The long‐term temperature trends since 1901 at ten meteorological stations on the Iberian Peninsula and one in the Canary Islands are analysed. These trends are identified by applying the Mann–Kendall trend test to the series of maximum and minimum temperatures, the variability of both, and the diurnal temperature range. A multidimensional scaling analysis is used to produce an automatic grouped systemization of all trends.The results appear to confirm the hypothesis of a local regionalization of the more global influences, yielding three types of regional trends in temperature variations since 1901: (i) less extreme in the north and north‐west; (ii) more extreme conditions in the south‐east and centre‐east; and (iii) an overall increase in the south‐west.
Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. 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 FletcherReeves) 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.
Characterization of Climatic Variations in Spain at the
Experiments were carried out using each one of these techniques and their combination. Time-lag spectra obtained by means of MVTSMM seems to indicate time stamps of some of the relevant Earth-climate and solar variations on the temperature record. The equations provided by GP approximated analytically the relative contribution of particular solar activity time-lags. These preliminary results, even if they still are insufficient to support or discredit possible physical mechanisms, are interesting and encouraging to explore more in that direction.
Abstract-Two blocks (1904-1921 and 1990-2007) of daily maximum temperature data from seventeen Spanish meteorological stations exhibit a multimodal Empirical Distribution Function (EDF). Most of the stations show important differences in their EDF for each one of the considered periods of time, a fact that reveals the complexity of climatic changes within the accepted general warming trend of the Iberian Peninsula.As a tentative approach to understand the underlying structure of data, each EDF has been decomposed on two normal distributed functions. The parameters describing these functions for each station and for each time period have been space-optimized and visualized using classical optimization and genetic programming. The changes in the geographical distribution of the classes derived from the analysis point towards a recent greater role of Mediterranean climates, spreading its influence to the interior of the Peninsula. The general picture, however, is much more complex than a linear warming and a number of stations even show negative trends.This study is considered to be a preliminary methodological exploration of future procedures destined to close the gap between data driven analysis and what models based upon first principles may tell.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.