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The ensemble canonical correlation (ECC) prediction method is used to predict summer (July–September) and winter (January–March) seasonal mean surface temperature (Tsurf) over the United States. The predictors are the global sea surface temperature (SST), sea level pressure over the Northern Hemisphere Tsurf, and soil moisture over the United States from one to two seasons lead, as well as the model outputs from the NCEP seasonal forecast model. The canonical correlation analysis (CCA) prediction is performed for each variable separately. The predicted Tsurf fields form an ensemble. The ensemble forecast is the weighted average of its members. Both the simple ensemble forecast and the superensemble forecast are tested. The simple ensemble mean is the equally weighted average of its members. The weighting function for the superensemble forecast is determined by linear regression analysis. Overall, both ensemble forecasts improve skill. On average, the superensemble gives the best performance. For ...

Ensemble Canonical Correlation Prediction of Surface Temperature over the United States