Abstract
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.