Abstract
Agriculture is expanding in tropical mountainous areas, yet its climatic effect is poorly understood. Here, we investigate how elevation regulates the biophysical climate impacts of deforestation over tropical mountainous areas by integrating satellite-observed forest cover changes into a high-resolution land–atmosphere coupled model. We show that recent forest conversion between 2000 and 2014 increased the regional warming by 0.022 ± 0.002 °C in the Southeast Asian Massif, 0.010 ± 0.007 °C in the Barisan Mountains (Maritime Southeast Asia), 0.042 ± 0.010 °C in the Serra da Espinhaço (South America) and 0.047 ± 0.008 °C in the Albertine Rift mountains (Africa) during the local dry season. The deforestation-driven local temperature anomaly can reach up to 2 °C where forest conversion is extensive. The warming from mountain deforestation depends on elevation, through the intertwined and opposing effects of increased albedo causing cooling and decreased evapotranspiration causing warming. As the elevation increases, the albedo effect increases in importance and the warming effect decreases, analogous to previously highlighted decreases of deforestation-induced warming with increasing latitude. As most new croplands are encroaching lands at low to moderate elevations, deforestation produces higher warming from suppressed evapotranspiration. Impacts of this additional warming on crop yields, land degradation and biodiversity of nearby intact ecosystems should be incorporated into future assessments.
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Data availability
Data on satellite-observed high-resolution forest cover change in the twenty-first century are available at http://earthenginepartners.appspot.com/science-2013-global-forest. GSOD surface air temperature data are available at ftp://ftp.ncdc.noaa.gov/pub/data/gsod. The ERA5 reanalysis product is available at https://cds.climate.copernicus.eu/. The FNL reanalysis product is available at https://rda.ucar.edu/datasets/ds083.2. All of the datasets are also available on request from Z.Z.
Code availability
We used the programmes MATLAB (R2014a) and ArcGIS (10.4) to generate all of the results. Analysis scripts are available at https://doi.org/10.6084/m9.figshare.13280150.
References
Chini, L. P., Hurtt, G. C. & Frolking, S. Harmonized Global Land Use for Years 1500–2100, V1 (ORNL DAAC, 2014).
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
Zeng, Z. et al. Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century. Nat. Geosci. 11, 556–562 (2018).
Zeng, Z., Gower, D. & Wood, E. F. Accelerating forest loss in Southeast Asian Massif in the 21st century: a case study in Nan Province, Thailand. Glob. Change Biol. 24, 4682–4695 (2018).
Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision Working Paper No. 12-03 (FAO, 2012).
Grogan, K., Pflugmacher, D., Hostert, P., Mertz, O. & Fensholt, R. Unravelling the link between global rubber price and tropical deforestation in Cambodia. Nat. Plants 5, 47–53 (2019).
Mahmood, R. et al. Land cover changes and their biogeophysical effects on climate. Int. J. Climatol. 34, 929–953 (2014).
Lawton, R. O., Nair, U. S., Pielke, R. A. Sr & Welch, R. M. Climatic impact of tropical lowland deforestation on nearby montane cloud forests. Science 294, 584–587 (2001).
Ray, D. K., Nair, U. S., Lawton, R. O., Welch, R. M. & Pielke, R. A. Sr Impact of land use on Costa Rican tropical montane cloud forests: sensitivity of orographic cloud formation to deforestation in the plains. J. Geophys. Res. 111, D02108 (2006).
Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
Lee, X. et al. Observed increase in local cooling effect of deforestation at higher latitudes. Nature 479, 384–387 (2011).
Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).
Chen, F. The Noah Land Surface Model in WRF: A Short Tutorial (RAL, TIIMES, NCAR, 2007); https://go.nature.com/3lcWcsS
Skamarock, W. C. et al. A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN-475+STR (UCAR, 2008).
Li, D., Bouzeid, E., Barlage, M., Chen, F. & Smith, J. A. Development and evaluation of a mosaic approach in the WRF–Noah framework. J. Geophys. Res. Atmos. 118, 11918–11935 (2013).
Scott, J. C. The Art of Not Being Governed: An Anarchist History of Upland Southeast Asia (Yale Univ. Press, 2014).
Hersbach, H. & Dee, D. ERA5 reanalysis is in production. ECMWF Newsl. 147, 7 (2016).
NCEP GDAS/FNL 0.25 Degree Global Tropospheric Analyses and Forecast Grids (National Centers for Environmental Prediction, National Weather Service, NOAA & US Department of Commerce, accessed 1 August 2018); https://doi.org/10.5065/D65Q4T4Z
Song, X. P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).
Betts, R. A. Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 408, 187–190 (2000).
Peng, S. S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).
Cohn, A. S. et al. Forest loss in Brazil increases maximum temperatures within 50 km. Environ. Res. Lett. 14, 084047 (2019).
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
Mittermeier, R. A. et al. in Biodiversity Hotspots (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, 2011).
Freeman, B. G. & Freeman, A. M. Rapid upslope shifts in new Guinean birds illustrate strong distributional responses of tropical montane species to global warming. Proc. Natl Acad. Sci. USA 111, 4490–4494 (2014).
Freeman, B. G., Scholer, M. N., Ruizgutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
Mora, C. et al. Global risk of deadly heat. Nat. Clim. Change 7, 501–506 (2017).
Kohler, T. & Maselli, D. Mountains and Climate Change: From Understanding to Action (Geographica Bernensia, 2009).
Huber, U. M. et al. Global Change and Mountain Regions: An Overview of Current Knowledge (Springer Science & Business Media, 2006).
Beniston, M. in Climate Variability and Change in High Elevation Regions: Past, Present & Future (ed. Diaz, H. F.) 5–31 (Springer, 2003).
Practical Chemotherapy of Malaria: Report of a WHO Scientific Group (WHO, 1990).
Deutsch, C. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).
Special Report on Climate Change and Land (IPCC, 2019).
Egan, P. A. & Price, M. F. Mountain Ecosystem Services and Climate Change: A Global Overview of Potential Threats and Strategies for Adaptation (UNESCO, 2017).
Liu, Z., Ballantyne, A. P. & Cooper, L. A. Biophysical feedback of global forest fires on surface temperature. Nat. Commun. 10, 214 (2019).
Forrester, M. M. & Maxwell, R. Impact of lateral groundwater flow and subsurface lower boundary conditions on atmospheric boundary layer development over complex terrain. J. Hydrometeorol. 21, 1133–1160 (2020).
Leung, L. R., Kuo, Y. H. & Tribbia, J. Research needs and directions of regional climate modeling using WRF and CCSM. Bull. Am. Meteorol. Soc. 87, 1747–1751 (2006).
Bukovsky, M. S. & Karoly, D. J. Precipitation simulations using WRF as a nested regional climate model. J. Appl. Meteorol. Clim. 48, 2152–2159 (2009).
Caldwell, P., Chin, H. N. S., Bader, D. C. & Bala, G. Evaluation of a WRF dynamical downscaling simulation over California. Clim. Change 95, 499–521 (2009).
Wang, J. L. & Kotamarthi, V. R. Downscaling with a nested regional climate model in near-surface fields over the contiguous United States. J. Geophys. Res. Atmos. 119, 8778–8797 (2014).
Wi, S. et al. Climate change projection of snowfall in the Colorado River basin using dynamical downscaling. Water Resour. Res. 48, W05504 (2012).
Kain, J. S. The Kain–Fritsch convective parameterization: an update. J. Appl. Meteorol. Clim. 43, 170–181 (2004).
Hong, S. Y. & Lim, J. O. J. The WRF single-moment 6-class microphysics scheme (WSM6). Asia Pac. J. Atmos. Sci. 42, 129–151 (2006).
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. & Clough, S. A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. 102, 16663–16682 (1997).
Dudhia, J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci. 46, 3077–3107 (1989).
Monin, A. S. & Obukhov, A. M. Basic laws of turbulent mixing in the surface layer of the atmosphere. Tr. Akad. Nauk SSSR Geophiz. Inst. 24, 163–187 (1954).
Hong, S.-Y., Noh, Y. & Dudhia, J. A new vertical diffusion package with explicit treatment of entrainment processes. Mon. Weather Rev. 134, 2318–2341 (2006).
Friedl, M. A. et al. Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83, 287–302 (2002).
Chen, Y. et al. Generation and evaluation of LAI and FPAR products from Himawari-8 Advanced Himawari Imager (AHI) data. Remote Sens. 11, 1517 (2019).
Land Cover CCI: Product User Guide Version 2.0 (ESA, 2017); https://go.nature.com/3nTzp6Y
Chen, J. et al. Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7–27 (2015).
Friedl, A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
Yasutomi, N., Hamada, A. & Yatagai, A. Development of a long-term daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation. Glob. Environ. Res. 15, 165–172 (2011).
Pielke, R. A. Sr, Davey, C. & Morgan, J. Assessing “global warming” with surface heat content. Eos 85, 210–211 (2004).
Acknowledgements
This study was supported by Lamsam-Thailand Sustain Development (B0891), the National Natural Science Foundation of China (42071022, 42001321), the China Postdoctoral Science Foundation (2020M672693), the start-up fund provided by the Southern University of Science and Technology (29/Y01296122) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20060401). P.C. acknowledges support from the European Research Council Synergy project SyG-2013-610028 IMBALANCE-P and the ANR CLAND Convergence Institute. D.C. was supported by Swedish BECC and MERGE. We thank Della Research Computing at Princeton University and the Taiyi Supercomputer at the Southern University of Science and Technology for providing computing resources. We sincerely appreciate D. S. Wilcove for constructive comments on this paper.
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Z.Z. designed the research and wrote the draft. Z.Z. and D.W. performed the analysis. Z.Z., D.W., L.Y. and M.L. performed the numerical simulations. All of the authors contributed to interpretation of the results and writing of the paper.
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Zeng, Z., Wang, D., Yang, L. et al. Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat. Geosci. 14, 23–29 (2021). https://doi.org/10.1038/s41561-020-00666-0
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DOI: https://doi.org/10.1038/s41561-020-00666-0
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