AGS-PRF: Leveraging High-Resolution Modeling and Dynamical Theory to Elucidate Changes in Extreme Precipitation in a Monsoonal Regime

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Monsoon low pressure systems (MLPS) are synoptic storms that generate the majority of monsoon rainfall over South Asia, including the heaviest rainfall events. This densely populated region is already prone to severe impacts from extreme precipitation, and the sensitivity of MLPS to warming is poorly understood. With coarse spatial resolutions, climate models do not produce realistic MLPS, as the simulated tropical precipitation extremes are highly dependent on how models portray convection via numerical parameterizations. This proposal seeks to better understand MLPS and how their changes affect the monsoonal precipitation extremes. The findings will advance the goal of improving regional predictions of how precipitation extremes will respond to increasing temperatures, with direct implications for climate change mitigation. The proposed activities include the mentoring of undergraduate and graduate students in research and public outreach activities to promote increased science literacy.Specifically, the study will utilize an idealized high-resolution, convection-permitting configuration of the System for Atmospheric Modeling framework with an extended equatorial beta-plane, fixed sea surface temperatures (SST), and a simple representation of land. The analysis will examine how the presence of tropical land and the accompanying synoptic dynamics influence the extreme precipitation sensitivity to uniform SST warming. The work will utilize a moist dynamical framework and draw on several alternative theories to characterize precipitation extremes and MLPS dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.