www.sciencemag.org/cgi/content/full/science.1184984/DC1

Supporting Online Material for

Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate

Christian Beer,* Markus Reichstein, Enrico Tomelleri, Philippe Ciais, Martin Jung,

Nuno Carvalhais, Christian Rödenbeck, M. Altaf Arain, Dennis Baldocchi, Gordon B. Bonan, Alberte Bondeau, Alessandro Cescatti, Gitta Lasslop,

Anders Lindroth, Mark Lomas, Sebastiaan Luyssaert, Hank Margolis, Keith W. Oleson, Olivier Roupsard, Elmar Veenendaal, Nicolas Viovy, Christopher Williams,

F. Ian Woodward, Dario Papale

*To whom correspondence should be addressed. E-mail: christian.beer@bgc-jena.mpg.de

Published 5 July 2010 on Science Express DOI: 10.1126/science.1184984

This PDF file includes:

Materials and Methods SOM Text Figs. S1 to S34 Tables S1 to S9 References

Gross Primary Production Estimation at Ecosystem Level

Estimates of GPP at the ecosystem level are based on the eddy covariance technique, the only

method that allows the direct observation of the CO2 flux between land surface and atmosphere

in a nondestructive continuous way, which has been deployed on more than 400 sites world-

wide now. The eddy covariance technique is based on high-frequency vertical wind vector and

CO2 and H2O concentration measurements using 3-D anemometer and Infraed Gas analyzers

installed at platforms well above the vegetation canopy (1,2). Half-hourly flux and meteorolog-

ical data from regional networks and individual sites were combined to create the FLUXNET

database for a workshop in La Thuile, Italy in 2007. Hence, data from the first measurement

years (e.g. 1993 in the US, 1996 in Europe) up to 2006 are included. The submission of site

data to the data base by networks or individual site investigators occurred on a voluntary basis,

i.e. the data set is a subset of all observations carried out globally. The networks Ameriflux,

CarboEurope and Fluxnet-Canada submitted their complete data sets. This resulted in a global

data set with 968 years of data from the 253 sites, with all major climate zones being represented

(although temperate climates being over-represented). After harmonization of variable names

and units as well as logical checks for obvious data errors (e.g. constant variables, impossible

values) each site-year was processed according to the methods in Reichstein et al. (3) and Papale

et al. (4), including storage correction, quality control, gap-filling and flux-partitioning. Uncer-

tainties in temporal integrals of GPP stem from uncertainties in the measurements themselves,

gap-filling procedures, and the algorithm to partition the observed net flux into GPP and ecosys-

tem respiration as discussed and analyzed in refs. (4–6). Usually the largest uncertainties in the

eddy covariance flux data are introduced by a potential so called selective systematic error (7),

i.e. an underestimation of fluxes during periods without sufficient turbulent exchange between

ecosystems and the atmosphere. This primarily occurs during night, and the friction velocity

4

(u*) which is an indirect measure of the turbulent exchange can be used as an indicator for these

situations. Thus, data below a site specific threshold of u* is being filtered out. However, this

u*-based filtering is a semi-empirical approach and subject to uncertainties itself. Hence, we

followed two ways to address this uncertainty: 1) Via a bootstrap approach, for each site year

a distribution of u*-thresholds is generated, and propagated into a distribution of GPP, after

gap-filling and flux-partitioning (4). 2) The GPP is estimated both, via a light response curve

approach using only day-time data (8) and the conventional night-time data based approach (3).

Similar to Lasslop et al. (8) differences can be large for individual sites but remain in most cases

below 70 gC/m2/a and are distributed symmetrically around zero, i.e. there is no systematic dif-

ference between night-time and day-time based GPP estimates after u*-filtering (Fig. 1). The

propagation of the uncertainties of the GPP estimates at site level into global GPP estimates has

been tested with the Model Tree Ensemble approach which has been trained separately with

day-time and night-time based estimates (see below).

Diagnostic modeling

The flux tower sites are classified by the IGBP land cover types. For each of these types, we

generally estimate parameters at daily to monthly time scales of a model M that explains GPP

by a set of environmental conditions x, M : x → GPP where x can contain the fraction of

absorbed photosynthetically active radiation (fAPAR) or climate variables, such as short-wave

radiation, air temperature, precipitation, or vapor pressure deficit, depending on the respective

model. M can be represented by the arithmetic mean (KGB), a single regression equation (MI-

AMI) or by a set of linear, non-linear or piece-wise linear equations (ANN, MTE) as explained

in more detail below. The so calibrated model is then run by global grids of the predictor vari-

ables with underlying global land cover maps. The WUE approach differs from this scheme.

With this approach, WUE instead of GPP is scaled from flux towers sites to the globe and then

5

0.20

0.15

0.10

0.05

-320 -280 -240 -200 -160 -120 -80 -40 0 40 80 120 160 200 240 280 320

Difference between day-time and night

-time based GPP [gC m

-2yr

-1]

0.00

Difference between day time and night time based GPP [gC m yr ]

Figure 1: Distribution of differences in GPP estimations by NEE partitioning methods (3, 8) atflux tower sites.

GPP calculated by using the water balance of catchment basins[m1].

Ensemble Building and Overall Uncertainty Estimation

For each land cover type a parameter distribution is derived by randomly resampling a subset of

the set of flux tower sites (except MTE approach). This distribution accounts for uncertainties

of the site level GPP estimates and the overall sampling uncertainty. In addition, we take into

consideration potential biases in the predictor variables used for the globally distributed calcu-

lations by applying n independent predictor datasets. In conjunction with the additional usage

of m different models such scheme potentially results in n · m of the above mentioned GPP

distributions.

However, instead of full factorial model runs, we concentrate on the most sensitive drivers

6

for each approach (see detailed approach description below). For instance, the LUE approach is

a linear regression of GPP against fAPAR*PAR. Therefore, this method is sensitive to fAPAR

and radiation inputs; the extrapolation of WUE directly depends on the maximum LAI; the

KGB approach is a look-up table for ecoregions, hence sensitive to the underlying land cover

map; etc. This is detailed individually for each approach.

The parameter distribution leads to a GPP distribution for each driving dataset applied to

each model (see detailed approach description below). Then the overall GPP distribution for

each approach, and the overall GPP distribution (Fig. 1(a)) is achieved by a superimposition of

vectors with equal length representing these GPP distributions. A robust measure of standard

deviation (i.e. the median absolute deviation times 1.48) and the 95% confidence interval inform

then about the overall uncertainty taking into account uncertainty in the GPP estimation at the

flux tower sites, the globally available predictor variables, and the diagnostic model.

Representativeness of FLUXNET Sites

The eddy covariance sites are by far not homogeneously distributed over the globe, resulting

in a potential lack of representativeness. We cannot exclude that this an unknown source of

uncertainty, but there are three lines of evidence, which indicate that that the issue is not as big

as it might appear from the geographical distribution.

• The sites do represent the climate-vegetation space quite well (Fig. 2). Nevertheless no-

table gaps exist in the semi-arid-tropical areas, e.g. savannah type ecosystems. Also at

the very cold end of the climate space site are missing, but this is to a large extent very

unproductive land (including inland ice).

• An earlier study based on climate and geoecological variables similarly indicated that the

ecological representativeness of the FLUXNET sites is much better than the geographical

7

(9).

• A study with artificial data from a biosphere model indicated that the missing geographi-

cal representativeness did not hamper the retrieval of the spatial pattern of GPP using the

MTE approach (10).

These indications lend confidence that our GPP estimates are not compromised by the un-

equal spatial sampling of sites by FLUXNET from the global terrestrial surface.

Figure 2: Used flux tower sites within the temperature-precipitation space (annual basis).

Up-scaling Approaches

We have exploited almost all known approaches of how to utilize relationships between GPP and

climate and vegetation indices at site level for estimating global maps and the terrestrial total.

The GPP distributions from the individual approaches presented in the following are useful

8

to understand the uncertainty due to a single driving dataset or due to the model parameter

uncertainty for a particular approach.

Model Tree Ensemble (MTE)

The model tree ensemble approach (10) is a machine learning algorithm where the target vari-

able (GPP) is predicted by a set of multiple linear regressions from explanatory variables. Each

regression model is only valid within certain conditions defined by a hierarchical stratification

(the model tree). We established four MTEs using monthly data for: (1) predicting GPP based

on nighttime NEE data (3) and without using climate data in the regressions, (2) predicting GPP

based on daytime NEE data (8) and without using climate data in the regressions, (3) predicting

GPP based on nighttime NEE data (3) using also climate data in the regressions, (4) predict-

ing GPP based on daytime NEE data (8) using also climate data in the regressions. Type (1)

and (2) are motivated by Jung et al (11) suggesting that GPP can be predicted from remotely

sensed fAPAR alone. Training for different flux partitioning methods was used for uncertainty

analysis. A quality control filtering of the monthly FLUXNET GPP data was carried out. The

following criteria were used to remove unreliable monthly data: (1) less than 20% of mea-

sured NEE and explanatory meteorological variables were subject to uncertain gap filling, (2)

removing the 5% most uncertain data due to the uncertainty of setting the u* threshold (4), (3)

removing outliers of the difference between flux estimates from two different flux partitioning

(Reichstein, Lasslop). Regarding point (3), a non-parametric outlier test was used (median ±1.5 times interquartile range) and applied for GPP, TER, and NEE.

For each ensemble, 2500 different model trees were generated of which 25 of the best inde-

pendent trees were selected as ensemble members. In total 29 explanatory variables of four gen-

eral types were used to train MTE for predicting GPP globally: (1) monthly SeaWiFS fAPAR,

the product of fAPAR and potential radiation, and precipitation and temperature (measured at

9

the sites), (2) annual characteristics of the fAPAR seasonal cycle that describe properties of veg-

etation structure such as minimum, maximum, mean, and amplitude, (3) characteristics of the

mean annual climate such as mean annual temperature, precipitation, sunshine hours, relative

humidity, potential evapotranspiration, climatic water balance, as well as their mean seasonal

cycles derived from a global database, and (4) the vegetation type according to the IGBP classi-

fication plus a flag regarding the photosynthetic pathway (C3, C4, C3/C4) (in-situ information).

Please note that only variables of type (1) are used in the multiple linear regressions; all re-

maining variables are only used for data stratification. Spatial runs are based on gap-filled

SeaWiFS fAPAR, a land cover map (12) reclassified into the respective IGBP classes used to

describe the vegetation at FLUXNET sites from in-situ information. We generate fractions of

each class globally at 0.5◦ resolution. Grasslands are further separated into C3, C4, and mixed

C3/C4 types using the results of Winslow et al. (13); fractions of C3 and C4 crop types are

estimated using a gridded database based on FAO crop statistics (14). Various long-term mean

climatic characteristics are from CRU and correspond to the period 1961-1990. Global grids of

monthly precipitation is from GPCC; while global grids of monthly temperature is taken from

CRU-PIK. Additional runs with different temperature and precipitation data were performed

for uncertainty analysis: temperature and precipitation from CRU-PIK, and temperature and

precipitation from ERA-Interim. For these additional runs, the site-level estimation of GPP by

nighttime NEE data (3) was used.

The set-up of various MTE applications allows for analyzing the uncertainty related to: (1)

different methods of partitioning NEE measurements into GPP and TER at flux tower sites, (2)

impact of different temperature and precipitation drivers, (3) the necessity of using meteorolog-

ical variables as regressors for GPP, (4) up-scaling from FLUXNET sites, because substantial

divergence of GPP estimates within one particular ensemble essentially means that GPP rela-

tionships are not well constrained. Hence, divergence of model trees of one ensemble can be

10

interpreted as a degree of extrapolation (10).

Variables that are only split variables are only used for data stratification and do not enter

regressions. Please note that not all variables are automatically selected by the model trees.

The ’type of variability’ refers to if and when the values of the respective variable change for

a given pixel. ’Static’ variables never change and can be used by MTE to stratify into spatial

domains (e.g. according to long term mean annual temperature). ’Monthly but static over years’

refers to mean seasonal cycles, i.e. the values change monthly but the same monthly values are

repeatedly used for all years. ’Yearly’ variables have the same value within a year but this value

is updated for each year, which is primarily used for variables describing vegetation structure to

capture possible effects of land cover change. ’Monthly’ variables exhibit different values for

each month and year, i.e. they are continuously updated each for month.

11

Table 1: List of explanatory variables used for the training of the model tree ensemblesVariable Type Type of variability

Climate Mean annual temperature Split static

Mean Annual precipitation sum Split static

Mean annual climatic water balance Split static

Mean annual Potential evaporation Split static

Mean annual sunshine hours Split static

Mean annual number of wet days Split static

Mean annual relative humidity Split static

Mean monthly temperature Split Monthly but static over years

Mean monthly precipitation sum Split Monthly but static over years

Mean monthly climatic water balance Split Monthly but static over years

Mean monthly Potential evaporation Split Monthly but static over years

Mean monthly sunshine hours Split Monthly but static over years

Mean monthly number of wet days Split Monthly but static over years

Mean monthly relative humidity Split Monthly but static over years

Vegetation structure Maximum fAPAR of year Split yearly

Minimum fAPAR of year Split yearly

Maximum Minimum fAPAR Split yearly

Mean annual fAPAR Split yearly

sum of fAPAR over the growing season Split yearly

Mean fAPAR of the growing season Split yearly

Growing season length derived from fA-PAR

Split yearly

Sum of fAPAR potential radiation of year Split yearly

Maximum of fAPAR potential radiationof year

Split & regression yearly

IGBP vegetation type Split static

Meteorology Temperature Split & regression monthly

Precipitation Split & regression monthly

Potential radiation Split & regression Monthly but static over years

Vegetation status fAPAR Split & regression monthly

fAPAR x Potential Radiation Split & regression monthly

12

105

110

115

120

125

130

135

MT

E11

MT

E12

MT

E21

MT

E22

MT

E23

MT

E24

Ter

rest

rial G

PP

[PgC

/a]

Figure 3: Detailed distributions of global GPP [Pg C a−1] separately by combinations of the pre-dictor datasets. Shown are the median, 25 and 75 percentiles, and the 95% confidence interval.

• MTE11: no climate data, just SeaWifS fAPAR, daytime-based GPP; 1998-2005

• MTE12: no climate data, just SeaWifS fAPAR, nighttime-based GPP; 1998-2005

• MTE21: daytime based GPP, CRU temp plus GPCC precip, SeaWifS fAPAR; 1998-2005

• MTE22: night-time based GPP, CRU temp + GPCC precip, SeaWifS fAPAR; 1998-2005

• MTE23: night-time based GPP, pure CRU, SeaWifS fAPAR; 1998-2005

• MTE24: night-time based GPP, pure ECMWF ERA-Interim, SeaWifS fAPAR; 1998-

2005

13

Table 2: Mean and standard deviation of model performance indicators over the 25 model treesof the ensemble. All measures are based on 10-fold cross-validations performed in the leavesof the model trees. Different number of site months used for versions including or excludingmeteorological data is due to data availability of measured meteorology at the sites.

MTE type 1 MTE type 2 MTE type 3 MTE type 4R2 0.847 [0.006] 0.845 [0.007] 0.859 [0.005] 0.856 [0.006]RMSE 1.134 [0.0237] 1.14 [0.027] 1.109 [0.019] 1.117 [0.023]N parameters 107.68 [14.92] 106.84 [15.54] 122.44 [11.9] 118.4 [12.77]Adj R2 0.843 [0.006] 0.841 [0.007] 0.854 [0.005] 0.852 [0.006]N 4209 4209 3800 3800

14

Artificial Neural Network (ANN)

The ANNs are purely empirical non-linear regression models consisting of nodes connected

by weights which effectively are the regression parameters. The ANNs used are feed-forward

backpropagation networks and the weights values are adjusted during the training phase by

back-propagating the error in the prediction of the output. The training is based on a dataset of

measured examples composed by the predicting variables and the output to estimate.

Training datasets have been composed by daily eddy covariance data; input variables used

have been (1) mean air temperature (Tair), (2) daytime VPD (VPD), (3) incoming radiation

(Rg), (4) FAPAR and (5-6) top of atmosphere incoming radiation (ToAR) and first derivative of

(ToAR) as signal for the seasonality (15) while the output value has been GPP derived using the

method described in Reichstein et al. (3). Daily measured data have been filtered by removing

daily values where GPP or one of the input was calculated using less than 75% of original data

or high quality gap-filled data (see ref. (3)).

Ten different ANNs have been trained for each combination between Plant Functional Types

based on the SYNMAP classification and Climatic regions according with the KG classifica-

tion. In total 14 PFT classes (ENF, EBF, DBF, MF, CSH, OSH, WSA, SAV, GRAC3, CROC3,

WSAC4, GRAC3C4, GRAC4 and CROC4; see (16) for abbreviations and C3, C4 refers to pho-

tosynthetic pathways) and 7 climatic classes (Tropical, Dry, Subtropical/Mediterranean, Tem-

perate, Temperate Continental, Boreal and Arctic) have been used.

To train each of the 10 ANNs for each PFT-Climate combination a subset of the training

dataset (75%) has been randomly extracted and 8 ANNs with different structure (different num-

ber of nodes in the hidden layer/layers) have been trained 30 times each one, starting from

different initial weights. In total 240 (8x30) ANNs have been trained for each extraction and

for each PFT-Climate combination and the one with best performance in predicting the 25%

of data not used in the training and the simplest structure (less parameters) has been selected,

15

resulting in 10 ANNs for each PFT-Climate class (one for each extraction).

The 10 ANNs selected has been applied to the pixels where the relative PFT-Climate com-

binations were present, using two different gridded datasets:

• ANN1: Tair, VPD and Rg from ERAInterim reanalysis and fAPAR from SeaWifs (years

1998-2005)

• ANN2: Tair, VPD and Rg from GMAO GEOS400 reanalysis and fAPAR from SeaWifs

(years 2000-2005)

Using this set-up of the training it has been possible to include uncertainty estimation due to

differences in the training dataset (through the 10 extractions), differences in the model structure

(through the different ANN setups) and differences in the gridded driving variables.

Since the ANNs were trained by local meteorological variables measured at the sites, the ap-

plication of multiple climate reanalysis datasets provides highest uncertainty ranges. In contrast,

the uncertainty estimation due to different fAPAR grids should be small (cf. also LUE approach)

because the ANNs were trained by the cutouts of these grids. However, even this new applica-

tion of GMAO reanalysis climate data results in a low 2 Pg C a−1difference of the median GPP,

and the 95% confidence interval increased only slightly from 8 to 10 Pg C a−1(Fig. 4).

16

125

126

127

128

129

130

131

132

133

AN

N1

AN

N2

Ter

rest

rial G

PP

[PgC

/a]

Figure 4: Detailed distributions of global GPP [Pg C a−1] separately by combinations of the pre-dictor datasets. Shown are the median, 25 and 75 percentiles, and the 95% confidence interval.

17

Light Use Efficiency (LUE)

The light use efficiency (LUE) model (17) relates GPP to the amount of absorbed photosyn-

thetically active radiation (APAR), constrained by minimum daily temperature (T) and daytime

vapor pressure deficit (VPD) (18, 19):

GPP = LUE · f(V PD) · g(T ) · APAR (1)

The model parameters were calibrated in a Bayesian framework (20) for each measurement

site in FLUXNET. This technique integrates the prior knowledge of parameter values and eddy

covariance measurements along with their associated uncertainty. This allows the estimation

of a posteriori probability distributions for model parameters (21). Prior model parameter un-

certainty was derived from the literature (19) and measurement uncertainties were assessed

according to the method developed by Lasslop et al. (22).

For the in-situ calibration, the site meteorology and remotely sensed fAPAR values corre-

sponding to the site’s geographic position were used. This calibration assumes correspondence

between the footprint of the eddy covariance station and the resolution of the fAPAR product.

The calibration results were stratified per vegetation type (PFT) and when a sufficient number

of sites were available, a simplified version of the Koeppen-Geiger climate classification were

also used (23).

Subsequently, the joint probability density functions of model parameters were randomly

resampled to apply the model spatially for the different classes. Downward shortwave radiation,

daytime vapor pressure deficit, and minimum temperature provide the meteorological forcing

of the model. Model calibration and the uncertainty propagation were performed independently

for each different fAPAR product (Fig. 5).

This analysis is accounting for flux measurement uncertainty by means of the data assim-

18

ilation technique that underpin it. Furthermore, the method allows estimating the uncertainty

related to the spatial distribution of the measurement network to be included because of the up-

scaling of model parameters done by means of stratifying the whole dataset by vegetation and

climate classes. Conversely, this does not account for specific conditions that may arise, such

as different photosynthetic pathways. As already shown by other authors (24) the use of diverse

meteorological drivers may influence the spatial patterns of the estimated GPP. Therefore, in

this study, we account for this by using forcing fields from GMAO and ECMWF. The choice of

a specific fAPAR dataset may significantly affect the model results (25) which is why we use

three independent fAPAR products based on MODIS, SeaWiFS and SPOT VGT here.

98

100

102

104

106

108

110

112

LUE

11

LUE

12

LUE

13

LUE

21

LUE

22

LUE

23

Ter

rest

rial G

PP

[PgC

/a]

Figure 5: Detailed distributions of global GPP [Pg C a−1] separately by combinations of the pre-dictor datasets. Shown are the median, 25 and 75 percentiles, and the 95% confidence interval.

• LUE11: MODIS fAPAR, ECMWF ERA-Interim; 2001-2005

• LUE12: SeaWiFS fAPAR, ECMWF ERA-Interim; 1998-2004

19

• LUE13: SPOT VGT fAPAR, ECMWF ERA-Interim; 2000-2003

• LUE21: MODIS fAPAR, DAO; 2001-2005

• LUE22: SeaWiFS fAPAR, DAO; 2000-2004

• LUE23: SPOT VGT fAPAR, DAO; 2000-2003

20

Water Use Efficiency (WUE) Approach

GPP of a whole catchment basin can be estimated by multiplying the mean water use efficiency

WUE =(

GPPET

)of the watershed with its annual water balance (ET) (26). Here, ET stands

for transpiration plus soil evaporation beneath the canopy. This concept has the fortune that a

matter flux at the regional scale (the catchment basin runoff), which integrates ecophysiological

processes at this large scale, constrains the envisaged carbon flux at the same scale. This is

a forthcoming for estimating a global number and uncertainty, but the coarse resolution limits

further spatially explicit analyzes. A further shortcoming is that not all land is covered by

watersheds for which runoff data is available.

Both, the mean WUE of the whole watershed and the ET need to be estimated. ET is

estimated by total annual precipitation of the catchment basin minus annual runoff minus annual

interception. Interception is calculated by an empirical relationship to LAI and precipitation for

each land cover type following (27). The mean WUE estimation is described below.

Water Use Efficiency Approach

At the ecosystem level, WUE can be expressed as the ratio between GPP and evapotranspiration

(ET), both of which estimated by means of the eddy covariance technique (28). For this purpose,

soil evaporation and interception need to be kept to a minimum, as they are not related to

stomatal conductance. The assumption that soil evaporation and interception are negligible after

three consecutive days without rainfall is fairly acceptable. A simple extrapolation of WUE to

the land surface made by multiplying point scale flux tower WUE estimates by the area of

each biome, however, is not valid because relationships of WUE to environmental conditions

are confounded by the effect of VPD on the water flux (29, 30) regardless of the performance

of carboxylation in relation to inner-leaf CO2 concentration. Therefore, (31) introduced the

concept of inherent WUE (IWUE) which approximates intrinsic WUE (carbon assimilation

21

divided by stomatal conductance) at the ecosystem level:

IWUE =GPP

ET· V PD. (2)

By making use of ecosystem-level measurements made by the global network of eddy co-

variance flux towers FLUXNET (31) report on a linear relationship of IWUE to both relative

soil water content at field capacity (θ) and maximum light absorption by leaves in forests:

WUEi = a · θ + b · (1− exp(−k · LAI)) , (3)

k being 0.7 for deciduous broad-leaved forests and 0.5 otherwise. This model is able to explain

over 50 % of IWUE variability in forests (RMSE=4.3 g/kg*hPa). In herbaceous ecosystems

only a relationship to LAI was found which also explains over 50 % of the IWUE variability

(31):

WUEi = b · (1− exp(−0.4 · LAI)) (4)

with b being 2 times higher for C4 ecosystems compared to C3 ones (32, 33). Both predictor

quantities can be assessed by using remotely sensed information on maximum leaf area index

(LAI) and land cover, and spatial details of the soil texture type. The combination of such

global fields with the empirical relationships found at flux tower sites enables a global mapping

of IWUE, from which a global map of mean WUE is achieved by dividing the IWUE map by

daylight VPD averaged during the growing season.

The land cover information is used to distinguish between ecosystems for application of

models described by Eq. 3 and Eq. 4. These models were derived by a holistic approach of con-

sidering ecosystems as aggregation of different plant types, such as trees and grasses, i.e. field

capacity of the soil and LAI of trees are used in the regression analysis with observed IWUE for

forests. The application of such approach, however, provoked a bias in C4-ecosystems when

there is a significant tree cover but dominant vegetation type is C4-herbaceous. The land cover

22

map indicated grassland or cropland but the remotely sensed LAI value would be much higher

than for pure herbs having a significant impact on IWUE of the catchment basin. We make use

of the MODIS Vegetation Continuous Fields product (34) for a linear mixing of Eq. 4 utilizing

C3 or C4 parameters in regions with combined occurrence of C4 grass and trees. Natural oc-

currence of C4 species (Fig. 6) is modeled by the bioclimatic zone approach following ref. (35).

An analogous linear mixing is applied for agricultural regions using the ratio of cultivated C3

and C4 species based on statistics from the FAO Statistical Yearbook 2004 (36) on a country

level (Fig. 7).

0

0.2

0.4

0.6

0.8

1

Figure 6: Climate driven occurrence of C4 herbaceous vegetation following ref. (35).

Uncertainties

GPP estimation at watershed scale by using the concept of WUE is uncertain in the following

ways:

• Uncertain remote sensing information about land cover and maximum annual LAI

• Uncertain global distribution of soil texture type

• Uncertain mean growing-season daylight VPD from climate reanalysis

23

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Figure 7: Ratio between agricultural C3 and C4 vegetation following the FAO Statistical Year-book 2004 (36).

• Uncertain precipitation data and uncertainties in the interception loss calculation

• Uncertainty in river runoff data is neglected in this study.

• GPP is derived by flux partitioning of NEE at flux tower sites following Reichstein et

al. (3). With the WUE approach we do not address this rather small (8) uncertainty

explicitly, cf. results by the MTE1 versus MTE2 approach.

• Representativeness of flux tower sites, uncertainties in eddy covariance data processing

(cf. respective sections).

• Uncertainties in parameter estimation by the regression analysis are addressed by esti-

mating a parameter distribution instead of only one parameter value (31).

Uncertainties in remote sensing data are considered by using land cover and LAI products

both based on two different optical sensors, SPOT Vegetation (37,38) and TERRA MODIS (39,

40). These independent remote sensing products were resized to a common pixel size (1/112◦ ),

and the land cover classification harmonized to the IGBP definition. Spatial details of soil

24

texture type (41) are translated into relative soil water content at field capacity by using statistics

from Cosby et al. (42). These values are interpolated then to match the resolution of remote

sensing products. The combination of the full parameter distribution of Eq. 3 and Eq. ?? with

the different remote sensing maps led to 1372 global maps of IWUE with a spatial resolution

of 1/112◦ which is immediately averaged to 10 minutes for reasons of memory handling. For

further estimation of WUE fields we used a map of daylight VPD during the growing season

from the Global Monitoring and Assimilation Office (GMAO) GEOS400 reanalysis fields (43,

44).

The long-term precipitation of the watersheds was estimated from grids by the Climate Re-

search Unit of the University of East Anglia (CRU CL 2.0), and from the Global Precipitation

Climatology Project (GPCP, http://www.gewex.org/gpcp.html). In concert with a long-term av-

erage of river runoff that was downloaded from the Global Runoff Data Centre of the Federal In-

stitute of Hydrology (http://www.bafg.de/cln 005/GRDC/) this led to two different basin-wide

water balance values, which was then multiplied to the 1372 IWUE maps. For land that could

not be assigned to any river discharge data, potential evapotranspiration (45) is used instead of

precipitation minus runoff if the ratio of precipitation to potential evapotranspiration is higher

than 1.2 (no water limitation of GPP). This is in particular true for most of the productive land

in south-east Asia. In so doing, GPP of 84 · 106km2 land was estimated by the WUE approach.

GPP of the remaining 38 · 106km2 was filled with the median form approaches MTE1, MT2,

ANN, LUE, and KGB, in total 27 Pg C a−1.

Correction for Missing Energy Balance

For the WUE approach, another uncertainty arises from the fact that water-fluxes from eddy

covariance are used: with higher water fluxes, one gets proportionally lower WUE estimates and

consequently lower calculated GPP at catchment scale. It is a known issue, that the observed

25

0.78 0.8 0.82 0.84 0.860

10

20

30

40

50

Correction factor

Fre

quen

cy

Figure 8: Distribution of FLUXNET correction factors for evapotranspiration to meet the energybalance at the site

energy balance at eddy covariance sites is not closed. This non-closure is most often related

to an underestimation of the turbulent fluxes, has been quantified to vary around 20%, but can

vary considerably between sites ( (46)). Hence for this study we calculated the energy balance

ratio (EBR, ( (46)) for each site year, and via a bootstrap approach estimated the mean EBR for

the sites and the distribution of the expected mean (Fig. 8) . With a Monte-Carlo approach we

propagated the uncertainty of the EBR into the global GPP estimates from the WUE approach

by multiplying the GPP distribution with the distribution of the EBR factors (Fig. 8).

GPP Results by the WUE Approach

Fig. 9 shows two boxplots with the GPP distributions that stem from a) model parameter distri-

butions and the two different precipitation datasets. These results show that the uncertainty of

land cover and maximum LAI on the global GPP number is as high as the uncertainty of model

parameters and precipitation. Further investigations need to clarify if there is a high or low bias

in the MODIS or SPOT VGT based maximum LAI values, respectively.

• WUE1: MODIS based land cover and LAI; long-term average

26

118

120

122

124

126

128

130

132

134

WU

E1

WU

E2

Ter

rest

rial G

PP

[PgC

/a]

Figure 9: Detailed distributions of global GPP [Pg C a−1] separately by combinations of the pre-dictor datasets. Shown are the median, 25 and 75 percentiles, and the 95% confidence interval.

• WUE2: SPOT VGT based land cover and LAI; long-term average

27

Longitude [deg]

La

titu

de

[d

eg

]

−180 −150 −120 −90 −60 −30 0 30 60 90 120 150 180−90

−60

−30

0

30

60

90

none

Trop.

Arid

T.Dry.

T.Hum.

Sn.Warm

Sn.Cold

Polar

Figure 10: Simplified Koeppen-Geiger map

Koppen-Geiger cross Biome (KGB)

The KGB is a look-up table approach that incorporates mean annual GPP estimates for different

climate classes, given by a simplified version of the Koeppen-Geiger classification (KG map

(23)), and for different biome types (B), based on local land cover classifications. The site level

estimates of GPP stem from the flux-partitioning method described in ref. (3). When a certain

KGxB combination is not present in the set of eddy-covariance sites used, such entry in the

look-up table is given by the mean of the respective KG class for all biomes. The resulting

look-up table is up-scaled making use of the global Koeppen-Geiger classification map Fig. 10

and land cover classification maps: 1) SYNMAP (12), 2) GLC2000 (37), and 3) MODIS (39).

The uncertainties from the KGB approach stem from: a) the u* threshold used for NEE ”u*-

correction”; b) the partitioning of NEE into GPP and TER (3); c) the filling of empty KGxB

combinations; d) the land cover datasets used to propagate GPP globally; e) the climate datasets

used for the Koeppen-Geiger classification. The uncertainty characterization focuses: a) the de-

termination of the u* threshold, by following a bootstrap approach, with 500 resamplings (47),

to estimate the distribution of the mean annual GPP per KGxPFT combination; b) the changes

in global GPP estimates driven by different land cover classification maps: 1) SYNMAP; 2)

28

0 50 100 150

SYNMAP

MODIS

GLC2000

GPP Pg C yr−1

Land

cov

er m

ap

ENFEBFDNF+DBFMFCSHOSHWSASAVGRAWETCRO

Figure 11: Distribution of global GPP estimates per land cover class

MODIS; 3) GLC2000.

Globally, land cover is an important factor for the uncertainties in the KGB approach and the

differences in GPP between land covers is statistically significant (Fig. 11). Despite the over-

all global differences, the dissimilarities between the three land cover maps embed significant

differences in the partial contributions of each class for the global GPP estimate.

• KGB1: GLC2000 land cover; long-term average

• KGB2: MODIS land cover; long-term average

• KGB3: Synmap land cover; long-term average

29

120

122

124

126

128

130

132

134

136

138

KG

B1

KG

B2

KG

B3

Ter

rest

rial G

PP

[PgC

/a]

Figure 12: Detailed distributions of global GPP [Pg C a−1] separately by combinations of thepredictor datasets. Shown are the median, 25 and 75 percentiles, and the 95% confidence inter-val.

30

MIAMI Model

Annual net primary productivity has been related to mean annual temperature and precipitation

in the MIAMI model by Lieth (48). We adopted the MIAMI model for GPP and normalized it

to reference conditions, i.e. a mean annual temperature of 15 ◦C and precipitation of 1000mm:

GPP = min [g (MAT ) , f (P )] , (5)

with

g (MAT ) = GPP15degC · 1 + ea1−a2·15degC

1 + ea1−a2·MAT(6)

f (P ) = GPP1000mm · 1− e−k·P

1− e−k·1000mm(7)

where MAT is mean annual temperature [ ◦C ], P is the annual precipitation and the other

variables are parameters to be estimated. We re-parameterized this model with the mean annual

GPP, mean annual temperature and precipitation estimates at the eddy covariance flux tower

sites. Annual data was only used when there was more than 85% of reliably filled (see ref. (3))

flux and meteorological data and the longest data gap within a year was shorter than 20 days.

When several years of data were available those were aggregated into one average, in order to

avoid giving too much weight to sites with multiple years. The parameterized model was then

applied globally on a grid with 0.5◦ latitudinal and longitudinal resolution by utilizing available

data sets for temperature and precipitation. The GPP of non-vegetated area has been set to zero,

but no effect of vegetation structure or species-specific function is taken into account. Uncer-

tainties within this approach may arise from uncertainties in a) the parameter estimation, b) the

eddy covariance flux data, particularly the partitioning of the net flux into GPP and ecosystem

respiration, and c) the meteorological data sets used to apply the parameterized model globally.

31

The parameter estimation uncertainty was addressed via a bootstrap approach (?, 47), with 500

resamplings of all 138 sites.

Uncertainties in the partitioning of the eddy covariance data stem from the eddy covariance

data itself (in particular during night-time) and from uncertain model assumptions underlying

the partitioning of the net CO2flux into GPP and ecosystem respiration. We addressed this by

using two fundamentally different flux-partitioning algorithms: One algorithm is using night-

time data to parameterize ecosystem respiration as function of temperature and time-varying

base respiration, which is then extrapolated into the day (3). The other is using day-time data

to parameterize a response function of NEE to incoming short wave radiation, air temperature

and vapor pressure deficit of the air (8). We parameterized the MIAMI model with the GPP es-

timates from both algorithms and did not find significant differences in the parameter estimates.

The model explained 71% of the variance in GPP, with the parameters depicted in Tab. 3. GPP

residuals still show a clear association with fAPAR (Fig. 14) demonstrating the importance of

remotely sensed information about vegetation function and structure. Global meteorological

data sets are much more uncertain with respect to precipitation, compared to temperature (24).

Hence we concentrated the uncertainty analysis on the effect of precipitation by using six differ-

ent sources: CRU CL 2.0, CRU TS 2.1, CMAP, DELAWARE, GPCP, ECMWF, ERA-interim.

Furthermore, CRU TS 2.1 temperature forcing was used. Figure 13 shows the effect of the

different precipitation data sets on the estimation of global GPP, as well as the model parameter

uncertainty, given a precipitation data set.

• MIAMI1: CRU temp, CRU CL 2.0 precip; long-term average

• MIAMI2: CRU temp, CRU TS 2.1 precip; 1998-2002

• MIAMI3: CRU temp, CMAP precip;

• MIAMI4: CRU temp, DELAWARE precip; 1998-2005

32

Table 3: Parameterization of the MIAMI modelParameter Estimate (±1SE)GPP15 1923 (128) gC m-2 yr-1GPP1000 1827 (63) gC m-2 yr-1a1 242 (2.89)a2 0.049 (0.0038) ◦C -1k -0.00025 (0.00019) mm-1

135

140

145

150

155

160

MIA

MI1

MIA

MI2

MIA

MI3

MIA

MI4

MIA

MI5

MIA

MI6

Ter

rest

rial G

PP

[PgC

/a]

Figure 13: Detailed distributions of global GPP [Pg C a−1] separately by combinations of thepredictor datasets. Shown are the median, 25 and 75 percentiles, and the 95% confidence inter-val.

• MIAMI5: CRU temp, GPCP precip; 1998-2005

• MIAMI6: CRU temp, ECMWF ERA-Interim precip;

33

Figure 14: Relationship of the residual GPP from the parameterized MIAMI model (observedminus modeled) with mean annual fAPAR from SEAWIFS. For visual clarity the fAPAR datahas been aggregated into 12 percentile classes, each of them represented by the circle and errorsbars as mean and +-1 standard error. Regression line, 95% confidence bands and the regressionstatistics are shown as well.

34

Summary Predictor Datasets Usage for Up-scaling Approaches

Table 4: Driving data for the six diagnostic models

Approach fAPAR rad. temp. precip. vapor runoff land spatial temporal

or LAI pressure cover resolution resolution

deficit

MTE1 X X X X - - X 0.5◦ monthly

MTE2 X - X X - - X 0.5◦ monthly

ANN X X X X X - X 0.5◦ daily

WUE X - - X X X X 1km to water-shed

long-term

KGB - - X X - - X 1km to 0.5◦ monthly

MIAMI - - X X - - - 0.5◦ monthly

35

Figure 15: Regression between GPP and ∆NEE based on measured NEE and derived GPP atflux tower sites. Shown is the example for the 30◦ latitudinal band around 55◦ N.

NEE Amplitude Approach

First, a linear relation between annual GPP and the seasonal NEE amplitude is determined

from flux tower sites for 30◦ -wide latitudinal moving bands (Fig. 15). Second, this linear

regression model is applied to the seasonal NEE amplitude derived from atmospheric CO2 con-

centrations and an inversion of their atmospheric transport (update of (49), Jena inversion,

s 99v3.1 monthly.nc).

36

−150 −100 −50 0 50 100 150

−80

−60

−40

−20

0

20

40

60

80

0

200

400

600

800

1000

1200

1400

1600

(a)

−150 −100 −50 0 50 100 150

−80

−60

−40

−20

0

20

40

60

80

0

500

1000

1500

2000

2500

3000

3500

(b)

Figure 16: 16(a), 16(b) Spatial details of annual GPP [gC/m2/a] in extra-tropical regions basedon an independent scaling from flux tower sites to the region based on the relationship of annualGPP to the seasonal amplitude of NEE.

37

Summary of Data used

Here we briefly summarize and cite the data and data products used from site-level to global.

At flux tower stations measurements of net ecosystem exchange (NEE) were taken to estimate

gross primary production (GPP) as described in refs. (3, 8). At these sites, meteorological

information in addition to NEE was available with 30-minutes temporal resolution. For the

application of the diagnostic models, we mainly used the fraction of absorbed photosyntheti-

cally active radiation (fAPAR) based on SeaWiFS (50), but for the LUE approach also based

on MODIS (40) and SPOT VGT (38). The land cover maps are SYNMAP (12) based on the

sensors AHVRR, MODIS, and SPOT Vegetation, and in addition GLC2000 (SPOT Vegetation),

MOD12 (39), and MODIS VCF (34). Two LAI datasets have been used, from the CYCLOPES

project based on SPOT Vegetation (38), and based on the MODIS sensor (40). Finally, climate

fields from ECMWF ERA-Iterim (51), GMAO GEOS400 (44, 52), CRU TS 2.1 (53), and CRU

CL 2.0 (54), as well as precipitation from the GEWEX project Global Precipitation Climatology

Project (GPCP, http://www.gewex.org/gpcp.html), the GPCC Global Precipitation Climatology

Centre (55), and Delaware (GHCN) (56) have been applied. We have used river runoff data

that was downloaded from the Global Runoff Data Centre of the Federal Institute of Hydrology

(http://www.bafg.de/cln 005/GRDC/).

38

Process-based biosphere models and modeling protocol

The models ORganizing Carbon and Hydrology in Dynamic EcosystEms (ORCHIDEE) (57),

and Community Land Model (CLM-CN) version 4 (58) represent key ecosystem processes gov-

erning biogeochemistry and biophysics of the land surface. Spatial heterogeneity is represented

as a nested sub-grid hierarchy in which grid cells are composed of fractions of land units, soil

and canopy columns, and up to 15 plant functional types (PFTs). Biogeophysical processes are

simulated for each sub-grid unit independently, and the surface variables and fluxes are then av-

eraged by area. In so doing, the PFT composition of each grid cell and related maximum LAI is

prescribed. The Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) (59,60),

LPJmL (61) and SDGVM (62,63) additionally predict the composition of natural PFTs as a con-

sequence of competition. These models were driven with differing climate datasets. We have

chosen for each model the maximum period within 1998-2005 to calculate average annual GPP.

This is 1998-2002, 1998-2003, and 1998-2004 for SDGVM, LPJ-DGVM and CLM-CN, re-

spectively. However, aggregation tests showed no difference in the presented results in Fig. 3 of

the main paper (not shown). In contrast, aggregation experiments with the LPJ-DGVM demon-

strated that the spatial partial correlations are subject to the spatial resolution, with deviations

to the 0.5◦ results when being higher than 1.5◦ by1.5◦ (not shown). Therefore, only global

models with approximate one degree or less were included into the analysis. All climate drivers

are either observation-based from CRU or normalized to CRU. Therefore, process model results

were only correlated to CRU climate data.

LPJ-DGVM and LPJmL

The LPJ-DGVM (59, 60) was driven by monthly CRU-PIK climate fields with 0.5◦ pixel size

(53, 64). As a non-gridded global input, annual CO2 concentrations were used that were de-

rived from ice-core measurements and atmospheric observations provided by the Carbon Diox-

39

ide Information Analysis Center (http://cdiac.esd.ornl.gov/index.html). In addition, globally

available soil texture classes with 0.5◦ resolution (65) were used. The model was run until the

carbon pools reached equilibrium based on transient climate (1901-1930) and under constant

pre-industrial atmospheric CO2 concentration (280 ppm). Weather generator algorithms were

employed to disaggregate monthly climate variables into daily values.

Inputs for the LPJmL (61) runs were similar, but extended CRU-PIK climate until 2005 was

used. The modeling protocol is described in Jung et al. (10), and the establishment of the land

use data set (crop distribution, irrigated areas, etc) is described within Fader et al. (66).

CLM-CN

The Community Land Model version 4 is described by (58). The model includes a carbon-

nitrogen biogeochemistry sub-model that simulates leaf area index, stem area index, and canopy

height (CLM4CN). For these simulations, a 57-year (1948-2004) meteorological dataset was

used to force the model in offline simulations uncoupled from a climate model, as in (67). Land

cover, atmospheric CO2, and atmospheric nitrogen deposition were held constant at values for

year 2000. The spatial resolution of the model is 1.25 degrees in longitude by 0.9375 degrees

in latitude. For the correlation analyses the model results were interpolated bi-linearly to the

0.5◦ by0.5◦ resolution that is used by the data-driven approaches and the other process models.

The simulations were driven using a 57-year (1948-2004) atmospheric forcing dataset (68,

) with 3-hourly and 1.875 degree resolution. The forcing dataset is derived from the Na-

tional Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-

NCAR) reanalysis with monthly mean near-surface air temperature and precipitation from the

reanalysis corrected to observed monthly mean values; downward solar radiation adjusted for

observed cloud cover and then for mean biases; surface specific humidity adjusted using the

adjusted surface air temperature and reanalysis relative humidity; and surface wind speed and

40

air pressure taken directly from the reanalysis data.

ORCHIDEE

The forcing data of ORCHIDEE (57) was the CRUNCEP data which is describe in http://dods.extra.cea.fr/data/p529viov/cruncep/readme.htm.

Vegetation was fixed to those of year 2000 and then the vegetation was first spinup to an hy-

pothetical equilibrium in year 1860 (using climate for year 1901 to 1910 in a loop) with the

CO2 of 1860. Then the model was run for 40 years with a constant climate from 1901 to 1910

with increasing CO2 from 1860 to 1900, then followed the transient simulation from 1901 to

2008 with increasing climate and CO2.

SDGVM

The SDGVM (62, 63) was driven by monthly fields from CRU TS 2.1 (0.5◦ ,1901-2002) but

ECMWF ERA-40 reanalysis data was used for the interpolation by a weather generator. The

land cover was prescribed according to the GLC2000 land cover map (37). Initialization for

1901 was done using a 500 year spin-up composed of randomized 1901-1920 climate.

41

Biome map

Figure 17: Biomes of the world as defined by Prentice et al. (69) based on the Olson ecorgionsmap (70). The cropland area came from the MODIS land cover map (39). These biomes areused for Tab. 1 and Tab. 2 of the main paper, Tab. 5, and Tab. 6.

42

Mean GPP per biome

For each up-scaling approach, a spatially explicit GPP distribution was estimated by propagat-

ing parameter uncertainty and by using several predictor datasets, i.e. for each 0.5◦ by 0.5◦ pixel

we estimated a GPP distribution. This distribution was represented by differing number of repli-

cates for each approach, but we randomly resampled 200 out of the original number, ranging

from 25 (MTE) to 2744 (WUE). These distributions were superimposed for estimating the me-

dian and median absolute deviation times 1.48 (uncertainty) of the global number, but also of

the biome specific results.

Table 5: Mean annual GPP per area [gC/m2/a] for all biomes and for each up-scaling scheme(columns 2-8). Column 9 shows the median. Column 10 shows the biome area in 1012m2 (69).Column 11 shows the median GPP [Pg C a−1] of each biome from the individual GPP distribu-tions. The sum of these biome-wide GPP results does not meet the 123 Pg C a−1 estimated byour up-scaling (Fig. 1(a) of the main paper) because the underlying biome areas do not neces-sarily agree. Column 12 shows the comparison to the estimates by Prentice et al. (69) [Pg C a−1]by doubling NPP values from Saugier et al. (71).

Biome MTE1 MTE2 ANN LUE WUE KGB MIAMI Median Area GPP GPP=2*NPP

Tropicalforests

2256.1 2412.1 2443.5 2112.1 2332.9 2223.9 2663.4 2332.9 17.5 40.8 43.8

Temperateforests

933 954.4 928.8 895.3 1013.8 1033.9 1093 954.4 10.4 9.9 16.2

Borealforests

604.6 620.8 505.7 462.8 690.4 535.1 697.8 604.6 13.7 8.3 5.2

Tropicalsavannahs&grasslands

1053.7 1134.9 1339.1 900.4 1041.6 1225.5 1693.7 1134.9 27.6 31.3 29.8

Temperategrasslands& shrub-lands

372.1 400.7 478.2 376.4 622 664.9 736.7 478.2 17.8 8.5 14

Deserts 151.1 231.5 290.5 134.5 228.7 427.3 421.4 231.5 27.7 6.4 7

Tundra 236.7 292.9 213.6 131.6 329.6 301.6 490.2 292.9 5.6 1.6 1

Croplands 1015.8 1029.4 1094.6 935.2 1133 1134.5 1413.9 1094.6 13.5 14.8 8.2

Wetlands 980.7 1051.2 1203.8 902.1 952 1165.2 1484.6 1051.2 0 0 0

Mean/Total 844.9 903.1 944.2 761.2 927.1 968 1188.3 908.4 133.8 121.7 125.2

43

Spatial details and latitudinal means of median GPP results bythe individual up-scaling approaches

Median GPP [gC/m2/a] of approach MTE1

0

500

1000

1500

2000

2500

3000

3500

Figure 18: Spatial distribution of median GPP by the MTE1 approach

44

Median GPP [gC/m2/a] of approach MTE2

0

500

1000

1500

2000

2500

3000

3500

Figure 19: Spatial distribution of median GPP by the MTE2 approach

Median GPP [gC/m2/a] of approach ANN

0

500

1000

1500

2000

2500

3000

3500

Figure 20: Spatial distribution of median GPP by the ANN approach

45

Median GPP [gC/m2/a] of approach LUE

0

500

1000

1500

2000

2500

3000

3500

Figure 21: Spatial distribution of median GPP by the LUE approach

Median GPP [gC/m2/a] of approach WUE

0

500

1000

1500

2000

2500

3000

3500

Figure 22: Spatial distribution of median GPP by the WUE approach

46

Median GPP [gC/m2/a] of approach KGB

0

500

1000

1500

2000

2500

3000

3500

Figure 23: Spatial distribution of median GPP by the KGB approach

Median GPP [gC/m2/a] of approach MIAMI

0

500

1000

1500

2000

2500

3000

3500

Figure 24: Spatial distribution of median GPP by the MIAMI approach

47

−50 −25 0 25 50 750

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Latitude [°]

GP

P [g

C/m

2 /a]

Data−drivenProcess models∆ NEE

Figure 25: Data-driven and process-based ranges and medians of latitudinal GPP means.

−50 −25 0 25 50 750

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Latitude [°]

GP

P [g

C/m

2 /a]

Data−driven rangeLPJLPJmLORCHIDEECLM−CNSDGVM∆ NEE

Figure 26: Data-driven ranges and individual process-based resullts of latitudinal GPP means.

48

−50 −25 0 25 50 750

500

1000

1500

2000

2500

3000

Latitude [°]

GP

P [g

C/m

2 /a]

RangeMTE1MTE2ANNLUEKGB∆ NEE

Figure 27: Data-driven ranges and individual results of latitudinal GPP means.

49

Correlation analyses

For each of the spatially explicit up-scaling approaches MTE1, MTE2, ANN, LUE, and KGB,

and for the median GPP of these approaches, we performed a partial correlation analysis be-

tween GPP and precipitation, air temperature and short-wave radiation. This analysis has been

done for 4.5◦ by 4.5◦ moving windows. The resolution of the data is 0.5◦ by 0.5◦ , i.e. for each

0.5◦ pixel, surrounding 81 pixels have been used. Additionally, three sets of climate variables

were applied: a) CRU air temperature, CRU precipitation and ECMWF ERA-Interim short-

wave radiation, b) ECMWF ERA-Interim air temperature, precipitation and short-wave radia-

tion, and c) CRU air temperature, GPCP precipitation and ECMWF ERA-Interim short-wave

radiation. Type b is the most consistent dataset, type c is most strongly based on observations,

and type a is in between these extremes: air temperature and precipitation are consistent and

based on observations.

In Tab. 2 and Fig. 3 of the main paper and in Tab. 6, Fig. 28 and Fig. 29 resulting significant

(p<0.01) correlations are shown. Fig. 2 of the main paper shows example maps for the case of

using the median GPP and climate set type a.

GPP-Climate correlations

50

(a) (b) (c)

(d) (e) (f)

Figure 28: Same as Fig. 3 of the main paper but complete for all climate variables.

(a) (b) (c)

(d) (e) (f)

Figure 29: Same as Fig. 28 but showing all model results individually.

51

Partial correlation LPJ GPP and precipitation

−1

−0.5

0

0.5

1

Partial correlation LPJ GPP and temperature

−1

−0.5

0

0.5

1

Partial correlation LPJ GPP and radiation

−1

−0.5

0

0.5

1

Figure 30: Partial correlation in the spatial domain between GPP from the LPJ-DGVM andeither CRU precipitation (above), CRU air temperature (middle), or ECMWF ERA-Interimshort-wave radiation (bottom) after applying a moving 4.5◦ by 4.5◦ spatial window and sub-sequent median filtering. Shown are significant correlations (p<0.01) of which the correlationcoefficient is higher/lower than ± 0.2.

52

Partial correlation LPJmL GPP and precipitation

−1

−0.5

0

0.5

1

Partial correlation LPJmL GPP and temperature

−1

−0.5

0

0.5

1

Partial correlation LPJmL GPP and radiation

−1

−0.5

0

0.5

1

Figure 31: Partial correlation in the spatial domain between GPP from LPJmL and either CRUprecipitation (above), CRU air temperature (middle), or ECMWF ERA-Interim short-wave ra-diation (bottom) after applying a moving 4.5◦ by 4.5◦ spatial window and subsequent medianfiltering. Shown are significant correlations (p<0.01) of which the correlation coefficient ishigher/lower than ± 0.2.

53

Partial correlation SDGVM GPP and precipitation

−1

−0.5

0

0.5

1

Partial correlation SDGVM GPP and temperature

−1

−0.5

0

0.5

1

Partial correlation SDGVM GPP and radiation

−1

−0.5

0

0.5

1

Figure 32: Partial correlation in the spatial domain between GPP from the SDGVM and eitherCRU precipitation (above), CRU air temperature (middle), or ECMWF ERA-Interim short-wave radiation (bottom) after applying a moving 4.5◦ by 4.5◦ spatial window and subsequentmedian filtering. Shown are significant correlations (p<0.01) of which the correlation coeffi-cient is higher/lower than ± 0.2.

54

Partial correlation ORC GPP and precipitation

−1

−0.5

0

0.5

1

Partial correlation ORC GPP and temperature

−1

−0.5

0

0.5

1

Partial correlation ORC GPP and radiation

−1

−0.5

0

0.5

1

Figure 33: Partial correlation in the spatial domain between GPP from ORCHIDEE and eitherCRU precipitation (above), CRU air temperature (middle), or ECMWF ERA-Interim short-wave radiation (bottom) after applying a moving 4.5◦ by 4.5◦ spatial window and subsequentmedian filtering. Shown are significant correlations (p<0.01) of which the correlation coeffi-cient is higher/lower than ± 0.2.

55

Partial correlation CLM GPP and precipitation

−1

−0.5

0

0.5

1

Partial correlation CLM GPP and temperature

−1

−0.5

0

0.5

1

Partial correlation CLM GPP and radiation

−1

−0.5

0

0.5

1

Figure 34: Partial correlation in the spatial domain between GPP from CLM-CN and either CRUprecipitation (above), CRU air temperature (middle), or ECMWF ERA-Interim short-wave ra-diation (bottom) after applying a moving 4.5◦ by 4.5◦ spatial window and subsequent medianfiltering. Shown are significant correlations (p<0.01) of which the correlation coefficient ishigher/lower than ± 0.2.

56

Table 6: Percentage of biome area that is climatically controlled, indicated by a partial cor-relation coefficient higher than 0.2 (or 0.5 in brackets). Columns 5 to 7 show the percentageof biome area with a negative partial correlation coefficient to the climate variables lower than-0.2 (or -0.5 in brackets). Here, CRU precipitation and temperature, and ECMWF ERA-Interim radiation was used for the correlation analysis.

Biome pos corr Pa pos corr Tb pos corr Rc neg corr P neg corr T neg corr R

Tropical forests 47 (26) 39 (26) 4 (1) 4 (1) 12 (3) 42 (21)

Temperate forests 66 (46) 43 (25) 6 (1) 3 (1) 5 (1) 38 (20)

Boreal forests 36 (19) 53 (30) 24 (9) 14 (5) 5 (1) 19 (7)

Tropical savannahs &grasslands

63 (43) 20 (7) 5 (1) 4 (1) 17 (3) 49 (25)

Temperate grasslands& shrublands

75 (53) 41 (21) 9 (3) 2 (1) 11 (3) 40 (19)

Deserts 70 (51) 26 (11) 7 (2) 2 (0) 13 (4) 34 (15)

Tundra 39 (26) 42 (30) 28 (12) 18 (9) 12 (6) 15 (7)

Croplands 66 (42) 29 (13) 7 (1) 2 (0) 12 (3) 39 (17)

aprecipitationbair temperaturecshort-wave radiation

57

Table 7: Percentage of biome area that is climatically controlled, indicated by a partial cor-relation coefficient higher than 0.2 (or 0.5 in brackets). Columns 5 to 7 show the percentageof biome area with a negative partial correlation coefficient to the climate variables lower than-0.2 (or -0.5 in brackets). Here, ECMWF ERA-Interim climate was used for the correlationanalysis.

Biome pos corr Pa pos corr Tb pos corr Rc neg corr P neg corr T neg corr R

Biome pos corr P pos corr T pos corr R neg corr P neg corr T neg corr R

Tropical forests 26 (12) 18 (10) 6 (1) 8 (2) 29 (12) 32 (14)

Temperate forests 53 (29) 37 (19) 10 (3) 1 (0) 9 (3) 25 (8)

Boreal forests 24 (7) 56 (31) 20 (6) 13 (4) 1 (0) 19 (6)

Tropical savannahs &grasslands

46 (24) 15 (6) 6 (2) 7 (1) 29 (10) 48 (25)

Temperate grasslands& shrublands

66 (41) 38 (20) 9 (3) 2 (0) 9 (2) 35 (14)

Deserts 57 (36) 19 (6) 12 (3) 3 (0) 19 (10) 24 (9)

Tundra 20 (10) 39 (25) 32 (14) 19 (8) 10 (4) 10 (4)

Croplands 48 (22) 24 (10) 7 (1) 3 (0) 14 (4) 30 (12)

aprecipitationbair temperaturecshort-wave radiation

58

Table 8: Percentage of biome area that is climatically controlled, indicated by a partial cor-relation coefficient higher than 0.2 (or 0.5 in brackets). Columns 5 to 7 show the percentageof biome area with a negative partial correlation coefficient to the climate variables lower than-0.2 (or -0.5 in brackets). Here, GPCP precipitation, CRU temperature, and ECMW ERA-Interim radiation was used for the correlation analysis, and the median reported.

Biome pos corr Pa pos corr Tb pos corr Rc neg corr P neg corr T neg corr R

Biome pos corr P pos corr T pos corr R neg corr P neg corr T neg corr R

Tropical forests 24 (10) 46 (35) 5 (1) 7 (2) 15 (4) 43 (24)

Temperate forests 34 (16) 44 (30) 10 (3) 10 (4) 12 (5) 35 (18)

Boreal forests 15 (4) 55 (34) 25 (9) 23 (10) 5 (1) 16 (5)

Tropical savannahs &grasslands

51 (29) 18 (8) 3 (0) 5 (1) 29 (11) 47 (22)

Temperate grasslands& shrublands

54 (33) 32 (18) 9 (2) 6 (2) 22 (9) 37 (16)

Deserts 51 (29) 15 (6) 14 (5) 3 (1) 30 (15) 26 (12)

Tundra 23 (12) 36 (28) 40 (21) 20 (10) 27 (18) 10 (4)

Croplands 40 (20) 34 (22) 8 (2) 8 (3) 20 (7) 40 (19)

aprecipitationbair temperaturecshort-wave radiation

59

Table 9: List of FLUXNET sites used

Site Vegetation type ReferenceAT-Neu GRA (72)AU-Fog WET -AU-Tum EBF (73)AU-Wac EBF (74)BE-Bra MF (75)BE-Lon CRO (76)BE-Vie MF (77)BR-Ban EBF -BR-Ji2 GRA (78)BR-Ma2 EBF (79)BR-Sa1 EBF (80)BR-Sa2 CRO (81)BR-Sa3 EBF (82)BR-Sp1 WSA (83)BW-Ma1 WSA (84)CA-Ca1 ENF (85)CA-Ca2 ENF (85)CA-Ca3 ENF (85)CA-Let GRA (86)CA-Man ENF (87)CA-Mer OSH (88)CA-NS1 ENF (89)CA-NS3 ENF (89)CA-NS6 OSH (89)CA-NS7 OSH (89)CA-Oas DBF (90)CA-Obs ENF (91)CA-Ojp ENF (92)CA-Qcu ENF (93)CA-Qfo ENF (91)CA-SJ1 ENF (94)CA-SJ2 ENF (94)CA-SJ3 ENF (94)CA-TP4 ENF (95)CA-WP1 MF (96)CH-Oe1 GRA (97)CH-Oe2 CRO (98)CN-Cha MF (99)

60

CN-Do1 WET (100)CN-Do2 WET (100)CN-Do3 WET (100)CN-HaM GRA (101)CN-Xfs GRA -DE-Bay ENF (102)DE-Geb CRO (103)DE-Gri GRA (104)DE-Hai DBF (105)DE-Har ENF (106)DE-Kli CRO -DE-Meh MF (107)DE-Tha ENF (108)DE-Wet ENF (109)DK-Lva GRA (104)DK-Ris CRO (110)DK-Sor DBF (111)ES-ES1 ENF (112)ES-ES2 CRO -ES-Lma SAV -ES-VDA GRA (104)FI-Hyy ENF (113)FI-Sii GRA (114)FI-Sod ENF (115)FR-Fon DBF -FR-Gri CRO (116)FR-Hes DBF (117)FR-LBr ENF (118)FR-Lq1 GRA (104)FR-Lq2 GRA (104)FR-Pue EBF (119)GF-Guy EBF (120)HU-Bug GRA (121)HU-Mat GRA (122)ID-Pag EBF (123)IE-Dri GRA -IL-Yat ENF (124)IT-Amp GRA (104)IT-Bci CRO (125)IT-Cpz EBF (126)IT-Lav ENF (127)

61

IT-Lec EBF -IT-Mbo GRA (128)IT-PT1 EBF (129)IT-Ren ENF (130)IT-Ro1 DBF (131)IT-Ro2 DBF (132)IT-SRo ENF (133)JP-Mas CRO (134)JP-Tak DBF (135)JP-Tef MF (136)JP-Tom MF (137)NL-Hor GRA (138)NL-Loo ENF (139)PL-Wet WET (140)PT-Mi1 EBF (141)PT-Mi2 GRA (141)RU-Fyo ENF (142)SE-Deg WET (143)SE-Fla ENF (144)SE-Nor ENF (145)UK-Gri ENF (146)UK-Ham DBF -UK-PL3 DBF -US-ARM CRO (147)US-Aud GRA -US-Bar DBF (148)US-Bkg GRA (149)US-Blo ENF (150)US-Bn2 DBF (151)US-Bo1 CRO (152)US-Bo2 CRO (152)US-Dk3 MF (153)US-Fpe GRA -Site Vegetation type ReferenceUS-FR2 WSA (154)US-Goo GRA -US-Ha1 DBF (155)US-Ho1 ENF (156)US-Ho2 MF (156)US-IB1 CRO (157)US-IB2 GRA (157)

62

US-KS2 CSH (158)US-LPH DBF (159)US-MMS DBF (160)US-Moz DBF (161)US-Me4 ENF (162)US-NC1 OSH (163)US-NC2 ENF (164)US-NR1 ENF (165)US-Ne1 CRO (166)US-Ne2 CRO (166)US-Ne3 CRO (166)US-Pfa MF (167)US-SO2 WSA (168)US-SO3 WSA (168)US-SO4 CSH -US-SP1 ENF (169)US-SP2 ENF (170)US-SP3 ENF (170)US-SRM WSA (171)US-Ton WSA (172)US-UMB DBF (173)US-Var GRA (174)US-WBW DBF (175)US-WCr DBF (176)US-Wrc ENF (177)VU-Coc EBF (178)

63

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