| Title |
Selecting climate simulations for impact studies based on multivariate patterns of climate change
|
|---|---|
| Published in |
Climatic Change, December 2015
|
| DOI | 10.1007/s10584-015-1582-0 |
| Pubmed ID | |
| Authors |
Thomas Mendlik, Andreas Gobiet |
| 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. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 164 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Unknown | 163 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 56 | 34% |
| Student > Ph. D. Student | 32 | 20% |
| Student > Master | 14 | 9% |
| Other | 8 | 5% |
| Student > Doctoral Student | 6 | 4% |
| Other | 15 | 9% |
| Unknown | 33 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Earth and Planetary Sciences | 41 | 25% |
| Environmental Science | 40 | 24% |
| Engineering | 24 | 15% |
| Agricultural and Biological Sciences | 6 | 4% |
| Physics and Astronomy | 2 | 1% |
| Other | 8 | 5% |
| Unknown | 43 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 March 2016.
All research outputs
#15,364,458
of 22,856,968 outputs
Outputs from Climatic Change
#5,345
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Outputs of similar age
#228,902
of 390,658 outputs
Outputs of similar age from Climatic Change
#54
of 66 outputs
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