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

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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
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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.