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Evaluating climate models' cloud feedbacks against expert judgement

Presentation Date
Wednesday, December 15, 2021 at 4:00pm
Online Only



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 generally have moderate total cloud feedbacks (0.4 – 0.6 Wm-2K-1) and 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 evaluation methodology developed 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.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. It is supported by the Regional and Global Model Analysis Program of the Office of Science at the DOE. IM Release #LLNL-ABS-825134.

Funding Program Area(s)