Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts

Doblas-Reyes, F. J., Weisheimer, A., Déqué, M., Keenlyside, Noel, McVean, M., Murphy, J., Rogel, D., Smith, P. and Palmer, T. N. (2009) Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts Quarterly Journal of the Royal Meteorological Society, 135 . pp. 1538-1559. DOI 10.1002/qj.464.

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Abstract

The relative merits of three forecast systems addressing the impact of model uncertainty on seasonal/annual forecasts are described. One system consists of a multi-model, whereas two other systems sample uncertainties by perturbing the parametrization of reference models through perturbed parameter and stochastic physics techniques. Ensemble re-forecasts over 1991 to 2001 were performed with coupled climate models started from realistic initial conditions. Forecast quality varies due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. Both the stochastic-physics and perturbed-parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Although the multi-model experiment has an ensemble size larger than the other two experiments, most of the assessment was done using equally-sized ensembles. The three ensembles show similar levels of skill: significant differences in performance typically range between 5 and 20%. However, a nine-member multi-model shows better results for seasonal predictions with lead times shorter than five months, followed by the stochastic-physics and perturbed-parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed-parameter ensemble gives more often better results. All systems suggest that spread cannot be considered a useful predictor of skill. Annual-mean predictions showed lower forecast quality than seasonal predictions. Only small differences between the systems were found. The full multi-model ensemble has improved quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions.

Document Type: Article
Keywords: model uncertainty; ensemble forecast; probability forecasts; seasonal prediction; forecast quality
Research affiliation: OceanRep > GEOMAR > FB1 Ocean Circulation and Climate Dynamics > FB1-ME Maritime Meteorology
OceanRep > The Future Ocean - Cluster of Excellence
Refereed: Yes
DOI etc.: 10.1002/qj.464
ISSN: 0035-9009
Projects: Future Ocean
Date Deposited: 08 Jan 2010 14:29
Last Modified: 29 Sep 2016 11:07
URI: http://eprints.uni-kiel.de/id/eprint/324

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