Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
doi:10.5194/tc-2016-280
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
26 Jan 2017
Review status
This discussion paper is under review for the journal The Cryosphere (TC).
Snow water equivalent in the Alps as seen by gridded datasets, CMIP5 and CORDEX climate models
Silvia Terzago1, Jost von Hardenberg1, Elisa Palazzi1, and Antonello Provenzale2 1Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, Torino, Italy
2Institute of Geosciences and Earth Resources, National Research Council of Italy, Via Moruzzi 1, Pisa, Italy
Abstract. The estimate of the current and future conditions of snow resources in mountain areas depends on the availability of reliable, high resolution, regional observation-based gridded datasets and of climate models capable of properly representing snow processes and snow-climate interactions. Owing to the sparseness of station-based reference observations, in past decades mainly passive microwave remote sensing and reanalysis products have 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 datasets 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 analyze the simulations from the regional and global climate models (RCMs, GCMs) participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the latest Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing snow water equivalent against the remote sensing and reanalysis datasets previously considered.

The results of the analysis show that the distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and renalysis datasets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of RMSE and standard deviation than lower resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent that are locally much larger than those indicated by the gridded datasets but these differences are smoothed out when snow water equivalent is spatially averaged over the Alpine domain. ERA-Interim driven RCM simulations show a snow annual cycle comparable in amplitude to those provided by the reference datasets while GCM-driven RCMs present a large positive bias. The snow reduction expected by mid-21st century in the RCP 8.5 scenario is weaker in higher-resolution RCM simulations than in GCM runs.


Citation: Terzago, S., von Hardenberg, J., Palazzi, E., and Provenzale, A.: Snow water equivalent in the Alps as seen by gridded datasets, CMIP5 and CORDEX climate models, The Cryosphere Discuss., doi:10.5194/tc-2016-280, in review, 2017.
Silvia Terzago et al.
Silvia Terzago et al.
Silvia Terzago et al.

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Short summary
The estimate of the current and future conditions of snow resources in mountain areas depends on the availability of reliable high resolution datasets and of climate models capable of properly representing snow processes and snow-climate interactions.This work considers the available snow water equivalent datasets from remote sensing, reanalyses, regional and global climate models for the Alps and explores their ability to provide a coherent view of the snowpack features and of its changes.
The estimate of the current and future conditions of snow resources in mountain areas depends on...
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