The Development of a Statistical Forecast Model for Changma

Abstract

Abstract Forecasting year-to-year variations in East Asian summer monsoon (EASM) precipitation is one of the most challenging tasks in climate prediction because the predictors are not sufficiently well known and the forecast skill of the numerical models is poor. In this paper, a statistical forecast model for changma (the Korean portion of the EASM system) precipitation is proposed that was constructed with three physically based predictors. A forward-stepwise regression was used to select the predictors that included sea surface temperature (SST) anomalies over the North Pacific, the North Atlantic, and the tropical Pacific Ocean. Seasonal predictions with this model showed high forecasting capabilities that had a Gerrity skill score of ~0.82. The dynamical processes associated with the predictors were examined prior to their use in the prediction scheme. All predictors tended to induce an anticyclonic anomaly to the east or southeast of Japan, which was responsible for transporting a large amount of moisture to the southern Korean Peninsula. The predictor in the North Pacific formed an SST front to the east of Japan during the summertime, which maintained a lower-tropospheric baroclinicity. The North Atlantic SST anomaly induced downstream wave propagation in the upper troposphere, developing anticyclonic activity east of Japan. Forcing from the tropical Pacific SST anomaly triggered a cyclonic anomaly over the South China Sea, which was maintained by atmosphere–ocean interactions and induced an anticyclonic anomaly via northward Rossby wave propagation. Overall, the model used for forecasting changma precipitation performed well (R = 0.85) and correctly predicted information for 16 out of 19 yr of observational data.