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:

Trends in Europe storm surge extremes match the rate of sea-level rise

Nature volume 603, pages 841–845 (2022)Cite this article

Subjects

Abstract

Coastal communities across the world are already feeling the disastrous impacts of climate change through variations in extreme sea levels1. These variations reflect the combined effect of sea-level rise and changes in storm surge activity. Understanding the relative importance of these two factors in altering the likelihood of extreme events is crucial to the success of coastal adaptation measures. Existing analyses of tide gauge records2,3,4,5,6,7,8,9,10 agree that sea-level rise has been a considerable driver of trends in sea-level extremes since at least 1960. However, the contribution from changes in storminess remains unclear, owing to the difficulty of inferring this contribution from sparse data and the consequent inconclusive results that have accumulated in the literature11,12. Here we analyse tide gauge observations using spatial Bayesian methods13 to show that, contrary to current thought, trends in surge extremes and sea-level rise both made comparable contributions to the overall change in extreme sea levels in Europe since 1960 . We determine that the trend pattern of surge extremes reflects the contributions from a dominant north–south dipole associated with internal climate variability and a single-sign positive pattern related to anthropogenic forcing. Our results demonstrate that both external and internal influences can considerably affect the likelihood of surge extremes over periods as long as 60 years, suggesting that the current coastal planning practice of assuming stationary surge extremes1,14 might be inadequate.

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
Buy now

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

Fig. 1: Historical trends in storm surge extremes.
Fig. 2: Temporal changes in return period.
Fig. 3: Attribution of trends in surge extremes.

Similar content being viewed by others

Data availability

The high-frequency tide gauge data used in this study for the period 1960–2013 are available from the Global Extreme Sea Level Analysis project (https://www.gesla.org/), whereas data for the period 2014–2018 are from the British Oceanographic Data Centre (https://www.bodc.ac.uk/data/hosted_data_systems/sea_level/uk_tide_gauge_network/) and the Copernicus Marine Environment Monitoring Service (https://resources.marine.copernicus.eu/product-detail/INSITU_GLO_NRT_OBSERVATIONS_013_030/). The ensemble of climate simulations is available from https://doi.org/10.5285/0cea8d7aca57427fae92241348ae9b03 (baseline folder). The observed annual maxima from tide gauge records, the ensemble of surge simulations, as well as the Bayesian solutions from BHM1 and BHM2 have been deposited in Zenodo (https://doi.org/10.5281/zenodo.5749736).

Code availability

The code that implements the BHM is available at Zenodo (https://doi.org/10.5281/zenodo.5035438).

References

  1. Hinkel, J. et al. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl Acad. Sci. USA 111, 3292–3297 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhang, K., Douglas, B. C. & Leatherman, S. P. Twentieth-century storm activity along the U.S. East Coast. J. Clim. 13, 1748–1761 (2000).

    Article  ADS  Google Scholar 

  3. Woodworth, P. L. & Blackman, D. L. Evidence for systematic changes in extreme high waters since the mid-1970s. J. Clim. 17, 1190–1197 (2004).

    Article  ADS  Google Scholar 

  4. Marcos, M., Tsimplis, M. N. & Shaw, A. G. P. Sea level extremes in southern Europe. J. Geophys. Res. Oceans 114, C01007 (2009).

    Article  ADS  Google Scholar 

  5. Haigh, I., Nicholls, R. & Wells, N. Assessing changes in extreme sea levels: application to the English Channel, 1900–2006. Cont. Shelf Res. 30, 1042–1055 (2010).

    Article  ADS  Google Scholar 

  6. Menéndez, M. & Woodworth, P. L. Changes in extreme high water levels based on a quasi-global tide-gauge data set. J. Geophys. Res. 115, C10011 (2010).

    ADS  Google Scholar 

  7. Weisse, R. et al. Changing extreme sea levels along European coasts. Coastal Eng. 87, 4–14 (2014).

    Article  Google Scholar 

  8. Wahl, T. & Chambers, D. P. Evidence for multidecadal variability in US extreme sea level records. J. Geophys. Res. Oceans 120, 1527–1544 (2015).

    Article  ADS  Google Scholar 

  9. Marcos, M. & Woodworth, P. L. Spatiotemporal changes in extreme sea levels along the coasts of the North Atlantic and the Gulf of Mexico. J. Geophys. Res. Oceans 122, 7031–7048 (2017).

    Article  ADS  Google Scholar 

  10. Rohmer, J. & Le Cozannet, G. Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach. Environ. Res. Lett. 14, 014008 (2019).

    Article  ADS  Google Scholar 

  11. 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).

  12. Intergovernmental Panel on Climate Change in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  13. Calafat, F. M. & Marcos, M. Probabilistic reanalysis of storm surge extremes in Europe. Proc. Natl Acad. Sci. USA 117, 1877–1883 (2020).

    Article  ADS  MathSciNet  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  14. Luxford, F. & Faulkner, D. Recommendations for future research and practice on non-stationarity in UK flooding. FRS18087/REA/R2. Environment Agency https://assets.publishing.service.gov.uk/media/6038f813e90e07055685020c/Recommendations_for_future_research_and_practice_on_non-stationarity_in_UK_flooding_-_report__2_.pdf (2020).

  15. Jevrejeva, S., Jackson, L. P., Grinsted, A., Lincke, D. & Marzeion, B. Flood damage costs under the sea level rise with warming of 1.5 °C and 2.0 °C. Environ. Res. Lett. 13, 074014 (2018).

    Article  ADS  Google Scholar 

  16. Kulp, S. A. & Strauss, B. H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 10, 4844 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tiggeloven, T. et al. Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. Nat. Hazards Earth Syst. Sci. 20, 1025–1044 (2020).

    Article  ADS  Google Scholar 

  18. Church, J. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. 13, 1137–1216 (Cambridge Univ. Press, 2013).

  19. Frederikse, T. et al. The causes of sea-level rise since 1900. Nature 584, 393–397 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Marcos, M., Jordà, G., Gomis, D. & Pérez, B. Changes in storm surges in southern Europe from a regional model under climate change scenarios. Glob. Planet. Change 77, 116–128 (2011).

    Article  ADS  Google Scholar 

  21. Conte, D. & Lionello, P. Characteristics of large positive and negative surges in the Mediterranean Sea and their attenuation in future climate scenarios. Glob. Planet. Change 111, 159–173 (2013).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  23. Vousdoukas, M. I. et al. Projections of extreme storm surge levels along Europe. Clim. Dyn. 47, 3171–3190 (2016).

    Article  Google Scholar 

  24. Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M. & Feyen, L. Extreme sea levels on the rise along Europe’s coasts. Earths Future 5, 304–323 (2017).

    Article  ADS  Google Scholar 

  25. Howard, T., Palmer, M. D. & Bricheno, L. M. Contributions to 21st century projections of extreme sea-level change around the UK. Environ. Res. Commun 1, 095002 (2019).

    Article  Google Scholar 

  26. Muis, S. et al. A high-resolution global dataset of extreme sea levels, tides, and storm surges, including future projections. Front. Mar. Sci. 7, 263 (2020).

    Article  Google Scholar 

  27. Greenland, S. et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur. J. Epidemiol 31, 337–350 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Button, K. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Coles, S. G. An Introduction to Statistical Modelling of Extreme Values 208 pp (Springer, 2001).

  30. Schlather, M. Models for stationary max-stable random fields. Extremes 5, 33–44 (2002).

    Article  MathSciNet  MATH  Google Scholar 

  31. McFadden, D. Modeling the choice of residential location. Transp. Res. Rec. 672, 72–77 (1978).

    Google Scholar 

  32. Tadesse, M. G., Wahl, T. & Cid, A. Data-driven modeling of global storm surges. Front. Mar. Sci. 7, 260 (2020).

    Article  Google Scholar 

  33. Tadesse, M. G. & Wahl, T. A database of global storm surge reconstructions. Sci. Data 8, 125 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Guillod, B. P. et al. A large set of potential past, present and future hydro-meteorological time series for the UK. Hydrol. Earth Syst. Sci. 22, 611–634 (2018).

    Article  ADS  Google Scholar 

  35. Stott, P. A. et al. Attribution of extreme weather and climate-related events. Wiley Interdiscip. Rev. Clim. Change 7, 23–41 (2016).

    Article  PubMed  Google Scholar 

  36. Dangendorf, S., Arns, A., Pinto, J. G., Ludwig, P. & Jensen, J. The exceptional influence of storm ‘Xaver’ on design water levels in the German Bight. Environ. Res. Lett. 11, 054001 (2016).

    Article  ADS  Google Scholar 

  37. Zappa, G., Shaffrey, L. C., Hodges, K. I., Sansom, P. G. & Stephenson, D. B. A multimodel assessment of future projections of North Atlantic and European extratropical cyclones in the CMIP5 climate models. J. Clim. 26, 5846–5862 (2013).

    Article  ADS  Google Scholar 

  38. Feser, F. et al. Storminess over the North Atlantic and northwestern Europe—a review. Q. J. R. Meteorol. Soc 141, 350–382 (2015).

    Article  ADS  Google Scholar 

  39. Barcikowska, M. J. et al. Euro-Atlantic winter storminess and precipitation extremes under 1.5 °C vs. 2 °C warming scenarios. Earth Syst. Dyn 9, 679–699 (2018).

    Article  ADS  Google Scholar 

  40. Vousdoukas, M. I. et al. Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. Nat. Commun. 9, 2360 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  41. Woodworth, P. L. et al. Towards a global higher-frequency sea level dataset. Geosci. Data J. 3, 50–59 (2017).

    Article  ADS  Google Scholar 

  42. Killick, R., Fearnhead, P. & Eckley, I. A. Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107, 1590–1598 (2012).

    Article  MathSciNet  CAS  MATH  Google Scholar 

  43. Kalnay, et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–470 (1996).

    Article  ADS  Google Scholar 

  44. Holgate, S. J. et al. New data systems and products at the Permanent Service for Mean Sea Level. J. Coast. Res. 29, 493–504 (2013).

    Google Scholar 

  45. Reich, B. J. & Shaby, B. A. A hierarchical max-stable spatial model for extreme precipitation. Ann. Appl. Stat 6, 1430–1451 (2012).

    Article  MathSciNet  PubMed  PubMed Central  MATH  Google Scholar 

  46. Stephenson, A. G., Shaby, B. A., Reich, B. J. & Sullivan, A. L. Estimating spatially varying severity thresholds of a forest fire danger rating system using max-stable extreme-event modeling. J. Appl. Meteorol. Climatol. 54, 395–407 (2015).

    Article  ADS  Google Scholar 

  47. Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).

    Article  Google Scholar 

  48. Guillod, B. P. et al. weather@home 2: validation of an improved global–regional climate modelling system. Geosci. Model Dev. 10, 1849–1872 (2017).

    Article  ADS  Google Scholar 

  49. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, 4407 (2003).

    Article  ADS  Google Scholar 

  50. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  ADS  Google Scholar 

  51. Hersbach, et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We acknowledge the GESLA project for assembling and making the tide gauge data available. F.M.C. was supported by the Natural Environment Research Council (NERC) National Capability funding. M.G.T. and T.W. were supported by the National Aeronautics and Space Administration (NASA) under the New (Early Career) Investigator Program (NIP) in Earth Science (grant number 80NSSC18K0743) and the NASA Sea Level Science Team (grant number 80NSSC20K1241). T.W. also acknowledges support from the National Science Foundation (under grant ICER-1854896). We acknowledge conversations with M. Marcos and also thank her for providing the tide gauge data (from the British Oceanographic Data Centre and the Copernicus Marine Environment Monitoring Service) for the period 2014–2018.

Author information

Authors and Affiliations

  1. National Oceanography Centre, Liverpool, UK

    Francisco M. Calafat

  2. Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USA

    Thomas Wahl & Michael Getachew Tadesse

  3. National Center for Integrated Coastal Research, University of Central Florida, Orlando, FL, USA

    Thomas Wahl & Michael Getachew Tadesse

  4. Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK

    Sarah N. Sparrow

Authors
  1. Francisco M. Calafat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Wahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Getachew Tadesse
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sarah N. Sparrow
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.M.C. conceived and designed the study, with input from all authors. T.W. and M.G.T. produced the ensemble of surge simulations. S.N.S. provided the ensemble of climate simulations. F.M.C. performed the analyses and wrote the manuscript, with contributions from all authors.

Corresponding author

Correspondence to Francisco M. Calafat.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Nadia Bloemendaal, Jérémy Rohmer and the other, anonymous, reviewer for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Uncertainty of estimated μ trends at individual locations.

Posterior standard deviations for the μ trends at tide gauge sites (a) and gridded locations (b). These standard deviations correspond to the μ trends shown in Fig. 1a, b.

Extended Data Fig. 2 Tide gauge stations and spatial knots.

Location of the tide gauge stations used in the analysis of extremes (red circles), along with the spatial knots used to construct the spatial residual process in the BHM (blue crosses).

Extended Data Fig. 3 Amplitude of the anthropogenic fingerprint.

Posterior (blue) and prior (red) distributions for the amplitude of the anthropogenic fingerprint (βext). The posterior has been estimated by fitting BHM2 to the tide gauge observations.

Extended Data Table 1 Scalar parameters of the BHM and prior distributions
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Text and Supplementary Figures 1–4.

Peer Review File

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Calafat, F.M., Wahl, T., Tadesse, M.G. et al. Trends in Europe storm surge extremes match the rate of sea-level rise. Nature 603, 841–845 (2022). https://doi.org/10.1038/s41586-022-04426-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-04426-5

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced 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