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dc.contributor.author
Balmaceda Huarte, Rocio
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dc.contributor.author
Bettolli, Maria Laura
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dc.date.available
2023-12-26T14:27:14Z
dc.date.issued
2022-12
dc.identifier.citation
Balmaceda Huarte, Rocio; Bettolli, Maria Laura; Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures; John Wiley & Sons Ltd; International Journal of Climatology; 42; 16; 12-2022; 8423-8445
dc.identifier.issn
0899-8418
dc.identifier.uri
http://hdl.handle.net/11336/221406
dc.description.abstract
Empirical statistical downscaling (ESD) under the perfect prognosis approach was carried out to simulate daily maximum (Tx) and minimum temperatures (Tn) in 101 meteorological stations over the different climatic regions of Argentina. To this end, three ESD families were evaluated: analogs (AN), generalized linear models (GLM) and artificial neural networks (ANN) considering a variety of predictor sets with multiple configurations driven by three different reanalyses (ERA, JRA, NCEP). ESD models were cross-validated using folds of nonconsecutive years (1979–2014) and then evaluated in a warmer set of years (independent warm period, 2015–2018) to assess their extrapolation capability. Depending on the aspect analysed, AN, GLM or ANN models were more/less skilful, but no method fulfilled all the features of both predicand variables. In this sense, the predictor set and model configuration were key factors. For each ESD method, the different predictor structures (point-wise, spatial-wise and combinations of them) introduced the main differences, regardless of the predictand variable, region and reanalysis choice. However, some specific results could be highlighted. ERA (NCEP)-driven ESD models were the most (least) skilful in representing Tx and Tn. In the case of Tn, models' skills considerably increased when humidity information was included in the predictor set. Our results showed that downscaling models were able to capture the general characteristics of Tx and Tn in all regions, with better performance in the latter variable. However, regions with complex topography (Argentinian Patagonia and the subtropical Andes) pose a further challenge for capturing the local variability of daily extreme temperatures. The performance of the ESD models in the atypical warm conditions was similar to the one during the cross-validated period, showing some extrapolation skill. The results of this work set a reference for future ESD developments and comparisons in Argentina.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons Ltd
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ANALOGS
dc.subject
ARTIFICIAL NEURAL NETWORKS
dc.subject
GENERALIZED LINEAR MODELS
dc.subject
MAXIMUM AND MINIMUM TEMPERATURE
dc.subject
PERFECT PROGNOSIS
dc.subject
REANALYSIS
dc.subject
REGIONAL CLIMATE DOWNSCALING
dc.subject
SOUTHERN SOUTH AMERICA
dc.subject.classification
Investigación Climatológica
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dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-12-26T11:19:04Z
dc.journal.volume
42
dc.journal.number
16
dc.journal.pagination
8423-8445
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Londres
dc.description.fil
Fil: Balmaceda Huarte, Rocio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
dc.description.fil
Fil: Bettolli, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
dc.journal.title
International Journal of Climatology
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/joc.7733
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.7733
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