Temporal and Spatial Characteristics of Short-Term Cloud Feedback on Global and...

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Yue, Q., B. Kahn, E. J. Fetzer, S. Wong, X. Huang, and M. M. Schreier (2019), Temporal and Spatial Characteristics of Short-Term Cloud Feedback on Global and Local Interannual Climate Fluctuations from A-Train Observations, J. Climate, 32, 1875-1893, doi:10.1175/JCLI-D-18-0335.1.
Abstract: 

Observations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks l for different cloud types, with respect to the interannual variability within the A-Train era (July 2002–June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter lGG from regressing the global-mean cloud-induced TOA radiation anomaly DRG with the global-mean surface temperature change DTGS; 2) the local feedback parameter lLL from regressing the local DR with the local surface temperature change DTS; and 3) the local feedback parameter lGL from regressing global DRG with local DTS. Observations show significant temporal variability in the magnitudes and spatial patterns in lGG and lGL, whereas lLL remains essentially time invariant for different cloud types. The global-mean net lGG exhibits a gradual transition from negative to positive in the A-Train era due to a less negative lGG from low clouds and an increased positive lGG from high clouds over the warm pool region associated with the 2015/16 strong El Niño event. Strong temporal variability in lGL is intrinsically linked to its dependence on global DRG, and the scaling of lGL with surface temperature change patterns to obtain global feedback lGG does not hold. Despite the shortness of the A-Train record, statistically robust signals can be obtained for different cloud types and regions of interest.

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