Improved ENSO Forecasting using Bayesian Updating and the North American Multi Model Ensemble (NMME)

AbstractThis study assesses the forecast skill of eight North American Multimodel Ensemble (NMME) models in predicting Nino-3/-3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemble mean of NMME (NMME-EM) shows a strong dependence on lead (initial) month and target month and is quite promising in terms of correlation, root-mean-square error (RMSE), standard deviation ratio (SDRatio), and probabilistic Brier skill score, especially at short lead months. However, the skill decreases in target months from late spring to summer owing to the spring predictability barrier. When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Nino-3/-3.4 in terms of correlation, RMSE, and SDRatio. For Nino-3.4, the BU-Model outperforms NMME-EM forecasts for almost all leads (1–12; particularly for short leads) and target months (from January to December). However, for Nino-3, the BU-Model does not outperform N...