Prediction of the MJO

The Madden-Julian Oscillation (MJO) is a special type of organized tropical convection which is distinct from other forms by its vast horizontal scale, sub-seasonal variability, and propagation over the Indo-Pacific basin. Enhanced or suppressed convection associated with the MJO affects global weather and climate, thereby providing a source of global sub-seasonal predictability. Therefore, skilful prediction of the MJO represents a key objective for sub-seasonal prediction and considerable efforts have been made on the prediction of the MJO over the past 20 years. Ensemble prediction systems have shown remarkable improvements in MJO forecast skill in recent years. Skilful dynamical MJO forecasts have been reported beyond 30 days, making dynamical forecasts of the MJO more skillful than empirical models. However, studies found that the predictive skill limit of the dynamical models is still 2-3 weeks lower than the potential predictability limit, which suggests that there is still room for further improvements in the dynamical prediction of the MJO. One of the major challenges for the MJO prediction is the apparent loss of prediction skill across the Maritime Continent. Recent studies showed relatively low skill in models when the MJO convection is located over the Indian Ocean initially and propagates over the MC. Another challenge in MJO prediction field is to understand how the key physical processes of the MJO simulated in operational models affect the MJO prediction. Although many recent studies have investigated the MJO prediction in multi-models, analyses have been limited to simple performance-oriented metrics rather than process-oriented diagnostics which can provide insights for model success or failure at predicting the MJO. The lack of process-oriented studies in MJO prediction research was partly due to the lack of multiple variables output from frequently initialized reforecasts. However, recent scientific advances in understanding the key processes of the MJO and its simulation (e.g., WGNE/GASS experiments), in addition to the effort by WCRP/WWRP S2S prediction project for providing frequently initialized reforecasts with multi-variables, yield unprecedented opportunity to identify the physics of the MJO in the predictions. Another challenge is the understanding of the role of the mean state and mean state biases on MJO prediction and predictability. Recent studies have demonstrated that the combination of the MJO-related horizontal wind and seasonal mean moisture distribution is the key that contributes to the horizontal moisture advection. How do the seasonal mean state and its bias impact on the MJO prediction and what are common systematic biases in models that effect MJO prediction is a question that should be addressed to improve the MJO prediction. A broad review, current status and challenges of MJO prediction will be presented.