Enhanced predictions of the Madden-Julian oscillation using the FuXi-S2S machine learning model: Insights into physical mechanisms

TL;DR

This paper demonstrates that the FuXi-S2S machine learning model improves MJO prediction accuracy during boreal winter by better capturing moisture transport dynamics, outperforming traditional models in specific phases.

Contribution

It introduces the FuXi-S2S ML model for MJO prediction and shows its superior performance over existing models, with insights into physical mechanisms behind the improvement.

Findings

Reduced biases in outgoing longwave radiation anomalies during days 15-20.

More accurate prediction of the meridional moisture gradient.

Enhanced understanding of physical mechanisms improving MJO forecasts.

Abstract

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.