Inter-annual variability of carbon and water fluxes in Amazonian forest, Cerrado and pasture sites

12 Pages·2017·2.47 MB·English

Universidade de São Paulo
Biblioteca Digital da Produção Intelectual - BDPI
Departamento de Ciências Atmosféricas - IAG/ACA Artigos e Materiais de Revistas Científicas - IAG/ACA

2013

Inter-annual variability of carbon and water
fluxes in Amazonian forest, Cerrado and
pasture sites, as simulated by terrestrial
biosphere models

Agricultura and Forest Meteorology, Amsterdam, v. 182/183, p. 145-155, 2013
http://www.producao.usp.br/handle/BDPI/46283

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AgriculturalandForestMeteorology182–183 (2013) 145–155

ContentslistsavailableatScienceDirect

Agricultural and Forest Meteorology

journal homepage: www.elsevier.com/locate/agrformet

Inter-annual variability of carbon and water fluxes in Amazonian

forest, Cerrado and pasture sites, as simulated by terrestrial

biosphere models

CelsovonRandowa,∗,MarceloZeria,NataliaRestrepo-Coupeb,MichelN.Muzac,

LuisGustavoG.deGonc¸alvesd,MarcosH.Costae,AlessandroC.Araujof,

AntonioO.Manzig,HumbertoR.daRochah,ScottR.Saleskai,M.AlafArainj,IanT.Bakerk,

BrunoP.Cestaroh,BradleyChristofferseni,PhilippeCiaisl,JoshuaB.Fisherm,

DavidGalbraithn,XiaodanGuano,BartvandenHurkp,KazuhitoIchiiq,

HewlleyImbuzeiroe,AtulJainr,NaomiLevines,GonzaloMiguez-Machot,BenPoulterl,

DeboraR.Robertiu,AlokSahoov,KevinSchaeferw,MingjieShio,HanqinTianx,

HansVerbeecky,Zong-LiangYango

aCentrodeCiênciadoSistemaTerrestre,InstitutoNacionaldePesquisasEspaciais,CachoeiraPaulista,SP,Brazil
bPlantF un ctionalB iol ogyand ClimateC hangeClu ster,Univ er sityofTech nology,Sy dney,Aus tralia
cInstitu toFederal deSanta Cat arina,Fl orianóp olis,SC, Brazil
dCentrod ePrevisã o deTem poeEstu dosClimáticos ,In stitutoNacionaldePesquisasEspaciais,CachoeiraPaulista,SP,Brazil
eDep.Ag ric ulturalE ngi neering ,F ederalU niversityo fVic¸osa,V ic¸osa,MG ,B razil
fEmbr apaAmazôn iaOriental,B elém,PA ,Brazil
gInstituto Nacionald ePesquis asdaA maz ônia(INPA),Manaus,AM,Brazil
hDepartam entode Ciê nciasAtm osfe ricas,IAG, Univers idadede São Paulo,SãoPaulo,Brazil
iDepartmentof Eco logyand Evolutionary Biolo gy,University of Ariz ona,T ucso n,AZ,U SA
jSchoolofGe ogr aphyan dEa rthSciences, McMaste rUniversit y, Hamilton ,ON,Ca nad a
kAtmos ph ericScience Dep artme nt,Colora doStateU niversity,F ortCollins, CO, USA
lLaboratoired esScien cesduClimat etdel’En viron nement,CE AOrm edesM eri siers,GifsurYvette,France
mJetPropuls ionL aborato ry, Californ ia Ins tituteofTechnolo gy,P asade na, CA,USA
nSch oolofGeog raphy,Unive rsityofLe eds,Leed s,U K
oCenter for Integrated EarthSyste m Scienc e,Dep artmentofGeologicalSciences,TheUniversityofTexasatAustin,Austin,TX,USA
pRoyalN eth erlandsM eteoro logicalI nstitute (KNMI),DeB ilt ,TheNethe rlands
qFacult yofSymbiot icSystemsScien ce,Fukus himaUn ive rsity ,Jap an
rDepartm en tofAtmo sphericS ciences, Universityo fIllinoisat Urbana-Champaign,Urbana,IL,USA
sDepartment of Organismica ndEvolu tionaryBio log y,Harv ar dUniversity,Cambrid ge,MA, US A
tCondensedM a tterPhysics Depa rtment,Univ ersityof Santiago deCompos tela,Santiag ode Compostela,Spain
uDeptofPhy sics,Fe deralUn iversityofSa ntaMaria, Sa ntaMari a,R S,Brazil
vDepa rtm entofC iviland Environme nt alEng ineerin g,Prin cetonU niv ersity,Princeton,NJ,USA
wNationalSn ow and IceD ataCenter,Un iversityofCo loradoatB oulder,Bou lder,CO,U SA
xSchoolof Forest ryan dW ildli feScien ces,Auburn U niversity, Au burn,AL ,USA
yLabora tor yofPlan tEco logy,Gh entUniv ersity,G hent,Belgiu m

a r t i c l e i n f o a b s t r a c t

Articlehistory: Thisstudyanalyzestheinter-annualvariability(IAV)ofsimulationsof21differentlandsurfacemodel
Receiv ed31July2012 form ulatio ns,driven bym eteorologica lcondition smea su redat8fluxt ow ers ,locatedi nrain forest, forest-
AReccceepivteedd i1n6 r Mevaisy e2d0 f1o3rm 12 May 2013 savanna ecoto ne and pa sture sites in A mazonia, a nd one in sa va nna site in Southea st ern B razil. A nnual
totalsofnetecosystemexchange(NEE)ofcarbonandevapotranspiration(ET),measuredandsimulated

byeachmodelforeachsite-year,werecomparedintermsofyear-to-yearvariabilityandpossiblerela-
Keywords: tio ntoc limate dri vers. Resultsh avesh ownthat mo stofm o delssimulati onsforan nual totalsof NEE
Land-surfacemodeling
andET,andIAVofthesefluxes,arefrequentlydifferentfrommeasurements.Theaverageofthemodel
Surfacefluxes
simulationsofannualfluxestendtorespondtoclimaticdriverssimilarlytotheobservations,butwith
Amazonia
Inter-annual variability nthoeticfoeraebslte sditisecsregproaunpci.eAs.l tAhnonuughal tmheeaesnusreemmbelnetso foaf lNlEmEo adreel snyeigealtdisvealys icmorilraerlarteesdu ltto, oannlnyutahl rreaeinmfaolld ienl

∗
Correspondingauthor.Tel.:[phone];fax:[phone].

E-mailaddresses:[email protected],[email protected](C.vonRandow)

.
0168-1923/$–seefrontmatter© 2013 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.agrformet.2013.05.015
146 C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155

formulationsreproduceasignificantnegativecorrelationofsimulatedNEEwithrainfall.FortheIAVofET,

towermeasurementsarecontrolledbyannualvariationsofradiationandthisfeatureiscapturedbythe

ensembleofthemodels,bothatindividualsitesandwhenallforestsitesaregrouped.However,simulated

ETvaluesarealsosignificantlycorrelatedtotheamountofprecipitationinmanymodelsandinthemodel

ensemble,whilethereisnosignificantcorrelationintheobservations.Ingeneral,thesurfacemodelsare

abletoreproducetheresponsesoffluxestoclimaticdrivers,butimprovementsarestillneededtobetter

capturetheirinter-annualvariability.

© 2013 Elsevier B.V. All rights reserved.
1. Introduction 1991),measurementsatthesitesinCentralAmazoniaappearto

have little decline in evapotranspiration. Also, forest photosyn-

The Earth’s atmosphere is in permanent interaction with the thesis appear unaffected by the dry season, even showing some

terrestrial biosphere, forming a coupled system. This interaction enhancementrelatedtohigheravailablesolarenergy(Restrepo-

plays a fundamental role in the climate system and in biogeo- Coupeetal.,2013;Rochaetal.,2009),asmanyoftheseforeststend

chemicalandhydrologicalcyclesthroughtheexchangeofenergy tohavesufficientdryseasonwatersupplyinmostyearsbecause

and mass (for example, water and carbon), between the vegeta- oftherelativelyhighwaterholdingcapacityofthesoilsandthe

tion and the atmospheric boundary layer. With the objective of abilityofdeeprootsystemstoaccesswaterdownto10+mdeep

understanding and predicting these exchanges and their influ- (Bruno et al., 2006; Lola da Costa et al., 2010; Markewitz et al.,

ence in the climate system, the main focus of many studies of 2010;Negrón-Juárezetal.,2007).However,thesitesinSouthern

surface–atmosphereinteractionistoquantifythefluxesoverter- Amazonia, with semi-deciduous forests or transitional forests to

restrial biomes, either by direct measurements in flux towers or Cerradovegetation(Braziliansavanna)anddeforestedareashave

byparameterizationusingland-surfacemodels.Thishasbeenone showndeclinesindryseasonfluxesandclearindicationsofsea-

key objective of the Large Scale Biosphere-Atmosphere Program sonalwaterstress,alsorelatedtomoreintensedryseasonclimate

inAmazonia(LBA),whichinitiatedthescientificinfrastructurefor atthesesites(Rochaetal.,2009;vonRandowetal.,2004).

long-termfluxmeasurementsinBrazilianAmazonia(Kelleretal., By combining information from flux tower observations and

2004). terrestrialprocess-basedmodels,wecanimproveourknowledge

ItisknownthatAmazoniaplaysakeyroleintheregionaland about the functioning of the ecosystems, interaction with the

global climate system, by largely contributing to global surface climate system and possibly identify missing mechanisms that

evapotranspiration (and therefore constituting a large source of couldimprovemodelsimulations(Keenanetal.,2012).Terrestrial

latent heat) and substantially acting in the global carbon cycle. ecosystemmodelsareimportanttoolstoaidstudiesofbiosphere-

HowevertheAmazonforestiscurrentlyfacingrisksduetodefor- atmosphereinteractionandresponsesofecosystemprocessesto

estationpressureandclimatechange(Davidsonetal.,2012;Malhi hypotheticalclimateconditions.Processesarerepresentedinmod-

et al., 2007). On the one hand, evidence from observational and elsofdifferentcomplexities,rangingfromasimplerepresentation

modelingstudies(e.g.Bettsetal.,1997;Nobreetal.,1991;Sampaio ofthetransferofmassandenergyinthesoil–plant–atmosphere

et al., 2007; von Randow et al., 2004; Zhao et al., 2001) show interface,tocomplexversionsthatsimulatechangesincomposi-

thatchangesonsurfacecovermayleadtoasignificantimpacton tion,structureandfunctionofvegetationandsoilbiogeochemistry.

regional and global climate. On the other hand, changes in rain- TheproportionsofIAVdirectlyrelatedtovariabilityinclimate

fall regimes, especially in the dry season, may induce important driversremainasanopenquestionandadetailedassessmentofthe

alterationsoftheterrestrialecosystem. relativerolesofclimateandfunctionalchangeontheinterannual

Carbon and water fluxes in the Amazonian ecosystem are variabilityofCO fluxacrossawiderangeofsitesandclimatezones
2

expected to be coupled to regional climate conditions, but the isstillneeded.TheIAVofcarbonexchangehasbeenfoundtocor-

dynamicmechanismsassociatedwiththeirinter-annualvariability relateclimaticdriverspoorly(Richardsonetal.,2007;Polleyetal.,

(IAV)remainnotfullyunderstood(Nobreetal.,2009).Historical 2010)orstrongly(Yuanetal.,2009;Desai,2010).Thestudyofthe

records of the Amazonian rivers show that IAV of precipitation relationsofclimaticvariablesandfluxesovertheAmazonregion

in Amazonia is significant and dynamically linked with consis- mayprovideimportantnewknowledgeandreducetheuncertainty

tentanomaliesinthesurfacewaterandenergybalancesoverthe abouttheresponsesofthevegetationtonaturalclimatevariations

basinandassociatedwiththeElNin˜o–SouthernOscillationphe- andpossiblefutureextremeconditions.

nomenonoroscillationsintheAtlanticseasurfacetemperature,SST The LBA-DMIP project was designed to synthesize and com-

(Fuetal.,2001;Marengo,1992;Marengoetal.,1998;Povedaetal., pareasuiteofsimulationsoflandsurfaceandterrestrialecosystem

2006;Richeyetal.,1989).However,itmustbeemphasizedthatthe modelsin8fluxtowersoftheLBAprogram,coveringtropicalrain-

combinedtropicalPacificandAtlanticSSTvariabilityexplainslittle forest, Cerrado and pasture sites (Gonc¸alves et al., 2013). In this

morethan50%ofinter-annualprecipitationvarianceoverAmazo- workweanalyzetheinter-annualvariabilityofthefluxesobserved

niaandnotmuchisknownaboutothermechanisms,internalor and simulated by the suite of participating models of the LBA-

externaltotheregion,responsiblefortheremainingunexplained DMIP project, at forest, Cerrado and pasture sites in Amazonia,

IAV(Nobreetal.,2009). withtheobjectiveofgivinginsightintothefollowingquestions:

OneoftheachievementsoftheLBAprogramwastheestablish- how do carbon and water exchange vary from year to year and

mentofanetworkofeddycovariancefluxtowersacrossBrazilian howdothemodelssimulatethisIAVinAmazoniansites?Aredif-

Amazonia, which are providing important knowledge about the ferencesbetweensimulationsandobservationsrelatedtospecific

characteristicsofenergy,waterandcarbonfluxesacrosstheregion sitesorvegetationcover?TheIAVofobservedandsimulatedfluxes

(Araújoetal.,2002;Bormaetal.,2009;Milleretal.,2004;Rocha ismainlyrelatedtowhichclimaticdrivers?

et al., 2002, 2004, 2009; Sakai et al., 2004; Saleska et al., 2003;

von Randow et al., 2004; Zeri and Sá, 2010). While land surface
2. Methods
modelshavehistoricallyrepresentedecosystemsofAmazoniaas

water-limited, predicting dry season declines in evapotranspira- Modeled and observed values at the 8 sites listed in
tionandphoto synthesis( e.g. Costaa ndFoley, 19 97;Nobreetal., Table 1 w ere obtained throug h the LB A-DMI P pro ject
C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155 147

Table1
Listof eddycovariancetowersitesausedintheLBA-MIPproject.

Siteshortcode Sitename Longitude[◦] Latitude[◦] Elev.[m] Biometype Dataavailability

K34 ManausKm34 −60.21 −0261 130 Tropicalrainforest 2002–2005
K67 Santarém Km6 7 −5496 −0285 130 Tropical rainforest 2002–2004
K83 Santarém Km83 −5497 −0302 130 Tropical rainforest 2001–2003
RJA ReservaJa rú −6193 −10.08 191 Tropical rainforest 2000–2002
BAN JavaesR iver –BananalIsland −50.16 −0982 120 Forest-S avannaeco tone 2004–2006
K77 Santar émKm 7 7 −5489 −0302 130 Pasture/Agricul ture 2001–2005
FNS FazendaN ossaS enhora −6236 −10.76 306 Pasture 1999–2001
PDG Reserva Pe-de- Gigante −4765 −2162 690 Savanna 2001–2003

a PrincipleInvestigatorsanddatareferencesforthesetowersitesareasfollows:

K34:Manzi,A.,Nobre,A.(INPA,Brazil)(Araújoetal.,2002),K67:Wofsy,S.(HarvardUniversity,USA),Saleska,S.(UofA,USA),Camargo,A.CENA/USP,Brazil)(Hutyraetal.,

2007;Saleskaetal.,2003),K83:GouldenM.(UCIrvine,USA),Miller,S.(SUNY,Albany,USA),daRocha,H.(USP,Brazil).(Gouldenetal.,2004;Milleretal.,2004;Rochaetal.,

2004),K77:Fitzjarrald,D.(SUNY,Albany,USA)(Sakaietal.,2004),RJA:Manzi,A.(INPA,Brasil),Aguiar,R.(UNIR,Brazil.)(Kruijtetal.,2004;vonRandowetal.,2004),FNS:

Waterloo,M.(VrijeUniversiteitAmsterdam,TheNetherlands),Manzi,A.(INPA,Brazil)(vonRandowetal.,2004),BAN:daRocha,H.(USP,Brazil)(Bormaetal.,2009),PDG:

daRocha,H.(USP,Brazil)(Rochaetal.,2002).

(http://www.climatemodeling.org/lba-mip/). On the scope of theeddycovariancetechniqueinAmazoniansites(Araújoetal.,

the LBA-DMIP project, data collected at 8 flux towers were con- 2002;Kruijtetal.,2004;Milleretal.,2004).Inthispaper,wemain-

sistently checked and gap-filled to drive and validate a suite of tainthedifferentapproachesfornighttimetreatmentateachsite

land-surfaceandterrestrialbiospheremodels.Hereweprovidea asreportedintheirreferencepapers(Table1).

briefdescriptionofthemethodsused,whiledetailsofthesiteloca- Also,evapotranspirationdata(estimatedfromlatentheatflux

tions, data processing and characteristics of all the participating measurements) is likely partially underestimated in some sites,

modelsarepresentedbyGonc¸alvesetal.(2013). eitherduetophysicallimitationsoftheinstrumentation(Massman

Thesitesincludeevergreenforests(K34,K67andK83),asemi-

deciduous broadleaf forest (RJA), a deciduous broadleaf forest

(forest-savanna ecotone, BAN), a savanna biome (PDG), and two

pasturesites(FNSandK77).SevenofeightsitesareintheBrazil-

ianAmazon,whileasavannasiteinthestateofSãoPaulowasalso

included.Themeteorologicalforcingdatacollectedatthesesites

were gap-filled according to a common protocol, providing con-

tinuous dataset for driving models. Also, carbon and latent heat

fluxescollectedusingtheeddy-covariancemethodwereaccumu-

latedintoannualtotalsofnetecosystemexchange(NEE)ofcarbon

and evapotranspiration (ET) and used to infer the magnitude of

inter-annualvariabilityofcarbonandwaterexchange.

Thesuiteofmodelformulationsincludes9dynamicvegetation

modelsand8landsurfacemodels(thatdonotsimulatedynamic

vegetationbutsimulatecarbonandwaterexchangeattimescales

varyingfromhourlytomonthly)thatweredrivenbythestandard-

izedmeteorologicalforcingdatafromthefluxtowers(Gonc¸alves

etal.,2013).Additionally,variantversionsofsomemodelswererun

(suchas,forexample,5differentmodelsderivedusingtheSimple

Biosphere Model, SiB (Sellers et al., 1986) as their basis), result-

inginatotalof21differentmodelformulationsreportedinthis

intercomparison(Table2).Table2includesmodelnumbersused

inFigs.1and2,presentedinSection3.

Allsimulationswereperformedusingstandardversionsofthe

models, using the gap-filled meteorological forcing data at each

site and locally observed values of soil texture and vegetation

characteristics,whereneeded,accordingtothestandardprotocol

described in Appendix 1 of Gonc¸alves et al. (2013). No parame-

teroptimizationormodelcalibrationwasperformedpriortothe

intercomparisonruns.

DuetolackofmeasurementsofCO storagewithinthecanopyin
2

somelocations,slightlydifferentapproacheswereusedtoinferNEE

fromtheturbulentcarbonfluxesmeasured(Fc)atthesites.When-

everavailable,thecanopystorageflux(Sc)wasaddedtoFctoinfer

thebioticNEE.Duringinstrumentmalfunctions,Sc wasmodeled

atRJAandK34,followingIwataetal.(2005).Forsiteswithlower

biomassandwherethefullinstrumentationwasnotavailable(FNS,

BANandPDG),weassumedthatannualNEEisequivalenttoannual
total sof Fc. lFiisgte. d1.i nBToaxbplleo1tsw oift hanthneuaslu iNteeto fEtceorsryesstterimal Ebxiocshpahnegree (mNoEdEe) lssilmisuteladtiendT aatb ltehe2 Esiatcehs

ThecorrectionofnighttimeNEEvaluesforperiodsoflowtur- boxplotisadistributionoftheannualsite-yeartotalssimulatedbyoneparticular
bulent mixingisa lso acomple xissu eand isp robably the big gest model,f or (a )forestsites (K 34, K67,K8 3andRJA );and (b)cerrad oo rpa sturesites

(BAN,FNS,K77,PDG).Shadedareasshowtheinter-quartilerangeofobservations
causeofuncertaintiesintheaccountingofcarbonexchangeusing
atthesites.
148 C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155

Table2

Summaryofmodelsanditsvariantsused.

Model# ModelAcronym Simulatesenergyandwaterfluxes Simulatescarbonfluxes Simulatesdynamicvegetation Reference

1 Biome-BGC X X Thorntonetal.(2002)

2 CLM35-DGVM X X X Levisetal.(2004)

3 CLM35 X X Olesonetal.(2008)

4 CLM4CN X X Thorntonetal.(2007)

5 DLEM X X X Tianetal.(2010)

6 ISAM X X Yangetal.(2009)

7 JULES X X X Clarketal.(2011)

8 LEAF2-HYDRO X Miguez-Machoetal.(2007)

9 Noah-MP X X X Niuetal.(2011)

10 ORCHIDEE X X X Krinneretal.(2005)

11 SSiB2 X X Zhanetal.(2003)

12 SiB3 X X Bakeretal.(2008)

13 SiBCASA X X Schaeferetal.(2008)

14 CN-CLASS X X X Arainetal.(2006)

15 ED2 X X X Medvigyetal.(2009)

16 PT-JPL X Fisheretal.(2008)

17 H-TESSEL X Balsamoetal.(2009)

18 IBIS X X X Kuchariketal.(2000)

19 LPJ X X X Sitchetal.(2003)

20 SiB2 X X Sellersetal.(1996)

21 SiB2(modified) X X Rochaetal.(inpreparation)

andLee,2002)orlossesonscalesoftheorderofmorethan30min. Kandaetal.,2004)whichdonotmovewiththewindfastenough

StudieshaveshownthattheatmosphericboundarylayerinAma- tobeadequatelysampledinthetimescalesusuallyusedineddy

zonia(vonRandowetal.,2002,2008)frequentlypresentsslowly covariance.

movinglargeeddiescausedbystrongconvectivemotionsand/or Whennecessary,theevapotranspirationfluxeswerecorrected

localcirculationsinducedbysurfaceheterogeneity,andturbulence toachieveenergybalanceclosuremaintainingtheBowenratioas

is organized into “turbulent organized structures” (Foken, 2008; measuredbytheeddyflux(vonRandowetal.,2004).Thisapproach

ispreferredwhenitislikelythattheunderestimationofthefluxesis

causednotbytheinstrumentlimitationsbutbecauseofafailureto

capturelow-frequencytransportoradvectionorfromamismatch

betweenfootprintsofthefluxmeasurementscomparedtothatof

theradiationmeasurements.FromthepreviousstudiesinAmazo-

nia(vonRandowetal.,2004;Finniganetal.,2003),weconcluded

thatthisapproachisappropriate.

ToevaluatetheIAVofobservedandmodeledfluxes,siteswere

separatedintwogroups:rainforestsites(K34,K67,K83andRJA)

andCerrado/pasturesites(BAN,K77,FNSandPDG),resultingina

totalof13site-yearsavailableintherainforestgroupand14site-

yearsintheCerrado/pasturegroup.Althoughgroupingofthesites

intobroadcategoriesmayaugmentthespuriousvariabilityineach

group,thisclassificationisnecessarybecausethedatasetislimited

for a more detailed analysis. Still, figures in the next section are

presentedshowingeachsiteindifferentcolors.Model‘biases’were

then calculated as the difference between annual simulated flux

andannualmeasuredfluxateachsite-year.

Finally,toanalyzepossibledriversofIAVatthesites,weinvesti-

gatepossiblerelationsbetweenthefluxes(asmeasuredormodeled

ateachsite-year)andclimatevariablesRn,Pandannualvaluesof

Budyko’sdrynessindex(D)givenby

R
D= n (1)
(cid:2)P
where Rn is the annual net radiation in MJ/m2, P is the annual
precipi tation inm m,and (cid:2)(= 245MJ/kg )is thelate nt he atof vapor-

ization.
3. Results

3.1. Inter-annualvariabilityandcomparisontoobservations

AftercomputingtheannualtotalsofNEE(tC/ha)andET(mm)

as measured and simulated by each model in each site-year, we

separated the results into categories “Forest sites” and “Cerrado

andpasturesites”,andbuiltboxplotsforeachmodelformulation,
lFiisgte. d2. inSaTmabel eas1 Fwigi.t h1,t bhuets fuoirt eanonfutearlr Eevstarpiaoltrbaionsspphirearteiomn o(EdTe)ls sliimstueldatiendT aatb tlehe2 .sites whi charedi splayi nFig .1
C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155 149

Fig.3. Frequencydistributionofmodelbias(modeled–measured)overallyearsattheLBA-DMIPsites,for(a)NEEatforestsites;(b)NEEatCerradoandpasturesites;(c)

Evapotranspirationatforestsitesand(d)evapotranspirationatCerradoandpasturesites.

TheshadedareasinFig.1showtheinter-quartilerangeofobser- simulatedthefluxesinthesecategories.InFig.3,thedistributions

vationsattheforestsites(Fig.1a)andattheCerradoandpasture aggregateallmodelsimulationsinonesite.

sites(Fig.1b),which,fortheforestcategory,spanannualNEEval- ResultsinFig.3ashowthat,fortheforestsites,themodelbias
uesfr om− 25t C/ha(n ega tive value srepresen tnets inkofc arbo nby is in gener al nor ma lly dis tribu ted from nega tive t o p ositive val-

ecosystem)inthefirstquartile,tonearlynull(nonetsinkorsource ues,althoughslightlyskewedtopositivevalues.Fig.3bshows,for

atsomesite-years)inthethirdquartile.FortheCerradoandpasture theCerradoandpasturesites,thatthemodelsgenerallysimulate
sit es,th eobserved in ter- quart ilerange isf rom −9to− 5tC /ha. high erNEEt hano bserved ,or,ra ther ,du etomos ttowersm easuring

Distributions of annual modeled NEE show that most models highcarbonuptake(thereforestronglynegativeannualNEE),the

havelowerIAVthanobserved(Fig.1).Also,somemodelshavelarge differencebetweenmodelandobservationsisfrequentlypositive.

biascomparedtotherangeofobservations,especiallyatCerrado Fig.3canddshowsthat,forevapotranspiration,themodelbiases

andpasturesites.Itshouldbenoted,however,thatthesebiasesmay distributionsarewiderandmorevariable,withconsiderablepos-

bepartlyduetothetendencyofmodelsgenerallybeingheldtocon- itivevaluesatsomesitesandnegativevaluesatothers.Asnoted

serveenergy,moistureandcarbonbalance,andeddycovariance from Fig. 2, there is a tendency of underestimation of annual ET

flux measurements being largely prone to uncertainties in those intheforestsites,andwecanseethatthisisalsothecaseforthe

balances(Araújoetal.,2010;Milleretal.,2004). Cerradosites.

The observed and modeled IAV of evapotranspiration is pre-

sentedinFig.2ForETinforestsites(Fig.2a),somemodelsappear 3.2. Relationswithclimatedrivers

to present similar ranges of IAV as measured in the flux towers,

butthemajorityofthemodelsunderestimatetheannualETmea- Studies of responses of carbon and water fluxes to climate

sured.FortheCerradoandpasturesites(Fig.2b),theperformance driversarefundamentallyimportanttounderstandtheinteraction

isslightlybetter:mostmodelssimulateIAVsimilartotherange between the terrestrial biosphere and the atmosphere, and pos-

observed,and,althoughsomearealsounderestimatingthefluxes, sible climate-carbon cycle feedback. Recent studies in Amazonia

theyagreebetterwiththeobservedfluxesforthiscategorythan haveaddressedaspectsofseasonalvariationsofcarbonandwater

fortheforestssites. fluxesandcontrolsofradiationorprecipitation(Costaetal.,2010;

Toidentifywithbetterclaritythedifferencesbetweenmodel Restrepo-Coupeetal.,2013;Rochaetal.,2009),butthereisstill

simulationsandfluxmeasurements,wepresentinFig.3thedis- littleinformationavailableaboutvariabilityonlongertimescales.

tributionofmodelbiasforeachsite.Modelbias,inthiscontext, InFigs.4–7generalrelationsbetweentheannualvaluesofcar-

iscalculatedasthedifferencebetweenannualfluxessimulatedby bonandwaterexchangewithclimatevariablesarepresented,as

each model and measured at the towers. Note that, in previous measured at each tower and as an ensemble mean of all model

figures,distributionswereaggregatingdataforall“forest”or“Cer- simulationsateachsite-year.Intheseplots,weagainaggregateall

rado/pasture”sites,andshowingthevariabilityofhoweachmodel site-yearsoftwocategories(“forestsites”and“Cerradoandpasture
150 C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155

Fig.4. AnnualNetEcosystemExchangeversusannualaverages(orsums,incaseofannualrainfall)ofclimatedriversasobserved(toppanels)oraveragedoverthesuiteof

LBA-DMIPparticipatingmodels(bottompanels),attheforestsitesK34,K67,K83andRJA.

Fig.5. SameasinFig.4,butfortheCerradositesBANandPDG,andforthepasturesitesFNSandK77.
C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155 151

Fig.6. AnnualEvapotranspirationversusannualaverages(orsums,incaseofannualrainfall)ofclimatedriversasobserved(toppanels)oraveragedoverthesuiteof

LBA-DMIPparticipatingmodels(bottompanels),attheforestsitesK34,K67,K83andRJA.

Fig.7. SameasinFig.6,butfortheCerradositesBANandPDG,andforthepasturesitesFNSandK77.
152 C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155

sites”)inoneplot,attemptingtoenlightengeneralmaindriversof modelsimulationsandeddycovariancefluxmeasurementsincom-

variabilityofannualfluxes. plexsurfacesshouldalwaysbemadewithcaution.

Fig. 4a–f shows the NEE against annual average net radiation Itisknownthateddyfluxmeasurementsareinherentlyuncer-

(Rn),totalprecipitation(P)andtheDrynessindex(D)forthefor- tain due to different sources of errors, such as random errors

est sites. There are similarities in the general responses of the associatedwiththestochasticnatureofturbulence,andsystem-

modelsimulationstotheclimatevariablesastowhatisobserved atic errors caused by inadequate system design or violation of

inthetowers,butalsosomedifferencesappear.Themagnitudeof assumptions in the methodology (as, e.g., low turbulence condi-

variabilityamongthesitesisbiggerthanthevariabilityofmodel tions,cold-airdrainage,gravitywavesorother3Dflowregimes).

simulations(notethescaleofthey-axisinthetoppanelsisbigger These errors have been studied in the flux sites by the different

thaninthebottompanels).Also,thereappearstobelittlerelation researchteamsresponsibleforthesesites(e.g.Araújoetal.,2002;

oftheobservedfluxeswithRn(Fig.4a),butthemodelsareclearly Kruijtetal.,2004;Milleretal.,2004;vonRandowetal.,2004;Zeri

radiation-controlled(Fig.4d).Ontheotherhand,itispossibleto andSá,2011),butfullaccountingofuncertaintiesatallthesites

seethatthesitessubjecttolesserannualrainfallhaveloweruptake usingaconsistentmethodologystillremainstobequantified.

(andsomesite-years,infact,resultedinasourceofcarbontothe In general, processes and environmental factors governing

atmosphere) than others. This results in a pattern of higher net inter-annualvariabilityinNEEarealsonotwellunderstood,largely

uptakeinsite-yearswithhigherannualrainfallorlesserD(Fig.4b because NEE is the difference between two large quantities, the

andc),whichiscapturedbythemodels(Fig.4eandf). GrossPrimaryProduction(GPP)andtheTerrestrialEcosystemRes-

Fig.5a–fshowstheNEEasmeasuredandmodeledattheCer- piration (TER), each with different major climatic drivers (and

radoandpasturesites,inrelationtotheclimatevariables.Inthis responding to processes on different scales) and different biotic

category,itishardtoseeaclearrelationwithanyoftheclimate controls.

variables.Itislikelythatthisgroupingofsiteswithverydifferent OurestimatesofthemagnitudeofIAV,representedbytheinter-

vegetationcoversandlimiteddatasetisnotsuitabletotheanalysis quartilerangeofobservedannualfluxes,showthatthevariability

proposedhere. ofNEEisofthesameorderofthemeanannualfluxesmeasured

Fig.6a–fshowstheannualevapotranspirationintheforestsites. atthesites,andabout10–25%ofthemean,fortheevapotranspi-

We can depict that Rn largely controls annual ET (Fig. 6a), and ration.TheseresultsaresimilartotheresultsobtainedbyKeenan

this pattern is well captured by the models for individual sites et al. (2012), who analyzed the IAV at 11 long-term flux sites in

(Fig.6d),butwithoutasignificantcorrelationwhenallforestsites North America. The authors also obtained that a suite of 16 ter-

aregrouped(seelater,inTable3).Thisresultcorroboratesprevi- restrialbiospheremodelshavedifficultyinreproducingtheIAV,

ousstudiesthatshowedthatthereisastrongcontrolofRnonETon possiblybecauseofmisrepresentationofspringcanopyphenology,

seasonalscales(Costaetal.,2010;Fisheretal.,2009;Rochaetal., soilthawandsnowpackmelting,andlaggedresponsetoextreme

2009).Thereisweakrelationwithprecipitationandwiththedry- climaticevents.

nessindexD,althoughthemodelsaresensitivetothesevariables Togaininsightaboutthemainclimaticdriversthataffectcarbon

(Fig.6b,cande,f). and water exchange in the different sites and biomes, we ana-

Finally,Fig.7a–fshowstheannualETintheCerradoandpasture lyzedinFigs.4–7therelationsbetweenannualNEEandETwith

sites.Themodelsimulationsappeartohaveageneralrelationwith climaticdriversnetradiation,precipitationanddrynessindex,as

theclimatevariables,butthisisnotsoclearlymeasuredatthesites measuredineachtowerorcomputedbyanaverageofallmodel

(Fig.7a–c).Itispossiblethatthisisalsorelatedtotheaggregation simulationsineachsite-year.However,itshouldbeacknowledged

ofdifferentvegetationcoversinthesamecategory. thatthefluxesunexplainedbytheclimatefactorsmaybeprimarily

To give better insight into how individual model simulations drivenbynon-climatefactorssuchasstandage,disturbancehis-

are related to the environmental drivers, Table 3 presents the tory,speciescomposition,orcanopyleafareaindex,reflectinglocal

correlationcoefficientsbetweentheenvironmentalvariablesand variationinnutrientandwateravailability.Whileitisnotpossible

thefluxes,consideringtheforestsgroup.Onlyvalueswithsignif- todevelopapredictiverelationshipoftheannualfluxeswiththese

icantcorrelations(p-value<0.05)arepresented.Thecorrelations drivers,ourresultsareusefultoevaluatetherelativeimportance
form easurements ofRn×E T andP ×N EEcorrobo rate theprevious ofpartic ular climat icfa ctorsa tin dividual sites .
res ults,showingco rre latio ns of0. 87 a nd− 0.63,respec tive ly.Asthe Otherstu dieshave analyz ed possiblec limaticandnon-climatic

drynessindexDisalsoinverselyrelatedtotheamountofprecipita- driversofNEEandETatterrestrialecosystems.(Jungetal.,2011;
tion,asi gnific an t posit ivecorrel ationis al soo bserved fo rD×NEE. Law et al ., 20 02; Yi et al., 2010) . In the stu dy of Ju ng et al.

Theresultsforindividualmodelsshowthatonlythreemodelfor- (2011),worldwidetowerfluxmeasurementswerescaledupusing

mulationsreproduceasignificantnegativecorrelationofNEEwith a machine learning technique providing global grid products of

rainfall,buttheensembleofallmodelsresultinacorrelationsim- energyfluxesandNEEanditscomponentsGrossPrimaryProduc-

ilartotheobservations.Itisalsointerestingtonotethatmostof tivity(GPP)andTerrestrialEcosystemRespiration(TER),andthey

themodelsandtheaverageofmodelsyieldETfluxescorrelated foundthattheIAVofNEEisdominatedbyvariabilityinGPPforthe

toP,butthetowermeasurementsresultedinannualETonlysig- majorityofthelandsurface,butnotforAmazonianregion,where

nificantlycorrelatedtotheamountofradiation,andnottoannual thedominantvariabilitycomesfromIAVofecosystemrespiration.

rainfall. Then,analyzingtheIAVofTER,theauthorsfoundthatitismore

stronglycorrelatedwithprecipitationthanwithtemperature,what

alsocorroboratesourresults.Thismayberelatedtosoilrespiration

4. Discussionandconcludingremarks intropicalforestsbeingmorelimitedbythemoisturecontentofthe

soillitterthanbyitstemperature.

This study analyzes simulations of 21 different land sur- The correlation coefficients of environmental variables and

face/terrestrial ecosystem model formulations, driven by meteo- fluxes simulated by individual models or measured at the forest

rologicalconditionsmeasuredat8fluxtowersthatweregathered sites,presentedinTable3,indicatethatthenegativecorrelation

in the scope the LBA-DMIP project (Gonc¸alves et al., 2013). The betweenNEEandannualrainfallissignificantinthisdataset.While

resultsshowthatthemagnitudeofcarbonandwaterexchangeand theaverageofthemodelsalsopromoteasimilarcorrelation,only

theIAVassimulatedbymostofthemodelsisdifferentthanwhat threeoftheindividualmodelsshowsignificantvalues.FortheET

isobservedinthetowers.However,directcomparisonsbetween fluxes,thesituationisreversed:measurementsdonotshowany
C.vonRandowetal./AgriculturalandForestMeteorology182–183 (2013) 145–155 153

Table3

Correlationcoefficientsbetweenpossibleenvironmentaldriversandfluxes(annualtotals),assimulatedbythesuiteofsurfacemodelsofLBA-DMIPintheforestsites.Only

valueswithsignificantcorrelations(p-value<0.05)areshown.

ModelAcronym Rn×NEE Rn×ET Precip×NEE Precip×ET D×NEE D×ET

Biome-BGC 0.56

CLM35-DGVM
CLM35 0.67 −0.70

CLM4CN
CN-CLASS 0.68 −0.64
DLEM 0.62 −0.63
ED2 −0.64
H-TE SSEL 0.72 −0.70
IBIS −0.71 0.56 −0.60
ISAM −0.75
JULES −0.75 0.74 0.70 −0.75

LEAF2-HYDRO 0.68 0.56
LPJ 0.61 −0.62

Noah-MP
PT-JPL 0.71 −0.71

ORCHIDEE
SiB2 0.62 −0.67
SiB2 (modif.) −0.63 −0.67 0.71 −0.56
SiB3 0.81 −0.75

SiBCASA
SSiB2 −0.75

Averageofmodels −0.55 0.83 −0.55 0.67
Observa tio ns 0.87 −0.63 0.71

significantcorrelationwithannualprecipitationaccordingtothe Betts, R.A., Cox, P.M., Lee, S.E., Woodward, F.I., 1997. Contrasting physiological
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some e xte nt, these g ener al respo nses, bu t im prov em ents are st ill Clar2k0, D(1.B2.),, M2 4e7r7ca–d2o4,8L9. ,M h. t,tpS:i t/c/dhx,.Sd.o,i J.oornge/s1,0C.1.D0.0,2 G/hedypn.e6y2, 1N1..,Be st,M.J., Pryor,M.,
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Acknowledgements Costa ,M. H.,Biajoli ,M.C.,Sanches,L.,Malhado,A.C.M.,Hutyra,L.R.,Rocha,H.R.,
Ag uiar, R.G., Ar aújo, A.C., 2010 . A tmospher ic versu s veget ation contro ls of
Amazon ian t ropical rain forest evapotranspi ration: are the we t and se a-
We thank innumerous scientists and field technicians who sonally dry rain for ests a ny diff erent? J. Geophys. R es. B ioge osci. 115, 1–9,
operate d the flux towers u nder the L BA p rogra m, funded b y the http://d x.do i.org /10.1029 /200 9JG001179 .
Brazilian Min istry of Scie nce, T echn olog y and Inn ovation , N ASA Costoan, Mve.Hg.e, taFotiloeny, cJo.Av.e, r19a9n7d. cWanaotepry bcaolanndcuec toafn cthe.e JA.mGeaozopnh yBsa.sRine:s .d1e0p2en(dDe2n0c)e,
and European Agencies. We also thank the LBA-Data-Model- 239 73–23989.
Intercomparison project funded by NASA’s Terrestrial Ecology Davidson,E.A.,deAraujo,A.C.,Artaxo,P.,Balch,J.K.,Brown,I.F.,Bustamante,M.M.C.,
Program(grantN NX09AL 52G),w hich organiz edthedata setsthat Coe,M .T.,D eF ries,R.S .,Kell er,M.,L on go,M., Mun ger,J.W .,Sc hroeder,W., Soares-
Filho ,B.S. ,Souza Jr.,C .M.,W ofsy ,S.C., 201 2.TheA mazo nbasinin tra nsition.
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