Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach
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The conventional approach to detecting and attributing climate change impacts on extreme weather events is generally based on frequentist statistical inference wherein a null hypothesis of no influence is assumed, and the alternative hypothesis of an influence is accepted only when the null hypothesis can be rejected at a sufficiently high (e.g., 95% or “p = 0.05”) level of confidence. Using a simple conceptual model for the occurrence of extreme weather events, we show that if the objective is to minimize forecast error, an alternative approach wherein likelihoods of impact are continually updated as data become available is preferable. Using a simple “proof-of-concept,” we show that such an approach will, under rather general assumptions, yield more accurate forecasts. We also argue that such an approach will better serve society, in providing a more effective means to alert decision-makers to potential and unfolding harms and avoid opportunity costs. In short, a Bayesian approach is preferable, both empirically and ethically.
We thank James V. Stone, Psychology Department, Sheffield University, Sheffield, England for kindly posting the Bayesian coin flipping routine (MatLab code version 7.5. downloaded from http://jim-stone.staff.shef.ac.uk/BayesBook/Matlab). We thank two anonymous reviewers for the helpful comments on the initial draft of this article.
- Berry DA (1987) Interim analysis in clinical trials: the role of the likelihood principle. Amer Stat 41:117–122Google Scholar
- Bindoff NL, Stott PA, Achuta Rao KM, Allen MR, Gillett N, Gutzler D, Hansingo K, Hegerl G, Hu Y, Jain S, Mokhov II, Overland J, Perlwitz J, Sebbari R, Zhang X (2013) Detection and attribution of climate change: from global to regional. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- Gigerenzer G, Edwards A, (2003) Simple tools for understanding risks: from innumeracy to insight, British Medical Journal, 327, 741–744Google Scholar
- Hegerl GC, Hoegh-Guldberg O, Casassa G, Hoerling MP, Kovats RS, Parmesan C, Pierce DW, Stott PA (2010) Good practice guidance paper on detection and attribution related to anthropogenic climate change. In: Stocker TF, Field CB, Qin D, Barros V, Plattner GK, Tignor M, Midgley PM, Ebi KL (eds) Meeting report of the intergovernmental panel on climate change expert meeting on detection and attribution of anthropogenic climate change. IPCC Working Group I Technical Support Unit, University of Bern, Bern p 8Google Scholar
- Herring SC, Hoerling MP, Kossin JP, Peterson TC, Stott PA (2015) Explaining extreme events of 2014 from a climate perspective. Bull Am Meteorol Soc 96(12):S1–S172Google Scholar
- IPCC (2013) Summary for policymakers. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- National Research Council (2016) Attribution of extreme weather events in the context of climate change. National Academies Press, Washington, DCGoogle Scholar
- Oppenheimer M, Oreskes N, Jamieson D, Brysse K, O’Reilly J, Shindell M, (2017) Assessing assessments: scientific knowledge for public policy, University of Chicago Press. (forthcoming)Google Scholar