Revisiting the Identification of Wintertime Atmospheric Circulation Regimes in the Euro-Atlantic Sector

TL;DR

This study revisits the identification of winter atmospheric circulation regimes in the Euro-Atlantic sector, revealing six regimes instead of four and demonstrating that incorporating a persistence constraint improves regime stability.

Contribution

It introduces a new clustering approach using full field data and a persistence constraint, leading to more accurate and stable regime identification than traditional EOF-based methods.

Findings

Six regimes identified instead of four with full data clustering.

Opposite regimes exhibit symmetry, enhancing understanding of circulation patterns.

Persistence constraints extend regime durations without altering occurrence rates.

Abstract

Atmospheric circulation is often clustered in so-called circulation regimes, which are persistent and recurrent patterns. For the Euro-Atlantic sector in winter, most studies identify four regimes: the Atlantic Ridge, the Scandinavian Blocking and the two phases of the North Atlantic Oscillation. These results are obtained by applying k-means clustering to the first several empirical orthogonal functions (EOFs) of geopotential height data. Studying the observed circulation in reanalysis data, it is found that when the full field data is used for the k-means cluster analysis instead of the EOFs, the optimal number of clusters is no longer four but six. The two extra regimes that are found are the opposites of the Atlantic Ridge and Scandinavian Blocking, meaning they have a low-pressure area roughly where the original regimes have a high-pressure area. This introduces an appealing symmetry in the clustering result. Incorporating a weak persistence constraint in the clustering procedure is found to lead to a longer duration of regimes, extending beyond the synoptic timescale, without changing their occurrence rates. This is in contrast to the commonly-used application of a time-filter to the data before the clustering is executed, which, while increasing the persistence, changes the occurrence rates of the regimes. We conclude that applying a persistence constraint within the clustering procedure is a superior way of stabilizing the clustering results than low-pass filtering the data.