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Navigating Cascades of Uncertainty — As Easy as ABC? Not Quite

    The uncertainties in scientific studies for climate risk management can be investigated at three levels of complexity: “ABC”. The most sophisticated involves “Analyzing” the full range of uncertainty with large multi-model ensemble experiments. The simplest is about “Bounding” the uncertainty by defining only the upper and lower limits of the likely outcomes. The intermediate approach, “Crystallizing” the uncertainty, distills the full range to improve the computational efficiency of the “Analyze” approach. Modelers typically dictate the study design, with decision-makers then facing difficulties when interpreting the results of ensemble experiments. We assert that to make science more relevant to decision-making, we must begin by considering the applications of scientific outputs in facilitating decision-making pathways, particularly when managing extreme events. This requires working with practitioners from outset, thereby adding “D” for “Decision-centric” to the ABC framework.

    References

    • Caminade, C, Kovats S, Rocklov J, Tompkins AM, Morse AP, Colón-González, FJ, Stenlund H, Martens P and Lloyd SJ [2014] Impact of climate change on global malaria distribution. Proceedings of the National Academy of Sciences of the United States of America, 111: 3286–3291. CrossrefGoogle Scholar
    • Christierson, BV, Vidal J-P and Wade SD [2012] Using ukcp09 probabilistic climate information for UK water resource planning. Journal of Hydrology, 424–425: 48–67. CrossrefGoogle Scholar
    • Clark, MP, Wilby RL, Gutmann ED, Vano JA, Gangopadhyay S, Wood AW, Fowler HJ, Prudhomme C, Arnold JR and Brekke LD [2016] Characterizing uncertainty of the hydrologic impacts of climate change. Current Climate Change Reports, 2: 55–64. CrossrefGoogle Scholar
    • Dale, A, Fant C, Strzepek K, Lickley M and Solomon S [2017] Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-saharan Africa. Earth’s Future, 5: 337–353. CrossrefGoogle Scholar
    • Defra, DECC, Met Office, BADC, Newcastle University, University of East Anglia, Environment Agency, Tyndall Centre & Proudman Oceanographic Laboratory (2009). Ukcp09. Available at: http://ukclimateprojections.metoffice.gov.uk/. Google Scholar
    • Deryng, D, Elliott J, Folberth C, Müller C, Pugh TAM, Boote KJ, Conway D, Ruane AC, Gerten D, Jones JW, Khabarov N, Olin S, Schaphoff S, Schmid E, Yang H and Rosenzweig C [2016] Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nature Climate Change, 6: 786. CrossrefGoogle Scholar
    • Éditeur Officiel du Québec (2017). Dam Safety Regulation: Dam Safety Act. Chapter S-3.1.01 R. 1, http://legisquebec.gouv.qc.ca/en/ShowDoc/cr/S-3.1.01,%20r.%201. Google Scholar
    • IPCC [2014] Climate Change 2014: Synthesis Report. In: Pachauri RKMeyer LA (eds.) Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland, p. 151. Google Scholar
    • MacLachlan, C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P and Madec G [2015] Global seasonal forecast system version 5 (Glosea5): A high-resolution seasonal forecast system. Quarterly Journal of the Royal Meteorological Society, 141: 1072–1084. CrossrefGoogle Scholar
    • Munsell, EB and Zhang F [2014] Prediction and uncertainty of hurricane sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system assimilating airborne doppler radar observations. Journal of Advances in Modeling Earth Systems, 6: 38–58. CrossrefGoogle Scholar
    • Office for Nuclear Regulation & Environment Agency (2017). Joint Advice Note: Principles for Flood and Coastal Erosion Risk Management, Version 1, http://www.onr.org.uk/documents/2017/principles-for-flood-and-coastal-erosion-risk-management.pdf. Google Scholar
    • Schneider, SH [1983] CO2, Climate and Society: A Brief Overview. In: Chen RSBoulding ESchneider SH (eds.) Social Science Research and Climate Change: An Interdisciplinary Appraisal. Dordrecht: Springer Netherlands. CrossrefGoogle Scholar
    • Taylor, KE, Stouffer RJ and Meehl GA [2011] An overview of Cmip5 and the experiment design. Bulletin of the American Meteorological Society, 93: 485–498. CrossrefGoogle Scholar
    • Veldkamp, TIE, Wada Y, Aerts JCJH, Döll P, Gosling SN, Liu J, Masaki Y, Oki T, Ostberg S, Pokhrel Y, Satoh Y, Kim H and Ward PJ [2017] Water scarcity hotspots travel downstream due to human interventions in the 20th and 21st century. Nature Communications, 8: 15697. CrossrefGoogle Scholar
    • Wade, S, Sanderson M, Golding N, Lowe J, Betts R, Reynard N, Kay A, Stewart L, Prudhomme C, Shaffrey L, Lloyg-Hughes B and Harvey B [2015] Developing H++ Climate Change Scenarios for Heat Waves, Droughts, Floods, Windstorms and Cold Snaps. London: Committee on Climate Change. Google Scholar
    • Warszawski, L, Frieler K, Huber V, Piontek F, Serdeczny O and Schewe J [2014] The inter-sectoral impact model intercomparison project (ISI–MIP): Project framework. Proceedings of the National Academy of Sciences of the United States of America, 111: 3228–3232. CrossrefGoogle Scholar
    • Wilby, RL and Dessai S [2010] Robust adaptation to climate change. Weather, 65: 180–185. CrossrefGoogle Scholar