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Timothy N. Stockdale


Two different coupled atmosphere–ocean GCMs are used to forecast SST anomalies with lead times of up to one year. The initialization procedure does not balance the ocean and atmosphere components, nor is the coupled model flux corrected to maintain the correct mean state. Rather, the coupled model is allowed to evolve freely during the forecast. The inevitable climate drift is estimated across an ensemble of forecasts and subtracted to give the true forecast. Although the climate drift is often bigger than the interannual signal, the method works. This is true for a drift toward both warmer and colder SSTs, as exemplified by the two models.

The best way of establishing the mean bias correction from a small sample of prior forecasts is discussed. In some circumstances the sample median may be a more robust estimator than the sample mean. For the limited set of forecasts here, use of the median bias in the cross-correlated forecasts reduces forecast error, when compared to use of the mean bias.

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Frédéric Vitart and Timothy N. Stockdale


The ECMWF Seasonal Forecasting System, based on ensembles of 200-day coupled GCM integrations, contains tropical disturbances that are referred to as model tropical storms in the present paper. Model tropical storms display a genesis location and a seasonal cycle generally consistent with observations, though the frequency of model tropical storms is significantly lower than observed, particularly over the North Atlantic and the eastern North Pacific. Several possible causes for the low number of model tropical storms are discussed.

The ECMWF Seasonal Forecasting System produces realistic forecasts of the interannual variability of tropical storm frequency over the North Atlantic and the western North Pacific, with strong linear correlations and low rms error obtained when comparing the forecasts to observations. The skill of the seasonal forecasting system in predicting the frequency of tropical storms is likely to be related to its skill in predicting sea surface temperatures. In particular, the model seems successful in predicting the occurrence and development of El Niño and La Niña events, and their impact on the large-scale circulation over the Atlantic. For the period 1991–99, a comparison with the statistical forecasts issued by the Colorado State Hurricane Forecast Team suggests that the ECMWF seasonal forecasting system produces a better June forecast of the total number of tropical storms over the North Atlantic. These results establish the feasibility of real-time forecasting of tropical storm statistics by dynamical methods.

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Jérôme Vialard, Frédéric Vitart, Magdalena A. Balmaseda, Timothy N. Stockdale, and David L. T. Anderson


Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.

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