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Machine learning-driven skillful decadal predictions of the German Bight storm surge climate
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  • Daniel Krieger,
  • Ralf Weisse,
  • Johanna Baehr,
  • Leonard F. Borchert
Daniel Krieger
Universitat Hamburg Institut fur Meereskunde

Corresponding Author:[email protected]

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Ralf Weisse
Helmholtz-Zentrum Hereon
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Johanna Baehr
Universität Hamburg, Center for Earth System Research and Sustainability
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Leonard F. Borchert
Universität Hamburg
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Abstract

The German Bight coastline is regularly affected by storm surges driven by extratropical cyclones. Decadal-scale predictions of the local surge climate would foster coastal protection and decision making in affected areas. We therefore examine the prediction skill of the Max-Planck-Institute Earth System Model (MPI-ESM) decadal prediction system for three storm surge metrics at Cuxhaven, Germany: the annual upper percentiles of surge heights and durations and the annual number of surges. To avoid dynamical downscaling from the coarse model output to local surge heights, we use machine learning and train a neural network on observed surge heights and reanalyzed fields of mean sea-level pressure (MSLP). We apply this network to MSLP output of our prediction system to generate decadal predictions of surge heights at Cuxhaven. We find that the prediction system falls short of generating skillful predictions for individual lead years, but can predict certain multi-year averages of surge metrics skillfully.
31 Jul 2024Submitted to ESS Open Archive
31 Jul 2024Published in ESS Open Archive