Features of temperature regime of the mountainous regions of Central Pribaikalie
N.N. VOROPAY1,2, O.V. VASILENKO1, A.YU. BIBAEVA1
1V.B. Sochava Institute of Geography, Siberian Branch, Russian Academy of Sciences, Irkutsk, Russia
2Institute of Monitoring of Climatic and Ecological Systems, Siberian Branch, Russian Academy of Sciences, Tomsk, Russia
Keywords: Pribaikalie, automatic monitoring, microclimate, mountain-depression landscape, air temperature
Abstract
The article presents an analysis of the spatiotemporal differentiation of temperature characteristics in the Primorsky Range landscapes, obtained during 15 years of microclimatic observations. The work is based on data from automatic monitoring of air temperature and relative humidity at a height of 2 m from the surface, soil surface temperature, and soil temperature at a depth of 40 cm. The analysis of the microclimatic monitoring data showed that the close relationship between soil and air temperature regimes depends on landscape conditions. Within the study area, the main climate-forming factors at the local level are vegetation, orography, and distance from Lake Baikal. Open areas, compared to forested ones, are characterized by greater amplitudes of air and soil temperatures in both diurnal and annual cycles. The warming and cooling effects of Lake Baikal on the temperature regime of the adjacent territory are manifested no higher than 1000 m along the slope of the Primorsky Range. This study statistically confirms the relationships between air temperature series at study sites in similar landscapes. Several groups of sites can be identified, each differing in temperature regime; the number and composition of these groups vary throughout the year, depending on limiting factors. The results of the study, which identified patterns in the distribution of air and soil temperatures, will be useful for restoring missing data in shorter series and can subsequently be applied to reconstruct temperature regimes in areas not covered by the microclimate observation network. Furthermore, the identified patterns will serve as a basis for creating parametrization of processes in mountainous regions and scaling the results of global climate model calculations at the local and regional levels.
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