Validating Climate Models with Spherical Convolutional Wasserstein Distance

TL;DR

This paper introduces a novel spherical convolutional Wasserstein distance for validating climate models, providing a more comprehensive measure of differences between model outputs and observational data.

Contribution

The paper presents a new similarity measure that captures spatial variability in climate data, enhancing model validation techniques.

Findings

Modest improvements in CMIP6 models' climatologies compared to CMIP5.

The new measure effectively quantifies local differences in climate variable distributions.

Application to CMIP data demonstrates its utility in model evaluation.

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.