Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4335-2021
https://doi.org/10.5194/gmd-14-4335-2021
Model description paper
 | 
09 Jul 2021
Model description paper |  | 09 Jul 2021

Recalibrating decadal climate predictions – what is an adequate model for the drift?

Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich

Related authors

Past and future European atmospheric extreme events under climate change – the ClimXtreme program's structure and results
Andreas Hense, Christoph Kottmeier, Petra Friederichs, Sebastian Buschow, Svenja Szemkus, Uwe Ulbrich, Jens Grieger, Joaquim G. Pinto, Hendrik Feldmann, Frank Kaspar, Deborah Niermann, Rike Lorenz, Florian Ruff, and Etor E. Lucio Eceiza
EGUsphere, https://doi.org/10.5194/egusphere-2026-2697,https://doi.org/10.5194/egusphere-2026-2697, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Extreme precipitation and flooding in Berlin under climate change and effects of selected grey and blue-green measures
Franziska Tügel, Katrin M. Nissen, Lennart Steffen, Yangwei Zhang, Uwe Ulbrich, and Reinhard Hinkelmann
Nat. Hazards Earth Syst. Sci., 25, 4673–4692, https://doi.org/10.5194/nhess-25-4673-2025,https://doi.org/10.5194/nhess-25-4673-2025, 2025
Short summary
Temporal dynamic vulnerability – impact of antecedent events on residential building losses to wind storm events in Germany
Andreas Trojand, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 25, 2331–2350, https://doi.org/10.5194/nhess-25-2331-2025,https://doi.org/10.5194/nhess-25-2331-2025, 2025
Short summary
Tree fall along railway lines: modelling the impact of wind and other meteorological factors
Rike Lorenz, Nico Becker, Barry Gardiner, Uwe Ulbrich, Marc Hanewinkel, and Benjamin Schmitz
Nat. Hazards Earth Syst. Sci., 25, 2179–2196, https://doi.org/10.5194/nhess-25-2179-2025,https://doi.org/10.5194/nhess-25-2179-2025, 2025
Short summary
Investigating the global and regional response of drought to idealized deforestation using multiple global climate models
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025,https://doi.org/10.5194/hess-29-1637-2025, 2025
Short summary

Cited articles

Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996. a
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F., and Jones, P. D.: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850, J. Geophys. Res.-Atmos., 111, D12106, https://doi.org/10.1029/2005JD006548, 2006. a
Bühlmann, P. and Yu, B.: Boosting with the L 2 loss: regression and classification, J. Am. Stat. Assoc., 98, 324–339, 2003. a
Bühlmann, P. and Hothorn, T.: Boosting algorithms: Regularization, prediction and model fitting, Stat. Sci., 22, 477–505, 2007. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Download
Short summary
Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Share