{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mendlik T"],"funding":["Austrian Science Fund FWF"],"pagination":["381-393"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4922546"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["135"],"pubmed_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 fo"],"journal":["Climatic change"],"pubmed_title":["Selecting climate simulations for impact studies based on multivariate patterns of climate change."],"pmcid":["PMC4922546"],"funding_grant_id":["I 1295-N29","I 1295"],"pubmed_authors":["Mendlik T","Gobiet A"],"additional_accession":[]},"is_claimable":false,"name":"Selecting climate simulations for impact studies based on multivariate patterns of climate change.","description":"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 fo","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016","modification":"2025-04-04T14:05:52.925Z","creation":"2019-03-27T02:17:01Z"},"accession":"S-EPMC4922546","cross_references":{"pubmed":["27429499"],"doi":["10.1007/s10584-015-1582-0"]}}