Common Error Patterns in the Regional Atmospheric Circulation Simulated by the CMIP Multi-Model Ensemble

The ability of global climate models to reproduce recurrent regional atmospheric circulation types is introduced as an overarching concept to explore potential dependencies between these models. If this approach is applied on a sufficiently large spatial domain, the similarity of the resulting error pattern can be compared from one model to another. By computing a pattern correlation matrix for a large multi-model ensemble from the Coupled Model Intercomparison Project Phases 5 and 6, groups of comparatively strong correlation coefficients are obtained for those models working with similar atmospheric components. Thereby, frequent shared error patterns are found within the ensemble, which also occur for nominally different atmospheric component models. The error pattern correlation coefficients describing these similarities are nearly unrelated to model performance and can be used as statistical dependency weights.

Automated summary

This article introduces the ability of global climate models to reproduce regional atmospheric circulation types, and finds significant similarities between models through pattern correlation matrix. These similarities, which are largely unrelated to model performance, can be used as statistical dependency weights.