Revisiting the identification of wintertime atmospheric circulation regimes in the Euro‐Atlantic sector

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, 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 are 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 time‐scale, 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 better way of stabilizing the clustering results than low‐pass filtering the data.

Jet Speed Variability Obscures Euro‐Atlantic Regime Structure

“…Figure 2a shows that a broad minima exists for both raw and residual PCs when using K = 5 or 6, which is consistent with the findings of, for example, Falkena et al. (2019). Both data sets have had their variance normalized prior to clustering and we see that the absolute value of the BIC, and its structure as a function of cluster number is essentially unchanged by our regression procedure; the statistical validity of a multimodal hypothesis appears unchanged.…”

“…The BIC is an information theoretic measure of model suitability, as discussed in section 2, and the minima of the BIC can be used to identify the optimal number of free parameters in a model, which in our case is precisely the cluster number. Figure 2a shows that a broad minima exists for both raw and residual PCs when using K = 5 or 6, which is consistent with the findings of, for example, Falkena et al (2019). Both data sets have had their variance normalized prior to 10.1029/2020GL087907…”

Flow dependence of wintertime subseasonal prediction skill over Europe

“…Another prospect for future works would be to evaluate the sensitivity of the results to the methodology. First, our strategy to identify wintertime weather regimes, although widely referenced in literature, may not be optimal (Falkena et al, 2020). The robustness of our results would be worth assessing when considering a different set of weather regimes.…”

Towards a Robust Detection of Interannual Ensemble Forecast Signals over the North Atlantic and Europe using Atmospheric Circulation Regimes

“…This is exemplified by the domain dependence of the regimes, particularly the negative counterpart of the Atlantic Ridge (AR−), when considering the six regimes identified using the ECMWF SEAS5 hindcast ensemble in Figure 1. Here, six regimes are considered instead of the commonly used four as this was identified to be the optimal number when using gridpoint data and allows for a more detailed description of the variability in the atmospheric circulation (Falkena et al, 2020). For ERA-Interim data no such domain dependence of the regimes is found.…”

“…Similar forms of regularisation, designed to increase persistence in time, have been successfully employed to detect robust and meaningful regimes in the climate context (e.g. Horenko, 2010;Falkena et al, 2020).…”