Oceanic emissions of dimethyl sulfide (CH3SCH3,
DMS) have long been recognized to impact aerosol particle composition and
size, the concentration of cloud condensation nuclei (CCN), and Earth's
radiation balance. The impact of oceanic emissions of methanethiol
(CH3SH, MeSH), which is produced by the same oceanic precursor as DMS,
on the volatile sulfur budget of the marine atmosphere is largely
unconstrained. Here we present direct flux measurements of MeSH oceanic
emissions using the eddy covariance (EC) method with a high-resolution
proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToFMS)
detector and compare them to simultaneous flux measurements of DMS emissions
from a coastal ocean site. Campaign mean mixing ratios of DMS and MeSH were
72 ppt (28–90 ppt interquartile range) and 19.1 ppt (7.6–24.5 ppt
interquartile range), respectively. Campaign mean emission fluxes of DMS (FDMS) and MeSH (FMeSH) were 1.13 ppt m s-1 (0.53–1.61 ppt m s-1 interquartile range) and 0.21 ppt m s-1 (0.10–0.31 ppt m s-1 interquartile range), respectively. Linear least squares regression of observed MeSH and DMS flux indicates the emissions are highly correlated
with each other (R2=0.65) over the course of the campaign,
consistent with a shared oceanic source. The campaign mean DMS to MeSH flux
ratio (FDMS:FMeSH) was 5.5 ± 3.0, calculated from the ratio of 304 individual coincident measurements of FDMS and FMeSH. Measured FDMS:FMeSH was weakly correlated (R2=0.15) with
ocean chlorophyll concentrations, with FDMS:FMeSH reaching a maximum
of 10.8 ± 4.4 during a phytoplankton bloom period. No other volatile
sulfur compounds were observed by PTR-ToFMS to have a resolvable emission
flux above their flux limit of detection or to have a gas-phase mixing ratio consistently above their limit of detection during the study period,
suggesting DMS and MeSH are the dominant volatile organic sulfur compounds
emitted from the ocean at this site.
The impact of this MeSH emission source on atmospheric budgets of sulfur
dioxide (SO2) was evaluated by implementing observed emissions in a coupled ocean–atmosphere chemical box model using a newly compiled MeSH
oxidation mechanism. Model results suggest that MeSH emissions lead to
afternoon instantaneous SO2 production of 2.5 ppt h-1, which results in a 43 % increase in total SO2 production compared to a case
where only DMS emissions are considered and accounts for 30% of the
instantaneous SO2 production in the marine boundary layer at the mean
measured FDMS and FMeSH. This contribution of MeSH to SO2
production is driven by a higher effective yield of SO2 from MeSH
oxidation and the shorter oxidation lifetime of MeSH compared to DMS. This
large additional source of marine SO2 has not been previously
considered in global models of marine sulfur cycling. The field measurements
and modeling results presented here demonstrate that MeSH is an important
contributor to volatile sulfur budgets in the marine atmosphere and must be measured along with DMS in order to constrain marine sulfur budgets. This
large additional source of marine–reduced sulfur from MeSH will contribute to particle formation and growth and CCN abundance in the marine atmosphere, with subsequent impacts on climate.
Introduction
Dimethyl sulfide (CH3SCH3, DMS) emissions from the ocean are the
most abundant source of reduced sulfur to the marine atmosphere (Andreae,
1990; Bates et al., 1987b, 1992; Carpenter et al., 2012). The role of DMS as
a driver of cloud condensation nuclei (CCN) production, which ultimately
impacts Earth's radiative budget in the marine atmosphere, has been studied
extensively (Bates
et al., 1987a; Carslaw et al., 2013; Charlson et al., 1987; Quinn and Bates,
2011). The oxidation of DMS in the atmosphere ultimately leads to the
production of methane sulfonic acid (CH3SO3H, MSA) and sulfur
dioxide (SO2), which can be further oxidized to sulfuric acid (H2SO4), contributing to particle formation and growth (Clarke
et al., 1998; Hoffmann et al., 2016; Schobesberger et al., 2013; Sipila et
al., 2010). Direct observations and mechanistic understanding of the
intermediate products in the oxidation of DMS are limited, leading to large
variability in estimates of SO2 yields (31 %–98 %), where SO2
is a precursor to sulfuric acid (H2SO4) and non-sea-salt sulfate aerosol (nssSO42-) (Faloona, 2009;
Hoffmann et al., 2016). Previous efforts to constrain the total budget of
SO2 in the marine boundary layer (MBL) have required assignment of a near 100 % yield of SO2 from DMS oxidation (Faloona et al., 2009). A 100 %
yield of SO2 appears inconsistent with known production of MSA from DMS
oxidation and with results from multiple laboratory studies (Faloona, 2009). The high yield of SO2 from DMS
necessary for closure of the SO2 budget in that study prompted
speculation on the existence of other unknown marine sulfur species which
could contribute to SO2 production (Gray et al., 2011). Existence
of other marine contributors to SO2 production would serve to reduce
the implied SO2 yield from DMS and would potentially bring that yield
closer to the 40 %–80 % range typically determined in laboratory and
modeling studies (Gray et al.,
2011). Implementation of oceanic MeSH emissions and oxidation to SO2 in
chemical transport models would result in an increase in sulfuric acid
production with subsequent impacts on new particle formation and growth and
CCN abundance.
DMS and MeSH oceanic production
Both DMS and the volatile reduced sulfur molecule methanethiol (CH3SH,
MeSH) are produced in seawater from the same precursor metabolite,
dimethylsulfoniopropionate (DMSP) (Kiene
and Linn, 2000a). Bacterial cleavage of dissolved DMSP (DMSPd)
primarily produces dissolved DMS (DMSd), and DMSP demethylation or
demethiolation produces dissolved MeSH (MeSHd) (Yoch, 2002). MeSHd is the dominant product of DMSPd consumption, with a
total yield on the order of 75 % compared to an approximately 10 % yield of DMSd (Kettle
et al., 2001; Kiene and Linn, 2000b, 2000a). The bacterium Pelagibacter HTCC1062 has been shown to simultaneously produce both DMS and MeSH, where the allocation between products may be related to the available supply of
DMSP, with DMS production enhanced when the supply of DMSP exceeded the
cellular demand for sulfur (Sun et al., 2016).
While yields of MeSHd are generally higher compared to DMSd,
MeSHd is also more rapidly consumed by heterotrophic bacteria and
phytoplankton, resulting in significantly faster turnover times (hours for
MeSHd, days for DMSd) and lower steady-state dissolved concentrations (Kiene,
1996). Both MeSHd and DMSd are persistently supersaturated in the
dissolved phase, resulting in ventilation to the atmosphere (Kettle et al., 2001; Lee and Brimblecombe, 2016). Surface ocean concentrations of
DMSd have been measured extensively and are on the order of 1–7 nM, with higher values in the summer (Lana et al., 2011). This extensive collection of measurements has permitted the development of global
climatologies of DMSd and emission fluxes for implementation in global chemical transport models (Galí
et al., 2018; Lana et al., 2011). In contrast, measurements of MeSHd
are sparse. Underway measurements from a transect of the Atlantic in
September and October of 1998 showed a mean MeSHd of 0.39 ± 0.34 nM and a maximum of 1.7 nM (Kettle
et al., 2001). Mean MeSHd during that study was approximately 20 % of
DMSd. In the Baltic Sea, mean MeSHd was 0.16 ± 0.12 nM
compared to 2.6 ± 1.6 nM for DMSd (Leck and Rodhe, 1991). MeSH emission fluxes
were estimated to be 10 % of DMS in that study. Significant variability in
DMSd-to-MeSHd ratios was observed in those studies, emphasizing the need for more detailed study of the biogeochemical factors that control
relative consumption and production of DMSd and MeSHd.
Atmospheric fate of DMS
Once emitted into the atmosphere, DMS is oxidized by hydroxyl (OH), nitrate (NO3), chlorine (Cl), and bromine oxide (BrO) radicals to produce lower-volatility oxidized products which can contribute to aerosol particle
formation and growth (Bates
et al., 1987a; Charlson et al., 1987; Quinn and Bates, 2011). Gas-phase mixing ratios of DMS in the MBL are typically on the order of 50–600 ppt,
with higher concentrations generally associated with regions of high
phytoplankton abundance and with diel maxima at night when oxidative loss is at a minimum (Kettle
et al., 2001; Kettle and Andreae, 2000; Kim et al., 2017; Lana et al., 2011;
Lawson et al., 2020; Sciare et al., 2000). Oxidation by OH, thought to be
the largest loss pathway, proceeds either through OH addition or hydrogen
abstraction. OH addition produces MSA, methane sulfinic acid (MSIA), dimethyl sulfoxide (DMSO), and SO2, while H abstraction is traditionally thought to primarily produce SO2 (Barnes
et al., 2006; Conley et al., 2009; Hoffmann et al., 2016). The branching
fraction of the OH oxidation channels is highly temperature dependent, with H abstraction favored at higher temperatures (∼70 % at 298 K). The DMS H abstraction product rapidly produces the methylthiomethyl
peroxy radical (MTMP, CH3SCH2OO) following recombination with
atmospheric oxygen. Until recently it was thought that MTMP primarily
participates in further bimolecular reactions with the hydroperoxyl radical
(HO2), nitric oxide (NO), or other peroxy radicals (RO2) which
efficiently produce the methyl thiyl radical (CH3S) and ultimately SO2 (Barnes et
al., 2006; Hoffmann et al., 2016). Theoretical and laboratory studies have
shown that MTMP can also undergo a series of intramolecular hydrogen shift
rearrangements and additions of O2 to form the stable product
hydroperoxymethyl thioformate (HPMTF; HOOCH2SCHO) at a rate that is competitive with bimolecular reactions (Berndt
et al., 2019; Wu et al., 2015). HPMTF has been shown to be globally
ubiquitous in the marine boundary layer from airborne observations (Veres
et al., 2020) and at a coastal ocean ground site (Vermeuel et al., 2020). Global
chemical transport modeling shows that 46 % of emitted DMS goes on to form HPMTF (Novak et
al., 2021). The atmospheric fate of HPMTF is an active topic of research, but ambient observations show that dry deposition to the ocean surface is a
significant loss term (lifetime ∼30 h, Vermeuel et al., 2020) and that
HPMTF is efficiently lost to clouds (Novak
et al., 2021; Veres et al., 2020; Vermeuel et al., 2020), resulting in a
35 % decrease in global SO2 production from DMS oxidation (Novak et al., 2021).
These previously unconsidered loss processes for DMS-derived sulfur may necessitate reevaluation of marine SO2 budgets (Bandy
et al., 2011; Faloona et al., 2009).
Atmospheric fate of MeSH
Comparatively little is known about the atmospheric abundance of MeSH. To
date, only two ambient observations of MeSH atmospheric mixing ratios have
been presented in the literature. Measurements in the remote southwestern Pacific showed MeSH mixing ratios from <10 ppt to 65 ppt which were
3 %–36 % of coincident DMS mixing ratios (Lawson
et al., 2020). DMS and MeSH emission fluxes were inferred in that study from
the rate of accumulation at night when oxidative loss was assumed to be
zero, which showed that MeSH emission fluxes were 14 %–24 % of the DMS flux.
MeSH mixing ratios in the coastal and inshore waters west of the Antarctic
Peninsula were up to 3.6 ppt, which was 3 % of coincident DMS (Berresheim, 1987). To our knowledge there have been no previous direct eddy covariance measurements of MeSH emission flux from the
ocean.
While the bimolecular rate constants of MeSH with the primary atmospheric
oxidants (OH, BrO, NO3, Cl) are known, there has been limited study on
the reactive intermediates or yields of stable products from MeSH oxidation (Butkovskaya and Setser,
1999; Tyndall and Ravishankara, 1991). However, oxidation of MeSH by OH (Reaction R1)
has been shown to produce the methyl thiyl radical (CH3S)
at a yield of 1.1 ± 0.2 (Tyndall and Ravishankara,
1989), providing a mechanistic link to known reactions in the DMS
H abstraction pathway (Reaction R1) (Barnes et al., 2006), as shown in the simplified reaction scheme in Fig. 1. CH3S production from DMS H abstraction is the major pathway for SO2
production from DMS, and the reactions of CH3S are therefore
well studied (Barnes et al., 2006). Other studies have shown
that the reaction of MeSH with OH occurs primarily at the S-H group (0.87 ± 0.03) forming CH3S, with a minor channel of
H abstraction from the methyl group (0.13 ± 0.03) (Butkovskaya and Setser, 2021). Recent ab initio/RKKM calculations determined the CH3S yield from MeSH + OH at 298 K and 760 torr to be 0.98 (Mai et al.,
2020). Given the slight inconsistency between the directly measured
CH3S yield of 1.1 ± 0.2 and the MeSH + OH branching
fractions, we take these experiments to provide upper and lower bounds on the CH3S yield of 0.87 and 1.1 and assume a
CH3S yield of 1 for Reaction (R1) throughout this analysis.
OH+CH3SH→CH3S+H2O
CH3S efficiently produces SO2 through a series of
competing reactions outlined in Reactions (R2)–(R8) (Barnes
et al., 2006; Hoffmann et al., 2016; Lucas, 2002; Mardyukov and Schreiner,
2018). As shown in Reaction (R2), CH3S experiences reversible addition
of O2, producing a methyl thiyl peroxy radical (CH3SOO) which can then undergo a series of unimolecular Reactions (R3)–(R4) to efficiently
produce SO2.
R2CH3S+O2↔CH3SOOR3CH3SOO→CH3S(O)OR4CH3SOO→SO2+CH3
Bimolecular reactions of CH3S with O3 and NO2 can
also occur, forming CH3SO in Reactions (R5)–(R6). CH3SO primarily proceeds to react with O3 in Reaction (R7), forming CH3S(O)O, which links back to the
SO2-producing channel through Reaction (R4).
R5CH3S+O3→CH3SO+O2R6CH3S+NO2→CH3SO+NOR7CH3SO+O3→CH3SOO+O2
A minor (∼1 % at open-ocean O3 mixing ratios of ∼20 ppb) non-SO2-producing reaction pathway from CH3S(O)O + O3 in Reaction (R8) can also occur to produce CH3SO3,
which can react further to produce SO3 and MSA (Barnes et
al., 2006).
CH3SOO+O3→CH3SO3
The atmospheric yield of SO2 from the oxidation of MeSH by OH under low
NOx was recently reported as 0.98 based on modeling results constrained
by a laboratory oxidation study, which is in good agreement with the
efficient production of SO2 from MeSH proposed in our reaction scheme (Chen et al., 2021). We exploit this link between MeSH oxidation by OH and known DMS oxidation chemistry to develop a MeSH oxidation mechanism for implementation in a 0-D chemical box model as described further in the subsequent text. A simplified reaction diagram for
the gas-phase oxidation of DMS and MeSH is shown in Fig. 1. The full set of reactions with rate equations is provided in Table S1 in the Supplement.
A simplified reaction scheme for the gas-phase oxidation of dimethyl sulfide (DMS) and methanethiol (MeSH) that focuses on pathways to
SO2 production. Reaction (R1) through Reaction (R7) described in Sect. 1.3 are
labeled with green text on the schematic. Other chemical pathways including oxidation by halogens and most condensed-phase reactions of DMS and its oxidation products are not shown in this simplified schematic. Refer to
Table S1 for a complete list of reactions and rate equations as implemented
in this work.
Study overview
Here we present eddy covariance (EC) flux measurements of DMS and MeSH
emissions at a coastal ocean site using a high-sensitivity Vocus PTR-TOF for detection (Krechmer et al., 2018).
Results from this study show that emission fluxes of DMS (FDMS) and MeSH
(FMeSH) were well correlated (R2=0.65) and that FMeSH is a
significant contributor to marine sulfur emissions (mean FMeSH=0.21 ppt m s-1 compared to mean FDMS=1.13 ppt m s-1). The average ratio of individual DMS to MeSH flux (FDMS:FMeSH)
measurements for the full campaign was 5.5 ± 3.0. We assess the impact
of the observed large MeSH emission flux on production of SO2 in the
marine atmosphere through a coupled ocean flux–atmospheric chemistry 0-D box model with a newly compiled MeSH oxidation mechanism. Modeling results
suggest that MeSH contributes approximately 30 % of instantaneous
afternoon SO2 production (PSO2). Together these results show that
MeSH emissions are an important contributor to sulfur budgets in the marine
atmosphere, and further field studies and laboratory oxidation mechanism investigations are warranted.
Measurements of DMS and MeSH gas-phase mixing ratios and EC flux were made continuously from the end of the Ellen Browning Scripps Memorial Pier
(hereafter SIO Pier) at the Scripps Institution of Oceanography in La Jolla, CA, USA, during September 2019. SIO Pier is 330 m long and extends over 100 m beyond the wave-breaking zone. The SIO Pier site has been used regularly for EC studies of ocean–atmosphere trace gas exchange (Kim et al., 2014; Novak et al., 2020; Porter et al., 2018; Vermeuel et al.,
2020). SIO Pier experiences a characteristic sea breeze circulation pattern during summer where winds are from the ocean at moderate wind speeds (0–6 m s-1) during daytime and are from land at night, which limits
nighttime flux determinations. DMS and MeSH were detected with a latest-generation Vocus PTR-TOF instrument (TOFWERK, Aerodyne), with a high-resolution time-of-flight (HTOF) mass analyzer (resolution ca. 5000 MΔM-1 for DMS and MeSH) (Krechmer et al., 2018). The Vocus was housed in a temperature-controlled trailer at the end of the pier and sampled through a 19 m-long perfluoroalkoxy (PFA) inlet (0.625 cm i.d.) enclosed in an opaque housing to prevent photochemistry in the sampling line. The inlet was pumped at 22 slpm, which maintained turbulent flow and short residence times in the sampling line (Reynolds number 4280, calculated volumetric evacuation time
1.7 s). The full inlet line was held at 40 ∘C, which was always above ambient temperatures to prevent condensation of water vapor on inlet
surfaces. A bypass line through the Vocus front end subsampled from the main
inlet at 5 slpm through a PFA tee located at the instrument interface. The
Vocus subsampled from this bypass at a flow rate of 100 sccm immediately in
front of the Vocus capillary inlet into the instrument drift tube. This
sampling configuration was used to reduce residence times in the sampling
lines as much as possible (total estimated inlet and instrument residence
time ∼1.9 s). In addition to the main inlet line, all
surfaces in contact with the ambient sample flow, including unions and
valves, were composed of PFA or polyetheretherketone (PEEK) in order to minimize known surface artifacts for MeSH sampling except for one stainless-steel union at the Vocus subsampling point (Perraud et al.,
2016). The ambient inlet sampling point was collocated with a sonic
anemometer recording 3-D winds at 10 Hz (Gil HS-50). The sonic anemometer and Vocus inlet were mounted on a 6.1 m long boom extended beyond the end of the pier to minimize flow distortions from the pier. The inlet
was mounted on the boom at a height of 13 m above the mean lower low tide
level. Ocean depth below the pier sampling point was ca. 6 m.
The Vocus was operated at a drift tube pressure of 1.5 mbar and an axial
electric field gradient of 41.5 V cm-1, resulting in a high overall effective field strength (E/N) of 143 Td. Mass spectra were recorded at 10 Hz for the full mass range of 19–500 m/Q. The second radio frequency (RF)-only focusing quadrupole in the instrument was operated at an amplitude of 275 V at the start of the campaign before being reduced to 235 and ultimately 215 V later
in the deployment. The reduced amplitude on the quadrupole increased
transmission of low mass (<50m/Q) ions as described by Krechmer et al. (2018). MeSH (m/Q 49) transmission was increased by 10 % at the reduced amplitude of 215 V, compared to 275 V, which was accounted for in
the data processing. Transmission efficiency of DMS was independent of the
quadrupole amplitude as its nominal mass (m/Q 63) is larger than the mass
discrimination window of the quadrupole. High-resolution peak fitting and integration of the mass spectra were performed in the Tofware 3.2.0 software developed by the instrument manufacturer (TOFWERK).
Additional ancillary measurements made continuously from the pier included
O3 mixing ratios, temperature, relative humidity, and incoming solar
irradiance. O3 mixing ratios were measured at 1 min time resolution
(POM, 2B Technologies) in line with the Vocus with a subsampling point immediately downstream of the Vocus subsampling point. Temperature and RH (Vaisala HMP110) were also measured inline downstream of the Vocus
subsampling point at 1 Hz time resolution. Incoming total solar irradiance
at 1 Hz time resolution (Licor LI-200R) was measured via a sensor mounted on
top of the trailer housing the Vocus. Measurements of sea surface temperature (SST), salinity, and chlorophyll are continuously collected at a
1 min time resolution from the end of the pier from an automated shore
station operated by the Southern California Coastal Ocean Observing System (Wright, 2016).
DMS and MeSH calibrations and limit of detections
Instrument calibration factors for DMS were determined during ambient
sampling by a two-point standard addition of a DMS gas standard (Praxair,
5.08 ppm ± 5 %) to the full sampling inlet every 2.5 to 4 h.
Campaign mean sensitivity to DMS was 3.9 cps ppt-1. Sensitivity to DMS
was independent of RH. MeSH was not directly calibrated for during field
sampling. Post-campaign calibrations for MeSH were performed in the laboratory using a MeSH compressed gas standard (Airgas, 6.11 ± 5 % ppm) yielding a calibration factor of 1.3 cps ppt-1 for dry conditions.
MeSH sensitivity was slightly humidity dependent, resulting in the sensitivity at 80 % RH being 11 % lower than at 0 % RH. Calibrations
for MeSH were performed with the same 19 m sampling line and flow conditions
used for ambient sampling. We expect that this reduced sensitivity for MeSH
at high RH is at least partially due to reactions on inlet surfaces, which is a known complication in MeSH sampling (Perraud et al., 2016). Ambient MeSH
calibration factors were determined by scaling the measured in-field DMS
calibration factors by the RH-dependent DMS:MeSH sensitivity ratio determined from the laboratory calibrations. We note that the observed
sensitivity ratio of DMS:MeSH is different from what would be predicted
based on measured proton transfer rate coefficients, which are approximately equal for DMS and MeSH (Williams et al.,
1998), emphasizing the need for direct calibration of MeSH.
Instrument backgrounds were determined by overflowing the full inlet line
with dry UHP N2 at the tip of the ambient sampling point. Ambient
sampling periods were subdivided into 30 min blocks and were matched to
the nearest temporal calibration and background determination point. The
campaign mean, median, and interquartile range for DMS were 72.6, 49.2, and
28.0–89.8 ppt, respectively. MeSH mean, median, and interquartile range mixing ratios were 19.1, 13.4, and 7.6–24.5 ppt. Limits of detection (LODs) at a signal-to-noise (S / N) ratio of 3 were 2.6 ppt for DMS and 3.6 ppt for MeSH for a 10 s averaging time following the calculation method of Bertram et al. (2011).
The flux (F) of trace gas across the interface is described by Eq. (1) as a function of both the gas-phase (Cg) and liquid-phase (Cl)
concentrations and the dimensionless gas over liquid Henry's law constant
(H), where Kt, the total transfer velocity for the gas, encompasses all
of the chemical and physical processes that govern air–sea gas exchange (Liss and Slater, 1974).
F=KtHCl-Cg
Both DMS and MeSH are persistently supersaturated in the liquid phase, leading to an emission flux to the atmosphere (where a positive flux value
indicates emission). Fluxes of DMS and MeSH were measured in the turbulent
planetary boundary layer with the well-established EC technique, where F is calculated as the time average of the instantaneous
covariances from the mean of vertical wind (w) and the scalar magnitude (x,
here either DMS or MeSH) as shown in Eq. (2). Overbars are means, and primes are the instantaneous variance from the mean. N is the total number of data
points during the flux-averaging period. Ambient data were subdivided into approximately 30 min flux-averaging periods for the EC flux calculation.
Fx=1N∑iNwi-w¯xi-x¯=w′x′
EC flux data processing and quality control
Several standard EC data processing steps, data filters, and quality control
checks were applied during flux analysis, including (1) filtering by wind direction for periods of onshore winds (true wind direction 200–360∘), (2) coordinate rotation of 3-D wind
components by the planar fit method to remove unintentional tilts in the
sonic mounting and account for local flow distortions (Wilczak et al., 2001), (3) application of a friction
velocity (U∗) threshold of 0.05 cm s-1 to reject periods of
low shear driven turbulence, (4) despiking of DMS and MeSH data using a mean absolute deviation filter before the EC flux calculation (Mauder et al., 2013), (5) linear detrending of the scalar time series, and (6) flux stationarity filtering with flux periods rejected if they were non-stationary at a 30 % threshold (Foken and Wichura, 1996). Additional discussion of
EC flux processing and quality control steps, including lag time determination, spectral analysis, frequency attenuation corrections, and
flux LOD determination, are provided in the Supplement. A total of 304 out of 696 flux measurement periods passed all quality control filters. Campaign mean flux of DMS was 1.13 ppt m s-1, with a mean of individual flux period limits of
detection at the 3σ confidence interval of 0.35 ppt m s-1. MeSH
mean flux was 0.21 ppt m ms-1 with a mean flux 3σ LOD of 0.11 ppt m s-1.
Coupled ocean–atmosphere flux chemical box modelMeteorological and chemical constraints
A coupled ocean–atmosphere 0-D chemical box model was developed using the Master Chemical Mechanism (MCM) v3.3.1 (http://mcm.york.ac.uk (last access: 1 April 2022), Jenkin
et al., 1997; Saunders et al., 2003) in the Framework for 0-D Atmospheric
Modeling (F0AM, Wolfe et al., 2016) with added
chlorine chemistry (Riedel
et al., 2014). The model was used to assess the impact of observed MeSH
emissions on production of secondary marine SO2. Model ability to
reproduce observed diel profiles of DMS and MeSH mixing ratios was also
tested. Measured emission fluxes of MeSH and DMS are coupled to the model to set the source term for those molecules. Ambient pressure and temperature
data were acquired from the NOAA National Data Buoy Center (stations LJPC1 and LJAC1) as well as from an on-site temperature and relative humidity data logger (OM-62, Omega Engineering). Chemical constraints included coincident
measurements of O3 and nitryl chloride (ClNO2) measured at the
same site in August 2018 (Vermeuel
et al., 2020), an assumed OH profile that followed the solar cycle with a
peak at 4.0 × 106 molecules cm-3, and constant concentrations of
other major trace gases as listed in Table S2. A constant
first-order dilution loss term was used with a 1 d lifetime to approximate mixing out of the boundary layer. A static boundary layer height
(BLH) of 500 m was assumed (Faloona
et al., 2005; Stull, 1988; Wei et al., 2018). Clear-sky conditions were also assumed (i.e., no heterogeneous loss from reactive uptake on cloud droplets; Vermeuel et al., 2020). An updated oxidation mechanism for DMS and MeSH was implemented, expanding upon the
default DMS oxidation scheme in the MCM v3.3.1 to include oxidation of MeSH
to form CH3S Reaction (R1) and to include HPMTF chemistry, detailed
in Table S1. The model was allowed to spin up for 2 d to allow reactive intermediates to reach equilibrium, with all reported values taken
from day 3 of the model run.
Box model conditionsPier Model Case
Two distinct model cases were developed which differ in how they treat the
diel profile of FDMS and FMeSH. The first (termed the “Pier Model Case”) used the observed diel profile of FDMS and FMeSH at Scripps
Pier to test model ability to reproduce observed diel profiles of DMS and MeSH gas-phase mixing ratios. Nighttime flux measurements from 23:00 to 09:00 were limited during this study due to persistent offshore wind conditions. Instead, we apply a constant nighttime emission flux taken as
the average of the 09:00–10:00 and 21:00–22:00 flux observations for DMS and
MeSH. A 3 h moving mean was also applied to the observed diel flux profiles to reduce the influence of experimental variability on the model.
This Pier Model Case is used in the analysis of diel profiles presented in Sect. 3.4.
Open Ocean Case
A second case (termed the “Open Ocean Model Case”) was developed to provide a general assessment of the relative contribution of DMS and MeSH emissions to
SO2 production as described in Sect. 3.5. This Open Ocean Case uses fixed values for FDMS and FMeSH taken as the campaign mean fluxes rather than a variable diel profile. This case avoids
the ambiguity of uncertain nighttime emission flux in the observations and
better represents conditions on the open ocean, where there is little diel variability in wind speed, and thus emission fluxes for DMS and MeSH are
expected to be relatively constant (Archer
and Jacobson, 2005).
Results and discussionMeteorology overview
Observed meteorology and ocean physical and biogeochemical parameters showed
minimal variance over the sampling period. Mean SST during the campaign was 23.3 ∘C (21.6 to 24.7 ∘C interquartile range). Observed mean and interquartile ranges of air
temperatures and relative humidity were 22 ∘C (19.5 to
23.6 ∘C) and 79.9 % (72.3 % to 88.3 %), respectively. Chlorophyll concentrations suggest moderate biological productivity with an observed
campaign mean of 1.86 µg L-1 (1.5 to 2.0 µg L-1 interquartile range). O3 mixing ratios showed a clear diel pattern
peaking in mid afternoon with a campaign mean of 32.6 ppbv (27.6 to 38.9 ppbv interquartile range). Wind speeds during onshore wind periods were from 0 to 6 m s-1, typically peaking in late afternoon with a campaign mean
of 2.8 m s-1. Clear-sky conditions were observed for most afternoons during the study period, with total solar irradiance peaking near noon. The
period from days of year (DOYs) 268–271 saw occasional overcast skies during the afternoon. Light rainfall during the early morning of DOY 271 was the only precipitation during the campaign. Morning and late-evening periods showed occasional presence of marine stratocumulus clouds which drove
day-to-day variability in solar irradiance during those times. The campaign
time series of wind speed, air temperature, SST, O3 mixing ratios, solar irradiance, and DMS and MeSH mixing ratios are presented in Fig. 2. Gaps in the DMS and MeSH time series are from instrument maintenance periods, power outages at the site, and periods where
the instrument was operated in an alternative sampling mode.
Campaign time series of (a) wind speed and near-surface air temperature, (b) chlorophyll concentration and sea surface temperature (SST), (c) incoming solar irradiance (from 400 to 1100 nm) and ozone (O3) mixing ratios, (d) mixing ratios of DMS at 10 s and 2 min time
resolution, and (e) mixing ratios of MeSH at 10 s and 2 min time
resolution.
DMS and MeSH gas-phase mixing ratios
The campaign mean, median, and interquartile range of DMS mixing ratios were
72.6, 49.2, and 28.0–89.8 ppt, respectively. MeSH mean, median, and interquartile range mixing ratios were 19.1, 13.4, and 7.6–24.5 ppt. Maximum
concentrations of DMS and MeSH during the campaign were 562 and 217 ppt, respectively, for 10 s time-averaged data. The correlation of observed DMS and MeSH mixing ratios at a 2 min averaging time colored by the hour of day
(local time, UTC - 7) of the observation are shown in Fig. 3. The slope of the correlation was 0.19, with a linear least squares regression
coefficient (R2) of 0.61. The ratio of DMS:MeSH mixing ratios reaches a
minimum near hours 05:00–07:00 following the build-up of MeSH overnight. Both DMS
and MeSH were observed to reach a maximum in concentration at night and minimum concentrations in the early afternoon as shown in the diel profiles
in Fig. 4. The observed diel profile is consistent with
expectations due to the significantly lower oxidative loss rate at night and
has been observed in other studies (Lawson
et al., 2020). MeSH varies by approximately a factor of 5 between its diel average maximum and minimum concentration compared to DMS, which varies by approximately less than a factor of 3. The larger diel variability in MeSH is due to its approximately 5 times faster bimolecular rate constant
with OH (kOH+MeSH=3.3×10-11 cm3 molecule-1 s-1 at 293 K) compared to DMS (kOH+DMS=7.8×10-12 cm3 molecule-1 s-1 at 293 K), resulting in a
lifetime of MeSH to oxidation by OH during the afternoon on the order of 3 h compared to 16 h for DMS (for [OH] =2×106 molecule cm-3). In the remote southwestern Pacific Ocean, Lawson et al. (2020) measured mean DMS and MeSH mixing ratios of 208 and 18 ppt, respectively, with maximum concentrations observed at night. They also found
that DMS and MeSH were correlated, with a slope of 0.07 and an R2 of
0.3 over the full campaign. We observe similar mean MeSH (19.1 ppt), lower
DMS (72.6 ppt), and a larger slope for the correlation of DMS and MeSH
(slope =0.19) in this study compared to the Lawson et al. (2020)
observations. Still, our results show general qualitative agreement with Lawson et al. (2020), with both showing that atmospheric MeSH is present at a
significant ratio relative to DMS.
Correlation of DMS and MeSH mixing ratios at a 2 min averaging
time colored by hour of day of the observation. Hour of day is in local time
(UTC - 7). The linear least squares best fit is plotted as the solid black line. The slope of the best fit line is 0.19, and R2=0.61.
Hourly averaged diel profiles of observed mixing ratios and eddy
covariance flux of (a) DMS and (b) MeSH. Shading represents the standard
deviation of the binned hourly means. Winds were primarily from the land for
hours 00:00–10:00, limiting air–sea flux measurement during those times.
DMS and MeSH emission flux
A total of 304 valid quality-controlled flux-averaging periods was measured during the campaign. Campaign mean emission fluxes of FDMS and FMeSH were 1.13 ppt m s-1 (0.53–1.61 ppt m s-1
interquartile range) and 0.21 ppt m s-1 (0.10–0.31 ppt m s-1
interquartile range), respectively. Both FDMS and FMeSH reached a steady maximum between hour of days 10 to 17 as shown in Fig. 4, which corresponds to the typical period of sustained peak wind speed. The
magnitudes of both FDMS and FMeSH were found to increase with wind speed as shown in Fig. 5, following expectations for supersaturated species at moderate wind speeds where flux magnitude is
controlled by physical transfer terms (Carpenter
et al., 2012; Huebert et al., 2004; Kim et al., 2017; Marandino et al.,
2007). Measurement of FDMS and FMeSH during nighttime was limited
due to persistent winds from the land at night throughout the campaign. Fewer than 15 % of the valid flux observations were between hours 22:00 and 07:00. Further, those nighttime flux measurements were smaller and showed high
variability compared to daytime measurements. DMS and MeSH fluxes were
highly correlated with each other (R2=0.65) as shown in Fig. 6a. Campaign mean FDMS:FMeSH calculated as the simple mean of the
ratio of individual FDMS and FMeSH observations was 5.5 ± 3.0.
Lawson et al. (2020) calculated the average emission flux of MeSH compared
to DMS (FMeSH/(FMeSH+FDMS)) to be between 14 % and 24 %, where fluxes were calculated from the nighttime accumulation of DMS and MeSH
when oxidative loss was assumed to be negligible. In this study using direct
eddy covariance flux measurements, we calculate the mean FMeSH/(FMeSH+FDMS) to be 16 %, which compares well to the Lawson et
al. (2020) result. As shown in Fig. 6b, FDMS:FMeSH is partially correlated (R2=0.15) with ocean chlorophyll
concentrations. The time series of chlorophyll concentrations shown in Fig. 2b shows that chlorophyll peaked at the immediate start of
gas-phase sampling from DOY 245 to 246 at ca. 3.5 to 4 µg L-1 before declining over the course of several days to roughly constant concentrations from 1 to 2.5 µg L-1 over the remainder of the campaign. The
profile of chlorophyll suggests a phytoplankton bloom peak, and decay was sampled in the first period of the campaign, which transitioned into a
roughly constant moderately biologically productive state for the remainder
of the campaign. FDMS:FMeSH during the period of peak
chlorophyll concentrations over the first 3 d of the campaign (DOYs 245–247) was 10.8 ± 4.4 compared to the mean ratio from the full campaign of 5.5 ± 3.0. The relative production and consumption of DMS
and MeSH in seawater is known to be a complex function of the speciation and
abundance of phytoplankton and bacteria as well as available organic sulfur
and other biogeochemical parameters (Kiene
et al., 2000; Kiene and Linn, 2000b). No other measured meteorological
parameters, including wind speed, SST, and solar irradiance, showed a significant correlation with FDMS:FMeSH. The underlying cause
of the correlation between FDMS:FMeSH and chlorophyll in our dataset is not clear without additional constraints on the ocean
biochemistry. However, this result highlights that biological activity can
drive variations in dissolved ratios of DMS and MeSH, resulting in variability in ambient FDMS:FMeSH emission ratios, and that further
study is needed to elucidate this mechanism and its spatiotemporal
variability. Measurements during an induced mesocosm phytoplankton bloom
experiment using seawater collected at SIO Pier immediately before this study showed that the ratio of gas-phase DMS to MeSH varied by more than a factor of 5 over the course of a phytoplankton bloom and decay (Kilgour et al., 2022).
DMS to MeSH ratios in that study were strongly linked to changes in
bacterial sulfur demand and changes in the available pool of dissolved
sulfur across the phytoplankton bloom and decay cycle. Sun et al. (2016) have also shown that the
bacterium Pelagibacter produces both DMS and MeSH from DMSP, where the relative yield of
products is related to the amount of excess DMSP compared to the cellular
demand for sulfur for biosynthesis. While induced mesocosm blooms and
incubation experiments are not fully representative of the ambient ocean,
these results demonstrate the controlling role of ocean biology in FDMS and FMeSH and ultimately in marine SO2 production,
which must be better constrained through further ambient observations.
Flux of (a) DMS and (b) MeSH as a function of wind speed. Open
circles are individual 30 min data points and closed circles are mean fluxes binned by wind speed at a 1 m s-1 bin spacing. Shaded regions
are ± 1σ of the binned mean.
(a) Correlation and linear least squares regressions of measured MeSH and DMS emission fluxes (FMeSH and FDMS, respectively). The slope of the best fit line is 4.15, and R2=0.65. (b) Correlation and
linear least squares regression of the emission flux ratio of DMS to MeSH (FDMS:FMeSH) with ocean chlorophyll concentrations. The slope of the
best fit line is 3.15, and R2=0.15. Points in (a) are colored by ocean chlorophyll concentrations and in (b) by mean wind speeds for each
flux period.
Chemical box model comparison to pier observations
We assessed the ability of the coupled ocean–atmosphere chemical box model described in Sect. 2.4.2 using the Pier Model Case to replicate
the observed mean diel profiles of DMS and MeSH mixing ratios from SIO Pier. The model and measurement diel profiles of DMS and MeSH are shown in Fig. 7. For MeSH the model agrees with measurements to within
25 % during daytime hours (10:00–21:00) when direct flux constraints were
possible but diverges significantly at night, when the model underpredicts MeSH. DMS is overpredicted by roughly 25 ppt during daytime (hours 09:00 to 20:00)
in the model. Modeled DMS also shows less day–night variability in concentration, varying by a factor of 1.25 compared to observations, which vary by approximately a factor of 2. The poorer model performance at night
is likely related to diel changes in coastal boundary layer dynamics,
including boundary layer height and advection, which are not captured in the
model. As noted, nighttime emission fluxes of DMS and MeSH are poorly
constrained by the EC flux measurements and may also contribute to the
larger disagreement at nighttime. We expect the most informative model test
case to be for MeSH mixing ratios during daytime, where the MeSH emission flux is well constrained by measurements and the oxidative lifetime of MeSH is
short (<3 h), resulting in modeled MeSH mixing ratios being
primarily driven by oxidation and not by the uncertain boundary layer
dynamics or nighttime emission fluxes. During daytime modeled and measured
MeSH mixing ratios agreed to within 25 %, while DMS mixing ratios were overpredicted by ∼50 %. One additional potential driver of
model overprediction of DMS during daytime is the exclusion of BrO chemistry
from the base model due to the lack of observational constraints of BrO at
the study site. BrO has been suggested to be an important oxidant of DMS
which peaks in concentration in the afternoon (Saiz-Lopez
et al., 2006, 2008). A model sensitivity run using an afternoon peak BrO
concentration ([BrO]max) of 1 ppt was performed, which brings modeled DMS to within 10 ppt of the observations during daytime but degrades model-to-observation agreement at night. The [BrO]max of 1 ppt was selected
as an intermediate value in the range of measured and modeled BrO in the
daytime marine boundary layer; however, mean daytime BrO mixing ratios of up to 4 ppt have been observed in some locations (Mahajan
et al., 2010; Saiz-Lopez et al., 2004; Wang et al., 2019). Implementing
higher BrO mixing ratios in this model would generally serve to decrease
modeled DMS and MeSH mixing ratios, especially during daytime. Daytime MeSH
mixing ratios are reduced by less than 0.5 ppt in the 1 ppt BrO sensitivity
test, as MeSH oxidation is still dominated by OH. Due to the lack of
observational constraint on BrO during our study, we elect to exclude BrO
chemistry from the model base case used in subsequent calculations.
Inclusion of BrO chemistry would have minimal impact on model MeSH as
described and would serve to reduce DMS lifetime, increase the yield of DMSO and MSA from DMS oxidation, and reduce the yield of SO2 from DMS
oxidation. While there are clear uncertainties in this modeling effort, especially during nighttime, the general model ability to reproduce observed DMS and especially MeSH mixing ratios during daytime when we have robust
constraints on the emission flux suggests that the DMS and MeSH oxidation
mechanism added to the MCM v3.3.1 in this work is suitably accurate for providing meaningful information on the oxidative fate of DMS and MeSH.
Observed and modeled diel profiles of (a) DMS and (b) MeSH mixing
ratios.
Impact of MeSH on marine sulfur dioxide production
Production of SO2 as a function of FDMS and FMeSH was assessed
using the Open Ocean Case of the coupled ocean–atmosphere 0-D box model described in Sect. 2.4.2. Chemical box modeling of MeSH emission and gas-phase oxidation suggests that MeSH contributes significantly to marine boundary layer SO2 concentration. For a model case where FMeSH is taken to be 0 and FDMS is taken to be the campaign mean
of 1.1 ppt m s-1, modeled afternoon (hours 12:00 to 16:00) mean SO2
mixing ratio is 29.5 ppt, and the instantaneous SO2 production rate
(PSO2) is 5.8 ppt h-1. When FMeSH is added to the model at the
observed campaign mean of 0.21 ppt m s-1, model afternoon mean SO2
increases to 46.5 ppt, and PSO2 increases to 8.3 ppt h-1. Model diel profiles of SO2 mixing ratios and PSO2 with and without
MeSH emissions are shown in Fig. 8. In the campaign mean case MeSH
emissions contribute 30 % of the overall SO2 production (or a 43 %
increase in total SO2 production compared to the FDMS-only case). The model yield of SO2 from MeSH oxidation was 0.99, which is comparable
to an experimentally constrained model determination of the atmospheric
yield of 0.98 (Chen et al., 2021). We
include the full MeSH oxidation mechanism for completeness due to its
overlap with known DMS chemistry and ease of implementation in the box
model. However, given our determined model yield of SO2 from MeSH of 0.99 and a recently determined yield of 0.98 constrained by laboratory oxidation
studies (Chen et al., 2021), future modeling
efforts may be justified in simplifying this mechanism by including only a
direct MeSH + OH → SO2 reaction at a yield of 1. Prior efforts
to constrain the total SO2 budget in the marine boundary layer required
a unit yield of SO2 from DMS (Faloona et al., 2009), which stands in contrast to the 40 %–80 % range typically determined in laboratory
and modeling studies (Faloona, 2009;
Gray et al., 2011). This discrepancy has prompted consideration that other
potential SO2 precursors might be present which might reduce the yield
of SO2 from DMS needed to close the SO2 budget (Bandy et
al., 2011). Oceanic MeSH emissions as observed in our study are likely one
such additional contributor to secondary marine SO2 which has not
been previously considered. Eddy covariance flux measurements of
ocean–atmosphere trace gas exchange have generally been limited to a small set of molecules (e.g., DMS, acetone, methanol, acetaldehyde) (Novak and Bertram, 2020). As demonstrated
with the MeSH measurements presented here, marine EC flux observations of
new molecules are critical for constraining marine sulfur and volatile organic compound (VOC) budgets. Global spatiotemporal variability of MeSH emission
flux magnitude and the ratio of FDMS:FMeSH are both highly uncertain
due to the sparsity of ambient observations, which will need to be better
constrained through future studies.
Modeled diel profiles of SO2 mixing ratios and instantaneous
SO2 production rates (PSO2) for a model case considering only DMS
emissions (FDMS only, blue traces) and one including both DMS and MeSH
emissions (FDMS and FMeSH, red traces). Emissions were taken as the measured campaign mean flux of DMS and MeSH.
Heterogeneous chemistry of the DMS oxidation product HPMTF is not included
in our base model case. HPMTF heterogeneous chemistry has been proposed to
be a potentially large sink for HPMTF which would reduce SO2 production
from DMS (Novak
et al., 2021; Veres et al., 2020; Vermeuel et al., 2020). These details of
HPMTF heterogeneous chemistry do not impact the yield of SO2 from MeSH
described previously but do impact the calculated relative production of
SO2 from MeSH compared to DMS. Inclusion of HPMTF heterogeneous
chemistry (at γ=0.01 and an aerosol surface area of 48 µm2 cm-3) reduces model SO2 production from DMS to 2.7 ppt h-1 compared to 5.8 ppt h-1 in the model base case. In the HPMTF
heterogeneous chemistry case MeSH oxidation accounts for 48 % of marine
SO2 production. Further details on HPMTF chemistry are given in Sect. S5 in the Supplement.
We also note that the yield of SO2 production from MeSH does not have a
temperature dependence, unlike DMS, which may result in MeSH being an
especially important source of SO2 in colder high-latitude regions. At lower temperatures, the DMS OH-addition reaction pathway becomes more
favored, resulting in less efficient production of SO2 from DMS
oxidation as production of highly soluble intermediates begins to dominate
compared to at higher temperatures. Model calculations presented here used
the measured diel temperature profile at SIO Pier during this study, which had a mean of 293 K.
Modeled daytime PSO2 as a function of DMS and MeSH emission flux
magnitude is shown in Fig. 9 with the interquartile ranges of FMeSH and FDMS measured SIO Pier overlayed as a constraint. These results highlight the potential variability in PSO2 in varying regimes of FDMS:FMeSH. Model results were from the base Open
Ocean Case described in Sect. 2.4.2. Given the potential biological control on FDMS:FMeSH, the temperature dependence of SO2 yield from DMS, and the impact of HPMTF heterogeneous chemistry on
SO2 yield from DMS, we expect there may be significant temporal and
regional variability in the relative contribution of DMS and MeSH to marine
PSO2 across the global oceans. This additional SO2 production from
MeSH will likely contribute to new particle formation and growth through
enhanced production of sulfuric acid (H2SO4), with subsequent impacts on CCN abundance in the MBL. Given the newly determined significance
of both MeSH emissions and HPMTF heterogeneous chemistry on marine SO2
production, a critical reevaluation of the global marine SO2 budget
is likely warranted.
Modeled SO2 production rate (PSO2) in the marine boundary
layer as a function of MeSH and DMS emission fluxes. The interquartile range
of measured FDMS and FMeSH at SIO Pier is plotted as a black square.
Limited observational evidence for other volatile organic sulfur
compounds
In addition to DMS and MeSH, several other volatile organic sulfur compounds
(VOSCs) have been reported to be significant in the marine atmosphere,
either from direct oceanic emissions or gas-phase oxidation of precursor species. Recent shipborne observations in the Arabian Sea reported high
mixing ratios of dimethyl sulfone (DMSO2, 40–120 ppt) and methane
sulfonamide (MSAM, 20–50 ppt) downwind of a biologically productive
upwelling region (Edtbauer et
al., 2020). Reported DMS in the same region was from 100 to 300 ppt. The authors in that study propose a direct oceanic emission source of MSAM as
there is no known gas-phase oxidation pathway to produce MSAM from DMS. For our observations at Scripps Pier, both MSAM and DMSO2 were found to
have no observable emission flux above the flux limit of detection at either
the standard 3σ LOD threshold or a more relaxed 1σ LOD
threshold. Gas-phase mixing ratios of MSAM, DMSO, and DMSO2 were also not found to be significant, with none of those species consistently
observed above the 10 s averaging time LOD of 2.4, 7.0, and 9.2 ppt, respectively. All determinations of MSAM, DMSO, and DMSO2 mixing ratio
and LOD assume they have the same detection sensitivity as DMS. Our
measurements of DMSO and DMSO2 are consistent with box model calculated
mixing ratios which show hourly maxima of 4.9 ppt and <0.1 ppt for
DMSO and DMSO2, respectively, which are both below the instrument LOD. The box model conditions used were the Pier Model Case with the addition of
[BrO]max of 1 ppt. Oxidation of DMS by BrO favors DMSO and DMSO2
production, and the inclusion of [BrO]max of 1 ppt was done as a test of the reasonable upper limit case for DMSO and DMSO2 production for
conditions during this study. DMSO2 was also measured at Palmer Station, Antarctica, in January to February of 1994 with mean and median
mixing ratios of 1.7 and 1.3 ppt, respectively (Berresheim, 1998). The higher DMSO2 mixing ratios observed in that study are likely at least
in part due to the much lower temperatures (mean 274.5 K), where the DMS + OH addition channel forming DMSO and DMSO2 is more favored. Dimethyl
disulfide (DMDS, CH3S2CH3) was also not consistently observed
above the LOD in our measurements.
While a direct oceanic emission source of MSAM to the atmosphere has been
proposed (Edtbauer et al., 2020),
we did not measure a detectable MSAM emission flux in this study. As noted
by Edtbauer et al. (2020), given the Henry's law constant of MSAM
(KH=3.3×104 M atm-1), the waterside concentration of MSAM necessary to drive a net emission flux of MSAM to the
atmosphere is on the order of 1700 nM. Given that maximum total dissolved
organic sulfur (DOS) in ocean surface waters is on the order of 200 nM (Ksionzek et al., 2016), even if all surface ocean DOS was in the form of MSAM, we still would not expect an
emission flux of MSAM to the atmosphere. This exercise suggests MSAM may
instead be formed through an unknown reaction pathway in the atmosphere
which was active in the Arabian Sea but not in our observations at SIO Pier
in coastal southern California. Taken together, our observations indicate that MSAM, DMSO, and DMSO2 emission fluxes and mixing ratios were small
at the SIO Pier site, consistent with model calculations.
This discussion on the lack of observations of other VOSCs only extends to
molecules that are detectable with the Vocus PTR instrument, such as DMSO,
DMSO2, MSAM, and DMDS. For example, it does not include inorganic
sulfur-containing molecules such as carbonyl sulfide (OCS) and carbon disulfide (CS2) which also have a known oceanic source but are not
readily detectable by PTR. In particular, our measurements do not include the recently observed DMS oxidation product HPMTF, which was found to be a
globally ubiquitous sulfur reservoir from airborne observations in the
marine atmosphere (Veres
et al., 2020) and in prior surface observations at SIO Pier (Vermeuel et al., 2020). HPMTF is not detectable with the Vocus PTR ion chemistry used in this study. HPMTF
was previously observed in the summer of 2018 at the SIO Pier site to have a
strong diel profile, peaking in the early afternoon with an average daytime
mixing ratio of 12.1 pptv (Vermeuel
et al., 2020). While observations of HPMTF were made 1 year prior to the DMS and MeSH observations reported here, it suggests that HPMTF may comprise up to 10 %–20 % of the total daytime VOSC concentration at this site during
summer.
Conclusions
We present the first direct eddy covariance flux measurement of MeSH
emissions from the ocean which show that MeSH emissions account for a mean
of 16 % (FMeSH/(FMeSH+FDMS)) of emitted volatile
organic sulfur measurable with the Vocus PTR-ToF during this study. DMS and
MeSH emission fluxes were correlated with each other (R2=0.65),
consistent with their shared oceanic source from the degradation of DMSP.
Measured FDMS:FMeSH was found to have a weak correlation
(R2=0.15) with chlorophyll concentrations, which highlights the
need for further study of biogeochemical cycling in the ocean surface which
may drive significant spatiotemporal variability in FMeSH. The
atmospheric implications of ocean MeSH emissions were assessed by
development of a MeSH oxidation mechanism and incorporation into a coupled
ocean–atmosphere 0-D chemical box model. Modeling results show that oxidation of MeSH by OH produces SO2 at a high yield
(∼99 %) and is an important contributor to SO2
production in the marine atmosphere, driving an increase in afternoon
PSO2 of 2.5 ppt hr-1 corresponding to 30 % of total afternoon SO2 production rates. In a model case including HPMTF heterogeneous
uptake to aerosols, SO2 yield from DMS is reduced and MeSH becomes even
more important, accounting for 48 % of marine SO2 production. Taken
together, these results demonstrate that MeSH is an important contributor to volatile sulfur budgets in the marine atmosphere and that further studies
are needed to constrain spatiotemporal trends of MeSH emission and oxidation
relative to DMS.
Data availability
DMS and MeSH flux and concentration data and
associated meteorological data from this study are archived at http://digital.library.wisc.edu/1793/82383 (Novak and Bertram, 2022)
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-6309-2022-supplement.
Author contributions
GAN, DBK, and THB designed the research. GAN carried out the ambient sampling campaign and analyzed the data. MPV
assisted with the ambient deployment. DBK conducted laboratory calibrations
and developed sampling methods. CMJ and MPV contributed to model
development. THB supervised the project. GAN and THB wrote the paper. All the authors reviewed and edited the paper.
Competing interests
At least of tne of the authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Financial support
This research has been supported by the National Science Foundation (grant no. AGS 1829667) and the National Science Foundation (grant no. CHE 1801971).
Review statement
This paper was edited by Eliza Harris and reviewed by two anonymous referees.
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