Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation. (August 2016)

• Record Type: Journal Article
• Authors: Carvalho, M.J.; Melo-Gonçalves, P.; Teixeira, J.C.; Rocha, A.
• Is Part Of: Physics and chemistry of the earth. Volume 94(2016)
• Page Start: 22
• Page End: 28
• Publication Date: 2016-08
• Subjects: Climate change -- Surface temperatures -- K-Means Clustering -- Precipitation -- Europe; Geophysics -- Periodicals; Geochemistry -- Periodicals; Earth sciences -- Periodicals; Geodesy -- Periodicals; Astrophysics -- Periodicals; 550
• DOI: 10.1016/j.pce.2016.05.001
• Languages: English
• ISSNs: 1474-7065

Abstract

Abstract: In order to study climate change on a regional scale using Earth System Models, it is useful to partition the spatial domain into regions according to their climate changes. The aim of this work is to divide the European domain into regions of similar projected climate changes using a simulation of daily total precipitation, minimum and maximum temperatures for the recent-past (1986–2005) and long-term future (2081–2100) provided by the Coupled Model Intercomparison Project (CMIP5). The difference between the long-term future and recent-past daily climatologies of these three variables is determined. Aiming to objectively identify the grid points with coherent climate changes, a K -Mean Cluster Analysis is applied to these differences. This method is performed for each variable independently (univariate version) and for the aggregation of the three variables (multivariate version). A mathematical approach to determine the optimal number of clusters is pursued. However, due to the method characteristics, a sensitivity test to the number of clusters is performed by analysing the consistency of the results. This is a novel method, allowing for the determination of regions based on the climate change of multiple variables. Results from the univariate application of this method are in accordance with results found in the literature, showing overall similar regions of changes. The regions obtained for the multivariate version are mainly defined by latitude over European land, with some features of land-sea interaction. Furthermore, all regions have statistically different distributions of at least one of the variables, providing confidence to the regions obtained. Highlights: We regionalized Europe based on climate change using a K-Means Clustering analysis. The methodology was able to produce climate change coherent areas. The mathematical determination of the region number was backed by sensitivity tests.