Teleconnection-driven extreme events: Relevant case studies

Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on timescales of several weeks for selected cases of extreme events that are linked to remote drivers and large-scale teleconnections. We here present a range of case studies, including heatwaves, cold spells, and tropical cyclones, where precursors and global linkages may have improved sub-seasonal predictability. These linkages include teleconnections from the tropics as well as the stratosphere, in addition to circumglobal teleconnections. For example, cold extremes can be induced by stratospheric or tropical teleconnections, while the predictability of tropical cyclones tends to be increased when an active Madden-Julian Oscillation signal is present. Prediction can further be improved through increased physical process understanding of the drivers of extremes, an improved representation of these processes in prediction systems, as well as the development of post-processing techniques that improve model output. While the case studies presented only represent some of the major types of extreme events, they provide an informative overview of the current ability of prediction models to benefit from teleconnections. These case studies clearly illustrate the potential for event – dependent advance warnings for a wide range of extreme events globally.The subseasonal predictability of extreme events allows for an extension of warning horizons, can provide advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.