Comparison of Short‐Term Cloud Feedbacks at Top of the Atmosphere and the Surface in Observations and AMIP6 Models

Author:

Wang Xiaocong1ORCID,Miao Hao2,Feng Juan3ORCID,Liu Yimin1ORCID

Affiliation:

1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing China

2. Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric Sciences Nanjing China

3. Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing China

Abstract

AbstractWe compared short‐term cloud feedback, defined at the top of the atmosphere (TOA), the atmospheric column (ATM), and the surface (SFC), between observations and models participating in Atmospheric Model Intercomparison Project Phase 6 (AMIP6) for the period 2000–2014. The globally averaged net cloud feedbacks observed at TOA, ATM, and SFC are −0.06 ± 0.63, −0.17 ± 0.70, and 0.11 ± 0.81 W m−2 K−1, respectively. While most models produced TOA cloud feedbacks that agreed with the observations within uncertainty ranges, the intermodel spread at SFC and within ATM was relatively larger. This demonstrates that models are diverse in how their TOA feedback is distributed between ATM and SFC. Because short‐term cloud feedback is mainly driven by El Niño–Southern Oscillation (ENSO), the global‐mean cloud feedback was further decomposed into components from the ENSO and non‐ENSO regions. Results show that cloud feedback in these two regions tends to be inversely related. Compared to observations, almost all models overestimated the longwave cloud feedback in the ENSO region due to the overestimation of cloud amount changes for high‐topped clouds. For these models, it is the offset between deviations in ENSO and non‐ENSO regions that leads to the overall agreement of global mean with observations. Sensitivity tests show that the main conclusions still hold when alternative kernels are used in estimating cloud feedback.

Publisher

American Geophysical Union (AGU)

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