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DOI

About The GTSMip Project

GTSMip

Project

Within GTSMip we produced global projections of extreme sea levels, including tides and storm surges. This project was led by Deltares, but carried out in close collaboration between researchers from different universities and institutes.

Aim

The projections cover the period from 1950-2050 and are derived with the Global Tide and Surge Model forced with a ~25km-resolution HighResMIP climate model ensemble. The resolution of climate models is important to accurately simulate the extra-tropical storms and tropical cyclones that drive storm surges and this is is the first time that such a high-resolution ensemble is used to assess changes in future storm surges across the globe.

Results

Validation against ERA5 reanalysis (1985-2014) shows that the model performs well globally, but also reveals a clear spatial bias. The median-ensemble change of the 1 in 10-year storm surge levels from 2021-2050 compared to 1951-1980 shows changes up to 0.1 m or 20%. These changes are not uniform across the globe with decreases along the coast of Mediterranean and northern Africa and southern Australia and increases along the south coast of Australia and Alaska. There are also increases along (parts) of the coasts of northern Caribbean, eastern Africa, China and the Korean peninsula, but with less agreement among the HighResMIP ensemble. Information resulting from this study can be used to inform broad-scale assessment of coastal impacts under future climate change.

GTSMip_ens_median

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How to run these scripts?

This pages provides the scripts that we used to produce the figures for Muis et al. (2023). The input data that was used to run this scripts can be found on the [https://doi.org/10.24381/cds.6edf04e0](Climate Data Store). It provides provides statistical indicators of tides, storm surges and sea level that can be used to characterize global sea level in present-day conditions and also to assess changes under climate change. The indicators calculated include extreme-value indicators (e.g. return periods including confidence bounds for total water levels and surge levels), probability indicators (e.g. percentile for total water levels and surge levels). You can use the CDS API to download with Python.

License

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

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