Introduction
The balance of CO2 emissions from fossil fuel combustion, land biotic
CO2 uptake, and ocean CO2 uptake determines long-term change in
the atmospheric CO2 burden. At the same time, fossil fuel combustion
consumes atmospheric O2, while the land biotic CO2 uptake is
accompanied by the emission of O2 into the atmosphere. Additionally,
today's ocean is considered to be a weak source of O2 because of recent
ocean warming (Bopp et al., 2002; Keeling and Garcia, 2002; Plattner et al.,
2002). The CO2 and O2 exchanges for biotic processes and fossil
fuel combustion are stoichiometrically related, and the fossil fuel
consumption rate can be reliably estimated from energy statistics.
Therefore, coupled measurements of atmospheric CO2 and O2 have
been used to constrain global CO2 budgets by simultaneously solving
the equations for the atmospheric CO2 and O2 budgets (Keeling and
Shertz, 1992; Battle et al., 2000, 2006; Manning and Keeling, 2006; Tohjima
et al., 2008; Ishidoya et al., 2012).
Atmospheric O2 measurements are also useful for understanding air–sea
gas exchange. This is based on the fact that the air–sea exchange of O2
is more than 1 order of magnitude faster than that of CO2; the chemical
equilibrium of dissolved inorganic carbon (dissolved CO2,
bicarbonate, and carbonate ions) in seawater suppresses the air–sea exchange
of CO2 (e.g., Keeling et al., 1993). For example, seasonality in
ocean biotic activity and sea surface temperature is poorly reflected in
air–sea CO2 exchange, while oceanic O2 fluxes show clear seasonal
variations. The role of atmospheric O2 as a tracer of air–sea gas
exchange is emphasized by the introduction of the tracer atmospheric
potential oxygen (APO), which is defined as APO = O2 +1.1×CO2 (Stephens et al., 1998), where 1.1 represents the
-O2 / CO2 exchange ratio associated with land biotic activity
(Severinghaus, 1995). Since APO is invariant with respect to land biotic
O2 and CO2 exchange and predominantly reflects air–sea gas
exchange, spatiotemporal variations in APO have been used to study gas
fluxes associated with ocean ventilation (Lueker et al., 2003) and ocean
spring bloom (Yamagishi et al., 2008) to estimate net ocean production
(Keeling and Shertz, 1992; Balkanski et al., 1999; Nevison et al., 2012) and
to validate ocean biogeochemical models (Stephens et al., 1998; Naegler et
al., 2007; Nevison et al., 2008, 2015, 2016).
The precision required to detect atmospheric O2 variation is at the
µmol mol-1 (ppm) level, which is considerably smaller than the
atmospheric O2 mole fraction of about 21 %. Keeling (1988) was the
first to develop an atmospheric O2 measurement technique with this
precision using an interferometer and showing the usefulness of O2
measurements to study the global carbon cycle. Since then, several O2
measurement techniques based on a mass spectrometer (Bender et al., 1994), a
paramagnetic analyzer (Manning et al., 1999), a fuel cell analyzer (Stephens
et al., 2007), and a vacuum ultraviolet absorption photometer (Stephens et
al., 2003) have been developed and applied to atmospheric observations (cf.
Keeling and Manning, 2014). When the change in atmospheric O2
concentration is compared with that of CO2, it is expressed as a
deviation of the O2 / N2 ratio from an arbitrary reference according
to
δO2/N2=O2/N2samO2/N2ref-1,
where the subscripts sam and ref represent sample and reference gases,
respectively, and the δ(O2 / N2) value is
usually converted to a “per meg” value, which approximates parts per
million, by multiplying it by 106 (Keeling and Shertz, 1992). The mole
fraction is not used as a measure of O2 abundance because the
changes in the mole fraction of major atmospheric constituents like
O2 are sometimes very confusing. For example, adding
1 µmol of O2 to an air parcel containing 1 mol of dry
air results in a 0.79 ppm increase in the O2 mole fraction, and
adding 1 µmol of CO2 results in not only a 1 ppm
increase in the CO2 mole fraction but also a 0.21 ppm decrease in
the O2 mole fraction. These confusing results are attributed to
influences of the changes in the total number of moles in the air parcel on
the mole fractions or a dilution effect (e.g., Keeling et al., 1998; Tohjima,
2000). However, adding 1 µmol of O2 to 1 mol of dry air,
which contains 0.2094 mol of O2 (Tohjima et al., 2005), always
results in a 4.77 per meg change in the δ(O2 / N2) value.
Schematic diagram of atmospheric O2 and CO2
measurement system used aboard the cargo ship NC2.
The National Institute of Environmental Studies, Japan (NIES) also developed
a technique to measure atmospheric O2 based on a gas chromatograph
equipped with a thermal conductivity detector (GC–TCD; Tohjima, 2000). NIES
began measuring atmospheric O2 and CO2 by collecting air samples
in glass flasks at two monitoring stations, Hateruma Island in July 1997 and
Cape Ochiishi in December 1998 (Tohjima et al., 2003, 2008). Additionally,
to extend the observation area, we started flask sampling aboard cargo ships
sailing between Japan and Australia–New Zealand (Oceanian route) and between
Japan and North America (North American route) in December 2011 (Tohjima et
al., 2005, 2012). In situ measurements of atmospheric O2 using
the GC–TCD technique also started aboard a cargo ship between Japan and
Australia–New Zealand in 2007 (Yamagishi et al., 2012).
These O2 and CO2 data from widespread Pacific regions were used to
investigate the spatial distribution of the climatological seasonal cycle of
APO and the annual mean values of APO (Tohjima et al., 2012). Latitudinal
transects of the data in the western Pacific region revealed that variation
in the magnitude of the bulge in annual mean APO was associated with the El
Niño–Southern Oscillation cycle (Tohjima et al., 2015). This analysis
was made possible by the relatively high spatiotemporal sampling density in
the western Pacific. In contrast, the spatiotemporally sporadic APO data
obtained from the North American route made it difficult to investigate
interannual variations in the northern and eastern North Pacific. In 2015,
Pickers et al. (2017) started in situ observations of atmospheric O2 and
CO2 with a fuel cell analyzer and nondispersive infrared analyzer
aboard a commercial container ship regularly traveling in the Atlantic Ocean
between Hamburg, Germany and Buenos Aires, Argentina. They also showed the
usefulness of continuous observation to reveal the spatiotemporal APO
distribution. Therefore, in December 2015, we initiated a program of in situ
measurements aboard a cargo ship, the New Century 2 (NC2), sailing between Japan (Tahara
port) and North America.
Since June 2014, atmospheric greenhouse gas measurements, including flask
sampling (seven flasks per round-trip), have been conducted aboard NC2 along the
North American route in the Pacific. We also had an opportunity to install
an atmospheric O2 measurement system aboard NC2. However, since the
onboard space allotted to us was limited, we had to make the measurement
system smaller by reducing the number of cylinders required for the system.
In addition, since it is difficult to load and unload high-pressure gas
cylinders on ocean-going ships, we needed to reduce the consumption rate of
reference gases to reduce the cylinder exchange frequency. With these
constraints, the GC–TCD technique, which requires at least 16 m3 of He
as a carrier gas for 1 year of continuous O2 observation, was unsuitable
for use aboard NC2. Therefore, we developed a low-flow system to perform in situ
atmospheric O2 and CO2 measurements aboard NC2. In this paper, we
present the details of the measurement system and report its fundamental
performance in laboratory testing. We also discuss a problem that occurred
when the measurement system was installed aboard NC2. Finally, we present 1 year
of atmospheric O2, CO2, and APO data and discuss the
longitudinal distribution of the seasonal APO cycle in the North Pacific.
Methods
Analytical system
Figure 1 is a schematic diagram of the in situ observation system used aboard NC2.
We used a fuel cell analyzer (Oxzilla II; Sable Systems, USA) and a
nondispersive infrared analyzer (LI-840A; LI-COR, USA) for the onboard
measurement of O2 and CO2, respectively. After passing through a
polypropylene cartridge filter with a mesh size of 7 µm
(MCP-7-C10S;
ADVANTEC, Japan), the sample air is drawn by a diaphragm pump (MOA-P108-HB;
Gast Manufacturing, USA) at a flow rate of about 8×103 cm3 min-1
and introduced into a spherical glass vessel with a volume of
about 2×103 cm3. The air is vented to the atmosphere
through a back-pressure regulator (6800AL; KOFLOC, Japan), which keeps the
pressure inside the spherical vessel at about 0.05 MPa above ambient
pressure. Water that condenses within the spherical vessel is drained from
the bottom by a peristaltic pump (7016-21; Masterflex, USA). The sample gas
for the O2 and CO2 measurements is drawn from the center of the
spherical vessel through 1/16 inch stainless steel (SUS) tubing. The
technique of sampling from a spherical glass vessel was adopted to reduce
the fractionation of the O2 / N2 ratio (Yamagishi et al., 2008).
Schematic diagram of the (a) first and (b) second
versions of the cold trap for reducing water vapor in samples.
The gas sampled from the spherical vessel is introduced into a two-stage
cold trap (-80 ∘C) to reduce the water vapor concentration to
less than 1 ppm. Details of the cold trap are presented in Sect. 2.2. The
dried sample gas and working reference gas, which is supplied from a
high-pressure cylinder, are introduced into the two fuel cells of the
O2 analyzer via two mass flow controllers (SEC-E40MK3; HORIBA STEC,
Japan) and a four-way two-position valve (AC4UWE; Valco Instruments Co. Inc.,
USA). The mass flow controllers regulate the flow rates of the two gas
streams with a precision of 0.01 cm3 min-1. The dried sample air
and working reference air alternately pass through each fuel cell at
intervals of 2 min by switching the four-way two-position valve. The CO2
analyzer is placed downstream of one of the fuel cells. The flow rates of
the outflows from the CO2 analyzer and the other fuel cell are
monitored by mass flow meters (SEF-E40; HORIBA STEC, Japan) with a precision
of 0.01 cm3 min-1. We adjusted the settings of the mass flow
controllers until the readings of the mass flow meters for the two airstreams matched. The outflows of the mass flow meters are combined and
vented to the atmosphere via a piezo actuator valve (PV-1202MC; HORIBA STEC,
Japan). The outlet pressures of the analyzers are kept at the same absolute
value at all times by actively matching them to a reference pressure using
the piezo actuator valve and a differential pressure sensor (model 204;
Setra Systems, USA).
Before the dried sample gas is introduced into the mass flow controller, it
passes through a multi-position valve (EMTCSD6MWM; Valco Instruments Co.
Inc., USA) to which three standard gases are connected. During the
calibration procedures, the multi-position valve selects the standard gases
instead of the sample air, which is vented to the ambient air via a needle
valve (2204; KOFLOC, Japan) at a flow rate about 10 cm3 min-1. The
48 L aluminum cylinder for the working reference gas and the three 10 L
aluminum cylinders for the standard gases are stored horizontally in
thermally insulated boxes.
Temporal variations in the output signals of the (a)
O2 analyzer and (b) CO2 analyzer when standard
air from a high-pressure cylinder was measured as sample air. The vertical
dashed lines denote the timing of valve switching. Panels (c) and
(d) are close-ups of areas marked with rectangles in panels
(a) and (b), respectively, and the horizontal axis shows
the time from valve switching.
Custom software developed in LabVIEW (National Instruments Co., USA)
running on a PC controls valve operation and the acquisition of digital data
from the O2 and CO2 analyzers and analog data from the mass flow
and pressure sensors.
Cold trap
Among the constituents of tropospheric air, water vapor shows the widest
range of variation, which causes apparent variations in the O2 mole
fraction of the air because atmospheric O2 is a major constituent
of air (∼ 21 %). For example, a water vapor increase of 1 ppm causes a 0.2 ppm apparent decrease in the O2 mole fraction.
Therefore, a two-stage cold trap was adopted to reduce the water vapor in
the sample air to less than 1 ppm. Figure 2a shows the first version of the
cold trap, which consisted of a free-piston Stirling cooler (FPSC) module
(SC-UE15R; Twinbird, Japan), two disk-shaped aluminum blocks, a 1/8 inch SUS
tube, and a drum-shaped glass vessel with a volume of about 1.0×103 cm3. The aluminum blocks had four grooves for the 1/8 inch SUS
tubes, including spare tubes to address clogging. The aluminum blocks
were placed between the cold head of the FPSC module and the glass vessel
and contact was tight. A platinum resistance thermometer was inserted into
the aluminum block, and a temperature controller (E5CC; OMRON, Japan)
regulated the FPSC module to maintain the aluminum blocks at
-80 ∘C. The sample air was dried by passing through the glass
vessel first and then the SUS tube.
When the first cold trap was used for preliminary measurements at NIES
during the summer (with high humidity), it worked without clogging for at
least 1 month, which is the typical duration for a round-trip using the
North American route. However, the measurements were often interrupted
because the SUS tube and inlet of the glass vessel clogged when it was used
aboard NC2, and the frequency of clogging increased as the season progressed
from winter to spring to summer.
Thus, we changed the cold trap to the second design shown in Fig. 2b. In the
second version of the cold trap, the cylindrical aluminum block with several
holes is in contact with the cold head of the FPSC module, and a cylindrical
glass vessel (volume of about 1.5 × 103 cm3) and 1/8 inch
SUS tube are inserted in the holes. The cold head and the aluminum block are
insulated by a polyethylene resin cover. We began to use this cold trap for
the shipboard measurements in September 2016, and the cold trap has not
clogged since that time due to the more complete chilling of the glass
vessel.
Calculation of ΔO2, ΔCO2, and δ(O2 / N2)
The O2 analyzer, equipped with two fuel cell sensors, was designed to
precisely measure the difference in the O2 mole fraction between two
airstreams. Therefore, the change in the O2 mole fraction of the
sample gas is reported as a relative change with respect to the working
reference gas, which is supplied from the high-pressure aluminum cylinder
(48 L). In previous studies (e.g., Stephens et al., 2007; Thompson et al.,
2007; van der Laan-Luijkx et al., 2010; Goto et al., 2013), the sample and
reference air are alternately introduced into each fuel cell sensor by
switching the four-way two-position valve at 1 to 5 min intervals. In this
study, we adopted 2 min for the valve-switching intervals in light of the
responses of the O2 and CO2 analyzer after valve switching, as
described below. Figure 3a shows the temporal variation in the differential
output signal of the O2 analyzer during a test run in which a reference
gas from a high-pressure cylinder was used as a sample gas. Although the
flow rate in this system (10 cm3 min-1) is more than 4 times
slower than the flow rates used in previous studies, the output signal shows
an almost rectangular shape. The signal plateaus at least 1 min after the
valve switching, and the output signal is averaged from the second minute of
the cycle (Fig. 3c). The deviation of the O2 mole fraction in the
sample gas from that of the working reference gas for the ith 2 min
interval, ΔO2,i, is computed based on the 1 min average
according to the following equation:
ΔO2,i=-1i-1vi-vi-1+vi+1vi-1+vi+122vi-vi-1+vi+1vi-1+vi+12222,
where vi represents the average of the output signal for the second
minute of the ith 2 min interval and the output signal represents the
difference of the sample gas minus working reference gas when i is an odd
number greater than 1.
In contrast to the O2 analyzer, the CO2 analyzer alternately
measures the sample and working reference gases. The temporal variation in
the output signal of the CO2 analyzer is depicted in Fig. 3b and d.
As shown in the figure, the output signal does not plateau until after more
than 90 s because of the relatively low flow rate in comparison with the
volume of the optical cell of the LI-840A analyzer (14.5 cm3).
Therefore, we compute an average of the output signal for the last 20 s of
each 2 min interval. Then, the deviation of the CO2 mole fraction of
the sample gas from the working reference gas, ΔCO2,i, is
computed according to
ΔCO2,i=-1i-1wi-wi-1+wi+1wi-1+wi+122,
where wi represents the average of the last 20 s of data for the ith
2 min interval. Again, the sample gas is introduced into the CO2
analyzer when i is an odd number greater than 1.
The variation in atmospheric O2 is expressed as the change in
δ(O2 / N2) with respect to an arbitrary reference value, and
δ(O2 / N2) is defined according to Eq. (1). Based on the
ΔO2 and ΔCO2 values, δ(O2 / N2) is
given by the following equation:
δO2O2N2N2=ΔO2SO21-SO2+ΔCO21-SO2,
where SO2 represents the O2 mole fraction in dry air (SO2=0.2094; Tohjima et al., 2005). In this calculation, we assume that only
O2 and CO2 show more than parts-per-million-level variation among all
constituents of dry air, except nitrogen.
The time series of δ(O2 / N2) and ΔCO2
calculated by Eqs. (2), (3), and (4) for “sample” gas provided from a
high-pressure cylinder against the working reference air are plotted in Fig. 4.
The standard deviations for δ(O2 / N2) and ΔCO2 calculated from 20 h of data are 3.8 per meg and 0.1 ppm,
respectively, which likely represents the best possible precision because
the measurements were taken in an air-conditioned laboratory.
Time series of δ(O2 / N2) and
ΔCO2 calculated by Eqs. (2), (3), and (4) for sample air
provided from a high-pressure cylinder against the working reference.
Preliminary measurements of atmospheric O2 and
CO2
We conducted preliminary observations of atmospheric O2 and
CO2 variations at Tsukuba, Japan during the period 10–17 July 2015
to examine the performance of the O2 and CO2 measurement system.
Outside air was drawn by the diaphragm pump from an air intake placed on top
of our laboratory building. Two standard gases with high (-270 per meg)
and low (-579 per meg) δ(O2 / N2) values were repeatedly
introduced into the O2 analyzer for 32 min each at intervals of 25 h.
We determined a single calibration line of linear response function for
δ(O2 / N2) values from all the measurements of the two
standard gases during the observation. As for the CO2 mole fraction, a
single calibration line of linear function was determined from measurements
of three standard gases with 387, 406, and 434 ppm only before the
observation. The δ(O2 / N2) value and CO2 mole fraction
were reported in our own original scales: the NIES δ(O2 / N2)
scale (Tohjima et al., 2008) and the NIES 09 CO2 scale (Machida et al.,
2011).
As shown in Fig. 5, the observed δ(O2 / N2)
revealed a diurnal cycle with an increase in daytime and decrease at
nighttime. This cycle was inversely correlated with the CO2 mole
fraction. A scatter plot of CO2 and δ(O2 / N2) shows a clear negative correlation with
the ΔO2 / ΔCO2 slope of
-1.189 ± 0.004, which is close to the land biotic O2 to
CO2 exchange ratio of -1.10 ± 0.05. Since the observation
was conducted in summer and coal consumption is limited in Tsukuba, the
ΔO2 / ΔCO2 slope means that the
observed CO2 changes can be predominantly attributed to
activity on land. During the observation, 32 min measurements of a check gas
(CPD-00012: -426 per meg for δ(O2 / N2) and
407.12 ppm for CO2) supplied from an aluminum 10 L cylinder were
repeated twice daily. The δ(O2 / N2) values
and the CO2 mole fractions of the check gas showed steady values
(Fig. 5); the average and the standard deviation (±1σ) were
-427.5 ± 4.1 per meg for δ(O2 / N2)
and 407.11 ± 0.11 ppm for CO2. Moreover, there was no
significant drift in the LI-840A analyzer during this observation. These
results indicate the stability of the O2 and CO2
measurement system.
Time series of δ(O2 / N2) (blue,
left axis) and CO2 mole fraction (red, right axis) observed at
Tsukuba during 10–17 July 2015. The δ(O2 / N2) and CO2 mole fraction of the
periodically measured check gas are also depicted as light blue and pink
circles, respectively.
In situ measurements aboard NC2
In December 2015, the measurement system was installed in a deckhouse aboard
NC2. An air intake was placed on a left-side deck rail of the navigation
bridge, and air was drawn via a DK tube (NITTA, Japan) with an outer
diameter of 10 mm and a length of about 50 m. A 48 L aluminum cylinder for the
working reference gas and three 10 L aluminum cylinders for the O2 and
CO2 standard gases were placed in thermally insulated boxes, which were
laid horizontally on a shelf to minimize the inhomogeneous distribution of
δ(O2 / N2) within the cylinders associated with temperature
and pressure gradients (Keeling et al., 1998, 2007).
The two standard gases with -579 per meg (tank CPD-00010) and -270
per meg (tank CPD-00011) were used for calibration of the O2
analyzer. Since the CO2 mole fractions of these two standard gases are
almost the same (∼ 407 ppm), we additionally used a third standard
gas with a CO2 mole fraction of 448.3 ppm (tank CPB-17350) to
calibrate the CO2 analyzer. During every 24 h period, these three
standard gases were measured for 32 min each. To determine the calibration
lines for both the O2 and CO2 analyzers precisely,
measurements of the three standard gases were repeated over 24 h when NC2
berthed at the port of Tahara, Japan.
Results and discussion
Influence of ship motion
After beginning the in situ measurements aboard NC2, we found that the ship motions
did not affect the response of the CO2 analyzer but did seriously
affect the response of the O2 analyzer. Figure 6a and b show temporal
variations in the output signal of the O2 analyzer for the standard gas
when NC2 was berthed at the port of Tahara and was cruising on the Pacific
Ocean, respectively. The output signal during the cruise (Fig. 6b and d)
shows apparent variations with peak-to-peak amplitudes of more than several
tens of ppm and peak-to-peak periods of about 20 s. Pickers (2016) reported
that similar apparent variations caused by ship motion were superimposed on
the output signals of individual fuel cells in an Oxzilla II analyzer. However,
in the Pickers instrument, the differential signal of both fuel cells did not
show apparent variations because the motion-induced variations were almost
compensated for completely by the differential signals.
Temporal variations in differential output signals of the O2
analyzer for six measurement cycles of O2 standard gas while the ship
was (a) in harbor and (b) cruising.
We installed a three-dimensional accelerometer on the O2 analyzer on 3 March 2016 to examine the relationship between the Oxzilla output signals and
the ship's motion. The apparent variations in the output signal were
associated with the variations in the acceleration of one axis or another
and both amplitudes were roughly proportional to each other. However, we
have not succeeded in describing the apparent variations with a linear
function of the measured accelerations. This is because the sensitivity of
the O2 analyzer to the acceleration along the three axes seems to be
unstable with time. Therefore, at this stage we cannot remove the apparent
variations associated with the ship motions with a simple algorithm.
Figure 7 shows temporal variations in the δ(O2 / N2) value
of the two standard gases relative to the working gas during the 1-year period
of this study. In the figure, each blue circle represents the 32 min average
of the standard gas during the voyages. The standard deviations of the
32 min averages were less than 13 per meg, suggesting that the averaging
procedure for several tens of minutes can effectively suppress the errors
caused by ship motion. For example, the expected standard deviation of the
hourly δ(O2 / N2) value for the standard gases is 9 per meg
(=13/21/2).
Time series of differences in δ(O2 / N2) for two standard gases, (a)
CPD-00011 and (b) CPD-00010, with respect to the working reference gas
as determined aboard NC2 during the 1-year period of this study. Blue circles
represent 32 min average values of standard gas measurements carried out at
24 h intervals. Black circles represent the averages of calibrations
conducted when NC2 was berthed at the port of Tahara, and the error bars
represent standard deviations.
The uncertainties in the 32 min averages of the standard gases are too large
for calibration of the O2 analyzer. In Fig. 7, the average δ(O2 / N2) values of the standard gases determined when NC2 was
berthed at the port of Tahara are also plotted as black circles with error
bars showing the standard deviations. Unfortunately, the standard deviations
for the standard gases were larger than the expected values obtained in our
laboratory, as discussed in Sect. 2.3. However, the standard errors were
lower than 2 per meg because the measurements were continued for more than 5 h
for the individual standard gases. Therefore, we calibrated the O2
analyzer using calibration lines based on the results at the port just
before and after each round-trip voyage.
Comparison between flask sampling and in situ observations
During the 1-year period from December 2015 through November 2016, the in situ
measurements of the atmospheric O2 and CO2 were conducted during
nine round-trip voyages, from NC2-123 to NC2-131, along the North American
route. The individual cruise tracks are depicted in Fig. 8a, in which thin
lines correspond to intervals of missing measurements. We obtained no in situ data
during the two westbound voyages of NC2-123 and NC2-128 because the cold
trap became clogged. Along with the in situ measurements, air samples were
collected in seven 2.5 L glass flasks at fixed longitudes (130,
145, 160, 175∘ W, 170,
155, and 145∘ E) during each westbound cruise.
(a) Cruise tracks of NC2 for nine round-trips (NC2-123–NC2-131)
during the period from December 2015 to
November 2016. Thin lines represent intervals during which in situ measurements
aboard NC2 were interrupted. (b) Longitudinal distribution of hourly
APO taken from the nine cruises. 5 h running averages are applied to
the hourly data to reduce signal noise.
The time series of δ(O2 / N2), CO2, and APO data taken
from the in situ measurements and the flask samplings are shown in Fig. 9. The APO
is computed based on the δ(O2 / N2) and CO2 mole
fraction in accordance with the following equation:
δAPO=δO2O2N2N2+1.1×XCO2SO2-1850,
where 1850 is an arbitrary APO reference point adopted by NIES. In Fig. 9,
each point for the in situ measurement represents the hourly average, and the data
outside the longitudinal range between 140∘ E and 128∘ W
are excluded because of significant contamination by anthropogenic emissions
from the coastal regions of both Japan and North America. The time series in
Fig. 9 clearly shows seasonal cycles and the in situ data seem to agree with the
flask data. The differences in the δ(O2 / N2), CO2, and
APO values between the in situ and the 39 flask measurements (in situ–flask) are
depicted in Fig. 10. The averaged differences with standard deviations were
-2.8 ± 9.4 per meg of δ(O2 / N2), -0.02 ± 0.33 ppm
of CO2, and -2.9 ± 9.5 per meg of APO. Taking into account
the uncertainties in the flask measurements (5 per meg for δ(O2 / N2) and 0.05 ppm for CO2 measurements; Tohjima et al.,
2003), we conclude that the uncertainties in the in situ measurements aboard NC2
were 8.0 per meg for δ(O2 / N2) and 0.33 ppm for CO2.
The differences between flask sampling and in situ measurements by the GC–TCD
method were reported as -0.6 ± 9.1 per meg of APO on the Oceanian
route (Tohjima et al., 2015), and 7.0 ± 9.9 per meg of δ(O2 / N2) at Cape Ochiishi (Yamagishi et al., 2008). From these
results, we conclude that the reliability of the O2 measurements in
this study is similar to that of the GC–TCD method.
Time series of (a) δ(O2 / N2), (b) CO2, and
(c) APO during the 1-year period from December 2015 to November 2016.
Lines indicate continuous observation, and circles indicate flask
measurements. Data obtained near the coasts of Japan and North America are
excluded. Vertical dashed lines separate each cruise and the top labels
represent cruise numbers.
The time series of the in situ data shown in Fig. 9 do not necessarily show
smooth changes, which may be partly attributed to the fact that the onboard
observations were conducted in the broad area of the North Pacific. For
example, the APO values are relatively low (< -180 per meg) during the
eastbound voyage NC2-125 in early March and high (> -100 per meg) during
the eastbound voyage NC2-127 in late May. The longitudinal distributions of
the 5 h running average of APO for the individual round-trip cruises are
depicted in Fig. 8b, which clearly shows these anomalously low (blue line of
NC2-125) and high (light blue line of NC2-127) APO distributions. Preliminary
analyses of the cause of these anomalies point to atmospheric transport and
the expected air–sea gas exchanges in the source regions. Detailed
discussions of the anomalous changes are beyond the scope of this paper and
will be presented in a future publication.
Distribution of seasonal cycles in the North Pacific
We investigated the longitudinal distribution of the seasonal amplitude of
APO in the middle latitudes of the North Pacific using 1 year of in situ data
within the area of 29–45∘ N and 140∘ E–130∘ W (Fig. 8a). The in situ data were binned into 10 longitudinal
bands (140–150∘ E, 150–160∘ E, …, 140–130∘ W) and fitted to the
following function using a least-squares method:
ft=a0+a1t+∑i=12a2isin2πit+a2i+1cos2πit,
where a1 is a detrending coefficient (-7.9 per meg yr-1)
determined from the APO values measured from the flask samples collected
aboard NC2 during the 2-year period 2014–2016.
Time series of differences in (a) δ(O2 / N2), (b) CO2, and
(c) APO between the in situ measurements and flask measurements
(in situ–flask). The red lines show average values.
Detrended seasonal variations in APO for the 10 longitudinal bins
within the rectangular area of 29–45∘ N and
140∘ E–130∘ W. Red circles represent hourly averages of
in situ APO data. Solid lines are average seasonal cycles determined by a
least-squares method. Dashed lines indicate 95 % confidence intervals
determined by a linear prediction model.
The detrended in situ data for the individual longitudinal bins and the fitted
average seasonal cycles are shown in Fig. 11. The APO seasonal amplitudes
for the 10 bins are plotted along with the longitude in Fig. 12a. For
comparison, we also plot the seasonal amplitudes of APO in the North Pacific
reported by Tohjima et al. (2012). In the previous study, APO data from the
flask samples collected in the Pacific during the period from 2002 to 2008
were binned into several rectangular regions and the seasonal cycles for the
binned data were examined in the same way as in this study. The seasonal
amplitudes in the previous study varied from 20 per meg to 110 per meg,
while those in this study varied from 51 per meg to 73 per meg. The
difference in the seasonal amplitudes seems to be explained by the
dependence of the latitudinal distribution, which is clearly shown in Fig. 12b,
in which all the seasonal amplitudes shown in Fig. 12a are plotted along
with the latitude. Such latitudinal dependence of the APO amplitude was
previously pointed out for data from the western Pacific (Tohjima et al.,
2005, 2012). On the other hand, this study reveals that the longitudinal
variability in the seasonal amplitude of APO in the North Pacific is rather
small. This preliminary analysis suggests that the temporally and spatially
dense atmospheric O2 and CO2 data obtained from the in situ
observations
aboard NC2 will enhance our understanding of air–sea gas exchange in the
North Pacific.