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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.The online version of this article (doi:10.1007/s10584-015-1582-0) contains supplementary material, which is available to authorized users.

SUBMITTER: Mendlik T

PROVIDER: S-EPMC4922546 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Selecting climate simulations for impact studies based on multivariate patterns of climate change.

Mendlik Thomas T Gobiet Andreas A

Climatic change 20151224

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, detec ...[more]

PMID: 27429499

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Dataset Information

Selecting climate simulations for impact studies based on multivariate patterns of climate change.

Publications

Selecting climate simulations for impact studies based on multivariate patterns of climate change.

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