Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/11580
Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment
Title: Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment
Authors: Widmann, MartinBedia, JoaquínGutiérrez Llorente, José ManuelBosshard, T.Hertig, ElkeMaraun, DouglasCasado Calle, María JesúsAutor AEMETRamos Calzado, PetraAutor AEMETCardoso, Rita M.Soares, Pedro M. M.Ribalaygua Batalla, JaimePagé, C.Fischer, Andreas M.Herrera García, SixtoHuth, Radan
Keywords: Bias adjustment; Downscaling; Model output statistics; Perfect prognosis; Regional climate; Spatialvariability; Validation
Issue Date: 2019
Publisher: Wiley; Royal Meteorological Society
Citation: International Journal of Climatology. 2019, 39(9), p. 3819-3845
Publisher version: https://dx.doi.org/10.1002/joc.6024
Abstract: The spatial dependence of meteorological variables is crucial for many impacts, for example, droughts, floods, river flows, energy demand, and crop yield. There is thus a need to understand how well it is represented in downscaling (DS) products. Within the COST Action VALUE, we have conducted a comprehensive analysis of spatial variability in the output of over 40 different DS methods in a perfect predictor setup. The DS output is evaluated against daily precipitation and temperature observations for the period 1979–2008 at 86 sites across Europe and 53 sites across Germany. We have analysed the dependency of correlations of daily temperature and precipitation series at station pairs on the distance between the stations. For the European data set, we have also investigated the complexity of the downscaled data by calculating the number of independent spatial degrees of freedom. For daily precipitation at the German network, we have additionally evaluated the dependency of the joint exceedance of the wet day threshold and of the local 90th percentile on the distance between the stations. Finally, we have investigated regional patterns of European monthly precipitation obtained from rotated principal component analysis. We analysed Perfect Prog (PP) methods, which are based on statistical relationships derived from observations, as well as Model Output Statistics (MOS) approaches, which attempt to correct simulated variables. In summary, we found that most PP DS methods, with the exception of multisite analog methods and a method that explicitly models spatial dependence yield unrealistic spatial characteristics. Regional climate model‐based MOS methods showed good performance with respect to correlation lengths and the joint occurrence of wet days, but a substantial overestimation of the joint occurrence of heavy precipitation events. These findings apply to the spatial scales that are resolved by our observation network, and similar studies with higher resolutions, which are relevant for small hydrological catchment, are desirable.
URI: http://hdl.handle.net/20.500.11765/11580
ISSN: 0899-8418
1097-0088
Appears in Collections:Artículos científicos 2019-2021


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