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Climatic Change

, Volume 128, Issue 3–4, pp 293–306 | Cite as

Analysing uncertainties in climate change impact assessment across sectors and scenarios

  • Calum BrownEmail author
  • Evan Brown
  • Dave Murray-Rust
  • George Cojocaru
  • Cristina Savin
  • Mark Rounsevell
Article

Abstract

Many models have been developed to explore the likely consequences of climate change. These models tend to focus on single physical or socio-economic sectors and their processes, and neglect the many feedbacks that occur between the different components of the real world. To overcome this problem, models are increasingly being combined in integrated assessment platforms (IAPs), of which the CLIMSAVE IAP is an example, modelling cross-sectoral impacts, adaptation and vulnerability to climate change in Europe by combining 10 different meta-models that focus on specific sectors. Where models are combined in this way, however, attention must be given to the potential errors and uncertainties that integration might introduce. We present a quantitative uncertainty analysis of selected outputs of the CLIMSAVE IAP based on creating and sampling from probability density functions (PDFs) of each of the IAP’s input variables to take account of model and scenario uncertainty. We find limited uncertainties in aggregate outputs of the IAP, which allow specific impacts to be predicted with definable levels of confidence. However, we also find substantial overlap between different socio-economic scenarios at the European scale, suggesting that changes to socio-economic conditions cannot reliably overcome climate-related uncertainty. Nevertheless, there is evidence that particular adaptation actions may significantly alter the impacts of climate change, especially at local or national scales.

Keywords

Climate Change Impact Flood Protection European Scale Credible Range Initial Sample Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was funded from the European Community’s Seventh Framework Programme ([FP7/2007-2013]) under grant agreement number 244031 (the CLIMSAVE Project). The authors would like to thank Paula Harrison and Rob Dunford for discussions about this work and comments on manuscript drafts, and two anonymous reviewers for their constructive and insightful comments.

Supplementary material

10584_2014_1133_MOESM1_ESM.pdf (347 kb)
ESM 1 (PDF 347 kb)
10584_2014_1133_MOESM2_ESM.pdf (613 kb)
ESM 2 (PDF 612 kb)
10584_2014_1133_MOESM3_ESM.pdf (322 kb)
ESM 3 (PDF 322 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Calum Brown
    • 1
    Email author
  • Evan Brown
    • 1
  • Dave Murray-Rust
    • 2
  • George Cojocaru
    • 3
    • 4
  • Cristina Savin
    • 3
  • Mark Rounsevell
    • 1
  1. 1.School of GeosciencesUniversity of EdinburghEdinburghUK
  2. 2.School of InformaticsUniversity of Edinburgh Appleton TowerEdinburghUK
  3. 3.TIAMASG FoundationBucharestRomania
  4. 4.National Institute of Research and Development for Pedology, Agrochemistry and Environment Protection– ICPA BucharestBucharestRomania

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