Validating Climate Models with Spherical Convolutional Wasserstein
  Distance
Robert C. Garrett Trevor Harris Bo Li Zhuo Wang
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.