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.

We introduce the spherical convolutional historical simulations coincide with observational measurements, Wasserstein distance to more comprehensively we can compare each model's synthetic climate measure differences between climate models and distribution to the distribution of observational or quasiobservational reanalysis data. This new similarity measure accounts data products (Raäisaänen, 2007), to assess for spatial variability using convolutional their reconstructive skill. For complete spatial coverage we projections and quantifies local differences in the compare against reanalysis data, a blend of observations distribution of climate variables. We apply this and short-range weather forecasts through data assimilation method to evaluate the historical model outputs (Bengtsson et al., 2004). This has become one popular of the Coupled Model Intercomparison Project climate model validation method (Flato et al., 2014).