Abstract

Increasing atmospheric moisture content is expected to intensify precipitation extremes under climate warming. However, extreme precipitation sensitivity (EPS) to temperature is complicated by the presence of reduced or hook-shaped scaling, and the underlying physical mechanisms remain unclear. Here, by using atmospheric reanalysis and climate model projections, we propose a physical decomposition of EPS into thermodynamic and dynamic components (i.e., the effects of atmospheric moisture and vertical ascent velocity) at a global scale in both historical and future climates. Unlike previous expectations, we find that thermodynamics do not always contribute to precipitation intensification, with the lapse rate effect and the pressure component partly offsetting positive EPS. Large anomalies in future EPS projections (with lower and upper quartiles of −1.9%/°C and 8.0%/°C) are caused by changes in updraft strength (i.e., the dynamic component), with a contrast of positive anomalies over oceans and negative anomalies over land areas. These findings reveal counteracting effects of atmospheric thermodynamics and dynamics on EPS, and underscore the importance of understanding precipitation extremes by decomposing thermodynamic effects into more detailed terms.

This study attributes extreme precipitation scaling into thermodynamic versus dynamic components and further decomposes the thermodynamic effects into more detailed terms to reveal the physics of extreme precipitation under climate warming.

Details

Title
Large anomalies in future extreme precipitation sensitivity driven by atmospheric dynamics
Author
Gu, Lei 1 ; Yin, Jiabo 2   VIAFID ORCID Logo  ; Gentine, Pierre 3   VIAFID ORCID Logo  ; Wang, Hui-Min 4   VIAFID ORCID Logo  ; Slater, Louise J. 5   VIAFID ORCID Logo  ; Sullivan, Sylvia C. 6   VIAFID ORCID Logo  ; Chen, Jie 2 ; Zscheischler, Jakob 7   VIAFID ORCID Logo  ; Guo, Shenglian 2 

 Wuhan University, State Key Laboratory of Water Resources Engineering and Management, Wuhan, P.R. China (GRID:grid.49470.3e) (ISNI:0000 0001 2331 6153); Huazhong University of Science and Technology, Hubei Key Laboratory of Digital River Basin Science and Technology, Wuhan, China (GRID:grid.33199.31) (ISNI:0000 0004 0368 7223) 
 Wuhan University, State Key Laboratory of Water Resources Engineering and Management, Wuhan, P.R. China (GRID:grid.49470.3e) (ISNI:0000 0001 2331 6153) 
 Columbia University, Department of Earth and Environmental Engineering, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729); Columbia University, Climate School, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729) 
 National University of Singapore, Department of Civil and Environmental Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431) 
 University of Oxford, School of Geography and the Environment, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948) 
 University of Arizona, Department of Chemical & Environmental Engineering, Tucson, USA (GRID:grid.134563.6) (ISNI:0000 0001 2168 186X) 
 Helmholtz Centre for Environmental Research, Department of Computational Hydrosystems, Leipzig, Germany (GRID:grid.7492.8) (ISNI:0000 0004 0492 3830) 
Pages
3197
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2821761674
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.