Implications of a Pervasive Climate Model Bias for Low‐Cloud Feedback

How low clouds respond to warming constitutes a key uncertainty for climate projections. Here we observationally constrain low-cloud feedback through a controlling factor analysis based on ridge regression. We find a moderately positive global low-cloud feedback (0.45 W m$^{-2}$K$^{-1}$, 90% range 0.18–0.72 W m$^{-2}$K$^{-1}$), about twice the mean value (0.22 W m$^{-2}$K$^{-1}$) of 16 models from the Coupled Model Intercomparison Project. We link this discrepancy to a pervasive model mean-state bias: models underestimate the low-cloud response to warming because (a) they systematically underestimate present-day tropical marine low-cloud amount, and (b) the low-cloud sensitivity to warming is proportional to this present-day low-cloud amount. Our results hence highlight the importance of reducing model biases in both the mean state of clouds and their sensitivity to environmental factors for accurate climate change projections.