Predictability of MJO Initiation in Data-Driven Models: Shared Instability Modes of Optimally Growing Initial Errors and Optimal Precursors

Data-driven models have achieved significant progress in sub-seasonal prediction, but predicting the initiation of the Madden-Julian Oscillation (MJO) remains a critical challenge, largely due to initial uncertainties from sparse observations over tropical oceans and complex multiscale interactions. Therefore, identifying sensitive areas in initial conditions is crucial to both reveal the underlying error growth mechanisms and provide guidance for target observations. Here, the FuXi-S2S model is applied to explore the initial sensitivity and instability modes of MJO initiation. First, the evaluation of prediction skill identifies the initiation of primary MJO events at a 3-pentad lead time as a critical bottleneck. Simulations initialized with optimized initial conditions within analysis uncertainty closely reproduce the observed MJO evolution, thereby validating the high initial sensitivity during the first 4 pentads. Subsequently, the conditional nonlinear optimal perturbation (CNOP) method is utilized to identify the optimally growing initial errors (OGIEs) and optimal precursors (OPRs). Analysis of OGIEs reveals three dominant types of error modes causing the largest forecast errors, indicating that the rapid growth of OGIEs is driven by the coupling of local low-level thermodynamic instability (temperature and moisture) and upstream upper-level dynamic forcing (wind). Moreover, the spatial structure and perturbation evolution of OPRs exhibit high consistency with OGIEs. The identification of these shared instability modes provides a theoretical foundation for target observations, suggesting that additional observations in sensitive areas can simultaneously reduce initial errors and capture precursors.