Articles | Volume 11, issue 1
https://doi.org/10.5194/gmd-11-351-2018
https://doi.org/10.5194/gmd-11-351-2018
Model description paper
 | 
25 Jan 2018
Model description paper |  | 25 Jan 2018

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Alexander Pasternack, Jonas Bhend, Mark A. Liniger, Henning W. Rust, Wolfgang A. Müller, 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
Arisido, M. W., Gaetan, C., Zanchettin, D., and Rubino, A.: A Bayesian hierarchical approach for spatial analysis of climate model bias in multi-model ensembles, Stoch. Env. Res. Risk A., 31, 1–13, 2017. 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, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Doblas-Reyes, F. J., Hagedorn, R., and Palmer, T. N.: The rationale behind the success of multi-model ensembles in seasonal forecasting – II. Calibration and combination, Tellus A, 57, 234–252, 2005. a
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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.
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