Open AccessAnalyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a G reen's function approach
Author(s) -
Zhou Chen,
Zelinka Mark D.,
Klein Stephen A.
Publication year - 2017
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1002/2017ms001096
Subject(s) - cloud feedback , cloud cover , environmental science , global warming , cloud forcing , cloud computing , climatology , atmospheric sciences , longwave , sea surface temperature , shortwave , liquid water content , cloud height , climate change , climate sensitivity , climate model , geology , radiative transfer , computer science , physics , operating system , oceanography , quantum mechanics
The spatial pattern of sea surface temperature (SST) changes has a large impact on the magnitude of cloud feedback. In this study, we seek a basic understanding of the dependence of cloud feedback on the spatial pattern of warming. Idealized experiments are carried out with an AGCM to calculate the change in global mean cloud‐induced radiation anomalies (Δ R cloud ) in response to imposed surface warming/cooling in 74 individual localized oceanic “patches”. Then the cloud feedback in response to a specific warming pattern can be approximated as the superposition of global cloud feedback in response to a temperature change in each region, weighted by the magnitude of the local temperature changes. When there is a warming in the tropical subsidence or extratropical regions, the local decrease of LCC results in a positive change in R cloud . Conversely, warming in tropical ascent regions increases the free‐tropospheric temperature throughout the tropics, thereby enhancing the inversion strength over remote regions and inducing positive global low‐cloud cover (LCC) anomalies and negative R cloud anomalies. The Green's function approach performs reasonably well in predicting the response of global mean ΔLCC and net Δ R cloud , but poorly for shortwave and longwave components of Δ R cloud due to its ineffectiveness in predicting middle and high cloud cover changes. The approach successfully captures the change of cloud feedback in response to time‐evolving CO 2 ‐induced warming and captures the interannual variations in Δ R cloud observed by CERES. The results highlight important nonlocal influences of SST changes on cloud feedback.