Advances in the subseasonal prediction of extreme events : relevant case studies across the globe

Domeisen, Daniela I.V. and White, Christopher J. and Afargan-Gerstman, Hilla and Muñoz, Ángel G. and Janiga, Matthew A. and Vitart, Frédéric and Wulf, C. Ole and Antoine, Salomé and Ardilouze, Constantin and Batté, Lauriane and Bloomfield, Hannah C. and Brayshaw, David J. and Camargo, Suzana J. and Charlton-Pérez, Andrew and Collins, Dan and Cowan, Tim and del Mar Chaves, Maria and Ferranti, Laura and Gómez, Rosario and González, Paula L.M. and González Romero, Carmen and Infanti, Johnna M. and Karozis, Stelios and Kim, Hera and Kolstad, Erik W. and LaJoie, Emerson and Lledó, Llorenç and Magnusson, Linus and Malguzzi, Piero and Manrique-Suñén, Andrea and Mastrangelo, Daniele and Materia, Stefano and Medina, Hanoi and Palma, Lluís and Pineda, Luis E. and Sfetsos, Athanasios and Son, Seok-Woo and Soret, Albert and Strazzo, Sarah and Tian, Di (2022) Advances in the subseasonal prediction of extreme events : relevant case studies across the globe. Bulletin of the American Meteorological Society, 103 (6). E1473–E1501. ISSN 0003-0007 (https://doi.org/10.1175/bams-d-20-0221.1)

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Abstract

Extreme weather events have devastating impacts on human health, economic activities, ecosys tems, 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 many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on timescales of 3-4 weeks, while this timescale is 2-3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. Tropical cyclones, on the other hand, can exhibit probabilistic predictability on timescales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden-Julian Oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event - dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

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