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Cloud feedback mechanisms and their representation in global climate models [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Science, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
Article numbers e465 : digital, PDF file
Additional Creators
Lawrence Livermore National Laboratory, United States. Department of Energy. Office of Science, National Aeronautics and Space Administration Announcement, Belgium. Office of the European Research Council Executive Agency (ERCEA), and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Cloud feedback—the change in top-of-atmosphere radiative flux resulting from the cloud response to warming—constitutes by far the largest source of uncertainty in the climate response to CO2 forcing simulated by global climate models (GCMs). In this paper, 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. Finally, 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.
Report Numbers
E 1.99:llnl-jrnl--707398
llnl-jrnl--707398
Subject(s)
Note
Published through SciTech Connect.
05/11/2017.
"llnl-jrnl--707398"
Wiley Interdisciplinary Reviews: Climate Change 8 4 ISSN 1757-7780 AM
Paulo Ceppi; Florent Brient; Mark D. Zelinka; Dennis L. Hartmann.
Funding Information
AC52-07NA27344
SC0012580
NNH14AX83I
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