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

Abstract

Abstract. 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 or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

Elevation-dependent warming in global climate model simulations at high spatial resolution

“…In mountain regions autumn is generally the "transition season" between snow-free and snow-covered surface. Climate warming is delaying the onset of snow cover at low and mid altitudes (Terzago et al 2014(Terzago et al , 2017) and this trend is expected to continue and intensify in the future, involving higher elevations. Therefore, larger snow free areas are expected in autumn, as many areas that used to be snow covered in the past might not be anymore in the future in this season.…”

Future snowfall in the Alps: projections based on the EURO-CORDEX regional climate models

“…S3). This fact might ultimately be connected to an overestimation of surface snow amount in several EURO-CORDEX RCMs as reported by Terzago et al (2017). As the precipitation ratio between simulations and observations depends approximately linearly on elevation, the calculation of P AF via a linear regression of the ratios against elevation (see Sect.…”

Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

“…Additionally, they also highlighted the importance of horizontal resolution [6]. Terzago et al provide the most comprehensive evaluation of SWE in RCMs (and GCMs) so far using multiple remote sensing products and reanalysis for the entire European Alps and found that RCMs overestimated SWE and only ERA-Interim driven RCMs had a comparable amplitude in the snow cycle while GCM-driven RCMs had large positive biases [7].In this study, we evaluate snow in the 0.11 • (~12.5 km) EURO-CORDEX RCMs using high-resolution observations. This is the first time, to the best of our knowledge, that a high-resolution comparison has been performed using such an extensive set of RCMs and covering the whole Alpine domain.…”

“…Additionally, they also highlighted the importance of horizontal resolution [6]. Terzago et al provide the most comprehensive evaluation of SWE in RCMs (and GCMs) so far using multiple remote sensing products and reanalysis for the entire European Alps and found that RCMs overestimated SWE and only ERA-Interim driven RCMs had a comparable amplitude in the snow cycle while GCM-driven RCMs had large positive biases [7].…”