Quantifying the Relative Contributions of CCN and IN to Extreme Monsoon Rainfall

The Indian summer monsoon, marked by extreme rainfall events during June–September, often triggers severe natural hazards such as floods. Accurate prediction of heavy rainfall is crucial to minimizing loss of life and property damage. While the roles of large-scale circulation, water vapor, and topography in monsoon convection have been studied, aerosol-cloud interactions remain poorly understood in this context. Aerosols, acting as cloud condensation nuclei (CCN) and ice nuclei (IN), influence cloud microphysics, precipitation mechanisms, and the hydrological cycle, intensifying weather and climate variability. Mixed-phase clouds, sensitive to aerosol effects, play a key role in regulating the Earth's radiation budget but are challenging to model due to complex processes like ice nucleation and particle growth. However, how uncertainty in aerosol data contributes to errors in quantitative precipitation forecasts (QPF) has yet to be thoroughly investigated. Understanding these interactions is critical for unraveling monsoon rainfall variability and enhancing forecast accuracy. To address this gap, this study uses the Weather Research and Forecasting (WRF) model with a triple-moment microphysics scheme to assess aerosol-cloud interactions in Indian summer monsoon precipitation. High-resolution simulations of monsoon depression events are performed under clean continental and urban (polluted) aerosol conditions, with model results compared to observations. Results of sensitivity simulations show that the microphysics can capture the observed rainfall pattern. However, in model performance it differs due to variations in mixing ratios of microphysics categories and associated dynamic and thermodynamic parameters. Polluted conditions significantly enhance extreme precipitation and updraft intensities, driven by increased ice-phase processes and larger snow and graupel hydrometeor sizes. These findings emphasize the pivotal role of aerosol concentrations in modulating extreme rainfall through intricate microphysical, thermodynamic, and dynamical interactions, offering new insights into improving the predictability of monsoon precipitation extremes.