A two-tier atmospheric circulation classification scheme for the European–North Atlantic region

A two-tier classification of large-scale atmospheric circulation was developed for the European–North-Atlantic domain. The classification was constructed using a combination of principal components and k-means cluster analysis applied to reanalysis fields of mean sea-level pressure for 1951–2004. Separate classifications were developed for the winter, spring, summer, and fall seasons. For each season, the two classification tiers were identified independently, such that the definition of one tier does not depend on the other tier having already been defined. The first tier of the classification is comprised of supertype patterns. These broad-scale circulation classes are useful for generalized analyses such as investigations of the temporal trends in circulation frequency and persistence. The second, more detailed tier consists of circulation types and is useful for numerous applied research questions regarding the relationships between large-scale circulation and local and regional climate. Three to five supertypes and up to 19 circulation types were identified for each season. An intuitive nomenclature scheme based on the physical entities (i.e., anomaly centers) which dominate the specific patterns was used to label each of the supertypes and types. Two example applications illustrate the potential usefulness of a two-tier classification. In the first application, the temporal variability of the supertypes was evaluated. In general, the frequency and persistence of supertypes dominated by anticyclonic circulation increased during the study period, whereas the supertypes dominated by cyclonic features decreased in frequency and persistence. The usefulness of the derived circulation types was exemplified by an analysis of the circulation associated with heat waves and cold spells reported at several cities in Bulgaria. These extreme temperature events were found to occur with a small number of circulation types, a finding that can be helpful in understanding past variability and projecting future changes in the occurrence of extreme weather and climate events.