Articles | Volume 14, issue 7
Geosci. Model Dev., 14, 4335–4355, 2021
https://doi.org/10.5194/gmd-14-4335-2021
Geosci. Model Dev., 14, 4335–4355, 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 et al.

Related authors

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)
Alexander Pasternack, Jonas Bhend, Mark A. Liniger, Henning W. Rust, Wolfgang A. Müller, and Uwe Ulbrich
Geosci. Model Dev., 11, 351–368, https://doi.org/10.5194/gmd-11-351-2018,https://doi.org/10.5194/gmd-11-351-2018, 2018
Short summary

Related subject area

Climate and Earth system modeling
EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021,https://doi.org/10.5194/gmd-14-5637-2021, 2021
Short summary
Vertical grid refinement for stratocumulus clouds in the radiation scheme of the global climate model ECHAM6.3-HAM2.3-P3
Paolo Pelucchi, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 14, 5413–5434, https://doi.org/10.5194/gmd-14-5413-2021,https://doi.org/10.5194/gmd-14-5413-2021, 2021
Short summary
Cloud Feedbacks from CanESM2 to CanESM5.0 and their influence on climate sensitivity
John G. Virgin, Christopher G. Fletcher, Jason N. S. Cole, Knut von Salzen, and Toni Mitovski
Geosci. Model Dev., 14, 5355–5372, https://doi.org/10.5194/gmd-14-5355-2021,https://doi.org/10.5194/gmd-14-5355-2021, 2021
Short summary
ATTRICI v1.1 – counterfactual climate for impact attribution
Matthias Mengel, Simon Treu, Stefan Lange, and Katja Frieler
Geosci. Model Dev., 14, 5269–5284, https://doi.org/10.5194/gmd-14-5269-2021,https://doi.org/10.5194/gmd-14-5269-2021, 2021
Short summary
Mitigation of the double ITCZ syndrome in BCC-CSM2-MR through improving parameterizations of boundary-layer turbulence and shallow convection
Yixiong Lu, Tongwen Wu, Yubin Li, and Ben Yang
Geosci. Model Dev., 14, 5183–5204, https://doi.org/10.5194/gmd-14-5183-2021,https://doi.org/10.5194/gmd-14-5183-2021, 2021
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.