2022/11/03 by Griffin Mooers, Tom Beucler, Mooers, Griffin +5
Earth and Planetary Sciences · Environmental Science · #Atmospheric and Oceanic Physics (physics.ao-ph) #Climate variability and models #FOS: Physical sciences #Meteorological Phenomena and Simulations #Oceanographic and Atmospheric Processes
paper · pdf · doi:10.48550/arxiv.2211.01613
openalex publication_date 2022/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Despite the importance of quantifying how the spatial patterns of extreme precipitation will change with warming, we lack tools to objectively analyze the storm-scale outputs of modern climate models. To address this gap, we develop an unsupervised machine learning framework to quantify how storm dynamics affect changes in precipitation extremes, without sacrificing spatial information. For the upper precipitation quantiles (above the 80th percentile), we find that the spatial patterns of extreme precipitation changes are dominated by spatial shifts in storm dynamical regimes rather than changes in how these storm regimes produce precipitation. Our study shows how unsupervised machine learning, paired with domain knowledge, may allow us to better understand the physics of the atmosphere and anticipate the changes associated with a warming world.