Presented on November 10, 2021, by

Professor J. David Neelin
Department of Atmospheric and Oceanic Sciences
University of California, Los Angeles (UCLA)

Abstract: Precipitation processes are notoriously complex, so it is not surprising that weather and climate models exhibit deficiencies in simulation of probability distributions of precipitation and their relationship to the water vapor and temperature environment. However, we rely on these models for projections of how the probabilities of extreme precipitation will change in a warming climate, so it is important to seek mechanistic understanding that can increase confidence in processes and associated diagnostics to improve models. Here we argue that it can be helpful to return to fundamental questions about what yields the characteristic shapes of probability distributions. This can be asked for different measures of precipitation including event accumulations, daily-average intensities and the size of spatial clusters. Many of these precipitation statistics can be captured by conceptually simple models, based on economical assumptions. These provide an understanding of the underlying processes and point, for instance, to the important role of the threshold-like transition from dry to precipitating conditions as a function of the thermodynamic environment. An overview will be provided of the dialogue between insights from the simple models and diagnostics of observations and complex model simulations.