Introduction
The global marine system plays a major role in regulating atmospheric
CO2, currently absorbing roughly 2 PgC from the atmosphere each year,
or roughly a quarter of anthropogenic CO2 emissions (Takahashi et al.,
2009; Wanninkhof et al., 2013; Sitch et al., 2015). Sea ice, which covers up
to 11.8 % of the global ocean's surface, has important implications for
the global carbon cycle (Weeks, 2010).
Sea ice does not have the same physical properties as freshwater ice (Gosink
et al., 1976). It is porous, with brine channels exchanging salt and gases
between the atmosphere and the water below. Compared to terrestrial
environments CO2 fluxes over sea ice are small. However, there are many
different types of sea ice and a large degree of uncertainty remains in the
physical processes controlling gas exchange in these regions (Miller et al.,
2015). The vast size of the sea ice ecosystem means that even small exchange
rates may produce important fluxes on the global scale, and therefore
improved measurement techniques and increased data collection/coverage are
essential to better characterize the baseline CO2 exchange for sea ice
regions. Such developments are also necessary for predicting how Arctic
carbon budgets will change as the current trend towards thinner, younger ice
cover and reduced sea ice extent continues (Kwok, 2007; Maslanik et al.,
2007; Comiso et al., 2017).
Eddy covariance (open-path IRGAs) and enclosure measurements of
FCO2 (mmol m-2 d-1) over landfast sea ice.
Study
Range
Low
High
Eddy covariance
Semiletov et al. (2004)
-38.6
-19.5
Zemmelink et al. (2006)∗
-18.2
-4.5
Semiletov et al. (2007)
-3.5
-0.4
Else et al. (2011)∗
20
36
Miller et al. (2011)∗
-60.5
70.3
Papakyriakou and Miller (2011)∗
-259.2
86.4
Sievers et al. (2015)
-110
295
Enclosure
Delille (2006)
-4
2
Nomura et al. (2010)
-1.0
0.7
Sejr et al. (2011)
0
1.1
Geilfus et al. (2012)
-2.65
2.1
Nomura et al. (2013)
-4.0
0.5
Geilfus et al. (2014)
-2.9
0.3
Delille et al. (2014)
-5.2
1.9
Geilfus et al. (2015)
-5.4
-0.04
Sievers et al. (2015)
0.9
2.2
∗ Results were reported as a range of daily averages (5 h average in
the case of Papakyriakou and Miller, 2011). Full measurement ranges are
necessarily larger, though unreported.
Over the last several decades the two main approaches for measuring
FCO2 over sea ice have been the enclosure method and the eddy
covariance (EC) method. The enclosure method works by measuring the change
in gas concentration over time within a chamber placed on the sea ice
(Miller et al., 2015). The main shortcoming with this method is that it
alters the environment which is being measured (e.g., affecting temperature,
radiation, pressure gradients, wind speed and turbulence, and
atmosphere–surface CO2 concentration differences). Proper technique can
minimize these artifacts, but even under the best conditions it is expected
that they will cause some underestimation of FCO2 (Miller et al.,
2015). Additionally, the measurements are spatially and temporally limited.
Measurements are confined to the region enclosed by the chamber (centimeter scale),
making it challenging to accurately measure fluxes over whole ecosystems
(meter to kilometer scale), which typically contain heterogeneity on scales larger than
the footprint of the chamber. Additionally, long-term measurements are not
feasible for enclosures due to the fact that they alter the underlying
environment and the degree of manual intervention they require.
The EC technique works by measuring vertical wind speed and gas
concentration at high frequencies. The covariance between fluctuations in
vertical wind and fluctuations in the gas concentration, averaged over a
period of time, represents a direct measurement of flux. Unlike the enclosure
method, it does not alter the environment in which it measures and is
practical for gathering long-term continuous measurements of flux over a
spatial scale that encompasses the natural heterogeneity of sea ice.
The enclosure and EC methods have not shown good agreement over sea ice,
for which flux magnitudes measured by EC systems have been consistently higher
than those measured by enclosures (Table 1). Over landfast sea ice the
enclosure method produces fluxes on the order of -5.4 to 2.2 mmol m-2 d-1
(Delille, 2006; Nomura et al., 2010, 2013; Sejr et al., 2011;
Geilfus et al., 2012, 2014, 2015; Delille et al., 2014; Sievers et al.,
2015), while EC often measures FCO2 uptake and effluxes of several
hundred mmol m-2 d-1 (Miller et al., 2011; Papakyriakou and
Miller, 2011; Sievers et al., 2015). Fluxes of this magnitude are
suspiciously high. They compare in magnitude with terrestrial fluxes (Christensen et al.,
2000; Lafleur et al., 2003; Sullivan et al., 2008) and fluxes over
open-ocean algal blooms (Yang et al., 2016a).
The most likely explanation for the large discrepancy between methods is a
failure of the open-path infrared gas analyzer (IRGA) used in the EC systems. Overestimation of
FCO2 by open-path EC in the low-flux marine environment has been
documented as far back as Broecker et al. (1986), and has since been
confirmed (Miller et al., 2010; Blomquist et al., 2014; Landwehr et al.,
2014). Closed-path eddy covariance systems may reduce measured FCO2
magnitude (e.g., Sievers et al., 2015 measured a mean FCO2 of 1.73±5 mmol m-2 d-1 over landfast sea ice), but have been shown
to be affected by the same problems as the open-path IRGAs (Blomquist et
al., 2014; Landwehr et al., 2014; Butterworth and Miller, 2016b). An
improved technique was developed by Miller et al. (2010) which used a
closed-path IRGA and dried sample airstream. This system addressed the
problems with previous EC systems by eliminating fluctuations in all
variables (pressure, temperature, and H2O) associated with the density
correction (Webb et al., 1980). Subsequent studies have confirmed the
effectiveness of this approach for measuring air–sea fluxes (Blomquist et
al., 2014; Landwehr et al., 2014; Butterworth and Miller, 2016b; Bell et
al., 2017).
Cavity ring-down spectroscopy (CRDS) may be suitable for measuring FCO2
over landfast sea ice, though it has yet to be field tested. Open water
results have confirmed flux detection limits for dried Picarro instruments
(G1301-f; G2311-f) to be in the range needed for measuring over landfast sea
ice (Blomquist et al., 2014; Yang et al., 2016b). The Los Gatos Research
FGGA on the other hand (in which drying is less feasible) has flux detection
limits that may not be suitable for measuring at the low flux magnitudes
expected over landfast sea ice (Yang et al., 2016b), though Prytherch et al. (2017),
using an undried FGGA, measured FCO2 in the Arctic marginal ice
zone that agreed with previous results (Butterworth and Miller, 2016a).
Because CRDS systems are expensive and have significant power demands,
systems based on closed-path IRGAs are currently more practical for making
continuous flux measurements in Arctic environments.
For this study, we applied the dried, closed-path IRGA design to measure
FCO2 from a permanent tower over sea ice in the Canadian Arctic. This
is the first EC system of this kind to measure FCO2 over landfast sea
ice. The benefits of a fixed tower are that it avoids the motion
contamination and flow distortion associated with ship-based EC.
Additionally, it is capable of collecting a long-term continuous flux
dataset in one area, thus enhancing our ability to address process-level
questions. Here we will present 4 months of flux data from the spring and
summer season (May to September 2017) as the region transitioned from full
ice cover to open water and describe the performance of the system. Our
primary goal in this paper is to describe the design and performance of the
system, while subsequent articles will more fully explore the insights
gained about CO2 exchange in the sea ice environment.
Methods
Site description
An eddy covariance system to measure fluxes of momentum, sensible heat,
latent heat, and CO2 was installed on a 10 m tower located on the
northwest side of Qikirtaarjuk Island, a low-lying island (500×200 m) in Dease Strait, roughly 35 km west of Cambridge Bay, Nunavut (Fig. 1).
Qikirtaarjuk Island is the southernmost island in a chain that extends
across Dease Strait, creating active tidal straits that produce polynyas in
the fall and early spring (Fig. 1). Except for the islands north of the
tower, the closest being Unihitak Island 3.5 km away, the tower has
unimpeded fetch on the order or 50 km from large-angled swaths to the east
and west. This ensures that much of the flux footprint represents only water
and not a mix of water and land.
Map (a) showing the location of Qikirtaarjuk Island, 35 km west
of Cambridge Bay, Nunavut. Satellite image (b) of Qikirtaarjuk Island
(28 June 2017), showing polynya development in the tidal straits. Landsat-8
image courtesy of the U.S. Geological Survey.
Instrument setup
The tower was configured with an array of instruments (Fig. 2) to measure
mean meteorological variables (logged as 1 min averages on a Campbell
Scientific CR1000 data logger) and high-frequency flux variables (10 Hz;
CR3000 data logger). Mean wind speed and direction were measured using a 2-D
propeller vane anemometer (RM Young; Marine Wind Monitor) mounted at 7.8 m
above ground level (a.g.l.). Three temperature and relative humidity probes
(Campbell Scientific HMP45C) were mounted at 9.6 m, 5 m, and 2.2 m a.g.l. A
net radiometer (Kipp & Zonen; CNR4) was mounted 2.8 m a.g.l.
Photograph of the tower (a) and the flux instruments mounted at
the top of the tower (b).
Wind vector
Measurements of momentum and sensible heat fluxes were obtained using a 3-D
ultrasonic anemometer (CSAT3; Campbell Scientific) mounted at 9.5 m a.g.l.,
oriented northwest (330∘), and roughly 15 m from the water's
edge. The ground level at the tower base was roughly 3 m above mean sea
level (a.s.l.), making the measurement height 12.5 m a.s.l. However,
3-D wind measurements show that the streamlines are
directionally dependent and bend upward/downward in proportion to the island
incline (a maximum of 6∘ from head-on winds). Therefore, wind and
flux measurements were considered representative of 9.5 m a.s.l. (similar to
vertical displacement seen in ship-based measurements; Yelland et al.,
2002). For each 20 min flux interval a double rotation was applied on
the wind vector to put it into a mean streamline coordinate system in which the
x axis was parallel to the mean wind (v‾=w‾=0) (Kaimal and
Finnigan, 1994).
H2O & CO2
Water vapor and CO2 concentrations used to calculate fluxes were
measured by three IRGAs. Two were open-path designs (EC150, Campbell
Scientific and LI-7500, LI-COR) and one was a closed-path design (LI-7200RS,
LI-COR, referred to herein as LI-7200). The EC150 was attached to the CSAT3
anemometer, making its measurements collocated with the wind vector
measurements. The LI-7500 was mounted 30 cm aft, 18 cm starboard, and at the
same height as the CSAT3. Mixing ratios for the IRGAs were calculated from
molar density, pressure, and temperature using the WPL correction (Webb et
al., 1980). This was even done for the LI-7200 (using T7200=0.8TIN+0.2TOUT
as suggested by LI-COR) because it was deemed
more reliable than the LI-7200's on-the-fly calculation of mixing ratio,
which inexplicably produced FCO2 with large contributions from low
frequencies.
Unlike the open-path IRGAs, the LI-7200 required a pump to pull air through
its cell. As pumps significantly increase power requirements, this has been
one of the barriers to closed-path IRGAs being used in remote Arctic EC
towers. The sample air for the LI-7200 was drawn from an inlet 5 cm aft of
the CSAT3 sampling volume. Ideally, flow through the LI-7200 should be fast
enough to fully flush its cell every sample (9.7 standard liters per minute
(slpm) for 10 Hz sampling). However, the maximum flow rate that could
consistently be achieved by our 12V DC diaphragm pump (UN814KNDC, KNF) was 7 slpm.
To ensure that flow remained constant, a mass flow controller
(MCRW-10SLPM-D-DB9/5M, Alicat) was installed immediately upstream of the
pump. It was expected that this flow rate should cause a minor loss of
signal at the high-frequency end of the spectrum (evaluated below).
Mean tube delay obtained from 574 inlet tests. Blue circles show
the state of the solenoid valve (0 = closed; 1 = opened) responsible for
releasing compressed N2 directly in front of the sample tube inlet to
the closed-path IRGA. The red triangles represent the decay in the CO2
mixing ratio from its pre-test steady state (1) to its settled value during
the test (0).
The mounting configuration of the LI-7200 was chosen to minimize signal
attenuation by tubing. The diameter of the tubing upstream of the LI-7200
was minimized (3.5 mm I.D.) to increase the Reynolds number (Re). With 7 slpm
flow Re in the sample line was 2800, in the transitional zone between laminar
(Re < 2100) and turbulent (Re > 4000). To mitigate the signal
smoothing that could occur with nonturbulent flow, the LI-7200 was mounted
near the top of the tower, reducing upstream tube length to 2.8 m.
Sea ice concentration from the AMSR2 SIC product and the mean
daily average from satellite and in situ images.
To test for tube delay and attenuation of high-frequency signal, inlet tests
with CO2-free air were performed regularly. Twice a day at 02:05 and
14:05 LST a normally closed, two-way solenoid valve installed at the base of
the CSAT3 would open and inject nine 10 s pulses of N2 directly in
front of the intake tube to the LI-7200. The mean delay for CO2 from
574 tests was 0.83±0.01 s (Fig. 3). The mean time constant (defined
as the time for signal to drop below 1/e strength) was 0.24±0.02 s.
This time constant (less than three samples at 10Hz) was low and suggested
minimal attenuation by the tubing. High-frequency loss of FCO2 was
characterized by estimating the flux lost by the open-path latent heat flux
measurement after applying a low-pass filter to H2O mixing ratio using
the CO2 time constant as a cutoff frequency (Goulden et al., 1996). The
ratio (Gc) of unfiltered to filtered latent heat flux indicated an
average high-frequency FCO2 loss of 1.7 %, in line with previous
studies (Ibrom et al., 2007; Butterworth and Miller, 2016b). To account for
this loss, while reducing the variability in Gc, a linear regression
between Gc and U10n was calculated (Gc=0.002U10n+1.0024) and used to compute a multiplier for each flux
interval based on the wind speed.
Sea ice
Images of sea ice were captured by a camera (Hero4, GoPro) mounted at the
top of the tower. An intervalometer was installed to take a picture once an
hour, indefinitely. These images were to be used to determine sea ice
concentration (SIC) and melt pond fraction. However, the external battery
packs for the camera failed late May 2017, and no images were collected by
the camera until the issue was fixed mid-July 2017. Because of this, we
relied on several other methods for estimating SIC. The AMSR2 passive
microwave SIC product (daily, 3.125 km) by the University of Bremen (Spreen et
al., 2008) was used to provide a picture of the seasonal ice breakup of the
area. The ice concentration from the three grid cells nearest the tower were
averaged. In addition to this product a variety of remotely sensed
(Landsat-8 and MODIS) and in situ images were collected. In situ photographs
were taken during site visits (four helicopter trips in June and July) and
from a motion-sensor-equipped trail camera (installed to identify wildlife
interactions with the installation, e.g., Fig. S1 of the Supplement, but which was frequently
set off by environmental conditions). Comparisons between the AMSR2 SIC
product and photographs confirms that it was generally accurate within the
Dease Strait region (Fig. 4), with the exception that it could not resolve
melt ponds as different from open water. This meant that during the melt
pond season (June) the product underestimated SIC due to the presence of
overlying water. Combining the photographs with the SIC product enabled
estimates of melt pond fraction. Additionally, the AMSR2 product continued
to measure SIC in mid-July, after images revealed full open water in front
of the tower (Fig. 4). This discrepancy was due to the fact the AMSR2
product had a footprint that extended beyond the immediate area in front of
the tower (which turns to open water more quickly than the rest of the
region).
Power
Power limitations often exert a large influence on experimental design in
Arctic field studies. With a closed-path IRGA and airstream drying, this
study required two pumps and a mass flow controller, and needed roughly 4 times the power required by an open-path system. With no external power to draw
from, all power needed to be generated on site. Three 150W solar panels
(EWS-150P-36, Enerwatt) and one 12V DC wind turbine (AIR Breeze, Primus)
were used to generate power that was stored in a battery bank of five 92AH
AGM batteries. The battery bank was housed in a large Pelican case, and
included charge controllers and circuit breakers for both the solar panels
(30A 12VDC EWC-30, Enerwatt) and turbine (Wind Control Panel, Primus). The
solar panels were arranged in a triangular formation to collect solar
radiation at different times throughout the long summer days. The turbine
was used as supplemental power to enable power generation when solar panels
were not active (night and winter). To conserve power the flux system was
design to shut off the more power-hungry equipment (gas analyzers, mass flow
controller, DC pumps, and sonic anemometer) when voltage in the battery bank
dropped below 11.8 V, and turn them back on again when voltage rose above
12.3 V. The system was fully operational 99.2 % of the time during the
study period. A schematic diagram (Fig. S2) of the power system is included
in the Supplement.
Drying
Like previous dried airstream systems, our system used a moisture exchanger
(Nafion PD-200T-12MPS, Permapure) to dry the sample air. However, instead of
a using a zero-air generator for purging water vapor from the counterflow
(which would have required AC power and compressed air), a desiccant (Du-Cal
Drierite) was used. Air was pumped (UN814KNDC, KNF) in a closed loop through
a large cylindrical tank containing 50 lbs. of desiccant, to the moisture
exchanger, and back to the tank. The advantage of using a closed loop design
is that the only moisture exposed to the desiccant was that which had passed
through the moisture exchanger membrane. This, along with the large mass of
desiccant, meant that the replacement of desiccant was required only once
every 40–60 days, depending on ambient humidity. The need for desiccant
replacement was determined with a small amount (1 lb.) of Indicating
Drierite (which changes color when exhausted) placed by a glass window on
the tank.
Data processing
Fluxes of momentum (τ, N m-2), sensible heat (HS, W m-2),
latent heat (HL, W m-2), and CO2 (FCO2,
mmol m-2 d-1) were calculated for 20 min intervals as
τ=ρa‾u′w′‾2+v′w′‾2,HS=ρa‾cpw′T′‾,HL=ρa‾Lvw′q′‾,FCO2=ρa‾w′c′‾,
where u,v, and w (m s-1) are the along-wind, cross-wind, and vertical wind
components, respectively, ρa‾ (mol m-3) is the
mean dry air density, cp (J kg-1 K-1) is the specific heat capacity
of air, T (K) is the dry air temperature, Lv (J kg-1) is the latent heat of
vaporization, q (kg kg-1) is the specific humidity, c is the CO2 mixing
ratio (µmolmol-1), primes indicate fluctuations about the mean,
and the overbar corresponds to the time average. The dry air temperature was
calculated from the sonic temperature after correction for the effect of
water vapor on air density and speed of sound (Schotanus et al., 1983).
The mean wind speed from the sonic anemometer was adjusted to neutral
stability at 10 m height using a semilogarithmic wind profile and assuming
a constant flux layer according to
U10n=(u∗/κ)ln(10/z0),
where u∗=(τ/ρ)1/2 is the friction velocity (m s-1)
measured by CSAT3, κ is the von Karman constant of 0.4,
z is the measurement height, and z0 is the roughness length (m) calculated as
z0=zexp-κU‾zu∗-ψmzL,
where ψm represents the stability function of Paulson (1970) for
unstable stratification and Grachev et al. (2007) for stable stratification,
both functions of z/L, where z is the measurement height and L is the Obukhov length,
calculated as
L=Tκgu∗3w′T′‾+0.61T1+0.61Qw′q′‾,
where g is the acceleration due to gravity, T is the air temperature, Q is the specific
humidity, and w′T′‾ and w′q′‾ are the turbulent
fluxes of temperature and water vapor (Andreas et al., 2010).
Quality control criteria were used to select intervals that passed the
underlying assumptions of eddy covariance. First, intervals were selected
that exhibited stationarity, following
RNcov=(w′x′‾)5‾-(w′x′‾)20(w′x′‾)20≤0.3,
where (w′x′‾)5‾ represents the mean of the four
5 min turbulent flux subintervals and (w′x′‾)20 represents the
turbulent flux of the whole 20 min interval (Blomquist et al., 2014). The
purpose of this criterion is to identify and remove intervals in which large-scale phenomena (e.g., mesoscale motions), outside the frequency range of
turbulent fluxes, are contributing to the measured flux.
A second quality control criterion selected for wind directions of
-150 to 150∘ (relative to the anemometer) to
eliminate winds from aft that were affected by flow distortion from the
instruments and tower frame. The size and shape of the island (0.2 km wide
and extending 0.5 km behind the tower) meant the remaining wind directions
from the back hemisphere had some degree of island contributing to their
flux footprint. To estimate the impact of the island on the flux
measurements, we ran the flux footprint model of Kljun et al. (2015). Using
the mean meteorological conditions from each 5∘ wind sector it was
found that on average the island accounted for 5 % of the footprint for
wind directions from the front hemisphere (-90 to 90∘).
From the back wind sectors (-150 to -90∘ and
90 to 150∘) 44 % of the flux footprint was
represented by the island. However, because extent of the footprint varied
with meteorological conditions, there were many periods during which these wind
directions saw minimal influence from the island. The island is bare rock,
which means that it should not act as either a source or a sink for
CO2. This means that the magnitudes of CO2 fluxes from these back
sectors are somewhat underestimated, by a factor that most likely scales
with the portion of the footprint that falls on the island. Future work is
planned to collect field data (i.e., coincident upwind pCO2w data for
varying wind directions) to determine if a linear scaling factor could be
used to correct flux magnitudes for these wind sectors.
Low-frequency contribution
One issue that was encountered during flux processing was unexpected low-frequency (between 10-3 to 10-2) contribution to
FCO2, separated from contributions from the typical frequency range for
turbulent fluxes by a distinct spectral gap (Fig. 5a). The spectral gap
suggests that the low-frequency contributions were the result of
larger scale motions (e.g., advection) and not representative of locally
meaningful fluxes. To remove this from the flux measurements a high-pass
filter (first-order Butterworth filter with cutoff frequency of 0.005 Hz
centered on the trough in the spectral gap) was applied to the CO2
mixing ratio prior to calculating FCO2. This reduced FCO2
magnitude by an average of 15.8 % (or 0.6 mmol m-2 d-1; Fig. 5a).
The choice to filter had to balance the need to remove spurious flux
with the possible removal of real flux operating at lower frequencies (Sakai
et al., 2001; Finnigan et al., 2003). To assess the loss of real flux we
applied the same high-pass filter to T and calculated HS. Filtering
caused an average flux loss of 3.5 %, represented in Fig. 5b as the area
between the unfiltered and filtered HS cospectra. This real flux loss
is substantially less than that lost by filtering FCO2, which indicates
that the filtering was appropriate in this instance. Future investigations
into the processes affecting FCO2 (e.g., melt pond fraction, sea ice
concentration) would benefit from a more subjective review of cospectra for
individual flux intervals. Because FCO2 is presented more broadly in
this paper, that level of scrutiny is not warranted here.
Median normalized frequency-weighted cospectra for FCO2
(a)
and HS (b). In (a) the purple line represents the cospectrum calculated
from the uncorrected LI-7200 CO2 mixing ratio. The green curve
represents the cospectrum calculated from the high-pass-filtered LI-7200
CO2 mixing ratio. The dashed gray line represents the theoretical
scalar cospectra from Kaimal et al. (1972). In (b) the blue curve represents
the cospectrum for HS calculated from an unfiltered air temperature
measurement. The red curve represents the cospectrum for HS
calculated from air temperature passed through the same high-pass filter
applied to the CO2 mixing ratio in the FCO2 calculation (i.e.,
first-order Butterworth filter with 0.005 Hz cutoff). The area below the shaded
region between the two curves represents the median loss of real low-frequency flux due to filtering.
Results
Meteorology
The period reported in this study ranges from 4 May to 1 September 2017,
encompassing the transition from full ice coverage to fully open water. From
4 May through 25 May the study area was characterized by snow-covered sea
ice. With air temperatures well below 0 ∘C,
this period represents winter ice conditions. From 25 May to 25 June melt ponds began to form. Comparing the
AMSR2 SIC product to the in situ photographs (which show no open water)
suggests that the melt ponds during this period ranged from 0 to 50 % of the surface area. Following this period there was an ice
breakup period (25 June to 7 July) which exhibited both ice and open water.
This breakup initially occurred directly in front (north) of the tower,
creating a polynya that was probably caused by tidal currents in the strait
funneled between the islands (Fig. 1). By the end of the breakup period the
area was ice-free for the remainder of the summer season.
Meteorological conditions from May to September 2017 shown as
3 h averages for (a) incoming solar radiation (W m-2), (b) air
temperature (∘C), (c) relative humidity (%), (d) atmospheric
pressure (kPa), (e) wind speed (m s-1), (f) wind direction (∘),
(g) CO2 mixing ratio (ppm), and (h) water vapor concentration (ppt).
The meteorology of the study area through the study period shows the strong
seasonal shift. The temperature over this period rose from its minimum of
-24 ∘C on 5 May, to its maximum of 18 ∘C on 13 August, with over half of the
time in between being within ±5∘ of 0 ∘C (Fig. 6b). Incoming
solar radiation exhibited strong diurnal trends, with over 75 % of days
experiencing peak daytime values greater than 500 W m-2, and
nighttime minimum values near zero (Fig. 6a). These oscillations did not
result in large diurnal temperature swings. Due to the low temperatures, the
relative humidity was typically high, with a mean of 86 % (Fig. 6c). Actual
water vapor content of the air was lowest in May, with a mean of 3.3 parts
per thousand (ppt), followed by a mean of 7.7 ppt from June to September
(Fig. 6h). The CO2 mixing ratio was roughly 410 ppm at the start of May
and decreased to 403 ppm by the end of August, indicative of the seasonal
trend that occurs when plant biomass consumes atmospheric CO2 in the
boreal growing season (Fig. 6g). Wind speed was moderate, ranging between 0
and 16.4 m s-1 with a mean of 5.6±2.7 m s-1. The
winds exhibited no distinct change in magnitude over the course of the
season (Fig. 6e), and were most commonly from the southeast
(105 to 135∘) and the west southwest (235 to 275∘)
(Figs. 6f, 7). These directions were generally
advantageous due to the large fetch in the east and west directions, the
only caveat being the very small island 1.5 km southeast of the tower.
Wind rose for May to September 2017 shown with 10∘ wind
direction bins. Color represents 10 m neutral wind speed and the size of the
bars indicates the frequency at which they occur.
Air–sea fluxes
Time series of τ, HS, HL, and FCO2 over the course of
this study are shown in Fig. 8. The τ ranged from 0 to 0.41 N m-2,
with a mean of 0.05±0.05 N m-2 (Fig. 8a). The range
in τ was similar through all different surface conditions, being most
strongly influenced by wind speed. The HS showed a diurnal trend
(increasing during the day, decreasing at night) throughout much of the
study period. The sea surface type also played a role in mean HS, with
the flux during full ice cover generally upward, then, during ice breakup
and early summer downward, and in the open water at the end of the summer upward again. HL acted similarly to HS, with upward fluxes
early in the season (full ice and melt ponds), transitioning to downward
fluxes during ice breakup, and upward fluxes under full open water
conditions.
The 3 h averages of measured fluxes, following the
meteorological conventions (negative fluxes indicate transport towards the
surface): (a) momentum flux (N m-2), (b) sensible heat flux
(W m-2), (c) latent heat flux (W m-2), and (d) CO2 flux (mmol m-2 d-1),
measured by the closed-path IRGA. Ice concentration from the AMSR2 ice product is shown by color, with red representing full ice cover
and blue representing open water. Demarcations (determined from satellite
and in situ images) of ice regimes (full ice, melt ponds, breakup, and open
water) are shown on top.
CO2 flux
FCO2 measured by the dried, closed-path system was low during periods
with sea ice cover. During full ice cover FCO2 hovered around zero, with
a mean of -0.03±1.21 mmol m-2 d-1. During melt season
FCO2 was slightly negative (i.e., downward), with a mean of -0.34±2.04 mmol m-2 d-1. During ice breakup, when the surface
was mixed ice and water, FCO2 was downward (mean of -2.9±4.9 mmol m-2 d-1). By the full open water period in August, FCO2
had switched direction and the water was outgassing CO2 to the
atmosphere (mean of 3.8±4.7 mmol m-2 d-1).
FCO2 values calculated from the different IRGAs are shown in Fig. 9. Both
open-path IRGAs yielded flux values with magnitudes much larger than those
from the dried LI-7200, sometimes orders of magnitude larger. For example,
during full ice conditions, when the dried LI-7200 measured near-zero
FCO2, the LI-7500 and EC150 had means of -22±58 and -32±103 mmol m-2 d-1, respectively.
The 12 h average CO2 fluxes calculated using EC150 (green
diamonds), LI-7500 (blue pluses), and dried LI-7200 (red line). Demarcations
(determined from satellite and in situ images) of ice regimes (full ice,
melt ponds, breakup, and open water) are shown on top.
Because comparisons of FCO2 from different gas analyzers do not have a
dependent variable, we used Pearson's correlation coefficient (r) to
describe the linear correlations between the two variables (Goodrich et al.,
2016). Comparisons of FCO2 from the closed-path LI-7200 against
FCO2 from the two open-path IRGAs showed no correlation (Fig. 10a, b),
with r=0.04 and r=0.15 for the LI-7500 and EC150, respectively. An
orthogonal regression of FCO2 (LI-7500) against FCO2 (EC150)
yielded a fit closer to 1 : 1 (Fig. 10c), but with more scatter and an r=0.44. On the other hand, the regression for HL (LI-7500) against
HL (EC150) showed a distinct 1 : 1 relationship (Fig. 10d). The
correlation coefficient for this case was 0.93, thus showing a strong linear
relationship. This suggests that the open-path IRGAs are better suited to
measuring HL than FCO2 in this environment.
Comparisons of FCO2 and HL from the three IRGAs: panel (a)
shows FCO2 (LI-7200) vs. FCO2
(LI-7500), panel (b) shows FCO2
(LI-7200) vs. FCO2 (EC150), panel (c) shows FCO2
(LI-7500) vs. FCO2
(EC150), and panel (d) shows HL (LI-7500) vs. HL (EC150). Because the
sample air to the LI-7200 was dried, comparisons to HL (LI-7200) were
omitted.
FCO2 values from all three IRGAs were also compared against heat fluxes,
HL and HS (Fig. 11). Negative relationships were found between
FCO2 from the open-path IRGAs and heat fluxes. FCO2 from the
dried, closed-path IRGA showed no relationship with HL or HS.
Bin averages of the relationships between FCO2 calculated
from all three IRGAs and (a) HL (LI-7500) and (b) HS.
Discussion
Sea ice flux comparisons
During the spring season prior to ice breakup, the dried, closed-path EC
system measured FCO2 of -0.25±1.75 mmol m-2 d-1.
When only considering sea ice conditions prior to melt pond formation,
FCO2 was -0.03±1.21 mmol m-2 d-1. These
measurements are within the range measured by previous enclosure
measurements, which taken together span from -5.4 to 2.2 mmol m-2 d-1
(Table 1; Delille, 2006; Nomura et al., 2010, 2013; Sejr et al.,
2011; Geilfus et al., 2012, 2014, 2015; Delille et al., 2014; Sievers et
al., 2015). The measurements also exhibit a sharp divergence from previous
open-path EC FCO2 measurements, which at tens to hundreds of mmol m-2 d-1
are several orders of magnitude larger (Semiletov et al., 2004;
Zemmelink et al., 2006; Else et al., 2011; Miller et al., 2011; Papakyriakou
and Miller, 2011). Unlike the dried, closed-path system, our open-path
systems installed at our site did measure FCO2 with similar magnitudes
to these previous open-path EC studies, with mean values of -22±58
(LI-7500) and -32±103 (EC150) mmol m-2 d-1.
This disagreement between simultaneous open-path and dried, closed-path systems at
our site suggests that the reason for discrepancies between previous chamber
and open-path EC measurements was not the result of different scales of
measurement, but was rather problems with the ability of open-path EC to resolve
fluxes. This is further demonstrated by the poor agreement between the two
open-path FCO2 results (Fig. 10c).
Heat fluxes
Previous undried EC studies over the open ocean have found relationships
between heat fluxes (HS and HL)
and FCO2 (Landwehr et al.,
2014; Sievers et al., 2015). However, in these instances the magnitude of
measured FCO2 at high latent heat fluxes exceeds values calculated from
bulk FCO2 formula. In their comparison of dried and undried closed-path
EC systems, Landwehr et al. (2014) concluded that such relationships
represented bias, and did not result from real physical phenomena. They also
found that the degree of bias was different for each individual IRGA
instrument. While our OP IRGAs found a relationship between heat fluxes and
FCO2 our dried, closed-path system did not (Fig. 11). This supports the
finding that these relationships represent bias, further evidence that
open-path IRGAs do not fully remove the effects of HS and HL in
the density correction and/or the instruments' built-in water vapor
corrections.
EC150
To the best of our knowledge, this study is the first published test of the EC150
against the LI7500 in a marine environment. The two instruments produced
similar HL (Fig. 10d), showing that both are capable instruments
for measuring water vapor flux. When it came to FCO2, the EC150 values
diverged from the dried, closed-path system to a similar degree as the
LI-7500 (Fig. 10b). Like the LI-7500, the EC150 also showed a strong negative
relationship between FCO2 and both HS and HL (Fig. 11). These
findings suggest that the EC150 is affected by the same problems that affect
the LI-7500.
However, FCO2 comparison between the EC150 and LI-7500 did not show
strong agreement (Fig. 10c). It is possible that the disagreement between
the two stems from differences in their design (e.g., the EC150 is not
necessarily affected by the same instrument-induced HS as the
LI-7500 (Burba et al., 2008) and presumably has a different set of equations
accounting for water vapor cross sensitivity). But the overall spurious,
high magnitudes for FCO2 appear to stem from problems inherent to the
open-path design. The EC150, like the LI-7500, appears to be more
appropriate for use in regions with larger magnitude FCO2.
Gas transfer velocity
While measuring FCO2 in the same range as chamber measurements shows
that the method ameliorates problems associated with open-path systems, it
alone is not a full accounting of measurement quality. To further assess the
performance of the flux system, we compared our open water results against
those from previous studies. To do this we calculated gas transfer velocity,
a coefficient which describes the efficiency of gas transport across the
air–sea interface. Gas transfer velocity is a more effective comparison
than FCO2 because it provides more context. It was calculated by
setting our measured flux equal to the bulk CO2 flux formula (FCO2
=ks[pCO2w-pCO2atm]) and rearranging the equation to form
k=FCO2s(pCO2w-pCO2atm),
where k is the gas transfer velocity, s is the solubility of CO2 in
seawater, and pCO2w and pCO2atm are the partial pressure of CO2 in water
and the atmosphere, respectively (Wanninkhof and McGillis, 1999). While
FCO2 and pCO2atm were continuously measured by the EC system, s and
pCO2w were not. They were however, measured aboard the research vessel
(RV) Martin Bergmann, which made several courses past the island in August 2017. For the
flux intervals that aligned temporally with these passes (all fully open
water), we calculated k660 (k adjusted to a Schmidt number (Sc) of 660). The
k660 values plotted against U10n (Fig. 12) showed good
agreement with previous parameterizations of k660 (Wanninkhof, 1992,
2014). Because the (RV) Martin Bergmann data showed that
pCO2w had high temporal and
spatial variability in this region, and because we only have three data
points, this result should not be interpreted as a new functional form to
the k vs. U10n relationship. What it does indicate is that the
dried, closed-path flux system is able to resolve FCO2 within an
expected range, based on previous results. Conversely, k660 values from the
open-path IRGAs were not similar to previous findings. For these three
intervals, the mean k660 was 247 cm h-1 for the EC150 and
-28 cm h-1 for the LI-7500 (where negative represents counter-gradient flux). This provides additional evidence that the open-path IRGAs
are not capable of resolving CO2 fluxes in this environment.
Gas transfer velocity (k660) plotted against 10 m neutral
wind speed for three periods in which the RV Martin Bergmann measured pCO2w
within 3 km of the tower and in which the magnitude of ΔpCO2
(i.e., pCO2w-pCO2atm)
was greater than 20 µatm. Parameterizations of Wanninkhof (1992) and
Wanninkhof (2014) are shown as blue and red lines, respectively.
The quality of the flux measurement was also assessed by comparing previous
years' measurements of pCO2w to estimated pCO2w (using FCO2)
from our study period. Measurements of pCO2w were collected aboard the
Canadian Coast Guard (CCGS) Ice Breaker Amundsen near Qikirtaarjuk Island during
five previous summers (2010, 2011, 2014, 2015, 2016). August measurements of
pCO2w (collected within a 10 km radius of the tower) ranged from 360 to
469 µatm, with a mean of 407±34 µatm. Estimates of
pCO2w from our study period were calculated by rearranging Eq. (9) so
that pCO2w = FCO2 k-1 s-1 + pCO2atm. As stated above,
pCO2atm and FCO2 were measured by the tower. Gas transfer velocity
was obtained using the Wanninkhof (2014) parameterization with measured
U10n, plus the mean Schmidt number (Sc) from the RV Martin Bergmann dataset (Sc =
1150). Solubility was also estimated as the mean value from the RV Martin Bergmann data
(s=48 mol m-3 atm-1).
Using FCO2 from the dried, closed-path IRGA yielded pCO2w
estimates ranging from 347 to 481 µatm (10th to 90th
percentile) and a median value of 407 µatm, over the course of summer
2017. This matched reasonably well with the range identified by the CCGS
Amundsen measurements. Comparatively, the pCO2w values from the open-path IRGAs
were not as tightly clustered around a central value (Fig. 13). The fluxes
from the LI-7500 and EC150 led to pCO2w estimates ranging from -1255
to 1101 µatm and -1252 to 906 µatm, respectively. These values
are far beyond the magnitude observed in this region (and with no physical
basis in the case of negative values), further evidence that the open-path
IRGAs are not capable of providing accurate FCO2 measurements in the
relatively low ΔpCO2 conditions typically found in the
marine environment.
Histograms showing the normalized frequency (%) of pCO2w
calculated using FCO2 from the EC150, LI-7500, and LI-7200. The limits
in the x axis were truncated at -400 and 1200 for better visualization.
However, roughly a quarter of pCO2w values from both the EC150 and
LI-7500 extend beyond these limits.
Drying
The drying system worked as desired, drying the sample air to dew point
temperatures of -27.5±7.6 ∘C, compared to the LI-7500 which
measured average ambient dew point temperatures of 6.0±7.6 ∘C. Perhaps more
importantly it reduced fluctuations in water vapor, reducing the standard
deviation in H2O mixing ratio by over an order of magnitude from 0.1±0.2 mmol mol-1 to 0.007±0.008 mmol mol-1. This
resulted in reducing the standard deviation of the CO2 mixing ratio
from 0.7±1.8 to 0.1±0.1 µmolmol-1.
This matches the LI-7200's specification for root mean square noise at a
sampling rate of 10 Hz, showing that preconditioning the sample air
completely removed the influence of other variables on the CO2
measurement.
As the comparisons between the LI-7200 and LI-7500/EC150 show, drying the
air did seem helpful in producing FCO2 that are more in line with
expected values. Interestingly, however, there were two occasions when the
desiccant's capacity ran out and the closed-path IRGA was receiving sample
air with near-ambient water vapor content (Fig. 6h). During those periods
of time, the LI-7200 still experienced standard deviations in H2O
mixing ratio 3.5 times lower than the open-path LI-7500. Additionally, there
was no increase in the variance of FCO2 from the LI-7200 during these
periods, and FCO2 magnitudes did not increase to open-path levels. This
suggests that even without a dry counterflow, the Nafion drier still
improves FCO2 measurements to an acceptable level by reducing
fluctuations in water vapor. This makes sense because spikes of moister or
drier air will still exchange H2O with a counterflow that is at mean
ambient humidity. Without a parallel, undried LI7200 it impossible to
quantify how much of the smoothing is from the Nafion compared to the
natural “stickiness” of H2O on tube walls. However, the impact of
H2O smoothing from tube walls alone was tested in Butterworth and
Miller (2016b) with an undried LI7200. It was found that tubing did not
fully remove the influence of water vapor on the CO2 flux, and instead
showed up as a spurious low-frequency contribution to the flux, which was
visible in the flux cospectra. In this study, when the desiccant was
exhausted, the cospectra did not indicate interference from water vapor,
suggesting that the Nafion played a critical role. This finding may
have important implications for the design of future systems (i.e., a system
could be designed that uses a Nafion and counterflow, but without a dry air
source), and should be investigated further.
Future work
In April 2018 as part of the Polar Knowledge Canada-funded CAT-TRAIN project
in collaboration with the Arctic Research Foundation, a mobile power
station/research lab was installed at the site. This new infrastructure will
be used to increase the functionality of the system. Specific additions that
are being considered are incorporating waterside pCO2w measurements, to
be used to calculate gas transfer velocity continuously through an annual
cycle. This would be particularly useful considering the pCO2w
measurements from the CCGS Amundsen and the RV Martin Bergmann, which showed that this region often
has ΔpCO2 of sufficient magnitude to measure accurate
gas transfer velocities.
Additionally, for data redundancy we plan to install a second closed-path
greenhouse gas analyzer (CRDS) capable of measuring CO2, H2O, and
methane (GGA-FGGA, Los Gatos Research), which we which have only deployed on
ships due to the large power consumption. Next spring a planned
intercomparison study taking place in Cambridge Bay will add simultaneous
enclosure measurements to verify agreement between the two methods. Lastly,
the system will be used (in forthcoming papers) to investigate annual gas
exchange cycles and process-level questions, including the processes
affecting FCO2 during spring melt and autumn freeze-up.
Conclusions
With its vast spatial extent, sea ice has the potential to play an important
role in the global CO2 cycle. Unfortunately, there has been significant
confusion around the importance of that role, largely because the community
studying sea ice gas fluxes has been unable to reconcile large fluxes
measured by eddy covariance with significantly smaller fluxes measured by
enclosure methods (Table 1). This problem is analogous to the problem faced
by researchers studying open water gas exchange, whereby for several decades
EC measurements could not be reconciled with tracer-based measurements
(e.g., Broecker et al., 1986). The open water problem was eventually
resolved by using closed-path EC systems with a dried sample airstream
(Miller et al., 2010), and EC measurements are now better aligned with other
techniques.
The dried, closed-path IRGA method has previously been applied to the
marginal ice zone (i.e., open water and drifting ice), where the open water
likely dominates the CO2 flux signal (Butterworth and Miller, 2016a).
In this study, for the first time we have applied sample drying techniques
to an installation capable of measuring CO2 fluxes throughout an annual
sea ice cycle. This allowed for measurements over many different surface
conditions, including landfast sea ice, ice break-up, and open water. Fluxes
measured during the open water season matched well with existing gas
transfer parameterizations, lending credibility to the method. During the
ice-covered season, this new measurement system closed the gap between EC
and enclosure methods, producing FCO2 with magnitudes in the range
found by enclosure studies. This finding suggests that modeling or upscaling
studies aiming to estimate the global CO2 exchange associated with
landfast sea ice should focus on the smaller range of CO2 fluxes
published by enclosure studies, at least until the EC method presented in
this paper can be applied to more sea ice environments.
The dried, closed-path EC method presented here represents a significant
advancement from previous attempts to measure FCO2 over sea ice. We
showed that incorporating the additional system complexity is feasible, even
in remote polar locations, by presenting an effective approach for drying
under low power requirements. The improved system can obtain long-term,
continuous FCO2 measurements over larger spatial scales than is
possible with enclosures and opens potential avenues for new research,
including a greater scrutiny of the ecosystem-scale processes affecting
CO2 fluxes in sea ice regions.