Quantitative estimates are obtained for regional features of the frequency distribution of extreme temperature, precipitation, and wind anomalies in the winter months in the Northern Hemisphere extratropical zone, associated with atmospheric circulation regimes (henceforth regimes) in the Euro-Atlantic sector. Using k-means cluster analysis for the daily geopotential height fields from the ERA5 reanalysis for 1979–2021, four most characteristic winter atmospheric circulation regimes are identified in the Euro-Atlantic sector corresponding to the North Atlantic Oscillation (NAO) in the positive (NAO+) and negative (NAO−) phases, Scandinavian blocking (SCAND), and the regime with anomalously high pressure over the North Atlantic and low pressure over Europe (ATL). Trends in the frequency of occurrence and in structures of regimes are analyzed. The NAO+ and NAO− regimes turned out to be asymmetrical in spatial structure with significantly different frequencies of occurrence (33% and 19%, respectively). The associated extreme weather anomaly fields are also asymmetric. For all regimes, an asymmetric response in the frequency of occurrence of positive and negative temperature anomalies is noted. Over the period under study, no statistically significant trends are found in the seasonal average frequency of occurrence of the regimes, or in the seasonal average fields of the regimes in the region of the geopotential height anomaly extremes, probably indicating the stability of these regimes under recent climate changes.

Various methods of cluster analysis are used for identifying large-scale atmospheric circulation regimes (weather regimes (WRs)). In this paper we compare the four most commonly used clustering methods: k-means (KM), Ward’s hierarchical clustering (HW), Gaussian mixture model (GM), and self-organizing maps (SOMs) to analyze WRs in the Euro-Atlantic (EAT) region. The data used for identifying WRs are 500 hPa geopotential height fields (z500) from the ERA5 reanalysis for 1940–2022. Four classical wintertime WRs are identified by the KM method—two regimes associated with positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO–), a regime associated with the Scandinavian blocking (SB), and a regime characterized by elevated pressure over the Northern Atlantic. For summer months, the KM method gets WRs that are similar in spatial structure to the classical winter ones. The SOM method yields results that are almost identical to the results of the KM method. Unlike KM and SOM methods, HW and GM do not fully catch the spatial structure of all of the four classical winter EAT WRs and their summer analogues. Compared to WRs of the KM and SOM methods, WRs obtained by HW and GM methods explain less z500 variance; they have different occurrences, persistence, and transition features. Summer and winter WRs obtained by HW and GM methods are less similar to each other compared to WRs provided by the KM method. Average spatial correlation coefficients between mean z500 fields of WRs obtained by KM and HW methods are 0.76 in winter and 0.83 in summer; 0.70 in winter and 0.72 in summer for KM and GM methods; and 0.41 in winter and 0.44 in summer for the regimes compared between HW and GM methods, respectively. There are statistically significant trends of the seasonal occurrence of WRs found by some of the studied clustering methods—a positive trend for the occurrence of the NAO+ regime and a negative trend for the occurrence of the NAO-regime.

Various methods of cluster analysis are used for identification of large-scale atmospheric circulation regimes or weather regimes (WRs). In this paper we compare four most commonly used clustering methods – k-means (KM), Ward’s hierarchical clustering (HW), Gaussian mixture model (GM) and self-organizing maps (SOM) to analyze WRs in Euro-Atlantic region. The data used for WRs identification are 500 hPa geopotential height fields (z500) from the ERA5 reanalysis for the 1940–2022 period. Four classical wintertime weather regimes are identified by the KM method – two regimes associated with positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO–), a regime associated with the Scandinavian blocking (SB) and a regime characterized by elevated pressure over the Northern Atlantics. For summer months KM method gets WRs that are similar by their spatial structure to the classical winter ones. The SOM method yields results that are almost identical to the results of KM method. Unlike KM and SOM methods, HW and GM do not catch the spatial structure of all four classical winter Euro-Atlantic weather regimes and their summer analogues. Compared to WRs of the KM and SOM methods, WRs obtained by HW and GM methods explain less z500 variance, they have different occurrences, persistence and transition features. Summer and winter WRs obtained by HW and GM methods are less similar to each other compared to WRs provided by KM method. Average spatial correlation coefficients between mean z500 fields of WRs obtained by KM and HW methods are 0.76 in winter and 0.83 in summer, 0.70 in winter and 0.72 in summer for KM and GM methods and 0.41 in winter and 0.44 in summer for the regimes between HW and GM methods, respectively. There are statistically significant trends of seasonal occurrence of WRs found by some of the studied clustering methods – a positive trend for the occurrence of the NAO+ regime and a negative trend for the occurrence of the NAO– regime.

A weather regimes approach is applied to study large-scale circulation patterns over the North Eurasian sector (NE, defined as 0–180° E and 40–80° N domain) during the 1940–2022 period using ERA5 reanalysis data. We identified four NE weather regimes (WRs) both for boreal winter and summer seasons using a k-means standard cluster analysis method for daily geopotential height fields (z500). Whereas the winter NE WRs do not have any significant long-term changes in occurrence, three summer NE WRs are found to have statistically significant linear trends of occurrence over the studied period, the most notable of which is a regime associated with anticyclonic activity centered over the Ural Mountains called Summer Ural High, which has a statistically significant trend of +2.4 days of seasonal occurrence per decade. Loops of statistically significant likely transitions are found for the NE WRs in both seasons, showing that the NE WRs evolution follows preferred paths and is not purely random. NE WRs’ seasonal occurrence is found to change depending on the phase of the El Nino oscillation and Northern Hemisphere sea-ice area anomalies in preceding seasons. El Nino events in autumn are associated with an increased occurrence of the Winter Scandinavian Blocking regime and a decreased occurrence of the Winter North Eastern High regime. Negative sea-ice area anomalies in autumn are associated with an increased occurrence of the Winter Ural High and a decreased occurrence of the Winter Scandinavian Low regime. NE WR’s are associated with considerable, up to several times, changes in probability of temperature and precipitation extremes over the NE region.