Validating Climate Models with Spherical Convolutional Wasserstein Distance

Robert C. Garrett, Trevor Harris, Zhuo Wang, Bo Li

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
Volume37
StatePublished - 2024
Event38th Conference on Neural Information Processing Systems, NeurIPS 2024 - Vancouver, Canada
Duration: Dec 9 2024Dec 15 2024

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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