Abstract
There is significant public and scientific interest in understanding whether and to what extent the severity and frequency of extreme events have increased in response to human influences on the climate system. As the science underpinning the field of event attribution continues to rapidly develop, there are growing expectations of faster and more accurate attribution statements to be delivered, even in the days to weeks after an extreme event occurs. As the research community looks to respond, a variety of approaches have been suggested, each with varying levels of conditioning to the observed state of the climate when the event of interest has occurred. One such approach to utilise unconditioned multi-model ensembles requires pre-computing estimates of the change in probability of occurrence for a wide range of possible ‘events’. In this study, we consider differences between event-as-class attribution statements with changes in the probability density of the distribution at the event threshold of interest. For the majority of extreme event attribution studies, it is likely that the two metrics are comparable once uncertainty estimates are considered. However, results show these two metrics can produce divergent answers from each other for moderate climatological anomalies if the present-day climate distribution experiences a substantial change in the underlying signal-to-noise ratio. As the emergent signals of climate change becomes increasingly clear, this study highlights the need for clear and explicit framing in the context of applying pre-computed attribution statements, particularly if attribution perspectives are to be included within the framework of future climate services.
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References
Allen M (2003) Liability for climate change. Nature 421:891–892. doi:10.1038/421891a
Bellprat O, Doblas-Reyes F (2016) Attribution of extreme weather and climate events overestimated by unreliable climate simulations. Geophys Res Lett 43, 2015GL067189. doi:10.1002/2015GL067189
Black MT, Karoly DJ, Rosier SM, et al (2016) The weather@home regional climate modelling project for Australia and New Zealand. Geosci Model Dev Discuss 1–28. doi: 10.5194/gmd-2016-100
Brasseur GP, Gallardo L (2016) Climate services: lessons learned and future prospects. Earths Future 4:79–89. doi:10.1002/2015EF000338
Christidis N, Stott PA, Zwiers FW (2015) Fast-track attribution assessments based on pre-computed estimates of changes in the odds of warm extremes. Clim Dyn 45:1547–1564. doi:10.1007/s00382-014-2408-x
Goddard L (2016) From science to service. Science 353:1366–1367. doi:10.1126/science.aag3087
Gregow H, Jylhä K, Mäkelä HM et al (2015) Worldwide survey of awareness and needs concerning reanalyses and respondents views on climate services. Bull Am Meteorol Soc 97:1461–1473. doi:10.1175/BAMS-D-14-00271.1
Hannart A, Carrassi A, Bocquet M et al (2016) DADA: data assimilation for the detection and attribution of weather and climate-related events. Clim Chang 136:155–174. doi:10.1007/s10584-016-1595-3
Hegerl G, Zwiers F (2011) Use of models in detection and attribution of climate change. Wiley Interdiscip Rev Clim Chang 2:570–591. doi:10.1002/wcc.121
Herring SC, Hoerling MP, Peterson TC, Stott PA (2014) Explaining extreme events of 2013 from a climate perspective. Bull Am Meteorol Soc 95:S1–S104. doi:10.1175/1520-0477-95.9.S1.1
Herring SC, Hoerling MP, Kossin JP et al (2015) Explaining extreme events of 2014 from a climate perspective. Bull Am Meteorol Soc 96:S1–S172. doi:10.1175/BAMS-ExplainingExtremeEvents2014.1
Hewitt C, Mason S, Walland D (2012) The global framework for climate services. Nat Clim Chang 2:831–832. doi:10.1038/nclimate1745
King AD, Donat MG, Fischer EM et al (2015a) The timing of anthropogenic emergence in simulated climate extremes. Environ Res Lett 10:94015. doi:10.1088/1748-9326/10/9/094015
King AD, van Oldenborgh GJ, Karoly DJ et al (2015b) Attribution of the record high Central England temperature of 2014 to anthropogenic influences. Environ Res Lett 10:54002. doi:10.1088/1748-9326/10/5/054002
King AD, Black MT, Min S-K, et al (2016) Emergence of heat extremes attributable to anthropogenic influences. Geophys Res Lett 2015GL067448. doi:10.1002/2015GL067448
Massey N, Jones R, Otto FEL et al (2015) weather@home—development and validation of a very large ensemble modelling system for probabilistic event attribution. Q J R Meteorol Soc 141:1528–1545. doi:10.1002/qj.2455
Meredith EP, Semenov VA, Maraun D et al (2015) Crucial role of Black Sea warming in amplifying the 2012 Krymsk precipitation extreme. Nat Geosci 8:615–619. doi:10.1038/ngeo2483
National Academies of Sciences, Engineering, and Medicine, Committee on Extreme Weather Events and Climate Change Attribution, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies (2016) Attribution of extreme weather events in the context of climate change. National Academies Press, Washington, D.C
Otto FEL (2016) Extreme events: the art of attribution. Nat Clim Chang 6:342–343. doi:10.1038/nclimate2971
Otto FEL, van Oldenborgh GJ, Eden J et al (2016) The attribution question. Nat Clim Chang 6:813–816. doi:10.1038/nclimate3089
Peterson TC, Stott PA, Herring S (2013) Explaining extreme events of 2012 from a climate perspective. Bull Am Meteorol Soc 94:S1–S74. doi:10.1175/BAMS-D-13-00085.1
Shepherd TG (2016) A common framework for approaches to extreme event attribution. Curr Clim Change Rep 2:28–38. doi:10.1007/s40641-016-0033-y
Stone DA, Allen MR (2005) The end-to-end attribution problem: from emissions to impacts. Clim Chang 71:303–318. doi:10.1007/s10584-005-6778-2
Stott P (2016) How climate change affects extreme weather events. Science 352:1517–1518. doi:10.1126/science.aaf7271
Stott PA, Stone DA, Allen MR (2004) Human contribution to the European heatwave of 2003. Nature 432:610–614. doi:10.1038/nature03089
Stott PA, Christidis N, Otto FEL et al (2016) Attribution of extreme weather and climate-related events. Wiley Interdiscip Rev Clim Chang 7:23–41. doi:10.1002/wcc.380
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi:10.1175/BAMS-D-11-00094.1
van Oldenborgh GJ, Otto FEL, Haustein K, Cullen H (2015) Climate change increases the probability of heavy rains like those of storm Desmond in the UK—an event attribution study in near-real time. Hydrol Earth Syst Sci Discuss 2015:13197–13216. doi:10.5194/hessd-12-13197-2015
Vautard R, Yiou P, van Oldenborgh G-J et al (2015) Extreme fall 2014 precipitation in the Cévennes mountains. Bull Am Meteorol Soc 96:S56–S60. doi:10.1175/BAMS-D-15-00088.1
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The author would like to thank Fraser Lott, David Frame and Friederike Otto and three reviewers for their helpful discussions and comments on earlier versions of the manuscript.
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Harrington, L.J. Investigating differences between event-as-class and probability density-based attribution statements with emerging climate change. Climatic Change 141, 641–654 (2017). https://doi.org/10.1007/s10584-017-1906-3
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DOI: https://doi.org/10.1007/s10584-017-1906-3