| description abstract | n Ecuador, forecasts of seasonal total rainfall could mitigate both flooding and drought disasters through warning systems if issued at useful lead time. In Ecuador, rainfall from December to April contributes most of the annual total, and it is crucial to agricultural and water management. This study examines the predictive skill for February?April and December?February seasonal rainfall totals using statistical and dynamical approaches. Fields of preceding observed sea surface temperature (SST) are used as predictors for a purely statistical prediction, and predictions of an atmospheric general circulation model (AGCM) are used as predictors with a model output statistics correction design using canonical correlation analysis. For both periods, results indicate considerable predictive skill in some, but not all, portions of the Andean and especially coastal regions. The skill of SST and AGCM predictors comes mainly through skillful rainfall anomaly forecasts during significant ENSO events. Atlantic Ocean SST plays a weaker predictive role. For the simultaneous diagnostic highest skill is obtained using the eastern Pacific Ocean domain, and for time-lagged forecasts highest scores are found using the global tropical ocean domain. This finding suggests that, while eastern Pacific SST is what matters most to Ecuadorian rainfall, at sufficient lead time these local SSTs become most effectively predicted using basinwide ENSO predictors. In Ecuador?s coastal region, and in some parts of the Andean highlands, skill levels are sufficient for warning systems to reduce economic losses associated with flood and drought. Accordingly, the Instituto Nacional Meteorologia e Hidrologia of Ecuador issues forecasts each month using methods described here?also implemented by countries of the Latin American Observatory partnership, among other South American organizations. | |
