The Madden-Julian Oscillation (MJO) is a key source of predictability for subseasonal-to-seasonal (S2S) forecasts, with important implications for early warning of high-impact weather and related disaster-risk reduction. However, in most S2S models, including IAP-CAS v1.3, the ensemble spread remains insufficient to capture atmospheric uncertainty, thereby limiting MJO forecast skill. An enhanced version of the IAP-CAS model has been developed to improve MJO forecasts by incorporating the Second-Order Exact Sampling (SOES) method into the initialization process, using large historical samples to extract the leading modes of uncertainty and generate physically consistent perturbations along the primary error-growth pathways with minimal computational cost. Based on selected MJO events during the winters of 2019–2023, a series of sensitivity experiments were designed to optimize the ensemble generation strategy. As a result, the upgraded model achieved an improvement in MJO forecast skill of up to 6 days. This improvement in MJO forecast skill is primarily attributed to a more realistic representation of the MJO moisture mode and background temperature stratification, leading to more accurate simulations of MJO-related convection and a demonstrable impact on precipitation forecasts over China. On one hand, the improved MJO forecasts enhance the forecasts of the position of the Western Pacific Subtropical High (WPSH), thereby increasing the accuracy of precipitation forecasts over Southern China. On the other hand, Rossby wave signals triggered by the MJO propagate into the mid- and high-latitudes, contributing to improved precipitation forecasts over central and northern China. This study underscores the importance of a well-designed ensemble generation strategy tailored to the model for S2S forecasts and reinforces the necessity of improving MJO forecast skill to support reliable forecasts of downstream climate systems at S2S timescales. However, the current SOES implementation remains an initial step, as the temporally static perturbations during nudging limit its ability to represent flow-dependent error growth, indicating the need for dynamically evolving perturbations to better regulate ensemble dispersion.
Liu et al. (Tue,) studied this question.