Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors

TitleEvaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors
Publication TypeMiscellaneous
Year of Publication2012
AuthorsMagnusson, L, Alonso-Balmaseda, M, Corti, S, Molteni, F, Stockdale, T
Secondary TitleTechnical Memorandum
Number676
Abstract

This study discusses and compares three different strategies used to deal with model error in seasonal and decadal forecasts. The strategies discussed are the so-called full initialisation, anomaly initialisation and flux correction. In the full initialisation the coupled model is initialised to a state close to the real-world attractor and after initialisation the model drifts towards its own attractor, giving rise to model bias. The anomaly initialisation aims to initialise the model close to its own attractor, by initialising only the anomalies. The flux correction strategy aims to keep the model trajectory close to the real-world attractor by adding empirical corrections. These three strategies have been implemented in the ECMWF coupled model, and are evaluated at seasonal and decadal time scales. The practical implications of the different strategies are also discussed. Results show that full initialisation results in a clear model drift towards a colder climate. The anomaly initialisation is able to reduce the drift, by initialising around the model mean state. However, the erroneous model mean state results in degraded seasonal forecast skill. The best results on the seasonal time scale are obtained using momentum-flux correction, mainly because it avoids the positive feedback responsible for a strong cold bias in the tropical Pacific. It is likely that these results are model dependent: the coupled model used here shows a strong cold bias in the Central Pacific, resulting from a positive coupled feedback between winds and SST. At decadal time scales it is difficult to decide whether any of the strategies is superior to the others.