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Climatic Change

, Volume 112, Issue 2, pp 217–231 | Cite as

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates

  • Simon N. GoslingEmail author
  • Glenn R. McGregor
  • Jason A. Lowe
Article

Abstract

The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach. This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate change projections are from a global climate model or a regional climate model. The results demonstrate the importance of presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk management framework.

Keywords

Regional Climate Model Emission Scenario Ensemble Member Generalize Extreme Value Climate Change Impact Assessment 
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

Acknowledgements

This study was supported with PhD funding from the UK Natural Environment Research Council (NERC) and a Cooperative Awards in Sciences of the Environment (CASE) award from the UK Met Office while the lead author was a PhD candidate at King’s College London, Department of Geography. Jason Lowe was supported by the Joint DECC and Defra Integrated Climate Programme - DECC/Defra (GA01101). Three anonymous reviewers are thanked for taking the time to read and comment on an earlier version of the manuscript.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Simon N. Gosling
    • 1
    Email author
  • Glenn R. McGregor
    • 2
  • Jason A. Lowe
    • 3
  1. 1.School of GeographyThe University of NottinghamNottinghamUK
  2. 2.School of EnvironmentThe University of AucklandAucklandNew Zealand
  3. 3.The Met Office Hadley CentreExeterUK

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