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  • Perspective
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Future climate risk from compound events

An Author Correction to this article was published on 20 June 2018

This article has been updated

Abstract

Floods, wildfires, heatwaves and droughts often result from a combination of interacting physical processes across multiple spatial and temporal scales. The combination of processes (climate drivers and hazards) leading to a significant impact is referred to as a ‘compound event’. Traditional risk assessment methods typically only consider one driver and/or hazard at a time, potentially leading to underestimation of risk, as the processes that cause extreme events often interact and are spatially and/or temporally dependent. Here we show how a better understanding of compound events may improve projections of potential high-impact events, and can provide a bridge between climate scientists, engineers, social scientists, impact modellers and decision-makers, who need to work closely together to understand these complex events.

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Fig. 1: Extended risk framework.
Fig. 2: Distribution of climatic drivers and associated hazards.
Fig. 3: Illustration of different possibilities to simulate potentially critical events.

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  • 20 June 2018

    In the version of this Perspective originally published, the names of the authors of reference 13 were presented incorrectly, with their first names in place of their last names; this has been corrected accordingly to read: “Diakakis, M., Deligiannakis, G., Katsetsiadou, K. & Lekkas, E.”.

References

  1. Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & García-Herrera, R.. The hot summer of 2010: redrawing the temperature record map of Europe. Science 332, 220–224 (2011).

    CAS  Google Scholar 

  2. Hauser, M., Orth, R. & Seneviratne, S. I. Role of soil moisture versus recent climate change for the 2010 heat wave in western Russia. Geophys. Res. Lett. 43, 2819–2826 (2016).

    Google Scholar 

  3. Witte, J. C. et al. NASA A-Train and Terra observations of the 2010 Russian wildfires. Atmos. Chem. Phys. 11, 9287–9301 (2011).

    CAS  Google Scholar 

  4. Grumm, R. H. The central European and Russian heat event of July–August 2010. Bull. Am. Meteorol. Soc. 92, 1285–1296 (2011).

    Google Scholar 

  5. Konovalov, I. B., Beekmann, M., Kuznetsova, I. N., Yurova, A. & Zvyagintsev, A. M. Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region. Atmos. Chem. Phys. 11, 10031–10056 (2011).

    CAS  Google Scholar 

  6. Shaposhnikov, D. et al. Mortality related to air pollution with the Moscow heat wave and wildfire of 2010. Epidemiology 25, 359–364 (2014).

    Google Scholar 

  7. Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017). This article provides the first global quantification of compound hot and dry summers and shows that they will occur more frequently in the future in many regions because of a stronger negative correlation between temperature and precipitation.

    Google Scholar 

  8. Otto, F. E. L., Massey, N., van Oldenborgh, G. J., Jones, R. G. & Allen, M. R. Reconciling two approaches to attribution of the 2010 Russian heat wave. Geophys. Res. Lett. 39, L04702 (2012).

    Google Scholar 

  9. Le Page, Y. et al. Global fire activity patterns (1996–2006) and climatic influence: an analysis using the World Fire Atlas. Atmos. Chem. Phys. 8, 1911–1924 (2008).

    Google Scholar 

  10. Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).

    CAS  Google Scholar 

  11. Moftakhari, H. R., Salvadori, G., AghaKouchak, A., Sanders, B. F. & Matthew, R. A. Compound effects of sea level rise and fluvial flooding. Proc. Natl Acad. Sci. USA 114, 9785–9790 (2017).

    CAS  Google Scholar 

  12. Wahl, T., Jain, S., Bender, J., Meyers, S. D. & Luther, M. E. Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nat. Clim. Change 5, 1093–1097 (2015). This article provides a quantification of flood risk associated with compound storm surge and heavy precipitation for US coasts and demonstrates that this risk has increased due to changes in the joint distributions of storm surge and precipitation.

    Google Scholar 

  13. Diakakis, M., Deligiannakis, G., Katsetsiadou, K. & Lekkas, E. Hurricane Sandy mortality in the Caribbean and continental North America. Disaster Prev. Manage 24, 132–148 (2015).

    Google Scholar 

  14. FEMA National Preparedness Report (Homeland Security, 2013).

  15. Orton, P. M. et al. A validated tropical‐extratropical flood hazard assessment for New York Harbor. J. Geophys. Res. Oceans 121, 8904–8929 (2016).

    Google Scholar 

  16. Sopkin, K. L. et al. Hurricane Sandy: Observations and Analysis of Coastal Change Report No. 2331-1258 (US Geological Survey, 2014).

  17. Emanuel, K. Assessing the present and future probability of Hurricane Harvey’s rainfall. Proc. Natl Acad. Sci. USA 114, 12681–12684 (2017).

    CAS  Google Scholar 

  18. Carlowicz, M. Harvey churned up and cooled down the gulf. Earth Observatory (3 September 2017).

  19. Welton, G. The impact of Russia’s 2010 grain export ban. Oxfam Policy Pract. Agric. Food Land 11(5), 76–107 (Oxfam International, 2011).

  20. Werrell, C. E., Femia, F. & Sternberg, T. Did we see it coming? State fragility, climate vulnerability, and the uprisings in Syria and Egypt. SAIS Rev. Int. Aff. 35, 29–46 (2015).

    Google Scholar 

  21. Houze, R. A., Rasmussen, K. L., Medina, S., Brodzik, S. R. & Romatschke, U. Anomalous atmospheric events leading to the summer 2010 floods in Pakistan. Bull. Am. Meteorol. Soc. 92, 291–298 (2011).

    Google Scholar 

  22. Lau, W. K. M. & Kim, K.-M. The 2010 Pakistan flood and Russian heat wave: teleconnection of hydrometeorological extremes. J. Hydrometeorol. 13, 392–403 (2012).

    Google Scholar 

  23. Milly, P. C. D., Wetherhald, R. T., Dunne, K. A. & Delworth, T. L. Increasing risk of great floods in a changing climate. Nature 415, 514–517 (2002).

    CAS  Google Scholar 

  24. Mehran, A. et al. Compounding impacts of human-induced water stress and climate change on water availability. Sci. Rep. 7, 6282 (2017).

    Google Scholar 

  25. Blöschl, G. et al. Changing climate shifts timing of European floods. Science 357, 588–590 (2017).

    Google Scholar 

  26. AghaKouchak, A., Cheng, L., Mazdiyasni, O. & Farahmand, A. Global warming and changes in risk of concurrent climate extremes: insights from the 2014 California drought. Geophys. Res. Lett. 41, 8847–8852 (2014). This is the first estimation of the likelihood of concurrent drought and heat, based on the California drought of 2014.

    Google Scholar 

  27. Williams, J. W., Jackson, S. T. & Kutzbach, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl Acad. Sci. USA 104, 5738–5742 (2007).

    CAS  Google Scholar 

  28. Leonard, M. et al. A compound event framework for understanding extreme impacts. WIREs Clim. Change 5, 113–128 (2014). This article introduces the concept of compound events to the wider climate science community.

    Google Scholar 

  29. Salvadori, G., Durante, F., De Michele, C., Bernardi, M. & Petrella, L. A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities. Water Resour. Res. 52, 3701–3721 (2016).

    Google Scholar 

  30. Kew, S., Selten, F., Lenderink, G. & Hazeleger, W. The simultaneous occurrence of surge and discharge extremes for the Rhine delta. Nat. Haz. Earth Syst. Sci. 13, 2017–2029 (2013).

    Google Scholar 

  31. Bender, J., Wahl, T., Müller, A. & Jensen, J. A multivariate design framework for river confluences. Hydrol. Sci. J 61, 471–482 (2016).

    Google Scholar 

  32. van den Hurk, B., van Meijgaard, E., de Valk, P., van Heeringen, K.-J. & Gooijer, J. Analysis of a compounding surge and precipitation event in the Netherlands. Environ. Res. Lett. 10, 035001 (2015). This article provides the first analyis of a compound surge and precipitation event with dynamical models.

    Google Scholar 

  33. Zheng, F., Westra, S. & Sisson, S. A. Quantifying the dependence between extreme rainfall and storm surge in the coastal zone. J. Hydrol. 505, 172–187 (2013).

    Google Scholar 

  34. Martius, O., Pfahl, S. & Chevalier, C. A global quantification of compound precipitation and wind extremes. Geophys. Res. Lett. 43, 7709–7717 (2016). This article presents a global quantification of compound precipitation and wind extremes.

    Google Scholar 

  35. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: A review. Earth Sci. Rev. 99, 125–161 (2010).

    CAS  Google Scholar 

  36. Jolly, W. M., Dobbertin, M., Zimmermann, N. E. & Reichstein, M. Divergent vegetation growth responses to the 2003 heat wave in the Swiss Alps. Geophys. Res. Lett. 32, L18409 (2005).

    Google Scholar 

  37. Peduzzi, P. et al. Global trends in tropical cyclone risk. Nat. Clim. Change 2, 289–294 (2012).

    Google Scholar 

  38. Moftakhari, H. R., AghaKouchak, A., Sanders, B. F. & Matthew, R. A. Cumulative hazard: The case of nuisance flooding. Earth's Future 5, 214–223 (2017).

    Google Scholar 

  39. Moezzi, M., Janda, K. B. & Rotmann, S.. Using stories, narratives, and storytelling in energy and climate change research. Energy Res. Soc. Sci 31, 1–10 (2017).

    Google Scholar 

  40. Cardona, O. D. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 65–108 (IPCC, Cambridge Univ. Press, 2012).

  41. Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (IPCC, Cambridge Univ. Press, 2012). This chapter of the IPCC SREX report was the first to provide a highlight on compound events in the IPCC context.

  42. Culley, S. et al. A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resour. Res. 52, 6751–6768 (2016).

    Google Scholar 

  43. Hazeleger, W. et al. Tales of future weather. Nat. Clim. Change 5, 107–113 (2015).

    Google Scholar 

  44. Prudhomme, C., Wilby, R. L., Crooks, S., Kay, A. L. & Reynard, N. S. Scenario-neutral approach to climate change impact studies: application to flood risk. J. Hydrol. 390, 198–209 (2010).

    Google Scholar 

  45. Wilby, R. L. & Dessai, S. Robust adaptation to climate change. Weather 65, 180–185 (2010).

    Google Scholar 

  46. Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Change 3, 816–821 (2013).

    Google Scholar 

  47. Delphine, D., Declan, C., Navin, R., Jeff, P. & Rachel, W. Global crop yield response to extreme heat stress under multiple climate change futures. Environ. Res. Lett. 9, 034011 (2014).

    Google Scholar 

  48. Gasparrini, A. et al. Projections of temperature-related excess mortality under climate change scenarios. Lancet Planet. Health 1, e360–e367 (2017).

    Google Scholar 

  49. Smith, L. A. What might we learn from climate forecasts? Proc. Natl. Acad. Sci. USA 99, 2487–2492 (2002).

    Google Scholar 

  50. Derbyshire, J. The siren call of probability: Dangers associated with using probability for consideration of the future. Futures 88, 43–54 (2017).

    Google Scholar 

  51. Whateley, S., Steinschneider, S. & Brown, C. A climate change range-based method for estimating robustness for water resources supply. Water Resour. Res. 50, 8944–8961 (2014).

    Google Scholar 

  52. Turner, S. W. D. et al. Linking climate projections to performance: a yield-based decision scaling assessment of a large urban water resources system. Water Resour. Res. 50, 3553–3567 (2014).

    Google Scholar 

  53. Steinschneider, S. et al. Expanded decision-scaling framework to select robust long-term water-system plans under hydroclimatic uncertainties. J. Water Resour. Plann. Manage 141, 04015023 (2015).

    Google Scholar 

  54. Ribot, J. Cause and response: vulnerability and climate in the Anthropocene. J. Peasant Stud. 41, 667–705 (2014).

    Google Scholar 

  55. Chandler, C., Cheney, P., Thomas, P., Trabaud, L. & Williams, D. Fire in Forestry (Forest Fire Behaviour and Effects Vol. 1, John Wiley & Sons, Inc., 1983).

  56. Lee, D. H. K. Seventy-five years of searching for a heat index. Environ. Res. 22, 331–356 (1980).

    CAS  Google Scholar 

  57. Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).

    Google Scholar 

  58. Milly, P. C. D. et al. Stationarity is dead: whither water management? Science 319, 573–574 (2008).

    CAS  Google Scholar 

  59. Garner, A. J. et al. Impact of climate change on New York City’s coastal flood hazard: Increasing flood heights from the preindustrial to 2300 CE. Proc. Natl Acad. Sci. USA 114, 11861–11866 (2017).

    CAS  Google Scholar 

  60. Lewis, S. C. & King, A. D. Evolution of mean, variance and extremes in 21st century temperatures. Weather Clim. Extremes 15, 1–10 (2017).

    Google Scholar 

  61. Wilby, R. L. & Wigley, T. Future changes in the distribution of daily precipitation totals across North America. Geophys. Res. Lett. 29, 39-1–39-4 (2002).

    Google Scholar 

  62. Embrechts, P., McNeil, A. & Straumann, D. in Risk Management: Value at Risk and Beyond 176–223 (Cambridge Univ. Press, Cambridge, 2001).

  63. Maraun, D. et al. Towards process-informed bias correction of climate change simulations. Nat. Clim. Change 7, 764–773 (2017).

    Google Scholar 

  64. Pathiraja, S., Westra, S. & Sharma, A. Why continuous simulation? The role of antecedent moisture in design flood estimation. Water Resour. Res. 48, W06534 (2012).

    Google Scholar 

  65. Vorogushyn, S. et al. Evolutionary leap in large-scale flood risk assessment needed. WIREs Water 2, e1266 (2018).

    Google Scholar 

  66. Montanari, A. et al. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrol. Sci. J. 58, 1256–1275 (2013).

    Google Scholar 

  67. Flato, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 741–866 (IPCC, Cambridge Univ. Press, 2013).

  68. Jakob, C. Accelerating progress in global atmospheric model development through improved parameterizations: challenges, opportunities, and strategies. Bull. Am. Meteorol. Soc. 91, 869–875 (2010).

    Google Scholar 

  69. Marotzke, J. et al. Climate research must sharpen its view. Nat. Clim. Change 7, 89–91 (2017).

    Google Scholar 

  70. Palmer, T. Build high-resolution global climate models. Nature 515, 338 (2014).

    CAS  Google Scholar 

  71. Haarsma, R. J. et al. High resolution model intercomparison project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev. 9, 4185–4208 (2016).

    Google Scholar 

  72. Baumberger, C., Knutti, R. & Hirsch Hadorn, G. Building confidence in climate model projections: an analysis of inferences from fit. WIREs Clim. Change 8, e454 (2017).

    Google Scholar 

  73. Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (IPCC, Cambridge Univ. Press, 2013).

  74. Zhang, X. et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim. Change 2, 851–870 (2011).

    Google Scholar 

  75. Zscheischler, J. et al. Impact of large-scale climate extremes on biospheric carbon fluxes: an intercomparison based on MsTMIP data. Glob. Biogeochem. Cycles 28, 585–600 (2014).

    CAS  Google Scholar 

  76. Katsouyanni, K. et al. Evidence for interaction between air pollution and high temperature in the causation of excess mortality. Arch. Environ. Health 48, 235–242 (1993).

    CAS  Google Scholar 

  77. Palmer, W. C. Meteorological Drought Vol. 30 (US Department of Commerce, Weather Bureau Washington, DC, 1965).

  78. Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim. 23, 1696–1718 (2010).

    Google Scholar 

  79. Van Wagner, C. Development and Structure of the Canadian Forest Fire Weather Index System Forestry Technical Report 35 (Canadian Forestry Service, 1987).

  80. Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M. & Vrac, M. Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy). Hydrol. Earth Syst. Sci. 21, 2701–2723 (2017).

    Google Scholar 

  81. Schroter, K., Kunz, M., Elmer, F., Muhr, B. & Merz, B. What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation. Hydrol. Earth Syst. Sci. 19, 309–327 (2015).

    Google Scholar 

  82. Eyring, V. et al. ESMValTool (v1.0) — a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP. Geosci. Model Dev. 9, 1747–1802 (2016).

    CAS  Google Scholar 

  83. Cortés-Hernández, V. E. et al. Evaluating regional climate models for simulating sub-daily rainfall extremes. Clim. Dynam. 47, 1613–1628 (2016).

    Google Scholar 

  84. Zscheischler, J., Orth, R. & Seneviratne, S. I. A submonthly database for detecting changes in vegetation-atmosphere coupling. Geophys. Res. Lett. 42, 9816–9824 (2015).

    Google Scholar 

  85. Sippel, S. et al. Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics. Earth Syst. Dynam 8, 387–403 (2017).

    Google Scholar 

  86. Vrac, M. & Friederichs, P. Multivariate—intervariable, spatial, and temporal–bias correction. J. Clim. 28, 218–237 (2015).

    Google Scholar 

  87. Cannon, A. J. Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Clim. Dynam. 50, 31–49 (2018).

    Google Scholar 

  88. Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).

    CAS  Google Scholar 

  89. Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction - the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).

    Google Scholar 

  90. Sippel, S. et al. A novel bias correction methodology for climate impact simulations. Earth Syst. Dynam.. 7, 71–88 (2016). https://doi.org/10.5194/esd-7-71-2016.

    Google Scholar 

  91. Kreibich, H. et al. Adaptation to flood risk: Results of international paired flood event studies. Earth's Future 5, 953–965 (2017).

    Google Scholar 

  92. Attema, J. J., Loriaux, J. M. & Lenderink, G. Extreme precipitation response to climate perturbations in an atmospheric mesoscale model. Environ. Res. Lett. 9, 014003 (2014).

    Google Scholar 

  93. Zscheischler, J. et al. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001 (2014).

    Google Scholar 

  94. Wernli, H., Dirren, S., Liniger, M. A. & Zillig, M. Dynamical aspects of the life cycle of the winter storm ‘Lothar’ (24–26 December 1999). Q. J. R. Meteorol. Soc. 128, 405–429 (2002).

    Google Scholar 

  95. Gettelman, A., Bresch, D. N., Chen, C. C., Truesdale, J. E. & Bacmeister, J. T. Projections of future tropical cyclone damage with a high-resolution global climate model. Climatic Change 146, 575–585 (2018).

    Google Scholar 

  96. Oppenheimer, M. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) Ch. 19 (IPCC, Cambridge Univ. Press, 2014).

  97. Lark, J. ISO31000: Risk Management: a Practical Guide for SMEs (International Organization for Standardization, 2015).

  98. IPCC Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012). This IPCC Special Report on Extremes defined a risk framework for IPCC reports, thereby highlighting the role of vulnerability and exposure in addition to hazards for changes in risks.

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Acknowledgements

Many ideas laid out in this paper emerged from a workshop ‘Addressing the challenge of compound events’ held in April 2017 at ETH Zurich. This workshop has also led to the recently approved EU COST Action DAMOCLES (CA17109). DAMOCLES will coordinate research activities laid out in this Perspective. We thank E. Fischer for presenting the initial idea that has led to Fig. 2 during the workshop. The workshop would not have been possible without generous funding from the World Climate Research Programme, the Australian Research Council Center of Excellence for Climate System Science (ARCCSS), ETH Zurich, the Vrije Universiteit Amsterdam and The Netherlands Organisation for Scientific Research (VIDI grant no. 016.161.324). The funding was primarily used to invite promising Early Career Scientists working on compound events to attend the workshop. S.W. was supported by ARC Discovery project DP150100411. B.J.J.M.v.d.H. acknowledges funding from the IMPREX research project supported by the European Commission under the Horizon 2020 Framework programme with grant no. 641811. S.I.S. acknowledges the European Research Council (ERC) DROUGHT-HEAT project funded by the European Community’s Seventh Framework Programme with grant no. 617518. This work contributes to the World Climate Research Programme (WCRP) Grand Challenge on Extremes.

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The article is a result of a workshop organized by J.Z., S.W., B.J.J.M.v.d.H., P.J.W., A.P. and S.I.S. Figure 1 and the definition of compound weather/climate events were created during the workshop. J.Z. wrote the first draft with input from S.W., B.J.J.M.v.d.H., S.I.S., P.J.W. and A.P. J.Z. created Figs. 2 and 3 with input from S.W. and S.I.S. All authors discussed the content of the manuscript.

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Correspondence to Jakob Zscheischler.

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Zscheischler, J., Westra, S., van den Hurk, B.J.J.M. et al. Future climate risk from compound events. Nature Clim Change 8, 469–477 (2018). https://doi.org/10.1038/s41558-018-0156-3

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