the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Sep 2025
Global dataset of storm surges and extreme sea levels for 1950–2024 based on the ERA5 climate reanalysis
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.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-471', Anonymous Referee #1, 16 Nov 2025
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RC2: 'Comment on essd-2025-471', Anonymous Referee #2, 10 Mar 2026
The paper presents an extension of a still water level hindcast from 40 to 75 years, made possible by the recent backward extension of the ERA5 atmospheric reanalysis. Its main objective is to assess how this longer dataset affects the estimation of extremes, which are often poorly constrained when derived from shorter records, particularly for long return periods. The extended hindcast represents a valuable resource for the community working on extreme sea level estimation, and the dataset itself constitutes a contribution worthy of publication. However, the manuscript requires substantial revisions to better validate the extended period and to more clearly demonstrate the implications for extreme value estimation. In particular, the results in Section 4 should be reorganized so that all validations against observations—including comparisons for individual events—are presented first, followed by a section dedicated to assessing the impact of the extended record on extreme value estimation (e.g., temporal variability and sampling uncertainty). Moreover, the validation against observations should be strengthened by including metrics specifically targeting the backward-extended period (1950–1978) and by focusing on selected long-record tide gauges, both within and outside tropical cyclone regions, in order to evaluate the added value of the extended dataset for better constraining return levels.
Data sets
Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections Sanne 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
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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.