Non-monotonic Dependencies of Cloud Microphysics and Precipitation on Aerosol Loading in Deep Convective Clouds

Abstract

Aerosol-cloud-precipitation interactions in deep convective clouds are investigated through numerical simulations of a heavy precipitation event that occurred over South Korea on 15–16 July 2017. The Weather Research and Forecasting model coupled with a bin microphysics scheme is used, and various aerosol number concentrations in the range N0 = 50–12800 cm-3 are considered. Precipitation amount as a function of aerosol loading shows a non-monotonic trend with the maximum peak occurring at a moderate level of aerosol loading (N0 = 800 cm-3), implying that changes in aerosol loading may trigger significant adjustments in cloud microphysical processes. Up to this optimal value of aerosol loading, an increase in the aerosol concentration results in a greater quantity of small droplets formed by nucleation, which are then brought above the freezing level, increasing the number of ice crystals. The supercooled drops and the ice crystals grow into snow particles through deposition and riming, and their melting enhances precipitation. Beyond the optimal value, on the other hand, a greater aerosol loading results in an enhanced generation of ice crystals while the overall growth of ice hydrometeors through deposition stagnates. Because of the smaller size of snow particles and supercooled drops, the riming between them is less efficient for the production of precipitating drops, leading to a slight suppression of precipitation. As for cloud microphysics-dynamics feedback, convection within the system is also non-monotonically reinforced with respect to an increase in aerosol loading. In contrast, cold pool becomes monotonically strengthened as aerosol loading increases.