Abstract. Extreme sea levels, generated by storm surges and high tides, can cause coastal flooding and erosion. Global datasets have been instrumental in mapping extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing timeseries of tides and storm surges based on a global hydrodynamic model covering the period 1950–2024. This is an extension of a previously published dataset that covered a shorter period (1979–2018). Using this dataset, we estimate extreme sea levels globally. Validation shows good agreement between observed and modelled extreme sea levels, with the level of agreement for the extended dataset being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of return periods, often resulting in smaller uncertainties than its 40-year precursor. This underscores the necessity for long timeseries and the strength of long-term modelling enabled by the ERA5 reanalysis extension. The present dataset can be used for assessing flood risk, climate variability and climate changes.
Received: 05 Aug 2025 – Discussion started: 03 Sep 2025
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
The article presents a 75-year global dataset /1950-2024) of coastal water levels and storm surges, generated using ERA5 reanalysis and consistent with the earlier 1979–2018 product. The extended record enables improved long-term statistics, more robust extreme-value estimation, and detailed reconstruction of historical storm events. Validation using GESLA observations and three past severe storms demonstrates generally reliable performance but also highlights systematic limitations. ERA5 underestimates the intensity of extreme storms especially tropical cyclones leading to underestimated surge heights. The global GTSM model cannot fully resolve complex coastlines or local bathymetry, contributing to discrepancies between modeled and observed extremes. As a result, the dataset’s extreme value statistics are best suited for first-order global assessments rather than local engineering applications. Downscaling and mean sea-level reconstructions can further improve regional accuracy.
The provided dataset complements the previous product provided by the same authors in 2020 with a significant extension of the priod covered giving better estimations of long term extreme-value statistics. Limitations of the product are well identified and well indicated in the concluding remarks of the manuscript. This is a good example of a paper suitable to be published in ESSD.
Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projectionsSanne Muis et al. https://doi.org/10.24381/cds.a6d42d60
GTSM-ERA5-E dataset - Data underlying the paper “Global dataset of storm surges and extreme sea levels for 1950-2024 based on the ERA5 climate reanalysis”Sanne Muis et al. https://doi.org/10.5281/ZENODO.14671593
Extreme sea levels during storms can lead to coastal flooding and sediment displacement. In the paper, we present an expanded global dataset of sea levels, produced using a global ocean model driven by ERA5 reanalysis – a state-of-the-art dataset of historical weather and climate. The strengths and limitations of the dataset are discussed by comparing it to observed sea levels. The dataset covers a period of 1950–2024, improving our understanding of the global frequency of extreme water levels.
Extreme sea levels during storms can lead to coastal flooding and sediment displacement. In the...
The article presents a 75-year global dataset /1950-2024) of coastal water levels and storm surges, generated using ERA5 reanalysis and consistent with the earlier 1979–2018 product. The extended record enables improved long-term statistics, more robust extreme-value estimation, and detailed reconstruction of historical storm events. Validation using GESLA observations and three past severe storms demonstrates generally reliable performance but also highlights systematic limitations. ERA5 underestimates the intensity of extreme storms especially tropical cyclones leading to underestimated surge heights. The global GTSM model cannot fully resolve complex coastlines or local bathymetry, contributing to discrepancies between modeled and observed extremes. As a result, the dataset’s extreme value statistics are best suited for first-order global assessments rather than local engineering applications. Downscaling and mean sea-level reconstructions can further improve regional accuracy.
The provided dataset complements the previous product provided by the same authors in 2020 with a significant extension of the priod covered giving better estimations of long term extreme-value statistics. Limitations of the product are well identified and well indicated in the concluding remarks of the manuscript. This is a good example of a paper suitable to be published in ESSD.