Introduction
Carbon dioxide (CO2) and methane (CH4) are two of the most
important greenhouse gases in the earth's atmosphere. Over the last few
decades, large efforts have gone into quantifying the impact of the ocean on
the CO2 and CH4 budgets. Air–sea fluxes of these gases are usually
estimated via a “bulk” formula, i.e. as the product of the waterside gas
transfer velocity kW and the air–sea concentration difference.
Globally, the open ocean takes up approximately a quarter of the
anthropogenic CO2 emissions (Le Quéré et al., 2015). This
estimate, limited in accuracy partly by uncertainties in kW, is
used in global models to constrain the terrestrial CO2 uptake (e.g.
Manning and Keeling, 2006; Canadell et al., 2007).
The shelf seas make up only a small fraction of the global oceans, but
support a significant portion of global primary productivity and draw a
substantial flux of atmospheric CO2 into the ocean (Chen et al., 2013).
Muller-Karger et al. (2005) estimated that the shelf seas might be
responsible for as much as 40 % of global oceanic carbon sequestration.
The majority of the atmospheric CO2 taken up by European shelf seas is
subsequently exported into the Atlantic Ocean (Thomas et al., 2004). Compared
to the open ocean, the coastal zone tends to be more spatially and temporally
heterogeneous, increasing the uncertainty in carbon flux estimates. Regions
influenced by riverine outflow and anthropogenic activities can be net
sources or sinks of atmospheric CO2 (Chen et al., 2013). Processes such
as respiration of allochthonous (terrestrial) organic carbon inputs,
benthic–pelagic coupling, variability in surfactant abundance, and
near-surface stratification are likely to have greater importance in shallow
waters. Furthermore, kW derived from the open ocean may not always
be applicable to shallow waters, where waves shoal and break more frequently,
and tidal flow and currents could become more important (e.g.
Upstill-Goddard, 2006). Monitoring of CO2 fluxes in such dynamic and
variable environments necessitates a continuous, high temporal resolution
methodology (Edson et al., 2008), such as the eddy covariance (EC) technique.
Based on seawater CH4 concentrations and global modelling, CH4
emission from the open ocean to the atmosphere has been estimated to be
0.4–18 Tg yr-1, an uncertain but probably small term in the global
CH4 budget (Bates et al., 1996; Bange et al., 1994; Lelieveld et al.,
1998). In certain regions such as the Arctic, however, ice melt can expose
underlying CH4-rich waters (e.g. Shakhova et al., 2010; Kitidis et al.,
2010). Enhanced mixing ratios of CH4 were measured on low-elevation
flights over regions of fractional ice cover and open leads in the Arctic,
suggesting a large surface source (Kort et al., 2012). On a per area basis,
shelf seas, rivers, and estuaries tend to have much greater CH4
emissions than the open ocean due to benthic methanogenesis (Bange,
2006; Upstill-Goddard et al., 2000; Middelburg et al., 2002). Global CH4
emissions from coastal regions are poorly quantified and may be influenced by
processes such as riverine outflow and tidal circulations. In shallow waters,
ebullition (bubbles rising from the sediment) represents an additional
pathway for CH4 transfer (Dimitrov, 2002; Kitidis et al., 2007). Some
bubbles are not fully dissolved in seawater before surfacing and this
transfer to the atmosphere is not accounted for in bulk flux calculations
based on aqueous CH4 concentrations.
Direct air–sea flux measurements would help to constrain CH4 cycling and
could also improve our understanding of the physical processes that drive gas
transfer. Thus far, estimates of kW from sparingly soluble gases
such as CO2 and 3He/SF6 (e.g. Sweeney et al., 2007; McGillis
et al., 2001; Nightingale et al., 2000) increase more rapidly with wind speed
than those derived from the more soluble dimethyl sulfide (e.g. Huebert et
al., 2004; Yang et al., 2011; Bell et al., 2013). This divergence may be due
to bubble-mediated gas exchange resulting from breaking waves (Blomquist et
al., 2006). CH4 is much less soluble than CO2 in seawater and
should thus be transferred even more efficiently by bubbles.
We measured air–sea CO2, CH4, momentum, and sensible heat fluxes by
the EC method at the Penlee Point Atmospheric Observatory (PPAO) during three
periods at three sampling heights: May–June 2014 (∼ 15 m above mean
sea level, a.m.s.l.), June–July 2014 (∼ 27 m), and April–June 2015
(∼ 18 m). The influences of sampling height and wind direction on
fluxes are examined in Sect. 3.2. To evaluate how representative our
measurements are of air–sea transfer, EC fluxes of momentum and sensible heat
are compared to open-ocean bulk formulae based on mean wind speed and air/sea
temperatures (Sect. 3.3). We illustrate wind direction and diel variations in
atmospheric CO2 and CH4 mixing ratios (Sect. 4.1). Marine CH4
emissions have not been quantified previously by EC and here we estimate the
detection limit of this measurement (Sect. 4.2). Focusing on the open water
wind sector, we elucidate the drivers for the variability in CO2 and
CH4 fluxes (Sects. 4.3 and 4.4).
Experimentation
Environmental setting
The Penlee Point Atmospheric Observatory (50∘19.08′ N,
4∘11.35′ W;
http://www.westernchannelobservatory.org.uk/penlee/) was established in
May 2014 by the Plymouth Marine Laboratory (PML) on the south-west coast of
the United Kingdom for long-term observations of air–sea exchange and
atmospheric chemistry. PPAO is in close proximity to two nearby long-term
marine stations that form the Western Channel Observatory
(http://www.westernchannelobservatory.org.uk). Meteorological variables
(wind, temperature, humidity, and pressure), sea surface temperature (SST),
salinity, chlorophyll, oxygen, and dissolved organic matter are measured
continuously from buoys stationed at L4 (50∘15.0′ N,
4∘13.0′ W) and E1 (50∘02.6′ N, 4∘22.5′ W),
which are about 6 and 18 km south of PPAO. Seawater pCO2 is measured on
weekly cruises to the L4 station and biweekly cruises to the E1 station
(Kitidis et al., 2012).
PPAO is situated on an exposed headland on the western edge of the Plymouth
Sound, which is primarily fed by the Tamar Estuary from the north-west and is
open to the Atlantic Ocean to the south-west (Fig. 1). South-south-west of
PPAO, the water depths increase steadily to ∼ 8, 15, 22, and 24 m
(relative to mean sea level) at horizontal distances of 100, 300, 1000, and
1300 m (www.channelcoast.org). North-easterly wind comes over the
Plymouth Sound to PPAO and is limited to a fetch of about 5 km. Air from the
south-east is affected by pollution from the European continent as well as
shipping emissions (Yang et al., 2016). In the south-westerly direction, the wind
fetch is up to thousands of km and the wind speed sometimes exceeds
20 m s-1. This brings in air that has much less anthropogenic
influence and is more representative of the background Atlantic atmosphere
(see Sect. 4.1).
Top left: location of the Penlee Point Atmospheric Observatory
(white cross). PPAO is ∼ 6 km south-south-west of the Plymouth
Marine Laboratory (red dot), ∼ 6 km north of the L4 station
(yellow star), and ∼ 18 km north of the E1 station (beyond the
southerly extent of the map). White dashed lines are commercial ferry routes.
Bottom left: a close up map showing the foreshore around the PPAO hut.
Right: PPAO with the telescopic mast fully raised. North is up in both maps
on the left.
The stone PPAO building (length, width, height of 3.5, 3.5, 3.0 m) is
approximately 11 m a.m.s.l., mains powered, vehicle-accessible,
and uses line-of-sight radioethernet to communicate with PML (6 km to the
north-north-east). A small strip of land and a narrow, rocky intertidal zone
separate the building from the sea. South-west and north-east of PPAO, the
horizontal distance to the water's edge is 30–60 m, depending on the tide.
South-east of PPAO, the distance to water is greater (about 70–90 m) due to
an exposed rocky outcrop. The local tidal amplitudes (semi-diurnal) are
∼ 5 m during spring tide and ∼ 2 m during neap tide. The
intertidal zone is only sparsely covered by macroalgae (< 10 % by
area), likely due to frequent exposure to large waves.
Summary of sampling periods, mast height above observatory rooftop
and above mean sea level (a.m.s.l.), and hourly eddy covariance CH4
fluxes (µmole m-2 d-1) for the south-west wind sector
(180–240∘). CH4 fluxes when the sampling height was
15 m a.m.s.l. are likely to be underestimates of air–sea transfer because a
significant portion of the flux footprint was over land (Sect. 3). For the
last period (2015), fluxes are computed from both the Windmaster Pro and R3
sonic anemometer (shown in that order). SE indicates standard error.
Sensor height (m)
EC flux
Falling tide
Rising tide
Time
Over roof
a.m.s.l.
Mean (SE)
Mean (SE)
Mean (SE)
14 May – 17 June 2014
1.4
∼ 15
16 (2)
14 (2)
20 (3)
17 June – 21 July 2014
13.3
∼ 27
24 (4)
21 (5)
29 (6)
21 April – 3 June 2015
3.6
∼ 18
25 (2), 30 (2)
19 (2), 22 (2)
33 (3), 38 (3)
Turbulent flux instrumentation
During May–June 2014, a sonic anemometer (Gill Windmaster Pro) and a
meteorology station (Gill Metpak Pro) were mounted on a metal pole about
1.4 m above the PPAO rooftop. A telescopic mast (retracted length of 2.8 m
and fully extended length of 12.3 m; Clark Masts) was installed on top of
the observatory roof (Fig. 1) on 17 June 2014. The Windmaster Pro anemometer
and the meteorology station were then moved to a cross bar on top of the
mast. In February 2015, another sonic anemometer (Gill R3) was installed at
the same height as the Windmaster Pro, about 80 cm apart in the horizontal.
The sonic anemometers measure 3-dimensional wind velocities (u, v: the
two horizontal components; w: the vertical component) at 10 Hz (Windmaster
Pro) and 20 Hz (R3). Table 1 summarises measurement periods and
corresponding sensor heights.
We deployed the Windmaster Pro and the R3 sonic anemometers side by side for
two reasons. First, signal dropouts at high frequencies were common for the
Windmaster Pro during moderate-to-heavy precipitation, which tended to
coincide with strong south-westerly winds. Valid flux measurements from the
Windmaster Pro, limited to mostly dry periods, may thus be biased towards
low-to-intermediate wind speeds. Second, initial drag coefficient
measurements from the Windmaster Pro at PPAO were lower than expected
compared to published results for air–sea momentum flux. The manufacturer
Gill report a firmware bug in the Windmaster Pro and recommend a bias
correction to the w axis (+16.6 % for positive w; 28.9 % for
negative w; see technical key note:
http://gillinstruments.com/data/manuals/KN1509_WindMaster_WBug_info.pdf).
This correction is not necessary for the R3 anemometer, which has
individually calibrated u, v, and w components. Simultaneous
deployments of these two anemometers allow us to evaluate the effectiveness
of the Windmaster Pro correction (Sect. 3.3).
CO2 and CH4 measurements
Atmospheric mixing ratios of CO2 and CH4 were measured by a Picarro
cavity-ringdown analyzer (G2311-f) at a frequency of 10 Hz (flux mode).
The inlet to this analyzer was mounted ∼ 30 cm below the centre volume
of the Windmaster Pro anemometer. An external dry vacuum pump drew sample air
via a ∼ 18 m long, 3/8′′ OD Teflon perfluoroalkoxy (PFA) tubing at a
flow rate of initially ∼ 30 L min-1. The pump performance
deteriorated over time due to constant exposure to sea salt. A high-performance particulate arrestance (HEPA) filter was installed immediately
upstream of the pump in late 2014, which resulted in a
∼ 15 L min-1 reduction of the main flow. The Picarro instrument
subsampled from the main flow via a ∼ 2 m long, 1/4′′ OD Teflon PFA
tubing at a rate of ∼ 5 L min-1. Airflow was fully turbulent
throughout the inlet.
The presence of water vapour (H2O) degrades the measurements of CO2
and CH4 via dilution, spectral interference and line broadening (Rella,
2010). Miller et al. (2010) and Blomquist et al. (2014) found that ambient
variability in H2O mixing ratio causes significant bias to the EC
measurements of air–sea CO2 flux. We followed the recommendation of
Blomquist et al. (2014) and dried the sampled air using a high-throughput
dryer (Nafion PD-200T-24M). H2O efficiently permeates through the Nafion
membrane while CO2 and CH4 essentially do not. Set up in
counter-flow mode (reflux configuration), the dryer utilises the lower
pressure of the Picarro exhaust air to dry the sample air. The ambient
H2O mixing ratio is typically on the order of 1 % at PPAO. With the
dryer inline the measured H2O mixing ratio was reduced by 5 to 10-fold.
The Picarro instrument reports mixing ratios of CO2 and CH4 in
sample air based on precisely controlled cavity temperature and pressure. An
internal, point-by-point correction by the instrument for residual humidity
yields the dry mixing ratios (CCO2 and CCH4),
which we use for flux computations. Air density fluctuations (i.e. Webb et
al., 1980) should thus not affect our measurements. Tuned by the manufacturer
prior to our first use, we checked the instrument calibration with CO2
and CH4 gas standards (BOC) and occasionally determined the instrument
backgrounds with nitrogen gas. CO2 and CH4 measurements were
unavailable between August 2014 and March 2015 due to faults in the Picarro
instrument.
Suitability of the site for air–sea transfer measurements
Eddy covariance flux processing
In the eddy covariance method, flux is determined from the correlation
between the vertical wind velocity (w) and the variable of interest (x):
w′x′‾. Here the primes indicate fluctuations from the means
while the overbar denotes temporal averaging. The coastal environment near
PPAO is complex and heterogeneous in both air and water phases. Shifts in
air mass and wind direction result in substantial changes in air temperature
and gas-mixing ratios. Thus we chose a relatively short averaging interval of
10 min (as used by e.g. Miller et al., 2010) to more easily satisfy the
homogeneity/stationarity requirements for eddy covariance (see Appendix A for
flux quality control).
For the computations of CO2 and CH4 fluxes
(w′CCO2′‾, w′CCH4′‾), an
lag correlation analysis is performed hourly to determine the time delay
between the instantaneous vertical winds and gas-mixing ratio measurements.
Most of the atmospheric variability in CO2 and CH4 is caused by
horizontal transport rather than the air–sea flux. Detrending the gas-mixing
ratios to remove low-frequency variability improves the accuracy of the lag-time
determination. Between May and July 2014, a delay of 1.9 ± 0.1 s
was found between w (Windmaster Pro anemometer) and CCO2.
After the installation of the HEPA filter, the delay increased to
3.3 ± 0.1 s. Lag times derived from w and CCH4 are much
noisier due to the smaller magnitude of the CH4 flux. We apply the lag
correction determined from the w : CCO2 analysis to the
CH4 flux calculation. Ten-minute segments of CO2 and CH4
fluxes that pass the quality control criteria (see Appendix A) are further
averaged to hourly intervals, which reduces random noise by a factor of
∼ N0.5, where N is the number of valid flux segments. Only hours
with at least three 10 min flux intervals are considered for further
analysis.
Evaluation of wind sectors
A double rotation (Tanner and Thurtell, 1969; Hyson et al., 1977) streamline
correction is applied to wind data in 10 min blocks prior to flux
computation. Tilt angles between the horizontal and vertical planes from this
calculation for sampling heights of 15, 18, and 27 m a.m.s.l. are shown in
Fig. 2. During onshore airflow, the mean tilt angle is positive as air is
forced upwards. The magnitude of this tilt for the south-westerly wind, which
blows perpendicularly across the Penlee headland and makes contact with water
again to the north-east, is comparable to shipboard measurements. The tilt
angle is negative in the north-west sector due to the presence of a small hill
behind the observatory building in that direction. A comparison of horizontal
wind speed between Penlee and the L4 buoy when the wind is from the south-west
does not show, within measurement uncertainties, a significant acceleration
in the Penlee measurement (e.g. as might be expected when air is forced over
a large superstructure). Thus the hill to the north-west of the site should not have
a major influence on our measurements during south-westerly conditions. A peak
in tilt angle near 120∘, more apparent at low sampling heights, is
likely caused by the exposed rocky outcrop in that direction. The impact of
this local topography is reduced with increasing sampling height.
Tilt angle vs. true wind direction at three sampling heights.
Lines represent averages (wind speed > 3 m s-1 only) and the
error bars indicate standard deviations within each wind direction bin. Wind
data are from the Windmaster Pro sonic anemometer.
From the friction velocity u∗=(u′w′‾2+v′w′‾2)1/4 and wind speed
(Utrue), we compute the drag coefficient CD=(u∗/Utrue)2. Bin-averaged CD at the three sampling heights as
a function of wind direction is shown in Fig. 3. At 15 and 18 m a.m.s.l.,
measured CD from about 80 to 150∘ are clearly elevated compared
to open-ocean values (which typically range between 0.5 × 10-3
and 2.5 × 10-3 depending on the wind speed; Edson et al.,
2013). This is likely because a part of the flux footprint overlapped with
the rocky outcrop in that direction, which has a greater roughness length
than the surface ocean. Likewise, high CD values between 250 and
40∘ are caused by land. The impact from the rocky outcrop to the
south-east is no longer obvious at a sampling height of 27 m a.m.s.l., when
the flux footprint shifts further away from the observatory. For winds
blowing from the north-east and south-west, measured CD is lower and much
closer to values expected for the open ocean. North-easterly winds are
relatively infrequent (∼ 8 % of the time) and limited in fetch;
also the air mass from that direction is affected by terrestrial pollution and
ship emissions. We thus focus on the more frequent (∼ 20 % of the
time) south-west wind sector (180–240∘) for most of this paper. In
Appendix B, we compute the theoretical flux footprints at different sampling
heights and during various atmospheric conditions/tidal cycles. For
south-westerly winds, land influence is predicted to be only a few percent
when the mast height is ≥ 18 m a.m.s.l.
Drag coefficient vs. true wind direction at three sampling
heights. Lines represent averages (wind speed > 3 m s-1
only) and the error bars indicate standard deviations within each wind
direction bin. Wind data are from the Windmaster Pro sonic anemometer.
Verification of momentum and sensible heat transfer
Here we compare the 10-m neutral drag coefficient (CD10N=(u*/U10N)2) and sensible heat fluxes to the fairly well established
open-ocean bulk formulae predictions. The 10-m neutral wind speed U10N
is determined using Businger–Dyer relationships (Businger, 1988) from the
wind speed and air temperature at PPAO, tidal-dependent sampling height, and
SST from L4. EC sensible heat flux is derived from the sonic temperature and
further corrected for humidity using the bulk latent heat flux. To avoid
sheltering by Rame Head to the west and near-shore processes, we limit our
CD10N observations to a narrower wind sector of 180–220∘.
Figure 4 shows the relationship between CD10N and U10N from the
Windmaster Pro sonic anemometer. Also shown are the predicted CD10N from
the COARE (Coupled Ocean-Atmosphere Response Experiment) model version 3.5 (Edson et al., 2013) and Smith (1980). When the
sensors were initially placed at 15 m a.m.s.l., measured CD10N values
were significantly higher than the open-ocean parameterisations at moderate
wind speeds, probably because land/foreshore was within the flux footprint. At
18 m a.m.s.l., the mean CD10N at intermediate-to-high wind speeds
was in close agreement with bulk predictions. Measured CD10N are sometimes
elevated at wind speeds less than ∼ 5 m s-1, possibly due to
increased flow distortion or minor land influence.
10 m neutral drag coefficient vs. 10 m neutral wind speed at
sampling heights of 15, 18, and 27 m a.m.s.l. (a) 10 min EC
measurements, (b) bin averages, with error bars indicating two standard
errors within each wind speed bin. Wind data are from the Windmaster Pro sonic
anemometer. Also shown are CD10N parameterized from the COARE
model version 3.5 (Edson et al., 2013) and Smith (1980).
At 27 m a.m.s.l., CD10N measurements from the Windmaster Pro within
the wind sector of 180–220∘ are limited (valid flux segments N=42), which appear to be lower than the open-ocean parameterisations by about
0.2 × 10-3. These low CD10N values may partly be due to
remaining uncertainties in the Windmaster Pro sonic anemometer even after
applying the bias correction to the w axis. Our coastal measurements show
that at a tilt angle of 5∘, the recommended w correction increases
u∗ from the Windmaster Pro by 6 % (and increases scalar fluxes
by 14 %). Relative to the R3 sonic anemometer, this reduces the low bias
in the Windmaster Pro u∗ from 9–10 to 3–4 %. The remaining
3–4 % bias can account for an approximate 0.1 × 10-3
underestimation of CD10N by the Windmaster Pro.
Figure 5 shows a comparison between the EC sensible heat flux and the bulk
sensible heat flux. The latter is computed from SST from the L4 buoy (1 m
depth), potential air temperature and U10N from PPAO, and the heat
transfer rate from the COARE model (Fairall et al., 2003). Measurement and
prediction are not far from the 1 : 1 line at a sampling height of
27 m a.m.s.l. (slope = 0.82; r2=0.72). A perfect agreement is
not expected here, as any spatial heterogeneity in SST along the 6 km
between L4 and PPAO (e.g. due to the Tamar Estuary outflow) or near-surface
vertical gradient in seawater temperature would contribute to the discrepancy
between measured and predicted sensible heat flux. At the initial sampling
height of 15 m a.m.s.l., measured sensible heat flux is often very large
and shows no correlation with the bulk flux estimate, most likely due to the
terrestrial influence within the flux footprint. At 18 m a.m.s.l., a better
coherence is observed but significant scatter remains, probably because the
largest horizontal variability in SST is close to shore (and occupies more of
the footprint at 18 m than at 27 m). Overall,
the comparisons above suggest that the mean measured fluxes at a sampling
height > = 18 m during south-westerly winds are within 20 % of
the expected open-ocean air–sea transfer rates.
EC sensible heat flux vs. bulk sensible heat flux computed using
SST from the L4 station. For June–July 2014 (27 m a.m.s.l.), the
colour-coding indicates the sea-air temperature difference, while the marker
size corresponds to wind speed (1–12 m s-1).
Results and discussion
Variability in CO2 and CH4 mixing ratios
Mixing ratios of CO2, and CH4 (CCO2 and
CCH4) varied at PPAO depending on wind direction (Fig. 6). On
average between May and July 2014, CCO2 and CCH4
were generally higher for winds blowing from land than for winds blowing from
the sea, likely due to the much greater terrestrial emissions of these gases
and also different boundary layer dynamics. Mean CCO2 and
CCH4 from the south-west sector (180–240∘) are similar
to “well-mixed” atmospheric observations from sites such as Mauna Loa and
Mace Head, consistent with the long atmospheric lifetime of these gases. Mean
diel cycles in CCO2 and CCH4 between May and
July 2014 during onshore (110–240∘) and offshore (300–60∘)
wind flows are shown in Fig. 7. CCO2 and CCH4 for
onshore winds show little diel variability, consistent with the relatively
small air–sea CO2 and CH4 fluxes (on a per area basis).
CCO2 and CCH4 for offshore winds increased at night
and peaked in the early morning. Night-time wind speeds tend to be low in
offshore flow, with an average of ∼ 3 m s-1 during these months.
The resultant low atmospheric turbulence favours the formation of a shallow
nocturnal boundary layer, which traps surface emissions. Between about 11:00
and 20:00 UTC, CCO2 was lower for offshore winds than for
onshore winds, probably due to terrestrial photosynthesis. Similar diel
cycles in CCO2 and CCH4 are often observed at
terrestrial sites (e.g. Winderlich et al., 2014). Clear day/night differences
were also apparent in the mixing ratios of oxygenated volatile organic
compounds measured from the rooftop of PML (Yang et al., 2013). While not the
focus of this work, it is worth noting that the elevated atmospheric CO2
and CH4 in the early morning will influence their air–sea fluxes in
coastal regions during offshore conditions.
Atmospheric mixing ratios of CO2 and CH4 as a function
of wind direction. Error bars indicate two standard errors within each wind
direction bin. CO2 and CH4 mixing ratios were generally lower for
south-westerly winds (180–240∘) than for northerly wind sectors.
Mean diel cycles in the mixing ratios of CO2 and CH4.
Error bars indicate two standard errors within each hour bin. Diel
variability for both gases is small during onshore flow (marine winds,
110–240∘). Mixing ratios of CO2 and CH4 during
offshore flow (wind from land, 300–60∘) increase at night and
peak in the early morning.
Detection limit of CH4 flux measurement
In this section, we examine the eddy covariance flux detection limit of
CH4 and its dependence on instrumental noise as well as ambient
variability. Blomquist et al. (2014) estimated an hourly CO2 flux
detection limit of ∼ 1 mmole m-2 d-1 for a prototype
version of the Picarro analyzer (G-1301-f) with a Nafion dryer at a wind
speed of 8 m s-1 and in a neutral atmosphere. This represents an order
of magnitude improvement over previous CO2 sensors (e.g. Licor) and is
lower in magnitude than the typical air–sea CO2 flux. Based on
terrestrial eddy covariance measurements, Peltola et al. (2014) estimated the
CH4 flux detection limit using the Picarro analyzers G-1301-f and
G-2311-f to be ∼ 170 µmole m-2 d-1 for an
averaging interval (T) of 30 min
(∼ 120 µmole m-2 d-1 at T=60 min). In
comparison, the expected emissions of CH4 (FCH4) based on
dissolved CH4 in the open ocean are generally less than
10 µmole m-2 d-1 (e.g. Forster et al., 2009).
We estimate the air–sea CH4 flux detection limit using an empirical and
a theoretical approach. First, following Spirig et al. (2005), we compute the
variability in the CCH4 : w covariance at a time lag far
away from the true lag (i.e. +300 s). During periods of consistent
south-westerly winds, the 1σ of this null CH4 flux is
15 µmole m-2 d-1 at T=10 min. The flux detection
limit (defined as 3σ) should thus be
18 µmole m-2 d-1 (= 3 × 15/60.5) for an
hourly average and 4 µmole m-2 d-1 for a daily average.
Based on theory and scalar flux observations, Blomquist et al. (2010, 2012)
attributed total uncertainty in eddy covariance flux (δFC) to
ambient variance (σCa2) and sensor noise (σCn2):
δFC=2σWTσCa2τWC+σCn2τCn1/2=2σWTσCa2τWC+φCn41/2.
Here τWC and τCn are the integral timescales
for ambient variance and noise variance. The noise term in Eq. (1)
relates to φCn, the band-limited noise. According
to the manufacturer the precision of the Picarro G2311-f is ≤ 3 ppb
for CH4 at a sampling rate of 10 Hz. The variance spectra of CH4
during two periods of south-westerly winds are shown in Fig. 8. Variance below
∼ 0.025 Hz largely follows the expected -5/3 slope for atmospheric
transport. At frequencies above ∼ 0.025 Hz, the Picarro shows a
“pink” background noise that approximately scales to a -1/5 slope. The
integrated variance from 0.025 to 5 Hz is ∼ 1.1 ppb2, while the
average φCn between 1 and 5 Hz is
∼ 0.23 ppb2 Hz-1. Considering noise alone (i.e.
σCa2=0), for a neutral atmosphere at a wind
speed of 10 m s-1 and a sampling height of 20 m a.m.s.l., Eq. (1)
predicts an uncertainty in hourly CH4 flux of
11 µmole m-2 d-1 (Fig. 9). From the expected air–sea
CH4 flux, using similarity theory we can estimate the variability in
CCH4 caused by air–sea exchange in a neutral atmosphere as 3|FCH4|/u∗ (e.g. Fairall et al., 2000; Blomquist et al.,
2010). For FCH4=2–20 µmole m-2 d-1 and
u∗=0.3 m s-1, this corresponds to a predicted variability
of 0.006–0.057 ppb. Figure 9 shows that if the ambient variability in
CCH4 were in this range, the hourly flux uncertainty would be
dominated by sensor noise.
Variance spectra of CH4 on two days of south-westerly winds.
Variance at frequencies above ∼ 0.025 Hz is dominated by
noise, while ambient variability accounts for most of the low-frequency
variance.
The observed ambient variability in CCH4 tends to be more than
an order of magnitude greater than is predicted from similarity theory, which
is likely related to processes other than air–sea flux (e.g. spatial
heterogeneity and horizontal atmospheric transport). We estimate σCa2 as the second point of the autocovariance of
CCH4 (the difference between the first and second points of the
autocovariance equates to σCn2 of
∼ 1 ppb2). At PPAO, the minimum CH4 ambient variability
during onshore flow is 0.2 ppb (σCa2=0.04 ppb2), which corresponds to a predicted hourly flux uncertainty
of 20 µmole m-2 d-1 (Fig. 9). This is close to our
empirical estimate of the CH4 flux detection limit above. With
increasing σCa (i.e. more variable
CCH4), the flux uncertainty increases substantially and becomes
much greater than FCH4, while the relative importance of σCn2 decreases. Thus, we expect the 10-fold greater
CH4 flux detection limit estimated by Peltola et al. (2014) to be due to
the higher variability in CCH4 over land than at our marine site
(for onshore winds only). Over the open ocean where
σCa in CH4 is likely to be even lower than at PPAO,
the flux detection limit for CH4 should slightly decrease.
Estimated uncertainty in hourly averaged EC flux of CH4.
Typical observed and predicted (based on similarity theory for the open
ocean) values of the ambient variability in CH4 mixing ratio are shown
by the horizontal bars.
Time series of (a) wind speed and direction, (b) CO2 flux and
mixing ratio, and (c) CH4 flux and mixing ratio during June–July 2014
(sampling height of 27 m a.m.s.l.). Cyan shading indicates onshore winds.
Fluxes are limited to the south-west wind sector only. Also shown are
pCO2 and atmospheric CO2 mixing ratio from the L4 station. Negative
CO2 fluxes on the order of a few mmole m-2 d-1 were observed
during the windy periods on 27 June and 4 July. By late July, observed
CO2 fluxes were indistinguishable from zero, consistent with near
saturation of seawater pCO2 at the L4 station. CH4 flux has a
positive mean, suggesting sea-to-air emission.
From the analysis above, it seems that an improvement in the precision of the
CH4 instrument will only marginally reduce the uncertainty in CH4
flux. Blomquist et al. (2010) arrived at a similar conclusion in an analysis
of air–sea carbon monoxide flux. At present, the relative CH4 flux
uncertainty is best minimised by measuring in regions of large flux (i.e.
high seawater supersaturation and strong winds) and minimal ambient
variability (i.e. spatially homogenous environment).
Blomquist et al. (2010) and Yang et al. (2011) estimated the high-frequency
loss in dimethylsulfide flux of typically less than 5 % from the same
type of Nafion dryer as used in this study. Flux attenuation by the tubing
itself should be negligible given the turbulent flow. Considering the other
larger random uncertainties in our CO2 and CH4 fluxes, we present
the measured fluxes without any attenuation correction in this paper.
As Fig. 10, but during April–June 2015 (sampling height of
18 m a.m.s.l.). Fluxes were computed from both the Windmaster Pro and the
R3 sonic anemometers. Large air-to-sea flux of CO2 is observed during
high wind speed events, while CH4 flux is almost always positive.
CO2 flux
Air–sea CO2 fluxes measured at sampling height of 27 m a.m.s.l.
between June and July 2014 were generally small (Fig. 10). Diurnal land–sea
breezes were common and durations of onshore winds tended to be short during
this period. CO2 fluxes from the south-west (negative = into the
ocean) ranged between 3 and -9 mmole m-2 d-1 (mean of
-3 mmole m-2 d-1) during the relatively windy periods on
27 June and 4 July. Seawater pCO2 at the L4 station ranged between 326
and 345 µatm (mean of 337 µatm) from 9 June to
7 July 2014. The atmospheric CO2 mixing ratio at L4 agrees well with
Picarro measurements at PPAO during onshore flow (Fig. 10). Using the air–sea
difference in partial pressure of CO2 (ΔpCO2), SST and
salinity at L4, as well as wind speed at PPAO, we compute the expected
air–sea CO2 flux as kW⋅α⋅ΔpCO2, where
α is the solubility of CO2 and kW is the gas transfer
velocity from Nightingale et al. (2000) adjusted for Schmidt number. The
expected air–sea CO2 flux of -1 to -5 mmole m-2 d-1
(mean of -3 mmole m-2 d-1) on 27 June and 4 July are of the
same magnitude as our EC measurements. The mean EC CO2 flux could not be
distinguished from zero in the second half of July, consistent with the
increase in seawater pCO2 at L4. The spring algal bloom ended abruptly
in early July 2014, with chlorophyll a concentration dropping from
∼ 3 to less than 1 mg m-3
(http://www.westernchannelobservatory.org.uk/buoys.php). The rapid
warming of seawater from ∼ 13 ∘C in June to
∼ 18 ∘C in July aided a rapid approach towards air–sea
CO2 equilibrium by the middle of July 2014.
Air-to-sea CO2 fluxes as substantial as -90 mmole m-2 d-1
were observed between April and June 2015 (sampling height of
18 m a.m.s.l., Fig. 11). For the south-west sector, the mean fluxes
(standard errors) computed from the Windmaster Pro and the R3 sonic
anemometers were -19.3(±1.4) and
-23.7(±1.4) mmole m-2 d-1 respectively during this period.
The reduced mean flux from the Windmaster Pro was primarily
caused by signal dropouts in this anemometer during moderate-to-heavy
precipitation, which tended to coincide with high wind speeds (and greater
air–sea transfer). When both sonic anemometers were functional, CO2
fluxes computed from the Windmaster Pro and the R3 demonstrate excellent
agreement (slope = 0.98, r2=0.95). Example CO2 cospectra over
about half a day from 24 April (wind speed of 8 m s-1) and 10 May 2015
(wind speed of 6 m s-1) are shown in Fig. 12. The observed cospectra
are fairly well described by theoretical fits for a neutral atmosphere
(Kaimal et al., 1972). Minimal (< 10 %) flux loss at high frequencies is
evident, as expected. Hourly CO2 flux (reversed in sign for clarity)
during this period clearly increased with wind speed (Fig. 13). Unfortunately
seawater pCO2 was not measured during this period for comparison. For
reference, pCO2 measurements from L4 in May 2014 had a mean
(1σ) of 306(26) µatm, implying a ΔpCO2 close to
-100 µatm. We compute the predicted CO2 fluxes at SST of
12.5 ∘C (mean from the E1 station) and ΔpCO2 of -50
and -100 µatm. During most of this period, EC CO2 flux is
fairly close to prediction using ΔpCO2=-100 µatm.
Towards late May/beginning of June, the magnitude of CO2 flux appeared
to be smaller at high wind speeds. A reduction in ΔpCO2 as
occurred in 2014 could explain the declining CO2 fluxes in 2015.
Measured CO2 flux from the south-west between May and June 2014 (sampling
height of 15 m a.m.s.l.) varied from a mean (±1 standard error) of
about 40(±8) mmole m-2 d-1 at night to
-55(±11) mmole m-2 d-1 during the day (Fig. 14). Mean wind
speeds were fairly similar between day and night at around 5 m s-1
during this period. The pronounced diel variability and large magnitude of
the CO2 flux suggest that these fluxes were likely affected by
photosynthesis and respiration from land upwind of the observatory building
and/or organisms living on the foreshore. As atmosphere–land exchange of
CO2 can be more than an order of magnitude greater than air–sea CO2
flux on a per area basis (e.g. Goulden et al., 1996), a relatively small
terrestrial contribution to the flux footprint (> 5 % spatially) could
significantly bias the EC measurement. At sampling heights ≥
18 m a.m.s.l., CO2 fluxes show much less diel variation, as would be
largely expected for air–sea transfer (Fig. 14). However, the possibility of
minor influence from land/foreshore on measurements at 18 m a.m.s.l. cannot
be entirely ruled out. Such local effects might explain some of the scatter
in CO2 fluxes at wind speeds below ∼ 5 m s-1, i.e. when the
flux footprint was probably closer to land.
Overall, except at the lowest sampling height, air–sea CO2 fluxes by EC
show the expected magnitude and direction in the mean. High resolution
CO2 fluxes demonstrate significant temporal variability, which is often
not well captured by the weekly seawater sampling at L4. We plan to make
more regular measurements of seawater pCO2, SST, and salinity within the
flux footprint in the future (e.g. as discrete water samples or using a
semi-automated dissolved measurement system on Plymouth Marine
Laboratory's research vessel Quest), which will enable a direct estimate of the
CO2 gas transfer velocity in a coastal environment.
CH4 flux
We use historical observations to assess the validity of the EC CH4
fluxes since dissolved CH4 was not measured during 2014–2015. Surface
CH4 saturation values of around 2000 % were measured at the mouth of
the Tamar Estuary in spring 2001 by Upstill-Goddard and Barnes (2016). At a
SST of 10 ∘C and wind speed of 10 m s-1, CH4 saturation
of 2000 % implies a predicted CH4 flux of
∼ 0.2 mmol m-2 d-1 (kW from Nightingale et al.,
2000). Moving further out from the estuary mouth, dissolved CH4
concentration is expected to decrease due to dilution and oxidation. A strong
inverse relationship between CH4 concentration and salinity has been
demonstrated by previous investigators (e.g. Upstill-Goddard et al., 2000),
with higher CH4 concentrations found in fresher waters. According to the
compilation by Bange (2006), typical seawater saturations of CH4
range from 110 to 340 % in the shelf waters of the North Sea, resulting in
fluxes on the order of 10 µmole m-2 d-1.
Over the three measurement periods presented here, mean EC CH4 fluxes
ranged between 16 and 30 µmole m-2 d-1 in the south-west
wind sector, with peak emissions above
∼ 50 µmole m-2 d-1 (Figs. 10 and 11). As with
CO2, during April–June 2015 the smaller mean CH4 flux computed
from the Windmaster Pro anemometer than from the R3 is primarily due to
signal dropouts in the former during rainy, windy conditions (Table 1). The
cospectra of CH4 are noisier than those of CO2 (Fig. 12) but
demonstrate the expected spectral shape. The lowest mean CH4 fluxes were
observed at a sampling height of 15 m a.m.s.l., when the flux footprint
should be the closest to shore. This suggests that surface waters, rather
than the foreshore/land, are the predominant source of CH4 at PPAO. In
other words, the EC CH4 fluxes during the low mast period in May–June
2014 are likely underestimates of air–sea transfer.
Mean CO2 and CH4 cospectra over about half a day for
24 April (wind speed of 8 m s-1) and 10 May 2015 (wind speed of 6 m s-1). Measurements were made at 18 m a.m.s.l. and from the
south-westerly
direction. Theoretical spectral fits (Kaimal et al., 1972) are also shown.
CH4 fluxes from the north-east wind sector (the direction of Plymouth
Sound) are on average 2–3 times higher than fluxes from the south-west
(Fig. 15), suggesting higher CH4 concentrations in the Tamar Estuary
outflow than in open water. CH4 fluxes from the south-west show a
significant but weak relationship with wind speed (r=0.33 during
June–July 2014; r=0.25 during April–June 2015; p < 0.05). The
weak relationship between CH4 flux and wind speed was likely in part due
to variable seawater CH4 concentrations. CH4 emissions do not
obviously vary with time of day but they tend to be higher during incoming
(rising) tide than during outgoing (falling) tide. In Fig. 16, CH4
fluxes from the south-westerly direction (April–June 2015) are plotted against
hours after low water (low tide occurs at hour zero; high tide occurs near
hour six). The median, 25, and 75 % percentiles within each hour bin
are also shown. The largest average CH4 emissions are observed in the
first ∼ 4 h after low tide, while CH4 fluxes during the falling
tide are lower and less variable. Mean CH4 fluxes were also
∼ 50 % higher during spring tide (here limited to daily tidal
amplitude > 4 m) than during neap tide (daily tidal amplitude < 3 m).
These patterns are consistent with an incoming tidal current that pushes the
CH4-rich surface outflow from the Tamar Estuary around the Rame
peninsula (Uncles et al., 2015).
Relationship between CO2 flux (R3 sonic anemometer; reversed
in sign) and wind speed during April–June 2015 (sampling height of
18 m a.m.s.l.). Predicted CO2 fluxes assuming ΔpCO2 of
-50 and -100 µatm are also shown.
Diel variations in CO2 fluxes at three sampling heights for
south-westerly winds (180–240∘). Error bars correspond to standard
errors within each hourly bin. At a sampling height of 15 m a.m.s.l., large
diel variability in CO2 flux was observed most likely due to a local,
terrestrial influence. Fluxes measured at ≥ 18 m a.m.s.l. exhibit much
less diel variability.
To further examine the influence of the Tamar estuarine plume, a
3-dimensional hydrodynamic Finite Volume Community Ocean Model (FVCOM, Chen
et al., 2003) was run for April–June 2011 with tidal forcing at the
boundaries (TPXO, Egbert et al., 2010), surface wind (Met Office Unified
Model, Davies et al., 2005), surface heating (NCEP Reanalysis-2, Kanamitsu et
al., 2002), and river input (E-HYPE, Donnelly et al., 2012) at variable
resolution (15 km at the open boundaries near the shelf edge and 150 m near
the Plymouth Sound). The model predicts that within 1 km south-south-west of
Penlee, surface layer (∼ 0.2 m thick) salinity tends to be lower
during rising tide (about 33.4–33.7) than during falling tide (about
33.9–34.1). This suggests a greater freshwater influence from the Tamar at
the surface during rising tide, qualitatively consistent with our CH4
flux observations. Natural processes other than direct air–sea gas transfer
(e.g. ebullition) could also contribute to the variability in CH4
fluxes. Quantifications of the temporal/spatial seawater CH4
distribution within the PPAO flux footprint and measurements of the
pelagic/benthic cycling of CH4 is essential for addressing this uncertainty.
CH4 emissions of a few tens of µmole m-2 d-1 at
PPAO are higher than estimates for the open ocean (e.g. Forster et al., 2009)
and are lower than previous measurements over other aquatic systems. Kitidis
et al. (2007) measured a CH4 emission of
63 µmole m-2 d-1 using a floating chamber in the Ria de
Vigo (a large coastal embayment), consistent with wind-driven turbulent
diffusivity models for the conditions at the time of the chamber deployment.
These authors also estimated fluxes up to
170 µmole m-2 d-1 during periods when the chamber was
not deployed. With an open path sensor Podgrajsek et al. (2014) recently
measured CH4 emissions from a Swedish lake using the EC technique. Lake
CH4 emissions range from near zero during the day to over
20 mmole m-2 d-1 at night (3 orders of magnitude higher than
observations at PPAO). Aircraft mixing ratio measurements suggest that
CH4 emissions from the partially ice-covered Arctic are 4–5 times larger
than mean emissions at PPAO (Kort et al., 2012). Our observations and
estimates of the CH4 flux uncertainty suggest that an EC system such as
the one employed here should be able to quantify emissions from those
CH4 hotspots.
Hourly CH4 flux as a function of wind direction at all three
sampling heights. Larger CH4 emissions are generally observed when
winds are from the north-east (direction of Plymouth Sound) compared to from
the south-west (open water), likely due to elevated seawater CH4
concentrations in the estuarine outflow.