, Volume 113, Issue 3-4, pp 387-405

Summer temperature extremes in northeastern Spain: spatial regionalization and links to atmospheric circulation (1960–2006)

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Abstract

A procedure for classifying daily summer temperature extremes in northeastern Spain into homogenous regions has been presented and evaluated. This procedure employed daily temperature series from a dense network of 128 weather stations spanning the period from 1960 to 2006. Characteristics of temperature extremes included temperature frequency (e.g., warm days), intensity (e.g., warmest day), and duration (e.g., maximum length of hot spell). Following the results of the principal components analysis and Ward's method of clustering, the study area was divided into four homogenous sub-regions in terms of both the geographic and climatic meanings: the Mediterranean region, the mainland and the Cantabrian region, the moderately elevated areas westward and southward, and the mountainous region. Based on an internal cluster validation measure (Silhouette width), the quality of clustering was evaluated and ensured. The temporal evolution of the long-term (1960–2006) temperature extremes clearly showed a different behavior amongst these sub-regions. The Mediterranean and the highly elevated regions revealed the strongest signals in both daytime and nighttime extremes. For mainland areas, considerable differences in the behavior of the daytime and nighttime temperature extremes were evident. The influence of atmospheric circulation on spatial and temporal variability of temperature extremes was also explored. The variability of summer temperature extremes in NE Spain appears to be mainly driven by the Scandinavian (SCA), the Western Mediterranean Oscillation (WeMO), and the East Atlantic (EA) patterns, with a tendency toward increasing during the positive (negative) phases of the EA (WeMO and SCA) circulation modes. In such a region with complex geography and climate, regionalization of summer temperature extremes can be advantageous for extracting finer-scale information, which may prove useful for the vulnerability assessments and the development of local adaptation strategies in areas such as health, ecosystems and agriculture.