Skillful decadal prediction of German Bight storm activity

Krieger, Daniel; Brune, Sebastian; Pieper, Patrick; Weisse, Ralf; Baehr, Johanna

doi:https://doi.org/10.5194/nhess-22-3993-2022

Articles | Volume 22, issue 12

https://doi.org/10.5194/nhess-22-3993-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-22-3993-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 12

Research article

|

14 Dec 2022

Research article |

| 14 Dec 2022

Skillful decadal prediction of German Bight storm activity

Daniel Krieger, Sebastian Brune, Patrick Pieper, Ralf Weisse, and Johanna Baehr

Data sets

ERA5 monthly averaged data on pressure levels from 1959 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.6860a573

MPI-ESM-LR1.2 decadal hindcast ensemble 3-hourly German Bight MSLP D. Krieger and S. Brune http://hdl.handle.net/21.14106/04bc4cb2c0871f37433a73ee38189690955e1f90

MPI-ESM-LR1.2 decadal hindcast ensemble seasonal mean North Atlantic MSLP D. Krieger and S. Brune http://hdl.handle.net/21.14106/d3a13dc153db18a6de49fd9758b794ec0508e5c0

MPI-ESM-LR1.2 decadal hindcast ensemble yearly German Bight storm activity D. Krieger and S. Brune http://hdl.handle.net/21.14106/e14ca8b63ccb46f2b6c9ed56227a0ac097392d0d

MPI-ESM-LR_1.2.01p5 decadal predictions localEnKF large ensemble: 3-hourly mean surface atmosphere values members 17 to 80 S. Brune, H. Pohlmann, W. A. Müller, D. M. Nielsen, L. Hövel, V. Koul, and J. Baehr http://hdl.handle.net/21.14106/42fdc24ed1c5558f9394225f128969cacd5a6eb5

Short summary

Accurate predictions of storm activity are desirable for coastal management. We investigate how well a climate model can predict storm activity in the German Bight 1–10 years in advance. We let the model predict the past, compare these predictions to observations, and analyze whether the model is doing better than simple statistical predictions. We find that the model generally shows good skill for extreme periods, but the prediction timeframes with good skill depend on the type of prediction.