Temperature Control of Spring CO₂ Fluxes at a Coniferous Forest and a Peat Bog in Central Siberia

Abstract

Climate change impacts the characteristics of the vegetation carbon-uptake process in the northern Eurasian terrestrial ecosystem. However, the currently available direct CO₂ flux measurement datasets, particularly for central Siberia, are insufficient for understanding the current condition in the northern Eurasian carbon cycle. Here, we report daily and seasonal interannual variations in CO₂ fluxes and associated abiotic factors measured using eddy covariance in a coniferous forest and a bog near Zotino, Krasnoyarsk Krai, Russia, for April to early June, 2013–2017. Despite the snow not being completely melted, both ecosystems became weak net CO₂ sinks if the air temperature was warm enough for photosynthesis. The forest became a net CO₂ sink 7–16 days earlier than the bog. After the surface soil temperature exceeded ~1 °C, the ecosystems became persistent net CO₂ sinks. Net ecosystem productivity was highest in 2015 for both ecosystems because of the anomalously high air temperature in May compared with other years. Our findings demonstrate that long-term monitoring of flux measurements at the site level, particularly during winter and its transition to spring, is essential for understanding the responses of the northern Eurasian ecosystem to spring warming.

1. Introduction

Boreal forests and peatlands are the major terrestrial biomes and large carbon (C) reservoirs in northern Eurasia, occupying 49% and 25% of Russia’s land area, respectively [1,2]. Both ecosystems are considered essential C sinks in the global C cycle [2,3,4,5,6,7,8]. The role of annual or seasonal C-sink capacity in boreal forests can vary depending on temperature and environmental factors (e.g., age, management) [5,9,10]. In case of peatlands, quantifying the seasonal C-uptake capacity seems more complex than in forests because of various ecohydrological factors (e.g., vegetation, soil microbes) [11,12,13,14]. Considering only CO₂ exchange processes, both boreal forests and peatlands in northern Eurasia are generally considered as net CO₂ sinks during the snow-free season [15,16,17,18].

During the seasonal transition from winter to spring, rapidly increasing radiation, temperature, and water availability after snowmelt affect plant photosynthesis and associated processes, thereby impacting the net ecosystem exchange of CO₂ (NEE). For instance, gross CO₂ uptake rates gradually increase from April to May owing to the increase in photochemical efficiency and more favorable meteorological conditions during that period [19,20]. The photosynthetic apparatus of boreal forests is adapted to quickly respond to positive temperatures and spring snowmelt, resulting in rapid recovery of physiological activity [20,21,22,23,24]. Similarly, a reactivation of photosynthesis for peat mosses occurs immediately after they are exposed from the snow cover [18,25,26,27]. At the beginning of snowmelt, the surface soil temperature exceeds 0 °C but remains close to 0 °C until snowmelt completion [21,25]. Once the snow has melted, surface soil temperature increases rapidly, and its diurnal cycle becomes pronounced [28]. Overall, the above mentioned studies demonstrate that the photosynthetic capacity of both boreal forests and peatlands are strongly influenced by the interannual variability in abiotic and environmental conditions during spring. Therefore, CO₂ flux data for the winter-to-spring transition period are crucial to understanding the C cycle in northern Eurasia.

Over the past five decades, substantial air temperature warming during spring has reduced the extent of snow cover in northern Eurasia [4,29,30,31,32,33]. A study by Pulliainen et al. [22] showed that during 1979–2014, earlier snowmelt induced by temperature warming increased spring ecosystem productivity in boreal forests. Earlier snowmelt impacts the earlier start of phenological development and may result in enhanced vegetation productivity [30]. However, other studies have shown that since the 2000s, the temperature-warming trend has slowed down in high-northern latitudes [31]. A slowed temperature increase may weaken C uptake in boreal forests [32]. Moreover, snow-cover reduction in northern Eurasia differs depending on the considered period or region [33]. These variations imply a need for continuous monitoring of the CO₂ flux at the ecosystem scale in northern Eurasia, particularly during winter and the transition to spring.

Eddy covariance (EC) flux measurements can provide direct information about biosphere-atmosphere interaction and photosynthesis-related processes at the ecosystem scale [34,35]. Boreal coniferous forests and peat bogs are major biomes in the Russian middle taiga [36]. However, EC flux data are still sparse for Siberia [37]. This lack of data motivated the first initiative of EC measurements in Siberia during 1999–2003 [15,38]. Various studies have quantified seasonal and inter-annual variabilities in CO₂ fluxes in Siberian boreal forests [15,28,36]. In addition, comparisons of CO₂ fluxes over the different peatland types have been reported [38,39,40]. In particular, Arneth et al. [21] highlighted differences in carbon- and energy-flux dynamics between the Scots pine forest and peat bog in central Siberia for April to May 1999–2000. However, no EC flux measurements were performed for the period of 2003 to mid-2012 in this area. To quantify the long-term biogeochemical cycle in northern Eurasia, EC flux measurements were subsequently re-initiated in a coniferous forest and a bog adjacent to the Zotino Tall Tower Observatory (ZOTTO) [40]. CO₂ flux measurements collected after mid-2012 were reported by Winderlich et al. [41] and Park et al. [42]; however, these studies analyzed the 2012–2013 growing season only. Despite the importance of the snow to the snow-free period in influencing the C cycle, to our best knowledge, there has been no subsequent investigation highlighting springtime after the study of Arneth et al. [21] in central Siberia.

Here, we report five years of springtime CO₂ flux data (April to early June 2013–2017) near Zotino in Russia. Our objectives are: (a) to characterize the difference in seasonal CO₂ fluxes and their responses to abiotic factors between the two ecosystems and among years; (b) to identify the factors explaining the CO₂ fluxes in a coniferous forest and a bog in spring; and (c) to examine the effects of temperature on the strength of net CO₂ sink and the timing of the start of CO₂ uptake. In particular, we focus on the seasonal variations of CO₂ fluxes for the three years (2014–2016). Lastly, we discuss the effect of spring warming on snowmelt and the ability of net CO₂ sink in both ecosystems.

2. Materials and Methods

2.1. Study Site

The Zotino forest flux tower (hereafter ZF; 60°48ʹ25″ N, 89°21ʹ27″ E, elevation 110 m a.s.l.) is situated 900 m to the north-northeast of the ZOTTO (Figure 1). The average canopy height of the forest is approximately 20 m, and the measurement height is 30.3 m (

Table 1. Site characteristics of the forest and bog flux towers at Zotino.

	ZF	ZB
Geographical location	60.48′25″ N 89.21′27″ E	60.49′03″ N 89.23′20″ E
Elevation altitude (m a.s.l)	110	66
Vegetation type	Coniferous forest	Ombrotrophic bog
Plant species	Pinus sylvestris	Sphagnum
Vegetation height (m)	20 for dominant trees 5 for understory	2.5 for Scots pines 0.5 for dwarf shrubs
Measurement height (m) 1	29.7	10.1
Tower height (m) 2	29.4	9.8
Zero plan displacement 3 height (m)	13.4	1.675 for pine trees 0.335 for dwarf shrubs
Roughness length (m) 4	3	0.375 for pine trees 0.075 for dwarf shrubs
Leaf area index (m−2 m−2)	1–3.5 5	not available
Soil type	Podzol	Histosol

¹ Measurement height was taken as the height measured to the centre of the sonic anemometers. ² Tower heights were taken as the distance from the ground fundament panels to the top of the tower plate. ³ Zero plan displacement height was computed as 0.67 × vegetation height. ⁴ Roughness length was computed as 0.15 × vegetation height. ⁵ Values obtained from the inventory and remote-sensing measurements [43,44].

). The dominant tree species is Scots pine (Pinus sylvestris), ranging in age from 80 to 180 years, whereas the patchy distributed regrowth Scots pine (height < 5 m) in understory is represented by younger pine trees of several age groups (40 years). The main ground vegetation within the footprint area is lichen (Cladina stellaris and Cladina rangiferina), with patches of dwarf shrub (Vaccinium vitis-idaea).

The Zotino bog flux tower (hereafter ZB, 60°49′03″ N, 89°23′20″ E, 66 m a.s.l.) is situated approximately 2 km to the northeast of the ZOTTO. The average canopy height at the site was approximately 2.5 m, and the measurement height was 9.9 m (Table 1 and Figure 1). The type of peat is classified as an ombrotrophic [21]. The landscape was covered by a pine-dwarf, shrub-sphagnum (in Siberia, called ryam), hollow-ridge complex. They had a height of 0.4–0.6 m and were covered by plant communities consisting of dwarf pine (Pinus sylvestris f. litwinowii), which dominates the trees and dwarf shrubs (Chamaedaphne calyculata). The peat’s calibrated age at the bottom of the bog ranged from 9397 ± 134 y BP at the edges to 13617 ± 190 y BP at the bog’s center. The peat depth showed a wide range from 1.60 m to 5.10 m, increasing toward the bog’s center.

2.2. Measurement System

Identical micrometeorological measurement systems were installed at the forest and bog sites (

Table 2. Instrument setup and sensor types of the flux towers at the Zotino sites.

	ZF	ZB
Sonic anemometer	USA-1, METEK GmbH, Elmshorn, Germany
CO2/H2O gas analyser	LI-7200, LiCor Biosciences, Lincoln NE, USA
Time lag	0.8 (CO2), 1.2 (H2O)	0.9 (CO2), 1.3 (H2O)
Flow rate (L/min)	13
Sampling frequency (Hz)	20
Long/short wave up/downwelling radiation	CNR1, Kipp & Zonen, Deft, The Netherlands
Up/downward photosynthetically active radiation	PQS1, Kipp & Zonen, Deft, The Netherlands
Air temperature and relative humidity	KPK 1/6-ME-H38, Mela, Bondorf, Germany
Barometric pressure	Pressure Transmitter, 61302 V, R.M. Young Company, Traverse City, USA
Soil temperature	RTD temperature probe, Pt100, JUMO
Soil moisture	ThetaProbe ML2x, Delta-T devices, Cambridge, England
Ground heat flux	Heat flux plates RIMCO HP3/CN3, McVan Instruments, Mulgrave, Victoria, Australia
Precipitation	Tipping bucket rain gauge, Adolf Thies GmbH & Co. KG, Göttingen Germany

). The EC system consisted of a three-dimensional ultrasonic anemometer (USA-1, Metek GmbH, Elmshorn, Germany) with integrated 55 W heating and closed-path infrared gas analyser (LI-7200, LiCor Biosciences, Lincoln, NE, USA) to measure CO₂ and H₂O fluxes at 20 Hz frequency. An external diaphragm vacuum pump transported air to the gas analyser with a flow rate of 13 L min⁻¹ at ambient atmospheric pressure. At the top of the towers, sensors measured the four radiation components and photosynthetically active radiation (PAR), air temperature (T_a), relative humidity, and atmospheric pressure. Soil or peat temperature (T_s) was measured by PT100 soil-temperature probes at six depths (0.02, 0.04, 0.08, 0.16, 0.32, and 0.64 m for forest and 0.04, 0.08, 0.16, 0.32, 0.64, and 1.28 m for bog, respectively). Soil moisture was measured by six sensors at both sites: two sensors at 0.08 m and one sensor at each depth of 0.16, 0.32, 0.64, and 1.28 m at the ZF and six sensors at a depth of 0.08 m at the ZB. Data collected from the EC system and meteorological measurements were stored on a data logger (CR3000, Campbell Scientific Inc., Logan, UT, USA). Details in setup of the EC system have been described by Park et al. [42]. Snow depths at both sites were measured manually at locations nearby the towers; however, the measurement intervals were irregular.

2.3. Post-Processing of Data and Quality Control

We applied the data processing scheme for the closed-path analysers mentioned in Mammarella et al. [45]. In the raw data post-processing step, spike detection, 2D coordinate rotation, and time lag adjustment as well as calculation of half-hourly turbulent fluxes were performed using the EddyUH software [45]. The thresholds of friction velocity (u*) for ZF and ZB were 0.2 m s⁻¹ and 0.1 m s⁻¹, respectively. Details of the post-processing steps and quality control have been described by Park et al. [42]. The high-quality data available after the quality check and u* filtering led to a data coverage of 42–61% for ZF and 19–51% for ZB for 2013–2017. To calculate the cumulative CO₂ flux (NEE_cum), we gap-filled the half-hourly data using the REddyProc [46] in R [47]. Storage flux calculation was applied only for ZF data.

2.4. Data Anlaysis and Statistical Model

In this study, ‘spring’ is defined from the day of year (DOY) of 91–165 (01.04–14.06), 2013–2017. Daily CO₂ fluxes were summed for each 24-h period with gap-filled data. Regarding the EC convention, a negative NEE or CO₂ flux refers to net CO₂ uptake by the ecosystem, whereas a positive NEE indicates CO₂ release from the ecosystems to the atmosphere.

We used surface albedo (Alb) as a proxy for the status of snowmelt, as suggested in previous studies [22]. We determined the final day of snowmelt on which the 3-day moving average of Alb fell below 0.15 (15%) for the first time. To detect the final day of snowmelt, Shibistova et al. [28] used the daily mean soil temperature at a depth of 0.05 m and a 15-day moving average as the final day of snowmelt; however, in the present study, we used diurnal patterns of surface or peat temperature at a depth of 0.04 (T_s04). To examine the effect of temperature accumulation on vegetation productivity, we defined the accumulated growing degree days for air temperature (CGDD_Ta) and surface soil or peat temperature (CGDD_Ts04). We computed the cumulative sums of daily mean T_a and T_s04 above 0 °C (base temperature 0 °C) from DOY 99–165.

To identify the abiotic variables that are important in explaining the variability in daily CO₂ flux, we used the multivariate adaptive regression splines (MARS) regression model [48], specifically, the earth package [49] in R [47]. Daily CO₂ fluxes with corresponding abiotic variables were used as training data for both sites from DOY of 99 to 165 in 2013–2017 for ZF and the same days in 2014–2016 for ZB, respectively. Several previous studies have shown that spring CO₂ uptake is associated with four environmental factors: air temperature (T_a), T_s04, beginning day of snowmelt, and final day of snowmelt [21,22,23,24]. Therefore, we included T_a, T_s04, and Alb to construct the MARS model. For ZF, we used nine abiotic variables as training data: PAR, Alb, T_a, midday vapour pressure deficit (VPD), T_s04, and soil or peat temperature at depths of 0.08 m (T_s08), 0.16 m (T_s16), 0.32 m (T_s32), and 0.64 m (T_s64). For ZB, a total of seven abiotic variables were used as training data: the same as those for ZF but excluding T_s08 and T_s16 due to the long-term data gaps (>3 months) in the data for these two variables.

The MARS is a non-parametric regression method that can deal with both linear and nonlinear relationships and interactions between variables in the data using hinge functions [48]. Variable selection algorithms search the variables using both forward and backward stepwise selections. Variable importance is determined by a selection algorithm and is based on the number of model subsets (nsubsets), generalized-cross validation (GCV) score, and residual sum of squares (RSS). The GCV and RSS were scaled from 0 to 100. Higher GCV scores indicate variables with more explanatory power. The largest summed decrease of the RSS is scaled to 100, meaning that a large net decrease in the RSS is more important than a low RSS. Higher nsubsets means that variables are included in more subsets because they are important. Further statistical theory and application are described in detail at http://www.milbo.org/doc/earth-notes.pdf (accessed on 26 July 2021).

To investigate the impacts of cold spring weathers on the net ecosystem productivity (NEP), we used a rectangular hyperbolic light-response functional relationship [50] between PAR and NEP. We examined the differences in curve shapes and fit parameters for the year with the most frequent cold spring weather days (2014) and the year with the least frequent cold spring weather days (2015) with the following Equation (1):

$$\mathrm{NEP}=\frac{{\mathsf{\alpha }\ast \mathrm{A}}_{\max }\ast \mathrm{PAR}}{{\mathrm{A}}_{\max }+\mathsf{\alpha }\ast \mathrm{PAR}}+{\mathrm{R}}_{\mathrm{d}}$$

(1)

where PAR (µmol photon m⁻² s⁻¹) is the incident photosynthetically active radiation, A_max (µmol CO₂ m⁻² s⁻¹) is the light-saturation point of CO₂ uptake, and α is the initial slope of the NEP-PAR (net CO₂ uptake at light saturation), and R_d is the ecosystem respiration during the day (µmol CO₂ m⁻² s⁻¹). We used daytime data for which potential global radiation (R_pot) exceeded 20 W m⁻². To make it easier to visualize, we used NEP, which is opposite in sign to NEE. Three model parameters were estimated for the selected two periods estimated using the Levenberg–Marquardt method, implemented in the minpack.lm package [51] in R [47]. The Levenberg–Marquardt method is a widely used algorithm for analyzing non-linear light-response curves [52,53]. NEP_sat was calculated with the obtained three parameters and by fixing PAR at 1500 µmol photon m⁻² s⁻¹. The definition of NEP_sat was the similar concept addressed by Migliavacca et al. [52] and Musavi et al. [54]. NEP_sat helps to understand NEP at light saturation when PAR as 1500 µmol photon m⁻² s⁻¹ corresponds to light saturation.

3. Results

3.1. Abiotic Controls of Spring CO₂ Fluxes

Temporal evolutions of daily CO₂ fluxes and associated abiotic drivers in 2016 showed that CO₂ flux variations generally corresponded to the rapid increasing T_a and T_s04 after snowmelt had completed (Figure 2). We confirmed that these highly fluctuating features of fluxes and spring meteorological conditions were similar in other years (Supplementary Figure S1). CO₂ fluxes at ZF increased faster than at ZB after snowmelt. Similar features were observed in other years. Alb at ZB was remarkedly different from that at ZF because of the higher radiation absorption by forest. Sporadic warm spells temporarily led to a rapid net uptake of CO₂ at ZF. In contrast, net CO₂ uptake rates at ZB did not increase as rapidly as those at ZF because the Sphagnum peat was still under snow cover and therefore less affected by changes in T_a. During the snowmelt period (DOY 127–145), the forest transitioned from a net CO₂ source to a net CO₂ sink. However, CO₂ fluxes were still highly variable during this time, fluctuating between net CO₂ source and net CO₂ sink.

In a typical springtime, T_a was highly variable, ranging from −5 °C in April to 25 °C in May (Figure 2). The amplitude of T_a was ~30 °C, which is typical of boreal spring weather. At the end of the snowmelt stage, the increase in T_a was considerable (by ~15 °C). This may have led to the disappearance of snow cover on the ground surface, resulting in the observed increase in T_s04 during this period to above 5 °C. As shown in Figure 2, we also confirmed that T_s04 at ZB increased above 15 °C, which was ~5 °C warmer than at ZF for every spring in the study period (Supplementary Figure S1).

At both ecosystems, the start of net CO₂ uptake occurred during periods with cold or frozen soil (Figure 2). However, the magnitude of daily CO₂ uptake was lower than −1 g C m⁻² d⁻¹ until thawing of the surface soil (T_s04 < 0 °C) or snowmelt was complete. CO₂ uptake rates remained almost neutral and constituted a net CO₂ sink only when T_s04 recovered above 0 °C (DOY 120). As shown in Figure 2, data for both sites reveal that the transition from net CO₂ source to net CO₂ sink can be disrupted temporarily by cold spells (DOY 110–135). This feature was more distinct in forest ecosystem.

In 2016, the transition from net CO₂ source to net CO₂ sink occurred earlier in the forest than in the bog (Figure 2). Although the soil was still frozen, if the temperature was favourable (~5 °C) for photosynthesis, the forest ecosystem transformed into a weak net CO₂ sink. For instance, the start of net CO₂ uptake at ZF was on DOY 114 in 2016. Mean T_a from DOY 113 to 119 was 7.7 °C, which favoured the triggering of the start of net CO₂ uptake. Compared with ZF, ZB was a not yet an active CO₂ sink, probably because the ground layer peat mosses were partially covered by snow.

We used the MARS model to examine the relative importance of factors controlling CO₂ fluxes over the study period. The fitting of this model showed fairly good predictive performance (84–89%;

Table 3. The order of important variables with statistics (nsubsets, GCV, and RSS; see details in Section 2.4) of CO₂ flux estimated by using the MARS model in spring period (DOY 99–DOY 155) from 2013 to 2017 at the ZF and ZB sites. For ZF, a total of 285 daily means of variables (T_a, T_s04, T_s08, T_s16, T_s32, T_s64, Alb, PAR, and VPD) were used as initial data. The percentage of variance in daily CO₂ fluxes explained by the model (R²) was 84%. For ZB, a total of 216 daily means of variables were used for the period DOY 111–155 in 2013 and DOY 99–115 in 2014–2016. Peat temperatures at depths of 0.08 and 0.15 m were excluded for the initial data of the MARS model due to the long-term data gaps (>3 months) for 2014–2015. The percentage of variance in daily CO₂ fluxes explained by the model (R²) was 89%.

Site	ZF			Site	ZB
Variable	nsubsets	GCV	RSS	Variable	nsubsets	GCV	RSS
Ts4	11	100.0	100.0	Ts4	12	100.0	100.0
PAR	19	59.0	60.5	Alb	11	46.1	48.7
Alb	9	44.8	46.9	Ts64	9	29.5	33.0
Ta	7	30.9	33.5	PAR	9	29.5	33.0
Ts16	6	25.8	28.4	Ts32	8	26.3	29.7
Ts64	5	17.2	21.0

). For ZF, the MARS model identified six variables (T_s04, PAR, Alb, T_a, T_s16, and T_s64) as the important influences on CO₂ fluxes out of the nine analysed. The percentage of variance in daily CO₂ fluxes explained by the model (R²) was 84%. For ZB, the MARS model identified five variables (T_s04, Alb, T_s64, PAR, and T_s32) as the important influences on CO₂ fluxes out of the seven variables analysed, with R² of 89% at ZB. Although we did not use ecological model, results show that the MARS model captured the controlling factors of CO₂ fluxes fairly well by considering non-linear relationships between modelled CO₂ fluxes and abiotic drivers.

3.2. Impacts of Cold Weather on Photosynthesis-Related Parameters

During the springtime, T_a was highly variable, with several apparent cold and warm phases (Figure 2). To examine the overall impacts of cold weather on CO₂ fluxes, we compared the photosynthesis-related parameters between the year having the most frequent cold spring weather days (2014) and the year having the least frequent cold spring weather days (2015) using the PAR-NEP relationship (Figure 3 and

Table 4. Model parameters of the rectangular light-response function for the year with the most frequent cold weather days (2014) and for the year with the least frequent cold weather days (2015) for both ZF and ZB. Numbers in parentheses denote the standard errors of the parameters. n is the number of half-hourly data. As NEP_sat is a function of A_max, $\alpha$, R_d, and PAR at saturation PAR of 1500 $\mu$mol photon m⁻² s⁻¹, the standard deviations of NEP_sat were taken as the averaged maximum and minimum standard errors of each parameter.

Site	Year	Amax $[\mathit{\mu }$mol m−2 s−1]	NEPsat $[\mathit{\mu }$mol m−2 s−1]	$\mathit{\alpha }$	Rd $[\mathit{\mu }$mol m−2 s−1]	n	Residual Standard Error
ZF	2014	9.22 (0.94)	4.84 (0.70)	0.010 (0.002)	−0.90 (0.21)	761	1.83
	2015	11.95 (0.63)	6.39 (0.76)	0.018 (0.003)	−1.90 (0.36)	659	2.12
ZB	2014	3.25 (0.14)	1.96 (0.18)	0.009 (0.002)	−0.68 (0.11)	815	0.77
	2015	6.12 (0.28)	3.41 (0.40)	0.014 (0.002)	−1.30 (0.26)	569	1.24

). Here, we defined cold spring weather as the day with a minimum daily T_a < 0 °C. For both sites, 2014 had the most frequent cold weather days (13 days for ZF and 15 days for ZB), and 2015 had the least frequent cold weather days (0 days for ZF and 2 days for ZB) compared with the entire study period (6 days for ZF and 10 days for ZB, 5-year mean values).

It is a typical feature that half-hourly CO₂ fluxes during spring daytime are highly scattered due to large temperature changes or cloud cover (Figure 3). Nevertheless, the light-response curves of both ecosystems showed typical shapes of rectangular hyperbola curve. The curve showed a rapid and linear increase in NEP at lower PAR levels, with a slow increase to reach maximum NEP at high PAR levels (Figure 3). Bogs fixed CO₂ at much lower PAR levels compared with forests. Under the same environmental conditions, for instance, needle-leaves at the top of the forest canopy are likely to warm sufficiently to photosynthesise compared with the understory vegetation, such as that found at the bog site. Due to the wider range of NEP for forest than those for bog, residual standard errors for ZF were larger than those for ZB (Table 4). Mean T_a values after snowmelt in 2014 and 2015 were ~5 °C and ~13.5 °C, respectively. As a result, both ecosystems seem to have higher A_max and NEP_sat in 2015 than in 2014. A_max from the rectangular hyperbola photosynthetic light-response function had a higher light saturation level than the one from the non-rectangular hyperbola curve because the latter had a stronger flattening at the light-compensation point. Therefore, NEP_sat better represents values of the maximum CO₂ uptake rate of ecosystems. Compared with the bog, the forest had approximately three times higher A_max and NEP_sat.

Quantum yields (α) at both ecosystems were similar in 2014 and increased by more than 50% in 2015 (Table 4). In this period, the light-response curves for ZB reached at light-saturation point earlier than for ZF, indicating that the bog reached its maximum ability to fix CO₂ earlier than for the forest due to lower light-use efficiency (Figure 3). Interestingly, the relative changes in NEP_sat of ZF and ZB in 2015 were approximately +32% and 74%, respectively. Presumably, the contribution of soil microbes in the ecosystem respiration term under the warm spring in 2015 may be greater than in 2014.

3.3. Interannual Variability of Spring Cumulative NEE_cum

Overall variations in spring NEE_cum were more distinct at ZF compared with ZB, as shown by daily CO₂ fluxes in Figure 2. Spring NEE_cum between 2014 to 2016 ranged from −27.6 to −37.2 g C m⁻² at ZF and from −7.7 to −14.9 g C m⁻² at ZB (Figure 4 and

Table 5. Cumulative net ecosystem exchange of CO₂ (NEE_cum; g C m⁻²), the day of year (DOY) of transition from NEE_cum source to NEE_cum sink (NEE_cum,tran; DOY), cumulative growing degree days of air temperature (CGDD_Ta; °C) corresponding to NEE_cum,tran, cumulative growing degree days of surface soil temperature (CGDD_T04; °C) corresponding to NEE_cum,tran, and mean slope ($\frac{\partial NE{E}_{cum}}{\partial DOY}$; g C m⁻² d⁻²) after reading peak of NEE_cum for ZF and ZB for the period DOY 99–155 in 2014–2016.

Year	NEEcum (g C m−2)		NEEcum,tran (DOY)		CGDDTa (°C)		CGDDT04 (°C)		$\frac{\partial \mathit{N}\mathit{E}{\mathit{E}}_{\mathit{c}\mathit{u}\mathit{m}}}{\partial \mathit{D}\mathit{O}\mathit{Y}}$ g C m−2 d−2
	ZF	ZB	ZF	ZB	ZF	ZB	ZF	ZB	ZF	ZB
2014	−27.6	−7.7	128	138	104.5	164.6	0.3	41.7	−1.05	−0.41
2015	−37.2	−14.9	134	141	137.6	211.4	0.3	75.25	−1.62	−0.92
2016	−28.3	−6.8	126	142	83.0	141.9	0.85	55.42	−0.86	−0.31

). Compared with the features at ZB, NEE_cum at ZF showed larger amplitudes and higher uptake rates, which indicate that forest is a stronger spring CO₂ sink than the bog. When thick snow depth rapidly decreased with increasing temperature, between DOY 120 and 130, 2014–2016, both ecosystems released CO₂ in the atmosphere.

To compare the rate of change of interannual variability in NEE_cum, the mean slopes of the curve ($\frac{\partial NE{E}_{cum}}{\partial DOY}$) following peak NEE_cum were examined (Table 5). Both ecosystems showed steeper declining slopes after the peak of NEE_cum in the three years studied, particularly the steepest in the warmest year (2015); $\frac{\partial NE{E}_{cum}}{\partial DOY}$ was −1.62 g C m⁻² d⁻² for ZF and −0.92 g C m⁻² d⁻² for ZB. For both ecosystems, 2015 was the year with the largest NEE_cum for both the 3-year period (2014–2016) and the entire study period. In 2015, T_a in May was ~10 °C, which was 4.2 °C higher than the 5-year mean (5.8 °C) (Figure 5). For both ecosystems, NEE_cum was strong and negatively correlated with mean T_a in May (R² = 0.69, P = 0.081 for ZF and R² = 0.79, P = 0.110 for ZB). Unusually high mean T_a in May corresponded to the highest NEE_cum, meaning that the ecosystems were the strongest CO₂ sinks in the warmest year.

Rapid increasing trends in accumulated growing degree days of air and soil temperatures were followed by the steep decreasing slope in NEE_cum,tran. These characteristics showed similar variations and trends over the three years. For instance, in 2014, transition day (DOY) from NEE_cum source to NEE_cum sink (NEE_cum,tran) at ZF was DOY 128, corresponding to CGDD_Ta at 104.5 °C. At this point, T_s04 was positive but stayed close to 0 °C. This implies that T_a plays an important role in NEE_cum transition time, as we observed in Figure 2. Before surface T_s04 stays nearly 0 °C, the forest was net CO₂ source. Rapid increasing phase in NEE_cum followed a steep and a linear increase of CGDD_T04 > 4 °C. CGDD_T04 increased more than 7 °C in two days (DOY 135–137). After DOY 136, the slope of NEE_cum/DOY was −1.05 g C m⁻² d⁻². From this point, ZF entered the growing season, and photosynthesis had fully recovered from the cold season. Both photosynthesis and ecosystem respiration continuously increase over the growing season. At ZB, NEE_cum,tran was 10 days later (DOY 138) than the NEE_cum,tran at ZF. At this time, peats were already warmed up, showing positive temperatures (CGDD_Ta = 164.6 °C). Similar to forest, daily mean T_s04 also increased approximately 7 °C in two days (DOY 128–130) immediately after CGDD_T04 exceeded 1 °C. Presumably, the ground surface at ZF is still partly covered by snow, and shading by trees may result in warmer surface conditions later than at ZB. If mosses were exposed to the open space without shading, then they could receive more direct solar radiation on the surface, which may result in earlier soil warm-up conditions. Characteristics of NEE_cum,tran and heat accumulation explained by CGDD_Ta and CGDD_T04 imply that the bog seems to have a higher level of temperature accumulation than forest, because soil microbial activity requires a suitable temperature level, corresponding to daily T_s04 > ~7 °C and CGDD_T04 of ~41 °C.

In 2015, NEE_cum,tran at ZF occurred in DOY 134, corresponding to CGDD_Ta at 137.6 °C. Although the spring mean T_a in 2015 was the highest, NEE_cum,tran in 2015 occurred the latest compared with other years. Perhaps, warm temperature conditions increase ecosystem respiration, leading to the forest remaining a net CO₂ source for longer. To find a reasonable answer, we will intend to examine components of the flux partitioning for a future study. Anomalously high T_a also led to the highest T_s04. CGDD_Ta during DOY 100–150 matched with the transition point of CGDD_T04 > ~4 °C. Compared with ZF, CGDD_T04 corresponding to NEE_cum,tran at ZB varied 40–75 °C. CGDD_T04 at ZB showed more rapid enhancement in other years, possibly associated with the soil microbial responses to warm temperature conditions.

4. Discussion

4.1. Identifying the Major Drivers of CO₂ Fluxes

At our sites, spring CO₂ fluxes were mainly controlled by air temperature (Table 2). A rapid rise in CO₂ uptake occurred after surface snow melted, and T_s04 warmed above 1 °C (Figure 2). We confirmed that abiotic controlling factors of CO₂ fluxes identified from the MARS model agreed with previous findings. In boreal coniferous forests, spring photosynthesis recovery is related to air temperature, surface soil temperatures, first day of snowmelt, and final day of snowmelt [20,22,23,24,25,26,29]. The coniferous forest was already a net CO₂ sink before the end of snowmelt and while part of the soil surface was still frozen (Figure 2). In a wider context, this result agrees with the finding of Parazoo et al. [55], who showed that spring thaw was the crucial trigger for the start of spring photosynthesis and net CO₂ uptake in boreal forests in Arctic ecosystem. In northern peatland, peat temperature and snowmelt completion have been shown to affect the rapid increase in vegetation productivity [11,18,21,25]. In addition, PAR and temperature have been identified as controlling factors on the CO₂ flux of peat during the growing season [26,56,57].

In our study, T_s04 was identified as the primary driver of CO₂ fluxes for both ecosystems (Table 2). From a biophysical point of view, light (i.e., PAR) is known to be the primary driver of the beginning of net CO₂ sink or photosynthesis. In a previous study, a data-driven approach similar to the one used here identified PAR as the most important driver of CO₂ fluxes at the ZF site [42]. During the growing season, including the snow-melting period, PAR strongly controls the growth of Sphagnum [57]. In the boreal winter-to-spring transition period, snow cover decreases as temperature rises, as shown in reverse patterns of surface reflectance and air temperature [21,22,23]. This reflects that, in reality, T_s04 and Alb are indirect drivers of CO₂ fluxes.

We chose the MARS model to identify the relative importance of controlling factors of CO₂ fluxes because of its simplicity. In our study site, we often met a long-term data gap, particularly in the winter and early season, meaning that estimates of a reliable annual carbon budget with uncertainty are challenging. To overcome this limitation, further efforts for utilizing major variables identified from the MARS model and/or incorporating with other machine-learning methods suitable for a relatively smaller dataset (e.g., support vector machine) are desirable. With such efforts, we can develop an advanced version for the site-specific gap-filling algorithm.

4.2. Potential Drivers of Spring CO₂ Fluxes

We observed that T_a, T_s04, Alb, and PAR affect seasonal variations of CO₂ fluxes at a boreal forest and a bog. However, there is still room for exploring unknown or missing drivers of spring CO₂ fluxes. For instance, a recent study by Koebsch et al. [58] suggested that biological drivers seem more important for determining the variability in maximum gross primary productivity compared with abiotic drivers in boreal peatlands. Peichl et al. [59] also emphasized the important role of phenology on seasonal variation of photosynthesis in European peatlands. In addition, it is likely that the rapid increase in net CO₂ uptake after the snowmelt completion at ZB (Figure 2) may be associated with increases in peat temperatures in the rooting zone (0.1–0.2 m), leaf nitrogen, and chlorophyll a concentration [25]. This suggests that chlorophyll a could be a potential biochemical driver of spring CO₂ fluxes in peatlands.

In peatland, hydrological drivers (e.g., precipitation, soil moisture, and water-table depth) and their regime are also crucial for understanding photosynthesis-related processes. Previous studies [11,60,61] suggest that the water-table depth is an important control on the growing season NEE and the annual CO₂ balance, although this control appears to be site dependent. In contrast to previous studies, Strachan et al. [62] did not find significant relationships between peatland productivity or cumulative CO₂ exchange and early season temperature, the timing of snowmelt, or growing season length at an ombrotrophic bog located in Canada. Our data was limited in analyzing the roles of hydrological factors on seasonality of CO₂ uptake in peatland; thus further efforts to examine the roles of biotic and abiotic drivers in regulating peatland C cycle at ZB are desirable. In addition, measurement of other important components of C flux, such as methane, are necessary to better understand the annual C balance in peatland [1,17,63,64].

4.3. Role of Air and Soil Temperatures on Vegetation CO₂ Uptake Capacity in Northern Eurasia

Consistent with characteristics of abiotic drivers of CO₂ fluxes shown in Figure 2, NEE_cum,tran at ZF requires suitable air temperature accumulation (CGDD_Ta > ~100 °C) more than soil temperature (Figure 4, Table 5). In previous studies, 5-day running mean of daily mean air temperature and cumulative temperature were good predictors of the commencement of spring photosynthesis in boreal coniferous forests [22,23].

We confirmed that both ecosystems have a zero-curtain period at T_s04 that remained close to 0 °C around until mid-April (Figure 2). This feature is a typical phenomenon in high-northern latitude or alpine ecosystems during the winter-to-spring transition period [21,23,25,60,65,66,67]. Although the surface snow may not have fully disappeared, warm spells may affect the timing of the start of net CO₂ uptake in boreal forests (Figure 2). Such phenomena may be a piece of evidence that Scots pines growing in high latitudes have adapted to water-limited environments in severe and long winters. Generally, low soil-temperature conditions during spring constrain water uptake and root activity in boreal conifers [24,66,68,69]. However, there is observational evidence that pine trees in winter use stem-stored water regardless of snowmelt termination or available soil water to maintain their metabolism [22,70]. It is also possible that different tree species may have different strategies for the process of spring photosynthesis recovery [71].

Cold weather appears to temporarily reduce CO₂ uptake rates over the study period. However, plants recovered CO₂ uptake rates as soon as T_a increased above 0 °C (Figure 2). We found a similar pattern in other years (Supplementary Figure S1), which tend to be more distinct in the forest than in the bog. In addition, our data reveal that colder spring weather conditions resulted in a reduction of the maximum photosynthetic capacity (Figure 3). In previous studies, intermittent cold temperatures, for instance, frost events, delayed the photosynthesis recovery process of conifers, and the effect is more pronounced if frost is severe [20,72,73]. In peatlands, chilling can reduce chlorophyll a concentration [25,74]. It is unclear to find these features from daily CO₂ fluxes in our study; however, Figure 3 and Table 4 seem to show clues; cold weather temporarily suppresses photosynthetic capacity during spring. Overall, our data showed reasonable ranges and PAR-NEP fit parameters compared with the previous study [64].

A warmer climate may result in an earlier start of spring photosynthesis and increased vegetative productivity as well as annual CO₂ uptake in boreal coniferous forests [29,30,73]. However, the effect of warm temperatures on the net CO₂ uptake capacity of peatlands is likely to be more complex than that of forests because of soil hydrological conditions. Precipitation, water table depth, vegetation type, and phenology are recognised as other important controls on CO₂ fluxes, and their effects on net CO₂ uptake seem to be highly site dependent [11,12,14,37,59].

In this study, both ecosystems showed the highest vegetation productivity in 2015 (Figure 5). Despite the small amount of data and highly deviated data in 2015, both ecosystems showed negative relationships between NEE_cum and mean T_a in May. Based on the spatial distribution of 2m temperature anomalies over the study period (2013–2017), it is highly likely that vegetations in Zotino absorb the highest CO₂ uptake among the years because of anomalously warm temperature in May (Supplementary Figure S2). Anomalous warm temperatures were spread over the western Siberia, including the Zotino site. A study by Alekseychik et al. [37] seems to support that our finding is possible. They reported that the spring in 2015 was warmer; monthly mean air temperature in May was 4.1 °C higher than the long-term average in a similar type of bog in Finland. Besides, Liu et al. [14] and Pulliainen et al. [29] found that net CO₂ uptake in boreal ecosystem during the warm spring was greater than the normal year, implying that vegetation is sensitive to the changes in air temperature, and it is a crucial driver to understand spring C uptake. To obtain the robust relationship between NEE_cum and mean T_a in May, we plan to analyze the longer term of vegetation indices using remote sensing data together with flux data. In addition, Alekseychik et al. [37] also found that despite the very warm spring in 2015, cloudy and rainy weather conditions in summer in that year resulted in a lower CO₂ uptake for the growing season compared with other years. This implies that the high vegetation productivity induced by the anomalously warm temperatures in May 2015 is likely to have been compensated by the cool and wet ensuing summer. Our forthcoming research will examine the annual net CO₂ balance through comparison of the results of the present study with those from other sites in Siberia and for different weather conditions.

5. Conclusions

CO₂ flux observations at the Zotino sites showed that distinct differences exist in flux magnitude, the timing of start of net CO₂ uptake, and the rate of CO₂ uptake between the boreal forest and bog. In spring, the boreal forest generally changed from net CO₂ source to net CO₂ sink approximately 1–2 weeks earlier than the bog. We found similar aspects for the transition from NEE_cum source to NEE_cum sink. Rapid net CO₂ uptakes for both ecosystems covaried with T_a, corresponding to T_s04 values > 5 °C. To change into complete NEEcum sink, ZF seems to require CGDDTa of ~80 to 137 °C and ZB requires CGDDTa of 141 to 211 °C. Compared with ZF, seasonal variations in NEE-cum at ZB were smaller. This implies that forests appear more sensitive to the changes in air temperature than bogs under the same spring weather conditions. We confirmed that cold spring weather reduced the maximum photosynthetic capacity in both ecosystems. During the study period, abnormally warm air temperatures in spring 2015 resulted in the highest vegetation productivity. Overall, results suggest that the CO₂ uptake capacities of boreal forest and bog are sensitive to rising air temperature in springtime. This strong relationship between air temperature and NEE seems to support that a linear relationship between NEE estimated from the atmospheric inversion method and air temperature proposed by Rödenbeck et al. [75].

Our analysis is limited in analyzing NEE; thus, future work will examine the effects of anomalously warm spring weather on both photosynthesis and respiration with respect to the annual C balance. Besides, we will investigate the linkage between hydrological conditions (e.g., snow depth and rainfall) in the previous winter and seasonal/annual CO₂ balances in the following year. To overcome the limited number of EC flux measurement, remote-sensing-based phenology and photosynthesis products will be utilized. We anticipate direct CO₂ flux measurements at Zotino will be valuable for evaluating biogeochemical models. Further, it will provide insights into the prediction for the future terrestrial C cycle in northern Eurasia, particularly in remote and relatively undisturbed natural ecosystems.