Atmospheric
marine aerosol particles impact Earth's albedo and climate. These particles
can be primary or secondary and come from a variety of sources, including sea
salt, dissolved organic matter, volatile organic compounds, and
sulfur-containing compounds. Dimethylsulfide (DMS) marine emissions
contribute greatly to the global biogenic sulfur budget, and its oxidation
products can contribute to aerosol mass, specifically as sulfuric acid and
methanesulfonic acid (MSA). Further, sulfuric acid is a known nucleating
compound, and MSA may be able to participate in nucleation when bases are
available. As DMS emissions, and thus MSA and sulfuric acid from DMS
oxidation, may have changed since pre-industrial times and may change in a
warming climate, it is important to characterize and constrain the climate
impacts of both species. Currently, global models that simulate aerosol size
distributions include contributions of sulfate and sulfuric acid from DMS
oxidation, but to our knowledge, global models typically neglect the impact
of MSA on size distributions.
In this study, we use the GEOS-Chem-TOMAS (GC-TOMAS) global aerosol
microphysics model to determine the impact on aerosol size distributions and
subsequent aerosol radiative effects from including MSA in the size-resolved
portion of the model. The effective equilibrium vapor pressure of MSA is
currently uncertain, and we use the Extended Aerosol Inorganics Model (E-AIM)
to build a parameterization for GC-TOMAS of MSA's effective volatility as a
function of temperature, relative humidity, and available gas-phase bases,
allowing MSA to condense as an ideally nonvolatile or semivolatile species or
too volatile to condense. We also present two limiting cases for MSA's
volatility, assuming that MSA is always ideally nonvolatile (irreversible
condensation) or that MSA is always ideally semivolatile (quasi-equilibrium
condensation but still irreversible condensation). We further present
simulations in which MSA participates in binary and ternary nucleation with
the same efficacy as sulfuric acid whenever MSA is treated as ideally
nonvolatile. When using the volatility parameterization described above (both
with and without nucleation), including MSA in the model changes the global
annual averages at 900 hPa of submicron aerosol mass by 1.2 %, N3
(number concentration of particles greater than 3 nm in diameter) by
-3.9 % (non-nucleating) or 112.5 % (nucleating), N80 by 0.8 %
(non-nucleating) or 2.1 % (nucleating), the cloud-albedo aerosol indirect
effect (AIE) by -8.6 mW m-2 (non-nucleating) or -26 mW m-2
(nucleating), and the direct radiative effect (DRE) by -15 mW m-2
(non-nucleating) or -14 mW m-2 (nucleating). The sulfate and
sulfuric acid from DMS oxidation produces 4–6 times more submicron mass than
MSA does, leading to an ∼10 times stronger cooling effect in the DRE.
But the changes in N80 are comparable between the contributions from MSA and
from DMS-derived sulfate/sulfuric acid, leading to comparable changes in the
cloud-albedo AIE.
Model–measurement comparisons with the Heintzenberg et al. (2000) dataset
over the Southern Ocean indicate that the default model has a missing source
or sources of ultrafine particles: the cases in which MSA participates in
nucleation (thus increasing ultrafine number) most closely match the
Heintzenberg distributions, but we cannot conclude nucleation from MSA is the
correct reason for improvement. Model–measurement comparisons with
particle-phase MSA observed with a customized Aerodyne high-resolution
time-of-flight aerosol mass spectrometer (AMS) from the ATom campaign show
that cases with the MSA volatility parameterizations (both with and without
nucleation) tend to fit the measurements the best (as this is the first use
of MSA measurements from ATom, we provide a detailed description of these
measurements and their calibration). However, no one model sensitivity case
shows the best model–measurement agreement for both Heintzenberg and the
ATom campaigns. As there are uncertainties in both MSA's behavior (nucleation
and condensation) and the DMS emissions inventory, further studies on both
fronts are needed to better constrain MSA's past, current, and future impacts
upon the global aerosol size distribution and radiative forcing.
Introduction
Atmospheric marine particles contribute significantly to the global aerosol
budget and impact the planetary albedo and climate (Quinn et al., 2015;
Reddington et al., 2017). The number concentration, size, and chemical
composition of these marine particles determine their ability to affect
climate, by either absorbing and scattering incoming solar radiation (the
direct radiative effect – DRE; Charlson et al., 1992; Erlick et al., 2001)
or indirectly, by modifying cloud properties (the cloud-albedo aerosol
indirect effect – AIE; de Leeuw et al., 2011). For the DRE, the magnitude
and relative division between absorbing and scattering will depend on both
the particle size and composition (Bond et al., 2006, 2013); peak
efficiencies for scattering and absorbing solar radiation are typically
reached with particles between 100 and 1 µm in diameter (Seinfeld
and Pandis, 2006). The cloud-albedo AIE refers to aerosols' ability to alter
the reflectivity (albedo) of clouds by changing properties such as the cloud
droplet number concentration (CDNC) (Twomey, 1974). Typically, particles act
as cloud condensation nuclei (CCN) if they are larger than 40–100 nm; the
ability of a particle to act as a CCN is also dependent upon particle
hygroscopicity (Petters and Kreidenweis, 2007). The number of particles in
these size ranges depends on primary emissions, as well as nucleation,
condensation, and coagulation (Pierce and Adams, 2009a). To improve model
estimates of the DRE and cloud-albedo AIE, models must account for nucleation
and condensational growth of marine particles.
Biologically productive oceans emit volatile organic compounds (VOCs),
primary biological particles, primary organic particles, and halocarbons
(Quinn et al., 2015). Sources of marine particles often indicate organic
species present (e.g., Heintzenberg et al., 2001; O'Dowd et al., 2007;
Frossard et al., 2014; Wang et al., 2017) that could dominate submicron
aerosol mass (O'Dowd et al., 2004; Facchini et al., 2008). Sulfur-containing
organic compounds in the form of dimethylsulfide (DMS; CH3SCH3)
and organosulfates (Bates et al., 1992; Quinn et al., 2015) are important
precursors and contributors to marine aerosol. DMS accounts for approximately
one-fifth of the global sulfur budget (Fiddes et al., 2017), with DMS flux estimates
ranging from 9 to 35 Tg yr-1 of sulfur (Belviso et al., 2004; Elliott,
2009; Woodhouse et al., 2010; Tesdal et al., 2016), although global DMS
fluxes remain uncertain (Tesdal et al., 2016; Royer et al., 2015). DMS and
its oxidation products have been the focus of many studies determining the
gas-phase chemistry (e.g., Barnes et al., 2006, and references therein),
gas-phase kinetics (e.g., Wilson and Hirst, 1996, and references therein),
and possible impact on the aerosol size distribution and radiative budget
(e.g., Korhonen et al., 2008; Woodhouse et al., 2013). Much of this research
has stemmed from efforts to test the hypothesis that DMS emissions may
regulate climate through a temperature–emissions feedback (the CLAW
hypothesis; Charlson et al., 1987).
The main products of DMS from oxidation by the hydroxyl radical are sulfur
dioxide (SO2) and methanesulfonic acid (CH3S(O)2OH,
MSA) (Andreae et al., 1985). SO2 can further oxidize to create
sulfuric acid (H2SO4). The relative yields of SO2 and
MSA from DMS oxidation are still uncertain, with reported branching ratios
from oxidation of DMS by OH addition of SO2: MSA varying
across 75:25, 65:4, 27:6, and 38:11 (Yin et al., 1990; Chin et al.,
1996; Sørensen et al., 1996; Arsene et al., 2001). The effective
equilibrium vapor pressure of sulfuric acid in the presence of water in the
troposphere is negligible compared to sulfuric acid concentrations under all
atmospherically relevant conditions (Marti et al., 1997), allowing sulfuric
acid to readily condense onto particles of all sizes and participate in
particle nucleation (e.g., Kulmala et al., 2000). Gas-phase concentrations of
MSA have been observed to be 10 %–100 % of sulfuric acid
concentrations in coastal marine boundary layers (Eisele and Tanner, 1993;
Berresheim et al., 2002; Maudlin III et al., 2003), and MSA can contribute to
the growth of pre-existing marine particles, at times contributing over half
as much bulk aerosol mass as non-sea-salt sulfate to the total aerosol burden
(e.g., Preunkert et al., 2008; Legrand et al., 2017). To our knowledge, the
effective equilibrium vapor pressure of MSA, which should depend on
temperature, relative humidity, and availability of bases, has not previously
been well quantified for the range of potential atmospheric conditions. Also
to our knowledge, MSA has not yet been observed in the field to directly
contribute to aerosol nucleation, although Dall'Osto et al. (2018) observed
new particle formation events over Greenland that suggest that MSA could be
involved in a portion of the events. Bork et al. (2014) determined through
the Atmospheric Cluster Dynamics Code kinetic model (McGrath et al., 2012;
Olenius et al., 2013) that the presence of MSA could increase the molecular
cluster formation rates by as much as 1 order of magnitude for a
MSA–H2SO4–DMA (DMA: dimethylamine) system under
atmospherically relevant MSA concentrations. This enhancement is predicted to
be typically less than 300 % at 258 K and less than 15 % at 298 K
for the case of [DMA] = 109 molec. cm-3 (Bork et al., 2014).
Chen et al. (2015) observed an MSA–H2O–TMA (TMA: trimethylamine)
system to nucleate in the laboratory, but at an efficiency lower than that of
the H2SO4–H2O system. Chen and Finlayson-Pitts (2017)
further observed nucleation of MSA/H2O systems with TMA, DMA, MA
(MA: methylamine), and ammonia. To our knowledge, global models that simulate
aerosol number concentrations (e.g., D'Andrea et al., 2013; Kodros et al.,
2018; Ma and Yu, 2015; Regayre et al., 2018; Xausa et al., 2018) only track
the effect of sulfuric acid and aqueous sulfate from DMS/SO2
oxidation on the aerosol size distribution and not MSA. Thus, the potential
contribution towards nucleation and/or size-resolved particle growth by MSA
and the resulting radiative impacts has not yet been quantified.
The effective volatility (equilibrium vapor pressure above the particle-phase
mixture) of MSA will modulate its impact on the aerosol size distribution.
Condensational growth of vapors to the particle phase is controlled by both
the volatility of the condensing species and the concentration of the species
in the gas phase. Riipinen et al. (2011) presented two limiting cases of
growth for gas-phase condensable material.
Compounds with low enough saturation vapor concentrations (C*; Donahue
et al., 2006) may be considered essentially nonvolatile to condense
irreversibly through kinetic, gas-phase-diffusion-limited condensation
(Riipinen et al., 2011; Zhang et al., 2012). This type of growth is referred
to as “kinetic condensation” by Riipinen et al. (2011) and can be thought
of as effectively nonvolatile condensation. The effective volatility required
to achieve effectively nonvolatile condensation typically must be less than
C*<≈10-3µg m-3 (e.g., low- and extremely
low-volatility organic compounds; LVOCs and ELVOCs) (Pierce et al., 2011;
Donahue et al., 2011). The contribution to growth from effectively
nonvolatile condensation is proportional to the Fuchs-corrected particle
surface area (Pandis et al., 1991). We will refer to this type of
condensation as “ELVOC-like” condensation in this work.
In contrast, semi-volatile species (e.g., semi-volatile organic compounds;
SVOCs) with average C* between 100 and
102µg m-3 (Murphy et al., 2014) quickly reach
equilibrium between gas and particle phases for all particle sizes. As a
result, the contribution to growth is proportional to the aerosol mass
distribution (Pierce et al., 2011; Riipinen et al., 2011; Donahue et al.,
2011; Zhang et al., 2012), limiting the growth of ultrafine particles. This
type of growth is referred to as “thermodynamic condensation” by Riipinen
et al. (2011) and “quasi-equilibrium” growth by Zhang et al. (2012); we
will refer to this type of condensation as “SVOC-like” condensation in this
work.
An important characteristic for growth in these regimes is that under
ELVOC-like condensation, particles in the kinetic regime
(Dp < ∼50 nm) all grow in diameter at the same
rate (e.g., nm h-1) regardless of diameter, whereas in the continuum
regime (Dp > ∼1µm), particle growth
rates are proportional to 1/Dp. Conversely, SVOC-like
condensation growth rates scale with Dp for all particle sizes,
favoring the largest particles. Thus, if MSA participates in ELVOC-like
condensation, ultrafine particles are able to grow more quickly to
climatically relevant sizes (e.g., CCN) as compared to SVOC-like
condensation. In reality, MSA's contribution towards growth likely lies
between these two limiting cases: as MSA is an acid, its volatility will
depend on not only temperature, but also relative humidity and gas-phase
bases (e.g., Barsanti et al., 2009; Yli-Juuti et al., 2013; Hodshire et al.,
2016).
In this study, we use the GEOS-Chem-TOMAS global chemical-transport model to
estimate the contribution of MSA to the aerosol size distribution and
resulting radiative effects. We examine (1) MSA condensation assumptions,
testing the limiting cases of growth (ELVOC-like versus SVOC-like) as well as
a parameterization of volatility dependent on temperature, water vapor, and
gas-phase bases built from a phase-equilibrium model and (2) how the
contribution of MSA changes depending on whether or not it is allowed to
participate in nucleation. We further use global measurements of aerosol size
distributions as compiled by Heintzenberg et al. (2000) and MSA mass as
observed on the ATom mission to compare the various model assumptions. Our
goals are to determine the sensitivity of the aerosol size distribution and
radiative impacts implied by the various assumptions, and to see whether the
assumptions can be constrained by observations. This study is a first look at
how MSA might impact the global aerosol size distribution and associated
climate effects by considering the sensitivity of its assumed volatility and
ability to impact nucleation. Along with our model analyses of MSA, we
provide a detailed overview of the calibration applied to an Aerodyne
high-resolution time-of-flight aerosol mass spectrometer (AMS) for detecting
MSA during the ATom mission in the Supplement as a general reference for the
AMS community.
MethodsModel description
In this work, we use the GEOS-Chem chemical transport model version 10.01
(http://geos-chem.org, last access: 5 March 2019) coupled to the online
TwO-Moment Aerosol Sectional (TOMAS) microphysical module (Adams and
Seinfeld, 2002; GEOS-Chem-TOMAS as described in Kodros et al., 2016, 2017) to
test the sensitivity of the aerosol size distribution to the addition of a
marine secondary organic aerosol (SOA) species, represented in this work by
methanesulfonic acid (MSA), of varying effective volatility and nucleation
capability. The version of GEOS-Chem-TOMAS (GC-TOMAS) used here has 47
vertical levels, a horizontal resolution of 4∘× 5∘
(∼400 km at midlatitudes), and GEOS-FP reanalysis
(http://gmao.gsfc.nasa.gov, last access: 5 March 2019) for
meteorological inputs. GC-TOMAS uses 15 size sections spanning dry diameters
from approximately 3 to 10 µm and explicitly tracks total particle
number as well as sulfate, sea salt, dust, hydrophilic OA, hydrophobic OA,
internally mixed BC, externally mixed BC, and water mass (Lee and Adams,
2012). Biomass burning emissions are simulated using the Fire INventory from
NCAR version 1.0 (FINNv1) (Wiedinmyer et al., 2011). Dust emissions follow
the parameterization of the DEAD scheme (Zender et al., 2003); sea-salt
aerosol emissions follow the parameterization of Jaeglé et al. (2011).
Anthropogenic emissions except for ammonia, black carbon, and organic aerosol
are from the Emissions Database for Global Atmospheric Research (EDGAR;
Janssens-Maenhout et al., 2010). In Europe, Canada, the US, and Asia,
anthropogenic emissions are overwritten by the European Monitoring and
Evaluation Programme (Centre on Emissions Inventories and Projections, 2013),
the Criteria Air Contaminant Inventory
(http://www.ec.gc.ca/air/default.asp?162lang=En&n=7C43740B-1, last
access: 5 March 2019), the National Emission Inventory from the U.S. EPA
(https://www.epa.gov/air-emissions-inventories, last access:
5 March 2019), and the MIX (Li et al., 2017) inventories, respectively. Black
and organic carbon emissions from fossil-fuel and biofuel combustion
processes are from Bond et al. (2007). Grid-box gas-phase concentrations of
NH3 are used in determining the volatility regime of MSA in the MSA
parameterization (Sect. 2.2): global anthropogenic, biofuel, and natural
ammonia sources are from the Global Emissions InitiAtive (GEIA) (Bouwman et
al., 1997). Anthropogenic ammonia emissions are overwritten over Europe,
Canada, the US, and Asia using the same regional inventories discussed above
for these regions. Ammonia emissions from biomass burning are from FINNv1
(above). All simulations are run for 2014, with 1 month of model spinup that
is not included in the analysis. All results are presented as annual or
monthly averages.
We use the default (at the time of this model version) GEOS-Chem DMS
emissions inventory (Kettle et al., 1999; Kettle and Andreae, 2000) for this
study. We acknowledge that the updated DMS inventory of Lana et al. (2011)
includes more up-to-date measurements than the default DMS inventory for
GEOS-Chem v10.01. Their work found that the default climatology overpredicted
DMS emissions in some latitudes/seasons but underpredicted DMS emissions in
other latitudes/seasons. We found, however, that using the Lana emission
inventory led to minor differences in MSA impacts spatially, but overall,
similar magnitudes of changes were observed. The Supplement Sect. S2 provides
more analysis of the two different emissions inventories.
In the standard GEOS-Chem DMS mechanism, DMS reacts with OH through the OH
addition pathway to form molar yields of 0.75 SO2 and 0.25 MSA
(Chatfield and Crutzen, 1990; Chin et al., 1996). As discussed in the
introduction, laboratory studies have reported variable yields of
SO2 and MSA from DMS oxidation by OH addition. We do not test the
sensitivity of our simulations to other pathways, and this is a source of
uncertainty. DMS also reacts with the nitrate radical (NO3) to form
a molar yield of 1 SO2. SO2 can then (1) react further
in the model with OH to form gas-phase sulfuric acid, (2) undergo aqueous
oxidation with H2O2 or O3 to form condensed sulfate,
or (3) be lost through dry and wet deposition processes (Pierce et al.,
2013). Pierce et al. (2013) found that in GC-TOMAS (v8.02.02), 26 % of
global SO2 formed sulfate through aqueous chemistry and 13 %
formed sulfuric acid through gas-phase reaction with OH (the rest was lost
through dry and wet deposition). The sulfate formed through aqueous chemistry
is added to CCN-sized particles when activated in clouds, whereas the
sulfuric acid formed from OH reactions participates in nucleation and
irreversible condensation to particles of all sizes. Prior to this work, the
DMS/SO2-oxidized sulfuric acid and sulfate were included in the
size-resolved portion of the GC-TOMAS model, but MSA was not. In this study,
we include MSA in the size-resolved microphysics of the model. The
contribution of MSA from DMS towards the sulfate budget and the size
distribution as a function of particle size will then depend on both MSA's
volatility and ability to participate in nucleation, as discussed below. A
discussion of alternative oxidation pathways of DMS and the potential
importance of aqueous-phase DMS chemistry (currently not included in
GEOS-Chem) is provided in Sect. 2.6.
Nucleation is simulated via a ternary nucleation scheme involving water,
sulfuric acid, and ammonia (Napari et al., 2002), scaled with a global tuning
factor of 10-5 (Jung et al., 2010; Westervelt et al., 2013). In
ammonia-limited regions (less than 1 pptv), a binary nucleation scheme
involving water and sulfuric acid (Vehkamäki et al., 2002) is instead
used. When MSA is assumed to participate in nucleation, it is treated as an
extra source of sulfuric acid for the ternary and binary nucleation schemes
within the model. Growth and loss of nucleated particles between 1 and 3 nm
are simulated using the parameterization of Kerminen et al. (2004) (Lee et
al., 2013), with growth in this size range controlled by the
pseudo-steady-state sulfuric acid (Pierce and Adams, 2009b) and MSA when it
participates in nucleation.
SOA in GC-TOMAS is traditionally formed from terrestrial biogenic sources,
with the biogenic source represented by 10 % of the monoterpene
emissions, totalling 19 Tg(SOA) yr-1; we further include
100 Tg(SOA) yr-1 spatially correlated with CO to represent
anthropogenic SOA and anthropogenically controlled biogenic SOA (Spracklen et
al., 2011; D'Andrea et al., 2013). The default GC-TOMAS setting is for SOA to
form through effective nonvolatile condensation (ELVOC-like condensation)
onto pre-existing particles at the time of emission of the parent compound.
However, it is possible to instead have SOA form in GC-TOMAS through
quasi-equilibrium condensation (SVOC-like condensation, but still
irreversible, e.g., not allowing for re-evaporation, in the model) by
distributing the SOA across aerosol sizes proportional to the aerosol mass
distribution. In this work, we assume ELVOC-like SOA condensation as it
performed best relative to size-distribution measurements in D'Andrea et
al. (2013).
MSA volatility assumptions, calculations, and parameterization
As the effective volatility of MSA is uncertain, we use the Extended Aerosol
Inorganics Model (E-AIM; http://www.aim.env.uea.ac.uk/aim/aim.php, last
access: 5 March 2019, Clegg et al., 1992; Clegg and Seinfeld, 2006a, b;
Wexler and Clegg, 2002) to build a parameterization for GC-TOMAS of MSA's
potential volatility as a function of temperature, relative humidity, and
available gas-phase bases. E-AIM calculates the MSA equilibrium vapor
pressure above the particle mixture (Ceq in units of
µg m-3), and thus we get an MSA volatility parameterization in
terms of Ceq (Fig. 1). We also consider two ideal assumptions of
MSA volatility: (1) MSA condenses as an ELVOC-like species, condensing
irreversibly to aerosol of all sizes, with net condensation of MSA
proportional to the Fuchs-corrected aerosol surface area. Conversely, (2) MSA
condenses as an SVOC-like species, where the net condensation of MSA is
proportional to the aerosol mass distribution.
E-AIM prediction of MSA equilibrium vapor pressure above the
particle mixture (Ceq) under conditions with (a) no free ammonia and
(b) high free ammonia (3 times as many moles of ammonia as MSA). (a) The
dashed line at 90 % RH indicates the cut-off for representing MSA as a
VOC-like (left of the line) or an SVOC-like (right of the line) species. (b) The dashed line is described by Eq. (1) in the text. Above the dashed line,
MSA is treated as an SVOC-like species; below the dashed line, MSA is
treated as an ELVOC-like species.
As MSA is a strong acid (pKa=-1.96; Haynes, 2017), we must
consider the amount of atmospheric gas-phase base present; ammonia is used in
E-AIM as the representative base. Although Chen and Finlayson-Pitts (2017)
found in laboratory experiments that MSA had different rates of new particle
formation with amines than ammonia, GC-TOMAS currently does not include any
amine species, and thus we do not attempt to account for these variations.
Figures S1 and S2 in the Supplement provide global annual and seasonally
averaged NH3 concentrations from GEOS-Chem-TOMAS. The effective
volatility of MSA also depends on the ambient temperature (Donahue et al.,
2006) and relative humidity (RH) (Chen et al., 2018). We run E-AIM for
between 10 % and 100 % RH and between 240 and 310 K. Figure 1 shows
the resulting volatility as a function of RH and temperature for conditions
with no free ammonia and excess ammonia (3 times as many moles of free
ammonia than moles of MSA). At low-base conditions (Fig. 1a), MSA acts
essentially as a VOC (will all stay in the vapor phase) below 90 % RH
and condenses as an ideally SVOC-like species above 90 % RH for the
entire input temperature range. Conversely, for excess-base conditions, we
see that MSA transitions between volatilities as a function of both
temperature and RH. We parameterize a transition between ELVOC-like behavior
and SVOC-like behavior for excess-base conditions along the Ceq=10-2µg m-3 line using the dashed line in Fig. 1b, given
by
Ttrans(RH)=a-b⋅RH+c⋅RH2-d⋅RH3+e⋅RH4,
where RH is the relative humidity, T is the temperature,
Ttrans is the transition temperature, and a, b, c, d, and
e are fit coefficients, whose values are listed in Table 1. If T>Ttrans, then MSA is treated as an ideally SVOC-like species that
undergoes quasi-equilibrium condensation in GC-TOMAS. If T<Ttrans, then MSA is low to extremely low in volatility and will be
treated as an ideally ELVOC-like species that undergoes
gas-phase-diffusion-limited condensation in GC-TOMAS. We do not include a
volatile region under excess-base conditions: the high-temperature, low-RH
regions that this would be applicable to are globally limited and likely only
occur over desert regions, where MSA formation is likely negligible. Although
E-AIM predicts that MSA's volatility will vary smoothly across the volatility
space as a function of temperature and RH, for simplicity, we only assume
three condensational regimes: SVOC-like condensation, ELVOC-like
condensation, and VOC-like (no condensation).
Fit coefficients for the MSA volatility parameterization equation.
When using this parameterization in GC-TOMAS, we use a gas-phase ammonia
mixing ratio of 10 pptv as a cutoff between the no-ammonia and
excess-ammonia cases, as this roughly marks the transition from acidic to
neutral aerosol (Croft et al., 2016, Supplement Fig. S4). The gas-phase MSA
production rate is explicitly tracked in the model, but not the MSA gas-phase
concentrations. At the time of production, the model will then determine
whether to treat MSA condensation as an effectively volatile species (no MSA
condensing), an SVOC-like species (with all of the MSA produced condensing to
the mass distribution), or an ELVOC-like species (with all of the MSA
produced condensing to the Fuchs surface area and participating in the
nucleation calculation in some simulations), based on the current T, RH,
and available ammonia. For both SVOC-like and ELVOC-like condensation, the
condensation is irreversible; we do not let MSA partition back to the gas
phase once it is condensed as gas-phase MSA is not tracked in the model. Even
this simple parameterization is a significant increase in the physical
representation of MSA volatility over assuming a fixed volatility.
Descriptions of simulations
The different GEOS-Chem-TOMAS (GC-TOMAS) simulations in this study are
summarized in Table 2. The default (DEFAULT_NoMSA) simulation represents a
default GEOS-Chem-TOMAS simulation with only sulfate and sulfuric acid from
DMS/SO2 oxidation included in TOMAS; DEFAULT_NoMSA will be the
comparison simulation for all other cases. PARAM_NoNuc uses the volatility
parameterization from E-AIM (Sect. 2.2), treating MSA as a non-nucleating
ELVOC, an SVOC, or a VOC, depending upon the temperature, RH, and amount of
ammonia in the gas phase. ELVOC_NoNuc treats MSA condensation as ELVOC-like
condensation. SVOC_NoNuc treats MSA condensation as SVOC-like condensation
(but irreversible, Sect. 2.2). PARAM_Nuc and ELVOC_Nuc are identical to
PARAM_NoNuc and ELVOC_NoNuc except that MSA is allowed to participate in
nucleation with the properties of sulfuric acid, providing an upper bound on
the role of MSA in nucleation. For PARAM_Nuc, MSA only participates in
nucleation when MSA is in the ELVOC-like regime; for ELVOC_Nuc, MSA is
always able to participate in nucleation. Finally, to determine the
contribution of sulfate and sulfuric acid from DMS/SO2 oxidation
alone to the default size distribution, we run a case with DMS emissions
turned off (NoDMS_NoMSA).
Description of simulations.
SimulationDescriptionDEFAULT_NoMSADefault model simulation: MSA does not contribute to the particle size distribution in GEOS-Chem-TOMAS (GC-TOMAS). The default GC-TOMAS v10.01 DMS emissions are used, and SO2, sulfate, and sulfuric acid from DMS influences the particle size distribution.PARAM_NoNuc (NoNuc: does not nucleate particles)Parameterization for MSA from E-AIM simulations: volatility is based on NH3, T, and RH. MSA can act as non-volatile and non-nucleating, semivolatile, or volatile (no condensation).ELVOC_NoNucMSA is assumed to be non-volatile and condenses proportionally to the surface area distribution.SVOC_NoNucMSA is assumed to be semivolatile and condenses proportional to the mass distribution.ELVOC_NucLike ELVOC_NoNuc, but MSA acts like sulfuric acid in nucleation.NoDMS_NoMSAAll DMS emissions are turned off in the model; all other parameters are the same as the DEFAULT_NoMSA case.DEFAULT_NoMSA_LanaDefault case using the Lana et al. (2011) DMS emissions inventory.DEFAULT_NoMSA_2xDMSDefault case with global DMS emissions increased by a factor of 2.PARAM_NoNuc_LanaUse the settings of PARAM_NoNuc with the Lana et al. (2010) DMS emissions inventory.PARAM_NoNuc_2xDMSIncrease DMS emissions by a factor of 2, using the settings of PARAM_NoNuc
In the Supplement, we test the sensitivity of the model to the DMS
concentration with two additional DMS inventories: the first is the DMS
emissions inventory of Lana et al. (2011) and the second is the default DMS
emissions inventory increased globally by a factor of 2. As the sulfate and
sulfuric acid from DMS/SO2 oxidation is included in the default
case simulation, we run new default simulations with the new DMS inventories
(DEFAULT_NoMSA_Lana and DEFAULT_NoMSA_2xDMS). We use the PARAM_NoNuc
case settings to determine the change in MSA's impact on the size
distribution under the new DMS emissions inventories (PARAM_NoNuc_Lana and
PARAM_NoNuc_2xDMS). However, the results for the contribution of MSA to the
size distribution do not qualitatively change between the default DMS
emissions inventory and the Lana DMS emission inventory. The contribution of
MSA towards the submicron aerosol mass and thus the aerosol DRE in the 2xDMS
case is roughly double that of the base DMS case (DEFAULT_NoMSA), but N3 and
N80 do not significantly change for our tested metrics. Hence, we will not
include these model results in the main portion of the paper. See the
Supplement, Sect. S2, Tables S1–S2, and Figs. S3–S5 for a brief analysis of
the different inventories.
Analysis of simulated radiative effects
We calculate aerosol DRE and cloud-albedo AIE following Kodros et al. (2016).
The all-sky DRE is calculated offline using the monthly mean aerosol mass and
number distributions from the GC-TOMAS output. The refractive indices are
from GADS (Global Aerosol Dataset; Koepke et al., 1997). Aerosol optical
depth (AOD), single-scattering albedo, and the asymmetry parameter are
calculated from Mie code (Bohren and Huffman, 1983). Optical properties and
the monthly mean albedo and cloud fractions from GEOS5 are used as inputs to
the offline version of the Rapid Radiative Transfer Model for Global Climate
Models (RRTMG: Iacono et al., 2008) that has been implemented for the
standard (non-TOMAS) version of GEOS-Chem (Heald et al., 2014). We assume an
internal mixture, spherical particles, non-absorptive OA (brown carbon is not
considered in this work), and a core-shell morphology. We note that the
mixing state may vary both regionally and temporally, and that using only one
mixing state globally for the full year is a limitation of our analysis of
the DRE.
The cloud-albedo AIE is calculated as follows: first, the CDNC is found using
the activation parameterization of Abdul-Razzak and Ghan (2002) for the
monthly mean aerosol mass and number distribution from the GC-TOMAS output. A
constant updraft velocity of 0.5 m s-1 is assumed. We again assume the
aerosol species are internally mixed within each TOMAS size bin to determine
κ, the hygroscopicity parameter, as a volume-weighted average of the
individual aerosol species (Petters and Kreidenweis, 2007). For the
cloud-albedo AIE, we use an effective cloud drop radius of 10 µm as
a control and then perturb this value with the ratio of the CDNC of each
sensitivity case to the default case to the one-third power, following the
methods of Rap et al. (2013), Scott et al. (2014), and Kodros et al. (2016):
rperturbed=CDNCbasecaseCDNCsensitivitycase1/3×10µm.
RRTMG is again used to determine the changes in the top-of-the-atmosphere
radiative flux from the changes in effective cloud drop radii, with monthly
mean meteorological data needed as inputs again informed by GEOS5. For more
details on the methods used for the DRE and cloud-albedo AIE calculations,
refer to Kodros et al. (2016) and references therein.
Measurement comparisons
Heintzenberg et al. (2000) compiled 30 years (between ∼ 1970 and 1999)
of physical marine aerosol data from both sampling sites and field campaigns
to create annual global size distribution parameters, fitting the size
distributions to bimodal lognormal distributions for latitudinal bands spaced
15∘ apart. We compare their fitted size distributions for 30–45,
45–60, and 60–75∘ S to the annual zonal-mean size distributions
for the DEFAULT_NoMSA case and each sensitivity case from the model. (There
are no data available from Heintzenberg et al., 2000, for
75–90∘ S.) We note that changes in the aerosol size distributions
between the measurement years and our simulated year (2014) are possible,
even for these remote latitudes, and may result in apparent simulation errors
and/or apparent model-to-measurement agreement biases.
The first and second Atmospheric Tomography Missions (ATom-1 and ATom-2)
(https://espo.nasa.gov/missions/atom/content/ATom, last access:
5 March 2019) took place from 28 July to 22 August 2016 and from 26 January
to 22 February 2017, respectively. Carrying a comprehensive gas and particle
chemistry payload, the NASA DC-8 aircraft systematically sampled the remote
atmosphere, profiling continuously between 0.2 and 12 km. The data for both
missions are publicly available (Wofsy et al., 2018). As a part of the
instrumentation on board, a highly customized Aerodyne high-resolution
time-of-flight aerosol mass spectrometer (AMS in the following; DeCarlo et
al., 2006; Canagaratna et al., 2007) continuously measured the composition of
submicron (PM1), non-refractory aerosol at 1 Hz time resolution. The
principles of operation and instrument/aircraft-operation specifics have been
described in detail elsewhere (Dunlea et al., 2009; Kimmel et al., 2011;
Schroder et al., 2018; Nault et al., 2018), and only the aspects specific to
MSA quantification are discussed here.
The instrument flew in the same configuration for all four ATom missions. MSA
data from the third and fourth ATom missions, ATom-3 and ATom-4, were not
used in this study, but the calibration details discussed in Sect. S5 apply
to these missions, as well. Overall sensitivity (as determined daily from the
ionization efficiency of nitrate, IENO3), relative ionization
efficiencies, and particle transmission (all determined periodically in the
field) were stable over all four deployments. Particle-phase MSA
concentrations for all ATom flights are reported based on the intensity of
the highly specific marker ion CH3SO2+ (Phinney et al., 2006;
Zorn et al., 2008). The quantification of MSA PM1 concentrations from
the signal intensity of the CH3SO2+ fragment is described in
detail in the Supplement, Sect. S5. Positive matrix factorization (Paatero,
1994; Ulbrich et al., 2009) of the ATom-1 organic aerosol (OA) and sulfate
data confirmed the specificity of the marker ion for MSA and the consistency
of the field mass spectra with those acquired in the MSA calibrations.
Importantly, it also confirmed that the AMS response to MSA is independent of
the aerosol acidity, which varied significantly over the range of conditions
found in ATom. Further details are provided in Sect. S5.
For the data presented here, the AMS raw data were processed at 1 min
resolution. Under those conditions, the detection limit of MSA was in the
range 1.5–3 ng s m-3 (0.3–0.6 pptv), and will decrease with the
square root of the number of averaged 1 min data points. The uncertainty in
the MSA quantification as detailed in the Supplement, Sect. S5, is comparable
to that of sulfate; hence, the overall uncertainty in the quantification is
estimated to be ±35 % (2 standard deviations; Bahreini et al., 2009).
We compare our sensitivity simulations to the ATom-1 and ATom-2 data as
follows: we subtract the DEFAULT_NoMSA sulfate mass (that accounts for
sulfate and sulfuric acid from DMS/SO2 oxidation but not MSA) for
the months of August (ATom-1) and February (ATom-2) from the sulfate mass for
the months of August and February for each sensitivity case that includes MSA
for each grid box. The resultant differences in sulfate mass represent the
model-predicted contributions of MSA to the total sulfur budget for each
case. This is an imperfect approach, as the additional aerosol mass from the
contribution of MSA will change the size distribution, therefore changing
rates of wet and dry deposition, and is a limitation of this study. We then
compare the measured and predicted MSA mass by first averaging every ATom
data point that falls within a given GC-TOMAS grid box. We then compare each
averaged data point to that model grid box. The ATom data used in our
analysis lie within 150–180∘ W (the Pacific Ocean basin) and
10–40∘ W (the Atlantic Ocean basin), and thus we use zonal averages
of these longitude bands for both the ATom data and the GC-TOMAS output. We
note that comparing monthly mean simulated values from 2014 to airborne
measurements from a single point in time in 2016 and 2017 contributes to the
apparent simulation errors. We also note that we use the full size range
(3–10 µm) of sulfate from the model output, whereas the ATom data
are submicron. However, the model-predicted percent difference in MSA mass
between the full range and the submicron mass is well under 1 % (not
shown).
To evaluate model performance, we calculate the log-mean bias (LMB), the
slope of the log–log regression (m), and the coefficient of determination
(R2) between each cosampled GC-TOMAS grid box and averaged measurement
point that falls within that GC-TOMAS grid box. The LMB is calculated through
LMB=∑iNlog10Si-log10OiN,
where Si and Oi are the simulated and observed MSA masses,
respectively, for each data point i, and N is the number of data points.
A LMB of 1 means that on average, the model overestimates the measurements by
a factor of 101 (10); a LMB of -1 means that on average, the model
underestimates the measurements by a factor of 10-1 (0.1); a LMB of 0
indicates no bias between the model and measurements (100=1.00). LMB,
m, and R2 are summarized in Fig. 8 (discussed in Sect. 3.4). Since MSA
is observed only in the particle phase in the ATom measurements, we do not
include the NoDMS_NoMSA (no DMS emissions in the model) sensitivity case in
our analysis of the ATom data. We present the aggregated results of the two
campaigns, as well as results for each campaign and ocean basin. The ATom-1
mission provided more data points than the ATom-2 missions (1258 versus
1000), and thus the aggregate results are slightly skewed towards the ATom-1
results.
Study caveats
This study is intended to examine the sensitivity of the aerosol size
distribution and radiative impacts implied by the various sensitivity
treatments of MSA (Table 2). However, our treatments of DMS and MSA still
fall short of what is currently known about organic condensational behavior.
Assuming idealized semivolatile condensation with no re-evaporation due to
conditional changes (e.g., change in temperatures, RH) may overestimate the
amount of MSA able to condense on particles, but it may also underestimate
particle-phase MSA if conditions for condensation switch from unfavorable to
favorable after MSA chemical production. Further, relying on E-AIM
simulations to construct our volatility parameterization could have hidden
biases due to an incomplete understanding of the system. We are also
neglecting known as well as gas-phase and aqueous-phase oxidation pathways of
DMS that are currently not included in GEOS-Chem. The standard GEOS-Chem
model does not include DMS oxidation through the OH or halogen addition
pathways to dimethylsulfoxide (DMSO). DMSO chemistry reduces the yield of
sulfate formation from DMS/SO2 oxidation (Breider et al., 2014) by
increasing the yields of both gas-phase and aqueous-phase MSA as well as
aqueous-phase dimethyl sulfone (DMSO2), another stable oxidation
product (Hoffmann et al., 2016). To reduce the number of parameters for this
study, we do not include the DMSO pathway. We acknowledge that neglecting
this pathway will slightly bias our estimates of the contributions to the
aerosol size distribution of sulfate and MSA mass high and low, respectively.
Further, aqueous-phase production of MSA would condense on CCN-sized
particles, similar to aqueous-phase sulfate (Sect. 2.1), shifting the size
distribution to larger sizes. Heterogeneous oxidation may limit the lifetime
of MSA in the particle phase (Mungall et al., 2017; Kwong et al., 2018),
although the reactive uptake coefficients from these studies are somewhat
dissimilar, indicating a need for further study of the system. Regardless,
neglecting heterogeneous chemistry could overestimate the estimate of the
contribution of MSA to aerosol mass. Finally, if MSA does participate in
nucleation, it is unlikely that it will behave exactly like sulfuric acid, as
it is treated here. All of the limitations described above are important and
require further testing in detailed chemical models and chemical-transport
models in order to determine their effects.
Another limitation of this study is our reliance upon the current ammonia
inventory in GEOS-Chem as well as our cutoff value of 10 ppt of ammonia
between the no ammonia and excess ammonia regimes (Sect. 2.2). Uncertainties
in the ammonia inventories over the oceans could change our results, as could
a different cutoff value. As this study is focused on MSA sensitivities, we
will leave sensitivities of MSA to ammonia for a future study. It is
important to note that other bases such as amines could also have an
important effect on MSA's effective volatility (e.g., Chen and
Finlayson-Pitts, 2017). However, the standard GEOS-Chem currently does not
account for gas-phase bases beyond ammonia, and this sensitivity will also be
left for a future study.
We do not test the sensitivity of our simulations to the binary and ternary
nucleation schemes used in this study, including potential sensitivity to
the global tuning factor of 10-5 that was developed for continental
regions (Jung et al., 2010; Westervelt et al., 2013). This source of
uncertainty should be tested in future studies, as well.
Results and discussion
Figure 2 shows the global annual mean percent change (at 900 hPa and
zonally) for submicron mass by adding MSA for the PARAM_NoNuc, ELVOC_NoNuc,
SVOC_NoNuc, PARAM_Nuc, and ELVOC_Nuc simulations. Figure 3 shows the
global annual mean percent change in N3 and N80 due to addition of MSA at
900 hPa and zonally for all model levels for each of these cases, and Fig. 4
shows the corresponding global annual cloud-albedo AIE and DRE of MSA.
Figure 5 shows the global annual mean percent contribution from
DMS/SO2 oxidation (at 900 hPa and zonally) alone (not including
MSA) to submicron mass, N3, N80, AID, and DRE. Figure 6 and Table S3
summarize the results of Figs. 2, 3, 4, and 5. All of the numerical
statistics presented in Sect. 3.1–3.4 are for the annual mean, either
globally or between 30 and 90∘ S. Each case with MSA is analyzed for
the change relative to DEFAULT_NoMSA to determine the impact that MSA has on
the size distribution and resulting radiative effects (positive values
indicate that the inclusion of MSA increases a given metric). For reference,
Fig. S6 provides the absolute number concentration for N3 and N80 at 900 hPa
and zonally for all model levels for the DEFAULT_NoMSA simulation. We will
refer back to these figures in the following sections.
Global annual mean percent change in submicron aerosol mass due to
the addition of MSA at 900 hPa (first column) and global zonal annual mean
percent change (second column) between DEFAULT_NoMSA and
PARAM_NoNuc (first row), ELVOC_NoNuc (second
row), SVOC_Nuc (third row), PARAM_Nuc (fourth
row), and ELVOC_Nuc (fifth row) (warm colors indicate an
increase in submicron mass as compared to DEFAULT_NoMSA).
Global annual mean percent change in N3 and N80 at 900 hPa (first
and third columns) and global zonal annual mean percent change (second and
fourth columns) between DEFAULT_NoMSA and PARAM_NoNuc (first row),
ELVOC_NoNuc (second row), SVOC_Nuc (third row), PARAM_Nuc (fourth row),
and ELVOC_Nuc (fifth row) (warm colors indicate an increase in N3/N80 as
compared to DEFAULT_NoMSA). First and second columns: N3 (the number
concentration of particles with diameters larger than 3 nm). Third and
fourth columns: N80.
Volatility-dependent impact of MSA if MSA does not participate in
nucleation
The top rows of Figs. 2 and 3 show the global annual mean percent change at
900 hPa and zonally from adding MSA using the volatility parameterization
without nucleation (PARAM_NoNuc–DEFAULT_NoMSA) for submicron aerosol mass
(Fig. 2) and N3 and N80 (Fig. 3). By adding MSA with these assumptions, we
predict at 900 hPa an increase in submicron mass of 0.7 % globally and
1.3 % between 30 and 90∘ S; a decrease in N3 of -3.9 %
globally and -8.5 % between 30 and 90∘ S; and an increase in
N80 of 0.8 % globally and 1.7 % between 30 and 90∘ S (Fig. 6
and Table S3). These MSA impacts are limited by ammonia availability.
Figures S1 and S2 show that many oceanic regions are predicted to have annual
and seasonal ammonia mixing ratios of less than 10 ppt. Below 10 pptv of
ammonia, MSA condensation as SVOC-like or VOC-like (no condensation)
(Fig. 1a) and MSA condensation will only be SVOC-like if
RH > 90 %; under these conditions for the majority of the
year, MSA will be a VOC-like species over Antarctica (low-RH conditions) and
often an SVOC-like species over the southern-oceans boundary layer (high-RH
conditions). Only in the Southern Hemisphere (SH) winter months does ammonia
exceed 10 ppt over appreciable regions in the southern oceans (Fig. S2);
during this time, MSA condensation is ELVOC-like due to cold temperatures
(Fig. 1b). As shown in D'Andrea et al. (2013), ideal-SVOC material largely
condenses primarily to accumulation-mode particles, which in turn suppresses
N3 through increased coagulation and reduced nucleation and has little impact
on N80. In the midlatitudes, the annual and seasonal ammonia concentrations
often exceed 10 ppt, and thus MSA condensation will be either ELVOC-like
under low-temperature and/or high-RH conditions or SVOC-like under
high-temperature and/or low-RH conditions. D'Andrea et al. (2013) showed that
adding ELVOC material can increase N80 by increasing growth of ultrafine
particles, but also can suppress N3 through the same coagulation/nucleation
feedbacks. This combination of ammonia-rich and ammonia-poor regions led to
MSA giving an overall weak increase in N80 with a large suppression of N3 in
some regions. We note that these results are somewhat sensitive to the
simulated ammonia concentrations and may be sensitive to the ammonia cutoff
of 10 ppt in the MSA-volatility parameterization. As there are already
uncertainties in many other dimensions, we do not attempt to quantify the
sensitivity of MSA towards ammonia in this work.
Global annual mean change in W m-2 for the aerosol indirect
effect (cloud-albedo AIE, denoted as “AIE”; first column) and the direct
radiative effect (DRE; second column) between DEFAULT_NoMSA and PARAM_NoNuc
(first row), ELVOC_NoNuc (second row), SVOC_Nuc (third row), PARAM_Nuc
(fourth row), and ELVOC_Nuc (fifth row) (warm colors indicate an increase in
the AIE/DRE as compared to DEFAULT_NoMSA).
The idealized volatility cases, ELVOC_NoNuc (Figs. 2 and 3, second row) and
SVOC_NoNuc (Figs. 2 and 3, third row), help to highlight and further explain
MSA's volatility-dependent contribution towards growth. In both of these
cases, 100 % of the formed MSA goes to the particle phase, unlike with
the MSA volatility parameterization, where MSA may not condense in the
absence of a base at lower RHs. Hence, the global annual MSA mass is nearly
double in these cases compared to when using the parameterization (Table 2;
Fig. 2). The addition of MSA in the ELVOC_NoNuc case allows for an increase
in condensable material that condenses to the Fuchs-corrected surface area
through ELVOC-like condensation, which increases the growth rate of all
particle sizes. Conversely, MSA in SVOC_NoNuc allows for an increase in
SVOC-like material that will condense preferentially to larger particles
through SVOC-like condensation (but still irreversible condensation). In both
the ELVOC_NoNuc and SVOC_NoNuc cases, N3 concentrations are reduced due to
increased coagulational losses and decreased nucleation rates because of the
added MSA mass (D'Andrea et al., 2013). When MSA condensation is treated as
ELVOC-like, the smaller particles grow more quickly into the larger sizes, so
N80 increases by 9.1 % globally and by 22.2 % between 30 and
90∘ S at 900 hPa (Fig. 6 and Table S3). When MSA condensation is
instead treated as SVOC-like, the largest particles uptake MSA preferentially
to smaller particles, and the N80 are not greatly impacted by the addition of
MSA. The slight boost in N80 for SVOC_NoNuc in the tropical upper
troposphere (UT) is due to the very low accumulation-mode concentration in
this region: the SVOC material condenses to ultrafine particles in this
region.
The changes in DRE and cloud-albedo AIE resulting from the addition of MSA
for these three no-MSA-nucleation cases (Fig. 4, top three rows) depend
roughly on the changes in N80 (the activation diameter for determining CDNC
will depend on local particle hygroscopicity and concentrations). The DRE
generally scales linearly with aerosol mass (Fig. 2, top three rows). As MSA
is assumed to have the same properties as sulfate, which is assumed to be
purely scattering, any increases in MSA mass result in a negative radiative
effect. However, the DRE also depends on aerosol size; the scattering
efficiency peaks between ∼ 300 and 900 nm, depending upon the aerosol
composition and shape (Seinfeld and Pandis, 2016, their Fig. 15.8). The
change in DRE when MSA is included using the volatility parameterization
(PARAM_NoNuc) is less negative than that of ELVOC_NoNuc and SVOC_NoNuc at
-15 mW m-2 globally (-26 mW m-2 between 30 and
90∘ S), because the parameterization yielded a smaller mass increase
than the ideal volatility simulations. ELVOC_NoNuc and SVOC_NoNuc have
almost identical changes in submicron aerosol mass (Fig. 6; Table S3), but
the DRE is -25 mW m-2 globally (-44 mW m-2 between 30 and
90∘ S) for SVOC_NoNuc and -0.02 W m-2 globally
(-34 mW m-2 between 30 and 90∘ S) for ELVOC_NoNuc (Fig. 6;
Table S3). MSA will preferentially condense to larger aerosol when its
condensation is SVOC-like, and so even though ELVOC_NoNuc shows a larger
increase in N80, SVOC_NoNuc increases the fraction of particulate mass in
the peak scattering efficiency regime.
Global annual mean changes between the NoDMS_NoMSA and
DEFAULT_NoMSA simulations. First row: percent change in submicron aerosol
mass at 900 hPa (left) and zonally (right). Second row: percent change in N3
at 900 hPa (left) and zonally (right). Third row: percent change in N80 at
900 hPa (left) and zonally (right). Fourth row: change in W m-2 in the
radiative effects. This figure gives the contribution from sulfate and
sulfuric acid produced from DMS/SO2 oxidation to the aerosol mass,
number, and radiative effects. Warm colors indicate that sulfate and sulfuric
acid produced from DMS/SO2 oxidation increase the metric.
The cloud-albedo AIE instead scales the aerosol number concentration of
particles large enough to act as CCN: PARAM_NoNuc's cloud-albedo AIE
(-8.6 mW m-2 globally, -17 mW m-2 between 30 and
90∘ S) reflects the small increase in N80 (0.8 % globally and
1.7 % between 30 and 90∘ S at 900 hPa) (Fig. 6; Table S3). The
larger increase in N80 for ELVOC_NoNuc results in the larger cooling
tendency in the cloud-albedo AIE, at -0.075 W m-2 globally
(-150 mW m-2 between 30 and 90∘ S), and the slight decrease
in N80 for SVOC_NoNuc results in the slight warming tendency in cloud-albedo
AIE at 7.5 mW m-2 globally (11 mW m-2 between 30 and
90∘ S) (Fig. 6; Table S3).
These annual results show in Fig. 6 and Table S3 that if MSA does not take
part in nucleation, the submicron aerosol mass will increase, causing a
cooling tendency in the DRE, and N3 will decrease regardless of the
volatility assumed. However, the changes in N80 are sensitive to the
volatility assumption and will only increase if MSA condensation is
ELVOC-like at least over some spatial and temporal scales, thereby causing a
further cooling tendency in the cloud-albedo AIE.
Annual mean changes due to MSA at 900 hPa for each MSA simulation
relative to the DEFAULT_NoMSA simulation for submicron aerosol mass, N3, and
N80, all expressed as percent changes, and radiative forcing changes in
cloud-albedo AIE (denoted as “AIE”) and DRE, both expressed as changes in
W m-2. Positive values for any metric for PARAM_NoNuc (P_NN),
ELVOC_NoNuc (E_NN), SVOC_NoNuc (S_NN), PARAM_Nuc (P_N), and ELVOC_Nuc
(E_N) all indicate that the addition of MSA increases that metric relative
to the DEFAULT_NoMSA simulation. The DEFAULT_NoMSA-NoDMS_NoMSA (NoDMS)
columns show the contribution of the sulfate and sulfuric acid from
DMS/SO2 oxidation present in the DEFAULT_NoMSA simulation;
positive values of a metric indicate that the sulfate and sulfuric acid
increase that metric compared to a simulation with no DMS emissions.
Numerical values for each bar are provided in Table S3 in the Supplement.
Volatility-dependent impact of MSA if MSA does participate in
nucleation
To test the potential influence on aerosol size distributions if MSA
contributes to nucleation, we allow MSA to participate in binary and ternary
nucleation with the same efficacy as sulfuric acid. This provides an upper
bound in the potential contribution of MSA towards nucleation (at least for
the nucleation schemes tested here). Figures 2, 3, and 4 (fourth rows) show
the global annual mean percent changes between DEFAULT_NoMSA and PARAM_Nuc.
MSA will have the same effective volatility as discussed for PARAM_NoNuc
(Sect. 3.1), but will now participate in nucleation under ELVOC-like regimes.
For PARAM_Nuc, we can clearly see that when the ammonia concentrations reach
above 10 ppt in the SH winter months over the Southern Ocean (Fig. S4), MSA
acts as an ELVOC-like species and contributes strongly to nucleation in these
sulfuric-acid-poor regions. The addition of MSA in ELVOC_Nuc has the largest
impact on N3, N80, and the cloud-albedo AIE of any of our cases, with an
increase in N3 of 153.4 % globally (397.7 % between 30 and
90∘ S), an increase in N80 of 23.8 % globally (56.3 %
between 30 and 90∘ S), and a decrease for the cloud-albedo AIE of
-0.18 W m-2 globally (-0.39 W m-2 between 30 and
90∘ S). MSA in PARAM_Nuc also has a large increase in N3
(112.5 % globally and 309.9 % between 30 and 90∘ S at
900 hPa), but only increases N80 by 2.1 % globally (4.4 % between 30
and 90∘ S), again indicating that MSA often undergoes SVOC-like or
ELVOC-like condensation within the volatility parameterization.
The increase in N80 from MSA in PARAM_Nuc is about double that of the
increase from MSA in PARAM_NoNuc, and the change in cloud-albedo AIE is
similarly slightly double for PARAM_Nuc. The global annual changes in
submicron mass and the DRE are quite similar between the two PARAM cases.
However, N80 increases more over the Northern Hemisphere (NH) high-latitude
ocean regions for PARAM_Nuc than for PARAM_NoNuc, and as a result, the
northern oceans experience a stronger regional negative cloud-albedo AIE when
MSA is allowed to participate in nucleation. As noted in Sect. 3.1, there are
uncertainties from the ammonia concentrations and cutoff point of 10 ppt for
PARAM_Nuc, but we will not attempt to quantify them here.
These results indicate that if MSA does participate in nucleation, the
largest climate-relevant change is anticipated to be an increased cooling
tendency for the cloud-albedo AIE as compared to if MSA does not participate
in nucleation. The change in DRE will be similar though, as MSA mass is not
predicted to significantly change between non-nucleating and nucleating
cases. This study provides an upper bound on the contribution of MSA to
nucleation: if MSA is less efficient at nucleating than sulfuric acid, it is
present in relatively sulfuric-acid poor regions and would still be able to
increase N3 concentrations (although possible by less than predicted here).
Microphysical feedbacks (increased condensation and coagulation sinks from
increased N80) will then limit the effect that small changes in N3 can have
on N80 and radiative effects.
Comparison of MSA impacts to the contribution from SO2 formed in
DMS oxidation
By removing DMS from the simulation entirely (NoDMS_NoMSA case; Figs. 5 and
6 and Table S3), we determine the baseline contribution of the simulated
sulfuric acid and sulfate from DMS/SO2 oxidation to the aerosol
size distribution in GEOS-Chem-TOMAS at 900 hPa. The sulfate and sulfuric
acid from DMS/SO2 oxidation provides larger changes in submicron
mass and N80 than MSA does in any of our sensitivity cases. The contribution
of SO2 from DMS to submicron mass is 4–6 times that of the MSA
contribution. However, about 2/3 of this mass increase from
DMS/SO2 comes through aqueous oxidation of SO2 to
sulfate, which adds mass (but not number) to already-CCN-sized particles
(Pierce et al., 2013) suppressing nucleation and growth. The remaining ∼1/3 of the mass comes from gas-phase formation of sulfuric acid, which
nucleates particles and condenses irreversibly to the Fuchs-corrected surface
area, potentially increasing the number of CCN-sized particles. Overall, N3
and N80 increase due to the inclusion of the DMS/SO2 pathway (N3 by
7.3 % and N80 by 12.2 % globally and N3 by 19.5 % and N80 by
24.3 % between 30 and 90∘ S at 900 hPa). The increases in both
N3 and N80 are strongly damped by the formation of aqueous sulfate. The
changes in N3 at 900 hPa indicate the relative importance of the sulfuric
acid produced by DMS/SO2 oxidation for nucleation compared to other
sources of sulfuric acid. N3 generally increases in remote regions where
sulfuric acid from DMS/SO2 oxidation would be the main source of
sulfuric acid. There are also regions of decrease in N3 in remote regions:
the condensation and coagulation sinks increase from aqueous sulfate
formation, and in some regions this competition effectively scavenges N3
faster than sulfuric acid from DMS/SO2 oxidation forms new
particles. Because of the large increase in submicron mass from the sulfuric
acid and sulfate from DMS/SO2 oxidation, the DRE from
DMS/SO2 is -120 mW m-2 globally (-173 mW m-2
between 30 and 90∘ S), about 5 times larger than MSA for any of our
assumptions. On the other hand, the cloud-albedo AIE cooling tendency of
-46 mW m-2 globally and -38 mW m-2 between 30 and
90∘ S was within the range of cloud-albedo AIEs from MSA that we
predicted, which is due to the N80 damping of DMS/SO2 due to
aqueous sulfate formation. Thus, overall we predict the DRE from MSA to be at
least 5 times weaker than from DMS/SO2, but the cloud-albedo AIE
may be of similar magnitude depending on the properties of MSA.
Comparison of simulated annual mean particle number size
distributions to the annual zonal particle number size distributions compiled
in Heintzenberg et al. (2000) (black lines) for the southern oceans. No data
were available in Heintzenberg et al. (2000) for 75–90∘ S. We match
the grid boxes sampled in their study to the GEOS-Chem-TOMAS grid boxes; due
to sparseness of data, we do not attempt to discuss seasonal variabilities in
this comparison.
1:1 (black dashed line) plots for the simulated mean MSA mass for
the months of August/February and measured MSA mass during the ATom-1/Atom-2
campaigns (28 July–22 August 2016/26 January–22 February 2017). Each
subpanel gives the calculated log-mean bias (LMB), slope (m), and coefficient
of determination (R2) between the ATom data and the sensitivity
simulation. The red and green dashed lines indicate 5:1 and 1:5 lines.
Simulated MSA mass is calculated by subtracting the total sulfate mass for
the base case from each sensitivity case.
Analysis of model–measurement comparisons
Figure 7 shows the comparison between the annual zonal-mean particle number
size distributions compiled in Heintzenberg et al. (2000; hereon referred to
as Heintzenberg) and the GC-TOMAS-simulated annual-mean particle number size
distributions within the boundary layer for the latitude bands of 30–45,
45–60, and 60–75∘ S (no data were provided in Heintzenberg between
75 and 90∘ S). We focus this comparison on the southern-oceans
region as this region has the strongest influence from DMS and its oxidation
products. It is also less likely to be influenced by changing anthropogenic
emissions that may have occurred between the time of the measurements
compiled in Heintzenberg (between ∼ 1970 and 1999) and 2014 (the year
of the model run) than higher latitudes (e.g., Pierce and Adams et al.,
2009a; Gordon et al., 2017). We see that all model simulations underpredict
both the Aitken and accumulation modes of Heintzenberg, but that the
simulations that allow MSA to participate in nucleation (ELVOC_Nuc and
PARAM_Nuc) give the best model-to-measurement agreements for the Aitken mode
for each latitude band, with ELVOC_Nuc performing the best across the model
cases. Further, ELVOC_Nuc shows the highest number of particles in the
accumulation mode, particularly between 60 and 75∘ S. These results
point to the necessity of another source of ultrafine particles over the
southern oceans than is being currently accounted for in the model. These
particles may be produced locally from ultrafine sea spray (Pierce and Adams,
2007), local nucleation (not necessarily through MSA), or entrainment of
ultrafine particles from the free troposphere (Clarke et al., 2002).
For the ATom mission, Fig. 8 provides 1:1 plots for each sensitivity
case's predicted MSA mass versus the observed MSA mass from the aggregated
ATom-1 and ATom-2 campaigns. Each subplot provides the LMB, m, and R2
statistics for the given sensitivity case. LMB, m, and R2 statistics are
also provided for each campaign and ocean basin in Figs. S7–S10; Figs. S11–S14 show the zonally averaged simulated MSA concentrations for each
basin and campaign with the corresponding particle-phase MSA measurements
overlaid. Figure 8 indicates that for the aggregated campaigns, the model
cases in which MSA always condenses to the particle phase (the
SVOC_NoNuc, ELVOC_NoNuc, and
ELVOC_Nuc cases) overpredict MSA mass, with positive LMBs
between 0.27 and 0.3 (overpredictions of a factor of 1.9–2). The
PARAM_NoNuc and PARAM_Nuc cases do not allow
MSA to condense to the particle phase under low-base/high-temperature/low-RH
conditions (Fig. 1). As a result, the PARAM cases instead slightly
underpredict MSA mass, with LMBs of -0.1 and -0.08 (underpredictions by a
factor of 0.79 and 0.83). Overall, when the parameterization is not used,
too much MSA mass is allowed to condense relative to the observations. Given
the large improvement in LMB through the use of the parameterization (with
roughly similar R2 and m values), we feel that these results support the
use of the volatility parameterization of MSA.
The R2 values are quite low across cases, with the parameterization
cases giving the highest R2 values, at 0.09. The m values are
similarly low, with the SVOC_NoNuc, ELVOC_NoNuc, and ELVOC_Nuc cases
giving the highest m values, at 0.33–0.34. However, we are comparing
monthly grid-box mean model predictions to individually grid-box averaged
measurements taken during a different year than the simulation year. Further,
using monthly mean model predictions on the y axis (Fig. 8) decreases
variability, which reduces the slope. These considerations contribute to
lower values of R2 and m.
The Heintzenberg and ATom model–measurement comparisons disagree on which
MSA assumptions lead to the best performance in GC-TOMAS. However, the
Heintzenberg analysis considers number size distribution, whereas the ATom
analysis considers total particle-phase MSA mass. The model–measurement
improvement for the Heintzenberg study is most strongly seen within the
Aitken mode (the smallest reported particle sizes). Aitken-mode-sized
particles contribute little to total mass compared to larger particles.
Further, it is not possible to determine from this study whether the source
of ultrafine particles that could explain the size of the Aitken modes in
Heintzenberg comes from MSA, another primary or secondary source. On the
other hand, the ATom comparison suggests that using the MSA volatility
parameterization helps predict the MSA mass concentrations more accurately.
Conclusions
We used the GEOS-Chem chemical-transport model coupled to the TOMAS aerosol
microphysics module to test the sensitivity of the aerosol size distribution
and resulting changes in the direct and indirect effects to the
condensational and nucleating behavior of methanesulfonic acid (MSA), an
oxidation product of dimethylsulfide (DMS). GEOS-Chem-TOMAS (GC-TOMAS)
normally simulates sulfuric acid and sulfate from DMS/SO2
oxidation, but does not include MSA within the size-resolved portion of the
model; we used this setup as our default model case (DEFAULT_NoMSA). We
considered both the global annual mean size distributions and the annual mean
in the southern-oceans regions (30–90∘ S) at 900 hPa for each
sensitivity case compared to DEFAULT_NoMSA. We further evaluated the model
output against two different measurement sets: zonal-mean number size
distributions compiled from ship-based measurements taken in the southern
oceans and particle-phase MSA mass concentrations obtained from aircraft data
over the Atlantic and Pacific Ocean basins for the months of August and
February.
As the effective volatility of MSA is uncertain, we used the Extended Aerosol
Inorganics Model (E-AIM) to build a parameterization for GC-TOMAS of MSA's
potential volatility as a function of temperature, relative humidity, and
available gas-phase base. For simplicity, we only allowed MSA to condense as
ideally nonvolatile or semivolatile, or to be volatile and not condense at
all under the parameterization. If MSA was ideally nonvolatile, it
contributed to the size distribution through condensation proportional to the
Fuchs-corrected aerosol surface area distribution (effectively nonvolatile or
ELVOC-like condensation). If MSA was instead ideally semivolatile, it
contributed to the size distribution through condensation proportional to the
aerosol mass distribution (quasi-equilibrium or SVOC-like condensation).
Regardless of the volatility treatment, condensed MSA was not allowed to
evaporate back to the gas phase, as gas-phase MSA was not explicitly tracked
in the model. Along with the parameterization, we tested limiting volatility
cases, allowing MSA to only be ELVOC-like or SVOC-like. We also performed
separate simulations in which MSA could participate in nucleation, using both
the MSA volatility parameterization and the ELVOC-like and SVOC-like MSA
assumptions. (MSA participated in nucleation only when it was under
ELVOC-like conditions in the parameterization; it always participated in
nucleation in the ELVOC simulation.) When using the volatility
parameterization, including MSA in the model changed the global annual
averages of submicron aerosol mass by 1.2 %, N3 by -3.9 %
(non-nucleating) or 112.5 % (nucleating), N80 by 0.8 %
(non-nucleating) or 2.1 % (nucleating), the aerosol indirect effect by
-8.6 mW m-2 (non-nucleating) or -26 mW m-2 (nucleating),
and the direct radiative effect by -15 mW m-2 (non-nucleating) or
-14 mW m-2 (nucleating). Across all simulations, including MSA in
the model changed the global annual averages of submicron aerosol mass by
0.7 % to 1.2 %, N3 by -3.9 % to 153.4 %, N80 by
-0.2 % to 23.8 %, the aerosol indirect effect by -0.18 to
0.0075 W m-2, and the direct radiative effect by -25 to
-13 mW m-2, depending on the assumed volatility and nucleating
ability of MSA.
The contribution from the sulfuric acid and sulfate from DMS/SO2
oxidation to the submicron aerosol mass is 4–6 times that of the
contribution from DMS/MSA, leading to a global cooling from the DRE 5–10
times that of MSA, at -120 mW m-2. However, because much of the aerosol
mass from DMS/SO2 is added through aqueous sulfate formation, which
suppresses nucleation and growth, the changes in N3, N80, and the
cloud-albedo AIE from DMS/SO2 oxidation products are smaller and on the
order of changes in these metrics from including MSA in the model.
The model–measurement annual zonal number size distribution comparisons to
the ship-based measurements compiled in Heintzenberg et al. (2000) of the
southern-oceans region (Fig. 11) show an underprediction of the Aitken mode
across cases, with the best agreement in the Aitken mode coming from the
cases that allow MSA to act as a nucleating nonvolatile compound (ELVOC_Nuc
and PARAM_Nuc). These results indicate the necessity of another source of
ultrafine particles over the southern oceans that is currently not being
accounted for in the model. However, it is not possible to conclude based on
this study where the source of extra ultrafines is coming from. More studies
over the oceans detailing the chemical compositions of the smallest particle
sizes are needed in order to help determine the origins of nucleating
material in these remote regions.
The model–measurement comparisons of total particle-phase MSA mass from the
aircraft data taken during the ATom-1 and ATom-2 campaigns compared to the
predicted mean MSA mass indicate that PARAM_Nuc and PARAM_NoNuc cases
perform the best, and that the cases in which MSA is always allowed to
condense to the particle phase overpredict MSA mass. As the Heintzenberg and
the ATom model–measurement comparisons are based on dissimilar metrics
(number size distribution versus particle-phase MSA mass) over dissimilar
spatial extents (surface-based ground and ship measurements versus aircraft
measurements continuously profiling between 0.2 and ∼13 km), we cannot
definitively state that any one sensitivity case appears to best fit both the
Heintzenberg and ATom measurements. Along with these model–measurement
comparisons, we provided a detailed description of the calibration for
detecting MSA applied to the Aerodyne high-resolution time-of-flight aerosol
mass spectrometer (AMS) present during the ATom campaigns in the Supplement
as a reference for the AMS community.
As there are uncertainties in both MSA's behavior (nucleation and
condensation) and the DMS emissions inventory, further
modeling and
measurement studies on both fronts are needed to better constrain MSA's
current and future impact upon the global aerosol size distribution and
radiative effect. Under the simulation tested in this work, MSA tends to have
small (<-0.1 W m-2) global annual radiative effects (DRE and
cloud-albedo AIE); in general, the forcings are predicted to be cooling
effects. The contributions to the size distribution and radiative effects
increase in magnitude in the southern oceans, where MSA concentrations are
highest and more pristine conditions exist. Although small, the radiative
effects from MSA and the associated size distribution dependencies should be
well characterized to more fully understand the role of changing DMS
emissions in a changing climate. This study provides a first look at some of
these potential dependencies and indicates possible directions for future
modeling and measurement studies.
Data availability
Data for the ATom campaigns are posted publicly at
10.3334/ORNLDAAC/1581 (Wofsy et al., 2018). The GEOS-Chem model is
available at http://wiki.seas.harvard.edu/geos-chem/ (last access:
5 March 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-3137-2019-supplement.
Author contributions
ALH, JRP, and BC defined the scientific questions and scope of this work.
ALH and BC performed all GEOS-Chem model simulations and offline calculations
with help from JKK, BC, and JRP. JRP performed the E-AIM calculations. PCJ,
BAN, JCS, and JLJ carried out the primary measurements and data processing
for the ATom field campaign, as well as campaign supervision and design. ALH
prepared the primary text with substantial contributions from JRP, JKK, BC,
PCJ, and JLJ. PCJ provided the detailed description provided in the
Supplement of the calibration method used for detecting MSA during the ATom
field campaign, with additional contributions from JLJ.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research was supported by the US Department of Energy's Atmospheric
System Research, an Office of Science, Office of Biological and Environmental
Research program, under grant no. DE-SC0011780, and by the U.S. National
Oceanic and Atmospheric Administration, an Office of Science, Office of
Atmospheric Chemistry, Carbon Cycle, and Climate program, under cooperative
agreement award no. NA17OAR430001. Betty Croft was supported under the
Climate Change and Atmospheric Research program at 1164 NSERC, as part of the
NETCARE project. The CU-Boulder group was supported by NASA NNX15AH33A and
NNX15AJ23G. Edited by: Veli-Matti
Kerminen
Reviewed by: three anonymous referees
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