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Correction of Atmospheric Dynamical Seasonal Forecasts using the Leading Ocean-Forced Spatial Patterns

Correction of Atmospheric Dynamical Seasonal Forecasts using the Leading Ocean-Forced Spatial Patterns

Hai Lin(1), Jacques Derome(2) and Gilbert Brunet(1)
(1)Recherche en prevision numerique, Meteorological Service of Canada, Dorval, Quebec, Canada
(2)Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

In a series of seasonal forecasts with two global atmospheric models, the atmospheric response to the specified sea surface temperature (SST) anomalies was found to be model dependent, not only in its amplitude but also in its spatial structure. The forced variability contains the information that may be useful for seasonal predictions at sufficiently long lead times. A singular value decomposition (SVD) analysis was performed for each model's ensemble mean forecast 500 mb geopotential height over the Northern Hemisphere and the corresponding tropical Pacific SST. The leading SVD patterns represent the dominant forced patterns associated with the tropical Pacific SST anomalies in the particular GCM. It was observed that while the spatial structures of the leading response modes were model dependent, their amplitudes were correlated with that of the observed Pacific/North American (PNA) pattern and the North Atlantic Oscillation(NAO). A statistical approach to correct the ensemble forecasts was formulated based on the regression of the model's leading forced SVD patterns and the observed 500mb geopotential height. This technique was applied to the winter forecasts from the two models. The performance of the corrected forecasts was assessed by comparing its cross-validated skill with that of the original GCM ensemble mean forecasts. The results show, in particular, that the corrected forecasts predict the amplitude of the NAO with statistically significant skill.