Temporal and Spatial Characteristics of Short-Term Cloud Feedback on Global and Local Interannual Climate Fluctuations from A-Train Observations

• Published in: Journal of climate Vol. 32; no. 6; pp. 1875 - 1893
• Main Authors: Yue, Qing, Kahn, Brian H., Fetzer, Eric J., Wong, Sun, Huang, Xianglei, Schreier, Mathias
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 15-03-2019
• Subjects: Climate change; Cloud types; Clouds; El Nino; El Nino events; El Nino phenomena; Estimates; Feedback; Greenhouse effect; Interannual variability; Local climates; Low clouds; Parameters; Radiation; Scaling; Short term; Spatial variability; Spatial variations; Studies; Surface temperature; Temperature changes; Temporal variability; Temporal variations
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI-D-18-0335.1

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