Possible evidence of increased global cloudiness due to aerosol-cloud interactions

Abstract. Aerosol-cloud interactions remain a large source of uncertainty in global climate models due to uncertainty in how pre-industrial clouds, aerosols, and the environment behaved. We employ three machine learning models, a random forest, a stochastic gradient boosting, and an extreme gradient boosting regressor to derive a pre-industrial proxy for warm cloudiness predicted using only their environmental controls. We train our models on boundary layer stability, relative humidity of the free atmosphere, upper level vertical motion, and sea surface temperature to predict a simulated, pristine cloud fraction as a one-for-one proxy for a pre-industrial warm cloud fraction. Using a multivariate linear regression as a proxy for sensitivity studies, we show that the non-linear signatures derived using the simple machine learning models are pivotal in deriving an accurate estimate. We find that aerosols may have increased global cloudiness by 1.27 % since pre-industrial times, leading to −0.42 (0.39–0.46 at 95 % confidence intervals) of cooling. Our methodology reduces the covariability between aerosol, the environment, and cloud adjustments by aiming only to estimate an initial, unperturbed state of the cloud based on the environment alone.