Global Impacts of MJO Large-Scale Precipitation on Tropical Cyclones, Atmospheric Rivers, and Extreme Rainfall

The Madden-Julian Oscillation (MJO) is the most dominate mode of subseasonal-to-seasonal (S2S) variability, which bridges global weather and climate (Zhang, 2013). The MJO has been recognized as a source of predictability of the global weather on the S2S time scales and can influence onset of the El Nino (e.g., Kerns and Chen, 2021, Jauregui and Chen 2024a, 2024b). We developed a new capability by tracking the multiscale systems like the MJO, atmospheric rivers (ARs), jet stream, the ITCZ, easterly waves, tropical cyclones (TCs), and mesoscale convective systems (MCSs) using Multiscale Objects-Tracking and AI Climate Modeling for Extremes (Mosaic4E). One of the unique capabilities of Mosaic4E is the MJO Large-scale Precipitation Tracking (LPT, Kerns and Chen 2016, 2022) that can identify the MJO large-scale convective heating over the Northern Hemisphere (NH) and Southern Hemisphere (SH) can be used to study teleconnection patterns that are fundamental to extreme rainfall, heat waves and drought, which is not possible with the traditional MJO RMM index. When the MJO convection/precipitation is in the NH, it has a direct impact on the blocking patterns influencing the heatwaves and flooding and drought events. The MJO influence on the global high-impact weather involving heavy rainfall and flooding such as tropical cyclones (TCs) and the atmospheric rivers (ARs) are investigated using Mosaci4E. It is found that the number of TC activities increases 50-100% when MJO LPTs ended up over NH than when is in SH. Similar results are found for the MJO LPTs impacts on the ARs and extreme rainfall over the CONUS. The MJO-LPT represents the S2S time scale bridging the weather and climate and is a key for better understanding and predicting extreme events. The multiscale tracking capability will be enhanced by AI/ML tools in Mosaic4E for identifying, understanding, and predicting the extreme events. Mosaic4E is developed and tested using satellite and in situ observations as well as the ERA5 reanalysis data from 1979-2024. Mosaic4E has shown high skills in coastal flooding over the US and is currently tested globally. The satellite observation and reanalysis data are used to evaluate global weather and climate models.  Results show that most models overproduce precipitation over land in non-LPTs and underestimate large-scale precipitation over the oceans compared with the observations. For example, the MJO contributes up to 40-50% of the observed annual precipitation over the Indio-Pacific warm pool region, which are usually much less in the models because of models’ inability to represent the MJO dynamics. Furthermore, the spatial variability of precipitation ENSO is more pronounced in the observations than models.