El Nino prediction has made using the KMA/SNU ENSO prediction system (Kang and Kug, 2000). The system is based on the intermediate ocean and statistical atmosphere model. The ocean model differs from the Cane and Zebiak (1987) model in the parameterization of subsurface temperature and the basic state. The statistical atmosphere model is developed based on the singular value decomposition (SVD) of wind stress and SST. The initialization of the model is done by combining observed SST and wind stress. Wind stress is calculated by using 925hPa wind of NCEP/NCAR reanalysis data. Using calculated wind stress for initialization has a better forecast skill than the case of FSU wind stress in recent prediction. (Kug et al., 2001). In addition, the present prediction is attended with random noise to consider weather noise and to generate many sets of prediction. Our approach for random noise is similar to Kirtman and Schopf (1998).

Figure 1 shows the Nino3 SST forecast with 12- month lead, with random noise (thin solid lines) and their ensemble mean (thick solid line) of 20 forecasts. The forecasts indicate that the NINO3 SST slowly develop to warm phase till next fall. However, the magnitude is weak. Figure 2 shows seasonal SST forecast in tropical Pacific basin for Mar. 2002 - Feb. 2003. The forecast results indicate normal state or weak warm state appeared in the eastern tropical Pacific for next fall and winter.

Reference:

Cane, M. A., S. E. Zebiak, 1987: Prediction of El Nino events using a physical model, In Atmospheric and Oceanic Variability, H. Cattle, Ed., Royal Meteorological Society press, 153-182

Kang, I.-S. and J.-S. Kug, 2000: An El-Nino prediction system with an intermediate ocean and statistical atmosphere model, Geophys. Res. Lett., 27, 1167-1170.

Kug, J.-S., I.-S. Kang and S. E. Zebiak 2001: Impacts of model assimilated wind stress data in the initialization of an intermediate ocean model and the ENSO predictability, Geophys. Res. Lett., 28, 3713

Kirtman, B. P. and P. S. Schopf, 1998: Decadal variability in ENSO predictability an prediction, J. Climate, 11, 2804-2822