Validating Climate Models with Spherical Convolutional Wasserstein Distance

Published: 25 Sept 2024, Last Modified: 06 Nov 2024NeurIPS 2024 spotlightEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Climate Models, Wasserstein Distance, Convolution, Functional Data
TL;DR: We create a Wasserstein distance variant based on spherical convolutions of functional data and apply the method to climate model validation.
Abstract: The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data. This new similarity measure accounts for spatial variability using convolutional projections and quantifies local differences in the distribution of climate variables. We apply this method to evaluate the historical model outputs of the Coupled Model Intercomparison Project (CMIP) members by comparing them to observational and reanalysis data products. Additionally, we investigate the progression from CMIP phase 5 to phase 6 and find modest improvements in the phase 6 models regarding their ability to produce realistic climatologies.
Supplementary Material: zip
Primary Area: Machine learning for physical sciences (for example: climate, physics)
Submission Number: 8285
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