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.
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Scientific Reports Open Access 17 May 2022
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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).
The code that implements the BHM is available at Zenodo (https://doi.org/10.5281/zenodo.5035438).
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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.
The authors declare no competing interests.
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Extended data figures and tables
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.
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).
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.
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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
Scientific Reports (2022)