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Author(s): Sari Juutinen, Mika Aurela, Juha-Pekka Tuovinen, Viktor Ivakhov, Maiju Linkosalmi, 
Aleksi Räsänen, Tarmo Virtanen, Juha Mikola, Johanna Nyman, Emmi Vähä, Marina 
Loskutova, Alexander Makshtas & Tuomas Laurila 
Title: Variation in CO2 and CH4 fluxes among land cover types in heterogeneous Arctic 
tundra in northeastern Siberia 
Year: 2022 
Version: Published version 
Copyright:  The Author(s) 2022 
Rights: CC BY 4.0 
Rights url: http://creativecommons.org/licenses/by/4.0/ 
Please cite the original version: 
Juutinen, S., Aurela, M., Tuovinen, J.-P., Ivakhov, V., Linkosalmi, M., Räsänen, A., Virtanen, T., 
Mikola, J., Nyman, J., Vähä, E., Loskutova, M., Makshtas, A., and Laurila, T.: Variation in CO2 and 
CH4 fluxes among land cover types in heterogeneous Arctic tundra in northeastern Siberia, 
Biogeosciences, 19, 3151–3167, https://doi.org/10.5194/bg-19-3151-2022, 2022. 
Biogeosciences, 19, 3151–3167, 2022
https://doi.org/10.5194/bg-19-3151-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
R
esearch
article
Variation in CO2 and CH4 fluxes among land cover types in
heterogeneous Arctic tundra in northeastern Siberia
Sari Juutinen1,2, Mika Aurela1, Juha-Pekka Tuovinen1, Viktor Ivakhov3, Maiju Linkosalmi1, Aleksi Räsänen4,5,
Tarmo Virtanen4, Juha Mikola4,6, Johanna Nyman1, Emmi Vähä1, Marina Loskutova7, Alexander Makshtas7, and
Tuomas Laurila1
1Finnish Meteorological Institute, Climate System Research, Erik Palménin aukio 1, 00560 Helsinki, Finland
2Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 2, 80100 Joensuu, Finland
3Voeikov Main Geophysical Observatory, Ulitsa Karbysheva, 7, St Petersburg, 194021, Russia
4Ecosystems and Environment Research Programme, University of Helsinki, Viikinkaari 1, 00790 Helsinki, Finland
5Natural Resources Institute Finland (Luke), Paavo Havaksen tie 3, 90570 Oulu, Finland
6Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland
7Department of Ocean-Atmosphere Interaction and Laboratory of Experimental Climatology of the Arctic, Arctic and
Antarctic Research Institute, Bering Str., 38, St. Petersburg, 199397, Russia
Correspondence: Sari Juutinen (sari.juutinen@uef.fi)
Received: 6 January 2022 – Discussion started: 26 January 2022
Revised: 26 May 2022 – Accepted: 1 June 2022 – Published: 4 July 2022
Abstract. Arctic tundra is facing unprecedented warming,
resulting in shifts in the vegetation, thaw regimes, and poten-
tially in the ecosystem–atmosphere exchange of carbon (C).
However, the estimates of regional carbon dioxide (CO2) and
methane (CH4) budgets are highly uncertain. We measured
CO2 and CH4 fluxes, vegetation composition and leaf area
index (LAI), thaw depth, and soil wetness in Tiksi (71◦ N,
128◦ E), a heterogeneous site located within the prostrate
dwarf-shrub tundra zone in northeastern Siberia. Using the
closed chamber method, we determined the net ecosystem
exchange (NEE) of CO2, ecosystem respiration in the dark
(ER), ecosystem gross photosynthesis (Pg), and CH4 flux
during the growing season. We applied a previously devel-
oped high-spatial-resolution land cover map over an area of
35.8 km2 for spatial extrapolation. Among the land cover
types varying from barren to dwarf-shrub tundra and tun-
dra wetlands, the NEE and Pg at the photosynthetically ac-
tive photon flux density of 800 µmol m−2 h−1 (NEE800 and
Pg800) were greatest in the graminoid-dominated habitats,
i.e., streamside meadow and fens, with NEE800 and Pg800 of
up to −21 (uptake) and 28 mmol m−2 h−1, respectively. Vas-
cular LAI was a robust predictor of both NEE800 and Pg800
and, on a landscape scale, the fens were disproportionately
important for the summertime CO2 sequestration. Dry tun-
dra, including the dwarf-shrub and lichen tundra, had smaller
CO2 exchange rates. The fens were the largest source of CH4,
while the dry mineral soil tundra consumed atmospheric
CH4, which on a landscape scale amounted to −9 % of the
total CH4 balance during the growing season. The largest
seasonal mean CH4 consumption rate of 0.02 mmol m−2 h−1
occurred in sand- and stone-covered barren areas. The high
consumption rate agrees with the estimate based on the eddy
covariance measurements at the same site. We acknowledge
the uncertainty involved in spatial extrapolations due to a
small number of replicates per land cover type. This study
highlights the need to distinguish different land cover types
including the dry tundra habitats to account for their differ-
ent CO2 and CH4 flux patterns, especially the consumption
of atmospheric CH4, when estimating tundra C exchange on
a larger spatial scale.
1 Introduction
It is uncertain whether the Arctic tundra is a sink or a source
of atmospheric carbon (C). The current estimates suggest a
sink of 13–110 Tg C yr−1, but their uncertainty range crosses
the zero balance (McGuire et al., 2012; Virkkala et al., 2021).
Published by Copernicus Publications on behalf of the European Geosciences Union.
3152 S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types
Improving these estimates is vital, because the Arctic tun-
dra covers a vast area of 7.6 million km2 (Walker, 2000) that
is experiencing substantial warming (IPCC, 2013; Chen et
al., 2021). Warming can alter C exchange, either amplifying
or mitigating climate change through ecosystem–atmosphere
interactions. Some local-scale studies suggest that the Arctic
tundra is shifting from a small sink to a source of C (Webb et
al., 2016; Euskirchen et al., 2017). It is likely that the climate
change response of the ecosystem carbon dioxide (CO2) sink
strength and methane (CH4) emissions, whether an increase
or a decrease, depends on site-specific changes in thawing,
wetness, temperature, and vegetation (McGuire et al., 2018).
Dynamics of C exchange need to be quantified across the arc-
tic habitats to improve the upscaling of arctic CO2 and CH4
balances and to monitor how ecosystems respond to environ-
mental changes.
The uncertainty in the Arctic C balance estimates arises
from the sparse and uneven observation network, which pro-
vides poor support for model-based spatial extrapolation (cf.
McGuire et al., 2018; Virkkala et al., 2021; Kuhn et al.,
2021). On a local scale, landscape heterogeneity and the re-
lated difficulty of mapping the spatial distribution of habitats
and their C fluxes add to this uncertainty (McGuire et al.,
2012; Treat et al., 2018; Saunois et al., 2020). Furthermore,
year-to-year variations in seasonal features, particularly the
timing of spring, summer temperatures, and snow depth
have been found to cause substantial variation in the annual
CO2 and CH4 balances (Aurela et al., 2004; Humphreys and
Lafleur, 2011; Zhang et al., 2019). Fine-scale spatial hetero-
geneity in soil water saturation, thaw depth, vegetation char-
acteristics, and soil organic content is typical of the tundra
landscape (e.g., Virtanen and Ek, 2014; Mikola et al., 2018;
Lara et al., 2020). These factors control CO2 and CH4 ex-
change, and on an annual scale, tundra wetlands typically act
as net CO2 sinks while upland tundra areas have a close-to-
neutral CO2 balance (e.g., Marushchak et al., 2013; Virkkala
et al., 2021). While tundra wetlands are substantial sources
of CH4, dry tundra acts as a small sink or small source of
atmospheric CH4 (Bartlett and Harriss, 1993; Kuhn et al.,
2021).
Mineral soil tundra barrens, however, have been found to
have high consumption rates of atmospheric CH4, which is
due to the high-affinity methane oxidizing bacteria (Emmer-
ton et al., 2014; Jørgensen et al., 2014; D’Imperio et al.,
2017; Oh et al., 2020). These bacteria can utilize atmospheric
CH4 as energy source at low atmospheric concentrations,
opposite to the low-affinity methane oxidizers that require
higher CH4 concentrations and occur in wetlands (e.g., Oh
et al., 2020). A modeling exercise that introduced the high-
affinity methanotrophy for mineral-rich soils resulted in a
doubling of the circumpolar soil CH4 sink above 50◦ N com-
pared to previous estimates (Oh et al., 2020). Thus, distin-
guishing dry and wet tundra with their moisture and vege-
tation characteristics is crucial when mapping C exchange
within the tundra biome. Treat et al. (2018) tested spatial res-
olution requirements for such mapping on a landscape level
and found that a 20 m pixel size captured the spatial variation
in a reasonable manner, while a coarser resolution resulted
in underestimation of both the landscape-scale CO2 uptake
and CH4 emissions. In addition, understanding the spatial
heterogeneity of ecosystem C exchange substantially im-
proves analyses of eddy covariance (EC) measurements that,
while in principle representing spatially integrated fluxes,
may provide biased gas flux balances in a highly heteroge-
neous source and sink environment, as the spatial integra-
tion of EC involves non-uniform weighting of the surface el-
ements that contribute to the measured flux (Tuovinen et al.,
2019).
The aim of this study was to assess the spatial patterns and
magnitudes of CO2 and CH4 fluxes within heterogeneous
prostrate dwarf-shrub tundra in Tiksi, located in northeast-
ern Russia. Growing season fluxes of CO2 (ecosystem net
exchange, photosynthesis, and respiration) and CH4 were de-
termined using the chamber method to answer the questions:
(i) what is the magnitude of these fluxes in different land
cover types and (ii) how do they depend on vegetation char-
acteristics and soil wetness? In addition, we extrapolated the
plot-level measurements in space and compared them with
the ecosystem-level data measured with the EC technique.
2 Materials and methods
2.1 Study site
The study site is located near the Tiksi Observatory in
Sakha (Yakutia) (see Uttal et al., 2016), northeastern Rus-
sia (71.5943◦ N, 128.8878◦ E), 500 m inland from the Laptev
Sea coast and, on average, 7 m above sea level (Fig. 1a). The
area belongs to the middle-Arctic prostrate dwarf-shrub tun-
dra subzone (Walker, 2000) and has continuous permafrost.
At the end of the growing season, the maximum thaw depth
is ca. 40 cm (Mikola et al., 2018). The climate in Tiksi
is defined by cold winters and cool summers. The long-
term mean annual temperature and mean annual precipitation
were −12.7 ◦C and 232 mm, respectively, during the climate
normal period 1981–2010. The growing season lasts about
3 months, the soils typically freeze in the end of September,
and the permanent snow falls in October and thaws in June
(AARI, 2018).
Bedrock is alkaline, resulting in high plant species rich-
ness. Vegetation consists of mosses, lichens, grasses, sedges,
prostrate dwarf-shrubs such as willows (Salix spp.), dwarf
birch (Betula nana) and Diapensia lapponica, and forb
species (Table 1). The average height of dwarf-shrub species
is 4–6 cm, and the leaf area index (LAI) of vascular plants
reaches up to 1 m2 m−2 in the fen and meadow habitats with
graminoid vegetation (Juutinen et al., 2017). The land cover
at the site has been classified a priori and mapped based on a
combination of field inventories and high-spatial-resolution
Biogeosciences, 19, 3151–3167, 2022 https://doi.org/10.5194/bg-19-3151-2022
S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types 3153
Figure 1. (a) Location of the study area in Tiksi, Yakutia, Russia. (b) Land cover map with the chamber flux measurement points (dark dots)
and the EC mast (cross), and photos of the land cover types: (c) lichen tundra with barren ground and patches of vegetation, (d) dwarf-shrub
tundra, (e) bog, (f) wet and dry fen, (g) graminoid tundra, and (h) meadow by the stream. See Tuovinen et al. (2019) for the EC footprint
climatology.
satellite images (Mikola et al., 2018). The a priori land cover
types (LCTs) consist of wet fen, dry fen, graminoid tundra,
bog, meadow at the stream bank, dwarf-shrub tundra, and
lichen tundra that consists of barren ground with rocks, sand,
and patches of vegetation (Table 1, Fig. 1c–h; for a closer
view see Appendix Fig. A1). The depth of organic layer is
negligible in lichen tundra and a few centimeters in dwarf-
shrub tundra, meadow, and graminoid tundra. In bog, dry fen,
and wet fen, the organic layer depth is at least the maximum
depth of the active layer, ca. 30–40 cm. Soil organic content
can reach ca. 40 % in tundra wetlands (Mikola et al., 2018).
A section of the wet and dry fen within the EC footprint area
is disturbed by vehicle tracks that create open water surfaces,
and there is also an area of eroded bare-peat surface on a dry
fen.
2.2 CO2 and CH4 flux measurements
Fluxes of CO2 and CH4 were measured using static cham-
bers equipped with a fan and set on pre-installed collars
of 50 cm× 50 cm in area. The measurement points (collars)
were set to cover the heterogeneity in land cover, and in each
study year, there were 1–4 measurement points per LCT (Ta-
ble 2). Most of the data were collected during a study cam-
paign during 15 July–16 August 2014 (12 collars). The grow-
ing season had started earlier due to a warm period, and the
daily mean air temperature had stayed over 5 ◦C since 5 July
(Fig. 2 and Tuovinen et al., 2019). The net ecosystem ex-
change of CO2 (NEE) and ecosystem respiration of CO2 in
the dark (ER) were measured using transparent and opaque
chambers (transparent chamber covered with a hood), re-
spectively, allowing the partitioning of ecosystem gross pho-
tosynthesis (Pg) and ER. Fluxes of CH4 were determined
from closures of both transparent and opaque chambers, but
because there was no difference between them when per-
https://doi.org/10.5194/bg-19-3151-2022 Biogeosciences, 19, 3151–3167, 2022
3154 S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types
Table 1. Soil and vegetation characteristics of the land cover types (LCT) and their proportions in the EC impact area (90 % of the cumulative
footprint).
LCT Soil properties and plant taxa Proportion (%)2
Lichen tundra1 Mixture of vegetated patches, stones, and bare ground. 8 barren, 11 sparse vegetation
Lichens, e.g., genera Thamnolia, Flavocetraria, Alectoria, Stereocaulon, dwarf
shrubs Dryas octopetala, Vaccinium vitis-ideae, Salix polaris, and Diapen-
sia lapponica, and forbs Oxytropis spp., Astragalus spp., Pedicularis spp.,
Artemisia spp., Minuartia sp.
Dwarf-shrub tundra Shallow organic layer on mineral soil ground
Feather mosses, lichens, Salix polaris, Vaccinium vitis-ideae, Vaccinium ulig-
inosum, Dryas octopetala, Cassiope tetragona, Betula nana, Polygonum
viviparum, Pedicularis spp., Carex spp.
18
Meadow Shallow organic layer on mineral soil ground
Calamagrostis sp., Festuca sp., Salix spp. Polygonum viviparum, Bistorta ma-
jor, Polemonium sp., Valeriana sp.
1.4
Graminoid tundra Shallow peat layer on mineral soil ground
Feather mosses, Sphagnum spp., Carex spp., Eriophorum spp., Calamagrostis
spp., Salix spp., B. nana, Saxifraga spp., Ranunculus spp., Bistorta major, Stel-
laria sp., Valeriana sp., Polemonium sp., Comarum palustre
13
Bog Dry hummock habitat at the tundra peatland
Sphagnum spp., feather mosses, Salix spp., Vaccinium uliginosum, Vac-
cinium vitis-idaea, Betula nana, Rhododendron tomentosum, Cassiope tetrag-
ona, Carex spp., Polygonum viviparum, Stellaria sp.
23
Dry fen Intermediate wet tundra peatland habitat
Sphagnum spp., Carex spp., Salix spp., Saxifraga spp., Comarum palustre,
Epilobium spp., Ranunculus spp., Pedicularis spp., Stellaria sp.
10
Wet fen Wet tundra peatland habitat with open pools
Brown mosses, Carex spp., Eriophorum spp., Ranunculus sp., Caltha palustris,
Pedicularis sp., Saxifraga sp.
15
1 Combines the bare and lichen tundra LCTs defined in Juutinen et al. (2017), Mikola et al. (2018), and Tuovinen et al. (2019). 2 Proportion within the 90 % coverage of the mean
EC footprint area during the growing season of 2014 (Tuovinen et al., 2019).
formed consecutively, the data from opaque chamber mea-
surements were used for flux calculations. In addition, CH4
fluxes were measured during shorter campaigns in 2012,
2013, 2016, and 2019 (Table 2). These data also included
the plots disturbed by vehicle tracks and an eroded bare-peat
surface, which were measured in 2019.
In 2012 and 2013, four air samples were taken from the
chambers using syringes. The samples were stored in glass
vials prior to the analysis. First, a vial was flushed with the
sample and then filled to overpressure. The samples were
analyzed for CH4 concentration using a TSVET 500 M gas
chromatograph (Chromatek, Russia) with a flame ionization
detector at the laboratory of the Voeikov Main Geophysical
Observatory within a month of sampling. Each measurement
was accompanied by calibration using standard gas mixtures
with known CH4 concentrations (the NOAA2004 scale). The
vials were tested prior to the field sampling using a standard
gas: after 2 weeks, the vials were still overpressurized and
the sample CH4 concentrations were within ±3 ppb of the
initial standard gas concentration. Since July 2014, CH4 and
CO2 concentrations inside the chambers were recorded every
second during closures of about 5 min using a gas analyzer
(DLT-100, Los Gatos Research, Inc., San Jose, CA, USA)
(see Appendix Fig. A2 for examples).
Gas fluxes between the ecosystem and the atmosphere
were calculated from the linear concentration change in the
chamber head space over time, accounting for temperature,
volume, and atmospheric pressure. The concentration change
during each chamber closure was evaluated visually to de-
termine the closure start time and to remove cases showing
nonlinearity due to leaks, ebullition, or saturation. The first
data points were generally neglected when determining the
Biogeosciences, 19, 3151–3167, 2022 https://doi.org/10.5194/bg-19-3151-2022
S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types 3155
Figure 2. (a) Air temperature accumulation with the threshold Tair
and surface Tsoil of 0 ◦C, (b) seasonal dynamics of NDVI in the
study area (16 d MODIS data), (c) weekly means of soil temperature
at a depth of 5 cm in wet fen and (d) in lichen tundra, (e) water-table
level relative to the ground surface in wet fen, and (f) LCT-specific
means of thaw depth in the measurement collars in 2014. Rocks in
the ground prevented detecting the thaw depth of lichen tundra.
slope of concentration change over time. The linearity was
screened also on the basis of the coefficient of determina-
tion of the fit (R2). An R2 greater than 0.9 was required, ex-
cept for nearzero fluxes. There were a few ebullition cases at
the vehicle track measurement points that had only sparse
or no vegetation cover, and those measurements were in-
cluded in the final data. When determining the NEE fluxes
measured using the transparent chamber, data were screened
for variation in photosynthetically active photon flux density
(PPFD), measured during the chamber closure, and the flux
measurement was rejected if the PPFD variation exceeded
100 µmol m−2 s−1 during the closure.
The fluxes of CO2 and CH4 were also measured by the
micrometeorological EC method, which provides continu-
ous data of the atmosphere–biosphere fluxes averaged on
an ecosystem scale. The EC system consisted of a three-
dimensional sonic anemometer (USA-1, METEK GmbH,
Elmshorn, Germany), a closed-path CH4 analyzer (RMT-
200, Los Gatos Research, Inc., San Jose, CA, USA), and
a closed-path CO2 /H2O analyzer (LI-7000, LI-COR, Inc.,
Lincoln, NE, USA). The fluxes were calculated as 30 min av-
erages and processed using standard methods (Aubinet et al.,
2012). The EC measurement system and the post-processing
procedures have been presented in more detail by Tuovinen
et al. (2019).
Supporting meteorological measurements including air
temperature (Tair) (HMP, Vaisala), soil temperature (Tsoil)
(IKES, Nokeval), PPFD (PQS1, Kipp & Zonen), and
water table level relative to the ground surface (WT)
(8438.66.2646, Trafag) were collected by a Vaisala QML
datalogger as 30 min averages. We also present meteorolog-
ical data for the period 2011–2019 to relate the conditions
during the measurement campaigns in 2012, 2013, 2014,
2016, and 2019, to longer-term variations.
2.3 Vegetation and topographic wetness index
On a site level, vegetation and soil characteristics were in-
ventoried in plots assigned into a systematic grid outside the
area covered by the gas flux measurement points in 2014
(see Juutinen et al., 2017; Mikola et al., 2018). The projec-
tion cover (%) of plant species and species groups, and the
mean canopy height of each species group were recorded.
Eight species groups were included in the inventory: Sphag-
num mosses, feather mosses, brown mosses, dwarf shrubs,
Betula nana, Salix species, forbs, and graminoids. A subset
of the plots was harvested, and vascular plant leaves were
scanned to determine the one-sided LAI for empirical re-
lationships between LAI and %-cover and canopy height,
which were used to estimate the LAI in the collars (see Juu-
tinen et al., 2017). In the collars, the projection cover and
canopy height of each species group were recorded weekly
during the gas flux measurement campaign during 15 July–
16 August 2014. Because there were no observational vege-
tation data for the other years than 2014, the green chromatic
coordinate (GCC) calculated from digital photographs was
used as a proxy for the amount of green above-ground vascu-
lar plants (e.g., Richardson, 2019). The GCC was calculated
from the digital numbers of red (R), green (G), and blue (B)
color channels as the proportion of green in the RGB images,
GCC=G / (R+G+B), of the vegetation inside the collars.
The photographs were taken at the time of measurements.
We determined an empirical relationship between LAI and
GCC by using a data set of harvested plots with digital pho-
tographs and measured LAI data (n= 91). For the LAI esti-
mation, we used a linear relationship (R2 = 0.46, p<0.001)
between LAI and GCC determined using the entire data set
(see Fig. A3 for the data and equation).
To quantify the potential soil wetness at each measure-
ment point, we calculated the mean topographic wetness in-
dex (TWI) based on a 2 m spatial resolution digital elevation
model (Mikola et al., 2018). To characterize differences be-
tween growing seasons as manifested by vegetation green-
ness, the MODIS Normalized Difference Vegetation Index
(NDVI) with 16 d temporal and 500 m spatial resolution was
calculated for a circular area with a 300 m radius from the
flux tower using Google Earth Engine (Gorelick et al., 2017).
NDVI was derived for 2011–2019 to place the measurement
https://doi.org/10.5194/bg-19-3151-2022 Biogeosciences, 19, 3151–3167, 2022
3156 S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types
Table 2. Measurement periods, measured fluxes (CH4, ER, NEE), and number of measurement points in each land cover type (LCT) across
the study years.
LCT 2012 2013 2014 2016 2019
18–21 Jul 5 Jul–3 Sep 15 Jul–16 Aug 30 May, 4–5 Aug, 13–14 Sep 28 Aug–1 Sep
CH4 CH4 ER, NEE, CH4 CH4 CH4
Wet fen (vehicle track) 4 6 3 3 5 (2)
Dry fen (bare peat) 2 4 3 3 2 (1)
Bog 2 3 1 1
Meadow 1 2 2
Dwarf-shrub tundra 1 1 1
Lichen tundra (snow∗) 1 2 2 (2) 2
∗ Measured only on 30 May 2016.
years in the context of year-to-year variation in weather and
plant growth.
2.4 Data analyses
When examining the role of the LCTs in CO2 and CH4
exchange, we applied the land cover classification pre-
sented by Mikola et al. (2018). The data collected in
15 July–16 August 2014 were used for examining gas ex-
change in relation to the variation in LAI, GCC, WT, and
TWI among the collars. The light-normalized Pg and NEE
at PPFD= 800 µmol m−2 s−1 (Pg800 and NEE800, respec-
tively), were estimated by fitting a hyperbolic response func-
tion of CO2 vs PPFD utilizing the ER and NEE flux data:
NEE= ER−Pgmax×PPFD/(β +PPFD), (1)
where Pgmax is the asymptotic maximum of photosynthetic
CO2 uptake rate, and β is the half-saturation PPFD. Fluxes
of CH4 are expressed as temporal averages for each collar.
We used a sign convention where a positive flux means net
release to the atmosphere and a negative flux denotes net
uptake by the ecosystem. Fluxes of CH4 measured over all
study years, 2012–2019, were averaged for each LCT.
Regression analyses were used to test the relationships be-
tween gas flux estimates and vascular LAI, GCC, WT, and
TWI. All CH4 flux data from the years 2012–2014, 2016, and
2019 were used to quantify the mean growing season CH4
flux for each LCT and examine the relationship between CH4
and GCC and TWI. To find the main factors and gradients in
the plant community, gas flux, and environmental variables
data measured in the flux collars in 2014, we performed a de-
trended correspondence analysis (DCA) of the species group
data with a post-hoc fit of environmental variables, includ-
ing gas fluxes, WT, LAI, GCC, elevation, and thaw depth as
supplementary variables. The DCA was performed on log-
arithmically transformed, centered species data (species or
species groups) using Canoco 5 (Šmilauer and Leps, 2014).
We compared the LCT-specific flux estimates obtained
from the chamber measurements with the estimates based on
EC measurements during the same period (15 July–16 Au-
gust 2014). Partitioning of the EC-based CO2 fluxes to Pg
and ER and the estimates of Pg800 and NEE800 were cal-
culated similarly to those derived from the chamber data
(Eq. 1). The EC flux data were classified into five wind sec-
tors (30–125, 125–185, 185–239, 239–310, 310–360◦) based
on the mean EC flux footprint, modeled for the growing sea-
son of 2014 by Tuovinen et al. (2019). The sectors distin-
guished areas dominated by different LCTs, especially tun-
dra heaths and wetlands, and similarly those with a large and
small vascular LAI. For each sector, the footprint-weighted
areal proportions of LCTs and mean vascular LAI were de-
rived from the high-spatial-resolution LCT and LAI maps
(Mikola et al., 2018). For this comparison, sector averages
of Pg800, ER, NEE800, and CH4 flux were calculated from
the chamber data by weighting the LCT-specific flux esti-
mates with the above-mentioned LCT proportions in each
sector. Because there were no chamber measurement points
within graminoid tundra, we applied wet fen (for CO2) and
dry fen (for CH4) flux estimates for the graminoid tundra
based on the observed similarities in LAI and soil wetness,
respectively. Overall, graminoid tundra can be considered
part of the fen continuum in terms of soil characteristics (no-
tably high organic content) and CH4 exchange (Mikola et al.,
2018; Tuovinen et al., 2019).
Finally, to synthesize the CO2 and CH4 exchange variabil-
ity across the tundra, we upscaled the LCT-specific NEE800,
Pg800, ER, and CH4 flux (2014 data) estimate averages to the
35.8 km2 area surrounding our study site, for which a LCT
map was produced by Mikola et al. (2018).
3 Results
3.1 Environmental conditions
In 2014, when we collected most of the flux data, temperature
sum accumulation (with a 0 ◦C Tair threshold) took place at
a near-average rate during the thaw period (the period when
soil surface temperature was continuously above 0 ◦C), but
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S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types 3157
the spring and mid-growing season were warmer than on
average (Fig. 2a). The average air temperature was 15 ◦C
during the gas flux measurements. Accordingly, the MODIS
NDVI showed an early start of greening (Fig. 2b), and veg-
etation development had already started at the beginning of
the measurement period. In 2011–2019, which includes all
the CH4 measurement years, the thaw period lasted for 74–
124 d, creating a temperature sum range of 642–1003 ◦C
days (Fig. 2a). Surface soils thawed between 28 May and
9 July and froze again between 21 September and 1 Octo-
ber. Among the observation years, the years 2012 and 2019
had notably longer and warmer thaw periods than the other
years. The driest habitat, lichen tundra, with least snow ac-
cumulation, thawed 10–15 d earlier than the other habitats
and had an about 3 ◦C higher soil temperature at the depth of
5 cm than wet fen (Fig. 2c–d). Water table level, measured at
a wet fen location, showed only subtle interannual variation
(Fig. 2e). In 2014, the active layer depth, measured close to
the collars during the flux measurement period, was deepest
in the end of August, reaching ca. 40 cm in wet fen but re-
maining <30 cm in the dwarf-shrub tundra (Fig. 2f). Lichen
tundra had rocks underneath the loose surface layer, which
made it impossible to measure the actual thaw depth.
3.2 Exchange of CO2 and CH4
Among different LCTs, the estimates of Pg800 varied
from about 5 mmol m−2 h−1 in lichen tundra to 22 and
27 mmol m−2 h−1 in wet fen and meadow, respectively (Ta-
ble 3a). Pg800 was strongly and positively correlated with the
vascular plant LAI and the greenness index GCC (Fig. 3).
There was also a positive correlation between Pg800 and both
WT and TWI, possibly because the highest LAI occurred at
the wet fen and meadow plots. However, the TWI values for
the two meadow plots located on an elevated bank of the
stream were disproportionately high in relation to the WT
at these plots, probably because of insufficient spatial accu-
racy or an artifact of the digital elevation model. Ecosystem
respiration was highest in the two meadow plots, on aver-
age 18 mmol m−2 h−1. The relationship between ER and LAI
was weaker than between Pg800 and LAI (Fig. 3). NEE800
varied from about zero in the lichen tundra plots to a net CO2
uptake of 16 mmol m−2 h−1 in the meadow and wet fen plots
(Table 3a). NEE800 was more tightly linked to Pg800 than
to ER and it was correlated with LAI, GCC, WT, and TWI
(Fig. 3).
There was substantial consumption of atmospheric CH4
in the barren tundra, where the mean of all measured fluxes
was −0.018 mmol m−2 h−1, and in the vegetated lichen tun-
dra with a mean of −0.005 mmol m−2 h−1 (Table 3c, Figs. 4
and 5). Minor consumption occurred in the bog, meadow,
and dwarf-shrub tundra plots (mean fluxes from −0.0002 to
−0.001 mmol m−2 h−1), while efflux to the atmosphere was
observed in the dry fen (mean 0.04 mmol m−2 h−1) and wet
fen plots (mean 0.17 mmol m−2 h−1). Fluxes were also high
in the eroded bare-peat plot within the dry fen habitat and the
vehicle-track plots in wet fen (Table 3c).
Variation among the plot means (Fig. 3 for 2014) and LCT
means (Fig. 5 for all years) of CH4 flux was related to WT,
and CH4 emissions occurred when TWI was greater than 4.
The two meadow plots that showed net consumption of CH4
had an unrealistically high TWI relative to their WT (see
above and Figs. 3 and 5). Variation in CH4 fluxes was in-
coherently related to the variation in LAI and GCC because
of the high emission cases in plots with little vegetation, in-
cluding the wettest wet fen, vehicle-track, and bare-peat plots
(Fig. 5).
The DCA ordination of species groups with a post-hoc fit
of environmental variables (elevation, WT, thaw depth, LAI,
GCC, and CO2 and CH4 exchange) showed that species dis-
tributed along a moisture gradient. Axis 1 explained 49 % of
the variation in the species data and distinguished the wet
and dry LCTs (Fig. 6). Graminoids and brown mosses oc-
curred in the wet end of the gradient, while evergreen dwarf-
shrubs, Betula nana, and lichens occurred in the dry end of
it. The barren plot (the other lichen tundra plot) with its neg-
ligible vegetation differed most from the other plots. Axis 2
explained additional 14 % of the variation in the species data
(Fig. 6). The supplementary variables WT, vascular plant
LAI, thaw depth, GCC, Pg800, NEE800, and CH4 flux corre-
lated positively with Axis 1 having post-hoc correlations (r)
of 0.6–0.9, as derived from the DCA-weighted correlation
matrix, while plot’s elevation and ER had positive correla-
tions with Axis 2 (r = 0.8 and 0.4, respectively).
In both the southern (125–185◦) and southwestern
(185–239◦) wind sectors, vegetation mainly consisted of
graminoids, as the LCTs dry fen, wet fen, graminoid tun-
dra, and meadow comprised 80 % of the total EC footprint-
weighted area (Fig. 7a). The northern sector (310–360◦)
was characterized by lichen tundra and bare ground that ac-
counted for 68 % of the footprint-weighted LCT areas, while
all the other LCTs covered less than 18 % in total. The other
wind direction sectors had more even LCT distributions. The
differences between the sectors were similar in the EC-based
and spatially weighted chamber-based averages of CO2 ex-
change (Fig. 7b–d). Both Pg800 and NEE800 were largest in
the southern and south-western sectors and clearly smallest
in the barren–lichen tundra-dominated sector in the north.
The chamber-based estimates of CO2 exchange were, how-
ever, lower: on average, Pg800 was 57 %, ER 93 %, and
NEE800 44 % of the mean EC-based fluxes among the wind
direction sectors.
The southern and southwestern wind sectors with abun-
dant dry and wet fens and graminoid tundra had clearly the
largest CH4 fluxes (Fig. 7f). The estimate based on chamber
measurements was 30 % and 50 % larger than the mean EC-
based flux in the east sector (dominated by dry fen and bog)
and south sector (dominated by dry fen and wet fen), respec-
tively. In contrast, the chamber-based estimate was smaller
than the EC flux for the other sectors, which were dominated
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3158 S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types
Figure 3. Variation in the estimates of Pg800, ER, and NEE800 (Eq. 1) and collar means of CH4 fluxes in relation to variation in the collar
means of LAI, GCC, WT, and TWI during 6 July–16 August 2014. Error bars denote the standard error of estimate (n= 15 or 16). Fitted
regression lines and adjusted coefficients of determination (R2adj.) are included for the significant linear relationships. The two meadow plots
were not included in the TWI regressions.
by graminoid tundra, lichen tundra, and barren ground. Both
the EC- and chamber-based measurements showed consump-
tion of atmospheric CH4 in the northernmost sector, of which
barren ground and lichen tundra covered 50 % and 20 %, re-
spectively. The mean EC flux was three times the chamber-
based estimate.
Within the extended study area of 35.8 km2, the LCT-
weighted mean NEE800 was −4.6 mmol m−2 h−1 (up-
take relative to the atmosphere). The corresponding mean
Pg800 was 11 mmol m−2 h−1, and the mean CH4 flux was
0.05 mmol m−2 h−1 (Table 3a). Relative to their spatial cover
(28 % in total), wet and dry fens were disproportionally
important for the landscape-level Pg800, NEE800, and CH4
emissions, because the fens contributed 47 % of total Pg800
and 74 % of NEE800, and were the largest source of CH4 (Ta-
ble 3b). Consumption of CH4 by barren and lichen tundra,
dwarf-shrub tundra, and meadow tundra soils contributed
−9 % of the CH4 balance, and the barren ground dominated
the sink. It should be noted that these data represent the grow-
ing season conditions when both the CH4 emissions and con-
sumption of atmospheric CH4 were at their highest during
the year due to high temperatures, thawed soils and active
vegetation.
4 Discussion
The studied tundra site in Tiksi in northeastern Siberia has
heterogeneous land cover, which is reflected as equally het-
erogeneous CO2 and CH4 exchange. We found that the LAI
of vascular plants was a robust predictor of Pg800 and NEE800
across the LCTs. On the one hand, the tundra wetlands had a
disproportionate role in the landscape-level CO2 uptake ca-
pacity. The fens also dominated the landscape’s CH4 emis-
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S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types 3159
Table 3. (a) Means, medians, and standard deviations (sd) of LCT specific CO2 and CH4 fluxes in 2014 calculated from collar specific esti-
mates (CO2) or seasonal means (CH4). There were 15 or 16 data points per each collar and 1–3 collars per LCT (see Table 2). (b) Proportions
of LCTs in landscape totals of area, and Pg800, NEE800, and CH4 fluxes based on the LCT means (part a). (c) LCT specific means, medians,
and standard deviations of CH4 fluxes based on multiyear data (n is number of the observations).
(a) LCT specific CO2 and CH4 fluxes in 2014 together with the landscape means (mmol m−2h−1)
LCT Wet fen Dry fen Bog Meadow Dwarf-s. t. Lichen t. Barren Gram. 1 Mean2
Pg800
Mean 21.93 14.6 15.27 26.45 8.64 7.85 2.11 21.93 11.21
Median 19.23 15.62 26.45
SD 3.91 1.02 9.51
ER
Mean 6.44 6.99 9.34 17.66 7.8 7.2 3.85 6.44 6.6
Median 5.75 7.37 17.66
SD 0.98 0.39 3.06
NEE800
Mean −15.49 −7.61 −5.93 −8.79 −0.85 0.55 0.55 −15.49 −4.61
Median −13.99 −8.25 −8.79
SD 3.39 0.64 6.45
CH4
Mean 0.29 0.05 0.0001 −0.001 −0.003 −0.005 −0.02 0.05 0.05
Median 0.12 0.03 0.001
SD 0.3 0.04 0.0003
(b) Proportions of LCTs in the landscape totals of area and CO2 and CH4 fluxes in 2014 (%)
LCT Wet fen Dry fen Bog Meadow Dwarf-s. t. Lichen t. Barren Gram.1
Area3 16 12 9 0.4 27 11 15 3
Pg800 32 15 12 1 21 5 7 7
NEE800 55 19 12 1 5 -1 -2 11
CH4 94 11 0 0 −2 −1 −6 3
(c) LCT specific CH4 fluxes across the study years 2012–2019 (mmol m−2 h−1)
LCT Wet fen Dry fen Bog Meadow Dwarf-s. t. Lichen t. Barren
(vehicle track) (bare peat)
Mean 0.17 (0.2) 0.04 (0.06) −0.002 −0.0002 −0.001 −0.006 −0.018
Median 0.04 (0.08) 0.03 (0.02) −0.0008 −0.001 −0.0005 −0.005 −0.016
SD 0.29 (0.46) 0.06 (0.11) 0.004 0.004 0.007 0.005 0.013
n 183 (30) 118 (15) 58 43 29 37 47
1 Graminoid tundra contribution estimated using values for wet fen (CO2) and dry fen (CH4), 2 Area-weighted mean. 3 Water not shown.
sions. On the other hand, our results highlight the substantial
CH4 consumption of atmospheric CH4 within the dry tundra
areas, particularly in barrens. The CH4 consumption by dry
tundra contributed −9 % of the total CH4 balance estimated
for this landscape from the data collected during the grow-
ing season. This finding is in agreement with other studies
and suggests distinguishing non-vegetated dry tundra habi-
tats when upscaling plot-scale CH4 fluxes (Table 4). In Tiksi,
the barren was characterized by sand and rocks underlain by
schists (Fig. A1). The consumption of CH4 was smaller if the
sand and stones were partly covered with vegetation (Figs. 5
and A1).
The land-cover-categorical approach serves to distinguish
the basic features of spatial variation in CO2 and CH4 fluxes,
and the extreme ends of the moisture and vegetation gradi-
ents from barren to wet fen are clearly distinguishable, also
in terms of CO2 and CH4 exchange (Fig. 6). Overall, mi-
crorelief, moisture gradient, vegetation types, and ecosys-
tem functions are connected. For instance, barren areas are
wind swept and thus have minimal snow accumulation, while
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3160 S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types
Table 4. Summary of reported CH4 fluxes in mineral soil dry tundra.
Location Habitat type Mean Min Max Reference
(µmol m−2 h−1)
Narsarsuaq, Greenland Low elevation heath vegetation −1.2 −4.0 −0.2 St Pierre et al. (2019)
Narsarsuaq, Greenland High elevation heath vegetation −2.6 −11.9 3.6 St Pierre et al. (2019)
Disko Island, Greenland Low elevation heath vegetation −3.8 −12.1 −1.1 St Pierre et al. (2019)
Disko Island, Greenland High elevation heath vegetation −3.5 −12.1 −1.3 St Pierre et al. (2019)
Tierra del Fuego, Argentina Alpine tundra 0.5 −16.6 10.3 Sá et al. (2019)
Disko Island, Greenland Dry tundra heath1 −4.0 −4.4 −2.5 D’Imperio et al. (2017)
Disko Island, Greenland Bare ground1 −9.0 −15.0 −3.8 D’Imperio et al. (2017)
Disko Island, Greenland Betula nana and Salix sp. heath −4.0 Christiansen et al. (2014)
Axel Heiberg Island, CA Vegetated ice-wedge polygon −2.7 −0.3 Lau et al. (2015)
Lake Hazen, Ellesmere I., CA Polar desert2 −3.6 −7.0 0.0 Emmerton et al. (2014)
Zackenberg Valley, Greenland Moist tundra −3.1 −7.0 −2.0 Jørgensen et al. (2014)
Zackenberg Valley, Greenland Dry tundra and barren ground −7.0 −16.0 −4.0 Jørgensen et al. (2015)
Zackenberg Valley, Greenland Tundra heath −1.3 −6.0 0.0 Christensen et al. (2000)
Okse Bay, Ellesmere I., CA Polar desert3 −0.5 Brummel et al. 2014
Petterson R., Ellesmere I., CA Polar desert3 −0.04 Brummel et al. (2014)
Dome, Ellesmere I., CA Polar desert3 −0.5 Brummel et al. (2014)
BAWLD-CH4 Synthesis Dry tundra −2.9 5.2 Kuhn et al. (2021)
BAWLD-CH4 Synthesis Boreal forest −2.6 −0.5 Kuhn et al. (2021)
Tiksi, RU Barren and lichen tundra4 −29 Tuovinen et al. (2019)
Tiksi, RU Lichen tundra mean −11.3 −57.9 −0.4 This study
Tiksi, RU Barren −18.1 −57.9 −3.0 This study
Tiksi, RU Vegetated −6.0 −34.7 −0.4 This study
Tiksi, RU Meadow −1.0 −21.1 24.5 This study
Tiksi, RU Dwarf-shrub tundra −0.2 −2.9 20.3 This study
Tiksi, RU Bog −2.1 −14.8 6.6 This study
1 Mean estimated from a figure. 2 Minimum and maximum estimated from a figure. 3 Three day measurement. 4 Estimated from EC measurements with a
statistical model.
in wet depressions snow accumulation further increases soil
moisture (Fig. 6; Callaghan et al., 2011). The spatial extrap-
olation of fluxes, however, is here sensitive to a small num-
ber of chamber measurement points as there is large within-
LCT variation in fluxes and LAI, as observed in the wet fen
and meadow data. Moreover, the LCTs share common fea-
tures and form a continuum as shown by the DCA ordination
(Fig. 6). Mikola et al. (2018) used a larger soil and vegetation
data set from Tiksi and also found that the neighboring LCTs
overlapped in terms of soil and plant attributes. Despite the
limited number of observations, our conclusions drawn from
the chamber data are corroborated by the temporally match-
ing section of EC data, which show high similarity to the
chamber data (Fig. 7). Furthermore, the statistical analysis
of EC data by Tuovinen et al. (2019) showed that it is pos-
sible to find significant differences between different LCT
categories representing high and low CH4 emitters and CH4
sinks. However, for spatial modeling of ecosystem functions,
maps of key variables, such as LAI and WT, that drive CO2
and CH4 exchange would be preferable to categorical LCT
classification (Räsänen et al., 2021).
The spatial pattern of the growing season Pg800 and
NEE800 was strongly related to the corresponding pattern of
the LAI of vascular plants (Fig. 3). Hence, the abundance of
graminoid (Cyperaceae and Poaceae) vegetation was asso-
ciated with a large NEE800, which varied from near zero in
lichen tundra up to −25 mmol m−2 h−1 in wet fen. Ecosys-
tem respiration had a smaller role than Pg in determining
NEE, but we note that our data cover only a section of the
growing season with warmer temperatures and half- to full-
grown vegetation. The importance of ER is likely to be differ-
ent when considering the full annual balance (e.g., Hashemi
et al., 2021). While our data represent only the growing sea-
son, a similar relationship has also been found between the
annual NEE and LAI at a tundra site with a mixture of wet
and dry tundra in northeastern Europe (Marushchak et al.,
2013), in a multi-site EC study in Alaskan tundra (McFad-
den et al., 2003), in Canadian low arctic tundra wetlands
(Lafleur et al., 2012), and across tundra sites (Street et al.,
2007; Shaver et al., 2007).
The magnitude of Pg800 and NEE800 in the fen and
meadow plots of this study were similar to the maximum Pg
and NEE found in a tundra wetland in Seida in northeastern
Europe (Marushchak et al., 2013), at low tundra wetland sites
in eastern Canada (Lafleur et al., 2012), and at a wetland-
dominated but more continental site (with an equally long
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S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types 3161
Figure 4. Instantaneous CH4 fluxes in each LCT. The data are a
composite of all study years. Barren surfaces are indicated among
the lichen tundra data. The eroded bare-peat and vehicle-track plots
(crosses) are plotted as part of the dry fen and wet fen data, respec-
tively. Note that the panel groups have different y-axis scales.
Figure 5. LCT mean (±SE) CH4 fluxes in relation to the cor-
responding mean (±SE) TWI (excluding the meadow) and mean
GCC. Data from years 2012–2019; see Table 3c for the number of
measurements.
growing season) in northeastern Siberia (van der Molen et
al., 2007). The vegetation and Pg800 of lichen tundra and
dwarf-shrub tundra in our study resembled those observed
within the polygon rim habitat of the polygon tundra in the
Lena River delta, while those of meadow, dry fen, and wet
fen resembled the wet polygon center habitats (Eckhardt et
al., 2019). In our study, the spatial variation of ecosystem
respiration resulted from the variation in vascular plant LAI,
soil organic content, and water saturation: the highest ER oc-
curred in the mineral soil meadow plots with a high LAI,
suggesting substantial autotrophic respiration and likely deep
rooting and large root biomass contributing to ecosystem res-
piration (Fig. 3).
Figure 6. DCA ordination diagram based on species (species
groups) data from the measurement collars in 2014. The explained
variation in the species data is indicated for the axes. The scores are
indicated for species groups (crosses), sample plots (small circles),
and post-hoc fits of the supplementary variables (blue arrows). Land
cover types of the sample plots are indicated in gray and the plots
assigned to each LCT are circled (dashed ellipses).
Our chamber-based estimate of the average CH4 flux
within the 35.8 km2 upscaling area was 0.05 mmol m−2 h−1,
which is close to 0.04 mmol m−2 h−1 obtained by Tuovinen
et al. (2019), who combined EC data with footprint model-
ing to statistically determine LCT group-specific CH4 fluxes.
Within this upscaling area, we estimate that 28 % of the area
emitted CH4, while the other habitats either consumed atmo-
spheric CH4 (barren and lichen tundra, dwarf-shrub tundra,
meadow) or were close to neutral (bog) relative to the at-
mosphere (Fig. 4, Table 3a–b). The relationship between the
vascular plant LAI and CH4 flux was confused by the oc-
currence of large CH4 fluxes in plots with little or no veg-
etation. Those fluxes were observed at the wettest fen plot
and the bare-peat and vehicle track plots (Figs. 4–5). A high
LAI, a high WT, and a high CH4 emission systematically
co-occurred in wet fen (Fig. 6). In addition, in the bare-peat
and vehicle-track plots, erosion or anthropogenic disturbance
may have created CH4 flux hotspots due to permafrost scars,
water saturation, and recently thawed organic matter (e.g.,
Bubier et al., 1995; McCalley et al., 2014; Wickland et al.,
2020). These are small-scale landscape features, while on a
larger scale our data encourage applying indices of wetness
and vegetation as a means of CH4 flux upscaling in a tundra
environment.
The recognition of CH4 consuming tundra habitats is im-
portant for accurately estimating the net CH4 balance of tun-
dra. The substantial uptake of atmospheric CH4 by lichen
tundra (here a mixture of bare ground and sparse vegetation)
in Tiksi was inferred by Tuovinen et al. (2019) based on a
source allocation analysis of EC data: the average flux of
the consuming area was estimated at −0.03 mmol m−2 h−1,
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3162 S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types
Figure 7. Footprint-weighted mean contribution of each LCT to the
EC measurements divided into wind direction sectors (a), and com-
parison of EC and chamber-based sector means of CO2 exchange
(Pg800, ER, and NEE800) (b–d) vascular plant LAI (e), and CH4
fluxes (f). The chamber-based data are weighted by the LCT pro-
portions shown in panel a. All data were measured in 2014. Map
of LAI (Tuovinen et al., 2019) and the LAI measured in the collars
were used to estimate the EC – and chamber – related sector means,
respectively, in panel (e).
which corresponded to −22 % of the total upscaled CH4
balance. In this study, the average seasonal CH4 flux was
−0.02 mmol m−2 h−1 in the barren tundra and an order of
magnitude lower in meadow and dwarf-shrub tundra. This
difference between the estimates likely originates from the
LCT-weighting and the small sample of the chamber-based
data and, in general, demonstrates the inherent sensitivity in-
volved in upscaling of fluxes of opposite direction.
High consumption of atmospheric CH4 in barrens is asso-
ciated with the high affinity methanotrophs (Emmerton et al.,
2014; Jørgensen et al., 2014; D’Imperio et al., 2017; St Pierre
et al., 2019). Our summary of the CH4 fluxes in mineral-
rich dry tundra (Table 4) shows that the consumption rates
in Tiksi are higher than those observed elsewhere. This may
be due to a local feature associated with the parent material
of the ground. Similar rates, however, have been recorded
at other dry tundra sites with little or no vegetation. For in-
stance, on Disko Island, Greenland, which consists of similar
land cover types to Tiksi, CH4 uptake by bare ground was
0.005–0.01 mmol m−2 h−1 during the growing season, while
a mean uptake of 0.003–0.004 mmol m−2 h−1 was observed
in dry tundra heath (D’Imperio et al., 2017). These consump-
tion rates associated with tundra barrens and high-affinity
methanotrophs can be even higher than those measured on
north-boreal forest soils (e.g., Lohila et al., 2016).
5 Conclusions
Our results provide new observations of carbon exchange for
the prostrate dwarf-shrub tundra sub-zone, which covers a
substantial area of the Arctic. These data augment the knowl-
edge on the functional diversity, namely the distribution of
different land cover types and their emission factors, across
the vast Arctic tundra and will lend support to bottom-up and
top-down extrapolations across the Arctic. Graminoid vege-
tation that favored the wet and moist habitats, such as wet
fens, was characterized by large CO2 uptake and CH4 emis-
sions. In addition, our data support the observation of no-
table consumption of atmospheric CH4 in barren tundra that
has substantial coverage across the Arctic. The heterogeneity
of landscape and the related large spatial variability of CO2
and CH4 fluxes observed in this study encourage to moni-
tor the Arctic sites for changes in habitat type distribution.
Such changes can include the forming of meadows and wet
fens and appearance of new vegetation communities, such as
erect shrubs, that benefit from warming-induced changes in
thaw depth and soil wetness. The spatial extrapolation based
on a small number of measurement points involves inherent
uncertainty but still allowed us to identify key relationships
between CO2 and CH4 fluxes and vegetation and moisture
features, which can be utilized in more robust upscaling stud-
ies that make use of EC measurements.
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S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types 3163
Appendix A
Figure A1. Examples of the barren (a, c) and lichen tundra (b, d) plots with close views (c, d). Vegetation consists of lichens Flavocetraria
sp., Thamnolia sp., Alectoria sp., dwarf-shrubs Dryas octopetala, Vaccinium vitis-idaea, Cassiope tetragona, and graminoids and forbs such
as Carex spp. and Polygonum viviparum.
Figure A2. Examples of gas concentration variations during chamber closures measured using the gas analyzer (DLT-100, Los Gatos Re-
search, Inc., San Jose, CA, USA). The examples represent lichen tundra, barren, and wet fen.
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3164 S. Juutinen et al.: Variation in CO2 and CH4 fluxes among land cover types
Figure A3. Relationship between GCC and vascular plant LAI in the harvested plots. LCTs are indicated with symbols. In the LCT-specific
regressions (not shown), the coefficient of determination (R2adj.) was lowest for dry fen (0.06) and highest for wet fen (0.54). Regression
slopes varied from 8.3 for dry fen to 17.8 for the combined graminoid tundra and meadow LCT.
Data availability. The flux data used in this study can be accessed
via the Zenodo data https://doi.org/10.5281/zenodo.5825705 (Juu-
tinen, 2022).
Author contributions. TL, MA, and SJ designed the study. TL, MA,
and AM took care of the overall site governance and maintenance.
VI, ML, TL, JM, JN, EV, TL, TV, and MA conceived the field mea-
surements of CO2 and CH4, vegetation, and environmental vari-
ables. In addition, ML calculated green chromatic coordinates, and
MA and JPT postprocessed the EC data and JPT modeled the foot-
print and estimated footprint LCT fractions. AR and TV processed
and modeled the land cover data and estimated TWI and NDVI for
the plots and area. SJ compiled the chamber flux data and con-
ducted the data analyses and spatial extrapolations and wrote the
manuscript with contributions from all co-authors.
Competing interests. The contact author has declared that neither
they nor their co-authors have any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements. We thank Galina Chumachenko,
Olga Dmitrieva, and Evgeniy Volkov at the Tiksi Observa-
tory and the Yakutian Hydrometeorological Service for their kind
assistance in carrying out and organizing the field campaigns and
Lauri Rosenius for assistance in the field work. We sincerely thank
Katharina Jentzsch and the anonymous reviewer for their time and
insightful suggestions.
Financial support. This research has been supported by the
Academy of Finland (grant nos. 269095, 285630, 291736, and
296888), the European Commission, Seventh Framework Pro-
gramme (PAGE21 (grant no. 282700)), and the NordForsk (DE-
FROST Nordic Centre of Excellence grant).
Review statement. This paper was edited by Paul Stoy and re-
viewed by Katharina Jentzsch and one anonymous referee.
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