A weather-type-based cross-time-scale diagnostic framework for coupled circulation models

Ángel G. Muñoz, Xiaosong Yang, Gabriel Andres Vecchi, Andrew W. Robertson, William F. Cooke

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple time scales. To illustrate the approach, three sets of 32-yr-long simulations are analyzed for northeastern North America and for the March-May season using the Geophysical Fluid Dynamics Laboratory's Low Ocean-Atmosphere Resolution (LOAR) and Forecast-Oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. 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 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. Moreover, the intraseasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, interexperiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple time scales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics.

Original languageEnglish (US)
Pages (from-to)8951-8972
Number of pages22
JournalJournal of Climate
Issue number22
StatePublished - Nov 1 2017

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


  • Atmospheric circulation
  • Climate models
  • Coupled models
  • Model errors
  • Model evaluation/performance


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