UBC Faculty Research and Publications

Model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon.. Gu, Lianhong; Grant, Robert; Desar, Ankur; Davis, Kenneth J.; Dragoni, Danilo; Dietze, Michael; Ciais, Philippe; Ma, Siyan; Luo, Yiqi; Lokupitiya, Erandathie; Li, Longhui; Law, Beverly E.; Liu, Shuguang; Li, Zhengpeng; Izaurralde, R. Cesar; Lafleur, Peter; Kucharik, Chris; Riciutto, Dan M.; Price, David T.; Oechel, Walter C.; Monson, Russell K.; Poulter, Benjamin; Peng, Changhui; McCaughey, Harry; Matamala, Roser; Margolis, Hank; Chen, Guangsheng; Tonitto, Christina; Verma, Shashi B.; Chen, Jing Ming; Verbeeck, Hans; Tian, Hanqin; Sun, Jianfeng; Sprintsin, Michael; Sahoo, Alok Kumar; Riley, William; Fischer, Marc L.; Flanagan, Lawrence B.; Hollinger, David; Schwalm, Christopher R.; Williams, Christopher A.; Anderson, Ryan; Barr, Alan G.; Black, T. Andrew; Arain, M. Altaf; Baker, Ian 2010

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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, G00H05, doi:10.1029/2009JG001229, 2010  A model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis Christopher R. Schwalm,1 Christopher A. Williams,1 Kevin Schaefer,2 Ryan Anderson,3 M. Altaf Arain,4 Ian Baker,5 Alan Barr,6 T. Andrew Black,7 Guangsheng Chen,8 Jing Ming Chen,9 Philippe Ciais,10 Kenneth J. Davis,11 Ankur Desai,12 Michael Dietze,13 Danilo Dragoni,14 Marc L. Fischer,15 Lawrence B. Flanagan,16 Robert Grant,17 Lianhong Gu,18 David Hollinger,19 R. César Izaurralde,20 Chris Kucharik,21 Peter Lafleur,22 Beverly E. Law,23 Longhui Li,10 Zhengpeng Li,24 Shuguang Liu,25 Erandathie Lokupitiya,5 Yiqi Luo,26 Siyan Ma,27 Hank Margolis,28 Roser Matamala,29 Harry McCaughey,30 Russell K. Monson,31 Walter C. Oechel,32 Changhui Peng,33 Benjamin Poulter,34 David T. Price,35 Dan M. Riciutto,18 William Riley,36 Alok Kumar Sahoo,37 Michael Sprintsin,9 Jianfeng Sun,33 Hanqin Tian,8 Christina Tonitto,38 Hans Verbeeck,39 and Shashi B. Verma40 Received 23 November 2009; revised 23 July 2010; accepted 29 July 2010; published 9 December 2010.  1 Graduate School of Geography, Clark University, Worcester, Massachusetts, USA. 2 National Snow and Ice Data Center, University of Colorado at Boulder, Boulder, Colorado, USA. 3 Numerical Terradynamic Simulation Group, University of Montana, Missoula, Montana, USA. 4 School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada. 5 Atmospheric Science Department, Colorado State University, Fort Collins, Colorado, USA. 6 Climate Research Division, Atmospheric Science and Technology Directorate, Saskatoon, Saskatchewan, Canada. 7 Faculty of Land and Food Systems, University of British Columbia, Vancouver, B. C., Canada. 8 School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama, USA. 9 Department of Geography and Program in Planning, University of Toronto, Toronto, Ontario, Canada. 10 Laboratoire des Sciences du Climat et de l’Environnement, CE Orme des Merisiers, Gif sur Yvette, France. 11 Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA. 12 Center for Climatic Research, University of Wisconsin‐Madison, Madison, Wisconsin, USA. 13 Department of Plant Biology, University of Illinois‐Urbana Champaign, Urbana, Illinois, USA. 14 Department of Geography, Indiana University, Bloomington, Indiana, USA. 15 Atmospheric Science Department, Lawrence Berkeley National Laboratory, Berkeley, California, USA. 16 Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta, Canada. 17 Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada. 18 Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. 19 Northern Research Station, USDA Forest Service, Durham, New Hampshire, USA. 20 Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, Maryland, USA.  21  Department of Agronomy and Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin‐ Madison, Madison, Wisconsin, USA. 22 Department of Geography, Trent University, Peterborough, Ontario, Canada. 23 College of Forestry, Oregon State University, Corvallis, Oregon, USA. 24 ASRC Research and Technology Solutions, Sioux Falls, South Dakota, USA. 25 Earth Resources Observation and Science, Sioux Falls, South Dakota, USA. 26 Department of Botany and Microbiology, University of Oklahoma, Norman, Oklahoma, USA. 27 Department of Environmental Science, Policy and Management and Berkeley Atmospheric Science Center, University of California, Berkeley, Berkeley, California, USA. 28 Centre d’études de la forêt, Faculté de foresterie, de géographie et de géomatique, Université Laval, Québec, Quebec, Canada. 29 Argonne National Laboratory, Biosciences Division, Argonne, Illinois, USA. 30 Department of Geography, Queen’s University, Kingston, Ontario, Canada. 31 Department of Ecology and Evolutionary Biology, University of Colorado at Boulder, Boulder, Colorado, USA. 32 Department of Biology, San Diego State University, San Diego, California, USA. 33 Department of Biology Sciences, University of Quebec at Montreal, Montreal, Quebec, Canada. 34 Swiss Federal Research Institute WSL, Birmensdorf, Switzerland. 35 Northern Forestry Centre, Canadian Forest Service, Edmonton, Alberta, Canada. 36 Climate and Carbon Sciences, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA. 37 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA. 38 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA. 39 Laboratory of Plant Ecology, Ghent University, Ghent, Belgium. 40 School of Natural Resources, University of Nebraska‐Lincoln, Lincoln, Nebraska, USA.  Copyright 2010 by the American Geophysical Union. 0148‐0227/10/2009JG001229  G00H05  1 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  [1] Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models’ ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step. Citation: Schwalm, C. R., et al. (2010), A model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis, J. Geophys. Res., 115, G00H05, doi:10.1029/2009JG001229.  1. Introduction [2] There is a continued need for models to improve consistency and agreement with observations [Friedlingstein et al., 2006], both overall and under more frequent extreme climatic events related to global environmental change such as drought [Trenberth et al., 2007]. Past validation studies of terrestrial biosphere models have focused only on few models and sites, typically in close proximity and primarily in forested biomes [e.g., Amthor et al., 2001; Delpierre et al., 2009; Grant et al., 2005; Hanson et al., 2004; Granier et al., 2007; Ichii et al., 2009; Ito, 2008; Siqueira et al., 2006; Zhou et al., 2008]. Furthermore, assessing model‐data agreement relative to drought requires, in addition to high‐quality observed CO2 exchange data, a reliable drought metric as well as a natural experiment across sites and drought conditions. [3] Drought is a reoccurring phenomenon in all climates [Larcher, 1995] and is characterized by a partial loss in plant function due to water limitation and heat stress. For terrestrial CO2 exchange, drought typically reduces photosynthesis more than respiration [Baldocchi, 2008; Ciais et al., 2005; Schwalm et al., 2010], resulting in decreased net carbon uptake from the atmosphere. In the recent past drought conditions have become more prevalent globally [Dai et al., 2004] and in North America [Cook et al., 2004b]. Both incidence and severity of drought [Seager et al., 2007] as well as heatwaves [Meehl and Tebaldi, 2004] are expected to further increase in conjunction with global warming [Houghton et al., 2001; Huntington, 2006; Sheffield and Wood, 2008; Trenberth et al., 2007]. [4] In this study, we evaluate model performance using terrestrial CO2 flux data and simulated fluxes collected from 1991 to 2007. This timeframe included two widespread droughts in North America: (1) the turn‐of‐the‐century drought from 1998 to 2004 that was centered in the western  interior of North America [Seager, 2007] and (2) a smaller‐ scale drought event in the southern continental Untied States from winter of 2005/2006 through October 2007 [Seager et al., 2009]. During these events Palmer Drought Severity Index values [Cook et al., 2007; Dai et al., 2004] and precipitation anomalies [Seager, 2007; Seager et al., 2009] were highly negative over broad geographic areas. Ongoing eddy covariance measurements [Baldocchi et al., 2001], active throughout the aforementioned drought periods, provided flux data across gradients of time, space, seasonality, and drought. We use these data to examine model skill relative to site‐specific drought severity, climatic season, and time. We also link model behavior to model architecture and site‐specific attributes. Specifically, we address the following questions: Are current state‐of‐the‐art terrestrial biosphere models capable of simulating CO2 exchange subject to gradients in dryness and seasonality? Are these models able to reproduce the seasonal variation of observed CO2 exchange across sites? Are certain characteristics of model structure coincident with better model‐data agreement? Which biomes are simulated poorly/well?  2. Methods 2.1. Observed and Simulated CO2 Exchange [5] Modeled and observed net ecosystem exchange (NEE, net carbon balance including soils where positive values indicate outgassing of CO2 to the atmosphere) data were analyzed from 21 terrestrial biosphere models (Table 1) and 44 eddy covariance (EC) sites spanning ∼220 site‐years and 10 biomes in North America (Table 2). All terrestrial biosphere models analyzed simulated carbon cycling with process based formulations of varying detail for component carbon fluxes. Simulated NEE was based on model‐specific  2 of 22  3 of 22 AR + HR  GPP ‐ AR  NPP ‐ HR  GPP ‐ AR  NPP ‐ HR  Net Primary Production (NPP)  Net Ecosystem Exchange (NEE)  Ecosystem Respiration (R)  Air Temperature Soil Temperature Precipitation Soil Moisture Surface Incident Shortwave Radiation Surface Incident Longwave Radiation Vegetation Carbon AR + HR  Air Temperature GPP  Air Temperature Soil Temperature Precipitation Soil Moisture Evaporation Soil Carbon Soil Nitrogen  First or Greater Order Model  Autotrophic Respiration (AR)  Daily 4 9 3 Semiprognostic Yes Enzyme Kinetic Model  Half‐hourly 4 7 11 Prognostic Yes Enzyme Kinetic Model  Temporal Resolution Vegetation Pools Soil Pools Soil Layers Canopy Phenology Nitrogen Cycle Gross Primary Productivity (GPP) Heterotrophic Respiration (HR)  BEPS  AgroIBIS  Model Attribute  Table 1. Summary of Model Characteristics  Soil Temperature Soil Moisture Surface Incident Shortwave Radiation Vapor Pressure Deficit  NPP ‐ HR  Air Temperature Soil Temperature Precipitation Soil Moisture Surface Incident Shortwave Radiation Surface Incident Longwave Radiation Vegetation Carbon AR + HR Air Temperature Soil Temperature Soil Moisture Soil Carbon Vegetation Carbon LAI Surface Incident GPP ‐ AR Shortwave Radiation Vapor Pressure Deficit CO2 Vegetation Carbon Leaf Nitrogen LAI  First or Greater Order Model  Soil Temperature Soil Moisture Soil Carbon  Air Temperature Vegetation Carbon Leaf Nitrogen  Half‐hourly 3 7 7 Prognostic Yes Enzyme Kinetic Model  Can‐IBIS  Daily 7 4 1 Prognostic Yes Stomatal Conductance Model  Biome‐BGC  Model  AR + HR  AR + HR  GPP ‐ R  NPP ‐ HR  GPP ‐ AR  Air Temperature Vegetation Carbon Leaf Nitrogen GPP  Fraction of Instantaneous GPP  DNDC  Air Temperature Precipitation Soil Moisture Potential Evaporation Vegetation Carbon Soil Nitrogen Leaf Nitrogen fPAR NPP ‐ HR  AR + HR  Soil Temperature  Daily Daily 6 3 3 9 2 10 Semiprognostic Prognostic Yes Yes Light Use Stomatal Efficiency Model Conductance Model Decay Methane Decay Methane Air Temperature Soil Temperature Soil Temperature Precipitation Soil Moisture Soil Litter and Soil Carbon Vegetation Carbon Soil Carbon Soil Nitrogen Soil Nitrogen Moisture  DLEM  Fraction of Instantaneous GPP  First or Greater Order Model  Half‐hourly 4 3 3 Prognostic Yes Enzyme Kinetic Model  CN‐CLASS Half‐hourly 9 4 9 Prognostic Yes Enzyme Kinetic Model  ED2  GPP ‐ R  GPP ‐ AR  AR + HR  NPP ‐ HR  GPP ‐ AR  AR + HR  Soil Temperature Decay Methane Soil Moisture CO2 Diffusion Soil Carbon Dissolved Carbon Soil Nitrogen Loss Soil Temperature Soil Moisture Surface Incident Shortwave Radiation Surface Incident Longwave Radiation Soil Carbon Vegetation Carbon Soil Nitrogen Leaf Nitrogen Air Temperature Air Temperature Soil Temperature Soil Temperature Vegetation Carbon Vegetation Carbon Leaf Nitrogen GPP Leaf Nitrogen  Hourly 9 9 15 Prognostic Yes Enzyme Kinetic Model  Ecosys  Air Temperature Precipitation Soil Carbon Soil Nitrogen Soil Moisture Vegetation Carbon Leaf Nitrogen LAI NPP ‐ HR  AR + HR  Proportional to Growth  Monthly 8 5 10 Prognostic Yes Light Use Efficiency Model Soil Temperature Soil Moisture Soil Carbon Dissolved Carbon Loss Vegetation Carbon Soil Nitrogen  EDCM  G00H05 SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05  EPIC  Model Attribute  4 of 22  Net Ecosystem Exchange (NEE) Biomes Simulated Sites Simulated Months Simulated Source  Ecosystem Respiration (R) Net Primary Production (NPP)  AR + HR  Nil  GPP ‐ R  5 9 909 Riley et al. [2002]  Light Use Efficiency Model  NPP ‐ HR  Croplands U.S.‐Ne3 48 Causarano et al. [2007]  Fraction of Instantaneous GPP  Half‐hourly 0 1 0 Prescribed No Stomatal Conductance Model First or Greater Order Model  ISOLSM  6 10 945 Liu et al. [1999]  BEPS  AR + HR  Temporal Resolution Daily Vegetation Pools 3 Soil Pools 0 Soil Layers 15 Canopy Phenology Prognostic Nitrogen Cycle Yes Nil Gross Primary Productivity (GPP) Heterotrophic CO2 Diffusion Dissolved Carbon Respiration Loss Air (HR) Temperature Soil Temperature Precipitation Soil Moisture Nil Autotrophic Respiration (AR)  Croplands 5 192 Kucharik and Twine [2007]  AgroIBIS  Biomes Simulated Sites Simulated Months Simulated Source  Model Attribute  Table 1. (continued)  GPP ‐ AR  GPP ‐ AR  6 10 825 Hanson et al. [2004]  9 29 2126 Sitch et al. [2003]  NPP ‐ HR  AR + HR  NPP ‐ HR  Model  Soil Temperature Soil Moisture Soil Carbon  Half‐hourly 8 8 0 Prognostic No Enzyme Kinetic Model  ORCHIDEE  9 31 2082 Arain et al. [2006]  CN‐CLASS  10 35 2332 Krinner et al. [2005]  GPP ‐ R  GPP ‐ AR  AR + HR  Air Temperature Air Temperature Vegetation Carbon Soil Moisture Vegetation Carbon  Daily 3 2 2 Prognostic No Stomatal Conductance Model Soil Temperature Soil Moisture Soil Carbon  LPJ  10 27 1978 Williamson et al. [2008]  Can‐IBIS  AR + HR  Air Temperature Soil Temperature Soil Moisture Vegetation Carbon GPP  Soil Temperature Soil Moisture Soil Carbon  Half‐hourly 4 5 14 Prognostic No Enzyme Kinetic Model  LoTEC  8 36 2001 Thornton et al. [2005]  Biome‐BGC  SiB3  10 31 2258 Baker et al. [2008]  GPP ‐ R  GPP ‐ AR  Forced Annual Balance  Fraction of Instantaneous GPP  Zero‐order Model  Half‐hourly 0 0 10 Prescribed Yes Enzyme Kinetic Model  Model  10 33 2246 Tian et al. [2010]  DLEM  Soil Temperature Soil Carbon  Half‐hourly 4 1 10 Prognostic Yes Enzyme Kinetic Model  SiBcrop  10 39 2450 Grant et al. [2005]  Ecosys  10 35 2402 Schaefer et al. [2009]  Air Temperature Soil Moisture CO2 Relative Humidity GPP ‐ R  AR + HR  Croplands 5 192 Lokupitiya et al. [2009]  GPP ‐ R  GPP ‐ AR  Forced Annual Balance  Air Temperature Air Temperature Vegetation Carbon Soil Moisture GPP Vegetation Carbon  Soil Temperature Soil Moisture Soil Carbon  10 min 8 5 15 Prescribed No Enzyme Kinetic Model  SiBCASA  Croplands 5 192 Li et al. [2010]  DNDC  Hourly 3 5 10 Prognostic No Stomatal Conductance Model First or Greater Order Model  TECO  6 10 658 Liu et al. [2003]  EDCM  Half‐hourly 0 0 0 Prescribed No Stomatal Conductance Model First or Greater Order Model  Triplex‐FLUX  10 44 2800 Zhan et al. [2003]  GPP ‐ R  GPP ‐ AR  10 35 2414 Weng and Luo [2008]  GPP ‐ R  GPP ‐ AR  3 7 291 Zhou et al. [2008]  GPP ‐ R  Fraction of Instantaneous GPP  Air Temperature Air Temperature Fraction of Soil Moisture Annual GPP Vegetation Surface Incident Carbon Shortwave Radiation Relative Humidity LAI fPAR CO2 Forced Annual AR + HR AR + HR Balance  Zero‐order Model  Half‐hourly 0 0 3 Prescribed No Stomatal Conductance Model  SSiB2  6 25 1684 Medvigy et al. [2009]  ED2  G00H05 SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05  5 of 22  1 1 1 1 1 2 2 2  Eastern Peatland ‐ Mer Bleue  Sask. ‐ SSA Old Aspen Sask. ‐ SSA Old Black Spruce Sask. ‐ SSA Old Jack Pine Quebec Mature Boreal Forest Site Sask. ‐ 1994 Harvested Jack Pine Sask. ‐ 2002 Harvested Jack Pine Sask. ‐ SSA 1975 Harvested Young Jack Pine Ontario ‐ Turkey Point Middle‐ aged White Pine Ontario ‐ Turkey Point Mature White Pine Western Peatland ‐ LaBiche‐Black Spruce/Larch Fen OK ‐ ARM Southern Great Plains Site ‐ Lamont AK ‐ Atqasuk AK ‐ Barrow NC ‐ Duke Forest ‐ Hardwoods NC ‐ Duke Forest ‐ Loblolly Pine MA ‐ Harvard Forest EMS Tower (HFR1) ME ‐ Howland Forest (Main Tower)  CA‐Mer  CA‐Oas CA‐Obs CA‐Ojp CA‐Qfo CA‐SJ1 CA‐SJ2 CA‐SJ3  U.S.‐Me3  IN ‐ Morgan Monroe State Forest MO ‐ Missouri Ozark Site OR ‐ Metolius ‐ Intermediate Aged Ponderosa Pine 2  1 1 1  1  1  USA  USA USA USA  USA  USA  USA  USA  1 1  USA USA USA USA USA  USA  Canada  Canada  Canada  Canada Canada Canada Canada Canada Canada Canada  Canada  Canada  Canada  Canada  1 1 1 1 1  1  1  1  2  1  2  Canada  Canada  Country  44.32  39.32 38.74 44.45  46.08  41.84  41.86  45.20  70.47 71.32 35.97 35.98 42.54  36.61  54.95  42.71  42.71  53.63 53.99 53.92 49.69 53.91 53.94 53.88  45.41  49.71  48.22  49.53  49.87  49.87  Latitude  165 300 960  −124.90 −82.16 −112.94  60  −68.74  275 219 1253 1005  −86.41 −92.20 −121.56 −121.61  480  16 1 160 163 303  −157.41 −156.63 −79.10 −79.09 −72.17  −89.98  310  −97.49  227  540  −112.47  −88.24  219  −80.36  227  219  −80.35  −88.22  530 629 579 382 580 518 511  −106.20 −105.12 −104.69 −74.34 −104.66 −104.65 −104.64  70  180  −125.29  −75.52  300  Elevation (m a.s.l.)  −125.33  Longitude  ENF  DBF DBF ENF  CSH  GRA  CRO  ENF  WET WET DBF ENF DBF  CRO  MF  ENF  ENF  DBF ENF ENF ENF ENF ENF ENF  WET  GRA  MF  ENF  ENF  ENF  IGBP Class  Warm summer continental Humid subtropical Humid subtropical Dry‐summer subtropical  Hot summer continental  Tundra Tundra Humid subtropical Humid subtropical Warm summer continental Warm summer continental Hot summer continental  Humid subtropical  Warm summer continental Warm summer continental Continental subarctic  Warm summer continental Warm summer continental Warm summer continental Continental subarctic Continental subarctic Continental subarctic Continental subarctic Continental subarctic Continental subarctic Continental subarctic  Maritime temperate  Maritime temperate  Maritime temperate  Köppen‐Geiger Climate Classification  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  U.S.‐MMS U.S.‐MOz U.S.‐Me2  U.S.‐Los  U.S.‐IB2  U.S.‐IB1  U.S.‐Ho1  U.S.‐Atq U.S.‐Brw U.S.‐Dk2 U.S.‐Dk3 U.S.‐Ha1  U.S.‐ARM  CA‐WP1  CA‐TP4  CA‐TP3  IL ‐ Fermi National Accelerator Laboratory ‐ Batavia (Agricultural Site) IL ‐ Fermi National Accelerator Laboratory ‐ Batavia (Prairie Site) WI ‐ Lost Creek  1  CA‐Let  CA‐Gro  CA‐Ca3  2  1  British Columbia ‐ Campbell River ‐ Mature Forest Site British Columbia ‐ Campbell River ‐ Clearcut Site British Columbia ‐ Campbell River ‐ Young Plantation Site Ontario ‐ Groundhog River ‐ Mature Boreal Mixed Wood Lethbridge  CA‐Ca1  CA‐Ca2  Priority  Name  Site ID  Table 2. Summary of Site Characteristicsa  G00H05 G00H05  USA  Daytime Data Coverage (%)  1 1 1 1 1 1 1  Annual NEE Error (g C m2−) 61.1 31.5 37.9 33.5 14.3 21.6 28.5 16.1 16.6 21.0 15.3 6.1 17.7 29.5 16.4  OK ‐ Shidler‐ Oklahoma MI ‐ Sylvania Wilderness Area  CA ‐ Tonzi Ranch  MI ‐ University of Michigan Biological Station CA ‐ Vaira Ranch ‐ Ione  WI ‐ Willow Creek  Annual NEE (g C m2−)  −244.3 571.7 91.2 −36.5 −132.9 −68.5 −158.0 −56.3 −29.9 −13.7 28.0 117.0 −82.0 −133.2 −195.8  U.S.‐Shd U.S.‐Syv  U.S.‐Ton  U.S.‐UMB  6 of 22  U.S.‐WCr  Site ID  CA‐Ca1 CA‐Ca2 CA‐Ca3 CA‐Gro CA‐Let CA‐Mer CA‐Oas CA‐Obs CA‐Ojp CA‐Qfo CA‐SJ1 CA‐SJ2 CA‐SJ3 CA‐TP4 CA‐WP1  U.S.‐Var  99 96 91 93 96 79 94 89 91 93 87 89 92 95 96  USA  USA  USA  USA USA  USA  USA  USA  USA  USA  USA  USA  CA ‐ Sky Oaks ‐ Old Stand  1  1  1  1  2  USA  Country  U.S.‐SO2  U.S.‐PFa  U.S.‐Ne3  U.S.‐Ne2  U.S.‐Ne1  U.S.‐NR1  U.S.‐Me5  U.S.‐Me4  2  Priority  1  Name  OR ‐ Metolius ‐ Second Young Aged Pine OR ‐ Metolius ‐ Old Aged Ponderosa Pine OR ‐ Metolius ‐ First Young Aged Pine CO ‐ Niwot Ridge Forest (LTER NWT1) NE ‐ Mead ‐ Irrigated Continuous Maize Site NE ‐ Mead ‐ Irrigated Maize ‐ Soybean Rotation Site NE ‐ Mead ‐ Rainfed Maize ‐ Soybean Rotation Site WI ‐ Park Falls/WLEF  Site ID  Table 2. (continued)  26 23 27 34 46 56 56 45 50 40 31 47 34 43 50  Nighttime Data Coverage (%)  45.81  38.41  45.56  38.43  36.93 46.24  33.37  45.95  41.18  41.16  41.17  40.03  44.44  44.50  Latitude  3050 361 361 361 485  −105.55 −96.48 −96.47 −96.44 −90.27  6.1 4.4 2.2 4.1 0.7 1.3 3.8 5.6 3.4 4 0.8 1.3 4.3 3.5 2.7  LAI  1256 1222 1554 427 335 935 460 470 461 819 344 537 694 959 481  Annual Precipitation (mm)  520  129  −120.95 −90.08  234  177  350 540  −84.71  −120.97  −96.68 −89.35  1392  1183  −121.57  −116.62  915  Elevation (m a.s.l.)  −121.62  Longitude  8.7 8.8 9.5 3.3 6.5 6.2 2.3 1.6 1.5 2.7 0.6 0.1 0.8 8.6 1.7  Mean Annual Air Temperature (°C)  DBF  GRA  DBF  WSA  GRA MF  CSH  MF  CRO  CRO  CRO  ENF  ENF  ENF  IGBP Class  1998–2006 2001–2006 2002–2006 2004–2006 1997–2006 1999–2006 1997–2006 2000–2006 2000–2006 2004–2006 2002–2005 2003–2006 2004–2005 2002–2007 2003–2007  Measurement Period  Hot summer continental Hot summer continental Hot summer continental Warm summer continental Dry‐summer subtropical Humid subtropical Warm summer continental Dry‐summer subtropical Warm summer continental Dry‐summer subtropical Warm summer continental  Dry‐summer subtropical Dry‐summer subtropical Dry‐summer subtropical Continental subarctic  Köppen‐Geiger Climate Classification  ENFT ENFT ENFT MF GRA WET DBF ENFB ENFB ENFB ENFB ENFB ENFB ENFT WET  Biome  Schwalm et al. [2007] Schwalm et al. [2007] Schwalm et al. [2007] McCaughey et al. [2006] Flanagan et al. [2002] Lafleur et al. [2003] Barr et al. [2004] Griffis et al. [2003] Griffis et al. [2003] Bergeron et al. [2007] Zha et al. [2009] Zha et al. [2009] Zha et al. [2009] Peichl and Arain [2007] Syed et al. [2006]  Source  G00H05 SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05  7 of 22  Annual NEE Error (g C m2−) 74.4 29.5 ‐ ‐ ‐ 139.0 65.9 33.4 31.3 42.0 19.2 66.3 48.9 65.8 32.7 ‐ 10.6 27.0 41.8 41.8 43.3 41.1 25.6 22.0 34.7 52.0 42.4 110.6 54.1  Annual NEE (g C m2−)  −128.4 −133.2 −12.8 −72.0 −718.1 −350.0 −217.4 −223.0 −269.0 −86.0 −78.0 −346.1 −305.7 −536.0 −198.0 −612.3 −206.0 −37.2 −424.0 −382.0 −258.0 45.0 22.4 −75.5 48.5 −67.8 −132.0 7.3 −222.6 89 95 50 49 48 75 78 70 92 80 82 97 94 63 83 55 97 89 93 96 94 85 87 96 53 77 86 80 48  Daytime Data Coverage (%) 36 43 22 29 1 37 34 47 46 49 54 46 33 46 28 41 48 44 42 51 55 30 30 49 51 25 39 22 55  Nighttime Data Coverage (%) 3.1 3.5 1.1 1.5 7 5.6 3.38 5.2 1.29 5.38 4.24 4.9 3.91 3.62 0.52 2.1 1.1 4.2 6.5 6.5 6.2 4.05 3 5.9 4.1 0.6 4.23 2.4 5.36  LAI 629 959 118 108 1091 1126 1122 818 718 818 666 1109 730 434 423 641 350 663 832 823 627 736 695 1179 700 549 629 563 712  Annual Precipitation (mm) 15.6 8.6 −10.6 −10.9 15.1 14.7 7.9 6.6 10.1 10.4 3.8 12.4 13.3 7.6 8.5 8.3 7.6 2.5 11.1 10.8 10.9 5.1 13.8 14.8 4.4 16.4 7.4 15.9 5.3  Mean Annual Air Temperature (°C) 2000–2006 2002–2007 1999–2006 1999–2002 2003–2005 1998–2005 1991–2006 1996–2004 2005–2007 2004–2007 2000–2006 1999–2006 2004–2007 2002–2007 2004–2005 1996–2000 1999–2002 1998–2007 2001–2006 2001–2006 2001–2006 1997–2005 1998–2006 1997–2001 2001–2006 2001–2007 1998–2006 2001–2007 1998–2006  Measurement Period CRO ENFT TUN TUN DBF ENFT DBF ENFT CRO GRA WET DBF DBF ENFT ENFT ENFT ENFT ENFT CRO CRO CRO MF SHR GRA MF WSA DBF GRA DBF  Biome  Fischer et al. [2007] Peichl and Arain [2007] Oberbauer et al. [2007] Harazono et al. [2003] Siqueira et al. [2006] Siqueira et al. [2006] Urbanski et al. [2007] Richardson et al. [2009] Post et al. [2004] Post et al. [2004] Sulman et al. [2009] Schmid et al. [2000] Gu et al. [2006] Thomas et al. [2009] Vickers et al. [2009] Irvine et al. [2004] Irvine et al. [2004] Bradford et al. [2008] Verma et al. [2005] Verma et al. [2005] Verma et al. [2005] Davis et al. [2003] Luo et al. [2007] Suyker et al. [2003] Desai et al. [2005] Ma et al. [2007] Schmid et al. [2003] Ma et al. [2007] Cook et al. [2004a]  Source  a Sources: IGBP classification, Loveland et al. [2001]; Köppen‐Geiger, Peel et al. [2007]; LAI for USA sites, http://public.ornl.gov/ameriflux/; LAI for Canadian sites, Chen et al. [2006] and Schwalm et al. [2006]. Annual precipitation and mean annual air temperature are measurement period averages of meteorological inputs used to drive model simulations. NEE values show yearly integrals and associated error: one standard deviation based on uncertainty due to random noise and the friction velocity threshold aggregated to yearly values and summed in quadrature [Barr et al., 2009]. Data coverages are percentages of half‐hourly NEE measurements that satisfied quality control standards (friction velocity threshold) for day‐ and nighttime separately. Priority: (1) Primary sites with complete (includes ancillary and biological data templates) records; (2) Secondary chronosequence sites. Standardized Precipitation Index available only for Priority 1 sites excluding US‐Atq, US‐Brw, US‐Dk2, US‐IB1, and US‐Shd. CA‐TP3, US‐Atq, US‐Brw, US‐Dk2, and US‐Me4 sites used postprocessing protocol from the La Thuile and Asilomar FLUXNET Synthesis data set (http://www.fluxdata.org/) [Moffat et al., 2007; Papale et al., 2006] and lack NEE uncertainties. Biome is combination of IGBP class and Köppen‐Geiger climate. US‐Atq and US‐Brw‐Arctic wetlands classified as tundra biome CA‐WP1, treed fen (IGBP mixed forest) grouped with wetlands biome US‐Los, shrub wetland site (IGBP closed shrublands) grouped with wetlands biome US‐SO2, closed shrublands grouped with shrublands (open or closed) biome. IGBP class and biome codes: CRO, croplands; CSH, closed shrublands; GRA, grasslands, ENF, evergreen needleleaf forest; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreen needleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woody savanna; SHR, shrublands; TUN, tundra; WET, wetlands.  U.S.‐ARM CA‐TP4 U.S.‐Atq U.S.‐Brw U.S.‐Dk2 U.S.‐Dk3 U.S.‐Ha1 U.S.‐Ho1 U.S.‐IB1 U.S.‐IB2 U.S.‐Los U.S.‐MMS U.S.‐MOz U.S.‐Me2 U.S.‐Me3 U.S.‐Me4 U.S.‐Me5 U.S.‐NR1 U.S.‐Ne1 U.S.‐Ne2 U.S.‐Ne3 U.S.‐PFa U.S.‐SO2 U.S.‐Shd U.S.‐Syv U.S.‐Ton U.S.‐UMB U.S.‐Var U.S.‐WCr  Site ID  Table 2. (continued)  G00H05 SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05  G00H05  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  runs using gap‐filled observed weather at each site and locally observed values of soil texture according to a standard protocol [Ricciuto et al., 2009] (http://www.nacarbon. org/nacp/), including a target NEE of zero integrated over the last 5 years of the simulation period. In addition, a mean model ensemble (hereafter: MEAN) was also analyzed. MEAN was calculated as the mean monthly value across all simulations. Furthermore, in contrast to other models, the parameter values used in the model LoTEC were optimized using data assimilation [Ricciuto et al., 2008]. LoTEC simulations were however retained when calculating MEAN as their effect on model skill was negligible due to the relatively small number of site‐months simulated. [6] Gaps in the meteorological data record occurred at EC sites due to data quality control or instrument failure. Missing values of air temperature, humidity, shortwave radiation, and precipitation data, i.e., key model inputs, were filled using DAYMET [Thornton et al., 1997] before 2003 or the nearest available climate station in the National Climatic Data Center’s Global Surface Summary of the Day (GSOD) database. Daily GSOD and DAYMET data were temporally downscaled to hourly or half‐hourly values using the phasing from observed mean diurnal cycles calculated from a 15 day moving window. The phasing used a sine wave assuming peak values at 1500 local standard time (LST) and lowest values at 0300 LST. In the absence of station data a 10 day running mean diurnal cycle was used [Ricciuto et al., 2009] (http://nacp.ornl.gov/docs/Site_Synthesis_Protocol_v7. pdf). [7] EC data were produced by AmeriFlux and Fluxnet‐ Canada investigators and processed as a synthesis product of the North American Carbon Program (NACP) Site Level Interim Synthesis (http://www.nacarbon.org/nacp/). The observed NEE were corrected for storage, despiked (i.e., outlying values removed), filtered to remove conditions of low turbulence (friction velocity filtered), and gap‐filled to create a continuous time series [Barr et al., 2004]. The time series included estimates of random uncertainty and uncertainty due to friction velocity filtering [Barr et al., 2004, 2009]. In this analysis, NEE was aggregated to monthly values using only non‐gap‐filled data, i.e., observed values deemed spurious and subsequently infilled were not considered. Coincident modeled NEE values were similarly excluded. This removed the influence of gap‐filling algorithms in the comparison of observed and modeled NEE. [8] Drought level was quantified using the 3 month Standard Precipitation Index (SPI) [McKee et al., 1993]. Monthly SPI values were taken from the U.S. Drought Monitor (http://drought.unl.edu/DM/) whereby each tower was matched to nearby meteorological station(s) indicative of local drought conditions given proximity, topography, and human impact. This study used three drought levels: dry required SPI < −0.8, wet corresponded to SPI > +0.8, otherwise normal conditions existed. Climatic season was defined by four seasons of 3 months each with winter given by December, January, and February. 2.2. Model Skill [9] Model‐data mismatch was evaluated using normalized mean absolute error (NMAE) [Medlyn et al., 2005], the reduced c2 statistic (c2) [Taylor, 1996] as well as Taylor  diagrams and skill (S) [Taylor, 2001]. The first metric quantifies bias, the “average distance” between observations and simulations in units of observed mean NEE: NMAE ¼  X NEEobs À NEEsim nNEEobs  ijkl  ;  ð1Þ  where the overbar indicates averaging across all values, n is sample size, the subscript obs is for observations and sim is for modeled estimates. The summation is for any arbitrary data group (denoted by subscripts on the summation operator only) where subscript i is for site, j is for model, k is for climatic season, l is for drought level. [10] The second metric used to evaluate model performance was the reduced c2 statistic. This is the squared difference between paired model and data points over observational error normalized by degrees of freedom: 2 ¼    1 X NEEobs À NEEsim 2 ; n ijkl 2NEE  ð2Þ  where d NEE is uncertainty of monthly NEE (see section 2.3), 2 normalizes the uncertainty in observed NEE to correspond to a 95% confidence interval, the summation is across any arbitrary data group (denoted by subscripts on the summation operator). c2 values are linked to model‐data mismatch where a value of unity indicates that model and data are in agreement relative to data uncertainty. [11] A final characterization of model performance used Taylor diagrams [Taylor, 2001]; visual displays based on pattern matching, i.e., the degree to which simulations matched the temporal evolution of monthly NEE. Taylor plots are polar coordinate displays of the linear correlation coefficient (r), centered root mean squared error (RMSE; pattern error without considering bias), and the standard deviation of NEE (s). Taylor diagrams were constructed for the mean model ensemble (MEAN) and across‐site mean model performance using the full data record for each combination of site and model (ranging from 7 to 178 months). More generally, each polar coordinate point for any arbitrary data group can be scored: S¼  2ð1 þ Þ ðnorm þ 1=norm Þ2  ;  ð3Þ  where S is the model skill metric bound by zero and unity where unity indicates perfect agreement, and snorm is the ratio of simulated to observed standard deviation [Taylor, 2001]. [12] To scale model skill metrics across gradients of site, biome, model, seasonality, and dryness level we aggregated across data groups weighting each by sample size. For example, c2 for model I, denoted by subscript j = I, is given by 2j¼I ¼  X nikl 2  ikl  ikl  nj¼I  ð4Þ  where the summation is over all sites, seasons, and levels of dryness where model I was used as denoted by subscripts i, k, and l, respectively; nj=I is the total site‐months simulated  8 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  Table 3. Model Structural and Site History Predictors Used to Classify Taylor Skill With Regression Tree Analysisa Predictor  Value  Model temporal resolution Canopy  Daily, half‐hourly or less, hourly, monthly Prognostic, semiprognostic, prescribed. Prescribed canopy from remote sensing, semiprognostic has some prescribed input into canopy leaf biomass but calculates phenology with other prognostic variables. Number of pools, both dynamic and static Number of pools, both dynamic and static Number of layers True if the model has a nitrogen cycle; otherwise false. True if the simulated long‐term NEE integral approaches zero; otherwise false. Fraction of annual GPP, fraction of instantaneous GPP, explicitly calculated, nil, proportional to growth AR + HR, explicitly calculated, forced annual balance Enzyme kinetic model, light use efficiency model, nil, stomatal conductance model Explicitly calculated, first or greater order model, zero‐order model Explicitly calculated, GPP ‐ R, NPP ‐ HR Explicitly calculated, fraction of instantaneous GPP, GPP ‐ AR, light use efficiency model Low, average, high Values correspond to terciles of the total amount of first‐order functional arguments for the following model‐generated variables/outputs: AR, canopy leaf biomass, R, evapotranspiration, GPP, HR, NEE, NPP, soil moisture. True if the below listed management activity or disturbance or event occurred on site; otherwise false. Grazed, fertilized, fire, harvest, herbicide, insects and pathogens, irrigation, natural regeneration, pesticide, planted, residue management, thinning Young, intermediate, nil, mature, multicohort. Values based on stand age in forested sites; stands without a clear dominant stratum are treated as multicohort; nonforest types have nil.  Number of vegetation pools Number of soil pools Number of soil layers Nitrogen Steady state Autotrophic respiration (AR) Ecosystem respiration (R) Gross primary productivity (GPP) Heterotrophic respiration (HR) Net ecosystem exchange (NEE) Net primary productivity (NPP) Overall model complexity  Site history  Stand age class  a Taylor skill (S; equation (3)) was divided into three classes using terciles. Model structural predictants are from the Metadata for Forward (Ecosystem) Model Intercomparison survey collated by the NACP Site Synthesis (http://daac.ornl.gov/SURVEY8/ survey_results.shtml). Site history data are from http://public.ornl.gov/ameriflux/, http://www.fluxnet.org, and Schwalm et al. [2006].  with model I; and c2j=I is aggregated c2 for model I. We did not evaluate model performance for any data group with n < 3. In sum, Taylor displays and skill examined models’ ability to mimic the monthly trajectory of observed NEE, the calculation of NMAE quantified bias in units of mean observed NEE, and c2 values quantified how well model‐ data mismatch scales with flux uncertainty. 2.3. Observational Flux Uncertainty [13] We calculated the standard error of monthly NEE (d NEE) [Barr et al., 2009] by combining random uncertainty and uncertainty associated with the friction velocity threshold (uTh * ), a value used to identify and reject spurious nighttime NEE measurements. Random uncertainty was estimated following Richardson and Hollinger [2007]: (1) generate synthetic NEE data using the gap‐filling model [Barr et al., 2004, 2009] for a given site‐year, (2) introduce gaps as in the observed data with uTh * filtering, (3) add noise, (4) infill gaps using the gap‐filling model, and (5) repeat the process 1000 times for each site. The random uncertainty component of dNEE was then the standard deviation across all 1000 realizations aggregated to months. [14] The uTh * uncertainty component of d NEE was also estimated using Monte Carlo methods. Here 1000 realiza-  tions of NEE were generated using 1000 draws from a distribution of uTh * . This distribution was based on binning the raw flux data with respect to climatic season, temperature, and site‐year and estimating uTh * in each bin [Papale et al., 2006]. The standard deviation across all realizations gave the uTh * uncertainty component of d NEE. Both components were combined in quadrature to one standard error of monthly NEE (= dNEE) [Barr et al., 2009]. 2.4. Relating Model Skill to Model Structure and Site History [15] The models evaluated here range widely in their emphasis and structure (Table 1). Some focus on biophysical calculations (SiB3, BEPS), where others emphasize biogeochemistry (DLEM), or ecosystem dynamics (ED2). However, as terrestrial biosphere models simulate carbon cycling with hydrological variables, most models contain both biophysics and biogeochemistry. This motivated characterizing model structure with definite attributes, e.g., prognostic versus prescribed canopy phenology, number of soil pools, and type of NEE algorithm (Table 3). To resolve how such characteristics and site history impacted model skill we calculated S for all observed combinations of site, model, seasonality, and drought level and cross‐referenced  9 of 22  G00H05  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE Table 4. Normalized Mean Absolute Error by Climatic Season, Drought Level, and Biomea Climatic Season  Drought Level  Biomeb  Winter  Spring  Summer  Fall  Dry  Normal  Wet  Overall  CRO DBF ENFB ENFT GRA MF SHR TUN WET WSA Overall  1.90 0.81 1.52 −6.34 −25.46 1.10 −87.37 −1.43 1.80 −2.73 2.42  4.64 93.7 −1.12 −0.66 −0.84 −7.48 −1.37 −11.07 −5.07 −0.75 −1.35  −0.79 −0.52 −0.69 −0.50 −1.11 −0.47 −3.03 −20.63 −0.59 −1.47 −0.61  12.73 −2.14 −1.92 −0.76 5.19 57.70 −140.17 6.38 −4.72 10.56 −1.94  −1.43 −1.01 −0.87 −0.63 −1.52 −1.42 −1.82 19.22 −1.21 −1.39 −0.97  −1.54 −1.00 −1.15 −0.72 −1.32 −1.04 −2.18 −24.06 −1.20 −1.32 −1.01  −1.59 −0.95 −3.43 −0.63 −3.07 −1.15 −41.13 −1.81 −2.38 −1.51 −1.00  −1.55 −1.00 −1.12 −0.68 −1.51 −1.12 −2.88 −20.15 −1.27 −1.37 −1.00  a Drought level was based on monthly values of 3 month Standard Precipitation Index (SPI): dry value were < −0.8; wet > +0.8. Otherwise normal conditions existed. b Biome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreen needleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woody savanna; SHR, shrubland; TUN, tundra; WET, wetland.  these with 13 site history variables and 14 model attributes (Table 3). Only 20 models were available for this exercise; MEAN and the optimized LoTEC were excluded. We used S as it is bound by zero (no agreement) and unity (perfect agreement) in contrast to NMAE and c2 which are unbound. The Taylor skill metric (S) was first discretized into three classes based on terciles. These classes, representing three tiers of model‐data agreement, were then related to biome, climatic season, drought level, site history, and model structure using regression tree analysis (RTA) as a supervised classification algorithm. RTA is a form of binary recursive partitioning [Breiman et al., 1984] that successively splits the data (Taylor skill classes as the response; all other attributes as predictors) into subsets (nodes) by minimizing within‐subset variation. The result is a pruned tree‐ like topology whereby predicted values (Taylor skill metric class) are derived by a top‐to‐bottom traversal following the rules (branches) that govern subset membership until a predicted value is reached (terminal node). The splitting rules at each node as well as its position allow for a calculation of relative variable importance [Breiman et al., 1984] with the most important variable given a score of 100. Variables of high importance were further analyzed using conditional means, i.e., comparing mean values for  each predictor value, with statistical differences determined using Bonferroni corrections for multiple comparisons [Hochberg and Tamhane, 1987].  3. Results 3.1. Model‐Data Agreement Relative to Climatic Season, Dryness, and Biome [16] Overall agreement across n = 31025 months was better in forested than nonforested biomes; both NMAE (Table 4) and c2 values (Table 5) were closer to zero and unity, respectively. At the biome level, model skill was loosely ranked in five tiers: evergreen needleleaf forests in the temperate zone, mixed forests > deciduous broadleaf forests, evergreen needleleaf forests in the boreal zone > grasslands, woody savannahs > croplands, shrublands, wetlands > tundra. These rankings were robust across models used in the majority of biomes, although some divergence was apparent for croplands and shrublands (Figure 1). Relative to seasonality and drought level models were most consistent with observations during periods of peak biological activity (climatic summer) and under dry conditions (Figure 2). However, across the three levels of dryness, changes in model‐data agreement were negligible  Table 5. Reduced c2 Statistic by Climatic Season, Drought Level, and Biomea Climatic Season Biome  b  CRO DBF ENFB ENFT GRA MF SHR WET WSA Overall  Drought Level  Winter  Spring  Summer  Fall  Dry  Normal  Wet  Overall  3.22 5.29 21.25 4.39 10.89 3.74 13.34 23.65 0.61 8.18  10.66 10.74 17.75 7.90 11.38 4.67 27.98 27.27 5.81 11.95  39.75 8.77 4.98 3.27 25.01 2.05 12.52 11.74 11.88 11.27  49.71 4.55 6.61 2.26 17.22 2.02 11.2 7.54 3.39 9.45  14.43 5.58 11.64 4.71 13.97 2.92 9.26 21.51 6.73 8.10  23.54 7.86 12.02 4.29 10.99 3.24 21.31 17.36 4.64 9.98  32.75 8.67 18.51 4.60 26.01 2.98 10.31 12.91 6.35 12.72  25.8 7.34 12.61 4.45 16.07 3.08 16.26 17.47 5.37 10.26  a Drought level was based on monthly values of 3 month Standard Precipitation Index (SPI): dry value were < −0.8; wet > +0.8. Otherwise normal conditions existed. b Biome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreen needleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woody savanna; SHR, shrubland; WET, wetland.  10 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  Figure 1. Normalized mean absolute error (NMAE) by biome for each model. Biomes in ascending order based on model‐specific NMAE; biomes on the left show better average agreement with observations. NMAE is normalized by mean observed flux. Across all sites, seasons, and drought levels within a given biome this value is negative (NEE < 0), indicating a sink. NMAE values closer to zero coincide with a higher degree of model‐data agreement. Woody savannahs and shrublands not shown: only one site each. Tundra (n = 2 sites) has NMAE < −10 for all models. CN‐CLASS croplands value is off‐scale (= −8.98). Black cross, no observations; white circle, undersampled (n < 100 months). for NMAE (∼4% change, Table 4) but more pronounced for c2 (from 8.10 to 12.72, Table 5). Averaged over just the warm season (excluding climatic winter) dry conditions were coincident with worse model‐data agreement, e.g., NMAE was −0.99, −0.91, and −0.84 for dry, normal, and wet, respectively. In biomes with a clear seasonal cycle in leaf area index (LAI) a loss of model skill occurred during climatic spring and fall (Tables 4 and 5), especially for NMAE. 3.2. Skill Metrics by Model [17] Regardless of metric, model skill was highly variable. Of the three model skill metrics, NMAE was related to both Taylor skill and c2 (r = −0.65; p < 0.0001). Jointly, high Taylor skill co‐occurred with NMAE and c2 values closer to zero and unity, respectively (Figure 3). Across models NMAE ranged from −0.42 of the overall mean observed flux to −2.18 for LoTEC and DNDC, respectively. Values of c2 varied from 2.17 to 29.87 for LoTEC and CN‐CLASS, respectively. Alternatively, the degree of model‐data mismatch (the distance between observations and simulations) was at least 2.17 times the observational flux uncertainty. Similarly, Taylor skill showed a high degree of scatter (Figure 4), although two crop only models (SiBcrop and AgroIBIS), LoTEC, and ISOLSM were more conservative  and showed a general high degree of consistency with observations. [18] Among crop models, SiBCrop and AgroIBIS performed well, especially in climatic spring and during wet conditions. In contrast, the crop only DNDC model exhibited poor model‐data agreement with c2 > 15 in climatic spring and summer as well as across all drought levels. Although four crop only simulators were analyzed, the best agreement in croplands (NMAE and c2 closer to zero and unity, respectively) was achieved by SiB3 and Ecosys, models used in multiple biomes. Based on all three skill metrics the LoTEC model (NMAE = −0.42, c2 = 2.17, S = 0.95) was most consistent with observations across all sites, dryness levels, and climatic seasons. This platform was optimized using a data assimilation technique, unique among model runs evaluated here, and was applied at 10 sites. In addition, the mean model ensemble (MEAN) performed well (NMAE = −0.74, c2 = 3.35, S = 0.80). For individual models (n = 12) used at a wider range of sites (at least 24 sites), model consistency with observations was highest for Ecosys (NMAE = −0.69, c2 = 7.71, S = 0.94) and lowest for CN‐CLASS (NMAE = −1.50, c2 = 29.87, S = 0.48). [19] Site‐level model‐data agreement also showed a high degree of variability (Figure 4). At three croplands sites (US‐Ne1, US‐Ne2, and US‐Ne3) Taylor skill ranged from zero to unity. Both NMAE and c2 exhibited similar  11 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  Figure 2. Normalized mean absolute error (NMAE) by climatic season and drought level. NMAE is normalized by mean observed flux such that most values are negative (NEE < 0), indicating a sink. Positive values indicate a source (NEE > 0). These occur in winter for all models as well as spring and fall for all crop only models: AgroIBIS, DNDC, EPIC, SiBcrop. Such values are displayed on the same color bar but with opposite sign. Off‐scale values: AgroIBIS and SiBcrop in fall are −7.1 and −11.1, respectively. DNDC in fall and spring is −11.4 and −8.7, respectively. Black cross, no observations; white circle, undersampled (n < 100 months). scatter by site (not shown). Even for the best predicted site (US‐Syv), S ranged from 0.19 to 0.95. Only two forested sites (CA‐Qfo and CA‐TP4) were predicted well (S > 0.5) by all models; whereas only one tundra site (US‐Atq) was consistently poorly predicted (S < 0.5). Despite the wide range in model performance, model skill (NMAE, c2, and S) was not correlated with the number of sites (p > 0.5) or biomes (p > 0.3) simulated, i.e., using a more general rather than a specialized model did not result in a loss in model performance. Also, model‐data agreement was not better at sites with longer data records (p > 0.1). [20] The steady state protocol had negligible effect on model skill. Long‐term simulated NEE by site and model varied from −2904 to 2227 g C m−2 yr−1 with 90% of all values between −600 and 100 g C m−2 yr−1. The extreme values were primarily croplands simulated outside of crop only models. Overall, only 5 models achieved steady state (simulated NEE → 0) over the full simulation: Biome‐BGC, LPJ, SiBCASA, SiB3, and TECO. Similar to simulated values, observed annual integrals at the 44 sites examined did not show steady state (Table 1) and varied from −718 to 571 g C m−2 yr−1. Nonetheless, model skill was not related to how close model spinup and initial conditions approximated steady state or how close a given site was to an observed NEE of zero. All three skill metrics were uncorrelated with long‐term observed or simulated average  annual NEE (p > 0.05). However, two models did show significant relationships: For Ecosys, c2 increased (decrease in model skill) and S decreased as observed or simulated NEE approached zero; a system closer to steady state was coincident with less model‐data agreement. BEPS was similar, showing lower S and more negative NMAE (decrease in model skill) for sites closer to steady state. 3.3. Model and Site‐Specific Consistency With Observations Using Taylor Diagrams [21] Average model performance (both across‐site and across‐model) was evaluated using Taylor diagrams based on all simulated and observed monthly NEE data. Better model performance was indicated by proximity to the benchmark, representing the observed state. The benchmark was normalized by observed standard deviation such that the distance of s and RMSE from the benchmark was in observed s units. Similar to model skill metrics, forested sites were better predicted than nonforested ones. The MEAN model showed r ≥ 0.2, apart from CA‐SJ2 and US‐Atq, but generally (33 of 44 sites) underpredicted the variability associated with monthly NEE at forested (Figure 5) and nonforested (Figure 6) sites. Similarly, 40 of 44 sites were predicted with RMSE < s. Also 8 (6 forested and two croplands sites: CA‐Obs, CA‐Qfo, CA‐TP4, US‐Ho1, US‐IB1, US‐MMS, US‐Ne3, US‐UMB) of the  12 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  Figure 3. Model skill metrics for all 22 models. Skill metrics are Taylor skill (S; equation (3)), normalized mean absolute error (NMAE), and reduced c2 statistic (c2). Better model‐data agreement corresponds to the upper left corner. Benchmark represents perfect model‐data agreement: S = 1, NMAE = 0, and c2 = 1. Gray interpolated surface added and model names jittered to improve readability. 44 sites were predicted with r ≥ 0.95 and RMSE < 1. The worst predicted site was CA‐SJ2 with r = −0.67, s = 4.3, and RMSE = 5.1. [22] Overall model performance, aggregated across sites, was similar (Figure 7). Most models underpredicted variability and showed RMSE < s. Of all 22 models only DNDC exhibited r < 0.2. Based on proximity to the benchmark, i.e., a high S value (Figure 3), the best models were: EPIC (crop only model used on one site), ISOLSM (used on 9 sites), LoTEC (data assimilation model), SiBcrop and AgroIBIS (crop only models), EDCM (used on 10 sites), Ecosys and SiBCASA (models used on most sites, 39 and 35, respectively), and MEAN (mean model ensemble for all 44 sites). All of these “best” models had r > 0.75, RMSE < 0.75 and slightly underpredicted variability; except the crop only models and Ecosys where variability was overpredicted. Models whose average behavior was furthest away from the benchmark were DNDC followed by BEPS. 3.4. Links Between Model Skill, Model Structure, and Site History [23] Biome classification was the most important factor in the distribution of model skill (Figure 8) sampled across all combinations of site, model, climatic season, and drought (n = 3132 groups). Climatic season and stand age, the highest scored site‐specific attribute, followed biome as lead determinants of model skill. Of the 12 evaluated site disturbances (Table 3) only grazing, which occurred on croplands, grasslands, and woody savannahs, achieved an importance score of at least 25. Apart from drought and  grazing activity, the remaining determinants were model‐ specific: the number of soil layers, vegetation pools, canopy phenology, and soil pools. Two carbon flux calculations also had a variable score > 25, with NEE being the highest. [24] Comparing mean S for these relatively important model attributes (Figure 9) revealed three instances where model structure showed a statistically significant relationship with model skill: prescribed canopy phenology, a daily time step, and calculating NEE as the difference between GPP and ecosystem respiration. Models using canopy characteristics and phenology prescribed from remotely sensed products achieved higher skill (S = 0.54) than either prognostic or semiprognostic models (S = 0.43; p < 0.05). Using a daily time step showed lower model skill (S = 0.40) relative to nondaily time steps (S = 0.50; p < 0.05). Finally, calculating NEE as the difference between GPP and total ecosystem respiration showed greater skill (S = 0.50) than other calculation methods (S = 0.42; p < 0.05). None of the other model attributes we studied showed statistically significant relationships between model structure and skill. [25] While not statistically significant, both vegetation pools and soil layers exhibited a weak pattern whereby the simplest and most complex models showed higher skill than models of intermediate complexity (Figure 9). Models with no soil model (zero soil layers) or no vegetation pools showed greater skill than models with the simplest soil model or smallest number of vegetation pools. As the number of soil layers or pools increased, so did model skill, indicating that a more comprehensive treatment of biological and physical processes can improve model skill. For vegetation pools, there was a limit where increased com-  13 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  characteristics considered here did not explain the underlying cause of biome and seasonal differences in model skill.  4. Discussion 4.1. Effect of Parameter Sets on Model Performance [27] Model parameter sets are a large source of variability in terms of model performance [Jung et al., 2007b]. They influence output and accuracy [Grant et al., 2005] and are more important for accurately simulating CO2 exchange than capturing effects of interannual climatic variability [Amthor et al., 2001]. For at least some of the models studied here this can be related to the use of biome‐specific parameters relative to within‐biome variability [Purves and Pacala, 2008]. A corollary occurs in the context of EC observations as tower footprints can exhibit heterogeneity, particularly in soils, that is not reproduced in site‐specific parameters [Amthor et al., 2001]. [28] The importance of model parameter sets was visible in this intercomparison in two ways. First, biome had the highest variable importance score. Insomuch as models rely on biome‐specific parameter values, this finding indicates that model parameter sets are a key factor in the distribution of model skill. This extends to plant functional types due to the high degree of overlap between both. Furthermore, the variability (Figure 4) in model skill across parameter sets, i.e., across models, underscores that biomes may be too heterogeneous in time [Stoy et al., 2005, 2009] and space to be well‐represented by constant parameters relative to, e.g., within‐biome climate variability [Hargrove et al., 2003]. Second, the general high degree of site‐specific variation in model skill (Figure 4) suggested that model parameter sets may need to be refined to capture local, site‐specific realities.  Figure 4. Boxplots of Taylor skill by model and site. Taylor skill (S; equation (3)) is a single value summary of a Taylor diagram where unity indicates perfect agreement with observations. Panels show interquartile range (blue box), median (solid red line), range (whiskers), and outliers (red cross; values more than 1.5 × interquartile range from the median). (top) Only models (n = 21) used on at least two sites shown. (bottom) Only sites (n = 32) simulated with at least 10 unique models, excluding the mean model ensemble (MEAN) and the assimilated LoTEC, shown. Models and sites sorted by median Taylor skill.  plexity beyond eight pools did not improve model‐data agreement. [26] Despite these effects, model attributes were of secondary importance. The change in S relative to biome varied from 0.28 to 0.55; a much larger range than seen for model attributes. Similarly, the high variable importance scores for biome and climatic season, as well as the lower score for drought level, corroborated the relationships between these factors and model skill as seen with NMAE and c2. While the regression tree algorithm achieved an accuracy of 68.5% for predicting Taylor skill class, the site history and model  4.2. Effect of Model Structure on Model Performance [29] In general, models with the highest model‐data agreement all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step. Models that exhibited all of these structural characteristics (SiBCASA, SiB3, and ISOLSM) showed high degrees of model‐data agreement across all three skill metrics. Similarly, Ecosys, which used a prognostic canopy but otherwise had similar structural characteristics as SiBCASA, also performed well. Relative to model complexity, consistency with observations was highest in those models with either the simplest structure (e.g., one soil carbon pool in ISOLSM) or the most complex (e.g., SiBCASA with 13 carbon pools). Models with a prognostic canopy seem to perform better with more carbon pools and soil layers (e.g., Ecosys). No model with a prognostic canopy and a low number of carbon pools and soil layers placed in the top tercile of model skill for any skill metric, except SiBcrop and AgroIBIS for Taylor skill in croplands. Using multimodel ensembles (MEAN) or data assimilation to optimize model parameter sets (LoTEC) can compensate for differences in model structure to improve model skill. [30] The relationships between model structure and model skill were consistent across all biomes. As a whole, the models performed better at forested sites than nonforested sites, but the same models showed the highest consistency  14 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  Figure 5. Taylor diagram of normalized mean model performance for forested sites. Each circle (n = 26 sites) is the site‐specific mean model ensemble (MEAN). Benchmark (red square) corresponds to observed normalized monthly NEE; units of s and RMSE are multiples of observed s. Color coding of site letter and circles indicates biome: evergreen needleleaf forest‐ temperate zone (red), deciduous broadleaf forest (brown), mixed (deciduous/evergreen) forest (blue), evergreen needleleaf forest‐boreal zone (black). Outlying sites (evergreen needleleaf forest‐boreal zone) not shown: CA‐SJ1 (r = 0.81, s = 3.9, RMSE = 3.1) and CA‐SJ2 (r = −0.67, s = 4.3, RMSE = 5.1). with observations in each biome (Ecosys and SiB3). This is true even for agriculture sites, where Ecosys and SiB3 scored as high as crop only models. This suggests that any model with requisite structural attributes can successfully simulate carbon flux in all types of ecosystems. 4.3. Links Between Model Performance and Environmental Factors [31] Model skill was only weakly linked to drought, showing high variability across dryness level by biome and model. Only during the warm season (all climatic seasons excluding winter) did aggregate model skill decline under drought conditions. While this points to process uncertainty [Sitch et al., 2008], ecosystem response to longer‐term drought can exhibit lags and positive feedbacks [Arnone et al., 2008; Granier et al., 2007; Thomas et al., 2009; Williams et al., 2009] that were not explicitly included in the drought metric used here but did influence simulation behavior through model structure, e.g., soil moisture model and soil resolution. [32] In spring and fall, especially for biomes with a significant deciduous component, models showed a decline in model skill (Table 4) relative to periods of peak biological activity (climatic summer) [see also Morales et al., 2005]. While this was more pronounced for NMAE (Table 4) than c2 (Table 5), phenological cues are known to influence the annual carbon balance at multiple scales [Barr et al., 2007;  Delpierre et al., 2009; Keeling et al., 1996]. The loss of model skill seen in this study during spring and fall was likely linked to poor treatment of leaf initiation and senescence as well as season‐specific effects of soil moisture and soil temperature on canopy photosynthesis [Hanson et al., 2004]. In this study seasonality was second only to biome in driving model skill (Figure 8). This and the lack of link between model skill and site history strongly implicate phenology as a needed refinement of terrestrial biosphere simulators. [33] The evergreen needleleaf forest biome diverged in performance based on whether the sites were located in the temperate or boreal zones. A similar divergence was reported using Biome‐BGC, LPJ, and ORCHIDEE to simulate gross CO2 uptake across a temperature gradient in Europe [Jung et al., 2007a]; average relative RMSE was higher for evergreen needleleaf forests in the boreal zone. This was linked to an overestimation of LAI at the boreal sites and relationships between resource availability and leaf area [Friedlingstein et al., 2006; Jung et al., 2007a; Sitch et al., 2008]. Additionally, recent observations in the circumboreal region, where all boreal evergreen needleleaf forested sites are located, suggest that transient effects of climate change, e.g., increased severity and intensity of natural disturbances (fire, pest outbreaks) and divergence from climate normals in temperature, have already occurred [Soja et al., 2007] and influence resource availability. We speculate the loss of  15 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  Figure 6. Taylor diagram of normalized mean model performance for nonforested sites. Each circle (n = 16 sites) is the site‐specific mean model ensemble (MEAN). Benchmark (red square) corresponds to observed normalized monthly NEE; units of s and RMSE are multiples of observed s. Color coding of site letter and circles indicates biome: croplands (red), grasslands (brown), wetlands (blue), all other biomes (black). model skill in boreal relative to temperate evergreen needleleaf forests was linked to insufficient characterization of cold temperature sensitivity of metabolic processes and water flow in plants as well as freeze‐thaw dynamics [Schaefer et al., 2007, 2009] and that this was exacerbated by the effects of transient climate change. 4.4. Effects of Site History and Protocol on Model Evaluation [34] Disturbance regime and how a model treats disturbance are known to impact model performance [Ito, 2008]. In this study, stand age impacted model skill whereas site history was of marginal importance (Figure 8). However, CA‐SJ2, the worst predicted site (Figure 5), was harvested in 2000 and scarified in 2002, and US‐SO2, a second poorly predicted shrubland site (Figure 6), suffered catastrophic wildfire during the analysis period. The poor model performance for recently disturbed sites followed from assumed steady state as used in some simulations and the absence of modeling logic to accommodate disturbance. However, the distribution of site history metrics was skewed; only few sites were burned, harvested, or in the early stages of recovery from disturbance when NEE is more nonlinear relative to established stands. Furthermore, age class was biased toward older stands; of the 17 forested sites only one was classified as a young stand. Other site characteristics were also unbalanced; all nonforested biomes occurred on five or less sites; with only one site each for  shrublands and woody savannahs. While regression trees are inherently robust, additional observed and simulated fluxes in rapidly growing young forested stands, recently burned or harvested sites, and undersampled biomes are desirable to better characterize model performance. [35] Aspects of the NACP site synthesis protocol and analysis framework also influenced the interpretation of our results. First, this analysis focused solely on non‐gap‐filled data to allow the model‐data intercomparison to inform model development. However, the low turbulence (friction velocity) filtering removed more data at night than during the day. Average data coverage across all sites was 82% for daytime and 39% at night, respectively (Table 2), so our analysis is skewed toward daytime conditions. Second, each model that used remotely sensed inputs (such as LAI) repeated an average seasonal cycle calculated from site‐ specific time series based on all pixels within 1 km of the tower site. This likely deflated relevant variable importance scores (Figure 8) and precluded a full comparison of prescribed versus prognostic LAI. While only few models used such inputs (Table 1), removing the inherent bias of an invariant seasonal cycle over multiple years may improve model performance. Incorporating disturbance information to recreate historical land use and disturbance, especially for recent site entries, could also improve model performance. Last, despite the model simulation protocol’s emphasis on steady state, this condition was not achieved for most sites (Table 2), even when discounting observational uncertainty,  16 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  Figure 7. Taylor diagram of normalized across‐site average model performance. Model s and RMSE were normalized by observed s. Each circle (n = 22 models) corresponds to the mean across all sites. Benchmark (red square) corresponds to observed normalized monthly NEE; units of s and RMSE are multiples of observed s. Color coding of model letter and circles indicates generality of model performance: specialist models used only in croplands (n ≤ 5 sites; black), generalist models used across a range of biomes and sites (n ≥ 30 sites, blue), all other models (red). The correlation for DNDC (r = −0.13) is displayed as zero for readability.  Figure 8. Variable importance scores for model‐specific (blue) and site‐specific (green) predictors. Scores were generated from a regression tree with the Taylor skill classes based on terciles (n = 3132) as the response. Only the 12 of 28 predictants with score > 25 shown; see Table 3 for complete listing of evaluated model structural and site attributes. 17 of 22  G00H05  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  G00H05  Figure 9. Bar graphs of mean Taylor skill by model attribute. Whiskers represent one standard error of the mean. Only model‐specific attributes with variable important scores >25 shown. Note y axis on right panels starts at 0.4. or most models. None of the four crop only models achieved steady state. This followed from site history of croplands in general where active management precluded any system steady state, e.g., DNDC allowed for prescribed initial soil carbon pools. For those models (5 of the 21 evaluated) that achieved steady state in initialization this resulted in an inherent bias between simulated and observed NEE for all sites regardless of site history. However, as biome and seasonality largely governed the distribution of model skill, this bias was too small to manifest itself in this study. Relaxing the steady state assumption [Carvalhais et al., 2008] or initializing using observed wood biomass and the quasi‐steady state assumption [Schaefer et al., 2008] could improve these models’ performance.  5. Conclusion [36] We used observed CO2 exchange from 44 eddy covariance towers in North America with simulations from 21 terrestrial biosphere models and a mean model ensemble to examine model skill across gradients in dryness, seasonality, biome, site history, and model structure. Models’ ability to match observed monthly net ecosystem exchange was generally poor; the mean squared distance between observations and simulations was ∼10 times observational error. Overall, forested sites were better predicted than nonforested sites. Weaknesses in model performance concerned model parameter sets and phenology, especially for biomes with a clear seasonal cycle in leaf area index. Drought was weakly linked to model skill with abnormally dry conditions during the growing season showing marginally worse model‐data agreement compared to nondry conditions. Sites with disturbances during the analysis  period and undersampled biomes (grasslands, shrublands, wetlands, woody savannah, and tundra) also showed a large divergence between observations and simulations. The highest degree of model‐data agreement occurred in temperate evergreen forests in all climatic seasons and during summer across all biomes. Overall skill was higher for models that estimated net ecosystem exchange as the difference between gross primary productivity and ecosystem respiration, used prescribed canopy phenology, and did not use a daily time step. The model ensemble (mean simulated value across all models) and an optimized model (parameters tuned using data assimilation) also performed well. Models with preferred structural attributes included generalist models (models used at multiple sites and biomes, e.g., SiB3, Ecosys) that exhibited high degrees of model‐data agreement across all biomes, indicating that a single model can successfully simulate carbon flux in all types of ecosystems. That is, different model architectures were not needed for different types of ecosystems and model choice is recast as a function of ease of parameterization and initialization. [37] Acknowledgments. C.R.S., C.A.W., and K.S. were supported by the U.S. National Science Foundation grant ATM‐0910766. We would like to thank the North American Carbon Program Site‐Level Interim Synthesis team, the Modeling and Synthesis Thematic Data Center, and the Oak Ridge National Laboratory Distributed Active Archive Center for collecting, organizing, and distributing the model output and flux observations required for this analysis. This study was in part supported by the U.S. National Aeronautics and Space Administration (NASA) grant NNX06AE65G, the U.S. National Oceanic and Atmospheric Administration (NOAA) grant NA07OAR4310115, and the U.S. National Science Foundation (NSF) grant OPP‐0352957 to the University of Colorado at Boulder.  18 of 22  G00H05  SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE  References Amthor, J. S., et al. (2001), Boreal forest CO2 exchange and evapotranspiration predicted by nine ecosystem process models: Intermodel comparisons and relationships to field measurements, J. Geophys. Res., 106(D24), 33,623–33,648, doi:10.1029/2000JD900850. Arain, M. A., F. Yaun, and T. A. Black (2006), Soil‐plant nitrogen cycling modulated carbon exchanges in a western temperate conifer forest in Canada, Agric. For. Meteorol., 140, 171–192, doi:10.1016/j.agrformet. 2006.03.021. Arnone, J. A., et al. 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(sverma1@unl.edu)  22 of 22  

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