Boreal low frequency variability in CMIP5 models

We assess the capability of CMIP5 models in representing primary modes of boreal extratropical low-frequency variability. Rotated principal component analysis is applied to monthly-mean output from historical simulations of 16 models and NCEP-NCAR reanalyses (NNR) to isolate the leading patterns of variability in 500 hPa height. For each model dataset, NAO-like and PNA-like patterns are identified using pattern correlation analysis (against NNR patterns). The relative pattern correspondence is further quantified via cluster analyses of NAO-like and PNA-like patterns, respectively. Dynamical diagnostic analyses are performed in an attempt to delineate the mechanistic distinctions among various model clusters.

We find that, for both NAO and PNA, ~20% of the model patterns lay within the same cluster as NNR. Composite structural differences among clusters chiefly consist of (a) spatial displacements of or (b) regional magnitude disparities in the primary anomaly features. While all models replicate the basic aspects of PNA-like variability, a few models fail to replicate the NAO pattern at all. Interestingly, models with a well-resolved stratosphere tend to perform more poorly than those without. Model biases in the low frequency mode structure have important consequences for the representation of associated regional anomalies in surface air temperature and storm track behavior. Composite analyses reveal that differences among clusters are linked to variations in dynamical structures and their relation to the climatological-mean flow. In summary, we find that some state-of-the-art models have important deficiencies in representing low frequency variability modes and some of these deficiencies are associated with the failure of models to adequately replicate the observed climatological stationary waves and their dynamical linkage to prominent low frequency modes of variability. >