Perspective | Published:

Future climate risk from compound events


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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Change history

  • 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.”.


  1. 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).

  2. 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).

  3. 3.

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

  4. 4.

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

  5. 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).

  6. 6.

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

  7. 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.

  8. 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).

  9. 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).

  10. 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).

  11. 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).

  12. 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.

  13. 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).

  14. 14.

    FEMA National Preparedness Report (Homeland Security, 2013).

  15. 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).

  16. 16.

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

  17. 17.

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

  18. 18.

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

  19. 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. 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).

  21. 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).

  22. 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).

  23. 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).

  24. 24.

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

  25. 25.

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

  26. 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.

  27. 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).

  28. 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.

  29. 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).

  30. 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).

  31. 31.

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

  32. 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.

  33. 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).

  34. 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.

  35. 35.

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

  36. 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).

  37. 37.

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

  38. 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).

  39. 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).

  40. 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. 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. 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).

  43. 43.

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

  44. 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).

  45. 45.

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

  46. 46.

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

  47. 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).

  48. 48.

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

  49. 49.

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

  50. 50.

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

  51. 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).

  52. 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).

  53. 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).

  54. 54.

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

  55. 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. 56.

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

  57. 57.

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

  58. 58.

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

  59. 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).

  60. 60.

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

  61. 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).

  62. 62.

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

  63. 63.

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

  64. 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).

  65. 65.

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

  66. 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).

  67. 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. 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).

  69. 69.

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

  70. 70.

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

  71. 71.

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

  72. 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).

  73. 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. 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).

  75. 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).

  76. 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).

  77. 77.

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

  78. 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).

  79. 79.

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

  80. 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).

  81. 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).

  82. 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).

  83. 83.

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

  84. 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).

  85. 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).

  86. 86.

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

  87. 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).

  88. 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).

  89. 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).

  90. 90.

    Sippel, S. et al. A novel bias correction methodology for climate impact simulations. Earth Syst. Dynam.. 7, 71–88 (2016).

  91. 91.

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

  92. 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).

  93. 93.

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

  94. 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).

  95. 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).

  96. 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. 97.

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

  98. 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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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.

Author information

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.

Competing interests

The authors declare no competing interests.

Correspondence to Jakob Zscheischler.

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Further reading

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.