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Supporting Information
Appendix S1: projected climate change by the three GCMs
We conducted cell-by-cell temporal trend analysis on the major climate variables
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from the bias-corrected results of the three GCMs using the non-parametric Mann-
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Kendall test. The results show considerable differences between the projected
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climatologies by the three GCMs (Fig. S1). All three predict significant (P < 0.01),
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region-wide warming trends over the 21st century (Fig. S1a); however, the magnitudes of
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warming trends differ: HadCM3 has the strongest warming trend followed by CCSM3
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and PCM. Although the magnitudes of warming trends in CCSM3 and HadCM3 differ,
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both models predict that the eastern and southern Amazon will experience the highest
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rates of warming during the 21st century (Fig. S1a), and that these regions will also
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experience significant upward trends in the vapor pressure deficit (VPD) (Fig. S1b). In
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contrast, PCM predicts that most of the basin will experience relatively mild changes in
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temperature, and little or no changes in VPD.
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With regard to rainfall, the HadCM3 projection indicates that approximately half
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(53%) of the region, mainly the eastern and southeastern Amazon, will suffer significant
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reductions in precipitation during the 21st century (Fig. S1c). In contrast, PCM and
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CCSM3 predict that significant portions of the basin (47% and 62% of the region,
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respectively), located mainly in southern and western portion of the basin, will
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experience significant increases in precipitation. Comparison of these predictions against
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the nineteen GCM projections examined by Malhi et al. (2009) indicate that they span the
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range of precipitation predictions for the Amazon region: the PCM projection presents a
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slightly warmer but wetter future climate, while the HadCM3 projection represents an
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extremely hot and dry scenario and the CCSM3 projection falls in between.
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Fig. S1. Maps of temporal trends from 2009 to 2100 in (a) annual air temperature, (b)
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vapor pressure deficit and (c) precipitation from the bias-corrected projections of three
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GCMs (i.e. PCM, CCSM3, and HadCM3); grey areas denote non-significant trends with
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90% confidence.
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Appendix S2: spatial patterns of the Business-As-Usual and Governance land-use
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scenarios in Amazonia
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The Global Land-Use dataset (GLU) incorporates the SAGE-HYDE 3.3.1 dataset
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and provides global land-use transitions on a 1° grid from 1700 to 1999 (Hurtt et al.,
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2006). The Business-As-Usual (BAU) and Governance (GOV) datasets provide yearly 1-
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km horizontal resolution land-use maps from 2002 to 2050. Consistent land-use transition
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data for the entire period (1700–2100) were produced by following the procedure: (1) the
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1-km BAU and GOV land-use maps were used to calculate the fractions of the three
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land-use types for each 1° grid cell; (2) linear interpolation was used to connect the 1999
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GLU land-use map to the 2008 GOV land-use map to produce continuous land-use maps
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for the 2000–2008 period; (3) the land-use transition rates between 2000 and 2008 were
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computed from the interpolated land-use maps; (4) the BAU and GOV land-use maps for
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the 2009–2050 period were converted to their respective land-use transition rates for the
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same period; and (5) BAU and GOV land-use transition rates were extrapolated from
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2050 to 2100 by assuming the continuation of the same transition rates in 2050 until no
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forest left in the grid cells.
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Fig. S2. Spatial patterns of land-use composition in 2100 under the (a) GOV and (b)
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BAU scenarios; the projected rates of land-use transformation were derived and extended
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from Soares-Filho et al. (2006).
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Appendix S3: evaluation of the biosphere models' performance
In this study, we assessed the three biosphere models' predictive capabilities by
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comparing modeled above-ground live biomass (AGB) and percent tree cover against
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two sets of satellite remote sensing (RS) derived AGB estimates (Baccini et al., 2012,
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Saatchi et al., 2011) and the MODIS Collection 5 MOD44B percent tree cover product
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(DiMiceli et al., 2011), respectively. The Baccini et al. (2012), Saatchi et al. (2011), and
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MOD44B data have nominal spatial resolutions of 500m, 1km, 500m, respectively. We
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first aggregated these data to 1-degree and then compared them with our model results.
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Both of the two RS biomass products were produced using a combination of data from
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spaceborne LiDAR, optical and microwave imagery, and in situ inventory plots. An
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evaluation of models' performance against site-level measurements of carbon fluxes and
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aboveground biomass dynamics can be found in an ongoing study (Levine et al., The role
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of short-term climate variability in governing Amazonian biomass dynamics, in
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preparation) and Powell et al. (2013).
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Although there are non-negligible discrepancies between the two RS products,
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across the models, and between the models and RS products, the model predictions of
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AGB show a similar spatial gradient to the satellite-derived estimates of regional AGB,
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with AGB increasing from the southern and southeastern dry savanna zones to the
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western and northeastern dense, moist forest regions (Fig. S3). ED2 AGB agrees well
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with the RS AGB in these high biomass regions, but tends to underestimate AGB in low
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biomass regions (Fig. S3a,d-f). IBIS AGB estimates are systematically lower than the RS
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estimates in areas of high biomass but higher in areas of low biomass (Fig. S3b,d-f),
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while JULES AGB is systematically higher than the RS AGB (Fig. S3c-f). A recent study
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by Levine et al. (2014) compared site-level AGB estimates from the ED2 model and the
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above two RS biomass products with the measurements of the RAINFOR network(Malhi
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et al., 2002), and found that these model and RS estimates are qualitatively consistent
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with that observed in the RAINFOR plots.
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To more quantitatively describe the vegetation structure, we calculate the percent
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tree cover (𝑓𝑡𝑟𝑒𝑒𝑐𝑜𝑣𝑒𝑟 ) using the fully projected tree foliage cover by following Kucharik
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et al. (2000): 𝑓𝑡𝑟𝑒𝑒𝑐𝑜𝑣𝑒𝑟 = 1 − exp⁡(−0.5 ∙ 𝐿𝐴𝐼𝑡𝑟𝑒𝑒 ), where 0.5 is the canopy extinction
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coefficient, and 𝐿𝐴𝐼𝑡𝑟𝑒𝑒 is the total leaf area index of all tree plant function types. Percent
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tree cover from all models generally compares well with the RS based estimate (Fig. S4).
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𝑓𝑡𝑟𝑒𝑒𝑐𝑜𝑣𝑒𝑟 shows the similar spatial gradient as AGB in models and RS estimates (Fig.
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S3,S4); the higher biomass regions have higher 𝑓𝑡𝑟𝑒𝑒𝑐𝑜𝑣𝑒𝑟 , while the lower biomass
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regions have lower 𝑓𝑡𝑟𝑒𝑒𝑐𝑜𝑣𝑒𝑟 . The inset quantile-quantile (Q-Q) plot (Fig. S4) shows that
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IBIS and JULES have generally higher tree cover fractions than ED2 and the MODIS
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product. Although ED2 predicts lower tree cover in the lower tree cover region relative
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to the MODIS product, ED2's predictions of tree cover fraction are close to the MODIS
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values in the areas with middle to high tree cover fractions (Fig. S4).
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Despite the uncertainty in the AGB and 𝑓𝑡𝑟𝑒𝑒𝑐𝑜𝑣𝑒𝑟 estimates of models and remote
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sensing products and some discrepancies between the model and remote sensing results,
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the overall similar spatial patterns and gradients from the model and remote sensing
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results suggest that all three biosphere models are able to reasonably capture the present-
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day composition and spatial variability of Amazonian ecosystems.
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Fig. S3. Spatial patterns of present-day (2000~2008) above-ground biomass across
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Amazonia from model estimates of (a) ED2, (b) IBIS, and (c) JULES, and remote
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sensing based estimates of (d) Baccini et al. (2012) and (e) Saatchi et al. (2011), and (f)
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the quantile-quantile plots of model estimates against remote sensing (RS) based
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estimates.
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Fig. S4. Spatial patterns of present-day (2000~2008) percent tree cover across Amazonia
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from (a) ED2, (b) IBIS, (c) JULES, and (d) MODIS collection 5 MOD44B product. The
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inset graph shows the quantile-quantile plot of model estimates against remote sensing
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based estimates.
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Appendix S4: evaluation of association between water stress regime and AGB from
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model simulations and remote sensing estimates across the Amazon.
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Table S1. Summary of the strength of association between water stress (MCWD) and
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AGB from model simulations and remote sensing estimates across the Amazon; the
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strength of association is quantified by Pearson's simply linear correlations and Kendall's
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Tau (i.e. the rank correlation).
Water Stress
Regime
MCWD
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***
Statistical
Test
Pearson's r
Kendall's τ
AGBED2
AGBIBIS
AGBJULES
AGBBaccini
AGBSaatchi
0.75***
0.67***
0.61***
0.65***
0.63***
0.61***
0.45***
0.45***
0.48***
0.42***
P<0.01
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