Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes
- Autores
- Osman, Marisol; Beerli, Remo; Büeler, Dominik; Grams, Christian M.
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The prediction skill of sub-seasonal forecast models is evaluated for seven year-round weather regimes in the Atlantic–European region. Reforecasts based on models from three prediction centers are considered and verified against weather regimes obtained from ERA-Interim reanalysis. Results show that predicting weather regimes as a proxy for the large-scale circulation outperforms the prediction of raw geopotential height. Greenland blocking tends to have the longest year-round skill horizon for all three models, especially in winter. On the other hand, the skill is lowest for the European blocking regime for all three models, followed by the Scandinavian blocking regime. Furthermore, all models struggle to forecast flow situations that cannot be assigned to a weather regime (so-called no regime), in comparison with weather regimes. Related to this, variability in the occurrence of no regime, which is most frequent in the transition seasons, partly explains the predictability gap between transition seasons and winter and summer. We also show that models have difficulties in discriminating between related regimes. This can lead to misassignments in the predicted regime during flow situations in which related regimes manifest. Finally, we document the changes in skill between model versions, showing important improvements for the ECMWF and NCEP models. This study is the first multi-model assessment of year-round weather regimes in the Atlantic–European domain. It advances our understanding of the predictive skill for weather regimes, reveals strengths and weaknesses of each model, and thus increases our confidence in the forecasts and their usefulness for decision-making.
Fil: Osman, Marisol. Karlsruhe Institute of Technology; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Beerli, Remo. No especifíca;
Fil: Büeler, Dominik. Institute for Atmospheric and Climate Science; Suiza
Fil: Grams, Christian M.. Karlsruhe Institute of Technology; Alemania - Materia
-
BLOCKING
EUROPE
NORTH ATLANTIC OSCILLATION
WINDOWS OF OPPORTUNITY - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/237117
Ver los metadatos del registro completo
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Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimesOsman, MarisolBeerli, RemoBüeler, DominikGrams, Christian M.BLOCKINGEUROPENORTH ATLANTIC OSCILLATIONWINDOWS OF OPPORTUNITYhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The prediction skill of sub-seasonal forecast models is evaluated for seven year-round weather regimes in the Atlantic–European region. Reforecasts based on models from three prediction centers are considered and verified against weather regimes obtained from ERA-Interim reanalysis. Results show that predicting weather regimes as a proxy for the large-scale circulation outperforms the prediction of raw geopotential height. Greenland blocking tends to have the longest year-round skill horizon for all three models, especially in winter. On the other hand, the skill is lowest for the European blocking regime for all three models, followed by the Scandinavian blocking regime. Furthermore, all models struggle to forecast flow situations that cannot be assigned to a weather regime (so-called no regime), in comparison with weather regimes. Related to this, variability in the occurrence of no regime, which is most frequent in the transition seasons, partly explains the predictability gap between transition seasons and winter and summer. We also show that models have difficulties in discriminating between related regimes. This can lead to misassignments in the predicted regime during flow situations in which related regimes manifest. Finally, we document the changes in skill between model versions, showing important improvements for the ECMWF and NCEP models. This study is the first multi-model assessment of year-round weather regimes in the Atlantic–European domain. It advances our understanding of the predictive skill for weather regimes, reveals strengths and weaknesses of each model, and thus increases our confidence in the forecasts and their usefulness for decision-making.Fil: Osman, Marisol. Karlsruhe Institute of Technology; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Beerli, Remo. No especifíca;Fil: Büeler, Dominik. Institute for Atmospheric and Climate Science; SuizaFil: Grams, Christian M.. Karlsruhe Institute of Technology; AlemaniaJohn Wiley & Sons Ltd2023-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/237117Osman, Marisol; Beerli, Remo; Büeler, Dominik; Grams, Christian M.; Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 149; 755; 7-2023; 2386-24080035-9009CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1002/qj.4512info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:17:55Zoai:ri.conicet.gov.ar:11336/237117instacron: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:34982025-09-29 10:17:55.373CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes |
title |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes |
spellingShingle |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes Osman, Marisol BLOCKING EUROPE NORTH ATLANTIC OSCILLATION WINDOWS OF OPPORTUNITY |
title_short |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes |
title_full |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes |
title_fullStr |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes |
title_full_unstemmed |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes |
title_sort |
Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes |
dc.creator.none.fl_str_mv |
Osman, Marisol Beerli, Remo Büeler, Dominik Grams, Christian M. |
author |
Osman, Marisol |
author_facet |
Osman, Marisol Beerli, Remo Büeler, Dominik Grams, Christian M. |
author_role |
author |
author2 |
Beerli, Remo Büeler, Dominik Grams, Christian M. |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
BLOCKING EUROPE NORTH ATLANTIC OSCILLATION WINDOWS OF OPPORTUNITY |
topic |
BLOCKING EUROPE NORTH ATLANTIC OSCILLATION WINDOWS OF OPPORTUNITY |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
The prediction skill of sub-seasonal forecast models is evaluated for seven year-round weather regimes in the Atlantic–European region. Reforecasts based on models from three prediction centers are considered and verified against weather regimes obtained from ERA-Interim reanalysis. Results show that predicting weather regimes as a proxy for the large-scale circulation outperforms the prediction of raw geopotential height. Greenland blocking tends to have the longest year-round skill horizon for all three models, especially in winter. On the other hand, the skill is lowest for the European blocking regime for all three models, followed by the Scandinavian blocking regime. Furthermore, all models struggle to forecast flow situations that cannot be assigned to a weather regime (so-called no regime), in comparison with weather regimes. Related to this, variability in the occurrence of no regime, which is most frequent in the transition seasons, partly explains the predictability gap between transition seasons and winter and summer. We also show that models have difficulties in discriminating between related regimes. This can lead to misassignments in the predicted regime during flow situations in which related regimes manifest. Finally, we document the changes in skill between model versions, showing important improvements for the ECMWF and NCEP models. This study is the first multi-model assessment of year-round weather regimes in the Atlantic–European domain. It advances our understanding of the predictive skill for weather regimes, reveals strengths and weaknesses of each model, and thus increases our confidence in the forecasts and their usefulness for decision-making. Fil: Osman, Marisol. Karlsruhe Institute of Technology; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Beerli, Remo. No especifíca; Fil: Büeler, Dominik. Institute for Atmospheric and Climate Science; Suiza Fil: Grams, Christian M.. Karlsruhe Institute of Technology; Alemania |
description |
The prediction skill of sub-seasonal forecast models is evaluated for seven year-round weather regimes in the Atlantic–European region. Reforecasts based on models from three prediction centers are considered and verified against weather regimes obtained from ERA-Interim reanalysis. Results show that predicting weather regimes as a proxy for the large-scale circulation outperforms the prediction of raw geopotential height. Greenland blocking tends to have the longest year-round skill horizon for all three models, especially in winter. On the other hand, the skill is lowest for the European blocking regime for all three models, followed by the Scandinavian blocking regime. Furthermore, all models struggle to forecast flow situations that cannot be assigned to a weather regime (so-called no regime), in comparison with weather regimes. Related to this, variability in the occurrence of no regime, which is most frequent in the transition seasons, partly explains the predictability gap between transition seasons and winter and summer. We also show that models have difficulties in discriminating between related regimes. This can lead to misassignments in the predicted regime during flow situations in which related regimes manifest. Finally, we document the changes in skill between model versions, showing important improvements for the ECMWF and NCEP models. This study is the first multi-model assessment of year-round weather regimes in the Atlantic–European domain. It advances our understanding of the predictive skill for weather regimes, reveals strengths and weaknesses of each model, and thus increases our confidence in the forecasts and their usefulness for decision-making. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07 |
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/11336/237117 Osman, Marisol; Beerli, Remo; Büeler, Dominik; Grams, Christian M.; Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 149; 755; 7-2023; 2386-2408 0035-9009 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/237117 |
identifier_str_mv |
Osman, Marisol; Beerli, Remo; Büeler, Dominik; Grams, Christian M.; Multi-model assessment of sub-seasonal predictive skill for year-round Atlantic–European weather regimes; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 149; 755; 7-2023; 2386-2408 0035-9009 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1002/qj.4512 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
John Wiley & Sons Ltd |
publisher.none.fl_str_mv |
John Wiley & Sons Ltd |
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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1844614136192827392 |
score |
13.070432 |