2007Russian Meteorology and HydrologyRequires access

The MGO climate model for Siberia

I. M. Shkolnik, V. P. Meleshko, V. M. Kattsov

Open publisher page 9 citations

Abstract

Validation results of the MGO regional climate model (RCM) with 50-km resolution for Siberia are discussed. For the specification of side boundary conditions, the reanalysis data are used. It is shown that the model satisfactorily simulates the sea-level pressure and temperature fields for all seasons and the year as a whole. The lowest computational errors in the simulation of regional surface temperature arise in the fall and winter; in spring and summer, the temperature errors are slightly higher. The model slightly underestimates the variability of daily mean temperature in winter relative to the reanalysis data. In summer, on the contrary, the RCM-simulated variability exceeds the variability in reanalysis. In winter, the space distribution of model precipitation is in qualitative agreement with the data of observational analysis; in summer, the space variability of model precipitation is significantly higher than that of precipitation in the reanalysis, especially in the mountains. Agreement between time changes in precipitation and temperature anomalies in RCM and in the reanalysis is better in the areas with a relatively large number of weather stations. The model can be used for estimation of future climate changes in the above-mentioned region.

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What this paper is about

Validation results of the MGO regional climate model (RCM) with 50-km resolution for Siberia are discussed. For the specification of side boundary conditions, the reanalysis data are used. It is shown that the model satisfactorily simulates the sea-level pressure and temperature fields for all seasons and the year as a whole. The lowest computational errors in the simulation of regional surface temperature arise in the fall and winter; in spring and summer, the temperature errors are slightly higher. The model slightly underestimates the variability of daily mean temperature in winter relative to the reanalysis data. In summer, on the contrary, the RCM-simulated variability exceeds the variability in reanalysis. In winter, the space distribution of model precipitation is in qualitative agreement with the data of observational analysis; in summer, the space variability of model precipitation is significantly higher than that of precipitation in the reanalysis, especially in the mountains. Agreement between time changes in precipitation and temperature anomalies in RCM and in the reanalysis is better in the areas with a relatively large number of weather stations. The model can be used for estimation of future climate changes in the above-mentioned region.

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Available abstract

Validation results of the MGO regional climate model (RCM) with 50-km resolution for Siberia are discussed. For the specification of side boundary conditions, the reanalysis data are used. It is shown that the model satisfactorily simulates the sea-level pressure and temperature fields for all seasons and the year as a whole. The lowest computational errors in the simulation of regional surface temperature arise in the fall and winter; in spring and summer, the temperature errors are slightly higher. The model slightly underestimates the variability of daily mean temperature in winter relative to the reanalysis data. In summer, on the contrary, the RCM-simulated variability exceeds the variability in reanalysis. In winter, the space distribution of model precipitation is in qualitative agreement with the data of observational analysis; in summer, the space variability of model precipitation is significantly higher than that of precipitation in the reanalysis, especially in the mountains. Agreement between time changes in precipitation and temperature anomalies in RCM and in the reanalysis is better in the areas with a relatively large number of weather stations. The model can be used for estimation of future climate changes in the above-mentioned region.

Key concepts: Precipitation, Climatology, Environmental science, Climate model, Atmospheric sciences, Climate change, Meteorology, Geology

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