Cloud feedback mechanisms and their representation in global climate models

被引:211
作者
Ceppi, Paulo [1 ]
Brient, Florent [2 ]
Zelinka, Mark D. [3 ]
Hartmann, Dennis L. [4 ]
机构
[1] Univ Reading, Dept Meteorol, Reading, Berks, England
[2] Meteo France, Ctr Natl Rech Meteorol, CNRS, Toulouse, France
[3] Lawrence Livermore Natl Lab, Cloud Proc Res Grp, Livermore, CA USA
[4] Univ Washington, Dept Atmospher Sci, Seattle, WA 98195 USA
基金
美国国家科学基金会;
关键词
GENERAL-CIRCULATION MODEL; SEA-SURFACE TEMPERATURE; OPTICAL DEPTH FEEDBACK; LARGE-EDDY SIMULATION; 1998; EL-NINO; SELF-AGGREGATION; TROPOSPHERIC ADJUSTMENT; STATISTICAL-ANALYSES; THERMAL-EQUILIBRIUM; PHYSICAL-MECHANISMS;
D O I
10.1002/wcc.465
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Cloud feedbackthe change in top-of-atmosphere radiative flux resulting from the cloud response to warmingconstitutes by far the largest source of uncertainty in the climate response to CO2 forcing simulated by global climate models (GCMs). We review the main mechanisms for cloud feedbacks, and discuss their representation in climate models and the sources of intermodel spread. Global-mean cloud feedback in GCMs results from three main effects: (1) rising free-tropospheric clouds (a positive longwave effect); (2) decreasing tropical low cloud amount (a positive shortwave [SW] effect); (3) increasing high-latitude low cloud optical depth (a negative SW effect). These cloud responses simulated by GCMs are qualitatively supported by theory, high-resolution modeling, and observations. Rising high clouds are consistent with the fixed anvil temperature (FAT) hypothesis, whereby enhanced upper-tropospheric radiative cooling causes anvil cloud tops to remain at a nearly fixed temperature as the atmosphere warms. Tropical low cloud amount decreases are driven by a delicate balance between the effects of vertical turbulent fluxes, radiative cooling, large-scale subsidence, and lower-tropospheric stability on the boundary-layer moisture budget. High-latitude low cloud optical depth increases are dominated by phase changes in mixed-phase clouds. The causes of intermodel spread in cloud feedback are discussed, focusing particularly on the role of unresolved parameterized processes such as cloud microphysics, turbulence, and convection. (C) 2017 Wiley Periodicals, Inc.
引用
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页数:21
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