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A framework for testing the ability of models to project climate change and its impacts

Author

Listed:
  • J. Refsgaard
  • H. Madsen
  • V. Andréassian
  • K. Arnbjerg-Nielsen
  • T. Davidson
  • M. Drews
  • D. Hamilton
  • E. Jeppesen
  • E. Kjellström
  • J. Olesen
  • T. Sonnenborg
  • D. Trolle
  • P. Willems
  • J. Christensen

Abstract

Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • J. Refsgaard & H. Madsen & V. Andréassian & K. Arnbjerg-Nielsen & T. Davidson & M. Drews & D. Hamilton & E. Jeppesen & E. Kjellström & J. Olesen & T. Sonnenborg & D. Trolle & P. Willems & J. Christens, 2014. "A framework for testing the ability of models to project climate change and its impacts," Climatic Change, Springer, vol. 122(1), pages 271-282, January.
    • Handle: RePEc:spr:climat:v:122:y:2014:i:1:p:271-282
    DOI: 10.1007/s10584-013-0990-2
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    References listed on IDEAS

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    1. Fredrik Boberg & Jens H. Christensen, 2012. "Overestimation of Mediterranean summer temperature projections due to model deficiencies," Nature Climate Change, Nature, vol. 2(6), pages 433-436, June.
    2. J. Refsgaard & K. Arnbjerg-Nielsen & M. Drews & K. Halsnæs & E. Jeppesen & H. Madsen & A. Markandya & J. Olesen & J. Porter & J. Christensen, 2013. "The role of uncertainty in climate change adaptation strategies—A Danish water management example," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 18(3), pages 337-359, March.
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    Cited by:

    1. Yi, Xuan & Zou, Rui & Guo, Huaicheng, 2016. "Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake," Ecological Modelling, Elsevier, vol. 327(C), pages 74-84.
    2. M. Godsoe & M. Ladd & R. Cox, 2019. "Assessing Canada’s disaster baselines and projections under the Sendai Framework for Disaster Risk Reduction: a modeling tool to track progress," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(1), pages 293-317, August.
    3. Hooftman, Danny A.P. & Bullock, James M. & Jones, Laurence & Eigenbrod, Felix & Barredo, José I. & Forrest, Matthew & Kindermann, Georg & Thomas, Amy & Willcock, Simon, 2022. "Reducing uncertainty in ecosystem service modelling through weighted ensembles," Ecosystem Services, Elsevier, vol. 53(C).

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