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Evaluation of European snow cover as simulated by an ensemble of regional
climate models
Sven Kotlarski, Daniel Lüthi and Christoph Schär
Institute for Atmospheric and Climate Science, ETH Zurich, sven.kotlarski@env.ethz.ch
1. Introduction
In many regions land surface characteristics are strongly
influenced by the presence of seasonal snow cover. A
correct description of snow characteristics is therefore of
major importance for the simulation of surface-atmosphere
energy fluxes in regional climate models (RCMs). At the
same time snow modelling poses special challenges due to
the non-linear processes involved and their pronounced
spatial variability. If the future evolution of regional snow
cover is of interest, the ability of RCMs to reproduce
present-day conditions should be investigated beforehand.
The present contribution evaluates the performance of a set
of state-of-the-art RCMs with respect to snow cover extent
and snow depth in Europe. A special focus is laid on the
European Alps, an area with a pronounced topography and a
high economic significance of snow cover.
2. Data and Methods
The investigated RCM experiments were carried out within
the ENSEMBLES project for the period 1960-2000 at a
horizontal resolution of approx. 25 km. In all cases the
lateral boundary forcing was provided by the ERA40 reanalysis.
The simulated snow characteristics (snow cover
extent, snow depth, number of snow days) in Europe are
compared to different observational datasets. For this
purpose both ground based and remote-sensed datasets are
used.
3. Results
The detailed comparison of simulated and observed snow
cover characteristics reveals an overall good performance of
the RCMs. The basic characteristics of seasonal snow cover
on a European scale are well reproduced. Still, pronounced
differences exist between individual RCMs, for instance
with respect to winter snow extent (Figure 1).
Furthermore, important biases appear for individual
models in some regions. For instance, the timing of spring
snowmelt is often shifted by several weeks in the models.
One example of these biases is shown in Figures 2 and 3
which depict the mean annual cycle (1981-2000) of the
number of snow days for distinct elevation intervals in
Germany. With respect to the observational dataset most
models show a pronounced delay of the spring snowmelt
and an overestimation of the total number of snow days in
wintertime.
In some cases model deficiencies can be traced back to the
treatment of snow in the land surface scheme of the
respective RCM. Furthermore, biases in atmospheric
parameters (temperature and precipitation) can partly be
linked to shortcomings in the representation of surface
snow cover.
4. Conclusions
The detailed validation of the simulated snow
characteristics in an ensemble of state-of-the-art RCMs
reveals an overall good model performance for the period
1961-2000. However, important biases in basic parameters
in individual regions (e.g., the timing of spring snowmelt)
question the direct use of the simulated snow parameters
in impact studies (e.g., hydrological modeling).
Regarding regional climate change scenarios for the 21 st
century confidence has been gained that state-of-the-art
RCMs are able to capture basic climate change effects on
snow extent and snow depth. These experiments will be
analyzed in a second step.
Figure 1. Mean extent of winter (DJF) snow cover in 8 ENSEMBLES RCMs (1961-2000). White: Areas with
a mean winter snow depth of more then 3 cm w.e.