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 fo

SUBMITTER: Mendlik T 

PROVIDER: S-EPMC4922546 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature