1038/ngeo1129]; Dubinkina S, 2011, INT J BIFURCAT CHAOS, V21, P3611, DOI 10.1142/S0218127411030763; Barbante C, 2006, NATURE, V444, P195, DOI 10.1038/nature05301; Esper J, 2009, CLIMATIC CHANGE, V94, P267, DOI [10.1007/s10584-008-9492-z, 10.1007/s10584-008-9492-Z]; Fegyveresi JM, 2011, J GLACIOL, V57, P629; Frank DC, 2010, NATURE, V463, P527, DOI 10.1038/nature08769; Gergis J., J CLIMATE UNPUB; Giraudeau, 2011, B AM METEOROLOGICAL, V92, P1487; Gonzalez-Rouco F, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL018264; Goodwin ID, 2004, CLIM DYNAM, V22, P783, DOI 10.1007/s00382-004-0403-3; Goosse H, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2003GL019140; Goosse H, 2009, CLIM DYNAM, V33, P999, DOI 10.1007/s00382-008-0500-9; Goosse H, 1999, J GEOPHYS RES-OCEANS, V104, P23337, DOI 10.1029/1999JC900215; Goosse H, 2004, CLIM DYNAM, V23, P153, DOI 10.1007/s00382-004-0424-y; Goosse H, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL022368; Goosse H, 2012, GLOBAL PLANET CHANGE, V84-85, P35, DOI 10.1016/j.gloplacha.2011.07.002; Goosse H, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012737; Goosse H., 2012, CLIMATE DYNAMICS; Goosse H, 2010, GEOSCI MODEL DEV, V3, P603, DOI 10.5194/gmd-3-603-2010; Hegerl GC, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P663; HUGHES MK, 1994, CLIMATIC CHANGE, V26, P109, DOI 10.1007/BF01092410; Jungclaus JH, 2010, CLIM PAST, V6, P723, DOI 10.5194/cp-6-723-2010; Kaufman DS, 2009, SCIENCE, V325, P1236, DOI 10.1126/science.1173983; Khim BK, 2002, QUATERNARY RES, V58, P234, DOI 10.1006/qres.2002.2371; Knudson KP, 2011, QUATERNARY SCI REV, V30, P3124, DOI 10.1016/j.quascirev.2011.07.017; Kreutz KJ, 2000, J GEOPHYS RES-ATMOS, V105, P4047, DOI 10.1029/1999JD901069; Lamy F, 2010, NAT GEOSCI, V3, P695, DOI [10.1038/ngeo959, 10.1038/NGEO959]; Lara A., 2000, DENDROCHRONOLOGIA AM, P193; Lefebvre W, 2008, CLIM DYNAM, V30, P59, DOI 10.1007/s00382-007-0273-6; Le Quesne C, 2006, J CLIMATE, V19, P5731, DOI 10.1175/JCLI3935.1; Ljungqvist FC, 2010, GEOGR ANN A, V92A, P339, DOI 10.1111/j.1468-0459.2010.00399.x; Mann ME, 2005, J CLIMATE, V18, P447, DOI 10.1175/JCLI-3276.1; Mann ME, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017814; Mann ME, 2008, P NATL ACAD SCI USA, V105, P13252, DOI 10.1073/pnas.0805721105; Mann ME, 2009, SCIENCE, V326, P1256, DOI 10.1126/science.1177303; Marsland SJ, 2003, OCEAN MODEL, V5, P91, DOI 10.1016/S1463-5003(02)00015-X; Masson-Delmotte V, 2011, CLIM PAST, V7, P397, DOI 10.5194/cp-7-397-2011; Masson V, 2000, QUATERNARY RES, V54, P348, DOI 10.1006/qres.2000.2172; Masson-Delmotte V, 2008, J CLIMATE, V21, P3359, DOI 10.1175/2007JCLI2139.1; Masson-Delmotte V, 2004, HOLOCENE, V14, P145, DOI 10.1191/0959683604hl697ft; Massonnet F., 2012, CRYOSPHERE DISCUSSIO, V6, P2931; Mayewski PA, 2004, ANN GLACIOL, V39, P127, DOI 10.3189/172756404781814249; McGregor S, 2010, CLIM PAST, V6, P1; Miller GH, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2011GL050168; Mohtadi M, 2007, QUATERNARY SCI REV, V26, P1055, DOI 10.1016/j.quascirev.2006.12.008; Monaghan AJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009094; Monaghan AJ, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL032630; Moreno PI, 2009, QUATERNARY SCI REV, V28, P25, DOI 10.1016/j.quascirev.2008.10.009; Moy CM, 2008, QUATERNARY SCI REV, V27, P1335, DOI 10.1016/j.quascirev.2008.03.006; Mulvaney R, 2002, ANN GLACIOL, V35, P45, DOI 10.3189/172756402781817176; Muto A, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL048086; Neukom R, 2012, HOLOCENE, V22, P501, DOI 10.1177/0959683611427335; Neukom R, 2011, CLIM DYNAM, V37, P35, DOI 10.1007/s00382-010-0793-3; Nielsen SHH, 2004, GEOLOGY, V32, P317, DOI 10.1130/G20334.1; O'Donnell R, 2011, J CLIMATE, V24, P2099, DOI 10.1175/2010JCLI3656.1; Oerter H, 2000, ANN GLACIOL, V30, P27, DOI 10.3189/172756400781820705; Opsteegh JD, 1998, TELLUS A, V50, P348, DOI 10.1034/j.1600-0870.1998.t01-1-00007.x; Orsi AJ, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051260; Raddatz TJ, 2007, CLIM DYNAM, V29, P565, DOI 10.1007/s00382-007-0247-8; Renssen H, 2005, HOLOCENE, V15, P951, DOI 10.1191/0959683605hl869ra; Schmidt GA, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007781; Schmidt GA, 2011, GEOSCI MODEL DEV, V4, P33, DOI 10.5194/gmd-4-33-2011; Schneider D., J CLIMATE IN PRESS; Schneider DP, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027057; Schneider DP, 2012, CLIM DYNAM, V38, P323, DOI 10.1007/s00382-010-0985-x; Sepulveda J, 2009, QUATERNARY RES, V72, P400, DOI 10.1016/j.yqres.2009.06.010; Servonnat J, 2010, CLIM PAST, V6, P445, DOI 10.5194/cp-6-445-2010; Shevenell AE, 2011, NATURE, V470, P250, DOI 10.1038/nature09751; Siddall M, 2012, EARTH PLANET SC LETT, V315, P12, DOI 10.1016/j.epsl.2011.04.032; Sime LC, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010395; Sime LC, 2009, NATURE, V462, P342, DOI 10.1038/nature08564; Sinclair K., J CLIMATE IN PRESS; Stager JC, 2012, CLIM PAST, V8, P877, DOI 10.5194/cp-8-877-2012; Steig EJ, 2009, NATURE, V457, P459, DOI 10.1038/nature07669; Steig EJ, 1998, SCIENCE, V282, P92, DOI 10.1126/science.282.5386.92; Stenni B, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2000JD000317; Stenni B., 2010, NATURE GEOSCIENCE, V4, P46, DOI DOI 10.1038/NGE01026; Sterken M, 2012, QUATERNARY SCI REV, V31, P93, DOI 10.1016/j.quascirev.2011.10.017; Swingedouw D, 2011, CLIM DYNAM, V36, P1349, DOI 10.1007/s00382-010-0803-5; Thomas ER, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040104; Thompson DWJ, 2011, NAT GEOSCI, V4, P741, DOI [10.1038/ngeo1296, 10.1038/NGEO1296]; Thompson DWJ, 2002, SCIENCE, V296, P895, DOI 10.1126/science.1069270; Turner J., 2009, ACCE ANTARCTIC CLIMA, P555; Turner J, 2005, INT J CLIMATOL, V25, P279, DOI 10.1002/joc.1130; van Leeuwen PJ, 2009, MON WEATHER REV, V137, P4089, DOI 10.1175/2009MWR2835.1; Vaughan DG, 2003, CLIMATIC CHANGE, V60, P243, DOI 10.1023/A:1026021217991; Verleyen E, 2011, EARTH-SCI REV, V104, P199, DOI 10.1016/j.earscirev.2010.10.006; VILLALBA R, 1990, QUATERNARY RES, V34, P346, DOI 10.1016/0033-5894(90)90046-N; von Gunten L, 2009, HOLOCENE, V19, P873, DOI 10.1177/0959683609336573; Widmann M, 2010, CLIM PAST, V6, P627, DOI 10.5194/cp-6-627-2010; Wilmes SB, 2012, CLIM PAST, V8, P373, DOI 10.5194/cp-8-373-2012; Xiong L, 2000, CLIMATIC CHANGE, V45, P339, DOI 10.1023/A:1005525903714; Zagorodnov V., 2011, CRYOSPHERE DISCUSSIO, V5, P3053; Zhong Y, 2011, CLIM DYNAM, V37, P2373, DOI 10.1007/s00382-010-0967-z	113	1	1	PERGAMON-ELSEVIER SCIENCE LTD	OXFORD	THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND	0277-3791		QUATERNARY SCI REV	Quat. Sci. Rev.	NOV 8	2012	55						75	90		10.1016/j.quascirev.2012.09.003		16	Geography, Physical; Geosciences, Multidisciplinary	Physical Geography; Geology	038PH	WOS:000311186100006	
J	Sun, GD; Mu, M				Sun, Guodong; Mu, Mu			Responses of soil carbon variation to climate variability in China using the LPJ model	THEORETICAL AND APPLIED CLIMATOLOGY			English	Article							FINITE-AMPLITUDE PERTURBATIONS; NONLINEAR OPTIMAL PERTURBATION; GLOBAL VEGETATION MODEL; DATA ASSIMILATION; SENSITIVITY; ECOSYSTEM; STORAGE; FUTURE; OPTIMIZATION; GRASSLAND	In this study, we explored the maximal response of soil carbon in a part of China to climate change, including variations in climatology and climate variability, under the condition of global warming. A conditional nonlinear optimal perturbation (CNOP) approach was employed to discuss the above issue using the Lund-Potsdam-Jena (LPJ) model. The variation in the soil carbon was compared with those caused by a linear temperature or precipitation perturbation. The key difference between the CNOP-type and the linear perturbations depended on whether the perturbations brought the variation in the temperature or the precipitation variability in comparison with the reference temperature or the precipitation variability. The model results demonstrated that the variations in the soil carbon resulted from the CNOP-type and linear temperature perturbations in south of the study region, which was corresponding to part of South China, had different variations. We examined three components of the soil carbon in the LPJ model: fast-decomposing soil carbon, slow-decomposing soil carbon, and litter below the ground. The variations of these components derived by the two types of temperature perturbations were different in the chosen region. The reduction in the litter below the ground may be the main cause of decreased soil carbon in arid and semi-arid regions as a result of the two types of temperature perturbations. The different impacts of the two types of temperature perturbations in the south of the study region may be mainly caused by the variations in the fast-decomposing soil carbon. The variations in the soil carbon caused by the two types of precipitation perturbations were similar. In the arid and semi-arid regions, the soil carbon increased due to the two types of precipitation perturbations. This research implies that the variation in temperature variability plays a crucial role in the variations of the soil carbon and its components in the study region.	[Sun, Guodong] Chinese Acad Sci, Inst Atmospher Phys, LASG, Beijing 100029, Peoples R China; [Sun, Guodong; Mu, Mu] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China; [Mu, Mu] Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Wave, Qingdao 266071, Peoples R China	Sun, GD (reprint author), Chinese Acad Sci, Inst Atmospher Phys, LASG, Beijing 100029, Peoples R China.	sungd@mail.iap.ac.cn			National Natural Science Foundation of China [40905050, 40830955]; state Key Development Program for Basic Research [2012CB955202]	The authors thank Prof. Weidong Guo for the valuable discussion and recommendation. Funding was provided by grants from National Natural Science Foundation of China (No. 40905050, 40830955), and by grants from the state Key Development Program for Basic Research (Grant No. 2012CB955202).	Cao MK, 2003, ACTA BOT SIN, V45, P552; Cao MK, 2003, GLOBAL CHANGE BIOL, V9, P536, DOI 10.1046/j.1365-2486.2003.00617.x; Clein JS, 2000, GLOBAL CHANGE BIOL, V6, P127, DOI 10.1046/j.1365-2486.2000.06009.x; Conant RT, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001661; Davidson EA, 2006, NATURE, V440, P165, DOI 10.1038/nature04514; DUAN QY, 1992, WATER RESOUR RES, V28, P1015, DOI 10.1029/91WR02985; Eglin T, 2010, TELLUS B, V62, P700, DOI 10.1111/j.1600-0889.2010.00499.x; Eswaran H, 2000, GLOBAL CLIMATE CHANGE AND PEDOGENIC CARBONATES, P15; FOLEY JA, 1995, TELLUS B, V47, P310, DOI 10.1034/j.1600-0889.47.issue3.3.x; Gerber S, 2004, GLOBAL CHANGE BIOL, V10, P1223, DOI 10.1111/j.1529-8817.2003.00807.x; Giesecke T, 2010, J BIOGEOGR, V37, P1394, DOI 10.1111/j.1365-2699.2010.02296.x; Johnson DW, 2007, SOIL SCI SOC AM J, V71, P1639, DOI 10.2136/sssaj2006.0365; Kicklighter DW, 1999, TELLUS B, V51, P343, DOI 10.1034/j.1600-0889.1999.00017.x; KRUGER J, 1993, J PHYS OCEANOGR, V23, P679, DOI 10.1175/1520-0485(1993)023<0679:SAATFD>2.0.CO;2; Lal R, 2004, SCIENCE, V304, P1623, DOI 10.1126/science.1097396; Lettens S, 2005, GLOBAL CHANGE BIOL, V11, P2128, DOI 10.1111/j.1365-2486.2005.001074.x; Li KR, 2004, SCI CHINA SER D, V47, P49, DOI 10.1360/02yd0029; Li Z, 2001, WATER AIR SOIL POLL, V129, P45, DOI 10.1023/A:1010356828990; LLOYD J, 1994, FUNCT ECOL, V8, P315, DOI 10.2307/2389824; Matthews HD, 2005, J CLIMATE, V18, P1609, DOI 10.1175/JCLI3359.1; Mitchell TD, 2005, INT J CLIMATOL, V25, P693, DOI 10.1002/joc.1181; Mu M, 2007, NONLINEAR PROC GEOPH, V14, P409; Mu M, 2004, J PHYS OCEANOGR, V34, P2305, DOI 10.1175/1520-0485(2004)034<2305:TSASOT>2.0.CO;2; Mu M, 2003, NONLINEAR PROC GEOPH, V10, P493; Mu M, 2010, NONLINEAR PROC GEOPH, V17, P211; Ni J, 2006, ECOL MODEL, V191, P469, DOI 10.1016/j.ecolmodel.2005.05.019; Ni J, 2000, GLOBAL ECOL BIOGEOGR, V9, P463, DOI 10.1046/j.1365-2699.2000.00206.x; Notaro M, 2008, CLIM DYNAM, V30, P845, DOI 10.1007/s00382-007-0329-7; Pan Z, 2010, ECOL MODEL, V221, P876, DOI 10.1016/j.ecolmodel.2009.11.013; Piao SL, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002274; Post J, 2008, ENVIRON MODELL SOFTW, V23, P125, DOI 10.1016/j.envsoft.2007.05.010; Prentice IC, 2001, 3 IPCC; SCHLESINGER WH, 1990, NATURE, V348, P232, DOI 10.1038/348232a0; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Storn R, 1997, J GLOBAL OPTIM, V11, P341, DOI 10.1023/A:1008202821328; Thomey ML, 2011, GLOBAL CHANGE BIOL, V17, P1505, DOI 10.1111/j.1365-2486.2010.02363.x; Trumbore SE, 2008, SCIENCE, V321, P1455, DOI 10.1126/science.1160232; Wan YF, 2011, AGR ECOSYST ENVIRON, V141, P23, DOI 10.1016/j.agee.2011.02.004; Wang SP, 2011, AGR ECOSYST ENVIRON, V142, P329, DOI 10.1016/j.agee.2011.06.002; Wang SQ, 2003, TELLUS B, V55, P416, DOI 10.1034/j.1600-0889.2003.00039.x; [闻新宇 WEN XinYu], 2006, [大气科学, Chinese Journal of Atmospheric Sciences], V30, P894; Wu HB, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI [10.1029/2001GB001844, DOI 10.1029/2001GB001844]; Yang YH, 2010, GLOBAL CHANGE BIOL, V16, P3036, DOI 10.1111/j.1365-2486.2009.02123.x; Yurova AY, 2010, GLOBAL CHANGE BIOL, V16, P823, DOI 10.1111/j.1365-2486.2009.01992.x; Zhang S, 2000, MON WEATHER REV, V128, P4031, DOI 10.1175/1520-0493(2000)129<4031:UODANO>2.0.CO;2; Zobler L, 1986, 87802 NASA, V87802, P32	46	0	0	SPRINGER WIEN	WIEN	SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA	0177-798X		THEOR APPL CLIMATOL	Theor. Appl. Climatol.	OCT	2012	110	1-2					143	153		10.1007/s00704-012-0619-9		11	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	017LD	WOS:000309590500012	
J	Ziehn, T; Scholze, M; Knorr, W				Ziehn, T.; Scholze, M.; Knorr, W.			On the capability of Monte Carlo and adjoint inversion techniques to derive posterior parameter uncertainties in terrestrial ecosystem models	GLOBAL BIOGEOCHEMICAL CYCLES			English	Article							DATA ASSIMILATION; BIOSPHERE	Terrestrial ecosystem models (TEMs) contain the coupling of many biogeochemical processes with a large number of parameters involved. In many cases those parameters are highly uncertain. In order to reduce those uncertainties, parameter estimation methods can be applied, which allow the model to be constrained against observations. We compare the performance and results of two such parameter estimation techniques - the Metropolis algorithm (MA) which is a Markov Chain Monte Carlo (MCMC) method and the adjoint approach as it is used in the Carbon Cycle Data Assimilation System (CCDAS). Both techniques are applied here to derive the posterior probability density function (PDF) for 19 parameters of the Biosphere Energy Transfer and Hydrology (BETHY) scheme. We also use the MA to sample the posterior parameter distribution from the adjoint inversion. This allows us to assess if the commonly made assumption in variational data assimilation, that everything is normally distributed, holds. The comparison of the posterior parameter PDF derived by both methods shows that in most cases an approximation of the PDF by a normal distribution as used by the adjoint approach is a valid assumption. The results also indicate that the global minimum has been identified by both methods for the given set up. However, the adjoint approach outperforms the MA by several orders of magnitude in terms of computational time. Both methods show good agreement in the PDF of estimated net carbon fluxes for the decades of the 1980s and 1990s.	[Ziehn, T.; Scholze, M.; Knorr, W.] Univ Bristol, Sch Earth Sci, Bristol, Avon, England; [Scholze, M.] Univ Hamburg, Hamburg, Germany	Ziehn, T (reprint author), CSIRO Marine & Atmospher Res, Ctr Australian Weather & Climate Res, PMB 1, Aspendale, Vic 3195, Australia.	tilo.ziehn@csiro.au	Ziehn, Tilo/A-3094-2012		NERC National Centre for Earth Observation	This work was supported by the NERC National Centre for Earth Observation.	Best Mi., 2011, GEOSCI MODEL DEV DIS, V4, P595, DOI DOI 10.5194/GMDD-4-595-2011; Clark DB, 2011, GEOSCI MODEL DEV DIS, V4, P641, DOI DOI 10.5194/GMDD-4-641-2011; FLETCHER R, 1963, COMPUT J, V6, P163; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Gelman A., 2003, BAYESIAN DATA ANAL; Giering R, 1998, ACM T MATH SOFTWARE, V24, P437, DOI 10.1145/293686.293695; GLOBALVIEW-CO<INF>2</INF>, 2008, GLOBALVIEW CO2 COOP; Heimann M., 1995, 10 DTSCH KLIM; Kaminski T., 2010, LIV PLAN S EUR SPAC; Kaminski T, 2003, LECT NOTES COMPUT SC, V2668, P95; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P207, DOI 10.1029/1998GB001059; Knorr W, 2001, ADV GLOB CHANGE RES, V7, P273; Knorr W, 2000, GLOBAL ECOL BIOGEOGR, V9, P225, DOI 10.1046/j.1365-2699.2000.00159.x; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; MOSEGAARD K, 1995, J GEOPHYS RES-SOL EA, V100, P12431, DOI 10.1029/94JB03097; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Sambridge M, 2002, REV GEOPHYS, V40, DOI 10.1029/2000RG000089; Scholze M., 2003, THESIS M PLANCK I ME; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; TALAGRAND O, 1987, Q J ROY METEOR SOC, V113, P1311, DOI 10.1256/smsqj.47811; Tarantola A., 1987, INVERSE PROBLEM THEO; Tarantola A., 2005, INVERSE PROBLEM THEO; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Vetterling W. T., 1996, NUMERICAL RECIPES FO, P90; von Storch H., 1999, STAT ANAL CLIMATE RE; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Ziehn T, 2011, GLOBAL BIOGEOCHEM CY, V25, DOI 10.1029/2010GB003886; Ziehn T, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047182	28	0	0	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0886-6236		GLOBAL BIOGEOCHEM CY	Glob. Biogeochem. Cycle	SEP 14	2012	26								GB3025	10.1029/2011GB004185		13	Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences	Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences	007MX	WOS:000308893500001	
J	Miguez-Macho, G; Fan, Y				Miguez-Macho, Gonzalo; Fan, Ying			The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							DATA ASSIMILATION SYSTEM; SEQUENCE BENEATH FOREST; LAND-SURFACE MODEL; FLOODPLAIN LAKE; METHANE EMISSIONS; STORM RUNOFF; RIVER-BASIN; HEADWATER CATCHMENTS; CONTINENTAL-CRUST; GLOBAL EVALUATION	Observational studies across the Amazon report a common occurrence of shallow water table in lowland valleys and groundwater-surface water exchange from small headwater catchments to large floodplains. In this study, we assess groundwater's role in the Amazon surface water dynamics using a continental-scale coupled groundwater-surface water model (LEAF-Hydro-Flood) forced by ERA-Interim reanalysis, at 2 km and 4 min resolution over 11 years (2000-2010). The simulation is validated with observed streamflow, water table depth and flooding extent. A parallel simulation without groundwater is conducted to isolate its effect. Our findings support the following hypotheses. First, in the headwater catchments, groundwater dominates streamflow; the observed variations in its dominance across the Amazon can be explained by the varying water table depth. Second, over large floodplains, there are two-way exchanges between floodwater and groundwater as infiltration in the wet season and seepage in the dry season, and the direction and magnitude are controlled by the water table depth. Third, the Amazon harbors large areas of wetlands that are rarely under floodwater and difficult to observe by remote sensing, but are maintained by a persistently shallow water table. Fourth, due to its delayed and muted response to rainfall, groundwater seepage persists in the dry season, buffering surface waters through seasonal droughts. Our simulations shed new lights on the spatial-temporal structures of the hidden subsurface hydrologic pathways across the Amazon and suggest possible mechanisms whereby groundwater actively participates in the Amazon water-carbon cycle such as CO2 outgassing from groundwater seeps and CH4 emission from groundwater-supported wetlands.	[Fan, Ying] Rutgers State Univ, Dept Earth & Planetary Sci, New Brunswick, NJ 08854 USA; [Miguez-Macho, Gonzalo] Univ Santiago de Compostela, Fac Phys 15782, Nonlinear Phys Grp, Galicia, Spain	Fan, Y (reprint author), Rutgers State Univ, Dept Earth & Planetary Sci, New Brunswick, NJ 08854 USA.	yingfan@rci.rutgers.edu			Ministerio de Educacion y Ciencia de Espana (Spanish Ministry of Education and Science) [CGL2006-13828, NSF-AGS-1045110, EPA-STAR-RD834190]	Financial support comes from Ministerio de Educacion y Ciencia de Espana (Spanish Ministry of Education and Science) CGL2006-13828, NSF-AGS-1045110, and EPA-STAR-RD834190. Computational support is provided by CESGA (Centro de Supercomputacion de Galicia) Supercomputer Center at the Universidade de Santiago de Compostela, Galicia, Spain. We thank Dai Yamazaki for helpful discussions on solving the diffusive wave equation at large scales, and John Melack for directing us to the LBA flooding data. Finally we thank the two anonymous referees for their insightful and in-depth reviewers and the many constructive comments.	Aalto R, 2003, NATURE, V425, P493, DOI 10.1038/nature02002; Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2; AHNERT F, 1970, AM J SCI, V268, P243; Alkama R, 2010, J HYDROMETEOROL, V11, P583, DOI 10.1175/2010JHM1211.1; Alsdorf D, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029447; Anyah RO, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009087; Balsamo G, 2009, J HYDROMETEOROL, V10, P623, DOI 10.1175/2008JHM1068.1; BARTLETT KB, 1988, J GEOPHYS RES-ATMOS, V93, P1571, DOI 10.1029/JD093iD02p01571; Bates PD, 2010, J HYDROL, V387, P33, DOI 10.1016/j.jhydrol.2010.03.027; Bauer P, 2006, WATER RESOUR RES, V42, DOI 10.1029/2005WR004234; Beighley RE, 2009, HYDROL PROCESS, V23, P1221, DOI 10.1002/hyp.7252; Belger L, 2011, BIOGEOCHEMISTRY, V105, P171, DOI 10.1007/s10533-010-9536-0; Bergamaschi P, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007268; BETSON RP, 1969, WATER RESOUR RES, V5, P574, DOI 10.1029/WR005i003p00574; Betts AK, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010761; Bierkens MFP, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028396; BONGERS F, 1985, VEGETATIO, V63, P13, DOI 10.1007/BF00032183; Bonnet MP, 2008, J HYDROL, V349, P18, DOI 10.1016/j.jhydrol.2007.10.055; Borma LS, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2007JG000641; Bourrel L, 2009, HYDROL PROCESS, V23, P3161, DOI 10.1002/hyp.7384; Carabajal CC, 2006, PHOTOGRAMM ENG REM S, V72, P287; do Carmo JB, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL025436; Chapelon N, 2002, CLIM DYNAM, V19, P141, DOI 10.1007/s00382-001-0213-9; Cherry J.A., 1979, GROUNDWATER; Choi HI, 2010, J HYDROMETEOROL, V11, P797, DOI 10.1175/2010JHM1221.1; CLAPP RB, 1978, WATER RESOUR RES, V14, P601, DOI 10.1029/WR014i004p00601; Clapperton CM, 1993, QUATERNARY GEOLOGY G; Coe M., 2002, J GEOPHYS RES, V107, P1, DOI [10.1029/2001JD000740, DOI 10.1029/2001JD000740]; Coe MT, 2008, HYDROL PROCESS, V22, P2542, DOI 10.1002/hyp.6850; Coomes DA, 1996, VEGETATIO, V122, P167, DOI 10.1007/BF00044699; Costa MH, 1997, J GEOPHYS RES-ATMOS, V102, P23973, DOI 10.1029/97JD01865; Cuartas L. A., 2008, THESIS I NAC PESQUI; Cullmann J, 2006, J HYDROL, V328, P297, DOI 10.1016/j.jhydrol.2005.12.027; Davidson EA, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001202; Decharme B, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009376; Decharme B, 2010, J HYDROMETEOROL, V11, P601, DOI 10.1175/2010JHM1212.1; Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828; DEVOL AH, 1990, J GEOPHYS RES-ATMOS, V95, P16417, DOI 10.1029/JD095iD10p16417; DEVRIES JJ, 1995, J HYDROL, V170, P15, DOI 10.1016/0022-1694(95)02684-H; DEVRIES JJ, 1994, J HYDROL, V155, P39, DOI 10.1016/0022-1694(94)90157-0; Do Nascimento NR, 2008, EUR J SOIL SCI, V59, P911, DOI 10.1111/j.1365-2389.2008.01049.x; DUNNE T, 1970, WATER RESOUR RES, V6, P1296, DOI 10.1029/WR006i005p01296; Dunne T, 1998, GEOL SOC AM BULL, V110, P450, DOI 10.1130/0016-7606(1998)110<0450:EOSBTF>2.3.CO;2; Dunne T., 2007, PHYS GEOGRAPHY S AM, P76; DUNNE T, 1970, WATER RESOUR RES, V6, P478, DOI 10.1029/WR006i002p00478; Eltahir EAB, 1999, WATER RESOUR RES, V35, P1199, DOI 10.1029/1998WR900071; Engle D, 2000, BIOGEOCHEMISTRY, V51, P71, DOI 10.1023/A:1006389124823; Eva HD, 2002, VEGETATION MAP S AM; Eva HD, 2004, GLOBAL CHANGE BIOL, V10, P731, DOI 10.1111/j.1529-8817.2003.00774.x; Fan Y, 2011, CLIM DYNAM, V37, P253, DOI 10.1007/s00382-010-0829-8; Fan Y., 2007, J GEOPHYS RES-ATMOSP, V112, DOI 10.1029/2006JD008111; Fan Y, 2010, HYDROL EARTH SYST SC, V14, P2039, DOI 10.5194/hess-14-2039-2010; Ferguson IM, 2010, WATER RESOUR RES, V46, DOI 10.1029/2009WR008616; FOLEY JA, 2002, GLOBAL BIOGEOCHEMICA, V16, P1; FORSBERG BR, 1988, LIMNOL OCEANOGR, V33, P41; Frankenberg C, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006235; FREEZE RA, 1971, WATER RESOUR RES, V7, P347, DOI 10.1029/WR007i002p00347; Germer S, 2010, J HYDROL, V380, P473, DOI 10.1016/j.jhydrol.2009.11.022; Grogan James, 2006, Acta Amazonica, V36, P483, DOI 10.1590/S0044-59672006000400009; GULDEN LE, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029804; Gutowski WJ, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000392; Hamilton SK, 2007, GEOMORPHOLOGY, V89, P23, DOI 10.1016/j.geomorph.2006.07.024; Harbaugh A.W., 2000, 0092 US GEOL SURV; Heimsath AM, 1997, NATURE, V388, P358, DOI 10.1038/41056; Hess LL, 2003, REMOTE SENS ENVIRON, V87, P404, DOI 10.1016/j.rse.2003.04.001; HEWLETT JD, 1963, J GEOPHYS RES, V68, P1081, DOI 10.1029/JZ068i004p01081; Hodnett MG, 1997, HYDROL EARTH SYST SC, V1, P265; Hodnett MG, 1997, HYDROL EARTH SYST SC, V1, P279; Hooke RL, 2000, GEOL SOC AM BULL, V112, P1778, DOI 10.1130/0016-7606(2000)112<1778:TAUTOC>2.0.CO;2; Hunter NM, 2007, GEOMORPHOLOGY, V90, P208, DOI 10.1016/j.geomorph.2006.10.021; Jiang XY, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010756; Jirka S, 2007, J VEG SCI, V18, P183, DOI 10.1658/1100-9233(2007)18[183:RBSHAF]2.0.CO;2; Johnson MS, 2006, BIOGEOCHEMISTRY, V81, P45, DOI 10.1007/s10533-006-9029-3; Johnson MS, 2006, HYDROL PROCESS, V20, P2599, DOI 10.1002/hyp.6218; Johnson MS, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034619; Juarez RIN, 2009, J HYDROMETEOROL, V10, P289, DOI 10.1175/2008JHM1023.1; KALLIOLA R, 1991, ANN BOT FENN, V28, P225; KALLIOLA R, 1992, NATURWISSENSCHAFTEN, V79, P75, DOI 10.1007/BF01131806; Kirkby M.J., 1979, HYDROL SCI B, V24, P43, DOI DOI 10.1080/02626667909491834; Kollet SJ, 2006, ADV WATER RESOUR, V29, P945, DOI 10.1016/j.advwatres.2005.08.006; Lafleur P. M., 2008, GEOGRAPHY COMPASS, V2, P1027, DOI [10.1111/j.1749-8198.2007.00132.x, DOI 10.1111/J.1749-8198.2007.00132.X]; Lahteenoja O, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001508; Lam A, 2011, HYDROL EARTH SYST SC, V15, P2621, DOI 10.5194/hess-15-2621-2011; Latrubesse EM, 2005, GEOMORPHOLOGY, V70, P187, DOI 10.1016/j.geomorph.2005.02.005; Leopold L. B., 1953, US GEOL SURV PROF PA, V252; LEOPOLDO PR, 1995, FOREST ECOL MANAG, V73, P185, DOI 10.1016/0378-1127(94)03487-H; LESACK LFW, 1993, WATER RESOUR RES, V29, P759, DOI 10.1029/92WR02371; LESACK LFW, 1995, LIMNOL OCEANOGR, V40, P598; LESACK LFW, 1995, WATER RESOUR RES, V31, P329, DOI 10.1029/94WR02271; Liang X, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003090; Lo MH, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014191; Lo MH, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015134; Lo MH, 2008, ADV WATER RESOUR, V31, P1552, DOI 10.1016/j.advwatres.2008.06.007; Lo MH, 2010, WATER RESOUR RES, V46, DOI 10.1029/2009WR007855; Manning CE, 1999, REV GEOPHYS, V37, P127, DOI 10.1029/1998RG900002; Marani M, 2001, WATER RESOUR RES, V37, P2619, DOI 10.1029/2000WR000119; Maxwell RM, 2008, NAT GEOSCI, V1, P665, DOI 10.1038/ngeo315; Maxwell RM, 2005, J HYDROMETEOROL, V6, P233, DOI 10.1175/JHM422.1; McCarthy TS, 2006, J HYDROL, V320, P264, DOI 10.1016/j.jhydrol.2005.07.045; McClain ME, 1997, GLOBAL BIOGEOCHEM CY, V11, P295, DOI 10.1029/97GB01056; MEADE RH, 1991, ENVIRON GEOL WATER S, V18, P105, DOI 10.1007/BF01704664; Melack J. M., 2009, LBA ECO LC 07 CENTRA; Melack J. M., 2011, ECOL STUD, V210, P43, DOI [10.1007/978-90-481-8725-6_3, DOI 10.1007/978-90-481-8725-63]; Melack JM, 2004, GLOBAL CHANGE BIOL, V10, P530, DOI 10.1111/j.1529-8817.2003.00763.x; Mertes LAK, 1997, WATER RESOUR RES, V33, P1749, DOI 10.1029/97WR00658; Mertes LAK, 1996, GEOL SOC AM BULL, V108, P1089, DOI 10.1130/0016-7606(1996)108<1089:CFGATS>2.3.CO;2; Miguez-Macho G, 2008, B AM METEOROL SOC, V89, P663, DOI 10.1175/BAMS-89-5-663; Miguez-Macho G, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008112; Miguez-Macho G, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017540; Miller JB, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL029213; Mohamed YA, 2006, HYDROL EARTH SYST SC, V10, P507; Nepstad DC, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000360; Neu V, 2011, BIOGEOCHEMISTRY, V105, P133, DOI 10.1007/s10533-011-9581-3; Niu GY, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015139; Niu GY, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007522; Oleson K. W., 2010, NCARTN478STR; Papa F, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012674; Pelletier JD, 2009, WATER RESOUR RES, V45, DOI 10.1029/2008WR007319; Phillips OL, 2008, PHILOS T R SOC B, V363, P1819, DOI 10.1098/rstb.2007.0033; Poels R. L. H., 1987, SOILS WATER NUTR FOR; Prance G. T., 1987, BIOGEOGRAPHY QUATERN, P19; Press W.H., 1989, NUMERICAL RECIPES AR; Prigent C, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007847; RASANEN M, 1992, GEOL MAG, V129, P293; Rasanen M. E., 1990, TERRA NOVA, V2,, P320, DOI DOI 10.1111/J.1365-3121.1990.TB00084.X; RICHEY J E, 1989, Global Biogeochemical Cycles, V3, P191, DOI 10.1029/GB003i003p00191; Richey JE, 2002, NATURE, V416, P617, DOI 10.1038/416617a; Richey JE, 2011, BIOGEOCHEMISTRY, V105, P1, DOI 10.1007/s10533-011-9622-y; Richey JE, 2009, GEOPHYS MONOGR SER, V186, P489, DOI 10.1029/2008GM000734; RICHEY JE, 1989, SCIENCE, V246, P101, DOI 10.1126/science.246.4926.101; Rihani JF, 2010, WATER RESOUR RES, V46, DOI 10.1029/2010WR009111; Rodell M, 2004, B AM METEOROL SOC, V85, P381, DOI 10.1175/BAMS-85-3-381; Rojstaczer SA, 2008, GEOFLUIDS, V8, P128, DOI 10.1111/j.1468-8123.2008.00211.x; Romero-Saltos H, 2005, AM J BOT, V92, P443, DOI 10.3732/ajb.92.3.443; Schaller MF, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010636; Selhorst D., 2003, AGUA CRESCIMENTO FLO; Singh V.P., 2003, INT J SEDIMENT RES, V18, P196; Summerfield M.A., 1994, J GEOPHYS RES, V99, P13, DOI DOI 10.1029/94JB00715; TANAKA T, 1988, J HYDROL, V102, P139, DOI 10.1016/0022-1694(88)90095-9; Tomasella J, 2008, HYDROL PROCESS, V22, P2133, DOI 10.1002/hyp.6813; Trigg MA, 2009, J HYDROL, V374, P92, DOI 10.1016/j.jhydrol.2009.06.004; Troch PA, 2003, WATER RESOUR RES, V39, DOI 10.1029/2002WR001728; Vourlitis G. L., 2008, Water Resources Research, V44, pw03412, DOI 10.1029/2006WR005526; Walko RL, 2000, J APPL METEOROL, V39, P931, DOI 10.1175/1520-0450(2000)039<0931:CABHMF>2.0.CO;2; Walter BP, 2000, GLOBAL BIOGEOCHEM CY, V14, P745, DOI 10.1029/1999GB001204; Whalen SC, 2005, ENVIRON ENG SCI, V22, P73, DOI 10.1089/ees.2005.22.73; Whitmore T. C., 1992, INTRO TROPICAL RAINF; Wilson M, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030156; Winter T. C., 1998, 1139 US GEOL SURV; Yamazaki D, 2009, HYDROL EARTH SYST SC, V13, P2241; Yamazaki D, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR009726; Yang ZL, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015140; Yeh PJF, 2005, J CLIMATE, V18, P1861, DOI 10.1175/JCLI3330.1; York JP, 2002, ADV WATER RESOUR, V25, P221, DOI 10.1016/S0309-1708(01)00021-5; Yuan X, 2011, J HYDROMETEOROL, V12, P579, DOI 10.1175/2010JHM1302.1; YUAN X, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010180; Zeng XB, 2009, J HYDROMETEOROL, V10, P308, DOI 10.1175/2008JHM1011.1	157	3	3	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	AUG 14	2012	117								D15113	10.1029/2012JD017539		30	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	992CK	WOS:000307752900003	
J	Hooghiemstra, PB; Krol, MC; van Leeuwen, TT; van der Werf, GR; Novelli, PC; Deeter, MN; Aben, I; Rockmann, T				Hooghiemstra, P. B.; Krol, M. C.; van Leeuwen, T. T.; van der Werf, G. R.; Novelli, P. C.; Deeter, M. N.; Aben, I.; Rockmann, T.			Interannual variability of carbon monoxide emission estimates over South America from 2006 to 2010	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							VARIATIONAL DATA ASSIMILATION; LAND-USE CHANGE; CLIMATE-CHANGE; CO EMISSIONS; AMAZON DEFORESTATION; BRAZILIAN AMAZON; FIRE EMISSIONS; MODEL TM5; MOPITT; INVERSION	We present the first inverse modeling study to estimate CO emissions constrained by both surface and satellite observations. Our 4D-Var system assimilates National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) surface and Measurements Of Pollution In The Troposphere (MOPITT) satellite observations jointly by fitting a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. South American dry season (August and September) biomass burning emission estimates amount to 60, 92, 42, 16 and 93 Tg CO/yr for 2006 to 2010, respectively. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations in Sao Paulo state are underestimated in current inventories by 50-100%. We conclude that climatic conditions (such as the widespread drought in 2010) seem the most likely cause for the IAV in biomass burning CO emissions. However, socio-economic factors (such as the growing global demand for soy, beef and sugar cane ethanol) and associated deforestation fires, are also likely as drivers for the IAV of CO emissions, but are difficult to link directly to CO emissions.	[Hooghiemstra, P. B.; Krol, M. C.; Rockmann, T.] Univ Utrecht, Inst Marine & Atmospher Res Utrecht, NL-3584 CC Utrecht, Netherlands; [Hooghiemstra, P. B.; Krol, M. C.; Aben, I.] SRON Netherlands Inst Space Res, Utrecht, Netherlands; [Krol, M. C.] Wageningen Univ, Wageningen, Netherlands; [van Leeuwen, T. T.; van der Werf, G. R.] Vrije Univ Amsterdam, Fac Earth & Life Sci, Amsterdam, Netherlands; [Novelli, P. C.] Natl Ocean & Atmospher Adm, Climate Monitoring & Diagnost Lab, Boulder, CO USA; [Deeter, M. N.] Natl Ctr Atmospher Res, Div Atmospher Chem, Boulder, CO 80307 USA	Hooghiemstra, PB (reprint author), Univ Utrecht, Inst Marine & Atmospher Res Utrecht, Princetonpl 5, NL-3584 CC Utrecht, Netherlands.	p.b.hooghiemstra@gmail.com			Dutch User Support Programme [GO-AO/05]	This research was supported by the Dutch User Support Programme 2006-2010 under project GO-AO/05. The Dutch National Computer Facility (NCF) is acknowledged for computer resources. We would like to acknowledge the IASI team and the French Ether database for providing the ULB/LATMOS IASI CO product, and we would like to thank in particular M. George for helpful discussion on the IASI averaging kernels. We are most thankful to S. Basu for help during the model development phase. We are thankful to S. Myriokefalitakis for supplying the a priori fields of NMVOC-CO production. We also thank S. Houweling for optimized methane mixing ratio fields.	Aragao LEOC, 2008, PHILOS T R SOC B, V363, P1779, DOI 10.1098/rstb.2007.0026; Arellano AF, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006613; Arellano AF, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020311; Bala G, 2007, P NATL ACAD SCI USA, V104, P6550, DOI 10.1073/pnas.0608998104; Bergamaschi P, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012287; Bergamaschi P, 2000, J GEOPHYS RES-ATMOS, V105, P1909, DOI 10.1029/1999JD900818; Cardoso RS, 2012, BIOMASS BIOENERG, V36, P20, DOI 10.1016/j.biombioe.2011.09.027; Chevallier F, 2009, BIOGEOSCIENCES, V6, P103; Clerbaux C, 2009, ATMOS CHEM PHYS, V9, P6041; Davidson EA, 2007, NATURE, V447, P995, DOI 10.1038/nature05900; Deeter MN, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003186; Deeter MN, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013005; Duncan BN, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008459; EDGAR Project Team, 2010, EM DAT GLOB ATM RES; Edwards DP, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006655; Emmons LK, 2009, ATMOS CHEM PHYS, V9, P1795; Emmons LK, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007833; Fisher M., 1995, A8 U FLOR; Fortems-Cheiney A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014416; George M, 2009, ATMOS CHEM PHYS, V9, P8317; Giglio L, 2010, BIOGEOSCIENCES, V7, P1171; Gloudemans A M S, 2009, ATMOS CHEM PHYS, V9, P3799, DOI DOI 10.5194/ACP-9-3799-2009; Gloudemans AMS, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026804; HESTENES MR, 1952, J RES NAT BUR STAND, V49, P409, DOI 10.6028/jres.049.044; Hoffmann WA, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015424; Hooghiemstra PB, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD017043; Hooghiemstra PB, 2011, ATMOS CHEM PHYS, V11, P4705, DOI 10.5194/acp-11-4705-2011; HOUGHTON RA, 1995, GLOB CHANGE BIOL, V1, P275, DOI 10.1111/j.1365-2486.1995.tb00026.x; Huijnen V, 2010, GEOSCI MODEL DEV, V3, P445, DOI 10.5194/gmd-3-445-2010; Jiang Z, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015282; Jones DBA, 2009, ATMOS CHEM PHYS, V9, P3547; Kasibhatla P, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015581; Kopacz M, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2007JD009264; Kopacz M, 2010, ATMOS CHEM PHYS, V10, P855; Krol M, 2005, ATMOS CHEM PHYS, V5, P417; LANCZOS C, 1950, J RES NAT BUR STAND, V45, P255, DOI 10.6028/jres.045.026; Lapola DM, 2010, P NATL ACAD SCI USA, V107, P3388, DOI 10.1073/pnas.0907318107; Laurance WF, 2007, SCIENCE, V318, P1721; Lewis SL, 2011, SCIENCE, V331, P554, DOI 10.1126/science.1200807; Malhi Y, 2008, SCIENCE, V319, P169, DOI 10.1126/science.1146961; Meirink JF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009740; Meirink JF, 2008, ATMOS CHEM PHYS, V8, P6341; Morton DC, 2006, P NATL ACAD SCI USA, V103, P14637, DOI 10.1073/pnas.0606377103; Morton DC, 2008, GLOBAL CHANGE BIOL, V14, P2262, DOI 10.1111/j.1365-2486.2008.01652.x; Nepstad D, 2009, SCIENCE, V326, P1350, DOI 10.1126/science.1182108; NOBRE CA, 1991, J CLIMATE, V4, P957, DOI 10.1175/1520-0442(1991)004<0957:ADARCC>2.0.CO;2; Novelli PC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003031; Novelli PC, 1998, J GEOPHYS RES-ATMOS, V103, P19015, DOI 10.1029/98JD01366; Petron G, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001305; Petron G, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020560; Pison I, 2009, ATMOS CHEM PHYS, V9, P5281; Rovere E. L. L., 2011, WORLD DEV, V39, P1026, DOI [10.1016/j.worlddev.2010.01.004, DOI 10.1016/J.WORLDDEV.2010.01.004]; Sala OE, 2000, SCIENCE, V287, P1770, DOI 10.1126/science.287.5459.1770; SHUKLA J, 1990, SCIENCE, V247, P1322, DOI 10.1126/science.247.4948.1322; Sparovek G, 2010, ENVIRON SCI TECHNOL, V44, P6046, DOI 10.1021/es1007824; Spivakovsky CM, 2000, J GEOPHYS RES-ATMOS, V105, P8931, DOI 10.1029/1999JD901006; Stavrakou T, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006896; Torres O, 2010, ATMOS CHEM PHYS, V10, P3505; Turquety S, 2009, ATMOSPHERIC CHEM PHY, V9, P7413; van der Werf GR, 2009, BIOGEOSCIENCES, V6, P235; van der Werf GR, 2010, ATMOS CHEM PHYS, V10, P11707, DOI 10.5194/acp-10-11707-2010; van Leeuwen TT, 2011, ATMOS CHEM PHYS, V11, P3611, DOI 10.5194/acp-11-3611-2011; Walter A, 2011, ENERG POLICY, V39, P5703, DOI 10.1016/j.enpol.2010.07.043; Xu LA, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL046824	64	1	1	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	AUG 10	2012	117								D15308	10.1029/2012JD017758		25	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	988AQ	WOS:000307460900003	
J	Luo, YW; Ducklow, HW; Friedrichs, MAM; Church, MJ; Karl, DM; Doney, SC				Luo, Ya-Wei; Ducklow, Hugh W.; Friedrichs, Marjorie A. M.; Church, Matthew J.; Karl, David M.; Doney, Scott C.			Interannual variability of primary production and dissolved organic nitrogen storage in the North Pacific Subtropical Gyre	JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES			English	Article							STATION ALOHA; OCEAN; FIXATION; CARBON; SEA; PHOSPHORUS; MICROBES; NITRATE	The upper ocean primary production measurements from the Hawaii Ocean Time series (HOT) at Station ALOHA in the North Pacific Subtropical Gyre showed substantial variability over the last two decades. The annual average primary production varied within a limited range over 1991-1998, significantly increased in 1999-2000 and then gradually decreased afterwards. This variability was investigated using a one-dimensional ecosystem model. The long-term HOT observations were used to constrain the model by prescribing physical forcings and lower boundary conditions and optimizing the model parameters against data using data assimilation. The model reproduced the general interannual pattern in the observed primary production, and mesoscale variability in vertical velocity was identified as a major contributing factor to the interannual variability in the simulation. Several strong upwelling events occurred in 1999, which brought up nitrate at rates several times higher than other years and elevated the model primary production. Our model results suggested a hypothesis for the observed interannual variability pattern of primary production at Station ALOHA: Part of the upwelled nitrate input in 1999 was converted to and accumulated as semilabile dissolved organic nitrogen (DON), and subsequent recycling of this semilabile DON supported enhanced primary productivity for the next several years as the semilabile DON perturbation was gradually removed via export.	[Luo, Ya-Wei; Doney, Scott C.] Woods Hole Oceanog Inst, Dept Marine Chem & Geochem, Woods Hole, MA 02543 USA; [Ducklow, Hugh W.] Marine Biol Lab, Ctr Ecosyst, Woods Hole, MA 02543 USA; [Friedrichs, Marjorie A. M.] Virginia Inst Marine Sci, Coll William & Mary, Gloucester Point, VA 23062 USA; [Church, Matthew J.; Karl, David M.] Univ Hawaii, Sch Ocean & Earth Sci & Technol, Honolulu, HI 96822 USA	Luo, YW (reprint author), Woods Hole Oceanog Inst, Dept Marine Chem & Geochem, MS 25, Woods Hole, MA 02543 USA.	yluo@whoi.edu	Doney, Scott/F-9247-2010; Luo, Ya-Wei/E-7169-2011		Center for Microbial Oceanography, Research and Education (C-MORE) [NSF EF-0424599]; Hawaii Ocean Time series program [NSF OCE09-26766]; Gordon and Betty Moore Foundation; Marine Biological Laboratory	We thank all of the scientists and crew who have contributed to the Hawaii Ocean Time (HOT) series. This work was supported in part by the Center for Microbial Oceanography, Research and Education (C-MORE) (NSF EF-0424599), Hawaii Ocean Time series program (NSF OCE09-26766), the Gordon and Betty Moore Foundation, and the Marine Biological Laboratory. The computational resources were provided by the College of William and Mary SciClone cluster. We thank three anonymous reviewers for their constructive comments, which greatly helped us to improve the quality of this paper.	AZAM F, 1983, MAR ECOL PROG SER, V10, P257, DOI 10.3354/meps010257; Bidigare RR, 2009, J PLANKTON RES, V31, P1131, DOI 10.1093/plankt/fbp064; Calil PHR, 2010, J GEOPHYS RES-OCEANS, V115, DOI 10.1029/2009JC005360; Calil PHR, 2011, J GEOPHYS RES-OCEANS, V116, DOI 10.1029/2010JC006704; Carlson C. A., 2002, BIOGEOCHEMISTRY MARI, P91, DOI DOI 10.1016/B978-012323841-2/50006-3; CARLSON CA, 1994, NATURE, V371, P405, DOI 10.1038/371405a0; Casciotti KL, 2008, DEEP-SEA RES PT II, V55, P1661, DOI 10.1016/j.dsr2.2008.04.017; Church MJ, 2002, LIMNOL OCEANOGR, V47, P1; Church MJ, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003418; Corno G, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003730; Deutsch C, 2001, GLOBAL BIOGEOCHEM CY, V15, P483, DOI 10.1029/2000GB001291; Dore JE, 1996, DEEP-SEA RES PT II, V43, P385, DOI 10.1016/0967-0645(95)00105-0; Dore JE, 2002, LIMNOL OCEANOGR, V47, P1595; DUCKLOW HW, 1995, PHILOS T ROY SOC B, V348, P161; Friedrichs MAM, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003852; HEDGES JI, 1992, MAR CHEM, V39, P67, DOI 10.1016/0304-4203(92)90096-S; JACKSON GA, 1985, DEEP-SEA RES, V32, P223, DOI 10.1016/0198-0149(85)90030-5; Karl D, 1997, NATURE, V388, P533, DOI 10.1038/41474; Karl DM, 1996, DEEP-SEA RES PT II, V43, P129, DOI 10.1016/0967-0645(96)00005-7; Karl D. M., 2001, OCEANOGRAPHY, V14, P6; Karl DM, 2008, NITROGEN IN THE MARINE ENVIRONMENT, 2ND EDITION, P705, DOI 10.1016/B978-0-12-372522-6.00016-5; Karl DM, 2001, DEEP-SEA RES PT II, V48, P1529, DOI 10.1016/S0967-0645(00)00152-1; Karl DM, 1998, LIMNOL OCEANOGR, V43, P1270; Karl DM, 2008, MAR ECOL PROG SER, V364, P257, DOI 10.3354/meps07547; LAWSON LM, 1995, B MATH BIOL, V57, P593, DOI 10.1016/S0092-8240(05)80759-1; Letelier RM, 2000, J GEOPHYS RES-OCEANS, V105, P28723, DOI 10.1029/1999JC000306; Luo YW, 2010, AQUAT MICROB ECOL, V60, P273, DOI 10.3354/ame01427; McCarren J, 2010, P NATL ACAD SCI USA, V107, P16420, DOI 10.1073/pnas.1010732107; MONOD J, 1949, ANNU REV MICROBIOL, V3, P371, DOI 10.1146/annurev.mi.03.100149.002103; REDFIELD AC, 1958, AM SCI, V46, P205; Saba VS, 2010, GLOBAL BIOGEOCHEM CY, V24, DOI 10.1029/2009GB003655; Sverdrup HU, 1942, OCEANS THEIR PHYS CH; Vaillancourt RD, 2003, DEEP-SEA RES PT I, V50, P829, DOI 10.1016/S0967-0637(03)00059-1	33	0	0	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0148-0227		J GEOPHYS RES-BIOGEO	J. Geophys. Res.-Biogeosci.	AUG 10	2012	117								G03019	10.1029/2011JG001830		12	Environmental Sciences; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	988BJ	WOS:000307462900001	
J	Yuan, WP; Liang, SL; Liu, SG; Weng, ES; Luo, YQ; Hollinger, D; Zhang, HC				Yuan, Wenping; Liang, Shunlin; Liu, Shuguang; Weng, Ensheng; Luo, Yiqi; Hollinger, David; Zhang, Haicheng			Improving model parameter estimation using coupling relationships between vegetation production and ecosystem respiration	ECOLOGICAL MODELLING			English	Article						Bayesian inversion; Eddy covariance; Markov chain Monte Carlo; Gross primary production; Ecosystem respiration; Parameter estimation	WATER-VAPOR EXCHANGE; EDDY COVARIANCE MEASUREMENTS; TERRESTRIAL CARBON FLUXES; SOIL RESPIRATION; GAS-EXCHANGE; INTERANNUAL VARIABILITY; PHOTOSYNTHETIC CAPACITY; REGIONAL APPLICATIONS; TEMPERATURE RESPONSE; SEASONAL-CHANGES	Data assimilation techniques and inverse analysis have been applied to extract ecological knowledge from ecosystem observations. However, the number of parameters in ecosystem models that can be constrained is limited by conventional inverse analysis. This study aims to increase the number of parameters that can be constrained in parameter inversions by considering the internal relationships among ecosystem processes. Our previous study has reported thermal adaptation of net ecosystem exchange (NEE). Ecosystems tend to transfer from a carbon source to sink when the air temperature exceeds the mean annual temperature, and attain their maximum uptake when the temperature reaches the long-term growing season mean. Because NEE is the difference between gross primary production (GPP) and ecosystem respiration (ER), the adaptation of NEE indirectly indicates the coupling relationship between GPP and ER. Five assimilation experiments were conducted with (1) estimated GPP based on eddy flux measurements, (2) estimated GPP and coupling relationship between GPP and ER, (3) observed NEE measurements, (4) observed NEE measurements and internal relationship between GPP and ER and (5) observed NEE, estimated ER and GPP. The results show that the inversion method, using only estimated GPP based on eddy covariance towers, constrained 4 of 16 parameters in the terrestrial ecosystem carbon model, and the improved method using both GPP data and the internal relationship between GPP and ER allowed us to constrain 10 of 16 parameters. The improved method constrained the parameters for ER without additional ER observations, and accordingly improved the model performance substantially for simulating ER. Overall, our method enhances our ability to extract information from ecosystem observations and potentially reduces uncertainty for simulating carbon dynamics across the regional and global scales. (C) 2012 Elsevier B.V. All rights reserved.	[Yuan, Wenping; Liang, Shunlin; Zhang, Haicheng] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China; [Yuan, Wenping; Liang, Shunlin] Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing 100875, Peoples R China; [Liang, Shunlin] Univ Maryland, Dept Geog, College Pk, MD 20742 USA; [Liu, Shuguang] US Geol Survey, Earth Resources Observat & Sci Ctr, Sioux Falls, SD 57198 USA; [Weng, Ensheng; Luo, Yiqi] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA; [Hollinger, David] USDA, Forest Serv, No Res Stn, Durham, NH 03824 USA; [Yuan, Wenping; Liang, Shunlin] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China	Yuan, WP (reprint author), Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.	wenpingyuancn@yahoo.com	Weng, Ensheng/E-4390-2012; Hollinger, David/G-7185-2012		National Key Basic Research and Development Plan of China [2012CB955501, 2010CB833504]; National High Technology Research and Development Program of China (863 Program) [2009AA122101]; Fundamental Research Funds for the Central Universities; Office of Science (BER), US Department of Energy [DE-AI02-07ER64355]	This research was financially supported by National Key Basic Research and Development Plan of China (2012CB955501 and 2010CB833504), National High Technology Research and Development Program of China (863 Program) (2009AA122101) and the Fundamental Research Funds for the Central Universities. Howland research was supported by the Office of Science (BER), US Department of Energy under Interagency Agreement No. DE-AI02-07ER64355.	Aalto T, 2001, BOREAL ENVIRON RES, V6, P53; Agren G.I., 1998, THEORETICAL ECOSYSTE; Baldocchi D, 2008, AUST J BOT, V56, P1, DOI 10.1071/BT07151; Baldocchi DD, 2005, INT J BIOMETEOROL, V49, P377, DOI 10.1007/s00484-005-0256-4; Beer C, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003233; Bernacchi CJ, 2003, PLANT CELL ENVIRON, V26, P1419, DOI 10.1046/j.0016-8025.2003.01050.x; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Campell G.S., 1998, INTRO ENV BIOPHYSICS; Carswell FE, 2000, TREE PHYSIOL, V20, P179; Chabot B.F., 1985, PHYSL ECOLOGY N AM P; Chang M, 2003, FOREST HYDROLOGY INT; CHATFIELD C, 1995, J ROY STAT SOC A STA, V158, P419, DOI 10.2307/2983440; Davidson EA, 2006, GLOBAL CHANGE BIOL, V12, P230, DOI 10.1111/j.1365-2486.2005.01062.x; DeForest JL, 2009, SOIL BIOL BIOCHEM, V41, P1180, DOI 10.1016/j.soilbio.2009.02.029; Desai AR, 2008, AGR FOREST METEOROL, V148, P821, DOI 10.1016/j.agrformet.2007.11.012; Dreyer E, 2001, TREE PHYSIOL, V21, P223; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Gelman A, 1992, STAT SCI, V7, P457, DOI DOI 10.1214/SS/1177011136; Goulden ML, 1996, GLOB CHANGE BIOL, V2, P169, DOI 10.1111/j.1365-2486.1996.tb00070.x; Gove JH, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006021; Green EJ, 1999, FOREST SCI, V45, P528; Hagen SC, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006154; HARLEY PC, 1995, PLANT CELL ENVIRON, V18, P1146, DOI 10.1111/j.1365-3040.1995.tb00625.x; HARLEY PC, 1992, PLANT CELL ENVIRON, V15, P271, DOI 10.1111/j.1365-3040.1992.tb00974.x; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Haxeltine A, 1996, J VEG SCI, V7, P651, DOI 10.2307/3236377; HOBBIE SE, 1992, TRENDS ECOL EVOL, V7, P336, DOI 10.1016/0169-5347(92)90126-V; Hollinger DY, 1999, GLOB CHANGE BIOL, V5, P891, DOI 10.1046/j.1365-2486.1999.00281.x; Hollinger DY, 2005, TREE PHYSIOL, V25, P873; Hollinger DY, 2004, GLOBAL CHANGE BIOL, V10, P1689, DOI 10.1111/j.1365-2486.2004.00847.x; Janssens IA, 2003, GLOBAL CHANGE BIOL, V9, P911, DOI 10.1046/j.1365-2486.2003.00636.x; Janssens IA, 2001, AGR FOREST METEOROL, V106, P61, DOI 10.1016/S0168-1923(00)00177-5; JORDAN DB, 1984, PLANTA, V161, P308, DOI 10.1007/BF00398720; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kosugi Y, 2003, PLANT CELL ENVIRON, V26, P285, DOI 10.1046/j.1365-3040.2003.00960.x; Larcher W., 1995, PHYSL PLANT ECOLOGY; LEUNING R, 1995, PLANT CELL ENVIRON, V18, P1183, DOI 10.1111/j.1365-3040.1995.tb00628.x; LEUNING R, 1995, PLANT CELL ENVIRON, V18, P339, DOI 10.1111/j.1365-3040.1995.tb00370.x; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; Luo YQ, 2001, ECOL MONOGR, V71, P357, DOI 10.1890/0012-9615(2001)071[0357:ECDECP]2.0.CO;2; Luo YQ, 2007, ANNU REV ECOL EVOL S, V38, P683, DOI 10.1146/annurev.ecolsys.38.091206.095808; Ma SY, 2007, AGR FOREST METEOROL, V147, P157, DOI 10.1016/j.agrformet.2007.07.008; MacFarlane DW, 2000, ECOL MODEL, V134, P27, DOI 10.1016/S0304-3800(00)00329-X; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; Mosegaard K, 2002, INVERSE PROBL, V18, P29; Novick KA, 2004, OECOLOGIA, V138, P259, DOI 10.1007/s00442-003-1388-z; ODUM HT, 1956, LIMNOL OCEANOGR, V1, P102; PARTON WJ, 1987, SOIL SCI SOC AM J, V51, P1173; POTTER CS, 1993, GLOBAL BIOGEOCHEM CY, V7, P811, DOI 10.1029/93GB02725; RASTETTER EB, 1991, TREE PHYSIOL, V9, P101; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Reichstein M., 2005, GLOBAL CHANGE BIOL, V11, P1, DOI DOI 10.111/J.1365-2486.2005; Rey A, 1998, TREE PHYSIOL, V18, P441; Richardson AD, 2006, AGR FOREST METEOROL, V141, P219, DOI 10.1016/j.agrformet.2006.10.010; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; RUNNING SW, 1991, TREE PHYSIOL, V9, P147; RUNNING SW, 1988, ECOL MODEL, V42, P125, DOI 10.1016/0304-3800(88)90112-3; Santaren D, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002834; Schulz K, 2001, J CLIMATE, V14, P2551, DOI 10.1175/1520-0442(2001)014<2551:TPUOLS>2.0.CO;2; SELLERS PJ, 1992, REMOTE SENS ENVIRON, V42, P187, DOI 10.1016/0034-4257(92)90102-P; Stoy PC, 2006, AGR FOREST METEOROL, V141, P2, DOI 10.1016/j.agrformet.2006.09.001; Tang JW, 2005, GLOBAL CHANGE BIOL, V11, P1298, DOI 10.1111/j.1365-2486.2005.00987.x; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Van't Hoff J.H., 1899, LECT THEORETICAL PHY; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; Van Wijk MT, 2000, TREE PHYSIOL, V20, P115; Verbeeck H, 2006, TREE PHYSIOL, V26, P807; VONCAEMMERER S, 1994, PLANTA, V195, P88; Wang Q, 2004, GLOBAL CHANGE BIOL, V10, P37, DOI 10.1111/j.1365-2486.2003.00716.x; Wang YP, 2003, TELLUS B, V55, P290, DOI 10.1034/j.1600-0889.2003.00030.x; Wang YP, 1998, AGR FOREST METEOROL, V91, P89, DOI 10.1016/S0168-1923(98)00061-6; Wang YP, 2003, TELLUS B, V55, P270, DOI 10.1034/j.1600-0889.2003.00031.x; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; Watt K.E.F., 1966, SYSTEMS ANAL ECOLOGY; Weng E.S., 2007, NAT SCI FDN WORKSH D; White M.A, 2000, EARTH INTERACT, V4, P3; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Wu XW, 2009, J PLANT ECOL-UK, V2, P55, DOI 10.1093/jpe/rtp005; WULLSCHLEGER SD, 1993, J EXP BOT, V44, P907, DOI 10.1093/jxb/44.5.907; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Yuan W, 2011, BIOGEOSCIENCES, V8, P1453, DOI 10.5194/bg-8-1453-2011; Yuan WP, 2009, GLOBAL CHANGE BIOL, V15, P2905, DOI 10.1111/j.1365-2486.2009.01870.x; Zhang L, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001004	84	1	1	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0304-3800		ECOL MODEL	Ecol. Model.	AUG 10	2012	240						29	40		10.1016/j.ecolmodel.2012.04.027		12	Ecology	Environmental Sciences & Ecology	978UB	WOS:000306770200003	
J	Liu, YY; Dorigo, WA; Parinussa, RM; de Jeu, RAM; Wagner, W; McCabe, MF; Evans, JP; van Dijk, AIJM				Liu, Y. Y.; Dorigo, W. A.; Parinussa, R. M.; de Jeu, R. A. M.; Wagner, W.; McCabe, M. F.; Evans, J. P.; van Dijk, A. I. J. M.			Trend-preserving blending of passive and active microwave soil moisture retrievals	REMOTE SENSING OF ENVIRONMENT			English	Article						Soil moisture; Satellite; Active and passive microwave; Blending; Long term trend	IN-SITU OBSERVATIONS; AMSR-E; RADIOFREQUENCY INTERFERENCE; ERS SCATTEROMETER; DATA ASSIMILATION; TIBETAN PLATEAU; SATELLITE DATA; VALIDATION; PRODUCTS; MODEL	A series of satellite-based passive and active microwave instruments provide soil moisture retrievals spanning altogether more than three decades. This offers the opportunity to generate a combined product that incorporates the advantages of both microwave techniques and spans the observation period starting 1979. However, there are several challenges in developing such a dataset, e.g., differences in instrument specifications result in different absolute soil moisture values, the global passive and active microwave retrieval methods produce conceptually different quantities, and products vary in their relative performances depending on vegetation density. This paper presents an approach for combining four passive microwave products from the VU University Amsterdam/National Aeronautics and Space Administration and two active microwave products from the Vienna University of Technology. First, passive microwave soil moisture retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall Measuring Mission microwave imager (TMI) instruments were scaled to the climatology of the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) derived product and then all four were combined into a single merged passive microwave product. Second, active microwave soil moisture estimates from the European Remote Sensing (ERS) Scatterometer instrument were scaled to the climatology of the Advanced Scatterometer (ASCAT) derived estimates. Both were combined into a merged active microwave product. Finally, the two merged products were rescaled to a common globally available reference soil moisture dataset provided by a land surface model (GLDAS-1-Noah) and then blended into a single passive/active product. Blending of the active and passive data sets was based on their respective sensitivity to vegetation density. While this three step approach imposes the absolute values of the land surface model dataset to the final product, it preserves the relative dynamics (e.g., seasonality and inter-annual variations) of the original satellite derived retrievals. More importantly, the long term changes evident in the original soil moisture products were also preserved. The method presented in this paper allows the long term product to be extended with data from other current and future operational satellites. The multi-decadal blended dataset is expected to enhance our basic understanding of soil moisture in the water, energy and carbon cycles. (C) 2012 Elsevier Inc. All rights reserved.	[Liu, Y. Y.; McCabe, M. F.] Univ New S Wales, Sch Civil & Environm Engn, Sydney, NSW, Australia; [Dorigo, W. A.; Wagner, W.] Vienna Univ Technol, Inst Photogrammetry & Remote Sensing, A-1040 Vienna, Austria; [Liu, Y. Y.; Parinussa, R. M.; de Jeu, R. A. M.] Vrije Univ Amsterdam, Dept Hydrol & Geoenvironm Sci, Fac Earth & Life Sci, Amsterdam, Netherlands; [Liu, Y. Y.; Evans, J. P.] Univ New S Wales, Climate Change Res Ctr, Sydney, NSW, Australia; [Liu, Y. Y.; van Dijk, A. I. J. M.] CSIRO Land & Water, Black Mt Labs, Canberra, ACT, Australia	Liu, YY (reprint author), Univ New S Wales, Sch Civil & Environm Engn, Sydney, NSW, Australia.	yiliu001@gmail.com	Evans, Jason/F-3716-2011	Evans, Jason/0000-0003-1776-3429	European Space Agency (ESA) STSE [22086/08/I-EC]; European Union [226701]; University of New South Wales International Postgraduate Award (UIPA); CSIRO Water for a Healthy Country Flagship Program	This work has been undertaken as part of the European Space Agency (ESA) STSE funded Integrated Project WAter Cycle Multi-mission Observation Strategy (WACMOS, http://www.wacmos.org/, ESRIN/Contract No. 22086/08/I-EC) and is continued within ESA's Climate Change Initiative. We would like to thank Diego Fernandez-Prieto for his support The development of the long term soil moisture dataset was also supported by the European Union (FP7) funded Framework Programme for Research and Technological Development Carbo Extreme (Contract No. 226701). Yi Liu is funded by a University of New South Wales International Postgraduate Award (UIPA) and a scholarship from the CSIRO Water for a Healthy Country Flagship Program.	Albergel C, 2012, REMOTE SENS ENVIRON, V118, P215, DOI 10.1016/j.rse.2011.11.017; Albergel C, 2009, HYDROL EARTH SYST SC, V13, P115; Bartalis Z, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL091088; Brocca L, 2011, REMOTE SENS ENVIRON, V115, P3390, DOI 10.1016/j.rse.2011.08.003; Brocca L, 2010, REMOTE SENS ENVIRON, V114, P2745, DOI 10.1016/j.rse.2010.06.009; Ceballos A, 2005, HYDROL PROCESS, V19, P1549, DOI 10.1002/hyp.5585; Christy JR, 1998, J CLIMATE, V11, P2016, DOI 10.1175/1520-0442-11.8.2016; Crapolicchio R., 2005, 2004 ENVISAT ERS S; De Jeu R. A. M., 2003, RETRIEVAL LAND SURFA; Dee DP, 2009, Q J ROY METEOR SOC, V135, P1830, DOI 10.1002/qj.493; Dorigo WA, 2010, HYDROL EARTH SYST SC, V14, P2605, DOI 10.5194/hess-14-2605-2010; Dorigo WA, 2011, HYDROL EARTH SYST SC, V15, P1675, DOI 10.5194/hess-15-1675-2011; Draper CS, 2009, REMOTE SENS ENVIRON, V113, P703, DOI 10.1016/j.rse.2008.11.011; Entekhabi D, 2010, P IEEE, V98, P704, DOI 10.1109/JPROC.2010.2043918; Foster JL, 2011, INT J REMOTE SENS, V32, P1371, DOI 10.1080/01431160903548013; Gobron N, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043870; Gruhier C, 2010, HYDROL EARTH SYST SC, V14, P141; Guan L, 2004, J OCEANOGR, V60, P905, DOI 10.1007/s10872-004-5782-x; Helsel D. R, 2002, TECHNIQUES WATER RES; Holmes TRH, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010257; Hutchinson JMS, 2003, T ASAE, V46, P225; Jackson TJ, 2010, IEEE T GEOSCI REMOTE, V48, P4256, DOI 10.1109/TGRS.2010.2051035; JACKSON TJ, 1991, REMOTE SENS ENVIRON, V36, P203, DOI 10.1016/0034-4257(91)90057-D; Jackson TJ, 2002, J HYDROMETEOROL, V3, P688, DOI 10.1175/1525-7541(2002)003<0688:SSMRAM>2.0.CO;2; Kerr Y., 2000, EARTH OBSERVATION Q, V66, P18; Kirdyashev KP, 1979, RADIO ENG ELEC PHYS, V24, P37; Koster RD, 2009, J CLIMATE, V22, P4322, DOI 10.1175/2009JCLI2832.1; Kumar S, 2009, J HYDROL, V374, P171, DOI 10.1016/j.jhydrol.2009.06.012; Kuria DN, 2007, IEEE T GEOSCI REMOTE, V45, P1207, DOI 10.1109/TGRS.2007.894552; Lakshmi V, 1997, J APPL METEOROL, V36, P1309, DOI 10.1175/1520-0450(1997)036<1309:EOSSMI>2.0.CO;2; Lebel T, 2009, J HYDROL, V375, P3, DOI 10.1016/j.jhydrol.2009.03.020; Le Vine DA, 2007, IEEE T GEOSCI REMOTE, V45, P2040, DOI 10.1109/TGRS.2007.898092; Li L, 2004, IEEE T GEOSCI REMOTE, V42, P380, DOI 10.1109/TGRS.2003.817195; Liu YY, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL048684; Liu YY, 2009, WATER RESOUR RES, V45, DOI 10.1029/2008WR007187; Liu YY, 2011, HYDROL EARTH SYST SC, V15, P425, DOI 10.5194/hess-15-425-2011; Maritorena S, 2010, REMOTE SENS ENVIRON, V114, P1791, DOI 10.1016/j.rse.2010.04.002; Martinez-Fernandez J, 2005, J HYDROL, V312, P28, DOI 10.1016/j.jhydrol.2005.02.007; McCabe M. F., 2005, GEOPHYS RES LETT, V32, P1; Meesters AGCA, 2005, IEEE GEOSCI REMOTE S, V2, P121, DOI 10.1109/LGRS.2005.843983; Miralles DG, 2010, J HYDROMETEOROL, V11, P1423, DOI 10.1175/2010JHM1285.1; Mladenova I, 2011, REMOTE SENS ENVIRON, V115, P2096, DOI 10.1016/j.rse.2011.04.011; Naeimi V, 2009, J HYDROMETEOROL, V10, P555, DOI 10.1175/2008JHM1051.1; Njoku EG, 2003, IEEE T GEOSCI REMOTE, V41, P215, DOI 10.1109/TGRS.2002.808243; Njoku EG, 2005, IEEE T GEOSCI REMOTE, V43, P938, DOI 10.1109/TGRS.2004.837507; Owe M, 2008, J GEOPHYS RES-EARTH, V113, DOI 10.1029/2007JF000769; Pan M, 2008, REMOTE SENS ENVIRON, V112, P1282, DOI 10.1016/j.rse.2007.02.039; Parinussa RM, 2012, IEEE T GEOSCI REMOTE, V50, P2683, DOI 10.1109/TGRS.2011.2174643; Parinussa RM, 2011, HYDROL EARTH SYST SC, V15, P3135, DOI 10.5194/hess-15-3135-2011; Parinussa RM, 2011, IEEE GEOSCI REMOTE S, V8, P779, DOI 10.1109/LGRS.2011.2114872; Parrens M., 2011, HYDROL EARTH SYST SC, V8, P8565, DOI DOI 10.5194/HESSD-8-8565-2011; Pellarin T, 2009, HYDROL EARTH SYST SC, V13, P1887; Reichle RH, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008033; Reichle RH, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020938; Rodell M, 2004, B AM METEOROL SOC, V85, P381, DOI 10.1175/BAMS-85-3-381; Rudiger C, 2009, J HYDROMETEOROL, V10, P431, DOI 10.1175/2008JHM997.1; Rudiger C, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005837; Rudolf B., 2005, P 2 WORKSH INT PREC, P231; Rudolf B., 2010, GPCC STATUS REPORT D; Rustomji P, 2008, WATER RESOUR RES, V44, DOI 10.1029/2007WR006656; Scipal K, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035599; Shi XK, 2010, ENVIRON EARTH SCI, V61, P1289, DOI 10.1007/s12665-010-0504-8; Simmons A., 2007, ECMWF NEWSLETTER, V110, P25; Simmons A., 2007, ECMWF NEWSLETTER, V111, P5; Stoffelen A, 1998, J GEOPHYS RES-OCEANS, V103, P7755, DOI 10.1029/97JC03180; Uppala S, 2008, ECMWF NEWSL, V115, P12; Wagner W, 1999, REMOTE SENS ENVIRON, V70, P191, DOI 10.1016/S0034-4257(99)00036-X; Wagner W, 2007, HYDROGEOL J, V15, P121, DOI 10.1007/s10040-006-0104-6; Wagner W, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003663; WANG JR, 1980, IEEE T GEOSCI REMOTE, V18, P288, DOI 10.1109/TGRS.1980.350304; Wen J, 2003, PHYS CHEM EARTH, V28, P53, DOI 10.1016/S1474-7065(03)00007-X; Xu ZX, 2008, HYDROL PROCESS, V22, P3056, DOI 10.1002/hyp.6892; Young R., 2008, SOIL MOISTURE METEOR	73	4	4	ELSEVIER SCIENCE INC	NEW YORK	360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA	0034-4257		REMOTE SENS ENVIRON	Remote Sens. Environ.	AUG	2012	123						280	297		10.1016/j.rse.2012.03.014		18	Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology	016CX	WOS:000309496000025	
J	Keenan, TF; Davidson, E; Moffat, AM; Munger, W; Richardson, AD				Keenan, Trevor F.; Davidson, Eric; Moffat, Antje M.; Munger, William; Richardson, Andrew D.			Using model-data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling	GLOBAL CHANGE BIOLOGY			English	Article						climate change; data-informed model; Harvard forest; long-term trend; model benchmark; model-data fusion; Monte Carlo; multiple constraints	GLOBAL VEGETATION MODELS; LAND-SURFACE MODELS; EDDY COVARIANCE; LONG-TERM; FOREST CARBON; ESTIMATING PARAMETERS; MIDLATITUDE FOREST; DATA ASSIMILATION; SOIL RESPIRATION; DECIDUOUS FOREST	Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model-data fusion framework benchmarked with an artificial neural network (ANN). We used 18 similar to years of eddy-covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process-based carbon cycle model, both against the measured decadal trend and under future long-term climate change. The use of high-frequency eddy-covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long-term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data-informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long-term trend. Furthermore, we use the model-data fusion framework, and the ANN, to show that the long-term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model-data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data-informed process-based model.	[Keenan, Trevor F.; Richardson, Andrew D.] Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA; [Davidson, Eric] Woods Hole Res Ctr, Falmouth, MA 02540 USA; [Moffat, Antje M.] Max Planck Inst Biogeochem, D-07745 Jena, Germany; [Munger, William] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA; [Munger, William] Harvard Univ, Dept Earth & Planetary Sci, Cambridge, MA 02138 USA	Keenan, TF (reprint author), Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA.	tkeenan@oeb.harvard.edu	Keenan, Trevor/B-2744-2010; Richardson, Andrew/F-5691-2011	Keenan, Trevor/0000-0002-7579-7494; 	Office of Science (BER), US Department of Energy (DOE); National Science Foundation; Northeastern States Research Cooperative; US DOE BER, through the Northeastern Regional Center of the National Institute for Climate Change Research; NOAA's Climate Program Office, Global Carbon Cycle Program [NA11OAR4310054]	Carbon flux and biometric measurements at HFEMS have been supported by the Office of Science (BER), US Department of Energy (DOE) and the National Science Foundation Long-Term Ecological Research Programs. T. F. K. and A. D. R. acknowledge support from the Northeastern States Research Cooperative, and from the US DOE BER, through the Northeastern Regional Center of the National Institute for Climate Change Research. T. F. K., A. D. R., and J. W. M. acknowledge support from NOAA's Climate Program Office, Global Carbon Cycle Program, under award NA11OAR4310054. We thank Y. Ryu, M. Toomey, and S. Klosterman for useful feedback. We especially thank the many participants who have sustained the long-term data collection, and in particular the summer students engaged in collecting field data who were supported by NSF Research Experience for Undergraduates (REU) program, and the Harvard Forest Woods Crew for logistical and maintenance support.	Abramowitz G, 2007, J HYDROMETEOROL, V8, P989, DOI 10.1175/JHM628.1; BALDOCCHI D, 1994, TREE PHYSIOL, V14, P1069; Ball J.T., 1987, PROGR PHOTOSYNTHESIS, P221; Barford CC, 2001, SCIENCE, V294, P1688, DOI 10.1126/science.1062962; Barr A, 2009, EOS T AGU S, pB54A; Barrett DJ, 2005, AUST J BOT, V53, P689, DOI 10.1071/BT04139; Beer C, 2010, SCIENCE, V329, P834, DOI 10.1126/science.1184984; Bernacchi CJ, 2001, PLANT CELL ENVIRON, V24, P253, DOI 10.1046/j.1365-3040.2001.00668.x; Beven K, 2006, J HYDROL, V320, P18, DOI 10.1016/j.jhydrol.2005.07.007; Bishop C. M., 1995, NEURAL NETWORKS PATT; Bonan GB, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001593; Bousquet P, 2000, SCIENCE, V290, P1342, DOI 10.1126/science.290.5495.1342; BOWDEN RD, 1993, CAN J FOREST RES, V23, P1402, DOI 10.1139/x93-177; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; CAMPBELL GS, 1986, AGR FOREST METEOROL, V36, P317, DOI 10.1016/0168-1923(86)90010-9; Clark JS, 2001, SCIENCE, V293, P657, DOI 10.1126/science.293.5530.657; Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539; Cramer W, 2001, GLOBAL CHANGE BIOL, V7, P357, DOI 10.1046/j.1365-2486.2001.00383.x; dePury DGG, 1997, PLANT CELL ENVIRON, V20, P537, DOI 10.1111/j.1365-3040.1997.00094.x; Delworth TL, 2006, J CLIMATE, V19, P643, DOI 10.1175/JCLI3629.1; Denham KL, 2007, CLIMATE CHANGE, P501; Desai AR, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2010JG001423; Dietze MC, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2011JG001661; Dragoni D, 2011, GLOBAL CHANGE BIOL, V17, P886, DOI 10.1111/j.1365-2486.2010.02281.x; Fahey TJ, 2005, BIOGEOCHEMISTRY, V75, P109, DOI 10.1007/s10533-004-6321-y; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; FERNANDEZ IJ, 1993, CAN J SOIL SCI, V73, P317; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Franks SW, 1999, HYDROL EARTH SYST SC, V3, P477; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Fung IY, 2005, P NATL ACAD SCI USA, V102, P11201, DOI 10.1073/pnas.0504949102; Gaudinski JB, 2000, BIOGEOCHEMISTRY, V51, P33, DOI 10.1023/A:1006301010014; Gough CM, 2009, AGR FOREST METEOROL, V149, P1531, DOI 10.1016/j.agrformet.2009.04.006; Goulden ML, 1996, GLOB CHANGE BIOL, V2, P169, DOI 10.1111/j.1365-2486.1996.tb00070.x; Hanson PJ, 2004, ECOL MONOGR, V74, P443, DOI 10.1890/03-4049; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hayhoe K, 2008, MITIG ADAPT STRAT GL, V13, P425, DOI 10.1007/s11027-007-9133-2; Heimann M, 1998, GLOBAL BIOGEOCHEM CY, V12, P1, DOI 10.1029/97GB01936; Heimann M, 2008, NATURE, V451, P289, DOI 10.1038/nature06591; Jenkins J.C., 2004, NE319 USDA FOR SERV; Jolly WM, 2004, TREE PHYSIOL, V24, P1069; Keenan T, 2011, GLOBAL CHANGE BIOL, V17, P565, DOI 10.1111/j.1365-2486.2010.02254.x; Keenan TF, 2011, ECOL MODEL, V222, P737, DOI 10.1016/j.ecolmodel.2010.11.004; Keenan TF, 2012, GLOBAL CHANGE BIOL, V18, P1971, DOI 10.1111/j.1365-2486.2012.02678.x; Keenan TF, 2011, OECOLOGIA, V167, P587, DOI 10.1007/s00442-011-2106-x; Knorr W, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL029019; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; le Maire G, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001244; Lewis SL, 2009, NATURE, V457, P1003, DOI 10.1038/nature07771; Long SP, 2006, SCIENCE, V312, P1918, DOI 10.1126/science.1114722; Luo Y., 2008, B ECOL SOC AM, V89, P467, DOI DOI 10.1890/0012-9623; Luo YQ, 2011, GLOBAL CHANGE BIOL, V17, P843, DOI 10.1111/j.1365-2486.2010.02265.x; Magill AH, 2000, SOIL BIOL BIOCHEM, V32, P597, DOI 10.1016/S0038-0717(99)00186-8; McMahon SM, 2010, P NATL ACAD SCI USA, V107, P3611, DOI 10.1073/pnas.0912376107; Medvigy D, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000812; Metropolis N, 1949, J AM STAT ASSOC, V44, P355; Metropolis N., 1953, CHEM PHYS, V21, P1087, DOI DOI 10.1063/1.1699114; Moffat AM, 2010, GLOBAL CHANGE BIOL, V16, P2737, DOI 10.1111/j.1365-2486.2010.02171.x; Moffat AM, 2007, AGR FOREST METEOROL, V147, P209, DOI 10.1016/j.agrformet.2007.08.011; Munger JW, 1998, J GEOPHYS RES-ATMOS, V103, P8355, DOI 10.1029/98JD00168; Norman J.M., 1993, SCALING PHYSL PROCES, P41; O'Keefe J., 2000, PHENOLOGY WOODY SPEC; Pan YD, 2011, SCIENCE, V333, P988, DOI 10.1126/science.1201609; Phillips SC, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2008JG000858; Pilegaard K, 2011, AGR FOREST METEOROL, V151, P934, DOI 10.1016/j.agrformet.2011.02.013; Prentice I. C., 2001, CLIMATE CHANGE 2001, P183; Press W. H., 2007, NUMERICAL RECEIPES A; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Ricciuto DM, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001400; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; Richardson AD, 2006, AGR FOREST METEOROL, V136, P1, DOI 10.1016/j.agrformet.2006.01.007; Richardson AD, 2007, GLOBAL CHANGE BIOL, V13, P788, DOI 10.1111/j.1365-2486.2007.01330.x; Rojas R, 1996, NEURAL NETWORKS SYST; Salzer MW, 2009, P NATL ACAD SCI USA, V106, P20348, DOI 10.1073/pnas.0903029106; Savage K, 2009, AGR FOREST METEOROL, V149, P2012, DOI 10.1016/j.agrformet.2009.07.008; Schwalm CR, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001229; SINCLAIR TR, 1976, J APPL ECOL, V13, P813, DOI 10.2307/2402257; Siqueira MB, 2006, GLOBAL CHANGE BIOL, V12, P1189, DOI 10.1111/j.1365-2486.2006.01158.x; Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Urbanski S, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000293; van Oijen M, 2011, AGR FOREST METEOROL, V151, P1609, DOI 10.1016/j.agrformet.2011.06.017; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Wang YP, 2007, GLOBAL CHANGE BIOL, V13, P652, DOI 10.1111/j.1365-2486.2006.01225.x; Wang YP, 1998, AGR FOREST METEOROL, V91, P89, DOI 10.1016/S0168-1923(98)00061-6; Weng ES, 2011, ECOL APPL, V21, P1490, DOI 10.1890/09-1394.1; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; WOFSY SC, 1993, SCIENCE, V260, P1314, DOI 10.1126/science.260.5112.1314; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zaehle S, 2010, GLOBAL BIOGEOCHEM CY, V24, DOI 10.1029/2009GB003522	91	3	3	WILEY-BLACKWELL	HOBOKEN	111 RIVER ST, HOBOKEN 07030-5774, NJ USA	1354-1013		GLOBAL CHANGE BIOL	Glob. Change Biol.	AUG	2012	18	8					2555	2569		10.1111/j.1365-2486.2012.02684.x		15	Biodiversity Conservation; Ecology; Environmental Sciences	Biodiversity & Conservation; Environmental Sciences & Ecology	971UB	WOS:000306228300017	
J	Mattern, JP; Fennel, K; Dowd, M				Mattern, Jann Paul; Fennel, Katja; Dowd, Michael			Estimating time-dependent parameters for a biological ocean model using an emulator approach	JOURNAL OF MARINE SYSTEMS			English	Article						Statistical emulator; Polynomial chaos; Parameter estimation; Data assimilation; Time-dependent parameters; Biological model; 3D ocean model	MARINE ECOSYSTEM MODEL; MIDDLE ATLANTIC BIGHT; ENSEMBLE KALMAN FILTER; DATA ASSIMILATION; PHYTOPLANKTON GROWTH; POLYNOMIAL CHAOS; PHYSICAL MODEL; NORTH-ATLANTIC; SEA; ZOOPLANKTON	We use a statistical emulator technique, the polynomial chaos expansion, to estimate time-dependent values for two parameters of a 3-dimensional biological ocean model. We obtain values for the phytoplankton carbon-to-chlorophyll ratio and the zooplankton grazing rate by minimizing the misfit between simulated and satellite-based surface chlorophyll. The misfit is measured by a spatially averaged, time-dependent distance function. A cross-validation experiment demonstrates that the influence of outlying satellite data can be diminished by smoothing the distance function in time. The optimal values of the two parameters based on the smoothed distance function exhibit a strong time-dependence with distinct seasonal differences, without overfitting observations. Using these time-dependent parameters, we derive (hindcast) state estimates in two distinct ways: (1) by using the emulator-based interpolation and (2) by performing model runs with time-dependent parameters. Both approaches yield chlorophyll state estimates that agree better with the observations than model estimates with globally optimal, constant parameters. Moreover, the emulator approach provides us with estimates of parameter-induced model state uncertainty, which help determine at what time improvement in the model simulation is possible. The time-dependence of the analyzed parameters can be motivated biologically by naturally occurring seasonal changes in the composition of the plankton community. Our results suggest that the parameter values of typical biological ocean models should be treated as time-dependent and will result in a better representation of plankton dynamics in these models. We further demonstrate that emulator techniques are valuable tools for data assimilation and for analyzing and improving biological ocean models. (C) 2012 Elsevier B.V. All rights reserved.	[Mattern, Jann Paul; Dowd, Michael] Dalhousie Univ, Dept Math & Stat, Halifax, NS, Canada; [Mattern, Jann Paul; Fennel, Katja] Dalhousie Univ, Dept Oceanog, Halifax, NS, Canada	Mattern, JP (reprint author), Dalhousie Univ, Dept Math & Stat, Halifax, NS, Canada.	Paul.Mattern@dal.ca	Fennel, Katja/A-7470-2009	Fennel, Katja/0000-0003-3170-2331	ONR MURI [N00014-06-1-0739]; ACEnet; NSERC; CFI	This work was supported by the ONR MURI grant N00014-06-1-0739 to KF. KF is also acknowledging support from ACEnet, NSERC and CFI. MD acknowledges support from NSERC. We thank Carlisle Thacker for many constructive comments on an earlier version of this manuscript. We also thank an anonymous reviewer whose comments led to substantial improvements.	Allen JI, 2003, ANN GEOPHYS-GERMANY, V21, P399; Askey R., 1985, MEMOIRS AM MATH SOC, P319; Aumont O, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2001GB001745; BARLOW RG, 1993, DEEP-SEA RES PT II, V40, P459, DOI 10.1016/0967-0645(93)90027-K; Bianucci L, 2011, J GEOPHYS RES-OCEANS, V116, DOI 10.1029/2010JC006720; CHAN AT, 1980, J PHYCOL, V16, P428, DOI 10.1111/j.0022-3646.1980.00428.x; Chen K, 2010, J PHYS OCEANOGR, V40, P949, DOI 10.1175/2009JPO4262.1; Denman KL, 2003, PROG OCEANOGR, V57, P429, DOI 10.1016/S0079-6611(03)00109-5; Doney SC, 1996, DEEP-SEA RES PT II, V43, P591, DOI 10.1016/0967-0645(95)00104-2; Dowd M, 2007, J MARINE SYST, V68, P439, DOI 10.1016/j.jmarsys.2007.01.007; Dowd M, 2011, ENVIRONMETRICS, V22, P501, DOI 10.1002/env.1083; Evensen G., 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Fennel K, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL036147; Fennel K, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039818; Fennel K, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002456; FLAGG CN, 1994, DEEP-SEA RES PT II, V41, P411, DOI 10.1016/0967-0645(94)90030-2; Follows MJ, 2007, SCIENCE, V315, P1843, DOI 10.1126/science.1138544; Follows MJ, 2011, ANNU REV MAR SCI, V3, P427, DOI 10.1146/annurev-marine-120709-142848; Franks PJS, 1996, J MAR RES, V54, P631, DOI 10.1357/0022240963213718; Frolov S, 2009, DYNAM ATMOS OCEANS, V48, P16, DOI 10.1016/j.dynatmoce.2008.10.004; Geider RJ, 1997, MAR ECOL PROG SER, V148, P187, DOI 10.3354/meps148187; Geider RJ, 1998, LIMNOL OCEANOGR, V43, P679; Goebel NL, 2010, J MARINE SYST, V83, P221, DOI 10.1016/j.jmarsys.2010.05.002; Gregg WW, 2003, DEEP-SEA RES PT II, V50, P3143, DOI 10.1016/j.dsr2.2003.07.013; Haidvogel DB, 2008, J COMPUT PHYS, V227, P3595, DOI 10.1016/j.jcp.2007.06.016; Hooten MB, 2011, J AGR BIOL ENVIR ST, V16, P475, DOI 10.1007/s13253-011-0073-7; Hu JT, 2012, J MARINE SYST, V94, P145, DOI 10.1016/j.jmarsys.2011.11.016; Jones E, 2010, AUST METEOROL OCEAN, V59, P7; Kane J, 2005, J PLANKTON RES, V27, P401, DOI 10.1093/plankt/fbi009; Lawson LM, 1996, DEEP-SEA RES PT II, V43, P625, DOI 10.1016/0967-0645(95)00096-8; Lehmann MK, 2009, BIOGEOSCIENCES, V6, P1961; Losa SN, 2003, J MARINE SYST, V43, P31, DOI 10.1016/j.jmarsys.2003.06.001; Lucas DD, 2005, ATMOS CHEM PHYS, V5, P1505; Mannino A, 2008, J GEOPHYS RES-OCEANS, V113, DOI 10.1029/2007JC004493; MARRA J, 1990, J MAR RES, V48, P851; Marzouk YM, 2009, J COMPUT PHYS, V228, P1862, DOI 10.1016/j.jcp.2008.11.024; Mattern JP, 2010, J MARINE SYST, V79, P144, DOI 10.1016/j.jmarsys.2009.08.004; Mattern JP, 2010, REMOTE SENS-BASEL, V2, P794, DOI 10.3390/rs2030794; MCKAY MD, 1979, TECHNOMETRICS, V21, P239, DOI 10.2307/1268522; Moore JK, 2002, DEEP-SEA RES PT II, V49, P403, DOI 10.1016/S0967-0645(01)00108-4; Powell BS, 2008, OCEAN MODEL, V23, P130, DOI 10.1016/j.ocemod.2008.04.008; Previdi M, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000881; Rougier J, 2007, PHILOS T R SOC A, V365, P2133, DOI 10.1098/rsta.2007.2071; Scott V, 2011, OCEAN SCI, V7, P405, DOI 10.5194/os-7-405-2011; SMEDSTAD OM, 1991, PROG OCEANOGR, V26, P179, DOI 10.1016/0079-6611(91)90002-4; Thacker WC, 2012, OCEAN MODEL, V43-44, P52, DOI 10.1016/j.ocemod.2011.11.011; Wan XL, 2006, J SCI COMPUT, V27, P455, DOI 10.1007/s10915-005-9038-8; Wiener N, 1938, AM J MATH, V60, P897, DOI 10.2307/2371268; Xiu DB, 2002, SIAM J SCI COMPUT, V24, P619, DOI 10.1137/S1064827501387826; Xiu DB, 2003, J COMPUT PHYS, V187, P137, DOI 10.1016/S0021-9991(03)00092-5	50	4	4	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0924-7963		J MARINE SYST	J. Mar. Syst.	AUG	2012	96-97						32	47		10.1016/j.jmarsys.2012.01.015		16	Geosciences, Multidisciplinary; Marine & Freshwater Biology; Oceanography	Geology; Marine & Freshwater Biology; Oceanography	929TH	WOS:000303088100004	
J	Jones, SD; Le Quere, C; Rodenbeck, C				Jones, S. D.; Le Quere, C.; Roedenbeck, C.			Autocorrelation characteristics of surface ocean pCO(2) and air-sea CO2 fluxes	GLOBAL BIOGEOCHEMICAL CYCLES			English	Article							SUBPOLAR NORTH-ATLANTIC; CARBON-DIOXIDE; INTERANNUAL VARIABILITY; SPATIAL VARIABILITY; SOUTHERN-OCEAN; PACIFIC-OCEAN; TRANSPORT; VELOCITY; SEAWATER; TEMPERATURE	Understanding the variability and coherence of surface ocean pCO(2) on a global scale can provide insights into its physical and biogeochemical drivers and inform future samplings strategies and data assimilation methods. We present temporal and spatial autocorrelation analyses of surface ocean pCO(2) on a 5 degrees x 5 degrees grid using the Lamont-Doherty Earth Observatory database. The seasonal cycle is robust with an interannual autocorrelation of similar to 0.4 across multiple years. The global median spatial autocorrelation (e-folding) length is 400 +/- 250 km, with large variability across different regions. Autocorrelation lengths of up to 3,000 km are found along major currents and basin gyres while autocorrelation lengths as low as 50 km are found in coastal regions and other areas of physical turbulence. Zonal (east-west) autocorrelation lengths are typically longer than their meridional counterparts, reflecting the zonal nature of many major ocean features. Uncertainties in spatial autocorrelation in different ocean basins are between 42% and 73% of the calculated decorrelation length. The spatial autocorrelation length in air-sea fluxes is much shorter than for pCO(2) (200 +/- 150 km) due to the high variability of the gas transfer velocity.	[Jones, S. D.; Le Quere, C.] Univ E Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England; [Le Quere, C.] British Antarctic Survey, Cambridge CB3 0ET, England; [Roedenbeck, C.] Max Planck Inst Biogeochem, Jena, Germany	Jones, SD (reprint author), Univ E Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England.	s.jones3@uea.ac.uk			UK NERC [NE/F005733/1]	We thank all the people who contributed data to the LDEO database, in particular: Thorarinn S. Arnarson, Dorothee C. E. Bakker, Nicholas R. Bates, Richard Bellarby, Wei-Jun Cai, Francisco Chavez, David W. Chipman, Cathy E. Cosca, Brune Delille, Hein J. W. de Baar, Richard A. Feely, Gernot Friederich, John Goddard, Burke Hales, Mario Hoppema, Masao Ishii, Trus Johannessen, Arne Kortzinger, Nicolas Metzl, Takashi Midorikawa, Ludger Mintrop, P. P. Murphy, Timothy Newberger, Yukihiro Nojiri, Jon Olafsson, Are Olsen, Christopher L. Sabine, Ute Schuster, Tobias Steinhoff, Stewart C. Sutherland, Peter Salomeh, Colm Sweeney, Taro Takahashi, Rik Wanninkhof, Andrew Watson, Ray F. Weiss, C. S. Wong, and H. Yoshikawa-Inoue. The SSH anomaly products were produced and distributed by Aviso (http://www.aviso.oceanobs.com/), as part of the Ssalto ground processing segment. The SST data were Level 3 Standard measurements from the Aqua-MODIS satellite provided by NASA/GFSC/DAAC (http://oceancolor.gsfc.nasa.gov). The SeaWiFS Chlorophyll data were produced by NASA/GFSC/DAAC (http://oceancolor.gsfc.nasa.gov). We thank Ute Schuster, Andrew Manning, and the reviewers for their invaluable comments and suggestions. The auto-correlation calculations presented in this paper were carried out on the High Performance Computing Cluster supported by the Research Computing Service at the University of East Anglia. Steve Jones is supported by a PhD Studentship funded by UK NERC Project reference NE/F005733/1.	Bates NR, 1996, DEEP-SEA RES PT II, V43, P347, DOI 10.1016/0967-0645(95)00093-3; Bates NR, 1998, J GEOPHYS RES-OCEANS, V103, P15567, DOI 10.1029/98JC00247; Cosca CE, 2003, J GEOPHYS RES-OCEANS, V108, DOI 10.1029/2000JC000677; Dai MH, 2009, LIMNOL OCEANOGR, V54, P735, DOI 10.4319/lo.2009.54.3.0735; DASILVEIRA ICA, 1994, J GEOPHYS RES-OCEANS, V99, P22501, DOI 10.1029/94JC01776; Dickson R. R., 1994, J GEOPHYS RES, V12, P319, DOI DOI 10.1029/94JC00530; Fangohr S, 2007, J MARINE SYST, V66, P195, DOI 10.1016/j.jmarsys.2006.01.012; Flatau MK, 2003, J CLIMATE, V16, P2355, DOI 10.1175/2787.1; Fransson A, 2009, CONT SHELF RES, V29, P1317, DOI 10.1016/j.csr.2009.03.008; Gruber N, 2002, SCIENCE, V298, P2374, DOI 10.1126/science.1077077; Heywood KJ, 2002, NATURE, V415, P1011, DOI 10.1038/4151011a; Imawaki S, 2001, GEOPHYS RES LETT, V28, P17, DOI 10.1029/2000GL011796; Ingleby B, 2007, J MARINE SYST, V65, P158, DOI 10.1016/j.jmarsys.2005.11.019; Ishii M, 2009, DEEP-SEA RES PT II, V56, P591, DOI 10.1016/j.dsr2.2009.01.002; Jiang LQ, 2008, J GEOPHYS RES-OCEANS, V113, DOI 10.1029/2007JC004366; Kawabe M, 1995, J PHYS OCEANOGR, V25, P3103, DOI 10.1175/1520-0485(1995)025<3103:VOCPVA>2.0.CO;2; KEELING CD, 1976, TELLUS, V28, P538; KELLY KA, 1991, J GEOPHYS RES-OCEANS, V96, P16721, DOI 10.1029/91JC01380; Krasakopoulou E, 2009, CONT SHELF RES, V29, P1477, DOI 10.1016/j.csr.2009.03.015; Lefevre N, 2002, J GEOPHYS RES-OCEANS, V107, DOI 10.1029/2000JC000395; Leinweber A, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL037018; Lenton A, 2009, DEEP-SEA RES PT II, V56, P523, DOI 10.1016/j.dsr2.2008.12.008; Lenton A, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002620; Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689; Levitus S., 1982, 13 NOAA US DEP COMM; Li Z, 2005, J GEOPHYS RES-OCEANS, V110, DOI [10.1029/2004JC002723, 10.1029/2004GC002723]; Litt EJ, 2010, J PLANKTON RES, V32, P621, DOI 10.1093/plankt/fbp133; Fletcher SEM, 2006, GLOBAL BIOGEOCHEM CY, V20; Morales CE, 2004, DEEP-SEA RES PT II, V51, P2345, DOI 10.1016/j.dsr2.2004.08.008; MORAN PAP, 1950, BIOMETRIKA, V37, P17, DOI 10.2307/2332142; Olsen A, 2008, BIOGEOSCIENCES, V5, P535; Padin XA, 2010, BIOGEOSCIENCES, V7, P1587, DOI 10.5194/bg-7-1587-2010; Richardson PL, 2005, DEEP-SEA RES PT II, V52, P429, DOI 10.1016/j.dsr2.2004.11.001; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Santana-Casiano JM, 2009, DEEP-SEA RES PT II, V56, P533, DOI 10.1016/j.dsr2.2008.12.010; Sarma VVSS, 2003, J GEOPHYS RES-OCEANS, V108, DOI 10.1029/2001JC001062; Schmitz W. J., 1996, WHOI9603, VI; Schuster U, 2009, DEEP-SEA RES PT II, V56, P620, DOI 10.1016/j.dsr2.2008.12.011; Shim J, 2007, CONT SHELF RES, V27, P2623, DOI 10.1016/j.csr.2007.07.005; Simmons A., 2007, ECMWF NEWSLETTER, P25; Sweeney C, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002784; Sweeney C., 2002, LARGE SCALE CO2 OBSE, P155; Taft B.A., 1973, Journal of Physical Oceanography, V3, DOI 10.1175/1520-0485(1973)003<0347:CPABVO>2.0.CO;2; Takahashi T, 2009, DEEP-SEA RES PT II, V56, P554, DOI 10.1016/j.dsr2.2008.12.009; TAKAHASHI T, 1993, GLOBAL BIOGEOCHEM CY, V7, P843, DOI 10.1029/93GB02263; Takahashi T, 2002, DEEP-SEA RES PT II, V49, P1601, DOI 10.1016/S0967-0645(02)00003-6; Uppala SM, 2005, Q J ROY METEOR SOC, V131, P2961, DOI 10.1256/qj.04.176; van Sebille E, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL042847; Wanninkhof R., 1992, J GEOPHYS RES, V97, P7373, DOI DOI 10.1029/92JC00188; Watson AJ, 2006, TELLUS B, V58, P73, DOI 10.1111/j.1600-0889.2005.00167.x; Weiss R. F., 1974, MAR CHEM, V2, P203, DOI DOI 10.1016/0304-4203(74)90015-2; WICHURA MJ, 1988, APPL STAT-J ROY ST C, V37, P477, DOI 10.2307/2347330; Wong CS, 2010, DEEP-SEA RES PT I, V57, P687, DOI 10.1016/j.dsr.2010.02.003; Zhang LJ, 2010, CONT SHELF RES, V30, P293, DOI 10.1016/j.csr.2009.11.009; [Anonymous], 2011, GLOBALVIEW CO2 COOP	55	0	0	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0886-6236		GLOBAL BIOGEOCHEM CY	Glob. Biogeochem. Cycle	JUN 27	2012	26								GB2042	10.1029/2010GB004017		12	Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences	Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences	968OK	WOS:000305992200001	
J	Hilker, T; Hall, FG; Tucker, CJ; Coops, NC; Black, TA; Nichol, CJ; Sellers, PJ; Barr, A; Hollinger, DY; Munger, JW				Hilker, Thomas; Hall, Forrest G.; Tucker, Compton J.; Coops, Nicholas C.; Black, T. Andrew; Nichol, Caroline J.; Sellers, Piers J.; Barr, Alan; Hollinger, David Y.; Munger, J. W.			Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: II Model implementation and validation	REMOTE SENSING OF ENVIRONMENT			English	Article						Data assimilation; Photosynthesis; Eddy-flux; Multivariate function; Epsilon; Epsilon max; Global carbon cycle; Carbon modeling; Vegetation carbon cycle; Downregulation; CHRIS/Proba; PRI'; Multi-angular	CARBON-DIOXIDE FLUXES; GROSS PRIMARY PRODUCTION; DOUGLAS-FIR FOREST; LEAF-AREA INDEX; DECIDUOUS FOREST; BOREAL; MODIS; CANOPY; CANADA; VARIABILITY	Spatially explicit and temporally continuous estimates of photosynthesis will be of great importance for increasing our understanding of and ultimately closing the terrestrial carbon cycle. Current capabilities to model photosynthesis, however, are limited by accurate enough representations of the complexity of the underlying biochemical processes and the numerous environmental constraints imposed upon plant primary production. A potentially powerful alternative to model photosynthesis through these indirect observations is the use of multi-angular satellite data to infer light-use efficiency (epsilon) directly from spectral reflectance properties in connection with canopy shadow fractions. Hall et al. (this issue) introduced a new approach for predicting gross ecosystem production that would allow the use of such observations in a data assimilation mode to obtain spatially explicit variations in epsilon from infrequent polar-orbiting satellite observations, while meteorological data are used to account for the more dynamic responses of epsilon to variations in environmental conditions caused by changes in weather and illumination. In this second part of the study we implement and validate the approach of Hall et al. (this issue) across an ecologically diverse array of eight flux-tower sites in North America using data acquired from the Compact High Resolution Imaging Spectroradiometer (CHRIS) and eddy-flux observations. Our results show significantly enhanced estimates of epsilon and therefore cumulative gross ecosystem production (GEP) over the course of one year at all examined sites. We also demonstrate that epsilon is greatly heterogeneous even across small study areas. Data assimilation and direct inference of GEP from space using a new, proposed sensor could therefore be a significant step towards closing the terrestrial carbon cycle. (c) 2012 Elsevier Inc. All rights reserved.	[Hilker, Thomas; Hall, Forrest G.; Tucker, Compton J.; Sellers, Piers J.] NASA, Goddard Space Flight Ctr, Biospher Sci Branch, Greenbelt, MD 20771 USA; [Coops, Nicholas C.] Univ British Columbia, Fac Forest Resources Management, Vancouver, BC V6T 1Z4, Canada; [Black, T. Andrew] Univ British Columbia, Fac Land & Food Syst, Vancouver, BC V6T 1Z4, Canada; [Nichol, Caroline J.] Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland; [Barr, Alan] Environm Canada, Saskatoon, SK, Canada; [Hollinger, David Y.] US Forest Serv, No Res Stn, Durham, NH USA; [Munger, J. W.] Harvard Univ, Cambridge, MA 02138 USA	Hilker, T (reprint author), NASA, Goddard Space Flight Ctr, Biospher Sci Branch, Code 618, Greenbelt, MD 20771 USA.	thomas.hilker@nasa.gov	Coops, Nicholas/J-1543-2012; Hollinger, David/G-7185-2012		NASA; Canadian Carbon Program; Natural Sciences and Engineering Research Council of Canada (NSERC); BIOCAP	The ESA CHRIS/Proba images were provided by Dr. David G. Goodenough, Dr. Ray Merton, and Dr. Mathias Kneubuhler, all principal investigators of the Evaluation and Validation of CHRIS (EVC) Project. The Center for Remote Sensing and Department of Geography at Boston University are thanked for provision of the GOMS model. Partial funding for this study was provided by NASA's Terrestrial Ecology Program managed by Dr. Diane Wickland. This research is also partially funded by the Canadian Carbon Program, the Natural Sciences and Engineering Research Council of Canada (NSERC) and BIOCAP, and an NSERC-Accelerator grant to NCC.	Amiro BD, 2006, AGR FOREST METEOROL, V136, P237, DOI 10.1016/j.agrformet.2004.11.012; Baldocchi DD, 2003, GLOBAL CHANGE BIOL, V9, P479, DOI 10.1046/j.1365-2486.2003.00629.x; Barr AG, 2004, AGR FOREST METEOROL, V126, P237, DOI 10.1016/j.agrformet.2004.06.011; Bergeron O, 2007, GLOBAL CHANGE BIOL, V13, P89, DOI 10.1111/j.1365-2486.2006.01281.x; Bernacchi CJ, 2002, PLANT PHYSIOL, V130, P1992, DOI 10.1104/pp.008250; Black TA, 1996, GLOB CHANGE BIOL, V2, P219, DOI 10.1111/j.1365-2486.1996.tb00074.x; BUCK AL, 1981, J APPL METEOROL, V20, P1527, DOI 10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2; Chen JM, 1996, IEEE T GEOSCI REMOTE, V34, P1353, DOI 10.1109/36.544559; Chen JM, 2006, AGR FOREST METEOROL, V140, P257, DOI 10.1016/j.agrformet.2006.08.005; Coleman TF, 1996, SIAM J OPTIMIZ, V6, P418, DOI 10.1137/0806023; Coleman TF, 2002, COMPUT OPTIM APPL, V21, P177, DOI 10.1023/A:1013764800871; Coops NC, 2010, BIOSCIENCE, V60, P788, DOI 10.1525/bio.2010.60.10.5; DemmigAdams B, 1996, TRENDS PLANT SCI, V1, P21, DOI 10.1016/S1360-1385(96)80019-7; Field C.B., 1986, EC PLANT FORM FUNCTI, P25; Gomez-Chova L, 2008, APPL OPTICS, V47, pF46, DOI 10.1364/AO.47.000F46; Goulden ML, 1996, SCIENCE, V271, P1576, DOI 10.1126/science.271.5255.1576; Hall FG, 2008, REMOTE SENS ENVIRON, V112, P3201, DOI 10.1016/j.rse.2008.03.015; Hall FG, 2011, REMOTE SENS ENVIRON, V115, P1918, DOI 10.1016/j.rse.2011.03.014; Heinsch FA, 2006, IEEE T GEOSCI REMOTE, V44, P1908, DOI 10.1109/TGRS.2005.853936; Hilker T, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2011JG001692; Hilker T, 2009, REMOTE SENS ENVIRON, V113, P2463, DOI 10.1016/j.rse.2009.07.012; Hilker T, 2008, TREE PHYSIOL, V28, P825; Hilker T, 2009, REMOTE SENS ENVIRON, V113, P421, DOI 10.1016/j.rse.2008.10.003; Hilker T, 2010, REMOTE SENS ENVIRON, V114, P2863, DOI 10.1016/j.rse.2010.07.004; Hilker T, 2010, TREES-STRUCT FUNCT, V24, P819, DOI 10.1007/s00468-010-0452-7; Hollinger DY, 2004, GLOBAL CHANGE BIOL, V10, P1689, DOI 10.1111/j.1365-2486.2004.00847.x; Horii C. V., 2004, J GEOPHYS RES ATMOSP, V109; Humphreys ER, 2006, AGR FOREST METEOROL, V140, P6, DOI 10.1016/j.agrformet.2006.03.018; JARVIS PG, 1976, PHILOS T ROY SOC B, V273, P593, DOI 10.1098/rstb.1976.0035; Kidston J, 2010, BOUND-LAY METEOROL, V136, P193, DOI 10.1007/s10546-010-9507-y; LI XW, 1985, IEEE T GEOSCI REMOTE, V23, P705, DOI 10.1109/TGRS.1985.289389; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Lyapustin A, 2007, REMOTE SENS ENVIRON, V107, P12, DOI 10.1016/j.rse.2006.09.028; Ma JL, 2010, IEEE T GEOSCI REMOTE, V48, P2829, DOI 10.1109/TGRS.2010.2042813; Milker T., 2008, J GEOPHYS RES BIOGEO, P113; MONTEITH JL, 1977, PHILOS T ROY SOC B, V281, P277, DOI 10.1098/rstb.1977.0140; MONTEITH JL, 1972, J APPL ECOL, V9, P747, DOI 10.2307/2401901; Morgenstern K, 2004, AGR FOREST METEOROL, V123, P201, DOI 10.1016/j.agrformet.2003.12.003; Munger JW, 1996, J GEOPHYS RES-ATMOS, V101, P12639, DOI 10.1029/96JD00230; Running SW, 2004, BIOSCIENCE, V54, P547, DOI 10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2; Schwalm CR, 2006, AGR FOREST METEOROL, V140, P269, DOI 10.1016/j.agrformet.2006.06.010; Staebler RM, 2005, J APPL METEOROL, V44, P1161, DOI 10.1175/JAM2265.1; Thuillier G, 2003, SOL PHYS, V214, P1, DOI 10.1023/A:1024048429145; Turner DP, 2003, REMOTE SENS ENVIRON, V88, P256, DOI 10.1016/j.rse.2003.06.005; Verstraete MM, 1996, REMOTE SENS ENVIRON, V58, P201, DOI 10.1016/S0034-4257(96)00069-7; Xiao XM, 2005, ECOL APPL, V15, P954, DOI 10.1890/04-0470; Zha T, 2009, GLOBAL CHANGE BIOL, V15, P1475, DOI 10.1111/j.1365-2486.2008.01817.x; Zhao MS, 2005, REMOTE SENS ENVIRON, V95, P164, DOI 10.1016/j.rse.2004.12.011	48	1	1	ELSEVIER SCIENCE INC	NEW YORK	360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA	0034-4257		REMOTE SENS ENVIRON	Remote Sens. Environ.	JUN	2012	121						287	300		10.1016/j.rse.2012.02.008		14	Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology	955WN	WOS:000305051700025	
J	Hall, FG; Hilker, T; Coops, NC				Hall, Forrest G.; Hilker, Thomas; Coops, Nicholas C.			Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: I. Model formulation	REMOTE SENSING OF ENVIRONMENT			English	Article						Photosynthesis; Light-use efficiency; Data assimilation; Multivariate model; CHRIS/Proba; Eddy covariance; Carbon cycle	NET PRIMARY PRODUCTION; MESOPHYLL CONDUCTANCE; TEMPERATURE RESPONSE; STOMATAL CONDUCTANCE; DECIDUOUS FOREST; IN-VIVO; PRODUCTIVITY; KINETICS; CLIMATE; LEAVES	Forest photosynthetic exchange rates at landscape scales have proven difficult to either accurately measure or estimate. Recent developments (Hall et al., 2011, 2008: Hilker et al., 2011a, 2010a) permit us to infer photosynthetic forest light use efficiency (epsilon) using multi-angle measurements of photochemical reflectance index (PRI) from the CHRIS/PROBA satellite imaging spectrometer, thus completing a long sought-after capability to remotely sense the major inputs driving gross primary production GPP i.e., epsilon and absorbed photosynthetically active radiation (APAR). In this first of two companion papers we introduce the theoretical underpinnings of an innovative approach that utilizes our recent developments to produce remotely sensed and spatially explicit maps of epsilon and GPP from space, and a data assimilation approach to extend the spatially explicit maps to diurnal, daily and annual time scales. We quantify GPP using the traditional radiation-limited approach of Monteith (1972); however we apply it in an innovative way. [I] Using CHRIS/PROBA we quantify epsilon at each satellite overpass for a 625 km(2) area at 30 m resolution. [II] We use a novel physiologically-based multivariate function of APAR, temperature and water vapor pressure deficit model (described herein) and use it to down-regulate epsilon at 30 minute intervals. [III] We use the CHRIS/PROBA images of spatial variation in epsilon, and NDVI to quantify APAR, hence produce snapshots of GPP. We use a data assimilation approach to extend epsilon and GPP to temporally continuous and spatially contiguous maps of vegetation carbon uptake. In the second part of this study (Hilker et al., 2011b) we demonstrate and validate our approach over eight different forest flux tower sites in North America. (c) 2012 Elsevier Inc. All rights reserved.	[Hall, Forrest G.; Hilker, Thomas] NASA, Goddard Space Flight Ctr, Biospher Sci Branch, Greenbelt, MD 20771 USA; [Coops, Nicholas C.] Univ British Columbia, Fac Forest Resources Management, Vancouver, BC V6T 1Z4, Canada	Hilker, T (reprint author), NASA, Goddard Space Flight Ctr, Biospher Sci Branch, Code 614-4, Greenbelt, MD 20771 USA.	Forrest.G.Hall@nasa.gov; thomas.hilker@nasa.gov	Coops, Nicholas/J-1543-2012		NASA; Canadian Carbon Program; Natural Sciences and Engineering Research Council of Canada (NSERC); BIOCAP	Partial funding for this study was provided by NASA's Terrestrial Ecology Program managed by Dr. Diane Wickland. This research is also partially funded by the Canadian Carbon Program, the Natural Sciences and Engineering Research Council of Canada (NSERC) and BIOCAP.	Bernacchi CJ, 2002, PLANT PHYSIOL, V130, P1992, DOI 10.1104/pp.008250; Bernacchi CJ, 2001, PLANT CELL ENVIRON, V24, P253, DOI 10.1046/j.1365-3040.2001.00668.x; Canadell JG, 2007, P NATL ACAD SCI USA, V104, P18866, DOI 10.1073/pnas.0702737104; DemmigAdams B, 1996, TRENDS PLANT SCI, V1, P21, DOI 10.1016/S1360-1385(96)80019-7; Drolet GG, 2008, REMOTE SENS ENVIRON, V112, P3064, DOI 10.1016/j.rse.2008.03.002; Drolet GG, 2005, REMOTE SENS ENVIRON, V98, P212, DOI 10.1016/j.rse.2005.07.006; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; FARQUHAR GD, 1982, ANNU REV PLANT PHYS, V33, P317, DOI 10.1146/annurev.pp.33.060182.001533; Field C.B., 1986, EC PLANT FORM FUNCTI, P25; GAMON JA, 1992, REMOTE SENS ENVIRON, V41, P35, DOI 10.1016/0034-4257(92)90059-S; Goetz SJ, 1996, AGR FOREST METEOROL, V78, P149, DOI 10.1016/0168-1923(95)02268-6; Goulden ML, 1996, SCIENCE, V271, P1576, DOI 10.1126/science.271.5255.1576; Hall FG, 2008, REMOTE SENS ENVIRON, V112, P3201, DOI 10.1016/j.rse.2008.03.015; Hall FG, 2011, REMOTE SENS ENVIRON, V115, P1918, DOI 10.1016/j.rse.2011.03.014; Harley P. C., 1991, MODELING PHOTOSYNTHE; Heinsch FA, 2006, IEEE T GEOSCI REMOTE, V44, P1908, DOI 10.1109/TGRS.2005.853936; Hilker T, 2009, REMOTE SENS ENVIRON, V113, P2463, DOI 10.1016/j.rse.2009.07.012; Hilker T, 2010, INSTRUM SCI TECHNOL, V38, P319, DOI 10.1080/10739149.2010.508357; Hilker T, 2008, TREE PHYSIOL, V28, P825; Hilker T, 2011, OECOLOGIA, V165, P865, DOI 10.1007/s00442-010-1901-0; Hilker T., 2011, J GEOPHYS RES BIOGEO, V116; Hilker T, 2010, REMOTE SENS ENVIRON, V114, P2863, DOI 10.1016/j.rse.2010.07.004; JARVIS PG, 1976, PHILOS T ROY SOC B, V273, P593, DOI 10.1098/rstb.1976.0035; LLOYD J, 1995, PLANT CELL ENVIRON, V18, P1129, DOI 10.1111/j.1365-3040.1995.tb00624.x; Martin TA, 1999, TREE PHYSIOL, V19, P435; Medlyn BE, 1998, TREE PHYSIOL, V18, P167; MONTEITH JL, 1977, PHILOS T ROY SOC B, V281, P277, DOI 10.1098/rstb.1977.0140; MONTEITH JL, 1972, J APPL ECOL, V9, P747, DOI 10.2307/2401901; Niinemets U, 2010, FOREST ECOL MANAG, V260, P1623, DOI 10.1016/j.foreco.2010.07.054; PENMAN HL, 1951, SYM SOC EXP BIOL, V5, P115; Pons TL, 2009, J EXP BOT, V60, P2217, DOI 10.1093/jxb/erp081; Running SW, 2004, BIOSCIENCE, V54, P547, DOI 10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2; Schwalm CR, 2006, AGR FOREST METEOROL, V140, P269, DOI 10.1016/j.agrformet.2006.06.010; SELLERS PJ, 1986, J ATMOS SCI, V43, P505, DOI 10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2; TUCKER CJ, 1986, INT J REMOTE SENS, V7, P1395; Turner DP, 2003, GLOBAL CHANGE BIOL, V9, P383, DOI 10.1046/j.1365-2486.2003.00573.x; VONCAEMMERER S, 1994, PLANTA, V195, P88; Waring RH, 2011, J PLANT ECOL-UK, V4, P101, DOI 10.1093/jpe/rtq041; Zhao MS, 2005, REMOTE SENS ENVIRON, V95, P164, DOI 10.1016/j.rse.2004.12.011	39	1	1	ELSEVIER SCIENCE INC	NEW YORK	360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA	0034-4257		REMOTE SENS ENVIRON	Remote Sens. Environ.	JUN	2012	121						301	308		10.1016/j.rse.2012.02.007		8	Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology	955WN	WOS:000305051700026	
J	Hu, JT; Fennel, K; Mattern, JP; Wilkin, J				Hu, Jiatang; Fennel, Katja; Mattern, Jann Paul; Wilkin, John			Data assimilation with a local Ensemble Kalman Filter applied to a three-dimensional biological model of the Middle Atlantic Bight	JOURNAL OF MARINE SYSTEMS			English	Article						Data assimilation; Ensemble Kalman Filter; State estimation; Regional Ocean Modeling System; Biological model; Satellite ocean chlorophyll data	MARINE ECOSYSTEM MODEL; OCEAN COLOR DATA; NORTH-ATLANTIC; SEA; PARAMETERIZATION; PHYTOPLANKTON; ALGORITHM; SYSTEM	A multivariate sequential data assimilation approach, the Localized Ensemble Kalman Filter (LEnKF), was used to assimilate daily satellite observations of ocean chlorophyll into a three-dimensional physical-biological model of the Middle Atlantic Bight (MAB) for the year 2006. Covariance localization was applied to make the EnKF analysis more effective by removing spurious long-range correlations in the ensemble approximation of the model's covariance. The model is based on the Regional Ocean Modeling System (ROMS) and coupled to a biological nitrogen cycle model, which includes seven state variables: chlorophyll, phytoplankton, nitrate, ammonium, small and large detrital nitrogen, and zooplankton. An ensemble of 20 model simulations, generated by perturbing the biological parameters according to assumed probability distributions, was used. Model fields of chlorophyll, phytoplankton, nitrate and zooplankton were updated at all vertical layers during LEnKF analysis steps, based on their cross-correlations with surface chlorophyll (the observed variable). The performance of the LEnKF scheme, its influence on the model's predictive skill and on surface particulate organic matter concentrations and primary production are investigated. Estimates of surface chlorophyll and particulate organic carbon are improved in the data-assimilative simulation when compared to one without any assimilation, as is the model's predictive skill. (C) 2011 Elsevier B.V. All rights reserved.	[Hu, Jiatang; Fennel, Katja; Mattern, Jann Paul] Dalhousie Univ, Dept Oceanog, Halifax, NS, Canada; [Mattern, Jann Paul] Dalhousie Univ, Dept Math & Stat, Halifax, NS, Canada; [Wilkin, John] Rutgers State Univ, Inst Marine & Coastal Sci, New Brunswick, NJ 08903 USA	Hu, JT (reprint author), Dalhousie Univ, Dept Oceanog, Halifax, NS, Canada.	jiatang.hu@dal.ca	Fennel, Katja/A-7470-2009; Wilkin, John/E-5343-2011	Fennel, Katja/0000-0003-3170-2331; Wilkin, John/0000-0002-5444-9466	ONR MURI [N00014-06-1-0739]; NSERC; CFI	This work was supported by the ONR MURI grant N00014-06-1-0739 to KF and JW. KF was also supported by NSERC and CFI. We thank Kimberly Hyde for making the satellite observations of ocean chlorophyll and POC available, and two anonymous reviewers for their constructive comments on an earlier version of this manuscript.	Allen JI, 2003, ANN GEOPHYS-GERMANY, V21, P399; Anderson JL, 1999, MON WEATHER REV, V127, P2741, DOI 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2; Bagniewski W, 2011, BIOGEOSCIENCES, V8, P1291, DOI 10.5194/bg-8-1291-2011; Brusdal K, 2003, J MARINE SYST, V40, P253, DOI 10.1016/S0924-7963(03)00021-6; Burgers G, 1998, MON WEATHER REV, V126, P1719, DOI 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2; Carmillet V, 2001, OCEAN MODEL, V3, P167, DOI 10.1016/S1463-5003(01)00007-5; Chen K, 2010, J PHYS OCEANOGR, V40, P949, DOI 10.1175/2009JPO4262.1; Druon JN, 2010, ESTUAR COAST SHELF S, V88, P488, DOI 10.1016/j.ecss.2010.05.010; Eknes M, 2002, J MARINE SYST, V36, P75, DOI 10.1016/S0924-7963(02)00134-3; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Evensen G., 2006, DATA ASSIMILATION EN; Evensen G, 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Evensen G., 2009, CONTROL SYST MAG IEE, V29, P82; Fairall CW, 2003, J CLIMATE, V16, P571, DOI 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2; Fennel K, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL036147; Fennel K, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039818; Fennel K, 2001, J MARINE SYST, V28, P45, DOI 10.1016/S0924-7963(00)00083-X; Fennel K, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002456; Fontana C, 2009, CONT SHELF RES, V29, P1397, DOI 10.1016/j.csr.2009.03.005; Friedrichs MAM, 2006, DEEP-SEA RES PT II, V53, P576, DOI 10.1016/j.dsr2.2006.01.026; Gaspari G, 1999, Q J ROY METEOR SOC, V125, P723, DOI 10.1256/smsqj.55416; Geider RJ, 1996, LIMNOL OCEANOGR, V41, P1; Haidvogel DB, 2008, J COMPUT PHYS, V227, P3595, DOI 10.1016/j.jcp.2007.06.016; Hamill TM, 2001, MON WEATHER REV, V129, P2776, DOI 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2; Higdon R.L., 1997, J COMP PHYS, V135, P31; Hooker S.B., 1992, SEAWIFS TECHNICAL RE, V1; Houtekamer PL, 1998, MON WEATHER REV, V126, P796, DOI 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2; Houtekamer PL, 2001, MON WEATHER REV, V129, P123, DOI 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2; Lehmann M.K., 2009, BIOGEOSCIENCES, V6, P1; Mattern JP, 2010, J MARINE SYST, V79, P144, DOI 10.1016/j.jmarsys.2009.08.004; Mattern J.P., 2008, THESIS U LUBECK; Mesinger F, 2006, B AM METEOROL SOC, V87, P343, DOI 10.1175/BAMS-87-3-343; Mukai A.Y., 2002, ERDCCHLTR0224; Natvik LJ, 2003, J MARINE SYST, V40, P127, DOI 10.1016/S0924-7963(03)00016-2; Nerger L, 2007, J MARINE SYST, V68, P237, DOI 10.1016/j.jmarsys.2006.11.009; Ourmieres Y, 2009, J MARINE SYST, V75, P100, DOI 10.1016/j.jmarsys.2008.08.003; PAULSON CA, 1977, J PHYS OCEANOGR, V7, P952, DOI 10.1175/1520-0485(1977)007<0952:IMITUO>2.0.CO;2; Previdi M, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000881; Shchepetkin A., 2009, COMPUTATIONAL KERNEL; Shchepetkin AF, 2005, OCEAN MODEL, V9, P347, DOI 10.1016/j.ocemod.2004.08.002; SMOLARKIEWICZ PK, 1984, J COMPUT PHYS, V54, P325, DOI 10.1016/0021-9991(84)90121-9; Spitz YH, 1998, J MARINE SYST, V16, P51, DOI 10.1016/S0924-7963(97)00099-7; Triantafyllou G, 2007, OCEAN SCI, V3, P397; Warner JC, 2008, COMPUT GEOSCI-UK, V34, P1284, DOI 10.1016/j.cageo.2008.02.012; Warner JC, 2005, OCEAN MODEL, V8, P81, DOI 10.1016/j.ocemod.2003.12.003	45	2	2	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0924-7963		J MARINE SYST	J. Mar. Syst.	JUN	2012	94						145	156		10.1016/j.jmarsys.2011.11.016		12	Geosciences, Multidisciplinary; Marine & Freshwater Biology; Oceanography	Geology; Marine & Freshwater Biology; Oceanography	898NG	WOS:000300748500013	
J	Jahangiri, HR; Zhang, DX				Jahangiri, Hamid Reza; Zhang, Dongxiao			Ensemble based co-optimization of carbon dioxide sequestration and enhanced oil recovery	INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL			English	Article						CO2 sequestration; Enhanced oil recovery; Co-optimization; Net present value; Ensemble based optimization	KALMAN FILTER; SCREENING CRITERIA; DATA ASSIMILATION; EOR; RESERVOIRS; RANKING; STORAGE	Sequestration of carbon dioxide (CO2) in depleted or partially depleted oil reservoirs is a plausible option to reduce CO2 emissions into the atmosphere. Carbon dioxide has been used as the injection fluid in enhanced oil recovery (EOR) operations. The goal of such projects is to improve the profitability by maximizing the oil production (to increase the revenue) and minimizing the CO2 injection (to decrease the costs). However, in sequestration projects, subsurface storage of the injected CO2 needs to be maximized. The objective of this study is to develop a framework to co-optimize the oil extraction and CO2 sequestration. In our work, factors such as the cost of capturing the produced CO2, CO2 transportation and recycling are taken into account. In the proposed framework, the net present value (NPV) of the project is selected as the optimization objective function. The ensemble-based optimization (EnOpt) algorithm has been chosen as the optimization algorithm and the well injection patterns and rates as the controlling variables. A synthetic case is used to demonstrate the applicability of the developed technique. Our results show that the oil recovery and the NPV can be increased significantly. The proposed methodology is fairly robust as it does not require adjoint programming and can be readily used with any reservoir simulator. The workflow presented in this work can be used to design and co-optimize the coupled CO2 sequestration and EOR. (C) 2012 Elsevier Ltd. All rights reserved.	[Zhang, Dongxiao] Peking Univ, Coll Engn, ERE, Beijing 100871, Peoples R China; [Zhang, Dongxiao] Peking Univ, Coll Engn, SKLTCS, Beijing 100871, Peoples R China; [Jahangiri, Hamid Reza] Univ So Calif, Mork Family Dept Chem Engn & Mat Sci, Los Angeles, CA 90089 USA	Zhang, DX (reprint author), Peking Univ, Coll Engn, ERE, Beijing 100871, Peoples R China.	dxz@pku.edu.cn	Zhang, Dongxiao/D-5289-2009		Stanford University; China National Key Special Science and Technology Program [2011ZX05009-006, 2011ZX05025]	This work is partially supported by the Global Climate and Energy Project (GCEP) at Stanford University via a grant to University of Southern California, Peking University, and China University of Geosciences at Wuhan as well as by China National Key Special Science and Technology Program through grants 2011ZX05009-006 and 2011ZX05025.	Asheim H., 1988, SPE EUR PETR C LOND; Bachu S, 2003, ENVIRON GEOL, V44, P277, DOI 10.1007/s00254-003-0762-9; BLUNT M, 1993, ENERG CONVERS MANAGE, V34, P1197, DOI 10.1016/0196-8904(93)90069-M; Brouwer D.R., 2004, SPE ANN TECHN C EXH; Bryant E., 1997, CLIMATE PROCESS CHAN, P209; Chang HB, 2010, J COMPUT PHYS, V229, P8011, DOI 10.1016/j.jcp.2010.07.005; Chen Y., 2008, SPE DOE IMPR OIL REC; Evensen G., 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Fathi Z., 1984, SOC PETROLEUM EN JUN, P333; [GeoQuest Schlumberger], 2008, ECLIPSE REF MAN 2008; Ghomian Y., 2008, SPE ANN TECHN C EXH; Gu Y, 2007, SPE J, V12, P438; Guler B., 2001, SPE ANN TECHN C EXH; Harding T.J., 1998, SPE J, V3, P99, DOI DOI 10.2118/36379-PA; Haugen V, 2008, SPE J, V13, P382; Jahangiri H., 2010, SPE W REG M AN CA; Jahangiri H.R., 2011, OFFSH TECHN C HOUST; Khan S.A., 1992, EXPERT SYSTEM AID CO; Kovscek AR, 2002, PETROL SCI TECHNOL, V20, P841, DOI 10.1081/LFT-120003717; Kovscek AR, 2005, ENERG CONVERS MANAGE, V46, P1941, DOI 10.1016/j.enconman.2004.09.009; Lim M.T., 1994, SPE DOE 9 S IMPR OIL; Malik Q.M., 2000, SPE DOE 8 S IMPR OIL; Naevdal G, 2005, SPE J, V10, P66; Nwaozo J, 2006, THESIS U OKLAHOMA; ORR FM, 1984, SCIENCE, V224, P563, DOI 10.1126/science.224.4649.563; Ramirez W.F., 1984, SOC PETROLEUM EN JUN, P328; Rastegar R., 2009, SPE RES SIM S WOODL; Sarma P., 2005, SPE RES SIM S WOODL; Shaw J, 2002, J CAN PETROL TECHNOL, V41, P51; Taber JJ, 1997, SPE RESERVOIR ENG, V12, P189; Taber JJ, 1997, SPE RESERVOIR ENG, V12, P199; Tavakkolian M., 2004, 28 ANN SPE INT TECHN; Wang C., 2007, SPE ANN TECHN C EXH; Wang C.C., 1984, SPE DOE 4 S ENH OIL; Zhang DX, 2007, SPE J, V12, P108	35	0	0	ELSEVIER SCI LTD	OXFORD	THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND	1750-5836		INT J GREENH GAS CON	Int. J. Greenh. Gas Control	MAY	2012	8						22	33		10.1016/j.ijggc.2012.01.013		12	Energy & Fuels; Engineering, Environmental; Meteorology & Atmospheric Sciences	Energy & Fuels; Engineering; Meteorology & Atmospheric Sciences	941NT	WOS:000303970600003	
J	Hooghiemstra, PB; Krol, MC; Bergamaschi, P; de Laat, ATJ; van der Werf, GR; Novelli, PC; Deeter, MN; Aben, I; Rockmann, T				Hooghiemstra, P. B.; Krol, M. C.; Bergamaschi, P.; de Laat, A. T. J.; van der Werf, G. R.; Novelli, P. C.; Deeter, M. N.; Aben, I.; Rockmann, T.			Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							VARIATIONAL DATA ASSIMILATION; ZOOM MODEL TM5; CARBON-MONOXIDE; TROPOSPHERIC CHEMISTRY; INVERSION; VALIDATION; SCIAMACHY; ALGORITHM; POLLUTION; AIRCRAFT	This paper compares two global inversions to estimate carbon monoxide (CO) emissions for 2004. Either surface flask observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) or CO total columns from the Measurement of Pollution in the Troposphere (MOPITT) instrument are assimilated in a 4D-Var framework. Inferred emission estimates from the two inversions are consistent over the Northern Hemisphere (NH). For example, both inversions increase anthropogenic CO emissions over Europe (from 46 to 94 Tg CO/yr) and Asia (from 222 to 420 Tg CO/yr). In the Southern Hemisphere (SH), three important findings are reported. First, due to their different vertical sensitivity, the stations-only inversion increases SH biomass burning emissions by 108 Tg CO/yr more than the MOPITT-only inversion. Conversely, the MOPITT-only inversion results in SH natural emissions (mainly CO from oxidation of NMVOCs) that are 185 Tg CO/yr higher compared to the stations-only inversion. Second, MOPITT-only derived biomass burning emissions are reduced with respect to the prior which is in contrast to previous (inverse) modeling studies. Finally, MOPITT derived total emissions are significantly higher for South America and Africa compared to the stations-only inversion. This is likely due to a positive bias in the MOPITT V4 product. This bias is also apparent from validation with surface stations and ground-truth FTIR columns. Our results show that a combined inversion is promising in the NH. However, implementation of a satellite bias correction scheme is essential to combine both observational data sets in the SH.	[Hooghiemstra, P. B.; Krol, M. C.; de Laat, A. T. J.; Aben, I.] SRON, Netherlands Inst Space Res, NL-3584 CA Utrecht, Netherlands; [Hooghiemstra, P. B.; Krol, M. C.; Rockmann, T.] Univ Utrecht, Inst Marine & Atmospher Res Utrecht, NL-3584 CC Utrecht, Netherlands; [Krol, M. C.] Wageningen Univ, Dept Meteorol & Air Qual, Wageningen, Netherlands; [Bergamaschi, P.] Commiss European Communities, Joint Res Ctr, Inst Environm & Sustainabil, I-21027 Ispra, VA, Italy; [de Laat, A. T. J.] Royal Netherlands Meteorol Inst, NL-3732 GK De Bilt, Netherlands; [van der Werf, G. R.] Free Univ Amsterdam, Fac Earth & Life Sci, NL-1081 HV Amsterdam, Netherlands; [Novelli, P. C.] NOAA, Climate Monitoring & Diagnost Lab, Boulder, CO 80303 USA; [Deeter, M. N.] Natl Ctr Atmospher Res, Div Atmospher Chem, Boulder, CO 80307 USA	Hooghiemstra, PB (reprint author), SRON, Netherlands Inst Space Res, Sorbonnelaan 2, NL-3584 CA Utrecht, Netherlands.	p.b.hooghiemstra@uu.nl			Dutch User Support Programme [GO-AO/05]	This research was supported by the Dutch User Support Programme 2006-2010 under project GO-AO/05. FTIR data used in this publication were obtained as part of the Network for the Detection of Atmospheric Composition Change (NDACC) and are publicly available (see http://www.ndacc.org). The Dutch National Computer Facility (NCF) is acknowledged for computer resources. We are most thankful to S. Basu for help during the model development phase. We are thankful to S. Myriokefalitakis for supplying the a priori fields of NMVOC-CO production. We also thank S. Houweling for optimized methane mixing ratio fields. Finally, we thank G. Maenhout for useful discussion during the writing process.	Arellano AF, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006613; Arellano AF, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020311; Bergamaschi P, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007268; Bergamaschi P, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012287; Bergamaschi P, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014180; Bergamaschi P, 2000, J GEOPHYS RES-ATMOS, V105, P1909, DOI 10.1029/1999JD900818; Bergamaschi P, 2005, ATMOS CHEM PHYS, V5, P2431; Chevallier F., 2007, Geophysical Research Letters, V34, DOI 10.1029/2007GL030463; Chevallier F, 2009, BIOGEOSCIENCES, V6, P103; Deeter MN, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003186; Deeter M.N., 2009, MOPITT MEASUREMENTS; Deeter MN, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013005; de Laat ATJ, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012698; DUNCAN BN, 2007, J GEOPHYS RES, V112, DOI DOI 10.1029/2007JD008459; Emmons LK, 2009, ATMOS CHEM PHYS, V9, P1795; Emmons LK, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007833; Fisher M., 1995, ESTIMATING COVARIANC; Fortems-Cheiney A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014416; Gonzi S, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012053; Heald CL, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD005185; HESTENES MR, 1952, J RES NAT BUR STAND, V49, P409, DOI 10.6028/jres.049.044; Hooghiemstra PB, 2011, ATMOS CHEM PHYS, V11, P4705, DOI 10.5194/acp-11-4705-2011; Houweling S, 1998, J GEOPHYS RES-ATMOS, V103, P10673, DOI 10.1029/97JD03582; Huijnen V, 2010, GEOSCI MODEL DEV, V3, P445, DOI 10.5194/gmd-3-445-2010; Jiang Z, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015282; Jones DBA, 2009, ATMOS CHEM PHYS, V9, P3547; Kasibhatla P, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015581; KOPACZ M., 2007, J GEOPHYS RES, V113, DOI [DOI 10.1029/2007JD009264, 10.1029/2007JD009264]; Kopacz M, 2010, ATMOS CHEM PHYS, V10, P855; Krol M, 2005, ATMOS CHEM PHYS, V5, P417; LANCZOS C, 1950, J RES NAT BUR STAND, V45, P255, DOI 10.6028/jres.045.026; Liu JH, 2010, ATMOS CHEM PHYS, V10, P12207, DOI 10.5194/acp-10-12207-2010; LOGAN JA, 1981, J GEOPHYS RES-OC ATM, V86, P7210, DOI 10.1029/JC086iC08p07210; Meirink JF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009740; Meirink JF, 2008, ATMOS CHEM PHYS, V8, P6341; Mukherjee C, 2011, ATMOS CHEM PHYS, V11, P5365, DOI 10.5194/acp-11-5365-2011; Muller JF, 2005, ATMOS CHEM PHYS, V5, P1157; Myriokefalitakis S, 2008, ATMOS CHEM PHYS, V8, P4965; Nedelec P, 2003, ATMOS CHEM PHYS, V3, P1551; Novelli PC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003031; Palmer PI, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003397; Peters W, 2010, GLOBAL CHANGE BIOL, V16, P1317, DOI 10.1111/j.1365-2486.2009.02078.x; Petron G, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001305; Petron G, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020560; Pison I, 2009, ATMOS CHEM PHYS, V9, P5281; Spivakovsky CM, 2000, J GEOPHYS RES-ATMOS, V105, P8931, DOI 10.1029/1999JD901006; Stavrakou T, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006896; Stavrakou T, 2009, ATMOS CHEM PHYS, V9, P3663; Martin MV, 2010, ATMOS CHEM PHYS, V10, P1491; van der Werf GR, 2010, ATMOS CHEM PHYS, V10, P11707, DOI 10.5194/acp-10-11707-2010; Yurganov L, 2010, ATMOS CHEM PHYS, V10, P3479	51	2	2	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	MAR 27	2012	117								D06309	10.1029/2011JD017043		23	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	918GE	WOS:000302236500005	
J	While, J; Totterdell, I; Martin, M				While, J.; Totterdell, I.; Martin, M.			Assimilation of pCO(2) data into a global coupled physical-biogeochemical ocean model	JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS			English	Article							QUALITY-CONTROL; SEA; CO2; THERMODYNAMICS; CALCIFICATION; DISSOCIATION; ATLANTIC; SEAWATER; SYSTEM; ACID	As part of the CARBOOCEAN project, measurements of the partial pressure of carbon dioxide (pCO(2)) in the ocean have been collected since 2001. Our aim was to use these data in a data assimilation context to improve the representation of pCO(2) and its associated prognostic variables in biogeochemical models. Based upon a sequential framework a new method for the assimilation of pCO(2) data was developed. This method had to account for oceanic pCO(2) being a derived quantity with a functional dependence on temperature, salinity, dissolved inorganic carbon (DIC), and alkalinity. Our method calculates pCO(2) increments using an analysis correction technique and then converts these into increments in model variables. Temperature and salinity are assumed to be error free, and so only DIC and alkalinity are updated. Furthermore, in DIC and alkalinity space increments are taken to be perpendicular to the local line of constant pCO(2). Our method was tested by assimilating pCO(2) data from 2006 into the NEMO-HadOCC biogeochemical model and then comparing the output from this model to a control with no pCO(2) assimilation. While lack of data prevented the assimilation system from correcting a significant systematic error in the subpolar North Atlantic, results from this experiment showed that pCO(2) assimilation, despite limited data, reduced RMS and mean errors and also brought the model closer to climatology in the subtropical North Atlantic.	[While, J.; Totterdell, I.] Met Off, Hadley Ctr Climate Predict & Res, Exeter EX1 3PB, Devon, England	While, J (reprint author), Met Off, Hadley Ctr Climate Predict & Res, FitzRoy Rd, Exeter EX1 3PB, Devon, England.	james.while@metoffice.gov.uk			CARBOOCEAN, European Commission [511176]	We would like to thank the CARBOOCEAN project for providing us with the data used in this report, particularly the following principal investigators: A. Borges, M. Gonzalez Davila, T. Johannessen, A. Krtzinger, A. Olsen, A. Omar, A. Rios, J.M. Santana-Casiano, U. Schuster, T. Steinhoff, D. Wallace, and A. Watson. This work was funded through the CARBOOCEAN project, European Commission grant 511176 (GOCE).	Bacastow R. B., 1981, CARBON CYCLE MODELLI, P95; Bloom SC, 1996, MON WEATHER REV, V124, P1256, DOI 10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2; Cummings J, 2009, OCEANOGRAPHY, V22, P96; Davies T, 2005, Q J ROY METEOR SOC, V131, P1759, DOI 10.1256/qj.04.101; Dickson A.G., 1994, HDB METHODS ANAL VAR; DICKSON AG, 1990, DEEP-SEA RES, V37, P755, DOI 10.1016/0198-0149(90)90004-F; Dickson AG, 2007, PICES SPECIAL PUBLIC, V3, P191; Fichefet T, 1997, J GEOPHYS RES-OCEANS, V102, P12609, DOI 10.1029/97JC00480; Davila MG, 2005, J GEOPHYS RES-OCEANS, V110, DOI 10.1029/2004JC002763; Gregg WW, 2008, J MARINE SYST, V69, P205, DOI 10.1016/j.jmarsys.2006.02.015; Hemmings JCP, 2008, J MAR RES, V66, P87, DOI 10.1357/002224008784815739; Iglesias-Rodriguez MD, 2008, SCIENCE, V320, P336, DOI 10.1126/science.1154122; Ingleby B, 2007, J MARINE SYST, V65, P158, DOI 10.1016/j.jmarsys.2005.11.019; Jackson DR, 2008, Q J ROY METEOR SOC, V134, P1567, DOI 10.1002/qj.294; Kara AB, 2000, J GEOPHYS RES-OCEANS, V105, P16803, DOI 10.1029/2000JC900072; LORENC AC, 1988, Q J ROY METEOR SOC, V114, P515, DOI 10.1002/qj.49711448012; LORENC AC, 1991, Q J ROY METEOR SOC, V117, P59, DOI 10.1002/qj.49711749704; Madec G., 2008, 27 I PIERR SIM LAPL; Martin M.J., 2007, Quarterly Journal of the Royal Meteorological Society, V133, DOI 10.1002/qj.74; MILLERO FJ, 1995, GEOCHIM COSMOCHIM AC, V59, P661, DOI 10.1016/0016-7037(94)00354-O; Nerger L, 2007, J MARINE SYST, V68, P237, DOI 10.1016/j.jmarsys.2006.11.009; Olsen A, 2008, BIOGEOSCIENCES, V5, P535; Omar AM, 2010, OCEAN SCI, V6, P77; Padin XA, 2010, BIOGEOSCIENCES, V7, P1587, DOI 10.5194/bg-7-1587-2010; Palmer JR, 2001, DEEP-SEA RES PT I, V48, P1169, DOI 10.1016/S0967-0637(00)00080-7; Riebesell U, 2000, NATURE, V407, P364, DOI 10.1038/35030078; ROY RN, 1993, MAR CHEM, V44, P249, DOI 10.1016/0304-4203(93)90207-5; Schuster U, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003941; Takahashi T, 2009, DEEP-SEA RES PT II, V56, P554, DOI 10.1016/j.dsr2.2008.12.009	29	1	1	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0148-0227		J GEOPHYS RES-OCEANS	J. Geophys. Res.-Oceans	MAR 23	2012	117								C03037	10.1029/2010JC006815		11	Oceanography	Oceanography	914FT	WOS:000301935600001	
J	Koffi, EN; Rayner, PJ; Scholze, M; Beer, C				Koffi, E. N.; Rayner, P. J.; Scholze, M.; Beer, C.			Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon-cycle data assimilation system	GLOBAL BIOGEOCHEMICAL CYCLES			English	Article							EDDY COVARIANCE TECHNIQUE; GLOBAL VEGETATION MODEL; TERRESTRIAL BIOSPHERE; TROPICAL FORESTS; CO2; TRANSPORT; INVERSION; DIOXIDE; EXCHANGE; LEAVES	This paper combines an atmospheric transport model and a terrestrial ecosystem model to estimate gross primary productivity (GPP) and net ecosystem productivity (NEP) of the land biosphere. Using atmospheric CO2 observations in a Carbon Cycle Data Assimilation System (CCDAS) we estimate a terrestrial global GPP of 146 +/- 19 GtC/yr. However, the current observing network cannot distinguish this best estimate from a different assimilation experiment yielding a terrestrial global GPP of 117 GtC/yr. Spatial estimates of GPP agree with data-driven estimates in the extratropics but are overestimated in the poorly observed tropics. The uncertainty analysis of previous studies was extended by using two atmospheric transport models and different CO2 observing networks. We find that estimates of GPP and NEP are less sensitive to these choices than the form of the prior probability for model parameters. NEP is also found to be significantly sensitive to the transport model and this sensitivity is not greatly reduced compared to direct atmospheric transport inversions, which optimize NEP directly.	[Koffi, E. N.] Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France; [Rayner, P. J.] Univ Melbourne, Sch Earth Sci, Melbourne, Vic 3010, Australia; [Scholze, M.] Univ Bristol, Dept Earth Sci, Bristol BS8 1RJ, Avon, England; [Beer, C.] Max Planck Inst Biogeochem, Biogeochem Model Data Integrat Grp, D-07745 Jena, Germany	Koffi, EN (reprint author), Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France.	ernest.koffi@lsce.ipsl.fr	Beer, Christian/D-2296-2013		European Commission through the IMECC Integrated Infrastructure Initiative [026188]; ARC [DP1096309]	We acknowledge the financial support of the European Commission through the IMECC Integrated Infrastructure Initiative (I3) project under the 6th Framework Program (contract 026188). PR is in receipt of an ARC Professorial Fellowship (DP1096309). We would like to sincerely thank both reviewers for thoughtful, thorough and constructive comments on the manuscript.	Andres RJ, 1996, GLOBAL BIOGEOCHEM CY, V10, P419, DOI 10.1029/96GB01523; Aubinet M., 2000, ADV ECOL RES, V30, P114; Baker DF, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2004GB002439; Baldocchi DD, 2003, GLOBAL CHANGE BIOL, V9, P479, DOI 10.1046/j.1365-2486.2003.00629.x; Beer C, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003233; Beer C, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL029006; Beer C, 2010, SCIENCE, V329, P834, DOI 10.1126/science.1184984; BEERLING DJ, 1995, GLOB CHANGE BIOL, V1, P289, DOI 10.1111/j.1365-2486.1995.tb00027.x; Brenkert A. L., 1998, CARBON DIOXIDE EMISS; Clark DA, 2001, ECOL APPL, V11, P371, DOI 10.2307/3060895; Clark DA, 2001, ECOL APPL, V11, P356, DOI 10.2307/3060894; COLLATZ GJ, 1992, AUST J PLANT PHYSIOL, V19, P519; Crisp D, 2004, ADV SPACE RES-SERIES, V34, P700, DOI 10.1016/j.asr.2003.08.062; Enting I. G., 2002, INVERSE PROBLEMS ATM, DOI [10.1017/CBO9780511535741, DOI 10.1017/CB09780511535741]; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; FARQUHAR GD, 1988, SYM SOC EXP BIOL, V42, P395; Foken T, 1996, AGR FOREST METEOROL, V78, P83, DOI 10.1016/0168-1923(95)02248-1; Frankenberg C, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL048738; Giering R, 1998, ACM T MATH SOFTWARE, V24, P437, DOI 10.1145/293686.293695; GILBERT JC, 1989, MATH PROGRAM, V45, P407, DOI 10.1007/BF01589113; GLOBALVIEW-CO2, 2004, GLOBALVIEW CO2 COOP; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; Gurney KR, 2003, TELLUS B, V55, P555, DOI 10.1034/j.1600-0889.2003.00049.x; Gurney KR, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002111; Hamazaki T., 2004, 20 ISPRS C INT SOC P; Hauglustaine DA, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003957; Heimann M., 1995, 10 DTSCH KLIM; Heimann M., 2003, 5 M PLANCK I BIOG; Houghton R A, 2008, TRENDS COMPENDIUM DA; HOUGHTON R A, 1987, Tellus Series B Chemical and Physical Meteorology, V39, P122; Imhoff ML, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007377; Imhoff ML, 2004, NATURE, V429, P870, DOI 10.1038/nature02619; Kaminski T, 2003, LECT NOTES COMPUT SC, V2668, P95; Kaminski T, 1999, J GEOPHYS RES-ATMOS, V104, P18555, DOI 10.1029/1999JD900146; Kaminski T, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001463; Kattge J, 2009, GLOBAL CHANGE BIOL, V15, P976, DOI 10.1111/j.1365-2486.2008.01744.x; Knorr W, 2001, ADV GLOB CHANGE RES, V7, P273; Knorr W, 2000, GLOBAL ECOL BIOGEOGR, V9, P225, DOI 10.1046/j.1365-2699.2000.00159.x; Krinner G, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2003GB002199; Lasslop G, 2010, GLOBAL CHANGE BIOL, V16, P187, DOI 10.1111/j.1365-2486.2009.02041.x; Law RM, 2003, TELLUS B, V55, P580, DOI 10.1034/j.1600-0889.2003.00053.x; Law RM, 1996, GLOBAL BIOGEOCHEM CY, V10, P783, DOI 10.1029/96GB01892; Le Quere C, 2003, TELLUS B, V55, P649, DOI 10.1034/j.1600-0889.2003.00043.x; LIETH H, 1973, HUM ECOL, V1, P303, DOI 10.1007/BF01536729; Luyssaert S, 2007, GLOBAL CHANGE BIOL, V13, P2509, DOI 10.1111/j.1365-2486.2007.01439.x; Malmstrom CM, 1997, GLOBAL BIOGEOCHEM CY, V11, P367, DOI 10.1029/97GB01419; Marland G, 2001, TRENDS COMPENDIUM DA; MASARIE KA, 1995, J GEOPHYS RES-ATMOS, V100, P11593, DOI 10.1029/95JD00859; McMurtrie RE, 2008, FUNCT PLANT BIOL, V35, P521, DOI 10.1071/FP08128; Nijssen B, 2001, J CLIMATE, V14, P1790, DOI 10.1175/1520-0442(2001)014<1790:GREOSM>2.0.CO;2; Nowak RS, 2004, NEW PHYTOL, V162, P253, DOI 10.1111/j.1469-8137.2004.01033.x; Oleson K. W., 2010, NCARTN478STR; Papale D, 2006, BIOGEOSCIENCES, V3, P571; Prince SD, 2001, ECOL APPL, V11, P1194, DOI 10.1890/1051-0761(2001)011[1194:NPPOUS]2.0.CO;2; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Rayner PJ, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003068; Rebmann C, 2005, THEOR APPL CLIMATOL, V80, P121, DOI 10.1007/s00704-004-0095-y; Reichstein M, 2005, GLOBAL CHANGE BIOL, V11, P1424, DOI 10.1111/j.1365-2486.2005.001002.x; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Takahashi T., 1999, P 2 INT S CO2 OC CTR, P9; Tarantola A., 1987, INVERSE PROBLEM THEO; Tarantola A., 2005, INVERSE PROBLEM THEO; WILSON MF, 1985, J CLIMATOL, V5, P119; Yuan WP, 2010, REMOTE SENS ENVIRON, V114, P1416, DOI 10.1016/j.rse.2010.01.022; Ziehn T, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047182	67	2	2	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0886-6236		GLOBAL BIOGEOCHEM CY	Glob. Biogeochem. Cycle	MAR 17	2012	26								GB1024	10.1029/2010GB003900		15	Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences	Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences	910UX	WOS:000301669600001	
J	Decker, M; Brunke, MA; Wang, Z; Sakaguchi, K; Zeng, XB; Bosilovich, MG				Decker, Mark; Brunke, Michael A.; Wang, Zhuo; Sakaguchi, Koichi; Zeng, Xubin; Bosilovich, Michael G.			Evaluation of the Reanalysis Products from GSFC, NCEP, and ECMWF Using Flux Tower Observations	JOURNAL OF CLIMATE			English	Article							MIXED HARDWOOD FOREST; CARBON-DIOXIDE; CO2 EXCHANGE; CHAPARRAL ECOSYSTEM; ATMOSPHERE EXCHANGE; PONDEROSA PINE; SOIL-MOISTURE; UNITED-STATES; GREAT-PLAINS; WATER-VAPOR	Reanalysis products produced at the various centers around the globe are utilized for many different scientific endeavors, including forcing land surface models and creating surface flux estimates, Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP- NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.	[Decker, Mark] Univ New S Wales, Climate Change Res Ctr, Sydney, NSW 2502, Australia; [Decker, Mark; Brunke, Michael A.; Wang, Zhuo; Sakaguchi, Koichi; Zeng, Xubin] Univ Arizona, Dept Atmospher Sci, Tucson, AZ USA; [Bosilovich, Michael G.] NASA, Global Modeling & Assimilat Off, Goddard Space Flight Ctr, Greenbelt, MD USA	Decker, M (reprint author), Univ New S Wales, Climate Change Res Ctr, Mathews Bldg,Level 4, Sydney, NSW 2502, Australia.	m.decker@unsw.edu.au	Bosilovich, Michael/F-8175-2012; Sakaguchi, Koichi/D-9557-2013	Sakaguchi, Koichi/0000-0001-9672-6364	NASA [NNX09A021G]; NOAA [NA10NES4400006]; NSF [AGS-0944101]	This work was supported by NASA (Grant NNX09A021G), NOAA (Grant NA10NES4400006), and NSF (Grant AGS-0944101). We thank Hoshin Gupta for suggesting the analysis of the mean square error decomposition and two anonymous reviewers for their insightful suggestions (including the impact of energy balance closure issue in tower measurements on reanalysis evaluations). We thank the scientists at NCAR CISL, as they provided web access to the reanalysis products from NCEP and ECMWF. Also, we thank NCAR for the use of the NCAR computers for obtaining data from the mass store system. The GLDAS data used in this study were acquired as part of the mission of NASA's Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). We thank all of the principal investigators for each of the towers utilized in this study, each contributed their data to the Flux Net database, as their efforts have produced an abundance of valuable data for the earth sciences community. Finally, we thank the Oak Ridge National Laboratory for providing access to the Flux Net data.	Aubinet M, 2000, ADV ECOL RES, V30, P113; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Berg AA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003334; Betts A. K, 2008, 568 ECMWF, V568; Betts AK, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000427; Bloom SC, 1996, MON WEATHER REV, V124, P1256, DOI 10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2; Bosilovich MG, 2008, J APPL METEOROL CLIM, V47, P2279, DOI 10.1175/2008JAMC1921.1; Brunke MA, 2003, J CLIMATE, V16, P619, DOI 10.1175/1520-0442(2003)016<0619:WBAAAL>2.0.CO;2; Decker M, 2009, J ADV MODEL EARTH SY, V1, DOI 10.3894/JAMES.2009.1.5; Dore S, 2003, GLOBAL CHANGE BIOL, V9, P84, DOI 10.1046/j.1365-2486.2003.00561.x; Ek MB, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003296; Falk M, 2008, TREE PHYSIOL, V28, P509; Fan Y, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004345; Fang H, 2009, README DOCUMENT GLOB; Fischer M. L, 2005, TR048 DOE ARM; Foken T, 2011, B AM METEOROL SOC, V92, pES13, DOI 10.1175/2011BAMS3130.1; Gholz HL, 2002, AGR FOREST METEOROL, V112, P87, DOI 10.1016/S0168-1923(02)00059-X; Gibson J. K., 1997, ECMWF REANALYSIS PRO, V1; Gilmanov TG, 2005, GLOBAL ECOL BIOGEOGR, V14, P271, DOI 10.1111/j.1466-822x.2005.00151.x; Goldstein AH, 2000, AGR FOREST METEOROL, V101, P113, DOI 10.1016/S0168-1923(99)00168-9; Goulden ML, 2006, GLOBAL CHANGE BIOL, V12, P2146, DOI 10.1111/j.1365-2486.2006.01251.x; Griffies S. M, 2004, 5 GFDL OC TROUP, V5; Gupta HV, 2009, J HYDROL, V377, P80, DOI 10.1016/j.jhydrol.2009.08.003; Hollinger DY, 1999, GLOB CHANGE BIOL, V5, P891, DOI 10.1046/j.1365-2486.1999.00281.x; Hollinger DY, 2004, GLOBAL CHANGE BIOL, V10, P1689, DOI 10.1111/j.1365-2486.2004.00847.x; Hollinger SE, 2005, AGR FOREST METEOROL, V130, P59, DOI 10.1016/j.agrformet.2005.01.005; Janowiak JE, 1998, J CLIMATE, V11, P2960, DOI 10.1175/1520-0442(1998)011<2960:ACOTNN>2.0.CO;2; Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2; Koster RD, 2000, J GEOPHYS RES-ATMOS, V105, P24809, DOI 10.1029/2000JD900327; Law BE, 2003, GLOBAL CHANGE BIOL, V9, P510, DOI 10.1046/j.1365-2486.2003.00624.x; Lin SJ, 2004, MON WEATHER REV, V132, P2293, DOI 10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2; Lipson DA, 2005, APPL ENVIRON MICROB, V71, P8573, DOI 10.1128/AEM.71.12.8573-8580.2005; Qian TT, 2006, J HYDROMETEOROL, V7, P953, DOI 10.1175/JHM540.1; Rienecker M. M, 2008, TECH REP SERIES GLOB; Rodell M, 2004, B AM METEOROL SOC, V85, P381, DOI 10.1175/BAMS-85-3-381; Ruiz-Barradas A, 2005, J CLIMATE, V18, P1808, DOI 10.1175/JCLI3343.1; Saha S, 2010, B AM METEOROL SOC, V91, P1015, DOI 10.1175/2010BAMS3001.1; Schmid HP, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003011; Schmid HP, 2000, AGR FOREST METEOROL, V103, P357, DOI 10.1016/S0168-1923(00)00140-4; Simmons A., 2006, ECMWF NEWSLETTER, P25; Sims DA, 2006, REMOTE SENS ENVIRON, V103, P289, DOI 10.1016/j.rse.2005.01.020; Steinfeld G, 2007, BOUND-LAY METEOROL, V123, P77, DOI 10.1007/s10546-006-9133-x; Sulman BN, 2009, BIOGEOSCIENCES, V6, P1115; Trenberth KE, 1998, CLIM DYNAM, V14, P213, DOI 10.1007/s003820050219; Uppala SM, 2005, Q J ROY METEOR SOC, V131, P2961, DOI 10.1256/qj.04.176; Urbanski S, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000293; White P. W, 2003, CY23R4 IFS; Wilson K, 2002, AGR FOREST METEOROL, V113, P223, DOI 10.1016/S0168-1923(02)00109-0; Xu LK, 2004, AGR FOREST METEOROL, V123, P79, DOI 10.1016/j.agrformet.2003.10.004	49	3	3	AMER METEOROLOGICAL SOC	BOSTON	45 BEACON ST, BOSTON, MA 02108-3693 USA	0894-8755		J CLIMATE	J. Clim.	MAR 15	2012	25	6					1916	1944		10.1175/JCLI-D-11-00004.1		29	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	910EP	WOS:000301620300010	
J	Goosse, H; Guiot, J; Mann, ME; Dubinkina, S; Sallaz-Damaz, Y				Goosse, Hugues; Guiot, Joel; Mann, Michael E.; Dubinkina, Svetlana; Sallaz-Damaz, Yoann			The medieval climate anomaly in Europe: Comparison of the summer and annual mean signals in two reconstructions and in simulations with data assimilation	GLOBAL AND PLANETARY CHANGE			English	Article						Europe; Last Millennium; seasonal climate signal; atmospheric circulation; data assimilation	NORTH-ATLANTIC OSCILLATION; LAND-COVER CHANGE; LAST MILLENNIUM; TEMPERATURE-VARIATIONS; LATE HOLOCENE; CARBON-CYCLE; CIRCULATION VARIABILITY; INTERMEDIATE COMPLEXITY; FENNOSCANDIAN SUMMERS; SURFACE-TEMPERATURE	The spatial pattern and potential dynamical origin of the Medieval Climate Anomaly (MCA, around 1000 AD) in Europe are assessed with two recent reconstructions and simulations constrained to follow those reconstructions by means of paleoclimate data assimilation. The simulations employ a climate model of intermediate complexity (LOVECLIM). The data assimilation technique is based on a particle filter using an ensemble of 96 simulations. The peak winter (and annual mean) warming during the MCA, in our analyses, is found to be strongest at high latitudes, associated with strengthened mid-latitude westerlies. Summer warmth, by contrast, is found to be greatest in southern Europe and the Mediterranean Sea, associated with reduced westerlies and strengthened southerly winds off North Africa. The results of our analysis thus underscore the complexity of the spatial and seasonal structure of the MCA in Europe. (C) 2011 Elsevier B.V. All rights reserved.	[Goosse, Hugues; Dubinkina, Svetlana; Sallaz-Damaz, Yoann] Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, B-1348 Louvain, Belgium; [Guiot, Joel] Aix Marseille Univ, CNRS, CEREGE, Aix En Provence, France; [Mann, Michael E.] Penn State Univ, Dept Meteorol & Earth, University Pk, PA 16802 USA; [Mann, Michael E.] Penn State Univ, Environm Syst Inst, University Pk, PA 16802 USA	Goosse, H (reprint author), Catholic Univ Louvain, Earth & Life Inst, Georges Lemaitre Ctr Earth & Climate Res, Chemin Cyclotron 2, B-1348 Louvain, Belgium.	hugues.goosse@uclouvain.be	Guiot, Joel/G-7818-2011	Guiot, Joel/0000-0001-7345-4466	F.R.S.-FNRS; Belgian Federal Science Policy Office; EU; NSF [ATM-0902133]; French National Research Agency [ANR-06-VULN-010]	H. Goosse is Research Associate with the Fonds National de la Recherche Scientifique (F.R.S.-FNRS-Belgium). This work is supported by the F.R.S.-FNRS and by the Belgian Federal Science Policy Office (Research Program on Science for a Sustainable Development) and by EU (project Past4future). M.E. Mann gratefully acknowledges support from the NSF Paleoclimate program (grant number ATM-0902133). J. Guiot acknowledges support from the French National Research Agency (program VMC, project ESCARSEL ANR-06-VULN-010). Aurelien Mairesse helped in the design of Fig. 1. The simulations were performed on the computers of the Institut de calcul intensif et de stockage de masse of the Universite catholique de Louvain.	Allan R, 2006, J CLIMATE, V19, P5816, DOI 10.1175/JCLI3937.1; Ammann CM, 2007, P NATL ACAD SCI USA, V104, P3713, DOI 10.1073/pnas.0605064103; Bard E, 2000, TELLUS B, V52, P985, DOI 10.1034/j.1600-0889.2000.d01-7.x; BARNSTON AG, 1987, MON WEATHER REV, V115, P1083, DOI 10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2; Bengtsson L, 2006, CLIM DYNAM, V27, P743, DOI 10.1007/s00382-006-0168-y; Berger A. L., 1978, J ATMOS SCI, V35, P2363; Bradley RS, 2003, SCIENCE, V302, P404, DOI 10.1126/science.1090372; Brewer S., 2006, CLIM PAST, V3, P355; BRIFFA KR, 1992, CLIM DYNAM, V7, P111; Brohan P, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006548; Brovkin V, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001662; Brovkin V, 2006, CLIM DYNAM, V26, P587, DOI 10.1007/s00382-005-0092-6; Buntgen U., 2006, J CLIMATOL, V19, P5607; Buntgen U, 2005, CLIM DYNAM, V25, P141, DOI 10.1007/s00382-005-0028-1; Casty C, 2007, CLIM DYNAM, V29, P791, DOI 10.1007/s00382-007-0257-6; CHARLSON RJ, 1991, TELLUS A, V43, P152, DOI 10.1034/j.1600-0870.1991.00013.x; Charman DJ, 2010, QUATERNARY SCI REV, V29, P1539, DOI 10.1016/j.quascirev.2009.02.017; Corona C, 2010, CLIM PAST, V6, P379, DOI 10.5194/cp-6-379-2010; Crespin E, 2009, CLIM PAST, V5, P389; Crowley TJ, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017801; Delaygue G, 2011, CLIM DYNAM, V36, P2201, DOI 10.1007/s00382-010-0795-1; Dubinkina S., 2011, INT J BIFURCAT CHAOS, V13, P2011; Feddema J, 2005, CLIM DYNAM, V25, P581, DOI 10.1007/s00382-005-0038-z; Fischer EM, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL027992; Folland CK, 2009, J CLIMATE, V22, P1082, DOI 10.1175/2008JCLI2459.1; Foukal P, 2006, NATURE, V443, P161, DOI 10.1038/nature05072; Gaillard MJ, 2010, CLIM PAST, V6, P483, DOI 10.5194/cp-6-483-2010; Gao CC, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010239; Gomez-Navarro J. J., 2010, CLIM PAST DISCUSS, V6, P2071; Gonzalez-Rouco JF, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL024693; Goosse H, 1999, J GEOPHYS RES-OCEANS, V104, P23337, DOI 10.1029/1999JC900215; Goosse H, 2006, CLIM PAST, V2, P99; Goosse H, 2005, QUATERNARY SCI REV, V24, P1345, DOI 10.1016/j.quascirev.2004.12.009; Goosse H, 2004, CLIM DYNAM, V23, P153, DOI 10.1007/s00382-004-0424-y; Goosse H, 2006, CLIM DYNAM, V27, P165, DOI 10.1007/s00382-006-0128-6; Goosse H, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012737; Goosse H, 2010, GEOSCI MODEL DEV, V3, P603, DOI 10.5194/gmd-3-603-2010; Goudie A., 1993, HUMAN IMPACT NATURAL; Gouriand I., 2007, TELLUS A, V59, P30; Graham NE, 2011, CLIM DYNAM, V37, P1217, DOI 10.1007/s00382-010-0914-z; Gray LJ, 2010, REV GEOPHYS, V48, DOI 10.1029/2009RG000282; Grudd H, 2008, CLIM DYNAM, V31, P843, DOI 10.1007/s00382-007-0358-2; Guiot J., GLOBAL PLANERY CHANG; Guiot J., 2010, PLOS ONE, V5, P1; Gunnarson BE, 2011, CLIM DYNAM, V36, P97, DOI 10.1007/s00382-010-0783-5; Hegerl G, 2011, NAT GEOSCI, V4, P99, DOI [10.1038/ngeo1057, 10.1038/NGEO1057]; Hegerl GC, 2007, J CLIMATE, V20, P650, DOI 10.1175/JCLI4011.1; Hiller A, 2001, HOLOCENE, V11, P491, DOI 10.1191/095968301678302931; Hodson DLR, 2010, CLIM DYNAM, V34, P1041, DOI 10.1007/s00382-009-0571-2; HUGHES MK, 1994, CLIMATIC CHANGE, V26, P109, DOI 10.1007/BF01092410; HURRELL JW, 1995, SCIENCE, V269, P676, DOI 10.1126/science.269.5224.676; Jacobeit J, 2003, CLIM DYNAM, V20, P341, DOI 10.1007/s00382-002-0278-0; Jansen E, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P433; Jiang H, 2005, GEOLOGY, V33, P73, DOI 10.1130/G21130.1; Jungclaus JH, 2010, CLIM PAST, V6, P723, DOI 10.5194/cp-6-723-2010; Kalnay E., 2003, ATMOSPHERIC MODELLIN; Kaplan JO, 2009, QUATERNARY SCI REV, V28, P3016, DOI 10.1016/j.quascirev.2009.09.028; Klimenko VV, 2001, DOKL EARTH SCI, V377, P190; Kremenetski KV, 2004, PALAEOGEOGR PALAEOCL, V209, P113, DOI 10.1016/j.palaeo.2004.02.018; Lamb H.H., 1965, PALAEOGEOGR PALAEOCL, V1, P13, DOI 10.1016/0031-0182(65)90004-0; Lean JL, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015880; Luterbacher J, 2004, SCIENCE, V303, P1499, DOI 10.1126/science.1093877; Mangini A, 2005, EARTH PLANET SC LETT, V235, P741, DOI 10.1016/j.epsl.2005.05.010; Mann ME, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008272; Mann ME, 2008, P NATL ACAD SCI USA, V105, P13252, DOI 10.1073/pnas.0805721105; Mann ME, 2009, SCIENCE, V326, P1256, DOI 10.1126/science.1177303; Matthews HD, 2004, CLIM DYNAM, V22, P461, DOI 10.1007/s00382-004-0392-2; Muscheler R, 2007, QUATERNARY SCI REV, V26, P82, DOI 10.1016/j.quascirev.2006.07.012; Opsteegh JD, 1998, TELLUS A, V50, P348, DOI 10.1034/j.1600-0870.1998.t01-1-00007.x; Pfister C, 1998, HOLOCENE, V8, P535, DOI 10.1191/095968398675289943; Pitman AJ, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039076; Pohlmann H, 2006, J CLIMATE, V19, P6062, DOI 10.1175/JCLI3941.1; Pongratz J, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036394; Pongratz J, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043010; Pongratz J, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003153; Ramankutty N, 1999, GLOBAL BIOGEOCHEM CY, V13, P997, DOI 10.1029/1999GB900046; Richter TO, 2009, QUATERNARY SCI REV, V28, P1941, DOI 10.1016/j.quascirev.2009.04.008; Robock A, 2000, REV GEOPHYS, V38, P191, DOI 10.1029/1998RG000054; Sanso B, 2008, BAYESIAN ANAL, V3, P1; Schmidt G.A., 2010, GEOSCI MODEL DEV DIS, V3, P1549, DOI DOI 10.5194/GMDD-3-1549-2010; Semenov VA, 2010, J CLIMATE, V23, P5668, DOI 10.1175/2010JCLI3347.1; Seppa H, 2009, CLIM PAST, V5, P523; Servonnat J., 2011, THESIS U VERSAILLES; Servonnat J, 2010, CLIM PAST, V6, P445, DOI 10.5194/cp-6-445-2010; Shabalova MV, 2003, CLIMATIC CHANGE, V58, P219, DOI 10.1023/A:1023474032539; Shapiro AI, 2011, ASTRON ASTROPHYS, V529, DOI 10.1051/0004-6361/201016173; Shindell DT, 2001, SCIENCE, V294, P2149, DOI 10.1126/science.1064363; Shindell DT, 2003, J CLIMATE, V16, P4094, DOI 10.1175/1520-0442(2003)016<4094:VASFOC>2.0.CO;2; Sicre MA, 2008, EARTH PLANET SC LETT, V268, P137, DOI 10.1016/j.eps1.2008.01.011; Smerdon JE, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047372; Snyder C, 2008, MON WEATHER REV, V136, P4629, DOI 10.1175/2008MWR2529.1; Sutton RT, 2005, SCIENCE, V309, P115, DOI 10.1126/science.1109496; Swingedouw D, 2011, CLIM DYNAM, V36, P1349, DOI 10.1007/s00382-010-0803-5; Tiljander M., 2003, BOREAS, V32, P567; Timmreck C, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040083; Trouet V, 2009, SCIENCE, V324, P78, DOI 10.1126/science.1166349; van der Schrier G., 2005, CLIM DYNAM, V24, P335, DOI DOI 10.1007/S00382-004-0506-X; Van Engelen A.F.V., 2001, HIST CLIMATE MEMORIE, P101; van Leeuwen PJ, 2009, MON WEATHER REV, V137, P4089, DOI 10.1175/2009MWR2835.1; Wanner H, 2001, SURV GEOPHYS, V22, P321, DOI 10.1023/A:1014217317898; Widmann M, 2010, CLIM PAST, V6, P627, DOI 10.5194/cp-6-627-2010	101	5	6	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0921-8181		GLOBAL PLANET CHANGE	Glob. Planet. Change	MAR	2012	84-85				SI		35	47		10.1016/j.gloplacha.2011.07.002		13	Geography, Physical; Geosciences, Multidisciplinary	Physical Geography; Geology	911WB	WOS:000301750700006	
J	Sakurai, G; Jomura, M; Yonemura, S; Iizumi, T; Shirato, Y; Yokozawa, M				Sakurai, Gen; Jomura, Mayuko; Yonemura, Seiichiro; Iizumi, Toshichika; Shirato, Yasuhito; Yokozawa, Masayuki			Inversely estimating temperature sensitivity of soil carbon decomposition by assimilating a turnover model and long-term field data	SOIL BIOLOGY & BIOCHEMISTRY			English	Article						Soil organic carbon; Decomposition rate; Temperature sensitivity; Turnover model; Long-term field experiment; Data assimilation; Particle filter; Metropolis-Hasting algorithm	ORGANIC-MATTER DECOMPOSITION; CLIMATE-CHANGE; QUALITY; LABILE; POOLS; PH; INCUBATION; FEEDBACKS; DYNAMICS; INPUTS	Change in temperature sensitivity of soil organic carbon (SOC) decomposition with change in soil qualities (i.e. decomposability or lability) is one of the most important issues to be evaluated for projection of future CO2 emissions from soils. We inversely estimated the temperature sensitivity of SOC decomposition rate by applying a hybrid of the Metropolis-Hasting algorithm and the particle filter method to the extended Rothamsted carbon model (RothC), together with long-term (9 years) experimental data on SOC obtained at five sites in Japanese upland soils. Contrary to the prediction of the Arrhenius kinetics theory, we found no significant differences in temperature sensitivity among soils with different qualities (represented as soil compartments in the RothC model). We also confirmed that there was a positive correlation between the relative temperature sensitivity of the humus compartment and future total CO2 emissions. The RothC model with default parameterization tended to overestimate future total CO2 emissions relative to the calibrated model, and the degree of overestimation was larger than that of underestimation. (C) 2011 Elsevier Ltd. All rights reserved.	[Sakurai, Gen; Yonemura, Seiichiro; Iizumi, Toshichika; Shirato, Yasuhito; Yokozawa, Masayuki] Natl Inst Agroenvironm Sci, Tsukuba, Ibaraki 3058604, Japan; [Jomura, Mayuko] Nihon Univ, Coll Bioresources Sci, Fujisawa, Kanagawa 2520880, Japan	Yokozawa, M (reprint author), Natl Inst Agroenvironm Sci, 3-1-3 Kannondai, Tsukuba, Ibaraki 3058604, Japan.	myokoz@affrc.go.jp			MEXT; MOE, Japan [S-8]	We are grateful to the three anonymous reviewers for their insightful comments. We are also grateful to Drs Shin'ya Nakano and Genta Ueno of The Institute for Statistical Mathematics (ISM) for their valuable advice on the Monte Carlo calculation. We thank Dr. Rota Wagai for information on chemical properties of SOC. We also thank Drs Tsuneo Kuwagata, Yasushi Ishigooka, Shin Fukui, and Masashi Okada for providing data on AMeDAS and climate change scenario. This study was performed under the ISM Cooperative Research Program (2010-ISM-CPP-1005) and was supported by the Innovative Program of Climate Change Projection for the 21st Century (KAKUSHIN Program) of MEXT and the Global Environment Research Fund (S-8) of MOE, Japan.	Agren GI, 2007, SOIL BIOL BIOCHEM, V39, P1794, DOI 10.1016/j.soilbio.2007.02.007; Aichi Prefectural Agricultural Experiment Station, 1981, DOJ KANK KIS KIJ CHO; Akita Prefectural Agricultural Experiment Station, 1985, DOJ KANK KIS KIJ CHO; Balesdent J, 1996, EUR J SOIL SCI, V47, P485, DOI 10.1111/j.1365-2389.1996.tb01848.x; Barak P, 1997, PLANT SOIL, V197, P61, DOI 10.1023/A:1004297607070; Biasi C, 2005, RAPID COMMUN MASS SP, V19, P1401, DOI 10.1002/rcm.1911; Bol R, 2003, J PLANT NUTR SOIL SC, V166, P300, DOI 10.1002/jpln.200390047; Bosatta E, 1999, SOIL BIOL BIOCHEM, V31, P1889, DOI 10.1016/S0038-0717(99)00105-4; Candy J. V., 2009, ADAPTIVE LEARNING SY; Conant RT, 2008, GLOBAL CHANGE BIOL, V14, P868, DOI 10.1111/j.1365-2486.2008.01541.x; Conen F., 2006, BIOGEOSCIENCES, V3, P154; Davidson EA, 2006, NATURE, V440, P165, DOI 10.1038/nature04514; Falloon P, 1998, SOIL BIOL BIOCHEM, V30, P1207, DOI 10.1016/S0038-0717(97)00256-3; Fang CM, 2005, NATURE, V433, P57, DOI 10.1038/nature03138; Fontaine S, 2004, ECOL LETT, V7, P314, DOI 10.1111/j.1461-0248.2004.00579.x; Gelman A., 2004, BAYESIAN DATA ANAL; Golchin A., 1997, ADV SOIL SCI, P245; GRESZTA J, 1992, WATER AIR SOIL POLL, V63, P51, DOI 10.1007/BF00475621; Hargreaves G. H., 1985, Applied Engineering in Agriculture, V1, P96; Hasumi H, 2004, K 1 COUPLED GCM MIRO; Hokkaido Prefectural Agricultural Experimental Station, 1984, DOJ KANK KIS KIJ CHO; Iizumi Toshichika, 2010, Journal of Agricultural Meteorology, V66, P131, DOI 10.2480/agrmet.66.2.5; Iizumi T., 2011, J GEOPHYS RES, V116, P1; JENKINSON DS, 1977, SOIL SCI, V123, P298, DOI 10.1097/00010694-197705000-00005; Jones DL, 2009, PLANT SOIL, V321, P5, DOI 10.1007/s11104-009-9925-0; Kirschbaum MUF, 2006, SOIL BIOL BIOCHEM, V38, P2510, DOI 10.1016/j.soilbio.2006.01.030; Kitagawa G, 1998, J AM STAT ASSOC, V93, P1203, DOI 10.2307/2669862; Knorr W, 2005, NATURE, V433, P298, DOI 10.1038/nature03226; Kusaba T., 2008, MISCELLANEOUS PUBLIC, V8, P1; Kuwagata T., 2011, J AGROMETEOROL, V67, P223; Kuzyakov Y, 2000, SOIL BIOL BIOCHEM, V32, P1485, DOI 10.1016/S0038-0717(00)00084-5; Leifeld J, 2008, SOIL BIOL BIOCHEM, V40, P2948, DOI 10.1016/j.soilbio.2008.08.019; Ludwig B, 2003, EUR J SOIL SCI, V54, P117, DOI 10.1046/j.1365-2389.2003.00496.x; Luo Y., 2001, NATURE, V613, P622; Melillo JM, 2002, SCIENCE, V298, P2173, DOI 10.1126/science.1074153; Mitchell TD, 2005, INT J CLIMATOL, V25, P693, DOI 10.1002/joc.1181; Motavalli PP, 1995, SOIL BIOL BIOCHEM, V27, P1589, DOI 10.1016/0038-0717(95)00082-P; Murayama S, 1996, JARQ-JPN AGR RES Q, V30, P153; Nguyen C., 2009, SUS AGR, V1, P97; Ogawa K., 1988, RES B HOKKAIDO NATL, V149, P57; Oita Prefectural Agricultural Experiment Station, 1984, DOJ KANK KIS KIJ CHO; Pietri JCA, 2009, SOIL BIOL BIOCHEM, V41, P1396, DOI 10.1016/j.soilbio.2009.03.017; R Development Core Team, 2011, R LANG ENV STAT COMP; Rey A, 2006, GLOBAL CHANGE BIOL, V12, P1894, DOI 10.1111/j.1365-2465.2006.01230.x; Saitama Prefectural Agricultural Experiment Station, 1986, DOJ KANK KIS KIJ CHO; SANCHEZ PA, 1989, DYNAMICS OF SOIL ORGANIC MATTER IN TROPICAL ECOSYSTEMS, P125; Scharnagl B, 2010, BIOGEOSCIENCES, V7, P763; Shirato Y, 2006, SOIL BIOL BIOCHEM, V38, P812, DOI 10.1016/j.soilbio.2005.07.008; Six J, 2002, AGRONOMIE, V22, P755, DOI 10.1051/agro:2002043; Skjemstad JO, 2004, AUST J SOIL RES, V42, P79, DOI 10.1071/SR03013; Solomon SD, 2007, CONTRIBUTION WORKING; Strickland MS, 2010, SOIL BIOL BIOCHEM, V42, P1385, DOI 10.1016/j.soilbio.2010.05.007; Vanhala P, 2007, SOIL BIOL BIOCHEM, V39, P2967, DOI 10.1016/j.soilbio.2007.05.022; von Lutzow M, 2009, BIOL FERT SOILS, V46, P1, DOI 10.1007/s00374-009-0413-8; Wetterstedt JAM, 2010, GLOBAL CHANGE BIOL, V16, P1806, DOI 10.1111/j.1365-2486.2009.02112.x; Xu T., 2006, GLOBAL BIOGEOCHEMICA, V20, P1	56	0	0	PERGAMON-ELSEVIER SCIENCE LTD	OXFORD	THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND	0038-0717		SOIL BIOL BIOCHEM	Soil Biol. Biochem.	MAR	2012	46						191	199		10.1016/j.soilbio.2011.11.005		9	Soil Science	Agriculture	898LH	WOS:000300743400025	
J	Finke, PA				Finke, Peter A.			On digital soil assessment with models and the Pedometrics agenda	GEODERMA			English	Article						Digital soil mapping; Digital soil assessment; Process modelling; Pedometrics; Calibration; Ensemble modelling	RUDIMENTARY MECHANISTIC MODEL; PEDOTRANSFER FUNCTIONS; LANDSCAPE DEVELOPMENT; BAYESIAN CALIBRATION; CARBON SEQUESTRATION; ACIDIFICATION MODEL; DATA ASSIMILATION; PREDICTION ERROR; WATER-FLOW; PART II	The unique selling point of pedometricians still is close to their cradle: ability to map. Irrespective of the many scientific achievements one can ask if offering mapping ability, allbeit in various contexts, is all a pedometrician can do to bring forward soil science's broader agenda. This paper identifies, within some of the hotter issues on the soil science agenda, activities that need the input of pedometricians. Digital soil mapping (DSM) has reached maturity although some issues like the optimal use of legacy data still need attention. The necessary shift from DSM to digital soil function mapping implies an increased need for process knowledge in mapping, but also an increased focus of pedometricians on the strengthening of process models. Most quality issues related to DSM have a counterpart in process modelling, and some of these issues need elaboration to construct well-calibrated and complete models, e.g. by making motivated choices between the inclusion of processes or model reduction. Reaching out to stakeholders is also an issue of increasing interest, as these are confronted with uncertain concurrent models to evaluate future scenarios. Questions are raised such as: Does the need for a state-of-the-art (SOTA) approach imply a choice for one model and how should this be made, or is SOTA implementable as some weighted average of screened concurrent models. Can observations and model results be combined in decision making and what techniques are needed. The above issues are illustrated with existing examples and new material from soil science and beyond. As DSM and process modelling share common ground, mutual benefits can be expected at the interface of both research fields such as (i) increased usage of process knowledge in DSM and (ii) probabilistic approaches to less well understood soil processes. Stakeholders will profit from the development of decision frameworks to choose applicable DSM-techniques and the increased application of ensembles of DSM-methods and models to narrow prediction error bandwidths. Additionally, they will profit from the development of decision support systems filled with outcomes of scenario studies of multiple, uncertain, and concurrent models as these provide an interesting alternative to the application of one selected model. (C) 2011 Elsevier B.V. All rights reserved.	Univ Ghent, Dept Geol & Soil Sci, Ghent, Belgium	Finke, PA (reprint author), Univ Ghent, Dept Geol & Soil Sci, Ghent, Belgium.	peter.finke@ugent.be					Ajami NK, 2010, STOCH ENV RES RISK A, V24, P831, DOI 10.1007/s00477-010-0381-4; Alcamo J., 2005, MILLENNIUM ECOSYSTEM, V2; Babak O, 2008, COMPUTAT GEOSCI, V12, P503, DOI 10.1007/s10596-008-9089-7; Batjes N.H., 1996, 9605 INT SOIL REF IN; Bertino L, 2003, INT STAT REV, V71, P223; Bierkens M., 2000, UPSCALING DOWNSCALIN; Bishop TFA, 1999, GEODERMA, V91, P27, DOI 10.1016/S0016-7061(99)00003-8; Bormann H., 2005, ADV GEOSCI, V5, P43, DOI [10.5194/adgeo-5-43-2005, DOI 10.5194/ADGEO-5-43-2005]; Bouma J, 2009, GEODERMA, V150, P224, DOI 10.1016/j.geoderma.2009.01.015; Cain J., 2001, PLANNING IMPROVEMENT; Carre F, 2007, GEODERMA, V141, P1, DOI 10.1016/j.geoderma.2007.01.018; Carre F, 2007, GEODERMA, V142, P69, DOI 10.1016/j.geoderma.2007.07.015; Dann RL, 2006, J ENVIRON QUAL, V35, P628, DOI 10.2134/jeq2005.0257; de Gruijter J. J., 2006, SAMPLING NATURAL RES; De Lannoy GJM, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006367; de Vries W, 1998, NUTR CYCL AGROECOSYS, V50, P223, DOI 10.1023/A:1009744429062; Dobos E., 2006, 22123 EUR EN; Doherty J., 2004, PEST MODEL INDEPENDE; Eckelmann W., 2006, 20 EUR SOIL BUR OFF; Ehlschlaeger CR, 1997, COMPUT GEOSCI, V23, P387, DOI 10.1016/S0098-3004(97)00005-8; EU, 2006, COM2006231FINALSEC20; EU, 2006, COM2006231; EU, 2006, COM2006232; Evensen G., 1994, J GEOPHYS RES-OCEANS, V99, P17905; Finke PA, 2008, J ARCHAEOL SCI, V35, P2786, DOI 10.1016/j.jas.2008.05.004; Finke PA, 2008, GEODERMA, V145, P462, DOI 10.1016/j.geoderma.2008.01.017; Finke PA, 2007, DEV SOIL SCI, V31, P523; Finke P.A., 2010, WORLD C SOIL SCI P 2; FINKE PA, 1993, HYDROL PROCESS, V7, P63, DOI 10.1002/hyp.3360070107; Finke PA, 1996, HYDROL PROCESS, V10, P661, DOI 10.1002/(SICI)1099-1085(199605)10:5<661::AID-HYP346>3.3.CO;2-I; Fujino J., 2001, YOK IFAC WORKSH MOD, P109; Goovaerts P., 1997, GEOSTATISTICS NATURA; Greenslade P., 1992, ECOSYSTEMS HUMAN WEL, V40, P297; Grimm R, 2010, GEODERMA, V155, P154, DOI 10.1016/j.geoderma.2009.05.006; Grunwald S, 2009, GEODERMA, V152, P195, DOI 10.1016/j.geoderma.2009.06.003; Guber AK, 2009, VADOSE ZONE J, V8, P1, DOI 10.2136/vzj2007.0144; HacktenBroeke MJD, 1996, AGR WATER MANAGE, V29, P201, DOI 10.1016/0378-3774(95)01190-0; Hartemink A. E, 2008, DIGITAL SOIL MAPPING; Hartemink AE, 2008, GEODERMA, V148, P123, DOI 10.1016/j.geoderma.2008.10.006; Hegerl GC, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P663; Hengl T, 2007, PRACTICAL GUIDE GEOS; Hengl T, 2004, INT J GEOGR INF SCI, V18, P183, DOI 10.1080/13658810310001620924; Hengl T, 2004, GEODERMA, V120, P75, DOI 10.1016/j.geoderma.2003.08.018; Heuvelink G. B. M., 1998, ERROR PROPAGATION EN; Heuvelink G.B.M., 2007, 22123 EUR EN; Heuvelink GBM, 2001, GEODERMA, V100, P269, DOI 10.1016/S0016-7061(01)00025-8; Heuvelink G.B.M., 2007, DEV SOIL SCI, V31, P97106; Heuvelink GBM, 2007, INT J GEOGR INF SCI, V21, P497, DOI 10.1080/13658810601063951; HOOSBEEK MR, 1992, GEODERMA, V55, P183, DOI 10.1016/0016-7061(92)90083-J; Hopkins JC, 1996, J THEOR BIOL, V183, P417, DOI 10.1006/jtbi.1996.0232; IMAGE-Team, 2001, IMAGE 2 2 IMPL SRES; Irvine K., 2005, WATER FRAMEWORK DIRE; Jansen MJW, 1998, NUTR CYCL AGROECOSYS, V50, P247, DOI 10.1023/A:1009748529970; Jones JW, 2007, AGR SYST, V94, P52, DOI 10.1016/j.agsy.2005.06.023; Kempen B, 2009, GEODERMA, V151, P311, DOI 10.1016/j.geoderma.2009.04.023; Klein M.T., 2006, MOL MODELLING HEAVY; Kros J, 1999, J ENVIRON QUAL, V28, P366; Kros J, 2002, STOCH ENV RES RISK A, V16, P279, DOI 10.1007/s00477-002-0098-0; Lagacherie P., 2007, DEV SOIL SCI, V31, P523; Leon A, 2009, AUST J SOIL RES, V47, P763, DOI 10.1071/SR08119; Leon-Rojas J.-M., 2003, P 2003 ACM S APPL CO, P227, DOI 10.1145/952532.952580; Leopold U, 2006, GEODERMA, V130, P368, DOI 10.1016/j.geoderma.2005.02.008; Leroy SS, 1998, J CLIMATE, V11, P640, DOI 10.1175/1520-0442(1998)011<0640:DCSSBA>2.0.CO;2; LOAGUE K, 1991, Journal of Contaminant Hydrology, V7, P51, DOI 10.1016/0169-7722(91)90038-3; Marker M., 2003, MODSIM 2003 INT C MO, V3, P440; Marker M., 2003, MODSIM 2003 INT C MO, V4, P440; Marker M., 2003, MODSIM 2003 INT C 2, V2, P440; Marker M., 2003, MODSIM 2003 INT C 1, P440; Matthews E., 2000, FOREST ECOSYSTEMS; Mayr T.R., 2008, DIGITAL SOIL MAPPING; McBratney AB, 2002, GEODERMA, V109, P41, DOI 10.1016/S0016-7061(02)00139-8; McBratney AB, 2003, GEODERMA, V117, P3, DOI 10.1016/S0016-7061(03)00223-4; McBratney A.B., 1986, 531986 CSIRO AUSTR D, P5064; Mckenzie NJ, 2007, DEV SOIL SCI, V31, P327; McNeill J.M., 2006, APPL GIS, V2, DOI [10.2104/ag060019, DOI 10.2104/AG060019]; Meehl GA, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P747; Min SK, 2007, J CLIMATE, V20, P2769, DOI 10.1175/JCLI4178.1; Minasny B, 1999, GEODERMA, V90, P3, DOI 10.1016/S0016-7061(98)00115-3; Minasny B, 2006, GEODERMA, V133, P138, DOI 10.1016/j.geoderma.2006.03.042; Minasny B, 2001, GEODERMA, V103, P161, DOI 10.1016/S0016-7061(01)00075-1; Moradkhani H, 2005, ADV WATER RESOUR, V28, P135, DOI 10.1016/j.advwatres.2004.09.002; Mora-Vallejo A, 2008, CATENA, V76, P44, DOI 10.1016/j.catena.2008.09.008; Moyeed RA, 2002, MATH GEOL, V34, P365, DOI 10.1023/A:1015085810154; Murphy JM, 2004, NATURE, V430, P768, DOI 10.1038/nature02771; Myers D.B., 2008, THESIS U MISSOURI US; Oldeman L.R., 1991, WORLD MAP STATUS HUM; Pachepsky Y, 2006, GEODERMA, V134, P253, DOI 10.1016/j.geoderma.2006.03.003; PARTON WJ, 1987, SOIL SCI SOC AM J, V51, P1173; Pearson K., 1895, PHILOS T R SOC A, VA 186, P186; Potting Jose, 2004, GEO 3 SCENARIOS 2002; Randall DA, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P589; Reible D, 2002, ENVIRON TOXICOL CHEM, V21, P875, DOI 10.1897/1551-5028(2002)021<0875:ALFRWM>2.0.CO;2; Reinds GJ, 2008, GEODERMA, V146, P475, DOI 10.1016/j.geoderma.2008.06.022; RIVM, 1995, GEN STRAT INT ENV AS; ROBERTSON A, 1959, BIOMETRICS, V15, P219, DOI 10.2307/2527670; Samouelian A, 2008, GEODERMA, V145, P401, DOI 10.1016/j.geoderma.2008.01.016; Sanchez PA, 2009, SCIENCE, V325, P680, DOI 10.1126/science.1175084; Sauer D, 2008, J PLANT NUTR SOIL SC, V171, P483, DOI 10.1002/jpln.200700023; Schaetzl RJ, 2006, SOIL SCI, V171, P152, DOI 10.1097/01.ss.0000187373.31026.04; Schils R., 2008, REV EXISTING INFORM; Schimmack W, 2006, SCI TOTAL ENVIRON, V368, P863, DOI 10.1016/j.scitotenv.2006.03.028; Schulp CJE, 2008, AGR ECOSYST ENVIRON, V127, P251, DOI 10.1016/j.agee.2008.04.010; Selle B, 2008, COMPUT GEOSCI-UK, V34, P699, DOI 10.1016/j.cageo.2007.06.007; Shibab K., 2005, P 2005 5 INT C INT S; Smith CS, 2007, BIOL CONSERV, V139, P333, DOI 10.1016/j.biocon.2007.06.025; Smith LA, 2002, P NATL ACAD SCI USA, V99, P2487, DOI 10.1073/pnas.012580599; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Stoorvogel JJ, 2009, GEODERMA, V149, P161, DOI 10.1016/j.geoderma.2008.11.039; Takeshi S., 1999, P JSCE JAPAN SOC CIV, V631, P97; UN Millennium Project, 2005, HALV HUNG IT CAN BE; UNEP, 2007, GEO 4 ENV DEV; UNEP, 2002, GEO 3 PRES FUT PERSP; United Nations Development Programme (UNDP), 2007, HUM DEV REP 2007 200; van der Linden PJ, 2001, CLIMATE CHANGE 2001; Van Engelen V., 2006, SOIL TERRAIN DATABAS; VANGRINSVEN HJM, 1995, ECOL MODEL, V83, P1, DOI 10.1016/0304-3800(95)00078-A; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; VEREECKEN H, 1992, SOIL SCI SOC AM J, V56, P1371; Vereecken H., 1991, NITRATE SOILS, P321; Viertl R., 1987, PROBABILITY BAYESIAN, P471; Vrugt JA, 2008, WATER RESOUR RES, V44, DOI 10.1029/2007WR006720; Vrugt JA, 2009, INT J NONLIN SCI NUM, V10, P273; Walley W.J., 2002, E1056TR2 R D ENV AG; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Wohling T, 2008, WATER RESOUR RES, V44, DOI 10.1029/2008WR007154; Wood S., 2000, PILOT ANAL GLOBAL EC	126	6	6	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0016-7061		GEODERMA	Geoderma	FEB	2012	171				SI		3	15		10.1016/j.geoderma.2011.01.001		13	Soil Science	Agriculture	911AF	WOS:000301689000002	
J	Lokupitiya, E; Paustian, K; Easter, M; Williams, S; Andren, O; Katterer, T				Lokupitiya, E.; Paustian, K.; Easter, M.; Williams, S.; Andren, O.; Katterer, T.			Carbon balances in US croplands during the last two decades of the twentieth century	BIOGEOCHEMISTRY			English	Article						Climate change; Cropland carbon cycling; Agriculture; Carbon budgets; Regional and national scale	SOIL ORGANIC-CARBON; ATMOSPHERIC CO2; AGRICULTURAL SOILS; UNITED-STATES; DATA ASSIMILATION; CROP RESIDUES; YIELD DATA; TEMPERATURE; MANAGEMENT; SEQUESTRATION	Carbon (C) added to soil as organic matter in crop residues and carbon emitted to the atmosphere as CO(2) in soil respiration are key determinants of the C balance in cropland ecosystems. We used complete and comprehensive county-level yields and area data to estimate and analyze the spatial and temporal variability of regional and national scale residue C inputs, net primary productivity (NPP), and C stocks in US croplands from 1982 to 1997. Annual residue C inputs were highest in the North Central and Central and Northern Plains regions that comprise similar to 70% of US cropland. Average residue C inputs ranged from 1.8 (Delta States) to 3.0 (North Central region) Mg C ha(-1) year(-1), and average NPP ranged from 3.1 (Delta States) to 5.4 (Far West region) Mg C ha(-1) year(-1). Residue C inputs tended to be inversely proportional to the mean growing season temperature. A quadratic relationship incorporating the growing season mean temperature and total precipitation closely predicted the variation in residue C inputs in the North Central region and Central and Northern Plains. We analyzed the soil C balance using the crop residue database and the Introductory Carbon Balance regional Model (ICBMr). Soil C stocks (0-20 cm) on permanent cropland ranged between 3.07 and 3.1 Pg during the study period, with an average increase of similar to 4 Tg C year(-1), during the 1990s. Interannual variability in soil C stocks ranged from 0 to 20 Tg C (across a mean C stock of 3.08 +/- A 0.01 Pg) during the study period; interannual variability in residue C inputs varied between 1 and 43 Tg C (across a mean input of 220 +/- A 19 Tg). Such interannual variation has implications for national estimates of CO(2) emissions from cropland soils needed for implementation of greenhouse gas (GHG) mitigation strategies involving agriculture.	[Lokupitiya, E.; Paustian, K.] Colorado State Univ, Dept Soil & Crop Sci, Ft Collins, CO 80523 USA; [Andren, O.; Katterer, T.] Swedish Univ Agr Sci, Dept Soil & Environm, SLU, Uppsala, Sweden; [Lokupitiya, E.; Paustian, K.; Easter, M.; Williams, S.] Colorado State Univ, Nat Resource Ecol Lab, Ft Collins, CO 80523 USA	Lokupitiya, E (reprint author), Colorado State Univ, Dept Atmospher Sci, 1371 Campus Delivery, Ft Collins, CO 80523 USA.	erandi@atmos.colostate.edu			Consortium for Agricultural Soil Mitigation of Greenhouse Gases (CASMGS); Cooperative State Research, Education, and Extension Service [2001-38700-1109]; National Institute for Climate Change Research (NICCR) [MTU 050516Z14]; Department of Energy (DoE) [DE-FG02-06ER64317]; U.S. Department of Agriculture	Support for this work was provided by the Consortium for Agricultural Soil Mitigation of Greenhouse Gases (CASMGS) funded by Cooperative State Research, Education, and Extension Service, U.S. Department of Agriculture, under Agreement No. 2001-38700-1109, National Institute for Climate Change Research (NICCR) under Contract No MTU 050516Z14, and the Department of Energy (DoE) under contract No DE-FG02-06ER64317.	Akaike A., 1974, IEEE T AUTOMAT CONTR, V19, P716, DOI [10.1109/TAC.1974.1100705, DOI 10.1109/TAC.1974.1100705]; Allen R.G., 1998, 56 FAO; Allmaras RR, 2004, SOIL SCI SOC AM J, V68, P1366; ALLMARAS RR, 1999, RES ENG TECHNOL SUST, V6, P7; Andren O., 2001, ASSESSMENT METHODS S, P495; Andren O, 2007, AMBIO, V36, P379, DOI 10.1579/0044-7447(2007)36[379:SCADAI]2.0.CO;2; Andren O, 2004, NUTR CYCL AGROECOSYS, V70, P231, DOI 10.1023/B:FRES.0000048471.59164.ff; Andren O, 2008, NUTR CYCL AGROECOSYS, V81, P129, DOI 10.1007/s10705-008-9177-z; Andren O, 1997, ECOL APPL, V7, P1226, DOI 10.2307/2641210; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; BARAN V, 2001, CAN PASTURES SLOW DO, V5, P3; Bolinder MA, 1999, PLANT SOIL, V215, P85, DOI 10.1023/A:1004765024519; Bolinder MA, 1997, AGR ECOSYST ENVIRON, V63, P61, DOI 10.1016/S0167-8809(96)01121-8; BRUCE RR, 1997, SOIL ORGANIC MATTER, P247; BRUHWILER L, 2005, ATMOS CHEM PHYS DISC, V5, P1891; BUCHANAN M, 1993, AGRON J, V85, P631; Burgess MS, 2002, SOIL SCI SOC AM J, V66, P1350; BUYANOVSKY GA, 1986, PLANT SOIL, V93, P57, DOI 10.1007/BF02377145; Campbell CA, 2001, CAN J SOIL SCI, V81, P39; Clapp CE, 2000, SOIL TILL RES, V55, P127, DOI 10.1016/S0167-1987(00)00110-0; DALY C, 1994, J APPL METEOROL, V33, P140, DOI 10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2; DENNING AS, 1995, NATURE, V376, P240, DOI 10.1038/376240a0; Eggleston H. S., 2006, 2006 IPCC GUIDELINES; Follett RF, 2001, SOIL TILL RES, V61, P77, DOI 10.1016/S0167-1987(01)00180-5; Goidts E, 2009, GLOBAL CHANGE BIOL, V15, P2981, DOI 10.1111/j.1365-2486.2009.01940.x; Graham RL, 2007, AGRON J, V99, P1, DOI 10.2134/argonj2005.0222; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; HALVORSON AD, 1997, SOIL ORGANIC MATTER, P361; Hargreaves G. H., 1985, Applied Engineering in Agriculture, V1, P96; HENDRIX PF, 1997, SOIL ORGANIC MATTER, P235; Jackson RB, 2004, P NATL ACAD SCI USA, V101, P15827, DOI 10.1073/pnas.0403631101; Johnson JMF, 2006, AGRON J, V98, P622, DOI 10.2134/agronj2005.0179; JUMA NG, 1997, SOIL ORGANIC MATTER, P273; Katterer T, 1998, BIOL FERT SOILS, V27, P258, DOI 10.1007/s003740050430; KERN JS, 1994, SOIL SCI SOC AM J, V58, P439; Lal R., 1998, POTENTIAL US CROPLAN; LAWES DA, 1977, J AGR SCI, V89, P751; LIN W, 2000, TECHNICAL B USDA, V1888; Lobell DB, 2003, SCIENCE, V299, P1032, DOI 10.1126/science.1077838; Lokupitiya E, 2007, AGRON J, V99, P673, DOI 10.2134/agronj2006.0143; Lokupitiya RS, 2006, ENVIRONMETRICS, V17, P339, DOI 10.1002/env.773; Lomander A, 1998, SOIL BIOL BIOCHEM, V30, P2023, DOI 10.1016/S0038-0717(98)00077-7; LOTT NA, 1993, WEATHER, V49, P370; Lyon DJ, 1997, SOIL ORGANIC MATTER, P343; Metz B., 2007, CLIMATE CHANGE 2007, P498; Michalak AM, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004422; MILES T, 2010, VEGETATION  IN PRESS; Ogle SM, 2010, GLOBAL CHANGE BIOL, V16, P810, DOI 10.1111/j.1365-2486.2009.01951.x; Ogle SM, 2007, ECOL MODEL, V205, P453, DOI 10.1016/j.ecolmodel.2007.03.007; PALMER TN, 1989, NATURE, V338, P54, DOI 10.1038/338054a0; Paustian K, 1997, SOIL USE MANAGE, V13, P230, DOI 10.1111/j.1475-2743.1997.tb00594.x; Paustian K., 1997, SOIL ORGANIC MATTER, P15; Paustian K, 2002, AGRICULTURAL PRACTICES AND POLICIES FOR CARBON SEQUESTRATION IN SOIL, P193; Paustian K., 2006, AGR ROLE GREENHOUSE; Peters SE, 1997, SOIL ORGANIC MATTER, P183; Peters W, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006157; PETERSON GA, 1997, SOIL ORGANIC MATTER, P371; Peterson TC, 1998, J GEOPHYS RES-ATMOS, V103, P25967, DOI 10.1029/98JD01168; PIERCE FJ, 1997, SOIL ORGANIC MATTER, P141; Potter C., 2007, EARTH INTERACT, V11, P1, DOI DOI 10.1175/EI228.1; Prince SD, 2001, ECOL APPL, V11, P1194, DOI 10.1890/1051-0761(2001)011[1194:NPPOUS]2.0.CO;2; Reilly JM, 1998, SOIL TILL RES, V47, P275, DOI 10.1016/S0167-1987(98)00116-0; Rodenbeck C, 2003, TELLUS B, V55, P488, DOI 10.1034/j.1600-0889.2003.00033.x; Rosenzweig C., 2000, CLIMATE CHANGE US AG; RUSSELL WA, 1991, ADV AGRON, V46, P245, DOI 10.1016/S0065-2113(08)60582-9; Saby NPA, 2008, GLOBAL CHANGE BIOL, V14, P2432, DOI 10.1111/j.1365-2486.2008.01658.x; Samani Z, 2000, J IRRIG DRAIN E-ASCE, V126, P265, DOI 10.1061/(ASCE)0733-9437(2000)126:4(265); Smith P, 2007, AGR ECOSYST ENVIRON, V118, P6, DOI 10.1016/j.agee.2006.06.006; Sperow M, 2003, CLIMATIC CHANGE, V57, P319, DOI 10.1023/A:1022888832630; Thornthwaite CW, 1948, GEOGR REV, V38, P55, DOI 10.2307/210739; Torbert HA, 2004, FIELD CROP RES, V88, P57, DOI 10.1016/j.fcr.2003.11.011; TRENBERTH KE, 1988, SCIENCE, V242, P1640, DOI 10.1126/science.242.4886.1640; Lobell DB, 2002, GLOBAL CHANGE BIOL, V8, P722, DOI 10.1046/j.1365-2486.2002.00503.x; USEPA, 2009, INV US GREENH GAS EM, P1990; Vaida F, 2005, BIOMETRIKA, V92, P351, DOI 10.1093/biomet/92.2.351; Vanotti MB, 1997, SOIL ORGANIC MATTER, P105; VANWESEMAEL B, 2010, PLANT SOIL IN PRESS; Walsh M., 2004, J IND ECOL, V7, P117, DOI DOI 10.1162/108819803323059433; WALTMAN SW, 1996, ESTIMATES SOIL ORGAN; Watson R, 2000, LAND USE LAND USE CH; West TO, 2008, SOIL SCI SOC AM J, V72, P285, DOI 10.2136/sssaj2007.0113; WYCH RD, 1983, CROP SCI, V23, P879; Young CE, 2001, AM J AGR ECON, V83, P1196, DOI 10.1111/0002-9092.00267; *NOAA, 2009, CO2 CONC DAT; *USDA, 2008, USDA TECHN B, V1921	85	2	3	SPRINGER	DORDRECHT	VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS	0168-2563		BIOGEOCHEMISTRY	Biogeochemistry	FEB	2012	107	1-3					207	225		10.1007/s10533-010-9546-y		19	Environmental Sciences; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	864GV	WOS:000298226700014	
J	Smith, P; Albanito, F; Bell, M; Bellarby, J; Blagodatskiy, S; Datta, A; Dondini, M; Fitton, N; Flynn, H; Hastings, A; Hillier, J; Jones, EO; Kuhnert, M; Nayak, DR; Pogson, M; Richards, M; Sozanska-Stanton, G; Wang, SF; Yeluripati, JB; Bottoms, E; Brown, C; Farmer, J; Feliciano, D; Hao, C; Robertson, A; Vetter, S; Wong, HM; Smith, J				Smith, Pete; Albanito, Fabrizio; Bell, Madeleine; Bellarby, Jessica; Blagodatskiy, Sergey; Datta, Arindam; Dondini, Marta; Fitton, Nuala; Flynn, Helen; Hastings, Astley; Hillier, Jon; Jones, Edward O.; Kuhnert, Matthias; Nayak, Dali R.; Pogson, Mark; Richards, Mark; Sozanska-Stanton, Gosia; Wang, Shifeng; Yeluripati, Jagadeesh B.; Bottoms, Emily; Brown, Chris; Farmer, Jenny; Feliciano, Diana; Hao, Cui; Robertson, Andy; Vetter, Sylvia; Wong, Hon Man; Smith, Jo			Systems approaches in global change and biogeochemistry research	PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES			English	Review						system biology; ecology; systems approach; global change; biogeochemistry; model	SOIL ORGANIC-MATTER; BAYESIAN CALIBRATION; PROJECTED CHANGES; CARBON-DIOXIDE; MODEL; EUROPE; EXCHANGE; UNCERTAINTIES; GRASSLANDS; SCENARIOS	Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.	[Smith, Pete; Albanito, Fabrizio; Bell, Madeleine; Bellarby, Jessica; Blagodatskiy, Sergey; Datta, Arindam; Dondini, Marta; Fitton, Nuala; Flynn, Helen; Hastings, Astley; Hillier, Jon; Jones, Edward O.; Kuhnert, Matthias; Nayak, Dali R.; Pogson, Mark; Richards, Mark; Sozanska-Stanton, Gosia; Wang, Shifeng; Yeluripati, Jagadeesh B.; Bottoms, Emily; Brown, Chris; Farmer, Jenny; Feliciano, Diana; Hao, Cui; Robertson, Andy; Vetter, Sylvia; Wong, Hon Man; Smith, Jo] Univ Aberdeen, Inst Biol & Environm Sci, Aberdeen AB24 3UU, Scotland	Smith, P (reprint author), Univ Aberdeen, Inst Biol & Environm Sci, 23 St Machar Dr, Aberdeen AB24 3UU, Scotland.	pete.smith@abdn.ac.uk	Smith, Jo/F-7763-2012; Smith, Pete/G-1041-2010; Blagodatsky, Sergey/F-2734-2010	Smith, Pete/0000-0002-3784-1124; 	Royal Society	This work was prepared for a conference, 'Predictive ecology: systems approaches' held at the Royal Society in London on 18-19 April 2011. P.S. is a Royal Society-Wolfson Research Merit Award holder.	Benton TG, 2012, PHILOS T R SOC B, V367, P200, DOI 10.1098/rstb.2011.0168; Betts RA, 2007, NATURE, V448, P1037, DOI 10.1038/nature06045; Coleman K., 1996, NATO ASI SERIES 1, V38, P237; Dailey AG, 2005, NUTR CYCL AGROECOSYS, V73, P257, DOI 10.1007/s10705-005-3031-3; Del Grosso SJ, 2006, J ENVIRON QUAL, V35, P1451, DOI 10.2134/jeq2005.0160; Evans MR, 2012, PHILOS T R SOC B, V367, P163, DOI 10.1098/rstb.2011.0191; Ewert F, 2005, AGR ECOSYST ENVIRON, V107, P101, DOI 10.1016/j.agee.2004.12.003; Falloon P, 2000, SOIL BIOL BIOCHEM, V32, P433, DOI 10.1016/S0038-0717(99)00172-8; Gedney N, 2006, NATURE, V439, P835, DOI 10.1038/nature04504; Gillett NP, 2008, NAT GEOSCI, V1, P750, DOI 10.1038/ngeo338; Gottschalk P, 2007, AGR ECOSYST ENVIRON, V121, P175, DOI 10.1016/j.agee.2006.12.026; Grimm V, 2012, PHILOS T R SOC B, V367, P298, DOI 10.1098/rstb.2011.0180; Hastings AF, 2010, AGR ECOSYST ENVIRON, V136, P97, DOI 10.1016/j.agee.2009.11.016; JENKINSON DS, 1992, SOIL BIOL BIOCHEM, V24, P295, DOI 10.1016/0038-0717(92)90189-5; Jones RJA, 2005, EUR J SOIL SCI, V56, P655, DOI 10.1111/j.1365-2389.2005.00728.x; Kennedy M, 2008, J R STAT SOC A STAT, V171, P109; Kennedy MC, 2001, J ROY STAT SOC B, V63, P425, DOI 10.1111/1467-9868.00294; Kennedy MC, 2006, RELIAB ENG SYST SAFE, V91, P1301, DOI 10.1016/j.ress.2005.11.028; LOAGUE K, 1991, Journal of Contaminant Hydrology, V7, P51, DOI 10.1016/0169-7722(91)90038-3; Lotka A. J., 1925, ELEMENTS PHYS BIOL; Medvigy D, 2012, PHILOS T R SOC B, V367, P222, DOI 10.1098/rstb.2011.0253; Morales P, 2005, GLOBAL CHANGE BIOL, V11, P2211, DOI 10.1111/j.1365-2486.2005.01036.x; Nakicenovic N., 2000, SPECIAL REPORT EMISS; Orzack SH, 2012, PHILOS T R SOC B, V367, P170, DOI 10.1098/rstb.2011.0265; Penfield S, 2012, PHILOS T R SOC B, V367, P291, DOI 10.1098/rstb.2011.0186; POSCH M, 2003, MANUAL DYNAMIC MODEL; Rastetter EB, 2010, ECOL APPL, V20, P1285, DOI 10.1890/09-0876.1; Reinds GJ, 2008, GEODERMA, V146, P475, DOI 10.1016/j.geoderma.2008.06.022; Riedo M, 2002, ECOL MODEL, V158, P83, DOI 10.1016/S0304-3800(02)00169-2; Rounsevell MDA, 2006, AGR ECOSYST ENVIRON, V114, P57, DOI 10.1016/j.agee.2005.11.027; Del Grosso SJ, 2001, MODELING CARBON AND NITROGEN DYNAMICS FOR SOIL MANAGEMENT, P303; Schroter D, 2005, SCIENCE, V310, P1333, DOI 10.1126/science.1115233; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Smith J, 2005, GLOBAL CHANGE BIOL, V11, P2141, DOI 10.1111/j.1365-2486.2005.001075.x; Smith J, 2010, CLIM RES, V45, P193, DOI 10.3354/cr00902; Smith J, 2010, CLIM RES, V45, P179, DOI 10.3354/cr00899; SMITH JU, 2007, ENV MODELLING INTRO, P180; SMITH JU, 1997, QUANTITATIVE APPROAC, V10, P87; Smith JU, 2002, EUR J SOIL SCI, V53, P405, DOI 10.1046/j.1365-2389.2002.00458.x; Smith P, 2006, CAN J SOIL SCI, V86, P159; Smith P, 2007, GLOBAL CHANGE BIOL, V13, P2605, DOI 10.1111/j.1365-2486.2007.01458.x; Smith P, 1997, GEODERMA, V81, P153, DOI 10.1016/S0016-7061(97)00087-6; Tett SFB, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2000JD000028; van Vuuren DP, 2005, ECOL ECON, V52, P43, DOI 10.1016/j.ecolecon.2004.06.009; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; van Oijen M, 2010, CLIMATIC CHANGE, V103, P55, DOI 10.1007/s10584-010-9917-3; Volterra V, 1931, ANIMAL ECOLOGY; Wattenbach M, 2010, AGR ECOSYST ENVIRON, V139, P419, DOI 10.1016/j.agee.2010.08.004; WATTENBACH M, 2006, IEMSS 3 BIENN M SUMM; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Yeluripati JB, 2009, SOIL BIOL BIOCHEM, V41, P2579, DOI 10.1016/j.soilbio.2009.08.021; Zimmermann M, 2007, EUR J SOIL SCI, V58, P658, DOI 10.1111/j.1365-2389.2006.00855.x; *BBSRC, 2011, SEARCH TERMS SYST BI	53	3	3	ROYAL SOC	LONDON	6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND	0962-8436		PHILOS T R SOC B	Philos. Trans. R. Soc. B-Biol. Sci.	JAN 19	2012	367	1586					311	321		10.1098/rstb.2011.0173		11	Biology	Life Sciences & Biomedicine - Other Topics	863PM	WOS:000298177900015	
J	Almanza, VH; Molina, LT; Sosa, G				Almanza, V. H.; Molina, L. T.; Sosa, G.			Soot and SO2 contribution to the supersites in the MILAGRO campaign from elevated flares in the Tula Refinery	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							MEXICO-CITY; FIELD CAMPAIGN; BLACK CARBON; DATA ASSIMILATION; RAMA MEASUREMENTS; DIFFUSION FLAMES; MEZQUITAL VALLEY; CLOUD MODEL; CROSS-FLOW; COMBUSTION	This work presents a simulation of the plume trajectory emitted by flaring activities of the Miguel Hidalgo Refinery in Mexico. The flame of a representative sour gas flare is modeled with a CFD combustion code in order to estimate emission rates of combustion by-products of interest for air quality: acetylene, ethylene, nitrogen oxides, carbon monoxide, soot and sulfur dioxide. The emission rates of NO2 and SO2 were compared with measurements obtained at Tula as part of MILAGRO field campaign. The rates of soot, VOCs and CO emissions were compared with estimates obtained by Instituto Mexicano del Petroleo (IMP). The emission rates of these species were further included in WRF-Chem model to simulate the chemical transport of the plume from 22 to 27 March of 2006. The model presents reliable performance of the resolved meteorology, with respect to the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), mean bias (BIAS), vector RMSE and Index of Agreement (IOA). WRF-Chem outputs of SO2 and soot were compared with surface measurements obtained at the three supersites of MILAGRO campaign. The results suggest a contribution of Tula flaring activities to the total SO2 levels of 18% to 27% at the urban supersite (T0), and of 10% to 18% at the suburban supersite (T1). For soot, the model predicts low contribution at the three supersites, with less than 0.1% at three supersites. According to the model, the greatest contribution of both pollutants to the three supersites occurred on 23 March, which coincides with the third cold surge event reported during the campaign.	[Almanza, V. H.; Sosa, G.] Inst Mexicano Petr, Mexico City 07730, DF, Mexico; [Molina, L. T.] Molina Ctr Energy & Environm, La Jolla, CA USA; [Molina, L. T.] MIT, Dept Earth Atmospher & Planetary Sci, Cambridge, MA USA	Sosa, G (reprint author), Inst Mexicano Petr, Mexico City 07730, DF, Mexico.	gsosa@imp.mx					Abdulkareem AS, 2009, ENERG SOURCE PART A, V31, P1004, DOI 10.1080/15567030801909318; Ackermann IJ, 1998, ATMOS ENVIRON, V32, P2981, DOI 10.1016/S1352-2310(98)00006-5; Almanza V. H., 2012, NUMERICAL ESTI UNPUB; Alzueta MU, 2001, COMBUST FLAME, V127, P2234, DOI 10.1016/S0010-2180(01)00325-X; Beychok M. R., 1995, FUNDAMENTALS STACK G; BILGER RW, 1975, COMBUST SCI TECHNOL, V11, P215, DOI 10.1080/00102207508946700; Bond TC, 2011, ATMOS CHEM PHYS, V11, P1505, DOI 10.5194/acp-11-1505-2011; Brookes SJ, 1999, COMBUST FLAME, V116, P486, DOI 10.1016/S0010-2180(98)00056-X; Castineira D, 2008, J ENVIRON ENG-ASCE, V134, P561, DOI 10.1061/(ASCE)0733-9372(2008)134:7(561); Chen F, 2001, MON WEATHER REV, V129, P569, DOI 10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2; Chen SH, 2002, J METEOROL SOC JPN, V80, P99, DOI 10.2151/jmsj.80.99; CIFUENTES E, 1994, SALUD PUBLICA MEXICO, V36, P3; D'Errico G., 2007, 200724005 SAE; de Foy B, 2009, ATMOS CHEM PHYS, V9, P9599; de Foy B, 2006, ATMOS CHEM PHYS, V6, P2321; de Foy B, 2009, ATMOS CHEM PHYS, V9, P4419; Doran JC, 2008, ATMOS CHEM PHYS, V8, P1377; DUDHIA J, 1989, J ATMOS SCI, V46, P3077, DOI 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2; Fast J, 2009, ATMOS CHEM PHYS, V9, P6191; FAST JD, 1995, J APPL METEOROL, V34, P2762, DOI 10.1175/1520-0450(1995)034<2762:MMAFDD>2.0.CO;2; Fast JD, 2007, ATMOS CHEM PHYS, V7, P2233; Galant S., 1984, 20 S INT COMB COMB I, P531; Garcia AZ, 2009, ATMOS CHEM PHYS, V9, P6479; General Electric Energy Flare Gas Reduction, 2011, FLAR GAS RED REC GLO; Glassman I, 2008, COMBUSTION, 4TH EDITION, P1; Gogolek P., 2010, EMISSIONS ELEVATED F; Grell GA, 2005, ATMOS ENVIRON, V39, P6957, DOI 10.1016/j.atmosenv.2005.04.027; Grell GA, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015311; Hong SY, 2006, MON WEATHER REV, V134, P2318, DOI 10.1175/MWR3199.1; IMP, 2006, PSMAIFF213931 IMP; Johnson MR, 2011, ENVIRON SCI TECHNOL, V45, P345, DOI 10.1021/es102230y; Kassem HI, 2011, INT COMMUN HEAT MASS, V38, P363, DOI 10.1016/j.icheatmasstransfer.2010.12.012; Law C., 2006, COMBUSTION PHYS, P99; Lawal MS, 2010, COMBUST SCI TECHNOL, V182, P1491, DOI 10.1080/00102202.2010.496379; LEUNG KM, 1991, COMBUST FLAME, V87, P289, DOI 10.1016/0010-2180(91)90114-Q; Li G, 2010, ATMOS CHEM PHYS, V10, P6551, DOI 10.5194/acp-10-6551-2010; LIN YL, 1983, J CLIM APPL METEOROL, V22, P1065, DOI 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2; Lutz AE, 1988, P COMBUST INST, V22, P1683; Marzouk OA, 2010, ENG APPL COMP FLUID, V4, P331; McEwen JDN, 2012, J AIR WASTE MANAGE, V62, P307, DOI 10.1080/10473289.2011.650040; Mellqvist J., 2010, H102 TERC PROJ; Mlawer EJ, 1997, J GEOPHYS RES-ATMOS, V102, P16663, DOI 10.1029/97JD00237; Moffet RC, 2009, P NATL ACAD SCI USA, V106, P11872, DOI 10.1073/pnas.0900040106; Molina LT, 2010, ATMOS CHEM PHYS, V10, P8697, DOI 10.5194/acp-10-8697-2010; Morvan D, 1998, COMBUST SCI TECHNOL, V140, P93, DOI 10.1080/00102209808915769; MOSS JB, 1995, COMBUST FLAME, V101, P491, DOI 10.1016/0010-2180(94)00233-I; Nordin N., 2001, THESIS CHALMERS U TE; OpenFOAM, 2012, OPENFOAM OPEN SOURCE; Parrish DD, 2012, ATMOS CHEM PHYS, V12, P3273, DOI 10.5194/acp-12-3273-2012; Pope SB, 1997, COMBUST THEOR MODEL, V1, P41, DOI 10.1088/1364-7830/1/1/006; PURI R, 1994, COMBUST FLAME, V97, P125, DOI 10.1016/0010-2180(94)90001-9; Querol X, 2008, ATMOS CHEM PHYS, V8, P111; Richter H, 2000, PROG ENERG COMBUST, V26, P565, DOI 10.1016/S0360-1285(00)00009-5; Rivera C, 2009, ATMOS CHEM PHYS, V9, P6351; Sazhin SS, 1996, FUEL, V75, P289, DOI 10.1016/0016-2361(95)00269-3; Skamarock W.C., 2005, NCARTN468STR; Skeie RB, 2011, ATMOS CHEM PHYS, V11, P6809, DOI 10.5194/acp-11-6809-2011; Sonibare JA, 2004, ENERG POLICY, V32, P1653, DOI 10.1016/j.enpol.2004.02.008; Stauffer D., 1990, MONTH WEATHER REV, V110, P1250; STOCKWELL WR, 1990, J GEOPHYS RES-ATMOS, V95, P16343, DOI 10.1029/JD095iD10p16343; Tie XX, 2007, ATMOS ENVIRON, V41, P1989, DOI 10.1016/j.atmosenv.2006.10.053; Tuccella P, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016302; Vazquez-Alarcon A., 2001, Agrociencia (Montecillo), V35, P267; Villasenor R, 2003, ATMOS ENVIRON, V37, P3713, DOI 10.1016/S1352-2310(03)00445-X; Weller HG, 1998, COMPUT PHYS, V12, P620, DOI 10.1063/1.168744; Wild O, 2000, J ATMOS CHEM, V37, P245, DOI 10.1023/A:1006415919030; WILLMOTT CJ, 1985, J GEOPHYS RES-OCEANS, V90, P8995, DOI 10.1029/JC090iC05p08995; Willmott CJ, 2005, CLIMATE RES, V30, P79, DOI 10.3354/cr030079; Woolley RM, 2009, FUEL, V88, P393, DOI 10.1016/j.fuel.2008.10.005; World Bank, 2011, GLOB GAS FLAR RED NE; ZHANG XM, 1993, ATMOS ENVIRON A-GEN, V27, P2295, DOI 10.1016/0960-1686(93)90399-J; Zhang Y, 2009, ATMOS CHEM PHYS, V9, P3777; Zhang YX, 2009, ATMOS ENVIRON, V43, P4622, DOI 10.1016/j.atmosenv.2009.05.039; [Anonymous], 2012, OPENFOAM USER GUIDE	74	1	1	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2012	12	21					10583	10599		10.5194/acp-12-10583-2012		17	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	035NR	WOS:000310954400036	
J	Ford, DA; Edwards, KP; Lea, D; Barciela, RM; Martin, MJ; Demaria, J				Ford, D. A.; Edwards, K. P.; Lea, D.; Barciela, R. M.; Martin, M. J.; Demaria, J.			Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model	OCEAN SCIENCE			English	Article							MARINE ECOSYSTEM MODEL; SEQUENTIAL DATA ASSIMILATION; EFFICIENT DATA ASSIMILATION; EXTENDED KALMAN FILTER; NORTH-ATLANTIC OCEAN; LOCAL SEIK FILTER; IN-SITU DATA; PHYTOPLANKTON GROWTH; BIOCHEMICAL-MODEL; CHLOROPHYLL DATA	As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physical-biogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO(2), but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.	[Ford, D. A.; Edwards, K. P.; Lea, D.; Barciela, R. M.; Martin, M. J.] Met Off, Exeter EX1 3PB, Devon, England; [Demaria, J.] ACRI ST, F-06904 Sophia Antipolis, France	Ford, DA (reprint author), Met Off, FitzRoy Rd, Exeter EX1 3PB, Devon, England.	david.ford@metoffice.gov.uk			ESA DUE GlobColour program; ARGANS [ARG/001-001/0812_CCN_METOFFICE]; EC [218812]; Climate Modelling User Group [4000100222/10/I-AM]; UK Natural Environment Research Council through the UK marine research institutes' strategic research programme Oceans [2025]; National Oceanography Centre, Southampton; US National Science Foundation [OCE-0628107, 462 OCE-0628379]; NASA [NNX-08AL92G]; Bermuda Bio-Optics Project (BBOP); SEA Semester program	The work presented here was funded by the ESA DUE GlobColour program and ARGANS under contract number ARG/001-001/0812_CCN_METOFFICE with continuation support from the EC FP7/2007-2013 program under grant agreement no. 218812 (MyOcean). The authors would also like to thank ESA for funding the Climate Modelling User Group (Contract 4000100222/10/I-AM) as part of its Climate Change Initiative, through which a part of the assessment presented here was conducted. The SeaWiFS and MODIS-Aqua data were available thanks to NASA and OrbView while ESA provided the MERIS data. Through the use of AMT data, this study was supported by the UK Natural Environment Research Council through the UK marine research institutes' strategic research programme Oceans 2025 awarded to Plymouth Marine Laboratory and the National Oceanography Centre, Southampton; this is contribution number 212 of the AMT programme. pCO<INF>2</INF> data were supplied by the British Oceanographic Data Centre and collected by Plymouth Marine Laboratory under the UK Natural Environment Research Council Knowledge Transfer Programme CARBON-OPS NE/E002021/1; the authors would like to thank Nick Hardman-Mountford and Gwen Moncoiffe for their work providing the data. HOT ALOHA data were obtained from http:// hahana.soest.hawaii.edu/hot/hot-dogs/bextraction. html. For contributing data to SeaBASS the authors thank Frank Muller-Karger and the team at the Institute for Marine Remote Sensing, University of South Florida, and Ramon Varela and Yrene Astor and their team at the Fundacion La Salle de Ciencias Naturales de Venezuela, for the bio-optical and other oceanographic data collected at the CARIACO Ocean Time Series; E. D'Asaro, I. Cetinic, K. Fennel, C. M. Lee, and M. J. Perry for collecting data as part of the North Atlantic Bloom Experiment 2008, with support from US National Science Foundation (OCE-0628107 and 462 OCE-0628379) and NASA (NNX-08AL92G); the Bermuda Bio-Optics Project (BBOP); and the SEA Semester program run by Sea Education Association. The authors would also like to thank Ian Totterdell for help with HadOCC; John Hemmings for discussions about the data assimilation scheme and for generating Fig. 1; Mike Bell for comments on the draft manuscript; and Anna Teruzzi and two anonymous reviewers for their comments in Ocean Science Discussions.	Allen JI, 2003, ANN GEOPHYS-GERMANY, V21, P399; Anderson LA, 2000, DEEP-SEA RES PT I, V47, P1787, DOI 10.1016/S0967-0637(00)00019-4; ANDERSON TR, 1993, LIMNOL OCEANOGR, V38, P1403; Bailey SW, 2006, REMOTE SENS ENVIRON, V102, P12, DOI 10.1016/j.rse.2006.01.015; Barnes C, 2011, J PLANKTON RES, V33, P13, DOI 10.1093/plankt/fbq088; Berger H, 2011, J APPL METEOROL CLIM, V50, P2309, DOI 10.1175/JAMC-D-11-019.1; Berx B, 2011, OCEANOGRAPHY, V24, P166; Besiktepe ST, 2003, J MARINE SYST, V40, P171, DOI 10.1016/S0924-7963(03)00018-6; Bloom SC, 1996, MON WEATHER REV, V124, P1256, DOI 10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2; Blower JD, 2009, OCEANOGRAPHY, V22, P70, DOI 10.5670/oceanog.2009.67; Bocquet M, 2010, MON WEATHER REV, V138, P2997, DOI 10.1175/2010MWR3164.1; Brankart JM, 2012, OCEAN SCI, V8, P121, DOI 10.5194/os-8-121-2012; Brasseur P, 2009, OCEANOGRAPHY, V22, P206; CAMPBELL JW, 1995, J GEOPHYS RES-OCEANS, V100, P13237, DOI 10.1029/95JC00458; Carmillet V, 2001, OCEAN MODEL, V3, P167, DOI 10.1016/S1463-5003(01)00007-5; Ciavatta S, 2011, J GEOPHYS RES-OCEANS, V116, DOI 10.1029/2011JC007219; Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539; Cummings J, 2009, OCEANOGRAPHY, V22, P96; Donlon CJ, 2012, REMOTE SENS ENVIRON, V116, P140, DOI 10.1016/j.rse.2010.10.017; Eknes M, 2002, J MARINE SYST, V36, P75, DOI 10.1016/S0924-7963(02)00134-3; ElMoussaoui A., 2011, MERCATOR OCEAN NEWSL; EPPLEY RW, 1972, FISH B-NOAA, V70, P1063; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Fanton d'Andon O. H., 2008, OCEAN OPTICS, VXIX; Fontana C, 2010, CONT SHELF RES, V30, P1761, DOI 10.1016/j.csr.2010.08.003; Garcia H. E., 2010, NOAA ATLAS NESDIS; Garcia H. E., 2006, NOAA ATLAS NESDIS, V4; Geider RJ, 1997, MAR ECOL PROG SER, V148, P187, DOI 10.3354/meps148187; Geider RJ, 1996, LIMNOL OCEANOGR, V41, P1; GHRSST Science Team, 2010, REC GHRSST DAT SPEC; Gregg WW, 2009, J MARINE SYST, V76, P16, DOI 10.1016/j.jmarsys.2008.05.006; Gregg WW, 2008, J MARINE SYST, V69, P205, DOI 10.1016/j.jmarsys.2006.02.015; Gregg WW, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2009GL041893; Hardman-Mountford NJ, 2008, ICES J MAR SCI, V65, P1498, DOI 10.1093/icesjms/fsn110; Hemmings JCP, 2008, J MAR RES, V66, P87, DOI 10.1357/002224008784815739; Hemmings JCP, 2004, J MARINE SYST, V50, P141, DOI 10.1016/j.jmarsys.2004.02.003; HOLLINGSWORTH A, 1986, TELLUS A, V38, P111; Hooker S. B., 1992, 104566 NAT AER SPAC, V104566; Hoteit I, 2003, ANN GEOPHYS-GERMANY, V21, P389; Hoteit I, 2005, ANN GEOPHYS-GERMANY, V23, P3171; Ingleby B, 2007, J MARINE SYST, V65, P158, DOI 10.1016/j.jmarsys.2005.11.019; INGLEBY NB, 1993, Q J ROY METEOR SOC, V119, P1195, DOI 10.1002/qj.49711951316; ISHIZAKA J, 1990, J GEOPHYS RES-OCEANS, V95, P20201, DOI 10.1029/JC095iC11p20201; Kalnay E, 2003, ATMOSPHERIC MODELING; Key RM, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2004GB002247; Le Traon PY, 2009, OCEANOGRAPHY, V22, P56; Levy M, 2001, GEOPHYS RES LETT, V28, P3725, DOI 10.1029/2001GL012947; LORENC AC, 1991, Q J ROY METEOR SOC, V117, P59, DOI 10.1002/qj.49711749704; Losa SN, 2003, J MARINE SYST, V43, P31, DOI 10.1016/j.jmarsys.2003.06.001; Madec G, 2008, NOTE POLE MODEL, V27, P1288; Maritorena S, 2010, REMOTE SENS ENVIRON, V114, P1791, DOI 10.1016/j.rse.2010.04.002; Martin M.J., 2007, Quarterly Journal of the Royal Meteorological Society, V133, DOI 10.1002/qj.74; MATEAR RJ, 1995, J MAR RES, V53, P571, DOI 10.1357/0022240953213098; MOREL A, 1977, LIMNOL OCEANOGR, V22, P709; Natvik LJ, 2003, J MARINE SYST, V40, P127, DOI 10.1016/S0924-7963(03)00016-2; Nerger L, 2007, J MARINE SYST, V68, P237, DOI 10.1016/j.jmarsys.2006.11.009; Nerger L, 2008, J MARINE SYST, V73, P87, DOI 10.1016/j.jmarsys.2007.09.007; O'Brien T, 2005, NMFSFSPO73 NOAA US D; Oke PR, 2008, J ATMOS OCEAN TECH, V25, P1004, DOI 10.1175/2007JTECHO558.1; O'Reilly JE, 1998, J GEOPHYS RES-OCEANS, V103, P24937, DOI 10.1029/98JC02160; Ourmieres Y, 2009, J MARINE SYST, V75, P100, DOI 10.1016/j.jmarsys.2008.08.003; Palmer JR, 2001, DEEP-SEA RES PT I, V48, P1169, DOI 10.1016/S0967-0637(00)00080-7; PARRISH DF, 1992, MON WEATHER REV, V120, P1747, DOI 10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2; Popova EE, 2002, DEEP-SEA RES PT I, V49, P1741, DOI 10.1016/S0967-0637(02)00091-2; Purser R. J., 2000, 430 NCEP OFF; Raick C, 2007, J MARINE SYST, V65, P561, DOI 10.1016/j.jmarsys.2005.06.006; Samuelsen A., 2011, MERCATOR OCEAN NEWSL; Simon E, 2012, J MARINE SYST, V89, P1, DOI 10.1016/j.jmarsys.2011.07.007; Storkey D, 2010, J OPER OCEANOGR, V3, P3; Takahashi T, 2009, DEEP-SEA RES PT II, V56, P554, DOI 10.1016/j.dsr2.2008.12.009; Talagrand O, 1997, J METEOROL SOC JPN, V75, P191; Taylor KE, 2001, J GEOPHYS RES-ATMOS, V106, P7183, DOI 10.1029/2000JD900719; Teruzzi A., 2011, MERCATOR OCEAN NEWSL; Timmermann R, 2005, OCEAN MODEL, V8, P175, DOI 10.1016/j.ocemod.2003.12.009; Torres R, 2006, J MARINE SYST, V60, P317, DOI 10.1016/j.jmarsys.2006.02.001; Totterdell I. J., 2012, DIAT HADOCC OC UNPUB; Triantafyllou G, 2003, J MARINE SYST, V40, P213, DOI 10.1016/S0924-7963(03)00019-8; Werdell P. J., 2003, EOS T AM GEOPHYS UN, V84, P377; While J, 2012, J GEOPHYS RES-OCEANS, V117, DOI 10.1029/2010JC006815	79	0	0	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1812-0784		OCEAN SCI	Ocean Sci.		2012	8	5					751	771		10.5194/os-8-751-2012		21	Meteorology & Atmospheric Sciences; Oceanography	Meteorology & Atmospheric Sciences; Oceanography	029CN	WOS:000310472500002	
J	Miyazaki, K; Eskes, HJ; Sudo, K; Takigawa, M; van Weele, M; Boersma, KF				Miyazaki, K.; Eskes, H. J.; Sudo, K.; Takigawa, M.; van Weele, M.; Boersma, K. F.			Simultaneous assimilation of satellite NO2, O-3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							ENSEMBLE KALMAN FILTER; ATMOSPHERIC DATA ASSIMILATION; OZONE MONITORING INSTRUMENT; CHEMISTRY TRANSPORT MODELS; CARBON-MONOXIDE; RETRIEVAL ALGORITHM; ERROR ANALYSIS; IN-SITU; INTEX-B; NADIR RETRIEVALS	We have developed an advanced chemical data assimilation system to combine observations of chemical compounds from multiple satellites. NO2, O-3, CO, and HNO3 measurements from the Ozone Monitoring Instrument (OMI), Tropospheric Emission Spectrometer (TES), Measurement of Pollution in the Troposphere (MOPITT), and Microwave Limb Sounder (MLS) satellite instruments are assimilated into the global chemical transport model CHASER for the years 2006-2007. The CHASER data assimilation system (CHASER-DAS), based on the local ensemble transform Kalman filter technique, simultaneously optimizes the chemical species, as well as the emissions of O-3 precursors, while taking their chemical feedbacks into account. With the available datasets, an improved description of the chemical feedbacks can be obtained, especially related to the NOx-CO-OH-O-3 set of chemical reactions. Comparisons against independent satellite, aircraft, and ozonesonde data show that the data assimilation results in substantial improvements for various chemical compounds. These improvements include a reduced negative tropospheric NO2 column bias (by 40-85%), a reduced negative CO bias in the Northern Hemisphere (by 40-90%), and a reduced positive O-3 bias in the middle and upper troposphere (from 30-40% to within 10%). These changes are related to increased tropospheric OH concentrations by 5-15% in the tropics and the Southern Hemisphere in July. Observing System Experiments (OSEs) have been conducted to quantify the relative importance of each data set on constraining the emissions and concentrations. The OSEs confirm that the assimilation of individual data sets results in a strong influence on both assimilated and non-assimilated species through the inter-species error correlation and the chemical coupling described by the model. The simultaneous adjustment of the emissions and concentrations is a powerful approach to correcting the tropospheric ozone budget and profile analyses.	[Miyazaki, K.; Eskes, H. J.; van Weele, M.; Boersma, K. F.] Royal Netherlands Meteorol Inst KNMI, NL-3732 GK De Bilt, Netherlands; [Miyazaki, K.; Sudo, K.; Takigawa, M.] Japan Agcy Marine Earth Sci & Technol, Yokohama, Kanagawa, Japan; [Sudo, K.] Nagoya Univ, Grad Sch Environm Studies, Nagoya, Aichi 4648601, Japan; [Boersma, K. F.] Eindhoven Univ Technol, Fluid Dynam Lab, NL-5600 MB Eindhoven, Netherlands	Miyazaki, K (reprint author), Royal Netherlands Meteorol Inst KNMI, Wilhelminalaan 10, NL-3732 GK De Bilt, Netherlands.	miyazaki@knmi.nl	Boersma, Klaas/H-4559-2012		JSPS; Ministry of the Environment, Japan [A-0903]; Netherlands Organisation for Scientific Research, NWO [864.09.001]	We would like to thank the two anonymous reviewers and the editor for their valuable comments. This research was supported by the JSPS Postdoctoral Fellowships for Research Abroad and the Environment Research and Technology Development Fund (A-0903) of the Ministry of the Environment, Japan. Folkert Boersma acknowledges funding by the Netherlands Organisation for Scientific Research, NWO Vidi grant 864.09.001.	Allen DJ, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2002JD002066; Arellano AF, 2007, ATMOS CHEM PHYS, V7, P5695; Arellano AF, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027371; Arellano AF, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020311; Beer R, 2006, IEEE T GEOSCI REMOTE, V44, P1102, DOI 10.1109/TGRS.2005.863716; Bethan S, 1996, Q J ROY METEOR SOC, V122, P929, DOI 10.1002/qj.49712253207; Boersma K. F., 2011, DUTCH OMI NO2 DOMINO; Boersma KF, 2007, ATMOS CHEM PHYS, V7, P2103; Boersma KF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008816; Boersma KF, 2005, ATMOS CHEM PHYS, V5, P2311; Boersma KF, 2009, ATMOS CHEM PHYS, V9, P3867; Boersma KF, 2011, ATMOS MEAS TECH, V4, P1905, DOI 10.5194/amt-4-1905-2011; Boersma KF, 2008, ATMOS ENVIRON, V42, P4480, DOI 10.1016/j.atmosenv.2008.02.004; Boersma KF, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003962; Bovensmann H, 1999, J ATMOS SCI, V56, P127, DOI 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2; Bowman KW, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2002JD002150; Bowman KW, 2006, IEEE T GEOSCI REMOTE, V44, P1297, DOI [10.1109/TGRS.2006.871234, 10.1109/TGRS.2006871234]; Bowman KW, 2009, ATMOS CHEM PHYS, V9, P3563; Boxe CS, 2010, ATMOS CHEM PHYS, V10, P9901, DOI 10.5194/acp-10-9901-2010; Boynard A, 2011, ATMOS ENVIRON, V45, P784, DOI 10.1016/j.atmosenv.2010.08.006; Bucsela EJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008838; Callies J., 2000, ESA B; Coheur PF, 2009, ATMOS CHEM PHYS, V9, P5655; Coman A, 2012, ATMOS CHEM PHYS, V12, P2513, DOI 10.5194/acp-12-2513-2012; Constantinescu EM, 2007, Q J ROY METEOR SOC, V133, P1229, DOI 10.1002/qj.76; Daniel JS, 1998, J GEOPHYS RES-ATMOS, V103, P13249, DOI 10.1029/98JD00822; DeCaria AJ, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005556; Dee DP, 2005, Q J ROY METEOR SOC, V131, P3323, DOI 10.1256/qj.05.137; Deeter M. N., 2011, MOPITT MEASUREMENTS; Deeter MN, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008929; Deeter MN, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003186; Deeter MN, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013005; Edwards DP, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007079; Elbern H, 2007, ATMOS CHEM PHYS, V7, P3749; Elbern H, 2001, J GEOPHYS RES-ATMOS, V106, P3569, DOI 10.1029/2000JD900448; Elguindi N, 2010, GEOSCI MODEL DEV, V3, P501, DOI 10.5194/gmd-3-501-2010; Errera Q, 2008, ATMOS CHEM PHYS, V8, P6169; ESA, 2012, ESA, VSP-1324/3; Eskes HJ, 2003, Q J ROY METEOR SOC, V129, P1663, DOI 10.1256/qj.02.14; Eskes HJ, 2003, ATMOS CHEM PHYS, V3, P1285; European Commission: Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL), 2011, REL VERS 4 2; Feng L, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009121; Flemming J, 2009, GEOSCI MODEL DEV, V2, P253; Folberth G, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021812; Foret G, 2009, ATMOS CHEM PHYS, V9, P8479; Fortems-Cheiney A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014416; Froidevaux L, 2006, IEEE T GEOSCI REMOTE, V44, P1106, DOI 10.1109/TGRS.2006.864366; Froidevaux L, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008771; Fu TM, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007853; Geer AJ, 2006, ATMOS CHEM PHYS, V6, P5445; GHIL M, 1991, ADV GEOPHYS, V33, P141, DOI 10.1016/S0065-2687(08)60442-2; Gloudemans A M S, 2009, ATMOS CHEM PHYS, V9, P3799, DOI DOI 10.5194/ACP-9-3799-2009; GRAEDEL TE, 1993, GLOBAL BIOGEOCHEM CY, V7, P1, DOI 10.1029/92GB02793; Grassi B, 2004, ANN GEOPHYS-GERMANY, V22, P2669; GUENTHER A, 1995, J GEOPHYS RES-ATMOS, V100, P8873, DOI 10.1029/94JD02950; Turquety S, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004821; Hains JC, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012399; Hanea R. G., 2004, J GEOPHYS RES, V109; Hanea RG, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004283; Heald CL, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD005185; HEIKES BG, 1992, J GEOPHYS RES-ATMOS, V97, P18001; Ho SP, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012242; HOFFMAN RN, 1983, TELLUS A, V35, P100; Holloway T, 2000, J GEOPHYS RES-ATMOS, V105, P12123, DOI 10.1029/1999JD901173; Hooghiemstra PB, 2011, ATMOS CHEM PHYS, V11, P4705, DOI 10.5194/acp-11-4705-2011; Houtekamer PL, 1998, MON WEATHER REV, V126, P796, DOI 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2; Houtekamer PL, 2001, MON WEATHER REV, V129, P123, DOI 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2; Hudman RC, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007912; Hunt BR, 2007, PHYSICA D, V230, P112, DOI 10.1016/j.physd.2006.11.008; IPCC, 2007, CLIMATE CHANGE 2007; Jackson DR, 2007, Q J ROY METEOR SOC, V133, P1771, DOI 10.1002/qj.140; Jaegle L, 2005, FARADAY DISCUSS, V130, P407, DOI 10.1039/b502128f; Jenkins GS, 2004, ATMOS CHEM PHYS, V4, P449; Jones DBA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003702; Jones DBA, 2009, ATMOS CHEM PHYS, V9, P3547; Jourdain L, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028284; Kalnay E, 2003, ATMOSPHERIC MODELING; Kaminski JW, 2008, ATMOS CHEM PHYS, V8, P3255; Kanamitsu M, 2002, B AM METEOROL SOC, V83, P1631, DOI 10.1175/Bams-83-11-1631; Kasibhatla P, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015581; Khattatov BV, 2000, J GEOPHYS RES-ATMOS, V105, P29135, DOI 10.1029/2000JD900466; KOPACZ M., 2007, J GEOPHYS RES, V113, DOI [DOI 10.1029/2007JD009264, 10.1029/2007JD009264]; Kopacz M, 2010, ATMOS CHEM PHYS, V10, P855; Lahoz WA, 2007, ATMOS CHEM PHYS, V7, P5745; Lamsal LN, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2010GL046476; Lamsal LN, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013351; Levelt PF, 2006, IEEE T GEOSCI REMOTE, V44, P1199, DOI 10.1109/TGRS.2006.872336; Lin CY, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009498; LIU SC, 1987, J GEOPHYS RES-ATMOS, V92, P4191, DOI 10.1029/JD092iD04p04191; Livesey N. J., 2011, D33509 JPL; LOGAN JA, 1981, J GEOPHYS RES-OC ATM, V86, P7210, DOI 10.1029/JC086iC08p07210; Lopez JP, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008811; LUO M, 2006, J GEOPHYS RES, V112, pS2448, DOI DOI 10.1029/2007JD008803; Mallet V, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006234; Martin RV, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003453; MARTIN RV, 2006, J GEOPHYS RES, V112, DOI DOI 10.1029/2006JD007831; Martin RV, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001480; Massart S, 2012, Q J ROY METEOR SOC, V138, P889, DOI 10.1002/qj.971; Menard R, 2000, MON WEATHER REV, V128, P2672, DOI 10.1175/1520-0493(2000)128<2672:AOSCTO>2.0.CO;2; Messina P, 2011, ATMOS ENVIRON, V45, P6674, DOI 10.1016/j.atmosenv.2011.08.056; Millet DB, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006853; Miyazaki K, 2012, ATMOS CHEM PHYS, V12, P2263, DOI 10.5194/acp-12-2263-2012; Miyoshi T, 2007, MON WEATHER REV, V135, P3841, DOI 10.1175/2007MWR1873.1; Muller JF, 2005, ATMOS CHEM PHYS, V5, P1157; Nassar R, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008819; Ohara T, 2007, ATMOS CHEM PHYS, V7, P4419; Olivier J. G.J., 2005, ENV SCI, V2, P81, DOI DOI 10.1080/15693430500400345; Osterman G., 2009, D38042; Osterman GB, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008801; Ott E, 2004, TELLUS A, V56, P415, DOI 10.1111/j.1600-0870.2004.00076.x; Ott LE, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD011880; Pajot B., 2011, ATMOS CHEM PHYS, V11, P29357, DOI [10.5194/acpd-11-29357-2011, DOI 10.5194/ACPD-11-29357-2011]; Parrington M, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036935; Parrington M, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009341; Pickering KE, 1998, J GEOPHYS RES-ATMOS, V103, P31203, DOI 10.1029/98JD02651; Pison I, 2009, ATMOS CHEM PHYS, V9, P5281; PRICE C, 1992, J GEOPHYS RES-ATMOS, V97, P9919; Randerson J. T., 2007, GLOBAL FIRE EMISSION; Richter A, 2002, ADV SPACE RES, V29, P1673, DOI 10.1016/S0273-1177(02)00100-X; Richter A, 2005, NATURE, V437, P129, DOI 10.1038/nature04092; Rodgers C. D., 2000, INVERSE METHODS ATMO; Rodgers CD, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002299; SANDU A, 2011, ATMOSPHERE, V3, P426; Sawada S., ATMOS ENV, V20, P821; Schopfer MPJ, 2007, J GEOPHYS RES-SOL EA, V112, DOI [10.1029/2007JD008773, 10.1029/2006JB004902]; Schumann U, 2007, ATMOS CHEM PHYS, V7, P3823; Segers AJ, 2005, Q J ROY METEOR SOC, V131, P477, DOI 10.1256/qj.04.92; Shindell DT, 2009, SCIENCE, V326, P716, DOI 10.1126/science.1174760; Shindell DT, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007100; Singh HB, 2009, ATMOS CHEM PHYS, V9, P2301; Singh K, 2011, GEOSCI MODEL DEV, V4, P299, DOI 10.5194/gmd-4-299-2011; Smit H. G., 1998, WMO GLOBAL ATMOSPHER, V130; STAJNER I, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008863; Staudt AC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002139; Stavrakou T, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006896; Sudo K, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001114; THOMPSON AM, 1992, SCIENCE, V256, P1157, DOI 10.1126/science.256.5060.1157; Thornton JA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003559; van der A RJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009021; van Loon M, 2000, ENVIRON MODELL SOFTW, V15, P603, DOI 10.1016/S1364-8152(00)00048-7; Worden J, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL027806; Worden J, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010560; Worden J, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004522; Zhao C, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL037123; Zhao Y, 2011, ATMOS CHEM PHYS, V11, P2295, DOI 10.5194/acp-11-2295-2011; Ziemke JR, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007089	146	0	0	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2012	12	20					9545	9579		10.5194/acp-12-9545-2012		35	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	029BS	WOS:000310470400009	
J	Kuppel, S; Peylin, P; Chevallier, F; Bacour, C; Maignan, F; Richardson, AD				Kuppel, S.; Peylin, P.; Chevallier, F.; Bacour, C.; Maignan, F.; Richardson, A. D.			Constraining a global ecosystem model with multi-site eddy-covariance data	BIOGEOSCIENCES			English	Article							DECIDUOUS FOREST; FLUX MEASUREMENTS; CARBON-DIOXIDE; ESTIMATING PARAMETERS; NONLINEAR INVERSION; VEGETATION MODEL; EXCHANGE; RESPIRATION; PHOTOSYNTHESIS; ASSIMILATION	Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO2 flux (NEE) and latent heat flux (LE) measurements from 12 temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root-mean-square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (Reco), and to a lesser extent the gross primary productivity (GPP), although we only assimilated net CO2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS sets of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global-scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based observations of normalized difference vegetation index (NDVI).	[Kuppel, S.; Peylin, P.; Chevallier, F.; Maignan, F.] UMR 8212 CEA CNRS UVSQ, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France; [Peylin, P.] UMR 7618 CNRS UPMC INRA, Lab Biogeochim & Ecol Milieux Continentaux, Paris, France; [Bacour, C.] Noveltis, F-31520 Ramonville St Agne, France; [Richardson, A. D.] Harvard Univ, Dept Organism & Evolutionary Biol, HUH, Cambridge, MA 02138 USA	Kuppel, S (reprint author), UMR 8212 CEA CNRS UVSQ, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France.	sylvain.kuppel@lsce.ipsl.fr			CARBONES project, within the EU's 7th Framework Program for Research and Development; Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA); NOAA's Climate Program Office (Global Carbon Cycle Program) [NA11OAR4310054]; USDA Forest Service for measurements at US-Bar site	This work has been supported by the CARBONES project, within the EU's 7th Framework Program for Research and Development. The Ph.D. program of S. Kuppel has been funded by the Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA). A. D. Richardson acknowledges support from NOAA's Climate Program Office (Global Carbon Cycle Program, under award NA11OAR4310054) and from the USDA Forest Service for measurements at US-Bar site. The sites PIs of DE-Hai, DK-Sor, FR-Fon, FR-Hes, JP-Tak, UK-Ham, US-Ha1, US-LPH, US-MOz, US-UMB and US-WCr are thanked for making their data available and for their useful suggestions. We are also grateful to M. Reichstein and G. Lasslop for the useful discussions regarding FluxNet data, P. Ciais, S. Zaehle, and J.-C. Calvet for the fruitful general discussions, and two anonymous reviewers for their helpful comments. We finally thank F. Delage and P. Rayner for their contribution to the development of the tangent linear model, and the computer team at LSCE for the computational time and resources provided.	Baldocchi D, 2008, AUST J BOT, V56, P1, DOI 10.1071/BT07151; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Ball J.T., 1987, PROGR PHOTOSYNTHESIS, P221; Botta A, 2000, GLOB CHANGE BIOL, V6, P709, DOI 10.1046/j.1365-2486.2000.00362.x; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; BYRD RH, 1995, SIAM J SCI COMPUT, V16, P1190, DOI 10.1137/0916069; Carvalhais N, 2010, GLOBAL CHANGE BIOL, V16, P2813, DOI 10.1111/j.1365-2486.2010.02173.x; Carvalhais N, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003033; Chevallier F, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030463; Chevallier F, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026496; COLLATZ GJ, 1991, AGR FOREST METEOROL, V54, P107, DOI 10.1016/0168-1923(91)90002-8; Cook BD, 2004, AGR FOREST METEOROL, V126, P271, DOI 10.1016/j.agrformet.2004.06.008; Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539; Curtis PS, 2002, AGR FOREST METEOROL, V113, P3, DOI 10.1016/S0168-1923(02)00099-0; Desai AR, 2008, AGR FOREST METEOROL, V148, P821, DOI 10.1016/j.agrformet.2007.11.012; DUCOUDRE NI, 1993, J CLIMATE, V6, P248, DOI 10.1175/1520-0442(1993)006<0248:SANSOP>2.0.CO;2; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Garrigues S, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000635; Giering R, 2005, FUTURE GENER COMP SY, V21, P1345, DOI 10.1016/j.future.2004.11.003; Granier A, 2008, ANN FOREST SCI, V65, DOI 10.1051/forest:2008052; Groenendijk M, 2011, AGR FOREST METEOROL, V151, P22, DOI 10.1016/j.agrformet.2010.08.013; Gu LH, 2012, AGR FOREST METEOROL, V152, P135, DOI 10.1016/j.agrformet.2011.09.014; Hadley JL, 2008, TREE PHYSIOL, V28, P615; Hansen MC, 2000, INT J REMOTE SENS, V21, P1331, DOI 10.1080/014311600210209; Heymann Y., 1993, CORINE LAND COVER TE; Ito A, 2006, ECOL RES, V21, P137, DOI 10.1007/s11284-005-0100-7; Jenkins JP, 2007, AGR FOREST METEOROL, V143, P64, DOI 10.1016/j.agrformet.2006.11.008; Kattge J, 2011, GLOBAL CHANGE BIOL, V17, P2905, DOI 10.1111/j.1365-2486.2011.02451.x; Knohl A, 2003, AGR FOREST METEOROL, V118, P151, DOI 10.1016/S0168-1923(03)00115-1; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Krinner G, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2003GB002199; Lasslop G, 2008, BIOGEOSCIENCES, V5, P1311; Maignan F, 2011, GEOSCI MODEL DEV, V4, P1103, DOI 10.5194/gmd-4-1103-2011; Medvigy D, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000812; Papale D, 2006, BIOGEOSCIENCES, V3, P571; PARTON WJ, 1988, BIOGEOCHEMISTRY, V5, P109, DOI 10.1007/BF02180320; Pilegaard K, 2001, AGR FOREST METEOROL, V107, P29, DOI 10.1016/S0168-1923(00)00227-6; Prevost-Boure NC, 2010, APPL SOIL ECOL, V46, P26, DOI 10.1016/j.apsoil.2010.06.004; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Reichstein M, 2005, GLOBAL CHANGE BIOL, V11, P1424, DOI 10.1111/j.1365-2486.2005.001002.x; Richardson AD, 2008, AGR FOREST METEOROL, V148, P38, DOI 10.1016/j.agrformet.2007.09.001; Richardson AD, 2012, GLOBAL CHANGE BIOL, V18, P566, DOI 10.1111/j.1365-2486.2011.02562.x; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; Santaren D, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002834; Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x; Slater AG, 2001, J HYDROMETEOROL, V2, P7, DOI 10.1175/1525-7541(2001)002<0007:TROSIL>2.0.CO;2; Stoy PC, 2006, AGR FOREST METEOROL, V141, P2, DOI 10.1016/j.agrformet.2006.09.001; Tarantola A., 1987, INVERSE PROBLEM THEO; THONING KW, 1989, J GEOPHYS RES-ATMOS, V94, P8549, DOI 10.1029/JD094iD06p08549; Urbanski S, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000293; Verbeeck H, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001544; Vermote E, 2009, IEEE T GEOSCI REMOTE, V47, P898, DOI 10.1109/TGRS.2008.2005977; Vermote EF, 2002, REMOTE SENS ENVIRON, V83, P97, DOI 10.1016/S0034-4257(02)00089-5; Wang YP, 2007, GLOBAL CHANGE BIOL, V13, P652, DOI 10.1111/j.1365-2486.2006.01225.x; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; Williams M, 2009, BIOGEOSCIENCES, V6, P1341	57	1	1	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1726-4170		BIOGEOSCIENCES	Biogeosciences		2012	9	10					3757	3776		10.5194/bg-9-3757-2012		20	Ecology; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	029CG	WOS:000310471800004	
J	Luo, YQ; Randerson, JT; Abramowitz, G; Bacour, C; Blyth, E; Carvalhais, N; Ciais, P; Dalmonech, D; Fisher, JB; Fisher, R; Friedlingstein, P; Hibbard, K; Hoffman, F; Huntzinger, D; Jones, CD; Koven, C; Lawrence, D; Li, DJ; Mahecha, M; Niu, SL; Norby, R; Piao, SL; Qi, X; Peylin, P; Prentice, IC; Riley, W; Reichstein, M; Schwalm, C; Wang, YP; Xia, JY; Zaehle, S; Zhou, XH				Luo, Y. Q.; Randerson, J. T.; Abramowitz, G.; Bacour, C.; Blyth, E.; Carvalhais, N.; Ciais, P.; Dalmonech, D.; Fisher, J. B.; Fisher, R.; Friedlingstein, P.; Hibbard, K.; Hoffman, F.; Huntzinger, D.; Jones, C. D.; Koven, C.; Lawrence, D.; Li, D. J.; Mahecha, M.; Niu, S. L.; Norby, R.; Piao, S. L.; Qi, X.; Peylin, P.; Prentice, I. C.; Riley, W.; Reichstein, M.; Schwalm, C.; Wang, Y. P.; Xia, J. Y.; Zaehle, S.; Zhou, X. H.			A framework for benchmarking land models	BIOGEOSCIENCES			English	Article							GLOBAL VEGETATION MODEL; CARBON-CYCLE FEEDBACK; TERRESTRIAL ECOSYSTEMS; NITROGEN DEPOSITION; CLIMATE-CHANGE; EVAPOTRANSPIRATION ALGORITHM; PERFORMANCE ANALYSIS; DATA ASSIMILATION; SOIL RESPIRATION; TROPICAL FORESTS	Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data-model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.	[Luo, Y. Q.; Li, D. J.; Niu, S. L.; Qi, X.; Xia, J. Y.] Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA; [Randerson, J. T.] Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA 92697 USA; [Abramowitz, G.] Univ New S Wales, Climate Change Res Ctr, Sydney, NSW, Australia; [Bacour, C.] Joint Unit CEA CNRS, Lab Climate Sci & Environm, Gif Sur Yvette, France; [Blyth, E.] Ctr Ecol & Hydrol, Wallingford OX10 8BB, Oxon, England; [Carvalhais, N.; Dalmonech, D.; Mahecha, M.; Reichstein, M.; Zaehle, S.] Max Planck Inst Biogeochem, Jena, Germany; [Carvalhais, N.] Univ Nova Lisboa, DCEA, P-2829516 Caparica, Portugal; [Ciais, P.; Peylin, P.] CE Orme Merisiers, CEA CNRS UVSQ, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France; [Fisher, J. B.] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA; [Fisher, R.; Lawrence, D.] Natl Ctr Atmospher Res, Boulder, CO 80307 USA; [Friedlingstein, P.] Univ Exeter, Coll Engn Math & Phys Sci, Exeter EX4 4QF, Devon, England; [Hibbard, K.] Pacific NW Natl Lab, Richland, WA 99352 USA; [Hoffman, F.; Norby, R.] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA; [Huntzinger, D.; Schwalm, C.] No Arizona Univ, Sch Earth Sci & Environm Sustainabil, Flagstaff, AZ 86011 USA; [Jones, C. D.] Met Off Hadley Ctr, Exeter EX1 3PB, Devon, England; [Koven, C.; Riley, W.] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Earth Sci, Berkeley, CA 94720 USA; [Piao, S. L.] Peking Univ, Dept Ecol, Beijing 100871, Peoples R China; [Prentice, I. C.] Macquarie Univ, Dept Biol Sci, Sydney, NSW 2109, Australia; [Wang, Y. P.] CSIRO Marine & Atmospher Res PMB 1, Aspendale, Vic 3195, Australia; [Wang, Y. P.] Ctr Australian Weather & Climate Res, Aspendale, Vic 3195, Australia; [Zhou, X. H.] Fudan Univ, Res Inst Changing Global Environm, Shanghai 200433, Peoples R China	Luo, YQ (reprint author), Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA.	yluo@ou.edu	Xia, Jianyang/A-7886-2008; Zhou, Xuhui/H-4332-2011; Mahecha, Miguel/F-2443-2010; Reichstein, Markus/A-7494-2011; Abramowitz, Gab/C-4977-2013; Lawrence, David/C-4026-2011; wang, yp/A-9765-2011	Mahecha, Miguel/0000-0003-3031-613X; Reichstein, Markus/0000-0001-5736-1112; Abramowitz, Gab/0000-0002-4205-001X; 	NASA's Carbon Cycle and Ecosystems Program; US Dept. of Energy's Office of Biological and Environmental Research; US Department of Energy, Terrestrial Ecosystem Sciences [DE SC0008270]; US National Science Foundation (NSF) [DEB 0444518, DEB 0743778, DEB 0840964, DBI 0850290, EPS 0919466]; National Aeronautics and Space Administration; UK DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]; European Community [238366]	ILAMB is sponsored by the Analysis, Integration and Modeling of the Earth System (AIMES) project of the International Geosphere-Biosphere Programme (IGBP). The ILAMB project has received support from NASA's Carbon Cycle and Ecosystems Program and US Dept. of Energy's Office of Biological and Environmental Research. Preparation of the manuscript by Y. L. was financially supported by US Department of Energy, Terrestrial Ecosystem Sciences grant DE SC0008270 and US National Science Foundation (NSF) grant DEB 0444518, DEB 0743778, DEB 0840964, DBI 0850290, and EPS 0919466. Contributions from JBF were by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. CDJ was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). S. Z. and D. D. were supported by the European Community's Seventh Framework Programme under grant agreement no. 238366 (Greencycles II).	Abramowitz G, 2007, J HYDROMETEOROL, V8, P989, DOI 10.1175/JHM628.1; Abramowitz G, 2008, J CLIMATE, V21, P5468, DOI 10.1175/2008JCLI2378.1; Abramowitz G, 2006, J HYDROMETEOROL, V7, P160, DOI 10.1175/JHM479.1; Abramowitz G, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL024419; Arora VK, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2010GL046270; Batjes NH, 2002, SOIL USE MANAGE, V18, P324, DOI 10.1079/SUM2002138; Bell JE, 2010, WATER RESOUR RES, V46, DOI 10.1029/2009WR007908; Blyth E, 2011, GEOSCI MODEL DEV, V4, P255, DOI 10.5194/gmd-4-255-2011; Blyth E, 2010, J HYDROMETEOROL, V11, P509, DOI 10.1175/2009JHM1183.1; Bonan GB, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001593; Bonan GB, 2008, SCIENCE, V320, P1444, DOI 10.1126/science.1155121; Bonan GB, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL042430; Bond-Lamberty B. P., 2010, GLOBAL DATABASE SOIL, DOI [10.3334/ORNLDAAC/984, DOI 10.3334/ORNLDAAC/984]; Boyero L, 2011, ECOL LETT, V14, P289, DOI 10.1111/j.1461-0248.2010.01578.x; Brown J., 2003, CIRCUMPOLAR ACTIVE L; Bryan GH, 2002, MON WEATHER REV, V130, P2917, DOI 10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2; Cadule P, 2010, GLOBAL BIOGEOCHEM CY, V24, DOI 10.1029/2009GB003556; Carvalhais N, 2010, GLOBAL CHANGE BIOL, V16, P2813, DOI 10.1111/j.1365-2486.2010.02173.x; Chave J, 2005, OECOLOGIA, V145, P87, DOI 10.1007/s00442-005-0100-x; Chen TH, 1997, J CLIMATE, V10, P1194, DOI 10.1175/1520-0442(1997)010<1194:CERFTP>2.0.CO;2; Chen Y, 2010, GLOBAL CHANGE BIOL, V16, P2024, DOI 10.1111/j.1365-2486.2009.02156.x; Dai A, 2009, J CLIMATE, V22, P2773, DOI 10.1175/2008JCLI2592.1; Dai YJ, 2003, B AM METEOROL SOC, V84, P1013, DOI 10.1175/BAMS-84-8-1013; Dargaville RJ, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001426; Dorigo WA, 2011, HYDROL EARTH SYST SC, V15, P1675, DOI 10.5194/hess-15-1675-2011; [FAO IIASA ISRIC ISS-CAS JRC], 2009, HARM WORLD SOIL DAT; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Fifer R. M., 1988, BENCHMARKING BEATING; Fisher JB, 2008, REMOTE SENS ENVIRON, V112, P901, DOI 10.1016/j.rse.2007.06.025; Frank E, 1998, MACH LEARN, V32, P63, DOI 10.1023/A:1007421302149; Frankenberg C, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL048738; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Ghosh AK, 1998, IETE J RES, V44, P3; Giglio L, 2010, BIOGEOSCIENCES, V7, P1171; Gobron N., 2004, 21386 EUR EN I ENV S; Heimann M, 1998, GLOBAL BIOGEOCHEM CY, V12, P1, DOI 10.1029/97GB01936; Hungate BA, 2003, SCIENCE, V302, P1512, DOI 10.1126/science.1091390; Hurtt GC, 2006, GLOBAL CHANGE BIOL, V12, P1208, DOI 10.1111/j.1365-2486.2006.01150.x; Jamasb T, 2003, ENERG POLICY, V31, P1609, DOI 10.1016/S0301-4215(02)00226-4; JANSSEN PHM, 1995, ECOL MODEL, V83, P55, DOI 10.1016/0304-3800(95)00084-9; Janssens IA, 2010, NAT GEOSCI, V3, P315, DOI [10.1038/NGEO844, 10.1038/ngeo844]; Jin YF, 2011, REMOTE SENS ENVIRON, V115, P2302, DOI 10.1016/j.rse.2011.04.031; Jung M, 2010, NATURE, V467, P951, DOI 10.1038/nature09396; Jung M, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001566; Kattge J, 2009, GLOBAL CHANGE BIOL, V15, P976, DOI 10.1111/j.1365-2486.2008.01744.x; Knapp AK, 2008, BIOSCIENCE, V58, P811, DOI 10.1641/B580908; Kurz WA, 2008, P NATL ACAD SCI USA, V105, P1551, DOI 10.1073/pnas.0708133105; Kurz WA, 2008, NATURE, V452, P987, DOI 10.1038/nature06777; Lefsky MA, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043622; Liu LL, 2010, ECOL LETT, V13, P819, DOI 10.1111/j.1461-0248.2010.01482.x; Lu M, 2011, NEW PHYTOL, V189, P1040, DOI 10.1111/j.1469-8137.2010.03563.x; Lu M, 2011, AGR ECOSYST ENVIRON, V140, P234, DOI 10.1016/j.agee.2010.12.010; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; Luo YQ, 2011, ECOL APPL, V21, P1429; Luo YQ, 2006, ECOLOGY, V87, P53, DOI 10.1890/04-1724; Luo YQ, 2011, GLOBAL CHANGE BIOL, V17, P843, DOI 10.1111/j.1365-2486.2010.02265.x; Luo YQ, 2007, ANNU REV ECOL EVOL S, V38, P683, DOI 10.1146/annurev.ecolsys.38.091206.095808; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; Luo YQ, 2011, TRENDS ECOL EVOL, V26, P96, DOI 10.1016/j.tree.2010.11.003; Maignan F, 2011, GEOSCI MODEL DEV, V4, P1103, DOI 10.5194/gmd-4-1103-2011; Matthews E, 1997, J GEOPHYS RES-ATMOS, V102, P18771, DOI 10.1029/96JD02956; McGuire AD, 2001, GLOBAL BIOGEOCHEM CY, V15, P183, DOI 10.1029/2000GB001298; Mittelmann HD, 2006, OPTIM METHOD SOFTW, V21, P105, DOI 10.1080/10556780500065366; Moody EG, 2008, J APPL METEOROL CLIM, V47, P2879, DOI 10.1175/2008JAMC1795.1; Moody EG, 2005, IEEE T GEOSCI REMOTE, V43, P144, DOI 10.1109/TGRS.2004.838359; Morgan JA, 2004, OECOLOGIA, V140, P11, DOI 10.1007/s00442-004-1550-2; Mu Q, 2007, REMOTE SENS ENVIRON, V111, P519, DOI 10.1016/j.rse.2007.04.015; Mu QZ, 2011, REMOTE SENS ENVIRON, V115, P1781, DOI 10.1016/j.rse.2011.02.019; Norby RJ, 2006, ECOLOGY, V87, P5, DOI 10.1890/04-1950; Norby RJ, 2010, P NATL ACAD SCI USA, V107, P19368, DOI 10.1073/pnas.1006463107; Norby RJ, 2011, ANNU REV ECOL EVOL S, V42, P181, DOI 10.1146/annurev-ecolsys-102209-144647; Notaro M, 2005, J CLIMATE, V18, P3650, DOI 10.1175/JCLI3501.1; Oleson K. W., 2010, NCARTN478STR; Ollinger SV, 2008, P NATL ACAD SCI USA, V105, P19336, DOI 10.1073/pnas.0810021105; Olson J. S., 1983, CARBON LIVE VEGETATI, P152; Oreskes N, 2003, MODELS IN ECOSYSTEM SCIENCE, P13; Owe M, 2008, J GEOPHYS RES-EARTH, V113, DOI 10.1029/2007JF000769; Peng CH, 2011, ECOL LETT, V14, P522, DOI 10.1111/j.1461-0248.2011.01603.x; Piao SL, 2011, GLOBAL CHANGE BIOL, V17, P3228, DOI 10.1111/j.1365-2486.2011.02419.x; Pitman AJ, 2003, INT J CLIMATOL, V23, P479, DOI 10.1002/joc.893; POST WM, 1982, NATURE, V298, P156, DOI 10.1038/298156a0; Prentice IC, 2011, GLOBAL BIOGEOCHEM CY, V25, DOI 10.1029/2010GB003906; Prentice IC, 2000, J BIOGEOGR, V27, P507, DOI 10.1046/j.1365-2699.2000.00425.x; Prince S. D., 2011, ISLSCP INITIATIVE 2, DOI [10.3334/ORNLDAAC/1023, DOI 10.3334/ORNLDAAC/1023]; Randerson JT, 2009, GLOBAL CHANGE BIOL, V15, P2462, DOI 10.1111/j.1365-2486.2009.01912.x; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Reich PB, 2007, ECOL APPL, V17, P1982, DOI 10.1890/06-1803.1; Riley WJ, 2011, BIOGEOSCIENCES, V8, P1925, DOI 10.5194/bg-8-1925-2011; Rodell M, 2005, EARTH INTERACT, V9, DOI 10.1175/EI126.1; Rustad LE, 2001, OECOLOGIA, V126, P543, DOI 10.1007/s004420000544; Rykiel EJ, 1996, ECOL MODEL, V90, P229, DOI 10.1016/0304-3800(95)00152-2; Saatchi SS, 2007, GLOBAL CHANGE BIOL, V13, P816, DOI 10.1111/j.1365-2486.2007.01323.x; Schwalm CR, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001229; Sherry RA, 2007, P NATL ACAD SCI USA, V104, P198, DOI 10.1073/pnas.0605642104; Simard M, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2011JG001708; Simon TA, 2009, CONCURR COMP-PRACT E, V21, P2213, DOI 10.1002/cpe.1486; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x; SMITH EP, 1995, ECOL MODEL, V77, P49, DOI 10.1016/0304-3800(93)E0074-D; Tang JY, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2008JG000757; Tarnocai C, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003327; Taylor KE, 2001, J GEOPHYS RES-ATMOS, V106, P7183, DOI 10.1029/2000JD900719; Thomas RQ, 2010, NAT GEOSCI, V3, P13, DOI 10.1038/NGEO721; Thonicke K, 2010, BIOGEOSCIENCES, V7, P1991, DOI 10.5194/bg-7-1991-2010; Thornton PE, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002868; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; van der Werf GR, 2004, SCIENCE, V303, P73, DOI 10.1126/science.1090753; Vinukollu RK, 2011, REMOTE SENS ENVIRON, V115, P801, DOI 10.1016/j.rse.2010.11.006; Wan SQ, 2001, ECOL APPL, V11, P1349, DOI 10.2307/3060925; Wang A, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002514; Wang YP, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL041009; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; Wang YP, 2010, BIOGEOSCIENCES, V7, P2261, DOI 10.5194/bg-7-2261-2010; Weng ESS, 2012, J GEOPHYS RES-BIOGEO, V117, DOI 10.1029/2012JG002040; Weng ES, 2011, ECOL APPL, V21, P1490, DOI 10.1890/09-1394.1; Woodhouse IH, 2006, IEEE T GEOSCI REMOTE, V44, P871, DOI 10.1109/TGRS.2006.872356; Wright IJ, 2005, GLOBAL ECOL BIOGEOGR, V14, P411, DOI 10.1111/j.1466-822x.2005.00172.x; Wu ZT, 2011, GLOBAL CHANGE BIOL, V17, P927, DOI 10.1111/j.1365-2486.2010.02302.x; Xia J., 2012, GLOB CHANGE IN PRESS; Yang YH, 2011, NEW PHYTOL, V190, P977, DOI 10.1111/j.1469-8137.2011.03645.x; Yuan H, 2011, REMOTE SENS ENVIRON, V115, P1171, DOI 10.1016/j.rse.2011.01.001; Zaehle S, 2010, GLOBAL BIOGEOCHEM CY, V24, DOI 10.1029/2009GB003522; Zaehle S, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2009GL041345; Zhou T, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB002939; Zinke P. J., 1986, NDP018 ORNL, P146	127	1	1	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1726-4170		BIOGEOSCIENCES	Biogeosciences		2012	9	10					3857	3874		10.5194/bg-9-3857-2012		18	Ecology; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	029CG	WOS:000310471800010	
J	Kaminski, T; Rayner, PJ; Vossbeck, M; Scholze, M; Koffi, E				Kaminski, T.; Rayner, P. J.; Vossbeck, M.; Scholze, M.; Koffi, E.			Observing the continental-scale carbon balance: assessment of sampling complementarity and redundancy in a terrestrial assimilation system by means of quantitative network design	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							ATMOSPHERIC CO2 MEASUREMENTS; QUANTIFY REGIONAL FLUXES; MODEL; CYCLE; UNCERTAINTIES; BIOSPHERE; TRANSPORT; SENSITIVITY; INVERSION; LEARN	This paper investigates the relationship between the heterogeneity of the terrestrial carbon cycle and the optimal design of observing networks to constrain it. We combine the methods of quantitative network design and carbon-cycle data assimilation to a hierarchy of increasingly heterogeneous descriptions of the European terrestrial biosphere as indicated by increasing diversity of plant functional types. We employ three types of observations, flask measurements of CO2 concentrations, continuous measurements of CO2 and pointwise measurements of CO2 flux. We show that flux measurements are extremely efficient for relatively homogeneous situations but not robust against increasing or unknown complexity. Here a hybrid approach is necessary, and we recommend its use in the development of integrated carbon observing systems.	[Kaminski, T.; Vossbeck, M.] FastOpt, D-22767 Hamburg, Germany; [Rayner, P. J.] Univ Melbourne, Sch Earth Sci, Melbourne, Vic, Australia; [Scholze, M.] Univ Bristol, Dept Earth Sci, Bristol, Avon, England; [Koffi, E.] LSCE IPSL, Gif Sur Yvette, France	Kaminski, T (reprint author), FastOpt, Lerchenstr 28A, D-22767 Hamburg, Germany.	thomas.kaminski@fastopt.com			European Community [026188]; ARC Professorial Fellowship [DP1096309]	We thank Han Dolman and Antoon Meesters as well as Christoph Gerbig for their reviewer comments and Dietrich Feist for his interactive comment. These comments improved the manuscript significantly. This work was supported in part by the European Community within the 6th Framework Programme for Research and Technological Development under contract no. 026188. PR is in receipt of an ARC Professorial Fellowship (DP1096309).	Carouge C, 2010, ATMOS CHEM PHYS, V10, P3119; Carouge C, 2010, ATMOS CHEM PHYS, V10, P3107; Enting I.G., 2002, INVERSE PROBLEMS ATM; Giering R, 1998, ACM T MATH SOFTWARE, V24, P437, DOI 10.1145/293686.293695; Groenendijk M, 2011, AGR FOREST METEOROL, V151, P22, DOI 10.1016/j.agrformet.2010.08.013; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; HARDT M, 1994, GEOPHYS J INT, V117, P716, DOI 10.1111/j.1365-246X.1994.tb02464.x; Hauglustaine DA, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003957; Heimann Martin, 1995, 10 M PLANCK I MET, V10; Kaminski T, 2010, TELLUS B, V62, P784, DOI 10.1111/j.1600-0889.2010.00483.x; Kaminski T, 2012, BIOGEOSCIENCES, V9, P3173, DOI 10.5194/bg-9-3173-2012; Kaminski T., 2008, ECOL STUD; Kaminski T, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001463; Kaminski T, 1999, J GEOPHYS RES-ATMOS, V104, P18535, DOI 10.1029/1999JD900147; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P207, DOI 10.1029/1998GB001059; Knorr W, 2000, GLOBAL ECOL BIOGEOGR, V9, P225, DOI 10.1046/j.1365-2699.2000.00159.x; Koffi EN, 2012, GLOBAL BIOGEOCHEM CY, V26, DOI 10.1029/2010GB003900; Nijssen B, 2001, J CLIMATE, V14, P1790, DOI 10.1175/1520-0442(2001)014<1790:GREOSM>2.0.CO;2; Pillai D, 2010, ATMOS CHEM PHYS, V10, P83; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Rayner PJ, 1999, TELLUS B, V51, P213, DOI 10.1034/j.1600-0889.1999.t01-1-00008.x; Rayner PJ, 2001, GEOPHYS RES LETT, V28, P175, DOI 10.1029/2000GL011912; Rayner PJ, 2011, PHILOS T R SOC A, V369, P1955, DOI 10.1098/rsta.2010.0378; Rayner PJ, 1996, TELLUS B, V48, P433, DOI 10.1034/j.1600-0889.1996.t01-3-00003.x; Rayner PJ, 2010, CURR OPIN ENV SUST, V2, P289, DOI 10.1016/j.cosust.2010.05.005; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Solomon S, 2007, IPCC 2007 CLIMATE CH; WILSON MF, 1985, J CLIMATOL, V5, P119	30	0	0	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2012	12	16					7867	7879		10.5194/acp-12-7867-2012		13	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	999CW	WOS:000308287700038	
J	Kaminski, T; Knorr, W; Scholze, M; Gobron, N; Pinty, B; Giering, R; Mathieu, PP				Kaminski, T.; Knorr, W.; Scholze, M.; Gobron, N.; Pinty, B.; Giering, R.; Mathieu, P-P			Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis	BIOGEOSCIENCES			English	Article							UNCERTAINTIES; BIOSPHERE; PHOTOSYNTHESIS; DESIGN	The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA's Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.	[Kaminski, T.; Giering, R.] FastOpt, D-22767 Hamburg, Germany; [Knorr, W.; Scholze, M.] Univ Bristol, Dept Earth Sci, Bristol BS8 1RJ, Avon, England; [Knorr, W.] Aristotle Univ Thessaloniki, Dept Meteorol & Climatol, Thessaloniki, Greece; [Knorr, W.] Dept Earth & Ecosyst Sci, S-22362 Lund, Sweden; [Scholze, M.] Univ Hamburg, D-20144 Hamburg, Germany; [Gobron, N.; Pinty, B.] Commiss European Communities, Inst Environm & Sustainabil, DG Joint Res Ctr, Global Environm Monitoring Unit, I-21020 Ispra, VA, Italy; [Mathieu, P-P] European Space Agcy, Earth Observat Sci & Applicat, I-00044 Frascati, Rm, Italy	Kaminski, T (reprint author), FastOpt, Lerchenstr 28A, D-22767 Hamburg, Germany.	thomas.kaminski@fastopt.com			European Space Agency [20595/07/I-EC]; ESA/ESRIN, Frascati	The authors thank two anonymous reviewers for their valuable comments and suggestions. The authors thank the European Space Agency for financing this project under contract number 20595/07/I-EC, Philippe Goryl and Olivier Colin from ESA/ESRIN, Frascati, for support with the ESA MERIS product, Monica Robustelli and Ioannis Andredakis for help with data processing, Reiner Schnur for provision of meteorological data, and Michael Vossbeck for his help with code administration.	Denman K, 2007, CLIMATE CHANGE 2007; Giering R, 1998, ACM T MATH SOFTWARE, V24, P437, DOI 10.1145/293686.293695; Gobron N, 2000, IEEE T GEOSCI REMOTE, V38, P2489, DOI 10.1109/36.885197; Gobron N, 2008, REMOTE SENS ENVIRON, V112, P1871, DOI 10.1016/j.rse.2007.09.011; Groenendijk M, 2011, AGR FOREST METEOROL, V151, P22, DOI 10.1016/j.agrformet.2010.08.013; Heimann Martin, 1995, 10 M PLANCK I MET, V10; Kaminski T, 2010, TELLUS B, V62, P784, DOI 10.1111/j.1600-0889.2010.00483.x; Kaminski Thomas, 2008, V203, P33; Kaminski T, 2003, LECT NOTES COMPUT SC, V2668, P95; Kaminski T, 1999, J GEOPHYS RES-ATMOS, V104, P18555, DOI 10.1029/1999JD900146; Kaminski T., 2012, ATMOS CHEM PHYS DISC, V12, P7211, DOI [10.5194/acpd-12-7211-2012, DOI 10.5194/ACPD-12-7211-2012]; Knorr W, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001119; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P207, DOI 10.1029/1998GB001059; Knorr W, 2000, GLOBAL ECOL BIOGEOGR, V9, P225, DOI 10.1046/j.1365-2699.2000.00159.x; Knorr W., 2008, P 2 MERIS A ATSR US; Nijssen B, 2001, J CLIMATE, V14, P1790, DOI 10.1175/1520-0442(2001)014<1790:GREOSM>2.0.CO;2; Pinty B, 2009, REMOTE SENS ENVIRON, V113, P10, DOI 10.1016/j.rse.2008.08.017; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Rayner PJ, 2011, PHILOS T R SOC A, V369, P1955, DOI 10.1098/rsta.2010.0378; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Scholze M., 2003, MODEL STUDIES RESPON; SCHULZE ED, 1994, ANNU REV ECOL SYST, V25, P629, DOI 10.1146/annurev.ecolsys.25.1.629; SELLERS PJ, 1985, INT J REMOTE SENS, V6, P1335; Tarantola A., 1987, INVERSE PROBLEM THEO; Verstraete MM, 1996, REMOTE SENS ENVIRON, V58, P201, DOI 10.1016/S0034-4257(96)00069-7; WILSON MF, 1985, J CLIMATOL, V5, P119; Ziehn T., 2012, GLOBAL BIOG IN PRESS; [Anonymous], 2008, GLOBALVIEW CO2 COOP	28	5	5	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1726-4170		BIOGEOSCIENCES	Biogeosciences		2012	9	8					3173	3184		10.5194/bg-9-3173-2012		12	Ecology; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	999DT	WOS:000308290200023	
J	Ghilain, N; Arboleda, A; Sepulcre-Canto, G; Batelaan, O; Ardo, J; Gellens-Meulenberghs, F				Ghilain, N.; Arboleda, A.; Sepulcre-Canto, G.; Batelaan, O.; Ardo, J.; Gellens-Meulenberghs, F.			Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite	HYDROLOGY AND EARTH SYSTEM SCIENCES			English	Article							LEAF-AREA INDEX; DATA ASSIMILATION SYSTEM; CARBON FLUXES; ECMWF MODEL; REGIONAL EVAPOTRANSPIRATION; SOUTHWESTERN FRANCE; GLOBAL DATABASE; CLIMATE MODELS; PRODUCTS; IMPACT	Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3-5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north-south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations.	[Ghilain, N.; Arboleda, A.; Sepulcre-Canto, G.; Gellens-Meulenberghs, F.] Royal Meteorol Inst Belgium, B-1180 Brussels, Belgium; [Batelaan, O.] Vrije Univ Brussel, Dept Hydrol & Hydraul Engn, B-1050 Brussels, Belgium; [Batelaan, O.] Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Heverlee, Belgium; [Ardo, J.] Lund Univ, Dept Phys Geog & Ecosyst Anal, S-22362 Lund, Sweden	Ghilain, N (reprint author), Royal Meteorol Inst Belgium, Ave Circulaire 3, B-1180 Brussels, Belgium.	nicolas.ghilain@meteo.be	Garcia Bustamante, Elena/H-4188-2012		RMI by EUMETSAT; ESA under PRODEX programme [e15066]; Belgian Sciences Policy; Belgian Science Policy through the PROBA-V preparatory programme [CB/34/18]	The authors thank the Editor and the anonymous referees for their fruitful comments that have allowed to improve the readability of the manuscript. They are grateful to F. J. Garcia-Haro, F. Camacho de Coca and the LSA-SAF operating team to provide the LSA-SAF LAI and FVC images in near-real time, as well as DSSF, DSLF and AL. We also thank G. Balsamo for providing the H-TESSEL code, the in-situ data providers who could provide us with the necessary material for benchmarking, and ESA Globcover Project led by MEDIAS France for providing the Globcover land cover. The MODIS MCD12Q1 data were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (http://lpdaac.usgs.gov/get_data). This study has been done in the framework of the LSA-SAF project, funded at RMI by EUMETSAT and by ESA (contract e15066) under the PRODEX programme supported by the Belgian Sciences Policy, and in the framework of PROBA-VET project, supported by the Belgian Science Policy through the PROBA-V preparatory programme (contract CB/34/18).	ALADIN international team, 1997, WMO B, V46, P317; Alton PB, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001430; Aubinet M, 2001, AGR FOREST METEOROL, V108, P293, DOI 10.1016/S0168-1923(01)00244-1; Balsamo G, 2009, J HYDROMETEOROL, V10, P623, DOI 10.1175/2008JHM1068.1; BELJAARS ACM, 1994, BOUND-LAY METEOROL, V71, P135, DOI 10.1007/BF00709223; Betts A. K., 2000, 295 ECMWF; Bicheron P., 2006, P REC ADV QUANT REM; Bicheron P., 2008, GLOBCOVER PARTNERS G; Blyth E., 2006, GLOBAL CHANGE NEWSL, V66, P9; Bonan GB, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2000GB001360; Boone A, 2009, B AM METEOROL SOC, V90, P1865, DOI 10.1175/2009BAMS2786.1; Brut A, 2009, BIOGEOSCIENCES, V6, P1389; Albergel C, 2010, HYDROL EARTH SYST SC, V14, P1109, DOI 10.5194/hess-14-1109-2010; Camacho-de-Coca F., 2008, LSA SAF PROD US MAN; Carrer D, 2010, IEEE T GEOSCI REMOTE, V48, P1714, DOI 10.1109/TGRS.2009.2034530; Champeaux JL, 2005, METEOROL APPL, V12, P29, DOI 10.1017/S1350482705001519; Cooley HS, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005160; Dorigo WA, 2007, INT J APPL EARTH OBS, V9, P165, DOI 10.1016/j.jag.2006.05.003; Edwards JM, 2009, GEOSCI MODEL DEV, V2, P123; Garcia-Haro F. J., 2005, P EUMETSAT MET SAT C; Garcia-Haro FJ, 2005, INT J REMOTE SENS, V26, P2135, DOI 10.1080/01431160512331337817; Garrigues S, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000635; Ge JJ, 2009, J CLIMATE, V22, P4427, DOI 10.1175/2009JCLI2868.1; Geiger B, 2008, IEEE T GEOSCI REMOTE, V46, P3841, DOI 10.1109/TGRS.2008.2001798; Geiger B, 2008, METEOROL APPL, V15, P411, DOI 10.1002/met.84; Ghent D, 2011, INT J REMOTE SENS, V32, P617, DOI 10.1080/01431161.2010.517794; Ghilain N, 2011, HYDROL EARTH SYST SC, V15, P771, DOI 10.5194/hess-15-771-2011; Gibelin AL, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006691; Grippa M, 2011, WATER RESOUR RES, V47, DOI 10.1029/2009WR008856; Gu YX, 2006, REMOTE SENS ENVIRON, V104, P283, DOI 10.1016/j.rse.2006.04.021; Ineichen P, 2009, INT J REMOTE SENS, V30, P5549, DOI 10.1080/01431160802680560; Jarlan L, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009370; Jiang L, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013204; Jimenez MA, 2008, MON WEATHER REV, V136, P5148, DOI 10.1175/2008MWR2461.1; Joffre R, 1996, ANN SCI FOREST, V53, P561, DOI 10.1051/forest:19960235; Jung M, 2006, REMOTE SENS ENVIRON, V101, P534, DOI 10.1016/j.rse.2006.01.020; Kahan DS, 2006, J HYDROL, V320, P238, DOI 10.1016/j.jhydrol.2005.07.011; Kalma JD, 2008, SURV GEOPHYS, V29, P421, DOI 10.1007/s10712-008-9037-z; Tchuente Armel Thibaut Kaptue, 2010, Remote Sensing of Environment, V114, DOI 10.1016/j.rse.2009.12.008; Kato H, 2007, J METEOROL SOC JPN, V85A, P187, DOI 10.2151/jmsj.85A.187; Kergoat L, 2011, ATMOS SCI LETT, V12, P129, DOI 10.1002/asl.325; Kutsch WL, 2008, BIOGEOSCIENCES, V5, P1797; Li ZL, 2009, SENSORS-BASEL, V9, P3801, DOI 10.3390/s90503801; Masson V, 2003, J CLIMATE, V16, P1261, DOI 10.1175/1520-0442-16.9.1261; Merbold L, 2009, BIOGEOSCIENCES, V6, P1027; Miller J, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026636; Nash J.E., 1970, J HYDROL, V10, P282, DOI DOI 10.1016/0022-1694(70)90255-6; NOILHAN J, 1989, MON WEATHER REV, V117, P536, DOI 10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2; Oleson KW, 2000, J HYDROMETEOROL, V1, P431, DOI 10.1175/1525-7541(2000)001<0431:TEORSP>2.0.CO;2; Pekel J. F., 2010, LSA SAF 4 US WORKSH; Pereira JS, 2007, BIOGEOSCIENCES, V4, P791; Rebmann C, 2010, AGR FOREST METEOROL, V150, P684, DOI 10.1016/j.agrformet.2009.11.001; Rodell M, 2004, B AM METEOROL SOC, V85, P381, DOI 10.1175/BAMS-85-3-381; Courault D., 2005, Irrigation and Drainage Systems, V19, P223, DOI 10.1007/s10795-005-5186-0; Sepulcre-Canto G., 2012, INT J REMOT IN PRESS; Simmons A., 2006, ECMWF NEWSLETTER, V10, P26; Sjostrom M, 2009, BIOGEOSCIENCES, V6, P129; Stisen S, 2008, REMOTE SENS ENVIRON, V112, P1242, DOI 10.1016/j.rse.2007.08.013; SUNI T, 2003, GLOBAL CHANGE BIOL, V9, P1410; Tian Y., 2004, Journal of Geophysical Research, V109, DOI 10.1029/2003JD003777; Trigo IF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010035; Trigo IF, 2011, INT J REMOTE SENS, V32, P2725, DOI 10.1080/01431161003743199; van den Hurk BJJM, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002846; Verger A, 2009, REMOTE SENS ENVIRON, V113, P2285, DOI 10.1016/j.rse.2009.06.009; VITERBO P, 1995, J CLIMATE, V8, P2716, DOI 10.1175/1520-0442(1995)008<2716:AILSPS>2.0.CO;2; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Wipfler EL, 2011, HYDROL EARTH SYST SC, V15, P1257, DOI 10.5194/hess-15-1257-2011; Yang W, 2006, REMOTE SENS ENVIRON, V104, P297, DOI 10.1016/j.rse.2006.04.016; Yang WZ, 2006, IEEE T GEOSCI REMOTE, V44, P1829, DOI 10.1109/TGRS.2006.871214	69	0	0	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1027-5606		HYDROL EARTH SYST SC	Hydrol. Earth Syst. Sci.		2012	16	8					2567	2583		10.5194/hess-16-2567-2012		17	Geosciences, Multidisciplinary; Water Resources	Geology; Water Resources	998ND	WOS:000308245800013	
J	Koohkan, MR; Bocquet, M				Koohkan, Mohammad Reza; Bocquet, Marc			Accounting for representativeness errors in the inversion of atmospheric constituent emissions: application to the retrieval of regional carbon monoxide fluxes	TELLUS SERIES B-CHEMICAL AND PHYSICAL METEOROLOGY			English	Article						inverse modelling; representativeness errors; carbon monoxide; 4D-Var	VARIATIONAL DATA ASSIMILATION; METEOROLOGICAL-OBSERVATIONS; SURFACE EMISSIONS; AIR-POLLUTION; PART I; MODEL; CO; ADJOINT; VALIDATION; PARAMETERS	A four-dimensional variational data assimilation system (4D-Var) is developed to retrieve carbon monoxide (CO) fluxes at regional scale, using an air quality network. The air quality stations that monitor CO are proximity stations located close to industrial, urban or traffic sources. The mismatch between the coarsely discretised Eulerian transport model and the observations, inferred to be mainly due to representativeness errors in this context, lead to a bias (average simulated concentrations minus observed concentrations) of the same order of magnitude as the concentrations. 4D-Var leads to a mild improvement in the bias because it does not adequately handle the representativeness issue. For this reason, a simple statistical subgrid model is introduced and is coupled to 4D-Var. In addition to CO fluxes, the optimisation seeks to jointly retrieve influence coefficients, which quantify each station's representativeness. The method leads to a much better representation of the CO concentration variability, with a significant improvement of statistical indicators. The resulting increase in the total inventory estimate is close to the one obtained from remote sensing data assimilation. This methodology and experiments suggest that information useful at coarse scales can be better extracted from atmospheric constituent observations strongly impacted by representativeness errors.	[Koohkan, Mohammad Reza; Bocquet, Marc] Univ Paris Est, CEREA, Joint Lab, Ecole Ponts ParisTech, Champs Sur Marne, France; [Koohkan, Mohammad Reza; Bocquet, Marc] EDF R&D, Champs Sur Marne, France; [Koohkan, Mohammad Reza; Bocquet, Marc] Paris Rocquencourt Res Ctr, INRIA, Paris, France	Bocquet, M (reprint author), Univ Paris Est, CEREA, Joint Lab, Ecole Ponts ParisTech, Champs Sur Marne, France.	bocquet@cerea.enpc.fr	Bocquet, Marc/E-1966-2011		Agence Nationale de la Recherche [ANR-08-SYSC-014]	This article is a contribution to the MSDAG project supported by the Agence Nationale de la Recherche, grant ANR-08-SYSC-014, and a contribution to the INSU/LEFE ADOMOCA-2 project. We are grateful to two anonymous reviewers for their useful comments and suggestions at the origin of several developments in the article.	Arellano AF, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027371; Bocquet M, 2011, Q J ROY METEOR SOC, V137, P1340, DOI 10.1002/qj.837; Bocquet M, 2012, Q J ROY METEOR SOC, V138, P664, DOI 10.1002/qj.961; Boutahar J, 2004, INT J ENVIRON POLLUT, V22, P17; Chevallier F, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006390; Desroziers G, 2001, Q J ROY METEOR SOC, V127, P1433, DOI 10.1256/smsqj.57416; Elbern H, 2007, ATMOS CHEM PHYS, V7, P3749; Emmons LK, 2010, GEOSCI MODEL DEV, V3, P43; FISHER M, 1995, Q J ROY METEOR SOC, V121, P1681, DOI 10.1002/qj.49712152709; Fortems-Cheiney A, 2009, ATMOS CHEM PHYS, V9, P8735; Fortems-Cheiney A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014416; GENEMIS, 1994, AB BAF MU MAG KASH B; Guenther A, 2006, ATMOS CHEM PHYS, V6, P3181; Guillas S, 2008, ATMOS ENVIRON, V42, P1338, DOI 10.1016/j.atmosenv.2007.10.027; Henne S, 2010, ATMOS CHEM PHYS, V10, P3561; Horowitz L.W., 2003, Journal of Geophysical Research, V108, DOI 10.1029/2002JD002853; Karamchandani P, 2009, ENVIRON FLUID MECH, V9, P59, DOI 10.1007/s10652-008-9097-0; Kopacz M, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2007JD009264; Kopacz M, 2010, ATMOS CHEM PHYS, V10, P855; Larssen S., 1999, AB BAF MU MAG KASH B; LEDIMET FX, 1986, TELLUS A, V38, P97; Menard R, 2000, MON WEATHER REV, V128, P2654, DOI 10.1175/1520-0493(2000)128<2654:AOSCTO>2.0.CO;2; MULHOLLAND M, 1995, ATMOS ENVIRON, V29, P497, DOI 10.1016/1352-2310(94)00245-G; Muller JF, 2005, ATMOS CHEM PHYS, V5, P1157; NAPPO CJ, 1982, B AM METEOROL SOC, V63, P761; Petron G, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001305; Petron G, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020560; Quelo D, 2007, ATMOS ENVIRON, V41, P5300, DOI 10.1016/j.atmosenv.2007.02.035; Saide P, 2011, TELLUS B, V63, P360, DOI 10.1111/j.1600-0889.2011.00529.x; Stavrakou T, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006896; Vestreng V, 2007, ATMOS CHEM PHYS, V7, P3663; Wu L, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD016198; Yumimoto K, 2006, ATMOS ENVIRON, V40, P6836, DOI 10.1016/j.atmosenv.2006.05.042; Zhang Y, 2012, ATMOS ENVIRON, V60, P656, DOI 10.1016/j.atmosenv.2012.02.041	34	0	0	CO-ACTION PUBLISHING	JARFALLA	RIPVAGEN 7, JARFALLA, SE-175 64, SWEDEN	0280-6509		TELLUS B	Tellus Ser. B-Chem. Phys. Meteorol.		2012	64								19047	10.3402/tellusb.v64i0.19047		17	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	979GU	WOS:000306806300001	
J	Lauvaux, T; Schuh, AE; Bocquet, M; Wu, L; Richardson, S; Miles, N; Davis, KJ				Lauvaux, T.; Schuh, A. E.; Bocquet, M.; Wu, L.; Richardson, S.; Miles, N.; Davis, K. J.			Network design for mesoscale inversions of CO2 sources and sinks	TELLUS SERIES B-CHEMICAL AND PHYSICAL METEOROLOGY			English	Article						carbon dioxide; atmospheric inversion; air-land interaction; mesoscale modelling; carbon cycle; data assimilation	CARBON-DIOXIDE; EXCHANGE; FLUXES; MODEL; UNCERTAINTIES; GROWTH	Recent instrumental deployments of regional observation networks of atmospheric CO2 mixing ratios have been used to constrain carbon sources and sinks using inversion methodologies. In this study, we performed sensitivity experiments using observation sites from the Mid Continent Intensive experiment to evaluate the required spatial density and locations of CO2 concentration towers based on flux corrections and error reduction analysis. In addition, we investigated the impact of prior flux error structures with different correlation lengths and biome information. We show here that, while the regional carbon balance converged to similar annual estimates using only two concentration towers over the region, additional sites were necessary to retrieve the spatial flux distribution of our reference case (using the entire network of eight towers). Local flux corrections required the presence of observation sites in their vicinity, suggesting that each tower was only able to retrieve major corrections within a hundred of kilometres around, despite the introduction of spatial correlation lengths (similar to 100 to 300 km) in the prior flux errors. We then quantified and evaluated the impact of the spatial correlations in the prior flux errors by estimating the improvement in the CO2 model-data mismatch of the towers not included in the inversion. The overall gain across the domain increased with the correlation length, up to 300 km, including both biome-related and non-biome-related structures. However, the spatial variability at smaller scales was not improved. We conclude that the placement of observation towers around major sources and sinks is critical for regional-scale inversions in order to obtain reliable flux distributions in space. Sparser networks seem sufficient to assess the overall regional carbon budget with the support of flux error correlations, indicating that regional signals can be recovered using hourly mixing ratios. However, the smaller spatial structures in the posterior fluxes are highly constrained by assumed prior flux error correlation lengths, with no significant improvement at only a few hundreds of kilometres away from the observation sites.	[Lauvaux, T.; Richardson, S.; Miles, N.; Davis, K. J.] Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA; [Schuh, A. E.] Colorado State Univ, Nat Resource Ecol Lab, Ft Collins, CO 80523 USA; [Bocquet, M.; Wu, L.] CEREA, Joint Lab Ecole Natl Ponts & Chaussees EDF R&D, Champs Sur Marne, France; [Wu, L.] CECEA CNRS UVSQ, IPSL LS, UMR8212, Lab Sci Climat & Environm, Saclay, France; [Schuh, A. E.] Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USA	Lauvaux, T (reprint author), Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA.	lauvaux@meteo.psu.edu	Bocquet, Marc/E-1966-2011		Office of Science (BER) US Department of Energy, Terrestrial Carbon Program; US National Aeronautics and Space Administration's Terrestrial Ecology Program; US National Oceanographic and Atmospheric Administration, Office of Global Programs; Global Carbon Cycle Program	We thank Arlyn Andrews from NOAA/ESRL for providing the data from the West Branch tall tower site (WBI) and the WLEF tower (LEF). We thank Peter J. Rayner for fruitful discussions. This work was supported by the Office of Science (BER) US Department of Energy, Terrestrial Carbon Program, the US National Aeronautics and Space Administration's Terrestrial Ecology Program, the US National Oceanographic and Atmospheric Administration, Office of Global Programs and Global Carbon Cycle Program.	Bocquet M, 2009, MON WEATHER REV, V137, P2331, DOI 10.1175/2009MWR2789.1; Canadell JG, 2007, P NATL ACAD SCI USA, V104, P18866, DOI 10.1073/pnas.0702737104; Chevallier F, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026496; Ciais P, 2010, CLIMATIC CHANGE, V103, P69, DOI 10.1007/s10584-010-9909-3; Gerbig C, 2009, BIOGEOSCIENCES, V6, P1949; Gerbig C, 2006, ATMOS CHEM PHYS, V6, P539; Gloor M, 2001, J GEOPHYS RES-ATMOS, V106, P17831, DOI 10.1029/2001JD900021; Gockede M, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012257; Gockede M, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014443; Gurney KR, 2009, ENVIRON SCI TECHNOL, V43, P5535, DOI 10.1021/es900806c; Hilton T. W., 2011, THESIS PENNSYLVANIA; Keenan TF, 2012, GLOBAL CHANGE BIOL, V18, P1971, DOI 10.1111/j.1365-2486.2012.02678.x; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P227, DOI 10.1029/1998GB001060; Lauvaux T, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039574; Lauvaux T, 2012, ATMOS CHEM PHYS, V12, P337, DOI 10.5194/acp-12-337-2012; Lauvaux T, 2008, ATMOS CHEM PHYS, V8, P3459; Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689; Lokupitiya E, 2009, BIOGEOSCIENCES, V6, P969; Michalak AM, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005970; Miles NL, 2012, J GEOPHYS RES-BIOGEO, V117, DOI 10.1029/2011JG001781; Ogle SM, 2010, GLOBAL CHANGE BIOL, V16, P810, DOI 10.1111/j.1365-2486.2009.01951.x; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Raupach MR, 2008, BIOGEOSCIENCES, V5, P1601, DOI 10.5194/bg-5-1601-2008; Ricciuto DM, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001400; Rodenbeck C, 2003, TELLUS B, V55, P488, DOI 10.1034/j.1600-0889.2003.00033.x; Rodgers C. D., 2000, INVERSE METHODS ATMO; Schuh AE, 2010, BIOGEOSCIENCES, V7, P1625, DOI 10.5194/bg-7-1625-2010; Schwalm CR, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001229; Uliasz M, 1994, ENVIRON MODEL, P71; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; West T, 2011, BIOGEOSCIENCES, V8, P631, DOI DOI 10.5194/BG-8-631-2011; Wu L, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD016198	32	2	2	CO-ACTION PUBLISHING	JARFALLA	RIPVAGEN 7, JARFALLA, SE-175 64, SWEDEN	0280-6509		TELLUS B	Tellus Ser. B-Chem. Phys. Meteorol.		2012	64								17980	10.3402/tellusb.v64i0.17980		12	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	961GZ	WOS:000305453600001	
J	Huijnen, V; Flemming, J; Kaiser, JW; Inness, A; Leitao, J; Heil, A; Eskes, HJ; Schultz, MG; Benedetti, A; Hadji-Lazaro, J; Dufour, G; Eremenko, M				Huijnen, V.; Flemming, J.; Kaiser, J. W.; Inness, A.; Leitao, J.; Heil, A.; Eskes, H. J.; Schultz, M. G.; Benedetti, A.; Hadji-Lazaro, J.; Dufour, G.; Eremenko, M.			Hindcast experiments of tropospheric composition during the summer 2010 fires over western Russia	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							OZONE MONITORING INSTRUMENT; BIOMASS BURNING EMISSIONS; RADIATIVE POWER; ATMOSPHERIC COMPOSITION; PARTICULATE MATTER; AIR-POLLUTION; GLOBAL-MODEL; HEAT-WAVE; SATELLITE; SYSTEM	The severe wildfires in western Russia during July-August 2010 coincided with a strong heat wave and led to large emissions of aerosols and trace gases such as carbon monoxide (CO), hydrocarbons and nitrogen oxides into the troposphere. This extreme event is used to evaluate the ability of the global MACC (Monitoring Atmospheric Composition and Climate) atmospheric composition forecasting system to provide analyses of large-scale pollution episodes and to test the respective influence of a priori emission information and data assimilation on the results. Daily 4-day hindcasts were conducted using assimilated aerosol optical depth (AOD), CO, nitrogen dioxide (NO2) and ozone (O-3) data from a range of satellite instruments. Daily fire emissions were used from the Global Fire Assimilation System (GFAS) version 1.0, derived from satellite fire radiative power retrievals. The impact of accurate wildfire emissions is dominant on the composition in the boundary layer, whereas the assimilation system influences concentrations throughout the troposphere, reflecting the vertical sensitivity of the satellite instruments. The application of the daily fire emissions reduces the area-average mean bias by 63% (for CO), 60% (O-3) and 75% (NO2) during the first 24 h with respect to independent satellite observations, compared to a reference simulation with a multi-annual mean climatology of biomass burning emissions. When initial tracer concentrations are further constrained by data assimilation, biases are reduced by 87, 67 and 90%. The forecast accuracy, quantified by the mean bias up to 96 h lead time, was best for all compounds when using both the GFAS emissions and assimilation. The model simulations suggest an indirect positive impact of O-3 and CO assimilation on hindcasts of NO2 via changes in the oxidizing capacity. However, the quality of local hindcasts was strongly dependent on the assumptions made for forecasted fire emissions. This was well visible from a relatively poor forecast accuracy quantified by the root mean square error, as well as the temporal correlation with respect to ground-based CO total column data and AOD. This calls for a more advanced method to forecast fire emissions than the currently adopted persistency approach. The combined analysis of fire radiative power observations, multiple trace gas and aerosol satellite observations, as provided by the MACC system, results in a detailed quantitative description of the impact of major fires on atmospheric composition, and demonstrate the capabilities for the real-time analysis and forecasts of large-scale fire events.	[Huijnen, V.; Eskes, H. J.] Royal Netherlands Meteorol Inst, NL-3730 AE De Bilt, Netherlands; [Flemming, J.; Kaiser, J. W.; Inness, A.; Benedetti, A.] European Ctr Medium Range Weather Forecasts ECMWF, Reading, Berks, England; [Leitao, J.] Univ Bremen, Inst Environm Phys, Bremen, Germany; [Heil, A.; Schultz, M. G.] Forschungszentrum Julich, D-52425 Julich, Germany; [Hadji-Lazaro, J.] Univ Paris 06, UPMC, Paris, France; [Hadji-Lazaro, J.] Univ Versailles St Quentin, CNRS, LATMOS, INSU,IPSL, Paris, France; [Dufour, G.; Eremenko, M.] Univ Paris Est Creteil & Paris Diderot, CNRS, INSU, LISA,UMR7583, Creteil, France	Huijnen, V (reprint author), Royal Netherlands Meteorol Inst, POB 201, NL-3730 AE De Bilt, Netherlands.	huijnen@knmi.nl	Schultz, Martin/I-9512-2012; Heil, Angelika/J-7182-2012; Kaiser, Johannes/A-7057-2012	Schultz, Martin/0000-0003-3455-774X; Heil, Angelika/0000-0002-8768-5027; Kaiser, Johannes/0000-0003-3696-9123	European Commission [218793]	We thank N. Chubarova and B. Holben and their staff for maintaining the Moscow AERONET site which provided data used in this investigation. Some of the satellite data were downloaded from the National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA). We acknowledge the free use of satellite retrieval data from www.temis.nl. D. Hurtmans, P. Coheur (ULB, Belgium) and C. Clerbaux, M. George (LATMOS, France) are acknowledged for scientific development, maintenance and distribution of the CO products from IASI, available from the Ether French atmospheric database (http://ether.ipsl.jussieu.fr). The work has been carried out within the MACC project, which is funded by the European Commission under the Seventh Research Framework Programme under contract number 218793.	Akagi SK, 2011, ATMOS CHEM PHYS, V11, P4039, DOI 10.5194/acp-11-4039-2011; Andersson E, 1999, Q J ROY METEOR SOC, V125, P697, DOI 10.1256/smsqj.55415; Andreae MO, 2001, GLOBAL BIOGEOCHEM CY, V15, P955, DOI 10.1029/2000GB001382; Benedetti A, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011115; Bhartia PK, 1996, J GEOPHYS RES-ATMOS, V101, P18793, DOI 10.1029/96JD01165; Boersma KF, 2007, ATMOS CHEM PHYS, V7, P2103; Boersma KF, 2011, ATMOS MEAS TECH, V4, P1905, DOI 10.5194/amt-4-1905-2011; Chubarova N, 2012, ATMOS MEAS TECH, V5, P557, DOI 10.5194/amt-5-557-2012; Deeter MN, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013005; Dole R, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2010GL046582; Dufour G, 2012, ATMOS MEAS TECH, V5, P611, DOI 10.5194/amt-5-611-2012; Duncan BN, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003195; Dupont R, 2012, ATMOS CHEM PHYS, V12, P169, DOI 10.5194/acp-12-169-2012; Elansky NF, 2011, IZV ATMOS OCEAN PHY+, V47, P672, DOI 10.1134/S000143381106003X; Elguindi N, 2010, GEOSCI MODEL DEV, V3, P501, DOI 10.5194/gmd-3-501-2010; Eremenko M, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034803; FAO (Food and Agriculture Organization) WRB (World Reference Base) Map of World Soil Resources, 2003, WRB WORLD REF BAS MA; Flemming J, 2011, ATMOS CHEM PHYS, V11, P1961, DOI 10.5194/acp-11-1961-2011; Flemming J, 2009, GEOSCI MODEL DEV, V2, P253; Fokeeva EV, 2011, IZV ATMOS OCEAN PHY+, V47, P682, DOI 10.1134/S0001433811060041; George M, 2009, ATMOS CHEM PHYS, V9, P8317; Grell G, 2011, ATMOS ENVIRON, V45, P6845, DOI 10.1016/j.atmosenv.2011.01.017; Guenther A, 2006, ATMOS CHEM PHYS, V6, P3181; Heil A., 2010, 628 ECMWF; Hodzic A, 2007, ATMOS CHEM PHYS, V7, P4043; Hollingsworth A, 2008, B AM METEOROL SOC, V89, P1147, DOI 10.1175/2008BAMS2355.1; Huijnen V, 2010, ATMOS CHEM PHYS, V10, P3273; Huijnen V, 2010, GEOSCI MODEL DEV, V3, P445, DOI 10.5194/gmd-3-445-2010; Hurtmans D, 2012, J QUANT SPECTROSC RA, V113, P1391, DOI 10.1016/j.jqsrt.2012.02.036; Ichoku C, 2005, IEEE T GEOSCI REMOTE, V43, P2636, DOI 10.1109/TGRS.2005.857328; Inness A., 2009, 587 ECMWF, V587; Kaiser JW, 2009, AIP CONF PROC, V1100, P645, DOI 10.1063/1.3117069; Kaiser JW, 2012, BIOGEOSCIENCES, V9, P527, DOI 10.5194/bg-9-527-2012; Koelemeijer RBA, 2001, J GEOPHYS RES-ATMOS, V106, P3475, DOI 10.1029/2000JD900657; Koelemeijer RBA, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000840; Konovalov IB, 2011, ATMOS CHEM PHYS, V11, P10031, DOI 10.5194/acp-11-10031-2011; Lathiere J, 2006, ATMOS CHEM PHYS, V6, P2129; Lee JD, 2006, ATMOS ENVIRON, V40, P7598, DOI 10.1016/j.atmosenv.2006.06.057; Leitao J, 2010, ATMOS MEAS TECH, V3, P475; Levelt PF, 2006, IEEE T GEOSCI REMOTE, V44, P1093, DOI 10.1109/TGRS.2006.872333; Matsueda M, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2010GL046557; McMillan WW, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD011973; Mebust AK, 2011, ATMOS CHEM PHYS, V11, P5839, DOI 10.5194/acp-11-5839-2011; Mei L, 2011, BIOGEOSCIENCES, V8, P3771, DOI 10.5194/bg-8-3771-2011; Menut L, 2010, ANN GEOPHYS-GERMANY, V28, P61; Miller DJ, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD016170; Morcrette JJ, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011235; Ordonez C, 2010, ATMOS CHEM PHYS, V10, P789; PARRISH DF, 1992, MON WEATHER REV, V120, P1747, DOI 10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2; Pfister GG, 2011, ATMOS CHEM PHYS, V11, P1769, DOI 10.5194/acp-11-1769-2011; Real E, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007576; Redler R, 2010, GEOSCI MODEL DEV, V3, P87; Remer LA, 2005, J ATMOS SCI, V62, P947, DOI 10.1175/JAS3385.1; Richter A, 2005, NATURE, V437, P129, DOI 10.1038/nature04092; Sandu Adrian, 2011, Atmosphere, V2, DOI 10.3390/atmos2030426; Schultz M. G., 2007, REPORTS EARTH SYSTEM; Sofiev M, 2009, ATMOS CHEM PHYS, V9, P6833; Solberg S, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009098; Stavrakou T, 2009, ATMOS CHEM PHYS, V9, P1037; Stiller G. P., 2000, WISSENSCHAFTLIC FZKA, VFZKA 6487; Stolbovoi V., 2002, LAND RESOURCES RUSSI; Turquety S, 2009, ATMOS CHEM PHYS, V9, P4897; Martin MV, 2010, ATMOS CHEM PHYS, V10, P1491; Martin MV, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007530; van der A. R. J., 2005, ATMOS CHEM PHYS DISC, V5, P4429, DOI [10.5194/acpd-5-4429-2005, DOI 10.5194/ACPD-5-4429-2005]; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; van der Werf GR, 2010, ATMOS CHEM PHYS, V10, P11707, DOI 10.5194/acp-10-11707-2010; van Donkelaar A, 2011, ATMOS ENVIRON, V45, P6225, DOI 10.1016/j.atmosenv.2011.07.068; Vautard R, 2005, ATMOS ENVIRON, V39, P2957, DOI 10.1016/j.atmosenv.2005.01.039; Verma S, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010108; Waters JW, 2006, IEEE T GEOSCI REMOTE, V44, P1075, DOI 10.1109/TGRS.2006.873771; Wiedinmyer C, 2011, GEOSCI MODEL DEV, V4, P625, DOI 10.5194/gmd-4-625-2011; Williams Jason E., 2012, Atmosphere, V3, DOI 10.3390/atmos3010132; Witte JC, 2011, ATMOS CHEM PHYS, V11, P9287, DOI 10.5194/acp-11-9287-2011; Wittrock F., 2006, THESIS U BREMEN GERM; Wittrock F, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026310; Wooster MJ, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006318; Yurganov LN, 2011, ATMOS CHEM PHYS, V11, P7925, DOI 10.5194/acp-11-7925-2011; Zhang Y, 2008, ATMOS CHEM PHYS, V8, P2895	79	2	2	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2012	12	9					4341	4364		10.5194/acp-12-4341-2012		24	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	942OT	WOS:000304055600032	
J	Valsala, V; Maksyutov, S; Telszewski, M; Nakaoka, S; Nojiri, Y; Ikeda, M; Murtugudde, R				Valsala, V.; Maksyutov, S.; Telszewski, M.; Nakaoka, S.; Nojiri, Y.; Ikeda, M.; Murtugudde, R.			Climate impacts on the structures of the North Pacific air-sea CO2 flux variability	BIOGEOSCIENCES			English	Article							INTERANNUAL VARIABILITY; EQUATORIAL PACIFIC; DATA ASSIMILATION; SOUTHERN-OCEAN; DECADAL CHANGE; ENSO; CARBON; MODEL; OSCILLATION; ATLANTIC	Some dominant spatial and temporal structures of the North Pacific air-sea CO2 fluxes in response to the Pacific Decadal Oscillation (PDO) are identified in three data products from three independent sources: an assimilated CO2 flux product and two forward model solutions. The interannual variability of CO2 flux is found to be an order of magnitude weaker compared to the seasonal cycle of CO2 flux in the North Pacific. A statistical approach is employed to quantify the signal-to-noise ratio in the reconstructed dataset to delineate the representativity errors. The dominant variability with a signal-to-noise ratio above one is identified and its correlations with PDO are examined. A tentative four-pole pattern in the North Pacific air-sea CO2 flux variability linked to PDO emerges in which two positively correlated poles are oriented in the northwest and southeast directions and contrarily, the negatively correlated poles are oriented in the northeast and southwest directions. This pattern is identified in three products, providing CO2 and pCO(2). Its relations to the interannual El Nino-Southern Oscillation (ENSO) and lower-frequency PDO are separately identified. A combined EOF analysis between air-sea CO2 flux and key variables representing ocean-atmosphere interactions is carried out to elicit robust oscillations in the North Pacific CO2 flux in response to the PDO. The proposed spatial and temporal structures of the North Pacific CO2 fluxes are insightful since they separate the secular trends of the surface ocean carbon from the interannual variability. The regional characterization of the North Pacific in terms of PDO and CO2 flux variability is also instructive for determining the homogeneous oceanic domains for the Regional Carbon Cycle and Assessment Processes (RECCAP).	[Valsala, V.] Indian Inst Trop Meteorol, Pune, Maharashtra, India; [Maksyutov, S.; Nakaoka, S.; Nojiri, Y.] Natl Inst Environm Studies, CGER, Tsukuba, Ibaraki, Japan; [Telszewski, M.] UNESCO, Intergovt Oceanog Commiss, Paris, France; [Ikeda, M.] Hokkaido Univ, EES, Sapporo, Hokkaido 060, Japan; [Murtugudde, R.] Univ Maryland, ESSIC, College Pk, MD 20742 USA	Valsala, V (reprint author), Indian Inst Trop Meteorol, Pune, Maharashtra, India.	valsala@tropmet.res.in	Nojiri, Yukihiro/D-1999-2010	Nojiri, Yukihiro/0000-0001-9885-9195	Global Environmental Research Account for National Institutes from Ministry of Environment, Japan; Divecha Climate Center	This work was carried out as part of the GOSAT carbon cycle research project at CGER, NIES. Computational resources were provided by the supercomputer facility at NIES. MT acknowledges the use of the High Performance Computing Cluster supported by the Research Computing Service at the University of East Anglia, Norwich, UK. MT, SN and YN were kindly supported by Global Environmental Research Account for National Institutes from Ministry of Environment, Japan. RM gratefully acknowledges the generous support by the Divecha Climate Center and the hospitality of CAOS at IISc, Bangalore.	Ashok K, 2007, J CLIMATE, V20, P3147, DOI 10.1175/JCLI4155.1; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Ballabrera-Poy J, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL016995; Buitenhuis E, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002511; Carton JA, 2008, MON WEATHER REV, V136, P2999, DOI 10.1175/2007MWR1978.1; Chavez FP, 2002, PROG OCEANOGR, V54, P205, DOI 10.1016/S0079-6611(02)00050-2; Di Lorenzo E, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL032838; Feely RA, 2002, DEEP-SEA RES PT II, V49, P2443, DOI 10.1016/S0967-0645(02)00044-9; Gruber N, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003349; Gurney KR, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002111; HURRELL JW, 1995, SCIENCE, V269, P676, DOI 10.1126/science.269.5224.676; Jones D., 2001, J CLIMATE, V14, P4113; Latif M, 1996, J CLIMATE, V9, P2407, DOI 10.1175/1520-0442(1996)009<2407:DCVOTN>2.0.CO;2; LATIF M, 1994, SCIENCE, V266, P634, DOI 10.1126/science.266.5185.634; Lehodey P, 2010, PROG OCEANOGR, V86, P302, DOI 10.1016/j.pocean.2010.04.021; Le Quere C, 2000, GLOBAL BIOGEOCHEM CY, V14, P1247; Le Quere C, 2007, SCIENCE, V316, P1735, DOI 10.1126/science.1136188; Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689; Le Quere C, 2005, GLOBAL CHANGE BIOL, V11, P2016, DOI 10.1111/j.1365-2468.2005.01004.x; Lovenduski NS, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002900; Mantua NJ, 1997, B AM METEOROL SOC, V78, P1069, DOI 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2; McKinley GA, 2006, J GEOPHYS RES-OCEANS, V111, DOI 10.1029/2005JC003173; McKinley GA, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002179; Metzl N, 2006, DEEP-SEA RES PT I, V53, P1548, DOI 10.1016/j.dsr.2006.07.006; Midorikawa T, 2006, J GEOPHYS RES-OCEANS, V111, DOI 10.1029/2005JC003095; Midorikawa T, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021952; Newman M, 2003, J CLIMATE, V16, P3853, DOI 10.1175/1520-0442(2003)016<3853:EVOTPD>2.0.CO;2; Nigam S, 2009, J CLIMATE, V22, P1174, DOI 10.1175/2008JCLI2322.1; Obata A, 2003, J GEOPHYS RES-OCEANS, V108, DOI 10.1029/2001JC001088; Park GH, 2010, TELLUS B, V62, P352, DOI 10.1111/j.1600-0889.2010.00498.x; Patra PK, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002257; Polovina JJ, 2001, PROG OCEANOGR, V49, P469, DOI 10.1016/S0079-6611(01)00036-2; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Rodgers KB, 2004, J CLIMATE, V17, P3761, DOI 10.1175/1520-0442(2004)017<3761:TPDVAI>2.0.CO;2; Takahashi T, 2009, DEEP-SEA RES PT II, V56, P554, DOI 10.1016/j.dsr2.2008.12.009; Takahashi T, 2006, J GEOPHYS RES-OCEANS, V111, DOI 10.1029/2005JC003074; Takahashi T., 2007, GLOBAL OCEAN SURFACE, P20; Telszewski M, 2009, BIOGEOSCIENCES, V6, P1405; Thomas H, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003167; Tjiputra JF, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002745; TRENBERTH KE, 1994, CLIM DYNAM, V9, P303, DOI 10.1007/BF00204745; Valsala V, 2008, J CLIMATE, V21, P2752, DOI 10.1175/2007JCLI2018.1; Valsala V, 2010, TELLUS B, V62, P821, DOI [10.1111/j.1600-0889.2010.00495.x, 10.1111/J.1600-0889.2010.00495.X]; Vimont DJ, 2005, J CLIMATE, V18, P2080, DOI 10.1175/JCLI3365.1; Wakita M, 2010, TELLUS B, V62, P608, DOI 10.1111/j.1600-0889.2010.00476.x; WANNINKHOF R, 1992, J GEOPHYS RES-OCEANS, V97, P7373, DOI 10.1029/92JC00188; Zeng JY, 2002, DEEP-SEA RES PT II, V49, P5303, DOI 10.1016/S0967-0645(02)00192-3; Zhang Y, 1997, J CLIMATE, V10, P1004, DOI 10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2	48	0	0	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1726-4170		BIOGEOSCIENCES	Biogeosciences		2012	9	1					477	492		10.5194/bg-9-477-2012		16	Ecology; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	891PL	WOS:000300229000032	
J	Hill, TC; Ryan, E; Williams, M				Hill, Timothy Charles; Ryan, Edmund; Williams, Mathew			The use of CO2 flux time series for parameter and carbon stock estimation in carbon cycle research	GLOBAL CHANGE BIOLOGY			English	Article						eddy correlation; EnKF; Markov Chain; MCMC; Metropolis; Metropolis hastings; MH; model-data fusion; Monte-Carlo	ENSEMBLE KALMAN FILTER; EDDY COVARIANCE MEASUREMENTS; TERRESTRIAL ECOSYSTEM MODEL; LAND-SURFACE MODELS; DATA ASSIMILATION; FOREST; EXCHANGE; UNCERTAINTY; INVERSION; WATER	Data assimilation (DA) is increasingly being employed to estimate the parameters and states of terrestrial ecosystem models from eddy covariance measurements of net carbon (C) fluxes. The length of the observation time series used varies for each study. The impact of these differences has not been quantified explicitly. Therefore, in this study, we investigate the importance of the time series length relative to observation noise and data gaps. Different length synthetic time series are used to determine the parameter and C stocks of a simple ecosystem C model. Two commonly used DA schemes are tested: the sequential Ensemble Kalman Filter (EnKF) and a batch Metropolis Markov chain Monte Carlo algorithm. Longer time series improve both the parameter and C pool estimates of the EnKF, while adversely affecting those of the Metropolis algorithm. For both DA approaches, the length of the time series has more influence on the parameter and pool estimates than the level of random noise or amount of data. In this study, the EnKF provides more robust parameter and C pool estimates than the Metropolis algorithm. Optimized parameters and states are often used as the basis for forecasting future responses. Despite having better parameter and C pool estimates, EnKF forecasts estimates have much larger uncertainties than the Metropolis algorithm forecast estimates. Finally, we suggest that the structure of simple box models, as used in this study, introduces a large degree of equifinality into DA. Neither DA scheme correctly accounts for the equifinality, but our results suggest that it is particularly problematic for the batch Metropolis algorithm.	[Hill, Timothy Charles; Williams, Mathew] Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland; [Hill, Timothy Charles; Williams, Mathew] Univ Edinburgh, NERC Natl Ctr Earth Observat, Edinburgh EH9 3JN, Midlothian, Scotland; [Ryan, Edmund] Univ Sheffield, Sch Math & Stat, Sheffield, S Yorkshire, England; [Ryan, Edmund] Univ Sheffield, NERC Natl Ctr Earth Observat, Sheffield, S Yorkshire, England	Hill, TC (reprint author), Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland.	thill@staffmail.ed.ac.uk			Natural Environment Research Council's (NERC) National Centre for Earth Observation (NCEO)	We thank the Natural Environment Research Council's (NERC) National Centre for Earth Observation (NCEO) which funded this research. We are grateful for discussions with S. Quegan, A. Richardson, P. Peylin and M. Reichstein for setting up the experimental design. We thank the Harvard Forest LTER and Harvard Forest for access to their datasets.	Abramowitz G, 2007, J HYDROMETEOROL, V8, P989, DOI 10.1175/JHM628.1; Baldocchi D, 2008, AUST J BOT, V56, P1, DOI 10.1071/BT07151; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Bayes T, 1973, PHILOS T ROY SOC LON, V53, P370, DOI DOI 10.1098/RSBL.1763.0053; Boose E, 2001, HARVARD FOREST DATA, VHF001; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Burba GG, 2008, GLOBAL CHANGE BIOL, V14, P1854, DOI 10.1111/j.1365-2486.2008.01606.x; Carvalhais N, 2008, GLOBAL BIOGEOCHEM CY, V22, P1; Dekker SC, 2001, J HYDROL, V246, P197, DOI 10.1016/S0022-1694(01)00368-7; Evensen G, 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Evensen G, 2009, IEEE CONTR SYST MAG, V29, P83, DOI 10.1109/MCS.2009.932223; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Friend AD, 2007, GLOBAL CHANGE BIOL, V13, P610, DOI 10.1111/j.1365-2486.2006.01223.x; Goulden ML, 1996, GLOB CHANGE BIOL, V2, P169, DOI 10.1111/j.1365-2486.1996.tb00070.x; Hill TC, 2011, ECOL APPL, V21, P1474, DOI 10.1890/09-0840.1; Hollinger DY, 2005, TREE PHYSIOL, V25, P873; Kalnay E, 2010, Q J ROY METEOR SOC, V136, P1644, DOI 10.1002/qj.652; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kutsch WL, 2008, ECOL APPL, V18, P1391, DOI 10.1890/06-1301.1; Metropolis N., 1953, CHEM PHYS, V21, P1087, DOI DOI 10.1063/1.1699114; Mo XG, 2008, ECOL MODEL, V217, P157, DOI 10.1016/j.ecolmodel.2008.06.021; Moncrieff JB, 1996, GLOB CHANGE BIOL, V2, P231, DOI 10.1111/j.1365-2486.1996.tb00075.x; MOSEGAARD K, 1995, J GEOPHYS RES-SOL EA, V100, P12431, DOI 10.1029/94JB03097; Munger W, 1999, HARVARD FOREST DATA, VHF004; Pan M, 2006, J HYDROMETEOROL, V7, P534, DOI 10.1175/JHM495.1; Quaife T, 2008, REMOTE SENS ENVIRON, V112, P1347, DOI 10.1016/j.rse.2007.05.020; Rastetter EB, 2010, ECOL APPL, V20, P1285, DOI 10.1890/09-0876.1; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; Richardson AD, 2006, AGR FOREST METEOROL, V136, P1, DOI 10.1016/j.agrformet.2006.01.007; Tang JY, 2008, J GEOPHYS RES-BIOGEO, V113, P1; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Trudinger CM, 2008, ENVIRONMETRICS, V19, P849, DOI 10.1002/env.910; Wang YP, 2007, GLOBAL CHANGE BIOL, V13, P652, DOI 10.1111/j.1365-2486.2006.01225.x; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Williams M, 1996, PLANT CELL ENVIRON, V19, P911, DOI 10.1111/j.1365-3040.1996.tb00456.x; Williams M, 1997, ECOL APPL, V7, P882, DOI 10.2307/2269440; Zobitz JM, 2008, ECOSYSTEMS, V11, P250, DOI 10.1007/s10021-007-9120-1	39	0	1	WILEY-BLACKWELL	MALDEN	COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA	1354-1013		GLOBAL CHANGE BIOL	Glob. Change Biol.	JAN	2012	18	1					179	193		10.1111/j.1365-2486.2011.02511.x		15	Biodiversity Conservation; Ecology; Environmental Sciences	Biodiversity & Conservation; Environmental Sciences & Ecology	869LG	WOS:000298598900016	
J	Jiang, F; Liu, Q; Huang, XX; Wang, TJ; Zhuang, BL; Xie, M				Jiang, Fei; Liu, Qian; Huang, Xiaoxian; Wang, Tijian; Zhuang, Bingliang; Xie, Min			Regional modeling of secondary organic aerosol over China using WRF/Chem	JOURNAL OF AEROSOL SCIENCE			English	Article						Secondary organic aerosol; WRF/Chem; Biogenic source; Anthropogenic source; China	CHEMICAL-TRANSPORT MODEL; RIVER DELTA REGION; BASIS-SET APPROACH; CARBONACEOUS AEROSOLS; ATMOSPHERIC CHEMISTRY; DATA ASSIMILATION; UNITED-STATES; SOA FORMATION; ALPHA-PINENE; MEXICO-CITY	Using a new generation air quality modeling system (WRF/Chem) fully coupled with secondary organic aerosol model (SORGAM), we investigate the spatial and temporal characteristics of secondary organic aerosol (SOA) as well as the relative contributions of anthropogenic and biogenic sources to the formation of SOA in 2006 over China. To improve SOA simulation, a parameterization scheme for the isoprene induced SOA formation was added in WRF/Chem. The simulated SOA concentrations show large temporal and spatial variability, with the highest levels occur in summer and the lowest concentrations occur in winter. The high SOA regions are located near 30 degrees N in central China in summer, with values exceeding 8 mu g m(-3), while they shift to South China, mainly in Pearl River Delta (PRD) region in winter, with the concentrations at or below 2 mu g m(-3). Across the whole country, the average ground level SOA concentrations are 0.94, 2.54, 1.41, 0.43, and 1.34 mu g m(-3) in spring, summer, autumn, winter, and year, respectively. Commonly, the SOA loading is mostly concentrated in the boundary layer (similar to 70%). Although the SOA concentrations are dominated by biogenic sources in summer, the contributions of anthropogenic sources exceed biogenic sources over most areas in winter. On the national level, the anthropogenic sources contribute 35% of total SOA, with 41%, 26%, 39%, and 59% in spring, summer, autumn and winter, respectively. The estimated annual SOA production reaches 3.05 Tg yr(-1) over China, accounting for about 4-25% of global SOA formation. The modeled OC and EC concentrations as well as SOC to OC ratios are compared with the measurements and previous studies. The results suggest that the spatial and temporal characteristic of OC and EC levels is well captured by the model. However, the simulated SOA concentrations in this study might be underestimated by 0-75%. The modeling SOA in this paper are in agreement with other field and modeling studies, also showing the importance of SOA in total organic aerosol in China. (C) 2011 Elsevier Ltd. All rights reserved.	[Jiang, Fei; Huang, Xiaoxian; Wang, Tijian; Zhuang, Bingliang; Xie, Min] Nanjing Univ, Sch Atmospher Sci, Nanjing 210093, Jiangsu, Peoples R China; [Jiang, Fei] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210093, Jiangsu, Peoples R China; [Liu, Qian] Hong Kong Polytech Univ, Dept Civil & Struct Engn, Hong Kong, Hong Kong, Peoples R China	Wang, TJ (reprint author), Nanjing Univ, Sch Atmospher Sci, Nanjing 210093, Jiangsu, Peoples R China.	tjwang@nju.edu.cn			National Key Basic Research Development Program of China [2011CB403406, 2010CB428503]; National Special Fund for Water [2008ZX07103-007]; National Special Fund for Weather Industry [GYHY200806001]; National Natural Science Foundation of China [40805059]; Jiangsu Higher Education Institutions	The authors wish to thank Professor K.S. Lam at the Hong Kong Polytechnic University for his valuable help. This work was supported by the National Key Basic Research Development Program of China (2011CB403406, 2010CB428503), the National Special Fund for Water (2008ZX07103-007), and the National Special Fund for Weather Industry (GYHY200806001), the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 40805059), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.	Aiken AC, 2008, ENVIRON SCI TECHNOL, V42, P4478, DOI 10.1021/es703009q; Andreae MO, 1997, SCIENCE, V276, P1052, DOI 10.1126/science.276.5315.1052; Beekmann M, 2007, ATMOS CHEM PHYS, V7, P55; Cao JJ, 2004, ATMOS ENVIRON, V38, P4447, DOI 10.1016/j.atmosenv.2004.05.016; Cao JJ, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008205; Carlton AG, 2009, ATMOS CHEM PHYS, V9, P4987; Castro LM, 1999, ATMOS ENVIRON, V33, P2771, DOI 10.1016/S1352-2310(98)00331-8; Chan CY, 2005, ATMOS ENVIRON, V39, P5113, DOI 10.1016/j.atmosenv.2005.05.009; Chan LY, 1998, ATMOS ENVIRON, V32, P159, DOI 10.1016/S1352-2310(97)00320-8; Chen F, 2001, MON WEATHER REV, V129, P569, DOI 10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2; Chen JJ, 2005, ATMOS ENVIRON, V39, P7731, DOI 10.1016/j.atmosenv.2005.05.049; Chen JJ, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007603; Chou M.-D., 1994, 1046063 NASA, P85; Chung SH, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001397; Claeys M, 2004, SCIENCE, V303, P1173, DOI 10.1126/science.1092805; Dan M, 2004, ATMOS ENVIRON, V38, P3443, DOI 10.1016/j.atmosenv.2004.02.052; Ding YH, 2005, METEOROL ATMOS PHYS, V89, P117, DOI 10.1007/s00703-005-0125-z; Done J, 2004, ATMOS SCI LETT, V5, P110, DOI 10.1002/asl.72; Duan FK, 2005, CHEMOSPHERE, V60, P355, DOI 10.1016/j.chemosphere.2004.12.035; Duan JC, 2007, ATMOS ENVIRON, V41, P2895, DOI 10.1016/j.atmosenv.2006.12.017; Edney EO, 2005, ATMOS ENVIRON, V39, P5281, DOI 10.1016/j.atmosenv.2005.05.031; Grell GA, 2005, ATMOS ENVIRON, V39, P6957, DOI 10.1016/j.atmosenv.2005.04.027; Grell GA, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015311; Guenther A, 2006, ATMOS CHEM PHYS, V6, P3181; Hallquist M, 2009, ATMOS CHEM PHYS, V9, P5155; Han ZW, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009436; Henze DK, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL025976; Henze DK, 2008, ATMOS CHEM PHYS, V8, P2405; Hodzic A, 2010, ATMOS CHEM PHYS, V10, P5491, DOI 10.5194/acp-10-5491-2010; Hoyle CR, 2007, ATMOS CHEM PHYS, V7, P5675; INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC), 2007, PHYS SCI BAS CLIM CH; Janjic ZI, 2002, NCEP OFFICE NOTE, V437, P61; Janjic Z.I., 1996, 11 C NUM WEATH PRED, P354; Jiang F, 2008, ATMOS ENVIRON, V42, P8717, DOI 10.1016/j.atmosenv.2008.08.034; Kanakidou M, 2005, ATMOS CHEM PHYS, V5, P1053; Karydis VA, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007890; Kleindienst TE, 2006, ENVIRON SCI TECHNOL, V40, P3807, DOI 10.1021/es052446r; Koo BY, 2003, ATMOS ENVIRON, V37, P4757, DOI 10.1016/j.atmosenv.2003.08.016; Kroll JH, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023637; Lane TE, 2008, ATMOS ENVIRON, V42, P7439, DOI 10.1016/j.atmosenv.2008.06.026; Lim HJ, 2002, ENVIRON SCI TECHNOL, V36, P4489, DOI 10.1021/es0206487; Lin P, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010902; LIN YL, 1983, J CLIM APPL METEOROL, V22, P1065, DOI 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2; Lipsky EM, 2006, ENVIRON SCI TECHNOL, V40, P155, DOI 10.1021/es050319p; Liu H.W., 2008, Plateau and Mountain Meteorology Research, V28, P68; Mauderly JL, 2008, INHAL TOXICOL, V20, P257, DOI 10.1080/08958370701866008 ; Mlawer EJ, 1997, J GEOPHYS RES-ATMOS, V102, P16663, DOI 10.1029/97JD00237; Murphy BN, 2009, ENVIRON SCI TECHNOL, V43, P4722, DOI 10.1021/es803168a; Ng NL, 2006, ENVIRON SCI TECHNOL, V40, P2283, DOI 10.1021/es052269u; Ng NL, 2008, ATMOS CHEM PHYS, V8, P4117; Niu YW, 2006, SCI CHINA SER B, V49, P466, DOI 10.1007/s11426-006-2010-0; Odum JR, 1996, ENVIRON SCI TECHNOL, V30, P2580, DOI 10.1021/es950943+; Offenberg JH, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL024623; PANDIS SN, 1992, ATMOS ENVIRON A-GEN, V26, P2269, DOI 10.1016/0960-1686(92)90358-R; PICCOT SD, 1992, J GEOPHYS RES-ATMOS, V97, P9897; Pun BK, 2009, ATMOS ENVIRON, V43, P402, DOI 10.1016/j.atmosenv.2008.08.040; Pun BK, 2003, ENVIRON SCI TECHNOL, V37, P3647, DOI 10.1021/es0341541; Qian Y, 2010, ATMOS CHEM PHYS, V10, P6917, DOI 10.5194/acp-10-6917-2010; Robinson AL, 2007, SCIENCE, V315, P1259, DOI 10.1126/science.1133061; Saikawa E, 2009, ATMOS ENVIRON, V43, P2814, DOI 10.1016/j.atmosenv.2009.02.017; Sakulyanontvittaya T, 2008, ENVIRON SCI TECHNOL, V42, P1623, DOI 10.1021/es702274e; Schell B, 2001, J GEOPHYS RES-ATMOS, V106, P28275, DOI 10.1029/2001JD000384; Schell B, 2000, Mitteilungen aus dem Institut fur Geophysik und Meteorologie der Universittit zu Koln; Simpson D, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008158; Skamarock W. C., 2008, DESCRIPTION ADV RES; STAUFFER DR, 1990, MON WEATHER REV, V118, P1250, DOI 10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2; Steven E.K., 2004; Stockwell WR, 1997, J GEOPHYS RES-ATMOS, V102, P25847, DOI 10.1029/97JD00849; Streets DG, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2003GB002040; Sun JY, 2010, ATMOS ENVIRON, V44, P131, DOI 10.1016/j.atmosenv.2009.03.020; Tie XX, 2007, ATMOS ENVIRON, V41, P1989, DOI 10.1016/j.atmosenv.2006.10.053; Tie XX, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003659; Tsigaridis K, 2003, ATMOS CHEM PHYS, V3, P1849; Tsimpidi AP, 2010, ATMOS CHEM PHYS, V10, P525; Volkamer R, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030752; Zhang Q, 2009, ATMOS CHEM PHYS, V9, P5131; Zhang Q, 2005, ATMOS CHEM PHYS, V5, P3289; Zhang XY, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009525; 王坚, 2006, [环境化学, Environmental Chemistry], V25, P518	79	1	1	ELSEVIER SCI LTD	OXFORD	THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND	0021-8502		J AEROSOL SCI	J. Aerosol. Sci.	JAN	2012	43	1					57	73		10.1016/j.jaerosci.2011.09.003		17	Engineering, Chemical; Environmental Sciences; Meteorology & Atmospheric Sciences	Engineering; Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences	868LL	WOS:000298524700005	
J	Sakamoto, T; Gitelson, AA; Wardlow, BD; Verma, SB; Suyker, AE				Sakamoto, Toshihiro; Gitelson, Anatoly A.; Wardlow, Brian D.; Verma, Shashi B.; Suyker, Andrew E.			Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data	REMOTE SENSING OF ENVIRONMENT			English	Article						Wide Dynamic Range Vegetation Index; Gross primary production; CO(2)-flux; Crop phenology	EVERGREEN NEEDLELEAF FOREST; CARBON-DIOXIDE EXCHANGE; NET PRIMARY PRODUCTION; TIME-SERIES DATA; RAIN-FED MAIZE; CO2 FLUX; PHOTOSYNTHETIC EFFICIENCY; VEGETATION INDEXES; REMOTE ESTIMATION; GROWING-SEASON	Accurate assessment of temporal changes in gross primary production (GPP) is important for carbon budget assessments and evaluating the impact of climate change on crop productivity. The objective of this study was to devise a simple remote sensing-based GPP model to quantify daily GPP of maize. In the model. (1) daily shortwave radiation (SW), derived from the reanalysis data (North American Land Data Assimilation System; NLDAS-2) and (2) smoothed Wide Dynamic Range Vegetation Index (WDRVI) data, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations were used as proxy variables of the incident photosynthetically active radiation (PAR) and the total canopy chlorophyll content, respectively. The model was calibrated and validated by using tower-based CO(2) flux observations over an 8-year period (2001 to 2008) for one rainfed and two irrigated sites planted to maize as part of the Carbon Sequestration Program at the University of Nebraska-Lincoln. The results showed the temporal features of the product SW*WDRVI closely related to the temporal GPP variations in terms of both daily variations and seasonal patterns. The simple GPP model was able to predict the daily GPP values and accumulated GPP values of maize with high accuracy. (C) 2011 Elsevier Inc. All rights reserved.	[Sakamoto, Toshihiro; Wardlow, Brian D.] Univ Nebraska, Natl Drought Mitigat Ctr, Sch Nat Resources, Lincoln, NE USA; [Gitelson, Anatoly A.] Univ Nebraska, Sch Nat Resources, Ctr Adv Land Management Informat Technol, Lincoln, NE USA; [Verma, Shashi B.; Suyker, Andrew E.] Univ Nebraska, Sch Nat Resources, Great Plains Reg Ctr Global Environm Change, Lincoln, NE USA; [Sakamoto, Toshihiro] Natl Inst Agroenvironm Sci, Ecosyst Informat Div, Tsukuba, Ibaraki 305, Japan	Sakamoto, T (reprint author), 3-1-3 Kannondai, Tsukuba, Ibaraki 3058604, Japan.	sakamt@affrc.go.jp	Gitelson, Anatoly/G-3452-2012		Office of Science (BER), U.S. Department of Energy [DE-FG02-03ER63639]; NASA NACP [NNX08AI75G]; Japanese Society for the Promotion of Science	We gratefully acknowledge the use of facilities and equipment provided by the National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln. CO<INF>2</INF> flux and other measurements were supported by the Office of Science (BER), U.S. Department of Energy Grant No. DE-FG02-03ER63639 and NASA NACP grant no. NNX08AI75G. The remote sensing-based analyses were financially supported by the Japanese Society for the Promotion of Science; JSPS Postdoctoral Fellowships for Research Abroad. We are grateful to Mr. Anthony Nguy-Robertson and Ms. Yi Peng for their valuable assistance in calculating GPP data.	Baret F, 2007, J EXP BOT, V58, P869, DOI 10.1093/jxb/erl231; CHEN B, 2009, BIOGEOSCIENCES DISCU, V6, P11317; Ciganda V, 2008, AGRON J, V100, P1409, DOI 10.2134/agronj2007.0322; Fang H., 2009, README DOCUMENT N AM; GAMON JA, 1992, REMOTE SENS ENVIRON, V41, P35, DOI 10.1016/0034-4257(92)90059-S; Gitelson AA, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2002GL016543; Gitelson AA, 2008, IEEE GEOSCI REMOTE S, V5, P133, DOI 10.1109/LGRS.2008.915598; Gitelson AA, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL031620; Gitelson AA, 2004, J PLANT PHYSIOL, V161, P165, DOI 10.1078/0176-1617-01176; Gitelson AA, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006017; Harazono Yoshinobu, 2009, Journal of Agricultural Meteorology, V65, P365; Harris A, 2010, REMOTE SENS ENVIRON, V114, P1856, DOI 10.1016/j.rse.2010.03.010; Houborg R, 2011, REMOTE SENS ENVIRON, V115, P1694, DOI 10.1016/j.rse.2011.02.027; Huete A, 2002, REMOTE SENS ENVIRON, V83, P195, DOI 10.1016/S0034-4257(02)00096-2; Inoue Y, 2008, REMOTE SENS ENVIRON, V112, P156, DOI 10.1016/j.rse.2007.04.011; JAHAN N, 2009, J GEOPHYS RES BIOGEO, V114; Kitamoto Tomoyuki, 2007, Journal of Agricultural Meteorology, V63, P171, DOI 10.2480/agrmet.63.171; Lokupitiya E, 2009, BIOGEOSCIENCES, V6, P969; MERZLYAK MN, 1995, J PLANT PHYSIOL, V145, P315; MONTEITH JL, 1972, J APPL ECOL, V9, P747, DOI 10.2307/2401901; Nagai S, 2010, ECOL RES, V25, P359, DOI 10.1007/s11284-009-0663-9; Nakaji T, 2007, REMOTE SENS ENVIRON, V109, P274, DOI 10.1016/j.rse.2007.01.006; Peng Y, 2011, REMOTE SENS ENVIRON, V115, P978, DOI 10.1016/j.rse.2010.12.001; Pinker RT, 2010, INT J REMOTE SENS, V31, P5221, DOI 10.1080/01431161.2010.496474; Rouse J., 1974, MONITORING VEGETATIO; Ruimy A, 1994, J GEOPHYS RES-ATMOS, V99, P5263, DOI 10.1029/93JD03221; Running SW, 2000, METHODS IN ECOSYSTEM SCIENCE, P44; Running SW, 2004, BIOSCIENCE, V54, P547, DOI 10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2; Running S.W., 1999, MODIS DAILY PHOTOSYN; Sakamoto T, 2011, IEEE T GEOSCI REMOTE, V49, P1926, DOI 10.1109/TGRS.2010.2095462; SAKAMOTO T, 2005, REMOTE SENS ENVIRON, V96, P336; Sakamoto T, 2010, REMOTE SENS ENVIRON, V114, P2146, DOI 10.1016/j.rse.2010.04.019; Schubert P, 2010, REMOTE SENS ENVIRON, V114, P1178, DOI 10.1016/j.rse.2010.01.005; Schubert P, 2010, REMOTE SENS ENVIRON, V114, P2137, DOI 10.1016/j.rse.2010.04.018; SIMS DA, 2006, J GEOPHYS RES BIOGEO, V111; Sjostrom M, 2009, BIOGEOSCIENCES, V6, P129; Suyker AE, 2004, AGR FOREST METEOROL, V124, P1, DOI 10.1016/j.agrformet.2004.01.011; Suyker AE, 2005, AGR FOREST METEOROL, V131, P180, DOI 10.1016/j.agrformet.2005.05.007; Thenkabail PS, 2005, REMOTE SENS ENVIRON, V95, P317, DOI 10.1016/j.rse.2004.12.018; TUCKER CJ, 1979, REMOTE SENS ENVIRON, V8, P127, DOI 10.1016/0034-4257(79)90013-0; Turner DP, 2006, IEEE T GEOSCI REMOTE, V44, P1899, DOI 10.1109/TGRS.2006.876027; Verma SB, 2005, AGR FOREST METEOROL, V131, P77, DOI 10.1016/j.agrformet.2005.05.003; Vermote EF, 2002, REMOTE SENS ENVIRON, V83, P97, DOI 10.1016/S0034-4257(02)00089-5; Vina A., 2005, GEOPHYS RES LETT, V32; Wu CY, 2009, AGR FOREST METEOROL, V149, P1015, DOI 10.1016/j.agrformet.2008.12.007; Xiao JF, 2010, REMOTE SENS ENVIRON, V114, P576, DOI 10.1016/j.rse.2009.10.013; Xiao X, 2002, INT J REMOTE SENS, V23, P3009, DOI 10.1080/01431160110107734; Xiao XM, 2004, REMOTE SENS ENVIRON, V89, P519, DOI 10.1016/j.rse.2003.11.008; Xiao XM, 2005, ECOL APPL, V15, P954, DOI 10.1890/04-0470; Xiao XM, 2004, REMOTE SENS ENVIRON, V91, P256, DOI 10.1016/j.rse.2004.03.010; Xiao XM, 2006, REMOTE SENS ENVIRON, V100, P95, DOI 10.1016/j.rse.2005.10.004; Yan HM, 2009, AGR ECOSYST ENVIRON, V129, P391, DOI 10.1016/j.agee.2008.10.017; Yuan WP, 2010, REMOTE SENS ENVIRON, V114, P1416, DOI 10.1016/j.rse.2010.01.022; ZHAO M, 2006, J GEOPHYS RES BIOGEO, V111; Zhao MS, 2005, REMOTE SENS ENVIRON, V95, P164, DOI 10.1016/j.rse.2004.12.011	55	2	2	ELSEVIER SCIENCE INC	NEW YORK	360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA	0034-4257		REMOTE SENS ENVIRON	Remote Sens. Environ.	DEC 15	2011	115	12					3091	3101		10.1016/j.rse.2011.06.015		11	Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology	865LB	WOS:000298311300012	
J	Keenan, TF; Carbone, MS; Reichstein, M; Richardson, AD				Keenan, Trevor F.; Carbone, Mariah S.; Reichstein, Markus; Richardson, Andrew D.			The model-data fusion pitfall: assuming certainty in an uncertain world	OECOLOGIA			English	Article						Model-data fusion; Data assimilation; Parameter estimation; Inverse analysis; Carbon cycle model	TERRESTRIAL ECOSYSTEM MODEL; SOIL RESPIRATION MEASUREMENTS; FOREST CARBON DYNAMICS; EDDY COVARIANCE DATA; LAND-SURFACE MODELS; SUB-ALPINE FOREST; PARAMETER-ESTIMATION; DATA ASSIMILATION; FLUX; INVERSION	Model-data fusion is a powerful framework by which to combine models with various data streams (including observations at different spatial or temporal scales), and account for associated uncertainties. The approach can be used to constrain estimates of model states, rate constants, and driver sensitivities. The number of applications of model-data fusion in environmental biology and ecology has been rising steadily, offering insights into both model and data strengths and limitations. For reliable model-data fusion-based results, however, the approach taken must fully account for both model and data uncertainties in a statistically rigorous and transparent manner. Here we review and outline the cornerstones of a rigorous model-data fusion approach, highlighting the importance of properly accounting for uncertainty. We conclude by suggesting a code of best practices, which should serve to guide future efforts.	[Keenan, Trevor F.; Richardson, Andrew D.] Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA; [Carbone, Mariah S.] Natl Ctr Ecol Anal & Synth, Santa Barbara, CA 93101 USA; [Reichstein, Markus] Max Planck Inst Biogeochem, Biogeochem Model Data Integrat Grp, D-07745 Jena, Germany	Keenan, TF (reprint author), Harvard Univ, Dept Organism & Evolutionary Biol, 26 Oxford St, Cambridge, MA 02138 USA.	tkeenan@oeb.harvard.edu	Richardson, Andrew/F-5691-2011; Keenan, Trevor/B-2744-2010; Carbone, Mariah/H-7389-2012; Reichstein, Markus/A-7494-2011	Keenan, Trevor/0000-0002-7579-7494; Reichstein, Markus/0000-0001-5736-1112	Northeastern States Research Cooperative; Office of Science (BER), U.S. Department of Energy [DE-AI02-07ER64355]; Office of Science (BER), U.S. Department of Energy through Northeastern Regional Center of the National Institute for Climatic Change Research; European Commission [FP7-ENV-2008-1-226701]; Kearney Foundation of Soil Science	TFK and ADR acknowledge support from the Northeastern States Research Cooperative, and from the Office of Science (BER), U.S. Department of Energy, through the Terrestrial Carbon Program under Interagency Agreement number DE-AI02-07ER64355, and through the Northeastern Regional Center of the National Institute for Climatic Change Research. MR acknowledges support from the CARBO-Extreme project of the European Commission (FP7-ENV-2008-1-226701). MSC acknowledges support from the Kearney Foundation of Soil Science.	Abramowitz G, 2007, J HYDROMETEOROL, V8, P989, DOI 10.1175/JHM628.1; Alton P, 2010, AGR FOREST METEOROL, V150, P182, DOI 10.1016/j.agrformet.2009.10.001; Baldocchi D, 2008, AUST J BOT, V56, P1, DOI 10.1071/BT07151; Barrett DJ, 2005, AUST J BOT, V53, P689, DOI 10.1071/BT04139; Beven K, 2006, J HYDROL, V320, P18, DOI 10.1016/j.jhydrol.2005.07.007; Beven KJ, 2008, J HYDROL, V354, P15, DOI 10.1016/j.jhydrol.2008.02.007; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Brown JD, 2010, PROG PHYS GEOG, V34, P75, DOI 10.1177/0309133309357000; Butts MB, 2004, J HYDROL, V298, P242, DOI 10.1016/j.hydrol.2004.03.042; Carbone MS, 2008, NEW PHYTOL, V177, P295; Carvalhais N, 2010, GLOBAL CHANGE BIOL, V16, P2813, DOI 10.1111/j.1365-2486.2010.02173.x; Carvalhais N, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003033; Cressie N, 2009, ECOL APPL, V19, P553, DOI 10.1890/07-0744.1; Desai AR, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2010JG001423; Desai AR, 2008, AGR FOREST METEOROL, V148, P821, DOI 10.1016/j.agrformet.2007.11.012; Efron B., 1993, INTRO BOOTSTRAP; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Franks SW, 1997, AGR FOREST METEOROL, V86, P63, DOI 10.1016/S0168-1923(96)02421-5; FRANKS SW, 1999, HYDROL EARTH SYST SC, V3, P488; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Hanson PJ, 2000, BIOGEOCHEMISTRY, V48, P115, DOI 10.1023/A:1006244819642; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hogberg Peter, 2010, New Phytol, V188, P9, DOI 10.1111/j.1469-8137.2010.03366.x; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kuzyakov Y, 2006, SOIL BIOL BIOCHEM, V38, P425, DOI 10.1016/j.soilbio.2005.08.020; Lasslop G, 2008, BIOGEOSCIENCES, V5, P1311; Luo Y., 2008, B ECOL SOC AM, V89, P467, DOI DOI 10.1890/0012-9623; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; Medvigy D, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000812; Moore DJP, 2008, AGR FOREST METEOROL, V148, P1467, DOI 10.1016/j.agrformet.2008.04.013; Pappenberger F, 2006, WATER RESOUR RES, V42, DOI 10.1029/2005WR004820; Press W. H., 2007, NUMERICAL RECIPES AR; RASTETTER EB, 2003, MODELS ECOSYSTEM SCI, P221; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Reichstein M, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003430; Richardson AD, 2008, AGR FOREST METEOROL, V148, P38, DOI 10.1016/j.agrformet.2007.09.001; Richardson AD, 2006, AGR FOREST METEOROL, V141, P219, DOI 10.1016/j.agrformet.2006.10.010; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; Richardson AD, 2005, AGR FOREST METEOROL, V131, P191, DOI 10.1016/j.agrformet.2005.05.008; Sacks WJ, 2007, OECOLOGIA, V151, P54, DOI 10.1007/s00442-006-0565-2; Santaren D, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002834; Savage K, 2008, FUNCT ECOL, V22, P1000, DOI 10.1111/j.1365-2435.2008.01414.x; Taylor J. R., 1997, INTRO ERROR ANAL STU; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Vargas R, 2011, BIOGEOCHEMISTRY, V102, P1, DOI 10.1007/s10533-010-9462-1; Wang YP, 2006, GLOBAL CHANGE BIOL, V12, P1, DOI DOI 10.1111/J.1365-2486.2006.01225.X; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Weng ES, 2011, ECOL APPL, V21, P1490, DOI 10.1890/09-1394.1; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Wu XW, 2009, J PLANT ECOL-UK, V2, P55, DOI 10.1093/jpe/rtp005; Xie H, 2009, WATER RESOUR RES, V45, DOI 10.1029/2007WR006773; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zhang L, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001004; ZHANG Q, 2005, REMOTE SENS ENVIRON, V99, P359; Zhou XH, 2010, NEW PHYTOL, V187, P184, DOI 10.1111/j.1469-8137.2010.03256.x; Zobitz JM, 2011, OECOLOGIA, V167, P599, DOI 10.1007/s00442-011-2107-9; Zobitz JM, 2008, ECOSYSTEMS, V11, P250, DOI 10.1007/s10021-007-9120-1	58	7	7	SPRINGER	NEW YORK	233 SPRING ST, NEW YORK, NY 10013 USA	0029-8549		OECOLOGIA	Oecologia	NOV	2011	167	3					587	597		10.1007/s00442-011-2106-x		11	Ecology	Environmental Sciences & Ecology	834SQ	WOS:000295984800001	
J	Zobitz, JM; Desai, AR; Moore, DJP; Chadwick, MA				Zobitz, J. M.; Desai, A. R.; Moore, D. J. P.; Chadwick, M. A.			A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC)	OECOLOGIA			English	Article						Data assimilation; Markov Chain Monte Carlo; NEE; Aquatic insects; Ecological models	NET ECOSYSTEM EXCHANGE; EUROPEAN FORESTS; CARBON BALANCE; RESPIRATION; FLUX; TEMPERATURE; NETWORK; BIOLOGY; CO2; EQUIFINALITY	Data assimilation, or the fusion of a mathematical model with ecological data, is rapidly expanding knowledge of ecological systems across multiple spatial and temporal scales. As the amount of ecological data available to a broader audience increases, quantitative proficiency with data assimilation tools and techniques will be an essential skill for ecological analysis in this data-rich era. We provide a data assimilation primer for the novice user by (1) reviewing data assimilation terminology and methodology, (2) showcasing a variety of data assimilation studies across the ecological, environmental, and atmospheric sciences with the aim of gaining an understanding of potential applications of data assimilation, and (3) applying data assimilation in specific ecological examples to determine the components of net ecosystem carbon uptake in a forest and also the population dynamics of the mayfly (Hexagenia limbata, Serville). The review and examples are then used to provide guiding principles to newly proficient data assimilation practitioners.	[Zobitz, J. M.] Augsburg Coll, Dept Math, Minneapolis, MN 55454 USA; [Desai, A. R.] Univ Wisconsin, Dept Atmospher & Ocean Sci, Madison, WI 53706 USA; [Moore, D. J. P.; Chadwick, M. A.] Kings Coll London, Dept Geog, London WC2R 2LS, England	Zobitz, JM (reprint author), Augsburg Coll, Dept Math, 2211 Riverside Ave, Minneapolis, MN 55454 USA.	zobitz@augsburg.edu; desai@aos.wisc.edu; dave.moore@kcl.ac.uk; michael.chadwick@kcl.ac.uk	Moore, David/A-6268-2013		Department of Energy (DOE) Office of Biological and Environmental Research (BER) National Institute for Climatic Change Research (NICCR) [050516Z19]	The authors would like to thank T. Quaife for helpful discussions, students of the Annual Summer Course in Flux Measurements and Modeling (NSF 0634649), and anonymous reviewers. ARD acknowledges funding support from Department of Energy (DOE) Office of Biological and Environmental Research (BER) National Institute for Climatic Change Research (NICCR) Midwestern Region Subagreement 050516Z19.	ABrook R, 1996, J BIOL EDUC, V30, P281, DOI 10.1080/00219266.1996.9655518; AKAIKE H, 1974, IEEE T AUTOMAT CONTR, VAC19, P716, DOI 10.1109/TAC.1974.1100705; Baldocchi D, 2008, AUST J BOT, V56, P1, DOI 10.1071/BT07151; Beven K, 2001, J HYDROL, V249, P11, DOI 10.1016/S0022-1694(01)00421-8; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Burnham K.P, 2002, MODEL SELECTION MULT; Cable JM, 2011, BIOGEOCHEMISTRY, V103, P71, DOI 10.1007/s10533-010-9448-z; Canadell JG, 2004, CLIMATIC CHANGE, V67, P147, DOI 10.1007/s10584-004-3765-y; Chadwick MA, 2001, LIMNOL OCEANOGR, V46, P532; Clark JS, 1998, AM NAT, V152, P204, DOI 10.1086/286162; Clark JS, 2005, MODELS ECOLOGICAL DA; Clark JS, 2005, ECOL LETT, V8, P2, DOI 10.1111/j.1461-0248.2004.00702.x; Daley R, 1994, ATMOSPHERIC DATA ANA; Davidson EA, 2006, GLOBAL CHANGE BIOL, V12, P154, DOI 10.1111/j.1365-2486.2005.01065.x; Desai AR, 2008, AGR FOREST METEOROL, V148, P821, DOI 10.1016/j.agrformet.2007.11.012; Doney SC, 2006, DEEP-SEA RES PT II, V53, P451, DOI 10.1016/j.dsr2.2006.01.019; Ellison AM, 2010, FRONT ECOL ENVIRON, V8, P362, DOI 10.1890/080209; Eugster W, 2000, GLOBAL CHANGE BIOL, V6, P84, DOI 10.1046/j.1365-2486.2000.06015.x; Evensen G, 2009, DATA ASSIMILATION: THE ENSEMBLE KALMAN FILTER, SECOND EDITION, P1, DOI 10.1007/978-3-642-03711-5; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Friend AD, 2007, GLOBAL CHANGE BIOL, V13, P610, DOI 10.1111/j.1365-2486.2006.01223.x; Heinsch FA, 2006, IEEE T GEOSCI REMOTE, V44, P1908, DOI 10.1109/TGRS.2005.853936; Hurtt GC, 1996, DEEP-SEA RES PT II, V43, P653, DOI 10.1016/0967-0645(96)00007-0; Janssens IA, 2001, GLOBAL CHANGE BIOL, V7, P269, DOI 10.1046/j.1365-2486.2001.00412.x; Jaynes ET, 2003, PROBABILITY THEORY L; Johnson JB, 2004, TRENDS ECOL EVOL, V19, P101, DOI 10.1016/j.tree.2003.10.013; Keller M, 2008, FRONT ECOL ENVIRON, V6, P282, DOI 10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2; Konishi S, 2008, SPRINGER SER STAT, P1, DOI 10.1007/978-0-387-71887-3; Litton CM, 2007, GLOBAL CHANGE BIOL, V13, P2089, DOI 10.1111/j.1365-2486.2007.01420.x; LLOYD J, 1994, FUNCT ECOL, V8, P315, DOI 10.2307/2389824; LORENZ EN, 1963, J ATMOS SCI, V20, P130, DOI 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; Mathieu PP, 2008, REMOTE SENS ENVIRON, V112, P1258, DOI 10.1016/j.rse.2007.02.040; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; Metz AM, 2008, CBE-LIFE SCI EDUC, V7, P317, DOI 10.1187/cbe.07-07-0046; Monson RK, 2002, GLOBAL CHANGE BIOL, V8, P459, DOI 10.1046/j.1365-2486.2002.00480.x; National Research Council, 2003, BIO2010 TRANSF UND E; Nemani RR, 2003, SCIENCE, V300, P1560, DOI 10.1126/science.1082750; Ogle Kiona, 2008, V69, P281, DOI 10.1007/978-3-540-72954-9_12; Olden JD, 2008, Q REV BIOL, V83, P171, DOI 10.1086/587826; Peters DPC, 2008, FRONT ECOL ENVIRON, V6, P229, DOI 10.1890/070098; Piao SL, 2008, NATURE, V451, P49, DOI 10.1038/nature06444; Piovesan G, 2000, J VEG SCI, V11, P923, DOI 10.2307/3236563; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Reichstein M, 2005, GLOBAL CHANGE BIOL, V11, P1424, DOI 10.1111/j.1365-2486.2005.001002.x; Richey M, 2010, AM MATH MON, V117, P383, DOI 10.4169/000298910X485923; Sacks WJ, 2006, GLOBAL CHANGE BIOL, V12, P240, DOI 10.1111/j.1365-2486.2005.01059.x; Saigusa N, 2002, AGR FOREST METEOROL, V112, P203, DOI 10.1016/S0168-1923(02)00082-5; Schimel DS, 2001, NATURE, V414, P169, DOI 10.1038/35102500; SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136; Sokal RR, 1995, BIOMETRY; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Steen L., 2005, MATH BIO 2010 LINKIN; SWEENEY BW, 1978, SCIENCE, V200, P444, DOI 10.1126/science.200.4340.444; Tarantola A., 2005, INVERSE PROBLEM THEO; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Valentini R, 2000, NATURE, V404, P861, DOI 10.1038/35009084; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; WOFSY SC, 1993, SCIENCE, V260, P1314, DOI 10.1126/science.260.5112.1314; Yi CX, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020490; Zobitz JM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000282	62	2	2	SPRINGER	NEW YORK	233 SPRING ST, NEW YORK, NY 10013 USA	0029-8549		OECOLOGIA	Oecologia	NOV	2011	167	3					599	611		10.1007/s00442-011-2107-9		13	Ecology	Environmental Sciences & Ecology	834SQ	WOS:000295984800002	
J	Zhang, L; Liu, HN; Zhang, N				Zhang Li; Liu Hongnian; Zhang Ning			Impacts of internally and externally mixed anthropogenic sulfate and carbonaceous aerosols on East Asian climate	ACTA METEOROLOGICA SINICA			English	Article						anthropogenic aerosols; externally mixed; internally mixed; climate effects	MIXING STATE; OPTICAL-PROPERTIES; CHEMISTRY/AEROSOL MODEL; ATMOSPHERIC AEROSOLS; CHEMICAL-COMPOSITION; DATA ASSIMILATION; RADIATION BUDGET; BLACK CARBON; HONG-KONG; CHINA	A coupled regional climate and aerosol-chemistry model, RIEMS 2.0 (Regional Integrated Environmental Model System for Asia), in which anthropogenic sulfate, black carbon, and organic carbon were assumed to be externally mixed (EM), internally mixed (IM) or partially internally mixed (IEM), was used to simulate the impacts of these anthropogenic aerosols on East Asian climate for the entire year of 2006. The distributions of aerosol mass concentration, radiative forcing and hence the surface air temperature and precipitation variations under three mixing assumptions of aerosols were analyzed. The results indicated that the mass concentration of sulfate was sensitive to mixing assumptions, but carbonaceous aerosols were much less sensitive to the mixing types. Modeled results were compared with observations in a variety of sites in East Asia. It was found that the simulated concentrations of sulfate and carbonaceous aerosols were in accord with the observations in terms of magnitude. The simulated aerosol concentrations in IM case were closest to observation results. The regional average column burdens of sulfate, black carbon, and organic carbon, if internally mixed, were 11.49, 0.47, and 2.17 mg m(-2), respectively. The radiative forcing of anthropogenic aerosols at the top of the atmosphere increased from -1.27 (EM) to -1.97 W m(-2) (IM) while the normalized radiative forcing (NRF) decreased from -0.145 (EM) to -0.139 W mg(-1) (IM). The radiative forcing and NRF were -1.82 W m(-2) and -0.141 W mg(-1) for IEM, respectively. The surface air temperature changes over the domain due to the anthropogenic sulfate and carbonaceous aerosols were -0.067, -0.078, and -0.072 K, with maxima of -0.47, -0.50, and -0.49 K, for EM, IM, and IEM, respectively. Meanwhile, the annual precipitation variations were -8.0 (EM), -20.6 (IM), and -21.9 mm (IEM), with maxima of 148, 122, and 102 mm, respectively, indicating that the climate effects were stronger if the sulfate and carbonaceous aerosols were internally mixed.	[Zhang Li; Liu Hongnian; Zhang Ning] Nanjing Univ, Sch Atmospher Sci, Nanjing 210093, Jiangsu, Peoples R China	Liu, HN (reprint author), Nanjing Univ, Sch Atmospher Sci, Nanjing 210093, Jiangsu, Peoples R China.	liuhn@nju.edu.cn			National Key Basic Research and Development (973) Program of China [2006CB400506, 2010CB428501]; National Natural Science Foundation of China [40775014]	Supported by the National Key Basic Research and Development (973) Program of China (2006CB400506 and 2010CB428501) and National Natural Science Foundation of China (40775014).	Adhikary B, 2008, ATMOS ENVIRON, V42, P8600, DOI 10.1016/j.atmosenv.2008.08.031; Andreae MO, 2008, ATMOS ENVIRON, V42, P6335, DOI 10.1016/j.atmosenv.2008.01.030; Bergin MH, 2001, J GEOPHYS RES-ATMOS, V106, P17969, DOI 10.1029/2001JD900073; Bohren C. F., 1983, ABSORPTION SCATTERIN, P287; Cao JJ, 2009, PARTICUOLOGY, V7, P161, DOI 10.1016/j.partic.2009.01.009; Chang LS, 2004, ATMOS ENVIRON, V38, P4467, DOI 10.1016/j.atmosenv.2004.05.006; Chang SC, 2010, ATMOS ENVIRON, V44, P1088, DOI 10.1016/j.atmosenv.2009.11.046; Chung CE, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006356; Chung CE, 2010, ATMOS CHEM PHYS, V10, P6007, DOI 10.5194/acp-10-6007-2010; Chung SH, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005441; Huang Y, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007114; Ebert M, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2000JD000195; FassiFihri A, 1997, ATMOS ENVIRON, V31, P1393, DOI 10.1016/S1352-2310(96)00341-X; Fu CB, 2005, B AM METEOROL SOC, V86, P257, DOI 10.1175/BAMS-86-2-257; FU CB, 2000, 122 TEACOM; GELBARD F, 1980, J COLLOID INTERF SCI, V76, P541, DOI 10.1016/0021-9797(80)90394-X; Ghan SJ, 2001, J GEOPHYS RES-ATMOS, V106, P5279, DOI 10.1029/2000JD900503; Giorgi F, 2003, CLIMATIC CHANGE, V58, P345, DOI 10.1023/A:1023946010350; Giorgi F, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001066; Gu Y, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006312; Han YM, 2008, ATMOS ENVIRON, V42, P2405, DOI 10.1016/j.atmosenv.2007.12.020; Han ZW, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009436; Hansen J, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001143; HAYWOOD JM, 1995, GEOPHYS RES LETT, V22, P603, DOI 10.1029/95GL00075; He KB, 2001, ATMOS ENVIRON, V35, P4959, DOI 10.1016/S1352-2310(01)00301-6; HEGG DA, 1994, J GEOPHYS RES-ATMOS, V99, P25903, DOI 10.1029/94JD02224; Heo JB, 2009, ATMOS CHEM PHYS, V9, P4957; Jacobson MZ, 2001, J GEOPHYS RES-ATMOS, V106, P1551, DOI 10.1029/2000JD900514; Jacobson MZ, 2001, NATURE, V409, P695, DOI 10.1038/35055518; JOSEPH CL, 1993, BHCOAT; Kiehl JT, 2000, J GEOPHYS RES-ATMOS, V105, P1441, DOI 10.1029/1999JD900495; Kim D, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009756; Kim YJ, 2009, ATMOS ENVIRON, V43, P5556, DOI 10.1016/j.atmosenv.2009.03.062; KIM YP, 1990, J COLLOID INTERF SCI, V135, P185, DOI 10.1016/0021-9797(90)90299-4; Kirkevag A, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000886; Lesins G, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000973; Li WJ, 2010, J ENVIRON SCI-CHINA, V22, P561, DOI 10.1016/S1001-0742(09)60146-7; Liao H, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005907; Liu HN, 2010, ADV ATMOS SCI, V27, P1276, DOI 10.1007/s00376-010-9188-y; Liu XH, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008216; Lu J, 2010, ATMOS CHEM PHYS, V10, P4033, DOI 10.5194/acp-10-4033-2010; Ma ZG, 2005, CHINESE J GEOPHYS-CH, V48, P519; Manktelow PT, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028668; Meng ZY, 2007, ATMOS ENVIRON, V41, P6901, DOI 10.1016/j.atmosenv.2007.07.049; Moteki N, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028943; Moteki N, 2007, AEROSOL SCI TECH, V41, P398, DOI 10.1080/02786820701199728; Naoe H, 2001, ATMOS ENVIRON, V35, P5765, DOI 10.1016/S1352-2310(01)00367-3; Novakov T, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005977; Okuda T, 2008, CHEMOSPHERE, V72, P917, DOI 10.1016/j.chemosphere.2008.03.033; Pathak RK, 2009, ATMOS CHEM PHYS, V9, P1711; PENNER JE, 1992, SCIENCE, V256, P1432, DOI 10.1126/science.256.5062.1432; Penner JE, 1998, CLIM DYNAM, V14, P839, DOI 10.1007/s003820050259; Qian Y, 2001, TELLUS B, V53, P171, DOI 10.1034/j.1600-0889.2001.d01-14.x; Qian Y, 2003, TELLUS B, V55, P914, DOI 10.1046/j.1435-6935.2003.00070.x; Qu WJ, 2009, SCI TOTAL ENVIRON, V407, P3518, DOI 10.1016/j.scitotenv.2009.02.012; Reddy MS, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004048; Schulz M, 2006, ATMOS CHEM PHYS, V6, P5225; Schwarz JP, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009042; Shiraiwa M, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029819; Shiraiwa M, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010546; So KL, 2007, ATMOS ENVIRON, V41, P9427, DOI 10.1016/j.atmosenv.2007.08.053; Stier P, 2005, ATMOS CHEM PHYS, V5, P1125; Stier P, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007147; Streets DG, 2008, ATMOS RES, V88, P174, DOI 10.1016/j.atmosres.2007.10.016; STROM J, 1992, J AEROSOL SCI, V23, P467, DOI 10.1016/0021-8502(92)90018-Q; Tan Q, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD005071; Vester BP, 2007, ATMOS ENVIRON, V41, P6102, DOI 10.1016/j.atmosenv.2007.04.021; WARREN DR, 1985, AEROSOL SCI TECH, V4, P31, DOI 10.1080/02786828508959037; Worringen A, 2008, APPL OPTICS, V47, P3835, DOI 10.1364/AO.47.003835; [吴涧 Wu Jian], 2005, [云南大学学报. 自然科学版, Journal of Yunnan University (Natural Science)], V27, P323; Xie SD, 2008, ATMOS CHEM PHYS, V8, P2701; Xiong Z, 2009, CHINESE SCI BULL, V54, P4253, DOI 10.1007/s11434-009-0669-2; Xiong Z, 2006, ADV ATMOS SCI, V23, P784, DOI 10.1007/s00376-006-0784-9; [熊喆 Xiong Zhe], 2004, [气候与环境研究, Climatic and environmental research], V9, P251; Xiong Z, 2003, ADV ATMOS SCI, V20, P815; Xu J, 2002, ATMOS ENVIRON, V36, P161, DOI 10.1016/S1352-2310(01)00455-1; Ye BM, 2003, ATMOS ENVIRON, V37, P499, DOI 10.1016/S1352-2310(02)00918-4; Yu H, 2006, ATMOS CHEM PHYS, V6, P613; Yu JZ, 2004, ATMOS ENVIRON, V38, P1511, DOI 10.1016/j.atmosenv.2003.11.035; Zhang H, 2009, ADV ATMOS SCI, V26, P57, DOI 10.1007/s00376-009-0057-5; Zhang Q, 2009, ATMOS CHEM PHYS, V9, P5131; Zhang XY, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009525; Zhou M, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005909; *IPCC AR4, 2007, 4 IPCC AR4, P153	84	2	2	SPRINGER HEIDELBERG	HEIDELBERG	TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY	0894-0525		ACTA METEOROL SIN	Acta Meteorol. Sin.	OCT	2011	25	5					639	658		10.1007/s13351-011-0508-7		20	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	865EM	WOS:000298293700008	
J	Richardson, AD; Dail, DB; Hollinger, DY				Richardson, Andrew D.; Dail, D. Bryan; Hollinger, D. Y.			Leaf area index uncertainty estimates for model-data fusion applications	AGRICULTURAL AND FOREST METEOROLOGY			English	Article						Carbon cycle; Data assimilation; Error analysis; Data-model fusion; Leaf area index; Uncertainty	BOREAL FORESTS; CARBON; ERROR; INSTRUMENTS; FLUXES	Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux site for illustrative purposes. We made measurements along two transects (one in a mature stand, one in a recently harvested shelterwood) before sunset on successive days using both the Li-Cor LAI-2000 plant canopy analyzer and digital hemispherical photography (DHP). The random measurement uncertainty (1 sigma) at a given point for a single measurement is about 5% for LAI-2000 and 10% for DHP. These uncertainties are small compared to potential systematic biases due to instrument calibration errors and data processing decisions, which are estimated to be 10-20% for each instrument. Sampling uncertainty (due to the spatial variability along each transect where we conducted our measurements) is an additional, but again relatively small. uncertainty. Assumptions about clumping parameters, for which standard literature values are typically used, remain large sources of uncertainty. This analysis can also be used to develop strategies to reduce measurement uncertainties. (C) 2011 Elsevier B.V. All rights reserved.	[Richardson, Andrew D.] Harvard Univ, Dept Organism & Evolutionary Biol, HUH, Cambridge, MA 02138 USA; [Dail, D. Bryan] Univ Maine, Dept Plant Soil & Environm Sci, Orono, ME 04469 USA; [Hollinger, D. Y.] US Forest Serv, USDA, No Res Stn, Durham, NH 03824 USA	Richardson, AD (reprint author), Harvard Univ, Dept Organism & Evolutionary Biol, HUH, 22 Divin Ave, Cambridge, MA 02138 USA.	arichardson@oeb.harvard.edu	Richardson, Andrew/F-5691-2011; Hollinger, David/G-7185-2012		Office of Science (BER), U.S. Department of Energy [DE-AI020-7ER64355, DE-FG02-00ER63002]	We thank the John Lee, site manager, Holly Hughes, Charles Rodrigues, and Michelle Day for assistance with the field measurements. This research was supported by the Office of Science (BER), U.S. Department of Energy, Interagency Agreement no. DE-AI020-7ER64355, and by the U.S. Department of Energy's Office of Science (BER) grant no. DE-FG02-00ER63002.	Harmon M. E., 2007, Principles and standards for measuring primary production, P238; BONAN GB, 1993, REMOTE SENS ENVIRON, V43, P303, DOI 10.1016/0034-4257(93)90072-6; Breda NJJ, 2003, J EXP BOT, V54, P2403, DOI 10.1093/jxb/erg263; Chave J, 2004, PHILOS T ROY SOC B, V359, P409, DOI 10.1098/rstb.2003.1425; Chen JM, 1996, AGR FOREST METEOROL, V80, P135, DOI 10.1016/0168-1923(95)02291-0; Chen JM, 1997, J GEOPHYS RES-ATMOS, V102, P29429, DOI 10.1029/97JD01107; Chen JM, 2006, AGR FOREST METEOROL, V140, P257, DOI 10.1016/j.agrformet.2006.08.005; CHEN JM, 1992, PLANT CELL ENVIRON, V15, P421, DOI 10.1111/j.1365-3040.1992.tb00992.x; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Gower ST, 1999, REMOTE SENS ENVIRON, V70, P29, DOI 10.1016/S0034-4257(99)00056-5; Hollinger DY, 2005, TREE PHYSIOL, V25, P873; Hollinger DY, 2004, GLOBAL CHANGE BIOL, V10, P1689, DOI 10.1111/j.1365-2486.2004.00847.x; Hyer EJ, 2004, AGR FOREST METEOROL, V122, P157, DOI 10.1016/j.agrformet.2003.09.013; LEBLANC SG, 2005, TRACING RAD ARCHITEC, P27; LEBLANC SG, 2005, DIGITAL HEMISPHERICA, P28; Petersen PH, 2001, CLIN CHEM LAB MED, V39, P589, DOI 10.1515/CCLM.2001.094; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; Richardson AD, 2006, AGR FOREST METEOROL, V136, P1, DOI 10.1016/j.agrformet.2006.01.007; Savage K, 2008, FUNCT ECOL, V22, P1000, DOI 10.1111/j.1365-2435.2008.01414.x; WELLES JM, 1991, AGRON J, V83, P818; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Williams M, 2001, ECOL APPL, V11, P1800, DOI 10.1890/1051-0761(2001)011[1800:PPOAAW]2.0.CO;2; WOODS KD, 1991, CAN J FOREST RES, V21, P974, DOI 10.1139/x91-135; Yanai RD, 2010, ECOSYSTEMS, V13, P239, DOI 10.1007/s10021-010-9315-8	26	3	3	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0168-1923		AGR FOREST METEOROL	Agric. For. Meteorol.	SEP 15	2011	151	9					1287	1292		10.1016/j.agrformet.2011.05.009		6	Agronomy; Forestry; Meteorology & Atmospheric Sciences	Agriculture; Forestry; Meteorology & Atmospheric Sciences	809DM	WOS:000294032000013	
J	Xiao, JF; Davis, KJ; Urban, NM; Keller, K; Saliendra, NZ				Xiao, Jingfeng; Davis, Kenneth J.; Urban, Nathan M.; Keller, Klaus; Saliendra, Nicanor Z.			Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates	JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES			English	Article							NET PRIMARY PRODUCTION; EDDY-COVARIANCE MEASUREMENTS; DIFFERENCE WATER INDEX; GREAT-LAKES REGION; ECOSYSTEM EXCHANGE; UNITED-STATES; NORTHERN WISCONSIN; VEGETATION INDEXES; FOREST ECOSYSTEMS; UPPER MIDWEST	Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes.	[Xiao, Jingfeng; Davis, Kenneth J.] Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA; [Urban, Nathan M.; Keller, Klaus] Penn State Univ, Dept Geosci, University Pk, PA 16870 USA; [Saliendra, Nicanor Z.] Univ Maryland Baltimore Cty, Ctr Urban Environm Res & Educ, Baltimore, MD 21250 USA	Xiao, JF (reprint author), Univ New Hampshire, Inst Study Earth Oceans & Space, Complex Syst Res Ctr, 8 Coll Rd, Durham, NH 03861 USA.	j.xiao@unh.edu			National Aeronautics and Space Administration (NASA)	This study was supported by the National Aeronautics and Space Administration (NASA). Terrestrial Ecology Program. Data collection was supported by NASA's Terrestrial Ecology Program and the Department of Energy's Office of Biological and Environmental Research, Terrestrial Carbon Program, and the National Institute for Climatic Change Research. We thank T. Hilton for assistance with data assimilation and A. Desai for helpful discussion. We also thank J. Chen, K. Cherrey, P. Curtis, A. Desai, C. Gough, and A. Noormets for contributions to the flux observations used in this study. We also thank the two anonymous reviewers and D.D. Baldocchi for constructive comments on earlier versions of the manuscript.	Ardia D., 2009, DEOPTIM DIFFERENTIAL; BALDOCCHI DD, 1988, ECOLOGY, V69, P1331, DOI 10.2307/1941631; Boelman NT, 2003, OECOLOGIA, V135, P414, DOI 10.1007/s00442-003-1198-3; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Butler MP, 2010, TELLUS B, V62, P550, DOI 10.1111/j.1600-0889.2010.00501.x; Chen JQ, 2008, AGR FOREST METEOROL, V148, P155, DOI 10.1016/j.agrformet.2007.08.016; Cook BD, 2004, AGR FOREST METEOROL, V126, P271, DOI 10.1016/j.agrformet.2004.06.008; Cook BD, 2009, REMOTE SENS ENVIRON, V113, P2366, DOI 10.1016/j.rse.2009.06.017; Davis KJ, 2003, GLOBAL CHANGE BIOL, V9, P1278, DOI 10.1046/j.1365-2486.2003.00672.x; DAVIS KJ, 2008, FIELD MEASUREMENTS L, V18, P207; Desai AR, 2008, AGR FOREST METEOROL, V148, P288, DOI 10.1016/j.agrformet.2007.08.001; Desai AR, 2005, AGR FOREST METEOROL, V128, P33, DOI 10.1016/j.agrformet.2004.09.005; Falge E, 2001, AGR FOREST METEOROL, V107, P43, DOI 10.1016/S0168-1923(00)00225-2; Fensholt R, 2003, REMOTE SENS ENVIRON, V87, P111, DOI 10.1016/j.rse.2003.07.002; FOLEY S, 2005, SCIENCE, V309, P570, DOI DOI 10.1126/SCIENCE.1111772; Friedl MA, 2002, REMOTE SENS ENVIRON, V83, P287, DOI 10.1016/S0034-4257(02)00078-0; Gao BC, 1996, REMOTE SENS ENVIRON, V58, P257, DOI 10.1016/S0034-4257(96)00067-3; Goetz SJ, 1996, AGR FOREST METEOROL, V78, P149, DOI 10.1016/0168-1923(95)02268-6; Gough CM, 2008, AGR FOREST METEOROL, V148, P158, DOI 10.1016/j.agrformet.2007.08.004; Gower ST, 1999, REMOTE SENS ENVIRON, V70, P29, DOI 10.1016/S0034-4257(99)00056-5; Heinsch F.A., 2003, USERS GUIDE GPP NPP; Homer C, 2004, PHOTOGRAMM ENG REM S, V70, P829; Huete A, 2002, REMOTE SENS ENVIRON, V83, P195, DOI 10.1016/S0034-4257(02)00096-2; Huete AR, 1997, REMOTE SENS ENVIRON, V59, P440, DOI 10.1016/S0034-4257(96)00112-5; Hurtt GC, 2002, P NATL ACAD SCI USA, V99, P1389, DOI 10.1073/pnas.012249999; Jackson TJ, 2004, REMOTE SENS ENVIRON, V92, P475, DOI 10.1016/j.rse.2003.10.021; Jung M, 2009, BIOGEOSCIENCES, V6, P2001; Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Landsberg JJ, 1997, FOREST ECOL MANAG, V95, P209, DOI 10.1016/S0378-1127(97)00026-1; LLOYD J, 1994, FUNCT ECOL, V8, P315, DOI 10.2307/2389824; Mahadevan P, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2006GB002735; Mo XG, 2008, ECOL MODEL, V217, P157, DOI 10.1016/j.ecolmodel.2008.06.021; Moffat AM, 2007, AGR FOREST METEOROL, V147, P209, DOI 10.1016/j.agrformet.2007.08.011; MONTEITH JL, 1977, PHIL T R S LONDON B, P277; MONTEITH JL, 1972, J APPL ECOL, V9, P747, DOI 10.2307/2401901; Myneni RB, 2001, P NATL ACAD SCI USA, V98, P14784, DOI 10.1073/pnas.261555198; Noormets A, 2008, AGR FOREST METEOROL, V148, P216, DOI 10.1016/j.agrformet.2007.08.002; PERSSON P, 1993, ADV SPACE RES, V13, P111, DOI 10.1016/0273-1177(93)90211-S; Pinker RT, 2002, ADV SPACE RES, V30, P2427, DOI 10.1016/S0273-1177(02)80296-4; Potter C, 2007, EARTH INTERACT, V11, DOI 10.1175/EI228.1; Price K. V., 2006, DIFFERENTIAL EVOLUTI; Prince SD, 1995, J BIOGEOGR, V22, P815, DOI 10.2307/2845983; Quaife T, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003097; RAICH JW, 1991, ECOL APPL, V7, P444; Reich PB, 1999, REMOTE SENS ENVIRON, V70, P69, DOI 10.1016/S0034-4257(99)00058-9; Ricciuto DM, 2008, AGR FOREST METEOROL, V148, P309, DOI 10.1016/j.agrformet.2007.08.007; Ruimy A, 1994, J GEOPHYS RES-ATMOS, V99, P5263, DOI 10.1029/93JD03221; Ruimy A, 1996, GLOBAL BIOGEOCHEM CY, V10, P269, DOI 10.1029/96GB00349; Running SW, 2004, BIOSCIENCE, V54, P547, DOI 10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2; SCHMID HP, 1994, BOUND-LAY METEOROL, V67, P293, DOI 10.1007/BF00713146; Stuart Chapin III F., 2002, PRINCIPLES TERRESTRI; Su HB, 2008, AGR FOREST METEOROL, V148, P186, DOI 10.1016/j.agrformet.2007.08.009; Sulman BN, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL044018; Sun G, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001573; Tian H, 1999, TELLUS B, V51, P414, DOI 10.1034/j.1600-0889.1999.00021.x; TUCKER CJ, 1985, REMOTE SENS ENVIRON, V17, P233, DOI 10.1016/0034-4257(85)90097-5; U.S. Climate Change Science Program and the Subcommittee on Global Change Research, 2007, 1 STAT CARB CYCL REP; Vermote E. F., 1999, MODIS ALGORITHM TECH; Veroustraete F, 2002, REMOTE SENS ENVIRON, V83, P376, DOI 10.1016/S0034-4257(02)00043-3; Xiao JF, 2010, REMOTE SENS ENVIRON, V114, P576, DOI 10.1016/j.rse.2009.10.013; Xiao JF, 2008, AGR FOREST METEOROL, V148, P1827, DOI 10.1016/j.agrformet.2008.06.015; Xiao JF, 2011, AGR FOREST METEOROL, V151, P60, DOI 10.1016/j.agrformet.2010.09.002; Xiao JF, 2009, EARTH INTERACT, V13, DOI 10.1175/2009EI275.1; Xiao XM, 2004, REMOTE SENS ENVIRON, V89, P519, DOI 10.1016/j.rse.2003.11.008; Zhang L, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001504; Zhao MS, 2005, REMOTE SENS ENVIRON, V95, P164, DOI 10.1016/j.rse.2004.12.011	67	1	1	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0148-0227		J GEOPHYS RES-BIOGEO	J. Geophys. Res.-Biogeosci.	SEP 1	2011	116								G03027	10.1029/2010JG001568		15	Environmental Sciences; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	816PW	WOS:000294615600001	
J	Weng, ES; Luo, YQ; Gao, C; Oren, R				Weng, Ensheng; Luo, Yiqi; Gao, Chao; Oren, Ram			Uncertainty analysis of forest carbon sink forecast with varying measurement errors: a data assimilation approach	JOURNAL OF PLANT ECOLOGY			English	Article						uncertainty analysis; data assimilation; Markov Chain Monte Carlo (MCMC) method; measurement error; carbon residence time; information contribution	ATMOSPHERIC CO2 ENRICHMENT; NET ECOSYSTEM EXCHANGE; EDDY-FLUX DATA; PARAMETER-ESTIMATION; PINE FOREST; TERRESTRIAL BIOSPHERE; NONLINEAR INVERSION; MODEL; COVARIANCE; VEGETATION	Aims Accurate forecast of ecosystem states is critical for improving natural resource management and climate change mitigation. Assimilating observed data into models is an effective way to reduce uncertainties in ecological forecasting. However, influences of measurement errors on parameter estimation and forecasted state changes have not been carefully examined. This study analyzed the parameter identifiability of a process-based ecosystem carbon cycle model, the sensitivity of parameter estimates and model forecasts to the magnitudes of measurement errors and the information contributions of the assimilated data to model forecasts with a data assimilation approach. Methods We applied a Markov Chain Monte Carlo method to assimilate eight biometric data sets into the Terrestrial ECOsystem model. The data were the observations of foliage biomass, wood biomass, fine root biomass, microbial biomass, litter fall, litter, soil carbon and soil respiration, collected at the Duke Forest free-air CO2 enrichment facilities from 1996 to 2005. Three levels of measurement errors were assigned to these data sets by halving and doubling their original standard deviations. Important Findings Results showed that only less than half of the 30 parameters could be constrained, though the observations were extensive and the model was relatively simple. Higher measurement errors led to higher uncertainties in parameters estimates and forecasted carbon (C) pool sizes. The long-term predictions of the slow turnover pools were affected less by the measurement errors than those of fast turnover pools. Assimilated data contributed less information for the pools with long residence times in long-term forecasts. These results indicate the residence times of C pools played a key role in regulating propagation of errors from measurements to model forecasts in a data assimilation system. Improving the estimation of parameters of slow turnover C pools is the key to better forecast long-term ecosystem C dynamics.	[Weng, Ensheng; Luo, Yiqi; Gao, Chao] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA; [Oren, Ram] Duke Univ, Nicholas Sch Environm, Durham, NC 27708 USA	Luo, YQ (reprint author), Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA.	yluo@ou.edu	Weng, Ensheng/E-4390-2012		Office of Science (BER), Department of Energy [DE-FG02-006ER64319]; Midwestern Regional Center of the National institute for Climatic Change Research at Michigan Technological University [DE-FC02-06ER64158]; National Science Foundation [DEB 0078325, DEB 0743778]	This research was financially supported by the Office of Science (BER), Department of Energy (DE-FG02-006ER64319) and through the Midwestern Regional Center of the National institute for Climatic Change Research at Michigan Technological University, under Award Number DE-FC02-06ER64158 and by National Science Foundation (DEB 0078325 and DEB 0743778). The model runs were performed at the Supercomputing Center for Education Er Research (OSCER), University of Oklahoma.	Allen AS, 2000, ECOL APPL, V10, P437, DOI 10.2307/2641105; Barrett DJ, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2002GB001860; Barrett DJ, 2005, AUST J BOT, V53, P689, DOI 10.1071/BT04139; BECK MB, 1987, WATER RESOUR RES, V23, P1393, DOI 10.1029/WR023i008p01393; Bernhardt ES, 2006, BIOGEOCHEMISTRY, V77, P91, DOI 10.1007/s10533-005-1062-0; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Bulygina N, 2009, WATER RESOUR RES, V45, DOI 10.1029/2007WR006749; Carvalhais N, 2010, GLOBAL CHANGE BIOL, V16, P2813, DOI 10.1111/j.1365-2486.2010.02173.x; Clark JS, 2001, SCIENCE, V293, P657, DOI 10.1126/science.293.5530.657; Doherty J, 2009, J HYDROL, V366, P119, DOI 10.1016/j.jhydrol.2008.12.018; Eriksson E, 1971, ANNU REV ECOL SYST, V2, P67, DOI 10.1146/annurev.es.02.110171.000435; Finzi AC, 2006, ECOSYSTEMS, V9, P215, DOI 10.1007/s10021-005-0078-6; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; GELFAND AE, 1990, J AM STAT ASSOC, V85, P398, DOI 10.2307/2289776; Gelman A, 1992, STAT SCI, V7, P457, DOI DOI 10.1214/SS/1177011136; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hendrey GR, 1999, GLOB CHANGE BIOL, V5, P293, DOI 10.1046/j.1365-2486.1999.00228.x; Hollinger DY, 2005, TREE PHYSIOL, V25, P873; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P207, DOI 10.1029/1998GB001059; Kucharik CJ, 2000, GLOBAL BIOGEOCHEM CY, V14, P795, DOI 10.1029/1999GB001138; Kullback L., 1951, ANN MATH STAT, V22, P79; Lasslop G, 2008, BIOGEOSCIENCES, V5, P1311; Lichter J, 2005, ECOLOGY, V86, P1835, DOI 10.1890/04-1205; Lichter J, 2008, GLOBAL CHANGE BIOL, V14, P2910, DOI 10.1111/j.1365-2486.2008.01701.x; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; Luo Y, 2001, ECOL APPL, V11, P239, DOI 10.2307/3061070; LUO YQ, 2011, ECOL APPL IN PRESS, DOI DOI 10.1890/09-1275.1; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; PARTON WJ, 1987, SOIL SCI SOC AM J, V51, P1173; Pielke RA, 2003, ECOLOGY, V84, P1351, DOI 10.1890/0012-9658(2003)084[1351:BPIPFD]2.0.CO;2; Pritchard SG, 2008, GLOBAL CHANGE BIOL, V14, P588, DOI 10.1111/j.1365-2486.2007.01523.x; Qian HF, 2010, GLOBAL CHANGE BIOL, V16, P641, DOI 10.1111/j.1365-2486.2009.01989.x; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Renyi A, 1960, P 4 BERK S MATH STAT, P547; Ricciuto DM, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2006GB002908; Ricciuto DM, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2010JG001400; Richardson AD, 2010, OECOLOGIA, V164, P25, DOI 10.1007/s00442-010-1628-y; Richardson AD, 2005, AGR FOREST METEOROL, V131, P191, DOI 10.1016/j.agrformet.2005.05.008; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Stoy PC, 2006, AGR FOREST METEOROL, V141, P2, DOI 10.1016/j.agrformet.2006.09.001; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Wang YP, 2007, GLOBAL CHANGE BIOL, V13, P652, DOI 10.1111/j.1365-2486.2006.01225.x; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; WENG E, 2011, ECOL APPL IN PRESS, DOI DOI 10.1890/09-1394.1; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Wu XW, 2009, J PLANT ECOL-UK, V2, P55, DOI 10.1093/jpe/rtp005; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zhang L, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001004; Zhou T, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB002939	49	1	1	OXFORD UNIV PRESS	OXFORD	GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND	1752-9921		J PLANT ECOL-UK	J. Plant Ecol.	SEP	2011	4	3					178	191		10.1093/jpe/rtr018		14	Plant Sciences; Ecology	Plant Sciences; Environmental Sciences & Ecology	818HR	WOS:000294743100008	
J	Stockli, R; Rutishauser, T; Baker, I; Liniger, MA; Denning, AS				Stoeckli, R.; Rutishauser, T.; Baker, I.; Liniger, M. A.; Denning, A. S.			A global reanalysis of vegetation phenology	JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES			English	Article							SURFACE PARAMETERIZATION SIB2; FOLIAGE CLUMPING INDEX; ENSEMBLE KALMAN FILTER; PLANT FUNCTIONAL TYPES; CLIMATE-CHANGE; LAND-COVER; LEAF-AREA; DATA ASSIMILATION; ATMOSPHERIC GCMS; BIOSPHERE MODEL	Simulations of the global water and carbon cycle are sensitive to the model representation of vegetation phenology. Current phenology models are empirical, and few predict both phenological timing and leaf state. Our previous study demonstrated how satellite data assimilation employing an Ensemble Kalman Filter yields realistic phenological model parameters for several ecosystem types. In this study the data assimilation framework is extended to global scales using a subgrid-scale representation of plant functional types (PFTs) and elevation classes. A reanalysis of vegetation phenology for 256 globally distributed regions is performed using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of photosynthetically active radiation (FPAR) absorbed by vegetation and leaf area index (LAI) data. The 9 . 10(8) quality screened observations (corresponding to <1% of the globally available MODIS data) successfully constrain a posterior PFT-dependent phenological parameter set. It reduces the global FPAR and LAI prediction error to 20.6% and 14.8%, respectively, compared to the prior prediction error. A 50 year long (1960-2009) daily 1 degrees x 1 degrees global phenology data set with a mean FPAR and LAI prediction error of 0.065 (-) and 0.34 (m(2) m(-2)) is generated. Temperate phenology is best explained by a combination of light and temperature. Tropical evergreen phenology is found to be largely insensitive to moisture and light variations. Boreal phenology can be accurately predicted from local to global scales, while temperate and mediterranean landscapes might benefit from a better subgrid-scale PFT classification or from a more complex canopy radiative transfer model.	[Stoeckli, R.; Liniger, M. A.] Fed Off Meteorol & Climatol MeteoSwiss, CH-8044 Zurich, Switzerland; [Rutishauser, T.] Univ Bern, Inst Geog, CH-3012 Bern, Switzerland; [Baker, I.; Denning, A. S.] Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USA	Stockli, R (reprint author), Fed Off Meteorol & Climatol MeteoSwiss, Krahbuhlstr 58, CH-8044 Zurich, Switzerland.	reto.stoeckli@meteoswiss.ch	Denning, Scott/F-4974-2011	Denning, Scott/0000-0003-3032-7875	NASA Energy and Water Cycle Study (NEWS) [NNG06CG42G]; NASA [NNX11AB87G]; MeteoSwiss	The NASA Energy and Water Cycle Study (NEWS) grant NNG06CG42G, NASA grant NNX11AB87G and MeteoSwiss provided funding for this study. The MODIS Science Team and the MODIS Science Data Support Team provided the MOD15A2, MOD44B and MOD12Q1 data. The NASA Science Mission Directorate (SMD) is acknowledged for granting the SMD-08-0810 and SMD-09-1256 requests with 250,000 and 460,000 CPU hours, respectively, on the NASA Center for Computational Sciences (NCCS) High-End Computing (HEC) Discover system. The National Center for Atmospheric Research (NCAR) Terrestrial Sciences Section (TSS) is acknowledged for providing computational resources on the Bluefire system for testing purposes. The Swiss Center for Scientific Computing (CSCS) is acknowledged for providing computational resources on the Rigi system for testing purposes.	Ahrends HE, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000650; Arora VK, 2005, GLOBAL CHANGE BIOL, V11, P39, DOI 10.1111/j.1365-2486.2004.00890.x; Asner GP, 2004, P NATL ACAD SCI USA, V101, P6039, DOI 10.1073/pnas.0400168101; Berrisford P., 2009, ERA REP SERIES, V1, P16; Betts AK, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005702; Betts AK, 2007, AGR FOREST METEOROL, V142, P156, DOI 10.1016/j.agrformet.2006.08.020; Bonan G. B., 2002, ECOLOGICAL CLIMATOLO; Bonan GB, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2000GB001360; Chen JM, 2005, REMOTE SENS ENVIRON, V97, P447, DOI 10.1016/j.rse.2005.05.003; Chuine I, 2000, J THEOR BIOL, V207, P337, DOI 10.1006/jtbi.2000.2178; Cleland EE, 2007, TRENDS ECOL EVOL, V22, P357, DOI 10.1016/j.tree.2007.04.003; Cohen WB, 2006, IEEE T GEOSCI REMOTE, V44, P1843, DOI 10.1109/TGRS.2006.876026; Cox P. M., 2001, 24 HADL CTR; Defries RS, 2000, GLOB CHANGE BIOL, V6, P247, DOI 10.1046/j.1365-2486.2000.00296.x; Demarty J, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030014; Dickinson RE, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008934; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Evensen G, 2004, OCEAN DYNAM, V54, P539, DOI 10.1007/s10236-004-0099-2; Evensen G, 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Evensen G, 2009, IEEE CONTR SYST MAG, V29, P83, DOI 10.1109/MCS.2009.932223; Fang HL, 2008, REMOTE SENS ENVIRON, V112, P75, DOI 10.1016/j.rse.2006.07.026; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Fisher JI, 2006, REMOTE SENS ENVIRON, V100, P265, DOI 10.1016/j.rse.2005.10.022; Foley JA, 1996, GLOBAL BIOGEOCHEM CY, V10, P603, DOI 10.1029/96GB02692; Friedl MA, 2002, REMOTE SENS ENVIRON, V83, P287, DOI 10.1016/S0034-4257(02)00078-0; Garrigues S, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000635; Gervois S, 2004, EARTH INTERACT, V8, P1, DOI DOI 10.1175/1087-3562(2004)8<1:ICIAGB>2.0.CO;02; Hansen M. C., 2003, EARTH INTERACT, V7, P1, DOI DOI 10.1175/1087-3562; Hansen MC, 2000, INT J REMOTE SENS, V21, P1331, DOI 10.1080/014311600210209; Heck P, 1999, PHYS CHEM EARTH PT B, V24, P609, DOI 10.1016/S1464-1909(99)00052-0; JARVIS PG, 1976, PHILOS T ROY SOC B, V273, P593, DOI 10.1098/rstb.1976.0035; Jolly WM, 2005, GLOBAL CHANGE BIOL, V11, P619, DOI 10.1111/j.1365-2486.2005.00930.x; Jonsson P, 2002, IEEE T GEOSCI REMOTE, V40, P1824, DOI 10.1109/TGRS.2002.802519; Justice CO, 2002, REMOTE SENS ENVIRON, V83, P3, DOI 10.1016/S0034-4257(02)00084-6; Keeling CD, 1996, NATURE, V382, P146, DOI 10.1038/382146a0; KEPERT JD, 2004, OCEAN DYNAM, V6, P561; Kim Y, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005436; Knorr W, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001119; Korner C, 2003, SCIENCE, V300, P1242, DOI 10.1126/science.1084460; Korner C, 2010, SCIENCE, V327, P1461, DOI 10.1126/science.1186473; Kramer K, 2000, INT J BIOMETEOROL, V44, P67, DOI 10.1007/s004840000066; Kucharik CJ, 2006, ECOL MODEL, V196, P1, DOI 10.1016/j.ecolmodel.2005.11.031; Kurner C., 2006, PLANT GROWTH CLIMATE, P48; Larcher W, 2003, PHYSL PLANT ECOLOGY; Law BE, 2002, AGR FOREST METEOROL, V113, P97, DOI 10.1016/S0168-1923(02)00104-1; Lawrence PJ, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000168; Lee JE, 2005, P NATL ACAD SCI USA, V102, P17576, DOI 10.1073/pnas.0508785102; Leff B., 2004, Global Biogeochemical Cycles, V18, pGB1009, DOI 10.1029/2003GB002108; Levis S, 2004, J CLIMATE, V17, P4531, DOI 10.1175/3218.1; Li H, 2009, Q J ROY METEOR SOC, V135, P523, DOI 10.1002/qj.371; Liang LA, 2011, REMOTE SENS ENVIRON, V115, P143, DOI 10.1016/j.rse.2010.08.013; Lokupitiya E, 2009, BIOGEOSCIENCES, V6, P969; Los SO, 2000, J HYDROMETEOROL, V1, P183, DOI 10.1175/1525-7541(2000)001<0183:AGYBLS>2.0.CO;2; Lu LX, 2001, J CLIMATE, V14, P900, DOI 10.1175/1520-0442(2001)014<0900:IOATWI>2.0.CO;2; Mahadevan P, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2006GB002735; Menzel A, 2006, GLOBAL CHANGE BIOL, V12, P1969, DOI 10.1111/j.1365-2486.2006.01193.x; Monsi M, 2005, ANN BOT-LONDON, V95, P549, DOI 10.1093/aob/mci052; Moradkhani H, 2005, ADV WATER RESOUR, V28, P135, DOI 10.1016/j.advwatres.2004.09.002; Myneni R. B., 1999, ALGORITHM THEORETICA; Myneni RB, 2002, REMOTE SENS ENVIRON, V83, P214, DOI 10.1016/S0034-4257(02)00074-3; Myneni RB, 2007, P NATL ACAD SCI USA, V104, P4820, DOI 10.1073/pnas.0611338104; Oleson KW, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000563; Penuelas J, 2009, SCIENCE, V324, P887, DOI 10.1126/science.1173004; Phillips OL, 2009, SCIENCE, V323, P1344, DOI 10.1126/science.1164033; Piao SL, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI [10.1029/2006GB002888, 10.1029/2005GB002634]; Pisek J, 2010, ISPRS J PHOTOGRAMM, V65, P341, DOI 10.1016/j.isprsjprs.2010.03.002; Poulter B, 2009, INT J REMOTE SENS, V30, P6575, DOI 10.1080/01431160903242005; Randerson JT, 2009, GLOBAL CHANGE BIOL, V15, P2462, DOI 10.1111/j.1365-2486.2009.01912.x; Rayner PJ, 2010, CURR OPIN ENV SUST, V2, P289, DOI 10.1016/j.cosust.2010.05.005; Richardson AD, 2009, ECOL APPL, V19, P1417, DOI 10.1890/08-2022.1; Rudiger C, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012912; Rutishauser T, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000382; Ryu Y, 2010, AGR FOREST METEOROL, V150, P1597, DOI 10.1016/j.agrformet.2010.08.009; Ryu Y, 2010, AGR FOREST METEOROL, V150, P63, DOI 10.1016/j.agrformet.2009.08.007; Saleska SR, 2007, SCIENCE, V318, P612, DOI 10.1126/science.1146663; Samanta A, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2009GL042154; Schaefer K, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002336; Scheifinger H, 2002, INT J CLIMATOL, V22, P1739, DOI 10.1002/joc.817; Schimel DS, 1997, P NATL ACAD SCI USA, V94, P8280, DOI 10.1073/pnas.94.16.8280; Sellers PJ, 1997, SCIENCE, V275, P502, DOI 10.1126/science.275.5299.502; Sellers PJ, 1996, J CLIMATE, V9, P676, DOI 10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2; Sellers PJ, 1996, J CLIMATE, V9, P706, DOI 10.1175/1520-0442(1996)009<0706:ARLSPF>2.0.CO;2; Shabanov NV, 2003, REMOTE SENS ENVIRON, V85, P410, DOI 10.1016/S0034-4257(03)00017-8; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Still CJ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2001GB001807; Stockli R, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000562; Stockli R, 2004, INT J REMOTE SENS, V25, P3303, DOI 10.1080/01431160310001618149; Stockli R, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2008JG000781; Studer S, 2007, INT J BIOMETEOROL, V51, P405, DOI 10.1007/s00484-006-0080-5; Taylor KE, 2001, J GEOPHYS RES-ATMOS, V106, P7183, DOI 10.1029/2000JD900719; Thornton PE, 2002, AGR FOREST METEOROL, V113, P185, DOI 10.1016/S0168-1923(02)00108-9; Thornton PE, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002868; Tsvetsinskaya EA, 2001, J CLIMATE, V14, P692, DOI 10.1175/1520-0442(2001)014<0692:ITEOSP>2.0.CO;2; Tucker CJ, 2005, INT J REMOTE SENS, V26, P4485, DOI 10.1080/01431160500168686; Uppala SM, 2005, Q J ROY METEOR SOC, V131, P2961, DOI 10.1256/qj.04.176; Ustin SL, 2010, NEW PHYTOL, V186, P795, DOI 10.1111/j.1469-8137.2010.03284.x; van Vliet AJH, 2003, INT J BIOMETEOROL, V47, P202, DOI 10.1007/s00484-003-0174-2; Wang YJ, 2004, REMOTE SENS ENVIRON, V91, P114, DOI 10.1016/j.rsc.2004.02.007; WILMOTT CJ, 2007, GLOBAL AIR TEMPERATU; Zhao MS, 2010, SCIENCE, V329, P940, DOI 10.1126/science.1192666; Zupanski M, 2005, MON WEATHER REV, V133, P1710, DOI 10.1175/MWR2946.1; *MESS P FOR, 1994, UTCS94230 MESS P FOR; *US GEOL SURV, 1996, GTOPO30 DIG MED	103	7	7	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0148-0227		J GEOPHYS RES-BIOGEO	J. Geophys. Res.-Biogeosci.	AUG 25	2011	116								G03020	10.1029/2010JG001545		19	Environmental Sciences; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	813LT	WOS:000294368800001	
J	Miyazaki, K; Maki, T; Patra, P; Nakazawa, T				Miyazaki, Kazuyuki; Maki, Takashi; Patra, Prabir; Nakazawa, Takakiyo			Assessing the impact of satellite, aircraft, and surface observations on CO2 flux estimation using an ensemble-based 4-D data assimilation system	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							ATMOSPHERIC DATA ASSIMILATION; TRANSFORM KALMAN FILTER; PARAMETER-ESTIMATION; TRANSPORT MODEL; CARBON-DIOXIDE; COVARIANCE; INVERSION; SINKS; STATE; CYCLE	The potential impacts of various types of CO2 concentration data obtained from surface, satellite (by the GOSAT project), and aircraft (by the CONTRAIL project) measurements on the estimation of surface CO2 fluxes have been investigated using an ensemble-based data assimilation approach. A four-dimensional ensemble Kalman filter with a 3 day assimilation window was used for analyzing surface fluxes of CO2 at every model grid point (horizontal resolution of 2.8 degrees). Observation system simulation experiments have demonstrated a way to make efficient use of various observations and have shown that conventional surface network data contribute to large flux error reductions in the continental areas of the northern extratropics, while GOSAT XCO2 and CONTRAIL profile data provide strong additional constraints. The GOSAT data show a large error reduction over North and South America, South Africa, and temperate and boreal Asia, but the correction in tropical fluxes is lower than expected because of the poor data coverage caused by cloud abstraction. The CONTRAIL data provide large error reductions over Europe and tropical and temperate Asia. The assimilation of the upper tropospheric data gathered by CONTRAIL results in distinct error reductions over Siberia. By combining the information obtained from all the data sets, the global flux estimation is significantly improved. Meanwhile, many sources of error in the observations and the transport model strongly decrease the usefulness of each observation, and this can become a limiting factor in real data assimilation; for example, realistic systematic errors in the GOSAT data can reduce their usefulness by a factor of 2.	[Miyazaki, Kazuyuki; Patra, Prabir; Nakazawa, Takakiyo] Japan Agcy Marine Earth Sci & Technol, Res Inst Global Change, Yokohama, Kanagawa 2360001, Japan; [Miyazaki, Kazuyuki] Royal Netherlands Meteorol Inst, NL-3730 AE De Bilt, Netherlands; [Maki, Takashi] Meteorol Res Inst, Tsukuba, Ibaraki 3050052, Japan; [Nakazawa, Takakiyo] Tohoku Univ, Grad Sch Sci, Dept Geophys, Sendai, Miyagi 9808578, Japan	Miyazaki, K (reprint author), Japan Agcy Marine Earth Sci & Technol, Res Inst Global Change, Yokohama, Kanagawa 2360001, Japan.	miyazaki@knmi.nl			Ministry of the Environment, Japan [A-0903]; JSPS	The data assimilation system was developed on the basis of the LETKF scheme developed by Takemasa Miyoshi. We are grateful to Toshinobu Machida, Masayuki Takigawa, and Takemasa Miyoshi for their helpful discussions. We would also like to thank the four anonymous reviewers for their valuable comments to substantially improve this paper. This research was supported by the Environment Research and Technology Development Fund (A-0903) of the Ministry of the Environment, Japan, and JSPS Postdoctoral Fellowships for Research Abroad.	Aksoy A, 2006, MON WEATHER REV, V134, P2951, DOI 10.1175/MWR3224.1; Anderson JL, 2009, TELLUS A, V61, P72, DOI 10.1111/j.1600-0870.2008.00361.x; Anderson JL, 2001, MON WEATHER REV, V129, P2884, DOI 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2; Bishop CH, 2009, TELLUS A, V61, P84, DOI 10.1111/j.1600-0870.2008.00371.x; BRENKERT AL, 1998, NDP058A CARB DIOX IN; BRENNINKMEIJER CAM, 2007, IGAC NEWSL, V37, P2; Chevallier F, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040108; Chevallier F, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007375; Connor BJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2006JD008336; Conway T., 1994, J GEOPHYS RES, V99, P831, DOI DOI 10.1029/94JD01951; Corbin KD, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008716; Corbin KD, 2009, ATMOS CHEM PHYS, V9, P3043; Dee DP, 1998, Q J ROY METEOR SOC, V124, P269, DOI 10.1256/smsqj.54511; Engelen RJ, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010739; Eskes HJ, 2003, Q J ROY METEOR SOC, V129, P1663, DOI 10.1256/qj.02.14; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Feng L, 2009, ATMOS CHEM PHYS, V9, P2619, DOI 10.5194/acp-9-2619-2009; Fertig EJ, 2007, TELLUS A, V59, P96, DOI 10.1111/j.1600-0870.2006.00205.x; FRANCEY RJ, 1995, NATURE, V373, P326, DOI 10.1038/373326a0; GLOBALVIEW-CO2, 2009, GLOBALVIEW CO2 COOP; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; Gurney KR, 2003, TELLUS B, V55, P555, DOI 10.1034/j.1600-0889.2003.00049.x; HOFFMAN RN, 1983, TELLUS A, V35, P100; Houtekamer PL, 2001, MON WEATHER REV, V129, P123, DOI 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2; HOUWELING S, 2010, ATMOS CHEM PHYS DISC, V10, P14737, DOI DOI 10.5194/ACPD-10-14737-2010; HUNGERSHOEFER K, 2010, ATMOS CHEM PHYS DISC, V10, P18561; Hunt BR, 2004, TELLUS A, V56, P273, DOI 10.1111/j.1600-0870.2004.00066.x; Hunt BR, 2007, PHYSICA D, V230, P112, DOI 10.1016/j.physd.2006.11.008; Kalnay E, 2010, DATA ASSIMILATION: MAKING SENSE OF OBSERVATIONS, P69, DOI 10.1007/978-3-540-74703-1_4; Kang JS, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014673; KEELING CD, 1995, NATURE, V375, P666, DOI 10.1038/375666a0; Law RM, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003050; Machida T, 2008, J ATMOS OCEAN TECH, V25, P1744, DOI 10.1175/2008JTECHA1082.1; Masarie KA, 2001, J GEOPHYS RES-ATMOS, V106, P20445, DOI 10.1029/2000JD000023; Matsueda H., 2008, PAP METEOROL GEOPHYS, V59, P1, DOI DOI 10.2467/MRIPAPERS.59.1; Miyazaki K, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011892; Miyazaki K, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009557; Miyoshi T, 2007, SOLA, V3, P89, DOI 10.2151/sola.2007-023; NUMAGUTI A, 1995, REPORTS NEW PROGRAM, P1; Olsen SC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003968; Ott E, 2004, TELLUS A, V56, P415, DOI 10.1111/j.1600-0870.2004.00076.x; Patra P. K., 2011, ATMOS CHEM PHYS DISC, V11, P5379, DOI [10.5194/acpd-11-5379-2011, DOI 10.5194/ACPD-11-5379-2011]; Patra PK, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002257; Patra PK, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003661; Peters W, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006157; Randerson JT, 1997, GLOBAL BIOGEOCHEM CY, V11, P535, DOI 10.1029/97GB02268; Rayner PJ, 2001, GEOPHYS RES LETT, V28, P175, DOI 10.1029/2000GL011912; Sawa Y, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010568; Stephens BB, 2007, SCIENCE, V316, P1732, DOI 10.1126/science.1137004; Takahashi T, 2002, DEEP-SEA RES PT II, V49, P1601, DOI 10.1016/S0967-0645(02)00003-6; Tong MJ, 2008, MON WEATHER REV, V136, P1649, DOI 10.1175/2007MWR2071.1; Yokota T, 2009, SOLA, V5, P160, DOI 10.2151/sola.2009-041; *NOAA ESRL, 2006, GLOBALVIEW CO2 COOP	53	1	1	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	AUG 23	2011	116								D16306	10.1029/2010JD015366		20	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	813LL	WOS:000294368000001	
J	Saito, M; Ito, A; Maksyutov, S				Saito, Makoto; Ito, Akihiko; Maksyutov, Shamil			Evaluation of Biases in JRA-25/JCDAS Precipitation and Their Impact on the Global Terrestrial Carbon Balance	JOURNAL OF CLIMATE			English	Article							NET PRIMARY PRODUCTION; SPACE-TIME CLIMATE; NCEP-NCAR; REANALYSIS PROJECT; GAUGE OBSERVATIONS; SIMULATION-MODEL; BIOSPHERE MODEL; PLANT-GROWTH; PART I; VARIABILITY	This study evaluates a modeled precipitation field and examines how its bias affects the modeling of the regional and global terrestrial carbon cycle. Spatial and temporal variations in precipitation produced by the Japanese 25-yr reanalysis (JRA-25)/Japan Meteorological Agency (JMA) Climate Data Assimilation System (JCDAS) were compared with two large-scale observation datasets. JRA-25/JCDAS captures the major distribution patterns of annual precipitation and the features of the seasonal cycle. Notable problems include over-and undersimulated areas of precipitation amount in South America, Africa, and Southeast Asia in the 30 degrees N-30 degrees S domain and a large discrepancy in the number of rainfall days. The latter problem was corrected by using a stochastic model based on the probability of the occurrence of dry and wet day series; the monthly precipitation amount was then scaled by the comparison data. Overall, the corrected precipitation performed well in reproducing the spatial distribution of and temporal variations in total precipitation. Both the corrected and original precipitation data were used to simulate regional and global terrestrial carbon cycles using the prognostic biosphere model Vegetation Integrative Simulator for Trace Gases (VISIT). Following bias correction, the model results showed differences in zonal mean photosynthesis uptake and respiration release ranging from -2.0 to +3.3 Pg C yr(-1), compared with the original data. The difference in the global terrestrial net carbon exchange rate was 0.3 Pg C yr(-1), reflecting the compensation of coincident increases or decreases in carbon sequestration and respiration loss. At the regional scale, the ecosystem carbon cycle and canopy structure, including seasonal variations in autotrophic and heterotrophic respiration and total biomass, were strongly influenced by the input precipitation data. The results highlight the need for precise precipitation data when estimating the global terrestrial carbon balance.	[Saito, Makoto; Ito, Akihiko; Maksyutov, Shamil] Natl Inst Environm Studies, Ctr Global Environm Res, Tsukuba, Ibaraki, Japan	Saito, M (reprint author), Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France.	makoto.saito@lsce.ipsl.fr					Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2; AMTHOR JS, 1984, PLANT CELL ENVIRON, V7, P561, DOI 10.1111/j.1365-3040.1984.tb01856.x; Annamalai H, 1999, MON WEATHER REV, V127, P1157, DOI 10.1175/1520-0493(1999)127<1157:TMEAVO>2.0.CO;2; Aonashi K, 1997, J METEOROL SOC JPN, V75, P597; Aumann HH, 2003, IEEE T GEOSCI REMOTE, V41, P253, DOI 10.1109/TGRS.2002.808356; Berg AA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003334; Betts AK, 2005, J HYDROMETEOROL, V6, P764, DOI 10.1175/JHM441.1; Bosilovich MG, 2008, J APPL METEOROL CLIM, V47, P2279, DOI 10.1175/2008JAMC1921.1; Bousquet P, 1999, J GEOPHYS RES-ATMOS, V104, P26161, DOI 10.1029/1999JD900342; Ciais P, 2005, NATURE, V437, P529, DOI 10.1038/nature03972; Costa MH, 1998, GEOPHYS RES LETT, V25, P155, DOI 10.1029/97GL03502; Cramer W, 1999, GLOBAL CHANGE BIOL, V5, P1, DOI 10.1046/j.1365-2486.1999.00009.x; DORMAN JL, 1989, J APPL METEOROL, V28, P833, DOI 10.1175/1520-0450(1989)028<0833:AGCOAR>2.0.CO;2; Falkowski P, 2000, SCIENCE, V290, P291, DOI 10.1126/science.290.5490.291; Fekete BM, 2004, J CLIMATE, V17, P294, DOI 10.1175/1520-0442(2004)017<0294:UIPATI>2.0.CO;2; FRASIER GW, 1987, J RANGE MANAGE, V40, P2, DOI 10.2307/3899350; Gibson J. K., 1997, ECMWF REANALYSIS PRO, V1, P72; Hatsushika H, 2006, J METEOROL SOC JPN, V84, P891, DOI 10.2151/jmsj.84.891; Hicke JA, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002391; Huffman GJ, 2001, J HYDROMETEOROL, V2, P36, DOI 10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2; Huffman GJ, 1997, B AM METEOROL SOC, V78, P5, DOI 10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2; Huffman GJ, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040000; Ichii K, 2007, GLOBAL CHANGE BIOL, V13, P67, DOI 10.1111/j.1365-2486.2006.01277.x; Ito A, 2002, ECOL MODEL, V151, P143, DOI 10.1016/S0304-3800(01)00473-2; Ito A, 2010, J PLANT RES, V123, P577, DOI 10.1007/s10265-009-0305-x; Jones CD, 2003, TELLUS B, V55, P642, DOI 10.1034/j.1600-0889.2003.01440.x; Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2; Keeling RF, 2002, P NATL ACAD SCI USA, V99, P7848, DOI 10.1073/pnas.122154899; Larcher W, 2003, PHYSL PLANT ECOLOGY; Lawless C, 2005, AGR FOREST METEOROL, V135, P302, DOI 10.1016/j.agrformet.2006.01.002; Lenters JD, 2000, J GEOPHYS RES-ATMOS, V105, P22393, DOI 10.1029/2000JD900277; Malhi Y, 1998, J GEOPHYS RES-ATMOS, V103, P31593, DOI 10.1029/98JD02647; McGuire AD, 2001, GLOBAL BIOGEOCHEM CY, V15, P183, DOI 10.1029/2000GB001298; Mitchell TD, 2005, INT J CLIMATOL, V25, P693, DOI 10.1002/joc.1181; Monsi M., 1953, JAPANESE J BOTANY, V14, P22; Nemani RR, 2003, SCIENCE, V300, P1560, DOI 10.1126/science.1082750; New M, 1999, J CLIMATE, V12, P829, DOI 10.1175/1520-0442(1999)012<0829:RTCSTC>2.0.CO;2; New M, 2000, J CLIMATE, V13, P2217, DOI 10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2; Onogi K, 2007, J METEOROL SOC JPN, V85, P369, DOI 10.2151/jmsj.85.369; Onogi K, 2005, Q J ROY METEOR SOC, V131, P3259, DOI 10.1256/qj.05.88; RACSKO P, 1991, ECOL MODEL, V57, P27, DOI 10.1016/0304-3800(91)90053-4; Randel WJ, 2000, J GEOPHYS RES-ATMOS, V105, P15509, DOI 10.1029/2000JD900155; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Roderick ML, 2001, OECOLOGIA, V129, P21, DOI 10.1007/s004420100760; Rotter R, 1999, CLIMATIC CHANGE, V43, P651, DOI 10.1023/A:1005541132734; Saleska SR, 2003, SCIENCE, V302, P1554, DOI 10.1126/science.1091165; Schimel DS, 2001, NATURE, V414, P169, DOI 10.1038/35102500; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; SELLERS PJ, 1986, J ATMOS SCI, V43, P305; Semenov MA, 1998, CLIMATE RES, V10, P95, DOI 10.3354/cr010095; Serreze MC, 2000, J CLIMATE, V13, P182, DOI 10.1175/1520-0442(2000)013<0182:ROMAPF>2.0.CO;2; Sheffield J, 2004, J CLIMATE, V17, P3814, DOI 10.1175/1520-0442(2004)017<3814:COTHRD>2.0.CO;2; Sloughter JM, 2007, MON WEATHER REV, V135, P3209, DOI 10.1175/MWR3441.1; SMITH WL, 1979, B AM METEOROL SOC, V60, P1177; Thornton PE, 2002, AGR FOREST METEOROL, V113, P185, DOI 10.1016/S0168-1923(02)00108-9; Thornton PE, 2007, J CLIMATE, V20, P3902, DOI 10.1175/JCLI4222.1; TOKUHIRO T, 2002, P 27 ANN DIAGN PRED, P2; Tourigny E, 2009, TELLUS A, V61, P323, DOI [10.1111/J.1600-0870.2008.00386.x, 10.1111/j.1600-0870.2008.00386.x]; Trenberth KE, 2008, J METEOROL SOC JPN, V86, P579; Trenberth KE, 2001, J CLIMATE, V14, P1499, DOI 10.1175/1520-0442(2001)014<1499:QORITT>2.0.CO;2; Trenberth KE, 1998, CLIM DYNAM, V14, P213, DOI 10.1007/s003820050219; van der Linden PJ, 2001, CLIMATE CHANGE 2001; VOSE RS, 1992, GLOBAL HIST CLIMATOL; Wilks D. S., 2006, STAT METHODS ATMOSPH; WILKS DS, 1990, J CLIMATE, V3, P1495, DOI 10.1175/1520-0442(1990)003<1495:MLEFTG>2.0.CO;2; Williams RJ, 1997, ECOLOGY, V78, P2542, DOI 10.1890/0012-9658(1997)078[2542:LPOWSI]2.0.CO;2; Xie PP, 2003, J CLIMATE, V16, P2197, DOI 10.1175/2769.1; Xie PP, 1997, B AM METEOROL SOC, V78, P2539, DOI 10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2; Zaehle S, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002395; Zhao X, 2010, J CLIMATE, V23, P1034, DOI 10.1175/2009JCLI2597.1	71	4	4	AMER METEOROLOGICAL SOC	BOSTON	45 BEACON ST, BOSTON, MA 02108-3693 USA	0894-8755		J CLIMATE	J. Clim.	AUG	2011	24	15					4109	4125		10.1175/2011JCLI3918.1		17	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	806QE	WOS:000293823900021	
J	Hilker, T; Coops, NC; Hall, FG; Nichol, CJ; Lyapustin, A; Black, TA; Wulder, MA; Leuning, R; Barr, A; Hollinger, DY; Munger, B; Tucker, CJ				Hilker, Thomas; Coops, Nicholas C.; Hall, Forrest G.; Nichol, Caroline J.; Lyapustin, Alexei; Black, T. Andrew; Wulder, Michael A.; Leuning, Ray; Barr, Alan; Hollinger, David Y.; Munger, Bill; Tucker, Compton J.			Inferring terrestrial photosynthetic light use efficiency of temperate ecosystems from space	JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES			English	Article							PHOTOCHEMICAL REFLECTANCE INDEX; CARBON-DIOXIDE FLUXES; GROSS PRIMARY PRODUCTION; BOREAL FOREST STANDS; DOUGLAS-FIR FOREST; LEAF-AREA INDEX; WATER-VAPOR; MODIS; SATELLITE; MODELS	Terrestrial ecosystems absorb about 2.8 Gt C yr(-1), which is estimated to be about a quarter of the carbon emitted from fossil fuel combustion. However, the uncertainties of this sink are large, on the order of +/- 40%, with spatial and temporal variations largely unknown. One of the largest factors contributing to the uncertainty is photosynthesis, the process by which plants absorb carbon from the atmosphere. Currently, photosynthesis, or gross ecosystem productivity (GEP), can only be inferred from flux towers by measuring the exchange of CO(2) in the surrounding air column. Consequently, carbon models suffer from a lack of spatial coverage of accurate GEP observations. Here, we show that photosynthetic light use efficiency (epsilon), hence photosynthesis, can be directly inferred from spaceborne measurements of reflectance. We demonstrate that the differential between reflectance measurements in bands associated with the vegetation xanthophyll cycle and estimates of canopy shading obtained from multiangular satellite observations (using the CHRIS/PROBA sensor) permits us to infer plant photosynthetic efficiency, independently of vegetation type and structure (r(2) = 0.68, compared to flux measurements). This is a significant advance over previous approaches seeking to model global-scale photosynthesis indirectly from a combination of growth limiting factors, most notably pressure deficit and temperature. When combined with modeled global-scale photosynthesis, satellite-inferred epsilon can improve model estimates through data assimilation. We anticipate that our findings will guide the development of new spaceborne approaches to observe vegetation carbon uptake and improve current predictions of global CO(2) budgets and future climate scenarios by providing regularly timed calibration points for modeling plant photosynthesis consistently at a global scale.	[Hilker, Thomas; Coops, Nicholas C.] Univ British Columbia, Fac Forest Resources Management, Vancouver, BC V6T 1Z4, Canada; [Hilker, Thomas] NASA, Goddard Space Flight Ctr, Biospher Sci Branch, Greenbelt, MD 20771 USA; [Hall, Forrest G.; Lyapustin, Alexei] Univ Maryland Baltimore Cty, Joint Ctr Earth Syst Technol, Baltimore, MD 21228 USA; [Nichol, Caroline J.] Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland; [Wulder, Michael A.] Canadian Forest Serv, Victoria, BC V8Z 1M5, Canada; [Leuning, Ray] CSIRO Marine & Atmospher Res, Canberra, ACT 2601, Australia; [Barr, Alan] Environm Canada, Climate Res Branch, Saskatoon, SK 27N 3H5, Canada; [Hollinger, David Y.] US Forest Serv, NE Res Stn, Durham, NH 03824 USA; [Munger, Bill] Harvard Univ, Dept Earth & Planetary Sci, Cambridge, MA 02138 USA	Hilker, T (reprint author), Univ British Columbia, Fac Forest Resources Management, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada.	thomas.hilker@nasa.gov	Leuning, Ray/A-2793-2008; Hollinger, David/G-7185-2012; Coops, Nicholas/J-1543-2012		Canadian Carbon Program; Natural Sciences and Engineering Research Council of Canada (NSERC); BIOCAP; NSERC	The ESA CHRIS/PROBA images were provided by David G. Goodenough, Ray Merton, and Mathias Kneubuhler, all principal investigators of the Evaluation and Validation of CHRIS (EVC) Project. This research is partially funded by the Canadian Carbon Program, the Natural Sciences and Engineering Research Council of Canada (NSERC) and BIOCAP, and an NSERC-Accelerator grant to N.C.C.	Amiro BD, 2006, AGR FOREST METEOROL, V136, P237, DOI 10.1016/j.agrformet.2004.11.012; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Barr AG, 2004, AGR FOREST METEOROL, V126, P237, DOI 10.1016/j.agrformet.2004.06.011; Bergeron O, 2007, GLOBAL CHANGE BIOL, V13, P89, DOI 10.1111/j.1365-2486.2006.01281.x; Black TA, 1996, GLOB CHANGE BIOL, V2, P219, DOI 10.1111/j.1365-2486.1996.tb00074.x; Chen JM, 1996, IEEE T GEOSCI REMOTE, V34, P1353, DOI 10.1109/36.544559; Chen JM, 2006, AGR FOREST METEOROL, V140, P257, DOI 10.1016/j.agrformet.2006.08.005; Demmig-Adams B, 1998, PLANT CELL PHYSIOL, V39, P474; DemmigAdams B, 1996, TRENDS PLANT SCI, V1, P21, DOI 10.1016/S1360-1385(96)80019-7; Drolet GG, 2008, REMOTE SENS ENVIRON, V112, P3064, DOI 10.1016/j.rse.2008.03.002; Drolet GG, 2005, REMOTE SENS ENVIRON, V98, P212, DOI 10.1016/j.rse.2005.07.006; Fletcher PA, 2004, P SOC PHOTO-OPT INS, V5546, P141, DOI 10.1117/12.561097; Fuentes DA, 2006, REMOTE SENS ENVIRON, V103, P312, DOI 10.1016/j.rse.2005.10.028; GAMON JA, 1992, REMOTE SENS ENVIRON, V41, P35, DOI 10.1016/0034-4257(92)90059-S; Gamon JA, 1997, OECOLOGIA, V112, P492, DOI 10.1007/s004420050337; Goetz SJ, 1998, CAN J FOREST RES, V28, P375, DOI 10.1139/cjfr-28-3-375; Gomez-Chova L, 2008, APPL OPTICS, V47, pF46, DOI 10.1364/AO.47.000F46; GOSHTASBY A, 1988, IMAGE VISION COMPUT, V6, P255, DOI 10.1016/0262-8856(88)90016-9; Goulden ML, 1997, TREE PHYSIOL, V17, P537; Gruninger J, 2004, P SOC PHOTO-OPT INS, V5425, P1, DOI 10.1117/12.543794; Hall FG, 2008, REMOTE SENS ENVIRON, V112, P3201, DOI 10.1016/j.rse.2008.03.015; Hall FG, 1996, INT J REMOTE SENS, V17, P3077; Hall FG, 1997, J GEOPHYS RES-ATMOS, V102, P29567, DOI 10.1029/97JD02578; Hall FG, 2011, REMOTE SENS ENVIRON, V115, P1918, DOI 10.1016/j.rse.2011.03.014; HALL FG, 1995, ECOL APPL, V5, P993, DOI 10.2307/2269350; Hilker T, 2009, REMOTE SENS ENVIRON, V113, P2463, DOI 10.1016/j.rse.2009.07.012; Hilker T, 2008, TREE PHYSIOL, V28, P825; Hilker T, 2010, REMOTE SENS ENVIRON, V114, P2863, DOI 10.1016/j.rse.2010.07.004; Hilker T, 2008, REMOTE SENS ENVIRON, V112, P2777, DOI 10.1016/j.rse.2008.01.011; Hilker T, 2008, SCI TOTAL ENVIRON, V404, P411, DOI 10.1016/j.scitotenv.2007.11.007; Horii CV, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004326; Humphreys ER, 2006, AGR FOREST METEOROL, V140, P6, DOI 10.1016/j.agrformet.2006.03.018; Leprince S, 2007, IEEE T GEOSCI REMOTE, V45, P1529, DOI 10.1109/TGRS.2006.888937; Leuning R, 2005, AGR FOREST METEOROL, V129, P151, DOI 10.1016/j.agrformet.2004.12.004; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Lyapustin A, 2007, REMOTE SENS ENVIRON, V107, P12, DOI 10.1016/j.rse.2006.09.028; LYAPUSTIN A, 2009, TIME SERIES TECHNIQU; Ma JL, 2010, IEEE T GEOSCI REMOTE, V48, P2829, DOI 10.1109/TGRS.2010.2042813; Meroni M, 2008, SENSORS-BASEL, V8, P1740, DOI 10.3390/s8031740; MONTEITH JL, 1977, PHILOS T ROY SOC B, V281, P277, DOI 10.1098/rstb.1977.0140; MONTEITH JL, 1972, J APPL ECOL, V9, P747, DOI 10.2307/2401901; Morgenstern K, 2004, AGR FOREST METEOROL, V123, P201, DOI 10.1016/j.agrformet.2003.12.003; MYNENI RB, 1994, REMOTE SENS ENVIRON, V49, P200, DOI 10.1016/0034-4257(94)90016-7; Nakaji T, 2006, INT J REMOTE SENS, V27, P493, DOI 10.1080/01431160500329528; Nichol CJ, 2000, AGR FOREST METEOROL, V101, P131, DOI 10.1016/S0168-1923(99)00167-7; Peddle DR, 2005, INT J REMOTE SENS, V26, P4959, DOI 10.1080/01431160500213979; POTTER CS, 1993, GLOBAL BIOGEOCHEM CY, V7, P811, DOI 10.1029/93GB02725; Rahman AF, 2001, J GEOPHYS RES-ATMOS, V106, P33579, DOI 10.1029/2001JD900157; Running SW, 1999, REMOTE SENS ENVIRON, V70, P108, DOI 10.1016/S0034-4257(99)00061-9; Schwalm CR, 2006, AGR FOREST METEOROL, V140, P269, DOI 10.1016/j.agrformet.2006.06.010; SELLERS PJ, 1995, REMOTE SENS ENVIRON, V51, P3, DOI 10.1016/0034-4257(94)00061-Q; SELLERS PJ, 1994, INT J REMOTE SENS, V15, P3519; SELLERS PJ, 1985, INT J REMOTE SENS, V6, P1335; Shaker A, 2008, PHOTOGRAMM REC, V23, P275, DOI 10.1111/j.1477-9730.2008.00490.x; Staebler RM, 2005, J APPL METEOROL, V44, P1161, DOI 10.1175/JAM2265.1; Stylinski CD, 2002, OECOLOGIA, V131, P366, DOI 10.1007/s00442-002-0905-9; Thuillier G, 2003, SOL PHYS, V214, P1, DOI 10.1023/A:1024048429145; Toutin T, 2004, INT J REMOTE SENS, V25, P1893, DOI 10.1080/0143116031000101611; TOUTIN T, 1992, PHOTOGRAMM ENG REM S, V58, P417; Valentini R, 2000, NATURE, V404, P861, DOI 10.1038/35009084; Van Laake PE, 2005, REMOTE SENS ENVIRON, V94, P554, DOI 10.1016/j.rse.2004.11.011; Xiao XM, 2005, ECOL APPL, V15, P954, DOI 10.1890/04-0470	62	4	4	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0148-0227		J GEOPHYS RES-BIOGEO	J. Geophys. Res.-Biogeosci.	JUL 29	2011	116								G03014	10.1029/2011JG001692		11	Environmental Sciences; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	800PR	WOS:000293375900003	
J	Luo, YQ; Ogle, K; Tucker, C; Fei, SF; Gao, C; LaDeau, S; Clark, JS; Schimel, DS				Luo, Yiqi; Ogle, Kiona; Tucker, Colin; Fei, Shenfeng; Gao, Chao; LaDeau, Shannon; Clark, James S.; Schimel, David S.			Ecological forecasting and data assimilation in a data-rich era	ECOLOGICAL APPLICATIONS			English	Article						data assimilation; data-model fusion; ecological forecasting; inverse analysis; optimization; predictions; prognosis; projections	ENSEMBLE KALMAN FILTER; FOREST CARBON DYNAMICS; ECOSYSTEM MODEL; SPECIES DISTRIBUTIONS; CLIMATE-CHANGE; BAYESIAN CALIBRATION; TIME-SERIES; MULTIMODEL SUPERENSEMBLE; ENVIRONMENTAL-CHANGE; MEASLES EPIDEMICS	Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.	[Luo, Yiqi; Fei, Shenfeng; Gao, Chao] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA; [Ogle, Kiona; Tucker, Colin] Univ Wyoming, Dept Bot, Laramie, WY 82071 USA; [Ogle, Kiona] Univ Wyoming, Dept Stat, Laramie, WY 82071 USA; [LaDeau, Shannon] Cary Inst Ecosyst Studies, Millbrook, NY 12545 USA; [Clark, James S.] Duke Univ, Dept Biol, Durham, NC 27708 USA; [Schimel, David S.] NEON, Boulder, CO 80301 USA	Luo, YQ (reprint author), Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA.	yluo@ou.edu	Clark, James/G-6331-2011		National Science Foundation (NSF) [DEB 0743778, DEB 0840964, EF 0938795]; Office of Science (BER), Department of Energy [DE-FG02-006ER64319]; Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University [DE-FC02-06ER64158]	This study was financially supported by the National Science Foundation (NSF) under DEB 0743778, DEB 0840964, EF 0938795; by the Office of Science (BER), Department of Energy, Grants No. DE-FG02-006ER64319; and through the Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University, under Award Number DE-FC02-06ER64158.	Akimoto H, 2003, SCIENCE, V302, P1716, DOI 10.1126/science.1092666; ANDERSON RM, 1979, NATURE, V280, P361, DOI 10.1038/280361a0; Arhonditsis GB, 2008, J MARINE SYST, V73, P8, DOI 10.1016/j.jmarsys.2007.07.004; BAKER DF, 2008, ATMOS CHEM PHYS DISC, V8, P20051; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Beaumont LJ, 2005, ECOL MODEL, V186, P250, DOI 10.1016/j.ecolmodel.2005.01.030; Beaumont LJ, 2007, GLOBAL CHANGE BIOL, V13, P1368, DOI 10.1111/j.1365-2486.2007.01357.x; Beerling DJ, 1997, GLOBAL ECOL BIOGEOGR, V6, P439, DOI 10.2307/2997353; Bjornstad ON, 2002, ECOL MONOGR, V72, P169, DOI 10.2307/3100023; Borsuk ME, 2004, ECOL MODEL, V173, P219, DOI 10.1016/j.ecolmodel.2003.08.020; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Carvalhais N, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003033; Chaves LF, 2007, PLOS NEGLECT TROP D, V1, DOI 10.1371/journal.pntd.0000033; Chen M, 2008, ECOL MODEL, V219, P317, DOI 10.1016/j.ecolmodel.2008.07.013; CHEN XY, 2008, WATER RESOURCES RES, V44; Clark JS, 2004, ECOL MONOGR, V74, P415, DOI 10.1890/02-4093; Clark JS, 2010, ECOL MONOGR, V80, P569, DOI 10.1890/09-1541.1; Clark JS, 2011, ECOL APPL, V21, P1523, DOI 10.1890/09-1212.1; Clark JS, 2005, ECOL LETT, V8, P2, DOI 10.1111/j.1461-0248.2004.00702.x; Clark JS, 2007, ECOL APPL, V17, P1942, DOI 10.1890/06-1039.1; Clark JS, 2001, SCIENCE, V293, P657, DOI 10.1126/science.293.5530.657; Clark JS, 2003, ECOLOGY, V84, P1979, DOI 10.1890/01-0618; Clark JS, 2004, ECOLOGY, V85, P3140, DOI 10.1890/03-0520; Clein JS, 2000, GLOBAL CHANGE BIOL, V6, P127, DOI 10.1046/j.1365-2486.2000.06009.x; Cook BI, 2005, GLOBAL CHANGE BIOL, V11, P919, DOI 10.1111/j.1365-2486.2005.00960.x; Cox PM, 2004, THEOR APPL CLIMATOL, V78, P137, DOI 10.1007/s00704-004-0049-4; Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539; Crozier LG, 2008, GLOBAL CHANGE BIOL, V14, P236, DOI 10.1111/j.1365-2486.2007.01497.x; Currie DJ, 2001, ECOSYSTEMS, V4, P216, DOI 10.1007/s10021-001-0005-4; DANTONIO CM, 1992, ANNU REV ECOL SYST, V23, P63, DOI 10.1146/annurev.ecolsys.23.1.63; Dormann CF, 2008, ECOL LETT, V11, P235, DOI 10.1111/j.1461-0248.2007.01142.x; Dowd M, 2007, J MARINE SYST, V68, P439, DOI 10.1016/j.jmarsys.2007.01.007; Evensen G., 2007, DATA ASSIMILATION EN; Ferguson NM, 2001, NATURE, V413, P542, DOI 10.1038/35097116; Ferrari MJ, 2008, NATURE, V451, P679, DOI 10.1038/nature06509; Fisher R. A., 1959, STAT METHODS SCI INF; Foley JA, 1998, GLOB CHANGE BIOL, V4, P561, DOI 10.1046/j.1365-2486.1998.t01-1-00168.x; Forrester J. W., 1961, IND DYNAMICS; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Gao C, 2011, ECOL APPL, V21, P1461, DOI 10.1890/09-1234.1; Gelb A, 1974, APPL OPTIMAL ESTIMAT; Glass M, 2000, NEUROSCIENCE, V97, P505, DOI 10.1016/S0306-4522(00)00008-7; Gleick P.H., 2002, WORLDS WATER 2002 20; Grenfell BT, 2001, NATURE, V414, P716, DOI 10.1038/414716a; Hanson PJ, 2004, ECOL MONOGR, V74, P443, DOI 10.1890/03-4049; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hijmans RJ, 2006, GLOBAL CHANGE BIOL, V12, P2272, DOI 10.1111/j.1365-2486.2006.01256.x; Hood SM, 2007, INT J WILDLAND FIRE, V16, P679, DOI 10.1071/WF06122; Jones C, 2005, GLOBAL CHANGE BIOL, V11, P154, DOI 10.1111/j.1365-2486.2004.00885.x; Kalman R. E., 1960, J BASIC ENG, V82, P35, DOI DOI 10.1115/1.3662552; Kalnay E, 2003, ATMOSPHERIC MODELING; Kearney M, 2009, ECOL LETT, V12, P334, DOI 10.1111/j.1461-0248.2008.01277.x; Keeling MJ, 2001, SCIENCE, V294, P813, DOI 10.1126/science.1065973; Keeling MJ, 2003, NATURE, V421, P136, DOI 10.1038/nature01343; Kermack WO, 1933, P R SOC LOND A-CONTA, V141, P94, DOI 10.1098/rspa.1933.0106; Kirilenko A, 2007, ENVIRON MODELL SOFTW, V22, P1498, DOI 10.1016/j.envsoft.2006.11.001; Klemedtsson L, 2008, BIOGEOCHEMISTRY, V89, P61, DOI 10.1007/s10533-007-9169-0; Knapp AK, 2001, SCIENCE, V291, P481, DOI 10.1126/science.291.5503.481; Krishnamurti TN, 1999, SCIENCE, V285, P1548, DOI 10.1126/science.285.5433.1548; Law T, 2009, ECOL MODEL, V220, P979, DOI 10.1016/j.ecolmodel.2009.01.001; Liao CZ, 2008, NEW PHYTOL, V177, P706, DOI 10.1111/j.1469-8137.2007.02290.x; Liu M, 2009, SCI CHINA SER D, V52, P257, DOI 10.1007/s11430-009-0010-6; Lokupitiya RS, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009679; Lotka A., 1924, ELEMENTS PHYS BIOL; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; Luo Y., 2006, SOIL RESP ENV; May RM., 2001, STABILITY COMPLEXITY; May RM, 1981, THEORETICAL ECOLOGY; McGuire AD, 2000, GLOBAL CHANGE BIOL, V6, P141, DOI 10.1046/j.1365-2486.2000.06017.x; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; Mo XG, 2008, ECOL MODEL, V217, P157, DOI 10.1016/j.ecolmodel.2008.06.021; Monson RK, 2006, NATURE, V439, P711, DOI 10.1038/nature04555; Moore DJP, 2008, AGR FOREST METEOROL, V148, P1467, DOI 10.1016/j.agrformet.2008.04.013; Morin X, 2008, J ECOL, V96, P784, DOI 10.1111/j.1365-2745.2008.01369.x; Nemani RR, 2003, SCIENCE, V300, P1560, DOI 10.1126/science.1082750; Ogle K, 2009, TREE PHYSIOL, V29, P587, DOI 10.1093/treephys/tpn051; Ogle K, 2009, ECOL APPL, V19, P577, DOI 10.1890/08-0560.1; Ourmieres Y, 2009, J MARINE SYST, V75, P100, DOI 10.1016/j.jmarsys.2008.08.003; PACALA SW, 1993, CAN J FOREST RES, V23, P1980, DOI 10.1139/x93-249; PARTON WJ, 1987, SOIL SCI SOC AM J, V51, P1173; PARTON WJ, 1993, GLOBAL BIOGEOCHEM CY, V7, P785, DOI 10.1029/93GB02042; Pascual MA, 1996, ECOLOGY, V77, P337, DOI 10.2307/2265613; Prasad AM, 2006, ECOSYSTEMS, V9, P181, DOI 10.1007/s10021-005-0054-1; Prihodko L, 2008, AGR FOREST METEOROL, V148, P268, DOI 10.1016/j.agrformet.2007.08.006; Quaife T, 2008, REMOTE SENS ENVIRON, V112, P1347, DOI 10.1016/j.rse.2007.05.020; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Reichstein M, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003430; RIBBENS E, 1994, ECOLOGY, V75, P1794, DOI 10.2307/1939638; Ricciuto DM, 2008, AGR FOREST METEOROL, V148, P309, DOI 10.1016/j.agrformet.2007.08.007; Ricciuto DM, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2006GB002908; Schimel DS, 1997, ECOL MONOGR, V67, P251, DOI 10.1890/0012-9615(1997)067[0251:CSVIEP]2.0.CO;2; Schwartz MW, 2001, ECOSYSTEMS, V4, P568, DOI 10.1007/s10021-001-0030-3; Smith KF, 2005, FRONT ECOL ENVIRON, V3, P29, DOI 10.1890/1540-9295(2005)003[0029:ETTEID]2.0.CO;2; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Spitz YH, 1998, J MARINE SYST, V16, P51, DOI 10.1016/S0924-7963(97)00099-7; Svensson M, 2008, ECOL MODEL, V213, P331, DOI 10.1016/j.ecolmodel.2008.01.001; Tang JY, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2008JG000757; Todling R, 2000, GEOPH MONOG SERIES, V114, P49; Vallecillo S, 2009, ECOL APPL, V19, P538, DOI 10.1890/08-0348.1; Verhulst PF, 1838, CORRES MATH PHYSIQUE, V10, P113; Vitousek PM, 1997, SCIENCE, V277, P494, DOI 10.1126/science.277.5325.494; Volterra V, 1926, NATURE, V118, P558, DOI 10.1038/118558a0; Vukicevic T, 2001, TELLUS B, V53, P150, DOI 10.1034/j.1600-0889.2001.d01-13.x; Weng ES, 2011, ECOL APPL, V21, P1490, DOI 10.1890/09-1394.1; WILLIAMS M, 2009, BIOGEOSCIENCES DISCU, V6, P2785, DOI DOI 10.5194/BGD-6-2785-2009; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Williams SE, 2008, PLOS BIOL, V6, P2621, DOI 10.1371/journal.pbio.0060325; Williford CE, 2003, MON WEATHER REV, V131, P1878; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zhao Q, 2008, J MARINE SYST, V74, P443, DOI 10.1016/j.jmarsys.2008.03.006; Zhou T, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB002939; *HEINZ REP, 2008, STAT NAT EC MEAS LAN	113	7	7	ECOLOGICAL SOC AMER	WASHINGTON	1990 M STREET NW, STE 700, WASHINGTON, DC 20036 USA	1051-0761		ECOL APPL	Ecol. Appl.	JUL	2011	21	5					1429	1442				14	Ecology; Environmental Sciences	Environmental Sciences & Ecology	792RF	WOS:000292766100002	
J	Gao, C; Wang, H; Weng, ES; Lakshmivarahan, S; Zhang, YF; Luo, YQ				Gao, Chao; Wang, Han; Weng, Ensheng; Lakshmivarahan, S.; Zhang, Yanfen; Luo, Yiqi			Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics	ECOLOGICAL APPLICATIONS			English	Article						carbon cycle; data assimilation; ecological forecast; ensemble Kalman filter (EnKF); parameter estimation; uncertainty analysis	AIR CO2 ENRICHMENT; NET ECOSYSTEM EXCHANGE; TEMPERATE FOREST; ATMOSPHERIC CO2; LOBLOLLY-PINE; ELEVATED CO2; MODEL; PARAMETERS; TERM; SOIL	The ensemble Kalman filter (EnKF) has been used in weather forecasting to assimilate observations into weather models. In this study, we examine how effectively forecasts of a forest carbon cycle can be improved by assimilating observations with the EnKF. We used the EnKF to assimilate into the terrestrial ecosystem (TECO) model eight data sets collected at the Duke Forest between 1996 and 2004 (foliage biomass, fine root biomass, woody biomass, litterfall, microbial biomass, forest floor carbon, soil carbon, and soil respiration). We then used the trained model to forecast changes in carbon pools from 2004 to 2012. Our daily analysis of parameters indicated that all the exit rates were well constrained by the EnKF, with the exception of the exit rates controlling the loss of metabolic litter and passive soil organic matter. The poor constraint of these two parameters resulted from the low sensitivity of TECO predictions to their values and the poor correlation between these parameters and the observed variables. Using the estimated parameters, the model predictions and observations were in agreement. Model forecasts indicate 15 380-15 660 g C/m(2) stored in Duke Forest by 2012 (a 27% increase since 2004). Parameter uncertainties decreased as data were sequentially assimilated into the model using the EnKF. Uncertainties in forecast carbon sinks increased over time for the long-term carbon pools (woody biomass, structure litter, slow and passive SOM) but remained constant over time for the short-term carbon pools (foliage, fine root, metabolic litter, and microbial carbon). Overall, EnKF can effectively assimilate multiple data sets into an ecosystem model to constrain parameters, forecast dynamics of state variables, and evaluate uncertainty.	[Gao, Chao; Weng, Ensheng; Luo, Yiqi] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA; [Wang, Han] Univ Oklahoma, Sch Elect & Comp Engn, Norman, OK 73019 USA; [Lakshmivarahan, S.] Univ Oklahoma, Sch Comp Sci, Norman, OK 73019 USA; [Zhang, Yanfen] Univ Oklahoma, Newbourne Coll Earth & Energy, Norman, OK 73019 USA	Luo, YQ (reprint author), Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA.	yluo@ou.edu	Weng, Ensheng/E-4390-2012		Office of Science (BER), Department of Energy [DE-FG02-006ER64319]; Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University [DE-FC02-06ER64158]; National Science Foundation (NSF) [DEB 0743778]	We greatly appreciate two anonymous reviewers for their constructive comments. This research was financially supported by the Office of Science (BER), Department of Energy, Grant No. DE-FG02-006ER64319; through the Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University, under Award Number DE-FC02-06ER64158; and by the National Science Foundation (NSF) under DEB 0743778. The authors thank Xuhui Zhou and Garrett Street for their helpful comments on the manuscript.	Allen AS, 2000, ECOL APPL, V10, P437, DOI 10.2307/2641105; ARNOLD CP, 1986, B AM METEOROL SOC, V67, P687, DOI 10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2; Beezley JD, 2008, TELLUS A, V60, P131, DOI [10.1111/J.1600-0870.2007.00275.X, 10.1111/j.1600-0870.2007.00275.x]; Bernhardt ES, 2006, BIOGEOCHEMISTRY, V77, P91, DOI 10.1007/s10533-005-1062-0; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Clark JS, 2007, ECOL APPL, V17, P1942, DOI 10.1890/06-1039.1; COHN HENRY, 2005, P 46 ANN S FDN COMP, P379; DEE DP, 1995, MON WEATHER REV, V123, P1128, DOI 10.1175/1520-0493(1995)123<1128:OLEOEC>2.0.CO;2; Evensen G, 1996, MON WEATHER REV, V124, P85, DOI 10.1175/1520-0493(1996)124<0085:AOGADF>2.0.CO;2; EVENSEN G, 2003, OCEAN DYNAM, V53, P1616; Finzi AC, 2001, ECOLOGY, V82, P470, DOI 10.1890/0012-9658(2001)082[0470:FLPCAD]2.0.CO;2; Finzi AC, 2006, ECOLOGY, V87, P15, DOI 10.1890/04-1748; GERRIT B, 1998, MONTHLY WEATHER REV, V126, P1719; Gu YQ, 2006, J ENERG RESOUR-ASME, V128, P79, DOI 10.1115/1.2134735; Hamilton JG, 2002, OECOLOGIA, V131, P250, DOI 10.1007/s00442-002-0884-x; Hui DF, 2003, TREE PHYSIOL, V23, P433; Jiao JJ, 1996, WATER RESOUR BULL, V32, P75; Kalman R. E., 1960, J BASIC ENG, V82, P35, DOI DOI 10.1115/1.3662552; Karspeck AR, 2007, J CLIMATE, V20, P4638, DOI 10.1175/JCLI4245.1; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Lichter J, 2005, ECOLOGY, V86, P1835, DOI 10.1890/04-1205; Liu YQ, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005756; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; Luo YQ, 2011, ECOL APPL, V21, P1429; Luo YQ, 2007, ANNU REV ECOL EVOL S, V38, P683, DOI 10.1146/annurev.ecolsys.38.091206.095808; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; Martin JG, 1998, CAN J FOREST RES, V28, P1648, DOI 10.1139/cjfr-28-11-1648; Matamala R, 2000, GLOB CHANGE BIOL, V6, P967, DOI 10.1046/j.1365-2486.2000.00374.x; Mo XG, 2008, ECOL MODEL, V217, P157, DOI 10.1016/j.ecolmodel.2008.06.021; Naidu SL, 1998, CAN J FOREST RES, V28, P1116, DOI 10.1139/cjfr-28-8-1116; Pritchard SG, 2008, GLOBAL CHANGE BIOL, V14, P588, DOI 10.1111/j.1365-2486.2007.01523.x; Reichle RH, 2002, MON WEATHER REV, V130, P103, DOI 10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2; Richardson AD, 2007, GLOBAL CHANGE BIOL, V13, P788, DOI 10.1111/j.1365-2486.2007.01330.x; Roulier S, 2003, J ENVIRON QUAL, V32, P2341; Saltelli A, 2004, SENSITIVITY ANAL PRA; Tang JY, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011724; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; Weng ES, 2011, ECOL APPL, V21, P1490, DOI 10.1890/09-1394.1; Williams M, 2001, PLANT CELL ENVIRON, V24, P679, DOI 10.1046/j.1365-3040.2001.00715.x; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zhou T, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB002939	43	1	1	ECOLOGICAL SOC AMER	WASHINGTON	1990 M STREET NW, STE 700, WASHINGTON, DC 20036 USA	1051-0761		ECOL APPL	Ecol. Appl.	JUL	2011	21	5					1461	1473				13	Ecology; Environmental Sciences	Environmental Sciences & Ecology	792RF	WOS:000292766100004	
J	Hill, TC; Williams, M; Woodward, FI; Moncrieff, JB				Hill, T. C.; Williams, M.; Woodward, F. I.; Moncrieff, J. B.			Constraining ecosystem processes from tower fluxes and atmospheric profiles	ECOLOGICAL APPLICATIONS			English	Article						aircraft observations; boreal forest; BOREAS; carbon budget; ecosystem model; eddy covariance; planetary boundary layer; productivity; transpiration	EDDY COVARIANCE MEASUREMENTS; BLACK SPRUCE FOREST; CARBON-DIOXIDE; BOREAL FOREST; BOUNDARY-LAYER; STOMATAL CONDUCTANCE; SURFACE FLUXES; WATER-VAPOR; MODEL; CO2	The planetary boundary layer (PBL) provides an important link between the scales and processes resolved by global atmospheric sampling/modeling and site-based flux measurements. The PBL is in direct contact with the land surface, both driving and responding to ecosystem processes. Measurements within the PBL (e. g., by radiosondes, aircraft profiles, and flask measurements) have a footprint, and thus an integrating scale, on the order of; similar to 1-100 km. We use the coupled atmosphere-biosphere model (CAB) and a Bayesian data assimilation framework to investigate the amount of biosphere process information that can be inferred from PBL measurements. We investigate the information content of PBL measurements in a two-stage study. First, we demonstrate consistency between the coupled model (CAB) and measurements, by comparing the model to eddy covariance flux tower measurements (i.e., water and carbon fluxes) and also PBL scalar profile measurements (i.e., water, carbon dioxide, and temperature) from Canadian boreal forest. Second, we use the CAB model in a set of Bayesian inversions experiments using synthetic data for a single day. In the synthetic experiment, leaf area and respiration were relatively well constrained, whereas surface albedo and plant hydraulic conductance were only moderately constrained. Finally, the abilities of the PBL profiles and the eddy covariance data to constrain the parameters were largely similar and only slightly lower than the combination of both observations.	[Hill, T. C.; Williams, M.; Moncrieff, J. B.] Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland; [Hill, T. C.; Williams, M.; Moncrieff, J. B.] Univ Edinburgh, NERC Ctr Terr Carbon Dynam, Edinburgh EH9 3JN, Midlothian, Scotland; [Woodward, F. I.] Univ Sheffield, Dept Anim & Plant Sci, Sheffield S10 2TN, S Yorkshire, England; [Woodward, F. I.] Univ Sheffield, NERC Ctr Terr Carbon Dynam, Sheffield S10 2TN, S Yorkshire, England	Hill, TC (reprint author), Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland.	thill@staffmail.ed.ac.uk	Woodward, Ian/B-7762-2008		NERC at the Centre for Terrestrial Carbon Dynamics	The authors thank Paul Jarvis and Shaun Quegan for their helpful comments and support during the drafting of this paper and also two anonymous reviewers whose comments were very helpful in developing this paper. T. C. Hill was funded by a NERC Studentship at the Centre for Terrestrial Carbon Dynamics.	ANDERSON DW, 2000, COLLECTED DATA BOREA; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Barr AG, 1997, J GEOPHYS RES-ATMOS, V102, P29213, DOI 10.1029/97JD01104; Bayes T, 1973, PHILOS T ROY SOC LON, V53, P370, DOI DOI 10.1098/RSBL.1763.0053; Betts AK, 2001, J GEOPHYS RES-ATMOS, V106, P33529, DOI 10.1029/2001JD900047; Betts AK, 1997, J GEOPHYS RES-ATMOS, V102, P28901, DOI 10.1029/96JD03876; Bousquet P, 1999, J GEOPHYS RES-ATMOS, V104, P26161, DOI 10.1029/1999JD900342; Chen JM, 1997, J GEOPHYS RES-ATMOS, V102, P29429, DOI 10.1029/97JD01107; Cuxart J, 2006, BOUND-LAY METEOROL, V118, P273, DOI 10.1007/s10546-005-3780-1; DEARDORF.JW, 1966, J ATMOS SCI, V23, P503, DOI 10.1175/1520-0469(1966)023<0503:TCGHFI>2.0.CO;2; DENNING AS, 1995, NATURE, V376, P240, DOI 10.1038/376240a0; Draxier RR, 1998, AUST METEOROL MAG, V47, P295; Ewers BE, 2005, PLANT CELL ENVIRON, V28, P660, DOI 10.1111/j.1365-3040.2005.01312.x; Gelman A, 1995, BAYESIAN DATA ANAL; Goulden ML, 1997, J GEOPHYS RES-ATMOS, V102, P28987, DOI 10.1029/97JD01111; Goulden ML, 1996, GLOB CHANGE BIOL, V2, P169, DOI 10.1111/j.1365-2486.1996.tb00070.x; Gower ST, 1997, J GEOPHYS RES-ATMOS, V102, P29029, DOI 10.1029/97JD02317; Hall FG, 1997, J GEOPHYS RES-ATMOS, V102, P29567, DOI 10.1029/97JD02578; Hill TC, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009412; Hollinger DY, 2005, TREE PHYSIOL, V25, P873; HOLTSLAG AAM, 1983, J CLIM APPL METEOROL, V22, P517, DOI 10.1175/1520-0450(1983)022<0517:ASSFDE>2.0.CO;2; Jarvis PG, 1997, J GEOPHYS RES-ATMOS, V102, P28953, DOI 10.1029/97JD01176; JARVIS PG, 2000, COLLECTED DATA BOREA; KEELING CD, 1986, J GEOPHYS RES-ATMOS, V91, P7782, DOI 10.1029/JD091iD07p07782; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kutsch WL, 2008, ECOL APPL, V18, P1391, DOI 10.1890/06-1301.1; Lasslop G, 2008, BIOGEOSCIENCES, V5, P1311; Lin JC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004754; Lin JC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003161; Loescher HW, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006932; Macatangay R, 2008, ATMOS CHEM PHYS, V8, P2555; MACPHERSON JI, 2000, COLLECTED DATA BOREA; MAHRT L, 1984, BOUND-LAY METEOROL, V29, P1, DOI 10.1007/BF00119116; MAILHOT J, 1982, J ATMOS SCI, V39, P2249, DOI 10.1175/1520-0469(1982)039<2249:AFEMOT>2.0.CO;2; Martins DK, 2009, AGR FOREST METEOROL, V149, P1674, DOI 10.1016/j.agrformet.2009.05.005; Miller JR, 1997, J GEOPHYS RES-ATMOS, V102, P29475, DOI 10.1029/97JD02558; MOSEGAARD K, 1995, J GEOPHYS RES-SOL EA, V100, P12431, DOI 10.1029/94JB03097; Nakane K, 1997, J GEOPHYS RES-ATMOS, V102, P28785, DOI 10.1029/97JD02313; Newcomer J., 2000, COLLECTED DATA BOREA; Owen KE, 2007, GLOBAL CHANGE BIOL, V13, P734, DOI 10.1111/j.1365-2486.2007.01326.x; POTTER CS, 1993, GLOBAL BIOGEOCHEM CY, V7, P811, DOI 10.1029/93GB02725; PRIESTLEY CHB, 1947, PROC R SOC LON SER-A, V189, P543, DOI 10.1098/rspa.1947.0057; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Rayment MB, 2002, TREE PHYSIOL, V22, P219; Running SW, 1999, REMOTE SENS ENVIRON, V70, P108, DOI 10.1016/S0034-4257(99)00061-9; Savage K, 1997, J GEOPHYS RES-ATMOS, V102, P29279, DOI 10.1029/97JD02233; SAXTON KE, 1986, SOIL SCI SOC AM J, V50, P1031; Sellers PJ, 1997, J GEOPHYS RES-ATMOS, V102, P28731, DOI 10.1029/97JD03300; TROEN I, 1986, BOUND-LAY METEOROL, V37, P129, DOI 10.1007/BF00122760; Williams M, 2001, TREE PHYSIOL, V21, P287; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Williams M, 1996, PLANT CELL ENVIRON, V19, P911, DOI 10.1111/j.1365-3040.1996.tb00456.x; Williams M, 2001, ECOL APPL, V11, P1800, DOI 10.1890/1051-0761(2001)011[1800:PPOAAW]2.0.CO;2; WOFSY SC, 2000, COLLECTED DATA BOREA	54	3	3	ECOLOGICAL SOC AMER	WASHINGTON	1990 M STREET NW, STE 700, WASHINGTON, DC 20036 USA	1051-0761		ECOL APPL	Ecol. Appl.	JUL	2011	21	5					1474	1489				16	Ecology; Environmental Sciences	Environmental Sciences & Ecology	792RF	WOS:000292766100005	
J	Weng, ES; Luo, YQ				Weng, Ensheng; Luo, Yiqi			Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics	ECOLOGICAL APPLICATIONS			English	Article						carbon cycle; data assimilation; Duke Forest FACE; ecological forecasting; information theory; model uncertainty	NET ECOSYSTEM EXCHANGE; ATMOSPHERIC CO2 ENRICHMENT; WARM-TEMPERATE FOREST; PINE FOREST; VEGETATION DISTRIBUTION; TERRESTRIAL BIOSPHERE; DIOXIDE ENRICHMENT; DATA ASSIMILATION; SOIL RESPIRATION; CYCLE FEEDBACKS	Biogeochemical models have been used to evaluate long-term ecosystem responses to global change on decadal and century time scales. Recently, data assimilation has been applied to improve these models for ecological forecasting. It is not clear what the relative information contributions of model (structure and parameters) vs. data are to constraints of short-and long-term forecasting. In this study, we assimilated eight sets of 10-year data (foliage, woody, and fine root biomass, litter fall, forest floor carbon [C], microbial C, soil C, and soil respiration) collected from Duke Forest into a Terrestrial Ecosystem model (TECO). The relative information contribution was measured by Shannon information index calculated from probability density functions (PDFs) of carbon pool sizes. The null knowledge without a model or data was defined by the uniform PDF within a prior range. The relative model contribution was information content in the PDF of modeled carbon pools minus that in the uniform PDF, while the relative data contribution was the information content in the PDF of modeled carbon pools after data was assimilated minus that before data assimilation. Our results showed that the information contribution of the model to constrain carbon dynamics increased with time whereas the data contribution declined. The eight data sets contributed more than the model to constrain C dynamics in foliage and fine root pools over the 100-year forecasts. The model, however, contributed more than the data sets to constrain the litter, fast soil organic matter (SOM), and passive SOM pools. For the two major C pools, woody biomass and slow SOM, the model contributed less information in the first few decades and then more in the following decades than the data. Knowledge of relative information contributions of model vs. data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.	[Weng, Ensheng; Luo, Yiqi] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA	Weng, ES (reprint author), Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA.	wengensheng@gmail.com	Weng, Ensheng/E-4390-2012		Office of Science, Department of Energy [DE-FG02-006ER64319]; Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University [DE-FC02-06ER64158]; National Science Foundation [DBI 0850290, DEB 0840964, DEB 0743778]; Office of Science, U.S. Department of Energy [DE-FG02-95ER62083]; Southeast Regional Center of the National Institute for Global Environmental Change [DE-FC02-03ER63613]	This research was financially supported by the Office of Science, Department of Energy, Grant No. DE-FG02-006ER64319, and through the Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University, under Award Number DE-FC02-06ER64158, and by the National Science Foundation under DBI 0850290, DEB 0840964, and DEB 0743778. The data are from Duke FACE facility, which was supported by the Office of Science, U.S. Department of Energy, Grant No. DE-FG02-95ER62083, and through its Southeast Regional Center of the National Institute for Global Environmental Change under Cooperative Agreement No. DE-FC02-03ER63613. The runs for this research were performed at the OU Supercomputing Center for Education and Research at the University of Oklahoma. We thank Henry Neeman for his help with using super computer. We appreciate Wendy M. Martin and Oleksandra Hararuk for their help in writing.	Abramowitz G, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL024419; Allen AS, 2000, ECOL APPL, V10, P437, DOI 10.2307/2641105; Bachelet D, 2001, ECOSYSTEMS, V4, P164, DOI 10.1007/s10021-001-0002-7; Bernhardt ES, 2006, BIOGEOCHEMISTRY, V77, P91, DOI 10.1007/s10533-005-1062-0; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Carpenter SR, 2009, P NATL ACAD SCI USA, V106, P1305, DOI 10.1073/pnas.0808772106; Carvalhais N, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003033; Chadwick OA, 1999, NATURE, V397, P491, DOI 10.1038/17276; Cramer W, 1999, GLOBAL CHANGE BIOL, V5, P1, DOI 10.1046/j.1365-2486.1999.00009.x; Del Grosso S, 2008, ECOLOGY, V89, P2117, DOI 10.1890/07-0850.1; Desai AR, 2008, AGR FOREST METEOROL, V148, P821, DOI 10.1016/j.agrformet.2007.11.012; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Finzi AC, 2006, ECOSYSTEMS, V9, P215, DOI 10.1007/s10021-005-0078-6; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Friedlingstein P, 1999, GLOB CHANGE BIOL, V5, P755, DOI 10.1046/j.1365-2486.1999.00269.x; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Fung IY, 2005, P NATL ACAD SCI USA, V102, P11201, DOI 10.1073/pnas.0504949102; Gaudinski JB, 2000, BIOGEOCHEMISTRY, V51, P33, DOI 10.1023/A:1006301010014; GELFAND AE, 1990, J AM STAT ASSOC, V85, P398, DOI 10.2307/2289776; Gough CM, 2007, GLOBAL CHANGE BIOL, V13, P1935, DOI 10.1111/j.1365-2486.2007.01406.x; Gough CM, 2008, BIOSCIENCE, V58, P609, DOI 10.1641/B580708; HAREMON ME, 1986, ADV ECOL RES, V15, P133; Harrison SP, 2003, GLOBAL CHANGE BIOL, V9, P983, DOI 10.1046/j.1365-2486.2003.00640.x; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hendrey GR, 1999, GLOB CHANGE BIOL, V5, P293, DOI 10.1046/j.1365-2486.1999.00228.x; Jackson RB, 2009, ECOLOGY, V90, P3352, DOI 10.1890/08-1609.1; JAYNES ET, 1957, PHYS REV, V106, P620, DOI 10.1103/PhysRev.106.620; Jones CD, 2006, TELLUS B, V58, P603, DOI 10.1111/j.1600-0889.2006.00215.x; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P207, DOI 10.1029/1998GB001059; KOLMOGOR.AN, 1968, INT J COMPUT MATH, V2, P157, DOI 10.1080/00207166808803030; Kucharik CJ, 2000, GLOBAL BIOGEOCHEM CY, V14, P795, DOI 10.1029/1999GB001138; KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694; Lichter J, 2005, ECOLOGY, V86, P1835, DOI 10.1890/04-1205; Lichter J, 2008, GLOBAL CHANGE BIOL, V14, P2910, DOI 10.1111/j.1365-2486.2008.01701.x; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; LUO Y, 1994, PLANT CELL ENVIRON, V17, P1195, DOI 10.1111/j.1365-3040.1994.tb02017.x; Luo YQ, 2011, ECOL APPL, V21, P1429; Luo YQ, 2001, ECOL MONOGR, V71, P357, DOI 10.1890/0012-9615(2001)071[0357:ECDECP]2.0.CO;2; Luo YQ, 2001, NATURE, V413, P622, DOI 10.1038/35098065; Luo YQ, 2008, GLOBAL CHANGE BIOL, V14, P1986, DOI 10.1111/j.1365-2486.2008.01629.x; McCarthy HR, 2006, P NATL ACAD SCI USA, V103, P19356, DOI 10.1073/pnas.0609448103; Burke IC, 2003, MODELS IN ECOSYSTEM SCIENCE, P225; McGuire AD, 2001, GLOBAL BIOGEOCHEM CY, V15, P183, DOI 10.1029/2000GB001298; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; Palmroth S, 2006, P NATL ACAD SCI USA, V103, P19362, DOI 10.1073/pnas.0609492103; PARTON WJ, 1987, SOIL SCI SOC AM J, V51, P1173; PRENTICE IC, 1992, J BIOGEOGR, V19, P117, DOI 10.2307/2845499; Pretzsch H, 2008, ANN BOT-LONDON, V101, P1065, DOI 10.1093/aob/mcm246; Pritchard SG, 2008, GLOBAL CHANGE BIOL, V14, P588, DOI 10.1111/j.1365-2486.2007.01523.x; Randerson JT, 2009, GLOBAL CHANGE BIOL, V15, P2462, DOI 10.1111/j.1365-2486.2009.01912.x; Rastetter EB, 1996, BIOSCIENCE, V46, P190, DOI 10.2307/1312740; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Renyi A, 1961, P 4 BERK S MATH STAT, V1, P547; Schmid S, 2006, FOREST ECOL MANAG, V237, P65, DOI 10.1016/j.foreco.2006.09.028; Schroter D, 2005, SCIENCE, V310, P1333, DOI 10.1126/science.1115233; SHANNON CE, 1948, AT&T TECH J, V27, P379; Sherry RA, 2008, GLOBAL CHANGE BIOL, V14, P2923, DOI 10.1111/j.1365-2486.2008.01703.x; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x; Stockli R, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000562; Stoy PC, 2006, AGR FOREST METEOROL, V141, P2, DOI 10.1016/j.agrformet.2006.09.001; Vemap M, 1995, GLOBAL BIOGEOCHEMICA, V9, P407, DOI DOI 10.1029/95GB02746; Wang YP, 2007, GLOBAL CHANGE BIOL, V13, P652, DOI 10.1111/j.1365-2486.2006.01225.x; Weng ES, 2006, ENVIRON MODEL ASSESS, V11, P45, DOI 10.1007/s10666-005-9019-1; White L, 2008, APPL MATH COMPUT, V196, P686, DOI 10.1016/j.amc.2007.07.004; White L, 2006, APPL MATH COMPUT, V181, P864, DOI 10.1016/j.amc.2006.02.014; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Wu XW, 2009, J PLANT ECOL-UK, V2, P55, DOI 10.1093/jpe/rtp005; Wutzler T, 2007, BIOGEOSCIENCES, V4, P125; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zaehle S, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002395; Zhou T, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB002939	74	8	8	ECOLOGICAL SOC AMER	WASHINGTON	1990 M STREET NW, STE 700, WASHINGTON, DC 20036 USA	1051-0761		ECOL APPL	Ecol. Appl.	JUL	2011	21	5					1490	1505				16	Ecology; Environmental Sciences	Environmental Sciences & Ecology	792RF	WOS:000292766100006	
J	Spadavecchia, L; Williams, M; Law, BE				Spadavecchia, L.; Williams, M.; Law, B. E.			Uncertainty in predictions of forest carbon dynamics: separating driver error from model error	ECOLOGICAL APPLICATIONS			English	Article						carbon dynamics; data assimilation; ensemble Kalman filter; geostatistics; product-sum covariance model; process-based modeling	PONDEROSA PINE FORESTS; NET PRIMARY PRODUCTION; BAYESIAN CALIBRATION; MOUNTAINOUS TERRAIN; DATA ASSIMILATION; ECOSYSTEM MODEL; SOIL SCIENCE; WATER; OREGON; FLUX	We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO(2) flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small (similar to 10% of the total net flux), while parameterization uncertainty was larger, similar to 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was >100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly compensated for each other. The time scales on which precipitation errors occurred in the simulations were shorter than the temporal scales over which drought developed in the model, so drought events were reasonably simulated. The approach outlined here provides a means to assess the uncertainty and bias introduced by meteorological drivers in regional-scale ecological forecasting.	[Spadavecchia, L.; Williams, M.] Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland; [Spadavecchia, L.; Williams, M.] Univ Edinburgh, NERC Ctr Terr Carbon Dynam, Edinburgh EH9 3JN, Midlothian, Scotland; [Law, B. E.] Oregon State Univ, Corvallis, OR 97331 USA	Williams, M (reprint author), Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland.	mat.williams@ed.ac.uk	Law, Beverly/G-3882-2010		NERC; U.S. Department of Energy (DOE) [DE-FG02-06ER64318]	This research was supported by an NERC studentship for L. Spadavecchia. The Metolius AmeriFlux research was supported by the U.S. Department of Energy Terrestrial Carbon Program (DOE grant #DE-FG02-06ER64318). Special thanks to James Irvine and Meredith Kurpius for supplying flux and respiration data, and all at the Oregon Climate Service for useful advice on meteorological data sources. Thanks also to Martin De Kauwe, who provided useful remarks on the manuscript. J. P. Gosling also provided many useful remarks on the methodology. We also thank all at the Sisters Forest Rangers Station, especially Pam Fahey, who provided the vegetation classification data, and two anonymous reviewers for their constructive comments.	Allen RG, 1997, J HYDROL ENG, V2, P56, DOI 10.1061/(ASCE)1084-0699(1997)2:2(56); Ashraf M, 1997, AGR FOREST METEOROL, V84, P255, DOI 10.1016/S0168-1923(96)02358-1; Canham C. D., 2003, MODELS ECOSYSTEM SCI; DALY C, 1994, J APPL METEOROL, V33, P140, DOI 10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2; Deutsch C. V., 1998, GSLIB GEOSTATISTICAL; Eknes M, 2002, J MARINE SYST, V36, P75, DOI 10.1016/S0924-7963(02)00134-3; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Evensen G, 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Falge E, 2001, AGR FOREST METEOROL, V107, P43, DOI 10.1016/S0168-1923(00)00225-2; Fisher RA, 2008, AGR FOREST METEOROL, V148, P850, DOI 10.1016/j.agrformet.2007.12.001; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Fuentes M, 2006, ECOL APPL, V16, P99, DOI 10.1890/04-1157; Goovaerts P, 2000, J HYDROL, V228, P113, DOI 10.1016/S0022-1694(00)00144-X; Goovaerts P, 2001, GEODERMA, V103, P3, DOI 10.1016/S0016-7061(01)00067-2; Goovaerts P., 1997, GEOSTATISTICS NATURA; Goovaerts P, 1999, GEODERMA, V89, P1, DOI 10.1016/S0016-7061(98)00078-0; Heuvelink GBM, 2001, GEODERMA, V100, P269, DOI 10.1016/S0016-7061(01)00025-8; HUDSON G, 1994, INT J CLIMATOL, V14, P77, DOI 10.1002/joc.3370140107; Hungerford R. D., 1989, INT414 US FOR SERV; Irvine J, 2002, GLOBAL CHANGE BIOL, V8, P1183, DOI 10.1046/j.1365-2486.2002.00544.x; Irvine J, 2004, TREE PHYSIOL, V24, P753; Kennedy M, 2008, J R STAT SOC A STAT, V171, P109; Klemedtsson L, 2008, BIOGEOCHEMISTRY, V89, P61, DOI 10.1007/s10533-007-9169-0; Law BE, 2001, TREE PHYSIOL, V21, P299; Law BE, 2001, AGR FOREST METEOROL, V110, P27, DOI 10.1016/S0168-1923(01)00279-9; Law BE, 2001, GLOBAL CHANGE BIOL, V7, P755, DOI 10.1046/j.1354-1013.2001.00439.x; Law BE, 2001, AGR FOREST METEOROL, V108, P1, DOI 10.1016/S0168-1923(01)00226-X; Law BE, 2003, GLOBAL CHANGE BIOL, V9, P510, DOI 10.1046/j.1365-2486.2003.00624.x; Maybeck P. S., 1979, STOCHASTIC MODELS ES, V1; Murray F. W., 1967, J APPL METEOROL, V6, P203, DOI 10.1175/1520-0450(1967)006<0203:OTCOSV>2.0.CO;2; RUNNING SW, 1987, CAN J FOREST RES, V17, P472, DOI 10.1139/x87-081; RUNNING SW, 1994, ECOL APPL, V4, P238, DOI 10.2307/1941930; RUNYON J, 1994, ECOL APPL, V4, P226, DOI 10.2307/1941929; SAXTON KE, 1986, SOIL SCI SOC AM J, V50, P1031; Schwarz PA, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2004GB002234; Spadavecchia L, 2009, AGR FOREST METEOROL, V149, P1105, DOI 10.1016/j.agrformet.2009.01.008; Thiessen A. H., 1911, MONTHLY WEATHER REV, V19, P1082, DOI 10.1175/1520-0493(1911)39<1082b:PAFLA>2.0.CO;2; Thornton PE, 1997, J HYDROL, V190, P214, DOI 10.1016/S0022-1694(96)03128-9; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; Van Tuyl S, 2005, FOREST ECOL MANAG, V209, P273, DOI 10.1016/j.foreco.2005.02.002; Verbeeck H, 2006, TREE PHYSIOL, V26, P807; Vickers D, 2009, AGR FOREST METEOROL, V149, P73, DOI 10.1016/j.agrformet.2008.07.005; Williams M, 2001, TREE PHYSIOL, V21, P287; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Williams M, 1996, PLANT CELL ENVIRON, V19, P911, DOI 10.1111/j.1365-3040.1996.tb00456.x; Williams M, 1997, ECOL APPL, V7, P882, DOI 10.2307/2269440; Williams M, 2001, ECOL APPL, V11, P1800, DOI 10.1890/1051-0761(2001)011[1800:PPOAAW]2.0.CO;2	48	2	2	ECOLOGICAL SOC AMER	WASHINGTON	1990 M STREET NW, STE 700, WASHINGTON, DC 20036 USA	1051-0761		ECOL APPL	Ecol. Appl.	JUL	2011	21	5					1506	1522				17	Ecology; Environmental Sciences	Environmental Sciences & Ecology	792RF	WOS:000292766100007	
J	Wolf, A; Field, CB; Berry, JA				Wolf, Adam; Field, Christopher B.; Berry, Joseph A.			Allometric growth and allocation in forests: a perspective from FLUXNET	ECOLOGICAL APPLICATIONS			English	Article						allocation of biomass; allometry; data assimilation; FLUXNET; forest stand biomass; land-surface model; net primary productivity, NPP; partitioning of photosynthesis products; tree biomass compounds; tree plasticity	CARBON ALLOCATION; FINE-ROOT; GENERAL-MODEL; SHOOT RATIOS; CO2 BALANCE; BIOMASS; CLIMATE; STORAGE; TREES; ECOSYSTEMS	To develop a scheme for partitioning the products of photosynthesis toward different biomass components in land-surface models, a database on component mass and net primary productivity (NPP), collected from FLUXNET sites, was examined to determine allometric patterns of allocation. We found that NPP per individual of foliage (Gfol), stem and branches (Gstem), coarse roots (Gcroot) and fine roots (Gfroot) in individual trees is largely explained (r(2) = 67-91%) by the magnitude of total NPP per individual (G). Gfol scales with G isometrically, meaning it is a fixed fraction of G (similar to 25%). Root-shoot trade-offs were manifest as a slow decline in Gfroot, as a fraction of G, from 50% to 25% as stands increased in biomass, with Gstem and Gcroot increasing as a consequence. These results indicate that a functional trade-off between aboveground and belowground allocation is essentially captured by variations in G, which itself is largely governed by stand biomass and only secondarily by site-specific resource availability. We argue that forests are characterized by strong competition for light, observed as a race for individual trees to ascend by increasing partitioning toward wood, rather than by growing more leaves, and that this competition strongly constrains the allocational plasticity that trees may be capable of. The residual variation in partitioning was not related to climatic or edaphic factors, nor did plots with nutrient or water additions show a pattern of partitioning distinct from that predicted by G alone. These findings leverage short-term process studies of the terrestrial carbon cycle to improve decade-scale predictions of biomass accumulation in forests. An algorithm for calculating partitioning in land-surface models is presented.	[Wolf, Adam; Field, Christopher B.; Berry, Joseph A.] Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA	Wolf, A (reprint author), Princeton Univ, Dept Ecol & Evolutionary Biol, Princeton, NJ 08544 USA.	adamwolf@princeton.edu	Berry, Joseph/B-8211-2009				Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Baldocchi D., 2004, The global carbon cycle: integrating humans, climate and the natural world, P295; Bartelink HH, 1998, TREE PHYSIOL, V18, P91; Beer C, 2010, SCIENCE, V329, P834, DOI 10.1126/science.1184984; BELLASSEN V, 2009, ECOLOGICAL MODELLING, V221, P2458; Cannell M. G. R., 1982, WORLD FOREST BIOMASS; CANNELL MGR, 1994, ADV ECOL RES, V25, P59, DOI 10.1016/S0065-2504(08)60213-5; Cheng DL, 2007, ANN BOT-LONDON, V99, P95, DOI 10.1093/aob/mc1206; Chopping M, 2008, REMOTE SENS ENVIRON, V112, P2051, DOI 10.1016/j.rse.2007.07.024; HARMON ME, 1986, ADV ECOL RES, V15, P133, DOI 10.1016/S0065-2504(08)60121-X; Curtis PS, 2002, AGR FOREST METEOROL, V113, P3, DOI 10.1016/S0168-1923(02)00099-0; Dubayah RO, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG000933; Enquist BJ, 2002, SCIENCE, V295, P1517, DOI 10.1126/science.1066360; Enquist BJ, 1998, NATURE, V395, P163, DOI 10.1038/25977; Friedlingstein P, 1999, GLOB CHANGE BIOL, V5, P755, DOI 10.1046/j.1365-2486.1999.00269.x; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Fung IY, 2005, P NATL ACAD SCI USA, V102, P11201, DOI 10.1073/pnas.0504949102; Gielen B, 2005, TREE PHYSIOL, V25, P1399; Global Soil Data Task, 2000, GLOB SOIL DAT PROD; Goodwin NR, 2007, REMOTE SENS ENVIRON, V111, P481, DOI 10.1016/j.rse.2007.04.001; Gower ST, 2001, ECOL APPL, V11, P1395, DOI 10.2307/3060928; Hese S, 2005, REMOTE SENS ENVIRON, V94, P94, DOI 10.1016/j.rse.2004.09.006; Ishii H, 2002, FOREST ECOL MANAG, V169, P257, DOI 10.1016/S0378-1127(01)00751-4; Jackson RB, 1997, P NATL ACAD SCI USA, V94, P7362, DOI 10.1073/pnas.94.14.7362; Lacointe A, 2000, ANN FOR SCI, V57, P521; Law BE, 2001, GLOBAL CHANGE BIOL, V7, P755, DOI 10.1046/j.1354-1013.2001.00439.x; LEVIN SA, 1989, ANN BOT-LONDON, V64, P71; LINDER S, 1998, NPP BOREAL FOREST SI; Litton CM, 2007, GLOBAL CHANGE BIOL, V13, P2089, DOI 10.1111/j.1365-2486.2007.01420.x; Luyssaert S, 2008, NATURE, V455, P213, DOI 10.1038/nature07276; Luyssaert S, 2007, GLOBAL CHANGE BIOL, V13, P2509, DOI 10.1111/j.1365-2486.2007.01439.x; Maier CA, 2004, GLOBAL CHANGE BIOL, V10, P1335, DOI 10.1111/j.1529-8817.2003.00809.x; Matamala R, 2003, SCIENCE, V302, P1385, DOI 10.1126/science.1089543; Mitchell TD, 2005, INT J CLIMATOL, V25, P693, DOI 10.1002/joc.1181; MOHLER CL, 1978, J ECOL, V66, P599, DOI 10.2307/2259153; Mokany K, 2006, GLOBAL CHANGE BIOL, V12, P84, DOI 10.1111/j.1365-2486.2005.001043.x; Moorcroft PR, 2001, ECOL MONOGR, V71, P557, DOI 10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2; Niklas KJ, 2002, AM NAT, V159, P482, DOI 10.1086/339459; Ogle K, 2009, TREE PHYSIOL, V29, P587, DOI 10.1093/treephys/tpn051; Poorter H, 2000, AUST J PLANT PHYSIOL, V27, P595, DOI 10.1071/PP99173; Purves D, 2008, SCIENCE, V320, P1452, DOI 10.1126/science.1155359; RUNNING SW, 1991, TREE PHYSIOL, V9, P147; Ryan MG, 1996, TREE PHYSIOL, V16, P333; SCHULZE ED, 2005, BIOGEOSCI DISCUSS, V2, P1283; Sellers PJ, 1997, SCIENCE, V275, P502, DOI 10.1126/science.275.5299.502; Sokal R. R., 1995, BIOMETRY PRINCIPLES; Sun GQ, 2000, IEEE T GEOSCI REMOTE, V38, P2617, DOI 10.1109/36.885208; Thornthwaite CW, 1948, GEOGR REV, V38, P55, DOI 10.2307/210739; Tilman D., 1988, MONOGRAPHS POPULATIO, V26; West GB, 2009, P NATL ACAD SCI USA, V106, P7040, DOI 10.1073/pnas.0812294106; West GB, 1999, NATURE, V400, P664, DOI 10.1038/23251; Wirth C, 2009, ECOL STUD-ANAL SYNTH, V207, P1, DOI 10.1007/978-3-540-92706-8; Wolf A, 2010, REMOTE SENS ENVIRON, V114, P1205, DOI 10.1016/j.rse.2010.01.013	53	0	0	ECOLOGICAL SOC AMER	WASHINGTON	1990 M STREET NW, STE 700, WASHINGTON, DC 20036 USA	1051-0761		ECOL APPL	Ecol. Appl.	JUL	2011	21	5					1546	1556				11	Ecology; Environmental Sciences	Environmental Sciences & Ecology	792RF	WOS:000292766100010	
J	Saide, P; Bocquet, M; Osses, A; Gallardo, L				Saide, Pablo; Bocquet, Marc; Osses, Axel; Gallardo, Laura			Constraining surface emissions of air pollutants using inverse modelling: method intercomparison and a new two-step two-scale regularization approach	TELLUS SERIES B-CHEMICAL AND PHYSICAL METEOROLOGY			English	Article							GEOPHYSICAL-DATA ASSIMILATION; ATMOSPHERIC TRACER SOURCE; CO2 SOURCES; RECONSTRUCTION; TRANSPORT; QUALITY; RESOLUTION; SCIAMACHY; SANTIAGO; ADJOINT	When constraining surface emissions of air pollutants using inverse modelling one often encounters spurious corrections to the inventory at places where emissions and observations are colocated, referred to here as the colocalization problem. Several approaches have been used to deal with this problem: coarsening the spatial resolution of emissions; adding spatial correlations to the covariance matrices; adding constraints on the spatial derivatives into the functional being minimized; and multiplying the emission error covariance matrix by weighting factors. Intercomparison of methods for a carbon monoxide inversion over a city shows that even though all methods diminish the colocalization problem and produce similar general patterns, detailed information can greatly change according to the method used ranging from smooth, isotropic and short range modifications to not so smooth, non-isotropic and long range modifications. Poisson (non-Gaussian) and Gaussian assumptions both show these patterns, but for the Poisson case the emissions are naturally restricted to be positive and changes are given by means of multiplicative correction factors, producing results closer to the true nature of emission errors. Finally, we propose and test a new two-step, two-scale, fully Bayesian approach that deals with the colocalization problem and can be implemented for any prior density distribution.	[Saide, Pablo] Univ Iowa, Ctr Global & Reg Environm Res, CGRER, Iowa City, IA 52242 USA; [Bocquet, Marc] Univ Paris Est, CEREA Joint Lab Ecole Ponts ParisTech, Champs Sur Marne, France; [Bocquet, Marc] EDF R&D, Champs Sur Marne, France; [Bocquet, Marc] INRIA, Paris Rocquencourt Res Ctr, Paris, France; [Osses, Axel] Univ Chile, Dept Ingn Matemat, Santiago, Chile; [Osses, Axel; Gallardo, Laura] Univ Chile, CNRS, Ctr Modelamiento Matemat, UMI 2807, Santiago, Chile; [Gallardo, Laura] Univ Chile, Dept Geofis, Santiago, Chile	Saide, P (reprint author), Univ Iowa, Ctr Global & Reg Environm Res, CGRER, Iowa City, IA 52242 USA.	pablo-saide@uiowa.edu	Bocquet, Marc/E-1966-2011		Inter-American Institute for Global Change Research (IAI) [CRN II 2017]; US National Science Foundation [GEO-0452325]; Agence Nationale de la Recherche [ANR-08-SYSC-014]	This work is being carried out with the aid of a grant from the Inter-American Institute for Global Change Research (IAI) CRN II 2017 which is supported by the US National Science Foundation (Grant GEO-0452325), the STIC-AMSUD project 'Air-quality prediction with data assimilation in Argentina and Chile" and Fulbright-CONICYT scholarship number 15093810. This paper is also a contribution to the MSDAG project supported by the Agence Nationale de la Recherche, grant ANR-08-SYSC-014. We appreciate the valuable comments from G.R. Carmichael and from two anonymous reviewers.	Bocquet M, 2005, NONLINEAR PROC GEOPH, V12, P219; Bocquet M, 2005, Q J ROY METEOR SOC, V131, P2191, DOI 10.1256/qj.04.67; Bocquet M, 2009, MON WEATHER REV, V137, P2331, DOI 10.1175/2009MWR2789.1; Bocquet M, 2010, MON WEATHER REV, V138, P2997, DOI 10.1175/2010MWR3164.1; Bousquet P, 1999, J GEOPHYS RES-ATMOS, V104, P26161, DOI 10.1029/1999JD900342; Carmichael GR, 2008, J COMPUT PHYS, V227, P3540, DOI 10.1016/j.jcp.2007.02.024; Chai TF, 2009, ATMOS ENVIRON, V43, P5046, DOI 10.1016/j.atmosenv.2009.06.052; Chang ME, 1997, J GEOPHYS RES-ATMOS, V102, P16023, DOI 10.1029/97JD00964; Corvalan RM, 2002, J AIR WASTE MANAGE, V52, P167; Davoine X, 2007, ATMOS CHEM PHYS, V7, P1549; Dubovik O, 2008, ATMOS CHEM PHYS, V8, P209; Elbern H, 2007, ATMOS CHEM PHYS, V7, P3749; Gaspari G, 1999, Q J ROY METEOR SOC, V125, P723, DOI 10.1256/smsqj.55416; Golub G H, 1996, MATRIX COMPUTATION; Grell G. A., 1995, NCARTN398STR; HANSEN PC, 1993, SIAM J SCI COMPUT, V14, P1487, DOI 10.1137/0914086; Henze DK, 2009, ATMOS CHEM PHYS, V9, P5877; Hoelzemann JJ, 2001, PHYS CHEM EARTH PT B, V26, P807, DOI 10.1016/S1464-1909(01)00089-2; Hoelzemann JJ, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010369; Houweling S, 2004, ATMOS CHEM PHYS, V4, P523; Issartel JP, 2003, ATMOS CHEM PHYS, V3, P2111; Jorquera H, 2010, ATMOS ENVIRON, V44, P42, DOI 10.1016/j.atmosenv.2009.09.040; Kalnay E, 2003, ATMOSPHERIC MODELING; Kaminski T, 1999, J GEOPHYS RES-ATMOS, V104, P18555, DOI 10.1029/1999JD900146; Kopacz M, 2010, ATMOS CHEM PHYS, V10, P855; Lindley SJ, 2000, ATMOS ENVIRON, V34, P375, DOI 10.1016/S1352-2310(99)00325-8; Mallet V, 2007, ATMOS CHEM PHYS, V7, P5479; MULHOLLAND M, 1995, ATMOS ENVIRON, V29, P497, DOI 10.1016/1352-2310(94)00245-G; Peters W, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006157; Petron G, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001305; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Saide P., 2009, ATMOS CHEM PHYS DISC, V9, P6325, DOI 10.5194/acpd-9-6325-2009; Sandu A, 2005, AEROSOL SCI TECH, V39, P677, DOI 10.1080/02786820500182289; Schmitz R, 2005, ATMOS ENVIRON, V39, P2035, DOI 10.1016/j.atmosenv.2004.12.033; SEIBERT P, 2000, AGU GEOPHYSICAL MONO, P147; Twomey S. A., 1977, INTRO MATH INVERSION; Zhu CY, 1997, ACM T MATH SOFTWARE, V23, P550, DOI 10.1145/279232.279236	37	5	5	WILEY-BLACKWELL	MALDEN	COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA	0280-6509		TELLUS B	Tellus Ser. B-Chem. Phys. Meteorol.	JUL	2011	63	3					360	370		10.1111/j.1600-0889.2011.00529.x		11	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	785BK	WOS:000292202200007	
J	Liu, JJ; Fung, I; Kalnay, E; Kang, JS				Liu, Junjie; Fung, Inez; Kalnay, Eugenia; Kang, Ji-Sun			CO2 transport uncertainties from the uncertainties in meteorological fields	GEOPHYSICAL RESEARCH LETTERS			English	Article							TRANSFORM KALMAN FILTER; ATMOSPHERIC CO2; DATA ASSIMILATION; MODEL; REANALYSIS; SURFACE; CYCLE	Inference of surface CO2 fluxes from atmospheric CO2 observations requires information about large-scale transport and turbulent mixing in the atmosphere, so transport errors and the statistics of the transport errors have significant impact on surface CO2 flux estimation. In this paper, we assimilate raw meteorological observations every 6 hours into a general circulation model with a prognostic carbon cycle (CAM3.5) using the Local Ensemble Transform Kalman Filter (LETKF) to produce an ensemble of meteorological analyses that represent the best approximation to the atmospheric circulation and its uncertainty. We quantify CO2 transport uncertainties resulting from the uncertainties in meteorological fields by running CO2 ensemble forecasts within the LETKF-CAM3.5 system forced by prescribed surface fluxes. We show that CO2 transport uncertainties are largest over the tropical land and the areas with large fossil fuel emissions, and are between 1.2 and 3.5 ppm at the surface and between 0.8 and 1.8 ppm in the column-integrated CO2 (with OCO-2-like averaging kernel) over these regions. We further show that the current practice of using a single meteorological field to transport CO2 has weaker vertical mixing and stronger CO2 vertical gradient when compared to the mean of the ensemble CO2 forecasts initialized by the ensemble meteorological fields, especially over land areas. The magnitude of the difference at the surface can be up to 1.5 ppm. Citation: Liu, J., I. Fung, E. Kalnay, and J.-S. Kang (2011), CO2 transport uncertainties from the uncertainties in meteorological fields, Geophys. Res. Lett., 38, L12808, doi:10.1029/2011GL047213.	[Liu, Junjie; Fung, Inez] Univ Calif Berkeley, Dept Earth & Planetary Sci, Berkeley, CA 94720 USA; [Kalnay, Eugenia; Kang, Ji-Sun] Univ Maryland, College Pk, MD 20742 USA	Liu, JJ (reprint author), CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA.	junjie.liu@jpl.nasa.gov	KANG, JI-SUN/F-4395-2010		DOE [DEFG0207ER64337, ER64437]; NASA [NNH09ZDA001N-TERRAQUA]	This project is supported by DOE grants DEFG0207ER64337 and ER64437, and NASA grant NNH09ZDA001N-TERRAQUA. We want to thank Kana Kanamitsu and Kei Yoshimura from Scripps Institute of Oceanography, UC San Diego; Jack Woolen from NCEP; and Michael Wehner from DOE Lawrence Berkeley National Lab for the help with running the DOE/NCEP Reanalysis 2 system and the usage of the supercomputers in National Energy Research Scientific Computing Center (NERSC). We also want to thank Yu-Heng Tseng for initially porting the CAM3.5 onto the NERSC machines. All the calculations were carried out at the supercomputers in NERSC.	Andres RJ, 1996, GLOBAL BIOGEOCHEM CY, V10, P419, DOI 10.1029/96GB01523; Baker DF, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2004GB002439; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Chevallier F, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL044652; Collins WD, 2006, J CLIMATE, V19, P2122, DOI 10.1175/JCLI3761.1; Dee DP, 2009, Q J ROY METEOR SOC, V135, P1830, DOI 10.1002/qj.493; FUNG I, 1983, J GEOPHYS RES-OC ATM, V88, P1281, DOI 10.1029/JC088iC02p01281; GERBIG C, 2007, ATMOS CHEM PHYS DISC, V7, P13121, DOI DOI 10.5194/ACPD-7-13121-2007; Gurney KR, 2003, TELLUS B, V55, P555, DOI 10.1034/j.1600-0889.2003.00049.x; Gurney KR, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002111; Hunt BR, 2007, PHYSICA D, V230, P112, DOI 10.1016/j.physd.2006.11.008; Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2; Kang JS, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014673; Krol M, 2005, ATMOS CHEM PHYS, V5, P417; Lin SJ, 2004, MON WEATHER REV, V132, P2293, DOI 10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2; Liu J, 2008, NONLINEAR PROC GEOPH, V15, P645; Miyoshi T, 2007, MON WEATHER REV, V135, P3841, DOI 10.1175/2007MWR1873.1; Olsen SC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003968; Peylin P, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000857; Prather MJ, 2008, P NATL ACAD SCI USA, V105, P19617, DOI 10.1073/pnas.0806541106; Rasch PJ, 1997, J GEOPHYS RES-ATMOS, V102, P28127, DOI 10.1029/97JD02087; Stephens BB, 2007, SCIENCE, V316, P1732, DOI 10.1126/science.1137004; Takahashi T, 2002, DEEP-SEA RES PT II, V49, P1601, DOI 10.1016/S0967-0645(02)00003-6; TANS PP, 1990, SCIENCE, V247, P1431, DOI 10.1126/science.247.4949.1431; Uppala SM, 2005, Q J ROY METEOR SOC, V131, P2961, DOI 10.1256/qj.04.176	25	3	3	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0094-8276		GEOPHYS RES LETT	Geophys. Res. Lett.	JUN 30	2011	38								L12808	10.1029/2011GL047213		6	Geosciences, Multidisciplinary	Geology	787MS	WOS:000292381400001	
J	Soetaert, K; Gregoire, M				Soetaert, Karline; Gregoire, Marilaure			Estimating marine biogeochemical rates of the carbonate pH system-A Kalman filter tested	ECOLOGICAL MODELLING			English	Article						Data assimilation; Parameter estimation; Kalman filter; Biogeochemical rates; Marine ecosystems	PHOTOSYNTHESIS LIGHT MODELS; TOTAL INORGANIC CARBON; BERING-SEA SHELF; AQUATIC SYSTEMS; SURFACE WATERS; IN-SITU; PARAMETER-ESTIMATION; EMILIANIA-HUXLEYI; DATA ASSIMILATION; ECOSYSTEM MODEL	Oxygen (O-2), nitrate (NO3), dissolved inorganic carbon (DIC) or pCO(2), and pH or total alkalinity (TA), are useful indices of marine chemical, physical and biological processes operating on varying time-scales. Although these properties are increasingly being monitored at high frequency, they have not been extensively used for studying ecosystem dynamics. We test whether we can estimate time-evolving biogeochemical rates (e.g. primary production, respiration, calcification and carbonate dissolution, and nitrification) from synthetic high frequency time-series of O-2, NO3, DIC, pCO(2), TA or pH. More specifically, a Kalman filter has been implemented in a very simplified biogeochemical model describing the dynamics of O-2, NO3, DIC and TA and linking the concentration data to biogeochemical fluxes. Different sets of concentration data are assimilated and biogeochemical rates are estimated. The frequency of assimilation required to get acceptable results is investigated and is compared with the frequency of sampling in the field or in controlled experimental settings. Smoothing of the data to remove data noise before assimilation improves the estimation of the biogeochemical rates. The best estimated rates are obtained when assimilating O-2, NO3 and TA although the assimilation of DIC instead of TA also gives satisfactory results. In case pH or pCO(2) is assimilated rather than DIC or TA, the linearization of the (now nonlinear) observation equation introduces perturbations and the Kalman filter behaves suboptimal. We conclude that, given the resolution of data required, the tool has potential to estimate biogeochemical rates of the carbonate system under controlled settings. (C) 2011 Elsevier B.V. All rights reserved.	[Gregoire, Marilaure] Univ Liege, MARE Interfacultary Ctr Marine Res, Dep Oceanol, B-4000 Liege, Belgium; [Soetaert, Karline] Netherlands Inst Ecol, Ctr Estuarine & Marine Ecol, NL-4400 AC Yerseke, Netherlands	Gregoire, M (reprint author), Univ Liege, MARE Interfacultary Ctr Marine Res, Dep Oceanol, Sart Tilman B6C,Allee Chim 3, B-4000 Liege, Belgium.	mgregoire@ulg.ac.be	Soetaert, Karline/A-9839-2011		European Community' [211384]	Marilaure Gregoire is Senior Research Associate at the Belgian Foundation for Scientific Research (FNRS). The authors thank Alberto Borges for fruitful discussion and the two anonymous reviewers for the comments on the manuscript. This research is a contribution to the "European Project on Ocean Acidification" (EPOCA) which received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 211384. This is publication n<SUP>o</SUP> 5012 of the Netherlands Institute of Ecology (NIOO-KNAW), Yerseke and MARE publication n<SUP>o</SUP> 208.	Alvarez M, 2002, DEEP-SEA RES PT II, V49, P643, DOI 10.1016/S0967-0645(01)00116-3; ANDERSON TR, 1998, COASTAL SHELF SCI, V46, P99; Baehr MM, 2004, LIMNOL OCEANOGR, V49, P330; BECK B, 1976, J ENV ENG DIVISION, P909; BECK MB, 1987, WATER RESOUR RES, V23, P1393, DOI 10.1029/WR023i008p01393; Borges AV, 2001, PROG OCEANOGR, V51, P283, DOI 10.1016/S0079-6611(01)00071-4; BREWER PG, 1976, LIMNOL OCEANOGR, V21, P108; Broecker W. S., 1982, TRACERS SEA; Byrne RH, 2002, ANAL CHIM ACTA, V451, P221, DOI 10.1016/S0003-2670(01)01423-4; Carrilloa CJ, 2004, MAR CHEM, V84, P161, DOI 10.1016/j.marchem.2003.07.004; Chen M, 2008, ECOL MODEL, V219, P317, DOI 10.1016/j.ecolmodel.2008.07.013; Ciavatta S, 2008, ECOL MODEL, V212, P28, DOI 10.1016/j.ecolmodel.2007.10.025; COSBY BJ, 1984, ECOL MODEL, V23, P25, DOI 10.1016/0304-3800(84)90117-0; COSBY BJ, 1984, ECOL MODEL, V23, P1, DOI 10.1016/0304-3800(84)90116-9; DeGrandpre MD, 1997, LIMNOL OCEANOGR, V42, P21; Dickson AG, 2010, GUIDE BEST PRACTICES, P17; Ennola K, 1998, ECOL MODEL, V110, P135, DOI 10.1016/S0304-3800(98)00057-X; FRANKIGNOULLE M, 1994, LIMNOL OCEANOGR, V39, P458; Gattuso JP, 1999, P NATL ACAD SCI USA, V96, P13017, DOI 10.1073/pnas.96.23.13017; Hofmann AF, 2008, BIOGEOSCIENCES, V5, P227; Jazwinski AH, 1970, STOCHASTIC PROCESSES; Joassin P, 2011, J MARINE SYST, V85, P71, DOI 10.1016/j.jmarsys.2010.11.007; Kalman R. E., 1960, J BASIC ENG, V82, P35, DOI DOI 10.1115/1.3662552; KELLY MG, 1974, LIMNOL OCEANOGR, V19, P305; LAWS EA, 1991, DEEP-SEA RES, V38, P143, DOI 10.1016/0198-0149(91)90059-O; Lenartz F, 2007, J MARINE SYST, V68, P327, DOI 10.1016/j.jmarsys.2006.12.001; Liu XW, 2006, ENVIRON SCI TECHNOL, V40, P5036, DOI 10.1021/es0601843; Maybeck P. S., 1979, STOCHASTIC MODELS ES, V1; Merico A, 2004, DEEP-SEA RES PT I, V51, P1803, DOI 10.1016/j.dsr.2004.07.003; MILLERO FJ, 1995, GEOCHIM COSMOCHIM AC, V59, P661, DOI 10.1016/0016-7037(94)00354-O; Mo XG, 2008, ECOL MODEL, V217, P157, DOI 10.1016/j.ecolmodel.2008.06.021; Moore TS, 2009, ANNU REV MAR SCI, V1, P91, DOI 10.1146/annurev.marine.010908.163817; Moradkhani H, 2005, ADV WATER RESOUR, V28, P135, DOI 10.1016/j.advwatres.2004.09.002; MORRIS A. W., 1966, DEEP SEA RES OCEANOGR ABST, V13, P699, DOI 10.1016/0011-7471(66)90601-2; Mucci A., 1983, AM J SCI, V283, P781; Murata A, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL013906; ODUM HT, 1956, LIMNOL OCEANOGR, V1, P102; Oschlies A, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002094; Pastres R, 2003, ECOL MODEL, V170, P227, DOI 10.1016/S0304-3800(03)00230-8; PLATT T, 1986, DEEP-SEA RES, V33, P273, DOI 10.1016/0198-0149(86)90123-8; R Development Core Team, 2010, R LANG ENV STAT COMP; Raick C, 2007, J MARINE SYST, V65, P561, DOI 10.1016/j.jmarsys.2005.06.006; Reinelt W, 2002, AUTOMATICA, V38, P787, DOI 10.1016/S0005-1098(01)00269-2; Riebesell U, 2004, J OCEANOGR, V60, P719, DOI 10.1007/s10872-004-5764-z; SMITH SV, 1975, LIMNOL OCEANOGR, V20, P493; Soetaert K, 2007, MAR CHEM, V105, P30, DOI 10.1016/j.marchem.2006.12.012; Soetaert K, 2002, ECOL MODEL, V151, P177, DOI 10.1016/S0304-3800(01)00469-0; Soetaert K, 2001, DEEP-SEA RES PT II, V48, P3141, DOI 10.1016/S0967-0645(01)00035-2; STIGTER JD, 1994, ENVIRONMETRICS, V5, P315, DOI 10.1002/env.3170050310; TETT P, 1988, CRC HDB LAB MODEL EC, P177; Trudinger CM, 2008, ENVIRONMETRICS, V19, P849, DOI 10.1002/env.910; Tyrrell T, 1996, J MARINE SYST, V9, P83, DOI 10.1016/0924-7963(96)00019-X; Van den Meersche K, 2004, LIMNOL OCEANOGR, V49, P862; Volk T, 1985, GEOPHYS MONOGR SER, V32, P99, DOI DOI 10.1021/BA-1985-0209.CH009.GE0PHYS.M0N0GR; Wang ZHA, 2007, ANAL CHIM ACTA, V596, P23, DOI 10.1016/j.aca.2007.05.048; WANNINKHOF R, 1992, J GEOPHYS RES-OCEANS, V97, P7373, DOI 10.1029/92JC00188; Watanabe A, 2004, MAR CHEM, V85, P75, DOI 10.1016/j.marchem.2003.09.004; WELCH G, 2001, INTRO KALMAN FILTER; Zarchan P., 2000, PROGR ASTRONAUTICS A; Zheng DQ, 2009, ATMOS ENVIRON, V43, P2005, DOI 10.1016/j.atmosenv.2009.01.014	60	1	1	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0304-3800		ECOL MODEL	Ecol. Model.	JUN 24	2011	222	12					1929	1942		10.1016/j.ecolmodel.2011.03.012		14	Ecology	Environmental Sciences & Ecology	775XR	WOS:000291497300003	
J	Ziehn, T; Knorr, W; Scholze, M				Ziehn, T.; Knorr, W.; Scholze, M.			Investigating spatial differentiation of model parameters in a carbon cycle data assimilation system	GLOBAL BIOGEOCHEMICAL CYCLES			English	Article							GLOBAL VEGETATION MODEL; TERRESTRIAL BIOSPHERE; ECOSYSTEM DYNAMICS; UNCERTAINTIES; BALANCE; FLUXES	Better estimates of the net exchange of CO(2) between the atmosphere and the terrestrial biosphere are urgently needed to improve predictions of future CO(2) levels in the atmosphere. The carbon cycle data assimilation system (CCDAS) offers the capability of inversion, while it is at the same time based on a process model that can be used independent of observational data. CCDAS allows the assimilation of atmospheric CO(2) concentrations into the terrestrial biosphere model BETHY, constraining its process parameters via an adjoint approach. Here, we investigate the effect of spatial differentiation of a universal carbon balance parameter of BETHY on posterior net CO(2) fluxes and their uncertainties. The parameter, beta, determines the characteristics of the slowly decomposing soil carbon pool and represents processes that are difficult to model explicitly. Two cases are studied with an assimilation period of 1979 to 2003. In the base case, there is a separate beta for each plant functional type (PFT). In the regionalization case, beta is differentiated not only by PFT, but also according to each of 11 large continental regions as used by the TransCom project. We find that the choice of spatial differentiation has a profound impact not only on the posterior (optimized) fluxes and their uncertainties, but even more so on the spatial covariance of the uncertainties. Differences are most pronounced in tropical regions, where observations are sparse. While regionalization leads to an improved fit to the observations by about 20% compared to the base case, we notice large spatial variations in the posterior net CO(2) flux on a grid cell level. The results illustrate the need for universal process formulations in global-scale atmospheric CO(2) inversion studies, at least as long as the observational network is too sparse to resolve spatial fluctuations at the regional scale.	[Ziehn, T.; Knorr, W.; Scholze, M.] Univ Bristol, Dept Earth Sci, Bristol, Avon, England	Ziehn, T (reprint author), Univ Bristol, Dept Earth Sci, Wills Mem Bldg, Bristol, Avon, England.		Ziehn, Tilo/A-3094-2012		Natural Environment Research Council, U.K.	This work was supported by the QUEST programme of the Natural Environment Research Council, U.K.	Bird M, 2002, EUR J SOIL SCI, V53, P393, DOI 10.1046/j.1365-2389.2002.00444.x; Boden T. A., 2009, GLOBAL REGIONAL NATL, DOI [10.3334/CDIAC/00001, DOI 10.3334/CDIAC/00001]; Bopp L, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001445; Bousquet P, 2000, SCIENCE, V290, P1342, DOI 10.1126/science.290.5495.1342; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; FLETCHER R, 1963, COMPUT J, V6, P163; Friend AD, 1997, ECOL MODEL, V95, P249, DOI 10.1016/S0304-3800(96)00034-8; Giering R, 1998, ACM T MATH SOFTWARE, V24, P437, DOI 10.1145/293686.293695; Gurney KR, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003082; HEIMANN M, 1995, GLOBAL ATMOSPHERIC T; Houghton R A, 2008, TRENDS COMPENDIUM DA; Kaminski T, 2003, LECT NOTES COMPUT SC, V2668, P95; Kaminski T, 2001, Science, V294, P259; Knorr W, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040613; Knorr W, 2001, GLOBAL BIOGEOCHEM CY, V15, P207, DOI 10.1029/1998GB001059; Knorr W, 2000, GLOBAL ECOL BIOGEOGR, V9, P225, DOI 10.1046/j.1365-2699.2000.00159.x; Krinner G, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2003GB002199; Le Quere C, 2007, SCIENCE, V316, P1735, DOI 10.1126/science.1136188; Prentice I. C., 2001, CLIMATE CHANGE 2001, P183; Prentice IC, 2000, ECOL APPL, V10, P1553, DOI 10.1890/1051-0761(2000)010[1553:TCBOTT]2.0.CO;2; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Ricciuto DM, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2006GB002908; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Scholze M., 2003, THESIS M PLANCK I ME; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; TAKAHASHI T, 1999, 2 INT CO2 OC S; Tarantola A., 1987, INVERSE PROBLEM THEO; Tarantola A., 2005, INVERSE PROBLEM THEO; Vetterling W. T., 1996, NUMERICAL RECIPES FO, P90	30	2	2	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0886-6236		GLOBAL BIOGEOCHEM CY	Glob. Biogeochem. Cycle	JUN 18	2011	25								GB2021	10.1029/2010GB003886		13	Environmental Sciences; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences	Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric Sciences	780BP	WOS:000291826500001	
J	Zhang, HQ; Zhang, L; Pak, B				Zhang, Huqiang; Zhang, Liang; Pak, Bernard			Comparing surface energy, water and carbon cycle in dry and wet regions simulated by a land-surface model	THEORETICAL AND APPLIED CLIMATOLOGY			English	Article							NET PRIMARY PRODUCTION; SOIL-MOISTURE; TROPICAL DEFORESTATION; CLIMATE MODEL; IMPACTS; CIRCULATION; HYDROLOGY; CANOPIES; DIEBACK; FOREST	In this study, we analyze results from 47-year (1954-2000) offline simulations using an Australian land-surface model CSIRO Atmosphere Biosphere Land Exchange. We focus on exploring its surface mean climatology, interannual and decadal variations in Australia and Amazonia basin in South America which are distinguished by dry and wet climates respectively. Its skill is assessed by using observational datasets and four model products from the Global Land-surface Data Assimilation System. Surface evaporation and runoff climatologies are satisfactorily simulated, including surface energy and water partitions in dry and wet climates. In the Australian continent dominated by dry climate, slowly varying soil moisture processes are simulated in the southeast during austral winter. The model is skilful in reproducing the nonlinear relationship between rainfall and runoff variations in the southwestern part of the Australia. It shows that the significant downward trend of river inflow in the region is associated with enhanced surface evaporation which is caused by increased surface radiation and wind speed. In its carbon-cycle modeling, the model simulates an upward trend of NPP by about 0.69%/year over the Amazonia forest region in the 47-year period. By comparing two sets of the model results with/without CO(2) variations, it shows that 35% of such increases are caused by changes in climatic conditions, while 65% is due to the increase in atmospheric CO(2) concentration. Given the close linkage between climate, water and vegetation (carbon cycle), this work promotes an integrated modeling and evaluation approach for better representation of land-surface processes in Earth system studies.	[Zhang, Huqiang] Ctr Australian Weather & Climate Res, Melbourne, Vic 3001, Australia; [Zhang, Huqiang] CSIRO, Melbourne, Vic 3001, Australia; [Pak, Bernard] Ctr Australian Weather & Climate Res, Aspendale, Vic 3195, Australia; [Pak, Bernard] CSIRO, Aspendale, Vic 3195, Australia; [Zhang, Liang] China Meteorol Adm, Inst Arid Meteorol, Lanzhou, Peoples R China; [Pak, Bernard] CSIRO Marine & Atmospher Res, Melbourne, Vic, Australia	Zhang, HQ (reprint author), Ctr Australian Weather & Climate Res, GPO Box 1289K, Melbourne, Vic 3001, Australia.	h.zhang@bom.gov.au					Cox PM, 2004, THEOR APPL CLIMATOL, V78, P137, DOI 10.1007/s00704-004-0049-4; Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539; Dickinson RE, 1998, J CLIMATE, V11, P2823, DOI 10.1175/1520-0442(1998)011<2823:ICFACM>2.0.CO;2; DICKINSON RE, 1993, TN387STR NAT CTR ATM, P80; Dirmeyer PA, 1999, B AM METEOROL SOC, V80, P851, DOI 10.1175/1520-0477(1999)080<0851:TPPOTG>2.0.CO;2; Drosdowsky W, 2001, J CLIMATE, V14, P1677, DOI 10.1175/1520-0442(2001)014<1677:NACNGS>2.0.CO;2; Garnaut R, 2008, GARNAUT CLIMATE CHAN, P680; Ichii K, 2005, GLOBAL PLANET CHANGE, V48, P274, DOI 10.1016/j.gloplacha.2005.02.005; Jovanovic B, 2008, CLIMATIC CHANGE, V87, P517, DOI 10.1007/s10584-007-9324-6; Koster RD, 2006, J HYDROMETEOROL, V7, P590, DOI 10.1175/JHM510.1; Koster RD, 2004, SCIENCE, V305, P1138, DOI 10.1126/science.1100217; Kowalczyk E, 2007, CSIRO ATMOSPHERE BIO; Larcher W., 1980, PHYSL PLANT ECOLOGY, P303; Liang X, 1994, J GEOPHYS RES-ATMOS, V99, P14415, DOI 10.1029/94JD00483; Malhi Y, 2009, P NATL ACAD SCI USA, V106, P20610, DOI 10.1073/pnas.0804619106; MANABE S, 1969, MON WEATHER REV, V97, P739, DOI 10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2; Nemani RR, 2003, SCIENCE, V300, P1560, DOI 10.1126/science.1082750; Ngo-Duc T, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005434; OLESON KW, 2004, NCARTN461STR, P173; PURI K, 2006, BUR METEOROL RES REP, V123, P1; RAUPACH MR, 1989, Q J ROY METEOR SOC, V115, P609, DOI 10.1256/smsqj.48709; Rodell M, 2004, B AM METEOROL SOC, V85, P381, DOI 10.1175/BAMS-85-3-381; SELLERS PJ, 1986, J ATMOS SCI, V43, P505, DOI 10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2; Walko RL, 2000, J APPL METEOROL, V39, P931, DOI 10.1175/1520-0450(2000)039<0931:CABHMF>2.0.CO;2; WANG Y, 2007, BMRC RES REP, V133, P45; Wang YP, 2003, TELLUS B, V55, P290, DOI 10.1034/j.1600-0889.2003.00030.x; Wang YP, 1998, AGR FOREST METEOROL, V91, P89, DOI 10.1016/S0168-1923(98)00061-6; Zhang H, 2004, J HYDROMETEOROL, V5, P180, DOI 10.1175/1525-7541(2004)005<0180:IOLSMC>2.0.CO;2; Zhang H, 1996, J CLIMATE, V9, P1497, DOI 10.1175/1520-0442(1996)009<1497:IOTDPI>2.0.CO;2; Zhang H, 1996, J CLIMATE, V9, P2498, DOI 10.1175/1520-0442(1996)009<2498:IOTDPI>2.0.CO;2; Zhang H, 2003, J CLIMATE, V16, P2117, DOI 10.1175/1520-0442(2003)16<2117:LANIOS>2.0.CO;2; Zhang HQ, 2004, J CLIMATE, V17, P4190, DOI 10.1175/JCLI3141.1; Zhang HQ, 2009, CLIM DYNAM, V32, P473, DOI 10.1007/s00382-008-0388-4; Zhang LA, 2009, THEOR APPL CLIMATOL, V96, P375, DOI 10.1007/s00704-008-0047-z; Zhao MS, 2005, REMOTE SENS ENVIRON, V95, P164, DOI 10.1016/j.rse.2004.12.011; ZHNAG H, 2008, CLIM DYNAM, DOI DOI 10.1007/S00382-008-0472-9; Zobler L, 1986, 87802 NASA, V87802, P32; Zuo ZhiYan, 2007, Chinese Science Bulletin, V52, DOI 10.1007/s11434-007-0442-3	38	3	3	SPRINGER WIEN	WIEN	SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA	0177-798X		THEOR APPL CLIMATOL	Theor. Appl. Climatol.	JUN	2011	104	3-4					511	527		10.1007/s00704-010-0364-x		17	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	768LF	WOS:000290935400020	
J	Rayner, PJ; Koffi, E; Scholze, M; Kaminski, T; Dufresne, JL				Rayner, P. J.; Koffi, E.; Scholze, M.; Kaminski, T.; Dufresne, J. -L.			Constraining predictions of the carbon cycle using data	PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES			English	Article						carbon cycle; terrestrial uptake; data assimilation	CLIMATE-CHANGE; FEEDBACKS; PROJECTIONS; UNCERTAINTY; DIOXIDE; MODEL	We use a carbon-cycle data assimilation system to estimate the terrestrial biospheric CO(2) flux until 2090. The terrestrial sink increases rapidly and the increase is stronger in the presence of climate change. Using a linearized model, we calculate the uncertainty in the flux owing to uncertainty in model parameters. The uncertainty is large and is dominated by the impact of soil moisture on heterotrophic respiration. We show that this uncertainty can be greatly reduced by constraining the model parameters with two decades of atmospheric measurements.	[Scholze, M.] Univ Bristol, Dept Earth Sci, Bristol BS8 1RJ, Avon, England; [Kaminski, T.] Fastopt GmbH, D-20357 Hamburg, Germany; [Dufresne, J. -L.] Univ Paris 06, IPSL LMD, F-75252 Paris, France	Rayner, PJ (reprint author), Bat 701 Cea Saclay, F-91191 Gif Sur Yvette, France.	prayner@unimelb.edu.au			Australian Research Council [DP1096309]	P.R. is the recipient of an Australian Research Council Professorial Fellowship (DP1096309).	CADULE P, 2009, P 8 INT CARB DIOX C; Canadell JG, 2007, P NATL ACAD SCI USA, V104, P18866, DOI 10.1073/pnas.0702737104; Cox PM, 2004, THEOR APPL CLIMATOL, V78, P137, DOI 10.1007/s00704-004-0049-4; Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539; Denman KL, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P499; Dufresne JL, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL013777; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Friedlingstein P, 2003, TELLUS B, V55, P692, DOI 10.1034/j.1600-0889.2003.01461.x; Giering R, 1998, ACM T MATH SOFTWARE, V24, P437, DOI 10.1145/293686.293695; GLOOR M, 2010, ATMOS CHEM PHYS DISC, V10, P9045, DOI DOI 10.5194/ACPD-10-9045-2010; Gregory JM, 2009, J CLIMATE, V22, P5232, DOI 10.1175/2009JCLI2949.1; Jones CD, 2006, TELLUS B, V58, P603, DOI 10.1111/j.1600-0889.2006.00215.x; Jones CD, 2003, TELLUS B, V55, P642, DOI 10.1034/j.1600-0889.2003.01440.x; KEELING CD, 1995, NATURE, V375, P666, DOI 10.1038/375666a0; Knorr W, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040613; Knorr W, 2001, ADV GLOB CHANGE RES, V7, P273; KOFFI E, 2009, P 8 CARB DIOX C JEN; Le Quere C, 2007, SCIENCE, V316, P1735, DOI 10.1126/science.1136188; Meinshausen M, 2009, NATURE, V458, P1158, DOI 10.1038/nature08017; Rayner PJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002254; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Schuster U, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003941; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; THONING KW, 1989, J GEOPHYS RES-ATMOS, V94, P8549, DOI 10.1029/JD094iD06p08549; Thornton PE, 2009, BIOGEOSCIENCES, V6, P2099; Zaehle S, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2009GL041345	26	7	7	ROYAL SOC	LONDON	6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND	1364-503X		PHILOS T R SOC A	Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci.	MAY 28	2011	369	1943					1955	1966		10.1098/rsta.2010.0378		12	Multidisciplinary Sciences	Science & Technology - Other Topics	751LK	WOS:000289619100006	
J	Kang, JS; Kalnay, E; Liu, JJ; Fung, I; Miyoshi, T; Ide, K				Kang, Ji-Sun; Kalnay, Eugenia; Liu, Junjie; Fung, Inez; Miyoshi, Takemasa; Ide, Kayo			"Variable localization" in an ensemble Kalman filter: Application to the carbon cycle data assimilation	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							COVARIANCE INFLATION; THEORETICAL ASPECTS; ERROR STATISTICS; ATMOSPHERIC CO2; FLUXES; INVERSION; SYSTEM; SPACE; LAND; GCM	In ensemble Kalman filter, space localization is used to reduce the impact of long-distance sampling errors in the ensemble estimation of the forecast error covariance. When two variables are not physically correlated, their error covariance is still estimated by the ensemble and, therefore, it is dominated by sampling errors. We introduce a "variable localization" method, zeroing out such covariances between unrelated variables to the problem of assimilating carbon dioxide concentrations into a dynamical model using the local ensemble transform Kalman filter (LETKF) in an observing system simulation experiments (OSSE) framework. A system where meteorological and carbon variables are simultaneously assimilated is used to estimate surface carbon fluxes that are not directly observed. A range of covariance structures are explored for the LETKF, with emphasis on configurations allowing nonzero error covariance between carbon variables and the wind field, which affects transport of atmospheric CO2, but not between CO2 and the other meteorological variables. Such variable localization scheme zeroes out the background error covariance among prognostic variables that are not physically related, thus reducing sampling errors. Results from the identical twin experiments show that the performance in the estimation of surface carbon fluxes obtained using variable localization is much better than that using a standard full covariance approach. The relative improvement increases when the surface fluxes change with time and model error becomes significant.	[Kang, Ji-Sun; Kalnay, Eugenia; Miyoshi, Takemasa; Ide, Kayo] Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA; [Liu, Junjie; Fung, Inez] Univ Calif Berkeley, Dept Earth & Planetary Sci, Berkeley, CA 94720 USA	Kang, JS (reprint author), Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA.	jskang@atmos.umd.edu; ekalnay@atmos.umd.edu; jjliu@atmos.berkeley.edu; ifung@berkeley.edu; miyoshi@atmos.umd.edu; ide@atmos.umd.edu	Miyoshi, Takemasa/C-2768-2009; Ide, Kayo/F-8443-2010; KANG, JI-SUN/F-4395-2010	Miyoshi, Takemasa/0000-0003-3160-2525; 	U.S. Department of Energy under DOE [DEFG0207ER64437]; NASA [NNX08AD4oG, NNX07AM97G]; NOAA [NA09OAR4310178]; ONR [N000141010557]	We are grateful to the U.S. Department of Energy for the support of the research project, "Carbon data assimilation with coupled ensemble Kalman filter", under DOE grant DEFG0207ER64437. Support was also received from NASA grants NNX08AD4oG and NNX07AM97G, NOAA grant NA09OAR4310178, and ONR grant N000141010557. The SPEEDY model was kindly provided by Franco Molteni and Fred Kucharski. The very constructive suggestions of Andy Jacobson and two anonymous reviewers improved the paper content and presentation.	Anderson JL, 2009, TELLUS A, V61, P72, DOI 10.1111/j.1600-0870.2008.00361.x; Anderson JL, 2001, MON WEATHER REV, V129, P2884, DOI 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2; Anderson JL, 1999, MON WEATHER REV, V127, P2741, DOI 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2; Andres RJ, 1996, GLOBAL BIOGEOCHEM CY, V10, P419, DOI 10.1029/96GB01523; Annan JD, 2005, NONLINEAR PROC GEOPH, V12, P363; Baek SJ, 2006, TELLUS A, V58, P293, DOI 10.1111/j.1600-0870.2006.00178.x; Baker DF, 2010, ATMOS CHEM PHYS, V10, P4145, DOI 10.5194/acp-10-4145-2010; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Bishop CH, 2001, MON WEATHER REV, V129, P420, DOI 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2; Bousquet P, 2000, SCIENCE, V290, P1342, DOI 10.1126/science.290.5495.1342; Chevallier F, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040108; Chevallier F, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012311; Crisp D, 2004, ADV SPACE RES-SERIES, V34, P700, DOI 10.1016/j.asr.2003.08.062; Desroziers G, 2005, Q J ROY METEOR SOC, V131, P3385, DOI 10.1256/qj.05.108; Engelen RJ, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010739; Enting I. G., 2002, INVERSE PROBLEMS ATM, DOI [10.1017/CBO9780511535741, DOI 10.1017/CB09780511535741]; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Feng L, 2009, ATMOS CHEM PHYS, V9, P2619, DOI 10.5194/acp-9-2619-2009; Gaspari G, 1999, Q J ROY METEOR SOC, V125, P723, DOI 10.1256/smsqj.55416; Greybush SJ, 2011, MON WEATHER REV, V139, P511, DOI 10.1175/2010MWR3328.1; Gurney KR, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002111; Hamill TM, 2001, MON WEATHER REV, V129, P2776, DOI 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2; Hollingsworth A, 2008, B AM METEOROL SOC, V89, P1147, DOI 10.1175/2008BAMS2355.1; Houtekamer PL, 2001, MON WEATHER REV, V129, P123, DOI 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2; Hunt BR, 2007, PHYSICA D, V230, P112, DOI 10.1016/j.physd.2006.11.008; Kang J.-S., 2009, THESIS U MD COLL PAR; Li H, 2009, Q J ROY METEOR SOC, V135, P523, DOI 10.1002/qj.371; LIU J, 2011, 91 ANN M AM MET SOC; Liu JJ, 2009, MON WEATHER REV, V137, P3918, DOI 10.1175/2009MWR2791.1; Miyazaki K, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011892; MIYOSHI T, 2005, THESIS U MD COLL PAR; Molteni F, 2003, CLIM DYNAM, V20, P175, DOI 10.1007/s00382-002-0268-2; Ott E, 2004, PHYS LETT A, V330, P365, DOI 10.1016/j.physleta.2004.08.004; Peters W, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006157; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; Stephens BB, 2007, SCIENCE, V316, P1732, DOI 10.1126/science.1137004; Takahashi T, 2002, DEEP-SEA RES PT II, V49, P1601, DOI 10.1016/S0967-0645(02)00003-6; Wang H, 2009, ATMOS CHEM PHYS, V9, P7313; Whitaker JS, 2002, MON WEATHER REV, V130, P1913, DOI 10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2; YOKOTA TH, 2004, 24 INT S SPAC TECHN, P887; Zeng N, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB0022763; Zhang FQ, 2004, MON WEATHER REV, V132, P1238, DOI 10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2; Zupanski D, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008371; Zupanski M, 2006, TELLUS A, V58, P159, DOI 10.1111/j.1600-0870.2006.00173.x; Zupanski M, 2005, MON WEATHER REV, V133, P1710, DOI 10.1175/MWR2946.1	46	11	11	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	MAY 12	2011	116								D09110	10.1029/2010JD014673		15	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	764II	WOS:000290622700001	
J	Mitchell, S; Beven, K; Freer, J; Law, B				Mitchell, Stephen; Beven, Keith; Freer, Jim; Law, Beverly			Processes influencing model-data mismatch in drought-stressed, fire-disturbed eddy flux sites	JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES			English	Article							CARBON-CYCLE FEEDBACKS; DAILY SOLAR-RADIATION; PONDEROSA PINE; TERRESTRIAL CARBON; ECOSYSTEM MODEL; CLIMATE-CHANGE; COVARIANCE MEASUREMENTS; UNCERTAINTY ESTIMATION; DATA ASSIMILATION; SAMPLING ERRORS	Semiarid forests are very sensitive to climatic change and among the most difficult ecosystems to accurately model. We tested the performance of the Biome-BGC model against eddy flux data taken from young (years 2004-2008), mature (years 2002-2008), and old-growth (year 2000) ponderosa pine stands at Metolius, Oregon, and subsequently examined several potential causes for model-data mismatch. We used the Generalized Likelihood Uncertainty Estimation methodology, which involved 500,000 model runs for each stand (1,500,000 total). Each simulation was run with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in modeled estimates of net ecosystem CO(2) exchange (NEE) that were compared to measured eddy flux data. Simulations for the young stand exhibited the highest level of performance, though they overestimated ecosystem C accumulation (-NEE) 99% of the time. Among the simulations for the mature and old-growth stands, 100% and 99% of the simulations underestimated ecosystem C accumulation. One obvious area of model-data mismatch is soil moisture, which was overestimated by the model in the young and old-growth stands yet underestimated in the mature stand. However, modeled estimates of soil water content and associated water deficits did not appear to be the primary cause of model-data mismatch; our analysis indicated that gross primary production can be accurately modeled even if soil moisture content is not. Instead, difficulties in adequately modeling ecosystem respiration, mainly autotrophic respiration, appeared to be the fundamental cause of model-data mismatch.	[Mitchell, Stephen] Duke Univ, Nicholas Sch Environm, Durham, NC 27708 USA; [Beven, Keith] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England; [Beven, Keith] Uppsala Univ, Dept Earth Sci, Uppsala, Sweden; [Freer, Jim] Univ Bristol, Sch Geog Sci, Bristol BS8 1SS, Avon, England; [Law, Beverly] Oregon State Univ, Dept Forest Ecosyst & Soc, Coll Forestry, Corvallis, OR 97331 USA	Mitchell, S (reprint author), Duke Univ, Nicholas Sch Environm, Durham, NC 27708 USA.	stephen.mitchell@duke.edu	Freer, Jim/C-7335-2009; Law, Beverly/G-3882-2010; Beven, Keith/F-8707-2011		U.S. Department of Energy [DE-FG02-03ER63653]; National Science Foundation [0333257]; NASA at Oregon State University [NN604GR436, DEB-0218088)]; UK NERC [NER/L/S/2001/00658, NE/E002242/1]	The Metolius AmeriFlux sites are supported by the U.S. Department of Energy Terrestrial Carbon Program (grant DE-FG02-03ER63653). Discussions regarding Biome-BGC with Ron Neilson at the United States Forest Service Pacific Northwest Research Station were also very helpful, as was his generosity in letting us use his Linux cluster. Biome-BGC version 4.1.2 was provided by Peter Thornton at the National Center for Atmospheric Research (NCAR), and by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana. NCAR is sponsored by the National Science Foundation. Support for this research was provided by an NSF IGERT graduate fellowship to S. R. Mitchell (NSF award 0333257) in the Ecosystem Informatics IGERT program at Oregon State University, a NASA New Investigator Program grant to K. E. B. O'Connell (NN604GR436) at Oregon State University, and the H. J. Andrews LTER (DEB-0218088). Development of the GLUE methodology has been supported by UK NERC grant NER/L/S/2001/00658 and NE/E002242/1.	Allen CD, 1998, P NATL ACAD SCI USA, V95, P14839, DOI 10.1073/pnas.95.25.14839; Anthoni PM, 2002, AGR FOREST METEOROL, V111, P203, DOI 10.1016/S0168-1923(02)00021-7; Baldocchi D, 2008, AUST J BOT, V56, P1, DOI 10.1071/BT07151; Baldocchi DD, 2003, GLOBAL CHANGE BIOL, V9, P479, DOI 10.1046/j.1365-2486.2003.00629.x; Beven K, 2002, P ROY SOC A-MATH PHY, V458, P2465, DOI 10.1098/rspa.2002.0986; Beven K, 2001, J HYDROL, V249, P11, DOI 10.1016/S0022-1694(01)00421-8; Beven K, 2006, J HYDROL, V320, P18, DOI 10.1016/j.jhydrol.2005.07.007; BEVEN K, 1992, HYDROL PROCESS, V6, P279, DOI 10.1002/hyp.3360060305; Bork J, 1985, THESIS OREGON STATE; BRASWELL B, 2005, GLOBAL CHANGE BIOL, V2, P219; Breshears DD, 2005, P NATL ACAD SCI USA, V102, P15144, DOI 10.1073/pnas.0505734102; Chapin FS, 2006, ECOSYSTEMS, V9, P1041, DOI 10.1007/s10021-005-0105-7; Cox PM, 2000, NATURE, V408, P184, DOI 10.1038/35041539; DeLucia EH, 2007, GLOBAL CHANGE BIOL, V13, P1157, DOI 10.1111/j.1365-2486.2007.01365.x; Falge E, 2001, AGR FOREST METEOROL, V107, P43, DOI 10.1016/S0168-1923(00)00225-2; Fischlin A, 2007, CLIMATE CHANGE 2007, P211, DOI DOI 10.1007/S10113-010-0191-8; Franks SW, 1997, AGR FOREST METEOROL, V86, P63, DOI 10.1016/S0168-1923(96)02421-5; Freer J, 1996, WATER RESOUR RES, V32, P2161, DOI 10.1029/95WR03723; Freer JE, 2004, J HYDROL, V291, P254, DOI 10.1016/j.jhydrol.2003.12.037; Friedlingstein P, 2003, TELLUS B, V55, P692, DOI 10.1034/j.1600-0889.2003.01461.x; Irvine J, 2004, TREE PHYSIOL, V24, P753; Janssens IA, 2001, GLOBAL CHANGE BIOL, V7, P269, DOI 10.1046/j.1365-2486.2001.00412.x; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kremer RG, 1996, ECOL MODEL, V84, P151, DOI 10.1016/0304-3800(94)00140-5; Law BE, 1999, GLOB CHANGE BIOL, V5, P169, DOI 10.1046/j.1365-2486.1999.00214.x; Law BE, 2001, GLOBAL CHANGE BIOL, V7, P755, DOI 10.1046/j.1354-1013.2001.00439.x; Law BE, 2003, GLOBAL CHANGE BIOL, V9, P510, DOI 10.1046/j.1365-2486.2003.00624.x; Luyssaert S, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003377; Martinez-Vilalta J, 2002, ECOL MODEL, V155, P127, DOI 10.1016/S0304-3800(02)00025-X; Meigs GW, 2009, ECOSYSTEMS, V12, P1246, DOI 10.1007/s10021-009-9285-x; Mitchell S, 2009, ECOL MODEL, V220, P3259, DOI 10.1016/j.ecolmodel.2009.08.021; Moorcroft PR, 2006, TRENDS ECOL EVOL, V21, P400, DOI 10.1016/j.tree.2006.04.009; Oren R, 2006, GLOBAL CHANGE BIOL, V12, P883, DOI 10.1111/j.1365-2486.2006.01131.x; Page T, 2003, WATER AIR SOIL POLL, V142, P71, DOI 10.1023/A:1022011520036; Pinol J, 2007, ECOL MODEL, V207, P34, DOI 10.1016/j.ecolmodel.2007.03.020; Pinol J, 2005, ECOL MODEL, V183, P397, DOI 10.1016/j.ecolmodel.2004.09.001; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; RUNNING SW, 1988, ECOL MODEL, V42, P125, DOI 10.1016/0304-3800(88)90112-3; RYAN MG, 1991, ECOL APPL, V1, P157, DOI 10.2307/1941808; Sacks WJ, 2006, GLOBAL CHANGE BIOL, V12, P240, DOI 10.1111/j.1365-2486.2005.01059.x; Schulz K, 2001, J CLIMATE, V14, P2551, DOI 10.1175/1520-0442(2001)014<2551:TPUOLS>2.0.CO;2; Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x; Smithwick EAH, 2002, ECOL APPL, V12, P1303, DOI 10.2307/3099973; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; SPEAR RC, 1980, WATER RES, V14, P43, DOI 10.1016/0043-1354(80)90040-8; Thomas CK, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2009JG001010; Thornton PE, 2002, AGR FOREST METEOROL, V113, P185, DOI 10.1016/S0168-1923(02)00108-9; Thornton PE, 2000, AGR FOREST METEOROL, V104, P255, DOI 10.1016/S0168-1923(00)00170-2; Thornton PE, 1999, AGR FOREST METEOROL, V93, P211, DOI 10.1016/S0168-1923(98)00126-9; Thornton PE, 2005, ECOL MODEL, V189, P25, DOI 10.1016/j.ecolmodel.2005.04.008; Verbeeck H, 2006, TREE PHYSIOL, V26, P807; Vickers D, 2009, AGR FOREST METEOROL, V149, P73, DOI 10.1016/j.agrformet.2008.07.005; Wang W, 2009, ECOL MODEL, V220, P2009, DOI 10.1016/j.ecolmodel.2009.04.051; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; White M.A., 2000, EARTH INTERACT, V4, P1, DOI DOI 10.1175/1087-3562(2000)004<0003:PASAOT>2.0.CO;2; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Xu M, 2001, TREE PHYSIOL, V21, P309	57	3	3	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	0148-0227		J GEOPHYS RES-BIOGEO	J. Geophys. Res.-Biogeosci.	MAY 7	2011	116								G02008	10.1029/2009JG001146		15	Environmental Sciences; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	761QY	WOS:000290414300001	
J	Saide, PE; Carmichael, GR; Spak, SN; Gallardo, L; Osses, AE; Mena-Carrasco, MA; Pagowski, M				Saide, Pablo E.; Carmichael, Gregory R.; Spak, Scott N.; Gallardo, Laura; Osses, Axel E.; Mena-Carrasco, Marcelo A.; Pagowski, Mariusz			Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model	ATMOSPHERIC ENVIRONMENT			English	Article						PM10 and PM2.5 forecast; WRF-Chem CO tracer; Santiago de Chile; Data assimilation; Deterministic model	SUBTROPICAL WEST-COAST; AIR-POLLUTION; HIGH-RESOLUTION; BOUNDARY-LAYER; MEAN STRUCTURE; SOUTH-AMERICA; SANTIAGO; CHILE; PREDICTION; SYSTEM	This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of 150 mu g m(-3) (24 h running averages). The forecasting system is based on accurately simulating carbon monoxide (CO) as a PM10/PM2.5 surrogate, since during episodes and within the city there is a high correlation (over 0.95) among these pollutants. Thus, by accurately forecasting CO, which behaves closely to a tracer on this scale, a PM estimate can be made without involving aerosol-chemistry modeling. Nevertheless, the very stable nocturnal conditions over steep topography associated with maxima in concentrations are hard to represent in models. Here we propose a forecast system based on the WRF-Chem model with optimum settings, determined through extensive testing, that best describe both meteorological and air quality available measurements. Some of the important configurations choices involve the boundary layer (PBL) scheme, model grid resolution (both vertical and horizontal), meteorological initial and boundary conditions and spatial and temporal distribution of the emissions. A forecast for the 2008 winter is performed showing that this forecasting system is able to perform similarly to the authority decision for PM10 and better than persistence when forecasting PM10 and PM2.5 high pollution episodes. Problems regarding false alarm predictions could be related to different uncertainties in the model such as day to day emission variability, inability of the model to completely resolve the complex topography and inaccuracy in meteorological initial and boundary conditions. Finally, according to our simulations, emissions from previous days dominate episode concentrations, which highlights the need for 48 h forecasts that can be achieved by the system presented here. This is in fact the largest advantage of the proposed system. (C) 2011 Elsevier Ltd. All rights reserved.	[Saide, Pablo E.; Carmichael, Gregory R.; Spak, Scott N.] Univ Iowa, Ctr Global & Reg Environm Res, Iowa City, IA 52242 USA; [Gallardo, Laura] Univ Chile, Dept Geofis, Santiago, Chile; [Gallardo, Laura; Osses, Axel E.] Univ Chile, CNRS, Ctr Modelamiento Matemat, UMI 2807, Santiago, Chile; [Osses, Axel E.] Univ Chile, Dept Ingn Matemat, Santiago, Chile; [Mena-Carrasco, Marcelo A.] Univ Andres Bello, Ctr Sustainabil Res, Santiago, Chile; [Pagowski, Mariusz] NOAA, Earth Syst Res Lab, Boulder, CO USA; [Pagowski, Mariusz] Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA	Saide, PE (reprint author), Univ Iowa, Ctr Global & Reg Environm Res, Iowa City, IA 52242 USA.	pablo-saide@uiowa.edu	Mena-Carrasco, Marcelo/B-8483-2012; Spak, Scott/B-7331-2008	Spak, Scott/0000-0002-8545-1411	US National Science Foundation [GEO-0452325]; NSF [0748012]; FONDECYT Iniciacion [11090084]; Fulbright-CONICYT [15093810]	This work was carried out with the aid of a grant from the Inter-American Institute for Global Change Research (IAI) CRN II 2017 which is supported by the US National Science Foundation (Grant GEO-0452325), NSF grant number 0748012, FONDECYT Iniciacion grant 11090084, and Fulbright-CONICYT scholarship number 15093810. We also acknowledge Ricardo Munoz (Geophysics Department, University of Chile) for providing boundary layer depth estimates based on ceilometer data and two anonymous reviewers for their valuable comments.	Baklanov A, 2006, ATMOS CHEM PHYS, V6, P2005; Baklanov A, 2007, ATMOS CHEM PHYS, V7, P855; Baklanov A, 2008, ATMOS CHEM PHYS, V8, P523; CASSMASSI J, 1999, 2 COM NAC MED AMB; Corvalan RM, 2002, J AIR WASTE MANAGE, V52, P167; Dobson JE, 2000, PHOTOGRAMM ENG REM S, V66, P849; DUDHIA J, 1989, J ATMOS SCI, V46, P3077, DOI 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2; Fay B, 2006, ATMOS CHEM PHYS, V6, P2107; Finardi S, 2008, ENVIRON MODELL SOFTW, V23, P344, DOI 10.1016/j.envsoft.2007.04.001; Gallardo L, 2002, ATMOS ENVIRON, V36, P3829, DOI 10.1016/S1352-2310(02)00285-6; Garreaud RD, 2002, MON WEATHER REV, V130, P75, DOI 10.1175/1520-0493(2002)130<0075:CLATSW>2.0.CO;2; Garreaud RD, 2003, MON WEATHER REV, V131, P891, DOI 10.1175/1520-0493(2003)131<0891:CLATSW>2.0.CO;2; Gramsch E, 2006, ATMOS ENVIRON, V40, P5464, DOI 10.1016/j.atmosenv.2006.03.062; Grell GA, 2005, ATMOS ENVIRON, V39, P6957, DOI 10.1016/j.atmosenv.2005.04.027; Hamdi R, 2007, ATMOS CHEM PHYS, V7, P4513; Hong SY, 2010, Q J ROY METEOR SOC, V136, P1481, DOI 10.1002/qj.665; Hong SY, 2004, MON WEATHER REV, V132, P103, DOI 10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2; Janjic Z. I, 2002, 437 NCEP, P61; Jorquera H, 2010, ATMOS ENVIRON, V44, P42, DOI 10.1016/j.atmosenv.2009.09.040; Kanamitsu M, 2002, B AM METEOROL SOC, V83, P1631, DOI 10.1175/Bams-83-11-1631; Kim Y, 2010, ATMOS ENVIRON, V44, P523, DOI 10.1016/j.atmosenv.2009.10.045; Kistler R, 2001, B AM METEOROL SOC, V82, P247, DOI 10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2; MENACARRASCO M, 2007, THESIS U IOWA; Mlawer EJ, 1997, J GEOPHYS RES-ATMOS, V102, P16663, DOI 10.1029/97JD00237; Munoz RC, 2010, J APPL METEOROL CLIM, V49, P1728, DOI 10.1175/2010JAMC2347.1; Nakanishi M, 2004, BOUND-LAY METEOROL, V112, P1, DOI 10.1023/B:BOUN.0000020164.04146.98; PAGOWSKI M, Q J ROYAL M IN PRESS; Palau JL, 2005, ATMOS CHEM PHYS, V5, P2771; Perez C, 2006, ATMOS ENVIRON, V40, P5274, DOI 10.1016/j.atmosenv.2006.04.039; Perez P, 2008, ATMOS ENVIRON, V42, P8219, DOI 10.1016/j.atmosenv.2008.07.035; Perez P, 2006, ATMOS ENVIRON, V40, P2845, DOI 10.1016/j.atmosenv.2006.01.010; Perez P, 2002, ATMOS ENVIRON, V36, P4555, DOI 10.1016/S1352-2310(02)00419-3; Perez P, 2004, J AIR WASTE MANAGE, V54, P908; Rahn DA, 2010, ATMOS CHEM PHYS, V10, P4491, DOI 10.5194/acp-10-4491-2010; RUTLLANT J, 1995, ENVIRON MONIT ASSESS, V34, P223, DOI 10.1007/BF00554796; Rutllant JA, 2004, MON WEATHER REV, V132, P611, DOI 10.1175/1520-0493(2004)132<0611:EOSFDT>2.0.CO;2; Saide P., 2009, ATMOS CHEM PHYS DISC, V9, P6325, DOI 10.5194/acpd-9-6325-2009; Saide P, 2009, ATMOS ENVIRON, V43, P4914, DOI 10.1016/j.atmosenv.2009.07.013; Schmitz R, 2005, ATMOS ENVIRON, V39, P2035, DOI 10.1016/j.atmosenv.2004.12.033; Schroeder AJ, 2006, MON WEATHER REV, V134, P1237, DOI 10.1175/MWR3118.1; SEAMAN N, 2009, 9 WRF US WORKSH BOUL, P7; Seinfeld J., 2006, ATMOSPHERIC CHEM PHY; Shrestha KL, 2009, J ENVIRON SCI-CHINA, V21, P782, DOI 10.1016/S1001-0742(08)62341-4; Skamarock W. C., 2008, NCARTN475STR; Sukoriansky S, 2005, BOUND-LAY METEOROL, V117, P231, DOI 10.1007/s10546-004-6848-4; Wu WS, 2002, MON WEATHER REV, V130, P2905, DOI 10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2; ZAVERI RA, 2008, J GEOPHYS RES ATMOSP, V113; *AIRNOW, 2010, AIR QUAL IND GUID; *CONAMA, 2007, UPD ATM EM INV SANT; *CONAMA, 2010, EM INV SANT; *MINSEGPRES, 2010, DECR SUPR, V66; *SINCA, 2010, CHIL AIR QUAL INF SY	52	9	10	PERGAMON-ELSEVIER SCIENCE LTD	OXFORD	THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND	1352-2310		ATMOS ENVIRON	Atmos. Environ.	MAY	2011	45	16					2769	2780		10.1016/j.atmosenv.2011.02.001		12	Environmental Sciences; Meteorology & Atmospheric Sciences	Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences	767VH	WOS:000290886000018	
J	Peng, CH; Guiot, J; Wu, HB; Jiang, H; Luo, YQ				Peng, Changhui; Guiot, Joel; Wu, Haibin; Jiang, Hong; Luo, Yiqi			Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach	ECOLOGY LETTERS			English	Review						Carbon cycle; data assimilation; earth system modelling; ecological forecasting; global climate change; inverse modelling; palaeoclimatic reconstruction; sequential data assimilation; variational data assimilation	ENSEMBLE KALMAN FILTER; TERRESTRIAL ECOSYSTEM MODEL; LAST GLACIAL MAXIMUM; DATA ASSIMILATION; CARBON STORAGE; POLLEN DATA; SPECIES DISTRIBUTIONS; IMAGING SPECTROSCOPY; PARAMETER-ESTIMATION; CLIMATE-CHANGE	P>It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e. palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services.	[Peng, Changhui] NW A&F Univ, Coll Forestry, Lab Ecol Forecasting & Global Change, Yangling 712100, Shaanxi, Peoples R China; [Peng, Changhui; Guiot, Joel] Aix Marseille Univ, CNRS, ECCOREV FR 3098, F-13545 Aix En Provence 4, France; [Peng, Changhui] Univ Quebec, Inst Environm Sci, Dept Biol Sci, Montreal, PQ H3C 3P8, Canada; [Wu, Haibin] Chinese Acad Sci, Key Lab Cenozo Geol & Environm, Inst Geol & Geophys, Beijing 100029, Peoples R China; [Jiang, Hong] Zhejiang Agr & Forestry Univ, State Key Lab Subtrop Forest Sci, Hangzhou 311300, Zhejiang, Peoples R China; [Jiang, Hong] Zhejiang Agr & Forestry Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Hangzhou 311300, Zhejiang, Peoples R China; [Jiang, Hong] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210093, Jiangsu, Peoples R China; [Luo, Yiqi] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA	Peng, CH (reprint author), NW A&F Univ, Coll Forestry, Lab Ecol Forecasting & Global Change, Yangling 712100, Shaanxi, Peoples R China.	peng.changhui@uqam.ca	Peng, Changhui/G-8248-2012; Guiot, Joel/G-7818-2011	Guiot, Joel/0000-0001-7345-4466	China QianRen program; Institute of Ecology and Environment (INEE) of CNRS (French National Center for Scientific Research); National Science and Engineering Research Council of Canada (NSERC); Canada Research Chair Program	This work was conducted in China and France during the sabbatical leave of C. Peng. For financial support, we would like to thank the China QianRen program, the Institute of Ecology and Environment (INEE) of CNRS (French National Center for Scientific Research), the National Science and Engineering Research Council of Canada (NSERC) Discover Grant, and the Canada Research Chair Program. C. Peng acknowledges the financial and scientific support he received during his sabbatical leave at ECCOREV, France, and at Northwest A&F University, China. We are grateful to Bin He, the editor and three anonymous referees for their constructive comments and suggestions concerning the paper. We also thank Wenhua Zhang for her technical assistance and Brian Doonan for his editorial help.	Abramowitz G, 2007, J HYDROMETEOROL, V8, P989, DOI 10.1175/JHM628.1; ADAMS JM, 1990, NATURE, V348, P711, DOI 10.1038/348711a0; ARAUJO M, 2006, TRENDS ECOL EVOL, V22, P42, DOI DOI 10.1016/J.TREE.2006.09.010; Araujo MB, 2005, GLOBAL ECOL BIOGEOGR, V14, P529, DOI 10.1111/j.1466-822x.2005.00182.x; Arulampalam MS, 2002, IEEE T SIGNAL PROCES, V50, P174, DOI 10.1109/78.978374; Baker DF, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2004GB002439; BATES JM, 1969, OPER RES QUART, V20, P451, DOI 10.2307/3008764; Benestad RE, 2004, TELLUS A, V56, P89, DOI 10.1111/j.1600-0870.2004.00039.x; Bousquet P, 2000, SCIENCE, V290, P1342, DOI 10.1126/science.290.5495.1342; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Burgers G, 1998, MON WEATHER REV, V126, P1719, DOI 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2; Carvalhais N, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003033; Chen M, 2008, ECOL MODEL, V219, P317, DOI 10.1016/j.ecolmodel.2008.07.013; Cheung KKW, 2001, METEOROL APPL, V8, P315, DOI 10.1017/S1350482701003073; Clark JS, 2001, SCIENCE, V293, P657, DOI 10.1126/science.293.5530.657; CLEMEN RT, 1989, INT J FORECASTING, V5, P559, DOI 10.1016/0169-2070(89)90012-5; COSBY BJ, 1984, WATER SCI TECHNOL, V16, P561; Cressman G. P., 1959, MON WEATHER REV, V87, P367, DOI DOI 10.1175/1520-0493(1959)087<0367:A00AS>2.0.C0;2; Daley R., 1991, ATMOSPHERIC DATA ANA; Denning AS, 1999, TELLUS B, V51, P266, DOI 10.1034/j.1600-0889.1999.00012.x; Enting I.G., 2002, INVERSE PROBLEMS ATM; EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572; Evensen G., 2007, DATA ASSIMILATION EN; EVENSEN G, 1992, J GEOPHYS RES-OCEANS, V97, P17905, DOI 10.1029/92JC01972; Evensen G, 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Fang HL, 2005, REMOTE SENS ENVIRON, V94, P405, DOI 10.1016/j.rse.2004.11.001; FOLEY JA, 1995, TELLUS B, V47, P310, DOI 10.1034/j.1600-0889.47.issue3.3.x; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; Gandin L. S., 1963, OBJECTIVE ANAL METEO; GAO G, 2011, ECOL APPL IN PRESS; GARRETA V, 2009, CLIM DYNAM, V35, P371; Gauthier P, 2007, MON WEATHER REV, V135, P2339, DOI 10.1175/MWR3394.1; Gregory AW, 2001, J BUS ECON STAT, V19, P34, DOI 10.1198/07350010152472599; Guiot J, 2007, DEV MARINE GEOL, V1, P523, DOI 10.1016/S1572-5480(07)01018-4; GUIOT J, 2009, CLIM PAST DISCUSS, V5, P99; Guiot J, 2000, ECOL MODEL, V127, P119, DOI 10.1016/S0304-3800(99)00219-7; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; Haslett J, 2006, J ROY STAT SOC A STA, V169, P395, DOI 10.1111/j.1467-985X.2006.00429.x; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; Hatte C, 2005, CLIM DYNAM, V25, P315, DOI 10.1007/s00382-005-0034-3; Hatte C, 2009, CLIM PAST, V5, P147; Hazarika MK, 2005, REMOTE SENS ENVIRON, V94, P298, DOI 10.1016/j.rse.2004.10.004; Houtekamer PL, 2009, MON WEATHER REV, V137, P2126, DOI 10.1175/2008MWR2737.1; Hunt BR, 2004, TELLUS A, V56, P273, DOI 10.1111/j.1600-0870.2004.00066.x; Iverson LR, 1998, ECOL MONOGR, V68, P465, DOI 10.1890/0012-9615(1998)068[0465:PAOTSF]2.0.CO;2; Joos F, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002156; Kalman R. E., 1960, J BASIC ENG, V82, P35, DOI DOI 10.1115/1.3662552; Kalnay E., 2003, ATMOSPHERIC MODELLIN; Kaplan JO, 2001, GEOPHYS APPL VEGETAT; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; KOLOMYTS EG, 2008, GEOGRAPHY NATURE RES, V29, P209, DOI 10.1016/j.gnr.2008.09.002; LAPLACE PS, 1918, COURCIER, V7, P531; Levenberg K., 1944, Quarterly of Applied Mathematics, V2; Liang SL, 2008, ADVANCES IN LAND REMOTE SENSING: SYSTEM, MODELING, INVERSION AND APPLICATION, P313, DOI 10.1007/978-1-4020-6450-0_12; Liang SL, 2007, PROG PHYS GEOG, V31, P501, DOI 10.1177/0309133307084626; Liu YQ, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005756; LORENC A, 2007, Q J ROY METEOR SOC, V133, P347; LORENC AC, 1981, MON WEATHER REV, V109, P701, DOI 10.1175/1520-0493(1981)109<0701:AGTDMS>2.0.CO;2; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; LUO Y, 2011, ECOL APPL IN PRESS; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; MAKRIDAKIS S, 1983, MANAGE SCI, V29, P987, DOI 10.1287/mnsc.29.9.987; Mandel J, 2008, MATH COMPUT SIMULAT, V79, P584, DOI 10.1016/j.matcom.2008.03.015; MARQUARDT DW, 1963, J SOC IND APPL MATH, V11, P431; Mathieu PP, 2008, REMOTE SENS ENVIRON, V112, P1258, DOI 10.1016/j.rse.2007.02.040; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; Miyamoto M.M., 1985, Cladistics, V1, P186, DOI 10.1111/j.1096-0031.1985.tb00421.x; Mo XG, 2008, ECOL MODEL, V217, P157, DOI 10.1016/j.ecolmodel.2008.06.021; Ollinger SV, 2005, ECOSYSTEMS, V8, P760, DOI 10.1007/s10021-005-0079-5; Pan M, 2008, REMOTE SENS ENVIRON, V112, P1282, DOI 10.1016/j.rse.2007.02.039; Peng CH, 1998, QUATERNARY SCI REV, V17, P719, DOI 10.1016/S0277-3791(97)00045-0; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Potter CS, 2001, J GEOPHYS RES-ATMOS, V106, P33671, DOI 10.1029/2000JD000224; PRENTICE IC, 1992, J BIOGEOGR, V19, P117, DOI 10.2307/2845499; Prentice IC, 1996, CLIM DYNAM, V12, P185, DOI 10.1007/s003820050102; Quaife T, 2008, REMOTE SENS ENVIRON, V112, P1347, DOI 10.1016/j.rse.2007.05.020; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Reichle RH, 2002, MON WEATHER REV, V130, P103, DOI 10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2; Renzullo LJ, 2008, REMOTE SENS ENVIRON, V112, P1306, DOI 10.1016/j.rse.2007.06.022; Sacks WJ, 2006, GLOBAL CHANGE BIOL, V12, P240, DOI 10.1111/j.1365-2486.2005.01059.x; Sanders F., 1963, J APPL METEOROL, V2, P191, DOI 10.1175/1520-0450(1963)002<0191:OSPF>2.0.CO;2; Santaren D, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002834; Schaepman ME, 2007, PHOTOGRAMM ENG REM S, V73, P1179; Scholze M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008642; Smith B, 2001, GLOBAL ECOL BIOGEOGR, V10, P621, DOI 10.1046/j.1466-822X.2001.t01-1-00256.x; Stockli R, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000562; Tang JY, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011724; Tarantola A., 2005, INVERSE PROBLEM THEO; Thiemann M, 2001, WATER RESOUR RES, V37, P2521, DOI 10.1029/2000WR900405; Thuiller W, 2003, GLOBAL CHANGE BIOL, V9, P1353, DOI 10.1046/j.1365-2486.2003.00666.x; Thuiller W, 2009, ECOGRAPHY, V32, P369, DOI 10.1111/j.1600-0587.2008.05742.x; Trudinger CM, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000367; Turner DP, 2004, BIOSCIENCE, V54, P573, DOI 10.1641/0006-3568(2004)054[0573:IRSAEP]2.0.CO;2; VANCAMPO E, 1993, GLOBAL PLANET CHANGE, V8, P189, DOI 10.1016/0921-8181(93)90008-C; Verstraeten WW, 2008, ADV SPACE RES, V41, P20, DOI 10.1016/j.asr.2007.08.021; Vrugt JA, 2005, WATER RESOUR RES, V41, DOI 10.1029/2004WR003059; Wang YP, 2009, AGR FOREST METEOROL, V149, P1829, DOI 10.1016/j.agrformet.2009.07.009; WILLIAMS M, 2009, BIOGEOSCIENCES DISCU, V6, P2785, DOI DOI 10.5194/BGD-6-2785-2009; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Wu HB, 2007, CLIM DYNAM, V29, P211, DOI 10.1007/s00382-007-0231-3; Wu HB, 2009, GLOBAL CHANGE BIOL, V15, P82, DOI 10.1111/j.1365-2486.2008.01712.x; Wu HB, 2007, P NATL ACAD SCI USA, V104, P9720, DOI 10.1073/pnas.0610109104; Xu T, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002468; Zhou T, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB002939	105	8	9	WILEY-BLACKWELL	MALDEN	COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA	1461-023X		ECOL LETT	Ecol. Lett.	MAY	2011	14	5					522	536		10.1111/j.1461-0248.2011.01603.x		15	Ecology	Environmental Sciences & Ecology	749NK	WOS:000289474700011	
J	Hill, TC; Quaife, T; Williams, M				Hill, T. C.; Quaife, T.; Williams, M.			A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							LEAF-AREA INDEX; KALMAN FILTER; MODELS; WATER; FLUX; PARAMETER; SYSTEMS; FUSION; ENERGY; BIAS	We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The "disaggregation" approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a "zero-order" model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set "truth." Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality.	[Hill, T. C.; Williams, M.] Univ Edinburgh, Sch Geosci, Edinburgh EH9 3JN, Midlothian, Scotland; [Quaife, T.] Univ Exeter, Sch Geog, Penryn TR10 9EZ, Cornwall, England; [Quaife, T.] Univ Exeter, NERC Natl Ctr Earth Observat, Penryn TR10 9EZ, Cornwall, England; [Hill, T. C.; Williams, M.] Univ Edinburgh, NERC Natl Ctr Earth Observat, Edinburgh EH9 3JN, Midlothian, Scotland	Hill, TC (reprint author), Univ Edinburgh, Sch Geosci, Kings Bldgs,W Mains Rd, Edinburgh EH9 3JN, Midlothian, Scotland.	thill@staffmail.ed.ac.uk	Quaife, Tristan/C-1355-2008		Natural Environment Research Council (NERC) through National Centre for Earth Observation (NCEO); Natural Environment Research Council (NERC) through Centre for Terrestrial Carbon Dynamics (CTCD)	The Natural Environment Research Council (NERC) funded this work through the National Centre for Earth Observation (NCEO) and the Centre for Terrestrial Carbon Dynamics (CTCD). We are grateful to B. Huntley and R. Baxter for access to the Natural Environment Research Council's (NERC) Airborne Research and Survey Facility (ARSF) spatial maps of NDVI; C. Llyod, J. Evans, and R. Harding for access to meteorological data; and also M. Disney and A. Prieto-Blanco for providing processed NDVI data gathered as part of the NERC Arctic Biosphere Atmosphere at Multiple Scales (ABACUS) project. Finally, the authors would like to express their gratitude to the anonymous reviewers for their valuable comments.	Bayes T, 1973, PHILOS T ROY SOC LON, V53, P370, DOI DOI 10.1098/RSBL.1763.0053; Chen B, 2010, BIOGEOSCIENCES, V7, P2943, DOI 10.5194/bg-7-2943-2010; Chen BZ, 2007, J HYDROMETEOROL, V8, P123, DOI 10.1175/JHM566.1; Chen JM, 1999, REMOTE SENS ENVIRON, V69, P30, DOI 10.1016/S0034-4257(99)00006-1; Dee DP, 2005, Q J ROY METEOR SOC, V131, P3323, DOI 10.1256/qj.05.137; Dee DP, 1998, Q J ROY METEOR SOC, V124, P269, DOI 10.1256/smsqj.54511; Demarty J, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030014; Dowd M, 2007, J MARINE SYST, V68, P439, DOI 10.1016/j.jmarsys.2007.01.007; Evensen G, 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Evensen G, 2009, IEEE CONTR SYST MAG, V29, P83, DOI 10.1109/MCS.2009.932223; Fletcher BJ, 2010, OECOLOGIA, V163, P47, DOI 10.1007/s00442-009-1532-5; Fox A, 2009, AGR FOREST METEOROL, V149, P1597, DOI 10.1016/j.agrformet.2009.05.002; JARVIS PG, 1995, PLANT CELL ENVIRON, V18, P1079, DOI 10.1111/j.1365-3040.1995.tb00620.x; Kimball JS, 1999, J GEOPHYS RES-ATMOS, V104, P27789, DOI 10.1029/1999JD900085; Quaife T, 2008, REMOTE SENS ENVIRON, V112, P1347, DOI 10.1016/j.rse.2007.05.020; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Roy DP, 2008, REMOTE SENS ENVIRON, V112, P3112, DOI 10.1016/j.rse.2008.03.009; Saatchi SS, 2007, GLOBAL CHANGE BIOL, V13, P816, DOI 10.1111/j.1365-2486.2007.01323.x; Shaver GR, 2007, J ECOL, V95, P802, DOI 10.1111/j.1365-2745.2007.01259.x; Spadavecchia L, 2008, J ECOL, V96, P1238, DOI 10.1111/j.1365-2745.2008.01424.x; Stoy PC, 2009, LANDSCAPE ECOL, V24, P971, DOI 10.1007/s10980-009-9367-3; Tang JY, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011724; Tang JY, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2008JG000757; Trudinger CM, 2008, ENVIRONMETRICS, V19, P849, DOI 10.1002/env.910; van Leeuwen PJ, 2009, MON WEATHER REV, V137, P4089, DOI 10.1175/2009MWR2835.1; van Wijk MT, 2005, ECOL APPL, V15, P1462, DOI 10.1890/03-5354; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Wolfe RE, 1998, IEEE T GEOSCI REMOTE, V36, P1324, DOI 10.1109/36.701082; WOOD EF, 1993, J CLIMATE, V6, P839, DOI 10.1175/1520-0442(1993)006<0839:SWAEFI>2.0.CO;2; Zobitz JM, 2008, ECOSYSTEMS, V11, P250, DOI 10.1007/s10021-007-9120-1	31	2	2	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	APR 29	2011	116								D08117	10.1029/2010JD015268		12	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	757QR	WOS:000290103300001	
J	Ingwersen, J; Steffens, K; Hogy, P; Warrach-Sagi, K; Zhunusbayeva, D; Poltoradnev, M; Gabler, R; Wizemann, HD; Fangmeier, A; Wulfmeyer, V; Streck, T				Ingwersen, J.; Steffens, K.; Hoegy, P.; Warrach-Sagi, K.; Zhunusbayeva, D.; Poltoradnev, M.; Gabler, R.; Wizemann, H. -D.; Fangmeier, A.; Wulfmeyer, V.; Streck, T.			Comparison of Noah simulations with eddy covariance and soil water measurements at a winter wheat stand	AGRICULTURAL AND FOREST METEOROLOGY			English	Article						Land surface modelling; Energy partitioning; Bowen ratio; H-correction	DATA ASSIMILATION SYSTEM; ENERGY-BALANCE CLOSURE; SURFACE-HYDROLOGY MODEL; LAND-SURFACE; FLUXES; CARBON; SITES; PHOTOSYNTHESIS; CONDUCTANCE; SENSITIVITY	Weather and climate simulations are critically dependent on an accurate representation of land surface exchange processes. The present study tests the performance of the Noah land surface model (LSM) for energy partitioning and soil water dynamics on a winter wheat stand in southwest Germany. The model was parameterized field-specifically, i.e., data on leaf area index, green vegetation fraction, albedo, and soil texture were derived from measurements, and tested against a set of eddy covariance (EC) and soil water data collected in the 2009 season. With respect to energy partitioning, the field-specific parameterization performed fairly well during the main vegetation period. During ripening, starting in mid-July until harvest on 6 August, however, the sensible heat flux was distinctly underestimated. In August, the bias increased to -105.5 W m(-2). As a consequence, evapotranspiration was overestimated during this period. After introducing a time-variable minimum stomata! resistance (R(c,min)), the model performed much better. The model also delivered acceptable results during crop maturing. The Noah LSM was unable to simulate the observed soil water dynamics. While the measured soil water content profiles showed a distinct gradient with depth, the Noah LSM tended to deplete the soil profile uniformly. Our study shows that for cereal-dominated croplands phenology and crop development play a crucial role in energy partitioning, especially during the ripening stage. Disregarding the dynamics of the physiological properties of the crops in the Noah LSM leads to significant bias in energy partitioning. Finally, we critically discuss the current approach to correct measured EC flux data for the energy residual based on the Bowen ratio with regard to LSM calibration and performance. (C) 2010 Elsevier B.V. All rights reserved.	[Ingwersen, J.; Steffens, K.; Poltoradnev, M.; Gabler, R.; Streck, T.] Univ Hohenheim, Inst Soil Sci & Land Evaluat, D-70593 Stuttgart, Germany; [Hoegy, P.; Zhunusbayeva, D.; Fangmeier, A.] Univ Hohenheim, Inst Landscape & Plant Ecol, D-70593 Stuttgart, Germany; [Warrach-Sagi, K.; Wizemann, H. -D.; Wulfmeyer, V.] Univ Hohenheim, Inst Phys & Meteorol, D-70593 Stuttgart, Germany	Ingwersen, J (reprint author), Univ Hohenheim, Inst Soil Sci & Land Evaluat, D-70593 Stuttgart, Germany.	joachim.ingwersen@uni-hohenheim.de			German Research Foundation (DFG) [PAK 346]	We gratefully acknowledge the financial support by the German Research Foundation (DFG) in the frame of the integrated research project PAK 346 "Structure and function of agricultural landscapes under global climate change - Processes and projections on a regional scale". We thank the farmer Mr. Bosch for his committed cooperation, and we are thankful for the helpful comments of two anonymous reviewers.	Alfieri JG, 2008, MON WEATHER REV, V136, P4452, DOI 10.1175/2008MWR2524.1; Arora VK, 2003, AGR FOREST METEOROL, V118, P21, DOI 10.1016/S0168-1923(03)00073-X; BONAN G, 2008, ECOLOGICAL CLIMATOLO, P253; Chen F, 2007, J APPL METEOROL CLIM, V46, P694, DOI 10.1175/JAM2463.1; Chen F, 2001, MON WEATHER REV, V129, P587, DOI 10.1175/1520-0493(2001)129<0587:CAALSH>2.0.CO;2; Chen F, 2001, MON WEATHER REV, V129, P569, DOI 10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2; Choi M, 2009, AGR FOREST METEOROL, V149, P2082, DOI 10.1016/j.agrformet.2009.07.002; Coudert B, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028778; Coudert B, 2006, J HYDROMETEOROL, V7, P404, DOI 10.1175/JHM503.1; dePury DGG, 1997, PLANT CELL ENVIRON, V20, P537, DOI 10.1111/j.1365-3040.1997.00094.x; Doherty J., 2010, PEST MODEL INDEPENDE; DUDHIA J, 1993, MON WEATHER REV, V121, P1493, DOI 10.1175/1520-0493(1993)121<1493:ANVOTP>2.0.CO;2; Fan Y, 2006, J CLIMATE, V19, P1214, DOI 10.1175/JCLI3681.1; Foken T, 2008, ECOL APPL, V18, P1351, DOI 10.1890/06-0922.1; FOKEN TT, 2009, THEOR APPL CLIMATOL, P1; Guo JX, 2009, SCI CHINA SER D, V52, P108, DOI 10.1007/s11430-009-0002-6; Hillel D, 1998, ENV SOIL PHYS; Houborg R, 2009, AGR FOREST METEOROL, V149, P1875, DOI 10.1016/j.agrformet.2009.06.014; IUSS Working Group WRB, 2007, WORLD SOIL RES REP F, V103; Kato H, 2007, J METEOROL SOC JPN, V85A, P187, DOI 10.2151/jmsj.85A.187; MAHRT L, 1984, J CLIM APPL METEOROL, V23, P222, DOI 10.1175/1520-0450(1984)023<0222:TIOASO>2.0.CO;2; Mauder M, 2006, METEOROL Z, V15, P597, DOI 10.1127/0941-2948/2006/0167; MAUDER MM, 2004, EDDY COVARIANCE SOFT; Meier Uwe, 2001, BBCH MONOGRAPH, P158; Mitchell KE, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003823; MOORE CJ, 1986, BOUND-LAY METEOROL, V37, P17, DOI 10.1007/BF00122754; Nash J.E., 1970, J HYDROL, V10, P282, DOI DOI 10.1016/0022-1694(70)90255-6; Niyogi D, 2009, J APPL METEOROL CLIM, V48, P349, DOI 10.1175/2008JAMC1662.1; Olioso A, 2002, AGRONOMIE, V22, P651, DOI 10.1051/agro:2002054; PetersLidard CD, 1997, J GEOPHYS RES-ATMOS, V102, P4303, DOI 10.1029/96JD02948; Raddatz RL, 2007, AGR FOREST METEOROL, V142, P186, DOI 10.1016/j.agrformet.2006.08.022; Rebmann C, 2005, THEOR APPL CLIMATOL, V80, P121, DOI 10.1007/s00704-004-0095-y; Robock A, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003245; Ronda RJ, 2001, J APPL METEOROL, V40, P1431, DOI 10.1175/1520-0450(2001)040<1431:ROTCCI>2.0.CO;2; Ruppert J, 2006, BOUND-LAY METEOROL, V120, P39, DOI 10.1007/s10546-005-9043-3; SCHAAKE JC, 2004, J GEOPHYS RES; SCHOTANUS P, 1983, BOUND-LAY METEOROL, V26, P81, DOI 10.1007/BF00164332; SCHULZE ED, 1994, ANNU REV ECOL SYST, V25, P629, DOI 10.1146/annurev.ecolsys.25.1.629; Skamarock W. C., 2008, 475STR NCAR; Twine TE, 2000, AGR FOREST METEOROL, V103, P279, DOI 10.1016/S0168-1923(00)00123-4; WEBB EK, 1980, Q J ROY METEOR SOC, V106, P85, DOI 10.1002/qj.49710644707; Wilson K, 2002, AGR FOREST METEOROL, V113, P223, DOI 10.1016/S0168-1923(02)00109-0; *GERM SOIL SURV IN, 2005, BOD KART, P353; *LUBW, 2008, LAND UMW MESS NAT BA; *STAT OFF EUR UN, 2009, EUR	45	5	6	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0168-1923		AGR FOREST METEOROL	Agric. For. Meteorol.	MAR 15	2011	151	3					345	355		10.1016/j.agrformet.2010.11.010		11	Agronomy; Forestry; Meteorology & Atmospheric Sciences	Agriculture; Forestry; Meteorology & Atmospheric Sciences	717SU	WOS:000287068500008	
J	Roberts, WHG; Battisti, DS				Roberts, William H. G.; Battisti, David S.			A new tool for evaluating the physics of coupled atmosphere-ocean variability in nature and in general circulation models	CLIMATE DYNAMICS			English	Article						El Nino Southern Oscillation (ENSO); Tropical climate; Climate models	SEA-SURFACE TEMPERATURE; STOCHASTIC DYNAMICAL MODEL; DATA ASSIMILATION ANALYSIS; NINO SOUTHERN-OSCILLATION; EL-NINO; EQUATORIAL PACIFIC; PART II; ENSO; DIVERGENCE; ANOMALIES	Intermediate models of the coupled tropical atmosphere-ocean system have been used to illuminate the physics of interannual climate phenomenon such as El Nio Southern Oscillation (ENSO) in the tropical Pacific and to explore how the tropics might respond to a forcing such as changing insolation (Milankovitch) or atmospheric carbon dioxide. Importantly, most of the intermediate models are constructed as anomaly models: models that evolve on a prescribed climatological mean state, which is typically prescribed and done so on a rather ad hoc basis. Here we show how the observed climatological mean state fields [ocean currents and upwelling, sea surface temperature (SST) and atmospheric surface winds] can be incorporated into a linearized intermediate model of the tropical coupled atmosphere-ocean system: called Linear Ocean-Atmosphere Model (LOAM), it is a linearized version of the Zebiak and Cane model. With realistic, seasonally varying mean state fields, we find that the essential physics of the ENSO mode is very similar to that in the original model and to that in the observations and that the observed mean fields support an ENSO mode that is stable to perturbations. Thus, our results provide further evidence that ENSO is generated and maintained by stochastic (uncoupled) perturbations. The method that we have outlined can be used to assimilate any set of ocean and atmosphere climatological data into the linearized atmosphere-ocean model. In a companion paper, we apply this same method to incorporate mean field output from two global climate models into the linearised model. We use the latter to diagnose the physics of the leading coupled mode (ENSO) that is supported by the climate models, and to illuminate why the structure and variance in the ENSO mode changes in the models when they are forced by early Holocene and Last Glacial Maximum boundary conditions.	[Roberts, William H. G.; Battisti, David S.] Univ Washington, Dept Atmospher Sci, Seattle, WA 98195 USA	Battisti, DS (reprint author), Univ Washington, Dept Atmospher Sci, Seattle, WA 98195 USA.	wroberts@atmos.washington.edu; battisti@washington.edu	Battisti, David /A-3340-2013		Ocean and Atmosphere Research (OAR) Climate Program Office (CPO) of the National Oceanic and Atmospheric Administration [NA08OAR4310883]	This work was funded by a grant from the Ocean and Atmosphere Research (OAR) Climate Program Office (CPO) of the National Oceanic and Atmospheric Administration (NA08OAR4310883). We thank an anonymous reviewer for critical and constructive comments, and Sandy Tudhope and Daniel Vimont for thoughtful comments throughout our investigation.	BATTISTI DS, 1989, J ATMOS SCI, V46, P1687, DOI 10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2; BATTISTI DS, 1988, J ATMOS SCI, V45, P2889, DOI 10.1175/1520-0469(1988)045<2889:DATOAW>2.0.CO;2; BEHRINGER D, 2004, AMS 84 ANN M WASH ST; BOURASSA M, 2001, 11 C INT SEA ATM SAN; BURGERS G, 2005, SIMPLEST ENSO RECHAR; Carton JA, 2000, J PHYS OCEANOGR, V30, P311, DOI 10.1175/1520-0485(2000)030<0311:ASODAA>2.0.CO;2; Carton JA, 2000, J PHYS OCEANOGR, V30, P294, DOI 10.1175/1520-0485(2000)030<0294:ASODAA>2.0.CO;2; Chang P, 1996, PHYSICA D, V98, P301, DOI 10.1016/0167-2789(96)00116-9; Collins M, 2005, CLIM DYNAM, V24, P89, DOI 10.1007/s00382-004-0478-x; Fedorov AV, 2001, J CLIMATE, V14, P3086, DOI 10.1175/1520-0442(2001)014<3086:ASAOTO>2.0.CO;2; GILL AE, 1980, Q J ROY METEOR SOC, V106, P447, DOI 10.1256/smsqj.44904; Guilyardi E, 2006, CLIM DYNAM, V26, P329, DOI 10.1007/s00382-005-0084-6; Guilyardi E, 2009, B AM METEOROL SOC, V90, P325, DOI 10.1175/2008BAMS2387.1; Jin FF, 1997, J ATMOS SCI, V54, P811, DOI 10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2; Johnson GC, 2001, J PHYS OCEANOGR, V31, P839, DOI 10.1175/1520-0485(2001)031<0839:EPOHVD>2.0.CO;2; Johnson SD, 2000, J CLIMATE, V13, P3, DOI 10.1175/1520-0442(2000)013<0003:EDMMAP>2.0.CO;2; JOHNSON SD, 1999, THESIS U WASHINGTON; Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2; Mantua NJ, 1995, J CLIMATE, V8, P2897, DOI 10.1175/1520-0442(1995)008<2897:AVITZC>2.0.CO;2; McPhaden M. J., 1998, J GEOPHYS RES, V103, P14, DOI [10.1029/97JC02906, DOI 10.1029/97JC02906]; NEELIN JD, 1998, J GEOPHYS RES, V103, P261; PENLAND C, 1995, J CLIMATE, V8, P1999, DOI 10.1175/1520-0442(1995)008<1999:TOGOTS>2.0.CO;2; Philander SG, 2003, ANNU REV EARTH PL SC, V31, P579, DOI 10.1146/annurev.earth.31.100901.141255; PHILANDER SGH, 1992, J CLIMATE, V5, P308, DOI 10.1175/1520-0442(1992)005<0308:SOEWAG>2.0.CO;2; POULAIN PM, 1993, J PHYS OCEANOGR, V23, P601, DOI 10.1175/1520-0485(1993)023<0601:EOHDAV>2.0.CO;2; RASMUSSON EM, 1982, MON WEATHER REV, V110, P354, DOI 10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2; Reynolds RW, 2002, J CLIMATE, V15, P1609, DOI 10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2; ROBERTS WHG, 2010, J CLIM UNPUB; SCHOPF PS, 1988, J ATMOS SCI, V45, P549, DOI 10.1175/1520-0469(1988)045<0549:VIACOM>2.0.CO;2; SEAGER R, 1988, J GEOPHYS RES-OCEANS, V93, P1265, DOI 10.1029/JC093iC02p01265; SMITH NR, 1995, AUST METEOROL MAG, V44, P93; SMITH NR, 1995, J ATMOS OCEAN TECH, V12, P850, DOI 10.1175/1520-0426(1995)012<0850:AISFTO>2.0.CO;2; Thompson CJ, 2001, J CLIMATE, V14, P445, DOI 10.1175/1520-0442(2001)014<0445:ALSDMO>2.0.CO;2; Thompson CJ, 2000, J CLIMATE, V13, P2818, DOI 10.1175/1520-0442(2000)013<2818:ALSDMO>2.0.CO;2; THOMPSON CM, 1998, THESIS U WASHINGTON; van Oldenborgh GJ, 2005, OCEAN SCI, V1, P81; Wittenberg AT, 2004, J CLIMATE, V17, P2526, DOI 10.1175/1520-0442(2004)017<2526:EWSAFE>2.0.CO;2; ZEBIAK SE, 1986, MON WEA REV, V115, P21263; ZEBIAK SE, 1987, MON WEATHER REV, V115, P2262, DOI 10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2	39	2	2	SPRINGER	NEW YORK	233 SPRING ST, NEW YORK, NY 10013 USA	0930-7575		CLIM DYNAM	Clim. Dyn.	MAR	2011	36	5-6					907	923		10.1007/s00382-010-0762-x		17	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	729CB	WOS:000287924400006	
J	Chen, BZ; Coops, NC; Black, TA; Jassal, RS; Chen, JM; Johnson, M				Chen, Baozhang; Coops, Nicholas C.; Black, T. Andy; Jassal, Rachhpals S.; Chen, Jing M.; Johnson, Mark			Modeling to discern nitrogen fertilization impacts on carbon sequestration in a Pacific Northwest Douglas-fir forest in the first-postfertilization year	GLOBAL CHANGE BIOLOGY			English	Article						Douglas-fir; ecological modeling; ecosystem respiration; net ecosystem productivity; nitrogen fertilization; photosynthesis	NORTHERN HARDWOOD FORESTS; REDUCES SOIL RESPIRATION; NET PRIMARY PRODUCTIVITY; LEAF-AREA INDEX; HARVARD FOREST; TERRESTRIAL ECOSYSTEMS; NUTRIENT AVAILABILITY; TEMPERATE FORESTS; INTERANNUAL VARIABILITY; PINE PLANTATIONS	This study investigated how nitrogen (N) fertilization with 200 kg N ha-1 of urea affected ecosystem carbon (C) sequestration in the first-postfertilization year in a Pacific Northwest Douglas-fir (Pseudotsuga menziesii) stand on the basis of multiyear eddy-covariance (EC) and soil-chamber measurements before and after fertilization in combination with ecosystem modeling. The approach uses a data-model fusion technique which encompasses both model parameter optimization and data assimilation and minimizes the effects of interannual climatic perturbations and focuses on the biotic and abiotic factors controlling seasonal C fluxes using a prefertilization 9-year-long time series of EC data (1998-2006). A process-based ecosystem model was optimized using the half-hourly data measured during 1998-2005, and the optimized model was validated using measurements made in 2006 and further applied to predict C fluxes for 2007 assuming the stand was not fertilized. The N fertilization effects on C sequestration were then obtained as differences between modeled (unfertilized stand) and EC or soil-chamber measured (fertilized stand) C component fluxes. Results indicate that annual net ecosystem productivity in the first-post-N fertilization year increased by similar to 83%, from 302 +/- 19 to 552 +/- 36 g m-2 yr-1, which resulted primarily from an increase in annual gross primary productivity of similar to 8%, from 1938 +/- 22 to 2095 +/- 29 g m-2 yr-1 concurrent with a decrease in annual ecosystem respiration (R(e)) of similar to 5.7%, from 1636 +/- 17 to 1543 +/- 31 g m-2 yr-1. Moreover, with respect to respiration, model results showed that the fertilizer-induced reduction in R(e) (similar to 93 g m-2 yr-1) principally resulted from the decrease in soil respiration R(s) (similar to 62 g m-2 yr-1).	[Chen, Baozhang] Chinese Acad Sci, LREIS Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; [Chen, Baozhang; Coops, Nicholas C.] Univ British Columbia, Dept Forest Resources Management, Fac Forestry, Vancouver, BC V6T 1Z4, Canada; [Black, T. Andy; Jassal, Rachhpals S.] Univ British Columbia, Fac Land & Food Syst, Biometeorol & Soil Phys Grp, Vancouver, BC V6T 1Z4, Canada; [Chen, Jing M.] Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada; [Chen, Jing M.] Univ Toronto, Program Planning, Toronto, ON M5S 3G3, Canada; [Johnson, Mark] Univ British Columbia, Inst Resources Environm & Sustainabil, Dept Earth & Ocean Sci, Vancouver, BC V6T 1Z4, Canada	Chen, BZ (reprint author), Chinese Acad Sci, LREIS Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.		Johnson, Mark/B-4445-2008; Coops, Nicholas/J-1543-2012	Johnson, Mark/0000-0001-5070-7539; 	Natural Sciences and Engineering Research Council of Canada; Chinese Academy of Sciences; National Basic Research Program of China [2010CB950900, 2010CB950704]; National Key Technology R&D Program of China [2008BAK50B06-02]; Natural Sciences and Engineering Research Council (NSERC); Canadian Foundation for Climate and Atmospheric Sciences (CFCAS); BIOCAP Canada; Agrium Inc.	This research is financially supported by an Alexander Graham Bell Canada Scholarship (CGS) funded by the Natural Sciences and Engineering Research Council of Canada, 'One hundred talents' program funded by Chinese Academy of Sciences, the National Basic Research Program of China (Grant No. 2010CB950900 and 2010CB950704), National Key Technology R&D Program of China (2008BAK50B06-02), and Canada Carbon Program funds provided by the Natural Sciences and Engineering Research Council (NSERC), the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) and BIOCAP Canada. We thank TimberWest Forest Corp. for permission to work on their land and logistical support, and to Agrium Inc. for financial support and the supply of urea for plot experiments. We acknowledge Zoran Nesic, Dominic Lessard for help with EC and climate measurements, Nick Grant, Christian Brummer and Praveena Krishnan for data quality control, and the assistance of Andrew Sauter and Rick Ketler. We are grateful to the subject editor Dr Ivan Janssens and the two anonymous referees for their constructive and in-depth reviews, and for their comments/suggestions on how to improve this manuscript.	Aber J, 1998, BIOSCIENCE, V48, P921, DOI 10.2307/1313296; Aber J, 1991, TERRESTRIAL ECOSYSTE; Aber JD, 1995, WATER AIR SOIL POLL, V85, P1665, DOI 10.1007/BF00477219; ABER JD, 1989, BIOSCIENCE, V39, P378, DOI 10.2307/1311067; ABER JD, 1993, ECOL APPL, V3, P156, DOI 10.2307/1941798; Adams AB, 2005, FOREST ECOL MANAG, V220, P313, DOI 10.1016/j.foreco.2005.08.018; AGREN GI, 1988, ENVIRON POLLUT, V54, P185, DOI 10.1016/0269-7491(88)90111-X; Arain MA, 2006, AGR FOREST METEOROL, V140, P171, DOI 10.1016/j.agrformet.2006.03.021; ARNEBRANT K, 1990, SOIL BIOL BIOCHEM, V22, P309, DOI 10.1016/0038-0717(90)90105-9; Baggs EM, 2006, PLANT SOIL, V284, P1, DOI 10.1007/s11104-006-0047-7; BALLARD TM, 1986, 20 BC MIN FOR VIC; Barr AG, 2004, AGR FOREST METEOROL, V126, P237, DOI 10.1016/j.agrformet.2004.06.011; Bauer GA, 2004, FOREST ECOL MANAG, V196, P173, DOI 10.1016/j.foreco.2004.03.032; Bergh J, 1999, FOREST ECOL MANAG, V119, P51, DOI 10.1016/S0378-1127(98)00509-X; BLOOM AJ, 1985, ANNU REV ECOL SYST, V16, P363, DOI 10.1146/annurev.ecolsys.16.1.363; Bowden RD, 2000, J ENVIRON QUAL, V29, P268; Bowden RD, 2004, FOREST ECOL MANAG, V196, P43, DOI 10.1016/j.foreco.2004.03.011; Boxman AW, 1998, FOREST ECOL MANAG, V101, P65, DOI 10.1016/S0378-1127(97)00126-6; BRIX H, 1991, IMPROVING FOREST FER, P76; Brooks JR, 2009, ECOL APPL, V19, P1044, DOI 10.1890/08-0310.1; Burton AJ, 2004, GLOBAL CHANGE BIOL, V10, P1080, DOI 10.1111/j.1365-2486.2004.00737.x; Butnor JR, 2003, GLOBAL CHANGE BIOL, V9, P849, DOI 10.1046/j.1365-2486.2003.00630.x; Canary JD, 2000, FOREST ECOL MANAG, V138, P225, DOI 10.1016/S0378-1127(00)00398-4; CHAPIN FS, 1990, ANNU REV ECOL SYST, V21, P423, DOI 10.1146/annurev.es.21.110190.002231; Chen BZ, 2007, ECOL MODEL, V209, P277, DOI 10.1016/j.ecolmodel.2007.06.032; Chen BZ, 2007, J HYDROMETEOROL, V8, P123, DOI 10.1175/JHM566.1; Chen BZ, 2009, GLOBAL CHANGE BIOL, V15, P1962, DOI 10.1111/j.1365-2486.2008.01832.x; Chen BZ, 2009, BOUND-LAY METEOROL, V130, P137, DOI 10.1007/s10546-008-9339-1; Chen JM, 2006, AGR FOREST METEOROL, V140, P257, DOI 10.1016/j.agrformet.2006.08.005; Cleveland CC, 2006, P NATL ACAD SCI USA, V103, P10316, DOI 10.1073/pnas.0600989103; Compton JE, 2004, FOREST ECOL MANAG, V196, P143, DOI 10.1016/j.foreco.2004.03.017; Davidson EA, 2004, GLOBAL CHANGE BIOL, V10, P718, DOI 10.1111/j.1365-2486.2004.00762.x; De Schrijver A, 2008, NATURE, V451, pE1, DOI 10.1038/nature06578; de Vries W, 2009, FOREST ECOL MANAG, V258, pVII, DOI 10.1016/S0378-1127(09)00590-8; De Vries W, 2006, GLOBAL CHANGE BIOL, V12, P1151, DOI 10.1111/j.1365-2486.2006.01151.x; de Vries W, 2008, NATURE, V451, pE1, DOI 10.1038/nature06579; DIEGO S, 2007, GLOBAL BIOGEOCHEM CY, V21; Fahey TJ, 1998, ECOL MONOGR, V68, P183, DOI 10.1890/0012-9615(1998)068[0183:ROESNH]2.0.CO;2; Falk M, 2008, TREE PHYSIOL, V28, P509; FELLER MC, 1992, BIORESOURCE TECHNOL, V39, P9, DOI 10.1016/0960-8524(92)90050-8; FIELD CB, 1992, ANNU REV ECOL SYST, V23, P201, DOI 10.1146/annurev.ecolsys.23.1.201; Fisher R. F., 2000, ECOLOGY MANAGEMENT F; Footen PW, 2009, FOREST ECOL MANAG, V258, P2194, DOI 10.1016/j.foreco.2009.02.033; Franklin O, 2003, ECOSYSTEMS, V6, P644, DOI 10.1007/s10021-002-0149-x; Frey SD, 2004, FOREST ECOL MANAG, V196, P159, DOI 10.1016/j.foreco.2004.03.018; Galloway JN, 2002, AMBIO, V31, P64, DOI 10.1639/0044-7447(2002)031[0064:RNATWY]2.0.CO;2; Gartner BL, 2002, CAN J FOREST RES, V32, P439, DOI 10.1139/X01-218; HANLEY DP, 1996, WASHINGTON STATE U E, V1800; Hanson PJ, 2000, BIOGEOCHEMISTRY, V48, P115, DOI 10.1023/A:1006244819642; HAYNES BE, 1995, TREE PHYSIOL, V15, P317; Holland EA, 1997, J GEOPHYS RES-ATMOS, V102, P15849, DOI 10.1029/96JD03164; Hollinger DY, 2004, GLOBAL CHANGE BIOL, V10, P1689, DOI 10.1111/j.1365-2486.2004.00847.x; Humphreys ER, 2006, AGR FOREST METEOROL, V140, P6, DOI 10.1016/j.agrformet.2006.03.018; Hyvonen R, 2008, BIOGEOCHEMISTRY, V89, P121, DOI 10.1007/s10533-007-9121-3; Janssens IA, 2010, NAT GEOSCI, V3, P315, DOI [10.1038/NGEO844, 10.1038/ngeo844]; Janssens IA, 2009, NAT GEOSCI, V2, P318, DOI 10.1038/ngeo505; Jassal RS, 2010, GEODERMA, V157, P118, DOI 10.1016/j.geoderma.2010.04.002; Jassal RS, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2008JG000764; Jassal RS, 2010, AGR FOREST METEOROL, V150, P208, DOI 10.1016/j.agrformet.2009.10.005; Johnson DW, 2001, FOREST ECOL MANAG, V140, P227, DOI 10.1016/S0378-1127(00)00282-6; Ju WM, 2006, AGR FOREST METEOROL, V140, P136, DOI 10.1016/j.agrformet.2006.08.008; Koehler B, 2009, BIOGEOSCIENCES, V6, P2973; Korner C, 2000, ECOL APPL, V10, P1590, DOI 10.1890/1051-0761(2000)010[1590:BRTCE]2.0.CO;2; Leggett ZH, 2006, SOIL SCI SOC AM J, V70, P279, DOI 10.2136/sssaj2003.0232; Liu J, 2002, GLOBAL ECOL BIOGEOGR, V11, P115, DOI 10.1046/j.1466-822X.2002.00278.x; Liu LL, 2009, ECOL LETT, V12, P1103, DOI 10.1111/j.1461-0248.2009.01351.x; Magill AH, 2004, FOREST ECOL MANAG, V196, P7, DOI 10.1016/j.foreco.2004.03.033; Magnani F, 2007, NATURE, V447, P848, DOI 10.1038/nature05847; Maier CA, 2000, CAN J FOREST RES, V30, P347, DOI 10.1139/cjfr-30-3-347; Marshall DD, 2005, J FOREST, V103, P71; Meidinger DV, 1991, BC MINISTRY SPECIAL, V6; Micks P, 2004, FOREST ECOL MANAG, V196, P57, DOI 10.1016/j.foreco.2004.03.012; Mo J, 2008, GLOBAL CHANGE BIOL, V14, P403, DOI 10.1111/j.1365-2486.2007.01503.x; Mo JM, 2006, PLANT SOIL, V282, P135, DOI 10.1007/s11104-005-5446-7; Mo XG, 2008, ECOL MODEL, V217, P157, DOI 10.1016/j.ecolmodel.2008.06.021; Morgenstern K, 2004, AGR FOREST METEOROL, V123, P201, DOI 10.1016/j.agrformet.2003.12.003; Nadelhoffer KJ, 1999, NATURE, V398, P145, DOI 10.1038/18205; NIHLGARD B, 1985, AMBIO, V14, P2; Olsson P, 2005, GLOBAL CHANGE BIOL, V11, P1745, DOI 10.1111/j.1365-2486.2005.001033.x; Pan YD, 2009, FOREST ECOL MANAG, V259, P151, DOI 10.1016/j.foreco.2009.09.049; Paw UKT, 2004, ECOSYSTEMS, V7, P513; Phillips RP, 2007, NEW PHYTOL, V176, P655, DOI 10.1111/j.1469-8137.2007.02204.x; Pregitzer KS, 2008, GLOBAL CHANGE BIOL, V14, P142, DOI 10.1111/j.1365-2486.2007.01465.x; Raupach MR, 2005, GLOBAL CHANGE BIOL, V11, P378, DOI 10.1111/j.1365-2486.2005.00917.x; Reay DS, 2008, NAT GEOSCI, V1, P430, DOI 10.1038/ngeo230; Reich PB, 1998, OECOLOGIA, V114, P471, DOI 10.1007/s004420050471; Richardson AD, 2006, AGR FOREST METEOROL, V136, P1, DOI 10.1016/j.agrformet.2006.01.007; Sacks WJ, 2006, GLOBAL CHANGE BIOL, V12, P240, DOI 10.1111/j.1365-2486.2005.01059.x; SCHULZE ED, 1989, SCIENCE, V244, P776, DOI 10.1126/science.244.4906.776; Schwalm CR, 2007, GLOBAL CHANGE BIOL, V13, P370, DOI 10.1111/j.1365-2486.2006.01298.x; Stuart Chapin III F., 2002, PRINCIPLES TERRESTRI; Sutton MA, 2008, GLOBAL CHANGE BIOL, V14, P2057, DOI 10.1111/j.1365-2486.2008.01636.x; Tarantola A., 1987, INVERSE PROBLEM THEO; TESKEY RO, 1994, TREE PHYSIOL, V14, P1215; Townsend AR, 1996, ECOL APPL, V6, P806, DOI 10.2307/2269486; Vitousek PM, 1997, ECOL APPL, V7, P737, DOI 10.2307/2269431; VITOUSEK PM, 1991, BIOGEOCHEMISTRY, V13, P87; WALLENSTEIN MD, 2003, EUROPEAN GEOPHYSICAL, V5, P13087; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x	99	4	4	WILEY-BLACKWELL PUBLISHING, INC	MALDEN	COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA	1354-1013		GLOBAL CHANGE BIOL	Glob. Change Biol.	MAR	2011	17	3					1442	1460		10.1111/j.1365-2486.2010.02298.x		19	Biodiversity Conservation; Ecology; Environmental Sciences	Biodiversity & Conservation; Environmental Sciences & Ecology	714VU	WOS:000286837900015	
J	Gershgorin, B; Majda, AJ				Gershgorin, B.; Majda, A. J.			Filtering a statistically exactly solvable test model for turbulent tracers from partial observations	JOURNAL OF COMPUTATIONAL PHYSICS			English	Article						Turbulent advection-diffusion equation; Exactly solvable model; Nonlinear Kalman filter; Data assimilation	DATA ASSIMILATION; MATHEMATICAL STRATEGIES; COMPLEX-SYSTEMS; KALMAN FILTER; CRITERIA	A statistically exactly solvable model for passive tracers is introduced as a test model for the authors' Nonlinear Extended Kalman Filter (NEKF) as well as other filtering algorithms. The model involves a Gaussian velocity field and a passive tracer governed by the advection-diffusion equation with an imposed mean gradient. The model has direct relevance to engineering problems such as the spread of pollutants in the air or contaminants in the water as well as climate change problems concerning the transport of greenhouse gases such as carbon dioxide with strongly intermittent probability distributions consistent with the actual observations of the atmosphere. One of the attractive properties of the model is the existence of the exact statistical solution. In particular, this unique feature of the mode provides an opportunity to design and test fast and efficient algorithms for real-time data assimilation based on rigorous mathematical theory for a turbulence model problem with many active spatiotemporal scales. Here, we extensively study the performance of the NEKF which uses the exact first and second order nonlinear statistics without any approximations due to linearization. The role of partial and sparse observations, the frequency of observations and the observation noise strength in recovering the true signal, its spectrum and fat tail probability distribution are the central issues discussed here. The results of our study provide useful guidelines for filtering realistic turbulent systems with passive tracer through partial observations. (C) 2010 Elsevier Inc. All rights reserved	NYU, Dept Math, New York, NY 10012 USA; NYU, Ctr Atmosphere & Ocean Sci, Courant Inst Math Sci, New York, NY 10012 USA	Gershgorin, B (reprint author), NYU, Dept Math, 550 1St Ave, New York, NY 10012 USA.	borisg@cims.nyu.edu			National Science Foundation [DMS-0456713]; office of Naval Research [25-74200-F6607, N00014-05-1-0164]; Defense Advanced Projects Agency [N0014-07-1-0750]	The research of A.J. Majda is partially supported by National Science Foundation grant DMS-0456713, the office of Naval Research grants 25-74200-F6607, and N00014-05-1-0164, and the Defense Advanced Projects Agency grant N0014-07-1-0750. Boris Gershgorin is supported as a postdoctoral fellow through the last two agencies.	Anderson B. D. O., 1979, OPTIMAL FILTERING; Anderson JL, 2001, MON WEATHER REV, V129, P2884, DOI 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2; Anderson JL, 2003, MON WEATHER REV, V131, P634, DOI 10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2; Bensoussan A., 2004, STOCHASTIC CONTROL P; Bourlioux A, 2002, PHYS FLUIDS, V14, P881, DOI 10.1063/1.1430736; Castronovo E, 2008, J COMPUT PHYS, V227, P3678, DOI 10.1016/j.jcp.2007.12.016; CHUI CK, 1999, KALMAN FILTERING; Daley R., 1991, ATMOSPHERIC DATA ANA; Evensen G, 1997, MON WEATHER REV, V125, P1342, DOI 10.1175/1520-0493(1997)125<1342:ADAFSN>2.0.CO;2; Gardiner C. W., 1997, HDB STOCHASTIC METHO; Gershgorin B, 2010, PHYSICA D, V239, P1741, DOI 10.1016/j.physd.2010.05.009; Gershgorin B, 2010, J COMPUT PHYS, V229, P1, DOI 10.1016/j.jcp.2009.08.019; Gershgorin B, 2010, COMMUN MATH SCI, V8, P67; Gershgorin B, 2008, COMMUN MATH SCI, V6, P611; Gershgorin B, 2010, J COMPUT PHYS, V229, P32, DOI 10.1016/j.jcp.2009.09.022; GHIL M, 1991, ADV GEOPHYS, V33, P141, DOI 10.1016/S0065-2687(08)60442-2; Harlim J, 2008, J COMPUT PHYS, V227, P5304, DOI 10.1016/j.jcp.2008.01.049; Harlim J, 2010, MON WEATHER REV, V138, P1050, DOI 10.1175/2009MWR3113.1; Jazwinski AH, 1970, STOCHASTIC PROCESSES; KUBO R, 1963, J MATH PHYS, V4, P174, DOI 10.1063/1.1703941; Majda A.J., 1999, Physics Reports, V314, DOI 10.1016/S0370-1573(98)00083-0; Majda A.J., 2005, CRM MONOGRAPH SERIES, V25; Majda AJ, 2007, P NATL ACAD SCI USA, V104, P1124, DOI 10.1073/pnas.0610077104; Majda AJ, 2010, DISCRETE CONT DYN-A, V27, P441, DOI 10.3934/dcds.2010.27.441; Majda AJ, 2010, P NATL ACAD SCI USA, V107, P14958, DOI 10.1073/pnas.1007009107; Neelin JD, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2009GL041726; Ott E, 2004, TELLUS A, V56, P415, DOI 10.1111/j.1600-0870.2004.00076.x	27	9	9	ACADEMIC PRESS INC ELSEVIER SCIENCE	SAN DIEGO	525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA	0021-9991		J COMPUT PHYS	J. Comput. Phys.	FEB 20	2011	230	4					1602	1638		10.1016/j.jcp.2010.11.024		37	Computer Science, Interdisciplinary Applications; Physics, Mathematical	Computer Science; Physics	714BQ	WOS:000286782300044	
J	Mangold, A; De Backer, H; De Paepe, B; Dewitte, S; Chiapello, I; Derimian, Y; Kacenelenbogen, M; Leon, JF; Huneeus, N; Schulz, M; Ceburnis, D; O'Dowd, C; Flentje, H; Kinne, S; Benedetti, A; Morcrette, JJ; Boucher, O				Mangold, A.; De Backer, H.; De Paepe, B.; Dewitte, S.; Chiapello, I.; Derimian, Y.; Kacenelenbogen, M.; Leon, J-F; Huneeus, N.; Schulz, M.; Ceburnis, D.; O'Dowd, C.; Flentje, H.; Kinne, S.; Benedetti, A.; Morcrette, J. -J.; Boucher, O.			Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 3. Evaluation by means of case studies	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES			English	Article							CHEMICAL-TRANSPORT MODEL; OPTICAL-PROPERTIES; HEAT-WAVE; SEA-SALT; EMISSIONS; SIMULATIONS; RETRIEVALS; ASSIMILATION; VARIABILITY; VALIDATION	A near real-time system for assimilation and forecasts of aerosols, greenhouse and trace gases, extending the ECMWF Integrated Forecasting System (IFS), has been developed in the framework of the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The GEMS aerosol modeling system is novel as it is the first aerosol model fully coupled to a numerical weather prediction model with data assimilation. A reanalysis of the period 2003-2009 has been carried out with the same system. During its development phase, the aerosol system was first run for the time period January 2003 to December 2004 and included sea salt, desert dust, organic matter, black carbon, and sulfate aerosols. In the analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) at 550 nm over ocean and land (except over bright surfaces) was assimilated. This work evaluates the performance of the aerosol system by means of case studies. The case studies include (1) the summer heat wave in Europe in August 2003, characterized by forest fire aerosol and conditions of high temperatures and stagnation, favoring photochemistry and secondary aerosol formation, (2) a large Saharan dust event in March 2004, and (3) periods of high and low sea salt aerosol production. During the heat wave period in 2003, the linear correlation coefficients between modeled and observed AOD (550 nm) and between modeled and observed PM2.5 mass concentrations are 0.82 and 0.71, respectively, for all investigated sites together. The AOD is slightly and the PM2.5 mass concentration is clearly overestimated by the aerosol model during this period. The simulated sulfate mass concentration is significantly correlated with observations but is distinctly overestimated. The horizontal and vertical locations of the main features of the aerosol distribution during the Saharan dust outbreak are generally well captured, as well as the timing of the AOD peaks. The aerosol model simulates winter sea salt AOD reasonably well, however, showing a general overestimation. Summer sea salt events show a better agreement. Overall, the assimilation of MODIS AOD data improves the subsequent aerosol predictions when compared with observations, in particular concerning the correlation and AOD peak values. The assimilation is less effective in correcting a positive (PM2.5, sulfate mass concentration, Angstrom exponent) or negative (desert dust plume AOD) model bias.	[Mangold, A.; De Backer, H.; De Paepe, B.; Dewitte, S.] Royal Meteorol Inst Belgium, Observat Dept, B-1180 Brussels, Belgium; [Dewitte, S.; Chiapello, I.; Derimian, Y.; Kacenelenbogen, M.] Univ Lille 1, CNRS, Lab Opt Atmospher, F-59655 Villeneuve Dascq, France; [Huneeus, N.; Schulz, M.] CEA CNRS UVSQ, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France; [Ceburnis, D.; O'Dowd, C.] Natl Univ Ireland Galway, Sch Phys, Galway, Ireland; [Ceburnis, D.; O'Dowd, C.] Natl Univ Ireland Galway, Environm Change Inst, Ctr Climate & Air Pollut Studies, Galway, Ireland; [Flentje, H.] Meteorol Observ Hohenpeissenberg, D-82383 Hohenpeissenberg, Germany; [Kinne, S.] Max Planck Inst Meteorol, D-20146 Hamburg, Germany; [Boucher, O.] Met Off Hadley Ctr, Exeter EX1 3PB, Devon, England; [Benedetti, A.; Morcrette, J. -J.] European Ctr Medium Range Weather Forecasts, Reading RG2 9AX, Berks, England	Mangold, A (reprint author), Royal Meteorol Inst Belgium, Observat Dept, Av Circulaire 3, B-1180 Brussels, Belgium.	alexander.mangold@oma.be	Boucher, Olivier/J-5810-2012; Boucher, Olivier/K-7483-2012	Boucher, Olivier/0000-0003-2328-5769; Boucher, Olivier/0000-0003-2328-5769	DECC/Defra [GA01101]; European Commission [516099]	The authors would like to thank all members of the GEMS-AER team. We thank EPA Ireland for financial support of D. Ceburnis and for providing observation data. At LOA, Didier Tanre and Fabrice Ducos are thanked for their helpful contribution to the analysis of POLDER and AERONET data for the pollution aerosol case study. We also thank P. Goloub, S. Despiau, D. Tanre, V. E. Cachorro, and D. V. Henriques, who are the principal investigators of the AERONET/PHOTONS and Brewer sites used for this study. We thank ADEME for providing the PM2.5 data. O.B. was supported by the joint DECC and Defra Integrated Climate Programme-DECC/Defra (GA01101). In particular, we are grateful to A. Hollingsworth, who initiated and led the GEMS project. GEMS was funded by the European Commission's Framework Programme 6, contract 516099.	BARRIE L, 2004, ESA; Benedetti A, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011115; Breon FM, 1999, J APPL METEOROL, V38, P777, DOI 10.1175/1520-0450(1999)038<0777:CDFTSP>2.0.CO;2; Cheymol A, 2009, ATMOS CHEM PHYS, V9, P733; Collins WD, 2001, J GEOPHYS RES-ATMOS, V106, P7313, DOI 10.1029/2000JD900507; Dentener F, 2006, ATMOS CHEM PHYS, V6, P4321; De Paepe B, 2008, REMOTE SENS ENVIRON, V112, P2455, DOI 10.1016/j.rse.2007.11.005; Deuze JL, 2001, J GEOPHYS RES-ATMOS, V106, P4913, DOI 10.1029/2000JD900364; Dubovik O, 2000, J GEOPHYS RES-ATMOS, V105, P9791, DOI 10.1029/2000JD900040; Dubovik O, 2002, J ATMOS SCI, V59, P590, DOI 10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2; Engelstaedter S, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029971; Ginoux P, 2001, J GEOPHYS RES-ATMOS, V106, P20255, DOI 10.1029/2000JD000053; Hodzic A, 2006, ATMOS CHEM PHYS, V6, P1853; Hodzic A, 2007, ATMOS CHEM PHYS, V7, P4043; Holben BN, 2001, J GEOPHYS RES-ATMOS, V106, P12067, DOI 10.1029/2001JD900014; Hollingsworth A, 2008, B AM METEOROL SOC, V89, P1147, DOI 10.1175/2008BAMS2355.1; Huneeus N, 2009, GEOSCI MODEL DEV, V2, P213; Ignatov A, 2002, J ATMOS SCI, V59, P313, DOI 10.1175/1520-0469(2002)059<0313:ARFIAC>2.0.CO;2; Kacenelenbogen M, 2006, ATMOS CHEM PHYS, V6, P4843; Kaiser JW, 2009, AIP CONF PROC, V1100, P645, DOI 10.1063/1.3117069; Knippertz P, 2006, Q J ROY METEOR SOC, V132, P1153, DOI 10.1256/qj.05.109; Lane TE, 2008, ATMOS ENVIRON, V42, P7439, DOI 10.1016/j.atmosenv.2008.06.026; Levy RC, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007811; MONAHAN EC, 1982, J GEOPHYS RES-OC ATM, V87, P8898, DOI 10.1029/JC087iC11p08898; Morcrette JJ, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011235; Morcrette JJ, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL036041; Mulcahy JP, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011992; Mulcahy JP, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034303; O'Dowd CD, 2004, NATURE, V431, P676, DOI 10.1038/nature02959; ODOWD CD, 1993, J GEOPHYS RES-ATMOS, V98, P1137, DOI 10.1029/92JD02302; Reddy MS, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD004757; Remer LA, 2005, J ATMOS SCI, V62, P947, DOI 10.1175/JAS3385.1; Robinson AL, 2007, SCIENCE, V315, P1259, DOI 10.1126/science.1133061; Schulz M, 2004, EMISSION ATMOSPHERIC, P333; Smith M.H., 1998, J AEROSOL SCI     S1, V29, pS189, DOI DOI 10.1016/S0021-8502(98)00280-8; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Tegen I, 1997, J GEOPHYS RES-ATMOS, V102, P23895, DOI 10.1029/97JD01864; Tressol M, 2008, ATMOS CHEM PHYS, V8, P2133; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; Vautard R, 2007, ATMOS ENVIRON, V41, P173, DOI 10.1016/j.atmosenv.2006.07.039; Vignati E, 2010, ATMOS ENVIRON, V44, P670, DOI 10.1016/j.atmosenv.2009.11.013; Weaver C, 2007, J ATMOS SCI, V64, P808, DOI 10.1175/JAS3838.1; WEHRLI C, 2004, WMO GAW EXP WORKSH G, P36; Yoon YJ, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2005JD007044; Yttri KE, 2007, ATMOS CHEM PHYS, V7, P5711; Yu HB, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003914; Zhang JL, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009065	47	8	8	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	2169-897X		J GEOPHYS RES-ATMOS	J. Geophys. Res.-Atmos.	FEB 8	2011	116								D03302	10.1029/2010JD014864		23	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	720ZW	WOS:000287322200002	
J	Zhu, GF; Li, X; Su, YH; Lu, L; Huang, CL				Zhu, Gao-Feng; Li, Xin; Su, Yong-Hong; Lu, Ling; Huang, Chun-Lin			Seasonal fluctuations and temperature dependence in photosynthetic parameters and stomatal conductance at the leaf scale of Populus euphratica Oliv.	TREE PHYSIOLOGY			English	Article						Bayesian statistics; Farquhar et al; model; maximum electron transport rate (J(max)); maximum rate of Rubisco carboxylation (V(cmax)); photosynthesis; stomatal conductance; transpiration; Populus euphratica Oliv	MESOPHYLL DIFFUSION CONDUCTANCE; BIOCHEMICALLY BASED MODEL; H2O GAS-EXCHANGE; CARBON-DIOXIDE; BAYESIAN CALIBRATION; ELEVATED CO2; ELECTRON-TRANSPORT; NET PHOTOSYNTHESIS; DATA ASSIMILATION; DECIDUOUS FOREST	A combined model to simulate CO(2) and H(2)O gas exchange at the leaf scale was parameterized using data obtained from in situ leaf-scale observations of diurnal and seasonal changes in CO(2) and H(2)O gas exchange. The Farquhar et al.-type model of photosynthesis was parameterized by using the Bayesian approach and the Ball et al.-type stomatal conductance model was optimized using the linear least-squares procedure. The results show that the seasonal physiological changes in photosynthetic parameters (e.g., V(cmax25), J(max25), R(d25) and g(m25)) in the biochemical model of photosynthesis and m in the stomatal conductance model should be counted in estimating long-term CO(2) and H(2)O gas exchange. Overall, the coupled model successfully reproduced the observed response in net assimilation and transpiration rates.	[Li, Xin; Su, Yong-Hong; Lu, Ling; Huang, Chun-Lin] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China; [Zhu, Gao-Feng] Lanzhou Univ, Key Lab Western Chinas Environm Syst, Minist Educ, Lanzhou 730000, Peoples R China; [Zhu, Gao-Feng] Lanzhou Jiaotong Univ, Sch Math Phys & Software Engn, Lanzhou 730000, Peoples R China	Su, YH (reprint author), Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Donggang W Rd 320, Lanzhou 730000, Gansu, Peoples R China.	syh@lzb.ac.cn	Li, Xin/F-7473-2011; Huang, Chunlin/G-6715-2011	Li, Xin/0000-0003-2999-9818; 	National Natural Science Foundation of China [40925004, 40771036, 41001242, 40701054]; national high-tech program (863) project [2009AA12Z130]	This research was supported by National Natural Science Foundation of China (Nos. 40925004, 40771036, 41001242 and 40701054), the national high-tech program (863) project 'a common software for multi-source remote sensing data assimilation' (grant number: 2009AA12Z130).	Ball J.T., 1987, PROGR PHOTOSYNTHESIS, P221; Bernacchi CJ, 2001, PLANT CELL ENVIRON, V24, P253, DOI 10.1046/j.1365-3040.2001.00668.x; BERRY JA, 1981, ENCY PLANT PHYSL A, V12, P277; Bonan GB, 1998, J CLIMATE, V11, P1307, DOI 10.1175/1520-0442(1998)011<1307:TLSCOT>2.0.CO;2; Braswell BH, 2005, GLOBAL CHANGE BIOL, V11, P335, DOI 10.1111/j.1365-2486.2005.00897.x; Cable JM, 2009, PLANT SOIL, V320, P153, DOI 10.1007/s11104-008-9880-1; Cable JM, 2008, ECOSYSTEMS, V11, P961, DOI 10.1007/s10021-008-9172-x; Clark JS, 2006, TRENDS ECOL EVOL, V21, P375, DOI 10.1016/j.tree.2006.03.016; Clark JS, 2005, ECOL LETT, V8, P2, DOI 10.1111/j.1461-0248.2004.00702.x; COLLATZ GJ, 1991, AGR FOREST METEOROL, V54, P107, DOI 10.1016/0168-1923(91)90002-8; Dreyer E, 2001, TREE PHYSIOL, V21, P223; Dubois JJB, 2007, NEW PHYTOL, V176, P402, DOI 10.1111/j.1469-8137.2007.02182.x; Ethier GJ, 2004, PLANT CELL ENVIRON, V27, P137, DOI 10.1111/j.1365-3040.2004.01140.x; FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231; Flexas J, 2008, PLANT CELL ENVIRON, V31, P602, DOI 10.1111/j.1365-3040.2007.01757.x; Flexas J, 2007, PLANT CELL ENVIRON, V30, P1284, DOI 10.1111/j.1365-3040.2007.01700.x; Gao Q, 2004, AGR FOREST METEOROL, V126, P203, DOI 10.1016/j.agrformet.2004.07.004; Gao Q, 2002, PLANT CELL ENVIRON, V25, P1373, DOI 10.1046/j.1365-3040.2002.00926.x; Gedney N, 2006, NATURE, V439, P835, DOI 10.1038/nature04504; GELFAND AE, 1990, J AM STAT ASSOC, V85, P398, DOI 10.2307/2289776; Gelman A, 1992, STAT SCI, V7, P457, DOI DOI 10.1214/SS/1177011136; Gordon WS, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002098; HARLEY PC, 1995, PLANT CELL ENVIRON, V18, P1146, DOI 10.1111/j.1365-3040.1995.tb00625.x; HARLEY PC, 1992, PLANT CELL ENVIRON, V15, P271, DOI 10.1111/j.1365-3040.1992.tb00974.x; HARLEY PC, 1986, OECOLOGIA, V70, P393, DOI 10.1007/BF00379502; HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940; HATTON TJ, 1992, AUST J BOT, V40, P679, DOI 10.1071/BT9920679; JANES SC, 2006, TRENDS ECOL EVOL, V21, P375; June T, 2004, FUNCT PLANT BIOL, V31, P275, DOI 10.1071/FP03250; Kattge J, 2007, PLANT CELL ENVIRON, V30, P1176, DOI 10.1111/j.1365-3040.2007.01690.x; Kirschbaum MUF, 1999, ECOL MODEL, V118, P17, DOI 10.1016/S0304-3800(99)00020-4; Knorr W, 2005, GLOBAL CHANGE BIOL, V11, P1333, DOI 10.1111/j.1365-2486.2005.00977.x; Kosugi Y, 2003, PLANT CELL ENVIRON, V26, P285, DOI 10.1046/j.1365-3040.2003.00960.x; Kosugi Y, 2006, TREE PHYSIOL, V26, P1173; Lasch P, 2005, FOREST ECOL MANAG, V207, P59, DOI 10.1016/j.foreco.2004.10.034; Lehuger S, 2009, AGR ECOSYST ENVIRON, V133, P208, DOI 10.1016/j.agee.2009.04.022; LEUNING R, 1995, PLANT CELL ENVIRON, V18, P1183, DOI 10.1111/j.1365-3040.1995.tb00628.x; Leuning R, 2002, PLANT CELL ENVIRON, V25, P1205, DOI 10.1046/j.1365-3040.2002.00898.x; Leuning R, 1997, J EXP BOT, V48, P345, DOI 10.1093/jxb/48.2.345; LLOYD J, 1991, AUST J PLANT PHYSIOL, V18, P649; LONG SP, 1993, PLANTA, V189, P226; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; Medlyn BE, 2002, PLANT CELL ENVIRON, V25, P1155, DOI 10.1046/j.1365-3040.2002.00890.x; Medlyn BE, 2005, TREE PHYSIOL, V25, P839; METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114; Miao ZW, 2009, PLANT CELL ENVIRON, V32, P109, DOI 10.1111/j.1365-3040.2008.01900.x; Misson L, 2004, TREE PHYSIOL, V24, P529; Morales P, 2005, GLOBAL CHANGE BIOL, V11, P2211, DOI 10.1111/j.1365-2486.2005.01036.x; Niinemets U, 1997, PLANT CELL ENVIRON, V20, P845, DOI 10.1046/j.1365-3040.1997.d01-133.x; Niinemets U, 2009, J EXP BOT, V60, P2271, DOI 10.1093/jxb/erp063; Niinemets U, 1998, TREE PHYSIOL, V18, P681; Niinemets U, 2009, J EXP BOT, V60, P2433, DOI 10.1093/jxb/erp045; Niinemets U, 2004, PLANT CELL ENVIRON, V27, P569, DOI 10.1111/j.1365-3040.2003.01168.x; Nogues S, 2002, FUNCT PLANT BIOL, V29, P621, DOI 10.1071/PP01117; Ogle K, 2009, NEW PHYTOL, V182, P541, DOI 10.1111/j.1469-8137.2008.02760.x; Ogle Kiona, 2008, V69, P281, DOI 10.1007/978-3-540-72954-9_12; Patrick LD, 2009, PLANT CELL ENVIRON, V32, P1695, DOI 10.1111/j.1365-3040.2009.02029.x; Patrick LD, 2009, GLOBAL CHANGE BIOL, V15, P1214, DOI 10.1111/j.1365-2486.2008.01750.x; Rogers A, 2000, GLOB CHANGE BIOL, V6, P1005, DOI 10.1046/j.1365-2486.2000.00375.x; Sacks WJ, 2006, GLOBAL CHANGE BIOL, V12, P240, DOI 10.1111/j.1365-2486.2005.01059.x; Sala A, 1996, AGR FOREST METEOROL, V78, P203, DOI 10.1016/0168-1923(95)02250-3; Sellers PJ, 1996, J CLIMATE, V9, P676, DOI 10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2; Sharkey TD, 2007, PLANT CELL ENVIRON, V30, P1035, DOI 10.1111/j.1365-3040.2007.01710.x; Silva MES, 2006, THEOR APPL CLIMATOL, V85, P1, DOI 10.1007/s00704-005-0177-5; Su YH, 2009, PLANT CELL ENVIRON, V32, P1710, DOI 10.1111/j.1365-3040.2009.02036.x; Svensson M, 2008, ECOL MODEL, V213, P331, DOI 10.1016/j.ecolmodel.2008.01.001; Tcherkez GGB, 2006, P NATL ACAD SCI USA, V103, P7246, DOI 10.1073/pnas.0600605103; Tuzet A, 2003, PLANT CELL ENVIRON, V26, P1097, DOI 10.1046/j.1365-3040.2003.01035.x; TYUL F, 2008, AM STAT, V62, P40; Vano JA, 2006, J GEOPHYS RES-BIOGEO, V111, DOI 10.1029/2005JG000112; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; VONCAEMMERER S, 1981, PLANTA, V153, P376; von Caemmerer S, 2000, TECHNIQUES PLANT SCI; VONCAEMMERER S, 1994, PLANTA, V195, P88; Walker GR, 2002, HYDROGEOL J, V10, P68, DOI 10.1007/s10040-001-0181-5; Wang YP, 2001, GLOBAL CHANGE BIOL, V7, P495, DOI 10.1046/j.1365-2486.2001.00434.x; Warren CR, 2006, J EXP BOT, V57, P3057, DOI 10.1093/jxb/erl067; WHITE JD, 2006, EARTH INTERACT, V10, pNIL1; Williams M, 2005, GLOBAL CHANGE BIOL, V11, P89, DOI 10.1111/j.1365-2486.2004.00891.x; Wilson KB, 2000, TREE PHYSIOL, V20, P565; Wohlfahrt G, 1999, PLANT CELL ENVIRON, V22, P1281, DOI 10.1046/j.1365-3040.1999.00479.x; WULLSCHLEGER SD, 1993, J EXP BOT, V44, P907, DOI 10.1093/jxb/44.5.907; Xu LK, 2003, TREE PHYSIOL, V23, P865; Zhu GF, 2007, J HYDROL, V333, P239, DOI 10.1016/j.jhydrol.2006.08.013; Zhu GF, 2010, HYDROL EARTH SYST SC, V14, P419	85	2	2	OXFORD UNIV PRESS	OXFORD	GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND	0829-318X		TREE PHYSIOL	Tree Physiol.	FEB	2011	31	2					178	195		10.1093/treephys/tpr005		18	Forestry	Forestry	745KP	WOS:000289164600008	
J	Seto, CJ; McRae, GJ				Seto, C. J.; McRae, G. J.			Reducing Risk in Basin Scale CO2 Sequestration: A Framework for Integrated Monitoring Design	ENVIRONMENTAL SCIENCE & TECHNOLOGY			English	Review							DEEP SALINE AQUIFERS; FOSSIL-FUEL CO2; CARBON-DIOXIDE; INDUCED SEISMICITY; DATA ASSIMILATION; GREENHOUSE GASES; GEOLOGIC SEQUESTRATION; NUMERICAL-SIMULATION; DENVER EARTHQUAKES; SEDIMENTARY BASINS	Injection of CO2 into geological structures is a key technology for sequestering CO2 emissions captured from the combustion of fossil fuels. Current projects inject volumes on the order of megatonnes per year. However, injection volumes must be increased by several orders of magnitude for material reductions in ambient concentrations. A number of questions surrounding safety and security of injection have been raised about the large scale deployment of geological CO2 sequestration. They are site specific and require an effective monitoring strategy to mitigate risks of concern to stakeholders. This paper presents a model-based framework for monitoring design that can provide a quantitative understanding of the trade-offs between operational decisions of cost, footprint size, and uncertainty in monitoring strategies. Potential risks and challenges of monitoring large scale CO2 injection are discussed, and research areas needed to address uncertainties are identified. Lack of clear guidance surrounding monitoring has contributed to hampering the development of policies to promote the deployment of large scale sequestration projects. Modeling provides an understanding of site specific processes and allows insights into the complexity of these systems, facilitating the calibration of an appropriate plan to manage risk. An integrated policy for risk-based monitoring design, prior to large scale deployment of sequestration will ensure safe and secure storage through an understanding of the real risks associated with large scale injection.	[Seto, C. J.; McRae, G. J.] MIT, Dept Chem Engn, Cambridge, MA 02139 USA	Seto, CJ (reprint author), MIT, Dept Chem Engn, Cambridge, MA 02139 USA.	cjseto@mit.edu			Henry Luce Foundation	C.J.S. gratefully acknowledges the support of the Clare Boothe Luce Program of the Henry Luce Foundation.	Archie GE, 1942, T AM I MIN MET ENG, V146, P54; Bachu S, 2009, INT J GREENH GAS CON, V3, P494, DOI 10.1016/j.ijggc.2008.11.002; Bachu S, 2003, ENERG CONVERS MANAGE, V44, P3151, DOI 10.1016/S0196-8904(03)00101-8; BACHU S, 1994, ENERG CONVERS MANAGE, V35, P269, DOI 10.1016/0196-8904(94)90060-4; BAKER JM, 2008, CHALLENGES CAUTIONS, P21; Barlet-Gouedard V, 2006, IADC SPE DRILL C; BARTON CA, 1995, GEOLOGY, V23, P683, DOI 10.1130/0091-7613(1995)023<0683:FFAPAF>2.3.CO;2; BATZLE M, 1992, GEOPHYSICS, V57, P1396, DOI 10.1190/1.1443207; Beaubien SE, 2008, INT J GREENH GAS CON, V2, P373, DOI 10.1016/j.ijggc.2008.03.005; Benson SM, 2008, ELEMENTS, V4, P325, DOI 10.2113/gselements.4.5.325; Benson S.M., 2006, SPE ANN TECHN C EXH; BERGMAN PD, 1995, ENERG CONVERS MANAGE, V36, P523, DOI 10.1016/0196-8904(95)00058-L; Bertino L, 2003, INT STAT REV, V71, P223; Bielinski A, 2008, INT J GREENH GAS CON, V2, P319, DOI 10.1016/j.ijggc.2008.02.008; Birkholzer JT, 2009, INT J GREENH GAS CON, V3, P181, DOI 10.1016/j.ijggc.2008.08.002; Brady JL, 2008, SPE RESERV EVAL ENG, V11, P824; Bruant RG, 2002, ENVIRON SCI TECHNOL, V36, p240A, DOI 10.1021/es0223325; Caers J, 2006, MATH GEOL, V38, P81, DOI 10.1007/s11004-005-9005-9; CELIA MA, 2002, P 6 INT GREENH GAS C, P477; Celia MA, 2009, GROUND WATER, V47, P627, DOI 10.1111/j.1745-6584.2009.00590.x; CHADWICK A, 2006, NATO SCI SERIES, V4, P303; Chadwick R. A., 2004, 3D SEISMIC TECHNOLOG, P311; CHADWICK RA, 2009, P 9 INT GREENH GAS C, P2103; CHALATURNYK R, 2005, P 7 INT GREENH GAS C, P623; Chen WY, 2010, INT J GREENH GAS CON, V4, P341, DOI 10.1016/j.ijggc.2009.09.001; Chiaramonte L, 2008, ENVIRON GEOL, V54, P1667, DOI 10.1007/s00254-007-0948-7; Class H, 2009, COMPUTAT GEOSCI, V13, P409, DOI 10.1007/s10596-009-9146-x; Cortis A, 2008, INT J GREENH GAS CON, V2, P640, DOI 10.1016/j.ijggc.2008.04.008; Daley TM, 2007, GEOPHYSICS, V72, pA57, DOI 10.1190/1.2754716; Du J, 2008, SPE RESERV EVAL ENG, V11, P63; EBERHARTPHILLIPS D, 1984, J GEOPHYS RES, V89, P1191, DOI 10.1029/JB089iB02p01191; Emberley S, 2005, APPL GEOCHEM, V20, P1131, DOI 10.1016/j.apgeochem.2005.02.007; Energy Information Administration, 2009, INT EN OUTL 2009; Ennis-King J, 2005, SPE J, V10, P349; ENNISKING J, 2009, INT J GREENH GAS CON, V1, P86; Evensen G., 2007, DATA ASSIMILATION EN; FARRAR CD, 1995, NATURE, V376, P675, DOI 10.1038/376675a0; Finkbeiner T, 2001, AAPG BULL, V85, P1007; Freifeld BM, 2005, J GEOPHYS RES-SOL EA, V110, DOI 10.1029/2005JB003735; Gasda SE, 2004, ENVIRON GEOL, V46, P707, DOI 10.1007/s00254-004-1073-5; Gasda SE, 2009, COMPUTAT GEOSCI, V13, P469, DOI 10.1007/s10596-009-9138-x; Gasperikova E, 2008, GEOPHYSICS, V73, pWA105, DOI 10.1190/1.2985823; Gassmann F, 1951, VIERTELJAHRSSCHRIFT, V96, P1; GHIL M, 1991, ADV GEOPHYS, V33, P141, DOI 10.1016/S0065-2687(08)60442-2; Goldberg DS, 2008, P NATL ACAD SCI USA, V105, P9920, DOI 10.1073/pnas.0804397105; Gunter WD, 1997, MINER PETROL, V59, P121, DOI 10.1007/BF01163065; Gunter WD, 1997, ENERG CONVERS MANAGE, V38, pS217, DOI 10.1016/S0196-8904(96)00272-5; Gurdak J. J., 2009, 1337 US GEOL SURV, P63; HAMILTON DH, 1971, SCIENCE, V172, P333, DOI 10.1126/science.172.3981.333; Han DH, 2004, GEOPHYSICS, V69, P398, DOI 10.1190/1.1707059; HARRIS JM, 1995, GEOPHYSICS, V60, P667, DOI 10.1190/1.1443806; HEALY JH, 1968, SCIENCE, V161, P1301, DOI 10.1126/science.161.3848.1301; Hesse MA, 2008, J FLUID MECH, V611, P35, DOI 10.1017/S002211200800219X; Hesse MA, 2007, J FLUID MECH, V577, P363, DOI 10.1017/S0022112007004685; Hitchon B, 1999, ENERG CONVERS MANAGE, V40, P825, DOI 10.1016/S0196-8904(98)00146-0; Holloway S, 2001, ANNU REV ENERG ENV, V26, P145, DOI 10.1146/annurev.energy.26.1.145; HOUSE KZ, 2006, P NATL ACAD SCI USA, V103, P1291; Hoversten GM, 2004, GEOPHYSICS, V69, P406, DOI 10.1190/1.1707060; HOVORKA SD, 2006, GROUND WAT PROT COUN, V25; HOVORKA SD, 2003, P 6 INT C GREENH GAS, P583; HSIEH PA, 1981, J GEOPHYS RES, V86, P903, DOI 10.1029/JB086iB02p00903; Ide ST, 2007, INT J GREENH GAS CON, V1, P481, DOI 10.1016/S1750-5836(07)00091-6; IPCC, 2007, CLIM CHANG 2007 SYNT; Jaramillo P, 2009, ENVIRON SCI TECHNOL, V43, P8027, DOI 10.1021/es902006h; Johnson J.W., 2004, GEOL SOC LOND SPEC P, V233, P107, DOI 10.1144/GSL.SP.2004.233.01.08; Jorstad A, 1999, GEOPHYS PROSPECT, V47, P785, DOI 10.1046/j.1365-2478.1999.00154.x; Juanes R, 2006, WATER RESOUR RES, V42, DOI 10.1029/2005WR004806; Juanes R, 2010, TRANSPORT POROUS MED, V82, P19, DOI 10.1007/s11242-009-9420-3; JURICK L, 1972, BLACK TIDE SANTA BAR; Kharaka YK, 2006, GEOLOGY, V34, P577, DOI 10.1130/G22357.1; KLING GW, 1987, SCIENCE, V236, P169, DOI 10.1126/science.236.4798.169; Koide H, 1997, ENERG CONVERS MANAGE, V38, pS253, DOI 10.1016/S0196-8904(96)00278-6; KRUGER M, 2009, P 9 INT C GREENH GAS, P1933; Kutchko BG, 2007, ENVIRON SCI TECHNOL, V41, P4787, DOI 10.1021/es062828c; LeNeveu DM, 2008, ENERG CONVERS MANAGE, V49, P32, DOI 10.1016/j.enconman.2007.06.001; Levin I, 2007, TELLUS B, V59, P245, DOI 10.1111/j.1600-0889.2006.00244.x; Levin I, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL018477; Levine JS, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL031560; Lewicki JL, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011297; Macek I, 2005, ENVIRON EXP BOT, V54, P90, DOI 10.1016/j.envexpbot.2004.06.003; MacMinn CW, 2010, J FLUID MECH, V662, P329, DOI 10.1017/S0022112010003319; Majer EL, 2007, GEOTHERMICS, V36, P185, DOI 10.1016/j.geothermics.2007.03.003; Malczewski J, 2006, INT J GEOGR INF SCI, V20, P703, DOI 10.1080/13658810600661508; Male EJ, 2010, ENVIRON EARTH SCI, V60, P251, DOI 10.1007/s12665-009-0372-2; Mathieson Allan, 2010, Leading Edge, V29, DOI 10.1190/1.3304827; Mavko G, 1998, GEOPHYSICS, V63, P918, DOI 10.1190/1.1444402; MAVKO G., 2003, ROCK PHYS HDB TOOLS; McCoy Sean, 2008, THESIS CARNEGIE MELL; MCCOY ST, 2009, P 9 INT C GREENH GAS, P4151; McGrail BP, 2006, J GEOPHYS RES-SOL EA, V111, DOI 10.1029/2005JB004169; Metz B, 2005, CARBON DIOXIDE CAPTU; MIT, 2007, FUT COAL OPT CARB CO; Nicot JP, 2008, INT J GREENH GAS CON, V2, P582, DOI 10.1016/j.ijggc.2008.03.004; Nooner SL, 2007, INT J GREENH GAS CON, V1, P198, DOI 10.1016/S1750-5836(07)00018-7; Nordbotten JM, 2009, ENVIRON SCI TECHNOL, V43, P743, DOI 10.1021/es801135v; Nordbotten JM, 2005, TRANSPORT POROUS MED, V58, P339, DOI 10.1007/s11242-004-0670-9; Nordbotten JM, 2006, J FLUID MECH, V561, P307, DOI 10.1017/S0022112006000802; Nordbotten JM, 2005, ENVIRON SCI TECHNOL, V39, P602, DOI 10.1021/es035338i; Nordbotten JM, 2011, GEOLOGICAL STORAGE C; Oldenburg CM, 2004, VADOSE ZONE J, V3, P848; Price PN, 2009, INT J GREENH GAS CON, V3, P658, DOI 10.1016/j.ijggc.2009.03.002; Pruess K, 2004, VADOSE ZONE J, V3, P738; Pruess K, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023250; Pruess K, 2008, INT J GREENH GAS CON, V2, P37, DOI 10.1016/S1750-5836(07)00095-3; Pruess K, 2004, ENERGY, V29, P1431, DOI 10.1016/j.energy.2004.03.077; Riaz A, 2006, J FLUID MECH, V548, P87, DOI 10.1017/S0022112005007494; Riding JB, 2009, GEOL SOC SPEC PUBL, V313, P227, DOI 10.1144/SP313.14; Rodell M, 2004, J HYDROMETEOROL, V5, P1064, DOI 10.1175/JHM-395.1; Rouse JH, 2010, ENVIRON EARTH SCI, V60, P313, DOI 10.1007/s12665-010-0483-9; Sakurai S, 2006, PETROPHYSICS, V47, P483; Seto CJ, 2007, SPE RESERV EVAL ENG, V10, P393; STALKER L, 2009, P 9 INT C GREEH GAS, P2119; Stauffer PH, 2009, ENVIRON SCI TECHNOL, V43, P565, DOI 10.1021/es800403w; STEVENS SH, 1999, P 4 INT C GREENH GAS, P175; Strebelle S, 2002, MATH GEOL, V34, P1, DOI 10.1023/A:1014009426274; TENG TL, 1973, B SEISMOL SOC AM, V63, P859; UROSEVIC M, 2009, P 9 INT C GREENH GAS, P3135; Vasco DW, 2008, GEOPHYSICS, V73, pO1, DOI 10.1190/1.2789395; Verdon J, 2007, J FLUID MECH, V588, P29, DOI 10.1017/S0022112007007069; Viswanathan HS, 2008, ENVIRON SCI TECHNOL, V42, P7280, DOI 10.1021/es800417x; Wang S, 2004, ENERG CONVERS MANAGE, V45, P2833, DOI 10.1016/j.enconman.2004.01.002; Wang ZJ, 1998, GEOPHYSICS, V63, P1604, DOI 10.1190/1.1444457; Wells AW, 2007, APPL GEOCHEM, V22, P996, DOI 10.1016/j.apgeochem.2007.01.002; WHITE DJ, 2009, P 9 INT C GREENH GAS, P2349; Wilson M., 2004, IEA GHG WEYBURN CO2; WOOD W, 1983, NUCL SAFETY RISKS RE; XU SY, 1995, GEOPHYS PROSPECT, V43, P91, DOI 10.1111/j.1365-2478.1995.tb00126.x; Xu TF, 2004, APPL GEOCHEM, V19, P917, DOI 10.1016/j.apgeochem.2003.11.003; Xu TF, 2003, J GEOPHYS RES-SOL EA, V108, DOI 10.1029/2002JB001979; Yamamoto H, 2009, INT J GREENH GAS CON, V3, P586, DOI 10.1016/j.ijggc.2009.04.007; Zhang YQ, 2010, ENVIRON EARTH SCI, V59, P1447, DOI 10.1007/s12665-009-0131-4; Zhou YH, 2008, MON WEATHER REV, V136, P678, DOI 10.1175/2007MWR2064.1; Zoback MD, 2001, TECTONOPHYSICS, V336, P19, DOI 10.1016/S0040-1951(01)00091-9; Zoback MD, 2003, INT J ROCK MECH MIN, V40, P1049, DOI 10.1016/j.ijrmms.2003.07.001; *CAN CTR OCC HLTH, 2007, HLTH EFF CARB DIOX G; *CARB DIOX INF AN, 2003, TRENDS ONL COMP DAT; *CENTR FED LANDS H, 2006, INSAR APPL HIGHW TRA; *IEA GREENH GAS RD, IEAGHG MON SEL TOOL; *JOINT SCI AC STAT, 2008, CLIM CHANG AD TRANS; *NAT EN TECHN LAB, 2006, SEA FLOOR GRAV SURV; *US EPA, 2008, GEOL CO2 SEQU TECHN	141	9	9	AMER CHEMICAL SOC	WASHINGTON	1155 16TH ST, NW, WASHINGTON, DC 20036 USA	0013-936X		ENVIRON SCI TECHNOL	Environ. Sci. Technol.	FEB 1	2011	45	3					845	859		10.1021/es102240w		15	Engineering, Environmental; Environmental Sciences	Engineering; Environmental Sciences & Ecology	711HG	WOS:000286577100004	
J	Bellassen, V; Delbart, N; Le Maire, G; Luyssaert, S; Ciais, P; Viovy, N				Bellassen, V.; Delbart, N.; Le Maire, G.; Luyssaert, S.; Ciais, P.; Viovy, N.			Potential knowledge gain in large-scale simulations of forest carbon fluxes from remotely sensed biomass and height	FOREST ECOLOGY AND MANAGEMENT			English	Article						Remote sensing; Global vegetation model; ORCHIDEE; Carbon; Biomass; Height	GLOBAL VEGETATION MODEL; ABOVEGROUND BIOMASS; EDDY COVARIANCE; CANOPY HEIGHT; LIDAR; AIRBORNE; FOOTPRINT; SAR; PRODUCTIVITY; CLIMATE	Global vegetation models (GVMs) simulate CO2, water and energy fluxes at large scales, typically no smaller than 10 x 1 0 km. GVM simulations are thus expected to simulate the average functioning, but not the local variability. The two main limiting factors in refining this scale are (1) the scale at which the pedo-climatic inputs - temperature, precipitation, soil water reserve, etc. - are available to drive models and (2) the lack of geospatial information on the vegetation type and the age of forest stands. This study assesses how remotely sensed biomass or stand height could help the new generation of GVMs, which explicitly represent forest age structure and management, to better simulate this local variability. For the ORCHIDEE-FM model, we find that a simple assimilation of biomass or height brings down the root mean square error (RMSE) of some simulated carbon fluxes by 30-50%. Current error levels of remote sensing estimates do not impact this improvement for large gross fluxes (e.g. terrestrial ecosystem respiration), but they reduce the improvement of simulated net ecosystem productivity, adding 13.5-21% of RMSE to assimilations using the in situ estimates. The data assimilation under study is more effective to improve the simulation of respiration than the simulation of photosynthesis. The assimilation of height or biomass in ORCHIDEE-FM enables the correct retrieval of variables that are more difficult to measure over large areas, such as stand age. A combined assimilation of biomass and net ecosystem productivity could possibly enable the new generation of GVMs to retrieve other variables that are seldom measured, such as soil carbon content. (C) 2010 Elsevier BM. All rights reserved.	[Bellassen, V.; Delbart, N.; Luyssaert, S.; Ciais, P.; Viovy, N.] CEA Orme Merisiers, Lab Sci Climat & Environm, Commissariat Energie Atom, F-91191 Gif Sur Yvette, France; [Le Maire, G.] CIRAD, UPR 80, SC UMR Eco&Sols, F-34060 Montpellier 01, France; [Delbart, N.] Univ Paris 07, PRODIG UMR8586, Paris 7, France	Bellassen, V (reprint author), CEA Orme Merisiers, Lab Sci Climat & Environm, Commissariat Energie Atom, F-91191 Gif Sur Yvette, France.	valentin.bellassen@lsce.ipsl.fr	Luyssaert, Sebastiaan/F-6684-2011		French ministry for research	This work was made possible thanks to a research grant from the French ministry for research.	B Vanniere, 1984, TABLES PRODUCTION FO; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Balzter H, 2007, REMOTE SENS ENVIRON, V108, P224, DOI 10.1016/j.rse.2006.11.014; Balzter H, 2007, INT J REMOTE SENS, V28, P1173, DOI 10.1080/01431160600904998; Bellassen V, 2010, ECOL MODEL, V221, P2458, DOI 10.1016/j.ecolmodel.2010.07.008; Bellassen V, 2011, ECOL MODEL, V222, P57, DOI 10.1016/j.ecolmodel.2010.08.038; Boudreau J, 2008, REMOTE SENS ENVIRON, V112, P3876, DOI 10.1016/j.rse.2008.06.003; Breidenbach J, 2008, INT J REMOTE SENS, V29, P1511, DOI 10.1080/01431160701736364; Carvalhais N, 2010, GLOBAL CHANGE BIOL, V16, P2813, DOI 10.1111/j.1365-2486.2010.02173.x; Deleuze C, 2004, ANN FOREST SCI, V61, P327, DOI 10.1051/forest:2004026; Demarty J, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030014; Desai AR, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000264; Dhote JF, 2000, ANN FOR SCI, V57, P651, DOI 10.1051/forest:2000148; Drake JB, 2002, REMOTE SENS ENVIRON, V81, P378, DOI 10.1016/S0034-4257(02)00013-5; Dubois-Fernandez PC, 2008, IEEE T GEOSCI REMOTE, V46, P3208, DOI 10.1109/TGRS.2008.919143; Dufrene E, 2005, ECOL MODEL, V185, P407, DOI 10.1016/j.ecolmodel.2005.01.004; DURRIEU S, 2010, LIDAR EARTH FORESTS; Friedlingstein P, 1999, GLOB CHANGE BIOL, V5, P755, DOI 10.1046/j.1365-2486.1999.00269.x; Grant RF, 2007, TREE PHYSIOL, V27, P115; Hajnsek I, 2009, IEEE T GEOSCI REMOTE, V47, P481, DOI 10.1109/TGRS.2008.2009437; Hyde P, 2007, REMOTE SENS ENVIRON, V106, P28, DOI 10.1016/j.rse.2006.07.017; Intergovernmental Panel on Climate Change (IPCC), 2003, GOOD PRACT GUID LAND; Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2; Krinner G, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2003GB002199; Lefsky MA, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023971; Lefsky MA, 2005, REMOTE SENS ENVIRON, V95, P549, DOI 10.1016/j.rse.2004.12.022; Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689; LETOAN T, 2008, CANDIDATE EARTH EXPL; Lim KS, 2004, SCAND J FOREST RES, V19, P558, DOI 10.1080/02827580410019490; LINDNER M, 2004, SPECIFIC STUDY FORES; Loustau D, 2005, TREE PHYSIOL, V25, P813; Lucas RM, 2008, INT J REMOTE SENS, V29, P1553, DOI 10.1080/01431160701736497; Luyssaert S, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003377; Luyssaert S, 2010, GLOBAL CHANGE BIOL, V16, P1429, DOI 10.1111/j.1365-2486.2009.02056.x; Luyssaert S, 2007, GLOBAL CHANGE BIOL, V13, P2509, DOI 10.1111/j.1365-2486.2007.01439.x; Magnani F, 2007, NATURE, V447, P848, DOI 10.1038/nature05847; Masera OR, 2003, ECOL MODEL, V164, P177, DOI 10.1016/S0304-3800(02)00419-2; Means JE, 1999, REMOTE SENS ENVIRON, V67, P298, DOI 10.1016/S0034-4257(98)00091-1; Mitchell S, 2009, ECOL MODEL, V220, P3259, DOI 10.1016/j.ecolmodel.2009.08.021; Mitchell TD, 2005, INT J CLIMATOL, V25, P693, DOI 10.1002/joc.1181; Moorcroft PR, 2001, ECOL MONOGR, V71, P557, DOI 10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2; NABUURS GJ, 2008, MAPPING CONTINENT HI; Naesset E, 2009, REMOTE SENS ENVIRON, V113, P148, DOI 10.1016/j.rse.2008.09.001; Neeff T, 2005, FOREST SCI, V51, P585; Nelson R, 2003, PHOTOGRAMM ENG REM S, V69, P267; Nunery JS, 2010, FOREST ECOL MANAG, V259, P1363, DOI 10.1016/j.foreco.2009.12.029; Pan Y, 2010, BIOGEOSCIENCES DISCU, V7, P979, DOI DOI 10.5194/BGD-7-979-2010; Piao SL, 2009, NATURE, V458, P1009, DOI 10.1038/nature07944; PORTE A, 1999, MODELISATION EFFETS; Pretzsch H, 2002, FOREST ECOL MANAG, V162, P3, DOI 10.1016/S0378-1127(02)00047-6; Reineke LH, 1933, J AGRIC RES, V46, P0627; Rubinstein R. Y., 1981, SIMULATION MONTE CAR, V1; Saatchi S, 2007, IEEE T GEOSCI REMOTE, V45, P1726, DOI 10.1109/TGRS.2006.887002; SANTAREN D, 2006, OPTIMISATION PARAMET; Sarrat C, 2009, BIOGEOSCIENCES, V6, P633; Sato H, 2007, ECOL MODEL, V200, P279, DOI 10.1016/j.ecolmodel.2006.09.006; Sitch S, 2003, GLOBAL CHANGE BIOL, V9, P161, DOI 10.1046/j.1365-2486.2003.00569.x; Stephens P., 2007, IAPRS, V36, P389; Thornton PE, 2002, AGR FOREST METEOROL, V113, P185, DOI 10.1016/S0168-1923(02)00108-9; Vetter M, 2008, BIOGEOSCIENCES, V5, P561; WATT PJ, 2006, EVALUATION AIRBORNE; WILLMOTT CJ, 1982, B AM METEOROL SOC, V63, P1309, DOI 10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2; Zaehle S, 2006, ECOL APPL, V16, P1555, DOI 10.1890/1051-0761(2006)016[1555:TIOADI]2.0.CO;2; Zaehle S, 2010, GLOBAL BIOGEOCHEM CY, V24, DOI 10.1029/2009GB003522; *IFN, 2006, OBS FOR FRANC MISS 1, P12; *JRC, 2009, EUR FOR YIELD TABL D	66	2	2	ELSEVIER SCIENCE BV	AMSTERDAM	PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS	0378-1127		FOREST ECOL MANAG	For. Ecol. Manage.	FEB 1	2011	261	3					515	530		10.1016/j.foreco.2010.11.002		16	Forestry	Forestry	721DO	WOS:000287333000021	
J	Luo, YQ; Melillo, J; Niu, SL; Beier, C; Clark, JS; Classen, AT; Davidson, E; Dukes, JS; Evans, RD; Field, CB; Czimczik, CI; Keller, M; Kimball, BA; Kueppers, LM; Norby, RJ; Pelini, SL; Pendall, E; Rastetter, E; Six, J; Smith, M; Tjoelker, MG; Torn, MS				Luo, Yiqi; Melillo, Jerry; Niu, Shuli; Beier, Claus; Clark, James S.; Classen, Aimee T.; Davidson, Eric; Dukes, Jeffrey S.; Evans, R. Dave; Field, Christopher B.; Czimczik, Claudia I.; Keller, Michael; Kimball, Bruce A.; Kueppers, Lara M.; Norby, Richard J.; Pelini, Shannon L.; Pendall, Elise; Rastetter, Edward; Six, Johan; Smith, Melinda; Tjoelker, Mark G.; Torn, Margaret S.			Coordinated approaches to quantify long-term ecosystem dynamics in response to global change	GLOBAL CHANGE BIOLOGY			English	Article						climate change; data assimilation; earth system; experimentation; global change; process study; terrestrial ecosystems	SOIL ORGANIC-MATTER; CLIMATE-CHANGE; ELEVATED CO2; ATMOSPHERIC CO2; CARBON-STORAGE; EXPERIMENTAL DROUGHT; FIELD EXPERIMENTS; FOREST ECOSYSTEM; AMAZON FOREST; RISING CO2	Many serious ecosystem consequences of climate change will take decades or even centuries to emerge. Long-term ecological responses to global change are strongly regulated by slow processes, such as changes in species composition, carbon dynamics in soil and by long-lived plants, and accumulation of nutrient capitals. Understanding and predicting these processes require experiments on decadal time scales. But decadal experiments by themselves may not be adequate because many of the slow processes have characteristic time scales much longer than experiments can be maintained. This article promotes a coordinated approach that combines long-term, large-scale global change experiments with process studies and modeling. Long-term global change manipulative experiments, especially in high-priority ecosystems such as tropical forests and high-latitude regions, are essential to maximize information gain concerning future states of the earth system. The long-term experiments should be conducted in tandem with complementary process studies, such as those using model ecosystems, species replacements, laboratory incubations, isotope tracers, and greenhouse facilities. Models are essential to assimilate data from long-term experiments and process studies together with information from long-term observations, surveys, and space-for-time studies along environmental and biological gradients. Future research programs with coordinated long-term experiments, process studies, and modeling have the potential to be the most effective strategy to gain the best information on long-term ecosystem dynamics in response to global change.	[Luo, Yiqi; Niu, Shuli] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73069 USA; [Melillo, Jerry; Rastetter, Edward] Marine Biol Lab, Ctr Ecosyst, Woods Hole, MA 02543 USA; [Beier, Claus] Tech Univ Denmark DTU, Riso Natl Lab Sustainable Energy, Biosyst Dept, DK-4000 Roskilde, Denmark; [Clark, James S.] Duke Univ, Dept Biol, Durham, NC 27708 USA; [Clark, James S.] Duke Univ, Nicholas Sch Environm, Durham, NC 27708 USA; [Classen, Aimee T.] Univ Tennessee, Dept Ecol & Evolutionary Biol, Knoxville, TN 37996 USA; [Davidson, Eric] Woods Hole Res Ctr, Falmouth, MA 02540 USA; [Dukes, Jeffrey S.] Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USA; [Dukes, Jeffrey S.] Purdue Univ, Dept Biol Sci, W Lafayette, IN 47907 USA; [Evans, R. Dave] Washington State Univ, Sch Biol Sci, Pullman, WA 99164 USA; [Field, Christopher B.] Carnegie Inst Washington, Dept Global Ecol, Stanford, CA 94305 USA; [Czimczik, Claudia I.] Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA 92697 USA; [Keller, Michael] Natl Ecol Observ Network Inc, Boulder, CO 80301 USA; [Kimball, Bruce A.] ARS, US Arid Land Agr Res Ctr, USDA, Maricopa, AZ 85018 USA; [Kueppers, Lara M.] Univ Calif, Sch Nat Sci, Merced, CA 95343 USA; [Norby, Richard J.] Oak Ridge Natl Lab, Div Environm Sci, Oak Ridge, TN 37831 USA; [Pelini, Shannon L.] Harvard Univ, Petersham, MA 01366 USA; [Pendall, Elise] Univ Wyoming, Dept Bot, Laramie, WY 82071 USA; [Six, Johan] Univ Calif Davis, Dept Plant Sci, Davis, CA 95616 USA; [Smith, Melinda] Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT 06520 USA; [Tjoelker, Mark G.] Texas A&M Univ, Dept Ecosyst Sci & Management, College Stn, TX 77843 USA; [Torn, Margaret S.] Univ Calif Berkeley, Lawrence Berkeley Lab, Div Earth Sci, Berkeley, CA 94720 USA	Luo, YQ (reprint author), Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73069 USA.	yluo@ou.edu	Dukes, Jeffrey/C-9765-2009; Niu, Shuli/E-7550-2011; Clark, James/G-6331-2011; Classen, Aimee/C-4035-2008; Keller, Michael/A-8976-2012; Norby, Richard/C-1773-2012; Beier, Claus/E-6288-2013	Dukes, Jeffrey/0000-0001-9482-7743; Keller, Michael/0000-0002-0253-3359; 	DOE through Oak Ridge for Science and Education; NSF [EF 0938795, DBI 0850290, DEB 0840964, DEB 0743778]; Office of Science (BER), Department of Energy [DE-FG02-006ER64319]; Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University [DE-FC02-06ER64158]; CLIMAITE project	We thank Ding Guo for his help with references. The work was financially supported by DOE through Oak Ridge for Science and Education and NSF EF 0938795. The preparation of the manuscript by Y. L. and S. N. was also financially supported by NSF DBI 0850290, DEB 0840964, DEB 0743778; by the Office of Science (BER), Department of Energy, Grant No.: DE-FG02-006ER64319 and through the Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University, under Award Number DE-FC02-06ER64158. The participation of C. B. was financially supported by the CLIMAITE project.	Ainsworth EA, 2008, PLANT CELL ENVIRON, V31, P1317, DOI 10.1111/j.1365-3040.2008.01841.x; Austin AT, 2002, ECOLOGY, V83, P328, DOI 10.2307/2680017; Beier C., 2004, WATER AIR SOIL POLL, V4, P191, DOI 10.1023/B:WAFO.0000028354.34016.af; Brando PM, 2008, PHILOS T R SOC B, V363, P1839, DOI 10.1098/rstb.2007.0031; Calfapietra C, 2010, TRENDS PLANT SCI, V15, P5, DOI 10.1016/j.tplants.2009.11.001; CLARK JS, 2010, ECOLOGICAL IN PRESS; Coe M., 2002, J GEOPHYS RES, V107, P1, DOI [10.1029/2001JD000740, DOI 10.1029/2001JD000740]; Cox PM, 2004, THEOR APPL CLIMATOL, V78, P137, DOI 10.1007/s00704-004-0049-4; Davidson EA, 2006, NATURE, V440, P165, DOI 10.1038/nature04514; Davidson EA, 2008, GLOBAL CHANGE BIOL, V14, P2582, DOI 10.1111/j.1365-2486.2008.01694.x; Dietze M, 2007, ECOL MONOGR, V78, P331; Dukes JS, 2005, PLOS BIOL, V3, P1829, DOI 10.1371/journal.pbio.0030319; Dunne JA, 2004, ECOLOGY, V85, P904, DOI 10.1890/03-8003; Froberg M, 2008, BIOGEOCHEMISTRY, V89, P151, DOI 10.1007/s10533-008-9205-8; Goidts E, 2007, GEODERMA, V141, P341, DOI 10.1016/j.geoderma.2007.06.013; Hanson PJ, 2004, ECOL MONOGR, V74, P443, DOI 10.1890/03-4049; Hofmann A, 2009, EUR J SOIL SCI, V60, P250, DOI 10.1111/j.1365-2389.2008.01106.x; Hungate BA, 1996, PLANT SOIL, V187, P135; Jones BM, 2009, ARCT ANTARCT ALP RES, V41, P309, DOI 10.1657/1938-4246-41.3.309; Karhu K, 2010, SOIL BIOL BIOCHEM, V42, P72, DOI 10.1016/j.soilbio.2009.10.002; Kimball BA, 2005, GLOBAL CHANGE BIOL, V11, P2041, DOI 10.1111/j.1365-2486.2005.01028.x; Kimball BA, 2007, GLOBAL CHANGE BIOL, V13, P2171, DOI 10.1111/j.1365-2486.2007.01430.x; KIMBALL BA, 1992, CRIT REV PLANT SCI, V11, P265, DOI 10.1080/07352689209382347; Knapp AK, 2002, SCIENCE, V298, P2202, DOI 10.1126/science.1076347; Knops JMH, 2000, ECOLOGY, V81, P88, DOI 10.1890/0012-9658(2000)081[0088:DOSNAC]2.0.CO;2; Korner C, 2009, ANNU REV ECOL EVOL S, V40, P61, DOI 10.1146/annurev.ecolsys.110308.120217; Lambers H, 2008, TRENDS ECOL EVOL, V23, P95, DOI 10.1016/j.tree.2007.10.008; Lewin KF, 2009, GLOBAL CHANGE BIOL, V15, P388, DOI 10.1111/j.1365-2486.2008.01748.x; Li WH, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006355; Lin GH, 1999, OECOLOGIA, V119, P97, DOI 10.1007/s004420050765; Luo YQ, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001923; LUO Y, 1994, PLANT CELL ENVIRON, V17, P1195, DOI 10.1111/j.1365-3040.1994.tb02017.x; Luo YQ, 2009, GCB BIOENERGY, V1, P62, DOI 10.1111/j.1757-1707.2008.01005.x; Luo YQ, 1999, ECOLOGY, V80, P1568, DOI 10.1890/0012-9658(1999)080[1568:VOEFCE]2.0.CO;2; Mack MC, 2004, NATURE, V431, P440, DOI 10.1038/nature02887; Malhi Y, 2008, SCIENCE, V319, P169, DOI 10.1126/science.1146961; Malhi Y, 2009, P NATL ACAD SCI USA, V106, P20610, DOI 10.1073/pnas.0804619106; Maloney KO, 2008, RESTOR ECOL, V16, P503, DOI 10.1111/j.1526-100X.2007.00324.x; Marengo JA, 2004, THEOR APPL CLIMATOL, V78, P79, DOI 10.1007/S00704-004-0045-8; May R. M, 1999, NATURE HUMAN SOC QUE, P30; McKane RB, 1997, ECOLOGY, V78, P1170, DOI 10.1890/0012-9658(1997)078[1170:CEOTCS]2.0.CO;2; Mikkelsen TN, 2008, FUNCT ECOL, V22, P185, DOI 10.1111/j.1365-2435.2007.01362.x; Morgan JA, 2004, OECOLOGIA, V140, P11, DOI 10.1007/s00442-004-1550-2; Nepstad DC, 2007, ECOLOGY, V88, P2259, DOI 10.1890/06-1046.1; Niu SL, 2010, ECOLOGY IN PRESS; Norby RJ, 1999, PLANT CELL ENVIRON, V22, P683, DOI 10.1046/j.1365-3040.1999.00391.x; Parton WJ, 2007, NEW PHYTOL, V174, P823, DOI 10.1111/j.1469-8137.2007.02052.x; Pelini SL, 2009, P NATL ACAD SCI USA, V106, P11160, DOI 10.1073/pnas.0900284106; Pendall E, 2007, SOIL BIOL BIOCHEM, V39, P2628, DOI 10.1016/j.soilbio.2007.05.016; Purves DW, 2009, P R SOC B, V276, P1477, DOI 10.1098/rspb.2008.1241; Rastetter EB, 1996, BIOSCIENCE, V46, P190, DOI 10.2307/1312740; Rastetter EB, 2010, ECOLOGICAL IN PRESS; Rustad LE, 2008, SCI TOTAL ENVIRON, V404, P222, DOI 10.1016/j.scitotenv.2008.04.050; Saleska SR, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001573; Sankaran M, 2004, ECOL LETT, V7, P480, DOI 10.1111/j.1461-0248.2004.00596.x; Schafer KVR, 2003, GLOBAL CHANGE BIOL, V9, P1378, DOI 10.1046/j.1365-2486.2003.00662.x; SCHLESINGER WH, 1990, NATURE, V348, P232, DOI 10.1038/348232a0; Schuur EAG, 2008, BIOSCIENCE, V58, P701, DOI 10.1641/B580807; Shaver GR, 1999, POLAR RES, V18, P245, DOI 10.1111/j.1751-8369.1999.tb00300.x; Shen WJ, 2009, GLOBAL CHANGE BIOL, V15, P2274, DOI 10.1111/j.1365-2486.2009.01857.x; Six J, 2002, PLANT SOIL, V241, P155, DOI 10.1023/A:1016125726789; Smith MD, 2009, ECOLOGY, V90, P3279, DOI 10.1890/08-1815.1; SWITZER GL, 1979, ECOLOGY, V60, P1162, DOI 10.2307/1936964; Tarnocai C, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003327; Tjoelker MG, 2009, NEW PHYTOL, V181, P218, DOI 10.1111/j.1469-8137.2008.02624.x; Trumbore S, 2000, ECOL APPL, V10, P399, DOI 10.1890/1051-0761(2000)010[0399:AOSOMA]2.0.CO;2; Valladares F, 2008, ANNU REV ECOL EVOL S, V39, P237, DOI 10.1146/annurev.ecolsys.39.110707.173506; Volder A, 2010, GLOBAL CHANGE BIOL, V16, P3349, DOI 10.1111/j.1365-2486.2009.02152.x; Walker MD, 2006, P NATL ACAD SCI USA, V103, P1342, DOI 10.1073/pnas.0503198103; Wardle DA, 2005, NATURE, V435, P806, DOI 10.1038/nature03611; Wardle DA, 2004, SCIENCE, V305, P509, DOI 10.1126/science.1098778; Weng ES, 2010, ECOLOGICAL IN PRESS; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Yahdjian L, 2006, ECOLOGY, V87, P952, DOI 10.1890/0012-9658(2006)87[952:VSCPPR]2.0.CO;2; Zhou XH, 2009, ECOSYSTEMS, V12, P1369, DOI 10.1007/s10021-009-9296-7; Zhou XH, 2008, ECOL APPL, V18, P453, DOI 10.1890/07-0626.1	76	20	21	WILEY-BLACKWELL	MALDEN	COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA	1354-1013		GLOBAL CHANGE BIOL	Glob. Change Biol.	FEB	2011	17	2					843	854		10.1111/j.1365-2486.2010.02265.x		12	Biodiversity Conservation; Ecology; Environmental Sciences	Biodiversity & Conservation; Environmental Sciences & Ecology	702ES	WOS:000285878000015	
J	Boesch, H; Baker, D; Connor, B; Crisp, D; Miller, C				Boesch, Hartmut; Baker, David; Connor, Brian; Crisp, David; Miller, Charles			Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission	REMOTE SENSING			English	Article						trace gases; remote sensing; inverse theory	FSI WFM-DOAS; ATMOSPHERIC CO2; MIDTROPOSPHERIC CO2; DATA ASSIMILATION; SURFACE; SCIAMACHY; DIOXIDE; SINKS; SPECTROMETER; GASES	The global characteristics of retrievals of the column-averaged CO2 dry air mole fraction, X-CO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on X-CO2 retrieval precision and averaging kernels and their sensitivity to key parameters such as solar zenith angle (SZA), surface pressure, surface type and aerosol optical depth (AOD), for both nadir and sunglint observing modes. Realistic simulations have been carried out and the single sounding retrieval errors for X-CO2 have been derived from the formal retrieval error covariance matrix under the assumption that the retrieval has converged to the correct answer and that the forward model can adequately describe the measurement. Thus, the retrieval errors presented in this study represent an estimate of the retrieval precision. For nadir observations, we find single-sounding retrieval errors with values typically less than 1 part per million (ppm) over most land surfaces for SZAs less than 70 degrees and up to 2.5 ppm for larger SZAs. Larger errors are found over snow/ice and ocean surfaces due to their low albedo in the spectral regions of the CO2 absorption bands and, for ocean, also in the O-2 A band. For sunglint observations, errors over the ocean are significantly smaller than in nadir mode with values in the range of 0.3 to 0.6 ppm for small SZAs which can decrease to values as small as 0.15 for the largest SZAs. The vertical sensitivity of the retrieval that is represented by the column averaging kernel peaks near the surface and exhibits values near unity throughout most of the troposphere for most anticipated scenes. Nadir observations over dark ocean or snow/ice surfaces and observations with large AOD and large SZA show a decreased sensitivity to near-surface CO2. All simulations are carried out for a mid-latitude summer atmospheric profile, a given aerosol type and vertical distribution, a constant windspeed for ocean sunglint and by excluding the presence of thin cirrus clouds. The impact of these parameters on averaging kernels and X-CO2 retrieval errors are studied with sensitivity studies. Systematic biases in retrieved X-CO2, as can be introduced by uncertainties in the spectroscopic parameters, instrument calibration or deficiencies in the retrieval algorithm itself, are not included in this study. The presented error estimates will therefore only describe the true retrieval errors once systematic biases are eliminated. It is expected that it will be possible to retrieve X-CO2 for cloud free observations and for low AOD (here less than 0.3 for the wavelength region of the O-2 A band) with sufficient accuracy for improving CO2 surface flux estimates and we find that on average 18% to 21% of all observations are sufficiently cloud-free with only few areas suffering from the presence of persistent clouds or high AOD. This results typically in tens of useful observations per 16 day ground track repeat cycle at a 1 degrees x 1 degrees resolution. Averaging observations acquired along similar to 1 degrees intervals for individual ground tracks will significantly reduce the random component of the errors of the X-CO2 average product for ingestion into data assimilation/inverse models. If biases in the X-CO2 retrieval of the order of a few tenth ppm can be successfully removed by validation or by bias-correction in the flux inversion, then it can be expected that OCO-2 X-CO2 data can lead to tremendous improvements in estimates of CO2 surface-atmosphere fluxes.	[Boesch, Hartmut] Univ Leicester, Dept Phys & Astron, Leicester LE1 7RH, Leics, England; [Baker, David] Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA; [Connor, Brian] BC Consulting Ltd, Alexandra 9320, New Zealand; [Crisp, David; Miller, Charles] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA	Boesch, H (reprint author), Univ Leicester, Dept Phys & Astron, Univ Rd, Leicester LE1 7RH, Leics, England.	hartmut.boesch@le.ac.uk; baker@cira.colostate.edu; bcconsulting@xtra.co.nz; david.crisp@jpl.nasa.gov; charles.e.miller@jpl.nasa.gov	Boesch, Hartmut/G-6021-2012		Research Council UK	This work was partly performed by the Jet Propulsion Laboratory of the California Institute of Technology, under contract to the National Aeronautics and Space Administration. We gratefully acknowledge Denis O'Brien for use of his orbit simulator and we would like to thank Robert Parker and Austin Cogan for proof-reading. HB is funded by the Research Council UK.	Arnold GT, 2002, INT J REMOTE SENS, V23, P3763, DOI 10.1080/01431160110117373; Baker DF, 2010, ATMOS CHEM PHYS, V10, P4145, DOI 10.5194/acp-10-4145-2010; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Baldridge AM, 2009, REMOTE SENS ENVIRON, V113, P711, DOI 10.1016/j.rse.2008.11.007; Barkley MP, 2006, ATMOS CHEM PHYS, V6, P4483; Barkley MP, 2007, ATMOS CHEM PHYS, V7, P3597; Boesch H., 2006, J GEOPHYS RES, V111, pD23; Bovensmann H, 1999, J ATMOS SCI, V56, P127, DOI 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2; BURROWS JP, 1995, ACTA ASTRONAUT, V35, P445, DOI 10.1016/0094-5765(94)00278-T; Butz A, 2009, APPL OPTICS, V48, P3322, DOI 10.1364/AO.48.003322; Chahine M, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL024165; Chevallier F, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL024229; Chevallier F, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007375; Chevallier F, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006390; Christi MJ, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004058; Connor BJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2006JD008336; COX C, 1954, J MAR RES, V13, P198; Crevoisier C, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020141; Crevoisier C, 2009, ATMOS CHEM PHYS, V9, P4797; Crisp D, 2004, ADV SPACE RES-SERIES, V34, P700, DOI 10.1016/j.asr.2003.08.062; DEROOIJ WA, 1984, ASTRON ASTROPHYS, V131, P237; Eguchi N, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029529; Feng L, 2009, ATMOS CHEM PHYS, V9, P2619, DOI 10.5194/acp-9-2619-2009; Forster P, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P129; Geller M., 1995, P ASP C, P81; Geller M., 1992, NASA REFERENCE PUBLI; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; Hamazaki T, 2005, P SOC PHOTO-OPT INS, V5659, P73, DOI 10.1117/12.581198; Houweling S, 2005, ATMOS CHEM PHYS, V5, P3003; Kahn R., 2001, J GEOPHYS RES, V106, pD16; Kuang ZM, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014298; Le Quere C, 2007, SCIENCE, V316, P1735, DOI 10.1126/science.1136188; Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689; Livingston W., 1991, ATLAS SOLAR SPECTRUM; Machida T, 2008, J ATMOS OCEAN TECH, V25, P1744, DOI 10.1175/2008JTECHA1082.1; Maddy E., 2008, J GEOPHYS RES, V113, pD11; Marshak A, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009196; Matsueda H, 2002, TELLUS B, V54, P1, DOI 10.1034/j.1600-0889.2002.00304.x; Engelen RJ, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005982; Miller CE, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007659; Mishchenko MI, 1998, J QUANT SPECTROSC RA, V60, P309, DOI 10.1016/S0022-4073(98)00008-9; Natraj V, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009017; Natraj V, 2007, J QUANT SPECTROSC RA, V107, P263, DOI 10.1016/j.jqsrt.2007.02.011; Natraj V, 2007, J QUANT SPECTROSC RA, V103, P245, DOI 10.1016/j.jqsrt.2006.02.073; Ohyama H, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011465; Olsen SC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003968; Palmer P. I., 2008, ATMOS CHEM PHYS DISC, V8, P7339; Rayner PJ, 2001, GEOPHYS RES LETT, V28, P175, DOI 10.1029/2000GL011912; Rodgers C. D., 2000, INVERSE METHODS ATMO; Schneising O, 2008, ATMOS CHEM PHYS, V8, P3827; Chedin A, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003439; Spurr RJD, 2007, J QUANT SPECTROSC RA, V103, P431, DOI 10.1016/j.jqsrt.2006.05.001; Spurr RJD, 2004, J QUANT SPECTROSC RA, V83, P15, DOI 10.1016/S0022-4073(02)00283-2; Wallace L., 1993, ATLAS PHOTOSPHERIC S; Warneke T, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023825; Washenfelder RA, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007154; [Anonymous], 2010, PUBL REL CONC DAT CA	57	15	16	MDPI AG	BASEL	POSTFACH, CH-4005 BASEL, SWITZERLAND	2072-4292		REMOTE SENS-BASEL	Remote Sens.	FEB	2011	3	2					270	304		10.3390/rs3020270		35	Remote Sensing	Remote Sensing	978MK	WOS:000306745600004	
J	Freitas, SR; Rodrigues, LF; Longo, KM; Panetta, J				Freitas, S. R.; Rodrigues, L. F.; Longo, K. M.; Panetta, J.			Impact of a monotonic advection scheme with low numerical diffusion on transport modeling of emissions from biomass burning	JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS			English	Article							SYSTEM CATT-BRAMS; BRAZILIAN DEVELOPMENTS; ATMOSPHERIC TRANSPORT; CHEMISTRY MODELS; COUPLED AEROSOL; PLUME RISE; CONSERVATION; SENSITIVITY; ALGORITHMS	An advection scheme, which maintains the initial monotonic characteristics of a tracer field being transported and at the same time produces low numerical diffusion, is implemented in the Coupled Chemistry-Aerosol-Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CCATT-BRAMS). Several comparisons of transport modeling using the new and original (non-monotonic) CCATT-BRAMS formulations are performed. Idealized 2-D non-divergent or divergent and stationary or time-dependent wind fields are used to transport sharply localized tracer distributions, as well as to verify if an existent correlation of the mass mixing ratios of two interrelated tracers is kept during the transport simulation. Further comparisons are performed using realistic 3-D wind fields. We then perform full simulations of real cases using data assimilation and complete atmospheric physics. In these simulations, we address the impacts of both advection schemes on the transport of biomass burning emissions and the formation of secondary species from non-linear chemical reactions of precursors. The results show that the new scheme produces much more realistic transport patterns, without generating spurious oscillations and under-and overshoots or spreading mass away from the local peaks. Increasing the numerical diffusion in the original scheme in order to remove the spurious oscillations and maintain the monotonicity of the transported field causes excessive smoothing in the tracer distribution, reducing the local gradients and maximum values and unrealistically spreading mass away from the local peaks. As a result, huge differences (hundreds of %) for relatively inert tracers (like carbon monoxide) are found in the smoke plume cores. In terms of the secondary chemical species formed by non-linear reactions (like ozone), we found differences of up to 50% in our simulations.	[Freitas, S. R.; Rodrigues, L. F.; Panetta, J.] INPE, Ctr Weather Forecasting & Climate Studies, BR-12630000 Cachoeira Paulista, Brazil; [Longo, K. M.] INPE, Ctr Earth Syst Sci, Sao Jose Dos Campos, Brazil	Freitas, SR (reprint author), INPE, Ctr Weather Forecasting & Climate Studies, Rodovia Presidente Dutra,Km 39, BR-12630000 Cachoeira Paulista, Brazil.	saulo.freitas@cptec.inpe.br	Freitas, Saulo/A-2279-2012	Freitas, Saulo/0000-0002-9879-646X	CNPq [302696/2008-3, 309922/2007-0]	We acknowledge partial support of this work by CNPq (302696/2008-3, 309922/2007-0). The authors would like to thank the anonymous reviewers and the editor for the careful review of our manuscript and providing us with their comments and suggestions to improve its quality.	CROWLEY WP, 1968, MON WEATHER REV, V96, P1, DOI 10.1175/1520-0493(1968)096<0001:NAE>2.0.CO;2; Freitas SR, 2010, ATMOS CHEM PHYS, V10, P585; Freitas SR, 2009, ATMOS CHEM PHYS, V9, P2843; Freitas SR, 2011, GEOSCI MODEL DEV, V4, P419, DOI 10.5194/gmd-4-419-2011; Freitas SR, 2005, ENVIRON FLUID MECH, V5, P135, DOI 10.1007/s10652-005-0243-7; Freitas SR, 2007, ATMOS CHEM PHYS, V7, P3385, DOI 10.5194/acp-7-3385-2007; Gevaerd R., 2006, REV BRASILEIRA METEO, V21, P1; Hundsdorfer W., 2007, SPRINGER SER COMPUT, V33; Lanser D., 1998, MASR9805 CWI; Lauritzen PH, 2010, J COMPUT PHYS, V229, P1401, DOI 10.1016/j.jcp.2009.10.036; LAURITZEN PH, 2011, NUMERICAL TECHNIQUES, V80, P185; Leveque RJ, 1996, SIAM J NUMER ANAL, V33, P627, DOI 10.1137/0733033; Lin SJ, 1996, MON WEATHER REV, V124, P2046, DOI 10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2; Longo KM, 2010, ATMOS CHEM PHYS, V10, P5785, DOI 10.5194/acp-10-5785-2010; MCRAE GJ, 1982, J COMPUT PHYS, V45, P1, DOI 10.1016/0021-9991(82)90101-2; Medvigy D, 2005, ENVIRON FLUID MECH, V5, P109, DOI 10.1007/s10652-005-5275-5; Nair RD, 2010, J COMPUT PHYS, V229, P8868, DOI 10.1016/j.jcp.2010.08.014; Ovtchinnikov M, 2009, MON WEATHER REV, V137, P632, DOI 10.1175/2008MWR2626.1; Peuch V.-H., 1999, P MET FRANC WORKSH A, P33; PLUMB RA, 1992, J GEOPHYS RES-ATMOS, V97, P10145; Plumb RA, 2007, REV GEOPHYS, V45, DOI 10.1029/2005RG000179; PRATHER MJ, 1986, J GEOPHYS RES-ATMOS, V91, P6671, DOI 10.1029/JD091iD06p06671; ROOD RB, 1987, REV GEOPHYS, V25, P71, DOI 10.1029/RG025i001p00071; Seinfeld J.H., 1998, ATMOSPHERIC CHEM PHY; Skamarock WC, 2009, MON WEATHER REV, V137, P488, DOI 10.1175/2008MWR2583.1; Smagorinsky J., 1963, MON WEATHER REV, V91, P99, DOI 10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2; Stockwell W. R., 1997, J GEOPHYS RES, V102, P25, DOI 10.1029/97JD00849; Thuburn J, 1997, J GEOPHYS RES-ATMOS, V102, P6775, DOI 10.1029/96JD03514; TREMBACK CJ, 1987, MON WEATHER REV, V115, P540, DOI 10.1175/1520-0493(1987)115<0540:TFTUAS>2.0.CO;2; VANLEER B, 1977, J COMPUT PHYS, V23, P276, DOI 10.1016/0021-9991(77)90095-X; Walcek CJ, 2000, J GEOPHYS RES-ATMOS, V105, P9335, DOI 10.1029/1999JD901142; Walko RL, 2000, J APPL METEOROL, V39, P931, DOI 10.1175/1520-0450(2000)039<0931:CABHMF>2.0.CO;2; Wang HL, 2009, MON WEATHER REV, V137, P2547, DOI 10.1175/2009MWR2820.1; WILLIAMSON DL, 1992, J COMPUT PHYS, V102, P211, DOI 10.1016/S0021-9991(05)80016-6; Yanenko N. A., 1971, METHOD FRACTIONAL ST	35	0	0	AMER GEOPHYSICAL UNION	WASHINGTON	2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA	1942-2466		J ADV MODEL EARTH SY	J. Adv. Model. Earth Syst.		2011	3								M01001	10.1029/2011MS000084		26	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	931EM	WOS:000303198400019	
J	Williams, IN; Riley, WJ; Torn, MS; Berry, JA; Biraud, SC				Williams, I. N.; Riley, W. J.; Torn, M. S.; Berry, J. A.; Biraud, S. C.			Using boundary layer equilibrium to reduce uncertainties in transport models and CO2 flux inversions	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							CARBON-DIOXIDE EXCHANGE; ATMOSPHERIC CO2; INTERANNUAL VARIABILITY; VERTICAL PROFILES; WATER-VAPOR; LAND; CLIMATE; RECTIFIER; CHEMISTRY; DYNAMICS	This paper reexamines evidence for systematic errors in atmospheric transport models, in terms of the diagnostics used to infer vertical mixing rates from models and observations. Different diagnostics support different conclusions about transport model errors that could imply either stronger or weaker northern terrestrial carbon sinks. Conventional mixing diagnostics are compared to analyzed vertical mixing rates using data from the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, the CarbonTracker data assimilation system based on Transport Model version 5 (TM5), and atmospheric reanalyses. The results demonstrate that diagnostics based on boundary layer depth and vertical concentration gradients do not always indicate the vertical mixing strength. Vertical mixing rates are anti-correlated with boundary layer depth at some sites, diminishing in summer when the boundary layer is deepest. Boundary layer equilibrium concepts predict an inverse proportionality between CO2 vertical gradients and vertical mixing strength, such that previously reported discrepancies between observations and models most likely reflect overestimated as opposed to underestimated vertical mixing. However, errors in seasonal concentration gradients can also result from errors in modeled surface fluxes. This study proposes using the timescale for approach to boundary layer equilibrium to diagnose vertical mixing independently of seasonal surface fluxes, with applications to observations and model simulations of CO2 or other conserved boundary layer tracers with surface sources and sinks. Results indicate that frequently cited discrepancies between observations and inverse estimates do not provide sufficient proof of systematic errors in atmospheric transport models. Some previously hypothesized transport model biases, if found and corrected, could cause inverse estimates to further diverge from carbon inventory estimates of terrestrial sinks.	[Williams, I. N.] Univ Chicago, Dept Geophys Sci, Chicago, IL 60637 USA; [Riley, W. J.; Torn, M. S.; Biraud, S. C.] Univ Calif Berkeley, Lawrence Berkeley Lab, Div Earth Sci, Berkeley, CA 94720 USA; [Berry, J. A.] Carnegie Inst Washington, Stanford, CA USA	Williams, IN (reprint author), Univ Chicago, Dept Geophys Sci, 5734 S Ellis Ave, Chicago, IL 60637 USA.	inw@uchicago.edu			US Department of Energy [DE-AC02-05CH11231]	Funding for this study was provided by the US Department of Energy, BER Program, Contract # DE-AC02-05CH11231. CarbonTracker 2008 results provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov. Andy Jacobson assisted with the CarbonTracker data.	Baker DF, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2004GB002439; Bakwin PS, 2004, TELLUS B, V56, P301, DOI 10.1111/j.1600-0889.2004.00111.x; BAKWIN PS, 1995, TELLUS B, V47, P535, DOI 10.1034/j.1600-0889.47.issue5.2.x; Betts AK, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004420; BETTS AK, 1992, J GEOPHYS RES-ATMOS, V97, P18523; CHATFIELD C, 2004, ANAL TIME SERIES, P50; Chen BZ, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003925; Chou WW, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001295; CONWAY TJ, 1994, J GEOPHYS RES-ATMOS, V99, P22831, DOI 10.1029/94JD01951; Cooley HS, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005160; Dargaville RJ, 2003, TELLUS B, V55, P711, DOI 10.1034/j.1600-0889.2003.00038.x; Denman KL, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P499; DENNING AS, 1995, NATURE, V376, P240, DOI 10.1038/376240a0; Denning AS, 1999, TELLUS B, V51, P266, DOI 10.1034/j.1600-0889.1999.00012.x; Denning AS, 2008, AGR FOREST METEOROL, V148, P206, DOI 10.1016/j.agrformet.2007.08.012; Erickson DJ, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000435; FISCHER ML, 2007, EARTH INTERACT, V11, P1, DOI DOI 10.1175/EI231.1; Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1; FUNG I, 1983, J GEOPHYS RES-OC ATM, V88, P1281, DOI 10.1029/JC088iC02p01281; Gloor M, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007973; Gurney KR, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005373; Helliker BR, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004532; JONES RH, 1975, J APPL METEOROL, V14, P159, DOI 10.1175/1520-0450(1975)014<0159:ETVOTA>2.0.CO;2; Krol M, 2005, ATMOS CHEM PHYS, V5, P417; Lawrence MG, 2008, ATMOS CHEM PHYS, V8, P6037; Levy PE, 1999, AGR FOREST METEOROL, V98-9, P169, DOI 10.1016/S0168-1923(99)00096-9; Lloyd J, 2001, GLOBAL BIOGEOCHEM CY, V15, P267, DOI 10.1029/1999GB001211; Patra PK, 2009, ATMOS CHEM PHYS, V9, P1209; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; RAUPACH MR, 1991, VEGETATIO, V91, P105, DOI 10.1007/BF00036051; RAUPACH MR, 1992, AUST J BOT, V40, P697, DOI 10.1071/BT9920697; Riley WJ, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2009JG001003; SALTZMAN B, 1972, MON WEATHER REV, V100, P441, DOI 10.1175/1520-0493(1972)100<0441:NOTTOT>2.3.CO;2; Stephens BB, 2007, SCIENCE, V316, P1732, DOI 10.1126/science.1137004; Styles JM, 2002, TELLUS B, V54, P768, DOI 10.1034/j.1600-0889.2002.01336.x; Yang Z, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029742; Yi C, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004164; Yi CX, 2001, J ATMOS SCI, V58, P1288, DOI 10.1175/1520-0469(2001)058<1288:LTOOTD>2.0.CO;2	38	3	3	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2011	11	18					9631	9641		10.5194/acp-11-9631-2011		11	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	826QL	WOS:000295368700012	
J	Hooghiemstra, PB; Krol, MC; Meirink, JF; Bergamaschi, P; van der Werf, GR; Novelli, PC; Aben, I; Rockmann, T				Hooghiemstra, P. B.; Krol, M. C.; Meirink, J. F.; Bergamaschi, P.; van der Werf, G. R.; Novelli, P. C.; Aben, I.; Rockmann, T.			Optimizing global CO emission estimates using a four-dimensional variational data assimilation system and surface network observations	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							CARBON-MONOXIDE; TROPOSPHERIC CHEMISTRY; FIRE EMISSIONS; MODEL TM5; INVERSION; MOPITT; ADJOINT; FOREST; ASIA; ALGORITHM	We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60% in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, with the limited amount of data from the surface network, the system becomes data sparse resulting in a large solution space. Sensitivity studies have shown that model uncertainties (e. g., vertical distribution of biomass burning emissions and the OH field) and the prior inventories used, influence the inferred emission estimates. Also, since the observations only constrain total CO emissions, the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10 %. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes.	[Hooghiemstra, P. B.; Krol, M. C.; Rockmann, T.] Inst Marine & Atmospher Res Utrecht, Utrecht, Netherlands; [Hooghiemstra, P. B.; Krol, M. C.; Aben, I.] Netherlands Inst Space Res, Utrecht, Netherlands; [Krol, M. C.] Wageningen Univ, Wageningen, Netherlands; [Meirink, J. F.] Royal Netherlands Meteorol Inst, NL-3730 AE De Bilt, Netherlands; [Bergamaschi, P.] Joint Res Ctr, Inst Environm & Sustainabil, Ispra, Italy; [van der Werf, G. R.; Aben, I.] Free Univ Amsterdam, Fac Earth & Life Sci, Amsterdam, Netherlands; [Novelli, P. C.] NOAA, Climate Monitoring & Diagnost Lab, Boulder, CO 80303 USA	Hooghiemstra, PB (reprint author), Inst Marine & Atmospher Res Utrecht, Utrecht, Netherlands.	p.b.hooghiemstra@uu.nl			Dutch User Support Programme [GO-AO/05]	This research was supported by the Dutch User Support Programme 2006-2010 under project GO-AO/05. We also thank the MOPITT team for the MOPITT data. The Dutch National Computer Facility (NCF) is acknowledged for computer resources.	Arellano AF, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006613; Arellano AF, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020311; Bergamaschi P, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012287; Bergamaschi P, 2000, J GEOPHYS RES-ATMOS, V105, P1909, DOI 10.1029/1999JD900818; BERGAMASCHI P, 2010, J GEOPHYS RES, V115, P4703; Chevallier F, 2009, BIOGEOSCIENCES, V6, P103; Deeter MN, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007999; Deeter MN, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003186; Deeter MN, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013005; de Laat ATJ, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012698; Duncan BN, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008459; Emmons LK, 2009, ATMOS CHEM PHYS, V9, P1795; Enting I.G., 2002, INVERSE PROBLEMS ATM; Fisher M., 1995, ESTIMATING COVARIANC; Fortems-Cheiney A, 2009, ATMOS CHEM PHYS, V9, P8735; Gloudemans A M S, 2009, ATMOS CHEM PHYS, V9, P3799, DOI DOI 10.5194/ACP-9-3799-2009; Gonzi S, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012053; Heald CL, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD005185; HESTENES MR, 1952, J RES NAT BUR STAND, V49, P409, DOI 10.6028/jres.049.044; Houweling S, 1998, J GEOPHYS RES-ATMOS, V103, P10673, DOI 10.1029/97JD03582; Hudman RC, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL032393; Huijnen V, 2010, GEOSCI MODEL DEV, V3, P445, DOI 10.5194/gmd-3-445-2010; Jones DBA, 2009, ATMOS CHEM PHYS, V9, P3547; Kasibhatla P, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015581; Kasischke ES, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002300; Kopacz M, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2007JD009264; Kopacz M, 2010, ATMOS CHEM PHYS, V10, P855; Krol M, 2005, ATMOS CHEM PHYS, V5, P417; Labonne M, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029311; Lawrence MG, 2001, ATMOS CHEM PHYS, V1, P37; Lelieveld J, 2008, NATURE, V452, P737, DOI 10.1038/nature06870; LOGAN JA, 1981, J GEOPHYS RES-OC ATM, V86, P7210, DOI 10.1029/JC086iC08p07210; Meirink JF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009740; Meirink JF, 2008, ATMOS CHEM PHYS, V8, P6341; Muller JF, 2005, ATMOS CHEM PHYS, V5, P1157; Myriokefalitakis S, 2008, ATMOS CHEM PHYS, V8, P4965; Novelli PC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003031; Novelli PC, 1998, J GEOPHYS RES-ATMOS, V103, P19015, DOI 10.1029/98JD01366; OLIVIER JGJ, 2000, 410200051RIVM NRP; OLIVIER JGJ, 2003, 2 RIVM POET; Palmer PI, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003397; Palmer PI, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006697; Petron G, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001305; Pfister G, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL022995; Pison I, 2009, ATMOS CHEM PHYS, V9, P5281; Rodgers C. D., 2000, INVERSE METHODS ATMO; Sanhueza E, 1998, TELLUS B, V50, P51, DOI 10.1034/j.1600-0889.1998.00004.x; Schade GW, 1999, TELLUS B, V51, P909, DOI 10.1034/j.1600-0889.1999.t01-4-00004.x; Seinfeld J., 2006, ATMOSPHERIC CHEM PHY; Spivakovsky CM, 2000, J GEOPHYS RES-ATMOS, V105, P8931, DOI 10.1029/1999JD901006; Stavrakou T, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006896; Streets DG, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007118; Tangborn A, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010781; Turquety S, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007281; Martin MV, 2010, ATMOS CHEM PHYS, V10, P1491; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; van der Werf GR, 2004, SCIENCE, V303, P73, DOI 10.1126/science.1090753; van der Werf GR, 2010, ATMOS CHEM PHYS, V10, P11707, DOI 10.5194/acp-10-11707-2010; Yumimoto K, 2006, ATMOS ENVIRON, V40, P6836, DOI 10.1016/j.atmosenv.2006.05.042	59	8	8	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2011	11	10					4705	4723		10.5194/acp-11-4705-2011		19	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	770NU	WOS:000291094500009	
J	Ghilain, N; Arboleda, A; Gellens-Meulenberghs, F				Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.			Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data	HYDROLOGY AND EARTH SYSTEM SCIENCES			English	Article							SURFACE-ENERGY BALANCE; ECMWF MODEL; REGIONAL EVAPOTRANSPIRATION; CARBON-DIOXIDE; WATER; FOREST; CONDUCTANCE; EVAPORATION; EXCHANGE; SYSTEM	We present an evapotranspiration (ET) model developed in the framework of the EUMETSAT "Satellite Application Facility" (SAF) on Land Surface Analysis (LSA). The model is a simplified Soil-Vegetation-Atmosphere Transfer (SVAT) scheme that uses as input a combination of remote sensed data and atmospheric model outputs. The inputs based on remote sensing are LSA-SAF products: the Albedo (AL), the Downwelling Surface Shortwave Flux (DSSF) and the Downwelling Surface Long-wave Flux (DSLF). They are available with the spatial resolution of the MSG SEVIRI instrument. ET maps covering the whole MSG field of view are produced from the model every 30 min, in near-real-time, for all weather conditions. This paper presents the adopted methodology and a set of validation results. The model quality is evaluated in two ways. First, ET results are compared with ground observations (from CarboEurope and national weather services), for different land cover types, over a full vegetation cycle in the Northern Hemisphere in 2007. This validation shows that the model is able to reproduce the observed ET temporal evolution from the diurnal to annual time scales for the temperate climate zones: the mean bias is less than 0.02 mm h(-1) and the root-mean square error is between 0.06 and 0.10 mm h(-1). Then, ET model outputs are compared with those from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Land Data Assimilation System (GLDAS). From this comparison, a high spatial correlation is noted, between 80 to 90%, around midday. Nevertheless, some discrepancies are also observed and are due to the different input variables and parameterisations used.	[Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.] Royal Meteorol Inst, Brussels, Belgium	Gellens-Meulenberghs, F (reprint author), Royal Meteorol Inst, Brussels, Belgium.	f.meulenberghs@meteo.be			ESA; Belgian Science Policy [Nr. 15066/01/NL/Sfe(IC)]	The authors acknowledge ESA and Belgian Science Policy for co-funding this project with EUMETSAT (ESA/Contract Nr. 15066/01/NL/Sfe(IC)).	Anderson MC, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007506; Aubinet M, 2001, AGR FOREST METEOROL, V108, P293, DOI 10.1016/S0168-1923(01)00244-1; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Balsamo G, 2009, J HYDROMETEOROL, V10, P623, DOI 10.1175/2008JHM1068.1; BASTIAANSSEN WGM, 1998, J HYDROL, V213, P198, DOI DOI 10.1016/S0022-1694(98)00253-4; Beljaars ACM, 1997, J CLIMATE, V10, P1172, DOI 10.1175/1520-0442(1997)010<1172:CDFTVO>2.0.CO;2; BELJAARS ACM, 1994, BOUND-LAY METEOROL, V71, P135, DOI 10.1007/BF00709223; Betts A. K., 2000, 295 ECMWF; BOONE A, 2009, B AM METEOROL SOC, P1865; Brutsaert W, 1982, EVAPORATION ATMOSPHE; Albergel C, 2010, HYDROL EARTH SYST SC, V14, P1109, DOI 10.5194/hess-14-1109-2010; Carrer D, 2010, IEEE T GEOSCI REMOTE, V48, P1714, DOI 10.1109/TGRS.2009.2034530; CHEHBOUNI A, 1996, P INT WORKSH REM SEN; Dolman AJ, 1998, ANN SCI FOREST, V55, P255; Dolman AJ, 2010, NAT GEOSCI, V3, P296, DOI 10.1038/cgeo849; Drusch M, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL037716; Geiger B, 2008, IEEE T GEOSCI REMOTE, V46, P3841, DOI 10.1109/TGRS.2008.2001798; Geiger B, 2008, METEOROL APPL, V15, P411, DOI 10.1002/met.84; Gellens-Meulenberghs F, 2005, BOUND-LAY METEOROL, V115, P453, DOI 10.1007/s10546-004-5640-9; Gellens-Meulenberghs F., 2007, IAHS PUBL, P228; GENTINE P, 2007, AGR FOREST METEOROL, V143, P12; Goulden ML, 2004, ECOL APPL, V14, pS42; Granier A, 2000, AGR FOREST METEOROL, V100, P291, DOI 10.1016/S0168-1923(99)00151-3; INEICHEN P, 2009, INT J REMOTE SENS, V30; JARVIS PG, 1976, PHILOS T ROY SOC B, V273, P593, DOI 10.1098/rstb.1976.0035; Jimenez C, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011392; Joffre R, 1996, ANN SCI FOREST, V53, P561, DOI 10.1051/forest:19960235; KALMA JD, 2008, SURV GEOPHYS, V29; Knohl A, 2003, AGR FOREST METEOROL, V118, P151, DOI 10.1016/S0168-1923(03)00115-1; Lhomme JP, 1999, HYDROL EARTH SYST SC, V3, P259; Li ZL, 2009, SENSORS-BASEL, V9, P3801, DOI 10.3390/s90503801; Masson V, 2003, J CLIMATE, V16, P1261, DOI 10.1175/1520-0442-16.9.1261; Merbold L, 2009, BIOGEOSCIENCES, V6, P1027; Meyers TP, 2004, AGR FOREST METEOROL, V125, P105, DOI 10.1016/j.agrformet.2004.03.001; Miglietta F, 2009, BIOGEOSCIENCES, V6, P1975; Mu Q, 2007, REMOTE SENS ENVIRON, V111, P519, DOI 10.1016/j.rse.2007.04.015; Nash J.E., 1970, J HYDROL, V10, P282, DOI DOI 10.1016/0022-1694(70)90255-6; Pereira JS, 2007, BIOGEOSCIENCES, V4, P791; Porte A, 2000, ANN FOR SCI, V57, P73; Rebmann C, 2010, AGR FOREST METEOROL, V150, P684, DOI 10.1016/j.agrformet.2009.11.001; RODELL M, 2004, B AM METEOROL SOC, V85; ROSEMA A, 1993, REMOTE SENS ENVIRON, V46, P27, DOI 10.1016/0034-4257(93)90030-2; Courault D., 2005, Irrigation and Drainage Systems, V19, P223, DOI 10.1007/s10795-005-5186-0; Stisen S, 2008, REMOTE SENS ENVIRON, V112, P1242, DOI 10.1016/j.rse.2007.08.013; Stockli R, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000562; Su H, 2007, J METEOROL SOC JPN, V85A, P439, DOI 10.2151/jmsj.85A.439; Su Z, 2002, HYDROL EARTH SYST SC, V6, P85; Su Z., 2010, HYDROL EARTH SYST SC, V7, P7899, DOI DOI 10.5194/HESSD-7-7899-2010; Teuling AJ, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036584; TIMMERMANS J, 2010, ESA; Trigo IF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010035; TRIGO IF, 2011, INT J REMOT IN PRESS; Verger A, 2009, REMOTE SENS ENVIRON, V113, P2285, DOI 10.1016/j.rse.2009.06.009; VITERBO P, 1995, J CLIMATE, V8, P2716, DOI 10.1175/1520-0442(1995)008<2716:AILSPS>2.0.CO;2; Wagner W, 1999, IEEE T GEOSCI REMOTE, V37, P206, DOI 10.1109/36.739155; Wang KC, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008351; Wilson K, 2002, AGR FOREST METEOROL, V113, P223, DOI 10.1016/S0168-1923(02)00109-0; WOOD EF, 2010, ESA; *LSA SAF, 2010, PROD US MAN EV ET PU	59	3	3	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1027-5606		HYDROL EARTH SYST SC	Hydrol. Earth Syst. Sci.		2011	15	3					771	786		10.5194/hess-15-771-2011		16	Geosciences, Multidisciplinary; Water Resources	Geology; Water Resources	743BL	WOS:000288990400008	
S	Hofmann, EE; Cahill, B; Fennel, K; Friedrichs, MAM; Hyde, K; Lee, C; Mannino, A; Najjar, RG; O'Reilly, JE; Wilkin, J; Xue, JH		Carlson, CA; Giovannoni, SJ		Hofmann, Eileen E.; Cahill, Bronwyn; Fennel, Katja; Friedrichs, Marjorie A. M.; Hyde, Kimberly; Lee, Cindy; Mannino, Antonio; Najjar, Raymond G.; O'Reilly, John E.; Wilkin, John; Xue, Jianhong			Modeling the Dynamics of Continental Shelf Carbon	ANNUAL REVIEW OF MARINE SCIENCE, VOL 3	Annual Review of Marine Science		English	Review; Book Chapter						carbon cycling; continental shelf; coupled circulation-biogeochemical models; model-data comparisons; data assimilation; biogeochemical model initialization	DISSOLVED ORGANIC-MATTER; SUBMARINE GROUNDWATER DISCHARGE; CALIFORNIA CURRENT SYSTEM; SOUTHEASTERN BERING SEA; NORTH-ATLANTIC; GAS-EXCHANGE; ATMOSPHERIC DEPOSITION; NITROGEN DEPOSITION; PERMEABLE SEDIMENTS; INORGANIC NITROGEN	Continental margin systems are important contributors to global nutrient and carbon budgets. Effort is needed to quantify this contribution and how it will be modified under changing patterns of climate and land use. Coupled models will be used to provide projections of future states of continental margin systems. Thus, it is appropriate to consider the limitations that impede the development of realistic models. Here, we provide an overview of the current state of modeling carbon cycling on continental margins as well as the processes and issues that provide the next challenges to such models. Our overview is done within the context of a coupled circulation-biogeochemical model developed for the northeastern North American continental shelf region. Particular choices of forcing and initial fields and process parameterizations are used to illustrate the consequences for simulated distributions, as revealed by comparisons to observations using quantitative statistical metrics.	[Hofmann, Eileen E.] Old Dominion Univ, Ctr Coastal Phys Oceanog, Norfolk, VA 23508 USA; [Cahill, Bronwyn; Wilkin, John] Rutgers State Univ, Inst Marine & Coastal Sci, New Brunswick, NJ 08901 USA; [Fennel, Katja] Dalhousie Univ, Dept Oceanog, Halifax, NS B3H 4J1, Canada; [Friedrichs, Marjorie A. M.; Xue, Jianhong] Virginia Inst Marine Sci, Coll William & Mary, Gloucester Point, VA 23062 USA; [Hyde, Kimberly; O'Reilly, John E.] NOAA, Natl Marine Fisheries Serv, Narragansett Lab, Narragansett, RI 02882 USA; [Lee, Cindy] SUNY Stony Brook, Marine Sci Res Ctr, Stony Brook, NY 11794 USA; [Mannino, Antonio] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Najjar, Raymond G.] Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA; [Xue, Jianhong] Univ Texas Austin, Inst Marine Sci, Port Aransas, TX 78373 USA	Hofmann, EE (reprint author), Old Dominion Univ, Ctr Coastal Phys Oceanog, Norfolk, VA 23508 USA.	hofmann@ccpo.odu.edu; bronwyn@marine.rutgers.edu; katja.fennel@dal.ca; marjy@vims.edu; kimberly.hyde@noaa.gov; cindy.lee@sunysb.edu; antonio.mannino@nasa.gov; najjar@meteo.psu.edu; jay.oreilly@noaa.gov; wilkin@marine.rutgers.edu; jxue@mail.utexas.edu	Fennel, Katja/A-7470-2009; Wilkin, John/E-5343-2011	Fennel, Katja/0000-0003-3170-2331; Wilkin, John/0000-0002-5444-9466			Allen JI, 2001, SARSIA, V86, P423; Aller R, 1988, NITROGEN CYCLING COA, P301; ALLER RC, 1994, CHEM GEOL, V114, P331, DOI 10.1016/0009-2541(94)90062-0; Aller R.C., 1980, ADV GEOPHYS, V22, P238; Alvarez-Salgado XA, 2001, LIMNOL OCEANOGR, V46, P135; Arrigo KR, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035624; Borges AV, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023053; Cahill B, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL033595; Cai WJ, 1998, GEOCHIM COSMOCHIM AC, V62, P473, DOI 10.1016/S0016-7037(97)00363-3; Cai WJ, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026219; CAI WJ, 2011, ANN REV MAR IN PRESS, V3; Cai WJ, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017633; Calbet A, 2004, LIMNOL OCEANOGR, V49, P51; Chai F, 2009, J OCEANOGR, V65, P703; Chassignet EP, 2007, J MARINE SYST, V65, P60, DOI 10.1016/j.jmarsys.2005.09.016; Chen CTA, 2002, DEEP-SEA RES PT II, V49, P5365, DOI 10.1016/S0967-0645(02)00196-0; CONKRIGHT ME, 2002, 17 US DEP COMM NAT O; Cook T, 1998, ESTUAR COAST SHELF S, V46, P587, DOI 10.1006/ecss.1997.0283; Cornell SE, 2003, ATMOS ENVIRON, V37, P2173, DOI 10.1016/S1352-2310(03)00133-X; Dagg M, 2004, CONT SHELF RES, V24, P833, DOI 10.1016/j.csr.2004.02.003; Darecki M, 2004, REMOTE SENS ENVIRON, V89, P326, DOI 10.1016/j.rse.2003.10.012; D'Asaro E, 2007, J MARINE SYST, V66, P92, DOI 10.1016/j.jmarsys.2006.06.007; DeGrandpre MD, 2002, DEEP-SEA RES PT II, V49, P4355, DOI 10.1016/S0967-0645(02)00122-4; Diaz RJ, 2008, SCIENCE, V321, P926, DOI 10.1126/science.1156401; Dinniman MS, 2003, DEEP-SEA RES PT II, V50, P3103, DOI 10.1016/j.dsr2.2003.07.011; Doney SC, 2007, P NATL ACAD SCI USA, V104, P14580, DOI 10.1073/pnas.0702218104; Donner SD, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001396; Druon JN, 2010, ESTUAR COAST SHELF S, V88, P488, DOI 10.1016/j.ecss.2010.05.010; Duce RA, 2008, SCIENCE, V320, P893, DOI 10.1126/science.1150369; Ducklow H. W., 2004, SEA, V13, P269; Dumont E, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2005GB002488; Egbert GD, 2002, J ATMOS OCEAN TECH, V19, P183, DOI 10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2; Fairall CW, 2003, J CLIMATE, V16, P571, DOI 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2; Fennel K, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL036147; Fennel K, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039818; Fennel K, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2005GB002456; Fennel K, 2009, BIOGEOCHEMISTRY, V93, P159, DOI 10.1007/s10533-008-9270-z; Flather R. A., 1976, MEM SOC R SCI LIEGE, V6, P141; Frankignoulle M, 2001, GLOBAL BIOGEOCHEM CY, V15, P569, DOI 10.1029/2000GB001307; Frew NM, 2002, GEOPH MONOG SERIES, V127, P153; Friedrichs MAM, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003852; Friedrichs MAM, 2006, DEEP-SEA RES PT II, V53, P576, DOI 10.1016/j.dsr2.2006.01.026; Gattuso JP, 1998, ANNU REV ECOL SYST, V29, P405, DOI 10.1146/annurev.ecolsys.29.1.405; Gregg WW, 2004, REMOTE SENS ENVIRON, V93, P463, DOI 10.1016/j.rse.2003.12.012; Gruber N, 2006, DEEP-SEA RES PT I, V53, P1483, DOI 10.1016/j.dsr.2006.06.005; Guerzoni S, 1999, PROG OCEANOGR, V44, P147, DOI 10.1016/S0079-6611(99)00024-5; HAGY JD, 2004, ESTUAR COAST, V27, DOI DOI 10.1007/BF02907650; Hansell DA, 1998, GLOBAL BIOGEOCHEM CY, V12, P443, DOI 10.1029/98GB01928; Hartnett HE, 1998, NATURE, V391, P572, DOI 10.1038/35351; Hernandez-Ayon JM, 2007, LIMNOL OCEANOGR-METH, V5, P225; HOFMANN E, 2008, OCEANOGRAPHY, V21, P32; Honjo S, 2008, PROG OCEANOGR, V76, P217, DOI 10.1016/j.pocean.2007.11.003; Hood RR, 2006, DEEP-SEA RES PT II, V53, P459, DOI 10.1016/j.dsr2.2006.01.025; Hopkinson CS, 2002, DEEP-SEA RES PT II, V49, P4461, DOI 10.1016/S0967-0645(02)00125-X; Howarth RW, 1996, BIOGEOCHEMISTRY, V35, P75, DOI 10.1007/BF02179825; Huettel M, 1996, LIMNOL OCEANOGR, V41, P309; Huettel M, 1998, GEOCHIM COSMOCHIM AC, V62, P613, DOI 10.1016/S0016-7037(97)00371-2; IANSON A, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI DOI 10.1029/2001GB001451; JAHNKE RA, 2010, GLOBAL SYNTHESIS; Jiang LQ, 2008, J GEOPHYS RES-OCEANS, V113, DOI 10.1029/2007JC004366; Jickells T, 2006, BIOGEOSCIENCES DISCU, V3, P183; JOLIFF JK, 2009, J MARINE SYST, V76, P64, DOI DOI 10.1016/J.JMARSYS.2008.05.014; Kahru M, 2001, J GEOPHYS RES-OCEANS, V106, P2517, DOI 10.1029/1999JC000094; Keene WC, 2002, ATMOS ENVIRON, V36, P4529, DOI 10.1016/S1352-2310(02)00403-X; Keil RG, 1997, GEOCHIM COSMOCHIM AC, V61, P1507, DOI 10.1016/S0016-7037(97)00044-6; Kim HC, 2006, LIMNOL OCEANOGR, V51, P331; Koeve W, 2001, MAR CHEM, V74, P245, DOI 10.1016/S0304-4203(01)00016-0; Komada T, 2002, MAR CHEM, V79, P81, DOI 10.1016/S0304-4203(02)00056-7; Komada T, 2001, MAR CHEM, V76, P155, DOI 10.1016/S0304-4203(01)00055-X; Krishnamurthy A, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000334; Kristensen E, 2000, HYDROBIOLOGIA, V426, P1, DOI 10.1023/A:1003980226194; Kuypers MMM, 2005, P NATL ACAD SCI USA, V102, P6478, DOI 10.1073/pnas.0502088102; Laursen AE, 2002, CONT SHELF RES, V22, P1397, DOI 10.1016/S0278-4343(02)00008-0; Lehmann M.K., 2009, BIOGEOSCIENCES, V6, P1; Le Quere C, 2005, GLOBAL CHANGE BIOL, V11, P2016, DOI 10.1111/j.1365-2468.2005.01004.x; Liss P.S., 1997, SEA SURFACE GLOBAL C; Liu G, 2009, ICES J MAR SCI, V66, P420, DOI 10.1093/icesjms/fsn219; Liu KK, 2010, GLOB CHANGE IGBP SER, P1, DOI 10.1007/978-3-540-92735-8; Liu ML, 2008, J AM WATER RESOUR AS, V44, P1193, DOI 10.1111/j.1752-1688.2008.00243.x; Longhurst A, 1995, PROG OCEANOGR, V36, P77, DOI 10.1016/0079-6611(95)00015-1; LOYEPILOT MD, 1990, BIOGEOCHEMISTRY, V9, P117, DOI 10.1007/BF00692168; Lutjeharms JRE, 2003, CONT SHELF RES, V23, P1099, DOI 10.1016/S0278-4343(03)00106-7; Mackenzie FT, 2004, BIOGEOSCIENCES, V1, P11; Marchesiello P, 2003, J PHYS OCEANOGR, V33, P753, DOI 10.1175/1520-0485(2003)33<753:ESADOT>2.0.CO;2; Marra J, 2007, DEEP-SEA RES PT I, V54, P155, DOI 10.1016/j.dsr.2006.12.001; MELLOR GL, 1982, REV GEOPHYS, V20, P851, DOI 10.1029/RG020i004p00851; Mesinger F, 2006, B AM METEOROL SOC, V87, P343, DOI 10.1175/BAMS-87-3-343; Middelburg JJ, 1996, GLOBAL BIOGEOCHEM CY, V10, P661, DOI 10.1029/96GB02562; Millero FJ, 1998, MAR CHEM, V60, P111, DOI 10.1016/S0304-4203(97)00084-4; MILLIMAN JD, 1993, GLOBAL BIOGEOCHEM CY, V7, P927, DOI 10.1029/93GB02524; MOISAN JR, 2010, COUPLED CIRCULATION; Moll A, 1998, J MARINE SYST, V16, P151, DOI 10.1016/S0924-7963(97)00104-8; Moore WS, 2010, ANNU REV MAR SCI, V2, P59, DOI 10.1146/annurev-marine-120308-081019; Mopper K., 2000, EFFECTS UV RAD MARIN, P101, DOI 10.1017/CBO9780511535444.005; Muller-Karger FE, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021346; Najjar RG, 2010, ESTUAR COAST SHELF S, V86, P1, DOI 10.1016/j.ecss.2009.09.026; Nightingale PD, 2000, GLOBAL BIOGEOCHEM CY, V14, P373, DOI 10.1029/1999GB900091; Onitsuka G, 2009, J OCEANOGR, V65, P433, DOI 10.1007/s10872-009-0038-4; O'Reilly JE, 1998, J GEOPHYS RES-OCEANS, V103, P24937, DOI 10.1029/98JC02160; PACE ML, 1984, MAR BIOL, V82, P47, DOI 10.1007/BF00392763; Paerl HW, 2002, ESTUARIES, V25, P677, DOI 10.1007/BF02804899; Pan XJ, 2010, REMOTE SENS ENVIRON, V114, P2403, DOI 10.1016/j.rse.2010.05.015; Peliz A, 2003, J PHYS OCEANOGR, V33, P1633, DOI 10.1175/2415.1; Pomeroy LR, 2006, MAR ECOL PROG SER, V325, P301, DOI 10.3354/meps325301; Previdi M, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2008JG000881; RADACH G, 1993, PROG OCEANOGR, V31, P339, DOI 10.1016/0079-6611(93)90001-T; Reimers CE, 2004, CONT SHELF RES, V24, P183, DOI 10.1016/j.csr.2003.10.005; Rhoads D.C., 1974, Oceanography mar Biol, V12, P263; SARMIENTO J.S., 2006, OCEAN BIOGEOCHEMICAL; Seitzinger S, 2006, ECOL APPL, V16, P2064, DOI 10.1890/1051-0761(2006)016[2064:DALAWA]2.0.CO;2; Seitzinger SP, 1996, BIOGEOCHEMISTRY, V35, P235, DOI 10.1007/BF02179829; Shchepetkin AF, 1998, MON WEATHER REV, V126, P1541, DOI 10.1175/1520-0493(1998)126<1541:QMASBO>2.0.CO;2; Shchepetkin AF, 2005, OCEAN MODEL, V9, P347, DOI 10.1016/j.ocemod.2004.08.002; Shchepetkin AF, 2003, J GEOPHYS RES-OCEANS, V108, DOI 10.1029/2001JC001047; SHUERT PG, 1993, CONT SHELF RES, V13, P543, DOI 10.1016/0278-4343(93)90094-E; Slomp CP, 2004, J HYDROL, V295, P64, DOI 10.1016/j.jhyfrol.2004.02.018; SMITH SV, 1993, REV GEOPHYS, V31, P75, DOI 10.1029/92RG02584; Soetaert K, 2000, EARTH-SCI REV, V51, P173, DOI 10.1016/S0012-8252(00)00004-0; Spokes LJ, 2000, TELLUS B, V52, P37, DOI 10.1034/j.1600-0889.2000.00062.x; Stramska M, 2005, J GEOPHYS RES-OCEANS, V110, DOI 10.1029/2004JC002762; Stramska M, 2003, J GEOPHYS RES-OCEANS, V108, DOI 10.1029/2001JC001195; Thomas H, 2004, SCIENCE, V304, P1005, DOI 10.1126/science.1095491; Tsunogai S, 1999, TELLUS B, V51, P701, DOI 10.1034/j.1600-0889.1999.t01-2-00010.x; Walsh J.J., 1988, NATURE CONTINENTAL S; WALSH JJ, 1994, PROG OCEANOGR, V34, P335, DOI 10.1016/0079-6611(94)90019-1; WALSH JJ, 1986, CONT SHELF RES, V5, P259, DOI 10.1016/0278-4343(86)90018-X; WALSH JJ, 1981, NATURE, V291, P196, DOI 10.1038/291196a0; Wang ZA, 2005, CONT SHELF RES, V25, P1917, DOI 10.1016/j.csr.2005.04.004; WANNINKHOF R, 1992, J GEOPHYS RES-OCEANS, V97, P7373, DOI 10.1029/92JC00188; Ward BB, 2009, NATURE, V461, P78, DOI 10.1038/nature08276; Warner JC, 2005, OCEAN MODEL, V8, P81, DOI 10.1016/j.ocemod.2003.12.003; Wilkin JL, 2006, J PHYS OCEANOGR, V36, P1997, DOI 10.1175/JPO2968.1; WOLLAST R, 1989, CLIMATE GEOSCIENCES, P453	133	2	2	ANNUAL REVIEWS	PALO ALTO	4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0897 USA	1941-1405	978-0-8243-4503-7	ANNU REV MAR SCI	Annu. Rev. Mar. Sci.		2011	3						93	122		10.1146/annurev-marine-120709-142740		30	Geochemistry & Geophysics; Marine & Freshwater Biology; Oceanography	Geochemistry & Geophysics; Marine & Freshwater Biology; Oceanography	BTE33	WOS:000286638700004	
J	Schneising, O; Buchwitz, M; Reuter, M; Heymann, J; Bovensmann, H; Burrows, JP				Schneising, O.; Buchwitz, M.; Reuter, M.; Heymann, J.; Bovensmann, H.; Burrows, J. P.			Long-term analysis of carbon dioxide and methane column-averaged mole fractions retrieved from SCIAMACHY	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							FSI WFM-DOAS; HYPERSPECTRAL INFRARED OBSERVATIONS; MOLECULAR SPECTROSCOPIC DATABASE; ESTIMATING ATMOSPHERIC CO2; DATA ASSIMILATION SYSTEM; SATELLITE RADIANCES; 1ST YEAR; SPACE; CALIBRATION; ALGORITHM	Carbon dioxide (CO(2)) and methane (CH(4)) are the two most important anthropogenic greenhouse gases contributing to global climate change. SCIAMACHY onboard ENVISAT (launch 2002) was the first and is now with TANSO onboard GOSAT (launch 2009) one of only two satellite instruments currently in space whose measurements are sensitive to CO(2) and CH(4) concentration changes in the lowest atmospheric layers where the variability due to sources and sinks is largest. We present long-term SCIAMACHY retrievals (2003-2009) of column-averaged dry air mole fractions of both gases (denoted XCO(2) and XCH(4)) derived from absorption bands in the near-infrared/shortwave-infrared (NIR/SWIR) spectral region focusing on large-scale features. The results are obtained using an upgraded version (v2) of the retrieval algorithm WFM-DOAS including several improvements, while simultaneously maintaining its high processing speed. The retrieved mole fractions are compared to global model simulations (CarbonTracker XCO(2) and TM5 XCH(4)) being optimised by assimilating highly accurate surface measurements from the NOAA/ESRL network and taking the SCIAMACHY averaging kernels into account. The comparisons address seasonal variations and long-term characteristics. The steady increase of atmospheric carbon dioxide primarily caused by the burning of fossil fuels can be clearly observed with SCIAMACHY globally. The retrieved global annual mean XCO(2) increase agrees with CarbonTracker within the error bars (1.80 +/- 0.13 ppm yr(-1) compared to 1.81 +/- 0.09 ppm yr(-1)). The amplitude of the XCO(2) seasonal cycle as retrieved by SCIAMACHY, which is 4.3 +/- 0.2 ppm for the Northern Hemisphere and 1.4 +/- 0.2 ppm for the Southern Hemisphere, is on average about 1 ppm larger than for CarbonTracker. An investigation of the boreal forest carbon uptake during the growing season via the analysis of longitudinal gradients shows good agreement between SCIAMACHY and CarbonTracker concerning the overall magnitude of the gradients and their annual variations. The analysis includes a discussion of the relative uptake strengths of the Russian and North American boreal forest regions. The retrieved XCH(4) results show that after years of stability, atmospheric methane has started to rise again in recent years which is consistent with surface measurements. The largest increase is observed for the tropics and northern mid- and high-latitudes amounting to about 7.5 +/- 1.5 ppb yr(-1) since 2007. Due care has been exercised to minimise the influence of detector degradation on the quantitative estimate of this anomaly.	[Schneising, O.; Buchwitz, M.; Reuter, M.; Heymann, J.; Bovensmann, H.; Burrows, J. P.] Univ Bremen FB1, Inst Environm Phys IUP, Bremen, Germany	Schneising, O (reprint author), Univ Bremen FB1, Inst Environm Phys IUP, Bremen, Germany.	oliver.schneising@iup.physik.uni-bremen.de	Buchwitz, Michael/G-1510-2011		ESA; European Union [FP7/2007-2013, 218793, 212095]; DLR; University and the State of Bremen	The research leading to these results has received funding from the ESA project CARBONGASES which is part of The Changing Earth Science Network, the ESA projects GHG-CCI, ADVANSE, and ALANIS Methane, the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement no. 218793 and 212095 (MACC and CityZen), the DLR grant SADOS, and from the University and the State of Bremen.	Aumann HH, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021784; Barkley MP, 2006, ATMOS CHEM PHYS, V6, P3517; Barkley MP, 2006, ATMOS CHEM PHYS, V6, P4483; Barkley MP, 2007, ATMOS CHEM PHYS, V7, P3597; Barkley MP, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026807; Bergamaschi P, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007268; Bergamaschi P, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012287; Bloom AA, 2010, SCIENCE, V327, P322, DOI 10.1126/science.1175176; Bosch H, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007080; Bousquet P, 2006, NATURE, V443, P439, DOI 10.1038/nature05132; Bovensmann H, 1999, J ATMOS SCI, V56, P127, DOI 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2; BRAMSTEDT K, 2008, IFESCIATN200701; Buchwitz M, 2007, ATMOS CHEM PHYS, V7, P4249; Buchwitz M, 2000, J GEOPHYS RES-ATMOS, V105, P15247, DOI 10.1029/2000JD900171; Buchwitz M, 2004, P SOC PHOTO-OPT INS, V5235, P375, DOI 10.1117/12.514219; Buchwitz M, 2005, ATMOS CHEM PHYS, V5, P941; Buchwitz M, 2006, ATMOS CHEM PHYS, V6, P2727; Buchwitz M, 2005, ATMOS CHEM PHYS, V5, P3313; Burrows J. P., 1990, TOP M OPT REM SENS A, V4, P71; BURROWS JP, 1995, ACTA ASTRONAUT, V35, P445, DOI 10.1016/0094-5765(94)00278-T; BURROWS JP, 1991, P SOC PHOTO-OPT INS, V1490, P146, DOI 10.1117/12.46619; Chedin A, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014082; Crevoisier C, 2009, ATMOS CHEM PHYS, V9, P4797; Crevoisier C, 2009, ATMOS CHEM PHYS, V9, P6337; Dlugokencky E. J., 2010, ATMOSPHERIC METHANE; Dlugokencky EJ, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039780; Frankenberg C, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014849; Frankenberg C, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006235; Frankenberg C, 2005, SCIENCE, V308, P1010, DOI 10.1126/science.1106644; Frankenberg C, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034300; Frankenberg C, 2008, ATMOS CHEM PHYS, V8, P5061; Gloudemans AMS, 2005, ATMOS CHEM PHYS, V5, P2369; Herman JR, 1997, J GEOPHYS RES-ATMOS, V102, P16911, DOI 10.1029/96JD03680; Houweling S, 2005, ATMOS CHEM PHYS, V5, P3003; Houweling S, 2004, ATMOS CHEM PHYS, V4, P523; Jenouvrier A, 2007, J QUANT SPECTROSC RA, V105, P326, DOI 10.1016/j.jqsrt.2006.11.007; KEPPELALEKS G, 2010, ATMOS CHEM PHYS DISC, V10, P30569, DOI DOI 10.5194/ACPD-10-30569-2010; Krol M, 2005, ATMOS CHEM PHYS, V5, P417; Kulawik SS, 2010, ATMOS CHEM PHYS, V10, P5601, DOI 10.5194/acp-10-5601-2010; Lichtenberg G, 2006, ATMOS CHEM PHYS, V6, P5347; Maddy ES, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009402; MASARIE KA, 1995, J GEOPHYS RES-ATMOS, V100, P11593, DOI 10.1029/95JD00859; Engelen RJ, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004777; Engelen RJ, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005982; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Peters W, 2010, GLOBAL CHANGE BIOL, V16, P1317, DOI 10.1111/j.1365-2486.2009.02078.x; Rayner PJ, 2001, GEOPHYS RES LETT, V28, P175, DOI 10.1029/2000GL011912; Reuter M, 2010, ATMOS MEAS TECH, V3, P209; Rigby M, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL036037; Rothman LS, 2009, J QUANT SPECTROSC RA, V110, P533, DOI 10.1016/j.jqsrt.2009.02.013; Rothman LS, 2005, J QUANT SPECTROSC RA, V96, P139, DOI 10.1016/j.jqsrt.2004.10.008; Schneising O, 2009, ATMOS CHEM PHYS, V9, P443; Schneising O, 2008, ATMOS CHEM PHYS, V8, P3827; Chedin A, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003439; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Stephens BB, 2007, SCIENCE, V316, P1732, DOI 10.1126/science.1137004; Strow LL, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006146; TILSTRA LG, 2007, P ENVISAT S MONTR SW; Yang Z, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029742; Yokota T, 2009, SOLA, V5, P160, DOI 10.2151/sola.2009-041	60	26	28	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2011	11	6					2863	2880		10.5194/acp-11-2863-2011		18	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	742YW	WOS:000288982300028	
J	Nassar, R; Jones, DBA; Kulawik, SS; Worden, JR; Bowman, KW; Andres, RJ; Suntharalingam, P; Chen, JM; Brenninkmeijer, CAM; Schuck, TJ; Conway, TJ; Worthy, DE				Nassar, R.; Jones, D. B. A.; Kulawik, S. S.; Worden, J. R.; Bowman, K. W.; Andres, R. J.; Suntharalingam, P.; Chen, J. M.; Brenninkmeijer, C. A. M.; Schuck, T. J.; Conway, T. J.; Worthy, D. E.			Inverse modeling of CO2 sources and sinks using satellite observations of CO2 from TES and surface flask measurements	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							TROPOSPHERIC EMISSION SPECTROMETER; ATMOSPHERIC CARBON-DIOXIDE; REGIONAL-SCALE FLUXES; UPDATED EMISSIONS; INTERANNUAL VARIABILITY; OBSERVING SYSTEMS; TRANSPORT MODEL; NORTH-AMERICA; FOREST; AIRCRAFT	We infer CO2 surface fluxes using satellite observations of mid-tropospheric CO2 from the Tropospheric Emission Spectrometer (TES) and measurements of CO2 from surface flasks in a time-independent inversion analysis based on the GEOS-Chem model. Using TES CO2 observations over oceans, spanning 40 degrees S-40 degrees N, we find that the horizontal and vertical coverage of the TES and flask data are complementary. This complementarity is demonstrated by combining the datasets in a joint inversion, which provides better constraints than from either dataset alone, when a posteriori CO2 distributions are evaluated against independent ship and aircraft CO2 data. In particular, the joint inversion offers improved constraints in the tropics where surface measurements are sparse, such as the tropical forests of South America. Aggregating the annual surface-to-atmosphere fluxes from the joint inversion for the year 2006 yields -1.13 +/- 0.21 PgC for the global ocean, -2.77 +/- 0.20 PgC for the global land biosphere and -3.90 +/- 0.29 PgC for the total global natural flux (defined as the sum of all biospheric, oceanic, and biomass burning contributions but excluding CO2 emissions from fossil fuel combustion). These global ocean and global land fluxes are shown to be near the median of the broad range of values from other inversion results for 2006. To achieve these results, a bias in TES CO2 in the Southern Hemisphere was assessed and corrected using aircraft flask data, and we demonstrate that our results have low sensitivity to variations in the bias correction approach. Overall, this analysis suggests that future carbon data assimilation systems can benefit by integrating in situ and satellite observations of CO2 and that the vertical information provided by satellite observations of mid-tropospheric CO2 combined with measurements of surface CO2, provides an important additional constraint for flux inversions.	[Nassar, R.; Worthy, D. E.] Environm Canada, Div Climate Res, Toronto, ON M3H 5T4, Canada; [Nassar, R.; Jones, D. B. A.] Univ Toronto, Dept Phys, Toronto, ON M5S 1A7, Canada; [Kulawik, S. S.; Worden, J. R.; Bowman, K. W.] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA; [Andres, R. J.] Oak Ridge Natl Lab, Div Environm Sci, Oak Ridge, TN 37831 USA; [Suntharalingam, P.] Univ E Anglia, Norwich NR4 7TJ, Norfolk, England; [Chen, J. M.] Univ Toronto, Dept Geog, Toronto, ON M5S 2E5, Canada; [Brenninkmeijer, C. A. M.; Schuck, T. J.] Max Planck Inst Chem, Air Chem Div, D-55128 Mainz, Germany; [Conway, T. J.] Natl Ocean & Atmospher Adm, Earth Syst Res Lab, Boulder, CO 80305 USA	Nassar, R (reprint author), Environm Canada, Div Climate Res, 4905 Dufferin St, Toronto, ON M3H 5T4, Canada.	ray.nassar@ec.gc.ca	ANDRES, ROBERT/B-9786-2012; Brenninkmeijer, Carl/B-6860-2013		Natural Sciences and Engineering Research Council (NSERC) of Canada; Jet Propulsion Laboratory California Institute of Technology	Work at the University of Toronto was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada. Work at the Jet Propulsion Laboratory California Institute of Technology was carried out under contract to NASA. We especially thank T. Machida and H. Matsueda of the CONTRAIL project for providing their aircraft CO<INF>2</INF> flask data for this work. Thanks to all of those who have contributed to the Carboscope (www.carboscope.eu) and CarbonTracker (www.esrl.noaa.gov/gmd/ccgg/carbontracker) websites for these excellent resources that make CO<INF>2</INF> flux inversion results publicly available. Lastly, we thank the anonymous reviewers for their helpful comments and suggestions.	Andres RJ, 2011, TELLUS B, V63, P309, DOI 10.1111/j.1600-0889.2011.00530.x; Arellano AF, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027371; BACASTOW RB, 1976, NATURE, V261, P116, DOI 10.1038/261116a0; Baker DF, 2006, GLOBAL BIOGEOCHEM CY, V20, DOI 10.1029/2004GB002439; Baker DF, 2006, TELLUS B, V58, P359, DOI 10.1111/j.1600-0889.2006.00218.x; Beer R, 2001, APPL OPTICS, V40, P2356, DOI 10.1364/AO.40.002356; Bey I, 2001, J GEOPHYS RES-ATMOS, V106, P23073, DOI 10.1029/2001JD000807; Bond-Lamberty B, 2007, NATURE, V450, P89, DOI 10.1038/nature06272; Bowman KW, 2006, IEEE T GEOSCI REMOTE, V44, P1297, DOI [10.1109/TGRS.2006.871234, 10.1109/TGRS.2006871234]; Brenninkmeijer CAM, 2007, ATMOS CHEM PHYS, V7, P4953; Buchwitz M, 2007, ATMOS CHEM PHYS, V7, P4249; Chahine M, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL024165; Chahine MT, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035022; Chevallier F, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD013887; Chevallier F, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007375; Chevallier F, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006390; Chevallier F, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL044652; Chevallier F, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012311; CONWAY TJ, 1994, J GEOPHYS RES-ATMOS, V99, P22831, DOI 10.1029/94JD01951; Corbett JJ, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD005030; Corbett JJ, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003751; Corbin KD, 2010, TELLUS B, V62, P521, DOI 10.1111/j.1600-0889.2010.00485.x; Crevoisier C, 2009, ATMOS CHEM PHYS, V9, P4797; Crisp D, 2004, ADV SPACE RES-SERIES, V34, P700, DOI 10.1016/j.asr.2003.08.062; Deng F, 2007, TELLUS B, V59, P179, DOI 10.1111/j.1600-0889.2006.00235.x; Denman KL, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P499; Endresen O, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007630; Endresen O, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004853; Engelen RJ, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010739; Enting IG, 1989, TELLUS B, V41, P111, DOI 10.1111/j.1600-0889.1989.tb00129.x; Fan S, 1998, SCIENCE, V282, P442, DOI 10.1126/science.282.5388.442; Friedl R., 1997, NASA REF PUBL, V1400; Gerbig C, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003018; Gerbig C, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003770; Gruber N, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003349; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; Hansen M, 1998, UMD GLOBAL LAND COVE, P1981; Hansen MC, 2000, INT J REMOTE SENS, V21, P1331, DOI 10.1080/014311600210209; Heald CL, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD005185; Heimann M, 2008, NATURE, V451, P289, DOI 10.1038/nature06591; Higuchi K, 2003, TELLUS B, V55, P115, DOI 10.1034/j.1600-0889.2003.00062.x; Houweling S, 2004, ATMOS CHEM PHYS, V4, P523; Houweling S, 2010, ATMOS CHEM PHYS, V10, P9981, DOI 10.5194/acp-10-9981-2010; Hungershoefer K, 2010, ATMOS CHEM PHYS, V10, P10503, DOI 10.5194/acp-10-10503-2010; Jacobson AR, 2007, GLOBAL BIOGEOCHEM CY, V21, DOI 10.1029/2006GB002703; JIANG Z, 2011, J GEOPHYS R IN PRESS; Jones DBA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003702; Jones DBA, 2009, ATMOS CHEM PHYS, V9, P3547; Kadygrov N, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011597; KEELING CD, 1995, NATURE, V375, P666, DOI 10.1038/375666a0; Kim BY, 2007, TRANSPORT RES D-TR E, V12, P325, DOI 10.1016/j.trd.2007.03.007; Kulawik SS, 2010, ATMOS CHEM PHYS, V10, P5601, DOI 10.5194/acp-10-5601-2010; Kurz WA, 2008, NATURE, V452, P987, DOI 10.1038/nature06777; Lee H, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2008JG000906; Machida T, 2008, J ATMOS OCEAN TECH, V25, P1744, DOI 10.1175/2008JTECHA1082.1; Matsueda H., 2008, PAP METEOROL GEOPHYS, V59, P1, DOI DOI 10.2467/MRIPAPERS.59.1; Miller CE, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007659; Nassar R, 2010, GEOSCI MODEL DEV, V3, P689, DOI 10.5194/gmd-3-689-2010; Nassar R, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011760; Negron-Juarez RI, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043733; Olsen SC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003968; PACALA S, 2010, VERIFYING CLIMATE AG; Pak BC, 2001, GEOPHYS RES LETT, V28, P4571, DOI 10.1029/2001GL013604; Palmer PI, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003397; Pan Y, 2011, BIOGEOSCIENCES, V8, P715, DOI 10.5194/bg-8-715-2011; Peters W, 2007, P NATL ACAD SCI USA, V104, P18925, DOI 10.1073/pnas.0708986104; Peters W, 2010, GLOBAL CHANGE BIOL, V16, P1317, DOI 10.1111/j.1365-2486.2009.02078.x; Phillips OL, 2009, SCIENCE, V323, P1344, DOI 10.1126/science.1164033; Rayner PJ, 2001, GEOPHYS RES LETT, V28, P175, DOI 10.1029/2000GL011912; Rodenbeck C, 2003, ATMOS CHEM PHYS, V3, P1919; RODENBECK C, 2005, TECHNICAL REPORTS, V6; RODGERS CD, 2000, INVERSE METHODS ATMO, P29; Schuck TJ, 2009, ATMOS MEAS TECH, V2, P449; Chedin A, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003439; Stephens BB, 2007, SCIENCE, V316, P1732, DOI 10.1126/science.1137004; Stinson G., 2011, GLOBAL CHANGE BIOL, DOI [10.1111/j.1365-2486.2010.02369.x, DOI 10.1111/J.1365-2486.2010.02369.X]; Suntharalingam P, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2005GB002466; Suntharalingam P, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004362; Takahashi T, 2009, DEEP-SEA RES PT II, V56, P554, DOI 10.1016/j.dsr2.2008.12.009; TANS PP, 1989, J GEOPHYS RES-ATMOS, V94, P5151, DOI 10.1029/JD094iD04p05151; Tollefson J, 2010, NATURE, V468, P488, DOI 10.1038/468488a; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; van der Werf GR, 2010, ATMOS CHEM PHYS, V10, P11707, DOI 10.5194/acp-10-11707-2010; Wilkerson JT, 2010, ATMOS CHEM PHYS, V10, P6391, DOI 10.5194/acp-10-6391-2010; WILKS DS, 2006, STAT METHODS ATMOSPH, P179; Worden J, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004522; Yevich R, 2003, GLOBAL BIOGEOCHEM CY, V17, DOI 10.1029/2002GB001952; Yokota T, 2009, SOLA, V5, P160, DOI 10.2151/sola.2009-041; Yoshida Y, 2011, ATMOS MEAS TECH, V4, P717, DOI 10.5194/amt-4-717-2011; *NOAA ESRL, 2004, GLOBALVIEW CO2 COOP	90	5	5	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2011	11	12					6029	6047		10.5194/acp-11-6029-2011		19	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	784UJ	WOS:000292183400007	
J	Gao, Y; Liu, X; Zhao, C; Zhang, M				Gao, Y.; Liu, X.; Zhao, C.; Zhang, M.			Emission controls versus meteorological conditions in determining aerosol concentrations in Beijing during the 2008 Olympic Games	ATMOSPHERIC CHEMISTRY AND PHYSICS			English	Article							4-DIMENSIONAL DATA ASSIMILATION; AREA MESOSCALE MODEL; AIR-QUALITY; ORGANIC AEROSOL; MINERAL DUST; POLLUTANTS; OZONE; SIMULATIONS; VARIABILITY; REDUCTION	A series of emission control measures were undertaken in Beijing and the adjacent provinces in China during the 2008 Beijing Olympic Games on 8-24 August 2008. This provides a unique opportunity for investigating the effectiveness of emission controls on air pollution in Beijing. We conducted a series of numerical experiments over East Asia for the period of July to September 2008 using a coupled meteorology-chemistry model (WRF-Chem). Model can generally reproduce the observed variation of aerosol concentrations. Consistent with observations, modeled concentrations of aerosol species (sulfate, nitrate, ammonium, black carbon, organic carbon, total particulate matter) in Beijing were decreased by 30-50% during the Olympic period compared to the other periods in July and August in 2008 and the same period in 2007. Model results indicate that emission controls were effective in reducing the aerosol concentrations by comparing simulations with and without emission controls. In addition to emission controls, our analysis suggests that meteorological conditions (e.g. wind direction and precipitation) were also important in producing the low aerosol concentrations appearing during the Olympic period. Transport from the regions surrounding Beijing determined the daily variation of aerosol concentrations in Beijing. Based on the budget analysis, we suggest that to improve the air quality over Beijing, emission control strategy should focus on the regional scale instead of the local scale.	[Gao, Y.; Liu, X.; Zhao, C.] Pacific NW Natl Lab, Richland, WA 99352 USA; [Gao, Y.; Zhang, M.] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Layer Phys & Atm, Beijing, Peoples R China	Liu, X (reprint author), Pacific NW Natl Lab, Richland, WA 99352 USA.	xiaohong.liu@pnnl.gov	zhao, chun/A-2581-2012; Wang, ZF/D-7202-2012	Wang, ZF/0000-0002-7062-6012	US Department of Energy (DOE), Office of Science; National Natural Science Foundation of China [20937001]; National Department Public Benefit Research Foundation (Ministry of Environmental Protection of China) [201009001, 201109002]; DOE by Battelle Memorial Institute [DE-AC06-76RLO 1830]	X. Liu acknowledges the funding support from the US Department of Energy (DOE), Office of Science, Scientific Discovery through Advanced Computing (SciDAC) Program M. Zhang was funded by the National Natural Science Foundation of China (No. 20937001) and the National Department Public Benefit Research Foundation (Ministry of Environmental Protection of China) (No. 201009001 and 201109002). The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830.	Ackermann IJ, 1998, ATMOS ENVIRON, V32, P2981, DOI 10.1016/S1352-2310(98)00006-5; An X, 2007, ATMOS CHEM PHYS, V7, P3103; Binkowski FS, 1995, J GEOPHYS RES-ATMOS, V100, P26191, DOI 10.1029/95JD02093; BROWN JF, 1993, PHOTOGRAMM ENG REM S, V59, P977; Cermak J, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL038572; Chan CK, 2008, ATMOS ENVIRON, V42, P1, DOI 10.1016/j.atmosenv.2007.09.003; Easter RC, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004571; Fast JD, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006721; Ghan S, 2001, J GEOPHYS RES-ATMOS, V106, P5295, DOI 10.1029/2000JD900502; Grell GA, 2005, ATMOS ENVIRON, V39, P6957, DOI 10.1016/j.atmosenv.2005.04.027; Guenther A, 2006, ATMOS CHEM PHYS, V6, P3181; GUSTAFSON WI, 2004, GEOPHYS RES LETT, V34, P19809, DOI DOI 10.1029/2007GL030021; Han ZW, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009436; Hsu KL, 1997, J APPL METEOROL, V36, P1176, DOI 10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2; Hsu KL, 1999, WATER RESOUR RES, V35, P1605, DOI 10.1029/1999WR900032; Kulmala M, 1998, J GEOPHYS RES-ATMOS, V103, P8301, DOI 10.1029/97JD03718; Li G, 2011, ATMOS CHEM PHYS, V11, P3789, DOI 10.5194/acp-11-3789-2011; LOVELAND TR, 1991, PHOTOGRAMM ENG REM S, V57, P1453; McKeen SA, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000697; Randerson J. T., 2005, GLOBAL FIRE EMISSION, DOI [10.3334/ORNLDAAC/849, DOI 10.3334/ORNLDAAC/849]; Schell B, 2001, J GEOPHYS RES-ATMOS, V106, P28275, DOI 10.1029/2001JD000384; Shrivastava M, 2011, ATMOS CHEM PHYS, V11, P6639, DOI 10.5194/acp-11-6639-2011; Sorooshian S, 2000, B AM METEOROL SOC, V81, P2035, DOI 10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2; STAUFFER DR, 1994, J APPL METEOROL, V33, P416, DOI 10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2; STAUFFER DR, 1990, MON WEATHER REV, V118, P1250, DOI 10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2; STAUFFER DR, 1991, MON WEATHER REV, V119, P734, DOI 10.1175/1520-0493(1991)119<0734:UOFDDA>2.0.CO;2; STOCKWELL WR, 1990, J GEOPHYS RES-ATMOS, V95, P16343, DOI 10.1029/JD095iD10p16343; Streets DG, 2007, ATMOS ENVIRON, V41, P480, DOI 10.1016/j.atmosenv.2006.08.046; Tang YH, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004201; Wang M, 2009, ATMOS CHEM PHYS, V9, P8247; Wang SX, 2010, ENVIRON SCI TECHNOL, V44, P2490, DOI 10.1021/es9028167; Wang T, 2010, ATMOS CHEM PHYS, V10, P7603, DOI 10.5194/acp-10-7603-2010; Wang T, 2009, ATMOS ENVIRON, V43, P5682, DOI 10.1016/j.atmosenv.2009.07.034; Wang Y, 2009, ATMOS CHEM PHYS, V9, P5237; Wesley M. L., 1989, ATMOS ENVIRON, V23, P1293; WHITBY ER, 1991, 600391020 ATM RES EX; Xin JY, 2010, CHINESE SCI BULL, V55, P1937, DOI 10.1007/s11434-010-3216-2; Yang Q, 2011, ENVIRON SCI TECHNOL, V45, P6404, DOI 10.1021/es200675v; Zaveri RA, 1999, J GEOPHYS RES-ATMOS, V104, P30387, DOI 10.1029/1999JD900876; Zaveri RA, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008782; Zhang Q, 2009, ATMOS CHEM PHYS, V9, P5131; Zhang XY, 2009, B AM METEOROL SOC, V90, P1633, DOI 10.1175/2009BAMS2804.1; Zhao C, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012745; Zhao C, 2010, ATMOS CHEM PHYS, V10, P8821, DOI 10.5194/acp-10-8821-2010; Zhao C, 2011, ATMOS CHEM PHYS, V11, P1879, DOI 10.5194/acp-11-1879-2011; *BEIJ MUN ENV PROT, 1994, BEIJ ENV B	46	3	3	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1680-7316		ATMOS CHEM PHYS	Atmos. Chem. Phys.		2011	11	23					12437	12451		10.5194/acp-11-12437-2011		15	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	863AD	WOS:000298134300031	
J	Claeyman, M; Attie, JL; Peuch, VH; El Amraoui, L; Lahoz, WA; Josse, B; Joly, M; Barre, J; Ricaud, P; Massart, S; Piacentini, A; von Clarmann, T; Hopfner, M; Orphal, J; Flaud, JM; Edwards, DP				Claeyman, M.; Attie, J-L; Peuch, V-H; El Amraoui, L.; Lahoz, W. A.; Josse, B.; Joly, M.; Barre, J.; Ricaud, P.; Massart, S.; Piacentini, A.; von Clarmann, T.; Hoepfner, M.; Orphal, J.; Flaud, J-M; Edwards, D. P.			A thermal infrared instrument onboard a geostationary platform for CO and O-3 measurements in the lowermost troposphere: Observing System Simulation Experiments (OSSE)	ATMOSPHERIC MEASUREMENT TECHNIQUES			English	Article							EXPLORER GEOTROPE MISSION; CHEMISTRY-TRANSPORT MODEL; AIR-QUALITY; ATMOSPHERIC COMPOSITION; DATA ASSIMILATION; OZONE; POLLUTION; RETRIEVALS; OBJECTIVES; EMISSIONS	This paper presents observing system simulation experiments (OSSEs) to compare the relative capabilities of two geostationary thermal infrared (TIR) instruments to measure ozone (O-3) and carbon monoxide (CO) for monitoring air quality (AQ) over Europe. The primary motivation of this study is to use OSSEs to assess how these infrared instruments can constrain different errors affecting AQ hindcasts and forecasts (emissions, meteorology, initial condition and the 3 parameters together). The first instrument (GEO-TIR) has a configuration optimized to monitor O-3 and CO in the lowermost troposphere (LmT; defined to be the atmosphere between the surface and 3 km), and the second instrument (GEO-TIR2) is designed to monitor temperature and humidity. Both instruments measure radiances in the same spectral TIR band. Results show that GEO-TIR could have a significant impact (GEO-TIR is closer to the reference atmosphere than GEO-TIR2) on the analyses of O-3 and CO LmT column. The information added by the measurements for both instruments is mainly over the Mediterranean Basin and some impact can be found over the Atlantic Ocean and Northern Europe. The impact of GEO-TIR is mainly above 1 km for O-3 and CO but can also improve the surface analyses for CO. The analyses of GEO-TIR2 show low impact for O-3 LmT column but a significant impact (although still lower than for GEO-TIR) for CO above 1 km. The results of this study indicate the beneficial impact from an infrared instrument (GEO-TIR) with a capability for monitoring O-3 and CO concentrations in the LmT, and quantify the value of this information for constraining AQ models.	[Claeyman, M.; Attie, J-L; Ricaud, P.] Univ Toulouse, Lab Aerol, UMR 5560, CNRS,INSU, Toulouse, France; [Claeyman, M.; Attie, J-L; Peuch, V-H; El Amraoui, L.; Lahoz, W. A.; Josse, B.; Joly, M.; Barre, J.] CNRS Meteo France & Toulouse, CNRM GAME, URA1357, Toulouse, France; [Lahoz, W. A.] NILU, N-2027 Kjeller, Norway; [Massart, S.; Piacentini, A.] CERFACS, F-31057 Toulouse, France; [von Clarmann, T.; Hoepfner, M.; Orphal, J.] Forschungszentrum Karlsruhe, Inst Meteorol & Klimaforsch, D-76021 Karlsruhe, Germany; [Flaud, J-M] Univ Paris Est, Lab Interuniv Syst Atmospher, UMR7583, CNRS, Creteil, France; [Edwards, D. P.] Natl Ctr Atmospher Res, Boulder, CO 80307 USA	Claeyman, M (reprint author), Univ Toulouse, Lab Aerol, UMR 5560, CNRS,INSU, Toulouse, France.	marine.claeyman@aero.obs-mip.fr	Orphal, Johannes/A-8667-2012; Hopfner, Michael/A-7255-2013; von Clarmann, Thomas/A-7287-2013	Orphal, Johannes/0000-0002-1943-4496; von Clarmann, Thomas/0000-0003-2219-3379	Centre National de Recherches Scientifiques (CNRS), Astrium-EADS; Centre National de Recherches Meteorologiques (CNRM) of Meteo-France; RTRA/STAE	This work was funded by the Centre National de Recherches Scientifiques (CNRS), Astrium-EADS, the Centre National de Recherches Meteorologiques (CNRM) of Meteo-France and the RTRA/STAE (POGEQA project). The authors acknowledge ETHER, the French atmospheric composition database (CNES and CNRS-INSU) and the Region Midi-Pyrenees (INFOAIR project).	AKIMOTO H, 2008, 2360001 JSAC JAMSTEC; ATLAS RM, 1997, CGC WMO WORSH GEN 7; Bechtold P, 2001, Q J ROY METEOR SOC, V127, P869, DOI 10.1256/smsqj.57308; BOVENSMANN H, 2005, OPERATIONAL ATMOSPHE; Branis M, 2009, ENVIRON MONIT ASSESS, V156, P377, DOI 10.1007/s10661-008-0491-3; Brunekreef B, 2002, LANCET, V360, P1233, DOI 10.1016/S0140-6736(02)11274-8; Buis S, 2006, CONCURR COMP-PRACT E, V18, P231, DOI 10.1002/cpe.914; Burrows JP, 2004, ADV SPACE RES, V34, P682, DOI 10.1016/j.asr.2003.06.067; Claeyman M, 2010, ATMOS CHEM PHYS, V10, P6097, DOI 10.5194/acp-10-6097-2010; Claeyman M, 2011, ATMOS MEAS TECH, V4, P297, DOI 10.5194/amt-4-297-2011; CLERBAUX C, 2008, POTENTIAL MTG IRS S4; Clerbaux C, 2009, ATMOS CHEM PHYS, V9, P6041; Clerbaux C, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL032300; Courtier P., 1991, ATMOSPHERIC MODELS, P193; Cristofanelli P, 2009, ATMOS CHEM PHYS, V9, P4603; Deeter MN, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008929; Dentener F, 2006, ATMOS CHEM PHYS, V6, P4321; Dufour A., 2004, ATMOS RES, V74, P161, DOI 10.1016/atmosres.2004.04.013; Edwards D. P., 2006, EOS T AM GEOPHYS UN, V87, P327, DOI [10.1029/2006E0330005, DOI 10.1029/2006EO330005]; Edwards DP, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011375; Edwards DP, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007079; El Amraoui L, 2008, Q J ROY METEOR SOC, V134, P217, DOI 10.1002/qj.191; El Amraoui L, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL031251; Eremenko M, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034803; Fisher M., 2001, TECHNICAL MEMORANDUM, V347; Fishman J, 2008, B AM METEOROL SOC, V89, P805, DOI 10.1175/2008BAMS2526.1; Flaud JM, 2004, ADV SPACE RES, V34, P688, DOI 10.1016/j.asr.2003.07.072; Foret G, 2009, ATMOS CHEM PHYS, V9, P8479; Geer AJ, 2006, ATMOS CHEM PHYS, V6, P5445; Hollingsworth A, 2008, B AM METEOROL SOC, V89, P1147, DOI 10.1175/2008BAMS2355.1; Honore C, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008761; Hopfner M, 1998, P SOC PHOTO-OPT INS, V3501, P186, DOI 10.1117/12.317753; Ignaccolo R, 2008, ENVIRONMETRICS, V19, P672, DOI 10.1002/env.946; Jacob DJ, 2000, ATMOS ENVIRON, V34, P2131, DOI 10.1016/S1352-2310(99)00462-8; Josse B, 2004, TELLUS B, V56, P339, DOI 10.1111/j.1600-0889.2004.00112.x; Krzyzanowski M, 2008, AIR QUAL ATMOS HLTH, V1, P7, DOI 10.1007/s11869-008-0008-9; Lahoz WA, 2005, Q J ROY METEOR SOC, V131, P503, DOI 10.1256/qj.03.109; Lahoz WA, 2007, ATMOS CHEM PHYS, V7, P5745; Lamarque JF, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005537; LEE S, 2010, GEOPHYS RES ABSTR, V12; LEFEVRE F, 1994, J GEOPHYS RES-ATMOS, V99, P8183, DOI 10.1029/93JD03476; LOUIS JF, 1979, BOUND-LAY METEOROL, V17, P187, DOI 10.1007/BF00117978; Martin RV, 2008, ATMOS ENVIRON, V42, P7823, DOI 10.1016/j.atmosenv.2008.07.018; Massart S, 2009, ATMOS CHEM PHYS, V9, P5073; Massart S, 2005, CR GEOSCI, V337, P1305, DOI 10.1016/j.crte.2005.08.001; Masutani M, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012528; Masutani M, 2010, DATA ASSIMILATION: MAKING SENSE OF OBSERVATIONS, P647, DOI 10.1007/978-3-540-74703-1_24; McKeen S, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005858; Menut L, 2010, ANN GEOPHYS-GERMANY, V28, P61; Oltmans SJ, 2006, ATMOS ENVIRON, V40, P3156, DOI 10.1016/j.atmosenv.2006.01.029; Orphal J, 2005, CR PHYS, V6, P888, DOI 10.1016/j.crhy.2005.09.003; Pagowski M, 2006, ATMOS ENVIRON, V40, P3240, DOI 10.1016/j.atmosenv.2006.02.006; Peuch V.-H., 1999, P MET FRANC WORKSH A, P33; PEUCH VH, 2009, AM GEOPH UN FALL M S; Peuch V.-H, 2010, MAGEAQ MONITORING AT; PHILLIPS D, 1962, J ASS COMPUT MAT, V9, P34; Pradier S, 2006, TELLUS B, V58, P163, DOI 10.1111/j.1600-0889.2006.00185.x; Rodgers C. D., 2000, SERIES ATMOSPHERIC O, V2; ROUIL L, 2008, B AM METEOROL SOC, V90, P73, DOI DOI 10.1175/2008BAMS2390.1; Seinfeld JH, 1997, ATMOSPHERIC CHEM PHY; Stiller GP, 2002, J QUANT SPECTROSC RA, V72, P249, DOI 10.1016/S0022-4073(01)00123-6; Stockwell WR, 1997, J GEOPHYS RES-ATMOS, V102, P25847, DOI 10.1029/97JD00849; Stoffelen A, 2006, Q J ROY METEOR SOC, V132, P1927, DOI 10.1256/qj.05.83; Stuhmann R, 2005, ADV SPACE RES, V36, P975, DOI 10.1016/j.asr.2005.03.091; TIKHONOV AN, 1963, DOKL AKAD NAUK SSSR+, V151, P501; Timmermans RMA, 2009, IEEE J-STARS, V2, P271, DOI 10.1109/JSTARS.2009.2034613; Turquety S, 2009, ATMOS CHEM PHYS, V9, P4897; Vaughan J, 2004, B AM METEOROL SOC, V85, P549, DOI 10.1175/BAMS-85-4-549; VISSCHEDIJK AHJ, 2005, GRIDDED EUROPEAN ANT; WHO, 2005, AIR QUAL GUID GLOB U	70	2	2	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1867-1381		ATMOS MEAS TECH	Atmos. Meas. Tech.		2011	4	8					1637	1661		10.5194/amt-4-1637-2011		25	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	814ME	WOS:000294457700009	
J	Velazco, VA; Buchwitz, M; Bovensmann, H; Reuter, M; Schneising, O; Heymann, J; Krings, T; Gerilowski, K; Burrows, JP				Velazco, V. A.; Buchwitz, M.; Bovensmann, H.; Reuter, M.; Schneising, O.; Heymann, J.; Krings, T.; Gerilowski, K.; Burrows, J. P.			Towards space based verification of CO2 emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation	ATMOSPHERIC MEASUREMENT TECHNIQUES			English	Article							INFRARED SATELLITE RADIANCES; ESTIMATING ATMOSPHERIC CO2; DATA ASSIMILATION SYSTEM; DIOXIDE EMISSIONS; SPECTROMETER; SCIAMACHY; MISSION; INVERSIONS; VALIDATION; INVENTORY	Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now being the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m. LT) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat has the potential to verify reported US annual CO2 emissions from large power plants (>= 5 MtCO(2) yr(-1)) with a systematic error of less than similar to 4.9% and a random error of less than similar to 6.7% for 50% of all the power plants. For 90% of all the power plants, the systematic error was less than similar to 12.4% and the random error was less than similar to 13 %. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Therefore, we recommend the CarbonSat constellation configuration that achieves daily global coverage.	[Velazco, V. A.; Buchwitz, M.; Bovensmann, H.; Reuter, M.; Schneising, O.; Heymann, J.; Krings, T.; Gerilowski, K.; Burrows, J. P.] Univ Bremen, Inst Environm Phys IUP, D-28359 Bremen, Germany	Velazco, VA (reprint author), Univ Wollongong, Ctr Atmospher Chem, Wollongong, NSW 2500, Australia.	voltaire@iup.physik.uni-bremen.de			Wirtschaftsfoerderung Bremen (WFB); University of Bremen	We sincerely thank the two anonymous referees for reviewing the paper and for their comments and suggestions to improve the manuscript. We acknowledge the Wirtschaftsfoerderung Bremen (WFB) and the University of Bremen for funding and supporting the CarbonSat constellation study. We also acknowledge NASA and the teams involved in producing the MODIS data. NARR data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. The satellite orbit simulations were done using STK (http://www.agi.com/products/by-product-type/applications/stk/). Hourly emissions data were freely provided by the US Environmental Protection Agency under the Clean Air Markets-Data and Maps (CAMD) program. V. V. thanks J. Peischl and S. Evans for helpful discussions.	Ackerman KV, 2008, ENVIRON SCI TECHNOL, V42, P5688, DOI 10.1021/es800221q; Amediek A, 2009, ATMOS MEAS TECH, V2, P755; Boesch H, 2011, REMOTE SENS-BASEL, V3, P270, DOI 10.3390/rs3020270; Bovensmann H, 2010, ATMOS MEAS TECH, V3, P781, DOI 10.5194/amt-3-781-2010; Bovensmann H, 1999, J ATMOS SCI, V56, P127, DOI 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2; Breon FM, 2010, CR GEOSCI, V342, P412, DOI 10.1016/j.crte.2009.09.012; Buchwitz M, 2007, ATMOS CHEM PHYS, V7, P4249; BURROWS JP, 1995, ACTA ASTRONAUT, V35, P445, DOI 10.1016/0094-5765(94)00278-T; Chedin A, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014082; Corbin KD, 2010, TELLUS B, V62, P506, DOI 10.1111/j.1600-0889.2010.00480.x; Crevoisier C, 2009, ATMOS CHEM PHYS, V9, P4797; Crisp D, 2004, ADV SPACE RES-SERIES, V34, P700, DOI 10.1016/j.asr.2003.08.062; Ellerman D., 2007, REV ENV ECON POLICY, V1, P66, DOI DOI 10.1093/REEP/REM003; Engelen RJ, 2004, J APPL METEOROL, V43, P373, DOI 10.1175/1520-0450(2004)043<0373:ICOISS>2.0.CO;2; EVANS S, 2009, RELIABLE ARE GHG COM; Forster P, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P129; Frankenberg C, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014849; Gurney KR, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005373; Gurney KR, 2002, NATURE, V415, P626, DOI 10.1038/415626a; HAMAZAKI T, 2004, P 20 ISPRS C IST TUR; Krings T, 2011, ATMOS MEAS TECH, V4, P1735, DOI 10.5194/amt-4-1735-2011; Kuang ZM, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014298; Kulawik SS, 2010, ATMOS CHEM PHYS, V10, P5601, DOI 10.5194/acp-10-5601-2010; Kuze A, 2009, APPL OPTICS, V48, P6716, DOI 10.1364/AO.48.006716; Marland G, 2008, J IND ECOL, V12, P136, DOI 10.1111/j.1530-9290.2008.00014.x; MARLAND G, 1984, TELLUS B, V36, P232; Engelen RJ, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004777; Engelen RJ, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005982; Miller CE, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007659; Oda T, 2011, ATMOS CHEM PHYS, V11, P543, DOI 10.5194/acp-11-543-2011; Palmer PI, 2009, NAT GEOSCI, V2, P247, DOI 10.1038/ngeo495; Peischl J, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013527; Reuter M, 2009, J APPL METEOROL CLIM, V48, P301, DOI 10.1175/2008JAMC1982.1; Reuter M, 2010, IEEE T GEOSCI REMOTE, V48, P2469, DOI 10.1109/TGRS.2010.2041240; Schneising O, 2011, ATMOS CHEM PHYS, V11, P2863, DOI 10.5194/acp-11-2863-2011; Chedin A, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003439; Petron G, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000602	37	5	5	COPERNICUS GESELLSCHAFT MBH	GOTTINGEN	BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY	1867-1381		ATMOS MEAS TECH	Atmos. Meas. Tech.		2011	4	12					2809	2822		10.5194/amt-4-2809-2011		14	Meteorology & Atmospheric Sciences	Meteorology & Atmospheric Sciences	867ZD	WOS:000298492400014	
J	Vargas, R; Carbone, MS; Reichstein, M; Baldocchi, DD				Vargas, Rodrigo; Carbone, Mariah S.; Reichstein, Markus; Baldocchi, Dennis D.			Frontiers and challenges in soil respiration research: from measurements to model-data integration	BIOGEOCHEMISTRY			English	Article						C-14 and C-13; Data assimilation; Isotopes; Model performance; Radiocarbon; Soil CO2 efflux; Soil sensors; Wavelet analysis	CARBON-DIOXIDE EXCHANGE; EDDY COVARIANCE TECHNIQUE; C-14 SAMPLE PREPARATION; NET ECOSYSTEM EXCHANGE; BLACK SPRUCE FOREST; CO2 EFFLUX; SEASONAL-VARIATION; TEMPERATE FOREST; BOREAL FOREST; TERRESTRIAL ECOSYSTEMS	Soil respiration, the flux of CO2 from the soil to the atmosphere represents a major flux in the global carbon cycle. Our ability to predict this flux remains limited because of multiple controlling mechanisms that interact over different temporal and spatial scales. However, new advances in measurement and analyses present an opportunity for the scientific community to improve the understanding of the mechanisms that regulate soil respiration. In this paper, we address several recent advancements in soil respiration research from experimental measurements and data analysis to new considerations for model-data integration. We focus on the links between the soil-plant-atmosphere continuum at short (i.e., diel) and medium (i.e., seasonal-years) temporal scales. First, we bring attention to the importance of identifying sources of soil CO2 production and highlight the application of automated soil respiration measurements and isotope approaches. Second, we discuss the need of quality assurance and quality control for applications in time series analysis. Third, we review perspectives about emergent ideas for modeling development and model-data integration for soil respiration research. Finally, we call for stronger interactions between modelers and experimentalists as a way to improve our understanding of soil respiration and overall terrestrial carbon cycling.	[Vargas, Rodrigo; Baldocchi, Dennis D.] Univ Calif Berkeley, Ecosyst Sci Div, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA; [Carbone, Mariah S.] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA; [Reichstein, Markus] Max Planck Inst Biogeochem, Jena, Germany	Vargas, R (reprint author), Univ Calif Berkeley, Ecosyst Sci Div, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA.	rvargas@berkeley.edu; baldocchi@berkeley.edu; mcarbone@icess.ucsb.edu; mreichstein@bgc-jena.mpg.de	Vargas, Rodrigo/C-4720-2008; Reichstein, Markus/A-7494-2011; Baldocchi, Dennis/A-1625-2009; Carbone, Mariah/H-7389-2012	Vargas, Rodrigo/0000-0001-6829-5333; Reichstein, Markus/0000-0001-5736-1112; 	National Science Foundation [DEB-0639235]; NOAA; European Research Council	RV and DDB acknowledge support from the National Science Foundation grant DEB-0639235. MSC was supported by the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by the University Corporation for Atmospheric Research. MR is grateful for funding from the European Research Council via the ERC Grant QUASOM. This study was improved by a workshop held at the Wartburg, Jena, Germany and the comments of three anonymous reviewers.	Austin AT, 2006, NATURE, V442, P555, DOI 10.1038/nature05038; Bahn M, 2008, ECOSYSTEMS, V11, P1352, DOI 10.1007/s10021-008-9198-0; Bahn M, 2009, NEW PHYTOL, V182, P451, DOI 10.1111/j.1469-8137.2008.02755.x; Baldocchi D, 2006, J GEOPHYS RES-BIOGEO, V111, DOI 10.1029/2005JG000063; BALDOCCHI DD, 1991, J GEOPHYS RES-ATMOS, V96, P7271, DOI 10.1029/91JD00269; Baldocchi DD, 2003, GLOBAL CHANGE BIOL, V9, P479, DOI 10.1046/j.1365-2486.2003.00629.x; BIRCH HF, 1959, PLANT SOIL, V9, P262; Bond-Lamberty B, 2010, NATURE, V464, P579, DOI 10.1038/nature08930; Borken W, 2006, GLOBAL CHANGE BIOL, V12, P177, DOI 10.1111/j.1365-2486.2005.001058.x; Bowling DR, 2008, NEW PHYTOL, V178, P24, DOI 10.1111/j.1469-8137.2007.02342.x; Carbone MS, 2007, NEW PHYTOL, V176, P124, DOI 10.1111/j.1469-8137.2007.02153.x; Carbone MS, 2007, GLOBAL CHANGE BIOL, V13, P466, DOI 10.1111/j.1365-2486.2006.01300.x; Carbone MS, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000611; Carbone MS, 2008, NEW PHYTOL, V177, P295; Carrara A, 2004, AGR FOREST METEOROL, V126, P15, DOI 10.1016/j.agrformet.2004.05.002; Carvalhais N, 2008, GLOBAL BIOGEOCHEM CY, V22, DOI 10.1029/2007GB003033; Cisneros-Dozal LM, 2006, GLOBAL CHANGE BIOL, V12, P194, DOI 10.1111/j.1365-2486.2005.001061.x; Czimczik CI, 2006, GLOBAL CHANGE BIOL, V12, P957, DOI 10.1111/j.1365-2486.2006.01107.x; Daly E, 2008, ADV WATER RESOUR, V31, P987, DOI 10.1016/j.advwatres.2008.04.001; Davidson EA, 2006, NATURE, V440, P165, DOI 10.1038/nature04514; Davidson EA, 2006, GLOBAL CHANGE BIOL, V12, P154, DOI 10.1111/j.1365-2486.2005.01065.x; Fierer N, 2003, SOIL SCI SOC AM J, V67, P798; Gaudinski JB, 2000, BIOGEOCHEMISTRY, V51, P33, DOI 10.1023/A:1006301010014; Goulden ML, 1997, TREE PHYSIOL, V17, P537; Goulden ML, 1996, GLOB CHANGE BIOL, V2, P169, DOI 10.1111/j.1365-2486.1996.tb00070.x; Grinsted A, 2004, NONLINEAR PROC GEOPH, V11, P561; Hahn V, 2006, GLOBAL CHANGE BIOL, V12, P972, DOI 10.1111/j.1365-2486.2006.001143.x; Hanson PJ, 2000, BIOGEOCHEMISTRY, V48, P115, DOI 10.1023/A:1006244819642; Hashimoto S, 2006, J FOREST RES-JPN, V11, P41, DOI 10.1007/s10310-005-0185-4; Hasselquist NJ, 2010, PLANT SOIL, V331, P17, DOI 10.1007/s11104-009-0183-y; Heimann M, 2008, NATURE, V451, P289, DOI 10.1038/nature06591; Hirano T, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003766; Hirsch AI, 2004, TELLUS B, V56, P312, DOI 10.1111/j.1600-0889.2004.00113.x; Hogberg P, 2006, TRENDS ECOL EVOL, V21, P548, DOI 10.1016/j.tree.2006.06.004; Hogberg P, 2008, NEW PHYTOL, V177, P220, DOI 10.1111/j.1469-8137.2007.02238.x; Hutchinson GL, 2003, SOIL SCI SOC AM J, V67, P166; Irvine J, 2008, GLOBAL CHANGE BIOL, V14, P2848, DOI 10.1111/j.1365-2486.2008.01682.x; Jarvis P, 2007, TREE PHYSIOL, V27, P929; Jassal R, 2005, AGR FOREST METEOROL, V130, P176, DOI 10.1016/j.agrformet.2005.03.005; Jassal RS, 2004, AGR FOREST METEOROL, V124, P219, DOI 10.1016/j.agrformet.2004.01.013; Kramer C, 2006, SOIL BIOL BIOCHEM, V38, P3267, DOI 10.1016/j.soilbio.2006.04.006; Kramer C, 2010, SOIL BIOL BIOCHEM, V42, P1028, DOI 10.1016/j.soilbio.2010.02.021; Kuzyakov Y, 2006, SOIL BIOL BIOCHEM, V38, P425, DOI 10.1016/j.soilbio.2005.08.020; Kuzyakov Y, 2010, GLOBAL CHANGE BIOL, V16, P3386, DOI 10.1111/j.1365-2486.2010.02179.x; Lavigne MB, 1997, J GEOPHYS RES-ATMOS, V102, P28977, DOI 10.1029/97JD01173; Levin I, 2000, RADIOCARBON, V42, P69; Lundegardh H, 1927, SOIL SCI, V23, P417, DOI 10.1097/00010694-192706000-00001; Luo YQ, 2009, ECOL APPL, V19, P571, DOI 10.1890/08-0561.1; Mahecha MD, 2010, J GEOPHYS RES-BIOGEO, V115, DOI 10.1029/2009JG001016; Moffat AM, 2007, AGR FOREST METEOROL, V147, P209, DOI 10.1016/j.agrformet.2007.08.011; Morner NA, 2002, GLOBAL PLANET CHANGE, V33, P185, DOI 10.1016/S0921-8181(02)00070-X; Muhr J, 2009, GLOBAL CHANGE BIOL, V15, P782, DOI 10.1111/j.1365-2486.2008.01695.x; Muhr J, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2009JG000998; Papale D, 2006, BIOGEOSCIENCES, V3, P571; Pataki DE, 2003, BIOSCIENCE, V53, P805, DOI 10.1641/0006-3568(2003)053[0805:TCIEFU]2.0.CO;2; Paterson E, 2009, NEW PHYTOL, V184, P19, DOI 10.1111/j.1469-8137.2009.03001.x; Pendall E, 2004, NEW PHYTOL, V162, P311, DOI 10.1111/j.1469-8137.2004.01053.x; Pumpanen J, 2004, AGR FOREST METEOROL, V123, P159, DOI 10.1016/j.agrformet.2003.12.001; Pumpanen J, 2008, SOIL SCI SOC AM J, V72, P1187, DOI 10.2136/sssaj2007.0199; Pumpanen J, 2003, SOIL SCI SOC AM J, V67, P402; Raich JW, 2000, BIOGEOCHEMISTRY, V48, P71, DOI 10.1023/A:1006112000616; RAICH JW, 1992, TELLUS B, V44, P81, DOI 10.1034/j.1600-0889.1992.t01-1-00001.x; Reichert P, 1997, WATER SCI TECHNOL, V36, P149; Reichstein M, 2005, GLOBAL CHANGE BIOL, V11, P1424, DOI 10.1111/j.1365-2486.2005.001002.x; Reichstein M, 2008, J PLANT NUTR SOIL SC, V171, P344, DOI 10.1002/jpln.200700075; Risk D, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001831; Risk D, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014002; ROCHETTE P, 1992, CAN J SOIL SCI, V72, P605; Ryan MG, 2005, BIOGEOCHEMISTRY, V73, P3, DOI 10.1007/s10533-004-5167-7; Santos GM, 2007, RADIOCARBON, V49, P255; Santos GM, 2007, NUCL INSTRUM METH B, V259, P293, DOI 10.1016/j.nimb.2007.01.172; Savage K, 2008, FUNCT ECOL, V22, P1000, DOI 10.1111/j.1365-2435.2008.01414.x; Savage KE, 2003, J EXP BOT, V54, P891, DOI 10.1093/jxb/erg121; Schuur EAG, 2006, GLOBAL CHANGE BIOL, V12, P165, DOI 10.1111/j.1365-2486.2005.01066.x; SIMUNEK J, 1993, WATER RESOUR RES, V29, P487, DOI 10.1029/92WR02225; Subke JA, 2006, GLOBAL CHANGE BIOL, V12, P921, DOI 10.1111/j.1365-2486.2006.01117.x; Tang JW, 2005, AGR FOREST METEOROL, V132, P212, DOI 10.1016/j.agrformet.2005.07.011; Tang JW, 2005, GLOBAL CHANGE BIOL, V11, P1298, DOI 10.1111/j.1365-2486.2005.00987.x; Torrence C, 1998, B AM METEOROL SOC, V79, P61, DOI 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2; Trumbore S, 2000, ECOL APPL, V10, P399, DOI 10.1890/1051-0761(2000)010[0399:AOSOMA]2.0.CO;2; Trumbore S, 2006, GLOBAL CHANGE BIOL, V12, P141, DOI 10.1111/j.1365-2486.2006.01067.x; Trumbore S, 2009, ANNU REV EARTH PL SC, V37, P47, DOI 10.1146/annurev.earth.36.031207.124300; Turcu VE, 2005, VADOSE ZONE J, V4, P1161, DOI 10.2136/vzj2004.0164; Valentini R, 1996, GLOB CHANGE BIOL, V2, P199, DOI 10.1111/j.1365-2486.1996.tb00072.x; Van Oijen M, 2005, TREE PHYSIOL, V25, P915; Vargas R, 2010, GLOBAL CHANGE BIOL, V16, P1589, DOI 10.1111/j.1365-2486.2009.02111.x; Vargas R, 2010, ECOL APPL, V20, P1569, DOI 10.1890/09-0693.1; Vargas R, 2008, NEW PHYTOL, V179, P460, DOI 10.1111/j.1469-8137.2008.02481.x; Vargas R, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000620; Wahl EH, 2006, ISOT ENVIRON HEALT S, V42, P21, DOI 10.1080/10256010500502934; Williams M, 2009, BIOGEOSCIENCES, V6, P1341; Wutzler T, 2008, BIOGEOSCIENCES, V5, P749; Xu LK, 2004, AGR FOREST METEOROL, V123, P79, DOI 10.1016/j.agrformet.2003.10.004	93	14	16	SPRINGER	DORDRECHT	VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS	0168-2563		BIOGEOCHEMISTRY	Biogeochemistry	JAN	2011	102	1-3					1	13		10.1007/s10533-010-9462-1		13	Environmental Sciences; Geosciences, Multidisciplinary	Environmental Sciences & Ecology; Geology	695GQ	WOS:000285359000001