Impacts of humidity initialization on MJO prediction: A study in an operational sub-seasonal to seasonal system

The Madden-Julian Oscillation (MJO) is a significant atmospheric phenomenon that influences weather patterns and climate variability over the tropics and beyond. Predicting the MJO is crucial for forecasting extreme weather events and making short-term climate predictions. However, MJO prediction remains challenging due to its complex nature and the various factors that affect its behavior. One of these factors is humidity, which plays a vital role in the development and evolution of the MJO. This study aims to investigate the impact of humidity initialization on MJO prediction using an operational sub-seasonal to seasonal (S2S) prediction system. The study utilizes the second finite-volume version of the Flexible Global Ocean-Atmosphere-Land System model (FGOALS-f2) S2S prediction system and 1.5-year real-time forcing datasets to conduct two numerical ensemble experi-ments. The control experiment does not include humidity initialization, while the sensitivity experiment ini-tializes three-dimensional humidity using nudging technology with time-varying weighting factor schemes. The multivariate MJO index is used to evaluate the MJO prediction skill. The results show that the MJO prediction skill is improved by more than five days and reached up to 23 days in the sensitivity experiments under real-time forcing. The study then discusses the possible physical linkage between humidity initialization and MJO pre-diction skill, highlighting the significant improvement during the second and third phases of MJO propagation, which exhibit robust convective humidity coupling behavior. Further analysis illustrates that the progress of MJO prediction skill is highly related to the realistic horizontal and vertical structure of moisture, which improvs the corresponding circulations and Outgoing Longwave Ra-diation (OLR). In conclusion, this study suggests that accurate humidity initialization can help improve MJO prediction in S2S prediction systems. This finding has significant implications for weather forecasting and short -term climate predictions. Accurate humidity initialization can lead to a better understanding and prediction of the MJO, which can help mitigate the impact of extreme weather events and improve short-term climate prediction.

Automated summary

This study investigates the impact of humidity initialization on the prediction of the Madden-Julian Oscillation (MJO). It finds that accurate humidity initialization can improve MJO prediction skill by more than five days, reaching up to 23 days under real-time forcing. This finding has significant implications for weather forecasting and short-term climate predictions.