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Volume 11, issue 1 | Copyright
Geosci. Model Dev., 11, 351-368, 2018
https://doi.org/10.5194/gmd-11-351-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 25 Jan 2018

Model description paper | 25 Jan 2018

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Alexander Pasternack1, Jonas Bhend2, Mark A. Liniger2, Henning W. Rust1, Wolfgang A. Müller3, and Uwe Ulbrich1 Alexander Pasternack et al.
  • 1Institute of Meteorology, Freie Universität Berlin, Berlin, Germany
  • 2Federal Office of Meteorology and Climatology (MeteoSwiss), Zürich, Switzerland
  • 3Max-Planck-Institute for Meteorology, Hamburg, Germany

Abstract. Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, 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 reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

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We propose a decadal forecast recalibration strategy (DeFoReSt) which simultaneously adjusts unconditional and conditional bias, as well as the ensemble spread while considering the typical setting of decadal predictions, i.e., model drift and a climate trend. We apply DeFoReSt to decadal toy model data and surface temperature forecasts from the MiKlip system and find consistent improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.
We propose a decadal forecast recalibration strategy (DeFoReSt) which simultaneously adjusts...
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