Local estimates of global climate change: A statistical downscaling approach

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For the purposes of estimating local changes in surface climate at selected stations in the central Argentina region, induced by an enhanced CO2 concentration, projected by general circulation models (GCM), a statistical method to derive local scale monthly mean minimum, maximum and mean temperature...

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Autores principales: Solman, S.A., Nuñez, M.N.
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 1999
Materias:
Argentina
Climate variables
Monthly temperature
Region
Regional climate change
South America
Statistical downscaling
Techniques
climate change
downscaling
general circulation model
regional climate
statistical analysis
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_08998418_v19_n8_p835_Solman
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paperaa:paper_08998418_v19_n8_p835_Solman
record_format dspace
spelling paperaa:paper_08998418_v19_n8_p835_Solman2023-06-12T16:48:27Z Local estimates of global climate change: A statistical downscaling approach Int. J. Climatol. 1999;19(8):835-861 Solman, S.A. Nuñez, M.N. Argentina Climate variables Monthly temperature Region Regional climate change South America Statistical downscaling Techniques climate change downscaling general circulation model regional climate statistical analysis Argentina For the purposes of estimating local changes in surface climate at selected stations in the central Argentina region, induced by an enhanced CO2 concentration, projected by general circulation models (GCM), a statistical method to derive local scale monthly mean minimum, maximum and mean temperatures from large-scale atmospheric predictors is presented. Empirical relationships are derived among selected variables from the NCEP re-analyses and local data for summer and winter months, tested against an independent set of observed data and subsequently applied to the HADAM and MPI GCM control runs. Finally, the statistical approach is applied to a climate change experiment performed with the MPI model to construct a local climate change scenario. The comparison between the estimated versus the observed mean temperature ffields shows good agreement and the temporal evolution of the estimated variables is well-captured, though, the estimated temperatures contain less interannual variability than the observations. For the present day climate simulation, the results from the HADAM and MPI GCMs are used. It is shown that the pattern of estimated temperatures obtained using the MPI large-scale predictors matches the observations for summer months, though minimum and mean temperatures are slightly underestimated in the southeast part of the domain. However, the differences are well within the range of the observed variability. The possible anthropogenic climate change at the local scale is assessed by applying the statistical method to the results of the perturbed run conducted with the MPI model. For summer and winter months, the local temperature increase is smaller for minimum temperature than for maximum temperature for almost all the stations, yielding an enhanced temperature amplitude in both seasons. The temperature amplitude (difference between maximum and minimum) for summer months was larger than for winter months. The estimated maximum temperature increase is found to be larger for summer months than for winter months for all the stations, while for the minimum, temperature increases for summer and winter months are similar. Fil:Solman, S.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Nuñez, M.N. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 1999 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion application/pdf eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_08998418_v19_n8_p835_Solman
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
language Inglés
orig_language_str_mv eng
topic Argentina
Climate variables
Monthly temperature
Region
Regional climate change
South America
Statistical downscaling
Techniques
climate change
downscaling
general circulation model
regional climate
statistical analysis
Argentina
spellingShingle Argentina
Climate variables
Monthly temperature
Region
Regional climate change
South America
Statistical downscaling
Techniques
climate change
downscaling
general circulation model
regional climate
statistical analysis
Argentina
Solman, S.A.
Nuñez, M.N.
Local estimates of global climate change: A statistical downscaling approach
topic_facet Argentina
Climate variables
Monthly temperature
Region
Regional climate change
South America
Statistical downscaling
Techniques
climate change
downscaling
general circulation model
regional climate
statistical analysis
Argentina
description For the purposes of estimating local changes in surface climate at selected stations in the central Argentina region, induced by an enhanced CO2 concentration, projected by general circulation models (GCM), a statistical method to derive local scale monthly mean minimum, maximum and mean temperatures from large-scale atmospheric predictors is presented. Empirical relationships are derived among selected variables from the NCEP re-analyses and local data for summer and winter months, tested against an independent set of observed data and subsequently applied to the HADAM and MPI GCM control runs. Finally, the statistical approach is applied to a climate change experiment performed with the MPI model to construct a local climate change scenario. The comparison between the estimated versus the observed mean temperature ffields shows good agreement and the temporal evolution of the estimated variables is well-captured, though, the estimated temperatures contain less interannual variability than the observations. For the present day climate simulation, the results from the HADAM and MPI GCMs are used. It is shown that the pattern of estimated temperatures obtained using the MPI large-scale predictors matches the observations for summer months, though minimum and mean temperatures are slightly underestimated in the southeast part of the domain. However, the differences are well within the range of the observed variability. The possible anthropogenic climate change at the local scale is assessed by applying the statistical method to the results of the perturbed run conducted with the MPI model. For summer and winter months, the local temperature increase is smaller for minimum temperature than for maximum temperature for almost all the stations, yielding an enhanced temperature amplitude in both seasons. The temperature amplitude (difference between maximum and minimum) for summer months was larger than for winter months. The estimated maximum temperature increase is found to be larger for summer months than for winter months for all the stations, while for the minimum, temperature increases for summer and winter months are similar.
format Artículo
Artículo
publishedVersion
author Solman, S.A.
Nuñez, M.N.
author_facet Solman, S.A.
Nuñez, M.N.
author_sort Solman, S.A.
title Local estimates of global climate change: A statistical downscaling approach
title_short Local estimates of global climate change: A statistical downscaling approach
title_full Local estimates of global climate change: A statistical downscaling approach
title_fullStr Local estimates of global climate change: A statistical downscaling approach
title_full_unstemmed Local estimates of global climate change: A statistical downscaling approach
title_sort local estimates of global climate change: a statistical downscaling approach
publishDate 1999
url http://hdl.handle.net/20.500.12110/paper_08998418_v19_n8_p835_Solman
work_keys_str_mv AT solmansa localestimatesofglobalclimatechangeastatisticaldownscalingapproach
AT nunezmn localestimatesofglobalclimatechangeastatisticaldownscalingapproach
_version_ 1769810394761658368

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