Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/213022COMPARTIR / EXPORTAR:
 SHARE
 CORE
BASE
|
|
| Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
| Título: | An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment |
Autor: | Gutiérrez, José M. CSIC ORCID ; San-Martín, Daniel; Herrera, Sixto CSIC ORCID CVN; Bedia, Joaquín CSIC ORCID; Casanueva, Ana CSIC ORCID; Manzanas, Rodrigo CSIC ORCID; Iturbide, Maialen CSIC ORCID; Casado, María Jesús; Turco, Marco; Cardoso, Rita M.; Pagé, C. | Palabras clave: | Bias adjustment CORDEX Downscaling Model output statistics Perfect prognosis Reproducibility Validation Weather generators |
Fecha de publicación: | 2019 | Editor: | John Wiley & Sons | Citación: | International Journal of Climatology 39(9): 3750-3785 (2019) | Resumen: | VALUE 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. | Descripción: | Special Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research: et al. | Versión del editor: | https://doi.org/10.1002/joc.5462 | URI: | http://hdl.handle.net/10261/213022 | DOI: | 10.1002/joc.5462 | Identificadores: | doi: 10.1002/joc.5462 e-issn: 1097-0088 issn: 0899-8418 |
| Aparece en las colecciones: | (IFCA) Artículos |
Ficheros en este ítem:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| accesoRestringido.pdf | 15,38 kB | Adobe PDF |  Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
155
checked on 09-abr-2024
WEB OF SCIENCETM
Citations
107
checked on 20-feb-2024
Page view(s)
174
checked on 17-abr-2024
Download(s)
29
checked on 17-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.