{"title":"Comparison of Cluster Analysis Methods for Identifying Weather Regimes in the Euro-Atlantic Region for Winter and Summer Seasons","authors":"B. A. Babanov, V. A. Semenov, I. I. Mokhov","doi":"10.1134/s0001433823060026","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>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.</p>","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1134/s0001433823060026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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