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Local surface cooling from afforestation amplified by lower aerosol pollution

Nature Geoscience volume 16, pages 781–788 (2023)Cite this article

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Abstract

Afforestation can play a key role in local climate mitigation by influencing local temperature through changes in land surface properties. Afforestation impacts depend strongly on the background climate, with contrasting effects observed across geographical locations, seasons and levels of greenhouse gas-induced warming. Meanwhile, atmospheric aerosols, which are a critical factor influencing regional climate, have varied substantially in recent decades and will continue to change. However, the impacts of aerosol changes on the local effects of afforestation remain unknown. Here, using multiple emissions scenario-based simulations, we show that lower anthropogenic emissions can modulate the local effects of afforestation through modifications in the surface energy balance. If current anthropogenic emissions are reduced to preindustrial levels, afforestation can produce additional cooling effects of up to 0.4 °C. The cooling effects of afforestation are projected to be most strongly affected in China if strict control measures on air pollution are adopted in the future. Our results demonstrate that the enhanced cooling effects of afforestation could partially counteract the warming effect of air quality control, with implications for countries that face the dual challenges of clean air and climate mitigation.

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Fig. 1: Impacts of idealized emissions reductions on the local effects of afforestation.
Fig. 2: Impacts of idealized emissions reductions on local responses of the surface energy balance to afforestation in eastern China.
Fig. 3: Impacts of future emissions reductions on the local effects of afforestation.
Fig. 4: The mechanisms for the impacts of emissions reductions on the local effects of afforestation.

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Data availability

The historical simulations of the Coupled Model Intercomparison Project Phase 6, the hist-piNTCF, hist-piAer, ssp370-lowNTCF, piClim-control and piClim-2xVOC simulations of the Aerosol Chemistry Model Intercomparison Project and the ssp370 simulation of the Scenario Model Intercomparison Project are available at https://esgf-node.llnl.gov/search/cmip6/. The dataset mapping the biophysical effects of vegetation cover changes is available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/ECOCLIM/Biophysical-effects-vgt-change/v2.0/. The observation-based estimate of the biogeochemical effects of afforestation is available at https://doi.org/10.5281/zenodo.5184884. The reprocessed Moderate-Resolution Imaging Spectroradiometer (MODIS) version 6 leaf area index dataset is available at http://globalchange.bnu.edu.cn/research/laiv6. The Earth’s Radiant Energy System (CERES) dataset for surface all-sky downward short-wave radiation is available at https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAF42Selection.jsp. The Climate Research Unit high-resolution gridded dataset version 4.06 for 2 m temperature and precipitation is available at https://crudata.uea.ac.uk/cru/data/hrg/. The MODIS collection 6 land-cover product MCD12C1 is available at https://lpdaac.usgs.gov/products/mcd12c1v006/.

Code availability

The processing MATLAB codes are available at https://doi.org/10.6084/m9.figshare.23528964.

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Acknowledgements

This research is supported by the Natural Science Foundation of China (42130602 awarded to W.G., 42005096 awarded to J.G., 42105023 awarded to B.Z. and 42175136 awarded to B.Q.) and Jiangsu Collaborative Innovation Center for Climate Change. We thank L. Horowitz for providing details on the GFDL-ESM4 model participating in the CMIP6 experiments.

Author information

Authors and Affiliations

  1. School of Atmospheric Sciences, Nanjing University, Nanjing, China

    Jun Ge, Xin Huang, Bo Qiu, Yipeng Cao & Weidong Guo

  2. Joint International Research Laboratory of Atmospheric and Earth System Sciences (JirLATEST)), Nanjing University, Nanjing, China

    Jun Ge, Xin Huang, Bo Qiu & Weidong Guo

  3. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‑FEMD), Nanjing University of Information Science and Technology, Nanjing, China

    Beilei Zan

  4. School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, China

    Beilei Zan

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Contributions

J.G. conceived and designed the overall study. J.G. performed the data analysis with help from X.H., B.Q. and Y.C. in the interpretation of the results. J.G. drafted the manuscript. X.H., B.Z. and B.Q. edited the manuscript. J.G., X.H., B.Q. and W.G. discussed and revised the manuscript.

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Correspondence to Jun Ge.

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Extended data

Extended Data Fig. 1 Simulated and observed local surface temperature responses to afforestation.

a, b, 5-year (2008–2012) mean annual surface temperature responses to afforestation (δLST) from the historical simulation (a) and the satellite-based observations (b). c, d, The zonally averaged δLST for each month from the historical simulation (c) and the satellite-based observations (d).

Extended Data Fig. 2 Impacts of idealized aerosol reductions on the local effects of afforestation.

a, 30-year (1985–2014) mean differences in local surface temperature responses (δLST) to afforestation (Δ(δLST)) between the historical and hist-piAer simulations (hist-piAer minus historical). The imbedded figure shows the frequency of Δ(δLST) across all coloured grid cells. b, Grid cells are labelled ‘enhanced cooling’ (δLST < 0°C and Δ(δLST) < 0°C), ‘attenuated cooling’ (δLST < 0°C and Δ(δLST) > 0°C), ‘enhanced warming’ (δLST > 0°C and Δ(δLST) > 0°C) and ‘attenuated warming’ (δLST > 0°C and Δ(δLST) < 0°C) when Δ(δLST) is statistically significant (p < 0.05) tested by the two-sided Student’s t-test; otherwise, grid cells are labelled ‘insignificant change’. The imbedded figure shows the frequency of the five categories across all coloured grid cells. c, Same as a but for differences in the metric \({{CO}}_{2}^{{BGP}}\) (Δ\({{CO}}_{2}^{{BGP}}\)). The imbedded figure shows the frequency of \(\Delta{\mathrm{CO}}_{2}^{\mathrm{BGP}}\) across all coloured grid cells. d, The ratio of Δ\({{CO}}_{2}^{{BGP}}\) to the observation-based estimate of the biogeochemical effects of afforestation.

Extended Data Fig. 3 Impacts of idealized emission reductions on climate and the albedo response to afforestation in eastern China.

a, c, e, g, i, Surface downwards shortwave radiation (DSR; a), albedo responses to afforestation (δα; c), 2-meter temperature (T2; e), precipitation (Pr; g) and snow amount (Snow; i) during 1850–2014 from the historical (red line) and hist-piNTCF (blue line) simulations. The time series is processed by a nine-point moving mean. b, d, f, h, j, Monthly and annual mean values of DSR (b), δα (d), T2 (f), Pr (h) and Snow (j) averaged over 1985–2014 from the historical (red line) and hist-piNTCF (blue line) simulations. The shading denotes the standard deviation across the 30 years. Eastern China is denoted by the black rectangle in Fig. 1c.

Extended Data Fig. 4 Impacts of idealized emission reductions on responses of the surface energy balance and leaf area index to afforestation.

30-year (1985–2014) mean differences in responses of net shortwave radiation (a), sensible heat flux (b), latent heat flux (c) and leaf area index (d) to afforestation between the historical and hist-piNTCF simulations (hist-piNTCF minus historical).

Extended Data Fig. 5 Multimodel mean impacts of emission reductions on climate.

a, b, c, 30-year (1985–2014) mean differences in downwards shortwave radiation (ΔDSR; a), 2-meter temperature (ΔT2; b) and precipitation (ΔPr; c) between the historical and hist-piNTCF simulations (hist-piNTCF minus historical). d, e, f, 30-year (2026–2055) mean ΔDSR (d), ΔT2 (e) and ΔPr (f) between the ssp370 and ssp370-lowNTCF simulations (ssp370-lowNTCF minus ssp370). The black dots denote that eight out of the nine models agree on the sign of the change.

Extended Data Fig. 6 Responses of leaf area index (LAI) to emission reduction-induced climate change.

a,b, GFDL-ESM4 simulated responses of LAI on forests (a) and openlands (b) to the combined changes in surface downwards shortwave radiation, 2-meter temperature and precipitation as a result of idealized emission reductions. c, d, Satellite-observed responses of LAI on forests (c) and openlands (d) to the combined changes in surface downwards shortwave radiation, 2-meter temperature and precipitation as a result of idealized emission reductions. The observation-based results (c and d) are shown at a resolution of 1°×1° for better visualization. Owing to the possible coexistence of forest and openland pixels within a 1°×1° grid, some locations may appear in both the forest (c) and openland (d) maps.

Extended Data Fig. 7 Impacts of doubling biogenic volatile organic compound emissions on climate and the local effects of afforestation.

30-year mean differences in biogenic volatile organic compound emissions (a), surface downwards shortwave radiation (b), 2-m temperature (c), precipitation (d) and local surface temperature responses (δLST) to afforestation (Δ(δLST); e) between the piClim-control and piClim-2xVOC simulations (piClim-2xVOC minus piClim-control). f, Grid cells are labelled ‘enhanced cooling’ (δLST < 0°C and Δ(δLST) < 0°C), ‘attenuated cooling’ (δLST < 0°C and Δ(δLST) > 0°C), ‘enhanced warming’ (δLST > 0°C and Δ(δLST) > 0°C) and ‘attenuated warming’ (δLST > 0°C and Δ(δLST) < 0°C) when Δ(δLST) is statistically significant (p < 0.05) tested by the two-sided Student’s t-test; otherwise, grid cells are labelled ‘insignificant change’. The imbedded figure shows the frequency of the five categories across all coloured grid cells.

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Ge, J., Huang, X., Zan, B. et al. Local surface cooling from afforestation amplified by lower aerosol pollution. Nat. Geosci. 16, 781–788 (2023). https://doi.org/10.1038/s41561-023-01251-x

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