Articles | Volume 16, issue 9
Model description paper
05 May 2023
Model description paper |  | 05 May 2023

The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework

Magnus Hieronymus

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Cited articles

Arns, A., Wahl, T., Haigh, I., Jensen, J., and Pattiaratchi, C.: Estimating extreme water level probabilities: A comparison of the direct methods and recommendations for best practise, Coastal Eng., 81, 51–66,, 2013. a
Arns, A., Dangendorf, S., Talke, J. J. S., Bender, J., and Pattiaratchi, C.: Sea-level rise induced amplification of coastal protection design heights, Sci. Rep.-UK, 7, 40171,, 2017. a
Bamber, J. L., Oppenheimer, M., Kopp, R. E., Aspinall, W. P., and Cooke, R. M.: Ice sheet contributions to future sea-level rise from structured expert judgment, P. Natl. Acad. Sci. USA, 116, 11195–11200,, 2019. a
Calafat, F. M. and Marcos, M.: Probabilistic reanalysis of storm surge extremes in Europe, P. Natl. Acad. Sci. USA, 117, 1877–1883,, 2020. a
Capellán-Pérez, I., Arto, I., Polanco-Martínez, J. M., González-Eguinob, M., and Neumann, M. B.: Likelihood of climate change pathways under uncertainty on fossil fuel resource availability, Energy Environ. Sci., 9, 2482–2496,, 2016. a
Short summary
A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.