Forecasts of ENSO evolution using spatial-temporal projection model

El Nino-Southern Oscillation (ENSO) prediction is one of the most debated and challenging tasks, whilst its real-time operational prediction skill still has room for improvement. In this study, spatial-temporal projection model is applied to predict Nino3.4 index at lead time of one to six months. By regressing variable fields onto Nino3.4 index month by month, physical-based predictability sources, that is, the mixed-layer oceanic temperature, sea surface temperature, thermocline depth, and accumulated westerly-wind-events index over the specific regions are detected as the predictors. Based on the temporal evolution of coupled modes of predictors and Nino3.4 index, the Nino3.4 index from JAS (July, August, and September) to DJF (December, January, and February) can be predicted once a year. The model could nicely reproduce the evolution of Nino3.4 index from JAS to DJF. It also achieved high prediction skills for the year-to-year DJF Nino3.4 index, with a root-mean-square error of 0.46 in the training period (1950-2000) and 0.52 in the independent-forecast period (2001-2016). Further investigation shows that the forecast is more reliable when the forecasted ENSO amplitude is much larger.