We review recent advances in classifications of circulation patterns as a specific research area within synoptic climatology. The review starts with a general description of goals of classification and the historical development in the field. We put circulation classifications into a broader context within climatology and systematize the varied methodologies and approaches. We characterize three basic groups of classifications: subjective (also called manual), mixed (hybrid), and objective (computer-assisted, automated). The roles of cluster analysis and principal component analysis in the classification process are clarified. Several recent methodological developments in circulation classifications are identified and briefly described: the introduction of nonlinear methods, objectivization of subjective catalogs, efforts to optimize classifications, the need for intercomparisons of classifications, and the progress toward an optimum, if possible unified, classification method. Among the recent tendencies in the applications of circulation classifications, we mention a more extensive use in climate studies, both of past, present, and future climates, innovative applications in the ensemble forecasting, increasing variety of synoptic-climatological investigations, and steps above from the troposphere. After introducing the international activity within the field of circulation classifications, the COST733 Action, we briefly describe outputs of the inventory of classifications in Europe, which was carried out within the Action. Approaches to the evaluation of classifications and their mutual comparisons are also reviewed. A considerable part of the review is devoted to three examples of applications of circulation classifications: in historical climatology, in analyses of recent climate variations, and in analyses of outputs from global climate models.

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Reconstructed daily mean sea level pressure patterns of the North Atlantic-European region are classified for the period 1850 to 2003 to explore long-term changes of the atmospheric circulation and its impact on long-term temperature variability in the central European region. Commonly used k-means clustering algorithms resulted in classifications of low quality because of methodological deficiencies leading to local optima by chance for complex datasets. In contrast, a newly implemented clustering scheme combining the concepts of simulated annealing and diversified randomization (SANDRA) is able to reduce substantially the influence of chance in the cluster assignment, leading to partitions that are noticeably nearer to the global optimum and more stable. The differences between conventional cluster analysis and the SANDRA scheme are significant for subsequent analyses of single clusters-in particular, for trend analysis. Conventional indices used to determine the appropriate number of clusters failed to provide clear guidance, indicating that no distinct separation between clusters of circulation types exists in the dataset. Therefore, the number of clusters is determined by an external indicator, the so-called dominance criteria for t-mode principal component analysis. Nevertheless, the resulting partitions are stable for certain numbers of clusters and provide meaningful and reproducible clusters. The resulting types of pressure patterns reveal pronounced long-term variability and various significant trends of the time series of seasonal cluster frequency. Tentative estimations of central European temperature changes based solely on seasonal cluster frequencies can explain between 33.9% (summer) and 59.0% (winter) of temperature variance on the seasonal time scale. However, the signs of long-term changes in temperature are correctly reproduced even on multidecadal-centennial time scales. Moreover, linear warming trends are reproduced, implying from one-third up to one-half of the observed temperature increase between 1851/52 and 2003 (except for summer, but with significant trends for spring and autumn), indicating that changes in daily circulation patterns contribute to the observed overall long-term warming in the central European region.

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Observed atmospheric circulation over the North Atlantic–European (NAE) region is examined using cluster analysis. A clustering algorithm incorporating a “simulated annealing” methodology is employed to improve on solutions found by the conventional k-means technique. Clustering is applied to daily mean sea level pressure (MSLP) fields to derive a set of circulation types for six 2-month seasons. A measure of the quality of this clustering is defined to reflect the average similarity of the fields in a cluster to each other. It is shown that a range of classifications can be produced for which this measure is almost identical but which partition the days quite differently. This lack of a unique set of circulation types suggests that distinct weather regimes in NAE circulation do not exist or are very weak. It is also shown that the stability of the clustering solution to removal of data is not maximized by a suitable choice of the number of clusters. Indeed, there does not appear to be any robust way of choosing an optimum number of circulation types. Despite the apparent lack of preferred circulation types, cluster analysis can usefully be applied to generate a set of patterns that fully characterize the different circulation types appearing in each season. These patterns can then be used to analyze NAE climate variability. Ten clusters per season are chosen to ensure that a range of distinct circulation types that span the variability is produced. Using this classification, the effect of forcing of NAE circulation by tropical Pacific sea surface temperature (SST) anomalies is analyzed. This shows a significant influence of SST in this region on certain circulation types in almost all seasons. A tendency for a negative correlation between El Niño and an anomaly pattern resembling the positive winter North Atlantic Oscillation (NAO) emerges in a number of seasons. A notable exception is November–December, which shows the opposite relationship, with positive NAO-like patterns correlated with El Niño.