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Climatic Change

, Volume 117, Issue 4, pp 951–960 | Cite as

The use of the land-sea warming contrast under climate change to improve impact metrics

  • Manoj M. Joshi
  • Andrew G. Turner
  • Chris Hope
Letter

Abstract

A favoured method of assimilating information from state-of-the-art climate models into integrated assessment models of climate impacts is to use the transient climate response (TCR) of the climate models as an input, sometimes accompanied by a pattern matching approach to provide spatial information. More recent approaches to the problem use TCR with another independent piece of climate model output: the land-sea surface warming ratio (φ). In this paper we show why the use of φ in addition to TCR has such utility. Multiple linear regressions of surface temperature change onto TCR and φ in 22 climate models from the CMIP3 multi-model database show that the inclusion of φ explains a much greater fraction of the inter-model variance than using TCR alone. The improvement is particularly pronounced in North America and Eurasia in the boreal summer season, and in the Amazon all year round. The use of φ as the second metric is beneficial for three reasons: firstly it is uncorrelated with TCR in state-of-the-art climate models and can therefore be considered as an independent metric; secondly, because of its projected time-invariance, the magnitude of φ is better constrained than TCR in the immediate future; thirdly, the use of two variables is much simpler than approaches such as pattern scaling from climate models. Finally we show how using the latest estimates of φ from climate models with a mean value of 1.6—as opposed to previously reported values of 1.4—can significantly increase the mean time-integrated discounted damage projections in a state-of-the-art integrated assessment model by about 15 %. When compared to damages calculated without the inclusion of the land-sea warming ratio, this figure rises to 65 %, equivalent to almost 200 trillion dollars over 200 years.

Keywords

CMIP3 Model Integrate Assessment Model Surface Temperature Change PAGE09 Model CMIP3 Ensemble 
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

AT is supported by a NERC Postdoctoral Fellowship, grant NE/H015655/1. We would like to thank D. Frame and R. Warren for useful discussions. We acknowledge the modelling groups, the PCMDI and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Manoj M. Joshi
    • 1
  • Andrew G. Turner
    • 2
  • Chris Hope
    • 3
  1. 1.Climatic Research Unit and Tyndall Centre for Climate Change Research, School of Environmental SciencesUniversity of East AngliaNorwichUK
  2. 2.NCAS ClimateUniversity of ReadingReadingUK
  3. 3.Judge Business SchoolUniversity of CambridgeCambridgeUK

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