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 2010-12-31

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A model‐data intercomparison of CO2exchange across NorthAmerica: Results from the North American Carbon Program sitesynthesisChristopher R. Schwalm,1Christopher A. Williams,1Kevin Schaefer,2Ryan Anderson,3M. Altaf Arain,4Ian Baker,5Alan Barr,6T. Andrew Black,7Guangsheng Chen,8Jing Ming Chen,9Philippe Ciais,10Kenneth J. Davis,11Ankur Desai,12Michael Dietze,13Danilo Dragoni,14Marc L. Fischer,15Lawrence B. Flanagan,16Robert Grant,17Lianhong Gu,18David Hollinger,19R. César Izaurralde,20Chris Kucharik,21Peter Lafleur,22Beverly E. Law,23Longhui Li,10Zhengpeng Li,24Shuguang Liu,25Erandathie Lokupitiya,5Yiqi Luo,26Siyan Ma,27Hank Margolis,28Roser Matamala,29Harry McCaughey,30Russell K. Monson,31Walter C. Oechel,32Changhui Peng,33Benjamin Poulter,34David T. Price,35Dan M. Riciutto,18William Riley,36Alok Kumar Sahoo,37Michael Sprintsin,9Jianfeng Sun,33Hanqin Tian,8Christina Tonitto,38Hans Verbeeck,39and Shashi B. Verma40Received 23 November 2009; revised 23 July 2010; accepted 29 July 2010; published 9 December 2010.1Graduate School of Geography, Clark University, Worcester,Massachusetts, USA.2National Snow and Ice Data Center, University of Colorado atBoulder, Boulder, Colorado, USA.3Numerical Terradynamic Simulation Group, University of Montana,Missoula, Montana, USA.4School of Geography and Earth Sciences, McMaster University,Hamilton, Ontario, Canada.5Atmospheric Science Department, Colorado State University, FortCollins, Colorado, USA.6Climate Research Division, Atmospheric Science and TechnologyDirectorate, Saskatoon, Saskatchewan, Canada.7Faculty of Land and Food Systems, University of British Columbia,Vancouver, B. C., Canada.8School of Forestry and Wildlife Sciences, Auburn University, Auburn,Alabama, USA.9Department of Geography and Program in Planning, University ofToronto, Toronto, Ontario, Canada.10Laboratoire des Sciences du Climat et de l’Environnement, CE Ormedes Merisiers, Gif sur Yvette, France.11Department of Meteorology, Pennsylvania State University,University Park, Pennsylvania, USA.12Center for Climatic Research, University of Wisconsin‐Madison,Madison, Wisconsin, USA.13Department of Plant Biology, University of Illinois‐UrbanaChampaign, Urbana, Illinois, USA.14Department of Geography, Indiana University, Bloomington, Indiana,USA.15Atmospheric Science Department, Lawrence Berkeley NationalLaboratory, Berkeley, California, USA.16Department of Biological Sciences, University of Lethbridge,Lethbridge, Alberta, Canada.17Department of Renewable Resources, University of Alberta,Edmonton, Alberta, Canada.18Environmental Sciences Division, Oak Ridge National Laboratory,Oak Ridge, Tennessee, USA.19Northern Research Station, USDA Forest Service, Durham, NewHampshire, USA.20Joint Global Change Research Institute, Pacific Northwest NationalLaboratory and University of Maryland, College Park, Maryland, USA.Copyright 2010 by the American Geophysical Union.0148‐0227/10/2009JG00122921Department of Agronomy and Nelson Institute Center forSustainability and the Global Environment, University of Wisconsin‐Madison, Madison, Wisconsin, USA.22Department of Geography, Trent University, Peterborough, Ontario,Canada.23College of Forestry, Oregon State University, Corvallis, Oregon,USA.24ASRC Research and Technology Solutions, Sioux Falls, SouthDakota, USA.25Earth Resources Observation and Science, Sioux Falls, South Dakota,USA.26Department of Botany and Microbiology, University of Oklahoma,Norman, Oklahoma, USA.27Department of Environmental Science, Policy and Management andBerkeley Atmospheric Science Center, University of California, Berkeley,Berkeley, California, USA.28Centre d’études de la forêt, Faculté de foresterie, de géographie et degéomatique, Université Laval, Québec, Quebec, Canada.29Argonne National Laboratory, Biosciences Division, Argonne,Illinois, USA.30Department of Geography, Queen’s University, Kingston, Ontario,Canada.31Department of Ecology and Evolutionary Biology, University ofColorado at Boulder, Boulder, Colorado, USA.32Department of Biology, San Diego State University, San Diego,California, USA.33Department of Biology Sciences, University of Quebec at Montreal,Montreal, Quebec, Canada.34Swiss Federal Research Institute WSL, Birmensdorf, Switzerland.35Northern Forestry Centre, Canadian Forest Service, Edmonton,Alberta, Canada.36Climate and Carbon Sciences, Earth Sciences Division, LawrenceBerkeley National Laboratory, Berkeley, California, USA.37Department of Civil and Environmental Engineering, PrincetonUniversity, Princeton, New Jersey, USA.38Department of Ecology and Evolutionary Biology, CornellUniversity, Ithaca, New York, USA.39Laboratory of Plant Ecology, Ghent University, Ghent, Belgium.40School of Natural Resources, University of Nebraska‐Lincoln,Lincoln, Nebraska, USA.JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, G00H05, doi:10.1029/2009JG001229, 2010G00H05 1of22[1] Our current understanding of terrestrial carbon processes is represented in variousmodels used to integrate and scale measurements of CO2exchange from remote sensingand other spatiotemporal data. Yet assessments are rarely conducted to determine how wellmodels simulate carbon processes across vegetation types and environmental conditions.Using standardized data from the North American Carbon Program we compare observedand simulated monthly CO2exchange from 44 eddy covariance flux towers in NorthAmerica 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 toevaluate model skill as a function of drought and seasonality. We evaluate models’ abilityto simulate the seasonal cycle of CO2exchange using multiple model skill metricsand analyze links between model characteristics, site history, and model skill. Overallmodel performance was poor; the difference between observations and simulations was∼10 times observational uncertainty, with forested ecosystems better predicted thannonforested. Model‐data agreement was highest in summer and in temperate evergreenforests. In contrast, model performance declined in spring and fall, especially inecosystems with large deciduous components, and in dry periods during the growingseason. Models used across multiple biomes and sites, the mean model ensemble, and amodel 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 usea daily time step.Citation: Schwalm, C. R., et al. (2010), A model‐data intercomparison of CO2exchange across North America: Results fromthe 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 con-sistency and agreement with observations [Friedlingsteinet al., 2006], both overall and under more frequent extremeclimatic events related to global environmental change suchas drought [Trenberth et al., 2007]. Past validation studiesof terrestrial biosphere models have focused only on fewmodels and sites, typically in close proximity and primarilyin 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; Zhouet al., 2008]. Furthermore, assessing model‐data agreementrelative to drought requires, in addition to high‐qualityobservedCO2exchangedata,areliabledroughtmetricaswellas 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 inplant function due to water limitation and heat stress. Forterrestrial CO2exchange, drought typically reduces photo-synthesismorethanrespiration[Baldocchi,2008;Ciaisetal.,2005; Schwalm et al., 2010], resulting in decreased netcarbon uptake from the atmosphere. In the recent pastdrought conditions have become more prevalent globally[Daietal., 2004] and in North America [Cook et al.,2004b]. Both incidence and severity of drought [Seageret al.,2007]aswellasheatwaves[Meehl and Tebaldi,2004] are expected to further increase in conjunction withglobal warming [Houghton et al., 2001; Huntington, 2006;Sheffield and Wood, 2008; Trenberth et al., 2007].[4] In this study, we evaluate model performance usingterrestrial CO2flux data and simulated fluxes collected from1991 to 2007. This timeframe included two widespreaddroughts in North America: (1) the turn‐of‐the‐centurydrought from 1998 to 2004 that was centered in the westerninterior of North America [Seager, 2007] and (2) a smaller‐scale drought event in the southern continental Untied Statesfrom winter of 2005/2006 through October 2007 [Seageret al., 2009]. During these events Palmer Drought SeverityIndex values [Cook et al., 2007; Dai et al., 2004] and pre-cipitation anomalies [Seager, 2007; Seager et al., 2009]were highly negative over broad geographic areas. Ongoingeddy covariance measurements [Baldocchi et al., 2001],active throughout the aforementioned drought periods,provided flux data across gradients of time, space, season-ality, and drought. We use these data to examine model skillrelative to site‐specific drought severity, climatic season,and time. We also link model behavior to model architectureand site‐specific attributes. Specifically, we address thefollowing questions: Are current state‐of‐the‐art terrestrialbiosphere models capable of simulating CO2exchangesubject to gradients in dryness and seasonality? Are thesemodels able to reproduce the seasonal variation of observedCO2exchange across sites? Are certain characteristics ofmodel structure coincident with better model‐data agree-ment? Which biomes are simulated poorly/well?2. Methods2.1. Observed and Simulated CO2Exchange[5] Modeled and observed net ecosystem exchange (NEE,net carbon balance including soils where positive valuesindicate outgassing of CO2to the atmosphere) data wereanalyzed from 21 terrestrial biosphere models (Table 1) and44 eddy covariance (EC) sites spanning ∼220 site‐years and10 biomes in North America (Table 2). All terrestrial bio-sphere models analyzed simulated carbon cycling withprocess based formulations of varying detail for componentcarbon fluxes. Simulated NEE was based on model‐specificSCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H052of22Table1.SummaryofModelCharacteristicsModelAttributeModelAgroIBISBEPSBiome‐BGCCan‐IBISCN‐CLASSDLEMDNDCEcosysED2EDCMTemporalResolutionHalf‐hourlyDailyDailyHalf‐hourlyHalf‐hourlyDailyDailyHourlyHalf‐hourlyMonthlyVegetationPools4473463998SoilPools7947339945SoilLayers11317321015910CanopyPhenologyPrognosticSemiprognosticPrognosticPrognosticPrognosticSemiprognosticPrognosticPrognosticPrognosticPrognosticNitrogenCycleYesYesYesYesYesYesYesYesYesYesGrossPrimaryProductivity (GPP)EnzymeKineticModelEnzymeKineticModelStomatalConductanceModelEnzymeKineticModelEnzymeKineticModelStomatalConductanceModelLightUseEfficiencyModelEnzymeKineticModelEnzymeKineticModelLightUseEfficiencyModelHeterotrophicRespiration (HR)FirstorGreaterOrderModelAirTemperatureSoilTemperaturePrecipitationSoilMoistureEvaporationSoilCarbonSoilNitrogenSoilTemperatureSoilMoistureSoilCarbonFirstorGreaterOrderModelFirstorGreaterOrderModelDecayMethaneAirTemperatureSoilTemperatureLitterandSoilCarbonSoilNitrogenSoilMoistureDecayMethaneSoilTemperaturePrecipitationSoilMoistureSoilCarbonVegetationCarbonSoilNitrogenDecayMethaneCO2DiffusionDissolvedCarbonLossSoilTemperatureSoilMoistureSurfaceIncidentShortwaveRadiationSurfaceIncidentLongwaveRadiationSoilCarbonVegetationCarbonSoilNitrogenLeafNitrogenSoilTemperatureSoilMoistureSoilCarbonSoilNitrogenSoilTemperatureSoilMoistureSoilCarbonDissolvedCarbonLossVegetationCarbonSoilNitrogenAutotrophicRespiration (AR)AirTemperatureSoilTemperaturePrecipitationSoilMoistureSurfaceIncidentShortwaveRadiationSurfaceIncidentLongwaveRadiationVegetationCarbonAirTemperatureGPPAirTemperatureVegetationCarbonLeafNitrogenAirTemperatureSoilTemperaturePrecipitationSoilMoistureSurfaceIncidentShortwaveRadiationSurfaceIncidentLongwaveRadiationVegetationCarbonFractionofInstantaneousGPPAirTemperatureVegetationCarbonLeafNitrogenGPPSoilTemperatureAirTemperatureSoilTemperatureVegetationCarbonLeafNitrogenAirTemperatureSoilTemperatureVegetationCarbonLeafNitrogenGPPProportionaltoGrowthEcosystemRespiration (R)AR+HRAR+HRAirTemperatureSoilTemperatureSoilMoistureSoilCarbonVegetationCarbonLAIAR+HRAR+HRAR+HRAR+HRAR+HRAR+HRAR+HRNetPrimaryProduction (NPP)GPP‐ARGPP‐ARSurfaceIncidentShortwaveRadiationVaporPressureDeficitCO2VegetationCarbonLeafNitrogen LAIGPP‐ARFractionofInstantaneousGPPGPP‐ARAirTemperaturePrecipitationSoilMoisturePotentialEvaporationVegetationCarbonSoilNitrogenLeafNitrogenfPARGPP‐ARGPP‐ARAirTemperaturePrecipitation SoilCarbonSoilNitrogenSoilMoistureVegetationCarbonLeafNitrogenLAINetEcosystemExchange (NEE)NPP‐HRNPP‐HRSoilTemperatureSoilMoistureSurfaceIncidentShortwaveRadiationVaporPressureDeficitNPP‐HRGPP‐RNPP‐HRNPP‐HRGPP‐RNPP‐HRNPP‐HRSCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H053of22Table1.(continued)ModelAttributeModelAgroIBISBEPSBiome‐BGCCan‐IBISCN‐CLASSDLEMDNDCEcosysED2EDCMBiomesSimulatedCroplands6810910Croplands1066SitesSimulated510362731335392510MonthsSimulated192945200119782082224619224501684658SourceKucharikandTwine[2007]Liuetal.[1999]Thorntonetal.[2005]Williamsonetal.[2008]Arainetal.[2006]Tianetal.[2010]Lietal.[2010]Grantetal.[2005]Medvigyetal.[2009]Liuetal.[2003]ModelAttributeModelEPICISOLSMLoTECLPJORCHIDEESiB3SiBCASASiBcropSSiB2TECOTriplex‐FLUXTemporalResolutionDailyHalf‐hourlyHalf‐hourlyDailyHalf‐hourlyHalf‐hourly10minHalf‐hourlyHalf‐hourlyHourlyHalf‐hourlyVegetationPools30438084030SoilPools01528051050SoilLayers15014201015103100CanopyPhenologyPrognosticPrescribedPrognosticPrognosticPrognosticPrescribedPrescribedPrognosticPrescribedPrognosticPrescribedNitrogenCycleYesNoNoNoNoYesNoYesNoNoNoGrossPrimaryProductivity (GPP)NilStomatalConductanceModelEnzymeKineticModelStomatalConductanceModelEnzymeKineticModelEnzymeKineticModelEnzymeKineticModelEnzymeKineticModelStomatalConductanceModelStomatalConductanceModelStomatalConductanceModelHeterotrophicRespiration (HR)CO2DiffusionDissolvedCarbonLossAirTemperatureSoilTemperaturePrecipitationSoilMoistureFirstorGreaterOrderModelSoilTemperatureSoilMoistureSoilCarbonSoilTemperatureSoilMoistureSoilCarbonSoilTemperatureSoilMoistureSoilCarbonZero‐orderModelSoilTemperatureSoilMoistureSoilCarbonSoilTemperatureSoilCarbonZero‐orderModelFirstorGreaterOrderModelFirstorGreaterOrderModelAutotrophicRespiration (AR)NilFractionofInstantaneousGPPAirTemperatureSoilTemperatureSoilMoistureVegetationCarbonGPPAirTemperatureSoilMoistureVegetationCarbonAirTemperatureVegetationCarbonFractionofInstantaneousGPPAirTemperatureSoilMoistureVegetationCarbonAirTemperatureVegetationCarbonGPPAirTemperatureSoilMoistureSurfaceIncidentShortwaveRadiationRelativeHumidityLAIfPARCO2AirTemperatureVegetationCarbonFractionofAnnualGPPEcosystemRespiration (R)AR+HRAR+HRAR+HRAR+HRAR+HRForcedAnnualBalanceAR+HRForcedAnnualBalanceForcedAnnualBalanceAR+HRAR+HRNetPrimaryProduction (NPP)LightUseEfficiencyModelNilGPP‐ARGPP‐ARGPP‐ARGPP‐ARAirTemperatureSoilMoistureCO2RelativeHumidityGPP‐ARGPP‐ARGPP‐ARFractionofInstantaneousGPPNetEcosystemExchange (NEE)NPP‐HRGPP‐RNPP‐HRNPP‐HRGPP‐RGPP‐RGPP‐RGPP‐RGPP‐RGPP‐RGPP‐RBiomesSimulatedCroplands569101010Croplands10103SitesSimulatedU.S.‐Ne391029353135544357MonthsSimulated48909825212623322258240219228002414291SourceCausaranoetal.[2007]Rileyetal.[2002]Hansonetal.[2004]Sitchetal.[2003]Krinneretal.[2005]Bakeretal.[2008]Schaeferetal.[2009]Lokupitiyaetal.[2009]Zhanetal.[2003]WengandLuo[2008]Zhouetal.[2008]SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H054of22Table2.SummaryofSiteCharacteristicsaSiteIDNamePriorityCountryLatitudeLongitudeElevation (ma.s.l.)IGBP ClassKöppen‐GeigerClimateClassificationCA‐Ca1BritishColumbia‐CampbellRiver‐MatureForestSite1Canada49.87−125.33300ENFMaritimetemperateCA‐Ca2BritishColumbia‐CampbellRiver‐ClearcutSite2Canada49.87−125.29180ENFMaritimetemperateCA‐Ca3BritishColumbia‐CampbellRiver‐YoungPlantationSite2Canada49.53−124.90165ENFMaritimetemperateCA‐GroOntario‐GroundhogRiver‐MatureBorealMixedWood1Canada48.22−82.16300MFWarmsummercontinentalCA‐LetLethbridge1Canada49.71−112.94960GRAWarmsummercontinentalCA‐MerEasternPeatland‐MerBleue1Canada45.41−75.5270WETWarmsummercontinentalCA‐OasSask.‐SSAOldAspen1Canada53.63−106.20530DBFContinentalsubarcticCA‐ObsSask.‐SSAOldBlackSpruce1Canada53.99−105.12629ENFContinentalsubarcticCA‐OjpSask.‐SSAOldJackPine1Canada53.92−104.69579ENFContinentalsubarcticCA‐QfoQuebecMatureBorealForestSite1Canada49.69−74.34382ENFContinentalsubarcticCA‐SJ1Sask.‐1994HarvestedJackPine2Canada53.91−104.66580ENFContinentalsubarcticCA‐SJ2Sask.‐2002HarvestedJackPine2Canada53.94−104.65518ENFContinentalsubarcticCA‐SJ3Sask.‐SSA1975HarvestedYoungJackPine2Canada53.88−104.64511ENFContinentalsubarcticCA‐TP3Ontario‐TurkeyPointMiddle‐agedWhitePine2Canada42.71−80.35219ENFWarmsummercontinentalCA‐TP4Ontario‐TurkeyPointMatureWhitePine1Canada42.71−80.36219ENFWarmsummercontinentalCA‐WP1WesternPeatland‐LaBiche‐BlackSpruce/LarchFen1Canada54.95−112.47540MFContinentalsubarcticU.S.‐ARMOK‐ARMSouthernGreatPlainsSite‐Lamont1USA36.61−97.49310CROHumidsubtropicalU.S.‐AtqAK‐Atqasuk1USA70.47−157.4116WETTundraU.S.‐BrwAK‐Barrow1USA71.32−156.631WETTundraU.S.‐Dk2NC‐DukeForest‐Hardwoods1USA35.97−79.10160DBFHumidsubtropicalU.S.‐Dk3NC‐DukeForest‐LoblollyPine1USA35.98−79.09163ENFHumidsubtropicalU.S.‐Ha1MA‐HarvardForestEMSTower(HFR1)1USA42.54−72.17303DBFWarmsummercontinentalU.S.‐Ho1ME‐HowlandForest(MainTower)1USA45.20−68.7460ENFWarmsummercontinentalU.S.‐IB1IL‐FermiNationalAcceleratorLaboratory‐Batavia(AgriculturalSite)1USA41.86−88.22227CROHotsummercontinentalU.S.‐IB2IL‐FermiNationalAcceleratorLaboratory‐Batavia(PrairieSite)1USA41.84−88.24227GRAHotsummercontinentalU.S.‐LosWI‐LostCreek1USA46.08−89.98480CSHWarmsummercontinentalU.S.‐MMSIN‐MorganMonroeStateForest1USA39.32−86.41275DBFHumidsubtropicalU.S.‐MOzMO‐MissouriOzarkSite1USA38.74−92.20219DBFHumidsubtropicalU.S.‐Me2OR‐Metolius‐IntermediateAgedPonderosaPine1USA44.45−121.561253ENFDry‐summersubtropicalU.S.‐Me32USA44.32−121.611005ENFSCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H055of22Table2.(continued)SiteIDNamePriorityCountryLatitudeLongitudeElevation (ma.s.l.)IGBP ClassKöppen‐GeigerClimateClassificationOR‐Metolius‐SecondYoungAgedPineDry‐summersubtropicalU.S.‐Me4OR‐Metolius‐OldAgedPonderosaPine2USA44.50−121.62915ENFDry‐summersubtropicalU.S.‐Me5OR‐Metolius‐FirstYoungAgedPine2USA44.44−121.571183ENFDry‐summersubtropicalU.S.‐NR1CO‐NiwotRidgeForest(LTERNWT1)1USA40.03−105.553050ENFContinentalsubarcticU.S.‐Ne1NE‐Mead‐IrrigatedContinuousMaizeSite1USA41.17−96.48361CROHotsummercontinentalU.S.‐Ne2NE‐Mead‐IrrigatedMaize‐SoybeanRotationSite1USA41.16−96.47361CROHotsummercontinentalU.S.‐Ne3NE‐Mead‐RainfedMaize‐SoybeanRotationSite1USA41.18−96.44361CROHotsummercontinentalU.S.‐PFaWI‐ParkFalls/WLEF1USA45.95−90.27485MFWarmsummercontinentalU.S.‐SO2CA‐SkyOaks‐OldStand1USA33.37−116.621392CSHDry‐summersubtropicalU.S.‐ShdOK‐Shidler‐Oklahoma1USA36.93−96.68350GRAHumidsubtropicalU.S.‐SyvMI‐SylvaniaWildernessArea1USA46.24−89.35540MFWarmsummercontinentalU.S.‐TonCA‐TonziRanch1USA38.43−120.97177WSADry‐summersubtropicalU.S.‐UMBMI‐UniversityofMichiganBiologicalStation1USA45.56−84.71234DBFWarmsummercontinentalU.S.‐VarCA‐VairaRanch‐Ione1USA38.41−120.95129GRADry‐summersubtropicalU.S.‐WCrWI‐WillowCreek1USA45.81−90.08520DBFWarmsummercontinentalSiteIDAnnualNEE(gCm2−)AnnualNEEError(gCm2−)DaytimeDataCoverage (%)NighttimeDataCoverage(%)LAIAnnualPrecipitation(mm)MeanAnnualAirTemperature(°C)MeasurementPeriodBiomeSourceCA‐Ca1−244.361.199266.112568.71998–2006ENFTSchwalmetal.[2007]CA‐Ca2571.731.596234.412228.82001–2006ENFTSchwalmetal.[2007]CA‐Ca391.237.991272.215549.52002–2006ENFTSchwalmetal.[2007]CA‐Gro−36.533.593344.14273.32004–2006MFMcCaugheyetal.[2006]CA‐Let−132.914.396460.73356.51997–2006GRAFlanaganetal.[2002]CA‐Mer−68.521.679561.39356.21999–2006WETLafleuretal.[2003]CA‐Oas−158.028.594563.84602.31997–2006DBFBarretal.[2004]CA‐Obs−56.316.189455.64701.62000–2006ENFBGriffisetal.[2003]CA‐Ojp−29.916.691503.44611.52000–2006ENFBGriffisetal.[2003]CA‐Qfo−13.721.0934048192.72004–2006ENFBBergeronetal.[2007]CA‐SJ128.015.387310.83440.62002–2005ENFBZhaetal.[2009]CA‐SJ2117.06.189471.35370.12003–2006ENFBZhaetal.[2009]CA‐SJ3−82.017.792344.36940.82004–2005ENFBZhaetal.[2009]CA‐TP4−133.229.595433.59598.62002–2007ENFTPeichlandArain[2007]CA‐WP1−195.816.496502.74811.72003–2007WETSyedetal.[2006]SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H056of22Table2.(continued)SiteIDAnnualNEE(gCm2−)AnnualNEEError(gCm2−)DaytimeDataCoverage (%)NighttimeDataCoverage (%)LAIAnnualPrecipitation(mm)MeanAnnualAirTemperature(°C)MeasurementPeriodBiomeSourceU.S.‐ARM−128.474.489363.162915.62000–2006CROFischeretal.[2007]CA‐TP4−133.229.595433.59598.62002–2007ENFTPeichlandArain[2007]U.S.‐Atq−12.8‐50221.1118−10.61999–2006TUNOberbaueretal.[2007]U.S.‐Brw−72.0‐49291.5108−10.91999–2002TUNHarazonoetal.[2003]U.S.‐Dk2−718.1‐4817109115.12003–2005DBFSiqueiraetal.[2006]U.S.‐Dk3−350.0139.075375.6112614.71998–2005ENFTSiqueiraetal.[2006]U.S.‐Ha1−217.465.978343.3811227.91991–2006DBFUrbanskietal.[2007]U.S.‐Ho1−223.033.470475.28186.61996–2004ENFTRichardsonetal.[2009]U.S.‐IB1−269.031.392461.2971810.12005–2007CROPostetal.[2004]U.S.‐IB2−86.042.080495.3881810.42004–2007GRAPostetal.[2004]U.S.‐Los−78.019.282544.246663.82000–2006WETSulmanetal.[2009]U.S.‐MMS−346.166.397464.9110912.41999–2006DBFSchmidetal.[2000]U.S.‐MOz−305.748.994333.9173013.32004–2007DBFGuetal.[2006]U.S.‐Me2−536.065.863463.624347.62002–2007ENFTThomasetal.[2009]U.S.‐Me3−198.032.783280.524238.52004–2005ENFTVickersetal.[2009]U.S.‐Me4−612.3‐55412.16418.31996–2000ENFTIrvineetal.[2004]U.S.‐Me5−206.010.697481.13507.61999–2002ENFTIrvineetal.[2004]U.S.‐NR1−37.227.089444.26632.51998–2007ENFTBradfordetal.[2008]U.S.‐Ne1−424.041.893426.583211.12001–2006CROVermaetal.[2005]U.S.‐Ne2−382.041.896516.582310.82001–2006CROVermaetal.[2005]U.S.‐Ne3−258.043.394556.262710.92001–2006CROVermaetal.[2005]U.S.‐PFa45.041.185304.057365.11997–2005MFDavisetal.[2003]U.S.‐SO222.425.68730369513.81998–2006SHRLuoetal.[2007]U.S.‐Shd−75.522.096495.9117914.81997–2001GRASuykeretal.[2003]U.S.‐Syv48.534.753514.17004.42001–2006MFDesaietal.[2005]U.S.‐Ton−67.852.077250.654916.42001–2007WSAMaetal.[2007]U.S.‐UMB−132.042.486394.236297.41998–2006DBFSchmidetal.[2003]U.S.‐Var7.3110.680222.456315.92001–2007GRAMaetal.[2007]U.S.‐WCr−222.654.148555.367125.31998–2006DBFCooketal.[2004a]aSources:IGBPclassification,Lovelandetal.[2001];Köppen‐Geiger,Peeletal.[2007];LAIforUSAsites,http://public.ornl.gov/ameriflux/;LAIforCanadiansites,Chenetal.[2006]andSchwalmetal.[2006].Annualprecipitationandmeanannualairtemperaturearemeasurementperiodaveragesofmeteorologicalinputsusedtodrivemodelsimulations.NEEvaluesshowyearlyintegralsandassociatederror:onestandarddeviationbasedonuncertaintyduetorandomnoiseandthefrictionvelocitythresholdaggregatedtoyearlyvaluesandsummedinquadrature[Barretal.,2009].Datacoveragesarepercentagesofhalf‐hourlyNEEmeasurementsthatsatisfiedqualitycontrolstandards(frictionvelocitythreshold)forday‐andnighttimeseparately.Priority:(1)Primarysiteswithcomplete(includesancillaryandbiologicaldatatemplates)records;(2)Secondarychronosequencesites.StandardizedPrecipitationIndexavailableonlyforPriority1sitesexcludingUS‐Atq,US‐Brw,US‐Dk2,US‐IB1,andUS‐Shd.CA‐TP3,US‐Atq,US‐Brw,US‐Dk2,andUS‐Me4sitesusedpostprocessingprotocolfromtheLaThuileandAsilomarFLUXNETSynthesisdataset(http://www.fluxdata.org/)[Moffatetal.,2007;Papaleetal.,2006]andlackNEEuncertainties.BiomeiscombinationofIGBPclassandKöppen‐Geigerclimate.US‐AtqandUS‐Brw‐ArcticwetlandsclassifiedastundrabiomeCA‐WP1,treedfen(IGBPmixedforest)groupedwithwetlandsbiomeUS‐Los,shrubwetlandsite(IGBPclosedshrublands)groupedwithwetlandsbiomeUS‐SO2,closedshrublandsgroupedwithshrublands(openorclosed)biome.IGBPclassandbiomecodes:CRO,croplands;CSH,closedshrublands;GRA,grasslands,ENF,evergreenneedleleafforest;ENFB,evergreenneedleleafforest‐borealzone;ENFT,evergreenneedleleafforest‐temperatezone;DBF,deciduousbroadleafforest;MF,mixed(deciduous/evergreen)forest;WSA,woodysavanna;SHR,shrublands;TUN,tundra;WET,wetlands.SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H057of22runs using gap‐filled observed weather at each site andlocally observed values of soil texture according to a stan-dard protocol [Ricciuto et al., 2009] (http://www.nacarbon.org/nacp/), including a target NEE of zero integrated overthe last 5 years of the simulation period. In addition, a meanmodel ensemble (hereafter: MEAN) was also analyzed.MEAN was calculated as the mean monthly value across allsimulations. Furthermore, in contrast to other models, theparameter values used in the model LoTEC were optimizedusing data assimilation [Ricciuto et al., 2008]. LoTECsimulations were however retained when calculating MEANas their effect on model skill was negligible due to the rel-atively small number of site‐months simulated.[6] Gaps in the meteorological data record occurred atEC sites due to data quality control or instrument failure.Missing values of air temperature, humidity, shortwaveradiation, and precipitation data, i.e., key model inputs, werefilled using DAYMET [Thornton et al., 1997] before 2003or the nearest available climate station in the NationalClimatic Data Center’s Global Surface Summary of the Day(GSOD) database. Daily GSOD and DAYMET data weretemporally downscaled to hourly or half‐hourly values usingthe phasing from observed mean diurnal cycles calculatedfroma15daymovingwindow.Thephasingusedasinewaveassuming peak values at 1500 local standard time (LST) andlowest values at 0300 LST. In the absence of station dataa10dayrunningmeandiurnalcyclewasused[Ricciutoetal.,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 ofthe North American Carbon Program (NACP) Site LevelInterim Synthesis (http://www.nacarbon.org/nacp/). Theobserved NEE were corrected for storage, despiked (i.e.,outlying values removed), filtered to remove conditions oflow turbulence (friction velocity filtered), and gap‐filled tocreate a continuous time series [Barr et al., 2004]. The timeseries included estimates of random uncertainty and uncer-tainty due to friction velocity filtering [Barr et al., 2004,2009]. In this analysis, NEE was aggregated to monthlyvalues using only non‐gap‐filled data, i.e., observed valuesdeemed spurious and subsequently infilled were not con-sidered. Coincident modeled NEE values were similarlyexcluded. This removed the influence of gap‐filling algo-rithms in the comparison of observed and modeled NEE.[8] Drought level was quantified using the 3 monthStandard Precipitation Index (SPI) [McKee et al., 1993].Monthly SPI values were taken from the U.S. DroughtMonitor (http://drought.unl.edu/DM/) whereby each towerwas matched to nearby meteorological station(s) indicativeof local drought conditions given proximity, topography,and human impact. This study used three drought levels: dryrequired SPI < −0.8, wet corresponded to SPI > +0.8, other-wise normal conditions existed. Climatic season was definedby four seasons of 3 months each with winter given byDecember, January, and February.2.2. Model Skill[9] Model‐data mismatch was evaluated using normalizedmean absolute error (NMAE) [Medlyn et al., 2005], thereduced c2statistic (c2)[Taylor, 1996] as well as Taylordiagrams and skill (S)[Taylor, 2001]. The first metricquantifies bias, the “average distance” between observationsand simulations in units of observed mean NEE:NMAE ¼XijklNEEobsC0 NEEsimnNEEobs; ð1Þwhere the overbar indicates averaging across all values, n issample size, the subscript obs is for observations and sim isfor modeled estimates. The summation is for any arbitrarydata group (denoted by subscripts on the summation oper-ator only) where subscript i is for site, j is for model, k is forclimatic season, l is for drought level.[10] The second metric used to evaluate model perfor-mance was the reduced c2statistic. This is the squareddifference between paired model and data points overobservational error normalized by degrees of freedom:C312¼1nXijklNEEobsC0 NEEsim2C14NEEC18C192; ð2Þwhered NEEisuncertaintyofmonthlyNEE(seesection2.3),2 normalizes the uncertainty in observed NEE to correspondto a 95% confidence interval, the summation is across anyarbitrary data group (denoted by subscripts on the summa-tion operator). c2values are linked to model‐data mismatchwhere a value of unity indicates that model and data are inagreement relative to data uncertainty.[11] A final characterization of model performance usedTaylor diagrams [Taylor, 2001]; visual displays based onpattern matching, i.e., the degree to which simulationsmatched the temporal evolution of monthly NEE. Taylorplots are polar coordinate displays of the linear correlationcoefficient (r), centered root mean squared error (RMSE;pattern error without considering bias), and the standarddeviation of NEE (s). Taylor diagrams were constructed forthe mean model ensemble (MEAN) and across‐site meanmodel performance using the full data record for each com-bination of site and model (ranging from 7 to 178 months).More generally, each polar coordinate point for any arbitrarydata group can be scored:S ¼21þ C26ðÞC27normþ1=C27normðÞ2; ð3Þwhere S is the model skill metric bound by zero and unitywhere unity indicates perfect agreement, and snormis theratio 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 aggregatedacross data groups weighting each by sample size. Forexample, c2for model I, denoted by subscript j = I, is givenbyC312j¼I¼XiklniklC312iklnj¼Ið4Þwhere the summation is over all sites, seasons, and levels ofdryness where model I was used as denoted by subscripts i,k, and l, respectively; nj=Iis the total site‐months simulatedSCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H058of22with model I; and c2j=Iis aggregated c2for model I. We didnot 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, thecalculation of NMAE quantified bias in units of meanobserved NEE, and c2values quantified how well model‐data mismatch scales with flux uncertainty.2.3. Observational Flux Uncertainty[13] We calculated the standard error of monthly NEE(dNEE)[Barr et al., 2009] by combining random uncertaintyand uncertainty associated with the friction velocitythreshold (u*Th), a value used to identify and reject spuriousnighttime NEE measurements. Random uncertainty wasestimated 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) introducegaps as in the observed data with u*Thfiltering, (3) add noise,(4) infill gaps using the gap‐filling model, and (5) repeat theprocess 1000 times for each site. The random uncertaintycomponent of dNEEwas then the standard deviation acrossall 1000 realizations aggregated to months.[14] The u*Thuncertainty component of dNEEwas alsoestimated using Monte Carlo methods. Here 1000 realiza-tions of NEE were generated using 1000 draws from adistribution of u*Th. This distribution was based on binningtherawfluxdatawithrespecttoclimaticseason,temperature,and site‐year and estimating u*Thin each bin [Papale et al.,2006]. The standard deviation across all realizations gavethe u*Thuncertainty component of dNEE. Both componentswere combined in quadrature to one standard error ofmonthly NEE (= dNEE)[Barr et al., 2009].2.4. Relating Model Skill to Model Structure and SiteHistory[15] The models evaluated here range widely in theiremphasis and structure (Table 1). Some focus on biophy-sical calculations (SiB3, BEPS), where others emphasizebiogeochemistry (DLEM), or ecosystem dynamics (ED2).However, as terrestrial biosphere models simulate carboncycling with hydrological variables, most models containboth biophysics and biogeochemistry. This motivatedcharacterizing model structure with definite attributes, e.g.,prognostic versus prescribed canopy phenology, number ofsoil pools, and type of NEE algorithm (Table 3). To resolvehow such characteristics and site history impacted modelskill we calculated S for all observed combinations of site,model, seasonality, and drought level and cross‐referencedTable 3. Model Structural and Site History Predictors Used to Classify Taylor Skill With Regression TreeAnalysisaPredictor ValueModel temporal resolution Daily, half‐hourly or less, hourly, monthlyCanopy Prognostic, semiprognostic, prescribed. Prescribed canopyfrom remote sensing, semiprognostic has someprescribed input into canopy leaf biomass butcalculates phenology with other prognostic variables.Number of vegetation pools Number of pools, both dynamic and staticNumber of soil pools Number of pools, both dynamic and staticNumber of soil layers Number of layersNitrogen True if the model has a nitrogen cycle; otherwise false.Steady state True if the simulated long‐term NEE integral approaches zero;otherwise false.Autotrophic respiration (AR) Fraction of annual GPP, fraction of instantaneous GPP,explicitly calculated, nil, proportional to growthEcosystem respiration (R) AR + HR, explicitly calculated, forced annual balanceGross primary productivity (GPP) Enzyme kinetic model, light use efficiency model, nil, stomatalconductance modelHeterotrophic respiration (HR) Explicitly calculated, first or greater order model,zero‐order modelNet ecosystem exchange (NEE) Explicitly calculated, GPP ‐ R, NPP ‐ HRNet primary productivity (NPP) Explicitly calculated, fraction of instantaneous GPP, GPP ‐ AR,light use efficiency modelOverall model complexity Low, average, highValues correspond to terciles of the total amount of first‐orderfunctional arguments for the following model‐generatedvariables/outputs: AR, canopy leaf biomass, R,evapotranspiration, GPP, HR, NEE, NPP, soil moisture.Site history True if the below listed management activity or disturbance orevent occurred on site; otherwise false.Grazed, fertilized, fire, harvest, herbicide, insects and pathogens,irrigation, natural regeneration, pesticide, planted, residuemanagement, thinningStand age class Young, intermediate, nil, mature, multicohort.Values based on stand age in forested sites; stands without a cleardominant stratum are treated as multicohort; nonforest typeshave nil.aTaylor skill (S; equation (3)) was divided into three classes using terciles. Model structural predictants are from the Metadata forForward (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].SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H059of22these 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 usedS as it is bound by zero (no agreement) and unity (perfectagreement) in contrast to NMAE and c2which are unbound.The Taylor skill metric (S) was first discretized into threeclasses based on terciles. These classes, representing threetiers of model‐data agreement, were then related to biome,climatic season, drought level, site history, and modelstructure using regression tree analysis (RTA) as a super-vised classification algorithm. RTA is a form of binaryrecursive partitioning [Breiman et al., 1984] that succes-sively splits the data (Taylor skill classes as the response; allother attributes as predictors) into subsets (nodes) by mini-mizing within‐subset variation. The result is a pruned tree‐like topology whereby predicted values (Taylor skill metricclass) are derived by a top‐to‐bottom traversal following therules (branches) that govern subset membership until apredicted value is reached (terminal node). The splittingrules at each node as well as its position allow for a cal-culation of relative variable importance [Breiman et al.,1984] with the most important variable given a score of100. Variables of high importance were further analyzedusing conditional means, i.e., comparing mean values foreach predictor value, with statistical differences determinedusing Bonferroni corrections for multiple comparisons[Hochberg and Tamhane, 1987].3. Results3.1. Model‐Data Agreement Relative to ClimaticSeason, Dryness, and Biome[16] Overall agreement across n = 31025 months wasbetter in forested than nonforested biomes; both NMAE(Table 4) and c2values (Table 5) were closer to zero andunity, respectively. At the biome level, model skill wasloosely ranked in five tiers: evergreen needleleaf forests inthe temperate zone, mixed forests > deciduous broadleafforests, evergreen needleleaf forests in the boreal zone >grasslands, woody savannahs > croplands, shrublands,wetlands > tundra. These rankings were robust acrossmodels used in the majority of biomes, although somedivergence was apparent for croplands and shrublands(Figure 1). Relative to seasonality and drought level modelswere most consistent with observations during periods ofpeak biological activity (climatic summer) and under dryconditions (Figure 2). However, across the three levels ofdryness, changes in model‐data agreement were negligibleTable 4. Normalized Mean Absolute Error by Climatic Season, Drought Level, and BiomeaBiomebClimatic Season Drought LevelOverallWinter Spring Summer Fall Dry Normal WetCRO 1.90 4.64 −0.79 12.73 −1.43 −1.54 −1.59 −1.55DBF 0.81 93.7 −0.52 −2.14 −1.01 −1.00 −0.95 −1.00ENFB 1.52 −1.12 −0.69 −1.92 −0.87 −1.15 −3.43 −1.12ENFT −6.34 −0.66 −0.50 −0.76 −0.63 −0.72 −0.63 −0.68GRA −25.46 −0.84 −1.11 5.19 −1.52 −1.32 −3.07 −1.51MF 1.10 −7.48 −0.47 57.70 −1.42 −1.04 −1.15 −1.12SHR −87.37 −1.37 −3.03 −140.17 −1.82 −2.18 −41.13 −2.88TUN −1.43 −11.07 −20.63 6.38 19.22 −24.06 −1.81 −20.15WET 1.80 −5.07 −0.59 −4.72 −1.21 −1.20 −2.38 −1.27WSA −2.73 −0.75 −1.47 10.56 −1.39 −1.32 −1.51 −1.37Overall 2.42 −1.35 −0.61 −1.94 −0.97 −1.01 −1.00 −1.00aDrought 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.bBiome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreenneedleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woodysavanna; SHR, shrubland; TUN, tundra; WET, wetland.Table 5. Reduced c2Statistic by Climatic Season, Drought Level, and BiomeaBiomebClimatic Season Drought LevelOverallWinter Spring Summer Fall Dry Normal WetCRO 3.22 10.66 39.75 49.71 14.43 23.54 32.75 25.8DBF 5.29 10.74 8.77 4.55 5.58 7.86 8.67 7.34ENFB 21.25 17.75 4.98 6.61 11.64 12.02 18.51 12.61ENFT 4.39 7.90 3.27 2.26 4.71 4.29 4.60 4.45GRA 10.89 11.38 25.01 17.22 13.97 10.99 26.01 16.07MF 3.74 4.67 2.05 2.02 2.92 3.24 2.98 3.08SHR 13.34 27.98 12.52 11.2 9.26 21.31 10.31 16.26WET 23.65 27.27 11.74 7.54 21.51 17.36 12.91 17.47WSA 0.61 5.81 11.88 3.39 6.73 4.64 6.35 5.37Overall 8.18 11.95 11.27 9.45 8.10 9.98 12.72 10.26aDrought 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.bBiome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreenneedleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woodysavanna; SHR, shrubland; WET, wetland.SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0510 of 22for NMAE (∼4% change, Table 4) but more pronounced forc2(from 8.10 to 12.72, Table 5). Averaged over just thewarm season (excluding climatic winter) dry conditionswere coincident with worse model‐data agreement, e.g.,NMAE was −0.99, −0.91, and −0.84 for dry, normal, andwet, respectively. In biomes with a clear seasonal cycle inleaf area index (LAI) a loss of model skill occurred duringclimatic spring and fall (Tables 4 and 5), especially forNMAE.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 bothTaylor skill and c2(r = −0.65; p < 0.0001). Jointly, highTaylor skill co‐occurred with NMAE and c2values closerto zero and unity, respectively (Figure 3). Across modelsNMAE ranged from −0.42 of the overall mean observedflux to −2.18 for LoTEC and DNDC, respectively. Valuesof c2varied from 2.17 to 29.87 for LoTEC and CN‐CLASS,respectively. Alternatively, the degree of model‐data mis-match (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 andAgroIBIS), LoTEC, and ISOLSM were more conservativeand showed a general high degree of consistency withobservations.[18] Among crop models, SiBCrop and AgroIBIS per-formed well, especially in climatic spring and during wetconditions. In contrast, the crop only DNDC model exhibitedpoor model‐data agreement with c2> 15 in climatic springand summer as well as across all drought levels. Althoughfour crop only simulators were analyzed, the best agreementin croplands (NMAE and c2closer to zero and unity,respectively) was achieved by SiB3 and Ecosys, modelsused in multiple biomes. Based on all three skill metrics theLoTEC model (NMAE = −0.42, c2= 2.17, S = 0.95) wasmost consistent with observations across all sites, drynesslevels, and climatic seasons. This platform was optimizedusing a data assimilation technique, unique among modelruns evaluated here, and was applied at 10 sites. In addi-tion, 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 forCN‐CLASS (NMAE = −1.50, c2= 29.87, S = 0.48).[19] Site‐level model‐data agreement also showed a highdegree of variability (Figure 4). At three croplands sites(US‐Ne1, US‐Ne2, and US‐Ne3) Taylor skill rangedfrom zero to unity. Both NMAE and c2exhibited similarFigure 1. Normalized mean absolute error (NMAE) by biome for each model. Biomes in ascendingorder based on model‐specific NMAE; biomes on the left show better average agreement with observa-tions. NMAE is normalized by mean observed flux. Across all sites, seasons, and drought levels within agiven biome this value is negative (NEE < 0), indicating a sink. NMAE values closer to zero coincidewith a higher degree of model‐data agreement. Woody savannahs and shrublands not shown: only onesite 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).SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0511 of 22scatter by site (not shown). Even for the best predicted site(US‐Syv), S ranged from 0.19 to 0.95. Only two forestedsites (CA‐Qfo and CA‐TP4) were predicted well (S > 0.5)by all models; whereas only one tundra site (US‐Atq) wasconsistently poorly predicted (S < 0.5). Despite the widerange in model performance, model skill (NMAE, c2, and S)was not correlated with the number of sites (p > 0.5) orbiomes (p > 0.3) simulated, i.e., using a more general ratherthan a specialized model did not result in a loss in modelperformance. Also, model‐data agreement was not better atsites with longer data records (p > 0.1).[20] The steady state protocol had negligible effect onmodel skill. Long‐term simulated NEE by site and modelvaried from −2904 to 2227 g C m−2yr−1with 90% of allvalues between −600 and 100 g C m−2yr−1. The extremevalues were primarily croplands simulated outside of croponly models. Overall, only 5 models achieved steady state(simulated NEE→0) over the full simulation: Biome‐BGC,LPJ, SiBCASA, SiB3, and TECO. Similar to simulatedvalues, observed annual integrals at the 44 sites examineddid not show steady state (Table 1) and varied from −718 to571 g C m−2yr−1. Nonetheless, model skill was not relatedto how close model spinup and initial conditions approxi-mated steady state or how close a given site was to anobserved NEE of zero. All three skill metrics were uncor-related with long‐term observed or simulated averageannual NEE (p > 0.05). However, two models did showsignificant relationships: For Ecosys, c2increased (decreasein model skill) and S decreased as observed or simulatedNEE approached zero; a system closer to steady state wascoincident with less model‐data agreement. BEPS wassimilar, showing lower S and more negative NMAE(decrease in model skill) for sites closer to steady state.3.3. Model and Site‐Specific Consistency WithObservations Using Taylor Diagrams[21] Average model performance (both across‐site andacross‐model) was evaluated using Taylor diagrams basedon all simulated and observed monthly NEE data. Bettermodel performance was indicated by proximity to thebenchmark, representing the observed state. The benchmarkwas normalized by observed standard deviation such that thedistance of s and RMSE from the benchmark was inobserved s units. Similar to model skill metrics, forestedsites were better predicted than nonforested ones. TheMEAN model showed r ≥ 0.2, apart from CA‐SJ2 andUS‐Atq, but generally (33 of 44 sites) underpredictedthe variability associated with monthly NEE at forested(Figure 5) and nonforested (Figure 6) sites. Similarly, 40 of44 sites were predicted with RMSE < s. Also 8 (6 forestedand two croplands sites: CA‐Obs, CA‐Qfo, CA‐TP4,US‐Ho1, US‐IB1, US‐MMS, US‐Ne3, US‐UMB) of theFigure 2. Normalized mean absolute error (NMAE) by climatic season and drought level. NMAE is nor-malized 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 springand fall for all crop only models: AgroIBIS, DNDC, EPIC, SiBcrop. Such values are displayed on thesame 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).SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0512 of 2244 sites were predicted with r ≥ 0.95 and RMSE < 1. Theworst 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 vari-ability and showed RMSE < s. Of all 22 models onlyDNDC exhibited r < 0.2. Based on proximity to thebenchmark, i.e., a high S value (Figure 3), the best modelswere: EPIC (crop only model used on one site), ISOLSM(used on 9 sites), LoTEC (data assimilation model), SiBcropand AgroIBIS (crop only models), EDCM (used on10 sites), Ecosys and SiBCASA (models used on most sites,39 and 35, respectively), and MEAN (mean model ensemblefor all 44 sites). All of these “best” models had r > 0.75,RMSE < 0.75 and slightly underpredicted variability; exceptthe crop only models and Ecosys where variability wasoverpredicted. Models whose average behavior was furthestaway 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 inthe distribution of model skill (Figure 8) sampled across allcombinations of site, model, climatic season, and drought(n = 3132 groups). Climatic season and stand age, thehighest scored site‐specific attribute, followed biome as leaddeterminants of model skill. Of the 12 evaluated site dis-turbances (Table 3) only grazing, which occurred on crop-lands, grasslands, and woody savannahs, achieved animportance score of at least 25. Apart from drought andgrazing activity, the remaining determinants were model‐specific: the number of soil layers, vegetation pools, canopyphenology, and soil pools. Two carbon flux calculationsalso had a variable score > 25, with NEE being the highest.[24] Comparing mean S for these relatively importantmodel attributes (Figure 9) revealed three instances wheremodel structure showed a statistically significant relation-ship with model skill: prescribed canopy phenology, a dailytime step, and calculating NEE as the difference betweenGPP and ecosystem respiration. Models using canopycharacteristics and phenology prescribed from remotelysensed products achieved higher skill (S = 0.54) than eitherprognostic 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 totalecosystem respiration showed greater skill (S = 0.50) thanother calculation methods (S = 0.42; p < 0.05). None of theother model attributes we studied showed statistically sig-nificant relationships between model structure and skill.[25] While not statistically significant, both vegetationpools and soil layers exhibited a weak pattern whereby thesimplest and most complex models showed higher skill thanmodels of intermediate complexity (Figure 9). Models withno soil model (zero soil layers) or no vegetation poolsshowed greater skill than models with the simplest soilmodel or smallest number of vegetation pools. As thenumber of soil layers or pools increased, so did model skill,indicating that a more comprehensive treatment of biologi-cal and physical processes can improve model skill. Forvegetation pools, there was a limit where increased com-Figure 3. Model skill metrics for all 22 models. Skill metrics are Taylor skill (S; equation (3)),normalized mean absolute error (NMAE), and reduced c2statistic (c2). Better model‐data agreementcorrespondstotheupperleftcorner.Benchmarkrepresentsperfectmodel‐dataagreement:S=1,NMAE=0,and c2= 1. Gray interpolated surface added and model names jittered to improve readability.SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0513 of 22plexity beyond eight pools did not improve model‐dataagreement.[26] Despite these effects, model attributes were of sec-ondary importance. The change in S relative to biome variedfrom 0.28 to 0.55; a much larger range than seen for modelattributes. Similarly, the high variable importance scores forbiome and climatic season, as well as the lower score fordrought level, corroborated the relationships between thesefactors and model skill as seen with NMAE and c2. Whilethe regression tree algorithm achieved an accuracy of 68.5%for predicting Taylor skill class, the site history and modelcharacteristics considered here did not explain the underly-ing cause of biome and seasonal differences in model skill.4. Discussion4.1. Effect of Parameter Sets on Model Performance[27] Model parameter sets are a large source of variabilityin terms of model performance [Jung et al., 2007b]. Theyinfluence output and accuracy [Grant et al., 2005] and aremore important for accurately simulating CO2exchangethan capturing effects of interannual climatic variability[Amthor et al., 2001]. For at least some of the modelsstudied here this can be related to the use of biome‐specificparameters relative to within‐biome variability [Purves andPacala, 2008]. A corollary occurs in the context of ECobservations as tower footprints can exhibit heterogeneity,particularly in soils, that is not reproduced in site‐specificparameters [Amthor et al., 2001].[28] The importance of model parameter sets was visiblein this intercomparison in two ways. First, biome had thehighest variable importance score. Insomuch as models relyon biome‐specific parameter values, this finding indicatesthat model parameter sets are a key factor in the distributionof model skill. This extends to plant functional types due tothe high degree of overlap between both. Furthermore, thevariability(Figure4)inmodelskillacrossparametersets,i.e.,across models, underscores that biomes may be too het-erogeneous in time [Stoy et al., 2005, 2009] and space to bewell‐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 inmodel skill (Figure 4) suggested that model parameter setsmay need to be refined to capture local, site‐specific realities.4.2. Effect of Model Structure on Model Performance[29] In general, models with the highest model‐dataagreement all used prescribed canopy phenology, calculatedNEE as the difference between GPP and ecosystem respira-tion, and did not use a daily time step. Models that exhibitedall of these structural characteristics (SiBCASA, SiB3, andISOLSM) showed high degrees of model‐data agreementacross all three skill metrics. Similarly, Ecosys, which useda prognostic canopy but otherwise had similar structuralcharacteristics as SiBCASA, also performed well. Relativeto model complexity, consistency with observations washighest 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 aprognostic canopy seem to perform better with more carbonpools and soil layers (e.g., Ecosys). No model with aprognostic canopy and a low number of carbon pools andsoil layers placed in the top tercile of model skill for anyskill metric, except SiBcrop and AgroIBIS for Taylor skill incroplands. Using multimodel ensembles (MEAN) or dataassimilation to optimize model parameter sets (LoTEC) cancompensate for differences in model structure to improvemodel skill.[30] The relationships between model structure and modelskill were consistent across all biomes. As a whole, themodels performed better at forested sites than nonforestedsites, but the same models showed the highest consistencyFigure 4. BoxplotsofTaylorskillbymodelandsite.Taylorskill (S; equation (3)) is a single value summary of a Taylordiagram where unity indicates perfect agreement withobservations. Panels show interquartile range (blue box),median (solid red line), range (whiskers), and outliers (redcross; values more than 1.5 × interquartile range from themedian). (top) Only models (n = 21) used on at least twosites shown. (bottom) Only sites (n = 32) simulated with atleast 10 unique models, excluding the mean model ensemble(MEAN) and the assimilated LoTEC, shown. Models andsites sorted by median Taylor skill.SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0514 of 22with observations in each biome (Ecosys and SiB3). This istrue even for agriculture sites, where Ecosys and SiB3scored as high as crop only models. This suggests that anymodel with requisite structural attributes can successfullysimulate carbon flux in all types of ecosystems.4.3. Links Between Model Performance andEnvironmental Factors[31] Model skill was only weakly linked to drought,showing high variability across dryness level by biome andmodel. Only during the warm season (all climatic seasonsexcluding winter) did aggregate model skill decline underdrought conditions. While this points to process uncertainty[Sitch et al., 2008], ecosystem response to longer‐termdrought can exhibit lags and positive feedbacks [Arnoneet al., 2008; Granier et al., 2007; Thomas et al., 2009;Williams et al., 2009] that were not explicitly included in thedrought metric used here but did influence simulationbehavior through model structure, e.g., soil moisture modeland soil resolution.[32] In spring and fall, especially for biomes with a sig-nificant deciduous component, models showed a decline inmodel skill (Table 4) relative to periods of peak biologicalactivity (climatic summer) [see also Morales et al., 2005].While this was more pronounced for NMAE (Table 4) thanc2(Table 5), phenological cues are known to influence theannual carbon balance at multiple scales [Barr et al., 2007;Delpierre et al., 2009; Keeling et al., 1996]. The loss ofmodel skill seen in this study during spring and fall waslikely linked to poor treatment of leaf initiation and senes-cence as well as season‐specific effects of soil moisture andsoil temperature on canopy photosynthesis [Hanson et al.,2004]. In this study seasonality was second only to biomein driving model skill (Figure 8). This and the lack of linkbetween model skill and site history strongly implicatephenology as a needed refinement of terrestrial biospheresimulators.[33] The evergreen needleleaf forest biome diverged inperformance based on whether the sites were located in thetemperate or boreal zones. A similar divergence was reportedusing Biome‐BGC, LPJ, and ORCHIDEE to simulate grossCO2uptake across a temperature gradient in Europe [Junget al., 2007a]; average relative RMSE was higher forevergreen needleleaf forests in the boreal zone. This waslinked to an overestimation of LAI at the boreal sites andrelationships between resource availability and leaf area[Friedlingstein et al., 2006; Jung et al., 2007a; Sitch et al.,2008]. Additionally, recent observations in the circumborealregion, where all boreal evergreen needleleaf forested sitesare located, suggest that transient effects of climate change,e.g., increased severity and intensity of natural disturbances(fire, pest outbreaks) and divergence from climate normalsin temperature, have already occurred [Soja et al., 2007] andinfluence resource availability. We speculate the loss ofFigure 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 toobserved normalized monthly NEE; units of s and RMSE are multiples of observed s. Color codingof site letter and circles indicates biome: evergreen needleleaf forest‐ temperate zone (red), deciduousbroadleaf forest (brown), mixed (deciduous/evergreen) forest (blue), evergreen needleleaf forest‐borealzone (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).SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0515 of 22model skill in boreal relative to temperate evergreenneedleleaf forests was linked to insufficient characterizationof cold temperature sensitivity of metabolic processes andwater flow in plants as well as freeze‐thaw dynamics[Schaefer et al., 2007, 2009] and that this was exacerbatedby the effects of transient climate change.4.4. Effects of Site History and Protocol on ModelEvaluation[34] Disturbance regime and how a model treats distur-bance are known to impact model performance [Ito, 2008].In this study, stand age impacted model skill whereas sitehistory was of marginal importance (Figure 8). However,CA‐SJ2, the worst predicted site (Figure 5), was harvestedin 2000 and scarified in 2002, and US‐SO2, a secondpoorly predicted shrubland site (Figure 6), suffered cata-strophic wildfire during the analysis period. The poor modelperformance for recently disturbed sites followed fromassumed steady state as used in some simulations and theabsence of modeling logic to accommodate disturbance.However, the distribution of site history metrics wasskewed; only few sites were burned, harvested, or in theearly stages of recovery from disturbance when NEE ismore nonlinear relative to established stands. Furthermore,age class was biased toward older stands; of the 17 forestedsites only one was classified as a young stand. Other sitecharacteristics were also unbalanced; all nonforested biomesoccurred on five or less sites; with only one site each forshrublands and woody savannahs. While regression trees areinherently robust, additional observed and simulated fluxesin rapidly growing young forested stands, recently burned orharvested sites, and undersampled biomes are desirable tobetter characterize model performance.[35] Aspects of the NACP site synthesis protocol andanalysis framework also influenced the interpretation of ourresults. First, this analysis focused solely on non‐gap‐filleddata to allow the model‐data intercomparison to informmodel development. However, the low turbulence (frictionvelocity) filtering removed more data at night than duringthe day. Average data coverage across all sites was 82% fordaytime and 39% at night, respectively (Table 2), so ouranalysis is skewed toward daytime conditions. Second, eachmodel 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 thetower site. This likely deflated relevant variable importancescores (Figure 8) and precluded a full comparison of pre-scribed versus prognostic LAI. While only few models usedsuch inputs (Table 1), removing the inherent bias of aninvariant seasonal cycle over multiple years may improvemodel performance. Incorporating disturbance informationto recreate historical land use and disturbance, especially forrecent site entries, could also improve model performance.Last, despite the model simulation protocol’s emphasis onsteady state, this condition was not achieved for most sites(Table 2), even when discounting observational uncertainty,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 toobserved normalized monthly NEE; units of s and RMSE are multiples of observed s. Color codingof site letter and circles indicates biome: croplands (red), grasslands (brown), wetlands (blue), all otherbiomes (black).SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0516 of 22Figure 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 listingof evaluated model structural and site attributes.Figure 7. Taylor diagram of normalized across‐site average model performance. Model s and RMSEwere normalized by observed s.Eachcircle(n = 22 models) corresponds to the mean across all sites.Benchmark (red square) corresponds to observed normalized monthly NEE; units of s and RMSE aremultiples of observed s. Color coding of model letter and circles indicates generality of model perfor-mance: specialist models used only in croplands (n ≤ 5 sites; black), generalist models used across a rangeof biomes and sites (n ≥ 30 sites, blue), all other models (red). The correlation for DNDC (r = −0.13) isdisplayed as zero for readability.SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0517 of 22or most models. None of the four crop only models achievedsteady state. This followed from site history of croplands ingeneral where active management precluded any systemsteady state, e.g., DNDC allowed for prescribed initial soilcarbon pools. For those models (5 of the 21 evaluated) thatachieved steady state in initialization this resulted in aninherent bias between simulated and observed NEE for allsites regardless of site history. However, as biome andseasonality 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 thequasi‐steady state assumption [Schaefer et al., 2008] couldimprove these models’ performance.5. Conclusion[36] We used observed CO2exchange from 44 eddycovariance towers in North America with simulations from21 terrestrial biosphere models and a mean model ensembleto examine model skill across gradients in dryness, sea-sonality, biome, site history, and model structure. Models’ability to match observed monthly net ecosystem exchangewas generally poor; the mean squared distance betweenobservations and simulations was ∼10 times observationalerror. Overall, forested sites were better predicted thannonforested sites. Weaknesses in model performanceconcerned model parameter sets and phenology, especiallyfor biomes with a clear seasonal cycle in leaf area index.Drought was weakly linked to model skill with abnormallydry conditions during the growing season showing mar-ginally worse model‐data agreement compared to nondryconditions. Sites with disturbances during the analysisperiod and undersampled biomes (grasslands, shrublands,wetlands, woody savannah, and tundra) also showed a largedivergence between observations and simulations. Thehighest degree of model‐data agreement occurred in tem-perate evergreen forests in all climatic seasons and duringsummer across all biomes. Overall skill was higher formodels that estimated net ecosystem exchange as the differ-ence between gross primary productivity and ecosystemrespiration, used prescribed canopy phenology, and did notuse a daily time step. The model ensemble (mean simulatedvalue across all models) and an optimized model (para-meters tuned using data assimilation) also performed well.Models with preferred structural attributes included gener-alist models (models used at multiple sites and biomes, e.g.,SiB3, Ecosys) that exhibited high degrees of model‐dataagreement across all biomes, indicating that a single modelcan successfully simulate carbon flux in all types of eco-systems. That is, different model architectures were notneeded for different types of ecosystems and model choiceis recast as a function of ease of parameterization andinitialization.[37] Acknowledgments. C.R.S., C.A.W., and K.S. were supportedby the U.S. National Science Foundation grant ATM‐0910766. We wouldlike to thank the North American Carbon Program Site‐Level InterimSynthesis team, the Modeling and Synthesis Thematic Data Center, andthe Oak Ridge National Laboratory Distributed Active Archive Centerfor collecting, organizing, and distributing the model output and flux obser-vations required for this analysis. This study was in part supported by theU.S. National Aeronautics and Space Administration (NASA) grantNNX06AE65G, the U.S. National Oceanic and Atmospheric Administra-tion (NOAA) grant NA07OAR4310115, and the U.S. National ScienceFoundation (NSF) grant OPP‐0352957 to the University of Colorado atBoulder.Figure 9. Bar graphs of mean Taylor skill by model attribute. Whiskers represent one standard error ofthe mean. Only model‐specific attributes with variable important scores >25 shown. Note y axis on rightpanels starts at 0.4.SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2EXCHANGE G00H05G00H0518 of 22ReferencesAmthor, J. S., et al. 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