Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina

Autores
Lovino, Miguel Angel; Müller, Omar Vicente; Berbery, Ernesto H.; Muller, Gabriela Viviana
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
It is generally agreed that models that better simulate historical and current features of climate should also be the ones that more reliably simulate future climate. This article describes the ability of a selection of global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to represent the historical and current mean climate and its variability over northeastern Argentina, a region that exhibits frequent extreme events. Two types of simulations are considered: Long-term simulations for 1901-2005 in which the models respond to climate forcing (e.g. changes in atmospheric composition and land use) and decadal simulations for 1961-2010 that are initialized from observed climate states. Monthly simulations of precipitation and temperature are statistically evaluated for individual models and their ensembles. Subsets of models that best represent the region´s climate are further examined. First, models that have a Nash-Sutcliffe efficiency of at least 0.8 are taken as a subset that best represents the observed temperature fields and the mean annual cycle. Their temperature time series are in phase with observations (r > 0.92), despite systematic errors that if desired can be corrected by statistical methods. Likewise, models that have a precipitation Pearson correlation coefficient of at least 0.6 are considered that best represent regional precipitation features. GCMs are able to reproduce the annual precipitation cycle, although they underestimate precipitation amounts during the austral warm season (September through April) and slightly overestimate the cold season rainfall amounts. The ensembles for the subsets of models achieve the best evaluation metrics, exceeding the performance of the overall ensembles as well as those of the individual models.
Fil: Lovino, Miguel Angel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Müller, Omar Vicente. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Berbery, Ernesto H.. University of Maryland; Estados Unidos
Fil: Muller, Gabriela Viviana. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Materia
COUPLED MODEL INTERCOMPARISON PROJECT PHASE 5 (CMIP5)
DECADAL SIMULATIONS
GLOBAL CLIMATE MODELS
LONG-TERM SIMULATIONS
PRECIPITATION
TEMPERATURE
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:11336/90979
Acceder
id CONICETDig_6fe435aa17cea72f078791019e396094
oai_identifier_str oai:ri.conicet.gov.ar:11336/90979
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern ArgentinaLovino, Miguel AngelMüller, Omar VicenteBerbery, Ernesto H.Muller, Gabriela VivianaCOUPLED MODEL INTERCOMPARISON PROJECT PHASE 5 (CMIP5)DECADAL SIMULATIONSGLOBAL CLIMATE MODELSLONG-TERM SIMULATIONSPRECIPITATIONTEMPERATUREhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1It is generally agreed that models that better simulate historical and current features of climate should also be the ones that more reliably simulate future climate. This article describes the ability of a selection of global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to represent the historical and current mean climate and its variability over northeastern Argentina, a region that exhibits frequent extreme events. Two types of simulations are considered: Long-term simulations for 1901-2005 in which the models respond to climate forcing (e.g. changes in atmospheric composition and land use) and decadal simulations for 1961-2010 that are initialized from observed climate states. Monthly simulations of precipitation and temperature are statistically evaluated for individual models and their ensembles. Subsets of models that best represent the region´s climate are further examined. First, models that have a Nash-Sutcliffe efficiency of at least 0.8 are taken as a subset that best represents the observed temperature fields and the mean annual cycle. Their temperature time series are in phase with observations (r > 0.92), despite systematic errors that if desired can be corrected by statistical methods. Likewise, models that have a precipitation Pearson correlation coefficient of at least 0.6 are considered that best represent regional precipitation features. GCMs are able to reproduce the annual precipitation cycle, although they underestimate precipitation amounts during the austral warm season (September through April) and slightly overestimate the cold season rainfall amounts. The ensembles for the subsets of models achieve the best evaluation metrics, exceeding the performance of the overall ensembles as well as those of the individual models.Fil: Lovino, Miguel Angel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Müller, Omar Vicente. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Berbery, Ernesto H.. University of Maryland; Estados UnidosFil: Muller, Gabriela Viviana. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaJohn Wiley & Sons Ltd2018-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/90979Lovino, Miguel Angel; Müller, Omar Vicente; Berbery, Ernesto H.; Muller, Gabriela Viviana; Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina; John Wiley & Sons Ltd; International Journal of Climatology; 38; 51; 4-2018; e1158-e11750899-84181097-0088CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.5441info:eu-repo/semantics/altIdentifier/doi/10.1002/joc.5441info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:08:12Zoai:ri.conicet.gov.ar:11336/90979instacron: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:34982025-10-22 11:08:12.256CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
title Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
spellingShingle Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
Lovino, Miguel Angel
COUPLED MODEL INTERCOMPARISON PROJECT PHASE 5 (CMIP5)
DECADAL SIMULATIONS
GLOBAL CLIMATE MODELS
LONG-TERM SIMULATIONS
PRECIPITATION
TEMPERATURE
title_short Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
title_full Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
title_fullStr Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
title_full_unstemmed Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
title_sort Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina
dc.creator.none.fl_str_mv Lovino, Miguel Angel
Müller, Omar Vicente
Berbery, Ernesto H.
Muller, Gabriela Viviana
author Lovino, Miguel Angel
author_facet Lovino, Miguel Angel
Müller, Omar Vicente
Berbery, Ernesto H.
Muller, Gabriela Viviana
author_role author
author2 Müller, Omar Vicente
Berbery, Ernesto H.
Muller, Gabriela Viviana
author2_role author
author
author
dc.subject.none.fl_str_mv COUPLED MODEL INTERCOMPARISON PROJECT PHASE 5 (CMIP5)
DECADAL SIMULATIONS
GLOBAL CLIMATE MODELS
LONG-TERM SIMULATIONS
PRECIPITATION
TEMPERATURE
topic COUPLED MODEL INTERCOMPARISON PROJECT PHASE 5 (CMIP5)
DECADAL SIMULATIONS
GLOBAL CLIMATE MODELS
LONG-TERM SIMULATIONS
PRECIPITATION
TEMPERATURE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv It is generally agreed that models that better simulate historical and current features of climate should also be the ones that more reliably simulate future climate. This article describes the ability of a selection of global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to represent the historical and current mean climate and its variability over northeastern Argentina, a region that exhibits frequent extreme events. Two types of simulations are considered: Long-term simulations for 1901-2005 in which the models respond to climate forcing (e.g. changes in atmospheric composition and land use) and decadal simulations for 1961-2010 that are initialized from observed climate states. Monthly simulations of precipitation and temperature are statistically evaluated for individual models and their ensembles. Subsets of models that best represent the region´s climate are further examined. First, models that have a Nash-Sutcliffe efficiency of at least 0.8 are taken as a subset that best represents the observed temperature fields and the mean annual cycle. Their temperature time series are in phase with observations (r > 0.92), despite systematic errors that if desired can be corrected by statistical methods. Likewise, models that have a precipitation Pearson correlation coefficient of at least 0.6 are considered that best represent regional precipitation features. GCMs are able to reproduce the annual precipitation cycle, although they underestimate precipitation amounts during the austral warm season (September through April) and slightly overestimate the cold season rainfall amounts. The ensembles for the subsets of models achieve the best evaluation metrics, exceeding the performance of the overall ensembles as well as those of the individual models.
Fil: Lovino, Miguel Angel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Müller, Omar Vicente. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Berbery, Ernesto H.. University of Maryland; Estados Unidos
Fil: Muller, Gabriela Viviana. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
description It is generally agreed that models that better simulate historical and current features of climate should also be the ones that more reliably simulate future climate. This article describes the ability of a selection of global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to represent the historical and current mean climate and its variability over northeastern Argentina, a region that exhibits frequent extreme events. Two types of simulations are considered: Long-term simulations for 1901-2005 in which the models respond to climate forcing (e.g. changes in atmospheric composition and land use) and decadal simulations for 1961-2010 that are initialized from observed climate states. Monthly simulations of precipitation and temperature are statistically evaluated for individual models and their ensembles. Subsets of models that best represent the region´s climate are further examined. First, models that have a Nash-Sutcliffe efficiency of at least 0.8 are taken as a subset that best represents the observed temperature fields and the mean annual cycle. Their temperature time series are in phase with observations (r > 0.92), despite systematic errors that if desired can be corrected by statistical methods. Likewise, models that have a precipitation Pearson correlation coefficient of at least 0.6 are considered that best represent regional precipitation features. GCMs are able to reproduce the annual precipitation cycle, although they underestimate precipitation amounts during the austral warm season (September through April) and slightly overestimate the cold season rainfall amounts. The ensembles for the subsets of models achieve the best evaluation metrics, exceeding the performance of the overall ensembles as well as those of the individual models.
publishDate 2018
dc.date.none.fl_str_mv 2018-04
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/11336/90979
Lovino, Miguel Angel; Müller, Omar Vicente; Berbery, Ernesto H.; Muller, Gabriela Viviana; Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina; John Wiley & Sons Ltd; International Journal of Climatology; 38; 51; 4-2018; e1158-e1175
0899-8418
1097-0088
CONICET Digital
CONICET
url http://hdl.handle.net/11336/90979
identifier_str_mv Lovino, Miguel Angel; Müller, Omar Vicente; Berbery, Ernesto H.; Muller, Gabriela Viviana; Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina; John Wiley & Sons Ltd; International Journal of Climatology; 38; 51; 4-2018; e1158-e1175
0899-8418
1097-0088
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.5441
info:eu-repo/semantics/altIdentifier/doi/10.1002/joc.5441
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
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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score 12.982451

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