## 2 Material and Methods
### Data Processing for Net Ecosystem Exchange of CO\({}_{2}\)  
For recent decades the eddy covariance method has been used to monitor greenhouse gas (including CO\({}_{2}\)) fluxes as the most common in meteorological and environmental studies [34]. The eddy covariance flux towers are currently distributed across multiple biomes of the world, with a special interest in the Arctic-boreal zone. The fluxes are determined using the standardized equipment and data processing technique, which provides not only the high consistency of measured fluxes but also the representative global pattern of emission and uptake of greenhouse gases [35]. For details on instrumentation and data processing, see Supplementary File S2. In Russia, the eddy-tower network for monitoring greenhouse gas fluxes is still sparse, and the towers are extremely inconsistently distributed over the territory. Specifically in Siberia, the regional and global carbon balance and their impact on the climate system remains most unexplored [22]. Net ecosystem CO\({}_{2}\) exchange (NEE) between the forest and the atmosphere is computed as:  
\[NEE=\overline{w\epsilon\epsilon}t+\int\limits_{o}^{h}\frac{\partial\overline{c} }{\partial t}dz \tag{1}\]  
where \(h\) is the eddy measurement level, \(w^{\prime}\) is vertical wind velocity fluctuations, and \(c^{\prime}\) is CO\({}_{2}\) density fluctuations. The first term in Equation (1) is eddy covariance flux, and the second is storage change in CO\({}_{2}\) below the eddy flux measurement level. The method used by [PERSON] et al. [36], calculating the change in CO\({}_{2}\) concentration at the top of the tower, was employed for estimating the second term in Equation (1). Figure 2: Study sites: the _Larix gmelini_ with _Pleurozium schrebei_ and _Aulacominium turgidum_ and dwarf-shrubs forest floor (**left**) and the _Pinus sylvestris_ with _Cladonia_, _Cladina_, and _Cetraria_ forest floor (**right**). Gaps in half-hourly NEE shorter than three half-hour periods were filled using linear interpolation. Longer gaps were filled with simple experimental regressions only for the purpose of integrating daily and seasonal NEE. A quality check for our half-hourly eddy measurements based on energy balance closure showed that the imbalance varied between 19 and 24% in the pine site in 1998-2000 [33] and up to 34% in the larch site or 17% for the daily integrated [13]. On average, in eddy covariance studies, energy imbalance varies from several percent up to 40%, on average about 20% [37; 38; 39; 40]. Among the main reasons for this imbalance are spatial landscape heterogeneity, incomplete detection of all turbulent and advective fluxes, and instrument malfunctions [41; 40; 42]. Half-hourly CO\({}_{2}\), water vapor and energy fluxes for the pine site processed in 1998-2000 (Carbon-Siberia, see Tellus 54 B 2002) and those for the Gmelin larch site processed in 2004-2008 (AsiaFlux, [[http://www.asiaflux.net](http://www.asiaflux.net)]([http://www.asiaflux.net](http://www.asiaflux.net)) (accessed on 6 February 2023)) were used in this study to calculate net ecosystem exchange (NEE), ecosystem respiration (R\({}_{\text{eco}}\)) and gross primary production (GPP), and energy balance components. R\({}_{\text{eco}}\) was calculated from air temperature using a Q\({}_{10}\)-type equation [43]. Half-hourly night-time NEE under PAR \(<\) 10 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) and friction velocity, u* \(>\) 0.2 m s\({}^{-1}\) were bin-averaged for the bin-width of the temperature of the air Ta = 1 \({}^{\circ}\)C. The relationship was of the exponential type:  
\[\text{NEE}_{\text{night}}=\text{k}_{0}\exp{(\text{k}_{1}\text{T}_{\text{a}})} \tag{2}\]  
where k\({}_{0}\) and k\({}_{1}\) are fitting parameters and T\({}_{\text{a}}\) is air temperature. Because the range of surface soil temperature in the larch forest did not even reach 10 \({}^{\circ}\)C during the vegetation season, we used the air temperature in Equation (2) to calculate R\({}_{10}\) and Q\({}_{10}\).