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Evaluating climate models' cloud feedbacks against expert judgement
  • Mark D. Zelinka,
  • Stephen A. Klein,
  • Yi Qin
Mark D. Zelinka
Lawrence Livermore National Laboratory (DOE), Lawrence Livermore National Laboratory (DOE)

Corresponding Author:[email protected]

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Stephen A. Klein
Lawrence Livermore National Laboratory (DOE), Lawrence Livermore National Laboratory (DOE)
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Yi Qin
Lawrence Livermore National Laboratory, Lawrence Livermore National Laboratory
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The persistent and growing spread in effective climate sensitivity (ECS) across global climate models necessitates rigorous evaluation of their cloud feedbacks. Here we evaluate several cloud feedback components simulated in 19 climate models against benchmark values determined via an expert synthesis of observational, theoretical, and high-resolution modeling studies. We find that models with smallest feedback errors relative to these benchmark values have moderate total cloud feedbacks (0.4–0.6 Wm$^{-2}$K$^{-1}$) and generally moderate ECS (3–4 K). Those with largest errors generally have total cloud feedback and ECS values that are too large or too small. Models tend to achieve large positive total cloud feedbacks by having several cloud feedback components that are systematically biased high rather than by having a single anomalously large component, and vice versa. In general, better simulation of mean-state cloud properties leads to stronger but not necessarily better cloud feedbacks. The Python code base provided herein could be applied to developmental versions of models to assess cloud feedbacks and cloud errors and place them in the context of other models and of expert judgement in real-time during model development.
27 Jan 2022Published in Journal of Geophysical Research: Atmospheres volume 127 issue 2. 10.1029/2021JD035198