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dc.contributor.author
Balmaceda Huarte, Rocio  
dc.contributor.author
Bettolli, Maria Laura  
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  
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  
dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
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  
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  
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