Non-monotonic Dependencies of Cloud Microphysics and Precipitation on Aerosol Loading in Deep Convective Clouds: A Case Study Using the WRF-bin Model

Aerosol-cloud-precipitation interaction in deep convective clouds is investigated through numerical simulations of a heavy precipitation event that occurred over South Korea on 15–16 July 2017. For this, the Weather Research and Forecasting (WRF) model coupled with a bin microphysics scheme is used. The precipitation amount as a function of aerosol loading shows a non-monotonic trend with the peak occurring at a moderate level of aerosol loading, 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 number 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 the Wegener-Bergeron-Findeisen process and riming. The subsequent melting of the snow particles increases the surface precipitation amount. Beyond the optimal value, on the other hand, a greater aerosol loading coincides with an enhanced generation of ice crystals while the overall growth of ice hydrometeors through deposition stagnates. Because the overall size of the particles is small, the riming between the snow particles and the supercooled drops may be insufficient for the production of precipitating drops, leading to a slightly decreased surface precipitation amount. As for cloud microphysics-dynamics feedback, the convection within the system is also non-monotonically reinforced with respect to an increase in aerosol loading. In contrast, the cold pool which is mainly formed by evaporation of raindrops in the lower layer becomes monotonically strengthened in a higher aerosol concentration range.