Long-Term Variability of Daily North Atlantic–European Pressure Patterns since 1850 Classified by Simulated Annealing Clustering

Abstract

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.

Spatial and temporal variability of climate extremes in Romania and associated large‐scale mechanisms

“…Tomozeiu et al, 2002;Efthymiadis et al, 2011). The most recent circulation types developed by Philipp et al (2007), using a more stable statistical scheme, have also been used to justify the trends in extremes on European regions (Jones and Lister, 2009;Ionita et al, 2013). Della-Marta et al (2007) investigated a combined influence of a number of forcing factors on the heat wave events over western Europe [sea level pressure (SLP), Atlantic sea surface temperatures (SST) and European precipitation] using the canonical correlation analysis (CCA) technique, considering precipitation as a proxy for soil moisture strengthening land-atmosphere feedback process.…”

The influence of the circulation on surface temperature and precipitation patterns over Europe

“…Philipp et al (2007) have developed a set of daily North Atlantic/European Circulation Types (CTs) covering the region 70 • to 25 • N by 70 • W to 50 • E using a technique involving simulated annealing (a clustering algorithm). The analysis was based on the reconstructed daily mean sea-level pressure (MSLP) maps from Ansell et al (2006) and used the full period of available data from 1850-2003.…”

“…Here, we will use the Philipp et al (2007) CTs for the four standard seasons to look at their surface temperature and precipitation response. We wish to assess whether the longterm change in temperature over Europe (towards warming) and precipitation (drying in Southern Europe and increases in wetness in Northern Europe, see e.g.…”

“…The daily surface network of temperature and precipitation data is discussed in Section 2. In Section 3, we briefly introduce the Philipp et al (2007) method of deriving circulation types and illustrate how the surface temperature data are transformed into anomalies for the time of year. Section 4 discusses a selection of the results and Sect.…”

Circulation Weather types as a tool in atmospheric, climate and environmental research

“…This has been possible by using either historical monthly mean grids going back to the eighteenth century (Beck et al, 2007) or daily fields that extend to 1871 in reanalysis products (e.g., Jones et al, 2013), to 1850 in historical data (Philipp et al, 2007) and to 1685 for some weather types (Barriopedro et al, 2014). These studies stress that changes in weather type frequencies can explain an important amount of temperature and precipitation variability.…”