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.
Similar content being viewed by others
References
Bouwer LM (2011) Have disaster losses increased due to anthropogenic climate change? Bull Amer Meteorol Soc 92(1):39
Bouwer LM (2013) Projections of future extreme weather losses under changes in climate and exposure. Risk Anal 33(5):915–930
Burke M, Davis WM, Diffenbaugh NS (2018) Large potential reduction in economic damages under UN mitigation targets. Nature 557(1):549–553
Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media, Berlin
Campiglio E, Dafermos Y, Monnin P, Ryan-Collins J, Schotten G, Tanaka M (2018) Climate change challenges for central banks and financial regulators. Nat Clim Chang 8:462–468
Chavas D, Yonekura E, Karamperidou C, Cavanaugh N, Serafin K (2012) US Hurricanes and economic damage: extreme value perspective. Nat Hazard Rev 14 (4):237–246
Cheng L, AghaKouchak A, Gilleland E, Katz RW (2014) Non-stationary extreme value analysis in a changing climate. Clim Change 127(2):353–369
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
Church JA, White NJ (2011) Sea-level rise from the late 19th to the early 21st century. Surv Geophys 32(4-5):585–602
Coles S (2001) An introduction to statistical modeling of extreme values, vol 208. Springer, Berlin
Cooley D (2009) Extreme value analysis and the study of climate change. Clim Change 97(1–2):77
Cooley D Aghakouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S (eds) (2013) Extremes in a changing climate. Springer, Berlin
Diffenbaugh NS, Scherer M, Trapp RJ (2013) Robust increases in severe thunderstorm environments in response to greenhouse forcing. Proc Nat Acad Sci USA 110(41):16361–16366
Elsner JB, Kossin JP, Jagger TH (2008) The increasing intensity of the strongest tropical cyclones. Nature 455 , pages= 92–95,
Estrada F, Botzen WW, Tol RS (2015) Economic losses from us hurricanes consistent with an influence from climate change. Nature Geoscience
Field CB, Barros V, Stocker TF, Dahe Q (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
Franzke CLE (2017) Impacts of a changing climate on economic damages and insurance. Economics of Disasters and Climate Change 1(1):95–110
Füssel HM (2010) Modeling impacts and adaptation in global iams. WIREs Clim Change 1(2):288–303
Gilleland E, Katz RW (2016) Extremes 2.0: an extreme value analysis package in r. J Stat Software 72:1–39. https://doi.org/10.18637/jss.v072.i08
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–S157
Hessl AE, McKenzie D, Schellhaas R (2004) Drought and pacific decadal oscillation linked to fire occurrence in the inland Pacific Northwest. Ecol Appl 14 (2):425–442
Heyerdahl EK, McKenzie D, Daniels LD, Hessl AE, Littell JS, Mantua NJ (2008) Climate drivers of regionally synchronous fires in the inland northwest (1651–1900). Int J Wildland Fire 17(1):40–49
Hoeppe P (2016) Trends in weather related disasters–consequences for insurers and society. Wea Clim Extr
Hsiang S (2016) Climate econometrics. Ann Rev Resour Econ 8:43–75
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
Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Menne MJ, Smith TM, Vose RS, Zhang HM (2017) Extended reconstructed sea surface temperature, version 5 (ersstv5): upgrades, validations, and intercomparisons. J Climate 30(20):8179–8205
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
Katz RW, Parlange MB, Naveau P (2002) Statistics of extremes in hydrology. Adv Water Resour 25(8-12):1287–1304
Klotzbach PJ (2007) Recent developments in statistical prediction of seasonal Atlantic basin tropical cyclone activity. Tellus 59(4):511–518
Klotzbach PJ, Bowen SG, Pielke R Jr, Bell M (2018) Continental United States hurricane landfall frequency and associated damage: observations and future risks. Bull Am Meteorol Soc 99:1359–1376
Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, Srivastava A, Sugi M (2010) Tropical cyclones and climate change. Nat Geosci 3(3):157–163
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 2016
Mantua NJ, Hare SR (2002) The pacific decadal oscillation. J Oceano 58 (1):35–44
McCabe GJ, Palecki MA, Betancourt JL (2004) Pacific and atlantic ocean influences on multidecadal drought frequency in the united states. Proc Nat Acad Sci USA 101(12):4136–4141. https://doi.org/10.1073/pnas.0306738101. http://www.pnas.org/content/101/12/4136
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
Monier E, Paltsev S, Sokolov A, Chen YHH, Gao X, Ejaz Q, Couzo E, Schlosser CA, Dutkiewicz S, Fant C, et al. (2018) Toward a consistent modeling framework to assess multi-sectoral climate impacts. Nat Commun 9(1):660
Morice CP, Kennedy JJ, Rayner NA, Jones PD (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the hadcrut4 data set. J Geophys Res 117(D8)
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 2018
Nadarajah S (2005) Extremes of daily rainfall in west Central Florida. Clim Change 69(2-3):325–342
Neumayer E, Barthel F (2011) Normalizing economic loss from natural disasters: a global analysis. Glob Environ Chang 21(1):13–24
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
Nordhaus W (2018) Evolution of modeling of the economics of global warming: changes in the dice model, 1992–2017. Clim Change 148(4):623–640
Nordhaus WD (1992) An optimal transition path for controlling greenhouse gases. Science 258(5086):1315–1319
Pielke RA Jr, Gratz J, Landsea CW, Collins D, Saunders MA, Musulin R (2008) Normalized hurricane damage in the united states: 1900–2005. Nat Hazard Rev 9(1):29–42
Riahi K, Van Vuuren DP, Kriegler E, Edmonds J, O’neill BC, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O, et al. (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Chang 42:153–168
Rogelj J, Den Elzen M, Höhne N, Fransen T, Fekete H, Winkler H, Schaeffer R, Sha F, Riahi K, Meinshausen M (2016) Paris agreement climate proposals need a boost to keep warming well below 2 c. Nature 534(7609):631
Rootzén H, Katz RW (2013) Design life level: quantifying risk in a changing climate. Wat Resources Res 49(9):5964–5972
Stern N (2007) The economics of climate change: the Stern review. Cambridge University Press, Cambridge
Stern N (2016) Economics: current climate models are grossly misleading. Nature 530(7591):407–409
Van Oldenborgh G, Te Raa L, Dijkstra H, Philip S (2009) Frequency-or amplitude-dependent effects of the atlantic meridional overturning on the tropical pacific ocean. Ocean Sci 5(3):293–301
Villarini G, Vecchi GA (2012) North atlantic power dissipation index (pdi) and accumulated cyclone energy (ace): Statistical modeling and sensitivity to sea surface temperature changes. J Climate 25(2):625–637
Walsh KJ, McBride JL, Klotzbach PJ, Balachandran S, Camargo SJ, Holland G, Knutson TR, Kossin JP, Tc Lee, Sobel A, et al. (2016) Tropical cyclones and climate change. WIREs Clim Change 7(1):65–89
Weinkle J, Landsea C, Collins D, Musulin R, Crompton RP, Klotzbach PJ, Pielke R (2018) Normalized hurricane damage in the continental United States 1900–2017. Nature Sustainability 1:808–813
Wilks DS (2011) Statistical methods in the atmospheric sciences, vol 100. Academic Press, Cambridge
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-019-02558-8