A statistical approach to Indian Ocean sea surface temperature prediction using a dynamical ENSO prediction

(1) In this study, a statistical prediction model has been developed to forecast monthly Sea Surface Temperature (SST) in the Indian Ocean. It is a linear regression model based on a lagged relationship between the Indian Ocean SST and the NINO3 SST. A new approach to the statistical modeling has been tried out, in which the model predictors are obtained from not only observed NINO3 SST but also predicted results produced by a dynamical El Nino model. The forecast skill of the present model is better than that of persistence prediction. In particular, the present model has a significantly improved predictive skill during the spring and summer seasons when the boreal summer Indian monsoon is affected by the Indian Ocean SST. INDEX TERMS: 4263 Oceanography: General: Ocean prediction; 4219 Oceanography: General: Continental shelf processes; 3339 Meteorology and Atmospheric Dynamics: Ocean/atmosphere interactions (0312, 4504); 3210 Mathematical Geophysics: Modeling; 1620 Global Change: Climate dynamics (3309). Citation: Kug, J.-S., I.-S. Kang, J.-Y. Lee, and J.-G. Jhun (2004), A statistical approach to Indian Ocean sea surface temperature prediction using a dynamical ENSO prediction, Geophys. Res. Lett., 31, L09212,