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 in 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.