On the nonlinearity of spatial scales in extreme weather attribution statements

  • Oliver Angélil
  • Daíthí Stone
  • Sarah Perkins-Kirkpatrick
  • Lisa V. Alexander
  • Michael Wehner
  • Hideo Shiogama
  • Piotr Wolski
  • Andrew Ciavarella
  • Nikolaos Christidis
Article

Abstract

In the context of ongoing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporal scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.

Keywords

Attribution Extremes C20C+ AGCMs 

Notes

Acknowledgements

OA, SP-K, and LVA were supported by Grant CE110001028. In addition SP-K was supported by DECRA grant DE140100952. DS and MW were supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under contract number DE-AC02- 05CH11231. HS was supported by the Program for Risk Information on Climate Change. PW was funded by the RSA National Research Foundation grant number 90964. AC and NC were supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101) and by the EUCLEIA project funded by the European Unions Seventh Framework Programme [FP7/20072013] under grant agreement number 607085.

Supplementary material

382_2017_3768_MOESM1_ESM.pdf (39.9 mb)
Supplementary material 1 (PDF 40,860 kb)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Oliver Angélil
    • 1
  • Daíthí Stone
    • 2
  • Sarah Perkins-Kirkpatrick
    • 1
  • Lisa V. Alexander
    • 1
  • Michael Wehner
    • 2
  • Hideo Shiogama
    • 3
  • Piotr Wolski
    • 4
  • Andrew Ciavarella
    • 5
  • Nikolaos Christidis
    • 5
  1. 1.Climate Change Research Centre and ARC Centre of Excellence for Climate System ScienceUNSW AustraliaSydneyAustralia
  2. 2.Lawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.National Institute for Environmental Studies, TsukubaIbarakiJapan
  4. 4.Climate Systems Analysis Group, Environmental and Geographical ScienceUniversity of Cape TownRondeboschSouth Africa
  5. 5.Met Office Hadley CentreExeterUK

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