Upstream Large-Scale Control of Subtropical Low-Cloud Climatology

Abstract

Abstract This study investigates the impact of the adjustment times of the atmospheric boundary layer (ABL) on the control of low-cloud coverage (LCC) climatology by large-scale atmospheric conditions in the subtropics. Using monthly data, we calculate back-trajectories and use machine learning statistical models with feature selection capabilities to determine the influence of local and upstream large-scale conditions on LCC for four physical cloud regimes: the stratocumulus (Sc) deck, the along-flow transition into the Sc deck (“Inflow”), the Sc-to-cumulus transition, and trade-cumulus clouds. All four regimes have unique local and upstream relationships with the large-scale meteorological variables within our parameter space, with upstream controls of LCC being the dominant processes in Sc deck and Sc-to-cumulus transition regimes. The time scales associated with these upstream controls across all regimes are consistent with known adjustment time scales of the ABL, determined in both modeling and observational studies. We find that low-level thermodynamic stratification (estimated inversion strength) is not the most important large-scale variable for LCC prediction in transition and trade-cumulus regimes despite its ubiquitous use as a proxy for LCC throughout the subtropics. Including upstream control provides significant improvements to the skill of statistical models predicting monthly LCC, increasing explained variance on the order of 15% in the Inflow, Sc deck, and transition regimes, but provides no improvement in the trade-cumulus regime.