ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-6665-2016Dimethyl sulfide in the summertime Arctic atmosphere: measurements and source sensitivity simulationsMungallEmma L.https://orcid.org/0000-0003-2567-5090CroftBettyhttps://orcid.org/0000-0002-7009-1767LizotteMartinehttps://orcid.org/0000-0002-7639-2819ThomasJennie L.MurphyJennifer G.https://orcid.org/0000-0001-8865-5463LevasseurMauriceMartinRandall V.https://orcid.org/0000-0003-2632-8402WentzellJeremy J. B.LiggioJohnAbbattJonathan P. D.jabbatt@chem.utoronto.cahttps://orcid.org/0000-0002-3372-334XDepartment of Chemistry, University of Toronto, Toronto, CanadaDepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, CanadaQuébec-Océan, Department of Biology, Université Laval, Québec, CanadaSorbonne Universités, UPMC Univ. Paris 06, Université Versailles St-Quentin, CNRS/INSU, LATMOS-IPSL, Paris, FranceAir Quality Processes Research Section, Environment Canada, Toronto, Ontario, CanadaJonathan P. D. Abbatt (jabbatt@chem.utoronto.ca)2June20161611666566801December201516December201526April201618May2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/6665/2016/acp-16-6665-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/6665/2016/acp-16-6665-2016.pdf
Dimethyl sulfide (DMS) plays a major role in the global sulfur cycle. In
addition, its atmospheric oxidation products contribute to the formation and
growth of atmospheric aerosol particles, thereby influencing cloud
condensation nuclei (CCN) populations and thus cloud formation. The pristine
summertime Arctic atmosphere is strongly influenced by DMS. However,
atmospheric DMS mixing ratios have only rarely been measured in the
summertime Arctic. During July–August, 2014, we conducted the first high
time resolution (10 Hz) DMS mixing ratio measurements for the eastern
Canadian Archipelago and Baffin Bay as one component of the Network on
Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian
Environments (NETCARE). DMS mixing ratios ranged from below the detection
limit of 4 to 1155 pptv (median 186 pptv) during the 21-day shipboard
campaign. A transfer velocity parameterization from the literature coupled
with coincident atmospheric and seawater DMS measurements yielded air–sea DMS
flux estimates ranging from 0.02 to 12 µmol m-2 d-1.
Air-mass trajectory analysis using FLEXPART-WRF and sensitivity simulations
with the GEOS-Chem chemical transport model indicated that local sources
(Lancaster Sound and Baffin Bay) were the dominant contributors to the DMS
measured along the 21-day ship track, with episodic transport from the Hudson
Bay System. After adjusting GEOS-Chem oceanic DMS values in the region to
match measurements, GEOS-Chem reproduced the major features of the measured
time series but was biased low overall (2–1006 pptv, median 72 pptv),
although within the range of uncertainty of the seawater DMS source. However,
during some 1–2 day periods the model underpredicted the measurements by
more than an order of magnitude. Sensitivity tests indicated that non-marine
sources (lakes, biomass burning, melt ponds, and coastal tundra) could make
additional episodic contributions to atmospheric DMS in the study region,
although local marine sources of DMS dominated. Our results highlight the
need for both atmospheric and seawater DMS data sets with greater spatial and
temporal resolution, combined with further investigation of non-marine DMS
sources for the Arctic.
Introduction
Despite the established importance of oceanic emissions of biogenic sulfur in
the form of dimethyl sulfide (DMS) to aerosol formation and growth in the
marine boundary layer e.g., key uncertainties remain about oceanic DMS
concentrations and the air–sea flux of DMS . DMS
emissions are responsible for about 15 % of the tropospheric sulfur budget
globally and up to 100 % in some remote areas .
Due to its low solubility and high volatility (small Henry's Law constant)
and its supersaturation in the ocean with respect to the atmosphere, DMS
partitions to the atmosphere after being produced by micro-organisms in
surface waters. In the atmosphere, DMS is oxidized to sulfuric acid and
methane sulfonic acid (MSA). These oxidation products can then participate in
new particle formation or
condense upon existing particles, causing them to grow larger and changing
particle hygroscopicity. The influence of DMS emissions on aerosol
concentrations is important since aerosols modify the climate directly by
scattering and absorbing radiation and indirectly by modifying cloud
radiative properties by acting as seeds for cloud droplet formation
.
Both composition and size affect the ability of an aerosol particle to act as
a cloud condensation nucleus (CCN), with bigger and more hygroscopic aerosol
particles preferentially activating as CCN .
(a) The Amundsen ship position with dates
indicated by colours. (b) Surface-layer atmospheric dimethyl sulfide
(DMS) mixing ratios from ship-based high-resolution time-of-flight chemical
ionization mass spectrometer (HR-ToF-CIMS) measurement with colour showing
magnitude of mixing ratios.
The summer Arctic atmosphere contains very few CCN through a combination of
limited local sources and efficient scavenging mechanisms
. At low CCN levels the radiative balance as
determined by cloud cover is very sensitive to CCN number
. Sea ice cover in the summer Arctic is in rapid
decline e.g.. With the decline in sea ice
comes an enhanced potential for sea–air exchange of compounds such as DMS
that may affect aerosol populations in the Arctic. In general, increased
numbers of CCN are associated with a cooling effect on climate. However,
portions of the Arctic can reside in a CCN-limited cloud–aerosol regime, with
the result that an increase in CCN could have a warming effect due to
increases in cloudiness in turn increasing the trapping of outgoing long-wave
radiation . In order to predict future changes
in CCN number, we need to understand the influence of sea–air exchange on
summertime Arctic aerosols.
Quantifying present-day atmospheric DMS mixing ratios (henceforth referred to
as DMSg) provides an important benchmark for interpreting future
measurements. Currently, only a few snapshots of DMSg in the Arctic exist
from a handful of shipboard studies conducted over the last 20 years,
none of which captured the most biologically productive time of June and July
. The data
span great distances in time and space and provide only a fragmented picture
of tropospheric DMSg levels in the Arctic. Understanding present-day
sources of DMSg is also relevant for predicting how these sources may
change in a future climate.
The lifetime of DMSg against OH oxidation of 1–2 days suggests that
DMSg may either undergo long-range transport before being oxidized or
remain in the same area under low wind conditions. Atmospheric transport
mixes DMSg within a region, effectively smoothing out atmospheric
concentration inhomogeneities due to inhomogeneity in the surface water DMS
(referred to henceforth as DMSsw). Transport can also bring
DMSg from regions further afield. For example, a study by
highlighted the importance of
transport in bringing DMSg from regions of open water to regions covered
by sea ice within the Arctic.
Despite the potential for an important role for atmospheric transport, few
source apportionment studies for sulfur in the Arctic have been carried out.
Previous work has focused almost exclusively on the aerosol phase. MSA in the aerosol phase is commonly assumed to arise from
oxidation of marine biogenic DMSg. However,
suggested that terrestrial sources in northern
Canada could also contribute MSA to Arctic aerosol. Previous studies indicate
that terrestrial emissions of DMSg from soils, vegetation, wetlands, and
lakes are less important than oceanic emissions
. However, these studies are based
on very few or even no measurements in the Canadian North, and the fluxes for
the Canadian tundra and boreal forest, which cover a very large surface area,
are highly unconstrained. Much of the Arctic Ocean is in close proximity to
land and is more subject to terrestrial influence than the open ocean in
other regions of the world .
Sources of DMSg other than seawater are not typically included in chemical
transport and climate models, despite evidence for several other sources of
DMSg. For example, significant levels of DMS have been measured in
Canadian lakes . DMS
emissions have also been observed from various continental sources such as
lichens , crops such as corn
, wetlands , and biomass
burning . Terrestrial
plants can be an important source of DMS as demonstrated by DMS levels in the
hundreds of pptv range measured from creosote bush in Arizona and from trees
and soils in the Amazonian rainforest
. One previous study based
on sulfur isotopes from Greenland included a pooled biogenic continental and
volcanic source (as the isotopic signatures of these two sources are not
easily distinguishable) and estimated this continental component to be 44 %
. In addition to the possibility of a continental
source, melt ponds have been suggested as a potentially important source of
DMS to the atmosphere . These fresh or brackish
ponds form from snowmelt on top of the sea ice in spring and summer, and
have been observed to have an extremely large areal extent, covering 30 %
of the sea ice on average in midsummer with up to 90 % coverage in some
regions . Here we present sensitivity studies
to examine the potential importance of these alternative sources of DMSg.
The goals of this study are (1) to present shipboard DMSg measurements
taken in the Canadian Arctic during July and August 2014 and (2) to explore
possible sources for the measured DMSg.
Section 2 outlines our measurement methodology. Section 3 presents the
measured DMSg time series along 3 weeks of the cruise. Section 3 also
includes concurrent measurements of DMSsw and the calculated DMS
air–sea flux estimates for the region. Section 4 presents sensitivity studies
with the GEOS-Chem chemical transport model and the FLEXPART-WRF particle
dispersion model, which examine the potential contribution of seawater and
non-marine sources to the measured DMSg.
MethodsMeasurements
Measurements of DMS were made during the first leg of the CCGS
Amundsen summer campaign under the aegis of NETCARE (Network on
Climate and Aerosols: Addressing Uncertainties in Remote Canadian
Environments). The research cruise started in Quebec City on 8 July 2014 and
ended in Kugluktuk on 14 August 2014. Measurements were made in Baffin Bay,
Lancaster Sound, and Nares Strait. The ship track is shown in
Fig. a.
DMS mixing ratios
DMSg measurements were made using a high-resolution time-of-flight
chemical ionization mass spectrometer (HR-ToF-CIMS, Aerodyne). The instrument
was housed in a container on the foredeck. The inlet was placed on a tower
9.44 m above the deck at the bow, which was itself nominally 6.6 m above
sea level (in total ca. 16 m a. s. l.). A diaphragm pump pulled air at
30 L min-1 through a 25 m long, 9.53 mm inner diameter PFA line
heated to 50∘ (Clayborn Labs). Flow rate through the line was
controlled by a critical orifice. The flow was subsampled and pulled to the
instrument inlet through another critical orifice restricting the flow to
2 L min-1. The flow through the sealed 210Po source of the
HR-ToF-CIMS, also controlled at 2 L min-1 by a critical orifice, was
supplied by a zero air generator (Parker Balston, model HPZA-18000, followed
by a Carbon Scrubber P/N B06-0263) via a mass flow controller supplying
2.4 L min-1. The zero air generator also supplied 9.8 sccm
(controlled by a mass flow controller) through a bubbler filled with benzene,
which was added to the flow through the radioactive source to provide the
reagent ion. The excess went to exhaust. Figure S1 in the Supplement shows a
flow schematic.
The use of benzene cations as a reagent ion for chemical ionization mass
spectrometry was first proposed by . This reagent
ion was successfully applied to the shipboard detection of DMSg by
. The ionization mechanism that prevails is the
transfer of charge from a benzene cation to an analyte ion which has an
ionization energy lower than that of benzene .
Due to space constraints on board the ship, a zero air generator was used
instead of cylinder nitrogen to produce our reagent ion flows. The use of
zero air introduced other potential reagent ions to the mass spectrum
(O2+, NO+, C6H7+, and H2O⋅H3O+; shown in
Fig. S2). To investigate the effect of this more complicated reagent ion
source, calibration experiments were carried out in the laboratory prior to
the campaign for both air and N2 at different sample flow relative
humidities and under different CIMS voltage configurations. The calibration
curves for DMS (detected as CH3SCH3+) showed a linear response under
all conditions. We found that the sensitivity of the instrument to DMS did
not depend on relative humidity. The average sensitivity measured by
one-point calibrations in the field (±1σ) was
80 ± 30 cps pptv-1. Actual uncertainties on the calibration
factor were less as a time-varying calibration factor was applied to the
data, as described below. Detection limits were below 4 pptv as the
background was consistently 2–3 pptv.
Background spectra were collected in the field by overflowing the inlet with
zero air from the zero air generator as shown in Fig. S1. The high mass
resolution of the instrument eliminated concern about unit mass isobaric
interferences as indicated in Fig. S3. Mass spectra were collected at 10 Hz.
One-point calibrations were performed nearly every day by overflowing the
inlet with zero air and adding a known amount of DMS from a standards
cylinder using a mass flow controller (499 ± 5 % ppb, Apel-Reimer).
Peak fitting was performed using the Tofware software package from Aerodyne
(version 2.4.4) in Igor Pro. Reported mixing ratios were calculated by first
normalizing analyte peak areas to reagent ion peak areas, then subtracting
backgrounds, and finally applying calibration factors obtained by linearly
interpolating the one-point daily calibrations. Text S1 provides details. To
remove artifacts that might have occurred due to enhanced DMS flux in the
ship's wake, the data were filtered such that values were removed when the
ship was moving (speed over ground greater than 2 m s-1) and the wind
direction was not within ±90∘ of the bow. This filtering removed
less than 12 % of data points.
Surface seawater DMS concentrations
Seawater concentrations of DMS were determined following procedures described
by and modified in
using purging, cryotrapping, and
sulfur-specific gas chromatography. Briefly, seawater was gently collected
directly from 12 L Niskin bottles in gas-tight 24 mL serum vials, allowing
the water to overflow. Subsamples of DMS were withdrawn from the 24 mL serum
vials within minutes of collection and sparged using an in line purge and
trap system with a Varian 3800 gas chromatograph (GC) equipped with a pulsed
flame photometric detector (PFPD). The GC was calibrated with injections of a
100 nM solution of hydrolyzed DMSP (Research Plus Inc.). The full data set
will be presented separately (M. Lizotte, personal communication, 2015).
Meteorological data
Basic meteorological measurements were made from a purpose built tower on the
ship's foredeck. Air temperature (8.2 m above deck), wind speed and
direction (9.4 m above deck), and barometric pressure (1.5 m above deck)
were measured using respectively a shielded temperature and relative
humidity probe (Vaisala™ HMP45C212), wind
monitor (RM Young 05103), and pressure transducer (RM
Young™ 61205V). Sensors were scanned every 2 s
and saved as 2 min averages to a micrologger (Campbell
Scientific™, model CR3000). Platform relative
wind was post-processed to true wind following .
Navigation data (ship position, speed over ground, course over ground, and
heading) necessary for the conversion were available from the ship's position
and orientation system (Applanix POS MV™ V4).
Periods when the tower sensors were serviced or when the platform relative
wind was beyond ±90∘ from the ship's bow were screened from the
meteorological data set. Screened periods accounted for less than 20 % of
total data but up to 45 % in some regions.
Sea surface temperature (SST) and salinity
SST was measured with the ship's Inboard Shiptrack
Water System, Seabird/Seapoint measurement system. There were no continuous
salinity measurements. An average salinity value of 29.7 PSU was used for
all calculations since the calculated transfer velocities had very low
sensitivity to changes in salinity for our study region.
Model descriptionsFLEXPART-WRF
A Lagrangian particle dispersion model based on FLEXPART
, FLEXPART-WRF website:
https://www.flexpart.eu/wiki/FpLimitedareaWrf,
was used to study the origin of air sampled by the ship. The model is driven
by meteorology from the Weather Research and Forecasting (WRF) model
and was run in backward mode to study the
emissions source regions and transport pathways influencing ship-based DMS
measurements. Specific details are in .
GEOS-Chem
The GEOS-Chem chemical transport model (www.geos-chem.org) was used to
conduct source sensitivity studies. We used GEOS-Chem version 9-02 at
2∘× 2.5∘ resolution with 47 vertical layers between
the surface and 0.01 hPa. The assimilated meteorology is taken from the
National Aeronautics and Space Administration (NASA) Global Modeling and
Assimilation Office (GMAO) Goddard Earth Observing System version 5.7.2
(GEOS-FP) assimilated meteorology product, which includes both hourly surface
fields and 3-hourly 3-D fields. Our simulations used 2014 meteorology and
allowed a 2-month spin-up prior to the simulation of July and August 2014.
The GEOS-Chem model includes a detailed oxidant–aerosol tropospheric
chemistry mechanism as originally described by .
Simulated aerosol species include sulfate–nitrate–ammonium
, carbonaceous aerosols
, dust , and sea salt . The
sulfate–nitrate–ammonium chemistry uses the ISORROPIA II thermodynamic model
, which partitions ammonia and nitric acid
between the gas and aerosol phases. The model includes natural and
anthropogenic sources of SO2 and NH3. DMS
emissions are based on the piece-wise linear sea–air flux
formulation (due to recent studies reporting a linear wind-speed dependence
for DMS;
)
and DMSsw concentrations from . In our
simulations, DMS emissions occurred only in the fraction of the grid box that
is covered by seawater and also free of sea ice. Biomass burning emissions
are from the Quick Fire Emissions Dataset (QFED2) ,
which provides daily open fire emissions at
0.1∘× 0.1∘. Oxidation of SO2 occurs in clouds by
reaction with H2O2 and O3 and in the gas phase with OH
and DMS oxidation occurs by reaction with OH
and NO3.
The GEOS-Chem model has been extensively applied to study the Arctic
atmosphere, in regard to aerosol acidity
, carbonaceous aerosol
, aerosol number ,
aerosol absorption , and mercury
.
Seawater DMS values in GEOS-Chem
The GEOS-Chem model uses the monthly mean DMSsw from the
climatology of , which was developed based on data
with very limited spatial coverage in the Canadian Arctic Archipelago and
Baffin Bay as shown by Fig. S1 in . In contrast, our
recent DMSsw measurements are spread quite evenly throughout the
21-day ship track and thus have a considerably greater spatial extent
throughout our study region than the sources used for the
climatology. The
climatology contains maximum DMSsw of 5 nM for our study region.
However, the DMSsw measured during our shipboard campaign was
generally between 5 and 10 nM and occasionally higher. Therefore, we used the
35 measured DMSsw values to create an updated DMSsw
field for use as a GEOS-Chem input in the study region. The measured values
were interpolated using the DIVA web application
(http://gher-diva.phys.ulg.ac.be/web-vis/diva.html) and a static field
was used for July and August. The climatology was
used for all other ocean regions. While our updated DMSsw has
improved spatial coverage and is a better temporal match to our study than
the data set, we acknowledge that there are
remaining uncertainties related to spatial and temporal resolution.
To our knowledge, there are no measurements of DMSsw in the
Hudson Bay System (comprising Hudson Bay, Foxe Basin and the Hudson Strait;
referred to as HBS hereafter). In our sensitivity simulations, we assess the
potential contribution of this source region to DMSg further north by
estimating the DMSsw based on primary productivity. We assumed
that (a) previously measured primary productivity values were representative
of the year of our cruise and (b) that the ratio of DMSsw in
Baffin Bay to DMSsw in other bodies of water is the same as the
ratio of primary productivity in Baffin Bay to primary productivity in other
bodies of water. In effect, we assumed a linear relationship between
DMSsw and primary productivity. This assumption is in keeping
with the parameterization for DMSsw. We
also note that use a related quantity, net
community productivity, to parameterize DMSsw, but net community
productivity data were not available for the HBS.
found that the waters of Hudson Strait are
as productive as those of the North Water (northern Baffin Bay), while Hudson
Bay and Foxe Basin are about a quarter as productive. Thus for our simulation
we set the DMSsw in Hudson Strait to be equal to that measured in
the North Water, and the DMSsw in Hudson Bay and Foxe Basin to a
quarter of that value. In the absence of measurements, it is not possible to
further constrain what the DMSsw values might be in the Hudson
Bay System.
Summary of past DMS atmospheric mixing ratio measurements in the
Arctic.
StudyThis workCruise NameIAOE-91Amundsen 2007Amundsen 2008Amundsen 2008ASCOS 2008Amundsen 2014SeasonAutumn (August,September, October)Autumn (Early October)Autumn (late September)Autumn (end of August, September)Autumn (August, beginning of September)Summer (late July and early August)LocationCentral Arctic OceanWestern Canadian ArcticEastern Canadian ArcticEastern Canadian ArcticCentral Arctic OceanEastern Canadian ArcticMethodGas chromatographyGas chromatographyGas chromatographyProton transfer reaction mass spectrometryProton transfer reaction mass spectrometryBenzene chemical ionization mass spectrometryMeasurement frequency392 samples in 64 days9 samples in 3 days18 samples in 3 days5 min1 min10 HzMedian25 (1.1)10 (0.44)30 (1.3)65.926185.825th percentile11 (0.48)41.215117.875th percentile53 (2.3)98.950262.5Minimum1.1 (0.047)Below detection (< 7 pptv)Below detection (< 7 pptv)0.34.0Below detection (< 4 pptv)Maximum380 (17)30 (1.3)94 (4.1)4741581155
The studies of and
report concentrations in nmol m-3. For
purposes of comparison, these have been converted to mixing ratios for an
atmospheric pressure and temperature of 101 kPa and 4∘ respectively.
Original (published) concentration values are reported in parentheses
following the calculated mixing ratios.
Flux estimate calculations
Our 35 concurrent measurements of DMS in the atmosphere and seawater along
the ship track in the Baffin Bay and Canadian Arctic Archipelago region allow
us to estimate the air–sea flux of DMS. The flux is defined as the rate of
transfer of a gas across a surface, in this case the surface of the ocean.
For liquid–gas surfaces, the flux is described by Eq. (),
F=-KWCg/KH-Cl
where Cg and Cl are the concentrations of the
chemical species of interest in the gas phase and liquid phase respectively,
KW is the transfer velocity, and KH is the dimensionless gas
over liquid form of the Henry's law constant .
The transfer velocity KW is described by Eq. ():
KW=1KHka+1kw-1,
where KW is composed of the single-phase transfer velocities for both the
water side (kw) and the air side (ka), representing
the rates of transfer in each phase.
The transfer velocity for each phase encapsulates the physical processes
controlling the flux in that phase. For soluble gases, the air-side processes
play a more important role and become increasingly relevant with increasing
solubility, while insoluble gases exhibit exclusively water-side control
. Air–sea fluxes are controlled by many
different factors, which has led to the development of a proliferation of
transfer velocity parameterizations, each addressing different issues. Some
are physically based, i.e. attempt to mathematically describe the processes
at play, while others are developed by fitting experimental or field data. It
is not clear whether parameterizations developed based on measurements of the
flux of a given gas can be applied to other gases. For example, bubbles
contribute less to the DMS flux than they do to the CO2 flux due to the
limited solubility of carbon dioxide in water, and so parameterizations
developed for CO2 might be expected to overestimate the DMS flux
. Indeed, recent studies have shown that the wind
speed dependence of the DMS transfer velocity is close to linear
.
Fluxes were calculated according to Eq. () using the transfer
velocity parameterizations of and
for water side and air side respectively
(adjusted to the ambient seawater Schmidt number of DMS; details are in
). Atmospheric concentrations were calculated
from measured mixing ratios using measured atmospheric temperature, pressure,
and the Henry's law constant for DMS at the in situ temperature. Fluxes were
multiplied by the fraction of open water in order to account for the capping
effect of sea ice . The sea ice cover near the
ship's location was estimated at a 0.5∘× 0.5∘
resolution by plotting the ship's course at hourly resolution on daily ice
charts obtained from the Canadian Ice Service
(http://www.ec.gc.ca/glaces-ice/). These estimates were
cross-referenced with daily photos taken aboard the ship to ensure accuracy.
Estimates were made on a scale from 1 to 10 with no fractional values.
DMS mixing ratio observations and estimated fluxes
Figure b and Table present the DMSg
mixing ratio data collected along the ship track. To our knowledge, these are
the first DMSg measurements for the Arctic during midsummer (July). These
summertime measurements exceed previous measurements made in late summer and
early autumn by a factor of 3–10 (Table ). This is
consistent with the expectation of higher biological productivity in the
summer than in other seasons . The time series
exhibits high temporal variability. Three episodes of elevated DMSg mixing
ratios with values of 400 pptv or above occurred along the ship track on
18–20, 26 July, and 1–2 August. Two episodes with DMSg mixing ratios with
values below 100 pptv occurred on 22–23 July and 5 August. Our values are
on the same order (hundreds of pptv) as measurements made at high latitudes
under bloom conditions in the Southern Ocean
, the North Atlantic
, and the northwestern Pacific
but are higher than measurements made in
the Tropical Pacific that were on the order of tens of pptv
.
Time series along Amundsen ship track of
(a) atmospheric DMS mixing ratio (10 Hz) from HR-ToF-CIMS,
(b) observed DMS surface seawater concentration,
(c) hourly-averaged wind speed at ship position (black) and
hourly-average GEOS wind speed at ship position (red), and (d) DMS
water-air flux estimates.
Summary of previous air–ocean DMS flux values in the
Arctic.
FluxDateLocationMethodAuthors0.02–12 µmol m-2 d-1Summer 2014 (July and August)Eastern Canadian ArcticEstimated from measurementsThis work0.1–2.6 µmol m-2 d-1Autumn 2007, 2008 (September to November)Beaufort Sea to Baffin bay through Lancaster SoundEstimated from measurements0.002–8.4 µmol m-2 d-1Autumn 1991 (August to October)Central Arctic Ocean and Greenland SeaEstimated from measurements0.007–11.5 µmol m-2 d-1Summer 1994 (July and August)Central Arctic Ocean east–west transectEstimated from measurements0.5 µmol m-2 d-1JanuaryNorth of 60∘ NGlobal model4–12 µmol m-2 d-1March–December 1996Gulf of AlaskaRegional model
Figure presents the time series of DMSg
along the ship track together with both measured and GEOS wind speeds,
DMSsw, and our flux estimates. Previous DMS flux estimates for
the Arctic are summarized in Table . The only other
summertime estimate falls within the same range as in this work of
ca. 0–10 µmol day-1 m-2. A
better constrained summer flux estimate for this region will require sampling
of DMSsw at higher spatial and temporal resolution, and ideally
direct continuous flux measurements using a technique such as eddy
covariance, but these are challenging measurements rendered more so by the
remoteness of Arctic Ocean.
Source sensitivity studies with GEOS-Chem and FLEXPART
In order to explore the provenance of the air masses being sampled on the
ship, we used FLEXPART-WRF backward runs as well as GEOS-Chem simulations.
Figure summarizes our understanding of the origins of air
masses arriving at the ship track. Figure a shows the time
series of DMSg from the GEOS-Chem simulation superimposed on the measured
DMSg time series, as well as the GEOS-Chem sea salt (a marine tracer) and
methyl ethyl ketone and carbon monoxide (MEK and CO, biomass burning tracers)
mixing ratios. Figure b shows the main land cover types in
the region. Figure c shows examples of potential
emissions sensitivity (PES) plots generated using FLEXPART-WRF that indicate
regions the air has passed over before being sampled. Periods highlighted
with a grey bar and numbered 1 through 3 were chosen as representative of
three types of influence: (1) marine influence from south of the Arctic
circle, (2) terrestrial influence from northern Canada, and (3) regional
marine influence from Baffin Bay. Sea salt tracer maxima indicate
marine-influenced air and reflect high winds, while MEK and CO maxima
indicate an influence from biomass burning. Biomass burning tracers provide a
convenient indication of continental influence on the air mass.
Figure shows agreement between the sources of the air
indicated by FLEXPART-WRF and by the GEOS-Chem tracers. For example, during
Period 2 the MEK tracer is high and FLEXPART-WRF shows continental influence,
while during Period 3 the sea salt tracer is high and FLEXPART-WRF shows
marine influence.
(a) Surface-layer atmospheric time series along
Amundsen ship track of (a) measured and GEOS-Chem (GC)
simulated DMS; (b) GC simulation of accumulation mode sea salt mass
concentration; (c) GC simulation of methyl ethyl ketone (MEK) mixing
ratio; (d) GC simulation of carbon monoxide (CO) mixing ratio.
(b) Olson Land Cover map of North America showing low-lying
tundra (red), other tundra (grey), forest (green), wetlands and marsh
(brown),
and inland water (dark blue). (c) FLEXPART-WRF potential
emissions sensitivity (PES) simulation plots showing the likely origin of the
air mass at the ship position. The colour scale in seconds corresponds to
time spent in the lower 300–1000 m (marked on each plot) before arriving at
the ship position. The three plots correspond to the three periods shown by
the numbers and shaded bars in (a), showing examples of
(1) transport from lower latitudes, including Hudson Bay (2) continentally
influenced air (3) local marine influence from Baffin Bay.
Model–measurement comparison
In comparing the simulated DMSg to our measurements, we assumed that the
major cause of discrepancies between measurements and model was the
representation of the DMS source in GEOS-Chem. Essentially, since the
GEOS-Chem model has realistic capabilities in the simulation of transport
and the chemical sinks of DMS are
relatively well understood , we chose to keep the
transport and sink parameterizations constant for our sensitivity studies and
focused on source sensitivity studies due to the considerable source-related
uncertainty.
Figure a shows that our GEOS-Chem simulations reproduce the
major features of the measured DMSg time series, with appropriate
magnitudes much of the time and an overall bias of -67 pptv. The poorest
model–measurement agreements occur on 1–2 and 6–7 August, as shown in
Figs. b and a, where GEOS-Chem
overestimates DMS mixing ratios by a factor of 2–3. This overestimation
coincides with high levels of the accumulation mode sea salt aerosol tracer
in GEOS-Chem as shown in Fig. b. The overestimation may be
due simulation errors related to the DMSsw field, excessive GEOS
wind speeds driving too large of a flux during this episode, or the
performance of the air–sea transfer velocity parameterization at high wind
speeds. Wind speeds in our GEOS-Chem simulations display considerable scatter
about the observed wind speeds along the ship-track time series but show a
linear relationship with a slope of 0.95 and R2=0.35 as in Fig. S4 and
reproduce major features of the wind time series as in
Fig. c. Overall, GEOS-Chem tended to
overestimate DMSg in Baffin Bay (largely open water at the time of the
campaign) and underestimate it in Lancaster Sound (where we encountered
between 10 and 100 % ice cover). It is worth noting that the effect of sea ice
on sea–air flux as hypothesized by is to
increase the flux at low wind speeds and decrease it at high wind speeds.
Implementation of this transfer velocity parameterization might be expected
to improve model–measurement agreement. More work is needed to assess how
best to parameterize air–sea flux in high-latitude regions and the marginal
ice zone in particular. Within these uncertainties, the seawater DMS source
could largely account for the measured DMSg. However, there are some
notable mismatches that cannot be accounted for by the uncertainties detailed
above. These are discussed in the following sections.
(a) GEOS-Chem (GC) simulated atmospheric surface-layer DMS
mixing ratio along Amundsen ship track as in
Fig. a, with indication of contributions from Baffin Bay
(blue), from Lancaster Sound (purple), and from other marine regions (red).
(b) Difference between measurement and simulated DMS mixing ratio
time series along the ship track showing model overprediction in blue and
underprediction in orange. (c) GC simulated DMS contributions along
ship track from sensitivity tests for additional DMS sources such as melt
ponds (blue), tundra (brown), and unknown sources possibly including forests,
soils, or lakes in proximity to biomass burning (green).
Seawater sources: Baffin Bay and Lancaster Sound as principal oceanic DMS source
Our model–measurement comparisons suggest that as expected, seawater makes
the dominant contribution to the measured DMSg. In this section, we
examine the potential regional contributions. Figure a
shows the relative contributions of various marine source regions to the
GEOS-Chem simulation of the DMSg along the ship track. Nearly 90 % of
the simulated DMSg could be explained by the DMS oceanic emissions from
Baffin Bay and Lancaster Sound when using the DMSsw field based
on our in situ measurements. The simulated DMSg values originating from Baffin
Bay and Lancaster Sound are shown in blue and purple respectively in
Fig. a. These local emissions also contributed the
majority of the highest mixing ratios observed during the campaign on 18 and
20 July. Overall, we conclude that the waters of Baffin Bay and Lancaster
Sound acted as a strong local source of DMSg throughout the campaign.
Transport from a seawater source: role of Hudson Bay System as an additional oceanic DMS source
Figure shows that the simulated influence of the HBS
is significant on 18–19 July, contributing up to 60 % of simulated DMSg
towards the end of that time period. This peak in DMS coincided with a
synoptic-scale storm system, which originated at lower latitudes and passed
over Lancaster Sound, where the ship was located at the time. This transport
pattern is visible in the FLEXPART-WRF retroplume for Period 1 in
Fig. c. These results suggest that DMS emissions from the
HBS are potentially an important source of atmospheric sulfur to the Arctic
atmosphere during episodic transport events associated with mid-latitude
storms travelling northward. Our simulated results depend on the assumption
that the DMSsw values in the HBS are similar to those observed at
higher latitudes. The potential for influence from the HBS is supported by
previous reports of high levels of DMSg in air masses transported
northward from the Hudson Bay region .
Measurements of both DMSsw and DMSg in the HBS are needed to
confirm this hypothesis.
Investigation of possible missing sources
The GEOS-Chem simulated DMSg time series underestimates the peaks in
measured DMSg on 17 and 26 July (shown in Fig. a). This
mismatch coincides with a minimum in the simulated marine tracer (sea salt),
suggesting that possibly a non-marine source of DMSg is not being
represented in the GEOS-Chem DMS parameterization. Since the emissions of
DMSg and sea salt aerosol are similarly dependent on wind speed and
fraction of open ocean and their lifetimes are similarly short, we expect the
DMSg and sea salt tracers in our simulation to covary when the DMSg is of
marine origin. We note that the GEOS wind speeds are in good agreement with
measured wind speeds during these time periods, as shown in
Fig. c. It is possible that this
model–measurement disagreement indicates that the model does not capture the
true relationship of DMSg to wind speed or that the GEOS-Chem simulation
is missing a coastal body of water at a sub-grid scale and that this water
body was emitting large quantities of DMS. However, the FLEXPART-WRF
retroplumes for 26 July (an example is shown as Period 2 of
Fig. c) indicate that the air mass had spent most of its
time over land surfaces and sea ice before reaching the ship's location. This
continental air-mass origin is further supported by high levels of simulated
continental tracers (e.g. MEK, shown in the third panel of
Fig. a) during these same periods.
The suggestion that DMSg may have a continental source is not new
, but it has not received very much attention. The
FLEXPART-WRF PES retroplumes indicate that the continental area influencing
the air masses sampled by the ship was northern Canada (primarily, regions to
the south and east of Baffin Bay, including Nunavut and the Northwest
Territories). The land cover in that region is shown in
Fig. b and is a mixture of tundra, boreal forest, wetlands,
and lakes. As well, there was a wide spatial extent of melt ponds to the
south and west of the ship track (shown in Fig. S5). To investigate the
impact that each of these sources could have had on the DMSg measured
during the campaign, we estimated the DMS emission potential of each land
cover type (including melt ponds) based on existing literature values. We
implemented these extra emissions in the GEOS-Chem model and performed
sensitivity tests to explore their potential to make additional contributions
to DMSg at the ship positions. These results are presented in the
following subsections.
(a) GEOS-Chem simulated July mean surface-layer atmospheric
DMS in Canada; (b) absolute change in simulated surface-layer DMS
with implementation of lake DMS emissions; (c) percent change in
simulated Canadian surface-layer DMS due to DMS emissions from wildfires;
(d) percent changes in simulated surface-layer DMS with the
implementation of lake DMS emissions.
Emissions from melt ponds
Melt ponds form on the surface of sea ice as the snowmelts. They cover much
of the surface of the sea ice by mid-summer and have been suggested as a
potentially important source of DMS to the atmosphere
. At the time of the campaign, the sea ice
regions to the west and south of our ship track, particularly in Lancaster
Sound, had considerable melt pond coverage as shown in Fig. S5. The melt pond
DMS source was implemented in GEOS-Chem by assuming that 50 % of sea ice
was covered by melt ponds and treating melt ponds as seawater with a
DMSsw concentration of 3 nM (expected to be an upper limit based
on . The transfer velocity
parameterization was used. The validity of assuming the same flux
parameterization applies to a shallow melt pond as to the open ocean is
untested, but it is a reasonable approximation for our sensitivity test.
The blue curve in Fig. c shows the simulated DMSg
contribution for the melt pond source. The simulated melt pond contribution
was greatest during 18–25 July when the ship was in Lancaster Sound. The
maximum simulated melt pond contribution was about 100 % on 23 July when
simulated and measured DMSg were very low. The strong contribution of the
melt ponds at this time was likely due to the ship's position at the ice edge
and advection of the arriving air mass over ice-covered regions. The simulated
melt pond source contributed an average about 20 % to the total simulated
DMSg over the remainder of the time series. Implementation of this source
reduced the overall normalized mean model–measurement bias by 9 %,
suggesting that melt ponds could serve to elevate the regional background
levels of DMSg. Further measurements of DMS concentrations in melt ponds
and, ideally, direct measurements of DMS fluxes from melt ponds are needed to
better constrain the impact this source might have on DMSg in the Arctic
summer.
Emissions from coastal tundra
Previous studies suggest that DMS emissions from lichens
and from coastal tundra, particularly in regions
where snow geese breed , may be quite large. For
lichens to emit reduced sulfur to the atmosphere, they require a source of
sulfur. In coastal regions this can be supplied by sea spray. We implemented
a tundra DMS source in GEOS-Chem by using the Olson Land Cover data
(http://edc2.usgs.gov/glcc/globdoc2_0.php) to calculate the fraction of
each GEOS-Chem grid box covered by the land type “barren tundra”. We then
assumed that 40 % of that tundra (to account for inland regions emitting
less due to less sulfate being deposited by sea spray) emitted DMS at a rate
of 480 nM m-2 h-1. We consider this
simulation to give us an upper limit to the potential influence of tundra DMS
emissions.
The results are presented as the brown curve in
Fig. c. The simulated DMSg at the ship track had
the largest contribution from tundra sources during 16–17 July, with a
maximum contribution to the simulated DMSg at the ship position of 6 %.
The percent contribution was lower than that of the melt pond source because
the tundra source acted to increase simulated DMSg during times when
levels were already high, but as can be seen in Fig. c
the absolute contribution of the simulated tundra source was comparable to or
greater than the melt pond source contribution. Like the melt pond source,
the possible tundra source reduces the overall normalized mean bias (by
14 %) and may contribute to the regional background levels of DMSg.
However, neither source can account for the large unexplained peaks in the
measured time series.
Emissions from lakes
To evaluate the potential contribution of DMS from lakes, the fresh water
fraction in each GEOS-Chem grid box in a rectangular domain spanning 48 to
75∘ N and -68 to -140∘ W was calculated using the Olson
Land Cover map, at 1 km × 1 km resolution. Based on the work of
, we assigned a mean value of 1 nM DMS to the
fresh water in that domain. We then applied the same
parameterization to the fraction of the grid box with lake coverage. The same
caveats apply to the use of transfer velocity parameterizations developed for
the open ocean for fluxes from lakes as to the application to melt ponds as
discussed above. In our simulation, the lake source was only locally
important as shown in Fig. . There was a modest contribution
to the absolute magnitude of DMSg in northern Quebec and Labrador, but the simulation
showed negligible effects of the lake source on surface-layer DMSg elsewhere. The percent change in surface-layer DMSg in
the Northwest Territories was quite large due to there being no other
simulated sources of DMSg in that location, but the absolute values of
DMSg are very small. However, as there are few measurements of DMS
concentrations in lakes in northern Canada, we cannot exclude the possibility
that the actual lake concentrations of DMSsw are much higher than
1 nM and that the unexplained peak in our time series is due to a lake
source of DMSg. This possibility is supported by high chlorophyll-α
levels in the lakes of northern Canada (shown in Fig. S6) and the fact that
the measurements of DMSsw in lakes that we used for this
sensitivity test were made more than 15 years ago, and the high northern
latitudes have warmed significantly since then .
Other potential DMS sources for the study area
Due to the paucity of measurements of DMS emissions from vegetation, boreal
soils, and Arctic wetlands, especially during and in proximity to biomass
burning events, this potential missing source is very difficult to evaluate.
The correlation between the measurement–model residual and the biomass
burning tracers in GEOS-Chem shown in Fig. a suggests that
DMSg was being co-transported with these biomass burning tracers. The
measurement–model difference and the MEK tracer have a similar peak on 26
July as shown in Fig. a. The FLEXPART-WRF retroplumes (e.g.
Period 2 in Fig. ) identify this time as being
continentally influenced.
DMS emissions have been reported from biomass burning
. Summer 2014 was a
particularly active wildfire season in northern Canada
. The simplest reason for the maxima in biomass
burning tracers during the unexplained DMSg peak on 26 July would be
emissions of DMS from biomass burning that are not represented in the model.
To gauge the importance of this source to DMSg in the Arctic, we used the
emission factor for DMS from boreal forest biomass burning reported by
. We indexed the simulated DMS emissions to CO
emissions, such that 3.66×10-5 molecules of DMS are emitted for
each molecule of CO emitted. Figure shows that the biomass
burning sensitivity test indicated that the biomass burning source of DMSg
had local influence only, like the modelled lake source. The reason for this
is that the emission factor for DMS from boreal forest fires is not very
large. As a result, this source acted to increase DMSg in the immediate
vicinity of the wildfires in the Northwest Territories but had a negligible
influence on the time series and is therefore not shown in
Fig. . The biomass burning source of DMSg was
likely not sufficient to directly influence the DMSg time series at the
ship position, unless the emission factor used in the model is an order of
magnitude too low. This seems unlikely as the emission factor we used was
derived from direct measurements in a biomass burning plume originating from
the boreal forest . Considerably larger DMS
emissions have been measured from other types of biomass burning in other
locations but we have no measurement evidence
to support a higher emissions factor in our present simulations. We note
that,
in particular, emissions from tundra fires are completely unconstrained and
might be quite different from emissions from boreal forest fires due to
different vegetation types and different types of burning (e.g. open flames
versus smoldering). Further study is required.
Although the available information suggests that direct DMS emissions from
fires seem unlikely to explain the bias, support for the hypothesis that
DMSg is being co-transported with biomass burning tracers is given by
improved model–measurement agreement indicated by Fig. c if
we assume the biomass burning plume contains equal amounts of DMSg and
MEK and add this DMSg “source” to the simulated DMSg. This revision
reduces the overall measurement–model bias by 24 % and reduces the
residual by 200 pptv for the 26 July. Alternatively, the air mass observed
at the ship could have passed over a strong near-land marine source, which is
missing in our simulations. However, the FLEXPART-WRF simulation indicates
that the air mass had travelled over nearly entirely ice-covered regions
before arriving at the ship, suggesting that a marine source is a less likely
explanation for the observed DMSg.
Emissions of reduced sulfur species from both soils and lakes are temperature
dependent , suggesting that the wild fires could
indirectly promote DMS emissions. Proximity to wild fires could increase the
temperature of the soil as well as changing the air quality, which might
stress biota. A mechanism whereby biomass burning increases the emission of
reduced sulfur species such as DMS from soils, lakes, and vegetation might
yield increased emissions but this requires further study and we do not have
any information that would allow implementation of this possible effect in
our simulations.
Conclusions
This study presents, to the best of our knowledge, the first measurements of
gaseous DMS mixing ratios in the summertime Arctic atmosphere of Baffin Bay
and parts of the Canadian Arctic Archipelago. Measured DMSg values were
greater than those measured in fall in the same region (consistent with
higher biological productivity in summer) and broadly consistent with
measurements in other parts of the ocean. We made flux estimates that fall
within the range of existing DMS air–sea flux estimates for the summertime
Central Arctic Ocean. The data presented here improve our knowledge of
atmospheric DMS levels in the summertime Arctic, but further study is needed
to understand spatial, seasonal, and interannual variability of DMS both in
the ocean and in the atmosphere.
We conducted sensitivity simulations with the GEOS-Chem chemical transport
model to examine the potential of various sources to contribute to DMSg
measured along the ship track. We found that local oceanic sources can
account for a large proportion (70 % overall) of the atmospheric
surface-layer DMS measured along our ship track in the Canadian Arctic
Archipelago and Baffin Bay during summer 2014. Our GEOS-Chem simulations
indicated that during transport events associated with synoptic-scale storms,
marine sources south of the Arctic Circle made strong and episodic
contributions (as much as 60 %) to DMS mixing ratios in the Canadian Arctic
Archipelago region. The role of transport in controlling DMS levels and the
potential for aerosol particle formation from DMSg has been argued
convincingly in a global sense by . We propose that it
may also be important episodically in the Arctic, e.g. transport from the
Hudson Bay System or the Northwest Territories. These origins for air at our
ship track are also supported by FLEXPART-WRF retroplume analysis.
GEOS-Chem simulations were biased low by 67 pptv over the ship-track time
series (representing between 10 and 100 % of the measured mixing ratios). We
investigated several additional sources (tundra, forests, lakes, and melt
ponds), which could contribute to surface-layer DMS mixing ratios. Our
sensitivity simulations indicated maximum contributions of 6 and 100 % from
tundra and melt ponds respectively to the simulated total DMSg for the
ship-track time series, suggesting that emissions of DMS from melt ponds and
coastal tundra could have important local, regional effects on DMS levels.
These sensitivity studies also suggest that terrestrial or near-terrestrial
sources could make additional contributions to DMSg in our study region.
These emissions may be related to changes in lake, forest, and soil emissions
due to the heat and stress associated with biomass burning. Flux measurements
from melt ponds and the boreal forest and lakes, particularly when under
stress from biomass burning events, are needed to evaluate this hypothesis.
Our findings have implications for our understanding of the sulfur cycle in
the summer Arctic and how it has changed in the recent past and will continue
to change in the future. For example, much of the discussion surrounding
changes in Arctic DMS has focused on the loss of sea ice
, but the loss of permafrost might also have a
large impact through changing nutrient levels in lakes
. The potential influence of the observed
atmospheric levels of DMS on new particle formation and subsequent growth
remains to be explored.
The Supplement related to this article is available online at doi:10.5194/acp-16-6665-2016-supplement.
J. Abbatt and M. Levasseur designed the
experiments and E. Mungall and M. Lizotte carried them out. J. Murphy, J.
Liggio and J. Wentzell facilitated the Amundsen campaign. B. Croft
and J. Thomas performed the GEOS-Chem and FLEXPART-WRF simulations
respectively. E. Mungall carried out the analysis, and E. Mungall prepared
the manuscript with the aid of B. Croft and contributions from all
co-authors.
Acknowledgements
The authors would like to acknowledge the financial support of NSERC for the
NETCARE project funded under the Climate Change and Atmospheric Research
program. As well, we thank ArcticNet for hosting NETCARE scientists on the
Amundsen, in particular the help of Keith Levesque, and all of the
crew and scientists aboard. Additionally, special thanks to Amir Aliabadi,
Ralf Staebler, Lauren Candlish, Heather Stark, Tonya Burgers, and Tim
Papakyriakou for ozone sondes and meteorological data. Thanks to Michelle Kim
and Tim Bertram of UCSD for invaluable discussions of ion chemistry. The
authors thank K. Tavis and P. Kim for their assistance in implementation of
the QFED2 database. Edited by: A. Perring
ReferencesAkagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S.,
Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and
domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys.,
11, 4039–4072, 10.5194/acp-11-4039-2011, 2011.
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, 1989.Alexander, B., Park, R. J., Jacob, D. J., Li, Q., Yantosca, R. M., Savarino,
J., Lee, C., and Thiemens, M.: Sulfate formation in sea-salt aerosols:
Constraints from oxygen isotopes, J. Geophys. Res.-Atmos., 110, D10307, 10.1029/2004JD005659, 2005.Alexander, B., Park, R. J., Jacob, D. J., and Gong, S.: Transition
metal-catalyzed oxidation of atmospheric sulfur: Global implications for the
sulfur budget, J. Geophys. Res.-Atmos., 114, D02309, 10.1029/2008JD010486, 2009.Allgood, C., Lin, Y., Ma, Y.-C., and Munson, B.: Benzene as a selective
chemical ionization reagent gas, Org. Mass Spectrom., 25, 497–502,
10.1002/oms.1210251003, 1990.Barnes, I., Hjorth, J., and Mihalopoulos, N.: Dimethyl Sulfide and Dimethyl
Sulfoxide and Their Oxidation in the Atmosphere, Chem. Rev., 106, 940–975,
10.1021/cr020529+, 2006.Bates, T. S., Lamb, B. K., Guenther, A., Dignon, J., and Stoiber, R. E.:
Sulfur
emissions to the atmosphere from natural sourees, J. Atmos. Chem., 14,
315–337, 10.1007/BF00115242, 1992.Bell, T. G., De Bruyn, W., Miller, S. D., Ward, B., Christensen, K. H., and
Saltzman, E. S.: Air-sea dimethylsulfide (DMS) gas transfer in the North
Atlantic: evidence for limited interfacial gas exchange at high wind speed,
Atmos. Chem. Phys., 13, 11073–11087, 10.5194/acp-13-11073-2013, 2013.Bell, T. G., De Bruyn, W., Marandino, C. A., Miller, S. D., Law, C. S.,
Smith, M. J., and Saltzman, E. S.: Dimethylsulfide gas transfer coefficients
from algal blooms in the Southern Ocean, Atmos. Chem. Phys., 15, 1783–1794,
10.5194/acp-15-1783-2015, 2015.Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore,
A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global
modeling of tropospheric chemistry with assimilated meteorology: Model
description and evaluation, 106, 23073–23095, 10.1029/2001JD000807, 2001.Blomquist, B. W., Fairall, C. W., Huebert, B. J., Kieber, D. J., and Westby,
G. R.: DMS sea-air transfer velocity: Direct measurements by eddy
covariance and parameterization based on the NOAA/COARE gas transfer
model, Geophys. Res. Lett., 33, L07601, 10.1029/2006GL025735, 2006.Blunden, J. and Arndt, D. S.: State of the Climate in 2014, B. Am.
Meteorol.
Soc., 96, ES1–ES32, 10.1175/2015BAMSStateoftheClimate.1, 2015.Breider, T. J., Mickley, L. J., Jacob, D. J., Wang, Q., Fisher, J. A., Chang,
R. Y.-W., and Alexander, B.: Annual distributions and sources of Arctic
aerosol components, aerosol optical depth, and aerosol absorption, J.
Geophys. Res.-Atmos., 119, 4107–4124, 10.1002/2013JD020996, 2014.Brioude, J., Arnold, D., Stohl, A., Cassiani, M., Morton, D., Seibert, P.,
Angevine, W., Evan, S., Dingwell, A., Fast, J. D., Easter, R. C., Pisso, I.,
Burkhart, J., and Wotawa, G.: The Lagrangian particle dispersion model
FLEXPART-WRF version 3.1, Geosci. Model Dev., 6, 1889–1904,
10.5194/gmd-6-1889-2013, 2013.Browse, J., Carslaw, K. S., Arnold, S. R., Pringle, K., and Boucher, O.: The
scavenging processes controlling the seasonal cycle in Arctic sulphate and
black carbon aerosol, Atmos. Chem. Phys., 12, 6775–6798,
10.5194/acp-12-6775-2012, 2012.Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A.,
Forster, P. M., Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre,
L. A., and Pierce, J. R.: Large contribution of natural aerosols to
uncertainty in indirect forcing, Nature, 503, 67–71,
10.1038/nature12674, 2013.Chang, R. Y.-W., Sjostedt, S. J., Pierce, J. R., Papakyriakou, T. N.,
Scarratt,
M. G., Michaud, S., Levasseur, M., Leaitch, W. R., and Abbatt, J. P. D.:
Relating atmospheric and oceanic DMS levels to particle nucleation events
in the Canadian Arctic, J. Geophys. Res.-Atmos., 116, D00S03,
10.1029/2011JD015926, 2011.Charlson, R. J., Lovelock, J. E., Andreae, M. O., and Warren, S. G.: Oceanic
phytoplankton, atmospheric sulphur, cloud albedo and climate, Nature, 326,
655–661, 10.1038/326655a0, 1987.Chen, H., Ezell, M. J., Arquero, K. D., Varner, M. E., Dawson, M. L., Gerber,
R. B., and Finlayson-Pitts, B. J.: New particle formation and growth from
methanesulfonic acid, trimethylamine and water, Phys. Chem. Chem. Phys.,
17, 13699, 10.1039/C5CP00838G, 2015.Croft, B., Martin, R. V., Leaitch, W. R., Tunved, P., Breider, T. J.,
D'Andrea, S. D., and Pierce, J. R.: Processes controlling the annual cycle of
Arctic aerosol number and size distributions, Atmos. Chem. Phys., 16,
3665–3682, 10.5194/acp-16-3665-2016, 2016.
Darmenov, A. and da Silva, A.: The quick fire emissions dataset
(QFED)–documentation of versions 2.1, 2.2 and 2.4, NASA Technical Report
Series on Global Modeling and Data Assimilation, NASA TM-2013-104606, 32,
183, 2013.Erickson, D. J., Ghan, S. J., and Penner, J. E.: Global ocean-to-atmosphere
dimethyl sulfide flux, J. Geophys. Res.-Atmos., 95, 7543–7552,
10.1029/JD095iD06p07543, 1990.
Fairlie, T. D., Jacob, D. J., and Park, R. J.: The impact of transpacific
transport of mineral dust in the United States, Atmos. Environ., 41,
1251–1266, 2007.Fairlie, T. D., Jacob, D. J., Dibb, J. E., Alexander, B., Avery, M. A., van
Donkelaar, A., and Zhang, L.: Impact of mineral dust on nitrate, sulfate, and
ozone in transpacific Asian pollution plumes, Atmos. Chem. Phys., 10,
3999–4012, 10.5194/acp-10-3999-2010, 2010.Ferland, J., Gosselin, M., and Starr, M.: Environmental control of summer
primary production in the Hudson Bay system: The role of
stratification, J. Marine Syst., 88, 385–400,
10.1016/j.jmarsys.2011.03.015, 2011.
Fisher, J. A., Jacob, D. J., Wang, Q., Bahreini, R., Carouge, C. C., Cubison,
M. J., Dibb, J. E., Diehl, T., Jimenez, J. L., Leibensperger, E. M., Lu, Z.,
Meinders, M. B. J., Pye, H. O. T., Quinn, P. K., Sharma, S., Streets, D. G., van Donkelaar, A., and Yantosca, R. M.:
Sources, distribution, and acidity of sulfate–ammonium aerosol in the Arctic
in winter–spring, Atmos. Environ., 45, 7301–7318, 2011.
Fisher, J. A., Jacob, D. J., Soerensen, A. L., Amos, H. M., Steffen, A., and
Sunderland, E. M.: Riverine source of Arctic Ocean mercury inferred from
atmospheric observations, Nat. Geosci., 5, 499–504, 2012.Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic
equilibrium model for K+–Ca2+–Mg2+–NH4+–Na+–SO42-–NO3-–Cl–H2O aerosols,
Atmos. Chem. Phys., 7, 4639–4659, 10.5194/acp-7-4639-2007, 2007.Gries, C., Iii, T. H. N., and Kesselmeier, J.: Exchange of reduced sulfur
gases
between lichens and the atmosphere, Biogeochemistry, 26, 25–39,
10.1007/BF02180402, 1994.Hines, M. E. and Morrison, M. C.: Emissions of biogenic sulfur gases from
Alaskan tundra, J. Geophys. Res.-Atmos., 97, 16703–16707,
10.1029/90JD02576, 1992.Hopke, P. K., Barrie, L. A., Li, S.-M., Cheng, M.-D., Li, C., and Xie, Y.:
Possible sources and preferred pathways for biogenic and non-sea-salt sulfur
for the high Arctic, J. Geophys. Res.-Atmos., 100, 16595–16603,
10.1029/95JD01712, 1995.Huebert, B. J., Blomquist, B. W., Yang, M. X., Archer, S. D., Nightingale,
P. D., Yelland, M. J., Stephens, J., Pascal, R. W., and Moat, B. I.:
Linearity of DMS transfer coefficient with both friction velocity and wind
speed in the moderate wind speed range, Geophys. Res. Lett., 37, L01605,
10.1029/2009GL041203, 2010.
IPCC: Summary for Policymakers, in: Climate Change 2013: The
Physical
Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate
Change, edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen,
S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., 1–30,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
2013.Jardine, K., Abrell, L., Kurc, S. A., Huxman, T., Ortega, J., and Guenther,
A.: Volatile organic compound emissions from Larrea tridentata
(creosotebush), Atmos. Chem. Phys., 10, 12191–12206,
10.5194/acp-10-12191-2010, 2010.Jardine, K., Yañez-Serrano, A., Williams, J., Kunert, N., Jardine, A.,
Taylor,
T., Abrell, L., Artaxo, P., Guenther, A., Hewitt, C., House, E., Florentino,
A. P., Manzi, A., Higuchi, N., Kesselmeier, J., Behrendt, T., Veres, P. R.,
Derstroff, B., Fuentes, J. D., Martin, S., and Andreae, M. O.: Dimethyl
Sulfide in the Amazon Rain Forest, Global Biogeochem. Cy.,
29, 19–32,
2014GB004969, 10.1002/2014GB004969, 2015.Jeffery, C. D., Robinson, I. S., and Woolf, D. K.: Tuning a physically-based
model of the air–sea gas transfer velocity, Ocean Model., 31, 28–35,
10.1016/j.ocemod.2009.09.001, 2010.Jodwalis, C. M., Benner, R. L., and Eslinger, D. L.: Modeling of dimethyl
sulfide ocean mixing, biological production, and sea-to-air flux for high
latitudes, J. Geophys. Res.-Atmos., 105, 14387–14399,
10.1029/2000JD900023, 2000.Johnson, M. T.: A numerical scheme to calculate temperature and salinity
dependent air-water transfer velocities for any gas, Ocean Sci., 6, 913–932,
10.5194/os-6-913-2010, 2010.Kameyama, S., Tanimoto, H., Inomata, S., Yoshikawa-Inoue, H., Tsunogai, U.,
Tsuda, A., Uematsu, M., Ishii, M., Sasano, D., Suzuki, K., and Nosaka, Y.:
Strong relationship between dimethyl sulfide and net community production in
the western subarctic Pacific, Geophys. Res. Lett., 40,
3986–3990, 10.1002/grl.50654, 2013.Kim, M. J., Zoerb, M. C., Campbell, N. R., Zimmermann, K. J., Blomquist, B.
W., Huebert, B. J., and Bertram, T. H.: Revisiting benzene cluster cations
for the chemical ionization of dimethyl sulfide and select volatile organic
compounds, Atmos. Meas. Tech., 9, 1473–1484, 10.5194/amt-9-1473-2016,
2016.Köhler, H.: The nucleus in and the growth of hygroscopic droplets,
T. Faraday Soc., 32, 1152–1161,
10.1039/TF9363201152, 1936.Kristiansen, N. I., Stohl, A., Olivié, D. J. L., Croft, B., Søvde, O.
A., Klein, H., Christoudias, T., Kunkel, D., Leadbetter, S. J., Lee, Y. H.,
Zhang, K., Tsigaridis, K., Bergman, T., Evangeliou, N., Wang, H., Ma, P.-L.,
Easter, R. C., Rasch, P. J., Liu, X., Pitari, G., Di Genova, G., Zhao, S. Y.,
Balkanski, Y., Bauer, S. E., Faluvegi, G. S., Kokkola, H., Martin, R. V.,
Pierce, J. R., Schulz, M., Shindell, D., Tost, H., and Zhang, H.: Evaluation
of observed and modelled aerosol lifetimes using radioactive tracers of
opportunity and an ensemble of 19 global models, Atmos. Chem. Phys., 16,
3525–3561, 10.5194/acp-16-3525-2016, 2016.Lana, A., Bell, T. G., Simó, R., Vallina, S. M., Ballabrera-Poy, J.,
Kettle,
A. J., Dachs, J., Bopp, L., Saltzman, E. S., Stefels, J., Johnson, J. E., and
Liss, P. S.: An updated climatology of surface dimethlysulfide concentrations
and emission fluxes in the global ocean, Global Biogeochem. Cy., 25, GB1004,
10.1029/2010GB003850, 2011.Leaitch, W. R., Sharma, S., Huang, L., Toom-Sauntry, D., Chivulescu, A.,
Macdonald, A. M., von Salzen, K., Pierce, J. R., Bertram, A. K., Schroder,
J. C., Shantz, N. C., Chang, R. Y.-W., and Norman, A.-L.: Dimethyl sulfide
control of the clean summertime Arctic aerosol and cloud, Elementa, 1,
000017, 10.12952/journal.elementa.000017, 2013.Leck, C. and Persson, C.: The central Arctic Ocean as a source of dimethyl
sulfide Seasonal variability in relation to biological activity, Tellus B,
48, 156–177, 10.1034/j.1600-0889.1996.t01-1-00003.x, 1996.Levasseur, M.: Impact of Arctic meltdown on the microbial cycling of sulphur,
Nat. Geosci., 6, 691–700, 10.1038/ngeo1910, 2013.Liao, H., Henze, D. K., Seinfeld, J. H., Wu, S., and Mickley, L. J.: Biogenic
secondary organic aerosol over the United States: Comparison of
climatological simulations with observations, J. Geophys. Res.-Atmos.,
112, D06201, 10.1029/2006JD007813,
2007.
Liss, P. S. and Merlivat, L.: Air-sea gas exchange rates: Introduction and
synthesis, in: The role of air-sea exchange in geochemical cycling,
113–127, Springer, 1986.Lizotte, M., Levasseur, M., Michaud, S., Scarratt, M. G., Merzouk, A.,
Gosselin, M., Pommier, J., Rivkin, R. B., and Kiene, R. P.: Macroscale
patterns of the biological cycling of dimethylsulfoniopropionate (DMSP) and
dimethylsulfide (DMS) in the Northwest Atlantic, Biogeochemistry, 110,
183–200, 10.1007/s10533-011-9698-4, 2012.Loose, B., McGillis, W. R., Perovich, D., Zappa, C. J., and Schlosser, P.: A
parameter model of gas exchange for the seasonal sea ice zone, Ocean Sci.,
10, 17–28, 10.5194/os-10-17-2014, 2014.Macdonald, R. W., Kuzyk, Z. A., and Johannessen, S. C.: It is not just about
the ice: a geochemical perspective on the changing Arctic Ocean, J.
Environ. Sci., 5, 1–14, 10.1007/s13412-015-0302-4, 2015.Mauritsen, T., Sedlar, J., Tjernström, M., Leck, C., Martin, M., Shupe,
M., Sjogren, S., Sierau, B., Persson, P. O. G., Brooks, I. M., and
Swietlicki, E.: An Arctic CCN-limited cloud-aerosol regime, Atmos. Chem.
Phys., 11, 165–173, 10.5194/acp-11-165-2011, 2011.Meinardi, S., Simpson, I. J., Blake, N. J., Blake, D. R., and Rowland, F. S.:
Dimethyl disulfide (DMDS) and dimethyl sulfide (DMS) emissions from
biomass burning in Australia, Geophys. Res. Lett., 30, 1454,
10.1029/2003GL016967, 2003.Nilsson, E. D. and Leck, C.: A pseudo-Lagrangian study of the sulfur budget
in the remote Arctic marine boundary layer, Tellus B, 54, 213–230,
10.1034/j.1600-0889.2002.01247.x, 2002.Nriagu, J. O., Holdway, D. A., and Coker, R. D.: Biogenic Sulfur and the
Acidity of Rainfall in Remote Areas of Canada, Science, 237,
1189–1192, 10.1126/science.237.4819.1189, 1987.Park, R. J., Jacob, D. J., Chin, M., and Martin, R. V.: Sources of
carbonaceous
aerosols over the United States and implications for natural visibility, J.
Geophys. Res.-Atmos., 108, 4355, 10.1029/2002JD003190, 2003.Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M., and Chin, M.:
Natural
and transboundary pollution influences on sulfate-nitrate-ammonium aerosols
in the United States: Implications for policy, J. Geophys. Res.-Atmos.,
109, D15204, 10.1029/2003JD004473,
2004.
Park, R. J., Jacob, D. J., Kumar, N., and Yantosca, R. M.: Regional
visibility
statistics in the United States: Natural and transboundary pollution
influences, and implications for the Regional Haze Rule, Atmos. Environ., 40,
5405–5423, 2006.Patris, N., Delmas, R., Legrand, M., De Angelis, M., Ferron, F. A.,
Stiévenard, M., and Jouzel, J.: First sulfur isotope measurements in central
Greenland ice cores along the preindustrial and industrial periods, J.
Geophys. Res.-Atmos., 107, ACH 6–1, 10.1029/2001JD000672, 2002.Pirjola, L., Kulmala, M., Wilck, M., Bischoff, A., Stratmann, F., and Otto,
E.:
Formation of sulphuric acid aerosols and cloud condensation nuclei: an
expression for significant nucleation and model comparison, J. Aerosol Sci.,
30, 1079–1094, 10.1016/S0021-8502(98)00776-9, 1999.Quinn, P. K. and Bates, T. S.: The case against climate regulation via
oceanic
phytoplankton sulphur emissions, Nature, 480, 51–56,
10.1038/nature10580, 2011.Rempillo, O., Seguin, A. M., Norman, A.-L., Scarratt, M., Michaud, S., Chang,
R., Sjostedt, S., Abbatt, J., Else, B., Papakyriakou, T., Sharma, S., Grasby,
S., and Levasseur, M.: Dimethyl sulfide air-sea fluxes and biogenic sulfur as
a source of new aerosols in the Arctic fall, J. Geophys. Res.-Atmos., 116,
D00S04, 10.1029/2011JD016336, 2011.Rhüland, K. and Smol, J. P.: Limnological Characteristics of 70 Lakes
Spanning Arctic Treeline from Coronation Gulf to Great Slave
Lake in the Central Northwest Territories, Canada, Int. Rev.
Hydrobiol., 83, 183–203, 10.1002/iroh.19980830302, 1998.Richards, S. R., Rudd, J. W. M., and Kelly, C. A.: Organic volatile sulfur in
lakes ranging in sulfate and dissolved salt concentration over five orders of
magnitude, Limnol. Oceanogr., 39, 562–572, 10.4319/lo.1994.39.3.0562,
1994.Rosel, A. and Kaleschke, L.: Exceptional melt pond occurrence in the years
2007
and 2011 on the Arctic sea ice revealed from MODIS satellite data, J.
Geophys. Res.-Oceans, 117, C05018, 10.1029/2011JC007869, 2012.
Scarratt, M. G., Levasseur, M., Schultes, S., Michaud, S., Cantin, G.,
Vezina,
A., Gosselin, M., and De Mora, S. J.: Production and consumption of
dimethylsulfide (DMS) in North Atlantic waters, Mar. Ecol.-Prog. Ser.,
204, 13–26, 2000.Sharma, S., Barrie, L. A., Hastie, D. R., and Kelly, C.: Dimethyl sulfide
emissions to the atmosphere from lakes of the Canadian boreal region, J.
Geophys. Res.-Atmos., 104, 11585–11592, 10.1029/1999JD900127,
1999a.Sharma, S., Barrie, L. A., Plummer, D., McConnell, J. C., Brickell, P. C.,
Levasseur, M., Gosselin, M., and Bates, T. S.: Flux estimation of oceanic
dimethyl sulfide around North America, J. Geophys. Res.-Atmos., 104,
21327–21342, 10.1029/1999JD900207, 1999b.Sharma, S., Chan, E., Ishizawa, M., Toom-Sauntry, D., Gong, S. L., Li, S. M.,
Tarasick, D. W., Leaitch, W. R., Norman, A., Quinn, P. K., Bates, T. S.,
Levasseur, M., Barrie, L. A., and Maenhaut, W.: Influence of transport and
ocean ice extent on biogenic aerosol sulfur in the Arctic atmosphere, J.
Geophys. Res.-Atmos., 117, D12209, 10.1029/2011JD017074, 2012.Simò, R. and Dachs, J.: Global ocean emission of dimethylsulfide
predicted
from biogeophysical data, Global Biogeochem. Cy., 16, 1078,
10.1029/2001GB001829, 2002.Simpson, R. M. C., Howell, S. G., Blomquist, B. W., Clarke, A. D., and
Huebert,
B. J.: Dimethyl sulfide: Less important than long-range transport as a
source of sulfate to the remote tropical Pacific marine boundary layer, J.
Geophys. Res.-Atmos., 119, 9142–9167, 10.1002/2014JD021643, 2014.Sjostedt, S. J., Leaitch, W. R., Levasseur, M., Scarratt, M., Michaud, S.,
Motard-Côté, J., Burkhart, J. H., and Abbatt, J. P. D.: Evidence for the
uptake of atmospheric acetone and methanol by the Arctic Ocean during
late summer DMS-Emission plumes, J. Geophys. Res.-Atmos., 117,
10.1029/2011JD017086, 2012.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang,
W., and Powers, J. G.: A description of the advanced research WRF version 2,
Tech. rep., DTIC Document, 2005.
Smith, S. R., Bourassa, M. A., and Sharp, R. J.: Establishing more truth in
true winds, J. Atmos. Ocean. Tech., 16, 939–952, 1999.Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G.: Technical
note:
The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos.
Chem. Phys., 5, 2461–2474, 10.5194/acp-5-2461-2005, 2005.Tanimoto, H., Kameyama, S., Iwata, T., Inomata, S., and Omori, Y.:
Measurement
of Air-Sea Exchange of Dimethyl Sulfide and Acetone by PTR-MS
Coupled with Gradient Flux Technique, Environ. Sci. Technol.,
48, 526–533,
10.1021/es4032562, 2013.Tesdal, J.-E., Christian, J. R., Monahan, A. H., and von Salzen, K.:
Sensitivity of modelled sulfate radiative forcing to DMS concentration and
air-sea flux formulation, Atmos. Chem. Phys. Discuss., 15, 23931–23968,
10.5194/acpd-15-23931-2015, 2015.Tilling, R. L., Ridout, A., Shepherd, A., and Wingham, D. J.: Increased
Arctic sea ice volume after anomalously low melting in 2013, Nat. Geosci.,
8, 643–646, 10.1038/ngeo2489, 2015.Tjernström, M., Leck, C., Birch, C. E., Bottenheim, J. W., Brooks, B. J.,
Brooks, I. M., Bäcklin, L., Chang, R. Y.-W., de Leeuw, G., Di Liberto,
L., de la Rosa, S., Granath, E., Graus, M., Hansel, A., Heintzenberg, J.,
Held, A., Hind, A., Johnston, P., Knulst, J., Martin, M., Matrai, P. A.,
Mauritsen, T., Müller, M., Norris, S. J., Orellana, M. V., Orsini, D. A.,
Paatero, J., Persson, P. O. G., Gao, Q., Rauschenberg, C., Ristovski, Z.,
Sedlar, J., Shupe, M. D., Sierau, B., Sirevaag, A., Sjogren, S., Stetzer, O.,
Swietlicki, E., Szczodrak, M., Vaattovaara, P., Wahlberg, N., Westberg, M.,
and Wheeler, C. R.: The Arctic Summer Cloud Ocean Study (ASCOS): overview and
experimental design, Atmos. Chem. Phys., 14, 2823–2869,
10.5194/acp-14-2823-2014, 2014.
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of
Clouds, J. Atmos. Sci., 34, 1149–1152,
10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977.Wang, Q., Jacob, D. J., Fisher, J. A., Mao, J., Leibensperger, E. M.,
Carouge, C. C., Le Sager, P., Kondo, Y., Jimenez, J. L., Cubison, M. J., and
Doherty, S. J.: Sources of carbonaceous aerosols and deposited black carbon
in the Arctic in winter-spring: implications for radiative forcing, Atmos.
Chem. Phys., 11, 12453–12473, 10.5194/acp-11-12453-2011, 2011.Wanninkhof, R., Asher, W. E., Ho, D. T., Sweeney, C., and McGillis, W. R.:
Advances in Quantifying Air-Sea Gas Exchange and Environmental Forcing*,
Annu. Rev. Mar. Sci., 1, 213–244,
10.1146/annurev.marine.010908.163742, 2009.Watts, S. F.: The mass budgets of carbonyl sulfide, dimethyl sulfide, carbon
disulfide and hydrogen sulfide, Atmos. Environ., 34, 761–779,
10.1016/S1352-2310(99)00342-8, 2000.Wentworth, G. R., Murphy, J. G., Croft, B., Martin, R. V., Pierce, J. R.,
Côté, J.-S., Courchesne, I., Tremblay, J.-É., Gagnon, J., Thomas,
J. L., Sharma, S., Toom-Sauntry, D., Chivulescu, A., Levasseur, M., and
Abbatt, J. P. D.: Ammonia in the summertime Arctic marine boundary layer:
sources, sinks, and implications, Atmos. Chem. Phys., 16, 1937–1953,
10.5194/acp-16-1937-2016, 2016.Yang, M., Blomquist, B. W., Fairall, C. W., Archer, S. D., and Huebert,
B. J.:
Air-sea exchange of dimethylsulfide in the Southern Ocean: Measurements
from SO GasEx compared to temperate and tropical regions, J.
Geophys. Res.-Oceans, 116, C00F05, 10.1029/2010JC006526, 2011.