Reducing the uncertainty in subtropical cloud feedback

Abstract:

Boundary layer clouds over the subtropical oceans are a key aspect of climate, but large uncertainties remain on how they will adjust to anthropogenic climate change and whether they will act to amplify or diminish global warming. Here, a statistical method is proposed to explain the subtropical cloud feedback in abrupt 4xCO2 experiments of 19 global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). First principles indicate that boundary layer cloudiness is largely controlled by sea surface temperature, the strength of the temperature inversion capping the clouds, horizontal surface temperature advection, free-tropospheric moisture, and subsidence. This motivates us to employ multi-linear regression to explain the subtropical cloud feedback in the CMIP5 ensemble using these parameters as predictor variables. We find that this linear framework is a valid approach to explain the shortwave cloud radiative effect feedback in models that realistically simulate interannual relationships between subtropical clouds and meteorology. These models produce a positive subtropical cloud feedback. Models that poorly simulate such relationships produce negative cloud feedbacks that are unlikely to occur in nature. Based on the observed cloud-meteorology relationships from two satellite cloud datasets and four atmospheric reanalyses, we conclude that the subtropical cloud feedback will likely be weakly positive due to an increase in sea-surface temperature in the perturbed climate, partially offset by a negative feedback that arises due to an increase in estimated inversion strength.