On the nonlinearity of spatial scales in extreme weather attribution statements
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.
KeywordsAttribution Extremes C20C+ AGCMs
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.
- Angélil O, Stone DA, Pall P (2014a) Attributing the probability of South African weather extremes to anthropogenic greenhouse gas emissions: Spatial characteristics. Geophysical Research Letters 41(9):3238–3243, DOI: 10.1002/2014GL059760, URL: http://doi.wiley.com/10.1002/2014GL059760Google Scholar
- Angélil O, Perkins S, Alexander L, Stone D, Donat M, Wehner M, Shiogama H, Ciavarella A, Christidis N (2016) Comparing regional precipitation and temperature extremes in climate model and reanalysis products. Weather Clim Extremes 13:35–43Google Scholar
- Bellprat O, Doblas-Reyes F (2016) Unreliable climate simulations overestimate attributable risk of extreme weather and climate events. Geophys Res LettGoogle Scholar
- Davison A, Hinkley D (1997) Bootstrap methods and their application. Cambridge university press, URL: http://www.citeulike.org/group/17501/article/12121847Google Scholar
- Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars aCM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer aJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, Mcnally aP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc 137(656):553–597. doi: 10.1002/qj.828
- Fischer EM, Knutti R (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat Clim Change 5(April):1–6. doi: 10.1038/nclimate2617
- Harrington LJ, Frame DJ, Fischer EM, Hawkins E, Joshi M, Jones CD (2016) Poorest countries experience earlier anthropogenic emergence of daily temperature extremes. Environ Res Lett 11(5):055007. doi: 10.1088/1748-9326/11/5/055007
- Herring SC, Hoerling MP, Kossin JP, Peterson TC, A SP, (2015) Extreme Events of 2014. Bulletin of the American Meteorological Society 96(12)Google Scholar
- National Academies of Sciences, Engineering, Medicine (2016) Attribution of extreme weather events in the context of climate change. The National Academies PressGoogle Scholar
- Rayner N, Parker D, Horton E, Folland C, Alexander L, Rowell D, Kent E, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late Nineteenth Century. Journal of Geophysical Research: Atmospheres 108(D14): doi: 10.1029/2002JD002670
- Seneviratne S, Nicholls N, Easterling DR, Goodess C, Kanae S, Kossin J, Luo Y, Marengo J, McInnes K, Rahimi M, Reichstein M, Sorteberg A, Vera C, Zhang X (2012) Changes in climate extremes and their impacts on the natural physical environment. Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation A Special Report of Working Groups I and II of the IPCC, Annex IIanaging the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation pp 109–230Google Scholar
- Shiogama H, Watanabe M, Imada Y, Mori M, Kamae Y, Ishii M, Kimoto M (2014) Attribution of the June-July 2013 heat wave in the southwestern United States. Sola 10:122–126. doi: 10.2151/sola.2014-025