Earth greening mitigates hot temperature extremes despite the effect being dampened by rising CO2
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
The escalating threat of climate-induced hot temperature extremes poses a global sustainability challenge that impacts ecosystems and public health. Although the enhancement of leaf area index (LAI; also known as Earth greening) is known to cool global mean air temperature, knowledge gaps exist regarding its mitigation effect on hot temperature extremes, particularly under rising CO2 during the past three decades. Our study, combining coupled land-atmosphere climate model (IPSL-CM) simulations with global observations, suggests that Earth greening has reduced the hot day frequency index (TX90p) and warm night frequency index (TN90p) by −0.26 ± 0.10 days decade−1 and −0.38 ± 0.11 days decade−1, respectively, offsetting 4.7% and 5.8% of observed trends globally. However, rising CO2 levels partly diminished these mitigation effects, without which Earth greening might have offset 7.7% of TX90p and 10.0% of TN90p. Our findings illuminate Earth greening's potential to mitigate hot temperature extremes, offering a pathway toward more resilient and sustainable climate adaptation and mitigation.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | One Earth |
| Vol/bind | 7 |
| Udgave nummer | 1 |
| Sider (fra-til) | 100-109 |
| ISSN | 2590-3330 |
| DOI | |
| Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:
This study was supported by the National Natural Science Foundation of China (grant 42071022 ), the start-up fund provided by Southern University of Science and Technology ( 29/Y01296122 ), Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control (No. 2023B1212060002), and Guangdong·Basic·and Applied· Basic Research Foundation (No.2022A1515240070). We thank the National Computer Center, France for providing computing resources. We thank OpenAI’s DALL-E, a deep learning model for image creation, for creating the background image of the graphical abstract.
Funding Information:
This study was supported by the National Natural Science Foundation of China (grant 42071022), the start-up fund provided by Southern University of Science and Technology (29/Y01296122), Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control (No. 2023B1212060002), and Guangdong·Basic·and Applied· Basic Research Foundation (No.2022A1515240070). We thank the National Computer Center, France for providing computing resources. We thank OpenAI's DALL-E, a deep learning model for image creation, for creating the background image of the graphical abstract. Z.Z. L.Z.X.L. and S.P. designed the research. L.L. performed numerical simulations. Y.F. and J.W. performed the analysis. J.W. wrote the draft. All authors contributed to the interpretation of the results and the writing of the paper. The authors declare no competing interests.
Publisher Copyright:
© 2023 Elsevier Inc.
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