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Agricultural and Forest Meteorology 182– 183 (2013) 177– 188

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

jou rn al hom ep age: www.elsev ier .com/ locate /agr formet

urface ecophysiological behavior across vegetation and moisture gradients inropical South America

.T. Bakera,∗, A.B. Harpera, H.R. da Rochab, A.S. Denninga, A.C. Araújom, L.S. Bormad, H.C. Freitase,.L. Gouldenf, A.O. Manzic, S.D. Millerg, A.D. Nobrec,d, N. Restrepo-Coupeh,l, S.R. Saleskah,

. Stöckli i, C. von Randowj, S.C. Wofsyk

Atmospheric Science Department, Colorado State University, Fort Collins, CO, USADepartamento de Cienciêas Atmosfericas, IAG, Universidade de São Paulo, São Paulo, Brazil

Instituto Nacional de Pesquisas da Amaz◦onia, Manaus, Amazonas, Brazil

Centro de Ciência do Sistema Terrestre/Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, BrazilUniversidade de São Paulo, São Paulo, BrazilDepartment of Earth System Science, University of California, Irvine, CA, USAAtmospheric Sciences Research Center, State University of New York at Albany, Albany, NY, USADepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USAClimate Services, Climate Analysis, MeteoSwiss, Zurich, SwitzerlandInstituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, São Paulo, BrazilDivision of Applied Sciences, Harvard University, Cambridge, MA, USAPlant Functional Biology and Climate Change Cluster, University of Technology, Sydney, New South Wales, AustraliaEmbrapa Amazonia Oriental, Belem, Para, Brazil

r t i c l e i n f o

rticle history:eceived 29 June 2011eceived in revised form 31 July 2012ccepted 26 November 2012

eywords:arbon cyclemazon ecophysiologyurface–atmosphere exchange

a b s t r a c t

Surface ecophysiology at five sites in tropical South America across vegetation and moisture gradients isinvestigated. From the moist northwest (Manaus) to the relatively dry southeast (Pé de Gigante, state ofSão Paulo) simulated seasonal cycles of latent and sensible heat, and carbon flux produced with the Sim-ple Biosphere Model (SiB3) are confronted with observational data. In the northwest, abundant moistureis available, suggesting that the ecosystem is light-limited. In these wettest regions, Bowen ratio is con-sistently low, with little or no annual cycle. Carbon flux shows little or no annual cycle as well; efflux anduptake are determined by high-frequency variability in light and moisture availability. Moving downgra-dient in annual precipitation amount, dry season length is more clearly defined. In these regions, a dry

season sink of carbon is observed and simulated. This sink is the result of the combination of increasedphotosynthetic production due to higher light levels, and decreased respiratory efflux due to soil dry-ing. The differential response time of photosynthetic and respiratory processes produce observed annualcycles of net carbon flux. In drier regions, moisture and carbon fluxes are in-phase; there is carbon uptakeduring seasonal rains and efflux during the dry season. At the driest site, there is also a large annual cycle

t flux

in latent and sensible hea

. Introduction

The Amazon Basin occupies a central position in our abil-ty to understand and predict interactions between earth andtmosphere across multiple spatial and temporal scales. Surface–tmosphere exchange in this region is important to weather and

limate both locally (Fu et al., 1999; Fu and Li, 2004; Li and Fu, 2004)nd globally (Gedney and Valdes, 2000; Werth and Avissar, 2002;chneider et al., 2006; Nobre et al., 2009). The dense forest andarge spatial extent means this region stores a significant fraction of

∗ Corresponding author. Tel.: +1 970 491 4948.E-mail address: baker@atmos.colostate.edu (I.T. Baker).

168-1923/$ – see front matter. Published by Elsevier B.V.ttp://dx.doi.org/10.1016/j.agrformet.2012.11.015

.Published by Elsevier B.V.

global terrestrial biomass (Houghton et al., 2001), and a significantfraction of global species diversity as well (Malhi et al., 2008). It hasbeen predicted that climate change may result in the conversion oflarge areas of the Amazonian forest to seasonal forest, savanna orgrassland, releasing much of the carbon stored at the surface andfurther altering the radiation characteristics of the atmosphere(Cox et al., 2000; Huntingford et al., 2004, 2008). However, con-sensus has not been reached on total conversion fraction or spatialorganization (Malhi et al., 2009; Salazar et al., 2007). Predictionssuch as these place a premium on our ability to understand the sur-

face ecophysiology of tropical systems. If we are to predict globalclimate under changing radiative conditions, we must be able totranslate our understanding of the physical system into numericalmodels, and tropical South America will play a significant role.

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78 I.T. Baker et al. / Agricultural and For

Recent work has debated which mechanism(s) are most respon-ible for determining variability in ecosystem function, and,ue to the tight coupling between the vegetated surface andurface–atmosphere exchange, variability in exchange of energy,oisture and carbon between the atmosphere and terrestrial bio-

phere in the Amazon Basin. It has been proposed that Amazonianorests are light-limited, and respond to relative drought with anncrease in ecophysiological function (Huete et al., 2006; Saleskat al., 2007). However, this finding has been challenged (Samantat al., 2010), citing problems with cloud and aerosol masking ofemotely sensed vegetation characteristics (i.e. Sellers et al., 1996a;os et al., 2000). Xu et al. (2011) discuss differential response in thereal extent and severity of Amazon Basin droughts in 2005 and010, while Brando et al. (2010) mention the possibility of differ-ntial response across vegetation gradients as well as interactionsetween multiple processes (leaf production, carbon allocation,espiration, mortality) that can combine to produce apparentlyonflicting observations. As of this writing, we do not feel that thessue is closed.

The behavior of the land surface is tightly coupled to the cyclesf wet and dry seasons that define seasonality in the region. In theropical Americas, there is an annual cycle, whereby convectiverecipitation associated with the Intertropical Convergence ZoneITCZ) is centered over the Amazon Basin during austral summerDecember, January, and February). In austral fall (March, April, and

ay) this feature moves northward and westward to a positionver Central America (Horel et al., 1989) where it remains duringoreal summer (June, July, and August). The northward position ofhe precipitation maximum coincides with the wet season north ofhe equator; south of the equator, the wet season is approximatelyoincident with austral summer. At the latitudinal extremities ofhis precipitation oscillation (Central America and southeasternrazil, approximately), annual precipitation variability is domi-ated by the annual cycle (Adler et al., 2003; Horel et al., 1989).etween these spatial endpoints annual precipitation is larger, thery season shorter or almost nonexistent, and interannual vari-bility dominates the precipitation variance (Horel et al., 1989).uperimposed on this mean pattern is variability in circulation andegetation behavior, which can be influenced by topography (Lut al., 2005) or other factors such as soil depth or type (von Randowt al., 2004). Recycling, or the precipitation of water at a site oregion that was locally evapotranspired rather than advected intohe region, is an important component of the Amazonian hydrologicycle and is estimated at 25–35% (Eltahir and Bras, 1994; Trenberth,999; Costa and Foley, 1999).

Seasonal cycles of observed water and heat flux across veg-tation and moisture gradients from forest to savanna has beenartitioned into two functional types (da Rocha et al., 2009; Costat al., 2010). In regions where annual precipitation is large and dryeason short, evaporation increases during seasonal drought. Latenteat flux is in phase with precipitation and evaporation decreasesuring the dry season in regions with a well-defined dry season and

ess annual precipitation. The authors in both papers postulate thatetter forests are light-limited, while evapotranspiration in drier

egions is controlled by soil moisture.In this manuscript, we simulate surface ecophysiology at a sub-

et of the stations investigated by da Rocha et al. (2009). Wevaluate the model’s ability to reproduce observed annual meanehavior across vegetation and moisture gradients. Additionally,e integrate carbon flux into the analysis to investigate full ecosys-

em behavior. The goals of this study are to (1) demonstrate anbility to capture mean annual cycles of biophysical behavior across

egetation and moisture gradients in model simulations and (2) usehe model’s ability to partition processes into component behaviors a means to formulating more detailed conceptual descriptionsf the mechanisms involved.

eteorology 182– 183 (2013) 177– 188

The paper is organized as follows: Methods, including model,sites, and data are introduced in Section 2. The behavior atindividual sites is discussed in Section 3, summarized in Section4, with conclusions in Section 5.

2. Methods

Historically, land surface models have had difficulty reprodu-cing annual cycles of energy, moisture, and carbon flux in tropicalecosystems. Saleska et al. (2003) showed that several modelsinverted the annual carbon flux cycle when compared to observeddata. Baker et al. (2008) demonstrated an ability to capture themean annual cycle of energy, moisture and carbon fluxes, at asingle point in the Tapajos River National Forest (Brazil), by incor-porating observed mechanisms into the Simple Biosphere Model(SiB3). With that as a starting point, in this paper we again con-front model results with observed quantities, this time at multiplesites and across vegetation and moisture gradients. We will focuson annual cycles of energy, moisture and carbon flux, but willevaluate behavior at shorter timescales to support conclusionswhere appropriate.

2.1. Model

The Simple Biosphere Model (SiB) was developed as a lowerboundary for atmospheric models (Sellers et al., 1986), and hasbeen coupled to General Circulation Models (GCMs; Sato et al.,1989; Randall et al., 1996) as well as mesoscale models (Denninget al., 2003; Nicholls et al., 2004; Wang et al., 2007; Corbin et al.,2008). The addition of ecosystem metabolism to the code (Sellerset al., 1996a; Denning et al., 1996) gives the model a high degreeof ecophysiological realism that is valuable to ecologists as well.SiB model output has been compared to eddy covariance obser-vations at sites in midlatitude forest (Baker et al., 2003; Schaeferet al., 2008), grassland (Colello et al., 1998; Hanan et al., 2005),and tropical forest (Baker et al., 2008; Schaefer et al., 2008). Themodel has a proven track record for simulating exchange betweenthe atmosphere and terrestrial biosphere, as evaluated in modelintercomparison studies (Schwalm et al., 2010).

As a ‘third generation’ land surface scheme (Sellers et al., 1997),SiB incorporates ecophysiological function as an additional con-straint on fluxes of latent (LE) and sensible (H) heat. Photosyntheticcarbon assimilation is based on enzyme kinetics developed byFarquhar et al. (1980), and stomatal conductance couples vegeta-tion behavior to the overall surface energy budget (Collatz et al.,1991, 1992; Sellers et al., 1996a; Randall et al., 1996). Soil heat andmoisture flux has been modified to follow the Community LandModel (CLM) (Dai et al., 2003). Root distribution follows Jacksonet al. (1996), and a fully prognostic canopy air space (CAS) for tem-perature and moisture follows Baker et al. (2003) and Vidale andStöckli (2005).

Long term Net Ecosystem Exchange (NEE) of carbon is the smallresidual between large photosynthetic and respiratory fluxes. InSiB, interannual NEE is constrained to zero (Denning et al., 1996)by constraining annual ecosystem respiration (autotrophic and het-erotrophic) to the previous year’s Gross Primary Productivity (GPP).This parameterization removes model dependence on carbon stor-age pools whose size may be unknown.

Remotely sensed information, such as Normalized DifferenceVegetation Index (NDVI; Brown et al., 2004; Tucker et al., 2005;Pinzon et al., 2006) was introduced into SiB (Sellers et al., 1996a,b;Randall et al., 1996) to describe spatiotemporally variable vegeta-

tion phenology. NDVI is used to obtain values of Leaf Area Index(LAI) and fraction of Photosynthetically Active Radiation absorbed(fPAR) (Sellers et al., 1992, 1996b). Due to model formulation,fPAR is the more important quantity for determination of potential

I.T. Baker et al. / Agricultural and Forest Meteorology 182– 183 (2013) 177– 188 179

F and pro icatest

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ig. 1. Site location and mean monthly incoming shortwave radiation, temperature

f months with less than 100 mm of precipitation, is shaded, and a dashed line indhis study is listed at the top of each panel.

hotosynthesis and transpiration rates in SiB. At LAI values above (m2 leaf per m2 ground), fPAR is nearly saturated (cf. Fig. 1 inellers et al., 1992), meaning that meteorological and soil moistureariability will play a larger role (when compared to leaf char-cteristics) in determining ecophysiological response in denselyegetated regions such as tropical forests. As observed LAI in Southmerican tropical forest is usually above ≈4 (Myneni et al., 2007;alhado et al., 2009; Miller et al., 2004), SiB is not acutely respon-

ive to variability in LAI in these regions. In other vegetation typeshere LAI/fPAR are lower (such as southeast Brazil), simulated

uantities show a stronger correlation with spectral vegetationndices.

Modifications to the code since SiB2 was introduced in 1996Sellers et al., 1996a,b) have been described elsewhere (Baker et al.,003, 2008; Hanan et al., 2005; Vidale and Stöckli, 2005). Bakert al. (2008) identified several mechanisms that were required forhe model to capture the annual cycles of energy, moisture, andarbon flux at the K83 site in the Tapajos River National Forest.hey are:

A soil reservoir large enough to store sufficient moisture to sus-tain ecophysiological function through periodic drought. Mostland surface models have a soil depth of 3–4 m, which was foundto be inadequate. A 10-m deep soil was found to be sufficient atthe Tapajos River K83 site, and has been incorporated into SiB as

the standard.Adequate soil moisture is a necessary, but not sufficient mecha-nism to allow vegetation function to survive seasonal drought.Removal of water by roots, usually tied directly to root mass

ecipitation, following Fig. 1 of da Rocha et al. (2009). Dry season, defined as number 10 cm (100 mm) of precipitation. Annual mean precipitation for the years used in

with depth in models, must be relaxed to allow water extractionby deep roots in excess of the amount suggested by root frac-tion. This phenomenon has been observed in multiple species(Oliveira et al., 2005), and allows retrieval of water stored deepin the soil. In SiB, we have developed a ‘relative root fraction’ sys-tem, wherein soil is extracted based on root density when wateris plentiful. When surface soil (where the majority of root massresides) dries, deeper roots are allowed to extract water at a rateexceeding their absolute root density.

Global maps of soil depth are nonexistent or unreliable, so SiBemploys rooting depth as a mechanism to impose heterogeneity ona global 10-m deep soil. Maximum rooting depth of different veg-etation is described in Canadell et al. (1996), while Jackson et al.(1996) give a global map of rooting depth and distribution associ-ated with discrete biome classes.

It has been postulated that hydraulic redistribution, or themovement of water across moisture gradients via roots, plays animportant role in Amazonian forests’ ability to survive seasonaldrought (Lee et al., 2005). In this case hydraulic redistributionfacilitates the movement of water downward during wet periods,increasing soil storage, and moves water upwards, against gravity,rewetting surface soils during seasonal drought. We do not considerhydraulic redistribution in our simulations for two reasons: (1)previous simulations (Baker et al., 2008) show that hydraulic redis-

tribution alone is not sufficient to reproduce observed seasonalityin SiB and (2) simulating hydraulic redistribution requires soil-to-root exchange coefficients that are unknown without detailedsoil/root surveys. We call the current version of the model SiB3.

180 I.T. Baker et al. / Agricultural and Forest M

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Fig. 2. Data availability for the sites used in this study.

.2. Observation sites

The behavior of observed energy and moisture fluxes across veg-tation and precipitation gradients in Amazonia was described ina Rocha et al., 2009), using data from seven stations in Brazil. Weimulated ecophysiological behavior at 5 of these 7 sites, listedn order of decreasing mean annual precipitation: Manaus (K34),aru (RJA), Tapajos River National Forest (K67 and K83), and Pé deigante (PEG) (Fig. 1). All towers are in the Amazon basin exceptEG, which is in São Paulo state. In the model, all sites are classifieds evergreen forest except PEG, which is classified as seasonal for-st. All sites were simulated for either 3 or 4 years over the period000–2005. Data availability for each site is shown in Fig. 2.

.2.1. Data availabilityNumerical simulations require gap-filled meteorology (pres-

ure, temperature, dewpoint, windspeed, longwave and shortwaveadiation, and precipitation) as model inputs. Missing data werenterpolated from neighboring values where gaps were short, androm climatology when gaps were long. Longwave radiation has aignificant impact on surface behavior, and is sporadically mea-ured at the sites used. Traditional techniques used to estimateongwave radiation at midlatitude sites are ineffective in the trop-cs; a new technique has been developed for determining incomingongwave (Restrepo-Coupe et al., in preparation), and we use itere.

Model simulations were evaluated against measured flux ofnergy (sensible heat), moisture (latent heat), and carbon taken athe tower sites. However, not all observations are available at eachite for all times; instrument failure, heavy rain, and low turbu-ence can all impair the ability of an eddy covariance instrument toccurately record data. NEE is the observation of choice for quan-ifying carbon sources and sinks of natural systems. This metricequires measurement of storage within the canopy air in additiono recording the flux of CO2 past a sensor situated above treetop. Theull measurement suite is available for some sites (i.e. K83; Millert al., 2004), but the lack of observations of canopy CO2 concen-ration at some other sites means that reliable NEE is not availableverywhere. Furthermore, at K34 complex terrain has been iden-ified as problematic to the calculation of NEE (von Randow et al.,008). Therefore, we use observed carbon flux measured above theanopy, rather than NEE, as the observational constraint, to main-ain consistency between sites. The prognostic canopy air spaceBaker et al., 2003; Vidale and Stöckli, 2005) makes it possible foriB3 to simulate the raw flux of CO2 past a sensor. Canopy storage isccounted for in SiB3, so model flux of carbon is analogous to whathe sensor sees. Since modeled NEE is constrained to a value of zeron an annual basis (Denning et al., 1996), we focus on the abilityf eddy covariance instruments to detect change and/or ecosystemesponse to variability on multiple timescales, and the ability of the

odel to reproduce this variability. We emphasize mean annual

ycles in this study.Evaluation of model simulations against eddy covariance flux

bservations can be problematic. Models are generally held to

eteorology 182– 183 (2013) 177– 188

energy, moisture and trace gas conservation through the formu-lation of their governing equations. However, determination ofenergy balance closure in eddy covariance data has been an ongoingissue (Wilson et al., 2002; Hollinger and Richardson, 2005; Fokenet al., 2006). Furthermore, the lack of closure in the eddy covarianceenergy budget can imply lack of closure in observed carbon budgetas well (Aranibar et al., 2006). The goal of this paper is not detailedanalysis of observational techniques and data. Instead, we wish toexploit the acknowledged strength of eddy covariance observationsto capture ecosystem response to variability in forcing over multi-ple timescales (diurnal, synoptic, and monthly) for comparison tosimulations.

Monthly mean observed carbon flux shows a net negative value(terrestrial uptake) for almost all months at the stations evalu-ated here. However, it is well-known that drainage (Araújo et al.,2002), energy/carbon budget closure (von Randow et al., 2004),or the lack of storage observations all contribute uncertainty toobserved carbon flux. Therefore, we calculate the monthly anomalyfor comparing observed annual cycles of carbon flux to simula-tions. Anomaly in this context means the difference between themonthly value and the average over all months of the observationalrecord. This metric neglects determination of observed source/sinkon timescales longer than monthly, which is consistent with theannual balance property of SiB3 (Denning et al., 1996). Deviationfrom the monthly average carbon flux value is also used in plots ofdaily average. No adjustment is made to observed latent or sensibleheat flux.

3. Analysis

If we are to use a model to parse out elements of ecophysiolog-ical behavior, we must first evaluate the model against availableobservations. In this section we will show that SiB3 demonstratescompetence when confronted with observational data across all 5sites. Once established against observations, model representationof component mechanisms and interpretation of ecophysiologicalfunction will have more credence.

The mean seasonality (precipitation, radiation, and tempera-ture) at these sites is described in da Rocha et al. (2009), but will bebriefly summarized here (Fig. 1), as a review of the climatologicalregime gives context to the discussion of biophysical behavior. SitesK34, K67 and K83 are all very near the equator, while RJA is locatedat approximately 10◦ south latitude. Site PEG is the farthest south, atapproximately 20◦. The wettest locations are in the north and west(K34, RJA), with a general decrease in annual mean precipitationtoward the east and south. The driest site is PEG, in the southeastcorner of the domain. The dry season is somewhat correlated withannual precipitation; K34 has a dry season, but its length is short (4months, maximum) and monthly precipitation is frequently near orabove the climatological definition of 100 mm month−1 for a ‘drymonth’ (Keller et al., 2004) even during the dry season. There isa well-defined dry season at RJA of 5 months, even though annualprecipitation is large, and 3 of these months (June, July, and August)are extremely dry. Mean precipitation during May and Septemberat RJA is close to 100 mm. The Santarém sites (K83, K67) are sim-ilar to each other with regard to annual mean precipitation andlength of dry season (5–6 months). Precipitation at these sites is notinfrequent during dry months, and can exceed 100 mm during anindividual month. At PEG the dry season is longer, and precipitationis rare or nonexistent during most dry months.

3.1. Manaus: K34

This tower is located in the Cuieiras reserve of the InstitutoNacional de Pesquisas da Amazónia (INPA), located approximately60 km northwest of the city of Manaus, state of Amazonas. The site

I.T. Baker et al. / Agricultural and Forest Meteorology 182– 183 (2013) 177– 188 181

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ig. 3. Mean annual cycles of modeled and observed net radiation (Rnet), latent heaean precipitation. Locations are shown in Fig. 1, dry season is shaded as before.

s described in detail by Araújo et al. (2002), its location is shown inig. 1 and data was collected from 2002–2005 (Fig. 2). Annual pre-ipitation at K34 averages 2329 mm for the 4 years studied. Annualemperature variability is small, and both incoming and net radi-tion (Rnet) is highest during the dry season (Figs. 1 and 3, panel). Observed LE and H is nearly constant on an annual basis (Fig. 3,anel a), as is monthly carbon flux (Fig. 4, panel a). However, someycle is evident: Observed LE, H and Rnet all show maximum val-es during the dry season (Fig. 3, panel a). Observed carbon fluxhows very little annual cycle, with maximum relative efflux laten the wet season, with slight relative uptake from late dry seasonhrough early wet season (Fig. 4, panel a).

Comparing model to observations at K34, we see that simulatednet follows the seasonal cycle observed, with a consistent posi-ive bias (Fig. 3, panel a). The overall energy budget of the modelill reflect this bias, and can be almost completely accounted for

y excess simulated LE and wet season H (Fig. 3, panel a). Bothbserved and simulated ground heat flux (G, not shown) are verymall, with absolute value on the order of 1–2 W m−2 or less. Thennual cycle of model LE (Fig. 3, panel a) matches observed on aonthly basis. Simulated values are slightly higher, but maximum

alues occur during the wet season in both observations and sim-lation. Model H exceeds observed during the wet season (Fig. 3,anel a), and maximum model H takes place during the wet season,s opposed to the dry season in the observations. As in the obser-ations, simulated H is less than LE, and amplitude of the annual

ycle is small.

Simulated carbon flux closely matches the mean annual cyclebserved (Fig. 4, panel a). Amplitude is small, with relativeptake in January and in July–August. Simulated GPP and total

, and sensible heat (H) for the 5 stations superimposed on a histogram of monthly

respiration (Fig. 4, panel a) are large and do not show obviousseasonality. There is a suggestion of larger simulated GPP duringthe dry season, but total respiration follows a similar path. Carbonflux lacks an obvious annual cycle in both model and observations,suggesting that relative direction of carbon flux (uptake or efflux)at K34 is a function of high-frequency variability in meteorolog-ical forcing (radiation, precipitation), on synoptic- to monthlytimescales. This is supported by Fig. 5, which shows K34 dailyaverage values of LE, H, carbon flux, GPP/total respiration, and pre-cipitation for February 2002. LE, in both model and observations,shows maximum values in the relatively dry periods between days8–15 and 26–28. Modeled H follows observed generally, with apositive bias of between 10 and 25 W m−2 on a daily basis. Thissensible heat bias is seen in the monthly average, shown in Fig. 3(panel a). Modeled carbon flux matches observed quite well on adaily basis, keeping in mind we are showing observed anomaly toemphasize response to changes in forcing rather than the absolutevalue of uptake or efflux. In the simulations, daily respirationis almost invariant during the month; relative uptake/efflux isdetermined by high-frequency variability in GPP, as vegetationresponds to rapid changes in insolation. Since February is a verydamp month, we expect soils to be very moist; the large, almostinvariant respiration supports this. We might expect that theincreased GPP during days 8–12 and following day 20 is respond-ing to higher levels of light. Day 8 has very little precipitation,yet light levels are still low (only 3–4 h with insolation greater

than 300 W m−2; not shown), resulting in low GPP. This type ofhigh-frequency behavior is seen throughout the year.

Both observed and simulated behaviors are consistent witha light-limited environment. The temperature, humidity and soil

182 I.T. Baker et al. / Agricultural and Forest Meteorology 182– 183 (2013) 177– 188

F , supes show

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ig. 4. Mean annual cycles of modeled and observed carbon flux for the 5 stationshown in Fig. 1. Modeled Gross Primary Productivity (GPP) and total respiration are

oisture regimes are favorable for both photosynthesis and respi-ation year-round, as indicated by the large gross fluxes and lack ofeasonal cycles shown in Fig. 4 (panel a). During the dry season,educed precipitation is associated with higher radiation levels,hich elevates GPP. This response can also occur during short dryeriods in other months. Increased insolation is also correlatedith slightly elevated temperatures, which can enhance surface

espiratory processes. It appears that GPP responds more rapidlyhan respiration to changes in forcing, so that short-term variabilitynd the lag in respiration response combine to create short-term,mall amplitude net fluxes of carbon that lack an obvious seasonalycle.

.2. Tapajos River National Forest: K67 and K83

The K67 and K83 sites are located in Tapajos River National For-st, approximately 70 km south of the city of Santarém, Pará, BrazilFig. 1). These sites are described by Saleska et al. (2003), da Rochat al. (2004), Miller et al. (2004), Goulden et al. (2004), and Hutyrat al. (2007). The Tapajos sites, while quite close to each otherwithin 20 km or so), are distinct in that K83 was selectively loggedeginning in 2001, during the period used in this study. K83 and67 have been considered simultaneously in other studies: Saleskat al. (2003) considered data prior to logging, but Costa et al. (2010)o not distinguish between logged and non-logged intervals. This

s supported by Miller et al. (2007, 2011) who report that the selec-ive logging at K83 does not appreciably influence observed fluxesf carbon and energy when compared to K67. For this study we willonsider K67 and K83 in combination.

rimposed on a histogram of monthly mean precipitation. Locations of towers aren at the top of each panel; dry season is shaded.

Latent heat flux, both observed and simulated (Fig. 3, panelsb and c), increases at the outset of the dry season and decreasesslightly as seasonal drought progresses. Interestingly, simulatedH exceeds observed at K67 significantly in the wet season, andonly slightly in the dry season, although simulated Rnet is similarto observed. At K83, simulated wet season H is close to observed,and overestimated during the dry season, but observed Rnet exceedssimulated.

At these sites, an annual cycle in carbon flux has been observed(Saleska et al., 2003), wherein there is regular carbon efflux duringthe wet season and uptake during seasonal drought. Our simula-tions, corroborated by observed carbon flux (Fig. 4, panels b andc), shows annual amplitude of 80–100 g C m−2 in both the GPPand respiration cycles, but with a shift in phase that determinesthe annual carbon flux signal. Maximum respiratory flux at theTapajos River sites occurs late in the wet season or soon afterrains have diminished; soils are at maximum moisture levels, andincreased temperature warms the soil slightly (temperature cycleshown in Fig. 1, panels b and c). Without replenishing rains, sur-face litter and near-surface soil dries out, and respiration decreases.Annual minimum respiration occurs just prior to the onset ofthe rainy season. Photosynthetic processes show a similar annualcycle in amplitude, but phase-lagged to respiration by 2–3 months.Respiration is quickly responsive to cessation of rainfall, whilemechanisms described in Section 2 allow forest ecophysiological

function to be maintained for longer periods. This difference inresponse time, coupled with the annual rainfall amount, soil depth,and length of dry season determine the annual cycle in carbonflux.

I.T. Baker et al. / Agricultural and Forest Meteorology 182– 183 (2013) 177– 188 183

F months n pane

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ig. 5. Daily mean (modeled and observed) latent, sensible and carbon flux for the

ymbols, simulated value as solid lines. Modeled partition of carbon flux is shown i

.3. Reserva Jaru: RJA

The forest site at RJA, located 100 km north of Ji-Paraná inondônia state, Brazil (location shown in Fig. 1), is described byon Randow et al. (2004) and Andreae et al. (2002). von Randow

t al. (2004) report a relatively thin soil at RJA, with depth less than

m overlying a solid bedrock layer. For this reason we did not incor-orate the deep soil modifications at this site, as reported in Bakert al. (2008) and Section 2. We retained the root mechanisms for

of February 2002 at K34 (panels A–C). Observations are shown as solid lines withl D, daily precipitation in panel E.

water extraction as discussed in Baker et al. (2008), but limited soildepth to approximately 3.5 m.

Mean annual precipitation at RJA is large (2354 mm years−1 forthe years used in this study), but latitude (10◦ South), thin soil andpronounced dry season lead to differences in ecophysiological func-

tion when compared to K34. At RJA, wet season insolation is greaterthan K34 (Fig. 1, panel d) due to slightly longer day length. Dry sea-son day length at RJA is slightly shorter than at K34, and middayinsolation less as well. The seasonal cycle of net radiation displays

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84 I.T. Baker et al. / Agricultural and For

bimodal nature (Fig. 3, panel d), with maxima at the end of theet and dry seasons. Modeled Rnet captures the annual cycle, with

regular bias of 20–50 W m−2 on a monthly basis.Mean annual cycles of observed LE and H (Fig. 3, panel d) reveal

imited seasonality. LE is almost constant annually, with a slightncrease in magnitude in September and October, at the end ofhe dry or beginning of the wet season. Amplitude of the annual

cycle is small, with small increases corresponding to the relativeaxima in Rnet at the end of the dry and wet seasons. Simulated LE

s relatively constant and slightly larger than observed. However,he modeled LE decreases slightly at the end of the dry season,here observed LE increases. Simulated H shows seasonal max-

ma consistent with observed, but amplitude of the annual cycle isverestimated in addition to a positive bias.

The observed annual cycle of carbon flux anomaly is similar to34, showing little variability throughout the year (Fig. 4, panel). There are relative tendencies toward efflux at the end of thery and wet seasons, with relative minima (uptake) at the mid-oint of the year. Simulated carbon flux reproduces this generalattern, but overestimates the amplitude. Model GPP has a sig-ificant annual amplitude, reflecting the inability of the shallowoil to store sufficient moisture to maintain ecophysiological func-ion completely through annual drought. Interestingly, simulatedE does not respond as strongly as photosynthesis. From wet tory seasons, gradients in water vapor pressure from the canopy tooundary layer are maintained, even as overall humidity decreases.t RJA, at the very end of the dry season a slight decrease in LE iseen in the simulations. The large amplitude in simulated carbonux (Fig. 4, panel d) is due to phase incoherence between photo-ynthetic and respiratory response. Following the method outlinedn Baker et al. (2008), respiration is tightly linked to moisture lev-ls in near-surface soil; litter respiration is responsive to surfaceoil moisture levels, and relative root mass is greater near the sur-ace as well. As surface moisture is depleted at dry season onset,otal respiration decreases. There is no concurrent decrease in GPP,s roots are able to access water at deeper levels in the soil. Its only after several dry months, when total column soil mois-ure has been depleted, that GPP decreases. The lack of a largennual cycle in the observed carbon flux suggests that either thePP and respiration cycles are more tightly in phase, or else there

s much less amplitude in actual annual cycles than the modelmplies.

The hysteresis between morning and afternoon ecophysiologi-al function, as reflected by diurnal cycles of latent heat and carbonux, has been attributed to a circadian response in vegetationKeller et al., 2004). This feature is seen across multiple sites, bute limit model evaluation of this feature to RJA. The model doesot parameterize a purely circadian response, but imposes stressn potential photosynthesis by temperature, humidity, and soiloisture factors as described in Sellers et al. (1992). Simulated soiloisture stress operates on timescales of moistening and drying

round precipitation events, but temperature and humidity stressperate in regular diurnal cycles. We can explore the diurnal naturef the vegetation response (and compare simulated to naturalrocesses) by plotting monthly mean diurnal cycles of carbon fluxgainst monthly mean diurnal cycles of latent heat (Fig. 6). Hours 9,2 and 15 are plotted as triangles on the observed cycle, and we canee that the observed LE/Carbon flux cycle in the wet season (panel) moves in a ‘counterclockwise’ direction; LE increases followingunrise concurrently with carbon uptake. In the afternoon, therocess is reversed (concurrent decrease in LE and carbon uptake),ut shifted slightly toward larger latent heat. From the simulations,

e can see that this shift is due to differences in the diurnal cycles

f transpiration and photosynthesis. Following sunrise, carbonptake is light-limited and increases rapidly. As carboxylation rateecomes the limiting factor in carbon uptake at higher light levels

eteorology 182– 183 (2013) 177– 188

(Sellers et al., 1992), GPP reaches a plateau. Simulated transpirationshows more of a midday ‘peak’ as meteorological conditions (tem-perature, vapor pressure, turbulent exchange) evolve throughoutthe day. The combination of these two cycles with disparatediurnal shapes determines the shape of the CO2 flux-LE cycle.The simulated cycle, shown as a dashed line (hours not shown),shows a similar ‘counterclockwise’ pattern as was observed, butprecedes the observed by several hours. For example, at 0900 theobserved carbon flux is nearly neutral, but the simulation showsan uptake of 15 �mol m−2 s−1. This lag decreases somewhat duringthe day, so that by 1500 the observed and simulated values arequite similar. During the dry season (Fig. 6, panel B), both observedand simulated carbon flux/LE patterns resemble a ‘figure-8’. Inthe morning, carbon uptake is strong while latent flux increaseis minimal, due to much lower water vapor pressure (in both theCAS and atmosphere) when compared to the wet season. In theafternoon, latent heat flux decreases more rapidly than carbonuptake, resulting in a ‘figure-8’ diel pattern. Again, the simulatedcycle, while displaying the same diurnal cycle, precedes theobserved by several hours, and modeled maximum carbon uptakein September is underestimated. That the model can reproducethe same shape as the observed cycles suggests that the simulatedprocesses are similar to observed, albeit with some error intiming.

3.4. Cerrado; Pé de Gigante (PEG)

Carbon, energy, and moisture flux over a woodland savanna(cerrado Sensu stricto) site has been described by da Rocha et al.(2002) and da Rocha et al. (2009). The site is located in southeastBrazil, in São Paulo state, and has the largest temperature and radi-ation seasonality of all sites in this study (Fig. 1, panel e). Fluxeswere recorded in Vassununga state park, in a region that containsclosed canopy forest, and open shrubland in addition to woodlandsavanna.

Heterogeneity is a defining characteristic of savanna, and as suchposes challenges for simulations. In SiB3, the use of satellite datato specify phenology requires a single-layer canopy (Sellers et al.,1996a,b), so explicit representation of heterogeneous assemblagesof grasses, shrubs and trees is not possible. The site is simulatedas seasonal forest in SiB3. However, the spectral characteristics ofNDVI capture the inclusion of grass phenology to a degree.

The Pé de Gigante site is water-limited (da Rocha et al., 2002,2009), meaning that ecophysiological function is tightly coupled toprecipitation and soil moisture. In contrast to all the other sites,where incoming radiation is regulated by cloud amount, seasona-lity at PEG is also defined by latitude. The dry season occurs duringaustral winter, so that radiation levels are actually higher duringthe rainy season, and temperatures are warmer (Fig. 1, panel e).Latent heat is larger than sensible heat during the seasonal rains,but the Bowen ratio drops below one for a short period at theend of the dry season in both simulations and observations (Fig. 3,panel e).

Simulations and observations (Fig. 4, panel e) suggest relativeuptake of carbon at PEG until early in the dry season, at which timerespiration exceeds GPP. Simulations show that GPP drops rapidlyfollowing cessation of seasonal rains, while respiration subsidesat a lower rate. This is in contrast to the ecophysiological mecha-nisms postulated for forest sites, where the opposite occurs; GPPis maintained during the dry season while respiration decreasesquickly following cessation of rains. Our model simulations suggestseveral reasons for this behavior at PEG, including (1) reduced

annual precipitation and longer, more severe (meaning very fewprecipitation events) dry season result in smaller water storagein the soil, (2) seasonal forests have shallower rooting systemsthan tropical evergreen forests (Jackson et al., 1996), and therefore

I.T. Baker et al. / Agricultural and Forest Meteorology 182– 183 (2013) 177– 188 185

F on flu1

lrc

4

casdtWbmaeacgt

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niLK

ig. 6. Monthly mean diurnal composite of latent heat (X-axis) plotted against carb5 are indicated with triangles in the observations.

ack the ability to access water stored deep in the soil. For theseeasons, simulated GPP and respiration at PEG are in phase, andoupled tightly to water availability in the near-surface soil.

. Discussion

We can summarize model performance with a review of modelomparison to observed net radiation, latent and sensible heat flux,nd carbon flux observed at the 5 stations. We acknowledge thatimulated behavior does not match observed perfectly at these 5iverse sites, but results provide insight into physiological func-ion. Furthermore, very little local tuning to SiB3 was performed.

e modified soil depth at RJA in accordance with local knowledge,ut otherwise values from global maps were used to determineodel parameters. These include vegetation and soil type, as well

s parameters dependent on these values. These secondary param-ters influence model components such as photosynthetic functionnd soil process (hydraulic and thermal conductivity). We useontinuous spatial data sets as a means to facilitate regional- tolobal-scale simulation as an ultimate goal, rather than fine-tuninghe model for local application.

No consistent bias in net radiation was found (Fig. 3). At threeites (K34, RJA and PEG) simulated Rnet exceeded observed, at83 observed exceeded simulated, and at K67 the correspon-ence was close. However, at all sites the mean annual cycle ofbserved and simulated was similar – where there was bias, theagnitude was nearly constant. We believe these differences are

aused by the use of uniform tabular values in SiB3 to representeterogeneous forests with diverse species, values of leaf angleistribution and radiative properties. Within a particular vegeta-ion type (broadleaf evergreen forest, for example), heterogeneityn simulation canopy parameters will be imposed only by differ-nces in spectrally derived LAI/fPAR between the sites. In addition,iB3 ingests a single incoming shortwave measurement and parti-ions it into visible/near-infrared and direct/diffuse partitions. Weo not expect modeled albedo to exactly match observed in allases.

Comparisons of simulated and observed latent heat flux follow

et radiation trends at K34, K83, and RJA (Fig. 3). At K67 observed LE

s slightly larger than simulated, and at PEG modeled and simulatedE are very similar in magnitude and annual cycle. At K34, K67 and83 the annual cycles are similar as well. At RJA there is very little

x (Y-axis) for RJA, March (panel A) and September (panel B) 2000. Hours 9, 12, and

amplitude in the annual cycle of LE, but simulations show a slightdecrease at the end of the dry season where observations show aslight increase.

There is a positive bias in simulated sensible heat flux at all sta-tions (Fig. 3). This has been noted in SiB simulations before (Bakeret al., 2003), and is believe to be related to the leaf-to-canopy scal-ing scheme outlined in Sellers (1985). This bias is most notable insimulations of forests, such as are simulated in this study. Simu-lated annual cycles generally follow observed, and Bowen ratio, orrelative magnitude of sensible to latent heat is consistent betweenmodel and observations.

Annual mean carbon flux is shown in Fig. 4. At K34 modeled andobserved carbon flux has low amplitude and no obvious seasona-lity. At the Tapajos National Forest sites (K67 and K83) the modelcaptures the general form of the annual cycle (wet season efflux,dry season uptake), but precedes the time of uptake by 1–3 months.At RJA the model reproduces the basic form of the observed annualcycle, but with a larger amplitude, and at PEG SiB3 reproduces theobserved carbon flux with reasonable fidelity.

Given the historical performance of land surface models in SouthAmerica (cf. Fig. 2 in Saleska et al., 2003), we find these results to bevery encouraging. We have simulated, with a minimum of localizedtuning, the general form of annual cycles of energy, moisture, andcarbon flux at several sites across Brazil. These results provide someinsight into the mechanistic coupling of carbon cycle processes thatcombine to determine annual cycles of flux across vegetation andmoisture gradients.

5. Conclusions

Climatological control of ecophysiology is spatially het-erogeneous in Brazil. da Rocha et al. (2009) showed thatevapotranspiration in the wettest areas (central Amazon) is tightlylinked to radiation levels (light-limited), while water availabilityregulates ET in the drier regions to the south and east. Our sim-ulations reproduce this behavior. Forest sites K34, RJA, K67 andK83 maintain a consistently small Bowen ratio (sensible smallerthan latent heat); maximum annual values for both H and LE occur

during the dry season, when net radiation is greatest, and annualamplitude of LE/H cycles is relatively small. The dry season increasein both LE and H suggests an ecosystem response to increasedradiation levels, without ecosystem stress, since evaporation is

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86 I.T. Baker et al. / Agricultural and For

aintained. At the savanna site (PEG; simulated as a seasonal forestn SiB3), evaporation is tightly coupled to precipitation. Latent heatux decreases immediately with cessation of seasonal rains, andowen ratio exceeds one during the dry season. Simulated annualycle of latent and sensible heat at PEG is very similar to observed.

Vegetation couples carbon dynamics to the Bowen ratio by sto-atal regulation of transpiration. Overall carbon flux is defined by

he interaction of photosynthetic and respiratory processes. Weave demonstrated that SiB3 can simulate observed annual cyclesf carbon flux, and we use model diagnostics to partition GPP andespiration as a means to evaluate photosynthesis and respirationcross vegetation and moisture gradients. We do not address over-ll source/sink of CO2 on an annual or interannual basis for thesendividual sites. Local to regional-scale Net Ecosystem Exchange ofO2 over long timescales is dependent upon storage pools, whichre themselves the residual from large gross photosynthetic andespiratory fluxes. These pools cannot be determined from modelimulations performed on 3 or 4 years of observational data.

We find that a conceptual model of ecophysiological behaviormerges: In the wettest regions of the forest (K34), ecosystems areight- rather than water-limited. Gross carbon fluxes are continu-usly large, and small magnitude uptake or efflux is determinedy high-frequency variability in forcing. A dry week, for exam-le, may result in increased GPP due to higher light levels, whilelight drying of near-surface soils may result in a small decrease inespiration. Moving downgradient in precipitation (K67 and K83),nnual total rainfall is less, and the dry season obtains definition. Athese locations seasonality in carbon flux may be imposed by the

echanistic concepts outlined in Baker et al. (2008): A combinationf GPP elevation in response to enhanced light levels and respira-ion decrease as surface soil desiccates results in carbon uptakeuring the dry season. At these sites, seasonality in carbon flux isistinct while seasonality in energy and moisture flux is minimal.hotosynthetic function is not excessively compromised during theeasonal drought, and transpiration maintains the Bowen ratio atmall values. However, there is some suppression of GPP as the dryeason progresses, indicating a combination of light- and water-imitation may be at work here. Using the terminology of Costat al. (2010), we believe that here ET is controlled by a combi-ation of abiotic (meteorological) and biotic (soil moisture deficitestricting canopy conductance) factors. At drier sites (PEG), vege-ation has stress imposed upon it by the combination of even lessnnual precipitation and a longer dry season. The imposition ofater limitation in the drier regions has the effect of forcing the

arbon cycle into phase with the precipitation cycle. Water limita-ion also has the effect of imposing larger amplitude to the annualycles of latent and sensible heat flux. As vegetation experiencesater stress, evapotranspiration rates cannot be maintained, and

he Bowen ratio increases. This conceptual model is expressed inhe GPP/Respiration cycles shown in Fig. 4.

Tropical forests survive annual drought (dry season), as wells climatological variability around mean annual cycles of wetnd dry. Evapotranspiration is critical to precipitation recyclingot only locally, but across regional and continental scales (vaner Ent et al., 2010). It has been shown that simulations of atmo-pheric processes are responsive to improved physical realism athe land–atmosphere interface (Harper et al., 2010). The results oflimate simulations that predict large-scale conversion of Amazo-ian forest to grassland or savanna (Cox et al., 2000, 2004; Bettst al., 2004; Cowling et al., 2004; Huntingford et al., 2004, 2008)ill be more robust if they can show consistency with ecophys-

ological behavior under current conditions. It has been shown

hat land has more leverage than ocean in influencing the globaltmospheric CO2 growth rate (Friedlingstein et al., 2006; Bakert al., 2006; Gurney et al., 2008), and that the tropics play a majorole in the land response. Therefore, fundamental understanding of

eteorology 182– 183 (2013) 177– 188

tropical land surface response on a mechanistic level will be integralto our ability to predict both present-day climate and ecophysio-logical response to changing atmospheric forcing.

Our simulations have demonstrated an ability to rectify unre-alistic ecophysiological stress in forest ecosystems (Saleska et al.,2003; Baker et al., 2008) while maintaining reasonable responseacross vegetation and moisture gradients. But removing unrealisticstress on vegetation is only half of the battle; forests are conditionedto survive annual drought and, it is expected, anomalous drought aswell. But if sustained drought in Amazonia occurs during the 21stcentury due to a higher incidence of El Nı̃no conditions (Cattanioet al., 2002; Li and Fu, 2006) or a combination of climatologicaland sociological pressure on the ecosystem (Nepstad et al., 2008),it is realistic to expect that forest collapse, or a ‘tipping point’ maybe reached (Nobre and Borma, 2009; Nepstad et al., 2008). Previ-ously, models were unable to withstand even seasonal drought, inthe form of a dry season. Now that we have adjusted our modelphysics to achieve greater resiliency to seasonal drought, we needto ensure that we have not created models that are impervious todrought.

Acknowledgements

This research was sponsored by the National Science Foun-dation Science and Technology Center for Multi-Scale Modelingof Atmospheric Processes, managed by Colorado State Universityunder cooperative agreement No. ATM-0425247. This researchwas also funded by Department of Commerce/National Oceanicand Atmospheric Administration contract NA08AR4320893, NASAcontracts NNX06AC75G, NNX08AM56G and NNX09AL52G, Depart-ment of Energy contract DE-FG02-06ER64317, and NICCR contractMTU050516Z14.

References

Adler, R.F., Huffman, G.J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schnei-der, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., Nelkin, E., 2003. TheVersion 2 Global Precipitation Climatology Project (GPCP) monthly precipitationanalysis (1979–present). J. Hydrometeorol. 4, 1147–1167.

Andreae, M.O., Artaxo, P., Brandão, C., Carswell, F.E., Ciccioli, P., da Costa, A.L.,Culf, A.D., Esteves, J.L., Gash, J.H.C., Grace, J., Kabat, P., Lelieveld, J., Malhi, Y.,Manzi, A.O., Meixner, F.X., Nobre, A.D., Nobre, C., Ruivo, M.L.P., Silva Dias,M.A.F., Stefani, P., Valentini, R., von Jouanne, J., Waterloo, M.J., 2002. Bio-geochemical cycling of carbon, water, energy, trace gases, and aerosols inAmazonia: the LBA-EUSTACH experiments. J. Geophys. Res. 107 (D20), 8066,http://dx.doi.org/10.1029/2001JD000524.

Araújo, A.C., Nobre, A.D., Kruijt, B., Elbers, J.A., Dallarosa, R., Stefani, P., von Randow,C., Manzi, A.O., Culf, A.D., Gash, J.H.C., Velentini, R., Kabat, P., 2002. Comparativemeasurements of carbon dioxide fluxes from two nearby towers in a centralAmazonian rainforest: the Manaus LBA site. J. Geophys. Res. 107 (D20), 8090,http://dx.doi.org/10.1029/2001JD000676.

Aranibar, J.N., Berry, J.A., Riley, W.J., Patakis, D.E., Law, B.E., Ehleringer, J.R., 2006.Combining meteorology, eddy fluxes, isotope measurements, and modelingto understand environmental controls of carbon isotope discrimination at thecanopy scale. Glob. Change Biol. 12, 710–730.

Baker, I.T., Denning, A.S., Hanan, N., Prihodko, L., Vidale, P.-L., Davis, K., Bakwin, P.,2003. Simulated and observed fluxes of sensible and latent heat and CO2 at theWLEF-TV Tower using SiB2.5. Glob. Change Biol. 9, 1262–1277.

Baker, D.F., Law, R.M., Gurney, K.R., Rayner, P., Peylin, P., Denning, A.S., Bousquet,P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fung, I.Y., Heimann, M., John, J., Maki, T.,Maksyutov, S., Masarie, K., Prather, M., Pak, B., Taguchi, I., Zhu, Z., 2006. TransCom3 inversion intercomparison: impact of transport model errors on the interann-ual variability of regions CO2 fluxes, 1988–2003. Global Biogeochem. Cycles 20,GB1002, http://dx.doi.org/10.1029/2004GB002439.

Baker, I.T., Prihodko, L., Denning, A.S., Goulden, M., Milller, S., da Rocha, H., 2008.Seasonal drought stress in the Amazon: reconciling models and observations. J.Geophys. Res. 113, G00B01, http://dx.doi.org/10.1029/2007JG000644.

Betts, R.A., Cox, P.M., Collins, M., Harris, P.P., Huntingford, C., Jones, C.D., 2004. Therole of ecosystem–atmosphere interactions in simulated Amazonian precipita-tion decrease and forest dieback under global climate warming. Theor. Appl.

Climatol. 78 (1–3), 157–175.

Brando, P.M., Goetz, S.J., BAccini, A., Nepstad, D.C., Beck, P.S.A., Christman,M.C., 2010. Seasonal and interannual variability of climate and vegetationindices across the Amazon. Proc. Natl. Acad. Sci. USA 107 (33), 14685–14690,http://dx.doi.org/10.1037/pnas.0908741107.

est M

B

C

C

C

C

C

C

C

C

C

C

C

d

d

d

D

D

D

E

F

F

F

F

F

G

G

G

H

I.T. Baker et al. / Agricultural and For

rown, M.E., Pinzon, J., Tucker, C.J., 2004. New vegetation index dataset available tomonitor global change. Eos Trans. 85, 565.

anadell, J., Jackson, R.B., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996.Maximum rooting depth of vegetation types at the global scale. Oecologia 108(4), 583–595.

attanio, J.H., Davidson, E.A., Nepstad, D.C., Verchot, L.V., Ackerman, I.L., 2002. Unex-pected results of a pilot throughfall exclusion experiment on soil emissions ofCO2, CH4, N2O and NO in eastern Amazonia. Biol. Fert. Soils 36 (2), 102–108.

olello, G.D., Grivet, C., Sellers, P.J., Berry, J.A., 1998. Modelig of energy, water andCO2 flux in a temperate grassland ecosystem with SiB3: May–October 1987. J.Atmos. Sci. 55, 1141–1169.

ollatz, G.J., Ball, J.T., Grivet, C., Berry, J.A., 1991. Physiological and environmentalregulation of stomatal conductance, photosynthesis and transpiration: a modelthat includes a laminar boundary layer. Agric. Forest Meteorol. 54, 107–136.

ollatz, G.J., Ribas-Carbo, M., Berry, J.A., 1992. Coupled photosynthesis–stomatalconductance model for leaves of C4 plants. Aust. J Plant Physiol. 19 (5), 519–538.

orbin, K.D., Denning, A.S., Lu, L., Wang, J.-W., Baker, I.T., 2008. Possible representa-tion errors in inversions of satellite CO2 retrievals. J. Geophys. Res. 113, D02301,http://dx.doi.org/10.1029/2007JD008716.

osta, M.H., Foley, J.A., 1999. Trends in the hydrologic cycle of the Amazon basin. J.Geophys. Res. 104 (D12), 14189–14198.

osta, M.H., Biajoli, M.C., Sanches, L., Malhado, A.C.M., Hutyra, L.R., da Rocha,H.R., Aguiar, R.G., de Araújo, A.C., 2010. Atmospheric versus vegetation con-trols of Amazonian tropical rain forest evapotranspiration: Are the wet andseasonally dry rain forests any different? J. Geophys. Res. 115, G04021,http://dx.doi.org/10.1029/2009JG001179.

owling, S.A., Betts, R.A., Cox, P.M., Ettwein, V.J., Jones, C.D., Maslin, M.A., spall, S.A.,2004. Contrasting simulated past and future responses of the Amazonian forestto atmospheric change. Philos. Trans. Roy. Soc. B 359 (1443), 539–547.

ox, P.M., Betts, R.A., Jones, C.D., Spall, S.A., Totterdell, I.J., 2000. Acceleration of globalwarming due to carbon-cycle feedbacks in a coupled climate model. Nature 408,184–187.

ox, P.M., Betts, R.A., Collins, M., Harris, P.P., Huntingford, C., Jones, C.D., 2004.Amazonian forest dieback under climate-carbon cycle projections for the 21stcentury. Theor. Appl. Climatol. 78 (1–3), 137–156.

a Rocha, H.R., Freitas, H.C., Rosolem, R., Juarez, R., Tannus, R.N., Ligo, M.A., Cabral,O.M.R., Silva Dias, M.A.F., 2002. Measurements of CO2 exchange over a woodlandsavanna (Cerrado Sensu strictu) in southeast Brazil. Biota Neotropica 2 (1).

a Rocha, H.R., Goulden, M.L., Miller, S.D., Menton, M.C., Pinto, L.D.V.O., de Frietas,H.C., E Silva Figueira, A.M., 2004. Seasonality of water and heat fluxes over atropical forest in eastern Amazonia. Ecol. Appl. 14 (Suppl. 4), S22–S32.

a Rocha, H.R., Manzi, A.O., Cabral, O.M., Miller, S.D., Goulden, M.L., Saleska, S.R.,Coupe, N.R., Wofsy, S.C., Borma, L.S., Artaxo, P., Vourlitis, G., Noguiera, J.S.,Cardoso, F.L., Nobre, A.D., Kruijt, B., Frietas, H.C., von Randow, C., Aguiar,R.G., Maia, J.F., 2009. Patterns of water and heat flux across a biome gradi-ent from tropical forest to savanna in Brazil. J. Geophys. Res. 114, G00B12,http://dx.doi.org/10.1029/2007JG000640.

ai, Y., Zeng, X., Dickinson, R.E., Baker, I., Bonan, G., Bosilovich, M., Denning, S.,Dirmeyer, P., Houser, P., Niu, G., Oleson, K., Schlosser, A., Yang, Z.-L., 2003. Thecommon land model (CLM). Bull. Am. Meteorol. Soc. 84, 1013–1023.

enning, A.S., Collatz, G.J., Zhang, C., Randall, D.A., Berry, J.A., Sellers, P.J., Colello,G.D., Dazlich, D.A., 1996. Simulations of terrestrial carbon metabolism and atmo-spheric CO2 in a general circulation model. Part 1: surface carbon fluxes. TellusB 48B, 521–542.

enning, A.S., Nicholls, M., Prihodko, L., Baker, I., Vidale, P.L., Davis, K., Bakwin, P.,2003. Simulated variations in atmospheric CO2 over a Wisconsin forest using acoupled ecosystem–atmosphere model. Glob. Change Biol. 9 (9), 1241–1250.

ltahir, E., Bras, R.L., 1994. Precipitation recycling in the Amazon Basin. Quart. J. Roy.Meteor. Soc. 120 (518A), 861–880.

arquhar, G.D., von Caemmerer, S., Berry, J.A., 1980. A biochemical model of photo-synthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90.

oken, T., Wimmer, F., Mauder, M., Thomas, C., Liebethal, C., 2006. Some aspects ofthe energy balance closure problem. Atmos. Chem. Phys. 6, 4395–4402.

riedlingstein, P.F., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V., Cadule, P.,Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C., Joos, F., Kato, T., Kawamiya,M., Knoff, W., Lindsay, K., Matthews, H.D., Raddatz, T., Rayner, P., Reick, C., Roeck-ner, E., Schnitzler, K.-G., Schnur, R., Strassmann, K., Weaver, A.J., Yoshikawa, C.,Zeng, N., 2006. Climate-carbon cycle feedback analysis: results from the C4MIPmodel intercomparison. J. Clim. 19, 3337–3353.

u, R., Li, W., 2004. The influence of the land surface on the transitionfrom dry to wet season in Amazonia. Theor. Appl. Climatol. 78, 97–110,http://dx.doi.org/10.1007/s00704-004-0046-7.

u, R., Zhu, B., Dickinson, R.E., 1999. How do atmosphere and land surface influ-ence seasonal changes of convection in the tropical Amazon? J. Clim. 12 (5),1306–1321.

edney, N., Valdes, P.J., 2000. The effect of Amazonian deforestation on the northernhemisphere circulation and climate. Geophys. Res. Lett. 27 (19), 3053–3056.

oulden, M.L., Miller, S.D., da Rocha, H.R., Menton, M.C., de Freitas, H.C., SilvaFigueira, E., de Sousa, C.A.D., 2004. Diel and seasonal patterns of tropical forestCO2 exchange. Ecol. Appl. 14 (Suppl. 4), S42–S54.

urney, K.R., Baker, D., Rayner, P., Denning, A.S., 2008. Interannual variations in

continental-scale net carbon exchange and sensitivity to observing networksestimated from atmospheric CO2 inversions for the period 1980 to 2005. GlobalBiogeochem. Cycles 22 (3), http://dx.doi.org/10.1029/2007GB003082.

anan, N.P., Berry, J.A., Verma, S.B., Walter-Shea, E.A., Suyker, A.E., Burba, G.G.,Denning, A.S., 2005. Testing a model of CO2, water and energy exchange in

eteorology 182– 183 (2013) 177– 188 187

Great Plains tallgrass prairie and wheat ecosystems. Agric. Forest Meteorol. 131,162–179.

Harper, A.B., Denning, A.S., Baker, I.T., Branson, M.D., Prihodko, L., Randall, D.A., 2010.Role of deep soil moisture in modulating climate in the Amazon. Geophys. Res.Lett. 37, L05802, http://dx.doi.org/10.1029/2009GL042302.

Hollinger, D.Y., Richardson, A.D., 2005. Uncertainty in eddy covariance measure-ments and its application to physiological models. Tree Physiol. 25 (7), 873–885.

Horel, J.D., Hahmann, A.N., Geisler, J.E., 1989. An investigation of the annual cycle ofconvective activity over the tropical Americas. J. Clim. 2 (11), 1388–1403.

Houghton, R.A., Lawrence, K.T., Hackler, J.L., Brown, S., 2001. The spatial distributionof forest biomass in the Brazilian Amazon: a comparison of estimates. Glob.Change Biol. 7, 731–746.

Huete, A.R., Didan, K., Shimabukuro, Y.E., Ratana, P., Salexska, S.R., Hutyra,L.R., Yang, W., Nemani, R.R., Myneni, R., 2006. Amazon rainforestsgreen-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405,http://dx.doi.org/10.1029/2005GL025583.

Huntingford, C., Harris, P.P., Gedney, N., Cox, P.M., Betts, R.A., Marengo, J.A., Gash,J.H.C., 2004. Using a GCM analogue model to investigate the potential for Ama-zonian forest dieback. Theor. Appl. Climatol. 78 (1–3), 177–185.

Huntingford, C., Fisher, R.A., Mercado, L., Booth, B.B.B., Sitch, S., Harris, P.P., Cox,P.M., Jones, C.D., Betts, R.A., Malhi, Y., Harris, G.R., Collins, M., Moorcroft, P.,2008. Towards quantifying uncertainty in predictions of Amazon dieback. Philos.Trans. Roy. Soc. B 363 (1498), 1857–1864.

Hutyra, L.R., Munger, J.W., Saleska, S.R., Gottleib, E., Daube, B.C., Dunn, A.L., Amaral,D.F., de Camargo, P.B., Wofsy, S.C., 2007. Seasonal controls on the exchange ofcarbon and water in an Amazonian rain forest. J. Geophys. Res. 112 (G4), G04099,http://dx.doi.org/10.1029/2007JG000573.

Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996.A global Analysis of root distributions for terrestrial biomes. OECO 108, 389–411.

Keller, M., Alencar, A., Asner, G.P., Braswell, B., Bustamante, M., Davidson, E., Feld-pausch, T., Fernandes, E., Goulden, M., Kabat, P., Kruijt, B., Luizao, F., Miller,S., Markewitz, D., Nobre, A.D., Nobre, C.A., Filho, N.P., da Rocha, H., Silva Dias,P., von Randow, C., Vourlitis, G.L., 2004. Ecological research in the Large-ScaleBiosphere–Atmosphere Experiment in Amazonia: early results. Ecol. Appl. 14(Suppl. 4), S3–S16.

Lee, J-E., Olivieira, R.S., Dawson, T.E., Fung, I., 2005. Root functioning modifies sea-sonal climate. Proc. Natl. Acad. Sci. USA 102 (49), 17576–17581.

Li, W., Fu, R., 2004. Transition of the large-scale atmospheric and land surface condi-tions from the dry to the wet season over Amazonia as diagnosed by the ECMWFre-analysis. J. Clim. 17 (7), 2637–2651.

Li, W., Fu, R., 2006. Influence of cold air intrusions on the wet season onset overAmazonia. J. Clim. 19 (2), 257–275.

Los, S.O., Collatz, G.J., Sellers, P.J., Malmstrom, C.M., Pollack, N.H., DeFries, R.S.,Bounoua, L., Parris, M.T., Tucker, C.J., Dazlich, D.A., 2000. A global 9-year bio-physical land surface dataset from NOAA AVHRR data. J. Hydrometeorol. 1,183–199.

Lu, L.X., Denning, A.S., da Silva-Dias, M.A., da Silva-Dias, P., Longo, M., Freitas,S.R., Saatchi, S., 2005. Mesosscale circulations and atmospheric CO2 varia-tions in the Tapajos region, Para, Brazil. J. Geophys. Res. 110 (D21), D21101,http://dx.doi.org/10.1029/2004JD005757.

Malhado, A.C.M., Costa, M.H., Lima, F.Z., Portilho, K.C., Figueiredo, D.N., 2009. Sea-sonal leaf dynamics in an Amazonian tropical forest. Forest Ecol. Manage. 258(7), 1161–1165.

Malhi, Y., Roberts, J.T., Betts, R.A., Killeen, T.J., Li, W., Nobre, C.A., 2008. Climatechange deforestation and the fate of the Amazon. Science 319, 169–172,http://dx.doi.org/10.1126/science.1146961.

Malhi, Y., Arag ao, L.E.O.C., Metcalf, D.B., Paiva, R., Quesada, C.A., Almeida, S., Ander-son, L., Brando, P., Chambers, J.Q., da Costa, A.C.L., Hutyra, L.R., Oliveira, P., Patino, S., Pyle, E.H., Robertson, A.L., Teixeira, L.M., 2009. Comprehensive assessmentof carbon productivity, allocation and storage in three Amazonian forests. Glob.Change Biol., http://dx.doi.org/10.1111/j.1365-2486.2009.01780.x.

Miller, S.D., Goulden, M.L., Menton, M.C., da Rocha, H.R., de Freitas, H.C., Figueira,A.M.E.S., de Sousa, C.A.D., 2004. Biometric and micrometeorological measure-ments of tropical forest carbon balance. Ecol. Appl. 14 (Suppl. 4), S114–S126.

Miller, S.D., Goulden, M.L., da Rocha, H.R., 2007. The effect of canopy gaps on subcanopy ventilation and scalar fluxes in a tropical forest. Agric. Forest Meteorol.142, 25–34, http://dx.doi.org/10.1016/j.agrformet.2006.10.008.

Miller, S.D., Goulden, M.L., Hutyra, L.R., Keller, M., Saleska, S.R., Wofsy, S.C., Figueira,A.M.S., da Rocha, H.R., de Camargo, P.B., 2011. Reduced impact logging minimallyalters tropical rainforest carbon and energy exchange. Proc. Natl. Acad. Sci. USA108 (48), 19431–19435, http://dx.doi.org/10.1073/pnas.1105068108.

Myneni, R.B., Yang, W., Nemani, R.R., Huete, A.R., Dickinson, R.E., Knyazikhin, Y.,Didan, K., Fu, R., Juarez, R.I.N., Saatchi, S.S., Hashimoto, H., Ichii, K., Shabanov,N.V., Tan, B., Ratana, P., Privette, J.L., Morisette, J.T., Vermote, E.F., Roy, D.P., Wolfe,R.E., Friedl, M.A., Running, S.W., Votava, P., El-Saleous, N., Devadiga, S., su, Y.,Salomonson, V.V., 2007. Large seasonal swings in leaf area of Amazon rainforests.Proc. Natl. Acad. Sci. USA 104 (12), 4820–4823.

Nepstad, D.C., Stickler, C.M., Soares-Filho, B., Merry, F., 2008. Interactions amongAmazon land use, forests and climate: prospects for a near-term forest tippingpoint. Philos. Trans. Roy. Soc. B 363 (1498), 1737–1746.

Nicholls, M.E., Denning, A.S., Prihodko, L., Vidale, P.L., Baker, I., Davis, K., Bak-

win, P., 2004. A multiple-scale simulation of variations in atmospheric carbondioxide using a coupled biosphere–atmosphere model. J. Geophys. Res. 109(D18), D18117, http://dx.doi.org/10.1029/2003JD004482.

Nobre, C.A., Borma, L.D.S., 2009. ‘Tipping points’ for the Amazon forest. Curr. Opin.Environ. Sustain. 1, 28–36, http://dx.doi.org/10.1016/j.cosust.2009.07.003.

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N

O

P

R

R

S

S

S

S

S

S

S

S

88 I.T. Baker et al. / Agricultural and For

obre, P., Malagutti, M., Urbano, D.F., de Almeida, R.A.F., Giarolla, E., 2009. Ama-zon deforestation and climate change in a couple model simulation. J. Clim. 22,5685–5697, http://dx.doi.org/10.1175/2009JCLI2757.1.

liveira, R.S., Dawson, T.E., Burgess, S.S.O., Nepstad, D.C., 2005. Hydraulic redistri-bution in three Amazonian trees. OECO 145, 354–363.

inzon, J., Brown, M.E., Tucker, C.J., 2006. Satellite time series correction of orbitaldrift artifacts using empirical mode decomposition. In: Huang, N. (Ed.), Applica-tions of Empirical Mode Decomposition, Part II. (Chapter 10).

andall, D.A., Dazlich, D.A., Zhang, C., Denning, A.S., Sellers, P.J., Tucker, C.J., Bounoua,L., Berry, J.A., Collatz, G.J., Field, C.B., Los, S.O., Justice, C.O., Fung, I., 1996. Arevised land surface parameterization (SiB2) for GCMs. Part III: the greening ofthe Colorado State University General Circulation Model. J. Clim. 9 (4), 738–763.

estrepo-Coupe, N., Zeng, X., Baker, I., Rosolem, R., Sakaguchi, K., Christofferson, B.,de Goncalves, L.G., Muza, M., da Araujo, A.C., Cabral, O.M.R., de Camargo, P.B.,Fitzjarrald, D.R., Goulden, M.L., Kruijt, B., Maia, J.M.F., Manzi, A.O., Miller, S.D.,Nobre, A.D., von Randow, C., da Rocha, H.R., Sakai, R.K., Tota, J., Zanchi, G.B.,Saleska, S.R. An empirical model for downward longwave radiation applied at 6of the Brasil flux network sites. Agric. Forest Meteorol., in preparation.

alazar, L.F., Nobre, C.A., Oyama, M.D., 2007. Climate change consequences on thebiome distribution in tropical South America. Geophys. Res. Lett. 34, L09708,http://dx.doi.org/10.1029/2007GL029695.

aleska, S.R., Miller, S.D., Matross, D.M., Goulden, M.L., Wofsy, S.C., da Rocha, H.R.,de Camargo, P.B., Crill, P., Dauge, B.C., de Frietas, H.C., Hutyra, L.R., Keller, M.,Kirchoff, V., Menton, M., Munger, J.W., Pyle, E.H., Rice, A.H., Silva, H., 2003. Carbonin Amazon forests: unexpected seasonal fluxes and disturbance-induced losses.Science 302, 1554–1557.

aleska, S.R., Didan, K., Huete, A.R., da Rocha, H.R., 2007. Amazonforests green-up during 2005 drought. Science 318 (5850), 612,http://dx.doi.org/10.1126/science.1146663.

amanta, A.S., Ganguly, Hashimoto, H., Devadiga, S., Vermote, E.,Knyazikhin, Y., Nemani, R.R., Myneni, R.B., 2010. Amazon forest didnot green-up during the 2005 drought. Geophys. Res. Lett. 37, L05401,http://dx.doi.org/10.1029/2009GL042154.

ato, N.P.J., Sellers, D.A., Randall, E.K., Schneider, J., Shukla, J.L., Kinter, Y.T., Hou,E., Albertazzi, 1989. Effects of implementing the Simple Biosphere Model in aGeneral Circulation Model. J. Atmos. Sci. 46 (18), 2757–2782.

chaefer, K., Collatz, G.J., Tans, P., Denning, A.S., Baker, I., Berry, J., Prihodko, L., Suits,N., Philpott, A., 2008. The combined Simple Biosphere/Carnegie-Ames-StanfordApproach (SiBCASA) terrestrial carbon cycle model. J. Geophys. Res. 113, G03034,http://dx.doi.org/10.1029/2007JG000603.

chneider, E.K., Fan, M., Kirtman, B.P., Dirmeyer, P.A., 2006. Potential effects of Ama-zon deforestation on tropical climate. IGES/COLA Rep., 41 pp.

chwalm, C.R., Williams, C.A., Schaefer, K., Anderson, R., Arain, M.A., Baker, I., Barr,A., Black, T.A., Chen, G., Chen, J.M., Ciais, P., Davis, K.J., Desai, A., Dietze, M.,Dragoni, D., Fischer, M.L., Flanagan, L.B., Grant, R., Gu, L., Hollinger, D., Izau-rralde, R.C., Kucharik, C., Lafleur, P., Law, B.E., Li, L., Li, Z., Liu, S., Lokupitya,E., Luo, Y., Ma, S., Margolis, H., Matamala, R., McCaughey, H., Monson, R.K.,

Oechel, W.C., Peng, C., Poulter, B., Price, D.T., Ricciutto, D.M., Riley, W., Sahoo,A.K., Sprintsin, M., Sun, J., Tian, H., Tonitto, C., Verbeek, H., Verma, S.B., 2010.A model-data intercomparison of CO2 exchange across North America: resultsfrom the North American Carbon Program site synthesis. J. Geophys. Res. 115,G00H05, http://dx.doi.org/10.1029/2009JG001229.

eteorology 182– 183 (2013) 177– 188

Sellers, P.J., Mintz, Y., Sud, Y.C., Dalcher, A., 1986. A Simple Biosphere Model (SiB) foruse within General Circulation Models. J. Atmos. Sci. 43 (6), 505–531.

Sellers, P.J., Berry, J.A., Collatz, G.J., Field, C.B., Hall, F.G., 1992. Canopy reflectance,photosynthesis, and transpiration. III. A reanalysis using improved leafmodels and a new canopy integration scheme. Remote Sens. Environ. 42,1878–2216.

Sellers, P.J.D.A., Randall, G.J., Collatz, J.A., Berry, C.B., Field, D.A., Dazlich, C., Zhang,G.D., Colello, L., Bounoua, 1996a. A revised land surface parameterization (SiB2)for Atmospheric GCMs. Part I: model formulation. J. Clim. 9 (4), 676–705.

Sellers, P.J.S.O., Los, C.J., Tucker, C.O., Justice, D.A., Dazlich, G.J., Collatz, D.A., Randall,1996b. A revised land surface parameterization (SiB2) for atmospheric GCMs.Part II: the generation of global fields of terrestrial biophysical parameters fromsatellite data. J. Clim. 9 (4), 706–737.

Sellers, P.J., Dickinson, R.E., Randall, D.A., Betts, A.K., Hall, F.G., Berry, J.A., Collatz,G.J., Denning, A.S., Mooney, H.A., Nobre, C.A., Sato, N., Field, C.B., Henderson-Sellers, A., 1997. Modeling the exchanges of energy, water, and carbon betweencontinents and the atmosphere. Science 275, 502–509.

Sellers, P.J., 1985. Canopy reflectance, photosynthesis and respiration. Int. J. RemoteSens. 6 (8), 1335–1372.

Trenberth, K.E., 1999. Atmospheric moisture recycling: role of advection and localevaporation. J. Clim. 12 (5), 1368–1381.

Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D.A., Pak, E.W., Mahoney, R., Vermote,E.F., El Saleous, N., 2005. An extended AVHRR 8-km NDVI dataset compati-ble with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26 (20),4485–4498.

van der Ent, R.J., Savenjie, H.H.G., Schaefli, B., Steele-Dunn, S.C., 2010. Originand fate of atmospheric moisture over continents. Water Resour. Res. 46,http://dx.doi.org/10.1029/2010/WR009127.

Vidale, P.L., Stöckli, R., 2005. Prognostic canopy air solutions for land surfaceexchanges. Theor. Appl. Climatol. 80, 245–257.

von Randow, C., Manzi, A.O., Kruijt, B., de Oliveira, P.J., Zanchi, F.B., Silva, R.L., Hodnett,M.G., Gash, J.H.C., Elbers, J.A., Waterloo, M.J., Cardoso, F.L., Kabat, P., 2004. Com-parative measurements and seasonal variations in energy and carbon exchangeover forest and pasture in South West Amazonia. Theor. Appl. Climatol. 78,5–26.

von Randow, C., Kruijt, B., Holtslag, A.A.M., Oliveira, M.B.L., 2008. Exploring eddy-covariance and large-aperture scintillometer measurements in an Amazonianrain forest. Agric. Forest Meteorol. 148 (4), 680–690.

Wang, J.W., Denning, A.S., Lu, L., Baker, I.T., Corbin, K.D., Davis, K.J., 2007. Observa-tions and simulations of synoptic, regional, and local variations in atmosphericCO2. J. Geophys. Res. 112 (D4), D0418, http://dx.doi.org/10.1029/2006JD007410.

Werth, D., Avissar, R., 2002. The local and global effects of Amazon deforestation. J.Geophys. Res. 107 (D20), 087, http://dx.doi.org/10.1029/2001JD000717.

Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer,C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B.E., Kowalski,A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R.,Verma, S., 2002. Energy balance closure at FLUXNET sites. Agric. Forest Meteorol.

113, 223–243.

Xu, L., Samanta, A., Costa, M.H., Ganguly, S., Nemani, R.R., Myneni,R.B., 2011. Widespread decline in greenness of Amazonian vege-tation due to the 2010 drought. Geophys. Res. Lett. 38, L07402,http://dx.doi.org/10.1029/2011GL046824.