Weather Regimes: Recurrence and Quasi Stationarity

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  • 1 Laboratoire de Météorologie Dynamique du CNRS, Paris, France
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Abstract

Two different definitions of midlatitude weather regimes are compared. The first seeks recurrent atmospheric patterns. The second seeks quasi-stationary patterns, whose average tendency vanishes. Recurrent patterns are identified by cluster analysis, and quasi-stationary patterns are identified by solving a nonlinear equilibration equation. Both methods are applied on the same dataset: the NMC final analyses of 700-hPa geopotential heights covering 44 winters. The analysis is performed separately over the Atlantic and Pacific sectors.

The two methods give the same number of weather regimes—four over the Atlantic sector and three over the Pacific sector. However, the patterns differ significantly. The investigation of the tendency, or drift, of the clusters shows that recurrent flows have a systematic slow evolution, explaining this difference. The patterns are in agreement with the ones obtained from previous studies, but their number differs.

The cluster analysis algorithm used here is a partitioning algorithm, which agglomerates data around randomly chosen seeds and iteratively finds the partition that minimizes the variance within clusters, given a prescribed number of clusters. The authors develop a classifiability index, based on the correlation between the cluster centroids obtained from different initial pullings. By comparing the classifiability index of observations with that obtained from a multivariate noise model, an objective definition of the number of clusters present in the data is given. Although the classifiability index is maximal by prescribing two clusters in both sectors, it only differs significantly from that obtained with the noise model using four Atlantic clusters and three Pacific clusters. The partitioning clustering method turns out to give more statistically stable clusters than hierarchical clustering schemes.

Abstract

Two different definitions of midlatitude weather regimes are compared. The first seeks recurrent atmospheric patterns. The second seeks quasi-stationary patterns, whose average tendency vanishes. Recurrent patterns are identified by cluster analysis, and quasi-stationary patterns are identified by solving a nonlinear equilibration equation. Both methods are applied on the same dataset: the NMC final analyses of 700-hPa geopotential heights covering 44 winters. The analysis is performed separately over the Atlantic and Pacific sectors.

The two methods give the same number of weather regimes—four over the Atlantic sector and three over the Pacific sector. However, the patterns differ significantly. The investigation of the tendency, or drift, of the clusters shows that recurrent flows have a systematic slow evolution, explaining this difference. The patterns are in agreement with the ones obtained from previous studies, but their number differs.

The cluster analysis algorithm used here is a partitioning algorithm, which agglomerates data around randomly chosen seeds and iteratively finds the partition that minimizes the variance within clusters, given a prescribed number of clusters. The authors develop a classifiability index, based on the correlation between the cluster centroids obtained from different initial pullings. By comparing the classifiability index of observations with that obtained from a multivariate noise model, an objective definition of the number of clusters present in the data is given. Although the classifiability index is maximal by prescribing two clusters in both sectors, it only differs significantly from that obtained with the noise model using four Atlantic clusters and three Pacific clusters. The partitioning clustering method turns out to give more statistically stable clusters than hierarchical clustering schemes.

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