The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes

Autores
Pepler, Acacia S.; Díaz, Leandro Baltasar; Prodhomme, Chloé; Doblas Reyes, Francisco J.; Kumar, Arun
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values.
Fil: Pepler, Acacia S.. University Of New South Wales; Australia
Fil: Díaz, Leandro Baltasar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina
Fil: Prodhomme, Chloé. Institut Català de Ciències del Clima; España
Fil: Doblas Reyes, Francisco J.. Institut Català de Ciències del Clima; España. Institució Catalana de Recerca i Estudis Avancats; España. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España
Fil: Kumar, Arun. National Oceanic and Atmospheric Administration; Estados Unidos
Materia
Extremes
Seasonal forecasting
ENSO
Climate model
Ensemble
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/17818
Acceder
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network_name_str CONICET Digital (CONICET)
spelling The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremesPepler, Acacia S.Díaz, Leandro BaltasarProdhomme, ChloéDoblas Reyes, Francisco J.Kumar, ArunExtremesSeasonal forecastingENSOClimate modelEnsemblehttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values.Fil: Pepler, Acacia S.. University Of New South Wales; AustraliaFil: Díaz, Leandro Baltasar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; ArgentinaFil: Prodhomme, Chloé. Institut Català de Ciències del Clima; EspañaFil: Doblas Reyes, Francisco J.. Institut Català de Ciències del Clima; España. Institució Catalana de Recerca i Estudis Avancats; España. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; EspañaFil: Kumar, Arun. National Oceanic and Atmospheric Administration; Estados UnidosElsevier Science2015-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/17818Pepler, Acacia S.; Díaz, Leandro Baltasar; Prodhomme, Chloé; Doblas Reyes, Francisco J.; Kumar, Arun; The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes; Elsevier Science; Weather and Climate Extremes; 9; 9-2015; 68-772212-0947enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.wace.2015.06.005info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S2212094715300062info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:21Zoai:ri.conicet.gov.ar:11336/17818instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:21.929CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
title The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
spellingShingle The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
Pepler, Acacia S.
Extremes
Seasonal forecasting
ENSO
Climate model
Ensemble
title_short The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
title_full The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
title_fullStr The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
title_full_unstemmed The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
title_sort The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes
dc.creator.none.fl_str_mv Pepler, Acacia S.
Díaz, Leandro Baltasar
Prodhomme, Chloé
Doblas Reyes, Francisco J.
Kumar, Arun
author Pepler, Acacia S.
author_facet Pepler, Acacia S.
Díaz, Leandro Baltasar
Prodhomme, Chloé
Doblas Reyes, Francisco J.
Kumar, Arun
author_role author
author2 Díaz, Leandro Baltasar
Prodhomme, Chloé
Doblas Reyes, Francisco J.
Kumar, Arun
author2_role author
author
author
author
dc.subject.none.fl_str_mv Extremes
Seasonal forecasting
ENSO
Climate model
Ensemble
topic Extremes
Seasonal forecasting
ENSO
Climate model
Ensemble
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values.
Fil: Pepler, Acacia S.. University Of New South Wales; Australia
Fil: Díaz, Leandro Baltasar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina
Fil: Prodhomme, Chloé. Institut Català de Ciències del Clima; España
Fil: Doblas Reyes, Francisco J.. Institut Català de Ciències del Clima; España. Institució Catalana de Recerca i Estudis Avancats; España. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España
Fil: Kumar, Arun. National Oceanic and Atmospheric Administration; Estados Unidos
description Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values.
publishDate 2015
dc.date.none.fl_str_mv 2015-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/17818
Pepler, Acacia S.; Díaz, Leandro Baltasar; Prodhomme, Chloé; Doblas Reyes, Francisco J.; Kumar, Arun; The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes; Elsevier Science; Weather and Climate Extremes; 9; 9-2015; 68-77
2212-0947
url http://hdl.handle.net/11336/17818
identifier_str_mv Pepler, Acacia S.; Díaz, Leandro Baltasar; Prodhomme, Chloé; Doblas Reyes, Francisco J.; Kumar, Arun; The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes; Elsevier Science; Weather and Climate Extremes; 9; 9-2015; 68-77
2212-0947
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.wace.2015.06.005
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S2212094715300062
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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