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.
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References
Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419(6903):224–232. doi:10.1038/nature01092
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/10.1002/2014GL059760
Angélil O, Stone DA, Tadross M, Tummon F, Wehner MF, Knutti R (2014b) Attribution of extreme weather to anthropogenic greenhouse gas emissions: sensitivity to spatial and temporal scales. Geophys Res Lett 41(6):2150–2155. doi:10.1002/2014GL059234
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 13:35–43
Bellprat O, Doblas-Reyes F (2016) Unreliable climate simulations overestimate attributable risk of extreme weather and climate events. Geophys Res Lett
Christensen JH, Christensen OB (2003) Climate modelling: severe summertime flooding in Europe. Nature 421(6925):805–806. doi:10.1038/421805a
Christidis N, Stott PA, Scaife AA, Arribas A, Jones GS, Copsey D, Knight JR, Tennant WJ (2013) A new HadGEM3-A-based system for attribution of weather- and climate-related extreme events. J Clim 26:2756–2783. doi:10.1175/JCLI-D-12-00169.1
Davison A, Hinkley D (1997) Bootstrap methods and their application. Cambridge university press, URL: http://www.citeulike.org/group/17501/article/12121847
Davison A, Huser R (2015) Statistics of Extremes. Annual Review of Statistics and Its Application 2:203–235. doi:10.1179/003962659792003612, doi:10.1146/annurev-statistics-010814-020133
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
Dole R, Hoerling M, Perlwitz J, Eischeid J, Pegion P, Zhang T, Quan XW, Xu T, Murray D (2011) Was there a basis for anticipating the 2010 Russian heat wave? Geophys Res Lett 38(6):1–6. doi:10.1029/2010GL046582
Donat MG, Sillmann J, Wild S, Alexander L, Lippmann T, Zwiers FW (2014) Consistency of temperature and precipitation extremes across various global gridded in situ and reanalysis datasets. J Clim 27(13):5019–5035. doi:10.1175/JCLI-D-13-00405.1
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
Hawkins E (2012) Time of emergence of climate signals. Geophysical Research Letters 39:1–7. doi:10.1029/2011GL050087
Herring SC, Hoerling MP, Peterson TC, Stott PA (2014) Explaining extreme events of 2013 from a climate perspective. Bull Am Meteorol Soc 95(9):S1–S96
Herring SC, Hoerling MP, Kossin JP, Peterson TC, A SP, (2015) Extreme Events of 2014. Bulletin of the American Meteorological Society 96(12)
Hurrell JW, Hack JJ, Shea D, Caron JM, Rosinski J (2008) A new sea surface temperature and sea ice boundary dataset for the community atmosphere model. J Clim 21(19):5145–5153. doi:10.1175/2008JCLI2292.1
Jones PW (1999) First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon Weather Rev 127(9):2204–2210
Jones RH, Westra S, Sharma A (2010) Observed relationships between extreme sub daily precipitation, surface temperature, and relative humidity. Geophys Res Lett 37(September):1–5. doi:10.1029/2010GL045081
National Academies of Sciences, Engineering, Medicine (2016) Attribution of extreme weather events in the context of climate change. The National Academies Press
Pall P, Allen MR, Stone DA (2007) Testing the Clausius-Clapeyron constraint on changes in extreme precipitation under CO2 warming. Clim Dyn 28(4):351–363. doi:10.1007/s00382-006-0180-2
Pall P, Aina T, Stone DA, Stott PA, Nozawa T, Hilberts AGJ, Lohmann D, Allen MR (2011) Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470(7334):382–385. doi:10.1038/nature09762
Peterson TC, Stott PA, Herring SC (2012) Explaining extreme events of 2011 from a climate perspective. Bull Am Meteorol Soc 93(7):1041–1067. doi:10.1175/BAMS-D-12-00021.1
Peterson TC, Hoerling MP, Stott PA, Herring SC (2013) Explaining extreme events of 2012 from a climate perspective. Bull Am Meteorol Soc 94(9):S1–S74
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
Robine JM, Cheung SLK, Le Roy S, Van Oyen H, Griffiths C, Michel JP, Herrmann FR (2008) Death toll exceeded 70,000 in Europe during the summer of 2003. Comptes Rendus Biol 331(2):171–178. doi:10.1016/j.crvi.2007.12.001
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 109–230
Shiogama H, Watanabe M, Imada Y, Mori M, Ishii M, Kimoto M (2013) An event attribution of the 2010 drought in the South Amazon region using the MIROC5 model. Atmos Sci Lett 14(May):170–175. doi:10.1002/asl2.435
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
Stott PA, Jones GS (2009) Variability of high latitude amplification of anthropogenic warming. Geophys Res Lett 36(10):1–7. doi:10.1029/2009GL037698
Stott PA, Stone DA, Allen MR (2004) Human contribution to the European heatwave of 2003. Nature 432(7017):610–4. doi:10.1029/2001JB001029. http://dx.doi.org/10.1038/nature03089
Westra S, Fowler H, Evans J, Alexander LV, Berg P, Johnson F, Kendon E, Lenderink G, Roberts N (2014) Future changes to the intensity and frequency of short-duration extreme rainfall. Rev Geophys 52:522–555. doi:10.1002/2014RG000464
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.
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Angélil, O., Stone, D., Perkins-Kirkpatrick, S. et al. On the nonlinearity of spatial scales in extreme weather attribution statements. Clim Dyn 50, 2739–2752 (2018). https://doi.org/10.1007/s00382-017-3768-9
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DOI: https://doi.org/10.1007/s00382-017-3768-9