Bias Correction and Downscaling of future RCM Precipitation Projections using a MOS-Analog Technique
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URI: http://hdl.handle.net/10902/11158ISSN: 2169-897X
ISSN: 2169-8996
ISSN: 2156-2202
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Turco, Marco; Llasat Botija, María del Carmen; Herrera García, Sixto; Gutiérrez Llorente, José ManuelFecha
2017-03-06Derechos
©American Geophysical Union. Turco,M.,M.C.Llasat,S.Herrera,and J. M. Gutiérrez (2017), Biascorrection and downscaling offuture RCM precipitation projectionsusing a MOS-Analog technique, J.Geophys. Res. Atmos., 122, 2631-2648,doi:10.1002/2016JD025724
Publicado en
Journal of Geophysical Research. Atmospheres - Volume 122, Issue 5
16 March 2017
Pages 2631-2648
Editorial
John Wiley & Sons
Resumen/Abstract
In this study we assess the suitability of a recently introduced analog-based Model Output Statistics (MOS) downscaling method (referred to as MOS-Analog, Turco_et_al_2011) for climate change studies, and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis-driven RCM data provide the historical data (with day-to-day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) GCM-driven RCM values. First, we show that the MOS-Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971-2000) for all considered regions and precipitation indices, although for the worst-performing models the method is less effective. Second, we show that the MOS-Analog method broadly preserves the original RCM climate change signal for different future periods (2011-2040, 2041-2070, 2071-2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS-Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.
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