Probabilistic assessment and projections of US weather and climate risks and economic damages
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.
KeywordsWeather extremes Climate extremes Mortality Non-stationarity Generalized Pareto distribution
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.
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.
- Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media, BerlinGoogle Scholar
- Christensen P, Gillingham K, Nordhaus W (2018) Uncertainty in forecasts of long-run economic growth. Proc Nat Acad Sci USA. https://doi.org/10.1073/pnas.1713628115, http://www.pnas.org/content/early/2018/05/08/1713628115 CrossRefGoogle Scholar
- Cooley D Aghakouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S (eds) (2013) Extremes in a changing climate. Springer, BerlinGoogle Scholar
- Estrada F, Botzen WW, Tol RS (2015) Economic losses from us hurricanes consistent with an influence from climate change. Nature GeoscienceGoogle Scholar
- Guha-Sapir D, Below R (2002) The quality and accuracy of disaster data: a comparative analyse of 3 global data sets. Tech. Rep. 191, Disaster Management facility, World Bank, Working paper ID, URL https://dial.uclouvain.be/downloader/downloader.php?pid=boreal:179722&datastream=PDF_01, last Accessed 22 03 2018
- Guha-Sapir D, Hoyois P, Wallemacq P, Below R (2017) Annual disaster statistical review 2016. Tech. rep., Centre for Research on the Epidemology of Disasters (CRED), http://emdat.be/sites/default/files/adsr_2016.eps, last Accessed 12 01 2018
- Herring SC, Christidis N, Hoell A, Kossin JP, Schreck CJ III, Stott PA (2018) Explaining extreme events of 2016 from a climate perspective. Bull Am Meteorol Soc 99(1):S1–S157Google Scholar
- Hoeppe P (2016) Trends in weather related disasters–consequences for insurers and society. Wea Clim ExtrGoogle Scholar
- Hsiang S, Kopp R, Jina A, Rising J, Delgado M, Mohan S, Rasmussen DJ, Muir-Wood R, Wilson P, Oppenheimer M, Larsen K, Houser T (2017) Estimating economic damage from climate change in the United States. Science 356(6345):1362–1369. https://doi.org/10.1126/science.aal4369. http://science.sciencemag.org/content/356/6345/1362 CrossRefGoogle Scholar
- Katz RW (2015) Economic impact of extreme events, American geophysical union (AGU), chap 16, pp 205–217. https://doi.org/10.1002/9781119157052.ch16, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/9781119157052.ch16 CrossRefGoogle Scholar
- Kunreuther HC, Michel-Kerjan EO (2007) Climate Change, insurability of large-scale disasters and the emerging liability challenge. Tech. rep. National Bureau of Economic Research, last Accessed 21 02 2016Google Scholar
- Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque JF, Matsumoto K, Montzka SA, Raper SCB, Riahi K, Thomson A, Velders GJM, van Vuuren DP (2011) The rcp greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 109(1):213. https://doi.org/10.1007/s10584-011-0156-z CrossRefGoogle Scholar
- Munich Re (2018a) The natural disasters of 2018 in figures. Tech. rep., Munich Re, https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/the-natural-disasters-of-2018-in-figures.html https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/the-natural-disasters-of-2018-in-figures.html https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/the-natural-disasters-of-2018-in-figures.html , last Accessed 13 06 2019
- Munich Re (2018b) Topics geo: Natural catastrophes 2017: analyses, assessments, positions. Tech. rep., Munich Re, last Accessed 10 05 2018Google Scholar
- Newman M, Alexander MA, Ault TR, Cobb KM, Deser C, Lorenzo ED, Mantua NJ, Miller AJ, Minobe S, Nakamura H, Schneider N, Vimont DJ, Phillips AS, Scott JD, Smith CA (2016) The pacific decadal oscillation, revisited. J Climate 29(12):4399–4427. https://doi.org/10.1175/JCLI-D-15-0508.1 CrossRefGoogle Scholar
- Wilks DS (2011) Statistical methods in the atmospheric sciences, vol 100. Academic Press, CambridgeGoogle Scholar