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.

翻译：全球气候模型的验证对于确保模型输出的准确性和有效性至关重要。我们引入球面卷积Wasserstein距离，以更全面地衡量气候模型与再分析数据之间的差异。这种新的相似性度量通过卷积投影考虑了空间变异性，并量化了气候变量分布中的局部差异。我们将该方法应用于评估耦合模型比对项目（CMIP）成员的历史模型输出，将其与观测和再分析数据产品进行比较。此外，我们还研究了从CMIP第五阶段到第六阶段的进展，发现第六阶段模型在生成逼真的气候态方面有适度改进。