Seasonal and interannual climate variability affects agricultural activities and, therefore, grain production in Southern Brazil. This dependence on climate has motivated the development of a seasonal climate forecast model, based on statistical techniques such as the Canonical Correlation Analysis (CCA). This model is called SIMOC (Oceanic Modelling Statistical System) and it was adapted to forecast seasonal anomalies of rainfall. The predictors for this model are sea surface temperature in areas of the Pacific and the Atlantic oceans, and the predictand is precipitation over Southern Brazil. The Atlantic Ocean, as a predictor, seems to affect more the prediction in the correlation analysis when the lag increases. Considering the Pacific Ocean, the best forecast was obtained when the lag was smaller and at the beginning of the run, in opposition to the Atlantic Ocean. In general, the use of the two oceans together seems to improve the precision of the predictions as compared to the individual ocean cases. For the ENSO episode of 1997-1998, the model reproduced reasonably well the observed rainfall anomalies, showing the usefulness of SIMOC in seasonal climate forecast for the Southern Brazil.
Statistical model; Seasonal climate forecast; ENSO episode1997-1998
