Abstract
It has recently been shown that the slow adjustment of the atmospheric boundary layer (ABL) to perturbations of the large-scale atmospheric conditions translates into an upstream control of the low-cloud cover (LCC) variability at climatological time scales in the subtropics. In this study, we expand upon this recent study to investigate upstream control of the climatology of low-cloud-radiative effect (CRE), as well as cloud properties relevant to their radiative effect: cloud liquid water path (LWP) and cloud optical depth (COD). We use machine learning statistical models with feature selection capabilities (random forests) to determine the influence of the local and upstream large-scale conditions in monthly data. These conditions are determined using backtrajectories in monthly mean wind fields. Total CRE, dominated by the shortwave contribution, exhibits a dependence on upstream estimated inversion strength (EIS), but not on upstream sea surface temperature (SST) as LCC does. Upstream control of COD was present but was not as consistent as LCC or CRE across different regions and cloud regimes, while LWP does not exhibit strong upstream control at all. This implies that the control of other boundary layer properties could explain the differences in response between LCC and CRE to SST and EIS. Looking at five subtropical eastern basins, it appears that key lead times are region specific. The inclusion of upstream control provides significant improvements to the predictive skill of statistical models over local linear regression, with improvements in variance explained well over 20% in many cases.
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