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Data and code for: A century of reforestation reduced anthropogenic warming in the eastern United States

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Figshare2024-01-24 更新2026-04-28 收录
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https://figshare.com/articles/dataset/Data_and_code_for_A_century_of_reforestation_reduced_anthropogenic_warming_in_the_eastern_United_States/29937149
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Restoring and preserving the world’s forests are promising natural pathways to mitigate some aspects of climate change. In addition to regulating atmospheric carbon dioxide concentrations, forests modify surface and near-surface air temperatures through biophysical processes. In the eastern United States (EUS), widespread reforestation during the 20th century coincided with an anomalous lack of warming, raising questions about reforestation’s contribution to local cooling and climate mitigation. Using new cross-scale approaches and multiple independent sources of data, we uncovered links between reforestation and the response of both surface and air temperature in the EUS. Ground- and satellite-based observations showed that EUS forests cool the land surface by 1–2 °C annually compared to nearby grasslands and croplands, with the strongest cooling effect during midday in the growing season, when cooling is 2 to 5 °C. Young forests (20–40 years) have the strongest cooling effect on surface temperature. Surface cooling extends to the near-surface air, with forests reducing midday air temperature by up to 1 °C compared to nearby non-forests. Analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations surrounded by reforestation were up to 1 °C cooler than neighboring locations that did not undergo land cover change, and areas dominated by regrowing forests were associated with cooling temperature trends in much of the EUS. Our work indicates reforestation contributed to the historically slow pace of warming in the EUS, underscoring reforestation’s potential as a local climate adaptation strategy in temperate regions.All the data can be processed using open-source programs such as R or Python.
创建时间:
2024-01-24
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