Enhanced predictions of the Madden-Julian oscillation using the FuXi-S2S machine learning model: Insights into physical mechanisms
This work addresses the need for reliable MJO predictions to protect lives and mitigate societal impacts, representing an incremental improvement in forecasting.
This study tackled the problem of predicting the Madden-Julian Oscillation (MJO) by comparing the FuXi-S2S machine learning model with a numerical model, finding that FuXi-S2S reduced biases in intraseasonal outgoing longwave radiation anomalies over the tropical western Pacific during days 15-20 for initial strong MJO phase 3.
The Madden-Julian Oscillation (MJO) is the dominant mode of tropical atmospheric variability on intraseasonal timescales, and reliable MJO predictions are essential for protecting lives and mitigating impacts on societal assets. However, numerical models still fall short of achieving the theoretical predictability limit for the MJO due to inherent constraints. In an effort to extend the skillful prediction window for the MJO, machine learning (ML) techniques have gained increasing attention. This study examines the MJO prediction performance of the FuXi subseasonal-to-seasonal (S2S) ML model during boreal winter, comparing it with the European Centre for Medium- Range Weather Forecasts S2S model. Results indicate that for the initial strong MJO phase 3, the FuXi-S2S model demonstrates reduced biases in intraseasonal outgoing longwave radiation anomalies averaged over the tropical western Pacific (WP) region during days 15-20, with the convective center located over this area. Analysis of multiscale interactions related to moisture transport suggests that improvements could be attributed to the FuXi-S2S model's more accurate prediction of the area-averaged meridional gradient of low-frequency background moisture over the tropical WP. These findings not only explain the enhanced predictive capability of the FuXi-S2S model but also highlight the potential of ML approaches in advancing the MJO forecasting.