Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts.

TitleAddressing model uncertainty in seasonal and annual dynamical ensemble forecasts.
Publication TypeMiscellaneous
Year of Publication2008
AuthorsDoblas-Reyes, F, Weisheimer, A, Deque, M, Keenlyside, N, McVean, M, Murphy, JM, Rogel, P, Smith, DM, Palmer, TN
Secondary TitleTechnical Memorandum
Number560
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 parameterisation 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 between the systems due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. 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 the perturbed-parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed-parameter ensemble gives more often better results. Both the stochastic-physics and perturbed-parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Annual-mean predictions showed lower forecast quality than seasonal predictions, but a substantial number of cases had positive skill. Only small differences between the systems were found. The full multi-model ensemble has improved forecast quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions.