English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/213022
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

Title

An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment

AuthorsGutiérrez, José M. ; San-Martín, Daniel; Herrera, S. ; Bedia, Joaquín ; Casanueva, A.; Manzanas, R. ; Iturbide, Maialen; Casado, María Jesús; Turco, Marco; Cardoso, Rita M.; Pagé, C.
KeywordsBias adjustment
CORDEX
Downscaling
Model output statistics
Perfect prognosis
Reproducibility
Validation
Weather generators
Issue Date2019
PublisherJohn Wiley & Sons
CitationInternational Journal of Climatology 39(9): 3750-3785 (2019)
AbstractVALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process-based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis-driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method-to-method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO-CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal.
DescriptionSpecial Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research: et al.
Publisher version (URL)https://doi.org/10.1002/joc.5462
URIhttp://hdl.handle.net/10261/213022
DOIhttp://dx.doi.org/10.1002/joc.5462
Identifiersdoi: 10.1002/joc.5462
e-issn: 1097-0088
issn: 0899-8418
Appears in Collections:(IFCA) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.