Implications of a Pervasive Climate Model Bias for Low‐Cloud Feedback
Scientists at Imperial College London, in collaboration with scientists at PCMDI and elsewhere, derived a new observationally-based estimate of how strongly changes in low-level cloud properties amplify global warming. They found a moderately strong amplifying feedback that is generally underestimated by global climate models. This underestimate is tied to the fact that models systematically lack sufficient low-level cloudiness in the mean state.
Reductions in low cloud amounts with warming are proportional to present-day cloud amounts. Hence the pervasive “too-few” bias leads to a muted sensitivity of clouds to warming in most models. To provide more accurate future climate projections, it is imperative to reduce model biases in both present-day cloud properties and their sensitivities to environmental factors.
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.