We measured CO2 and CH4 fluxes using chambers and eddy covariance
(only CO2) from a moist moss tundra in Svalbard. The average net
ecosystem exchange (NEE) during the summer (9 June–31 August) was negative
(sink), with -0.139 ± 0.032 µmol m-2 s-1 corresponding
to -11.8 g C m-2 for the whole summer. The cumulated NEE over the whole
growing season (day no. 160 to 284) was -2.5 g C m-2. The CH4 flux
during the summer period showed a large spatial and temporal variability.
The mean value of all 214 samples was 0.000511 ± 0.000315 µmol m-2 s-1, which corresponds to a growing season estimate of 0.04 to
0.16 g CH4 m-2. Thus, we find that this moss tundra ecosystem is
closely in balance with the atmosphere during the growing season when regarding
exchanges of CO2 and CH4. The sink of CO2 and the
source of CH4 are small in comparison with other tundra ecosystems in
the high Arctic.
Air temperature, soil moisture and the greenness index contributed significantly to explaining the variation in ecosystem respiration (Reco), while active layer depth, soil moisture and the greenness index were the variables that best
explained CH4 emissions. An estimate of temperature sensitivity of
Reco and gross primary productivity (GPP) showed that the sensitivity
is slightly higher for GPP than for Reco in the interval 0–4.5 ∘C; thereafter, the difference is small up to about 6 ∘C and then begins to rise rapidly for Reco. The consequence of this, for a small increase in air temperature of 1∘
(all other variables assumed unchanged), was that the respiration increased
more than photosynthesis turning the small sink into a small source (4.5 g C m-2) during the growing season. Thus, we cannot rule out that the
reason why the moss tundra is close to balance today is an effect of the
warming that has already taken place in Svalbard.
Introduction
Climate warming is predicted to be most evident at high latitudes
(Friedlingstein et al., 2006), with profound effects on ecosystem
functioning. One of the high-latitude regions that are expected to
experience the most dramatic changes caused by climate change is the Arctic.
This region, which is located roughly north of the tree line, is characterized
by cold winters and cool summers and with mean annual temperatures below
zero. The summer periods are short, ranging between 3.5 and 1.5 months from
the southern boundary to the north, and July is normally the warmest month.
Annual precipitation is generally low, decreasing from about 250 mm in the
southern areas to 45 mm in polar deserts in the north (Callaghan et al.,
2006).
The permafrost soils in the Arctic store 1035 ± 150 Pg of organic
carbon in the top 0–3 m (Hugelius et al., 2014), which is more than the
average 2010–2019 of 860 Pg of carbon in the atmosphere (Friedlingstein et
al., 2020). The increased warming in these areas can induce higher
decomposition rates due to increased microbial activity, which will provide a
positive feedback to the climate system (Schuur et al., 2015). On the other
hand, warming can also increase photosynthesis and carbon uptake and thus
compensate for, or exceed, the effect of increased decomposition. Climate
warming is also affecting plant community composition and the length of the
growing season (Post et al., 2009), which also has an impact on the processes
regulating annual carbon emissions and uptake (Bosiö et al., 2014).
There is, however, a large uncertainty regarding the timing, magnitude and
possible sign of potential feedbacks caused by these changes (Myers-Smith et
al., 2020).
Understanding processes that are controlling the exchanges of greenhouse
gases in the Arctic is crucial for the assessment of potential feedback effects.
For this purpose, multiple year-round long-term studies including direct
measurements of CO2 and CH4 fluxes covering all seasons, winter,
spring, summer and autumn, would be ideal. This is a great challenge in the harsh climate of the Arctic and with limited support of key infrastructures
for, e.g., provision of electricity for operation of instruments.
In spite of these difficulties, a few year-round studies have been performed
during the last couple of decades. In the low Arctic, Oechel et al. (2014)
demonstrate the importance of the wintertime fluxes in a tussock tundra
ecosystem in Alaska. They found that the non-summer season emitted more
CO2 than the corresponding uptake during the summer, resulting in a net
source to the atmosphere of about 14 g C m-2 on an annual basis. They
also showed that the shoulder seasons, spring and autumn, roughly
outweighed the summer uptake. Euskirchen et al. (2012, 2017) measured net
CO2 exchange in three different tundra ecosystems: heath tundra,
tussock tundra and wet sedge tundra in northern Alaska over 3 years.
They found that the uptake of -51 to -95 g C m-2 during the summer
(June–August) was overturned by the respiration that occurred during the
winter period, resulting in net annual losses for all three ecosystems. Zhang
et al. (2019) reported 5 years of year-round flux measurements in a
heath ecosystem on west Greenland, and they found that the heath was an annual sink of -35 ± 15 g C m-2. One year with an anomalously deep
snowpack showed a 3-fold higher respiration during the winter as compared
to the other years, which resulted in a significantly lower net uptake during
that year.
Even fewer studies have been done on year-round studies in the high Arctic.
Lüers et al. (2014) quantified the annual CO2 budget using eddy
covariance measurements in a river catchment area near Ny-Ålesund on
Spitsbergen in the Svalbard archipelago, and they found that the ecosystem
was in C balance. The footprint area was a semi-polar desert with only
60 % vegetation cover and patches of bare soil and stones. Also in
Svalbard but further south in Adventdalen on a flat alluvial fen irregularly
covered with ice wedged polygons, Pirk et al. (2017) made year-round
measurements of CO2 fluxes and found it to be a net sink of -82 g C m-2. Because of the irregularities caused by the ice wedges and the
differences in wetness, they focused the analyses on the spatial variability
in two different directions, one wetter and one drier, and they estimated
the annual net ecosystem exchange to -91 and -62 g C m-2
for the respective areas.
The Arctic ecosystems constitute also a source of CH4 to the atmosphere
even if it is not a very large one. Saunois et al. (2020) estimated that the
northern high-latitude region (60–90∘ N) contributed 4 % of global emissions, and emissions from wetlands are
only part of the emissions from this region. However, in the light of the
vulnerability of the high-Arctic permafrost areas and considering the large
carbon pool and the predicted changes in climate, a quantification and
understanding of CH4 exchanges in these areas are still important.
Christensen et al. (2004) showed one example of a dramatic impact of the
climate warming on the CH4 emissions in a permafrost mire in sub-Arctic
Sweden. The warming which has been visible in this area for decades and its
impact on permafrost and vegetation changes were estimated to have caused an
increase in landscape CH4 emissions in the range 22 %–66 % in the
period 1970–2000.
Mastepanov et al. (2008) were the first to show the importance of emissions
also outside of the growing season. They observed a large burst of CH4
from a fen area in Zackenberg, Greenland, after the growing season and during
the time when the soil started to freeze. This finding was confirmed in a
later paper (Mastepanov et al., 2013), and the process was hypothetically
attributed to the subsurface CH4 pool. Hydrology and vegetation
composition play an important role for CH4 emission and dynamics.
McGuire et al. (2012) made a comprehensive summary of CH4 exchanges of
the Arctic tundra showing the difference between wet and dry ecosystems; the
wet tundra emitted 5.4 to 13.0 g CH4-C m-2 during summer and 8.5
to 20.2 g CH4-C m-2 annually. The corresponding values for the
dry/mesic tundra were 0.3 to 1.4 and 0.3 to 4.3 g CH4-C m-2, respectively. Bao et al. (2021) utilized year-round
measurements of CH4 fluxes from three sites of the AmeriFlux network in
northern Alaska to demonstrate the importance of the spring and autumn
seasons for the annual emission. The shoulder seasons contributed about
25 % of the annual emissions, and the autumn season had about 3-times-higher emissions than the spring season. These findings increasingly
emphasize the importance of year-round measurements to fully understand the
CH4 controls and dynamics.
The main aim of this study is to provide another piece of the puzzle
concerning CO2 and CH4 exchanges from different but widespread
ecosystem types in the high Arctic. We hypothesize that this moist tundra
ecosystem is a net carbon sink during the growing season and that the summer
emissions of methane will be at levels comparable with other methane-emitting high-Arctic ecosystems. We made flux measurements of CO2 and
CH4 in an moist moss tundra ecosystem situated at Kapp Linné on the
west coast of the Svalbard archipelago in 2015 and with an additional
campaign in 2016. The measurements in 2015 were done using both the eddy
covariance system (CO2) and chambers (CO2 and CH4) but only
chambers in 2016. We quantify ecosystem respiration (Reco), gross
primary productivity (GPP) and net ecosystem exchange (NEE) during the
growing season based on a combination of chamber and eddy covariance
measurements. The CH4 emission was only quantified for the summer
season. We also analyze the environmental controls of the fluxes.
Materials and methodsResearch site and measurements
This study was performed in the Svalbard archipelago near the weather
station Isfjord Radio (78∘03′08′′ N, 13∘36′04′′ E; altitude 7 m), which is located right on the foreland of Kapp
Linné on the island of Spitzbergen (Fig. S1). The tundra area where the
measurements were performed is located about 1 km southeast of the station.
The study area consists of moist moss tundra, a widespread ecosystem in
Svalbard (Vanderpuye et al., 2002; Ravolainin et al., 2020). The vegetation
is characterized by the moss species Tomentypnum nitens, Sanionia uncinata, and Aulacomnium palustre and a sparse cover of vascular
plants (20 %–40 %), dominated by Equisetum arvense, Salix polaris and Bistorta vivipara. Other vascular plant species were found
in the plots: Saxifraga cespitosa, Saxifraga oppositifolia, Silene acaulis, and some grass species, most likely
Alopecurus ovatus (previously A. borealis) and Poa arctica. The vegetation analysis was made from photographs of
chamber location plots taken between 26 June and 2 July 2015 (see Fig. S4a–y in the Supplement).
The net ecosystem exchange of CO2 was measured with an eddy covariance
(EC) system located centrally on the moss tundra (78∘03′28.6′′ N, 13∘38′40′′ E). The sonic anemometer (USA-1; Metek
GmbH, Germany) was mounted on top of a tripod (see Fig. S1) at 2.7 m height.
The CO2 and H2O concentrations were measured with an open-path
sensor (LI-7500; Li-Cor Inc., USA) placed just beneath the sonic anemometer and
inclined about 30∘ pointing towards the east. Radiation
components, incoming and outgoing shortwave and longwave (CNR-4; Kipp and
Zonen, the Netherlands), were measured at 2.0 m height above the ground with the
sensor directed towards the south. All sensors were connected to a data logger
(CR-1000; Campbell Scientific, USA) which was powered by a solar panel and a
battery. The EC sensors were sampled and stored at 10 Hz, and all other
sensors were sampled at 0.1 Hz with storage of 30 min mean values. These
measurements were made from 25 June to 17 September 2015. The total data
coverage during this period was 47 %, with a longer break in the
measurements between 28 July and 29 August. The impact of substantial gap
filling of measured EC data and partial modeling in order to complete the
full growing season is further discussed below.
The soil efflux of CO2 and CH4 was measured with a dark chamber
connected to a gas analyzer (Ultraportable Greenhouse Gas Analyzer; Los
Gatos Research, USA) on 24 locations within the EC average footprint area. A
circular thin-steel frame, 15 cm in diameter and 15 cm high, was inserted ca. 5 cm into the ground in each location. The sharp edge of the frames made it
easy to insert them into the ground without damaging the vegetation and with
minimal soil disturbance. A picture was taken of each frame (see Supplement)
for documentation of vegetation and for calculation of different indexes.
The chamber was also made from steel, and it had a rubber seal in the end
facing the frame (Fig. S2) to make it airtight when mounted on the frame.
The volume of the chamber and the part of the frame raised above the surface
was 5.3 L. A small fan was installed inside the chamber to provide good
mixing of the air during measurement. A small weight (stone) was placed on
top of the chamber during measurement to prevent it from moving due to wind
gusts. During concentration measurement air was circulated in a closed loop
between the chamber and the gas analyzer in ca. 10 m long, 4 mm diameter
polyethene tubes (see Fig. S2). The airflow through the analyzer was ca 1.2 L min-1. The chamber was ventilated in the free air about 1 min
before each measurement, which lasted for 5 min. The concentrations were
recorded and stored once per second by the gas analyzer. The time stamp of
the recorded data was used to identify measurement cycles for analysis of
fluxes.
The chamber measurement positions were selected in the following way. The
frames were grouped in two sections, one northeast and one southwest of
the flux tower, since it was expected that the main wind direction would be
along that direction. Each group was then split into three subsections with
four measurement points within each one of them. The locations were named
S1:1–S1:4, S2:1–S2:4, S3:1–S3:4, N1:1–N1:4, N2:1–N2:4 and N3:1–N3:4. The
four measurement points within each subsection were then placed along a
transect with 3–4 m between each point. This way it was possible to measure
all four chamber locations without having to move the whole measurement
system. Chamber measurements were made in three separate campaigns:
mid-summer (26 June to 2 July 2015), late summer (25–27 August 2015) and
early summer (14–15 June 2016). Each location was measured three times
during each one of the three campaigns – a total of 216 measurements. Besides
gas concentrations, also soil temperature (5 cm), soil moisture (0–5 cm) and
active layer depth were measured during each campaign.
Meteorological data needed for analyses and gap filling were obtained as
follows: hourly air temperature and relative humidity from Isfjord Radio,
half-hourly global radiation from Adventdalen, daily snow depth and ground
ice conditions from Svalbard Airport, and monthly precipitation from Isfjord
Radio and Barentsburg. The distances between the measurement site and these
stations are as follows: Isfjord Radio, 1 km; Barentsburg, 13 km; Svalbard Airport, 46 km; and Adventdalen, 50 km. Using data from the more distant locations,
Svalbard Airport and Adventdalen, introduces some additional uncertainty.
Concerning global radiation data, we could compare in situ-measured
half-hourly radiation with the corresponding data from Adventdalen for a
shorter period, and it showed general good agreement although with relatively
large scatter (y=0.84x+15.9; r2=0.57; n=580). According to
Dobler et al. (2021) the amount of precipitation in the area where Kapp
Linné and Svalbard Airport are located does not show any significant
differences on an annual basis. Vickers et al. (2020) analyzed timing of
snow cover in Svalbard, and they showed that the mean (2000–2019) first
snow-free day is very similar in areas where Kapp Linné and Svalbard Airport
are located. Thus, we are confident that using data from these relatively
remote locations does not introduce serious bias in our analyses. Data
sources are given in the Acknowledgements.
Data analysis
The raw data from the eddy covariance flux measurements were analyzed using
the EddyPro software version 6.1.0 (Li-Cor, 2016). A correction was made for
the impact of the additional heat flux in the sensor path of the open-path
analyzer on the flux calculations according Burba et al. (2008). Gap filling
during the measurement period was made using the REddyProc online eddy
covariance data processing tool developed at the Max Planck Institute for
Biogeochemistry (Wutzler et al., 2018) without u* correction since we could
not identify any threshold for u*. The u* threshold is generally low for low
and smooth vegetation (Pastorello et al., 2020), and for a wind-exposed site
like ours, it is not surprising that such threshold could not be found. Flux
partitioning was made with the daytime-based method according Lasslop et al. (2000). Only data of highest quality, i.e., class = 0, were retained for the
gap filling and further analyses. Gap filling outside of the EC measurement
period to obtain the carbon balance for a full growing season was made by
modeling using the Lloyd and Taylor (1994) model for Reco and an
empirical light response function for GPP (see below). The measured
respiration by chambers was used to obtain the parameters for Reco, and
EC data were used for fitting of the light response function for GPP.
For flux footprint calculations the roughness length (z0) is needed, and
it was calculated from the wind profile relationship in near-neutral
(-0.01<z/L>0.01) conditions:
z0=zmeuz⋅ku∗,
where zm is measurement height, u(z) is wind speed at height z, k is
von Karman's constant and u* is friction velocity. We used the flux
footprint prediction (FFP) online tool by Kljun et al. (2015) to calculate
the footprint climatology.
The fluxes from the chamber measurements were estimated from the time change
of the concentrations using linear regression. Every individual measurement
was inspected and evaluated manually. These inspections showed that 50 s for CO2 and 100 s for CH4 were optimal to obtain
near perfectly linear responses a few seconds after the chamber had been
placed on the frame. The slopes of the regressions were then used to
calculate fluxes per unit surface area. The flux detection limits for
CO2 and CH4 were calculated in the following way: first the
peak-to-peak variations in the respective gases were determined when the
chamber was ventilated in the free air and when conditions were steady. Then
20 sets of artificial “fluxes” for each gas species were estimated based on
100 randomly generated concentrations for each data set. The peak-to-peak
difference was used as seed (input) for the randomly generated values. The
95 % value of the distribution of these randomly generated fluxes was
taken as the flux detection limit for the respective gas.
The pictures of the vegetation inside of the chamber frames were analyzed
using the ImageJ (https://imagej.net, last access: 29 December 2020) public domain software.
The camera color channel information (digital numbers for red (R), green (G)
and blue (B) channels) was collected from the JPEG pictures. This type of
picture is for instance used in studies that are tracking the phenological
development of vegetation (e.g., Richardson et al., 2009). The so-called
green index (GI) is applied to detect differences in greenness of
vegetation:
GI=G/R+G+B.
This index was also estimated for the central footprint area (100 m radius)
of the flux measurement location using a picture taken at 160 m above the
altitude of the measurement area.
Forward stepwise linear regression (SigmaPlot 12.5) was used to analyze the
dependency of the CO2 and CH4 fluxes on environmental variables.
We tested for air temperature (Ta), soil moisture (θ), soil
temperature (Ts), active layer depth (ALD), measurement location
(Sid) and GI.
For gap filling of Reco we only had access to air temperature with full
annual coverage, and, thus, we could only use this driver for estimation of
the Reco. The measured chamber CO2 fluxes were fitted to the Lloyd
and Taylor (1994) model with air temperature (Ta) as an independent
variable:
FCO2=a⋅eb156.02-1Ta+46.02.
During the EC measurement period (25 June to 17 September 2015) the GPP was
estimated as
GPP=NEEf-Reco,
where NEEf is the gap-filled NEE according to Wutzler et al. (2018).
This way Reco and GPP become consistent with the measured and gap-filled NEE. For the time before and after this period, NEE was estimated as
the sum of modeled Reco and modeled GPP. The data for the GPP model
was derived from
GPPm=NEEm-Reco,
where NEEm is the measured net ecosystem exchange. The GPPm was
then fitted to a light response function:
GPPm=c1+c2⋅c3/c2+Rg.
Results
For CO2 exchanges and partitioning we combined the soil efflux
measurements with the chamber system with the eddy covariance flux
measurements. This was crucial for the partitioning and for gap filling
because from 20 April to 20 August at this location the sun is above the
horizon 24 h of the day, and this means that there were few occasions of
dark nighttime measurements with the eddy covariance system, and all of these
were collected at the very end of the summer. We consider the chamber
measurements that were distributed across the summer to be more
representative of Reco for this location.
For CH4 exchanges we do not have any eddy covariance measurements, so we
present only chamber data for this variable.
Weather
The mean annual temperature at Kapp Linné was -1.5 ∘C
during 2015, which was 3.5 ∘C higher than the long-term mean
(1961–1990) of -5.1 ∘C. The summer (June–August) mean of
5.5 ∘C was 2.0 ∘C higher than the
long-term mean for the same time period (Fig. 1). The summer precipitation
in 2015 was much lower, 58 mm as compared to the long-term precipitation,
which was 121 mm. The annual precipitation was also lower, 431 mm compared
to the long-term precipitation, which was 514 mm.
Monthly precipitation (a): long-term average 1961–1990 shown by black
bars and 2015 by grey bars. Data from Barentsburg for January–May and from Isfjord
Radio for June–December. Mean monthly air temperature (b): solid line
is long-term average 1961–1990 and dotted line is 2015. Data from Isfjord
Radio, which is located about 1 km west of the investigation area.
We defined the growing season (the period during which vegetation is
photosynthesizing) based on the permanence of the snow pack, which resulted
in start day no. 160 and end day no. 284 (Fig. 2). The summer period which
normally is defined as June through August was here defined as lasting
from 9 June (same as the start of the growing season) until the end of August
(Fig. 2).
Weather conditions during 2015. (a) Mean daily global
radiation at Adventdalen. (b) Mean daily air temperature at
Isfjord Radio (blue), snow depth (red) and ground ice conditions (green) at
Svalbard Airport close to Longyearbyen. The ground ice condition is scaled
from 0–20, where 0 is no snow or ice on the ground and 20 indicates a
complete cover of snow or ice.
Flux footprint and greenness
The footprint climatology shows a good representativity of the moss tundra
surface by the EC measurements, with 60 %–70 % of fluxes emanating from areas
well within the border of the tundra (Fig. 3). The mean green index for a
circular area with radius of 100 m centered at the flux tower was 0.34, which
corresponded exactly to the mean value for all chamber locations. The GI for
the 24 chamber locations varied between 0.316 and 0.369. We observed a good
(visual) correlation between GI and coverage of green plants (see Fig. S4a–y of chamber location pictures and GI).
The footprint climatology with red contour lines 10 %–90 %. The
area within the green line marks the heart of the moss tundra. The scale (m)
is shown on the outer borders of the picture (map source: https://Bing.com, Maxar Technologies).
CO2 exchanges
The CO2 fluxes from the chamber measurements showed quite large
variation over time (Fig. 4) and across sampling locations (Fig. 5). The
mean CO2 flux of all samples was 0.81 ± 0.11 µmol m-2 s-1. The uncertainty is given as the 95 % confidence limit.
Measured CO2 exchange (FCO2) from the 24 sampling points
using a dark chamber and portable gas analyzer. The dashed red line indicates
the CO2 flux detection limit, and the blue line represents 3 × SD of all
data points. The dashed vertical lines separate sampling periods from left
to right: 14–15 June, 26 June–2 July and 25–27 August, respectively.
Box plot of CO2 fluxes (FCO2) per sampling location
named N1–N3 and S1–S3. The boundaries of the grey boxes represent the 25th
and 75th percentiles, the line represents the median, and whiskers above and
below the boxes indicate the 10th and 90th percentiles. Outlying points
are also shown.
Of the tested environmental variables – Ta, θ, Ts, ALD,
Sid and GI – it was only Ta, θ and GI that contributed
positively and significantly in decreasing order to explaining the variability
of the CO2 flux (Table 1).
Result of stepwise linear regression with CO2 flux as
a dependent variable. The normality test failed, but significance in all variables
was confirmed with Wilcoxon signed-rank tests. Ta is air temperature,
θ is soil moisture and GI is the green index.
Ideally all of these variables should be used in a model to estimate
Reco for gap-filling purposes, but we could only use air temperature
since this was the only variable that we had access to with complete
coverage for a full year. The Lloyd and Taylor model (Eq. 3 and Fig. 6)
was thus used to estimate ecosystem respiration for 2015 using half-hourly
air temperature as input.
Measured ecosystem respiration (Reco; green dots) using
chambers plotted against air temperature. The red curve is the fitted
equation, and the blue curves are the corresponding boundaries when
considering the standard deviation of the parameters.
The modeled gross primary productivity (Eq. 6; GPPm) had a small
offset when global radiation was zero (Fig. 7). This offset was adjusted for
when the model was applied for gap filling so that GPP becomes zero during
nighttime.
Gross primary productivity (GPPm) plotted against global
radiation (Rg); red symbols are estimated values according to Eq. (5),
and the black symbols are the fitted model.
The diurnal course of NEE during June–August exhibits the normal pattern
with a successively increasing drawdown of CO2 during the first half of the
day, resulting in a maximum around noon. It should be noted that during June
until 20 August the sun was over the horizon 24 h, thus no dark period.
The positive values at the beginning and end of the diurnal courses are a
result of Reco being larger than GPP. As pointed out in Fig. 8, most of
the data of August were gap filled, causing some additional uncertainty.
However, the diurnal course seems reasonable although the peak during noon is
much lower as compared to July. This can be explained by the much lower
incoming radiation in August as compared to July; the mean global radiation
in July was 192 and 98 W m-2 in August. The mean air
temperature was similar during July and August. In September the incoming
radiation is very low, and thus GPP is also very low, which results in a NEE
that is dominated by the Reco. The positive NEE values around midnight
during June–September are in good accordance with the values from the
independent dark chamber measurements (Fig. 5).
The mean monthly diurnal course of net ecosystem exchange (NEE)
during the period of eddy covariance measurements 25 June to 17 September.
The error bars (every 2nd shown) are the 95 % confidence interval.
Notice that the main part of August was gap filled because of measurement
problems.
In order to assess the impact of the large gap in measured data in August
where we only had 2 d of measured fluxes at the end of the month, we
made a comparison between the gap-filled diurnal course based on Wutzler et
al. (2018) and our modeling using Eqs. (3) and (6). The results show
very good agreement between the two methods (see Supplement), giving support
to the realism and reliability of the gap-filled data.
The mean net CO2 flux during the growing season was -0.019 ± 0.024 µmol m-2 s-1, with uncertainty given as the 95 % confidence
limit. The cumulated NEE during the growing season ended up negative, with -2.5 g C m-2 (Fig. 9). The mean net CO2 flux during summer was -0.139 ± 0.032 µmol m-2 s-1 (95 % confidence limit), and the
cumulated NEE was -11.8 g C m-2 (Table 2).
Summary of seasonal C fluxes from Kapp Linné. Reco is
ecosystem respiration, GPP is gross primary productivity and NEE is net
ecosystem exchange.
The cumulated half-hourly net ecosystem exchange (NEE) during
the growing season.
Temperature sensitivity of Reco and GPP
The temperature sensitivity of the Reco is already given by the fitted
Lloyd and Taylor (1994) equation. In the absence of long time series of
measurements during multiple year were natural climate variability could be
used to assess temperature sensitivity of GPP, we approached this problem in
the following way. We normalize GPP for its dependence on radiation by
estimating the difference between the “measured” GPP and the model which
only depends on radiation (see Fig. 7). A stepwise linear regression with
normalized GPP as a dependent variable and air temperature, time of season, and
vapor pressure deficit as independent variables showed that of the total
explained variance, air temperature stood for 94 % and time of season and
and vapor pressure deficit for 3 % each. Thus, the resulting normalized
GPP shows effectively a dependence on air temperature (Fig. 10), with values
becoming more negative, i.e., showing increasing GPP with increasing
temperature. We fitted the same type of model to these data as for the
Reco to be able to compare sensitivities to temperature.
Normalized gross primary productivity (GPP) plotted against air
temperature and with the fitted exponential model.
In Fig. 11 we reversed the sign of the GPP temperature response function to
make it more easily comparable with the Reco response model. The
temperature sensitivity (µmol m-2 s-1 K-1) can be
estimated from the slope of these curves, and the sensitivity is slightly
higher for GPP than for Reco in the interval 0–4.5 ∘C; thereafter, the difference is small up to about 6 ∘C and then
begins to rise rapidly for Reco. We tested what impact this could
have by increasing the measured half-hourly air temperature by 1 ∘C and found that during the growing season the GPP
increased by -31.9 g C m-2 and Reco by 36.4 g C m-2. Thus, a
slightly larger increase in Reco as compared to GPP results in
the small sink of -2.5 g C m-2 turning into a source of 4.5 g C m-2.
Temperature sensitivity for ecosystem respiration (Reco)
(brown) and Rg-normalized (positive) gross primary productivity (GPP)
(green).
CH4 exchanges
The CH4 fluxes from the chamber measurements showed large variation
over time (Fig. 12) and across sampling locations (Fig. 13). The mean
CH4 flux of all samples was 0.00051 ± 0.00024 µmol m-2 s-1. The uncertainty is given as the 95 % confidence limit.
Setting all fluxes that fell within the flux detection limits to zero
changed the mean value with -0.2 %. Assuming that the mean flux was
representative for the whole of growing season 1, the total CH4 summer
emission was 0.039 to 0.164 g CH4 m-2.
Measured CH4 exchange (FCH4) from the 24 sampling
points using a dark chamber and portable gas analyzer. The dashed red lines
indicate CH4 flux detection limit (i.e., inside the limits of detection
the exact numbers are highly uncertain), and the blue line represents 3 × SD.
The dashed vertical lines are the same as in Fig. 4.
Box plot of CH4 fluxes (FCH4) per sampling location
named N1–N3 and S1–S3. The statistics includes also the data that fall within
the flux detection limits. The boundaries of the grey boxes represent the
25th and 75th percentiles, the line represent the median, and whiskers above
and below the boxes indicate the 10th and 90th percentiles. Outlying
points are also shown.
We also noticed a clear trend during the summer with the highest fluxes in
mid-June and then decreasing during the following two sampling occasions.
The respective mean values with 95 % confidence intervals for the three
sampling periods were 0.00121 ± 0.000512 (14–15 June), 0.000332 ± 0.000465 (26 June–2 July) and -0.00000781 ± 0.0000936 µmol m-2 s-1 (25–26 August).
For CH4 exchanges we found that ALD, θ and GI contributed significantly
to explaining the variance of the flux (Table 3). The CH4 flux responded
negatively to increasing ALD and positively to θ and GI.
Result of stepwise multiple linear regression with CH4 flux as
a dependent variable. The normality test failed, but significance in all variables
was confirmed with Wilcoxon signed-rank tests. ALD is active layer depth,
θ is soil moisture and GI is the green index.
VariableDelta-R2Probability (p)ALD0.175< 0.001θ0.0250.01GI0.0200.004DiscussionSeasonal CO2 fluxes
We focus our discussion mainly on comparison with other tundra sites located
in the North Atlantic area since these sites are influenced by the North
Atlantic Current with its impact on weather patterns and climate. This
limits the comparisons to sites in Greenland, Svalbard and northern
Scandinavia. However, we broaden the comparison a bit by adding two sites
from Alaska.
Lund et al. (2012) found that the start of the uptake period was strongly
correlated with the start of the snowmelt for the fen in Zackenberg, NE
Greenland. They defined the start of snowmelt as the day when snow depth was
< 0.1 m. This coincides very well with our definition of the start of
the growing season (see Fig. 2). Our results for the growing season NEE showing
a small net uptake of -2.5 g C m-2 are at the low end in comparison with
any other high-Arctic sites which all show a larger gain of carbon during the
growing seasons.
Lund et al. (2012) analyzed 10 years of EC flux measurements from a
heathland in Zackenberg, and they reported a NEE range of -39.7 to -4.3 g C m-2 for the growing season. It was only 2 years out of 10 that
showed NEE values close to zero but still indicating a small net uptake in
Zackenberg heath. Their measured growing season GPP was in the range of
-95.4 to -54.1 g C m-2, and the Reco was in the range of 37.7 to
63.8 g C m-2. Our corresponding values were -112.7 g C m-2 for GPP
and 110.2 g C m-2 for Reco. López-Blanco et al. (2017)
presented data over a period of 8 years of EC flux measurements from
Kobbefjord, SW Greenland, over an area of mixed fen and heath vegetation.
Their growing season ranges were -74.2 to -45.9 g C m-2 for NEE, -316.2 to -181.8 g C m-2 for
GPP, and 144.2 to 279.2 g C m-2 for Reco excluding 2011, which was anomalous because of a pest outbreak,
and 2014, which did not have a full growing season.
Our estimate of a small summer NEE of -11.8 g C m-2 (Table 2) is also
different in comparison with other tundra sites which show larger uptake
during the summer; for a fen type of vegetation in NE Greenland, Soegaard and
Nordstroem (1999) reported -96.3 g C m-2, while Rennermalm et al. (2005)
reported -50 g C m-2 for the same site but for a different year.
Groendahl et al. (2007) reported a range of -1.4 to -18.9 g C m-2 for
heath vegetation also on NE Greenland.
It is difficult to compare growing season values firstly because they are
rarely defined the same way. Only small differences in definition of the start
and end of the growing season can have a large impact on the NEE values since
NEE is the sum of two large components of almost equal size and of different
sign. Secondly, it is also difficult to compare GPP and Reco for any
season since the methods to split NEE into components differ from case to
case. The most reliable comparison is probably for summer season (June–August) since most studies represent this period best in terms of
measurement coverage and quality. And thirdly, there are differences in
vegetation type that can have a big impact on gas exchanges. Our moist moss
tundra is dominated by moss species, and mosses are not as efficient primary
producers as vascular plants, which makes the net uptake of carbon dioxide
small as compared to heath or wet fen systems.
The climate warming is predicted to be most evident at high latitudes such
as the Arctic region. Svalbard has experienced significant warming during
the last decades (1971–2017) of 3–5∘, with the largest increase in
the winter and smallest in the summer (Hanssen-Bauer et al., 2019). Our air
temperature observations in 2015 are in line with these results (Fig. 1). An
interesting question is whether such changes in temperature have also affected the
net carbon balance of the ecosystem. Our analysis of temperature sensitivity
of Reco and GPP shows that this could be the case for this site since
Reco is increasing more than GPP for temperatures above about 6 ∘C, which occurs quite frequently during the summer (see Fig. 2). Our analyses of the impact of a temperature increase of 1 ∘C showed that our small sink of -2.5 g C m-2 during
the growing season would be turned into a similarly small source of 4.5 g C m-2 for a 1∘ increase in air temperature. These results are in
line with those of Welker et al. (2004), who performed a warming experiment in
high-Arctic tundra ecosystems. They showed that the net ecosystem exchange
in the wet tundra ecosystem decreased by 20 % during the growing season under
a 2∘ warming treatment. This was in contrast to the dry and mesic
ecosystems which increased their net carbon uptake by 12 %–30 %.
CH4 fluxes
Our estimated growing season CH4 flux of 0.08 g C m-2 is very low
compared to most other methane-emitting tundra sites; the Zackenberg fen
site emitted CH4 in the range 1.4 to 4.9 g C m-2 – Mastepanov et
al. (2013), Jackowicz-Korczynski et al. (2010) and Jammet et al. (2015)
reported 20.1 to 25.1 g CH4 m-2 for the Stordalen mire in northern
Sweden. For three different sites in northern Alaska, Bao et al. (2021)
reported annual emissions between 1.8 and 8.5 g CH4 m-2, which
corresponds to 0.94 and 4.5 g CH4 m-2 for the growing season based
on their estimate that growing season emissions are 52.6 % of the annual
emissions. Sachs et al. (2008) measured CH4 exchanges with the EC method in
a northern Siberian polygon tundra and found generally low fluxes of about
18.5 mg CH4 m-2 d-1 with little variation over the growing
season. This rate adds up to 2.3 g CH4 m-2 for their 4-month-long growing season.
It should be pointed out that we did not perform measurements during the
shoulder seasons, meaning that we probably underestimate the seasonal total.
The importance of shoulder seasons was first pointed out by Mastepanov et al. (2008), who discovered a large burst of CH4 at and after the onset of
soil freezing. One interesting observation is that the main part of our
CH4 flux occurred during the sampling period 14–15 June 2016, which is
about 30 d after snowmelt. This is the time of the season when CH4
emissions normally are peaking (Mastepanov et al., 2013). After that, the
rates dropped to practically zero in late August (see Fig. 12).
The comparison between the different sites is hampered by the fact that
they in most cases belong to different bioclimatic subzones with differences
in climate and vegetation (Walker et al., 2005). The only site besides Kapp
Linné that belongs to subzone B is the one in Ny-Ålesund. The other high-Arctic sites, Adventdalen and Zackenberg, both belong to subzone C, and the
intermediate high/low-Arctic sites Kobbefjord and Disko Island belong to
subzone D respectively C/D. The low-Arctic site Atqasuk belongs to subzone D,
and the Imnavait Creek belongs to subzone E. The sub-Arctic Abisko is not
classified by Walker et al. (2005), but based on mean July air temperature it
should belong to subzone E. These differences in climate and vegetation
should be kept in mind when comparing results from different sites.
Environmental controls of fluxes
A key issue in the high Arctic is how ecosystems with soil that contains large
amounts of frozen carbon will respond to warming. A recent report about the
future climate of Svalbard (Hanssen-Bauer et al., 2019) shows that appalling
changes are at risk of occurring. By 2071–2100, compared to 1971–2000 the mean
annual temperature is estimated to increase by 7 to 10 ∘C for the medium- and high-emission scenarios,
respectively. Precipitation is also estimated to increase by 45 %
respectively 65 % for these scenarios. Such large changes will of course
also have a lot of other impacts, for instance shorter snow season,
more erosion and sediment transport, and changes in vegetation composition and
growth. Assessment of such large changes is very difficult and is
far beyond the scope of this paper. We have, however, shown that for a smaller
temperature increase of 1∘, the impact on the net carbon balance
during the growing season will be minute; the increase in gross primary productivity is compensated for by a corresponding or actually slightly
larger increase in ecosystem respiration. A similar compensation effect
was obtained for a heath site in Zackenberg by Lund et al. (2012). They used
multi-year measurements to assess the effect of changes in temperature on
the growing season fluxes
We found that air temperature was the main control of ecosystem respiration
followed by soil moisture and the greenness index (Table 1). We had expected
that soil temperature would contribute significantly to explaining the
variations in Reco but it did not. Cannonea et al. (2019) showed that
ground surface temperature at 2 cm depth contributed significantly to
explaining Reco in nearby Adventdalen during early, peak and late parts of
the growing season. In their study, soil moisture was also significant during
peak and late seasons. One possible explanation to this difference in
responses could be that our soil temperature was measured at 5 cm depth and
that air temperature was more representative for the microbial processes
taking place in or near the soil surface. Interestingly, GI contributed
significantly to explaining variations in Reco. The GI was clearly
correlated with the abundance of Salix polaris (see Supplement), and thus we interpret the
positive correlation between GI and Reco to be an effect of increasing
contribution by autotrophic respiration to the total respiration.
We found no significant correlation between CH4 emission and
temperature. The best explanation was by active layer depth followed by soil
moisture and GI (Table 3). But it should be pointed out that ALD and θ are not independent from each other and that ALD can be regarded as a
proxy for any seasonal variability, like plant phenology. Soil moisture
decreases with increasing active layer depth. The correlation between GI and
CH4 emission is probably also connected with abundance of the vascular
plant Salix polaris. Vascular plants have long been mentioned as a pathway for CH4
from the soil interior to the atmosphere in wet tundra ecosystems (e.g.,
Schimel, 1995), but it could also be an effect of mediation of soil by the
root exudation of organic acids as mentioned by Ström et al. (2012).
However, we have not found any studies supporting the latter hypothesis
concerning Salix polaris.
Conclusions
Our analyses of EC and chamber flux measurements have shown that the moss
tundra on Kapp Linné is a small sink of CO2 and a small source of
CH4 during the growing season. Realizing that the winter season also
emits CO2, we tentatively conclude that this moist moss tundra is a
source on an annual basis. Concerning the magnitude of the CO2
exchanges during summer, we find it to be anomalous compared to fens and
heath ecosystems located in the North Atlantic region which all are sinks
during the summer. The CH4 exchange is much lower than for other tundra
ecosystems in the region.
The temperature sensitivity for CO2 exchange was slightly higher for
GPP than for Reco in the low temperature range of 0–4.5 ∘C, almost similar up to 6 ∘C, and
thereafter it was considerably higher for Reco. The consequence of
this, for a small increase in air temperature of 1∘ (all other
variables assumed unchanged), was that the respiration increased more than
photosynthesis, turning the small sink into a small source. But a warmer
winter period would probably also result in an additionally increased loss
of carbon. We cannot rule out whether the reason why the moss tundra is close
to balance today is an effect of the warming that has already taken place in
Svalbard.
The analysis of which environmental factors controlled the small-scale
fluxes showed that air temperature dominated for Reco and active layer
depth for CH4, but we also found that the greenness index significantly
explained part of the variation in these fluxes. For Reco we attributed
this to an increased share of autotrophic respiration to the total, and for
CH4 we hypothesized that the abundance of the dwarf shrub Salix polaris affected the
exchange either through internal plant pathway for methane or through
increased provision of C substrate to the anaerobic microbial community
stimulating the production of methane. This finding is an indication that
modeling of CO2 as well as of CH4 fluxes can be improved by also
considering differences and changes in greenness of the vegetation.
Data availability
Data can be obtained from Zenodo (10.5281/zenodo.5704508, Lindroth et al., 2021).
The supplement contains some additional photographs of equipment, site and
color photographs of vegetation within the frames used for chamber
measurements. The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-3921-2022-supplement.
Author contributions
AL designed the study and wrote the manuscript. NP and AL performed the EC
measurements and analyzed the EC data. ISJ performed the vegetation
characterization. AL, CS, LK and MBN performed the chamber measurements. All
authors read and commented on the manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work did not receive any other funding except salaries for the authors
from their respective organizations. Observations of air temperature,
relative humidity, precipitation, ground ice conditions and snow depth were
obtained from the Norwegian Centre for Climate Services (NCCS) and provided
under license CC BY 4.0. Global radiation data from Adventdalen were obtained
from the University Centre in Svalbard (UNIS). We thank Jonas Åkerman, Lund University, for support with information about the site.
Review statement
This paper was edited by David Bowling and reviewed by two anonymous referees.
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