Understanding precipitation changes through unsupervised machine learning

Abstract

Despite the importance of quantifying how the spatial patterns of heavy 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, spatial machine-learning framework to quantify how storm dynamics affect changes in heavy precipitation. We find that changes in heavy precipitation (above the 80th percentile) are predominantly explained by changes in the frequency of these events, rather than by 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.

Keywords

• atmospheric dynamics
• climate change
• heavy precipitation
• unsupervised learning
• variational autoencoders