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Carbon dioxide (CO _2 ) fluxes by different methods vary largely at global, regional and local scales. The net CO _2 fluxes by three bottom-up methods (tower observation (TWR), biogeochemical models (GTM), and a data-driven model (SVR)), and an ensemble of atmospheric inversions (top-down method, INV) are compared in Yakutsk, Siberia for 2004–2013. The region is characterized by highly homogeneous larch forest on a flat terrain. The ecosystem around Yakutsk shows a net sink of CO _2 by all the methods (means during 2004–2007 were 10.9 g C m ^−2 month ^−1 by TWR, 4.28 g C m ^−2 month ^−1 by GTM, 5.62 g C m ^−2 month ^−1 and 0.863 g C m ^−2 month ^−1 by SVR at two different scales, and 4.89 g C m ^−2 month ^−1 by INV). Absorption in summer (June–August) was smaller by three bottom-up methods (ranged from 88.1 to 191.8 g C m ^−2 month ^−1 ) than the top-down method (223.6 g C m ^−2 month ^−1 ). Thus the peak-to-trough amplitude of the seasonal cycle is greater for the inverse models than bottom-up methods. The monthly-mean seasonal cycles agree among the four methods within the range of inter-model variations. The interannual variability estimated by an ensemble of inverse models and a site-scale data-driven model (the max-min range was 35.8 g C m ^−2 month ^−1 and 34.2 g C m ^−2 month ^−1 ) is more similar to that of the tower observation (42.4 g C m ^−2 month ^−1 ) than those by the biogeochemical models and the large-scale data-driven model (9.5 g C m ^−2 month ^−1 and 1.45 g C m ^−2 month ^−1 ). The inverse models and tower observations captured a reduction in CO _2 uptake after 2008 due to unusual waterlogging.

This study assessed trends in the variability of soil temperature (T _SOIL ) using spatially averaged observation records from Russian meteorological land stations. The contributions of surface air temperature (SAT) and snow depth (SND) to T _SOIL variation were quantitatively evaluated. Composite time series of these data revealed positive trends during the period of 1921–2011, with accelerated increases since the 1970s. The T _SOIL warming rate over the entire period was faster than the SAT warming rate in both permafrost and non-permafrost regions, suggesting that SND contributes to T _SOIL warming. Statistical analysis revealed that the highest correlation between SND and T _SOIL was in eastern Siberia, which is underlain by permafrost. SND in this region accounted for 50% or more of the observed variation in T _SOIL . T _SOIL in the non-permafrost region of western Siberia was significantly correlated with changes in SAT. Thus, the main factors associated with T _SOIL variation differed between permafrost and non-permafrost regions. This finding underscores the importance of including SND data when assessing historical and future variations and trends of permafrost in the Northern Hemisphere.