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

MPI-ESM-LR1.2 decadal hindcast ensemble 3-hourly German Bight MSLP D. Krieger and S. Brune

MPI-ESM-LR1.2 decadal hindcast ensemble seasonal mean North Atlantic MSLP D. Krieger and S. Brune

MPI-ESM-LR1.2 decadal hindcast ensemble yearly German Bight storm activity D. Krieger and S. Brune

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

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.
Final-revised paper