Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Autores
Blázquez, Josefina; Solman, Silvina
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analysed to assess the extent to which the biases may impact the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, a variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over northeast of Brazil (NEB), where most models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models’ underestimationof both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. These systematic errors could affect the climate change signal, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that theserelationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes is only statistically significant for the NEB region, where models with the largest wet biases present the greatest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that clouds could affect them. The results found in this study point out that the analysis of the bias behaviour could help in a better interpretation of the climate change signal.
Facultad de Ciencias Astronómicas y Geofísicas
Consejo Nacional de Investigaciones Científicas y Técnicas
Materia
Geofísica
Systematic errors
Climate change signal
South America
RCM CORDEX models
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/160178

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network_name_str SEDICI (UNLP)
spelling Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signalBlázquez, JosefinaSolman, SilvinaGeofísicaSystematic errorsClimate change signalSouth AmericaRCM CORDEX modelsPrecipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analysed to assess the extent to which the biases may impact the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, a variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over northeast of Brazil (NEB), where most models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models’ underestimationof both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. These systematic errors could affect the climate change signal, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that theserelationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes is only statistically significant for the NEB region, where models with the largest wet biases present the greatest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that clouds could affect them. The results found in this study point out that the analysis of the bias behaviour could help in a better interpretation of the climate change signal.Facultad de Ciencias Astronómicas y GeofísicasConsejo Nacional de Investigaciones Científicas y Técnicas2023-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf2907-2920http://sedici.unlp.edu.ar/handle/10915/160178enginfo:eu-repo/semantics/altIdentifier/issn/0930-7575info:eu-repo/semantics/altIdentifier/doi/10.1007/s00382-023-06727-5info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:13:48Zoai:sedici.unlp.edu.ar:10915/160178Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:13:49.221SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
title Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
spellingShingle Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
Blázquez, Josefina
Geofísica
Systematic errors
Climate change signal
South America
RCM CORDEX models
title_short Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
title_full Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
title_fullStr Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
title_full_unstemmed Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
title_sort Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal
dc.creator.none.fl_str_mv Blázquez, Josefina
Solman, Silvina
author Blázquez, Josefina
author_facet Blázquez, Josefina
Solman, Silvina
author_role author
author2 Solman, Silvina
author2_role author
dc.subject.none.fl_str_mv Geofísica
Systematic errors
Climate change signal
South America
RCM CORDEX models
topic Geofísica
Systematic errors
Climate change signal
South America
RCM CORDEX models
dc.description.none.fl_txt_mv Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analysed to assess the extent to which the biases may impact the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, a variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over northeast of Brazil (NEB), where most models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models’ underestimationof both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. These systematic errors could affect the climate change signal, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that theserelationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes is only statistically significant for the NEB region, where models with the largest wet biases present the greatest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that clouds could affect them. The results found in this study point out that the analysis of the bias behaviour could help in a better interpretation of the climate change signal.
Facultad de Ciencias Astronómicas y Geofísicas
Consejo Nacional de Investigaciones Científicas y Técnicas
description Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analysed to assess the extent to which the biases may impact the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, a variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over northeast of Brazil (NEB), where most models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models’ underestimationof both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. These systematic errors could affect the climate change signal, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that theserelationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes is only statistically significant for the NEB region, where models with the largest wet biases present the greatest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that clouds could affect them. The results found in this study point out that the analysis of the bias behaviour could help in a better interpretation of the climate change signal.
publishDate 2023
dc.date.none.fl_str_mv 2023-03-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_6501
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/160178
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dc.language.none.fl_str_mv eng
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info:eu-repo/semantics/altIdentifier/doi/10.1007/s00382-023-06727-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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Creative Commons Attribution 4.0 International (CC BY 4.0)
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