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Asymmetric influence of forest cover gain and loss on land surface temperature

Nature Climate Change volume 13, pages 823–831 (2023)Cite this article

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Abstract

The direct biophysical effects of fine-scale tree cover changes on temperature are not well understood. Here, we show how land surface temperature responds to subgrid gross tree cover changes. We find that in many forests, the biophysical cooling induced by enhanced evapotranspiration due to tree cover gain is greater in magnitude than the warming from tree cover loss. Therefore, the goal of no biophysical warming effects from tree cover changes could be achieved by regaining a fraction of previously lost tree cover areas. This percentage differs between different forest biomes, ranging from 75% in tropical to 83% in temperate forests. Neglecting this asymmetric temperature effect of fine-scale tree cover change ignores the fact that biophysical feedbacks continue to cause surface temperature changes even under net-zero tree cover changes. Thus, it is necessary to account for gross, rather than net, tree cover changes when quantifying the biophysical effects of forests.

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Fig. 1: Global tree cover changes and their direct biophysical effects on daily mean LST.
Fig. 2: Asymmetric biophysical effects of tree cover gain and loss on LST.
Fig. 3: Ratio of fractional tree cover gain to loss for LST neutrality.
Fig. 4: Asymmetric influences of tree cover gain and loss on surface energy balance.
Fig. 5: Influences of tree age on the asymmetric temperature effects of tree cover gain and loss.

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Data availability

The LST, land cover, evapotranspiration, albedo, forest age and energy flux data used for the analyses in this study are available online as follows: 30 m resolution GFW maps of twenty-first century forest cover change https://glad.earthengine.app/view/global-forest-change; MOD11C3 LST product https://lpdaac.usgs.gov/products/mod11c3v061/; MCD12C1 Land Cover dataset https://lpdaac.usgs.gov/products/mcd12c1v061/; MCD43C3 Albedo product https://lpdaac.usgs.gov/products/mcd43c3v061/; MOD16A2GF ET and LE product https://lpdaac.usgs.gov/products/mod16a2v061/; GLASS Shortwave Radiation product http://www.glass.umd.edu/Download.html; MOD13C2 NDVI product https://lpdaac.usgs.gov/products/mod13c2v061/; drivers of global forest loss https://www.science.org/doi/abs/10.1126/science.aau3445; GLASS ET product http://www.glass.umd.edu/Download.html; PML_V2 ET product https://data.tpdc.ac.cn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c/; the latest digital Köppen-Geiger world map http://koeppen-geiger.vu-wien.ac.at/present.htm; global map of planting years https://figshare.com/articles/dataset/A_global_map_of_planting_years_of_plantations/19070084/1; forest cover change data of Canada https://opendata.nfis.org/mapserver/nfis-change_eng.html; forest cover change data of northern Europe https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/change-maps; forest cover change data of eastern Europe https://glad.geog.umd.edu/dataset/eastern-europe-forset-cover-dynamics-1985-2012/; forest cover change data of the United States https://www.mrlc.gov/data; and forest cover change data of the whole tropical region https://forobs.jrc.ec.europa.eu/TMF/.

Code availability

The code used for this analysis is available in a Zenodo repository at https://doi.org/10.5281/zenodo.8088598 (ref. 95).

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Acknowledgements

We thank N. Coops from the University of British Columbia and S. Bartalev from the Russian Academy of Sciences for providing the regional tree cover datasets and editing the paper. This study was supported by the National Natural Science Foundation of China (grant nos. 41971275, 31971458 and U21A6001).

Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China

    Yongxian Su, Chaoqun Zhang, Liyang Liu, Jiashun Ren & Xueqin Yang

  2. Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Ecology, School of Atmospheric Sciences, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

    Yongxian Su, Chaoqun Zhang, Wenping Yuan, Xiaoping Liu, Liyang Liu, Jiashun Ren, Xueqin Yang & Xiuzhi Chen

  3. Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif sur Yvette, France

    Philippe Ciais & Liyang Liu

  4. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Zhenzhong Zeng

  5. European Commission, Joint Research Centre, Ispra, Italy

    Alessandro Cescatti

  6. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada

    Jiali Shang

  7. Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada

    Jing Ming Chen & Jane Liu

  8. CSIRO Environment, Clayton South, Victoria, Australia

    Ying-Ping Wang

  9. College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shushi Peng

  10. School of the Environment, Yale University, New Haven, CT, USA

    Xuhui Lee

  11. School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China

    Zaichun Zhu

  12. Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen, China

    Zaichun Zhu

  13. School of Geographical Sciences, Southwest University, Chongqing, China

    Lei Fan

  14. Department of Soil, Plant and Food Sciences, University of Bari “Aldo Moro”, Bari, Italy

    Raffaele Lafortezza

  15. Beijing Forestry University, Beijing, China

    Raffaele Lafortezza

  16. State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing, China

    Yan Li

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  1. Yongxian Su
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Y.S. and X.C. designed the study and wrote the initial manuscript. Y.S. and C.Z. collected the data and performed the analysis. P.C., Z. Zeng, A.C., J.S., J.M.C., J.L., Y.-P.W., W.Y., S.P., X. Lee, Z. Zhu and Y.L. contributed to discuss the scientific question and revise the manuscript. All authors reviewed and approved the manuscript.

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Correspondence to Xiuzhi Chen.

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Supplementary Methods, Figs. 1–28 and Tables 1 and 2.

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Su, Y., Zhang, C., Ciais, P. et al. Asymmetric influence of forest cover gain and loss on land surface temperature. Nat. Clim. Chang. 13, 823–831 (2023). https://doi.org/10.1038/s41558-023-01757-7

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