Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Increasing probability of record-shattering climate extremes

Abstract

Recent climate extremes have broken long-standing records by large margins. Such extremes unprecedented in the observational period often have substantial impacts due to a tendency to adapt to the highest intensities, and no higher, experienced during a lifetime. Here, we show models project not only more intense extremes but also events that break previous records by much larger margins. These record-shattering extremes, nearly impossible in the absence of warming, are likely to occur in the coming decades. We demonstrate that their probability of occurrence depends on warming rate, rather than global warming level, and is thus pathway-dependent. In high-emission scenarios, week-long heat extremes that break records by three or more standard deviations are two to seven times more probable in 2021–2050 and three to 21 times more probable in 2051–2080, compared to the last three decades. In 2051–2080, such events are estimated to occur about every 6–37 years somewhere in the northern midlatitudes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Purchase on Springer Link

Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Illustrative example of a simulated record-shattering event in Central North America.
Fig. 2: Drivers of record-shattering heatwave.
Fig. 3: Projected occurrence of record-shattering extremes in northern midlatitudes.
Fig. 4: Path-dependence of occurrence of record-shattering extremes.

Similar content being viewed by others

Data availability

All original CMIP5 and CMIP6 data, the CanESM2 and NCAR LENS ensembles, and the ERA5 reanalysis used in this study, are publicly available as follows: CMIP5 model data, https://esgf-node.llnl.gov/projects/cmip5/; CMIP6 model data, https://esgf-node.llnl.gov/projects/cmip6/; CanESM2 ensemble reanalysis, https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c; NCAR LENS ensemble, https://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html; and ECMWF5 ERA5 reanalysis, https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels. The output from CESM1.2 and CESM-CAM4 used in this analysis is available at https://data.iac.ethz.ch/Fischer_et_al_2021_RecordExtremes/.

Code availability

All computer code to reproduce the main results and all figures are available at https://data.iac.ethz.ch/Fischer_et_al_2021_RecordExtremes/

References

  1. Risser, M. D. & Wehner, M. F. Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophys. Res. Lett. 44, 12457–12464 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Van Oldenborgh, G. J. et al. Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environ. Res. Lett. 12, 124009 (2017).

    Article  Google Scholar 

  4. Overland, J.E. & Wang, M. The 2020 Siberian heat wave. Int. J. Climatol. 41, E2341–E2346 (2020).

    Google Scholar 

  5. Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & Vilà-Guerau de Arellano, J. G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).

    Article  CAS  Google Scholar 

  6. 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).

    Article  CAS  Google Scholar 

  7. Robine, J. M. et al. Death toll exceeded 70,000 in Europe during the summer of 2003. C. R. Biol. 331, 171–178 (2008).

    Article  Google Scholar 

  8. Garcia-Herrera, R., Trigo, R. M., Luterbacher, J., Schär, C. & Fischer, E. M. A review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Technol. 40, 267–306 (2010).

    Article  Google Scholar 

  9. Haden, V. R., Niles, M. T., Lubell, M., Perlman, J. & Jackson, L. E. What attitudes and beliefs motivate farmers to mitigate and adapt to climate change? PLoS ONE 7, e52882 (2012).

    Article  CAS  Google Scholar 

  10. Blennow, K., Persson, J., Tomé, M. & Hanewinkel, M. Climate change: believing and seeing implies adapting. PLoS ONE 7, e50182 (2012).

    Article  CAS  Google Scholar 

  11. Weber, E. U. Experience-based and description-based perceptions of long-term risk: why global warming does not scare us (yet). Climatic Change 77, 103–120 (2006).

    Article  Google Scholar 

  12. Coumou, D., Robinson, A. & Rahmstorf, S. Global increase in record-breaking monthly-mean temperatures. Climatic Change 118, 771–782 (2013).

    Article  Google Scholar 

  13. Meehl, G. A., Tebaldi, C., Walton, G., Easterling, D. & McDaniel, L. Relative increase of record high maximum temperatures compared to record low minimum temperatures in the U.S. Geophys. Res. Lett. 36, L23701 (2009).

    Article  Google Scholar 

  14. Elguindi, N., Rauscher, S. A. & Giorgi, F. Historical and future changes in maximum and minimum temperature records over Europe. Climatic Change 117, 415–431 (2013).

    Article  Google Scholar 

  15. King, A. D. Attributing changing rates of temperature record breaking to anthropogenic influences. Earth’s Future 5, 1156–1168 (2017).

    Article  Google Scholar 

  16. Power, S. B. & Delage, F. P. D. Setting and smashing extreme temperature records over the coming century. Nat. Clim. Change 9, 529–534 (2019).

    Article  Google Scholar 

  17. Perkins, S. E., Alexander, L. V. & Nairn, J. R. Increasing frequency, intensity and duration of observed global heatwaves and warm spells. Geophys. Res. Lett. 39, L20714 (2012).

    Google Scholar 

  18. Kunkel, K. E., Liang, X.-Z., Zhu, J. & Lin, Y. Can CGCMs simulate the twentieth-century “warming hole” in the central United States? J. Clim. 19, 4137–4153 (2006).

    Article  Google Scholar 

  19. Meehl, G. A., Arblaster, J. M. & Branstator, G. Mechanisms contributing to the warming hole and the consequent US east–west differential of heat extremes. J. Clim. 25, 6394–6408 (2012).

    Article  Google Scholar 

  20. Yu, S. et al. Attribution of the United States ‘warming hole’: aerosol indirect effect and precipitable water vapor. Sci. Rep. 4, 6929 (2014).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  22. Sun, Y. et al. Rapid increase in the risk of extreme summer heat in Eastern China. Nat. Clim. Change 4, 1082–1085 (2014).

    Article  Google Scholar 

  23. Mueller, B., Zhang, X. & Zwiers, F. W. Historically hottest summers projected to be the norm for more than half of the world’s population within 20 years. Environ. Res. Lett. 11, 044011 (2016).

    Article  Google Scholar 

  24. Christidis, N., Jones, G. S. & Stott, P. A. Dramatically increasing chance of extremely hot summers since the 2003 European heatwave. Nat. Clim. Change 5, 46–50 (2015).

    Article  Google Scholar 

  25. Moore, F. C., Obradovich, N., Lehner, F. & Baylis, P. Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change. Proc. Natl Acad. Sci. USA 116, 4905–4910 (2019).

    Article  CAS  Google Scholar 

  26. Changnon, S. A., Kunkel, K. E. & Reinke, B. C. Impacts and responses to the 1995 heat wave: a call to action. Bull. Am. Meteorol. Soc. 77, 1497–1506 (1996).

  27. Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).

    Article  CAS  Google Scholar 

  28. Seneviratne, S. I., Pal, J. S., Eltahir, E. A. B. & Schär, C. Summer dryness in a warmer climate: a process study with a regional climate model. Clim. Dynam. 20, 69–85 (2002).

    Article  Google Scholar 

  29. Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E. & Raymond, C. A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep. 2, 242–259 (2016).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  31. Fischer, E. M., Seneviratne, S. I., Lüthi, D. & Schär, C. Contribution of land–atmosphere coupling to recent European summer heat waves. Geophys. Res. Lett. 34, L06707 (2007).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl Acad. Sci. USA 108, 17905–17909 (2011).

    Article  CAS  Google Scholar 

  34. Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).

    Article  CAS  Google Scholar 

  35. Fischer, E. M., Sedláček, J., Hawkins, E. & Knutti, R. Models agree on forced response pattern of precipitation and temperature extremes. Geophys. Res. Lett. 41, 8554–8562 (2014).

    Article  Google Scholar 

  36. Pendergrass, A. G., Lehner, F., Sanderson, B. M. & Xu, Y. Does extreme precipitation intensity depend on the emissions scenario? Geophys. Res. Lett. 42, 8767–8774 (2015).

    Article  Google Scholar 

  37. Taleb, N. N. The black swan: the impact of the highly improbable. Rev. Austrian Econ. 21, 361–364 (2007).

    Google Scholar 

  38. Lin, N. & Emanuel, K. Grey swan tropical cyclones. Nat. Clim. Change 6, 106–111 (2016).

    Article  Google Scholar 

  39. Fouillet, A., Rey, G. & Laurent, F. Excess mortality related to the August 2003 heat wave in France. Int. Arch. Occup. Environ. Health 80, 16–24 (2006).

    Article  CAS  Google Scholar 

  40. Green, H. K., Andrews, N., Armstrong, B., Bickler, G. & Pebody, R. Mortality during the 2013 heatwave in England—how did it compare to previous heatwaves? A retrospective observational study. Environ. Res. 147, 343–349 (2016).

    Article  CAS  Google Scholar 

  41. Wetter, O. et al. The largest floods in the High Rhine basin since 1268 assessed from documentary and instrumental evidence. Hydrol. Sci. J. 56, 733–758 (2011).

    Article  Google Scholar 

  42. Sippel, S. et al. Quantifying changes in climate variability and extremes: pitfalls and their overcoming. Geophys. Res. Lett. 42, 9990–9998 (2015).

    Article  Google Scholar 

  43. Fischer, E. M., Beyerle, U. & Knutti, R. Robust spatially aggregated projections of climate extremes. Nat. Clim. Change 3, 1033–1038 (2013).

    Article  Google Scholar 

  44. Deser, C., Phillips, A. S., Alexander, M. A. & Smoliak, B. V. Projecting North American climate over the next 50 years: uncertainty due to internal variability. J. Clim. 27, 2271–2296 (2014).

    Article  Google Scholar 

  45. Kirchmeier-Young, M. C., Zwiers, F. W. & Gillett, N. P. Attribution of extreme events in Arctic Sea ice extent. J. Clim. 30, 553–571 (2017).

    Article  Google Scholar 

  46. Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  47. Swart, N. et al. The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci. Model Dev. Discuss. 12, 4823–4873 (2019).

    Article  CAS  Google Scholar 

  48. Ana, F. & De Haan, L. On the block maxima method in extreme value theory: PWM estimators. Ann. Stat. 43, 276–298 (2015).

    Google Scholar 

  49. Falk, M., Chokami, A. K. & Padoan, S. A. Some results on joint record events. Stat. Probab. Lett. 135, 11–19 (2018).

    Article  Google Scholar 

  50. Falk, M., Khorrami Chokami, A. & Padoan, S. Records for time-dependent stationary Gaussian sequences. J. Appl. Probab. 57, 78–96 (2020).

    Article  Google Scholar 

  51. Ahsanullah M. & Nevzorov V.B. in International Encyclopedia of Statistical Science (Ed. Lovric, M.) 1195–1202 (Springer, 2011).

Download references

Acknowledgements

We acknowledge funding received from the Swiss National Science Foundation (grant no. 200020_178778) (E.M.F). We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modelling groups for producing and making available their model output. We acknowledge Environment and Climate Change Canada’s Canadian Centre for Climate Modelling and Analysis for executing and making available the CanESM2 large ensemble simulations. We acknowledge the US CLIVAR working group on large ensembles for compiling the Multi-Model Large Ensemble Archive.

Author information

Authors and Affiliations

Authors

Contributions

E.M.F and R.K designed the study. E.M.F. performed the model analysis and S.S. implemented the analytical solution. All authors contributed to the interpretation of the results and the writing of the manuscript.

Corresponding author

Correspondence to E. M. Fischer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Raphaël Huser 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.

Extended data

Extended Data Fig. 1 Observed record-shattering events.

Regional average Tx7day time series for (top) the European 2003 heatwave region 35–60°N and 10°W-20°E, (middle) the CNA region used in Fig. 1, (bottom) the Russian 2010 heatwave region 40–70°N and 20–70°E. The solid line shows a regional average of EOBS v19 gridded observations38 and the dashed lines of ERA5 reanalysis product39. The two dots illustrate two record-shattering events, and the magnitude is expressed in standard deviations of the detrended time series.

Extended Data Fig. 2 Changing return levels in other large ensemble.

Same as Fig. 1b but based on CanESM2 large ensemble. Yellow, orange and dark red lines illustrate the non-stationary GEV estimates for 200-, 500-, and 1000-yr return periods from all members using global mean temperature as a covariate for the location and scale parameter (see Methods). Best estimate of 1000-yr return period estimated from a stationary GEV fit from all 50 members (dotted violet line) (1950–2019) along with 95% CIs.

Extended Data Fig. 3 Observed heatwaves and their drivers.

(a-c) Tx7day anomaly and (d-f) associated 500 hPa geopotential during the 2003 European summer heatwave, the 1995 Chicago heatwave and the 2010 Russian heatwave. Anomalies are calculated based on the ECMWF ERA5 reanalysis and expressed as anomalies relative to the period 1986–2005.

Extended Data Fig. 4 Record-shattering event in long-term context.

(top) Tx7day anomalies averaged across Central N America in all CESM1.2 members from 1850–2100. The red and yellow dots illustrate the two record-shattering events discussed in the main text. The red line and red dot highlight the event and corresponding simulations shown in Fig. 1a. The yellow line and yellow dot highlight the second most extreme record-shattering event. (bottom) The second most intense record-shattering event discussed in the main text is illustrated in the same way as the most extreme event in Fig. 1a. Best estimate of 1,000-yr return period estimated from a stationary GEV fit to the selected member (1850–2019) (dashed turquoise line) and from all 84 members (dotted violet line) (1950–2019) along with 95% CI.

Extended Data Fig. 5 Drivers of record-shattering extremes in long-term context.

Same as Fig. 2d–g but relative to a constant reference period expressed as the multimember average across 1981–2010. The record-shattering extremes exceeding the previous record by more than 2σ, 3σ, and 4σ are marked with yellow, orange and red dots, respectively.

Extended Data Fig. 6 Examples of record-shattering events in other large ensembles and other regions.

Record-shattering event (red dot) and annual maximum 7-day temperature anomalies averaged over (a,c,e) Central N America) and (b,d,f) Central Europe in the corresponding simulation before the event (black line).

Supplementary information

Supplementary Information

Supplementary Text, Tables 1 and 2, Figs. 1–7 and References.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fischer, E.M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Chang. 11, 689–695 (2021). https://doi.org/10.1038/s41558-021-01092-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-021-01092-9

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing