title: Selecting climate simulations for impact studies based on multivariate patterns of climate change.
abstract: In climate change impact research it is crucial to carefully select the meteorological input for impact models. We present a method for model selection that enables the user to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity. This is done in three steps: First, using principal component analysis for a multitude of meteorological parameters, to find common patterns of climate change within the multi-model ensemble. Second, detecting model similarities with regard to these multivariate patterns using cluster analysis. And third, sampling models from each cluster, to generate a subset of representative simulations. We present an application based on the ENSEMBLES regional multi-model ensemble with the aim to provide input for a variety of climate impact studies. We find that the two most dominant patterns of climate change relate to temperature and humidity patterns. The ensemble can be reduced from 25 to 5 simulations while still maintaining its essential characteristics. Having such a representative subset of simulations reduces computational costs for climate impact modeling and enhances the quality of the ensemble at the same time, as it prevents double-counting of dependent simulations that would lead to biased statistics.
  
  
  BACKGROUND
  The online version of this article (doi:10.1007/s10584-015-1582-0) contains supplementary material, which is available to authorized users.
authors.affiliation: Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria.
authors.forename: Thomas
authors.initials: T
authors.lastname: Mendlik
authors.affiliation: Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Gobiet