2017
DOI: 10.1002/wcc.465
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Cloud feedback mechanisms and their representation in global climate models

Abstract: 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). 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)… Show more

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Cited by 234 publications
(255 citation statements)
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References 183 publications
(443 reference statements)
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“…Despite differences in the baseline mean state cloud, for their warming response (Figures b, d, and f), UP, and both SP configurations show robust poleward and upward shifts of clouds, consistent with most CMIP5 models (Boucher et al, ); this can be interpreted as an effect of mixed phase clouds (Ceppi et al, ; Tsushima et al, ). However, microphysical tuning common to UP and SP2 removed a significant portion of baseline low cloud liquid content, especially at high latitudes.…”
Section: Response Of Clouds To a +4k Sst Warmingsupporting
confidence: 72%
“…Despite differences in the baseline mean state cloud, for their warming response (Figures b, d, and f), UP, and both SP configurations show robust poleward and upward shifts of clouds, consistent with most CMIP5 models (Boucher et al, ); this can be interpreted as an effect of mixed phase clouds (Ceppi et al, ; Tsushima et al, ). However, microphysical tuning common to UP and SP2 removed a significant portion of baseline low cloud liquid content, especially at high latitudes.…”
Section: Response Of Clouds To a +4k Sst Warmingsupporting
confidence: 72%
“…SST increases (Figure a) result in increasing clouds at the highest levels, for all optical thicknesses and a general pattern of decrease below this, particularly strong for very thin clouds at all levels and low level thin and very low level moderately thick clouds. This pattern is overall consistent with those from a range of atmospheric models coupled to mixed layer oceans under a doubling of CO 2 examined by Zelinka et al () and for CMIP5 models (Ceppi et al, ; Zelinka et al, ). Total reduction in cloud cover (i.e., the cloud cover inferred from the TOA under ISCCP assumptions) is approximately −0.5%/K, and the inferred λ is −1.28 W/m 2 /K.…”
Section: Resultssupporting
confidence: 89%
“…The GCM in this study cannot reproduce these boundary layer processes that are more characteristic of the real atmosphere. Furthermore, moisture effects were neglected, and the associated latent heat release and cloud feedbacks are likely to alter the precise sensitivity of the equilibration (e.g., Hoskins and Valdes 1990;Voigt and Shaw 2015;Ceppi et al 2017). It would therefore be insightful to repeat the above analysis in a more realistic coupled model.…”
Section: Discussionmentioning
confidence: 99%