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

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  • Thomas Mendlik

    (University of Graz)

  • Andreas Gobiet

    (University of Graz)

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.

Suggested Citation

  • Thomas Mendlik & Andreas Gobiet, 2016. "Selecting climate simulations for impact studies based on multivariate patterns of climate change," Climatic Change, Springer, vol. 135(3), pages 381-393, April.
    • Handle: RePEc:spr:climat:v:135:y:2016:i:3:d:10.1007_s10584-015-1582-0
    DOI: 10.1007/s10584-015-1582-0
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    References listed on IDEAS

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    1. Penny Whetton & Kevin Hennessy & John Clarke & Kathleen McInnes & David Kent, 2012. "Use of Representative Climate Futures in impact and adaptation assessment," Climatic Change, Springer, vol. 115(3), pages 433-442, December.
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    Cited by:

    1. Srishti Gaur & Rajnish Singh & Arnab Bandyopadhyay & Rajendra Singh, 2023. "Diagnosis of GCM-RCM-driven rainfall patterns under changing climate through the robust selection of multi-model ensemble and sub-ensembles," Climatic Change, Springer, vol. 176(2), pages 1-30, February.
    2. Saeed Golian & Conor Murphy, 2021. "Evaluation of Sub-Selection Methods for Assessing Climate Change Impacts on Low-Flow and Hydrological Drought Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 113-133, January.
    3. Joel Katzav & Erica L. Thompson & James Risbey & David A. Stainforth & Seamus Bradley & Mathias Frisch, 2021. "On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives," Climatic Change, Springer, vol. 169(1), pages 1-20, November.
    4. Francisco Estrada & Oscar Calder'on-Bustamante & Wouter Botzen & Juli'an A. Velasco & Richard S. J. Tol, 2021. "AIRCC-Clim: a user-friendly tool for generating regional probabilistic climate change scenarios and risk measures," Papers 2111.01762, arXiv.org.
    5. Gwon-Soo Bahn & Byung-Chul An, 2020. "Analysis of Environmental Purification Effect of Riparian Forest with Poplar Trees for Ecological Watershed Management: A Case Study in the Floodplain of the Dam Reservoir in Korea," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    6. Riwaz Kumar Adhikari & Abdullah Gokhan Yilmaz & Bandita Mainali & Phil Dyson & Monzur Alam Imteaz, 2022. "Methods of Groundwater Recharge Estimation under Climate Change: A Review," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    7. Seung Beom Seo & Young-Oh Kim, 2018. "Impact of Spatial Aggregation Level of Climate Indicators on a National-Level Selection for Representative Climate Change Scenarios," Sustainability, MDPI, vol. 10(7), pages 1-18, July.
    8. Alessandro Dosio & Christopher Lennard & Jonathan Spinoni, 2022. "Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations," Climatic Change, Springer, vol. 170(1), pages 1-24, January.
    9. Andrew C. Ross & Raymond G. Najjar, 2019. "Evaluation of methods for selecting climate models to simulate future hydrological change," Climatic Change, Springer, vol. 157(3), pages 407-428, December.

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