Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning

Abstract

Storm-resolving models (SRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult to quantify objective differences in how SRMs resolve complex atmospheric formations. This lack of appropriate tools for comparing model similarities is a problem in many disparate fields that involve simulation tools for complex data. To address this challenge we develop methods to estimate distributional distances based on both nonlinear dimensionality reduction and vector quantization. Our approach automatically learns appropriate notions of similarity from low-dimensional latent data representations that the different models produce. This enables an intercomparison of nine SRMs based on their high-dimensional simulation data and reveals that only six are similar in their representation of atmospheric dynamics. Furthermore, we uncover signatures of the convective response to global warming in a fully unsupervised way. Our study provides a path toward evaluating future high-resolution simulation data more objectively.Comment: 22 pages, 19 figures. Submitted to journal for consideratio