A typology of compound weather and climate events

Abstract

Compound weather and climate events describe combinations of multiple climate drivers and/or hazards that contribute to societal or environmental risk. Although many climate-related disasters are caused by compound events, the understanding, analysis, quantification and prediction of such events is still in its infancy. In this Review, we propose a typology of compound events and suggest analytical and modelling approaches to aid in their investigation. We organize the highly diverse compound event types according to four themes: preconditioned, where a weather-driven or climate-driven precondition aggravates the impacts of a hazard; multivariate, where multiple drivers and/or hazards lead to an impact; temporally compounding, where a succession of hazards leads to an impact; and spatially compounding, where hazards in multiple connected locations cause an aggregated impact. Through structuring compound events and their respective analysis tools, the typology offers an opportunity for deeper insight into their mechanisms and impacts, benefiting the development of effective adaptation strategies. However, the complex nature of compound events results in some cases inevitably fitting into more than one class, necessitating soft boundaries within the typology. Future work must homogenize the available analytical approaches into a robust toolset for compound-event analysis under present and future climate conditions.

Key points

  • Compound events — a combination of multiple drivers and/or hazards that contribute to societal or environmental risk — are responsible for many of the most severe weather-related and climate-related impacts.

  • A classification of compound events is proposed, distinguishing events that are preconditioned, multivariate, temporally compounding and spatially compounding.

  • The typology aids compound-event analysis by facilitating the selection of appropriate analysis and modelling tools.

  • Through altering the distribution of climate variables and their spatial and temporal dependencies, climate change affects the likelihood, nature and impacts of compound events.

  • Bottom-up approaches, which link sectoral impacts to physical hazards, can help understand and, ultimately, better prepare for emerging risks posed by compound events.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Elements of a compound weather and climate event.
Fig. 2: Preconditioned events.
Fig. 3: Multivariate events.
Fig. 4: Temporally compounding events.
Fig. 5: Spatially compounding events.
Fig. 6: Climate-change effects on compound events.

References

  1. 1.

    Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).

    Article  Google Scholar 

  2. 2.

    Kornhuber, K. et al. Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions. Nat. Clim. Change 10, 48–53 (2020). Identified an atmospheric driver behind spatially concurrent hazards, an insight that is highly relevant for assessing the risk of global crop failure (spatially compounding).

    Article  Google Scholar 

  3. 3.

    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 (Cambridge Univ. Press, 2012).

  4. 4.

    Benestad, R. E. & Haugen, J. E. On complex extremes: flood hazards and combined high spring-time precipitation and temperature in Norway. Clim. Change 85, 381–406 (2007).

    Article  Google Scholar 

  5. 5.

    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).

    Article  Google Scholar 

  6. 6.

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

    Article  Google Scholar 

  7. 7.

    Hendry, A. et al. Assessing the characteristics and drivers of compound flooding events around the UK coast. Hydrol. Earth Syst. Sci. 23, 3117–3139 (2019). Quantifies the compound flooding risk associated with high skew surges and high river discharge across the UK, revealing the atmospheric drivers behind compound-flooding events (multivariate event).

    Article  Google Scholar 

  8. 8.

    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).

    Article  Google Scholar 

  9. 9.

    Leonard, M. et al. A compound event framework for understanding extreme impacts. Wiley Interdiscip. Rev. Clim. Change 5, 113–128 (2014). First introduced a framework for a systematic analysis of compound events.

    Article  Google Scholar 

  10. 10.

    Lian, J. J., Xu, K. & Ma, C. Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China. Hydrol. Earth Syst. Sci. 17, 679–689 (2013).

    Article  Google Scholar 

  11. 11.

    Ward, P. J. et al. Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries. Environ. Res. Lett. 13, 084012 (2018).

    Article  Google Scholar 

  12. 12.

    Kjellstrom, T. et al. Heat, human performance, and occupational health: a key issue for the assessment of global climate change impacts. Annu. Rev. Public Health 37, 97–112 (2016).

    Article  Google Scholar 

  13. 13.

    Coffel, E. D., Horton, R. M. & de Sherbinin, A. Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century. Environ. Res. Lett. 13, 014001 (2018).

    Article  Google Scholar 

  14. 14.

    Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6, eaaw1838 (2020).

    Article  Google Scholar 

  15. 15.

    Analitis, A. et al. Effects of heat waves on mortality: effect modification and confounding by air pollutants. Epidemiology 25, 15–22 (2014).

    Article  Google Scholar 

  16. 16.

    Holden, Z. A. et al. Decreasing fire season precipitation increased recent western US forest wildfire activity. Proc. Natl Acad. Sci. USA 115, E8349–E8357 (2018).

    Article  Google Scholar 

  17. 17.

    Raymond, C. et al. Understanding and managing connected extreme events. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0790-4 (2020) Introduces the concept of ‘connected extreme events’, when the impacts of extreme weather and climate are amplified by physical interactions among events and across a complex set of societal factors.

  18. 18.

    Balch, J. K. et al. Social-environmental extremes: rethinking extraordinary events as outcomes of interacting biophysical and social systems. Earth’s Future https://agupubs.onlinelibrary.wiley.com/journal/23284277 (2020).

  19. 19.

    Tilloy, A., Malamud, B. D., Winter, H. & Joly-Laugel, A. A review of quantification methodologies for multi-hazard interrelationships. Earth-Sci. Rev. 196, 102881 (2019).

    Article  Google Scholar 

  20. 20.

    Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J. & Kirchner, J. W. The relative importance of different flood-generating mechanisms across Europe. Water Resour. Res. 55, 4582–4593 (2019). Illustrates that floods in Europe are rarely caused by rainfall extremes, but, rather, by snowmelt and by the concurrence of heavy precipitation with high antecedent soil moisture (preconditioned event).

    Google Scholar 

  21. 21.

    Berghuijs, W. R., Woods, R. A., Hutton, C. J. & Sivapalan, M. Dominant flood generating mechanisms across the United States. Geophys. Res. Lett. 43, 4382–4390 (2016).

    Article  Google Scholar 

  22. 22.

    Martius, O. et al. The role of upper-level dynamics and surface processes for the Pakistan flood of July 2010. Q. J. R. Meteorol. Soc. 139, 1780–1797 (2013).

    Article  Google Scholar 

  23. 23.

    Grams, C. M., Binder, H., Pfahl, S., Piaget, N. & Wernli, H. Atmospheric processes triggering the central European floods in June 2013. Nat. Hazards Earth Syst. Sci. 14, 1691–1702 (2014).

    Article  Google Scholar 

  24. 24.

    Cohen, J., Ye, H. & Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 42, 7115–7122 (2015).

    Article  Google Scholar 

  25. 25.

    McCabe, G. J., Clark, M. P. & Hay, L. E. Rain-on-snow events in the western United States. Bull. Am. Meteorol. Soc. 88, 319–328 (2007).

    Article  Google Scholar 

  26. 26.

    Merz, R. & Blöschl, G. A process typology of regional floods. Water Resour. Res. 39, 1340 (2003).

    Article  Google Scholar 

  27. 27.

    Rössler, O. et al. Retrospective analysis of a nonforecasted rain-on-snow flood in the Alps-A matter of model limitations or unpredictable nature? Hydrol. Earth Syst. Sci. 18, 2265–2285 (2014).

    Article  Google Scholar 

  28. 28.

    Payne, A. E. et al. Responses and impacts of atmospheric rivers to climate change. Nat. Rev. Earth Environ. 1, 143–157 (2020).

    Article  Google Scholar 

  29. 29.

    Forkel, M. et al. Extreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia. Environ. Res. Lett. 7, 044021 (2012).

    Article  Google Scholar 

  30. 30.

    Ruffault, J., Curt, T., Martin-Stpaul, N. K., Moron, V. & Trigo, R. M. Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean. Nat. Hazards Earth Syst. Sci. 18, 847–856 (2018).

    Article  Google Scholar 

  31. 31.

    Ren, D., Fu, R., Leslie, L. M. & Dickinson, R. E. Modeling the mudslide aftermath of the 2007 Southern California Wildfires. Nat. Hazards 57, 327–343 (2011).

    Article  Google Scholar 

  32. 32.

    Jacobs, L. et al. Reconstruction of a flash flood event through a multi-hazard approach: focus on the Rwenzori Mountains, Uganda. Nat. Hazards 84, 851–876 (2016).

    Article  Google Scholar 

  33. 33.

    Sippel, S. et al. Drought, heat, and the carbon cycle: a review. Curr. Clim. Chang. Rep. 4, 266–286 (2018).

    Article  Google Scholar 

  34. 34.

    Sippel, S. et al. Contrasting and interacting changes in simulated spring and summer carbon cycle extremes in European ecosystems. Environ. Res. Lett. 12, 075006 (2017).

    Article  Google Scholar 

  35. 35.

    Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).

    Article  Google Scholar 

  36. 36.

    Marino, G. P., Kaiser, D. P., Gu, L. & Ricciuto, D. M. Reconstruction of false spring occurrences over the southeastern United States, 1901–2007: an increasing risk of spring freeze damage? Environ. Res. Lett. 6, 024015 (2011).

    Article  Google Scholar 

  37. 37.

    Hufkens, K. et al. Ecological impacts of a widespread frost event following early spring leaf-out. Glob. Chang. Biol. 18, 2365–2377 (2012).

    Article  Google Scholar 

  38. 38.

    Pfleiderer, P., Menke, I. & Schleussner, C.-F. Increasing risks of apple tree frost damage under climate change. Clim. Change 157, 515–525 (2019).

    Article  Google Scholar 

  39. 39.

    Rao, M. P. et al. Dzuds, droughts, and livestock mortality in Mongolia. Environ. Res. Lett. 10, 074012 (2015).

    Article  Google Scholar 

  40. 40.

    Liu, B., Siu, Y. L. & Mitchell, G. Hazard interaction analysis for multi-hazard risk assessment: A systematic classification based on hazard-forming environment. Nat. Hazards Earth Syst. Sci. 16, 629–642 (2016).

    Article  Google Scholar 

  41. 41.

    Mahony, C. R. & Cannon, A. J. Wetter summers can intensify departures from natural variability in a warming climate. Nat. Commun. 9, 783 (2018).

    Article  Google Scholar 

  42. 42.

    Flach, M. et al. Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques. Earth Syst. Dyn. 8, 677–696 (2017).

    Article  Google Scholar 

  43. 43.

    Sadegh, M. et al. Multihazard scenarios for analysis of compound extreme events. Geophys. Res. Lett. 45, 5470–5480 (2018).

    Article  Google Scholar 

  44. 44.

    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).

    Article  Google Scholar 

  45. 45.

    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).

    Article  Google Scholar 

  46. 46.

    Wu, W. et al. Mapping dependence between extreme rainfall and storm surge. J. Geophys. Res. Ocean. 123, 2461–2474 (2018).

    Article  Google Scholar 

  47. 47.

    Bevacqua, E. et al. Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Sci. Adv. 5, eaaw5531 (2019).

    Article  Google Scholar 

  48. 48.

    Couasnon, A. et al. Measuring compound flood potential from river discharge and storm surge extremes at the global scale. Nat. Hazards Earth Syst. Sci. 20, 489–504 (2020).

    Article  Google Scholar 

  49. 49.

    Röthlisberger, M. & Martius, O. Quantifying the local effect of northern hemisphere atmospheric blocks on the persistence of summer hot and dry spells. Geophys. Res. Lett. 46, 10101–10111 (2019).

    Article  Google Scholar 

  50. 50.

    Berg, A. et al. Interannual coupling between summertime surface temperature and precipitation over land: processes and implications for climate change. J. Clim. 28, 1308–1328 (2015).

    Article  Google Scholar 

  51. 51.

    Schumacher, D. L. et al. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12, 712–717 (2019).

    Article  Google Scholar 

  52. 52.

    Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017). Reports an increase in the dependence between summer temperature and precipitation with global warming, leading to an elevated risk of extremely hot and dry summers on top of long-term climate trends (multivariate event).

    Article  Google Scholar 

  53. 53.

    Hao, Z., Hao, F., Singh, V. P. & Zhang, X. Statistical prediction of the severity of compound dry-hot events based on El Niño-Southern Oscillation. J. Hydrol. 572, 243–250 (2019).

    Article  Google Scholar 

  54. 54.

    Cai, W. et al. Climate impacts of the El Niño–Southern oscillation on South America. Nat. Rev. Earth Environ. 1, 215–231 (2020).

    Article  Google Scholar 

  55. 55.

    Hoerling, M. et al. Anatomy of an extreme event. J. Clim. 26, 2811–2832 (2013).

    Article  Google Scholar 

  56. 56.

    Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).

    Article  Google Scholar 

  57. 57.

    Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).

    Article  Google Scholar 

  58. 58.

    Coffel, E. D. et al. Future hot and dry years worsen Nile Basin water scarcity despite projected precipitation increases. Earths Future 7, 967–977 (2019).

    Article  Google Scholar 

  59. 59.

    Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

    Article  Google Scholar 

  60. 60.

    Zscheischler, J. et al. Carbon cycle extremes during the 21st century in CMIP5 models: future evolution and attribution to climatic drivers. Geophys. Res. Lett. 41, 8853–8861 (2014).

    Article  Google Scholar 

  61. 61.

    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).

    Article  Google Scholar 

  62. 62.

    von Buttlar, J. et al. Impacts of droughts and extreme temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones. Biogeosciences 15, 1293–1318 (2018).

    Article  Google Scholar 

  63. 63.

    Williams, A. P. & Abatzoglou, J. T. Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Curr. Clim. Chang. Rep. 2, 1–14 (2016).

    Article  Google Scholar 

  64. 64.

    Cook, M. A., King, C. W., Davidson, F. T. & Webber, M. E. Assessing the impacts of droughts and heat waves at thermoelectric power plants in the United States using integrated regression, thermodynamic, and climate models. Energy Rep. 1, 193–203 (2015).

    Article  Google Scholar 

  65. 65.

    Tschumi, E. & Zscheischler, J. Countrywide climate features during recorded climate-related disasters. Clim. Change 158, 593–609 (2020).

    Article  Google Scholar 

  66. 66.

    Otkin, J. A. et al. Flash droughts: a review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Am. Meteorol. Soc. 99, 911–919 (2018).

    Article  Google Scholar 

  67. 67.

    Stoffel, M. & Corona, C. Future winters glimpsed in the Alps. Nat. Geosci. 11, 458–460 (2018).

    Article  Google Scholar 

  68. 68.

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

    Article  Google Scholar 

  69. 69.

    Fink, A. H., Brücher, T., Ermert, V., Krüger, A. & Pinto, J. G. The European storm Kyrill in January 2007: synoptic evolution, meteorological impacts and some considerations with respect to climate change. Nat. Hazards Earth Syst. Sci. 9, 405–423 (2009).

    Article  Google Scholar 

  70. 70.

    Liberato, M. L. R. The 19 January 2013 windstorm over the North Atlantic: large-scale dynamics and impacts on Iberia. Weather Clim. Extremes 5, 16–28 (2014).

    Article  Google Scholar 

  71. 71.

    Raveh-Rubin, S. & Wernli, H. Large-scale wind and precipitation extremes in the Mediterranean: a climatological analysis for 1979–2012. Q. J. R. Meteorol. Soc. 141, 2404–2417 (2015).

    Article  Google Scholar 

  72. 72.

    Lin, N., Emanuel, K. A., Smith, J. A. & Vanmarcke, E. Risk assessment of hurricane storm surge for New York City. J. Geophys. Res. Atmos. 115, D18121 (2010).

    Article  Google Scholar 

  73. 73.

    Villarini, G., Vecchi, G. A. & Smith, J. A. Modeling the dependence of tropical storm counts in the North Atlantic basin on climate indices. Mon. Weather Rev. 138, 2681–2705 (2010).

    Article  Google Scholar 

  74. 74.

    Baldwin, J. W., Dessy, J. B., Vecchi, G. A. & Oppenheimer, M. Temporally compound heat wave events and global warming: an emerging hazard. Earths Future 7, 411–427 (2019).

    Article  Google Scholar 

  75. 75.

    Hughes, T. P. et al. Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat. Clim. Change 9, 40–43 (2019).

    Article  Google Scholar 

  76. 76.

    Barton, Y. et al. Clustering of regional-scale extreme precipitation events in southern Switzerland. Mon. Weather Rev. 144, 347–369 (2016).

    Article  Google Scholar 

  77. 77.

    Wang, S. S.-Y. et al. Consecutive extreme flooding and heat wave in Japan: Are they becoming a norm? Atmos. Sci. Lett. 20, e933 (2019).

    Google Scholar 

  78. 78.

    Matthews, T., Wilby, R. L. & Murphy, C. An emerging tropical cyclone–deadly heat compound hazard. Nat. Clim. Change 9, 602–606 (2019). Illustrates the risk of newly emerging compound events with global warming, in particular, a tropical cyclone followed by a deadly heatwave (temporally compounding).

    Article  Google Scholar 

  79. 79.

    Mailier, P. J., Stephenson, D. B., Ferro, C. A. T. & Hodges, K. I. Serial clustering of extratropical cyclones. Mon. Weather Rev. 134, 2224–2240 (2006). Models the serial clustering of extratropical cyclones with a point-process approach and links the strength of clustering with teleconnection indices (temporally compounding).

    Article  Google Scholar 

  80. 80.

    Pinto, J. G., Bellenbaum, N., Karremann, M. K. & Della-Marta, P. M. Serial clustering of extratropical cyclones over the North Atlantic and Europe under recent and future climate conditions. J. Geophys. Res. Atmos. 118, 12,476–12,485 (2013).

    Article  Google Scholar 

  81. 81.

    Priestley, M. D. K., Pinto, J. G., Dacre, H. F. & Shaffrey, L. C. The role of cyclone clustering during the stormy winter of 2013/2014. Weather 72, 187–192 (2017).

    Article  Google Scholar 

  82. 82.

    Vitolo, R., Stephenson, D. B., Cook, I. M. & Mitchell-Wallace, K. Serial clustering of intense European storms. Meteorol. Z. 18, 411–424 (2009).

    Article  Google Scholar 

  83. 83.

    Pinto, J. G. et al. Large-scale dynamics associated with clustering of extratropical cyclones affecting Western Europe. J. Geophys. Res. Atmos. 119, 13,704–13,719 (2014).

    Article  Google Scholar 

  84. 84.

    Priestley, M. D. K., Pinto, J. G., Dacre, H. F. & Shaffrey, L. C. Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe. Geophys. Res. Lett. 44, 514–521 (2017).

    Article  Google Scholar 

  85. 85.

    Mumby, P. J., Vitolo, R. & Stephenson, D. B. Temporal clustering of tropical cyclones and its ecosystem impacts. Proc. Natl Acad. Sci. USA 108, 17626–17630 (2011).

    Article  Google Scholar 

  86. 86.

    Villarini, G., Smith, J. A., Vitolo, R. & Stephenson, D. B. On the temporal clustering of US floods and its relationship to climate teleconnection patterns. Int. J. Climatol. 33, 629–640 (2013).

    Article  Google Scholar 

  87. 87.

    Gu, X., Zhang, Q., Singh, V. P., Chen, Y. D. & Shi, P. Temporal clustering of floods and impacts of climate indices in the Tarim River basin, China. Glob. Planet. Change 147, 12–24 (2016).

    Article  Google Scholar 

  88. 88.

    Mallakpour, I., Villarini, G., Jones, M. P. & Smith, J. A. On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States. Glob. Planet. Change 155, 98–108 (2017).

    Article  Google Scholar 

  89. 89.

    Davies, H. C. Weather chains during the 2013/2014 winter and their significance for seasonal prediction. Nat. Geosci. 8, 833–837 (2015).

    Article  Google Scholar 

  90. 90.

    Fairman, T. A., Nitschke, C. R. & Bennett, L. T. Too much, too soon? A review of the effects of increasing wildfire frequency on tree mortality and regeneration in temperate eucalypt forests. Int. J. Wildland Fire 25, 831–848 (2016).

    Article  Google Scholar 

  91. 91.

    Ben-Ari, T. et al. Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France. Nat. Commun. 9, 1627 (2018). Reveals the drivers of the 2016 extreme wheat loss in France as a combination of unusually warm temperatures in late autumn and unusually wet conditions in the following spring (temporally compounding).

    Article  Google Scholar 

  92. 92.

    Steptoe, H., Jones, S. E. O. & Fox, H. Correlations between extreme atmospheric hazards and global teleconnections: implications for multihazard resilience. Rev. Geophys. 56, 50–78 (2018).

    Article  Google Scholar 

  93. 93.

    Anderson, W. B., Seager, R., Baethgen, W., Cane, M. & You, L. Synchronous crop failures and climate-forced production variability. Sci. Adv. 5, eaaw1976 (2019).

    Article  Google Scholar 

  94. 94.

    Singh, D. et al. Climate and the global famine of 1876–78. J. Clim. 31, 9445–9467 (2018).

    Article  Google Scholar 

  95. 95.

    Boers, N. et al. Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature 566, 373–377 (2019).

    Article  Google Scholar 

  96. 96.

    Kornhuber, K. et al. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett. 14, 054002 (2019).

    Article  Google Scholar 

  97. 97.

    Mehrabi, Z. & Ramankutty, N. Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).

    Article  Google Scholar 

  98. 98.

    Lunt, T., Jones, A. W., Mulhern, W. S., Lezaks, D. P. M. & Jahn, M. M. Vulnerabilities to agricultural production shocks: an extreme, plausible scenario for assessment of risk for the insurance sector. Clim. Risk Manag. 13, 1–9 (2016).

    Article  Google Scholar 

  99. 99.

    Sun, X., Thyer, M., Renard, B. & Lang, M. A general regional frequency analysis framework for quantifying local-scale climate effects: A case study of ENSO effects on Southeast Queensland rainfall. J. Hydrol. 512, 53–68 (2014).

    Article  Google Scholar 

  100. 100.

    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).

    Article  Google Scholar 

  101. 101.

    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).

    Article  Google Scholar 

  102. 102.

    Guisado-Pintado, E. & Jackson, D. W. T. Multi-scale variability of storm Ophelia 2017: the importance of synchronised environmental variables in coastal impact. Sci. Total Environ. 630, 287–301 (2018).

    Article  Google Scholar 

  103. 103.

    van der Wiel, K. et al. Meteorological conditions leading to extreme low variable renewable energy production and extreme high energy shortfall. Renew. Sustain. Energy Rev. 111, 261–275 (2019).

    Article  Google Scholar 

  104. 104.

    Koks, E. E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. Nat. Commun. 10, 2677 (2019).

    Article  Google Scholar 

  105. 105.

    Haigh, I. D. et al. Spatial and temporal analysis of extreme sea level and storm surge events around the coastline of the UK. Sci. Data 3, 160107 (2016).

    Article  Google Scholar 

  106. 106.

    Mass, C. F. & Ovens, D. The Northern California wildfires of 8–9 October 2017: The role of a major downslope wind event. Bull. Am. Meteorol. Soc. 100, 235–256 (2019).

    Article  Google Scholar 

  107. 107.

    Vahedifard, F., AghaKouchak, A. & Jafari, N. H. Compound hazards yield Louisiana flood. Science 353, 1374 (2016).

    Article  Google Scholar 

  108. 108.

    Jongman, B. et al. Increasing stress on disaster-risk finance due to large floods. Nat. Clim. Change 4, 264–268 (2014).

    Article  Google Scholar 

  109. 109.

    Keef, C., Tawn, J. & Svensson, C. Spatial risk assessment for extreme river flows. J. R. Stat. Soc. Ser. C. Appl. Stat. 58, 601–618 (2009).

    Article  Google Scholar 

  110. 110.

    Quesada, B., Vautard, R., Yiou, P., Hirschi, M. & Seneviratne, S. I. Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat. Clim. Change 2, 736–741 (2012).

    Article  Google Scholar 

  111. 111.

    Ridder, N., de Vries, H. & Drijfhout, S. The role of atmospheric rivers in compound events consisting of heavy precipitation and high storm surges along the Dutch coast. Nat. Hazards Earth Syst. Sci. 18, 3311–3326 (2018).

    Article  Google Scholar 

  112. 112.

    Faranda, D., Messori, G. & Yiou, P. Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America. Clim. Dyn. 54, 2187–2201 (2020).

    Article  Google Scholar 

  113. 113.

    De Luca, P., Messori, G., Pons, F. M. E. & Faranda, D. Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America. Q. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.3757 (2020).

    Article  Google Scholar 

  114. 114.

    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).

    Article  Google Scholar 

  115. 115.

    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).

    Article  Google Scholar 

  116. 116.

    Zischg, A. P. et al. Effects of variability in probable maximum precipitation patterns on flood losses. Hydrol. Earth Syst. Sci. 22, 2759–2773 (2018).

    Article  Google Scholar 

  117. 117.

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

    Article  Google Scholar 

  118. 118.

    Zscheischler, J., Mahecha, M. D., Harmeling, S. & Reichstein, M. Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecol. Inform. 15, 66–73 (2013).

    Article  Google Scholar 

  119. 119.

    Hao, Z., Singh, V. & Hao, F. Compound extremes in hydroclimatology: a review. Water 10, 718 (2018).

    Article  Google Scholar 

  120. 120.

    Lloyd, E. A. & Shepherd, T. G. Environmental catastrophes, climate change, and attribution. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.14308 (2020).

    Article  Google Scholar 

  121. 121.

    Sadegh, M., Ragno, E. & AghaKouchak, A. Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework. Water Resour. Res. 53, 5166–5183 (2017).

    Article  Google Scholar 

  122. 122.

    Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019).

    Article  Google Scholar 

  123. 123.

    Croci-Maspoli, M. & Davies, H. C. Key dynamical features of the 2005/06 European winter. Mon. Weather Rev. 137, 664–678 (2009).

    Article  Google Scholar 

  124. 124.

    Schoelzel, C. & Friederichs, P. Multivariate non-normally distributed random variables in climate research–introduction to the copula approach. Nonlin. Process. Geophys. 15, 761–772 (2008).

    Article  Google Scholar 

  125. 125.

    Sarhadi, A., Burn, D. H., Concepción Ausín, M. & Wiper, M. P. Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula. Water Resour. Res. 52, 2327–2349 (2016). Introduces an approach to model multivariate events in a non-stationary environment with copulas.

    Article  Google Scholar 

  126. 126.

    Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D. & Diffenbaugh, N. S. Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions. Sci. Adv. 4, eaau3487 (2018).

    Article  Google Scholar 

  127. 127.

    Kwon, H.-H. & Lall, U. A copula-based nonstationary frequency analysis for the 2012–2015 drought in California. Water Resour. Res. 52, 5662–5675 (2016).

    Article  Google Scholar 

  128. 128.

    Couasnon, A., Sebastian, A. & Morales-Nápoles, O. A copula-based Bayesian network for modeling compound flood hazard from riverine and coastal interactions at the catchment scale: An application to the Houston Ship Channel, Texas. Water 10, 1190 (2018).

    Google Scholar 

  129. 129.

    Davison, A. C. & Huser, R. Statistics of extremes. Annu. Rev. Stat. Appl. 2, 203–235 (2015).

    Article  Google Scholar 

  130. 130.

    Hughes, J. P., Guttorp, P. & Charles, S. P. A non-homogeneous hidden Markov model for precipitation occurrence. J. R. Stat. Soc. Ser. C. Appl. Stat. 48, 15–30 (1999).

    Article  Google Scholar 

  131. 131.

    Davison, A. C., Padoan, S. A. & Ribatet, M. Statistical modeling of spatial extremes. Stat. Sci. 27, 161–186 (2012).

    Article  Google Scholar 

  132. 132.

    Touma, D., Michalak, A. M., Swain, D. L. & Diffenbaugh, N. S. Characterizing the spatial scales of extreme daily precipitation in the United States. J. Clim. 31, 8023–8037 (2018).

    Article  Google Scholar 

  133. 133.

    Blanchet, J. & Creutin, J. D. Co-occurrence of extreme daily rainfall in the French Mediterranean region. Water Resour. Res. 53, 9330–9349 (2017).

    Article  Google Scholar 

  134. 134.

    Le, P. D., Leonard, M. & Westra, S. Modeling spatial dependence of rainfall extremes across multiple durations. Water Resour. Res. 54, 2233–2248 (2018).

    Article  Google Scholar 

  135. 135.

    Vicente-Serrano, S. M. et al. A multiscalar global evaluation of the impact of ENSO on droughts. J. Geophys. Res. Atmos. 116, D20109 (2011).

    Article  Google Scholar 

  136. 136.

    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).

    Article  Google Scholar 

  137. 137.

    Gouldby, B. et al. Multivariate extreme value modelling of sea conditions around the coast of England. Proc. Inst. Civ. Eng. Marit. Eng. 170, 3–20 (2017).

    Article  Google Scholar 

  138. 138.

    Poschlod, B., Zscheischler, J., Sillmann, J., Wood, R. R. & Ludwig, R. Climate change effects on hydrometeorological compound events over southern Norway. Weather Clim. Extremes 28, 100253 (2020).

    Article  Google Scholar 

  139. 139.

    Zscheischler, J., Fischer, E. M. & Lange, S. The effect of univariate bias adjustment on multivariate hazard estimates. Earth Syst. Dyn. 10, 31–43 (2019).

    Article  Google Scholar 

  140. 140.

    Shepherd, T. G. et al. Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Clim. Change 151, 555–571 (2018).

    Article  Google Scholar 

  141. 141.

    Intergovernmental Panel on Climate Change (IPCC). Climate Change 2013: the Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2013).

  142. 142.

    Little, C. M. et al. Joint projections of US East Coast sea level and storm surge. Nat. Clim. Change 5, 1114–1120 (2015).

    Article  Google Scholar 

  143. 143.

    Mazdiyasni, O. & AghaKouchak, A. Substantial increase in concurrent droughts and heatwaves in the United States. Proc. Natl Acad. Sci. USA 112, 11484–11489 (2015).

    Article  Google Scholar 

  144. 144.

    Manning, C. et al. Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013). Environ. Res. Lett. 14, 094006 (2019).

    Article  Google Scholar 

  145. 145.

    Sharma, S. & Mujumdar, P. Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Sci. Rep. 7, 15582 (2017).

    Article  Google Scholar 

  146. 146.

    Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).

    Article  Google Scholar 

  147. 147.

    Lemordant, L. & Gentine, P. Vegetation response to rising CO2 impacts extreme temperatures. Geophys. Res. Lett. 46, 1383–1392 (2019).

    Article  Google Scholar 

  148. 148.

    Swann, A. L. S. Plants and drought in a changing climate. Curr. Clim. Change Rep. 4, 192–201 (2018).

    Article  Google Scholar 

  149. 149.

    Mankin, J. S. et al. Blue water trade-offs with vegetation in a CO2-enriched climate. Geophys. Res. Lett. 45, 3115–3125 (2018).

    Article  Google Scholar 

  150. 150.

    Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).

    Article  Google Scholar 

  151. 151.

    Swain, D. L., Langenbrunner, B., Neelin, J. D. & Hall, A. Increasing precipitation volatility in twenty-first-century California. Nat. Clim. Change 8, 427–433 (2018).

    Article  Google Scholar 

  152. 152.

    Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future 7, 892–910 (2019).

    Article  Google Scholar 

  153. 153.

    Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. USA 115, 6644–6649 (2018).

    Article  Google Scholar 

  154. 154.

    Yang, H. et al. Strong but intermittent spatial covariations in tropical land temperature. Geophys. Res. Lett. 46, 356–364 (2019).

    Article  Google Scholar 

  155. 155.

    Gaupp, F., Hall, J., Hochrainer-Stigler, S. & Dadson, S. Changing risks of simultaneous global breadbasket failure. Nat. Clim. Change 10, 54–57 (2020).

    Article  Google Scholar 

  156. 156.

    Berghuijs, W. R., Allen, S. T., Harrigan, S. & Kirchner, J. W. Growing spatial scales of synchronous river flooding in Europe. Geophys. Res. Lett. 46, 1423–1428 (2019).

    Article  Google Scholar 

  157. 157.

    Haarsma, R. J. et al. More hurricanes to hit western Europe due to global warming. Geophys. Res. Lett. 40, 1783–1788 (2013).

    Article  Google Scholar 

  158. 158.

    Bougeault, P. et al. The THORPEX interactive grand global ensemble. Bull. Am. Meteorol. Soc. 91, 1059–1072 (2010).

    Article  Google Scholar 

  159. 159.

    Deser, C. et al. Insights from earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).

    Article  Google Scholar 

  160. 160.

    Knippertz, P. & Wernli, H. A Lagrangian climatology of tropical moisture exports to the Northern Hemispheric extratropics. J. Clim. 23, 987–1003 (2010).

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the European COST Action DAMOCLES (CA17109). J.Z. acknowledges financial support from the Swiss National Science Foundation (Ambizione grant 179876). O.M. acknowledges support from the Swiss National Science Foundation (grant no. 178751). A portion of C.R.’s work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. E.B. acknowledges financial support from the European Research Council grant ACRCC (project 339390). A.M.R. was supported by the Scientific Employment Stimulus 2017 from the Fundação para a Ciência e a Tecnologia, Portugal (FCT, CEECIND/00027/2017). N.N.R. was funded by the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). This work contributes to the World Climate Research Programme (WCRP) Grand Challenge on Weather and Climate Extremes.

Author information

Affiliations

Authors

Contributions

J.Z., O.M. and A.M.R. drafted the first ideas of the classification. J.Z. and O.M. conceived the main structure, created Figs 1–5 and wrote the first draft of the manuscript. J.Z. created Fig. 6. J.Z. and S.W. wrote the ‘Methods for compound-event analysis’ section, with substantial input from E.B., A.J., D.M. and E.V. All authors made substantial contributions to the discussion of content.

Corresponding author

Correspondence to Jakob Zscheischler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Earth & Environment thanks Ali Sarhadi, Pradeep Mujumdar, Aloïs Tilloy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zscheischler, J., Martius, O., Westra, S. et al. A typology of compound weather and climate events. Nat Rev Earth Environ (2020). https://doi.org/10.1038/s43017-020-0060-z

Download citation