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

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...

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Autores: Domeisen, Daniela I. V., White, Christopher J., Afargan Gerstman, Hilla, Muñoz, Ángel G., Janiga, Matthew A., Lledó, Llorenç|||0000-0002-8628-6876, Manrique Suñén, Andrea, Palma, Lluis, Soret, Albert|||0000-0002-1962-2972
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/371614
Acceso en línea:https://hdl.handle.net/2117/371614
https://dx.doi.org/10.1175/BAMS-D-20-0221.1
Access Level:acceso abierto
Palabra clave:Extreme weather
Climate change
Heatwaves (Meteorology)
Madden-Julian oscillation
Severe storms
Ensembles
Forecast verification/skill
Probability forecasts/models/distribution
Flood events
Simulació per ordinador
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
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spelling Advances in the subseasonal prediction of extreme events: relevant case studies across the globeDomeisen, Daniela I. V.White, Christopher J.Afargan Gerstman, HillaMuñoz, Ángel G.Janiga, Matthew A.Lledó, Llorenç|||0000-0002-8628-6876Manrique Suñén, AndreaPalma, LluisSoret, Albert|||0000-0002-1962-2972Extreme weatherClimate changeHeatwaves (Meteorology)Madden-Julian oscillationSevere stormsEnsemblesForecast verification/skillProbability forecasts/models/distributionFlood eventsSimulació per ordinadorÀrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologiaExtreme 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 time scales 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 time scales of 3–4 weeks, while this time scale 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 time scales 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.Peer Reviewed"Article signat per 40 autors/es: Daniela I. V. Domeisen, Christopher J. White, Hilla Afargan-Gerstman, Ángel G. Muñoz, Matthew A. Janiga, Frédéric Vitart, C. Ole Wulff, Salomé Antoine, Constantin Ardilouze, Lauriane Batté, Hannah C. Bloomfield, David J. Brayshaw, Suzana J. Camargo, Andrew Charlton-Pérez, Dan Collins, Tim Cowan, Maria del Mar Chaves, Laura Ferranti, Rosario Gómez, Paula L. M. González, Carmen González Romero, Johnna M. Infanti, Stelios Karozis, Hera Kim, Erik W. Kolstad, Emerson LaJoie, Llorenç Lledó, Linus Magnusson, Piero Malguzzi, Andrea Manrique-Suñén, Daniele Mastrangelo, Stefano Materia, Hanoi Medina, Lluís Palma, Luis E. Pineda, Athanasios Sfetsos, Seok-Woo Son, Albert Soret, Sarah Strazzo, and Di Tian"American Meteorological Society20222022-06-0120222022-08-02journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://hdl.handle.net/2117/371614https://dx.doi.org/10.1175/BAMS-D-20-0221.1reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 776787 Sub-seasonal to Seasonal climate forecasting for Energyopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3716142026-05-27T15:37:01Z
dc.title.none.fl_str_mv Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
title Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
spellingShingle Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
Domeisen, Daniela I. V.
Extreme weather
Climate change
Heatwaves (Meteorology)
Madden-Julian oscillation
Severe storms
Ensembles
Forecast verification/skill
Probability forecasts/models/distribution
Flood events
Simulació per ordinador
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
title_short Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
title_full Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
title_fullStr Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
title_full_unstemmed Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
title_sort Advances in the subseasonal prediction of extreme events: relevant case studies across the globe
dc.creator.none.fl_str_mv Domeisen, Daniela I. V.
White, Christopher J.
Afargan Gerstman, Hilla
Muñoz, Ángel G.
Janiga, Matthew A.
Lledó, Llorenç|||0000-0002-8628-6876
Manrique Suñén, Andrea
Palma, Lluis
Soret, Albert|||0000-0002-1962-2972
author Domeisen, Daniela I. V.
author_facet Domeisen, Daniela I. V.
White, Christopher J.
Afargan Gerstman, Hilla
Muñoz, Ángel G.
Janiga, Matthew A.
Lledó, Llorenç|||0000-0002-8628-6876
Manrique Suñén, Andrea
Palma, Lluis
Soret, Albert|||0000-0002-1962-2972
author_role author
author2 White, Christopher J.
Afargan Gerstman, Hilla
Muñoz, Ángel G.
Janiga, Matthew A.
Lledó, Llorenç|||0000-0002-8628-6876
Manrique Suñén, Andrea
Palma, Lluis
Soret, Albert|||0000-0002-1962-2972
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Extreme weather
Climate change
Heatwaves (Meteorology)
Madden-Julian oscillation
Severe storms
Ensembles
Forecast verification/skill
Probability forecasts/models/distribution
Flood events
Simulació per ordinador
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
topic Extreme weather
Climate change
Heatwaves (Meteorology)
Madden-Julian oscillation
Severe storms
Ensembles
Forecast verification/skill
Probability forecasts/models/distribution
Flood events
Simulació per ordinador
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
description 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 time scales 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 time scales of 3–4 weeks, while this time scale 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 time scales 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-06-01
2022
2022-08-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/371614
https://dx.doi.org/10.1175/BAMS-D-20-0221.1
url https://hdl.handle.net/2117/371614
https://dx.doi.org/10.1175/BAMS-D-20-0221.1
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 776787 Sub-seasonal to Seasonal climate forecasting for Energy
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Meteorological Society
publisher.none.fl_str_mv American Meteorological Society
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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