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The detection and attribution of extreme reductions in vegetation growth across the global land surface
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Resumo(s)
Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite-derived vegetation index data and supporting tree-ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi-arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region- and biome-dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non-dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate-specific disaster prevention and mitigation plans among different regions of the globe in a changing climate.
Descrição
Funding Information: This study was supported by the National Natural Science Foundation of China (grant number 41988101), the U.S. Department of Energy (grant number DE-SC0022074), and the German Federal Ministry of Economics and Technology (grant number 50EE1904). S.M.M. was supported by the USGS Ecosystems Mission Area. C.H. was supported by the NERC National Capability award to UKCEH. J.P. was supported by the grant TED2021-132627B-I00 funded by MCIN, AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. Any use of trade, product, or firm names in this paper is for descriptive purposes only and does not imply endorsement by the U.S. Government. Funding Information: This study was supported by the National Natural Science Foundation of China (grant number 41988101), the U.S. Department of Energy (grant number DE‐SC0022074), and the German Federal Ministry of Economics and Technology (grant number 50EE1904). S.M.M. was supported by the USGS Ecosystems Mission Area. C.H. was supported by the NERC National Capability award to UKCEH. J.P. was supported by the grant TED2021‐132627B‐I00 funded by MCIN, AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. Any use of trade, product, or firm names in this paper is for descriptive purposes only and does not imply endorsement by the U.S. Government. Publisher Copyright: © 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
Palavras-chave
climate extremes coincidence analysis drought flood frost heatwave vegetation growth anomaly Global and Planetary Change Environmental Chemistry Ecology General Environmental Science SDG 13 - Climate Action SDG 15 - Life on Land