Model performance in simulating snow cover in Northern Eurasia
Anna Parvio1 and Kirsti Jylhä1
1
Finnish Meteorological Institute, Helsinki, Finland
Correspondence author e-mail: anna.parvio@fmi.fi
Observations of snow conditions serve as a sensitive indicator of global warming. With the help of
long time series, important information about climate change especially in northern areas has been
obtained. A reasonable description of snow variables is very important in atmospheric general
circulation models in order to assess possible changes in the future climatic conditions. It is
particularly crucial to describe the snow covered area and surface albedo realistically. Snow and
surface albedo are connected through a positive feedback mechanism: when the amount of snow
reduces, the surface albedo decreases which affects the radiation conditions and the whole
climate system.
In this work, the model performance of the atmospheric general circulation model ECHAM5
(Roeckner et al. 2003) and the ERA-40 re-analysis produced by the European Centre for MediumRange Weather Forecast (ECMWF) (Uppala et al. 2005) in simulating snow water equivalent and
surface albedo on snow covered areas in northern Eurasia considered. The aim of the study was
to find out the modelling differences between these two and to examine the ability of the ECHAM5
model to represent the snow conditions of the present-day climate. The ECHAM5 simulations
(originally used by Räisänen et al. 2007) were forced by observed sea surface temperature and
sea ice. The comparison between ECHAM5 and ERA-40 data was done for years 1986-1990. The
ERA-40 data was used also to study snow cover characteristics during a longer period 1971-2000.
According to the ERA-40 results for 1971-2000, the monthly mean snow water equivalent (SWE) in
March was largest in western Siberia and in Uralian and Scandinavian mountains (Fig. 1a), while
largest interannual variation occurred in northern Europe (Fig. 1b). For the period of comparisons,
1986-1990, ECHAM5 produced in many areas lower values of SWE than ERA-40 (Fig. 1c, see
also Fig. 2a). The differences were greatest in the areas where SWE was largest, in the middle
parts of the northern Eurasia. Also the interannual variation of snow water equivalent was smaller
in the ECHAM5 data than in the ERA-40 data. Especially in two last years of the period, 1989 and
1990, the values of snow water equivalent in North Europe were very low according to ERA-40
(Fig. 2b). High values of NAO-index (North Atlantic Oscillation) in those years (Hurrell 1995)
appeared to be linked with these low SWE values. However, the strongly positive phase of the
North Atlantic Oscillation at the end of the 1980s did not seem to affect the snow water equivalent
of ECHAM5. The observations used in ERA-40 snow analysis probably mainly accounted for the
differencies between ERA-40 and ECHAM5. It may also be possible that the forcing used in
ECHAM5 (observed sea surface temperature and sea ice) was not strong enough to produce
completely realistic distribution of snow.
The surface albedo of the snow covered areas is modified with snow albedo. In ERA-40 this snow
albedo is a prognostic variable (ECMWF, 2004) while in ECHAM5 the snow albedo is
parameterized (Roeckner et al. 2003). In snow covered areas the surface albedo of ECHAM5 is
mainly larger than the surface albedo of ERA-40 (Fig. 2c). The discrepancy between these two is
mainly caused by the differencies in modelling snow albedo and by factors related to vegetation.
ECHAM5 underestimates the snow masking effect of trees as a result of which the surface albedo
is too high especially in the boreal forests (Roesch and Roeckner, 2006). The surface albedo of
ERA-40 is probably more realistic than the surface albedo of ECHAM5.
Acknowledgements
This work was carried out under the SNOW-CLIM (Snow Mapping of Boreal and Sub-Arctic Zones:
Earth Observation Data-Based Multi-Source Information Systems and Application to Climatic
Studies) Project financed by the Academy of Finland.
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Figure legends
Figure 1. (a) Mean monthly snow water equivalent in northern Eurasia in March in 1971-2000
based on ERA-40 and (b) its standard deviation. (c) The difference between ECHAM5 and ERA40 results for the monthly mean snow water equivalent in March 1986-1990. Units: m.
Figure 2. (a) Spatial average of snow water equivalent from Jan 1986 to Dec 1990 in northern
Eurasia (40-85°N, 0-180°E) and (b) for northern Europe (55-72°N, 3-40°E).( c) Spatially averaged
surface albedo. Black curve: ERA-40 re-analysis, red curve: average of 10 ensemble simulations
of ECHAM5; [Aika = Years].
b
a
c
Figure 1. (a) Mean monthly snow water equivalent in northern Eurasia in March in 1971-2000
based on ERA-40 and (b) its standard deviation. (c) The difference between ECHAM5 and ERA40 results for the monthly mean snow water equivalent in March 1986-1990. Units: m.
.
Figure 2. (a) Spatial average of snow water equivalent from Jan 1986 to Dec 1990 in northern
Eurasia (40-85°N, 0-180°E) and (b) for northern Europe (55-72°N, 3-40°E).( c) Spatially averaged
surface albedo. Black curve: ERA-40 re-analysis, red curve: average of 10 ensemble simulations
of ECHAM5. [Aika = Years]