Characterization of climatic variations in Spain at the regional scale: A computational intelligence approach

Computational intelligence and other data mining techniques are used for characterizing regional and time-varying climatic variations in Spain in the period 1901-2005. Daily maximum temperature data from 10 climatic stations are analyzed (with and without missing values) using principal components (PC), similarity-preservation feature generation, clustering, Kolmogorov-Smirnov dissimilarity analysis and genetic programming (GP). The new features were computed using hybrid optimization (differential evolution and Fletcher-Reeves) and GP. From them, a scalar regional climatic index was obtained which identifies time landmarks and changes in the climate rhythm. The equations obtained with GP are simpler than those obtained with PC and they highlight the most important sites characterizing the regional climate. Whereas the general consensus is that there has been a clear and smooth trend towards warming during the last decades, the results suggest that the picture may probably be much more complicated than what is usually assumed.