Climate modeling efforts are critically dependent upon the veracity with which the general circulation models (GCMs) used are able to simulate the atmospheric circulation. A minimum expectation is that the GCMs employed are capable of realistically representing important dynamical characteristics of the current climate. Regional climate is strongly influenced by second order atmospheric circulation features such as storm track variability and anomalous weather regimes, both of which are dynamically linked to the midlatitude jet stream and are associated with alterations in surface weather. Extratropical climate is also influenced by annular modes of variability. In particular, the Northern Annular Mode is linked to both storm track variations and certain anomalous weather regimes. A proper representation of the dynamical behavior of such intraseasonal variability is, thus, essential for reliable climate simulations. A detailed diagnostic assessment of the dynamics of midlatitude subseasonal variability provides an important benchmark test for a climate model.

We perform a modern dynamical diagnosis of the parallel characteristics of anomalous weather regimes, storm tracks, and annular modes in extended integrations of NASA/GSFC GCMs. This includes a detailed intercomparison of the representation of these natural phenomena in AMIP-type simulations of the NASA/ NCAR and Aries (NSIPP) models. We diagnose their statistics, three-dimensional structure, and dynamical characteristics and contrast the results with parallel observational analyses (NCEP/NCAR) to isolate systematic errors. Eddy structure is assessed using linear regressions and major axis analysis. The envelope function is used to isolate (a) midwinter stormtrack suppression and (b) regional relationships between anomalous weather regimes and storm track variability. Dynamical characteristics are ascertained with a diagnostic suite that includes Eliassen-Palm and wave activity fluxes, E-vectors, deformation analyses, potential vorticity inversions, and local energetics analyses. Specific goals include (a) determining the extent to which the models are able to replicate the observed characteristics of these phenomena and (b), in cases where a specific shortcoming is identified, performing targeted dynamical diagnoses aimed at deducing the underlying physical reasons for the systematic errors.