Optimized Lagged Multiple Linear Regression Model for MJO Prediction: Considering the Surface and Subsurface Oceanic Processes over the Maritime Continent

The Madden-Julian Oscillation (MJO) is a key atmospheric component connecting global weather and climate. It functions as a primary source for subseasonal forecasts. Previous studies have highlighted the vital impact of oceanic processes on MJO propagation. However, few existing MJO prediction approaches adequately consider these factors. This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data. By introducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model, the MJO forecasting skill is considerably optimized. This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions. Further analysis suggests that, during the years in which MJO events propagate across the Maritime Continent over a more southerly path, the optimized statistical forecasting model obtains better improvements in MJO prediction.