Introduction
The deposition of soluble trace gases to the ocean surface is an important
component in the global budgets of several important biogeochemical elements.
For example, roughly 90–108 Tg yr-1 of SO2 is emitted to the
atmosphere from fossil fuel combustion and industrial processes, from
volcanic outgassing, and from the atmospheric photochemical oxidation of
biogenic dimethylsulfide DMS;. In the marine
atmosphere, SO2 oxidation contributes to the production and growth of
aerosols which influence the Earth's radiation budget via aerosol backscatter
of solar radiation and cloud optical properties. Global models estimate that
dry deposition of SO2 to the sea surface comprises slightly less than half
of the total removal from the atmosphere .
The parameterization of dry deposition of soluble gases in atmospheric
chemistry models is based largely on laboratory experiments,
micrometeorological theory, or field studies in terrestrial environments
. Relatively few direct flux
studies of soluble trace gas deposition to the sea surface have been carried
out due to a lack chemical sensors with sufficient sensitivity and response time
for eddy covariance flux measurements. reported
air–sea eddy covariance surface fluxes for SO2 using a fast-response
chemical ionization mass spectrometric technique developed by
. To our knowledge these are the only previous eddy
covariance measurements of SO2 surface fluxes over the ocean. Air–sea
fluxes of the highly soluble organic compounds acetone and methanol have also
been reported
.
In this study, we made eddy covariance flux measurements of SO2 deposition
to the coastal ocean from the Scripps Institute of Oceanography pier in La
Jolla, California. These measurements were accompanied by simultaneous
measurements of air–sea fluxes of momentum, water vapor, and sensible heat.
The goals of this study were (1) to directly determine the transfer
coefficient of SO2 and its wind speed dependence for comparison to
existing estimates; (2) to compare the transfer coefficients of SO2 with
those of momentum, water vapor, and sensible heat to assess the relative
importance of turbulent and diffusive resistance to SO2 deposition; and
(3) to attempt to detect the dependence of soluble gas deposition on molecular
diffusivity in the marine environment.
Background
Air–sea gas transfer of highly soluble gases
Gas transfer across a gas–liquid interface is commonly parameterized as
follows:
F=KCwα-Ca,
where F is the air–sea flux (mol m-2 s-1), Ca and Cw are bulk
air- and water-side concentrations (mol m-3), and α is the
dimensionless solubility (Cw/Ca at equilibrium). K represents the bulk
gas transfer coefficient reflecting the physical processes limiting exchange
on both sides of the interface, expressed in air-side units (m s-1). The
reciprocal of K, or resistance, can be partitioned into water-side and air-side processes, where
K-1=Rtotal=rw+ra=1kw+αka.
In the case of gases like SO2 with very high effective solubility
(α≫1) and negligible seawater
concentration (see below), the air side dominates the total resistance (i.e.,
ra≫rw) so the gas transfer equation becomes
F=ka[SO2]wα-[SO2]a≈ka[SO2]air,
where ka is the air-side gas exchange coefficient (m s-1), also
referred to as the deposition velocity. The transfer coefficient, ka
(hereafter referred to as kSO2), encapsulates the physical processes
controlling transport across the marine atmospheric surface layer to the
air–sea interface. This transport is governed by (1) turbulence in the
surface layer, (2) molecular diffusion close to the sea surface where
turbulence is suppressed by molecular viscosity, and (3) the resistance to
transfer across the air–sea interface at the water surface
. The transfer coefficient can be
expressed in terms of resistance to deposition, as follows:
ka-1=rtotal=rturbulence+rdiffusion+rsurface.
The turbulent resistance term, sometimes referred to as aerodynamic
resistance, is often approximated by the momentum transfer coefficient (or
drag coefficient) under the assumption that there is no diffusive barrier to
momentum transfer. Diffusive resistance is usually conceptualized in terms of
the surface renewal model, involving periodic exchange of patches of
near-surface air by turbulent eddies, with deposition of a trace gas to the
sea surface via non-steady-state diffusion .
This model implies a dependency on molecular diffusivity, as follows:
rdiffusion∝Scn,
where Sc is the Schmidt number defined as the kinematic viscosity of air
(ν) divided by the molecular diffusion coefficient (D) of the gas in air
and n is a constant. Early studies of soluble gas deposition to the ocean
suggested a Sc2/3 dependence based on boundary layer theory
. Current gas transfer models parameterize gas transfer
as a surface renewal process with a Sc1/2 dependence
. Laboratory experiments using
water-side-controlled gases show n ranging from 0.50 to 0.66 for smooth and
rough flow conditions .
Interfacial surface resistance, i.e., resistance to air–sea gas transfer
arising from physical–chemical interactions in a molecular scale layer at the
surface, is included here for completeness. We are aware of no evidence that
such processes are important at clean water surfaces for molecules such as
SO2 or H2O (see Sect. 2.2.3). The sea surface is often
“contaminated” by the presence of organic compounds and particulates collectively referred
to as the sea surface (or marine) microlayer. One could hypothesize that a
hydrophobic surface film of sufficient coverage and thickness could introduce
resistance to the transfer of small polar molecules such as SO2 or H2O,
but such effects have not yet been demonstrated. It is well known that the
microlayer can alter the surface tension of the sea surface, dampening the
formation of capillary waves and indirectly altering the turbulent and
diffusive resistance to transfer of momentum and gases
.
Physical chemical properties of SO2 relevant to gas transfer
The interpretation of the SO2 air–sea flux measurements in this study
is based on the following premises: (1) deposition of SO2 is controlled
entirely on the air side of the air–sea interface and (2) surface ocean
waters are always highly undersaturated in SO2 with respect to the
overlying atmosphere.
In this section we discuss the basis for these
assumptions.
Effective solubility of SO2 and the kinetics of ionic equilibria
Sulfur dioxide is not a highly soluble gas, but it has a very large effective
solubility in aqueous solution at elevated pH because of the dissociation of
aqueous SO2 into bisulfite and sulfite ions (HSO3-;
SO32-). Collectively, dissolved SO2 and its ionized forms
are referred to as S(IV). The equilibria governing the aqueous speciation of
SO2 are listed below, with equilibrium constants given for seawater
at 298 K .
SO2⇌SO2(aq)SO2(aq)+H2O⇌HSO3-+H+HSO3-⇌SO32-+H+
HSO2=[SO2(aq)]PSO2=1.17Matm-1K1=[HSO3-][H+][SO2(aq)]=2.7×10-2MK2=[SO32-][H+][HSO3-]=7.7×10-7M
Combining these equilibria yields an effective SO2 solubility, as follows:
Heff=HSO21+K1[H+]+K1K2[H+]2.
HSO2 is the Henry's law solubility (M atm-1), K1 and K2 are
equilibrium constants in Equations (7) and (8), R is the gas constant (L atm K-1 mol-1),
and T is temperature (K). At the pH of seawater,
Heff is 6.2×108 M atm-1.
As noted by , the kinetics of S(IV) ionization in seawater
are rapid, occurring on timescales much shorter than those for transport
across the water-side interfacial layer. Based on rate constants for the
forward and reverse reactions comprising the equilibria listed above, the
characteristic time for equilibration of dissolved SO2 with the ionic
forms of S(IV) is roughly 4.5×10-4 s , while
the timescale for diffusive transport through the interfacial layer on the
water side is on the order of seconds .
Consequently, SO2 behaves as a highly soluble gas during the air–sea
exchange process.
Placing a limit on the surface ocean concentration of S(IV)
To our knowledge, there are no published measurements of surface ocean S(IV).
Here we place an upper limit on surface ocean S(IV) based on rough estimates
for the sources of S(IV) to the ocean and the oxidation kinetics of S(IV) in
seawater. The sources of S(IV) to the surface ocean include (1) release of
hydrogen sulfide (H2S) from marine sediments or deep waters, followed by
oxidation to S(IV); (2) atmospheric deposition of SO2; (3) production of
H2S in surface waters from hydrolysis of photochemically produced carbonyl
sulfide (OCS) followed by oxidation; and (4) production of H2S in surface
waters from particulates and/or organisms. For the sediment source, we take
the upper limit of about 10-1 mol m-2 yr-1 from the global
compilation of sulfate reduction rates by . For the
atmospheric source, an atmospheric SO2 mixing ratio of 1 nmol mol-1
and a deposition velocity of 0.02 m s-1 yields a source of 2.6×10-2 mol m-2 yr-1. The other sources
are many orders of magnitude
smaller, based on surface ocean distributions and laboratory hydrolysis rates
of OCS .
Assuming that all of these sources are delivered to a shallow mixed layer of
10 m depth yields an upper limit on the S(IV) production rate (PS(IV)) of
about 10-2 mol m-3 yr-1. For the open ocean, the S(IV)
production rate is likely much lower, because the sulfide from sedimentary
sulfate reduction is not released directly into the surface ocean. The
kinetics of oxidation of S(IV) in seawater was measured in the laboratory by
. They report the following rate expression:
[S(IV)]dt=koxidation[S(IV)]2,
where [S(IV)] is the seawater concentration of S(IV) (M) and
koxidation is the S(IV) oxidation rate constant of
12.4. M-1 s-1 The steady-state surface ocean S(IV) can be
calculated as a balance between sources and oxidation, as follows:
PS(IV)=koxidation[S(IV)]2,S(IV)=PS(IV)koxidation,
yielding a steady-state S(IV) concentration of roughly 8.4×10-8 M. Based on the effective solubility of SO2 in seawater,
this represents an equilibrium SO2 gas-phase mixing ratio of only
0.1 fmol mol-1. That is several orders of magnitude lower than typical
atmospheric SO2 levels over the ocean
. Therefore, one can
justifiably assume that the sea surface is highly undersaturated in
SO2 with respect to the overlying atmosphere. It follows that the
bulk air–sea concentration difference for SO2 is essentially equal
to the air-side concentration (Eq. 3).
Surface resistance to SO2 deposition
In order for the molecular interface between water and air to play a
significant role in air–sea gas transfer, the surface must introduce a
resistance comparable to that across the turbulent and viscous layers above
it. The surface can be modeled as a diffusive air-side layer with a thickness
(L) equal to the mean free path of SO2 in air, about 120 nm. The
resistance across a flat planar surface layer can be estimated as
rsurf=LγD=1.2×10-7γ×1.3×10-5≈10-2γsm-1,
where γ and D are the accommodation coefficient and molecular
diffusion coefficient of SO2, respectively . The
timescales associated with turbulent and diffusive transport can be
estimated using the COAREG (Coupled Ocean–Atmosphere Response Experiment Gas) gas transfer model . For a
height of 10 m and a wind speed of 10 m s-1 under neutral conditions,
COAREG yields the following:
rturb+rdiff≅102sm-1.
An accommodation coefficient of 10-4 would therefore be required in
order for resistance at the surface to be comparable to that of the turbulent
and diffusive atmosphere above. Laboratory studies of SO2 uptake into
clean water droplets suggest that the mass accommodation coefficient is about
0.1 . At this value, the surface resistance is only
about 0.1 % of the overall resistance. Thus, surface resistance is not
expected to play a significant role in air–sea gas transfer across clean
water surfaces. The same is likely true for H2O, which is believed to have
an accommodation coefficient near unity, although there is considerable
scatter in laboratory experiments . As noted earlier,
the possibility of additional surface resistance for either SO2 or H2O
due to the presence of natural organic marine microlayers cannot be evaluated
due to lack of information about their properties.
Methods
Study site and experimental setup
This study was conducted at Scripps Pier located in La Jolla, California,
during April 2014. The local meteorology is characterized by a daily
westerly sea breeze with occasional frontal systems that generally approach
from the northwest. The pier structure extends 330 m from shore in the
west–northwest direction and the water depth at the end of the pier is
approximately 10 m. The end of the pier extends roughly 100 m past seaward of
breaking waves. Meteorological sensors and air inlets were mounted at the end
of a moveable 6 m boom mounted on the northwest corner of the pier. The boom
was positioned to extend approximately into the prevailing winds. The sensing
regions of the eddy covariance flux package and the air intake for SO2
detection were located approximately 10 m above the sea surface. The sensor
height was corrected for changes in tidal range during the experiment.
Instrumentation for sulfur dioxide detection, data acquisition, clean air
generator, and pumps were located in a trailer located at the end of the
pier. Three-dimensional winds and fast-response temperature measurements were
measured using a Campbell CSAT 3 sonic anemometer, with data collection at
50 Hz. Water vapor and air density were measured using an open-path infrared gas
analyzer (IRGA; LI-COR model LI-7500) at 5 Hz. The instrument was calibrated
using a dew point generator (LI-COR model LI-610). Sea surface temperature was
measured using a temperature probe array mounted on the pier with 9 probes
vertically spaced by about 1 m. The sea surface temperature was taken to be
the shallowest probe not exposed to air. Mean air temperatures were obtained
from the NOAA meteorological station at the end of the pier.
For SO2 detection, the air sampling inlet was similar to that used by
to measure DMS. The air inlet was a 0.25′′ O.D. PFA tee
fitting mounted just behind the sonic anemometer sensing region. Air was
drawn into the inlet at a flow rate of 8500 cc min-1 and dried by
passage through two counterflow Nafion membrane driers (Perma Pure LLC
model PD-625-24PP) connected in series just after the inlet. The air passed
from the driers through a 0.25′′ O.D., 13 m long PFA Teflon tube to a chemical
ionization mass spectrometer located in the trailer. In the trailer,
1000 cc min-1 of the 8500 cc min-1 airflow was drawn through the
ionization source of the mass spectrometer. A 200 cc min-1 stream of
ozonized dry air (Pen Ray UV lamp) was added to the 1000 cc min-1 prior
to entry into the ionization source. A continuous flow of isotopically
labeled gas standard (34SO2 in N2) was injected into the sampled
air stream at the inlet tee. This gas standard was delivered to the inlet
from an aluminum high-pressure cylinder located in the trailer, at a flow
rate ranging from 1 to 10 cc min-1 from a 1/8′′ O.D. PFA tube.
All flow rates were controlled and logged using mass flow controllers
interfaced to a PC. Air for the Nafion counterflow driers and ozone
generator was supplied by a pure air generator and compressor (Aadco model
737-11), located in the trailer. Pumping for the air inlet and ionization
source was provided by a carbon vane pump (Gast model 1023).
SO2 detection by chemical ionization mass spectrometry
Atmospheric SO2 was detected using a laboratory-built chemical ionization
mass spectrometer (CIMS) in negative ion mode. This instrument was described
previously for positive ion measurements of dimethylsulfide
. The instrument was modified for this study by replacing
a set of conical declustering lenses with a multi-lens ion funnel of the
design developed by . This resulted in an order of
magnitude improvement in ion transmission over the prior configuration of the
instrument. In the CIMS instrument, ionization was carried out in a 0.25′′
inch glass-lined stainless steel flow tube containing a 63Ni foil at
430 Torr and room temperature, with an airflow rate of 1000 cc min-1. Ions
from the source enter the declustering region containing the ion funnel
through a 250 µm diameter pinhole. The ion funnel is 127 mm long and
consists of 100 concentric rings decreasing in diameter from 25.4 to 1.5 mm . A DC gradient of 3 V cm-1 was applied to
transmit ions axially and two phases of radio frequency (RF; 2 MHz, 150 V p-p) were applied so
that adjacent rings in the funnel were 180∘ out of phase. The ion
funnel was operated at a pressure of 1 Torr. Ions exit the ion funnel via a
1 mm orifice into the first stage of a differentially pumped Extrel quadrupole
mass filter (19 mm). Ions are detected using a dynode, ion multiplier, pulse
amplifier/discriminator, and counting electronics (National Instruments model
USB 6343). Ion counts were logged locally by the mass spectrometer control
software and retransmitted as analog signals in real time with a fixed 2 s delay. The analog signals were logged by the multichannel data logger
along with data from the meteorological sensors. Sulfur dioxide was detected
in negative ion mode as SO5- (m/z 112), which was generated using the
following reaction scheme previously described by .
O2-+O3→O3-+O2O3-+CO2→CO3-+O2CO3-+SO2→SO3-+CO2SO3-+O2+N2→SO5-+N2
The addition of ozone minimizes the competing reaction O2-+SO2→SO4- and increases response to SO2
. When operating the ionization source at atmospheric
pressure there was interference at m/z 112 from the CO4(H2O)2-
cluster ion. This was essentially eliminated by dropping the pressure in the
source to 430 Torr.
Isotopically labeled 34SO2 delivered to the air inlet served as an
internal standard to account for any wall losses or variations in instrument
sensitivity due to changes in ambient conditions. The flow rate of the gas
standard was adjusted to achieve a 34SO2 level of roughly 100 pmol mol-1 after dilution into the ambient airflow. The gas standard was
prepared in our laboratory in a high-pressure aluminum gas cylinder (Scott
Marrin model 30A) and delivered via mass flow controller. These gas standards
were calibrated in the lab against a gravimetrically calibrated permeation
device using an inert dilution system described by .
The isotopically labeled standard was detected at m/z 114. The ambient
SO2 mixing ratio was calculated from the field data as follows:
XSO2=S112S114fstdftotalXtank,
where S112 and S114 are blank-corrected mass spectrometer signals,
fstd and ftotal are the gas flow rates of the isotopic standard and
inlet, and Xtank is the molar mixing ratio of 34SO2 in the
compressed cylinder. Because the air stream was dried in the inlet tube prior
to analysis, XSO2 represents the mixing ratio of SO2 in dry air.
Blanks involved sampling air through a carbonate-impregnated filter to
quantitatively remove ambient SO2. Whatman 41 filters for this purpose
were soaked in 1 % sodium carbonate solution and dried prior to use. During
this study the SO2 instrument exhibited sensitivity of approximately
150 Hz ppt-1.
Flux data acquisition, post-processing, and gas transfer calculations
The analog data streams from the meteorological and chemical sensors were
filtered with a Butterworth filter and logged at 50 Hz using a National
Instruments multichannel data logger. Post-processing consisted of (1) aligning the data to account for instrumental electronic delays and the delay
due to the airflow transit time through the inlet tube; (2) rotating the 3-D
winds for each flux interval into the frame of reference of the mean winds
and to account for tilt in the sonic anemometer (1.3∘); (3) converting
the data to geophysical units; (4) computing vertical fluxes of water vapor,
sensible heat, SO2 and momentum; (5) applying a high-frequency correction
to the SO2 fluxes to account for loss of fluctuations in the inlet
tubing; and (6) applying various quality control criteria to filter the resulting data
set for instrumental issues or unsuitable environmental conditions. Data
processing was carried out using Matlab (Mathworks). The inlet delay for
SO2 was determined experimentally in the laboratory prior to field
deployments to be roughly one second. The measured delay was consistent with
the offset required for maximizing the covariance between vertical wind and
SO2 concentration. Sulfur dioxide was measured as a dry mixing ratio since
the air stream was dried prior to entering the mass spectrometer and
converted to concentration (mol m-3) using the dry air density. Water
vapor concentrations measured by the LI-COR IRGA were corrected to account for air
density fluctuations and converted to concentration (mol m-3). The
saturation vapor pressure of seawater at the sea surface temperature was
calculated following . The mean air temperature was
corrected for the adiabatic lapse rate, and the sonic temperatures were
corrected for humidity. SO2, water vapor, temperature, and winds were
corrected to 10 m height and neutral stability using COARE
. The
data set was subdivided into 13 min flux intervals for processing. The
resulting data consisted of means and variances for air temperature, relative
humidity, SO2, and seawater surface temperature. Fluxes of momentum
(Reynolds stress, τ), water vapor, sensible heat, and SO2 were
calculated for each interval according to
FSO2=w′CSO2′‾,FH2O=ρ‾w′XH2O′‾,Fmom=ρ‾(w′u′)2‾+(w′v′)2‾,FSH=ρ‾cpw′T′‾,
where u, v, and w are the winds; cp is the heat capacity of air and ρ
is air density in kg m-3; and the other variables are defined
previously. T is the air temperature corrected for humidity and the adiabatic
lapse rate. Primed quantities with overbars represent the ensemble average of
the fluctuations about the mean.
Transfer velocities were computed following Eqs. (1) and (3), as follows:
kSO2=-FSO2[SO2]a,kH2O=FH2O(Xs‾-XH2O‾)ρdry‾,kmom=FmomU10ρ‾,kSH=FSH(Ts‾-T‾)ρ‾cp‾.
Xs is the calculated mixing ratio of water vapor corresponding to the
saturation vapor pressure of water at the sea surface temperature.
High-frequency correction for inlet tubing
High-frequency fluctuations in the mixing ratio of SO2 are attenuated
during the passage of ambient air through inlet tubing and membrane driers.
The attenuation characteristics of the inlet used in this study were
characterized by interrupting the addition of an SO2 gas standard to the
airflow, resulting in an exponential decay of the SO2 signal. A decay
constant (K) was obtained from the slope of a linear regression to a plot of
log(SO2) vs. time. The attenuation of the inlet was modeled as a first-order low-pass
Butterworth filter with a cut-off frequency, Fc=K/(2p), of
about 1.5 Hz. A high-frequency correction factor or gain, G, was computed for
each flux interval by applying the filter to the sonic temperature time
series data and taking the ratio of the filtered and unfiltered fluxes as
follows:
G=Funfiltered/Ffiltered.
Linear regression of the gain against wind speed yielded G=0.005U10+1.018. The SO2 flux for each interval was
multiplied by the gain using this relationship and the mean wind speed for the interval.
Quality control criteria
Several quality control criteria were applied to the data to identify and
eliminate flux intervals collected under unsuitable conditions or with
instrumental problems. They are described as follows.
Co-spectral shape: a cumulative sum of co-spectral density, normalized to
the total flux, was computed for each flux interval, summing from low to high
frequency. Intervals were rejected if (a) the cumulative sum at 0.004 Hz
exceeded the total flux or was opposite in sign or (b) the difference between
the cumulative flux at two consecutive frequencies exceeded 18 %. These criteria
identified most intervals with obvious deviations in co-spectral shape from
those defined in . Most of these intervals were caused
by electronic noise on the sonic anemometer signal.
Small air–sea differences: intervals with air–sea concentration
differences close to the propagated uncertainty of the analytical
measurements were eliminated. The criteria for water vapor, sensible heat,
and SO2 were 10-3 mol mol-1, 0.7 ∘C, 10 pmol mol-1.
Wind sector: intervals with mean wind directions deviating from onshore
by more than ±90∘ were rejected.
Stable atmospheric conditions: intervals with stable atmospheric
conditions, defined as z/L>0.07, were rejected .
Local SO2 contamination: intervals with sharp excursions in SO2
associated with local contamination due to nearby vessels were subjectively
identified and rejected.
Time series of meteorological and oceanographic parameters measured
on Scripps Pier during 6–27 April 2014. The grey bands indicate night. The
blue symbols (×, right y axis) are fluxes that passed quality control.
Observations
Meteorological and oceanic conditions
The field study was carried out from 6 to 27 April 2014. Time series of
meteorological and oceanographic parameters and fluxes measured during this
study are given in Fig. . Winds were generally light during the
study, with a mean wind speed of 3.8±2.0 m s-1 and a range of 0–9.7 m s-1. Air temperatures were
16.2±1.3 ∘C with a range from
12.9 to 19.9 ∘C and the average relative humidity was 80 %. Sea surface
temperatures averaged 16.5±0.9 ∘C with a range of 13.8–18.3 ∘C. The SO2 mixing ratio ranged from below detection
to 560 pmol mol-1 with a mean of 100±114 pmol mol-1. Sharp spikes in
SO2 were usually associated with military or commercial vessels passing
upwind of the pier. Low SO2 levels were associated with the occurrence of
morning fog. For the first few days of the study, a high-pressure region was
located over the study site (DOY 97–100), during which winds were light and
air temperatures were warm. Air mass back trajectories from this period
indicate that marine air masses flowed from the north, passing inland over
California before reaching the site. SO2 levels were relatively high
during this time likely due to fossil fuel combustion. After the
high-pressure system moved out of the region, airflow was from the
northwest, arriving at the study site directly from the ocean, and SO2
levels were relatively low during this period. There was a notable increase
in wind speed starting at DOY 106. On DOY 115 a low-pressure system passed
over the region with higher wind speeds.
The Scripps Pier site experiences a consistent diurnal sea breeze, with
offshore flow during the evening and extending to the early morning. Data
from periods with offshore flow were excluded from the analysis in the
quality control process. Due to the sea breeze locally and along the coast,
there is likely advection of polluted air offshore, and the SO2 levels
measured during onshore flow may be elevated compared to marine air from the
open ocean. The average air–sea temperature differential during the study was
0.56±1.55 ∘C with a range from -3.5 to 2.7 ∘C, with
positive values indicating a warmer ocean than atmosphere. Occasionally
air–sea temperature differentials exhibited diurnal variability which
reflected the changes in air temperatures. Starting on DOY 114, seawater
temperatures warmed and were significantly warmer than air temperatures for
the remaining 3 days of the study.
Air–sea differences and fluxes
All the observed SO2 fluxes were from the atmosphere to the ocean surface
(negative by convention) and ranged from 0 to -65 pmol m-2 s-1, with
the largest fluxes observed at the beginning and end of the deployment
associated with high SO2 levels and high wind speeds, respectively (Fig. ). All observed water vapor and sensible heat fluxes passing
quality control were upward, which was consistent with the positive (from the
ocean to the atmosphere) thermodynamic gradient for the duration of the
study. The warm seawater temperatures combined with the high winds and cold
temperatures on the last 2 days of the study resulted in large H2O and
heat fluxes.
Frequency-weighted co-spectra of vertical wind and SO2
concentration for flux intervals collected at Scripps Pier during three time
periods. (a, d, g) DOY 96–102; (b, e, h) DOY 104–109; (c, f, i) DOY 114–117. (a–c) Individual
co-spectra for 13 min flux intervals;
(d–f) same as top except co-spectra have been normalized to the average
flux during the interval. (g–i) Bin-averages of the flux-normalized
co-spectra (circles), ± 1 standard deviation (dotted line), and idealized
co-spectral shape from (dashed line).
Frequency-weighted co-spectra of vertical wind and SO2 are shown in Fig. . Fluxes measured during DOY 114–117 were significantly larger
than those measured during the rest of the campaign because of the strong
winds and large air–sea temperature differences observed during that period
(Fig. ). The co-spectra measured at Scripps Pier for all
parameters were similar in shape to the characteristic boundary layer
co-spectral shapes defined by .
Transfer velocities
The wind speed dependence of kmom observed in this study was
significantly greater than predicted using the open ocean parameterization
from the NOAA COARE (Fig. ). The relationship
between wind speed and surface roughness can vary significantly between the
open ocean and coastal environments because of bottom-generated turbulence,
as well as other influences related to fetch, tidal currents, surfactants, and wave
properties . Thus, the
turbulent properties of the atmospheric surface layer in coastal environments
are not well described by wind speed alone. To account for such effects, we
examined the relationship between transfer velocities and both wind speed and
friction velocity (u*) (Fig. ).
Momentum transfer velocities measured at Scripps Pier as a function
of wind speed with linear least squares regression and 95 % confidence
intervals (black). Blue line – COAREG parameterization of
.
Transfer velocities measured at Scripps Pier as a function of wind
and friction velocity. (a) Water vapor, sensible heat, and SO2 as a
function of U10 (black dots). (b) Water vapor, sensible heat,
and SO2 as a function of u* with linear least squares regressions and
95 % confidence intervals (black dots and black line). Red lines are a second-order least squares regression of transfer velocities computed with the
COAREG parameterization using measured drag coefficients
. Blue lines are transfer velocities
computed with the COAREG parameterization, allowing the model to calculate
friction velocities and drag coefficients.
The transfer velocities measured for water vapor, sensible heat, and SO2
(kH2O, kSH, kSO2) were all positively correlated with
friction velocity (Fig. , Table ). kmom was
significantly higher than the scalar parameters and kSO2 was lower than
kH2O and kSH. The regressions against friction velocity utilize
slightly different data sets in each case because these regressions utilize
flux measurement intervals that passed quality control for both the scalar
parameter (water vapor, sensible heat, SO2) and for momentum flux. This
means that the data sets used for the various parameters were not identical
either in terms of the number of flux intervals or the physical conditions
under which they were collected, i.e., temperature, wind speed, atmospheric
stability, sea state, etc. Ideally, the comparison of transfer velocities
would be carried out using intervals for which all four of the parameters
passed quality control. However, given the limited data set, this constraint
reduced the available data to an unacceptable degree. As an alternative, we
also compared the gas transfer velocities to each other by computing two-way
linear regressions between pairs of simultaneously measured transfer
velocities (Fig. 5, Table ). This analysis was in general
agreement with the k vs. u* analysis described earlier and showed
kSO2<kH2O, kSO2<kSH and no significant difference between
kSH and kH2O. Momentum transfer velocities were significantly
larger than all the scalar transfer velocities. The comparison of transfer
velocities from simultaneous intervals is a more robust approach to observing
differences in transfer velocities.
Two-way regression of transfer velocities against friction velocity
(k/u*).
Parameter
Regression slope ± CI
Number of
(α=.05) (cm m-1)
observations
Water vapor (kH2O/u*)
3.74±0.71
69
Sensible heat (kSH/u*)
3.14±0.89
36
Sulfur dioxide (kSO2/u*)
2.32±0.79
15
Momentum (kmom/u*)
5.06±0.40
80
Pairwise regression of transfer velocities using simultaneously
measured data from Figs. 3 and 4.
Parameter
Regression slope ± CI
Number
(α=.05)
of data points
Sulfur dioxide vs. water vapor (kSO2 vs. kH2O)
0.52±0.14
26
Sulfur dioxide vs. sensible heat (kSO2 vs. kSH)
0.64±0.15
20
Water vapor vs. sensible heat (kH2O vs. kSH)
1.17±0.15
64
Sulfur dioxide vs. momentum (kSO2 vs. kmom)
0.40±0.27
15
Water vapor vs. momentum (kH2O vs. kmom)
0.82±0.15
69
Sensible heat vs. momentum (kSH vs. kmom)
0.72±0.13
36
Discussion
This study demonstrates the successful measurement of SO2 deposition to
the sea surface using eddy covariance, with (1) co-spectra exhibiting a similar
shape to water vapor and sensible heat and (2) a linear relationship between
transfer velocities and wind speed or friction velocity. Virtually all of the
SO2 co-spectra indicated that the direction of flux was from air to sea,
even during periods of very low atmospheric SO2. This confirms the
assumption that seawater SO2 concentrations are highly undersaturated with
respect to atmospheric SO2. In general, we expect measurements of
kSO2 to be of higher precision than those of water vapor and sensible
heat because (1) the SO2 in seawater is negligible, so the air–sea
concentration gradient is equal to the bulk atmospheric concentration,
eliminating the need for a water-side measurement; and (2) the SO2 flux and
atmospheric concentration are determined simultaneously using a single sensor
with a linear response, so the absolute calibration of the sensor does not
influence the measured gas transfer velocity. These are advantages compared
to the measurement of transfer velocities for water vapor or sensible heat,
which require both air-side and water-side measurements in order to quantify
the air–sea concentration or temperature difference. The transfer velocities
for SO2 had significantly less scatter compared to the water vapor and
sensible heat transfer velocities at high wind speeds (Fig. ).
Two-way regressions of transfer velocities measured at Scripps Pier.
(a) Water vapor, sensible heat, and SO2 against each other.
(b) SO2, water vapor, and sensible heat regressed against momentum. The 95 %
confidence intervals are shown.
reported airborne eddy covariance measurements of
SO2 deposition over the equatorial Pacific. The data from their lowest
flight altitude of 30 m should be comparable to the data from this study. We
made this comparison as a function of u* rather than wind speed to account
for the differences in sea surface roughness between the coastal and open
ocean environments. The SO2 transfer velocities reported by
were roughly half those observed at Scripps over a
similar range of wind stress (Fig. , Table ). This
difference is considerably larger than expected from the scatter in the data
or estimated uncertainties in the flux measurements. Further investigation is
needed in order to determine whether a systematic difference exists in SO2
deposition to coastal vs. open ocean waters and, if so, what the cause might
be.
A few studies of direct air–sea exchange of highly soluble organic compounds
have also been carried out. Fluxes of acetone to the Pacific Ocean were
reported by and methanol fluxes to the Atlantic
Ocean were reported by . Surprisingly, the direction
and/or magnitude of air–sea fluxes observed in those studies were not
consistent with observed air–sea concentration differences based on bulk air
and seawater measurements. Both studies speculated that this was due to
near-surface water-side gradients, because assuming a zero sea surface
concentration gave reasonable gas transfer velocities with linear wind speed
dependence. For acetone, the resulting gas transfer velocities were
considerably lower than those observed in this study (Fig. , Table ). For methanol, the gas transfer velocities were similar to this
study, but with a slightly stronger dependence on wind stress. The anomalous
behavior of acetone and methanol is generally thought to be related to
near-surface biological or photochemical processes. The presumed near-surface
gradients are problematic in that they require strong localized
production and loss processes and have not yet been observed in the field. Given
the uncertainty introduced by these inferred gradients, more detailed
analysis of the similarities and differences in the data seem unwarranted.
Gas transfer velocities as a function of friction velocity for this
study and prior measurements of air–sea exchange of highly soluble,
air-side-controlled gases from , ,
, and this study. The grey line is the COAREG model calculated with the drag
coefficients measured during this study, using the Sc number of SO2.
One of the goals of this study was to compare observations of air-side-controlled gas transfer velocities to model parameterizations. The COAREG
air–sea gas transfer model utilizes
the open ocean COARE parameterization of friction velocity, based on wind
speed and stability . As a result, COAREG
substantially underestimates the observed transfer velocities for this
nearshore coastal site. As noted earlier, momentum transfer coefficients at
Scripps Pier were elevated compared to those typically encountered under open
ocean conditions. COAREG yields much better agreement with the field data
when drag coefficients based on the measured momentum fluxes were used (Figs. , ). In this study, the momentum transfer velocity was
significantly (roughly 50 %) larger than the transfer velocities of SO2,
H2O, and sensible heat observed under simultaneous or similar conditions.
This is reasonable, given that momentum can be transferred across the air–sea
interface via both viscous stress (analogous to diffusion of mass or heat)
and by pressure forces for which there is no analog in mass transfer.
Differences between the gas transfer velocities of SO2, H2O, and
sensible heat should reflect the role of molecular diffusivity in the viscous
layer adjacent to the sea surface. The diffusivity of SO2 in air is
roughly half that of H2O or sensible heat (Table ). Comparing
the relative magnitudes of kH2O, kSH, and kSO2 is therefore
a good test for the ability of gas transfer models to partition resistance
between turbulence and diffusion. Using the drag coefficients based on the
field data, COAREG gives kSO2/kH2O=0.82. Using the average
k/u* of the field observations (Fig. ) gives
kSO2/u*kH2O/u*=2.32±0.793.74±0.71=0.62±0.24.
The pairwise analysis of simultaneous measurements gives a ratio of
kSO2/kH2O of 0.52±0.14. Thus, the field observations and
model qualitatively agree that the resistance to SO2 transfer is greater
than that of H2O. Quantitatively, the COAREG result is just within the
95 % confidence interval of the k/u* result, but outside the uncertainty
range of the pairwise comparison. For kSO2/kSH the result is
similar, with better agreement between observations and model. COAREG
predicts a ratio of 0.85 while the field data yield 0.74±0.33 from the
ratio of average k/u* and 0.64±0.15 from the pairwise analysis.
Finally, for kH2O/kSH COAREG predicts a ratio of 1.03. This agrees
very well with the field observations, which give ratios of 1.19±0.41
from the average k/u* and 1.17±0.15 from the pairwise analysis. The
model–data agreement for kH2O/kSH is not surprising because their
Sc numbers are almost identical. Consequently, the ratio calculated by COAREG
should not be sensitive to either the partitioning between turbulent and
diffusive resistance or to the parameterization of diffusive resistance.
Slopes and intercepts of regressions to k vs. u* shown in Fig. 6.
References
Gas
Slope ± 95 %CI
Intercept ± 95 %CI
This study
SO2
2.74±0.62
0.07±0.11
Faloona et al. (2009)
SO2
1.20±0.50
0.10±0.12
Yang et al. (2013)
methanol
3.82±0.29
-0.22±0.08
Marandino et al. (2005)
acetone
1.28±0.34
0.05±0.07
Diffusion coefficients and Schmidt numbers (Sc) for H2O, sensible
heat, and SO2 in air, as calculated according to
and .
Parameters
H2O
Sensible
SO2
heat
Diffusion coefficient in air (298 K; cm2 s-1)
0.25
0.22
0.13
Sc number (298 K)
0.61
0.69
1.19
The field data suggest that the resistance to gas transfer of SO2 is
larger than expected from COAREG. This could indicate that COAREG
underestimates diffusive resistance or it could indicate some additional
unknown source of resistance, such as a surface resistance. It seems
unlikely, though not impossible, that surface resistance associated with the
sea surface microlayer would influence only SO2 and not H2O, but as
noted earlier, the properties of the sea surface microlayer are not well
known. We can estimate the magnitude of this anomalous resistance using the
field data and COAREG as follows:
rtotal_H2O=rturb+rdiff_H2O=rH2O_COAREG,rtotal_SO2=rturb+rdiff_SO2+ranom_SO2=rSO2_COAREG+ranom_SO2,rSO2_COAREGrH2O_COAREG=1.18.
The k/u* slopes of the field data give
rtotal_SO2rtotal_H2O=kH2O/u*kSO2/u*=1.61±0.63.
Solving these equations simultaneously yields
ranom/rtotalSO2=0.26±0.29.
The analysis using the pairwise data gives
ranom/rtotalSO2=0.38±0.17.
In other words, the field data allow for additional resistance for SO2
comprising 25 %–38 % of the total air-side SO2 resistance. However, given
the limited data set and the uncertainties associated with the regressions,
it seems premature to conclude that such anomalous resistance exists or to
speculate on its origin. It does seem likely that, with further work,
measurements such as these can provide useful constraints on air–sea gas
transfer models.