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Spatio-temporal climate regionalization using a self-organized clustering approach

Authors :
Julio Ramiro-Bargueno
C. Casanova-Mateo
Sancho Salcedo-Sanz
Mihaela I. Chidean
Antonio J. Caamaño
Source :
Theoretical and Applied Climatology. 140:927-949
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

Abstract

The authors present a novel self-organized climate regionalization (CR) method that obtains a spatial clustering of regions, based on the explained variance of physical measurements in their coverage. This method enables a microscopic characterization of the probabilistic spatial extent of climate regions, using the statistics of the obtained clusters. It also allows for the study of the macroscopic behaviour of climate regions through time by using the dissimilarity among different cluster size probability histograms. The main advantages of the presented method, based on the Second-Order Data-Coupled Clustering (SODCC) algorithm, are that SODCC is robust to the selection of tunable parameters and that it does not require a regular or homogeneous grid to be applied. Moreover, the SODCC method has higher spatial resolution, lower computational complexity, and allows for a more direct physical interpretation of the outputs than other existing CR methods, such as Empirical Orthogonal Function (EOF) or Rotated Empirical Orthogonal Function (REOF). These facts are illustrated with an example of winter wind speed regionalization in the Iberian Peninsula through the period (1979 − 2014). This study also reveals that the North Atlantic Oscillation (NAO) has a high influence over the wind distribution in the Iberian Peninsula in a subset of years in the considered period.

Details

ISSN :
14344483 and 0177798X
Volume :
140
Database :
OpenAIRE
Journal :
Theoretical and Applied Climatology
Accession number :
edsair.doi...........93240de64849788ce9dcdd955f4726ce
Full Text :
https://doi.org/10.1007/s00704-019-03082-6