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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-191
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-191
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 21 Sep 2020

Submitted as: model description paper | 21 Sep 2020

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This preprint is currently under review for the journal GMD.

Recalibrating Decadal Climate Predictions – What is an adequate model for the drift?

Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich Alexander Pasternack et al.
  • Institute of Meteorology, Freie Universität Berlin, Berlin, Germany

Abstract. Near-term climate predictions such as decadal climate 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 corrected. In this context, decadal climate predictions have further characteristic features, such as the long time horizon, the lead-time dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of re-forecasts and observations are available to estimate calibration parameters. With DeFoReSt (DecadalClimate Forecast Recalibration Strategy), Pasternack et al. (2018) proposed a parametric post-processing approach to tackle these problems. The original approach of DeFoReSt assumes third order polynomials in lead time to capture conditional and unconditional biases, second order for dispersion, first order for start time dependency. In this study, we propose not to restrict orders a priori but use a systematic model selection strategy to obtain model orders from the data based on non-homogeneous boosting. The introduced boosted recalibration estimates the coefficients of the statistical model, while the most relevant predictors are selected automatically by keeping the coefficients of the less important predictors to zero. Through toy model simulations with differently constructed systematic errors, we show the advantages of boosted recalibration over DeFoReSt. Finally, we apply boosted recalibration and DeFoReSt to decadal surface temperature forecasts from the MiKlip Prototype system. We show that boosted recalibration performs equally well as DeFoReSt and yet offers a greater flexibility.

Alexander Pasternack et al.

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Alexander Pasternack et al.

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Algorithms For Recalibrating Decadal Climate Predictions – What is an adequatemodel for the drift?, Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich https://doi.org/10.5281/zenodo.3975759

Alexander Pasternack et al.

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Latest update: 23 Oct 2020
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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.
Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To...
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