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Evaluating Climate Models' Cloud Feedbacks Against Export Judgment
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DOI：10.1029/2021JD035198.png)
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Abstract
En 中文
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 W m(-2) K-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 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 judgment in real-time during model development. Plain Language Summary Climate models strongly disagree with each other regarding how much warming will occur in response to increased greenhouse gases in the atmosphere. This is mainly because they disagree on the response of clouds to warming-a process known as the cloud feedback that can amplify or dampen warming initially caused by carbon dioxide. In this study, we compare many models' cloud feedbacks to those that have been determined by a recent expert assessment of the literature. We find that the models whose cloud feedbacks most strongly disagree with expert assessment tend to have more extreme cloud feedbacks and hence warm too much or too little in response to carbon dioxide. The models with total cloud feedbacks that are too large do not have a single massive feedback component but rather several components that are larger than in other models. Models that simulate current-climate clouds that look more like those in nature also simulate stronger amplifying cloud feedbacks, but doing a better job at simulating current-climate clouds does not, in general, guarantee a better simulation of cloud feedbacks.
Keywords:
TROPICAL ANVIL CLOUDS
EARTH SYSTEM MODEL
COUPLED MODEL
SENSITIVITY
VARIABILITY
SIMULATION
SPREAD
ECMWF
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