Tightening of tropical ascent and high clouds key to precipitation change in a warmer climate

Hui Su (), Jonathan H. Jiang, J. David Neelin, T. Janice Shen, Chengxing Zhai, Qing Yue, Zhien Wang, Lei Huang, Yong-Sang Choi, Graeme L. Stephens and Yuk L. Yung
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Hui Su: Jet Propulsion Laboratory, California Institute of Technology
Jonathan H. Jiang: Jet Propulsion Laboratory, California Institute of Technology
J. David Neelin: University of California, Los Angeles
T. Janice Shen: Jet Propulsion Laboratory, California Institute of Technology
Chengxing Zhai: Jet Propulsion Laboratory, California Institute of Technology
Qing Yue: Jet Propulsion Laboratory, California Institute of Technology
Zhien Wang: University of Wyoming
Lei Huang: Jet Propulsion Laboratory, California Institute of Technology
Yong-Sang Choi: Ewha Womans University
Graeme L. Stephens: Jet Propulsion Laboratory, California Institute of Technology
Yuk L. Yung: California Institute of Technology

Nature Communications, 2017, vol. 8, issue 1, 1-9

Abstract: Abstract The change of global-mean precipitation under global warming and interannual variability is predominantly controlled by the change of atmospheric longwave radiative cooling. Here we show that tightening of the ascending branch of the Hadley Circulation coupled with a decrease in tropical high cloud fraction is key in modulating precipitation response to surface warming. The magnitude of high cloud shrinkage is a primary contributor to the intermodel spread in the changes of tropical-mean outgoing longwave radiation (OLR) and global-mean precipitation per unit surface warming (dP/dTs) for both interannual variability and global warming. Compared to observations, most Coupled Model Inter-comparison Project Phase 5 models underestimate the rates of interannual tropical-mean dOLR/dTs and global-mean dP/dTs, consistent with the muted tropical high cloud shrinkage. We find that the five models that agree with the observation-based interannual dP/dTs all predict dP/dTs under global warming higher than the ensemble mean dP/dTs from the ∼20 models analysed in this study.

Date: 2017
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DOI: 10.1038/ncomms15771

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