Abstract

Vegetation change can alter surface energy balance and subsequently affect the local climate. This biophysical impact has been well studied for forestation cases, but the sign and magnitude for persistent earth greening remain controversial. Based on long-term remote sensing observations, we quantify the unidirectional impact of vegetation greening on radiometric surface temperature over 2001–2018. Here, we show a global negative temperature response with large spatial and seasonal variability. Snow cover, vegetation greenness, and shortwave radiation are the major driving factors of the temperature sensitivity by regulating the relative dominance of radiative and non-radiative processes. Combined with the observed greening trend, we find a global cooling of −0.018 K/decade, which slows down 4.6 ± 3.2% of the global warming. Regionally, this cooling effect can offset 39.4 ± 13.9% and 19.0 ± 8.2% of the corresponding warming in India and China. These results highlight the necessity of considering this vegetation-related biophysical climate effect when informing local climate adaptation strategies.

Using satellite observations over the recent two decades, the authors quantify the biophysical impact of earth greening on land surface temperature and show a considerable cooling effect in India and China, important for climate mitigation.

Details

Title
Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming
Author
Li, Yitao 1 ; Li, Zhao-Liang 2   VIAFID ORCID Logo  ; Wu, Hua 1   VIAFID ORCID Logo  ; Zhou, Chenghu 3   VIAFID ORCID Logo  ; Liu, Xiangyang 2 ; Leng, Pei 2 ; Yang, Peng 2 ; Wu, Wenbin 2 ; Tang, Ronglin 4 ; Shang, Guo-Fei 5 ; Ma, Lingling 6 

 Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419) 
 Chinese Academy of Agricultural Sciences, Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Beijing, China (GRID:grid.410727.7) (ISNI:0000 0001 0526 1937) 
 Guangdong Academy of Sciences, Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou Institute of Geography, Guangzhou, China (GRID:grid.464309.c) (ISNI:0000 0004 6431 5677) 
 Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
 Hebei GEO University, School of Land Science and Space Planning, Shijiazhuang, China (GRID:grid.443566.6) (ISNI:0000 0000 9730 5695) 
 Chinese Academy of Sciences, Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
Pages
121
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2762558137
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.