Using the regional climate model COSMO-CLM over Siberia
K. Klehmet and B. Rockel
Siberia, a vast landmass extending from the arctic, subarctic to temperate northern latitudes
of Russia, is largely underlain by permafrost and characterised by one of the most
continental climates on earth. In this region temperature rise have been among the most
pronounced globally with profound and spatially variable impacts on frozen ground and snow
cover. In order to determine and assess long-term climatic changes consistent datasets over
long time spans are necessary. This is hampered by the fact that observations for the arctic
to temperate latitudes of Siberia are quite limited with few long-term stations.
To complement existing data at high spatial and temporal resolution and to provide
information of climate and ground parameters for which no measurements have been taken
the regional climate model COSMO-CLM (CCLM) is used. We aim to perform a climate
reconstruction over a domain in Siberia of approximately 45° N - 85° N and 50° E - 150° E
with a horizontal resolution of ~ 25 km for the last 60 years. Before, it is necessary to find an
appropriate model configuration to ensure that the model is able to reproduce climate in that
region of the northern latitudes.
Since cryosphere plays a crucial role in determining regional climate in Siberia, a special
consideration of frozen ground and snow treatment in CCLM is needed. Therefore, we use
the integrated multi-layer snow model and additional soil layers up to a total soil depth of 92
m in the soil model. Test runs for the period of 1990-1999 are conducted with a horizontal
resolution of ~ 50 km. Boundary conditions are taken form the NCEP-1 Reanalysis of the
National Centers for Environmental Prediction. We consider the capability and limitation of
the current model configuration in reproducing the present-day air temperature, precipitation
and soil temperature in its temporal and spatial characteristics.
First simulations indicate a strong warm bias of 2m air temperature in winter months over the
whole model domain compared to gridded observation data. In a next step model output will
also be evaluated using satellite data, e.g. land surface temperature provided by ESA DUE
Permafrost.