全球变化知识资源中心

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 lambda 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 lambda(GG) from regressing the global-mean cloud-induced TOA radiation anomaly Delta R-G with the global-mean surface temperature change Delta T-GS; 2) the local feedback parameter lambda(LL) from regressing the local Delta R with the local surface temperature change Delta T-S; and 3) the local feedback parameter lambda(GL) from regressing global Delta R-G with local Delta T-S. Observations show significant temporal variability in the magnitudes and spatial patterns in lambda(GG) and lambda(GL), whereas lambda(LL) remains essentially time invariant for different cloud types. The global-mean net lambda(GG) exhibits a gradual transition from negative to positive in the A-Train era due to a less negative lambda(GG) from low clouds and an increased positive lambda(GG) from high clouds over the warm pool region associated with the 2015/16 strong El Nino event. Strong temporal variability in lambda(GL) is intrinsically linked to its dependence on global Delta R-G, and the scaling of lambda(GL) with surface temperature change patterns to obtain global feedback lambda(GG) 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.