Summer seasonal predictability of warm days in Argentina: statistical model approach

Autores
Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Predicting extreme temperature events can be very useful for different sectorsthat are strongly affected by their variability. The goal of this study is toanalyze the influence of the main atmospheric, oceanic, and soil moistureforcing on the occurrence of summer warm days and to predict extremetemperatures in Argentina northern of 40°S by fitting a statistical model. In apreliminary analysis, we studied trends and periodicities. Significant positivetrends, fundamentally in western Argentina, and two main periodicities ofsummer warm days were detected: 2?4 years and approximately 8 years.Lagged correlations allowed us to identify the key predictors: ElNiño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), andStandardized Precipitation Indices (SPI). We also noticed that the frequency ofwarm days in spring acts as a good predictor of summer warm days. Due to thecollinearity among many predictors, principal component regression was usedto simulate summer warm days. We obtained negative biases (i.e., the modeltends to underestimate the frequency of summer warm days), but the observedand simulated values of summer warm days were significantly correlated,except in northwest Argentina. Finally, we analyzed the predictability of thesummer warm days under ENSO neutral conditions, and we found newpredictors: the geopotential height gradient in 850 hPa (between the AtlanticAnticyclone and the Chaco Low) and the Atlantic Multidecadal Oscillation(AMO), while the PDO and SPI lost some relevance.
Fil: Collazo, Soledad Maribel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Barrucand, Mariana Graciela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Rusticucci, Matilde Monica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Materia
WARM DAYS
EXTREME TEMPERATURE
CLIMATE PREDICTION
PRINCIPAL COMPONENT REGRESSION
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:11336/149553
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/149553
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Summer seasonal predictability of warm days in Argentina: statistical model approachCollazo, Soledad MaribelBarrucand, Mariana GracielaRusticucci, Matilde MonicaWARM DAYSEXTREME TEMPERATURECLIMATE PREDICTIONPRINCIPAL COMPONENT REGRESSIONhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Predicting extreme temperature events can be very useful for different sectorsthat are strongly affected by their variability. The goal of this study is toanalyze the influence of the main atmospheric, oceanic, and soil moistureforcing on the occurrence of summer warm days and to predict extremetemperatures in Argentina northern of 40°S by fitting a statistical model. In apreliminary analysis, we studied trends and periodicities. Significant positivetrends, fundamentally in western Argentina, and two main periodicities ofsummer warm days were detected: 2?4 years and approximately 8 years.Lagged correlations allowed us to identify the key predictors: ElNiño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), andStandardized Precipitation Indices (SPI). We also noticed that the frequency ofwarm days in spring acts as a good predictor of summer warm days. Due to thecollinearity among many predictors, principal component regression was usedto simulate summer warm days. We obtained negative biases (i.e., the modeltends to underestimate the frequency of summer warm days), but the observedand simulated values of summer warm days were significantly correlated,except in northwest Argentina. Finally, we analyzed the predictability of thesummer warm days under ENSO neutral conditions, and we found newpredictors: the geopotential height gradient in 850 hPa (between the AtlanticAnticyclone and the Chaco Low) and the Atlantic Multidecadal Oscillation(AMO), while the PDO and SPI lost some relevance.Fil: Collazo, Soledad Maribel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaFil: Barrucand, Mariana Graciela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaFil: Rusticucci, Matilde Monica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaSpringer Wien2019-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/149553Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica; Summer seasonal predictability of warm days in Argentina: statistical model approach; Springer Wien; Theory & Application Climatology; 138; 3; 7-2019; 1853-18760177-798XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s00704-019-02933-6info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00704-019-02933-6info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:29:32Zoai:ri.conicet.gov.ar:11336/149553instacron: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 10:29:32.842CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Summer seasonal predictability of warm days in Argentina: statistical model approach
title Summer seasonal predictability of warm days in Argentina: statistical model approach
spellingShingle Summer seasonal predictability of warm days in Argentina: statistical model approach
Collazo, Soledad Maribel
WARM DAYS
EXTREME TEMPERATURE
CLIMATE PREDICTION
PRINCIPAL COMPONENT REGRESSION
title_short Summer seasonal predictability of warm days in Argentina: statistical model approach
title_full Summer seasonal predictability of warm days in Argentina: statistical model approach
title_fullStr Summer seasonal predictability of warm days in Argentina: statistical model approach
title_full_unstemmed Summer seasonal predictability of warm days in Argentina: statistical model approach
title_sort Summer seasonal predictability of warm days in Argentina: statistical model approach
dc.creator.none.fl_str_mv Collazo, Soledad Maribel
Barrucand, Mariana Graciela
Rusticucci, Matilde Monica
author Collazo, Soledad Maribel
author_facet Collazo, Soledad Maribel
Barrucand, Mariana Graciela
Rusticucci, Matilde Monica
author_role author
author2 Barrucand, Mariana Graciela
Rusticucci, Matilde Monica
author2_role author
author
dc.subject.none.fl_str_mv WARM DAYS
EXTREME TEMPERATURE
CLIMATE PREDICTION
PRINCIPAL COMPONENT REGRESSION
topic WARM DAYS
EXTREME TEMPERATURE
CLIMATE PREDICTION
PRINCIPAL COMPONENT REGRESSION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Predicting extreme temperature events can be very useful for different sectorsthat are strongly affected by their variability. The goal of this study is toanalyze the influence of the main atmospheric, oceanic, and soil moistureforcing on the occurrence of summer warm days and to predict extremetemperatures in Argentina northern of 40°S by fitting a statistical model. In apreliminary analysis, we studied trends and periodicities. Significant positivetrends, fundamentally in western Argentina, and two main periodicities ofsummer warm days were detected: 2?4 years and approximately 8 years.Lagged correlations allowed us to identify the key predictors: ElNiño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), andStandardized Precipitation Indices (SPI). We also noticed that the frequency ofwarm days in spring acts as a good predictor of summer warm days. Due to thecollinearity among many predictors, principal component regression was usedto simulate summer warm days. We obtained negative biases (i.e., the modeltends to underestimate the frequency of summer warm days), but the observedand simulated values of summer warm days were significantly correlated,except in northwest Argentina. Finally, we analyzed the predictability of thesummer warm days under ENSO neutral conditions, and we found newpredictors: the geopotential height gradient in 850 hPa (between the AtlanticAnticyclone and the Chaco Low) and the Atlantic Multidecadal Oscillation(AMO), while the PDO and SPI lost some relevance.
Fil: Collazo, Soledad Maribel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Barrucand, Mariana Graciela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Rusticucci, Matilde Monica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
description Predicting extreme temperature events can be very useful for different sectorsthat are strongly affected by their variability. The goal of this study is toanalyze the influence of the main atmospheric, oceanic, and soil moistureforcing on the occurrence of summer warm days and to predict extremetemperatures in Argentina northern of 40°S by fitting a statistical model. In apreliminary analysis, we studied trends and periodicities. Significant positivetrends, fundamentally in western Argentina, and two main periodicities ofsummer warm days were detected: 2?4 years and approximately 8 years.Lagged correlations allowed us to identify the key predictors: ElNiño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), andStandardized Precipitation Indices (SPI). We also noticed that the frequency ofwarm days in spring acts as a good predictor of summer warm days. Due to thecollinearity among many predictors, principal component regression was usedto simulate summer warm days. We obtained negative biases (i.e., the modeltends to underestimate the frequency of summer warm days), but the observedand simulated values of summer warm days were significantly correlated,except in northwest Argentina. Finally, we analyzed the predictability of thesummer warm days under ENSO neutral conditions, and we found newpredictors: the geopotential height gradient in 850 hPa (between the AtlanticAnticyclone and the Chaco Low) and the Atlantic Multidecadal Oscillation(AMO), while the PDO and SPI lost some relevance.
publishDate 2019
dc.date.none.fl_str_mv 2019-07
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/11336/149553
Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica; Summer seasonal predictability of warm days in Argentina: statistical model approach; Springer Wien; Theory & Application Climatology; 138; 3; 7-2019; 1853-1876
0177-798X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/149553
identifier_str_mv Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica; Summer seasonal predictability of warm days in Argentina: statistical model approach; Springer Wien; Theory & Application Climatology; 138; 3; 7-2019; 1853-1876
0177-798X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s00704-019-02933-6
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00704-019-02933-6
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 Springer Wien
publisher.none.fl_str_mv Springer Wien
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 13.070432

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