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Probabilistic assessment and projections of US weather and climate risks and economic damages

Abstract

Weather and climate extremes cause significant economic damages and fatalities. Over the last few decades, the frequency of these disasters and their economic damages have significantly increased in the USA. The prediction of the future evolution of these damages and their relation to global warming and US economic growth is essential for deciding on cost-efficient mitigation pathways. Here we show using a probabilistic extreme value statistics framework that both the increase in US Gross Domestic Product per capita and global warming are significant covariates in probabilistically modeling the increase in economic damages. We also provide evidence that the Pacific Decadal Oscillation affects the number of fatalities. Using the Intergovernmental Panel on Climate Change scenarios, we estimate the potential future economic risks. We find that by 2060, the extreme risks (as measured by 200-year effective return level) will have increased by 3–5.4 times. The damage costs due to extreme risks are projected to be between 0.1 and 0.7% of US Gross Domestic Product by 2060 and could reach 5–16% by 2100.

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Acknowledgments

We thank three anonymous reviewers for the helpful comments which improved the clarity of this manuscript. We acknowledge the EM-DAT database (EM-DAT: The Emergency Events Database - Universite catholique de Louvain (UCL) - CRED, D. Guha-Sapir - www.emdat.be, Brussels, Belgium) for providing us with the disaster data.

Funding

CF was financially supported by the German Research Foundation through the collaborative research center TRR181 at the University of Hamburg. MC was supported by statutory means by Cracow University of Economics.

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Correspondence to Christian L. E. Franzke.

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Franzke, C.L.E., Czupryna, M. Probabilistic assessment and projections of US weather and climate risks and economic damages. Climatic Change 158, 503–515 (2020). https://doi.org/10.1007/s10584-019-02558-8

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Keywords

  • Weather extremes
  • Climate extremes
  • Mortality
  • Non-stationarity
  • Generalized Pareto distribution