Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

• Published in: The Cryosphere
• Main Authors: S. Terzago, J. von Hardenberg, E. Palazzi, A. Provenzale
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2017
• Subjects: geo; envir; Gar’; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-11-1625-2017; https://www.the-cryosphere.net/11/1625/2017/tc-11-1625-2017.pdf; https://doaj.org/article/371c1043715f4d83a2a23adcd7263152

The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow–climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes – near-surface air temperature and precipitation – against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to ...