SUPPLEMENTARY INFORMATION

Transcription

1 SUPPLEMENTARY INFORMATION Direct impacts on local climate of sugar-cane expansion in Brazil Materials and Methods Study Area Nine Brazilian states contain 99.5% percent of the cerrado (Table S1a). More natural vegetation remains, and clearing is proceeding faster, in the four northeastern most states (Fig. 1a). 25.3% of the cerrado in these states was cleared by 2002, and an additional 5.6% by In contrast, 56.5% of the cerrado in the five states to the southwest was cleared by 2002, with an additional 3.4% lost by We restrict our analyses to these five states that, together, encompass 55% of the cerrado. Only the states of Pará (13%), Bahia (8%) and Tocantins (7%) to the north contain significant (>5%) portions of the cerrado. The PROBIO map, a landcover analysis using Landsat from 2002, estimated that the ratio of crops to pasture in non-naturally vegetated, non-sugarcane portions of the study area was roughly 0.4:1 (MMA, 2007). In 2005, 9.0% of the study area was planted with soybeans and the average density of cattle was 0.45 km -2 (Table S1b). Datasets We acquired 30m Canasat (2009) data for on sugarcane area in the cerrado region. Canasat became active in 2003 within São Paulo (SP) and in 2005 within Goiás (GO), Minas Gerais (MG), Mato Grosso (MT), Mato Grosso do Sul (MS), and Paraná. As of 2008, no sugarcane has been detected in the cerrado portion of Paraná and very little sugarcane is to be expected in the northern portions of the cerrado not currently monitored by Canasat. While Canasat splits sugarcane into four classes, the spectral properties were similar for each except for renovated or fallow sugarcane which more closely resembles the alternate crop/pasture mosaic. On average 9.2% of sugarcane is undergoing renovation. Because we are interested in the climate effects of the sugarcane crop as a whole, we included all sugarcane classes. We acquired 30m natural vegetation data from LAPIG 1 that divides the cerrado into areas cleared before 2002, cleared from , and intact in For each year of Canasat data and for the three LAPIG time periods, we calculated the fraction of sugarcane and natural vegetation from these 30m datasets within each km 2 grid-cell matching the MODIS swath. We restricted our analysis to the cerrado portions of four MODIS tiles: h12v10, h12v11, h13v10, and h13v11 which encompassed all the Canasat data. To estimate pasture and crop area, we compiled two additional sources of data. First, we compiled the PROBIO map (Conservation and Sustainable Use of Brazilian Biological Diversity Project) 2 derived primarily from 2002 Landsat TM data with categories for pasture and agriculture. Second, we compiled municipal level soybean fraction (Table 1001 Area planted, area harvested, quantity produced and average yield of beans, 1 st, 2 nd, and 3 rd crops) and cattle head data (Table 74 Production of animal by product type ) rough indicators of crops and pasture respectively for 2005 from IBGE 3. We mapped both onto the km 2 grid across the study area. We summarized areas from these datasets in Table S1. nature climate change 1

2 supplementary information For each of these MODIS tiles, we derived time-series from of 1km MODIS estimates of land surface temperature (MODIS/Terra Land Surface Temperature/Emissivity 8- Day L3 Global 1km SIN Grid V005), ET (MODIS/Terra Land Surface Evapotranspiration 8-day L4 Global 1km SIN Grid V005), shortwave white-sky albedo (MODIS/Terra+Aqua Albedo 16- Day L3 Global 1km SIN Grid V005), and enhanced vegetation index (MODIS/Terra Vegetation Indices 16-day L3 Global 1km SIN Grid V005). We clarify that white-sky, or diffuse, albedo is not equivalent to albedo and is used here as a proxy. Actual albedo is a mix of white-sky and black-sky components. Analyses We separated crop years on May 17th which was the average minimum EVI across the study area and averaged MODIS variables within each crop-year. Plotting out time-series for example grid-cells in Figure 2, we see that beginning in the 2007/08 crop year Canasat indicates that the grid-cell was completely filled with sugarcane that was available for harvest for the first time. Before this, there was no sugarcane available for harvest within the grid-cell. Inspection of the EVI time-series for this grid-cell reveals that the land-transition to sugarcane likely began in the beginning of the 2005/06 crop year. It is important to note that this was two years prior to the first year in which sugarcane was reported as present in Canasat. Since the EVI values during the 2006/07 crop year closely resemble those from the 2007/08 year, we can assume that sugarcane is present during the crop year before it is first reported as available for harvest in Canasat. Second, we assume that three crop years prior to the first year in which sugarcane was reported as present in Canasat, in this case 2004/05, the land-use prior to conversion to sugarcane was intact and can verify with the LAPIG data that no natural vegetation was present in the grid-cell in Similarly, following the example grid-cell shown in Figure 2a, LAPIG reported that in 2002 this grid-cell was completely filled with natural vegetation and was completely cleared by Inspection of the EVI time-series reveals that this clearing began in mid We verify using Canasat that no cane was available for harvest in the grid-cell in the 2008/09 crop year. To estimate changes in land-use fraction we used 2002 to 2008 for transitions to and from natural vegetation (to correspond with LAPIG) and 2005/06 to 2007/08 for transitions to sugarcane (to correspond with Canasat coverage for all five states). We compare these fractional changes with changes in MODIS variables from 2001/02 to 2007/08 for transitions from natural vegetation and from 2003/04 two years prior to 2005/06 to account for lags from land-use conversion evident in Canasat to 2007/08. The fractional changes were calculated objectively by computing at 30m the proportion of sugarcane or forest that occupy each 1km grid cell. Next we plot fractional changes and changes in MODIS variables against one another and fit linear regression slopes. We do this for the entire study area (Fig. S1) and group separately by state (Fig. S2-S7, Table S2). The example grid-cells shown in Figure 2 were randomly chosen from a subset of grid-cells undergoing complete conversion and with temperature changes within 0.1 C of the average temperature change for completely converted grid-cells in the regression model for the entire study area. To test for and minimize the influence from spatial autocorrelation, we compared these regressions with analogous regressions using only those pixels separated by 5km or more (Table 2 nature climate change

3 supplementary information S4). The patterns and significance of the results were unchanged suggesting that spatial autocorrelation was not driving the results. The regressions using the subset of pixels have wider 95% credible intervals and we present these more conservative credible intervals in the text and propagate them through the results and conclusions. The average size of Canasat expansion events was 0.88 km 2 and the average size of LAPIG deforestation events was 0.76 km 2 both of which are much smaller than the 25 km 2 area surrounding adjacent pixels when the subset is used. To test whether the slope estimates are robust we repeated compared slopes with the Theil- Sen slope estimator derived from taking the median of all pairs of slopes 4. The Theil-Sen estimators fell within the confidence intervals derived from the least-squares regression (Table S8). To test whether including transitions from the crop-pasture mosaic to natural vegetation (fractional changes less than 0), we repeated regressions using only positive fractional changes and the significance and patterns of the results were unchanged (Table S9). The MODIS variables are quite variable across the study area and from year-to-year making the grouping of grid-cells difficult. We accordingly base our analyses on changes in land-use and changes in MODIS variables. The continuous nature of fractional changes also allows us to include partially converted grid-cells. An analysis of the MODIS variables of gridcells before (2001/02 for natural vegetation and 2005/06 for sugarcane) and after (2007/08 ) conversion shows the extent of variability in the MODIS variables associated with the land-uses (Fig. S8-10). Uncertainty in MODIS variables The low R 2 in Table 1 suggest that there is a great deal of error incorporated into this analysis. A large part of this error likely comes from the MODIS variables themselves. The MODIS land surface temperature product is thought to be accurate within 1 degree Kelvin 5. The MODIS Albedo product are thought to have less than 5% absolute error 6. The MODIS ET product used in this study uses an improved ET algorithm 7. The data are available at ftp://ftp.ntsg.umt.edu/pub/modis/mirror/mod16/. The comparison between the improved algorithm and the old one was done both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is km 3 compared with other reported estimates of km 3 over the terrestrial land surface. The average RMSE of daily latent heat flux over the 46 towers was 24 Wm -2 by tower meteorological data and 25 Wm -2 by GMAO meteorological data with average biases of -3.0 Wm -2 (tower-specific) and -0.5 Wm -2 (GMAOdriven). MOD16 ET algorithm was validated at 10 Large Scale Biosphere-Atmosphere in Amazonia (LBA) eddy covariance flux tower sites, in different land uses and land covers (tropical rainforest, tropical dry forest, selective logged forest, seasonal flooded forest, pasture, cropland, savannas) between 2000 and The result shows that, compared with observed data, annual ET averages varies less than ±4% in savannas, ±5% in tropical forests and ±13% in pasture/agriculture. At the PDG (Cerrado sensu stricto) site, the 8-day MOD16 ET estimates can explain 83% (p< ) of variance in ET observations. RMSE is 0.55 mm day -1 with a mean bias of mm day -1. The annual ET observed in 2001 is 993 mm yr -1 and annual MOD16 ET estimates is 957 mm yr -1, with a bias of less than 4%. At the USE (Sugar Cane Plantations) site, 82% (p< ) of variance in the 8-day ET observations can be explained by MOD16 ET nature climate change 3

4 supplementary information estimates. RMSE is 0.46 mm day -1. Annual ET observed was 1025 mm yr -1 and annual ET estimated was 893 mm yr -1, with a bias of 13% lower than observed ET 8. To test the impact of this uncertainty in ET on our results, we added random noise from this higher RMSE of 0.55 mm day -1 to the ET data points and estimated slope credible intervals from regressions of these data. The increased standard error of the slope estimate resulted in wider credible intervals of instead of for transitions of from natural vegetation and instead of to for transitions to sugarcane. We used these more conservative wider credible intervals in the text. While we acknowledge that all remote sensing products have uncertainties, we did several analyses to show that the significant regression slopes were not artifacts of this uncertainty. First, we used the daily MODIS reflectance data to compile a time-series of cloud masks from We used this time-series to produce an average 8-day time-series for a single annual cycle for pixels in the study region containing at least some sugarcane (Figure S11, black line) or some natural vegetation (Figure S11, red line). This graph was used to subjectively identify the portion of the year (April through September) with less cloud contamination (Figure S11, gray are). We repeated the regressions using only this period to test whether our results were driven by portions of the year with high proportions of cloud contamination. The significance and patterns of the results were unchanged (Table S5). Second, the MODIS ET product contains several static inputs. This includes the MODIS land cover from To test whether our results were artifacts of these static inputs, we repeated the regressions using only pixels classified as savannas in the MODIS land cover dataset (Figure S12). This was the most common land classification. The significance and patterns of the results were unchanged (Table S6). Third, to test whether the dynamic remote sensing derived inputs to the ET product, such as red and near-infrared reflectance are capable of detecting land cover changes, we estimated regression parameters from the EVI in the MODIS VI product which is also derived from red and near-infrared reflectance. These results were consistent with Figure 2. Land cover changes from natural vegetation to the crop/pasture mosaic significantly decreased EVI while changes to sugarcane from the crop/pasture mosaic significantly increased EVI (Table S7). This suggests that the ET product is capable of capturing dynamic land use transitions. Alternate land-use classification We do not directly test transitions from natural vegetation to sugarcane because the number of such transitions is very small (Table S1). In contrast we separately compare transitions from natural vegetation to some other land-use and from some other land-use to sugarcane. We filter grid-cells to ensure that no fraction of sugarcane follows transitions from natural vegetation and no natural vegetation precedes transitions to sugarcane to ensure that these land-uses are not mixed in with this alternate land-use. While we lack explicit datasets on the nature of this alternate land-use (analogous to LAPIG and Canasat), land classifications (MMA, 2007) reveal that this landscape is dominated by pasture and agriculture. Soy constitutes a large portion of non-sugarcane agriculture 3 (Table S1) and the while non-sugarcane perennial agriculture such as orchards and forestry likely make up a very small percentage. Across Brazil, 4 nature climate change

5 supplementary information orchards cover only 3% of crop area. We therefore assume that the land-use following transitions from natural vegetation and preceding transitions to sugarcane is mostly pasture and other annual crops such as soy. Together, we refer to these land-use classifications as the crop/pasture mosaic. This assumption is corroborated by work in Sao Paulo that explicitly identified these other landuses 9. There, annual crops and pasture were the source for 40.2% and 56.5%, respectively, of the areas converted to sugarcane. An additional 3% was converted from citrus (2.9%) and tree plantations (0.1%). Only 0.2% was converted from natural vegetation. From the PROBIO map, we estimate that the ratio of crops to pasture in lands converted to sugarcane was 1.7:1. An average of 4.9% of sugarcane was converted from areas that were naturally vegetated in Likewise, the ratio of crops to pasture in lands converted from natural vegetation was 0.2:1, and 0.6% of converted natural vegetation was planted with sugarcane in 2008 (Table S1). To justify combining non-sugarcane crops and pasture into a single category, we repeated the analysis grouping by grid-cells identified as pasture or crop in the PROBIO map (Table S3) to test whether mixing pasture and crops into a single crop/pasture category is appropriate. Because results were similar whether crops and pasture were grouped or separated, we chose to combine them into a single category. We use this assumption that the crop/pasture mosaic is a homogenous land-use as an anchor to compare MODIS values across land-uses despite differences in subsets of grid-cells compared across different years and areas (Fig. S8-S10). Such reasoning is behind our statement in the text that the temperature and ET of sugarcane are more similar to each other than the alternative crop pasture mosaic and similar statements. Albedo and evapotranspiration interactions We compare the strength of albedo and ET changes by estimating the amount of energy dissipated by ET and the changes in energy resulting from a change in albedo. For example, clearing natural vegetation decreases ET by 0.60 ( ) mm/day. Evaporating 1 mm of water dissipates 2.26 MJ/m 2. Accordingly: W m ( ) mm/day X 2.26 MJ/m 2 / (24 X 60 X 60) sec/day = 15.7 ( ) This is the rate of energy dissipation that is eliminated by the decrease in ET. Likewise, clearing natural vegetation increases albedo by 1.73 ( )% albedo. Using the annual mean daily solar radiation sums for the region of 18 MJ/m 2 /day ( ) X 18 MJ/m 2 / (24 X 60 X 60) sec/day = 3.6 ( ) W m -2 This is the rate at which energy is absorbed following the increase in albedo following clearing. Similarly, conversion to sugarcane increases the ability to dissapate energy through changes in evapotranspiration by: 0.43 ( ) mm/day X 2.26 MJ/m 2 / ( 24 X 60 X 60) sec/day = 11.2 ( ) W m -2 nature climate change 5

6 supplementary information and results in the absorption of an additional ( ) X 18 MJ/m 2 / ( 24 X 60 X 60) sec/day = 0.4 ( ) W m -2 through changes in albedo. The net effect of the natural vegetation to crop/pasture conversion 15.7 W m W m -2 = 12.1 W m -2 is about the same as the net effect of the crop/pasture to sugarcane conversion 11.2 W m W m -2 = 10.8 W m -2 The cooler temperatures of the natural vegetation than sugarcane, despite the lower albedo of the former, implies that the greater ET of the natural vegetation more than compensates for its lower albedo. It is also possible that surface roughness contributes to the temperature differences. The rougher canopy of the natural vegetation is expected to more efficiently transfer sensible heat to the atmosphere, tending to make it cooler, even when the input radiation and the ET are about the same 11. More work is needed to quantify the contribution of surface roughness. We report changes in temperature, evapotranspiration, and albedo of the land surface under clear skies and during overpass time (10:30). However, altered evapotranspiration following land-use change can influence atmospheric albedo by changing the frequency of clouds or through precipitation changes and other influences on atmospheric circulation. In the Amazon, deforestation is thought to increase the frequency of shallow clouds at the expense of deep clouds While no studies have examined the impact of land use change on clouds specifically in the cerrado, global analyses reveal that the impact on clouds varies seasonally 15. A global study of the climate impacts from afforestation found no consistent relationship between land-cover change and cloud frequency, likely because regional cloud cover was not primarily determined by specific land-cover changes at these relatively fine spatial scales 16. More work is needed to determine whether these land-use changes effect cloud formation thereby further impacting local climate. To confirm that these results only apply to the day, we performed an additional analysis of the land surface temperature product during the ascending 22:30 nighttime pass. Analogous regressions using these data reveal that the impact of land conversion on temperature changes are indeed very different at night (Table S7). The signs of the changes are reversed from during the day and the effects are weaker while still significant. Because these results are only applicable under clear skies and during the day (overpass time), it is useful to determine what proportion of the year matches those conditions. Assuming that the entire daytime is similar to the 10:30 overpass time, this would hold true for about half of all hours of the day. Likewise, our analysis of the MODIS cloudmasks reveals that on average the sky in the study region is about half clear (53% over cane and 51% over natural vegetation). We do not have results for other times of year, however we have shown that nighttime temperature effects are small (Table S7) and expect that temperature effects under clouds are also small since evapotranspiration is expected to be diminished. Thus, if temperature effects 6 nature climate change

7 supplementary information during the day time and under clouds were negligible the average annual impact of the direct land-use effects would be about a quarter as strong. Comparing direct and indirect climate impacts To compare direct, local climate impacts to indirect impacts from carbon emissions, we use the following calculations. From Fernside et al. 17, there are 0.48 grams of Carbon for each gram of wood in the cerrado, and the ratio of the molecular weight of carbon to the molecular weights of CO 2 is 12/44. Lastly, there are Mg ha -1 in natural vegetation and 12.8 Mg C ha -1 in agriculture. The amount of CO 2 in these vegetation types is: Mg ha -1 X 0.48 C X 44/12 CO 2 /C = Mg CO 2 ha Mg C ha -1 X 44/12 CO 2 /C = 46.9 Mg CO 2 ha -1 Thus a transition from natural vegetation to agriculture releases: Mg CO 2 ha Mg CO 2 ha -1 = Mg CO 2 ha -1 We use sugarcane productivity of 87.1 tc ha -1, harvest rates of 5 out of 6 years, ethanol yields of 86.3 L tc -1, and annual saving estimates of 2181 kg CO 2 eq m 3 hydrous from sugarcane ethanol using 2005/2006 data 18 to calculate ha kg CO 2 eq m -3 X m 3 L -1 X 86.3 L tc -1 X 87.1 tc ha -1 X 5/6= kg CO 2 eq This savings estimate takes into account relative carbon emissions from the life cycles of ethanol and fossil fuels. Thus, the carbon payback time for converting a hectare of natural vegetation to sugarcane is kg CO 2 ha -1 / kg CO 2 eq ha -1 yr -1 = yr To calculate the indirect climate impact of historic clearing of the cerrado, we use the following calculation. We estimate 78,727,800 ha -1 of natural vegetation were cleared in our study area by Using a conversion of 2.13E12 kg CO 2 ppmv -1 19, we estimate the change in atmospheric CO 2 from these emissions was: kg CO 2 ha -1 X ha -1 = 5.51E E12 kg CO E E12 kg CO 2 / 2.13E12 kg CO 2 ppmv -1 = ppmv Using a radiative forcing efficiency (α) of 5.35 W m -2 (from Rotenberg and Yakir 20 ) and the following estimate of CO 2 radiative forcing: RF CO2 = (α) (ln [Cη/C 0 ]) (from Myhre et al. 21 ), we obtain: RF CO2 = W m -2 [with η = 0.45; 5.35 W m -2 X ln( /379) ] nature climate change 7

8 supplementary information Where C is the total (i.e. reference) CO 2 plus these additional emissions ( ppmv), η is the airborne fraction (0.45; from Canadell 22 ) and C 0 is a reference CO 2 concentration (379 ppmv, IPCC). Global surface temperature change ( Ts) resulting from the modification in radiative forcing: Ts = λ X RF CO2 = (1.1 K W -1 m -2 ) X (0.015) = C (with η = 0.45) Where λ (climate sensitivity parameter) is widely accepted as variable between K W -1 m -2. To estimate the maximum possible forcing we have used the largest possible λ. This is smaller than the estimate of direct effects under day time clear skies of 1.55 ( ) C as well as one quarter of this value assuming these temperature effects are negligible at these other times of the year. Using these values, 1 Pg C emissions would increase CO 2 concentrations to: 379 ppmv + (1 Pg C X 44/12 CO 2 /C X (2.13 Pg CO 2 ppmv -1 ) -1 X 0.45) = ppmv which would increase global temperatures by: 5.35 W m -2 X ln(379.77/379) X 1.1 K W -1 = C Supporting references 1 LAPIG. Laboratorio de Processamento de Imagens e Geoprocessamento. Programa Cerrado, Universidade Federal de Goias, Goiania, GO, Brazil., < (2008). 2 MMA. Mapa de cobertura vegetal dos biomas brasileiros., < > (2007). 3 IBGE. Instituto Brasileiro de Geografia e Estatística, < (2006). 4 Theil, H. A rank invariant method of linear and polynomial regression analysis. Nederlandse Akademie Wetenchappen Series A 53, (1950). 5 Wan, Z., Zhang, Y., Zhang, Q. & Li, Z. Validation of the land surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer. Remote Sensing of Environment 83, (2002). 6 Liang, S. et al. Validating MODIS land surface reflectance and albedo products: Methods and preliminary results. Remote Sensing of Environment 83, (2002). 7 Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS GLobal Terrestrial Evapotranspiration Algorithm. Remote Sensing of Environment (in review after resubmission) (2011). 8 Ruhoff, A. Predicting evapotranspiration in tropical biomes using MODIS remote sensing data. PhD thesis, Federal University of Rio Grande do Sul., (2011). 9 Rudorff, B. F. T. et al. Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data. Remote Sensing 2 (2010). 10 Pereira, E. B. Solar Radiation 57, 125 (1996). 11 Dickenson, R. E. & Henderson Sellers, A. Q.J.R. Meteorol. Soc. 114, (1988). 12 Chagnon, F. J. F. Geophys. Res. Lett 31 (2004). 8 nature climate change

9 supplementary information 13 Roy, S. B. Journal of Geophysical Research 113 (2009). 14 Wang, J. Proceedings of the National Academy of Sciences (2009). 15 Bathiany, S. Biogeosciences, 7 (2010). 16 Montenegro, A. Global Planetary Change 69 (2009). 17 Fearnside, P. M. et al. Biomass and greenhouse gas emissions from land use change in Brazil's Amazonian. Forest Ecology and Management 258, (2009). 18 Macedo, I. C., Seabra, J. E. A. & Silva, J. Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for Biomass and bioenergy 32, (2008). 19 Trenberth, K. E. Journal of Geophysical Research 86 (1981). 20 Rotenberg, E. & Yakir, D. Science 327 (2010). 21 Myhre, G. Geophys. Res. Lett 25, 2715 (1998). 22 Canadell, J. G. Proceedings of the National Academy of Sciences 104 (2007). Supporting tables and figures Table S1a Table S1b Natural vegetation in the cerrado Lands converted from nat. veg. ( ) State frac. intact 2008 frac. cleared frac. cleared by 2002 crop/pasture ratio % to sugarcane São Paulo Goiás Mato Grosso Mato Grosso do Sul Minas Gerais Maranhão Piauí Cerrado landcover State Total area (km2) Fraction of total cerrado Cerrado area (km2) crop/pasture ratio % soy cattle/km2 São Paulo Goiás Mato Grosso Mato Grosso do Sul Minas Gerais Maranhão Piauí Bahia Tocantins DISTRITO FEDERAL Amazonas Ceará Rio de Janeiro Pará Paraíba Paraná Pernambuco Rondônia Total nature climate change 9

10 supplementary information Bahia Tocantins Total Table S1c Area of sugarcane (km2) Lands converted to sugarcane ( ) State crop/pasture ratio % from natural vegetation São Paulo Goiás Mato Grosso Mato Grosso do Sul Minas Gerais Total Table S1. Areas of cerrado, natural vegetation, and sugarcane by state. Variable Land-use transition State Slope Int. s.e. n R 2 sig./p-value Temperature (C) From crop/past. to sugarcane SP *** Temperature (C) From crop/past. to sugarcane GO *** Temperature (C) From crop/past. to sugarcane MT *** Temperature (C) From crop/past. to sugarcane MS *** Temperature (C) From crop/past. to sugarcane MG *** Temperature (C) From nat. veg. to crop/past. SP Temperature (C) From nat. veg. to crop/past. GO *** Temperature (C) From nat. veg. to crop/past. MT *** Temperature (C) From nat. veg. to crop/past. MS *** Temperature (C) From nat. veg. to crop/past. MG *** ET (mm/day) From crop/past. to sugarcane SP *** ET (mm/day) From crop/past. to sugarcane GO *** ET (mm/day) From crop/past. to sugarcane MT *** ET (mm/day) From crop/past. to sugarcane MS *** ET (mm/day) From crop/past. to sugarcane MG *** ET (mm/day) From nat. veg. to crop/past. SP ET (mm/day) From nat. veg. to crop/past. GO *** ET (mm/day) From nat. veg. to crop/past. MT *** ET (mm/day) From nat. veg. to crop/past. MS *** ET (mm/day) From nat. veg. to crop/past. MG *** Albedo (%) From crop/past. to sugarcane SP *** Albedo (%) From crop/past. to sugarcane GO *** Albedo (%) From crop/past. to sugarcane MT *** Albedo (%) From crop/past. to sugarcane MS Albedo (%) From crop/past. to sugarcane MG *** Albedo (%) From nat. veg. to crop/past. SP *** Albedo (%) From nat. veg. to crop/past. GO *** Albedo (%) From nat. veg. to crop/past. MT *** Albedo (%) From nat. veg. to crop/past. MS *** Albedo (%) From nat. veg. to crop/past. MG *** Table S2. Results of linear regressions fit through scatter plots of fractional change in land-use against change in MODIS variables grouped by state. P-value is given when > nature climate change

11 supplementary information Variable Land-use transition Crops or pasture Slope Int. s.e. N R 2 sig. Temperature ( C) To sugarcane From crop *** Temperature ( C) To sugarcane From pasture *** Temperature ( C) From nat. vegetation To crop *** Temperature ( C) From nat. vegetation To pasture *** ET (mm/day) To sugarcane From crop *** ET (mm/day) To sugarcane From pasture *** ET (mm/day) From nat. vegetation To crop *** ET (mm/day) From nat.vegetation To pasture *** Albedo (%) To sugarcane From crop *** Albedo (%) To sugarcane From pasture *** Albedo (%) From nat. vegetation To crop *** Albedo (%) From nat. vegetation To pasture *** Table S3. Results of linear regressions fit through scatter plots of fractional change in land-use against change in MODIS variables grouped by crops or pasture. s.e. refers to the standard error of the regression. Sep. 1km 5km 1km 5km 1km 5km 1km 5km 1km 5km 1km 5km MODIS variable Land-use transition Slope Temperature ( C) from nat. veg. to crop/past Temperature ( C) from nat. veg. to crop/past Temperature ( C) from crop/past. to sugarcane Temperature ( C) from crop/past. to sugarcane ET (mm/day) from nat. veg. to crop/past ET (mm/day) from nat. veg. to crop/past ET (mm/day) from crop/past. to sugarcane 0.42 ET (mm/day) from crop/past. to sugarcane 0.43 Albedo (%) from nat. veg. to crop/past Albedo (%) from nat. veg. to crop/past Albedo (%) from crop/past. to sugarcane 0.19 Albedo (%) from crop/past. to sugarcane 0.20 s.e. p.val c.i < < < < < < < < < < < < Int. n R < <0.01 Table S4. Regressions repeated using pixels separated by >= 5km (Sep. = 5km) alongside those using every pixel (Sep. = 1km). Time Annual Clear-sky Annual Clear-sky Annual Clear-sky Annual Clear-sky Annual Clear-sky Annual Clear-sky MODIS variable Land-use transition Slope Temperature ( C) from nat. veg. to crop/past Temperature ( C) from nat. veg. to crop/past Temperature ( C) from crop/past. to sugarcane Temperature ( C) from crop/past. to sugarcane ET (mm/day) from nat. veg. to crop/past ET (mm/day) from nat. veg. to crop/past ET (mm/day) from crop/past. to sugarcane 0.42 ET (mm/day) from crop/past. to sugarcane 0.40 Albedo (%) from nat. veg. to crop/past Albedo (%) from nat. veg. to crop/past Albedo (%) from crop/past. to sugarcane 0.19 Albedo (%) from crop/past. to sugarcane 0.40 s.e. p.val c.i < < < < <0.01 < <0.01 < < < < < < < Int. n R < nature climate change 11

12 supplementary information Table S5. Regressions repeated using only clear-sky portions of the year (April through September; Time = Clear-sky) alongside those using all dates (Time = Annual). MOD12 All Savannas All Savannas All Savannas All Savannas All Savannas All Savannas MODIS variable Land-use transition Slope Temperature ( C) from nat. veg. to crop/past Temperature ( C) from nat. veg. to crop/past Temperature ( C) from crop/past. to sugarcane Temperature ( C) from crop/past. to sugarcane ET (mm/day) from nat. veg. to crop/past ET (mm/day) from nat. veg. to crop/past ET (mm/day) from crop/past. to sugarcane 0.42 ET (mm/day) from crop/past. to sugarcane 0.76 Albedo (%) from nat. veg. to crop/past Albedo (%) from nat. veg. to crop/past Albedo (%) from crop/past. to sugarcane 0.19 Albedo (%) from crop/past. to sugarcane 0.07 s.e. p.val c.i < < < < < < < < < < < < Int. n R < <0.01 Table S6. Regressions repeated using only areas classified as savanna in the MODIS Land Cover product which is a static input to the MODIS ET product (MOD12 = Savannas ) alongside those using all dates (MOD12 = All). MODIS variable Land-use transition Slope EVI from nat. veg. to crop/past EVI from crop/past. to sugarcane 0.06 Night Temp. ( C) from nat. veg. to crop/past Night Temp. ( C) from crop/past. to sugarcane 0.17 s.e. p.val c.i. <0.00 < < <0.00 < < Int. n R < Table S7. Regressions for EVI and Land Surface Temperature using the 22:30 ascending pass for nighttime temperatures. Sep. 1km 1km 1km 1km 1km 1km MODIS variable Land-use transition Slope Temperature ( C) from nat. veg. to crop/past Temperature ( C) from crop/past. to sugarcane ET (mm/day) from nat. veg. to crop/past ET (mm/day) from crop/past. to sugarcane 0.43 Albedo (%) from nat. veg. to crop/past Albedo (%) from crop/past. to sugarcane 0.20 c.i Theil-Sen slope Table S8. Slopes and confidence intervals (c.i.) for the 5km subset derived using the method of least-squares and reported in Figure 2 compared with slopes derived using the Theil-Sen method. Range -1>x<1 0>x<1-1>x<1 0>x<1-1>x<1 0>x<1 MODIS variable Land-use transition Slope Temperature ( C) from nat. veg. to crop/past Temperature ( C) from nat. veg. to crop/past ET (mm/day) from nat. veg. to crop/past ET (mm/day) from nat. veg. to crop/past Albedo (%) from nat. veg. to crop/past Albedo (%) from nat. veg. to crop/past s.e. p.val c.i < < < < < < Int. n R nature climate change

13 supplementary information Table S9. Regressions for transitions from natural vegetation to the crop/pasture mosaic repeated using all pixels (-1>x<1) and only pixels where fractional land cover change was positive 0 (0>x<1). Figure S1. Scatterplots of fractional changes in landcover versus changes in MODIS variables with means (solid blue lines), 50% upper and lower quantiles (dashed blue lines), fitted linear regressions (solid red lines) and x-intercepts (dashed red lines) for the entire study area. nature climate change 13

14 supplementary information Figure S2. Maps show fractional changes in sugarcane by state (left column) and changes in temperature (C) by state. Graphs show scatterplots of these data with fractional changes in sugarcane on the x-axis and change in temperature on the y-axis. Fitted regression lines (solid red lines) and x-intercepts (dashed red lines) are shown. 14 nature climate change

15 supplementary information Figure S3. Maps show fractional changes in sugarcane by state (left column) and changes in ET (mm/day) by state. Graphs show scatterplots of these data with fractional changes in sugarcane on the x-axis and change in temperature on the y-axis. Fitted regression lines (solid red lines) and x-intercepts (dashed red lines) are shown. nature climate change 15

16 supplementary information Figure S4. Maps show fractional changes in sugarcane by state (left column) and changes in albedo (%) by state. Graphs show scatterplots of these data with fractional changes in sugarcane on the x-axis and change in temperature on the y-axis. Fitted regression lines (solid red lines) and x-intercepts (dashed red lines) are shown. 16 nature climate change

17 supplementary information Figure S5. Maps show fractional changes in natural vegetation by state (left column) and changes in temperature (C) by state. Graphs show scatterplots of these data with fractional changes in natural vegetation on the x-axis and change in temperature on the y-axis. Fitted regression lines (solid red lines) and x-intercepts (dashed red lines) are shown. nature climate change 17

18 supplementary information Figure S6. Maps show fractional changes in natural vegetation by state (left column) and changes in ET (mm/day) by state. Graphs show scatterplots of these data with fractional changes in natural vegetation on the x-axis and change in temperature on the y-axis. Fitted regression lines (solid red lines) and x-intercepts (dashed red lines) are shown. 18 nature climate change

19 supplementary information Figure S7. Maps show fractional changes in natural vegetation by state (left column) and changes in albedo (%) by state. Graphs show scatterplots of these data with fractional changes in natural vegetation on the x-axis and change in temperature on the y-axis. Fitted regression lines (solid red lines) and x-intercepts (dashed red lines) are shown. nature climate change 19

20 supplementary information Figure S8. Average temperature (C) for grid-cells with a greater than 0.9 fractional change from natural vegetation (left of dashed line) and to sugarcane (right of dashed line) across the entire study area and by state. 20 nature climate change

21 supplementary information Figure S9. Average ET (mm/day X 100) for grid-cells with a greater than 0.9 fractional change from natural vegetation (left of dashed line) and to sugarcane (right of dashed line) across the entire study area and by state. nature climate change 21

22 supplementary information Figure S10. Average albedo (%) for grid-cells with a greater than 0.9 fractional change from natural vegetation (left of dashed line) and to sugarcane (right of dashed line) across the entire study area and by state. 22 nature climate change

23 supplementary information Figure S11. An 8-day time series of average cloud fraction computed from the daily MODIS cloud mask and averaged over The 1km data was averaged by pixels containing some sugarcane during the study (black line) and pixels containing some natural vegetation during the course of the study (red line). The dotted lines indicate average cloud fractions (sugarcane areas = 0.47; natural vegetation areas = 0.49). The gray bars indicate a subjectively defined clear-sky portion of the year from April through September. nature climate change 23

24 supplementary information Figure S12. The locations of land classified as savannas in the 2001 MODIS land cover product. This is the most common land class. This land cover product is a static input to the ET product. 24 nature climate change