Year: 2022
Type: preprint
Abstract: Global Storm-Resolving Models (GSRMs) 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 GSRMs resolve complex atmospheric formations. This lack of comprehensive 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 physically meaningful notions of similarity from low-dimensional latent data representations that the different models produce. This enables an intercomparison of nine GSRMs based on their high-dimensional simulation data (2D vertical velocity snapshots) 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, Office of Science, Division of Social and Economic Sciences, Office of Advanced Cyberinfrastructure, Division of Computer and Network Systems, Division of Information and Intelligent Systems, Division of Atmospheric and Geospace Sciences, Biological and Environmental Research, Division of Advanced Cyberinfrastructure
Awards: G130266955, STC: Center for Learning the Earth with Artificial Intelligence and Physics (LEAP), Collaborative Research: Advancing Understanding of Aerosol-Cloud Feedback Using the World's First Global Climate Model with Explicit Boundary Layer Turbulence, G2114340465, G2236757247, G2645555643, G266180865, G2665077330, G3226045097, MLWiNS: Ultra-Reliable Collaborative Computing for Autonomous Unmanned Aerial Vehicles, RI: Small: Deep Variational Data Compression, G367327614, XSEDE 2.0: Integrating, Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement, G3812616897, G4382344593, G4875486994, G48959804, G4932404565, G4960938176, G5068903224, Collaborative Research: HDR Elements: Software for a new machine learning based parameterization of moist convection for improved climate and weather prediction using deep learning, Collaborative Research: FW-HTF-RM: Intelligent Facilitation for Teams of the Future via Longitudinal Sensing in Context, G6806689448, NRT-DESE: Team Science for Integrative Graduate Training in Data Science and Physical Science, G7371768842, CAREER: Variational Inference for Resource-Efficient Learning, G7491227550, G7691771761, G8506127299
OpenAlex ID: W4293329560