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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/90979
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:39:44Zoai: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:34982025-09-29 09:39:45.153CONICET 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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1844613258167713792 |
score |
13.070432 |