A Probabilistic Forecast for Multi-year ENSO Using Bayesian Convolutional Neural Network

El Nio Southern Oscillation (ENSO) significantly influences Earth's system and regional climate patterns. Despite dedicated efforts spanning decades, the precise prediction of ENSO events through numerical modelling beyond one-year lead time remains a formidable challenge. The advent of deep learning-based approaches marks a transformative era in climate and weather prediction. However, many machine learning-based studies attempting ENSO prediction are confined to singular estimates, lacking adequate quantification of uncertainty in learned parameters and overlooking the crucial need for a nuanced understanding of ENSO prediction confidence. Here, we introduce a deep learning-based Bayesian Convolutional Neural Network (BCNN) model that provides probabilistic predictions for ENSO with a lead time of up to 24 months. The Bayesian layers within the CNN maintain the capability to predict a distribution of learned parameters. The inherent capacity for uncertainty modeling enhances the reliability of BNNs, making them particularly valuable in operational services. The model was initially trained using globally gridded monthly sea surface temperature and upper 300 m integrated ocean potential temperature predictors from 25 Climate Model Intercomparison Project (CMIP) models, including 11 CMIP5 and 14 CMIP6. To address systematic errors in the BCNN, reflecting those of the CMIP samples, a learning transfer technique was applied using Simple Ocean Data Assimilation (SODA) reanalysis predictors spanning from 1871 to 1980. Validation of the all-season correlation skill of the Nino3.4 index in the BCNN model demonstrates significantly higher accuracy compared to current state-of-the-art dynamical forecast systems. This research holds substantial socio-economic implications as it enhances our forecasting capabilities and rigorously quantifies forecast uncertainties, providing valuable insights for planning and policy-making.