Articles | Volume 6, issue 4
https://doi.org/10.5194/wcd-6-1461-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-6-1461-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2025
Research article |
| 18 Nov 2025
| 18 Nov 2025
Mechanistic insights into tropical circulation and hydroclimate responses to future forest cover change
Nora L. S. Fahrenbach
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Robert C. J. Wills
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Steven J. De Hertog
Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
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Cited articles
Abiodun, B. J., Pal, J. S., Afiesimama, E. A., Gutowski, W. J., and Adedoyin, A.: Simulation of West African monsoon using RegCM3 Part II: impacts of deforestation and desertification, Theor. Appl. Climatol., 93, 245–261, https://doi.org/10.1007/s00704-007-0333-1, 2008. a
Akkermans, T., Thiery, W., and Van Lipzig, N. P. M.: The regional climate impact of a realistic future deforestation scenario in the Congo Basin, J. Climate, 27, 2714–2734, https://doi.org/10.1175/JCLI-D-13-00361.1, 2014. a
Alkama, R., Forzieri, G., Duveiller, G., Grassi, G., Liang, S., and Cescatti, A.: Vegetation-based climate mitigation in a warmer and greener world, Nat. Commun., 13, https://doi.org/10.1038/s41467-022-28305-9, 2022. a, b, c
Allen, R. J., Evan, A. T., and Booth, B. B. B.: Interhemispheric aerosol radiative forcing and tropical precipitation shifts during the late twentieth century, J. Climate, 28, 8219–8246, https://doi.org/10.1175/JCLI-D-15-0148.1, 2015. a
Arnault, J., Mwanthi, A. M., Portele, T., Li, L., Rummler, T., Fersch, B., Hassan, M. A., Bahaga, T. K., Zhang, Z., Mortey, E. M., Achugbu, I. C., Moutahir, H., Sy, S., Wei, J., Laux, P., Sobolowski, S., and Kunstmann, H.: Regional water cycle sensitivity to afforestation: synthetic numerical experiments for tropical Africa, Front. Clim., 5, https://doi.org/10.3389/fclim.2023.1233536, 2023. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020. a
Baidya Roy, S. and Avissar, R.: Impact of land use/land cover change on regional hydrometeorology in Amazonia, J. Geophys. Res., 107, https://doi.org/10.1029/2000JD000266, 2002. a, b
Betts, R. A.: Offset of the potential carbon sink from boreal forestation by decreases in surface albedo, Nature, 408, 187–190, https://doi.org/10.1038/35041545, 2000. a
Bischoff, T. and Schneider, T.: Energetic constraints on the position of the intertropical convergence zone, J. Climate, 27, 4937–4951, https://doi.org/10.1175/JCLI-D-13-00650.1, 2014. a
Bonan, G. B.: Ecological Climatology: Concepts and Applications, Cambridge University Press, New York, https://doi.org/10.1017/CBO9781107339200, 2002. a
Bonan, G. B.: Forests and climate change: forcings, feedbacks, and the climate benefits of forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121, 2008. a, b, c, d
Bonan, G. B., Pollard, D., and Thompson, S. L.: Effects of boreal forest vegetation on global climate, Nature, 359, 716–718, https://doi.org/10.1038/359716a0, 1992. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Vuichard, N., Ghattas, J., and Cadule, P.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 LUMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1528, 2019. a
Boysen, L. R., Brovkin, V., Pongratz, J., Lawrence, D. M., Lawrence, P., Vuichard, N., Peylin, P., Liddicoat, S., Hajima, T., Zhang, Y., Rocher, M., Delire, C., Séférian, R., Arora, V. K., Nieradzik, L., Anthoni, P., Thiery, W., Laguë, M. M., Lawrence, D., and Lo, M.-H.: Global climate response to idealized deforestation in CMIP6 models, Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, 2020. a, b, c, d
Burakowski, E., Tawfik, A., Ouimette, A., Lepine, L., Novick, K., Ollinger, S., Zarzycki, C., and Bonan, G.: The role of surface roughness, albedo, and Bowen ratio on ecosystem energy balance in the Eastern United States, Agr. Forest Meteorol., 367–376, https://doi.org/10.1016/j.agrformet.2017.11.030, 2018. a, b
Byrne, M. P. and Schneider, T.: Energetic constraints on the width of the intertropical convergence zone, J. Climate, 29, 4709–4721, https://doi.org/10.1175/JCLI-D-15-0767.1, 2016. a
Chadwick, R., Boutle, I., and Martin, G.: Spatial patterns of precipitation change in CMIP5: why the rich do not get richer in the tropics, J. Climate, 26, 3803–3822, https://doi.org/10.1175/JCLI-D-12-00543.1, 2013. a
Chausson, A., Turner, B., Seddon, D., Chabaneix, N., Girardin, C. A. J., Kapos, V., Key, I., Roe, D., Smith, A., Woroniecki, S., and Seddon, N.: Mapping the effectiveness of nature-based solutions for climate change adaptation, Glob. Change Biol., 26, 6134–6155, https://doi.org/10.1111/gcb.15310, 2020. a
Cheng, Y. and Mccoll, K. A.: Thermally direct mesoscale circulations caused by land surface roughness anomalies, Geophys. Res. Lett., 50, https://doi.org/10.1029/2023GL105150, 2023. a
Claussen, M., Brovkin, V., and Ganopolski, A.: Biogeophysical versus biogeochemical feedbacks of large-scale land cover change, Geophys. Res. Lett., 28, 1011–1014, https://doi.org/10.1029/2000GL012471, 2001. a, b
Crook, J., Klein, C., Folwell, S., Taylor, C. M., Parker, D. J., Bamba, A., and Kouadio, K.: Effects on early monsoon rainfall in West Africa due to recent deforestation in a convection-permitting ensemble, Weather Clim. Dynam., 4, 229–248, https://doi.org/10.5194/wcd-4-229-2023, 2023. a, b, c, d
Cui, J., Lian, X., Huntingford, C., Gimeno, L., Wang, T., Ding, J., He, M., Xu, H., Chen, A., Gentine, P., and Piao, S.: Global water availability boosted by vegetation-driven changes in atmospheric moisture transport, Nat. Geosci., 15, 982–988, https://doi.org/10.1038/s41561-022-01061-7, 2022. a, b, c
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 LUMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2195, 2019. a
Davin, E. L. and De Noblet-Ducoudré, N.: Climatic impact of global-scale deforestation: radiative versus nonradiative processes, J. Climate, 23, 97–112, https://doi.org/10.1175/2009JCLI3102.1, 2010. a
Douville, H., Raghavan, K., Renwick, J., Allan, R., Arias, P., Barlow, M., Cerezo-Mota, R., Cherchi, A., Gan, T., Gergis, J., Jiang, D., Khan, A., Pokam Mba, W., Rosenfeld, D., Tierney, J., and Zolina, O.: Water cycle changes, in: Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Chapt. 8, Cambridge University Press, https://doi.org/10.1017/9781009157896.010, 1055–1210, 2021. a
Girardin, C. A. J., Jenkins, S., Seddon, N., Allen, M., Lewis, S. L., Wheeler, C. E., Griscom, B. W., and Malhi, Y.: Nature-based solutions can help cool the planet – if we act now, Nature, 593, 191–194, https://doi.org/10.1038/d41586-021-01241-2, 2021. a
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamaki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., Herrero, M., Kiesecker, J., Landis, E., Laestadius, L., Leavitt, S. M., Minnemeyer, S., Polasky, S., Potapov, P., Putz, F. E., Sanderman, J., Silvius, M., Wollenberg, E., and Fargione, J.: Natural climate solutions, P. Natl. Acad. Sci. USA, 114, 11645–11650, https://doi.org/10.1073/pnas.1710465114, 2017. a
Hajima, T., Ito, A., Abe, M., Arakawa, O., Suzuki, T., Komuro, Y., Ogura, T., Ogochi, K., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Ohgaito, R., Ito, A., Yamazaki, D., Takata, K., Watanabe, S., Kawamiya, M., and Tachiiri, K.: MIROC MIROC-ES2L model output prepared for CMIP6 LUMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.922, 2019. a
Hua, W. J. and Chen, H. S.: Recognition of climatic effects of land use/land cover change under global warming, Chinese Sci. Bull., 58, 3852–3858, https://doi.org/10.1007/s11434-013-5902-3, 2013. a
IPCC: Summary for policymakers, in: Mitigation of climate change. Contribution of working group III to the sixth assessment report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, https://doi.org/10.1017/9781009157926.001, 2022. a
Khanna, J. and Medvigy, D.: Strong control of surface roughness variations on the simulated dry season regional atmospheric response to contemporary deforestation in Rondônia, Brazil, J. Geophys. Res.-Atmos., 119, https://doi.org/10.1002/2014JD022278, 2014. a, b, c
Khanna, J., Medvigy, D., Fueglistaler, S., and Walko, R.: Regional dry-season climate changes due to three decades of Amazonian deforestation, Nat. Clim. Change, 7, 200–204, https://doi.org/10.1038/nclimate3226, 2017. a, b
Kim, J. B., Monier, E., Sohngen, B., Pitts, G. S., Drapek, R., McFarland, J., Ohrel, S., and Cole, J.: Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios, Environ. Res. Lett., 12, https://doi.org/10.1088/1748-9326/aa63fc, 2017. a
Laguë, M. M., Bonan, G. B., and Swann, A. L. S.: Separating the impact of individual land surface properties on the terrestrial surface energy budget in both the coupled and uncoupled land-atmosphere system, J. Climate, 32, 5725–5744, https://doi.org/10.1175/JCLI-D-18-0812.1, 2019. a
Laguë, M. M., Swann, A. L., and Boos, W. R.: Radiative feedbacks on land surface change and associated tropical precipitation shifts, J. Climate, 34, https://doi.org/10.1175/JCLI-D-20-0883.1, 2021. a
Lawrence, D. M., Hurtt, G. C., Arneth, A., Brovkin, V., Calvin, K. V., Jones, A. D., Jones, C. D., Lawrence, P. J., de Noblet-Ducoudré, N., Pongratz, J., Seneviratne, S. I., and Shevliakova, E.: The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design, Geosci. Model Dev., 9, 2973–2998, https://doi.org/10.5194/gmd-9-2973-2016, 2016. a, b, c, d, e
Lejeune, Q., Davin, E. L., Guillod, B. P., and Seneviratne, S. I.: Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation, Clim. Dynam., 44, 2769–2786, https://doi.org/10.1007/s00382-014-2203-8, 2015. a, b, c, d, e, f, g
Liang, Y., Xu, X., and Jia, G.: Deforestation drives desiccation in global monsoon region, Earths Future, 10, https://doi.org/10.1029/2022EF002863, 2022. a, b, c
Loughran, T. F., Ziehn, T., Law, R., Canadell, J. G., Pongratz, J., Liddicoat, S., Hajima, T., Ito, A., Lawrence, D. M., and Arora, V. K.: Limited mitigation potential of forestation under a high emissions scenario: results from multi-model and single model ensembles, J. Geophys. Res.-Biogeo., 128, https://doi.org/10.1029/2023JG007605, 2023. a
Ma, J. and Xie, S.-P.: Regional patterns of sea surface temperature change: a source of uncertainty in future projections of precipitation and atmospheric circulation, J. Climate, 26, 2482–2501, https://doi.org/10.1175/JCLI-D-12-00283.1, 2013. a
Nogherotto, R., Coppola, E., Giorgi, F., and Mariotti, L.: Impact of Congo Basin deforestation on the African monsoon, Atmos. Sci. Lett., 14, 45–51, https://doi.org/10.1002/asl2.416, 2013. a, b
Pitman, A. J., Avila, F. B., Abramowitz, G., Wang, Y. P., Phipps, S. J., and de Noblet-Ducoudre, N.: Importance of background climate in determining impact of land-cover change on regional climate, Nat. Clim. Change, 1, 472–475, https://doi.org/10.1038/NCLIMATE1294, 2011. a
Pongratz, J., Hartung, K., Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPIESM1.2-LR model output prepared for CMIP6 LUMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.772, 2019. a
Pongratz, J., Schwingshackl, C., Bultan, S., Obermeier, W., Havermann, F., and Guo, S.: Land use effects on climate: current state, recent progress, and emerging topics, Curr. Clim. Change Rep., 7, 99–120, https://doi.org/10.1007/s40641-021-00178-y, 2021. a
Portmann, R., Beyerle, U., Davin, E., Fischer, E. M., De Hertog, S., and Schemm, S.: Global forestation and deforestation affect remote climate via adjusted atmosphere and ocean circulation, Nat. Commun., 13, 5569, https://doi.org/10.1038/s41467-022-33279-9, 2022. a, b
Qin, Y., Wang, D., Ziegler, A. D., Fu, B., and Zeng, Z.: Impact of Amazonian deforestation on precipitation reverses between seasons, Nature, 639, 102–108, https://doi.org/10.1038/s41586-024-08570-y, 2025. a, b
Roe, S., Streck, C., Beach, R., Busch, J., Chapman, M., Daioglou, V., Deppermann, A., Doelman, J., Emmet-Booth, J., Engelmann, J., Fricko, O., Frischmann, C., Funk, J., Grassi, G., Griscom, B., Havlik, P., Hanssen, S., Humpenoder, F., Landholm, D., Lomax, G., Lehmann, J., Mesnildrey, L., Nabuurs, G.-J., Popp, A., Rivard, C., Sanderman, J., Sohngen, B., Smith, P., Stehfest, E., Woolf, D., and Lawrence, D.: Land-based measures to mitigate climate change: potential and feasibility by country, Glob. Change Biol., 27, 6025–6058, https://doi.org/10.1111/gcb.15873, 2021. a
Seager, R., Naik, N., and Vecchi, G. A.: Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming, J. Climate, 23, 4651–4668, https://doi.org/10.1175/2010JCLI3655.1, 2010. a, b
Seddon, N.: Harnessing the potential of nature-based solutions for mitigating and adapting to climate change, Science, 376, 1410–1416, https://doi.org/10.1126/science.abn9668, 2022. a
Seddon, N., Smith, A., Smith, P., Key, I., Chausson, A., Girardin, C., House, J., Srivastava, S., and Turner, B.: Getting the message right on nature-based solutions to climate change, Glob. Change Biol., 27, 1518–1546, https://doi.org/10.1111/gcb.15513, 2021. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 LUMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1393, 2019. a
Semazzi, F. and Song, Y.: A GCM study of climate change induced by deforestation in Africa, Clim. Res., 17, 169–182, https://doi.org/10.3354/cr017169, 2001. a
Smith, C., Baker, J. C. A., and Spracklen, D. V.: Tropical deforestation causes large reductions in observed precipitation, Nature, 615, 270–275, https://doi.org/10.1038/s41586-022-05690-1, 2023. a, b, c
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L., and Marsham, J. H.: The effects of tropical vegetation on rainfall, Annu. Rev. Env. Resour., 43, 193–218, https://doi.org/10.1146/annurev-environ-102017-030136, 2018. a
Sud, Y., Walker, G., Kim, J., Liston, G., Sellers, P., and Lau, W.: Biogeophysical consequences of a tropical deforestation scenario: a GCM simulation study, J. Climate, 9, 3225–3247, https://doi.org/10.1175/1520-0442(1996)009<3225:BCOATD>2.0.CO;2, 1996. a, b, c, d
Swann, A. L. S., Fung, I. Y., and Chiang, J. C. H.: Mid-latitude afforestation shifts general circulation and tropical precipitation, P. Natl. Acad. Sci. USA, 109, 712–716, https://doi.org/10.1073/pnas.1116706108, 2012. a, b, c
te Wierik, S. A., Cammeraat, E. L. H., Gupta, J., and Artzy-Randrup, Y. A.: Reviewing the impact of land use and land-use change on moisture recycling and precipitation patterns, Water Resour. Res., 57, https://doi.org/10.1029/2020WR029234, 2021. a
Varejao-Silva, M., Franchito, S., and Rao, V.: A coupled biosphere-atmosphere climate model suitable for studies of climatic change due to land surface alterations, J. Climate, 11, 1749–1767, https://doi.org/10.1175/1520-0442(1998)011<1749:ACBACM>2.0.CO;2, 1998. a
Wills, R. C. and Schneider, T.: Stationary eddies and the zonal asymmetry of net precipitation and ocean freshwater forcing, J. Climate, 28, 5115–5133, https://doi.org/10.1175/JCLI-D-14-00573.1, 2015. a, b, c
Wills, R. C. and Schneider, T.: How stationary eddies shape changes in the hydrological cycle: zonally asymmetric experiments in an idealized GCM, J. Climate, 29, 3161–3179, https://doi.org/10.1175/JCLI-D-15-0781.1, 2016. a, b, c
Wills, R. C., Byrne, M. P., and Schneider, T.: Thermodynamic and dynamic controls on changes in the zonally anomalous hydrological cycle, Geophys. Res. Lett., 43, 4640–4649, https://doi.org/10.1002/2016GL068418, 2016. a
Wiltshire, A., Liddicoat, S., and Robertson, E.: MOHC UKESM1.0-LL model output prepared for CMIP6 LUMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1564, 2019. a
Windisch, M. G., Humpenoeder, F., Lejeune, Q., Schleussner, C.-F., Lotze-Campen, H., and Popp, A.: Accounting for local temperature effect substantially alters afforestation patterns, Environ. Res. Lett., 17, https://doi.org/10.1088/1748-9326/ac4f0e, 2022. a
Zan, B., Ge, J., Mu, M., Sun, Q., Luo, X., and Wei, J.: Spatiotemporal inequality in land water availability amplified by global tree restoration, Nat. Water, 2, 863–874, https://doi.org/10.1038/s44221-024-00296-5, 2024. a, b, c
Zeng, Z., Piao, S., Li, L. Z. X., Wang, T., Ciais, P., Lian, X., Yang, Y., Mao, J., Shi, X., and Myneni, R. B.: Impact of Earth greening on the terrestrial water cycle, J. Climate, 31, 2633–2650, https://doi.org/10.1175/JCLI-D-17-0236.1, 2018. a, b
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 LUMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.15741, 2021. a
Short summary
Afforestation is a key strategy for climate change mitigation, yet the impacts on tropical hydroclimate remain uncertain. We find that potential future afforestation would increase evaporation and precipitation in the tropics, especially over Africa. However, it would reduce net precipitation (precipitation minus evaporation), which determines water availability. This happens because trees slow near-surface winds, while their influence on the energy budget would otherwise strengthen convection.
Afforestation is a key strategy for climate change mitigation, yet the impacts on tropical...
