Supporting Online Material for

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1 This PDF file includes: SOM Text Fig. S1 Table S1 References Supporting Online Material for Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests Gordon B. Bonan *To whom correspondence should be addressed. Published 13 June 2008, Science 320, 1444 (2008) DOI: /science

2 Supporting Online Material 1. Data in Figure 1 The data in Fig. 1A,B are revised from (S1) with updated soil carbon (S2). For comparison with Fig. 1C, flux tower estimates of net ecosystem production for mature temperate evergreen conifer forests are g C m -2 year -1 (mean, 250 g C m -2 year -1 ) and for temperate deciduous broadleaf forests are g C m -2 year -1 (mean, 270 g C m -2 year -1 ) (S3). Fig. 1D shows wintertime black sky albedo ( μm) at local solar noon averaged over 40º-50ºN for snow-covered and snow-free surfaces derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) (S4). Data in Fig. 1E are eddy covariance flux tower measurements during the growing season. Equilibrium evapotranspiration is directly proportional to available energy (net radiation minus soil heat flux) and is the rate of evapotranspiration from a well-watered, vegetated surface. 2. The Ecology of Climate Models The categorization of land surface models into first, second, and third generations is based on model development through the mid-1990s (S5). Since then, there has been extensive model development on the carbon cycle, vegetation dynamics, and representing subgrid-scale surface heterogeneity. 3. Tropical Deforestation Mesoscale model studies suggest that small-scale, heterogeneous deforestation may enhance clouds and precipitation (S6), but most large-scale climate model studies of Amazonia deforestation find that complete transformation of forest to pasture results in a warmer and drier climate (S7-S24). Fifteen of 18 studies report an increase in annual mean air temperature ranging from +0.3ºC to +3.8ºC (mean, +1.7ºC), all but one in excess of +0.5 C. Sixteen of 18 studies show decreased annual precipitation ranging from -146 mm to -643 mm (mean, -398 mm), and all studies show decreased evapotranspiration. Similar results are seen in tropical Africa and Asia (S24-S26). The climatic influence of tropical forests may extend to the extratropics through atmospheric teleconnections (S25-S27). 4. Boreal Deforestation Climate model studies show that boreal deforestation warms climate by increasing surface albedo (S24, S28-S31). Illustrative effects of boreal deforestation are shown in table S1. Replacing forest with bare ground increases surface albedo (mostly in winter and spring), reduces net radiation at the surface (mostly in spring and summer), decreases surface air temperature throughout the year, decreases latent heat flux year-round, and decreases sensible heat flux in spring and summer. 1

3 5. Temperate Deforestation Numerous climate model studies find that historical clearing of temperate forests and grasslands for cropland has likely cooled the Northern Hemisphere (S32-S42). Increase in surface albedo with introduction of crops is a primary driver of this cooling. Satellite data show an increase in annual mean surface albedo in these regions compared with natural vegetation (S43). Some studies indicate the dominant cooling is in northern latitudes during winter or spring, when deforestation unmasks the high albedo of snow (S32, S34-S36, S40, S41) and that decreased evapotranspiration (reduced atmospheric water vapor) augments the negative radiative forcing from albedo (S42). Others also find summer cooling throughout temperate latitudes where forest and grassland have been converted to cropland (S37). The magnitude of the cooling is sensitive to the albedo of cropland (S38, S39). Simulations of land use effects on the climate of the United States using three different atmospheric models, two different land surface models, and various depictions of land cover change find that despite differences in model configuration, land cover, and length of simulation, introduction of agriculture has likely decreased surface air temperature during summer (S44-S47). The cooling largely arises from increased surface albedo that decreases net solar radiation at the surface, but also higher latent heat flux with crops. Feedback with the atmosphere is evident in increased cloudiness that reduces incoming solar radiation at the surface. The cooling is larger for daily maximum temperature than for daily minimum temperature so that diurnal temperature range decreases. The summer cooling is robust across all model simulations, but its magnitude depends on the spatial extent of land cover change, formulation of latent heat flux, and feedbacks with the atmosphere that increase cloudiness (S47). Clearing of temperate forests and grasslands to cultivate crops is generally thought to cool climate, primarily because of higher albedo. However, the climate signal associated with crops is complicated and related to the timing of crop planting, growth, and harvesting relative to the phenology of natural vegetation (S48). Maintaining unvegetated soils in summer, for example, decreases latent heat flux and warms temperature (S24, S49). Irrigation cools climate (S50). Realistic crop management practices are not well represented in the current generation of models. 6. Carbon Cycle Feedback The effects of increasing atmospheric CO 2 on climate can be partitioned into (a) radiative effects and (b) physiological effects due to reduced stomatal conductance with higher atmospheric CO 2. Climate model simulations in which stomatal conductance decreases with a doubling of atmospheric CO 2 show decreased evapotranspiration and surface warming over large vegetated regions in summer (S51-S53). 2

4 7. Land Use Forcing Simulations of climate change for the twenty-first century are forced with greenhouse gas concentrations and other atmospheric constituents derived from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) (S54) or are forced with the SRES emissions (rather than concentrations) in carbon cycle-climate simulations (S55). The SRES narrative storylines describe different demographic, social, economic, technological, and environmental developments that govern emission of greenhouse gases and other atmospheric constituents over the twenty-first century. The B1 storyline is a low greenhouse gas emission scenario, with a multi-model ensemble mean global temperature change of 1.8ºC by the late twenty-first century while the A2 storyline has higher CO 2 emission and 3.1ºC warming (S54). Changes in land use and land cover are important sources and sinks of CO 2, and the two storylines have vastly different land cover change associated with socioeconomic trends (fig. S1). 3

5 Fig. S1. Land cover as represented in climate simulations for present-day, 2050, and 2100 using the IPCC SRES B1 and A2 storylines (S56). 4

6 Table S1. Seasonal and annual response to boreal deforestation (S24). Data are averaged for the region of deforestation and show the difference (bare ground - forested). DJF, December-February; MAM, March-May; JJA, June-August; SON, September- November. Season Variable DJF MAM JJA SON Annual Temperature (ºC) Net radiation (W m -2 ) Albedo (fraction) Latent heat flux (W m -2 ) Sensible heat flux (W m -2 )

7 References S1. I. C. Prentice et al., in Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, J. T. Houghton et al., Eds. (Cambridge Univ. Press, Cambridge, 2001) pp S2. C. L. Sabine et al., in The Global Carbon Cycle: Integrating Humans, Climate, and the Natural World, C. B. Field, M. R. Raupach, Eds. (Island Press, Washington, DC, 2004) pp S3. A. W. King et al., Eds., The First State of the Carbon Cycle Report (SOCCR): The North American Carbon Budget and Implications for the Global Carbon Cycle, (National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, 2007). S4. Y. Jin et al., Geophys. Res. Lett. 29, 1374, doi: /2001gl (2002). S5. P. J. Sellers et al., Science 275, 502 (1997). S6. S. Baidya Roy, R. Avissar, J. Geophys. Res. 107, 8037, doi: /2000jd (2002). S7. R. E. Dickinson, A. Henderson-Sellers, Q. J. R. Meteorol. Soc. 114, 439 (1988). S8. J. Lean, D. A. Warrilow, Nature 342, 411 (1989). S9. C. A. Nobre, P. J. Sellers, J. Shukla, J. Clim. 4, 957 (1991). S10. R. E. Dickinson, P. Kennedy, Geophys. Res. Lett. 19, 1947 (1992). S11. M. F. Mylne, P. R. Rowntree, Clim. Change 21, 317 (1992). S12. A. Henderson-Sellers et al., J. Geophys. Res. 98, 7289 (1993). S13. J. Lean, P. R. Rowntree, Q. J. R. Meteorol. Soc. 119, 509 (1993). S14. A. J. Pitman, T. B. Durbidge, A. Henderson-Sellers, K. McGuffie, Int. J. Climatol. 13, 879 (1993). S15. J. Polcher, K. Laval, J. Hydrol. 155, 389 (1994). S16. J. Polcher, K. Laval, Clim. Dyn. 10, 205 (1994). S17. K. McGuffie, A. Henderson-Sellers, H. Zhang, T. B. Durbidge, A. J. Pitman, Global Planet. Change 10, 97 (1995). S18. Y. C. Sud et al., J. Clim. 9, 3225 (1996). S19. J. Lean, P. R. Rowntree, J. Clim. 10, 1216 (1997). S20. A. N. Hahmann, R. E. Dickinson, J. Clim. 10, 1944 (1997). S21. M. H. Costa, J. A. Foley, J. Clim. 13, 18 (2000). S22. N. Gedney, P. J. Valdes, Geophys. Res. Lett. 27, 3053 (2000). S23. A. Voldoire, J. F. Royer, Clim. Dyn. 22, 857 (2004). S24. P. K. Snyder, C. Delire, J. A. Foley, Clim. Dyn. 23, 279 (2004). S25. D. Werth, R. Avissar, Geophys. Res. Lett. 32, L12704, doi: /2005gl (2005). S26. D. Werth, R. Avissar, Geophys. Res. Lett. 32, L20702, doi: /2005gl (2005). S27. D. Werth, R. Avissar, J. Geophys. Res. 107, 8087, doi: /2001jd (2002). S28. G. B. Bonan, D. Pollard, S. L. Thompson, Nature 359, 716 (1992). S29. G. Thomas, P. R. Rowntree, Q. J. R. Meteorol. Soc. 118, 469 (1992). S30. S. Chalita, H. Le Treut, Clim. Dyn. 10, 231 (1994). 6

8 S31. H. Douville, J.-F. Royer, Clim. Dyn. 13, 57 (1996). S32. J. E. Hansen et al., Proc. Natl. Acad. Sci. U.S.A. 95, (1998). S33. J. Hansen et al., Clim. Dyn. 29, 661 (2007). S34. V. Brovkin, A. Ganopolski, M. Claussen, C. Kubatzki, V. Petoukhov, Global Ecol. Biogeogr. 8, 509 (1999). S35. V. Brovkin et al., Clim. Dyn. 26, 587 (2006). S36. R. A. Betts, Atmos. Sci. Lett. 1, 39 (2001). S37. L. Bounoua, R. DeFries, G. J. Collatz, P. Sellers, H. Khan, Clim. Change 52, 29 (2002). S38. H. D. Matthews, A. J. Weaver, M. Eby, K. J. Meissner, Geophys. Res. Lett. 30, 1055, doi: /2002gl (2003). S39. H. D. Matthews, A. J. Weaver, K. J. Meissner, N. P. Gillett, M. Eby, Clim. Dyn. 22, 461 (2004). S40. S. Gibbard, K. Caldeira, G. Bala, T. J. Phillips, M. Wickett, Geophys. Res. Lett. 32, L23705, doi: /2005gl (2005). S41. R. A. Betts, P. D. Falloon, K. Klein Goldewijk, N. Ramankutty, Agric. For. Meteorol. 142, 216 (2007). S42. E. L. Davin, N. de Noblet-Ducoudré, P. Friedlingstein, Geophys. Res. Lett. 34, L13702, doi: /2007gl (2007). S43. G. Myhre, M. M. Kvalevåg, C. B. Schaaf, Geophys. Res. Lett. 32, L21410, doi: /2005gl (2005). S44. G. B. Bonan, Clim. Change 37, 449 (1997). S45. G. B. Bonan, Ecol. Appl. 9, 1305 (1999). S46. G. B. Bonan, J. Clim. 14, 2430 (2001). S47. K. W. Oleson, G. B. Bonan, S. Levis, M. Vertenstein, Clim. Dyn. 23, 117 (2004). S48. T. E. Twine, C. J. Kucharik, J. A. Foley, J. Hydrometeorol. 5, 640 (2004). S49. B. L. Lamptey, E. J. Barron, D. Pollard, Global Planet. Change 49, 203 (2005). S50. D. B. Lobell, G. Bala, C. Bonfils, P. B. Duffy, Geophys. Res. Lett. 33, L13709, doi: /2006gl (2006). S51. P. J. Sellers et al., Science 271, 1402 (1996). S52. L. Bounoua et al., J. Clim. 12, 309 (1999). S53. S. Levis, J. A. Foley, D. Pollard, J. Clim. 13, 1313 (2000). S54. G. A. Meehl et al., in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon et al., Eds. (Cambridge Univ. Press, Cambridge, 2007) pp S55. P. Friedlingstein et al., J. Clim. 19, 3337 (2006). S56. J. J. Feddema et al., Science 310, 1674 (2005). 7