Abstract
Climate models are important tools to assess current and future climate. While they have
been extensively used for studying temperature and precipitation, only recently regional climate
models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain
terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program
Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations
of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well
as with point (station) observations of snow depth and temperature for the European Alps. Large
scale snow cover dynamics were reproduced well with some over- and under-estimations depending
on month and RCM. The orography, temperature, and precipitation mismatches could on average
explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the
winter period November–April. Biases in average monthly snow depth were remarkably low for
reanalysis driven RCMs (when averaged over all stations within 400 m of altitude difference with RCM orography. Some
RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa.
In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to
their limited horizontal resolution. Detected shortcomings in the models depended on the considered
snow variable, season and individual RCM.