A Cross-Timescale Diagnostic Framework for Coupled Circulation Models

Approaches to diagnose numerical circulation models generally involve metrics that provide an overall summary of the performance of the model in reproducing the particular variables of interest, normally tied to specific spatial and temporal scales. Nonetheless, the evaluation of the goodness of a model is not always linked to the understanding of physical processes that may be correctly represented, distorted or even absent in the model universe. As physical mechanisms are frequently related to interactions at multiple time and spatial scales, cross-scale model diagnostic tools are not only desirable, but required. This study proposes an integrated diagnostic framework based on weather type's spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple timescales. To illustrate the approach, three sets of 32-year-long simulations are analyzed for Northeastern North America and for the March-May season, using the Geophysical Fluid Dynamics Laboratory's LOAR and FLOR coupled models. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime's spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach, and are associated with a misrepresentation of troughs centered north of the Great Lakes and deep coastal troughs. Overall, inter-experiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple timescales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables (e.g., rainfall climatology) via their simulated weather type characteristics.