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Terrestrial_biospheric_model_intercomparison

Ecological Modelling 232 (2012) 144–157

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Ecological

Modelling

j o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /e c o l m o d e

North American Carbon Program (NACP)regional interim synthesis:Terrestrial biospheric model intercomparison

D.N.Huntzinger a ,?,W.M.Post b ,Y.Wei b ,A.M.Michalak c ,T.O.West d ,A.R.Jacobson e ,f ,I.T.Baker g ,J.M.Chen h ,K.J.Davis i ,D.J.Hayes b ,F.M.Hoffman b ,A.K.Jain j ,S.Liu k ,A.D.McGuire l ,R.P.Neilson m ,

Chris Potter n ,B.Poulter o ,David Price p ,B.M.Raczka i ,H.Q.Tian q ,P.Thornton b ,E.Tomelleri r ,N.Viovy o ,J.Xiao s ,W.Yuan t ,N.Zeng u ,M.Zhao v ,R.Cook b

School of Earth Science and Environmental Sustainability,Northern Arizona University,P.O.Box 5694,Flagstaff,AZ 86011-5694,United States b

Earth Science Division,Oak Ridge National Laboratory,Oak Ridge,TN,United States c

Department of Global Ecology,Carnegie Institute for Science,Stanford,CA,United States d

Joint Global Change Research Institute,College Park,MD,United States e

NOAA Earth System Research Lab Global Monitoring Division,Boulder,CO,United States f

Cooperative Institute for Research in Environmental Sciences,University of Colorado,Boulder,CO,United States g

Department of Atmospheric Sciences,Colorado State University,Fort Collins,CO,United States h

Department of Geography and Program in Planning,University of Toronto,Toronto,Ontario,Canada i

Department of Meteorology,The Pennsylvania State University,University Park,PA,United States j

Atmospheric Sciences,University of Illinois,Urbana Champaign,Urbana,IL,United States k

United States Geologic Survey National Center for EROS,Sioux Falls,SD,United States l

U.S.Geological Survey,Alaska Cooperative Fish and Wildlife Research Unit,University of Alaska Fairbanks,Fairbanks,AK,United States m

Department of Botany and Plant Pathology,University of Utah,Salt Lake City,UT,United States n

NASA Ames Research Center,Moffett Field,CA,United States o

Laboratoire des Sciences du Climat et de l’Environnement,LSCE,Gif sur Yvette,France p

Northern Forestry Centre,Natural Resources Canada,Edmonton,Alberta,Canada q

Ecosystem Dynamics and Global Ecology Laboratory,Auburn University,Auburn,AL,United States r

Max Planck Institute for Biogeochemistry,Jena,Germany s

Earth Systems Research Center,Institute for the Study of Earth,Oceans,and Space,University of New Hampshire,Durham,NH,United States t

College of Global Change and Earth System Science,Beijing Normal University,Beijing,China u

Department of Atmospheric and Oceanic Science,University of Maryland,College Park,MD,United States v

Numerical Terradynamics Simulation Group,University of Montana,Missoula,MT,United States

a r t i c l e

i n f o

Article history:

Received 5October 2011

Received in revised form 7February 2012Accepted 8February 2012

Keywords:

Terrestrial biospheric models Intercomparison Carbon ?uxes

North American Carbon Program Regional

a b s t r a c t

Understanding of carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments,as well as through modeling activities.Terrestrial biosphere models (TBMs)have become an integral tool for extrapolating local observations and understanding to much larger terrestrial regions.Although models vary in their speci?c goals and approaches,their central role within carbon cycle science is to provide a better understanding of the mechanisms currently controlling carbon exchange.Recently,the North American Carbon Program (NACP)organized several interim-synthesis activities to evaluate and inter-compare models and observations at local to continental scales for the years 2000–2005.Here,we compare the results from the TBMs collected as part of the regional and continental interim-synthesis (RCIS)activities.The primary objective of this work is to synthesize and compare the 19participating TBMs to assess current understanding of the terrestrial carbon cycle in North America.Thus,the RCIS focuses on model simulations available from analyses that have been completed by ongoing NACP projects and other recently published studies.The TBM ?ux estimates are compared and evaluated over different spatial (1?×1?and spatially aggregated to different regions)and temporal (monthly and annually)scales.The range in model estimates of net ecosystem productivity (NEP)for North America is much narrower than estimates of productivity or respiration,with estimates of NEP varying between ?0.7and 2.2PgC yr ?1,while gross primary productivity and heterotrophic respiration vary between 12.2and 32.9PgC yr ?1and 5.6and 13.2PgC yr ?1,respectively.The range in estimates from the models appears to be driven by a combination of factors,including the representation of photosynthesis,the source and of environmental driver data and the temporal

?Corresponding author.Tel.:+19285231669;fax:+19285237423.E-mail address:deborah.huntzinger@https://www.wendangku.net/doc/d47417079.html, (D.N.Huntzinger).

D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157145 variability of those data,as well as whether nutrient limitation is considered in soil carbon decomposition. The disagreement in current estimates of carbon?ux across North America,including whether North America is a net biospheric carbon source or sink,highlights the need for further analysis through the use of model runs following a common simulation protocol,in order to isolate the in?uences of model formulation,structure,and assumptions on?ux estimates.

1.Introduction

North America has been identi?ed as both a signi?cant source (e.g.,fossil fuel emissions)and biospheric sink of atmospheric car-bon dioxide(CO2)(Gurney et al.,2002;CCSP,2007;Prentice,2001). However,as summarized in the State of the Carbon Cycle Report (SOCCR;CCSP,2007),estimates of the North American biosphere carbon sink vary widely,ranging from less than0.1PgC yr?1to over 2.0PgC yr?1.While some of the mechanisms responsible for this sink are understood(e.g.,forest regrowth),the current and future role of other mechanisms,such as extreme weather events(Jentsch et al.,2007),changes in land-use,CO2and nitrogen fertilization, natural disturbances(e.g.,Kurz et al.,2007;Bond-Lamberty et al., 2007),and other carbon-climate feedbacks(Friedlingstein et al., 2006;Pan et al.,1998)in controlling the North American carbon cycle are highly uncertain(CCSP,2007).Thus,a basic goal of carbon cycle studies has been to address key scienti?c questions ranging from carbon?ux diagnosis(What are net carbon sources and sinks, and how do they change with time?),to attribution(What are the processes controlling?ux variability?),and prediction(How might changes in climate and other factors alter future?uxes?).Under-standing the sources and sinks of carbon and their distribution across North America is critical for the successful management of the carbon cycle(CCSP,2007)and for useful predictions of its future evolution,and requires a strong understanding of carbon dynamics. Providing useful information about the carbon cycle and project-ing future CO2concentrations is also urgently needed for informing policies addressing fossil fuel emissions.

Understanding of carbon exchange between terrestrial ecosys-tems and the atmosphere can be improved through direct observations and experiments,as well as through modeling activities.Terrestrial biosphere models(TBMs),sometimes called forward models,have become an integral tool for extrapolating local observations and understanding to much larger terrestrial regions(Waring and Running,2007;Davis,2008),as well as for test-ing hypotheses about how ecosystems will respond to changes in climate and nutrient availability.Although TBMs vary in their spe-ci?c goals and approaches,their central role within carbon cycle research is to provide a better understanding of the mechanisms currently controlling carbon exchange.This understanding is then used as the basis of prediction and,ultimately to inform the devel-opment of any potential carbon management plans(Schimel et al., 2000).

The ultimate objective is to model all the processes that result in the net carbon exchange between the terrestrial system and the atmosphere,called the net ecosystem exchange(NEE).This includes many processes,most importantly gross primary produc-tion(GPP),autotrophic and heterotrophic respiration(Ra and Rh respectively,which together add up to ecosystem respiration,Re), and losses due to?re and other disturbance processes(herbivory, insects,disease,physical disturbance from storms,etc.)Therefore, understanding how TBM estimates of ecosystem photosynthesis, respiration,and net carbon exchange vary spatially and temporally is of great importance,not only for improving TBMs,but also for understanding their contribution to uncertainty in global climate simulations.By extension,it is also important to know why differ-ent TBMs product different estimates,even when forced with the same driving conditions.The former can be examined by bringing together existing model results and comparing them within a con-sistent framework,while the later requires a substantial,formal intercomparison effort.

Individual TBMs are often based on different simplifying assumptions,use different environmental driving data and ini-tial conditions,and formulate the processes controlling carbon exchange in different ways.Thus,there is diversity in both the com-plexity of the model structure and formulation,as well as model estimates of regional net carbon exchange.Each TBM,therefore,is a complex combination of scienti?c hypotheses and choices,and their estimates depend on these inherent assumptions(Beer et al., 2010).Available observations of carbon?ux components,as well as our current understanding of the processes controlling carbon exchange over regional scales,however,are not suf?cient to rank models in terms of which is“best”at representing current?uxes or predicting carbon exchange under future climate conditions (Melillo et al.,1995).Therefore,in order to move towards more robust estimates of carbon cycle dynamics,we must?rst compare estimates from a variety of model types,as well as evaluate esti-mates against those measurements that are available(Cramer et al., 1999;Melillo et al.,1995;Beer et al.,2010).

Recently,the North American Carbon Program(NACP)(Denning et al.,2005;Wofsy and Harriss,2002)organized several interim-synthesis activities to evaluate and inter-compare models and observations at local to continental scales for the time period of 2000through2005.These interim synthesis activities include three companion studies,each conducted on different spatial scales:(1) site-level analyses that examine process-based model estimates and observations at over30AmeriFlux1and Fluxnet-Canada2tower sites across North America;(2)a regional,mid-continent inten-sive study centered in the agricultural regions of the United States and focused on comparing inventory-based estimates of net car-bon exchange with those from atmospheric inversions;and(3) a regional and continental synthesis evaluating model estimates against each other and available inventory-based estimates across North America.A number of other interim syntheses are underway, including ones focusing on non-CO2greenhouse gases,the impact of disturbance on carbon exchange,and coastal carbon dynamics.

Here,we compare the model estimates from the regional and continental interim-synthesis(RCIS)activities.The primary objec-tive of this work is to synthesize and compare TBMs to assess current understanding of the terrestrial carbon cycle in North America.Thus,the RCIS focuses on“off-the-shelf”model simula-tions,i.e.,existing model results currently available from analyses that have been completed by ongoing NACP projects and other recently published studies.Although there is a challenge in inter-preting existing results compared to prescribing new simulations designed for the controlled comparison of different modeling sys-tem,there is also great value in using independent estimates to assess the overall spread or variability in model results.While it is necessary to limit variability between models(by,for example,pre-scribing consistent driver data and a detailed simulation protocol) in order to better understand what is driving the differences among model estimates,this approach provides an unrealistic assessment 1https://www.wendangku.net/doc/d47417079.html,/ameri?ux/.

2http://www.?uxnet-canada.ca/.

146 D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157

of the true uncertainty in our ability to model land-atmosphere car-bon exchange.Models differ structurally in how they represent the processes controlling carbon exchange between the land and atmo-sphere,in their input or driver data(land cover,climate),and in the parameter values used within their varying process descriptions. These varying approaches to modeling terrestrial carbon exchange result in a large degree of variability in the land-atmosphere?ux estimates.Thus,this work provides a valuable assessment of the current status of terrestrial carbon modeling in NA by bringing together model estimates that incorporate a wide range of mod-eling choices and input data.This work also serves as a starting point for analyses that compare these model results to different observational data products.Speci?cally,Raczka and Davis(per-sonal communication)evaluated?ux estimates of RCIS models against observations from30?ux towers across a wide range of NA ecosystems.In addition,Hayes et al.(2012)has assembled and analyzed available agricultural and forest biomass inventory-based data for NA and compared them alongside estimates from TBM and inverse approaches available from the RCIS.In addition,ongo-ing work is comparing TBM estimates of net ecosystem exchange to?ux estimates derived from atmospheric inversions.Flux esti-mates from atmospheric inverse models are more comprehensive, in the sense that all ecosystem sources and sinks,fossil fuel emis-sions,and any other processes emitting or absorbing CO2are,in principle,captured in the atmospheric signal(GCP,2010).Com-bined,the comparison of TBM estimates to different observational data products and modeling approaches can provide further insight into our ability to model land-atmosphere carbon dynamics.This manuscript provides the foundation for these types of compar-isons.

2.Overview of participating models

TBMs represent processes controlling carbon cycle dynamics; however,the level of detail with which processes are represented varies across models.Whereas some models are empirically or statistically-based with relatively simple relationships between driver variables and?ux,others are more complex,simulating the coupled carbon,nutrient,and water cycles in terrestrial ecosys-tems.Models also differ in their representation of soil properties, vegetation type,and environmental forcings,as well as how car-bon pools are initialized.Here we compare carbon?ux estimates over North America(NA)for the19TBMs that participated in the RCIS.Key features of the models participating in this study in terms of how they represent photosynthesis,autotrophic respira-tion,decomposition,and other processes affecting carbon?uxes are summarized in Tables1–3(see Supplemental Material for addi-tional model descriptions).The TBM?ux estimates are evaluated over different land cover regions of NA,and with respect to pho-tosynthetic formulation,soil carbon dynamics,and whether they explicitly account for the impact of?re disturbances on carbon pools and stocks.

TBMs can be divided into two general classes:diagnostic and prognostic models.In order to specify the internal(time-varying) state of the system,diagnostic models rely on forcing data(e.g.,leaf area)provided directly or indirectly from satellite or other external sources.In contrast,the internal states of the system in prognostic models are computed as part of the system equations.Therefore,in principle,prognostic models can be used to predict future condi-tions using external climate forcing alone,in addition to being used for diagnostic analyses(e.g.,reproducing past or measured?uxes).

The distinction between diagnostic and prognostic models is important.Diagnostic models frequently use observed leaf area index(LAI)as a speci?ed driving variable,along with empirical algorithms of varying complexity,to estimate?uxes over regional or global domains and changes in carbon pool over time(Table2, models:BEPS,CASA,NASA-CASA,CASA GFEDv2,EC-MOD,EC-LUE, ISAM,MODIS,MOD17+).Conversely,prognostic models determine the amount of leaf area as the result of carbon allocation and water balance dynamics within the model.As a result,they can project or estimate carbon cycle dynamics into the future under changing environmental conditions(Can-IBIS,CLM-CASA ,CLM-CN,DLEM,LPJ-wsl,MC1,ORCHIDEE,SiB3.1,TEM6,VEGAS2).In addition,some prognostic models also contain dynamic algorithms to estimate vegetation distribution over time(Can-IBIS,LPJ-wsl, MC1,ORCHIDEE,and VEGAS2).Although prognostic models can be used for future predictions,they are much less constrained by observations than diagnostic models.As a result,one would expect their results to be more variable(and perhaps less reliable)even when used in a diagnostic mode.

The model results submitted to the interim synthesis activity also vary in terms of the processes included,the choice of driv-ing data,and the types of algorithms employed to represent these processes(Tables1–3,Supplementary Information).For example, eight of the nineteen models represent photosynthesis using an enzyme kinetic formulation(Farquhar et al.,1980),normally at a sub-daily time step,while nine of the models use a light-use ef?ciency calculation at daily to monthly time steps.The models also differ in how they model soil carbon decomposition.Five of the models use a zero-order calculation,where decomposition is a function of temperature and moisture only.Two of the models omitted soil carbon decomposition altogether,and the remainder of the models represent decomposition through?rst-order kinet-ics,where decomposition depends on the magnitude of soil carbon stocks in addition to environmental drivers,and interactive pro-cesses such as N dynamics.In addition,models differ in the types of disturbance considered(e.g.,wind or storm,?re,disease)and how these disturbances are included within the model(e.g.,explicitly described or implicitly accounted for through vegetation indices). Most of the models in this study do not directly account for the impacts of?re,disease,or storm events on carbon?uxes or pools. In addition,those that do include the impact of?re disturbances (e.g.,Can-IBIS,TEM6,MC1,LPJ-wsl)do so in varying ways(refer to Table3and Supplementary Information).

This diversity in model structure and process representation makes evaluation and comparison of model performance challeng-ing.However,information on model differences helps to inform the analysis and was used here to de?ne subsets or groups of models based on speci?c de?ning characteristics,and aid in the interpre-tation of observed differences.

3.Methods for comparison

Prior to analysis,all model output was processed,as necessary, to a spatial resolution of one-degree by one-degree,temporally aggregated to monthly?uxes,and placed on a grid with a spatial extent of10–84?North,and50–120?West.Fluxes are compared for the six years covering the period of2000through2005.

3.1.Regional analysis of TBM output

Several of the model estimates lack full spatial coverage of North America(Fig.1);therefore,in order to better compare net?ux across models,1?×1??ux estimates were spatially aggregated to regions de?ned by the TransCom intercomparison study(Gurney et al.,2002)and the Global Land Cover classi?cation for2000 (GLC2000;Latifovic et al.,2004;NRCan and USGS,2003).The aggre-gation of?uxes to large contiguous regions,with similar land cover or biome types and climatic conditions,allows for the examination of regional differences between the models.This approach is similar

D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157147 Table1

Terrestrial biospheric models participating in the NACP regional interim synthesis.

Model Spatial range Native spatial

resolution Native temporal

resolution

Fluxes submitted Temporal range Selected references

Can-IBIS Canada and U.S.–30min GPP,NEE,NEP,NPP,Ra,Rh2000–2005Wang et al.(2011),

Kucharik et al.(2000),

and Foley et al.(1996) CLM-CASA Global 2.8?20min GPP,NPP,Rh,NEE,NEP2000–2004Randerson et al.(2009) CLM-CN Global 2.8?20min GPP,NPP,Rh,NEE,NEP2000–2004Thornton et al.(2009)

and Randerson et al.

(2009)

DLEM N.America32km Daily GPP,NEE,NPP,Ra,Rh2000–2005Tian et al.(2010)

ISAM N.America1?Weekly NEE,Rh,NPP2000–2005Jain and Yang(2005)

and Yang et al.(2009) LPJ-wsl N.America0.5?Daily GPP,NPP,Rh,NEE,CFire,NEEF2000–2005Bondeau et al.(2007)

and Sitch et al.(2003) MC1Global,Continental U.S.0.5?Monthly NPP,Rh,NEE,CFire,NEEF2000–2005Bachelet et al.(2000),

Daly et al.(2000),and

Lenihan et al.(2008) ORCHIDEE Global0.5?30min GPP,NPP,Rh,NEE,CO2Flux2000–2005Krinner et al.(2005)

and Viovy et al.(2000) SiB3Global1?Hourly NEE,GPP,Reco2000–2005Baker et al.(2008)

TEM6N.A.>45?N0.5?Monthly GPP,NPP,Rh,NEE,CFire,NECB2000–2005McGuire et al.(2010)

and Hayes et al.(2011) VEGAS2N.America1?Daily GPP,NPP,Ra,Rh,NEE,CFire2000–2005Zeng(2003)and Zeng

et al.(2004,2005)

BEPS N.America1?Hourly GPP,NEE,NEP,NPP,Rh2000–2004Chen et al.(1999)and

Ju et al.(2006)

CASA Global1?Monthly NEE2002–2003Randerson et al.(1997) NASA CASA Continental U.S.8km Monhly NPP,Rh,NEE,NEP2001–2004Potter et al.(2007) CASA GFEDv2Global1?Monthly GPP,NPP,Rh,CFire,NEE2000–2005van der Werf et al.

(2004,2006)

EC-LUE N.America1?Weekly GPP2004–2005Yuan et al.(2007)

EC-MOD N.America1?8-Day GPP,NEE2000–2006Xiao et al.(2008,2010,

2011)

MODIS N.America–8-Day GPP,annual NPP2000–2005Heinsch et al.(2003)

and Running et al.

(2004)

MOD17+Global0.5?Daily GPP,NEE,Reco2000–2004Reichstein et al.(2005) Gross primary productivity(GPP);net ecosystem exchange(NEE);net ecosystem productivity(NEP);net primary productivity(NPP),autotrophic respiration(Ra);het-erotrophic respiration(Rh);carbon emissions from?res(CFire);net ecosystem exchange including?re emissions(NEEF);net carbon?ux including?re and disturbance (CO2Flux);ecosystem respiration(Reco);net ecosystem carbon balance(NECB).

to that used by Kicklighter et al.(1999),where net primary produc-tivity(NPP)estimates were averaged across global biomes de?ned by the potential natural vegetation map developed by Melillo et al. (1993).The choice of land cover classi?cation for de?ning spatially contiguous regions is somewhat subjective.As with the Potsdam model intercomparison study(e.g.,Cramer et al.,1999;Kicklighter et al.,1999),landcover classi?cation is used here solely as a mask for?ux aggregation to smaller regions in order to examine regional differences among models.

The models used(or prognostically generated)different veg-etation maps with varying classi?cation schemes.Therefore,the choice of land cover scheme applied in this analysis does not re?ect how well a model predicts?ux for a particular biome type,but rather how predicted?uxes compare over large,spatially contigu-ous regions with similar land cover or climatic conditions.To avoid comparing models with limited spatial coverage in a region,only those models with at least80%representation(i.e.,those that esti-mate?uxes for at least80%of the cells)in a given land region were included in the comparison within that region.

3.2.Subsetting models based on model formulation

In addition to comparing aggregated carbon?uxes,?ux estimates were also compared by grouping models by their pho-tosynthetic formulation and treatment of soil carbon dynamics (Table2).Both the spread in model estimates and the across-model average for these different subsets were evaluated and compared. As mentioned above,the models in this study can be divided into two predominant photosynthetic formulation classes:light-use ef?ciency(LUE)and enzyme kinetic(EK).Light-use ef?ciency models estimate productivity by quantifying the fraction of pho-tosynthetically active radiation(fPAR)absorbed by the vegetation and then adjust the conversion of solar energy to photosynthesis or biomass production through climatological and physiological restrictions(e.g.,temperature,moisture).Thus,carbon?xation is a strong function of solar radiation and leaf area index(LAI),or a proxy such as normalized vegetative difference index(NDVI).In contrast,models with enzyme kinetic formulations are more phys-iologically based,simulating photosynthesis using equations that represent biochemical/biophysical reactions driven by absorbed PAR,atmospheric CO2concentration,leaf temperature,and leaf water status(Farquhar et al.,1980).Thus,EK models quantify pho-tosynthesis by emphasizing the light and enzyme limiting rates that affect photosynthesis.In addition to LUE and EK formulations,some models employ more statistical or regression-based approaches, modeling productivity as an empirical function of different envi-ronmental drivers.Photosynthetic formulation controls,to some extent,estimates of carbon uptake or productivity predicted by the models.Photosynthesis can also be in?uenced by other factors including driving meteorology,atmospheric CO2concentration, nutrient availability,and moisture and temperature limitations.

In addition to photosynthesis,models were grouped based on their treatment of soil carbon dynamics and decomposition.The CO2released(i.e.,heterotrophic respiration,Rh)from the decompo-sition of above and below-ground dead organic matter is controlled by three factors,including:substrate quality and quantity,moisture availability,and temperature(Waring and Running,2007).Thus, the degree to which these limitations are accounted for in the model

148 D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157

Table2

Comparison of environmental drivers,vegetation and soil distribution,phenology,compartments,and photosynthetic and soil carbon decomposition formulations among models.

Model a Vegetation

distribution Soil distribution Weather/climate

data

Phenology#PFTs#Veg

pools

#Soil

pools

Photo-synthetic

formulation b

Soil carbon

decomposition

Can-IBIS Dynamic CSL(Canada),

STATSGO(Alaska),

VEMAP(cont.U.S.)Canadian Forest

Services(CFS)

Prognostic1237EK1st Order

CLM-CASA’MODIS IGBP-DIS(GSDTG,

2000)

NCEP reanalysis Prognostic1535EK1st Order

CLM-CN MODIS IGBP-DIS(GSDTG,

2000)

NCEP reanalysis Prognostic1547EK1st Order,with N

DLEM Multiple sources

(Tian et al.,2010)Zobler(1986)/FAO

(1995/2003)

NARR and PRISM Prognostic21+1073EK1st Order,with N

ISAM Loveland and

Belward(1997)

and Haxeltine and

Prentice(1996)Zobler(1986)/FAO

(1995/2003)

Mitchell et al.

(2005)

–1358LUE1st Order,with N

LPJ-wsl Dynamic Zobler(1986)/FAO

(1995/2003)

CRU TS3.0Prognostic932EK1st Order

MC1Dynamic STATSGO PRISM Prognostic676Statistical1st Order,with N

ORCHIDEE Dynamic Zobler(1986)/FAO

(1995/2003)CRU05and NCEP

reanalysis

Prognostic1288EK1st Order,with N

SiB3IGBP IGBP-DIS(GSDTG,

2000)

NARR MODIS LAI1410EK Zero Order

TEM6Loveland et al.

(2000)and Hurtt

et al.(2006)IGBP-DIS(GSDTG,

2000)

CRU05and NCEP

reanalysis

Prognostic2313EK1st Order,with N

VEGAS2Dynamic Related to

vegetation CRU05and NCEP

reanalysis

Prognostic436LUE1st Order

BEPS GLC2000STATSGO(SSS,

2011)

NCEP reanalysis VGETATION LAI649EK1st Order,with N

CASA DeFries and

Townshend(1994)Zobler(1986)/FAO

(1995/2003)

Leemans and

Cramer(1991)and

Hansen et al.

(1999)

GIMMS NDVI

derived LAI

1135LUE1st Order

NASA CASA MODIS STATSGO(SSS,

2011)

NCEP reanalysis MODIS EVI1135LUE1st Order,with N

CASA GFEDv2MODIS Batjes(1996)IISAS,GISSTEMP,

and GPCPv2GIMMS NDVI

derived LAI

335LUE1st Order

EC-LUE––GMAO/DAO MODIS NDVI–––LUE–

EC-MOD MODIS––MODIS EVI,LAI700statistical Zero Order MODIS MODIS–DAO MODIS LAI–0–LUE–

MOD17+SYNMAP,Jung et al.

(2006)–ERA-Interim

reanalysis

MODIS LAI1000LUE Zero Order

Shaded boxes refer to model components that are not considered or needed within the model.

a Model acronyms are de?ned and additional model information is provided in Supplementary Information.

b Enzyme kinetic(EK)and light-use ef?ciency(LUE).

will likely impact their estimations of Rh and overall net carbon dynamics.

Some models lack soil carbon pools/layers altogether,and het-erotrophic respiration is thus not explicitly calculated.Others calculate soil respiration as an empirical function of moisture and temperature conditions(e.g.,zero-order).In most models,how-ever,soil organic matter decomposition is based on?rst-order kinetics,where the rate of decomposition is a function of the size of the soil carbon pool(e.g.,amount of carbon),a simple decom-position constant,as well as temperature and moisture limitations (Reichstein and Beer,2008).The in?uence of nitrogen(N)dynam-ics and cycling on soil carbon decomposition may or may not be considered by the model(Table2).In this analysis,two soil car-bon dynamics classi?cations are used:models with(1)dynamic soil carbon pools,with?rst-order soil carbon decomposition rates and(2)dynamic soil carbon pools that include nitrogen cycling and limitations,with?rst-order soil carbon decomposition rates.A few of the models consider zero-order soil decomposition,and there-fore lack soil carbon pools altogether and were not included in the comparison of heterotrophic respiration.

Models were also classi?ed by other factors that affect their dynamics,including whether they consider?re disturbances and land-use change;and whether transient CO2,or the combination of transient CO2and N deposition forcings are included within the model(Tables1and3).Although many of these classi?cations are not mutually exclusive(e.g.,many prognostic models use an EK photosynthetic formulation),their use of in model evaluation helps to identify potential sources of variability that drive differences in GPP and Rh,which translate into differences in net ecosystem productivity(NEP).

4.Results and discussion

4.1.Magnitude and distribution of carbon sources and sinks

The carbon?ux that all the models submitted to the RCIS have in common is net ecosystem production(NEP),where NEP is the difference between GPP and the sum of autotrophic and het-erotrophic respiration(Chapin et al.,2006).NEP does not include direct disturbance-induced carbon?uxes,which many models in this study do not consider.If a model does consider disturbances (Table3),however,this can alter carbon pools,and as a result, impact both NPP and Rh.In some models,such as Can-IBIS,the effects of disturbances on NEP are only accounted for at year’s end. As a result,if NEP is compared over the summer months(June,July, August),the?ux estimates from these months will not account for losses due to disturbance.Instead,disturbances will cause additions

D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157149 Table3

Components and processes(including disturbance events)in?uencing the estimation of net ecosystem productivity by each model.

Model a NEP b Land-use/land

cover change Fire c Insect,storm

damage d

Transient forcings e DIC,DOC,

PC losses f

Can-IBIS GPP?(Ra+Rh)–Prognostic–CO2,Ndep DOC CLM-CASA’GPP?(Ra+Rh)Prescribed

land-use

––CO2–

CLM-CN GPP?(Ra+Rh)Prescribed

land-use

Prognostic–CO2,Ndep–DLEM GPP?(Ra+Rh)Prescribed

land-use

––CO2,Ndep CH4loss ISAM NPP?Rh Prescribed

land-use

––CO2,Ndep–

LPJ-wsl GPP?(Ra+Rh)–Prognostic–CO2–

MC1NPP?Rh Prescribed

land-use,

prognostic forest

harvest

Prognostic–CO2,Ndep–

ORCHIDEE GPP?(Ra+Rh)?crop

harvest No land-use/land-

cover change,40%

of cropland

biomass is

harvested

––CO2–

SiB3.1GPP?(Ra+Rh)–––CO2–TEM6GPP?(Ra+Rh)Prescribed

land-use,and

forest harvest

Prescribed–CO2,Ndep DOC

VEGAS2GPP?(Ra+Rh)––Constant

background

mortality rate

from cold and

drought stress

CO2–

BEPS GPP?(Ra+Rh)–––CO2–CASA NPP?Rh–––––NASA CASA NPP?Rh–––CO2,Ndep–CASA GFEDv2NPP?Rh–Prescribed–––EC-LUE GPP only–––––EC-MOD-NEE–––––MOD17+GPP?Re–––––Shaded boxes refer to processes that are not included or considered in the model.

a Model acronyms are de?ned and additional model information is provided in Supplementary Information.

b Net ecosystem productivity(NEP),gross primary productivity(GPP),heterotrophi

c respiration(Rh),autotrophic respiration(Ra).

c Models without prognostic or prescribed.

e Transient atmospheric carbon dioxide concentration(CO2),transient nitrogen deposition(Ndep).

f Dissolved inorganic carbon(DIC),dissolved organic carbon(DOC),particulate carbon(PC).

to litter pools and removals of live vegetation at year end,which will affect the NEP in the following(and subsequent)years.

The spatial distribution of average summer(June,July,August) NEP predicted by the models is shown in Fig.1.Table3provides a list of processes or factors that in?uence each model’s estimate of productivity.Although,as mentioned above,the direct and indi-rect effects of?res in?uence some model estimates of carbon?ux and pools,direct CO2emissions from forest?res are not included in model NEP estimates.Throughout the following discussion a positive(+)sign on NEP indicates net uptake of carbon from the atmosphere by the land,while a negative(?)sign signi?es a net release of carbon from the land back to the atmosphere.During the growing season,the magnitude and spatial distribution of?uxes vary substantially among the models(Fig.1).Some models show strong carbon sources in the Midwest and Southeast portions of the U.S.(e.g.,MC1,LPJ-wsl),Central Plains,West,and Southwest(LPJ-wsl,MOD17+,DLEM),while others estimate large sinks particularly in the Southeast(e.g.,BEPS,EC-MOD,NASA-CASA,Can-IBIS).In the boreal regions of North America,however,there appears to be more consistency among the models.In these northern regions,most models show an overall sink of carbon during the summer months, although the strength of that sink varies across models(Fig.1).

The overall similarities and differences among modeled mean NEP estimates were quanti?ed for each one-degree cell by cal-culating the across-model standard deviation in estimated?ux.During the summer months of June,July,and August,the largest differences between NEP estimates are located in the Midwestern and Southeast regions of the continental U.S.(Fig.2).Much of the across-model spread in summertime NEP in the southeast is driven by differences in predicted GPP(Fig.2).Overall,as expected,the greatest difference in model estimates occurs in areas of larger?ux magnitude.

When?uxes are spatially aggregated to all of North America,the TBMs predict annual NEP ranging from?0.7to+1.7PgC yr?1for prognostic models and?0.3to+2.2PgC yr?1for diagnostic mod-els,with an overall model average of+0.65PgC yr?1for the North American continent(Table4).This model average is consistent with previous estimates of the strength of the North American sink of0.35–0.75PgC yr?1(Goodale et al.,2002;Houghton et al., 1999;CCSP,2007;Pacala et al.,2001;Xiao et al.,2011).Much of the spread in NEP estimates comes from the range in model esti-mates of photosynthesis or GPP,because the majority of models scale autotrophic respiration(Ra)based on their estimates of pho-tosynthesis.TBM estimates of GPP and heterotrophic respiration for North America vary considerably between12.2and32.9PgC yr?1 and5.6and13.2PgC yr?1,respectively(Table4).Overall,prognostic models exhibit greater across-model spread or variability in their net GPP estimates relative to diagnostic models.Prognostic mod-els also estimate a larger net GPP or uptake across North America compared to diagnostic models.

150 D.N.Huntzinger et al./Ecological Modelling 232 (2012) 144–

157

Fig.1.Long-term mean summer (June,July,August)net ecosystem productivity by model (2000–2005).A positive sign indicates net terrestrial carbon uptake from the atmosphere,while a negative sign signi?es net carbon release to the atmosphere.Prognostic models are shown above with a green background;diagnostic models are below with a purple background.

One potential reason for the narrower spread in GPP among the diagnostic models is that several of the diagnostic models (EC-LUE,EC-MOD,MOD17+)presented in this study are calibrated to ?ux tower data and use similar satellite observations for provid-ing LAI and fPAR.As a result,their ?ux estimates tend to be more similar among themselves relative to the differences among prog-nostic models.However,only three of the eight diagnostic models explicitly calibrate their models using ?ux tower data,so this is un-likely to be the only cause of similarly among the diagnostic models.

It is surprising that diagnostic models have a greater range and standard deviation in NEP than prognostic models,given that diag-nostic models have smaller ranges in the component ?uxes GPP and Rh (Table 4).This indicates that the production and respiration components are less correlated within diagnostic models.

Fluxes were also spatially aggregated to Boreal and Temperate North America;regions de?ned by the TransCom inverse model intercomparison (Gurney et al.,2003).The TransCom regions were chosen for comparison because they cover a majority of North America (minus Greenland,the Northern Queen Elizabeth

Islands,

Fig.2.Across-model standard deviation in long-term mean (2000–2005)summer (June,July,August)model estimates of (A)net ecosystem productivity and (B)gross primary productivity.

D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157151

Fig.3.Model estimates of the long-term mean(2000–2005)seasonal cycle of(A)net ecosystem productivity and(B)gross primary productivity for boreal and temperate North America.

Fig.4.Model estimates of annual gross primary productivity(GPP)for2000through2005for Boreal and Temperate North America.Prognostic models are shown in shades of green;diagnostic models are shown in purple.

152 D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157

Table4

Long-term mean(2000–2005)net ecosystem productivity,gross primary productivity,and heterotrophic respiration estimated by the models in PgC yr?1for North America. Not all models submitted all three?uxes(NEP,GPP,and Rh).To avoid comparing models with limited spatial coverage in a region,only those models with at least80% representation(i.e.,those that estimate?uxes for at least80%of the cells)in a given land region were included in the comparison within that region.

Prognostic models Diagnostic models

Number of models(min,max)Mean Std dev Number of models(min,max)Mean Std dev

Net ecosystem productivity(n=17)

North America9(?0.7,1.7)0.40.46(?0.3,2.2)0.90.7 Boreal NA10(?0.2,0.7)0.10.24(?0.4,0.6)0.10.3 Temperate NA10(?0.5,1.1)0.20.36(?0.1,1.6)0.70.6 Gross primary productivity(n=15)

North America8(12.2,32.9)20.0 6.66(12.2,18.7)14.8 1.9 Boreal NA9(2.2,11.6) 5.7 2.75(2.6,4.4) 3.60.6 Temperate NA8(7.7,21.3)12.3 4.06(8.2,12.6)10.0 1.0 Heterotrophic respiration(n=13)

North America8(5.6,13.2)8.2 2.32(7.4,8.6)8.2–Boreal NA9(1.3,4.6) 2.6 1.12(2.1,2.9) 2.4–Temperate NA9(3.4,7.5) 4.8 1.33(2.4,5.6) 4.5–

Central America,and parts of southern Mexico).Estimates of NEP and GPP by prognostic versus diagnostic models differ considerably in both the depth and timing of the seasonal cycle,with prog-nostic models estimating greater overall productivity during the summer months compared to diagnostic models(Fig.3).These sea-sonal cycle differences translate into large variability in net annual estimates of NEP for2000–2005,ranging from?0.4to0.7PgC yr?1 (Boreal NA)and?0.5to1.6PgC yr?1(Temperate NA)(Table4).

The differences among TBMs are even more apparent when comparing GPP over similar land regions.Overall,prognostic mod-els exhibit a signi?cantly greater across model variability in net annual uptake than diagnostic models(Table4and Fig.4).In order to examine regional differences among the models that may be contributing to variation in their estimates of North American net annual?ux,model estimates of NEP,GPP,and Rh were compared across biomes(Fig.5).To better compare model estimates,aggre-gated?uxes were normalized by the total land area covered by a given model for a given land cover region,and therefore the results are presented as gC m?2yr?1.Recall,that to be included in the com-parison for a given biome,a model must have at least80%spatial coverage within that region.

Model estimates vary considerably in their net annual estimates of?ux with the greatest discrepancies occurring in more pro-ductive regions(e.g.,mixed and deciduous forest,cultivated

and Fig.5.Model estimates of the long-term mean(2000–2005)net ecosystem productivity(NEP),gross primary productivity(GPP)and heterotrophic respiration(Rh)by biome. Biome or vegetative cover classi?cation based on the Global Land Cover2000classi?cation scheme.

D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157153

managed lands;Fig.5).Model estimates of the long-term mean annual NEP in mixed and deciduous forested regions varies from about?25gC m?2yr?1to+250gC m?2yr?1.One explanation for this difference is that models,and their estimates of GPP and Rh, have varying sensitivities to limitations,such as water availability and temperature.In low productivity systems(e.g.,shrublands), limitations are likely strong regardless of a given model’s sensitiv-ity to these limitations.In more productive systems(e.g.,forests and cultivated lands),however,a model’s sensitivity to limiting factors (e.g.water availability)will have a much larger effect,and slight dif-ferences in the sensitivity of GPP and Rh to these limitations could result in more divergent NEP estimates.In addition,from exami-nation of model estimates of long-term mean seasonal cycle at the biome level,it appears that across-model differences in growing season net uptake may be driving some of the average annual NEP and GPP variability among models.Conversely,a similar range in estimated NEP is seen in areas of cultivated and managed lands. For most models,NEP is calculated as the difference between GPP and ecosystem respiration(Rh+Ra).Model estimates of GPP and Rh vary considerably across biomes.However,in more productive areas(e.g.,deciduous shrublands,evergreen and needleleaf),the larger productivity results in more decomposable substrate.As a result,Rh tends to be highly correlated with GPP,which yields rel-atively similar estimates of NEP across models compared to other regions(variability Rh and GPP somewhat cancel each other out).

The potential factors driving the differences seen across models are examined further below by subsetting models based on shared model attributes.

4.2.Attribution of intermodel differences to model formulation

and driver data

Attribution of intermodel differences in net?ux and the long-term mean seasonal cycle of NEP can best be examined through the component?uxes of GPP(photosynthetic uptake)and respi-ratory release of carbon(Rh).Thus,in order to identify potential drivers of differences between models,we compare estimates of component?uxes(e.g.,GPP and Rh)by subsetting models based on differences in their photosynthetic and soil carbon decompo-sition formulations,as well as their treatment of?re disturbance, land cover change and external forcings,such as time-varying CO2 and N deposition.

4.2.1.Differences in gross primary productivity

It is generally assumed that the physiology of photosynthe-sis and the kinetics of Rubisco are relatively well understood at the leaf-level(Collatz et al.,1992;Dai et al.,2004;Farquhar and von Caemmerer,1982).However,there is a great deal of uncer-tainty as to how to scale leaf-level processes up to the canopy or ecosystem level(Chen et al.,1999;Baldocchi and Amthor,2001).In addition,there are uncertainties concerning the exact in?uence of factors such as nitrogen content,nitrogen allocation,and radiative transfer on productivity.These processes must be parameterized in models,and can lead to a potentially large spread in GPP esti-mates across a collection of models.The complications in modeling productivity leads to signi?cant disagreement among the model estimates of GPP,with peak growing season differences of greater than2PgC month?1in both Temperate and Boreal NA TransCom regions(Fig.3),and over1000gC m?2yr?1in regions of mixed and deciduous broadleaf forests and cultivated and managed lands (Fig.5).

Overall,models with photosynthetic formulations based on enzyme kinetics predict a greater mean annual GPP with a larger range in estimates than light-use ef?ciency-based mod-els(Fig.6).Whether photosynthetic formulation is the driving cause of variability in modeled GPP is not clear.For example,Medvigy et al.(2010)found that high-frequency meteorological data profoundly impacts simulated terrestrial carbon dynamics. Using the Ecosystem Demography model version2(ED2)forced with observed meteorology,as well as reanalysis weather,this study found that over an8-year period,differences in climatic driver data alone resulted in a10%difference in net GPP and25% difference in NEP.This work suggests that precipitation and radia-tion data with higher temporal variability yield lower overall GPP and cumulative above ground biomass,due to non-linearities in the photosynthetic functions.Conversely,climate drivers with lower variability,e.g.,from reanalysis weather products,may lead to higher GPP(Medvigy et al.,2010).Model estimates of GPP and NEP are also highly sensitive to biases in solar radiation(e.g.,Ricciuto et al.,in prep,Poulter et al.,2011;Zhao et al.,2011).Finally,many of the EK models examined in this study also model phenology prognostically,which could also explain much of the spread in GPP (Figs.4and6)among the prognostic models.Therefore,much of the spread in GPP estimates in this study is likely to be driven by a combination of differences,including the source of driver data,the temporal variability of meteorological data,prognostic representa-tion of phenology,and/or how changes in sunlight and precipitation affect productivity through the models’choice of photosynthetic formulation.

Disturbances can have a signi?cant and immediate in?uence on ecosystems by redistributing stocks among live and dead organic matter pools and,in the case of?re,the atmosphere.Disturbances can also greatly alter the natural community(e.g.,succession), which can in?uence biogeochemical cycling long after the direct impacts of a disturbance event have passed.To examine the poten-tial impacts of a model’s treatment of disturbance on GPP,models were grouped based on how they account for?re disturbances. Some models explicitly account for the effect of?re either prog-nostically or diagnostically(refer to Supplemental Information). However,a majority of the models in this study do not directly account for?re disturbances or do so implicitly through the use of satellite-based vegetative indices such as LAI or fPAR,which are themselves impacted by?re disturbance.

Overall,models that explicitly account for?re disturbances,and their associated impact on carbon pools,predict a greater mean annual GPP with a larger range in?ux estimates than models with-out disturbance included(Fig.6).The impacts of?re on a given ecosystem depend on a number of factors including the ecosystem type(e.g.,ponderosa pine forest versus grasslands),?re intensity and type(i.e.,stand replacing),and overall scale.For example,a large,stand-replacing?re would likely result in suppressed pro-ductivity(and GPP)for several years following the?re.Conversely, given the right conditions,a?re event could make more nitrogen available for growth(and thereby increase production of leaf tis-sue)and/or for photosynthesis(through higher leaf tissue N in the form of Rubisco).This,however,is balanced by any losses in leaf area during the?re.Many of the models that directly account for ?re also employ an enzyme kinetic approach in their formulation of photosynthesis.Although,how a model accounts for disturbances (including?re)impacts their estimates of carbon pools and stocks, it is not likely the dominant driver for the differences in GPP seen among the participating models in this study.

There are limited datasets with which to compare modeled GPP. Although MODIS-derived estimates of GPP(Heinsch et al.,2006; Running et al.,2004;Zhao et al.,2005)have been favorably com-pared to?ux tower measurements,tower-by-tower comparisons still show signi?cant residuals.MODIS GPP is fundamentally a mod-eled product,not a direct observation.The MODIS product and other LUE-based models are similar in their estimates of net uptake, and generally predict lower productivity than models in which pho-tosynthesis is more physiologically based(Figs.4and6and Table3). For example,when totalled over the growing season and annually,

154 D.N.Huntzinger et al./Ecological Modelling 232 (2012) 144–

157

Fig.6.Model estimates of (A)gross primary productivity (GPP)and (B)heterotrophic respiration (Rh)for Temperate North America,grouped by decomposition kinetics,photosynthetic formulation (enzyme kinetic versus light-use ef?ciency),and whether ?re disturbance,land-cover/land-use changes,and transient forcings were considered by the models.See Tables 2and 3for more information.

many of the prognostic models in this study estimate 1.2–2times the GPP predicted by the diagnostic or light-use ef?ciency based models.Razcka and Davis (personal communications)compared the TBM estimates in this study to ?ux tower measurements.They found that the mean GPP and ecosystem respiration (Ra +Rh)from the models is about 30–40%greater in most biomes (not includ-ing deciduous broadleaf forests)compared to those derived from eddy-covariance (EC)measurements at ?ux tower sites.As a result,although similarities exist between the lower end of the model-based GPP estimates and those derived from EC measurements,it is dif?cult to say whether these lower GPP estimates are more correct.

In addition to the in?uence of environmental drivers discussed above,whether a model accounts for time-varying CO 2and/or nitrogen deposition could contribute to the differences in net car-bon uptake simulated by the models (Fig.6).Friedlingstein et al.(2006),for example,showed greater carbon uptake by ecosystems in uncoupled TBMs as a result of increased atmospheric CO 2con-centration.

4.2.2.Variability in heterotrophic respiration

Heterotrophic respiration is also dif?cult to model at a funda-mental scale due to its dependence on poorly understood,complex processes,as well as the need to track diverse carbon pools of varying decomposability (Jastrow,1996;Oades,1988;Parton et al.,1987).While the overall magnitude in Rh is smaller than that of GPP,the variation among models is still large,with estimates differing by 50–600gC m ?2yr ?1(Fig.5).Models that estimate soil carbon decomposition based on zero-order kinetics (i.e.,decomposition

rate independent of concentration)do not explicitly calculate Rh,and they are therefore not included in Fig.6.Estimates of Rh from models with both ?rst-order soil carbon decomposition rates,which also include nitrogen cycling,tend to exhibit a shallower seasonal cycle and less overall soil C release than models without N cycling.Nitrogen limitations on microbial decomposition could result in slower decomposition rates (Thornton et al.,2007;Yang et al.,2009).However,this in turn would reduce the rate of N availability for plant growth.The models that consider nitrogen deposition (in addition to CO 2)do not have lower GPP and may have a slightly larger GPP than the models that do not include N depo-sition (Fig.6A).This added N from atmospheric deposition may,at least for North America,be enough to compensate for the reduction in N from decomposition,thus supplying the N required for GPP.Overall,the differences in modeled GPP and Rh do not translate into large differences in the long-term mean seasonal cycle of NEP (Fig.3),in part,because within many models respiration is highly correlated to GPP.This is also observed in other studies (e.g.,Poulter et al.,2011)where modeled Rh tends to respond proportionally to changes in GPP or productivity,resulting in a smaller net range in absolute NEP among the models (Table 4).

5.Conclusions

This study brings together estimates of land-atmosphere carbon exchange from nineteen prognostic and diagnostic TBMs,in order to assess the current understanding of the terrestrial carbon cycle in North America.The models differ substantially in their estimates of net ecosystem productivity,as well as gross primary productivity

D.N.Huntzinger et al./Ecological Modelling232 (2012) 144–157155

and respiration.Prognostic models exhibit greater overall range in their estimates and predict larger net uptake of carbon over North America relative to diagnostic models.

Photosynthetic formulation,the source and variability of cli-matic driver data,and how phenology is described all appear to in?uence the across-model difference in estimated?uxes,and the magnitude of overall carbon uptake predicted by the models. Much of the variability in modeled Rh is likely driven by vari-ability in GPP,because the majority of models scale respiration based on their estimates of photosynthesis.While this type of scaling may be appropriate for forested regions where GPP and Rh are closely linked,this assumption is probably not appropri-ate for more managed lands(e.g.,agricultural lands and forest plantations in the U.S.Southeast),where harvest,lateral trans-port,and other management activities can impact where carbon is respired.

For many biome types(e.g.,evergreen and needleleaf,decidu-ous and herbaceous shrublands),there is a large range in both GPP and Rh,but a relatively small range in model-estimated NEP.This trend in simulation results is consistent with the work of Raczka and Davis(2011,personal communication),which compares model derived estimates of GPP and respiration to those inferred from?ux tower observations.Thus,models that overestimate(or underesti-mate)GPP and Rh can still predict plausible values for NEP,but for the wrong reasons.For example,models that are calibrated to?ux tower observations may be“tuned”to NEP,particularly when GPP and Rh observations are scarce.The?ux tower records can help to interpret the cause of model difference,and suggest that the lower range of GPP in this collection of TBM models may be closer to tower-based observations.What we cannot tell from comparisons with observations,however,whether the model estimates repro-duce observations for the right reasons(i.e.,whether processes accurately are represented in the model).

Overall,?ux estimates are a function not only of model algorith-mic formulation,but also how models were calibrated(or tuned), initial conditions(e.g.,soil properties,vegetation,and land-use), driver data(e.g.,weather,CO2concentration),and their treat-ment of disturbances(e.g.,?re,wind,disease).The entire modeling framework contributes to the results,and therefore all of the com-ponents require evaluation.The study reveals the large variation in TBM estimates of long-term mean net ecosystem productiv-ity,as well as discrepancies in the magnitude and timing of the seasonal cycle.The results also provide a sobering picture of the current lack of consensus among model estimates of land-atmosphere carbon exchange across North America.Attributing the cross-model variability to differences in modeling approaches and driving data is dif?cult,however,given the focus on existing results from models run using a wide range of assumptions and inputs. Developing,improving,and evaluating TBMs such that they can provide useable forecasts(and past diagnoses)at near-term,inter-annual,decadal,and century timescales requires developments in quantitative model evaluation and rigorous benchmark develop-ment.While we were able to attribute some of this variation to model structure and aspects of model driver data,a more formal model-data comparison is required to more de?nitively quantify the impact of model formulation and supporting and driver data on the accuracy of the simulation outputs.Such efforts require substantial technical support for model participation,the devel-opment of consistent and optimal environmental driver datasets, a uni?ed intercomparison protocol,as well as coordination of the intercomparison effort across research groups.These types of efforts are underway,including several projects working to under-stand how model formulation and model choices impact overall model performance through the use of detailed simulation proto-col and controlled input environmental driver data(e.g.,Schwalm et al.,2010)and the Multi-Scale Synthesis and Terrestrial Model Intercomparison Project(MsTMIP),which directly builds of the NACP regional interim synthesis present here. Acknowledgements

The interim-synthesis activity represents a grass-roots effort by the carbon cycle community,conducted largely on a volun-teer basis.We would particularly like to thank all of the modeling teams that participated in the synthesis activities,sharing results from their ongoing work,and providing feedback during the work-shops.We also thank MAST-DC at Oak Ridge National Laboratory for data management support;MAST-DC(Project NNH06AE47I) is a Carbon Cycle Interagency Working Group Project funded by NASA’s Terrestrial Ecology Program.Funding was also provided by the National Aeronautics and Space Administration(NASA)under Grant No.NNX06AE84G“Constraining North American Fluxes of Car-bon Dioxide and Inferring their Spatiotemporal Covariances through Assimilation of Remote Sensing and Atmospheric Data in a Geosta-tistical Framework”issued through the ROSES A.6North American Carbon Program.

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