A condensed multiphase halogen and dimethyl sulfide (DMS)
chemistry mechanism for application in chemistry transport models is
developed by reducing the CAPRAM DMS module 1.0 (CAPRAM-DM1.0) and the
CAPRAM halogen module 3.0 (CAPRAM-HM3.0). The reduction is achieved by
determining the main oxidation pathways from analysing the mass fluxes of
complex multiphase chemistry simulations with the air parcel model SPACCIM (SPectral Aerosol Cloud Chemistry Interaction Model).
These simulations are designed to cover both pristine and polluted marine
boundary layer conditions. Overall, the reduced CAPRAM-DM1.0 contains 32
gas-phase reactions, 5 phase transfers, and 12 aqueous-phase reactions, of
which two processes are described as equilibrium reactions. The reduced
CAPRAM-HM3.0 contains 199 gas-phase reactions, 23 phase transfers, and 87
aqueous-phase reactions. For the aqueous-phase chemistry, 39 processes are
described as chemical equilibrium reactions. A comparison of simulations
using the complete CAPRAM-DM1.0 and CAPRAM-HM3.0 mechanisms against the
reduced ones indicates that the relative deviations are below 5 % for
important inorganic and organic air pollutants and key reactive species
under pristine ocean and polluted conditions. The reduced mechanism has been
implemented into the chemical transport model COSMO-MUSCAT and tested by
performing 2D simulations under prescribed meteorological conditions that
investigate the effect of stable (stratiform cloud) and more unstable
meteorological conditions (convective clouds) on marine multiphase
chemistry. The simulated maximum concentration of HCl is of the order of
109 molecules cm-3 and that of BrO is around
1×107 molecules cm-3, reproducing the range of
ambient measurements. Afterwards, the oxidation pathways of DMS in a cloudy
marine atmosphere have been investigated in detail. The simulations
demonstrate that clouds have both a direct and an indirect photochemical
effect on the multiphase processing of DMS and its oxidation products. The
direct photochemical effect is related to in-cloud chemistry that leads to
high dimethyl
sulfoxide (DMSO) oxidation rates and a subsequently enhanced formation of methane
sulfonic acid compared to aerosol chemistry. The indirect photochemical
effect is characterized by cloud shading, which occurs particularly in the
case of stratiform clouds. The lower photolysis rate affects the activation
of Br atoms and consequently lowers the formation of BrO radicals. The
corresponding DMS oxidation flux is lowered by up to 30 % under thick
optical clouds. Moreover, high updraught velocities lead to a strong vertical
mixing of DMS into the free troposphere predominately under cloudy
conditions. The photolysis of hypohalous acids (HOX, X = Cl, Br, or I) is
reduced as well, resulting in higher HOX-driven sulfite-to-sulfate oxidation
in aerosol particles below stratiform clouds. Altogether, the present model
simulations have demonstrated the ability of the reduced mechanism to be
applied in studying marine aerosol–cloud processing effects in regional
models such as COSMO-MUSCAT. The reduced mechanism can be used also by other
regional models for more adequate interpretations of complex marine field
measurement data.
Introduction
In the marine and coastal atmosphere the chemical composition of the
gas phase, particles, and clouds as well as the size-distribution of
particles are significantly influenced by emissions of sea spray aerosols
(SSA) and volatile organic compounds from the sea surface (Simpson et
al., 2015; Farmer et al., 2015; Quinn et al., 2015). Sea salt is an
important compound of SSA (Quinn et al.,
2015) and represents a primary source for reactive chlorine and bromine
compounds in the troposphere (Saiz-Lopez and von Glasow, 2012; Simpson et
al., 2015). For reactive iodine compounds, however, emissions of gaseous
iodine compounds from the ocean surface dominate (Carpenter et al., 2012, 2013; Carpenter and Nightingale, 2015; Saiz-Lopez et al.,
2012). Additionally, the ocean is the main source for dimethyl sulfide
(DMS), which is the biggest natural source for atmospheric sulfur
(Andreae, 1990; Lana et al., 2011). The oxidation of DMS is the key to
understanding the natural radiative forcing as it affects both aerosol and
cloud condensation nuclei (CCN) concentrations (Charlson et
al., 1987). The chemical systems of halogens and DMS interact with each
other strongly and are highly influenced by multiphase chemistry (Barnes
et al., 2006; Hoffmann et al., 2016; von Glasow and Crutzen, 2004). As
oceans cover around 70 % of the Earth's surface (Joshi et al., 2017;
Law et al., 2013) and are in strong interaction with densely populated
coastal areas (Kummu et al., 2016; von Glasow et al., 2013), this
ocean-related atmospheric chemical subsystem is important for both Earth's
climate and air quality.
The chemistry of reactive halogen compounds as well as of DMS is very
sensitive to anthropogenic pollution. The advection of NOx and ozone
has strong effects on the activation of reactive halogen compounds
(Hoffmann et al., 2019b; Shechner and Tas, 2017; Mahajan et al., 2009b, a; McFiggans et al., 2002) and on DMS oxidation
(Breider et al., 2010; Barnes et al., 2006; Chen et al., 2018). Moreover,
reactive halogen compounds can significantly influence the depletion of
NOx, ozone, SO2, volatile organic compounds (VOCs), and oxidized
volatile organic compounds (OVOCs) (von Glasow et al., 2002b, a; Sherwen
et al., 2017, 2016; Schmidt et al., 2016). As the NO3
radical concentration in anthropogenically influenced atmospheric
environments is enhanced (Brown and Stutz,
2012), the NO3 radical-related DMS oxidation is reinforced (Breider
et al., 2010; Chen et al., 2018), which influences the formation of sulfate
aerosol particles and correspondingly leads to an increase of aerosol
acidity (Muniz-Unamunzaga et al., 2018). The changed aerosol acidity
further affects the formation of secondary organic aerosol (SOA) (Surratt
et al., 2010, 2007; Gaston et al., 2014) as well as the
activation of reactive halogen compounds (Keene et al., 1998). Apart from
that, the ongoing reduction of fossil fuel combustion emissions in some
parts of the world will promote the oxidation of DMS as an important
contributor to the formation of sulfate aerosol particles even in the
Northern Hemisphere (Perraud et al., 2015). Therefore, it is important
that chemical transport models (CTMs) treat the crucial multiphase chemistry
pathways of both reactive halogen compounds and DMS.
Currently, only a couple of multiphase chemistry mechanisms of halogens and
DMS have been developed and applied within CTMs, e.g. EMAC, CAM–MECCA, and
GEOS-Chem (Chen et al., 2017, 2018; Jöckel et al., 2016;
Long et al., 2014). Nevertheless, the applied model core of these CTMs does
not treat aqueous-phase chemistry of halogens and DMS by default. In CTMs
that deal with the chemistry in the marine boundary layer (MBL) and the free
troposphere, the activation of reactive halogen compounds and its dependence
on aerosol acidity is often described by heterogeneous reactions. The
parameters of these reactions have been determined in laboratory studies for
aerosol solutions that are more ideal than they naturally occur, e.g. pure
sulfate or sodium chloride/bromide aerosol. Hence, the accuracy of the
description of these processes is restricted, and it cannot easily be assumed
that they are representative under heterogeneous atmospheric conditions (Ammann et al., 2013). The treatment of
multiphase chemistry in models allows for more detailed investigations
concerning complex sea spray aerosol matrices. However, the level of detail
for the implementation of aqueous-phase chemistry into CTMs is limited
because of numerical restrictions, since the implementation of aqueous-phase
chemistry usually consumes huge amounts of CPU time. Consequently, mostly
only specific small subsystems are investigated, including a low number of
halogen or DMS multiphase chemistry reactions (Chen et al., 2018, 2017). Both discussed aspects, consumption of CPU time and
investigating only small subsystems, highlight that an overall picture of
multiphase marine chemistry cannot be drawn by chemical transport modelling
yet and might lead to an over- or underestimation of important chemical
pathways.
To achieve the goal of adequately treating the multiphase chemistry of DMS
and reactive halogen compounds within CTMs, not only is a solution for the high
CPU consumption necessary but also the development of a condensed
multiphase chemistry mechanism dealing with the complexity of these chemical
systems. Currently, such an adequate mechanism does not exist and should be derived by reducing detailed multiphase chemistry mechanisms, because important chemical pathways could otherwise be missed resulting in a misinterpretation of field data.
In the present study, a reduced multiphase chemistry mechanism describing
halogen and DMS chemistry is developed through a manual reduction using box
model studies with the CAPRAM halogen module 3.0 (CAPRAM-HM3.0; Hoffmann et al., 2019a) and the CAPRAM DMS
module 1.0 (CAPRAM-DM1.0; Hoffmann et al., 2016). Both modules currently
contain the most detailed mechanisms dealing with the multiphase chemistry
of these chemical systems. During the reduction procedure, two mechanisms
are derived, which are afterwards combined into a single one. The combined
reduced mechanism is implemented into the CTM MUSCAT (MUltiScale
Chemistry Aerosol Transport; Wolke et al., 2004, 2012), which
now treats detailed marine multiphase chemistry. Finally, the combined
reduced mechanism is applied in idealized 2D simulations with a focus on
multiphase DMS oxidation in the MBL and the various effects of clouds on
halogens and DMS.
Reduction of CAPRAM-DM1.0 and CAPRAM-HM3.0Model setup
The reduction of the marine multiphase chemistry modules CAPRAM-DM1.0 and
CAPRAM-HM3.0 is achieved through modelling studies with the air parcel model
SPACCIM (SPectral Aerosol Cloud Chemistry Interaction Model; Sehili et
al., 2005; Wolke et al., 2005). SPACCIM is a model framework designed to
solve complex multiphase chemistry systems and has already been used for the
development of reduced aqueous-phase chemistry mechanisms (Deguillaume et al., 2010). The description of the
simultaneously occurring chemical and physical processes in tropospheric
cloud droplets and aqueous aerosol particles in SPACCIM is realized by
combining a complex size-resolved cloud microphysical model and a detailed
multiphase chemistry model. The standard atmospheric multiphase chemistry is
represented by the near-explicit gas-phase mechanism MCMv3.2 (Jenkin et
al., 2003; Saunders et al., 2003) and the near-explicit aqueous-phase
mechanism CAPRAM4.0 (Bräuer et al., 2019).
The goal of reducing CAPRAM-DM1.0 and CAPRAM-HM3.0 is that both modules
can be applied in different marine atmospheric environments in CTMs. To this
end, simulations are carried out under two environmental conditions: (i) pristine ocean and (ii) polluted coastal area. The simulations run for 48 h and are equivalent to former simulations of atmospheric marine
environments studied with CAPRAM (Bräuer et al., 2013; Hoffmann et
al., 2016, 2019b).
In the simulations with pristine ocean conditions, an air parcel is moved
along a predefined trajectory at a 900 hPa pressure level. The simulations
are carried out at different latitudes (15, 30,
45, 60, and 75∘) and in different seasons
of the year (summer and winter). The air temperature in the simulations is
adjusted accordingly. Furthermore, the simulations are performed at
different relative humidity levels (50 %, 70 %, and 90 %). In the
simulations with relative humidity levels of 70 % and 90 %, cloud
passages of the air parcel are considered. The cloud occurrence is modelled
by uplifting the air parcel to an 800 hPa pressure level either at noon and
midnight or in the early morning and evening of the first model day. Due to
adiabatic cooling, the relative humidity increases, reaching the critical
supersaturation so that a cloud is formed. After the cloud passage, the air
parcel descends again to 900 hPa. The updraught and downdraught of the air
parcel requires 0.5 h of modelling time in each case. The in-cloud
residential time of the air parcel is 2 h in the simulations with
cloud occurrences at noon and midnight and 3 h in those with cloud
occurrences in the early morning and evening of the first model day. The
simulations with 50 % relative humidity have no cloud passages of the air
parcel included. For a detailed description of the emission and
initialization of chemical species within the pristine ocean scenario, the
reader is referred to Bräuer et al. (2013) and
Hoffmann et al. (2016). The modelled concentration time profiles of
specific important trace gases and aerosol compounds within the pristine
boundary layer are given in Figs. S1–S10 in the Supplement.
The scenario at the polluted coastal area is divided into two
sub-simulations at 45∘ latitude and 70 % relative humidity. The
lower diversity of the simulations compared to the pristine ocean scenario
is chosen because previous model studies had revealed that high NOx
concentrations suppress gas-phase halogen radical cross-reactions and lead
to a domination of halogen nitrate and nitryl chloride photolysis in halogen
atom activation (Hoffmann et al., 2019a, b; Faxon
and Allen, 2013; Saiz-Lopez and von Glasow, 2012; von Glasow et al., 2013).
The effect of photolysis and temperature change does not affect these four
important halogen activation precursors (ClNO2, ClNO3, BrNO3,
and INO3). In the first simulation, the air parcel represents the
composition of a pristine marine environment, which is advected over a
polluted coastal urban area. The second simulation describes an air–sea
breeze circulation system. Details on the model configurations of the first
simulation are explicitly given in Hoffmann et
al. (2019b) and those of the second simulation in Hoffmann et
al. (2019a). The main model results of both simulations do not differ from
the present work.
The reduction of both modules is performed by analysing the modelled
10 min time-resolved source and sink fluxes of key chemical compounds of
marine multiphase chemistry. These cover all DMS oxidation intermediates
and, for halogen chemistry, all XY species (X, X2O2,
XNO2, XNO3, XO, XOO, OXO, XY, HOX, and HX, with X and/or Y = Cl, Br, or
I). At first, CAPRAM-DM1.0 is reduced and afterwards CAPRAM-HM3.0.
In the following Sect. 2.2 and 2.3, the development of the reduced
CAPRAM-DM1.0 and CAPRAM-HM3.0 is described.
The final reduced marine multiphase chemistry mechanism is evaluated through
control simulations, which are carried out by using not only the full but
also the reduced marine multiphase chemistry mechanism.
The goal of the mechanism reduction firstly is that the modelled
concentration of chemical species that are classically treated as important
in CTMs, e.g. ozone, SO2, NOx, sulfate, or nitrate, only deviate
from the modelled concentration obtained from the complete scheme by less
than 5 % on average over the full modelling time. Secondly,
concentrations of oxidants and important chemical compounds of marine
multiphase chemistry should only differ by less than 10 % on average.
Important chemical compounds of DMS multiphase oxidation are DMS, dimethyl
sulfoxide (DMSO), and methane sulfonic acid (MSA), which represent the key
stable compounds from DMS oxidation. Dimethyl sulfone (DMSO2) is not
considered, because, according to current scientific knowledge, the
oxidation of DMSO2 is negligible under atmospheric conditions of the
pristine ocean. Additionally, the deviation of the concentration of methane
sulfinic acid (MSIA) is not a reduction criterion. MSIA is very reactive, so
even slight changes will immediately result in differences in the MSA
and sulfate concentrations. In the case of halogen multiphase chemistry,
important species for the mechanism reduction are the Cl, Br, and I atoms as
well as the ClO, BrO, and IO radical and stable halogen compounds, which can
act as important reservoir or activation species for halogen radicals, i.e. hypohalous acids, nitryl chloride, and dihalogen molecules. These halogen
reservoir or activation species are of high importance as strong changes in
their budget will obviously affect the overall oxidation processes in the
MBL. For other halogen radicals, it has been shown by previous studies
(e.g. see Saiz-Lopez et al., 2012; Simpson et al., 2015) that these are
rapidly converted into the above-mentioned compounds and thus strong
concentration changes will show up in the concentration of X atoms or XO
radicals. Lastly, the pattern of the concentration time profile for all
species has to match between the reduced and the full mechanism (R2≥0.75).
Development of the reduced multiphase DMS chemistry module
The oxidation of DMS in the tropospheric multiphase system leads to gaseous
SO2, sulfuric acid, DMSO2, MSA, dissolved sulfate, or methane
sulfonate through a sequence of steps (Hoffmann et al., 2016; Barnes et
al., 2006). To cover the important intermediate oxidation steps, the
reduction consists of six individual ones:
consideration of main multiphase DMS oxidation pathways only;
lumping of simple reactions;
application of the pseudo-steady-state approximation;
neglect of production and oxidation of DMSO2 in the aqueous phase;
lumping of the aqueous-phase oxidation of MSIA;
reduction of oxidation and production pathways of specific chemical compounds
unimportant in the gas or aqueous phase.
In the following, these mechanism reduction steps are outlined in more
detail.
Main pathways of multiphase DMS oxidation
In the first reduction step, the main pathways of the multiphase oxidation
of DMS and its oxidation products are investigated by analysing the
time-resolved source and sink fluxes of all simulations. Main pathways are
defined here as chemical production or loss processes that contribute more
than 5 % to the overall average mass flux of the investigated compound.
This analytical approach has proven its applicability in manual mechanism
reduction (Deguillaume et al., 2010; Ervens et al., 2003). To provide a
reduced mechanism applicable for a wide range of conditions, a chemical
reaction is included in the reduced scheme in any case if the reaction is
important under a single simulation condition. The present analyses provide
the important DMS multiphase reaction pathways within the troposphere,
similar to a former CAPRAM study dealing with multiphase DMS chemistry
(Hoffmann et al., 2016). This approach determines the subsequent
reduction steps.
Lumping of simple reactions
According to current knowledge, in the gas phase, the oxidation of DMS
or its oxidation products through H-atom abstraction leads to a corresponding
peroxyl radical, which can be further oxidized into an alkoxy radical.
Recently, it was suggested that the methylthiomethyl peroxyl radical
(CH3SCH2O2) undergoes a rapid unimolecular H-shift (Wu et
al., 2015; Berndt et al., 2019). The final stable product will be an
oxidized organic sulfur compound that is characterized by an aldehyde and an
organic hydrogen peroxide functionality. This compound can be oxidized in
both gas and aqueous phases. Currently, the chemistry of this compound is not
further investigated and therefore not treated in CAPRAM-DM1.0. However,
when more laboratory data are available, further mechanisms have to consider
this chemistry to improve the modelling of DMS chemistry and its effects.
Then, the DMS oxidation-derived alkoxy radical can undergo thermal
decomposition, a reaction with oxygen, or isomerization (Lightfoot et
al., 1992). Nevertheless, the current gas-phase chemistry of
CAPRAM-DM1.0 is based on MCMv3.2, which only treats thermal
decomposition for the CH3SCH2O, i.e. C-C bond scission leading to
the release of formaldehyde (HCHO). Since the analyses of the mass fluxes
reveal that decomposition appears immediately after formation, further
decomposition products are directly incorporated in every reaction in which
an alkoxy radical is formed and the corresponding decomposition reaction is
deleted (see Reactions R1–R3).
R1CH3SCH2O2+NO→CH3SCH2O+NO2R2CH3SCH2O→CH3S+HCHOR3Σ:CH3SCH2O2+NO→CH3S+HCHO+NO2
Further simple integrated reactions correspond to reactions of DMS oxidation
products in the aqueous phase, i.e. reactions with water. The reduction of
CAPRAM3.0 has already revealed that peroxyl radical formations in the
aqueous phase are not reaction-rate determining steps (Deguillaume et al., 2010). The same is true if reactions of
oxidation intermediates occur with water only. Therefore, such aqueous-phase
reactions are deleted and the products are directly implemented on the
right-hand side of the affected reaction equations.
Application of the pseudo-steady-state approximation
The oxidation of DMS by the OH radical and the Cl atom occurs not only
through H-atom abstraction but also through the addition of these radicals onto
the sulfur atom. The formed DMS adduct is unstable and decomposes back into
DMS and the corresponding radical if it is not stabilized through a reaction
with oxygen, which adds to the sulfur atom (Barnes et al., 2006). It is
possible that the DMS adduct reacts with NOx or decomposes into methane
sulfenic acid (CH3SOH) and a methyl (CH3) radical (Barnes et
al., 2006; Yin et al., 1990). The first reduction step has already revealed
that even at polluted coastlines, with NOx concentrations above 10 ppb,
NOx-related decompositions are not of atmospheric importance though.
Overall, the analysis shows that oxygen is too reactive against DMS adducts.
The pseudo-steady-state approximation (PSSA) is a fundamental way to deal
with such reactive intermediates to derive the overall rate of a chemical
reaction (Seinfeld and Pandis, 2006).
1d[DMS]dt=-k1DMSX+k2DMS-X;(X=OHorCl)2d[DMS-X]dt=k1DMSX-k2[DMS-X]-k3[DMS-X][O2]3→d[DMS]dt=-k1k3[O2]k2+k3[O2][DMS][X]
As outlined, apart from the DMSO formation, a considerable amount of
CH3SOH is also formed through DMS adduct decomposition. Hence, a PSSA
is also effective for this reaction sequence. The implemented rate constant
of the DMS adduct decomposition within CAPRAM-DM1.0 is given temperature
independent. The same is true for the oxygen addition reaction. Therefore,
under tropospheric conditions both reactions can be aggregated into first-order reaction rate constants and merged. This merging gives a ratio between
decomposition and oxygen addition that is implemented into the overall
reaction (see Reactions R4–R7).
R4CH3SCH3+OH⇄CH3S(-OH)CH3R5CH3S(-OH)CH3+O2→CH3S(O)CH3+HO2R6CH3S(-OH)CH3⟶O2CH3SOH+CH3O2R7Σ:CH3SCH3+OH⟶O20.9CH3S(O)CH3+0.9HO2+0.1CH3SOH+0.1CH3O2
The PSSA is also applicable to the oxidation of DMSO by the Cl atom and for
the methylthiyl radical (CH3S) reaction with oxygen, leading to the
methylthio peroxyl radical (CH3SOO), which has multiple reaction
pathways. On the one hand, CH3SOO can react with NOx or decompose back into
CH3S or decompose into SO2 and CH3; on the other hand, it can rearrange itself into the sulfonyl
radical (CH3SO2). The first reduction step
reveals that the reaction with NOx is negligible since only a
decomposition into SO2 and the formation of the sulfonyl radical
occur. For that reason, the PSSA is also applied to these two reaction
pathways as well.
4d[CH3S]dt=-k1CH3SO2+k2CH3SOO5d[CH3SOO]dt=k1CH3SO2-k2[CH3SOO]-k3[CH3SOO]6→d[CH3S]dt=-k1k3k2+k3CH3SO2
Neglect of production and oxidation of DMSO2
Though the analysis of the sink and source fluxes has revealed that
aqueous-phase chemistry contributes a little more than 5 % to DMSO2
formation and oxidation, the modelled overall DMSO2 formation and
oxidation flux is negligible compared to that of MSIA. Furthermore,
DMSO2 has low reactivity towards OH oxidation in the gas (kOH<3.0×10-13 cm3 molecules-1 s-1;
Falbe-Hansen et al., 2000) and aqueous phases (kOH=1.77×107 l mol-1 s-1; Zhu et
al., 2003) Because of the low measured background gas-phase
concentrations of DMSO2 in the single-digit parts per trillion (ppt) range (Davis et al.,
1998; Berresheim et al., 1998), the gas-phase oxidation of DMSO2 by the
OH radical is likely to be suppressed through methane oxidation (k=3.5×10-15 to 6.4×10-15 cm3 molecules-1 s-1 in the temperature range of 270 to 298 K) and
dry and wet deposition can be assumed as the major atmospheric removal
processes for DMSO2. In order to shrink the mechanism, DMSO2
production in the aqueous phase as well as the oxidation of DMSO2 in
both the gas and aqueous phases are neglected as a consequence.
Lumping of the aqueous-phase oxidation of MSIA
In the fifth reduction step, the oxidation of MSIA in the aqueous phase has
been simplified. Having a pKa value of 2.28 (Wudl et al.,
1967), MSIA occurs in both its non-dissociated and its dissociated form under
atmospheric aerosol as well as cloud conditions. At this point, the only
important oxidant for the non-dissociated form is ozone. The deprotonated
MSIA reacts with both the OH and the
Cl2- radical via an electron transfer
reaction into aqueous CH3SO2. The formed CH3SO2 further
reacts with O2 into the methylsulfonylperoxyl radical
(CH3SO2O2) or decomposes into the CH3 radical and
dissolved SO2 that is immediately dissociated into
HSO3-/SO32-.
Because of its high atmospheric abundance, in our study the O2
concentration is modelled to be almost constant within the tropospheric
aqueous phase. Furthermore, both reaction rate constants are implemented as
temperature independent, which is why a ratio between these reactions can be
calculated. The reaction of the CH3SO2O2 with MSIA yields MSA
and the methylsulfonylalkoxyl radical (CH3SO3). The latter
decomposes into the CH3 radical and sulfate. Both reactions occur
immediately. As a whole, all reactions of the deprotonated MSIA oxidation
can be summarized into one reaction for each oxidant, covering the overall
MSIA loss (see below Reactions R8–R13 for MSIA oxidation by the OH radical).
R8CH3SO2-+OH⟶O2,aq,H2O0.9CH3SO2,aq+0.9OH-+0.1CH3O2,aq+0.1HSO3--0.1H2OR9CH3SO2,aq⟶O2,aqSO2,aq+CH3O2,aqR10CH3SO2,aq+O2,aq→CH3SO2O2,aqR11CH3SO2-+CH3SO2O2,aq→CH3SO3-+CH3SO3,aqR12CH3SO3,aq⟶O2,aqSO3,aq+CH3O2,aqR13Σ:CH3SO2-+OH⟶O2,aq,H2OCH3O2,aq+0.235HSO3-+0.765CH3SO3-+0.765SO3,aq+0.9OH--0.765CH3SO2--0.235H2O
Reduction of oxidation and production pathways of specific chemical compounds less important in gas or aqueous phase
In the last reduction step, the mechanism is again analysed for residual
multiphase chemistry pathways to be combined. These are, for example,
reactions of radicals that now treat only one fast reaction sequence and
thus are merged into the previous reaction.
Development of the reduced multiphase halogen chemistry module
The goal of reducing CAPRAM-HM3.0 is to enable the description of key
halogen chemistry affecting ozone, NOx, SO2, VOCs, and OVOCs by a
reduced mechanism that almost conserves the concentration time profile of
the reactive halogen compounds listed earlier. The following three reduction
steps are applied to achieve this:
consideration of chemical production or loss processes that contribute more than 5 % to the overall mass flux of halogen compounds only;
lumping of simple reaction sequences;
neglect of oxidation and production of specific chemical species modelled to be
unimportant in tropospheric gas- or aqueous-phase chemistry.
In the following, these mechanism reduction steps are outlined in more
detail.
Main pathways of multiphase halogen chemistry
An analysis of the main pathways is performed in a similar manner as for the
multiphase DMS chemistry. However, the development of a DMS-like stepwise
oxidation scheme is impossible, due to important side pathways and
interconnections with other chemical subsystems, e.g. NOx or HOx
chemistry and various halogen cross-interactions (Saiz-Lopez and von Glasow, 2012). Furthermore,
halogen multiphase chemistry is characterized by large differences in
aqueous-phase oxidation within aerosol particles and cloud droplets
(Bräuer et al., 2013; Hoffmann et al., 2019b; von Glasow et al.,
2002a). Therefore, an appropriate reduced representation of multiphase
halogen chemistry requires the focus of the reduction to be on different halogen
species. Hence, the determination of the main pathways is done for a huge
number of halogen compounds covering key halogen atoms, halogen radicals,
halogen nitrates, halogenated organics such as halogenated aldehydes, as
well as halogen oxo-carboxylic acids. Again, as for the CAPRAM-DM1.0
reduction, a chemical reaction is included in the reduced scheme in any case
if the reaction is important under a single simulation condition.
As already modelled in other studies, the analyses revealed that the Cl atom
is an important oxidant for VOCs and OVOCs, e.g. alkanes, non-oxidized
aromatic compounds, alcohols, and aldehydes (e.g. Hoffmann et al., 2019a;
Sherwen et al., 2016; Xue et al., 2015; Pechtl and von Glasow, 2007). In
order to restrict computational costs, chemical mechanisms in
state-of-the-art CTM applications do not contain a high number of organic
compounds as the near-explicit MCM. In order to still represent the
chemistry of important VOCs and OVOCs in CTMs, species of the same compound
classes or of equal reactivity are typically merged into “lumped” species in
condensed mechanisms applied in CTMs (Baklanov et al., 2014). Based on
these limitations, the reduced CAPRAM-HM3.0 has to be linkable with the
chemical mechanisms used in CTMs. A first screening on treated VOCs and
OVOCs in the gas-phase chemical mechanisms MOZART4.0 (Schultz et al., 2018), RACM2 (Goliff et
al., 2013), MECCA (Jöckel et al., 2016), GEOS-Chem (Wang et al., 2019), and SAPRC11 (Yan et
al., 2019) has been performed for the main VOCs and OVOCs for this purpose.
It has been shown that most of the mechanisms contain the same set of
primary VOC/OVOC compound classes; for example, aldehydes and alcohols are
often treated up to a carbon number of two. As outlined in Sect. 3, the
gas-phase mechanism MOZART4 is chosen for further modelling with
COSMO-MUSCAT. As a result, only the Cl atom oxidation of the lumped VOCs and
OVOCs that are treated within MOZART4 are considered further. These sets can
be applied to the other mechanisms, but they have to be adjusted if species are
missing. The chosen reaction rate constant is based on the first lumped
product. As the k values are higher with longer carbon chain, this approach is
suitable, as it gives a lower limit of the reaction rate constant. Thus, no
overestimation will occur. However, when the simulations with the reduced
version of CAPRAM-HM3.0 are compared to the simulations with the
non-condensed CAPRAM-HM3.0, this approach results in lower HCl but higher ClO
formation.
Lumping of simple reaction sequences
In the gas phase, halogen atoms react rapidly with O2 and CO, yielding
unstable molecules that, as the model simulations show, immediately
decompose again. Still, within CAPRAM-HM3.0, specific oxidation pathways
lead to unstable molecules (e.g. the oxidation of halogenated oxidized
organics). Consequently, in every reaction in which such an unstable
molecule occurs as a product it is replaced by the halogen atom and O2 or CO.
The further processing of halogenated organic peroxyl radicals in the gas
phase results in halogenated organic alkoxy radicals. As for DMS, the
halogenated organic alkoxy radical decomposition, which is modelled to
not be the overall rate-determining step is integrated into these reactions.
Overall, the recombination of the halogenated organic peroxyl radicals with
other organic peroxyl radicals (RO2) leads exclusively to halogenated
carbonyls (see Reactions R14–R17).
R14XCH2O2+RO2→0.2XCHO+0.2XCH2OH+0.6XCH2O;(X=ClorBr)R15XCH2OH+OH⟶O2XCHO+HO2+H2OR16XCH2O⟶O2XCHO+HO2R17Σ:XCH2O2+RO2⟶O2,OHXCHO+0.8HO2
If the analysis of the main pathways leads to only one further reaction of a
compound being left, this reaction was screened for two criteria: (i) does the follow-up reaction occur rapidly and (ii) is the overall
concentration of the product so low that it would not be a significant
interfering factor for the modelling? If both criteria are true, the overall
reaction is merged together. For example, the recombination of IO in the
aqueous phase leads to iodite (HIO2), which is an intermediate in the
conversion between iodide and iodate
(IO3-). It is quickly oxidized into
iodate by H2O2, which is ubiquitous in the marine atmospheric
multiphase (Jacob and Klockow, 1992; Benedict et al., 2012; Kim et al.,
2007; Yuan and Shiller, 2000), and it also has a very low modelled
concentration. Overall, the IO recombination together with the oxidation of
HIO2 by H2O2 results in iodate (see Reactions R18–R20).
R18IOaq+IOaq+H2O→HOIaq+HIO2,aqR19HIO2,aq+H2O2→H++IO3-+H2OR20Σ:IOaq+IOaq⟶H2O2,aqHOIaq+H++IO3-
Neglect of oxidation and production of specific chemical species modelled to be less important in tropospheric gas or aqueous-phase chemistry
For the reduction of the halogen chemistry part, less important chemical
halogen species are defined as such with low (<0.1 ppt for
non-radical species) modelled concentrations or high chemical stability
(kOH<6.4×10-15 cm3 molecules-1 s-1 that is the k298 of methane; Atkinson et al., 2006) in
order that their non-consideration does not affect the concentrations of the
target species under conditions in the lower troposphere. While typical
species with a rather high chemical stability are chlorinated and brominated
organics (e.g. CH3Cl), except bromoform (CHBr3), for which
oxidation in the lower troposphere is negligible, typical species with low
modelled concentrations are oxidized halogenated organics derived from the
OH oxidation of methylated halogens (e.g. ICHO or ICIO from CH3I
oxidation). As the reduced mechanism is developed to deal with tropospheric
multiphase chemistry, the oxidation of such species is not treated within
the reduced CAPRAM-HM3.0.
Evaluation of the reduction steps
By comparing simulations with the reduced and with the original CAPRAM-DM1.0
and CAPRAM-HM3.0 versions added to the multiphase chemistry mechanism
MCMv3.2/CAPRAM4.0, the performed reduction steps are evaluated. Being
referred to as pristine, breeze, and outflow scenarios, the evaluation
simulations are carried out for 45∘ latitude with a relative
humidity of 70 % under pristine ocean (Hoffmann et al., 2016) and
polluted coastal conditions (Hoffmann et al., 2019a). While the
scenarios pristine and breeze stand for simulations of the
pristine ocean as well as of an air–sea breeze circulation system, the outflow scenario
represents the advection of polluted air masses over a
marine environment. The evaluation simulations run for 96 h in total including
cloud passages between 11:00 and 13:00 LT (local time) and between 23:00 and 01:00 LT in both pristine and outflow scenarios as well as between 13:00 and 14:00 LT in the
third breeze scenario. The longer simulation time compared to that of
previous simulations was chosen in order to investigate the effect of a
longer modelling time on concentration divergence. The evaluation by these
three simulation cases is appropriate, because both reduced mechanisms
contain reactions that were both important and not important under the
different performed simulations. Thus, other possible evaluation simulations
would also treat reactions that are not necessary under specific conditions.
Regardless of the simulation setup, a similar performance is expected as a
consequence.
The investigation of the modelled evolution of the concentration time
profile of ozone is shown in Fig. 1, revealing an
excellent agreement for all the scenarios (R2=1). Moreover, the
average ozone concentrations diverge by less than 5 % throughout the
entire modelling time, demonstrating that the reduced mechanism is able to
reproduce the modelled ozone concentrations of the complex mechanism.
Modelled concentrations of ozone within the pristine,
breeze, and outflow scenarios compared between the simulations with the full
(solid lines) and reduced (dotted lines) CAPRAM-DM1.0 and CAPRAM-HM3.0
mechanisms.
The same analysis is performed for other air pollutants and key aerosol
compounds important for air quality modelling, which are NO, NO2,
SO2, HNO3, HCl, and DMS in the gas phase, and also for the dry mass as
well as the organic mass of the aerosols together with nitrate, sulfate,
chloride, and methane sulfonate. Furthermore, the analysis is performed for
reactive halogen compounds and the OH, NO3, and HO2 radicals.
Table 1 shows the average percentage deviation for
these chemical species. The main target species, except MSA in the breeze
scenario, do not exceed the 5 % threshold, which is also true for the OH,
HO2, and NO3 radicals in both the gas and aqueous phases. Even for
reactive halogen compounds, the deviation rarely exceeds the 5 % mark.
Average percentage deviations (%) of some inorganic and organic
target compounds between the simulations with the full and reduced
CAPRAM-DM1.0 and CAPRAM-HM3.0 mechanisms (deviations calculated throughout the
full SPACCIM simulation). Exceedances of the threshold are marked in italic.
However, especially in the outflow scenario, reactive bromine compounds
exceed the 10 % threshold being caused by missing brominated organics in
the reduced CAPRAM-HM3.0 that trap the bromine from further reaction. For
example, 3 ppt of bromine is trapped on average in brominated alcohols
formed through Br atom-related oxidation of alkenes and further RO2
recombination. Regarding the low concentration and the fact that alcohols
are further oxidized into carbonyls, only the formation of brominated
carbonyls is considered in the reduced CAPRAM-HM3.0 to minimize the
mechanism. Consequently, the bromine radical is recycled faster by the
following reaction sequence.
R21Brg+C2H4,g→→→BrCHOg+1.8HO2+HCHO-CH3O2R22BrCHOg⇌BrCHOaqR23BrCHOaq→H++Br-+COaqR24Brg+O3,g→BrOg+O2,gR25BrOg+HO2,g→HOBrg+O2,gR26HOBrg⇌HOBraqR27HOBraq+Br-+H+⇌Br2,aq+H2OR28Br2,aq⇌Br2,gR29Br2,g+hν→Brg+Brg
Overall, this increases the modelled concentrations of reactive bromine
compounds, particularly in the afternoon after cloud occurrence and under
high alkene as well as low ozone. However, the evolution of the
concentration time profile fits very well (R2=0.98, 0.95, 0.97, and
0.75 for Br, BrO, HOBr, and Br2, respectively). Apart from bromine,
larger differences also occur for HOCl in the breeze and outflow
scenarios. This is related to the restricted VOC and OVOC oxidations within
the reduced CAPRAM-HM3.0 in order to match the condensed gas-phase chemistry
mechanisms implemented in the CTMs. Therefore, the HCl concentration is more
than 2 % smaller, and more Cl atoms react with ozone to form ClO that quickly
reacts with HO2 to yield HOCl. As opposed to Br2 formation by
HOBr, the higher HOCl does not necessarily lead to a higher modelled
Cl2 formation, which is related to the significant higher chloride
content in sea spray aerosols compared to bromine. It so concludes that the
enhanced HOCl seems not to be a driving factor for Cl2 formation under
pristine ocean conditions.
Finally, the methane sulfonate anion (MS-) is around one-quarter lower
in the breeze scenario, which is caused by the
Cl2- radical oxidation. This reduction
revealed that the Cl2- radical is an
important oxidant for oxalic acid and MS-. Consequently, other OVOC
oxidations are discarded for treatment in the reduced CAPRAM-HM3.0. The
modelled Cl2- radical concentrations
are around 1 order of magnitude higher in the breeze scenario compared
to the pristine and outflow scenarios, resulting in much higher MS-
oxidation rates. Yet, the development of the MS concentration–time profile
agrees very well (R2=1). In addition, the MSA concentration
deviates by only 6 % at the end of the model simulations.
Since, the evaluation reveals that the reduced mechanism system is able to
reproduce similar results as the full mechanism system, it is basically
appropriate for implementation into CTMs. The reduced mechanisms of CAPRAM-DM1.0 and CAPRAM-HM3.0 will be called CAPRAM-DM1.0red and
CAPRAM-HM3.0red in the following text.
Also, the modelling studies reveal that computational (CPU) time is
decreased, especially within the pristine scenario (see
Fig. 2). Compared to the base runs, the CPU time is
reduced by 16 %, 5 %, and 6 % in the pristine,
breeze, and outflow scenarios, respectively. Overall, the CPU time reduction is
low, but it is because of the usage of MCMv3.2 and CAPRAM4.0 that the simulations still
treat more than 21 000 reactions. Furthermore, the calculation of
microphysical processes consumes a huge amount of CPU time, too. Therefore,
the still high CPU time is caused by requirements of the standard multiphase
chemistry mechanism. These high requirements cover the reduction of CPU time
achieved by the reduction efforts. Very low initialized NOx
concentrations in the pristine scenario induced the stronger decrease of
computation time unlike the other scenarios. When it comes to the breeze and outflow scenarios, the high initialized NOx concentrations
effectively suppress halogen radical cross-interactions in the gas phase.
These cross-interactions are very fast and establish reaction cycles that
induces high fluxes. Therefore, under high NOx conditions, a much lower
CPU time is required to solve these reactions. However, in the pristine scenario, the rapidly occurring gas-phase cross-interactions of halogen
radicals still exist, hindering stronger amplified CPU time reductions.
Required CPU time within the pristine, breeze, and
outflow scenarios considering the original multiphase chemistry mechanism system
MCMv3.2/CAPRAM4.0/CAPRAM-DM1.0/CAPRAM-HM3.0 and the multiphase chemistry
mechanism system with CAPRAM-DM1.0red and CAPRAM-HM3.0red. The CPU costs
include gas- and aqueous-phase chemistry, microphysics, model initialization,
and output.
Uncertainties of the new chemistry scheme
The downsizing of CAPRAM-HM3.0 and CAPRAM-DM1.0 solely, considering the
most important reactions, has led to two new reduced mechanisms, which
consist of reactions that sensitively impact the model outcome. Hence, the
uncertainty of these reactions can be crucial for the model results. A
discussion of the uncertainties of the mechanism development has already
been performed in the previous papers describing the mechanism development
of CAPRAM-HM2.0 (Bräuer et al., 2013), CAPRAM-HM3.0
(Hoffmann et al., 2019a, b), and CAPRAM-DM1.0
(Hoffmann et al., 2016) as well as in the cited laboratory work. That is
why only a short discussion is given here.
For the oxidation of DMS in the gas phase, most of the rate constants are
based on recommended values of the IUPAC database
(http://iupac.pole-ether.fr/, last access: 20 April 2016) or Jet Propulsion Laboratory panel (JPL; Burkholder et al., 2015). Nevertheless, the
application of the PSSA has modified the oxidation pathways, in particular
the OH-addition reaction for DMS. The incorporation of the oxygen
concentration might increase the uncertainty influencing the DMSO formation
rate. As oxygen is in excess under tropospheric conditions and the oxygen
concentration is treated in the new derived reaction rate constant
specifically, minor changes are expectable. By contrast, no recommended
values are available for the aqueous-phase reaction rate constants and can
hence be stated as more uncertain (Hoffmann et al., 2016). Further
laboratory work is required to minimize their uncertainties.
Regarding CAPRAM-HM3.0red, certain gas-phase reaction rate constants of
the halogen chemistry are based on recommended values (Atkinson et al.,
2006, 2007, 2008; Burkholder et al., 2015). However, for
the oxidation of VOCs/OVOCs by the Cl or Br atom, often only one reaction
rate constant has been measured by laboratory studies. This is also true for
many aqueous-phase chemistry reactions. The highest uncertainties are
related to iodine chemistry. Here, often reaction rate constants, which
might be of high atmospheric significance are based on estimations, only.
First applications in chemistry transport modelling with COSMO-MUSCAT
The CTM applied in this study is MUSCAT (Wolke et al., 2004, 2012). It is either coupled to the weather model COSMO (Consortium for Small-Scale Modelling; Steppeler et al., 2003; Baldauf et al., 2011) or
ICON (ICOsahedral Non-hydrostatic; Zängl et al.,
2015), which provide all required meteorological fields (e.g. wind,
temperature, relative humidity, liquid water content, and precipitation) to
MUSCAT that are necessary to calculate the advection, diffusion, and
physico-chemical interaction of particles and trace gases. While the
emission files of gases and aerosols within MUSCAT are generated by
preprocessors, the chemical mechanism is imported from ASCII files, which
allows for changes without code recompilation. In terms of dust (Heinold et al., 2007) and sea spray aerosols (Barthel et al., 2019), emissions can also be calculated online.
Implementation of CAPRAM-DM1.0red and CAPRAM-HM3.0red into COSMO-MUSCAT
Within the present study, the model framework COSMO-MUSCAT is used, which
has recently been extended to be able to treat multiphase chemistry in
clouds (Schrödner et al., 2014, 2018). The
chemistry of DMS and detailed halogen chemistry are still missing. In order
to consider them, the mechanisms CAPRAM-DM1.0red and CAPRAM-HM3.0red are
implemented into the atmospheric multiphase chemistry core of COSMO-MUSCAT,
in which gas-phase chemistry is described by the MOZART4 mechanism (Schultz et al., 2018) and aqueous-phase chemistry by the
CAPRAM3.0red mechanism (Deguillaume et al., 2010). MOZART4
treats comprehensive halogen and DMS gas-phase chemistry that is replaced,
as well as specific lumped VOCs and OVOCs. As outlined, these lumped species
notably cover the VOCs and OVOCs, where the oxidation by the Cl atom is
significant. To link CAPRAM-HM3.0red to MOZART4, the overall rate
constants of the Cl atom are derived for the following lumped VOCs: (i) BIGALK, (ii) ALKOH, (iii) C2H5CHO, (iv) BIGALD1, (v) XYL, and (vi) BZALD. The lumped species C2H5CHO represents all aldehydes with
more than three carbon atoms and is newly implemented in MOZART4, which
makes it consistent with CAPRAM3.0red. Accordingly, the oxidation pathways
are adjusted and the C2H5CHO oxidation by the OH radical and the
Br atom has also been implemented.
C2H5CHO+OH/Cl/Br→1.5CH3C(O)O2+H2O/HCl/HBr
Table 2 provides the implemented reaction rate
constants of lumped VOCs. A full mechanistic description of the adjusted
CAPRAM-DM1.0red and CAPRAM-HM3.0red is given in Tables S2–S10 in the Supplement.
Description of the lumped MOZART4 species and the corresponding
kinetic reaction rate constants.
SpeciesCommentkComment on kBIGALKalkanes with no. of C ≥ 42.05×10-10same as for butaneALKOHalcohols with no. of C ≥ 32.7×10-11e525/Tsame as for propanolC2H5CHOaldehydes with no. of C ≥ 34.9×10-12e405/TOH; same as for propionaldehyde1.3×10-10Cl; same as for propionaldehyde5.75×10-11e-610/TBr; same as for propionaldehydeBIGALD1unsaturated dialdehyde1.35×10-10same as for 2-butenedialXYLlumped xylenes1.4×10-10mean value of o-, p- and m-xyleneBZALDlumped aromatic aldehydes1.0×10-10same as for benzaldehyde
The recombination of halogenated peroxyl radicals as described above is
adjusted to fit into MOZART4. In the MCM, this recombination is implemented
for the sum of all RO2, whereas in MOZART4 it is often only for
CH3O2 and CH3C(O)O2. The ratio of the MCM is applied in
the RO2 recombination reaction, but RO2 is considered as
CH3O2. The reaction rate constant is adopted from the
corresponding CAPRAM-HM2.0 reaction (Bräuer et al., 2013).
XCH2O2+CH3O2→XCHO+1.4HO2+0.8HCHO+0.2CH3OH
Currently, the calculation of aqueous-phase chemical processes in MUSCAT is
limited to cloudy conditions, i.e. a liquid water content (LWC) of above
0.01 g m-3. Furthermore, the chemical equilibria are treated
dynamically as forward and backward reactions, which represents a critical
challenge for the numerical solver. Deviations from the equilibrium state of
rapid phase transfers and dissociations may lead to large chemical fluxes
and hence small time steps, i.e. high computational costs. The robustness of
the numerical integration is particularly affected at phase boundaries
between cloud and non-cloud grid cells. Pre-balancing the aqueous-phase
equilibria of CO2, NH3, HNO3, HCl, SO2, H2SO4,
and organic acids in cloud-free grid cells at a predefined threshold LWC
under cloud conditions (0.01 g m-3, abbreviated with “sub no. 1”)
results in a robust numerical integration. This approach is similar to the
pH calculation parameterization described by Alexander et al. (2012) but is designed to
describe cloud chemistry and its effect on cloud droplet acidity in detail.
In order to describe the activation of reactive halogen compounds by the
chemistry in deliquesced aerosols, an additional sub-mechanism is
introduced, assuming an LWC of 10 µg m-3 in cloud-free grid
cells (abbreviated with “sub no. 2”). A schematic on how both sub-mechanisms
enable the multiphase chemistry treatment in COSMO-MUSCAT can be seen in Fig. 3.
Schematic representation of the multiphase chemistry treatment in
COSMO-MUSCAT in an idealized 2D simulation.
The main halogen chemistry reactions treated in the “sub no. 2” sub-mechanism
are the following:
R32XYg⇌XYaqwithX=Cl,BrorI,andY=Cl,BrorIR33HOXg⇌HOXaqR34XNO3,g⇌XNO3,aqR35HOXaq+Y-+H+⇌XYaq+H2OR36XYaq+Z-⇌XZaq+Y-(withZ=Cl,BrorI)R37HXaq⇌H++X-R38HNO3,g⇌HNO3,aqR39N2O5,g⇌N2O5,aqR40N2O5,aq→NO2++NO3-R41NO2++H2O→NO3-+2H+R42NO2++Cl-⇌ClNO2,aqR43XNO3,aq+H2O→HOXaq+HNO3,aqR44HSO3-+HOXaq→HSO4-+X-+H+R45H2O2,g⇌H2O2,aqR46HSO3-+H2O2+H+→SO42-+2H++H2O
The activation of halogens is in accordance with the heterogeneous reactions
used in other CTMs (Badia et al., 2019; Hossaini et al., 2016; Jöckel
et al., 2016; Long et al., 2014; Muniz-Unamunzaga et al., 2018; Saiz-Lopez
et al., 2014; Wang et al., 2019). However, the “sub no. 2” sub-mechanism is
able to treat pH-dependent processes, including (i) the activation of
reactive halogen compounds and (ii) sulfite formation induced by HOX and
H2O2 in the MBL online. Tables S8–S10 provide
a complete overview of the treated aqueous-phase reactions and phase
transfers in both the “sub no. 1” and the “sub no. 2” sub-mechanism in CAPRAM-DM1.0red and CAPRAM-HM3.0red. Overall, COSMO-MUSCAT now represents
the CTM with the most detailed description of marine multiphase chemistry
(see Fig. 4).
Comparison of applied tropospheric DMS and halogen chemistry
mechanisms within the chemical transport models: CMAQ (Muniz-Unamunzaga
et al., 2018), TOMCAT (Hossaini et al., 2016), GEOS-Chem (Wang et al., 2019), CAM-Chem (Saiz-Lopez et al., 2014), CAM-MECCA (Long et al., 2014), EMAC (Jöckel et al., 2016), and WRF-Chem (Badia et al., 2019). EMAC is a chemistry climate model
that can be run as a CTM.
Evaluation of the 2D implementation
The evaluation of the implementation is carried out by two 2D simulations
(x–z cross section) dealing with a pristine ocean scenario under two
meteorological conditions, namely a convective and stable atmospheric layer,
which will result in modelled convective and stratiform clouds,
respectively. Both scenarios are further applied to investigate DMS
oxidation in more detail. The evaluation of the implementation is performed
by investigating the activation of halogen compounds in the MBL and
comparing it with available ambient measurements and other model data.
2D simulations are preferred over 3D ones, because they are based on the same
meteorological dynamics but have one lower degree of freedom. As a result,
the system under investigation is less computationally expensive.
Additionally, 2D simulations enable a comprehensive understanding of
multiphase chemistry in the atmospheric column, including vertical mixing
processes.
2D model setup
The chemical model setup, i.e. the initialization, deposition, and emission
of trace gases and VOCs, as well as the aerosol composition to describe the
atmospheric composition of the pristine ocean, is the same as the one used
during the mechanism reduction. Because of the lumped species within
MOZART4, specific VOC emissions are merged. Table S1
provides the emission rates. Initialized concentrations of gas-phase and
aerosol compounds represent ground values and are distributed vertically as
constant mass mixing ratios within the model domain of MUSCAT. These values
are used as constant boundary conditions on the left-hand side of the model
domain (see Fig. 3).
Two different meteorological cases are simulated: one with a high diversity
of clouds and a strong vertical wind velocity (further called unstable meteorological condition) and another with a stable cloud cover at the top
of the marine boundary layer and a weak vertical wind velocity (further
called stable meteorological condition). These two different
meteorological pristine ocean simulations are chosen to evaluate the
numerical robustness of the model. The meteorological scenarios are
initialized using radiosonde profiles (unstable meteorological condition
station: Camborne Observations, station identifier: 3808 on 12 Z 21 June 2016
and stable meteorological condition station: GVAC Sal Observations,
station identifier: 8594 on 12 Z 12 June 2017), which can be read and processed
by COSMO by default and are considered constant meteorological boundary
conditions on the left-hand side of the model domain (see
Fig. 3).
The model domain spans 400 horizontal grid cells with a resolution of
1.11 km per grid cell and 100 vertical levels with a resolution of 100 m.
Whereas COSMO is run on the full domain, only the inner 200 horizontal grid
cells (overall 222 km) and lowermost 15 vertical levels (overall 1500 m) are
used for the multiphase chemistry simulations with MUSCAT. MUSCAT uses a
smaller domain as the interaction is sufficient to describe the multiphase
chemistry in the marine boundary layer (MBL). Furthermore, the height of the
MBL is often lower than 1000 m (Norris, 1998; Carrillo et al., 2016),
which enables an investigation of the interactions between the MBL and the
free troposphere and significantly saves computation time compared to the
full domain. So, this chosen model setup can capture almost all essential
chemical processes as well as the distribution of important species in the
marine troposphere. Overall, the modelling domain for multiphase chemistry
encompasses 3000 grid cells.
Comparison with measurementsHCl gas-phase concentration
Firstly, the modelled concentration range of gaseous HCl is compared to
actual measurements. Figure 5 shows that very high
HCl concentrations are modelled, especially in the stable meteorological condition simulation above the MBL. These are results of the constant
vertical distributed mass mixing ratios of the initial values. Since the
evaluation focuses on the activation of HCl within the MBL, these values can
be neglected. The modelled values below 1000 m outside of clouds are of the
order of 109 molecules cm-3 after 12 h of modelling time and
thus in the range of both other modelled (Hossaini et al., 2016; Wang et
al., 2019) and measured values within the marine pristine boundary layer
(e.g. Keene and Savoie, 1999; Sander et al., 2013; Pszenny et al.,
2004). The higher LWC of clouds results in strongly reduced HCl gas-phase
concentrations due to the phase partitioning shift from the gas to the
aqueous phase. Further chemical cloud processing increases aerosol acidity,
yielding higher HCl gas-phase concentrations behind the cloud in wind
direction (see Fig. 5).
Simulated concentrations of HCl in the gas phase by COSMO-MUSCAT
(a) in the unstable meteorological condition simulation with convective
clouds and (b) in the stable meteorological condition simulation with
stratiform clouds after 12 h of modelling time. The x axis represents
the innermost horizontal grid cells divided by 100. The grey bars represent
measured values in most likely pristine marine environments (see Table S11
for further details). The black contour lines represent the simulated
clouds. The black line corresponds to a LWC of 0.01 g m-3 and the white line to 0.1 g m-3. The area framed by the white
line includes LWC above 0.1 g m-3.
BrO gas-phase concentration
Contrary to HCl, the measured concentrations of HBr over the pristine ocean
are missing. However, a high number of measured gas-phase BrO concentrations
in the MBL are available (Saiz-Lopez and von Glasow, 2012; Simpson et
al., 2015). Therefore, as a second step, the modelled gas-phase BrO
concentration range is compared to measurements.
Figure 6 shows that the modelled values outside of
the cloud grid cells ranging from 106 to 107 molecules cm-3 after 12 h of modelling time. Thus, they are in the range of
other modelled (Zhu et al., 2019) as well as measured
values within the pristine MBL (e.g. Leser et al., 2003; Read et al.,
2008; Chen et al., 2016). Apart from that, the vertical distribution
significantly differs between both simulations resulting into distinct
spatial pattern. At the left-hand side of the model domain, the BrO
concentration is similar, which is related to the activation of reactive
bromine species from the initialized marine aerosols. However, when clouds
are formed the profiles change. This is related to the high differences in
the vertical wind field (see Fig. 7a and b). Because of the stronger
updraughts in the unstable meteorological condition simulation, the reactive
halogen compounds are advected towards higher altitudes compared to the slow
vertical winds in the stable meteorological condition simulation. A second
remarkable difference is the much lower BrO concentration at the right-hand
side of the model domain in the stable meteorological condition
simulation. This effect is more explicitly discussed in Sect. 3.3.3.
Simulated concentrations of bromine monoxide in the gas phase by
COSMO-MUSCAT (a) in the unstable meteorological condition simulation with
convective clouds and (b) in the stable meteorological condition
simulation with stratiform clouds after 12 h of modelling time. The
x axis represents the innermost horizontal grid cells divided by 100. The
grey bars represent measured values in most likely pristine marine
environments (see Table S11 for further details). The black contour lines
represent the simulated clouds. The black line corresponds to a LWC of 0.01 g m-3 and the white line to 0.1 g m-3. The area
framed by the white line includes LWC above 0.1 g m-3.
Modelled vertical winds (cm s-1)(a) in the unstable meteorological condition simulation with convective clouds and (b) in the
stable meteorological condition simulation with stratiform clouds after 12 h of modelling time. The x axis represents the innermost horizontal grid
cells divided by 100. The black contour lines represent the simulated
clouds. The black line corresponds to a LWC of 0.01 g m-3 and the white line to 0.1 g m-3. The area framed by the white
line includes LWC above 0.1 g m-3. Further, the modelled concentrations
of DMS in the gas phase (109 molecules cm-3) are shown in (c) for the
unstable meteorological condition simulation with convective clouds and in (d) for the stable meteorological condition simulation with stratiform
clouds after 12 h of modelling time.
Simulated concentrations of DMSO (a) in the gas phase and (b) in
the aqueous phase under the stable meteorological condition simulation
with stratiform clouds after 12 h of modelling time. The x axis
represents the innermost horizontal grid cells divided by 100. The black
contour lines represent the simulated clouds. The black line corresponds to
a LWC of 0.01 g m-3 and the white line to 0.1 g m-3. The area framed by the white line includes LWC above 0.1 g m-3.
Apart from that, many field studies had the problem that the BrO
concentration in the MBL was always below the detection limit (Sander et al., 2003).
In a recent measurement study, the measured BrO in the MBL was also always
below the detection limit of 0.5 pptv, i.e. around 1.2×107 molecules cm-3 (Volkamer et al., 2015). Hence, the BrO
concentrations are modelled adequately. Since the activation of reactive
bromine is highly related to that of chlorine, the mechanism is able to
represent the activation of reactive halogen compounds within the MBL. A
comparison of reactive iodine compounds with measurements is not performed,
because the concentration range is highly sensitive to the chosen emission
values of molecular iodine and iodinated organics from the sea surface and
is thus uncertain.
Overall, the new marine multiphase chemistry model can represent marine
aerosol chemistry and linked halogen activation under consideration of
meteorological dynamics and shows a good agreement to other field as well as
model data. Thus, it is applicable for further detailed 3D studies.
Results of pristine ocean scenariosVertical wind and DMS distribution
Both scenarios are further applied to investigate the multiphase oxidation
pathways of DMS in a cloudy marine atmosphere in detail.
Figure 7 shows the modelled distribution of clouds
and the strength of the vertical wind field as well as the modelled DMS
concentration distribution for both simulations after 12 h of modelling
time. In the unstable meteorological condition simulation, the clouds
extend up to a height of more than 2000 m, whereas in the stable meteorological condition simulation, the top of the cloud is capped below
an inversion layer at around 1000 m. Also, the vertical winds are much
stronger in the unstable meteorological condition simulation. Because of
the strong vertical winds, gas-phase DMS concentrations of around
2×109 molecules cm-3 are transported into the lower
free troposphere. The strong inversion and low magnitude of the vertical
wind speed in the stable meteorological condition simulation hinders
effective DMS transportation into the free troposphere, resulting in a DMS
concentration of around 1×109 molecules cm-3 above
the MBL. This is consistent with the initialized background DMS
concentration. Below the inversion, DMS concentrations are more
homogeneously distributed, which is different to the unstable meteorological condition simulation, with a stronger variability related to
the vertical wind field, i.e. the peaking of DMS concentrations into higher
vertical levels because of strong updraughts (cp. Fig. 7).
Vertical DMSO distribution
If such homogeneously distributed DMS concentrations are modelled as in the
stable meteorological condition simulation in a clear sky atmosphere, a
similar concentration distribution for the first stable DMS oxidation
products will be modelled. Hence, the concentration distribution of DMSO is
a good indicator to investigate the effect of clouds on DMS oxidation. In
Fig. 8, the distribution of DMSO in the gas and
aqueous phases in the stable meteorological condition simulation after 12 h of modelling time is shown. Furthermore, the overall modelled DMSO
production and loss rates separated into gas- and aqueous-phase reactions
were added to the Supplement (see Fig. S11).
The stratiform clouds have a very high influence on the DMSO concentration
in both the gas and the aqueous phases. The spatial gas-phase DMSO
concentration distribution differs from the DMS concentration. Below the
optically thickest clouds, the gas-phase DMSO concentration is significantly
reduced, whereas above the cloud it slightly increases. In the cloud grid
cells, the gas-phase DMSO concentration is reduced significantly because of
the uptake into the aqueous phase. The reduced gas-phase concentrations
below the cloud cannot be explained by vertical or horizontal transportation,
because, as can be seen in Fig. 7, an updraught would
result in observable concentration peaks in the higher vertical levels.
Therefore, the gas-phase DMSO formation in the MBL is somehow influenced by
the cloud above, which makes it necessary to investigate the cloud-induced
effect on such crucial DMS oxidants in the pristine MBL.
Simulated oxidation rate of DMS by BrO in the stable meteorological condition simulation with stratiform clouds after 12 h
of modelling time. The x axis represents the innermost horizontal grid cells
divided by 100. The black contour lines represent the simulated clouds. The
black line corresponds to a LWC of 0.01 g m-3 and the
white line to 0.1 g m-3. The area framed by the white line includes LWC
above 0.1 g m-3.
Effects of stratiform clouds on DMS oxidation
Within the pristine MBL, the BrO radical is a primary DMS oxidant that forms
DMSO (Barnes et al., 2006; Breider et al., 2010; Hoffmann et al., 2016;
von Glasow and Crutzen, 2004; Chen et al., 2018). This radical is formed
through reaction of O3 by the Br atom that is activated by multiphase
chemistry. The following reactions are important pathways of activation in a
clear sky MBL (von Glasow and Crutzen, 2004; von Glasow et al., 2002b):
R47BrClg+hν→Brg+ClgR48Brg+O3,g→BrOg+O2,gR49BrOg+DMSg→DMSOg+BrR50BrOg+HO2,g→HOBrg+O2,gR51HOBrg+hν→Brg+OHgR52HOBrg⇌HOBraqR53HOBraq+Br-+H+⇌BrClaq+H2OR54BrClaq⇌BrClg
In the pristine marine boundary layer, two competing pathways determine the
main fate of the BrO radical through (i) a reaction with DMS and (ii) a
reaction with HO2. The oxidation of DMS leads to DMSO and the Br atom,
so that a cycle is established that continuously depletes O3 and forms
DMSO as long as DMS is emitted or ozone is available. This cycle is
disturbed by the reaction of BrO with HO2, yielding HOBr, which can be
photolysed back into the Br atom again or converted by multiphase chemistry
into BrCl or Br2. Overall, the photolysis of HOBr, Br2 and BrCl
determine the DMS to DMSO conversion. Clouds suppress the photolysis of
Br2, BrCl, and HOBr due to the reflection of incoming solar radiation.
The thicker the cloud, the lower the radiation flux below. Consequently, Br
atom activation below the cloud is hindered, affecting the BrO concentration
and thus the reaction rate of BrO with DMS that yields DMSO (see
Fig. 9). Due to a longer lifetime against further
oxidation and corresponding horizontal advection, the DMSO concentration
profile is shifted towards the right compared to BrO. The lowest oxidation
flux between DMS and BrO is modelled between grid cell 2.0 and 2.15. The
effect on DMSO concentration is modelled between grid cell 2.1 and 2.4.
The photolysis of BrCl and Br2 is highly sensitive to cloud shading and
thus has a high impact on the formation of reactive bromine and the linked
DMS oxidation. Moreover, model studies suggest that BrCl photolysis is an
important contributor to Cl atom activation in the MBL (Wang et al.,
2019; von Glasow et al., 2002b). Hence, the outlined model results reveal
that the shading effect of clouds is also very important for the atmospheric
Cl atom concentration budget, affecting the atmospheric oxidation capacity
within the MBL.
Formation of MSA and aqueous sulfate
DMSO is rapidly oxidized into MSIA and thus a similar MSIA profile is
modelled. As MSIA is highly reactive in the gas and aqueous phases as well
as highly soluble, it is rapidly oxidized into methane sulfonate (MS-)
in both the aerosol and the cloud phases. There, O3 is the preferred
oxidant in the aerosol phase, whereas in cloud droplets it is the OH radical
(Hoffmann et al., 2016). The MS- formed in cloud can be transported
towards the ground by downdraughts. However, comparing the DMS concentrations
in Fig. 7 with the DMSO concentrations in
Fig. 8, the up- and downdraughts in the stable meteorological condition simulations have little effect on the
concentration distribution in height. The strongest effect relates to the
advection from the left-hand to the right-hand side of the model domain and
continuous emission from the surface. In the grid cells left of the cloud,
the DMSO concentration is high and consequently the aerosol particle
chemistry of DMSO and of the subsequent oxidation product MSIA leads to a
sharp increase of MS- formation at the grid cells below the left cloud
edge (see Fig. 10a). Due to the advection of the
stable MS- to the right-hand side of the model domain, the spatial
profiles of DMSO (Fig. 8) and MS- differ. The
high modelled chemical fluxes in cloud droplets indicate the highest
MS- concentrations to be within and below the cloud grid cells.
Simulated aqueous-phase concentrations of (a) methane sulfonate
and (b) sulfate in the stable meteorological condition simulation with
stratiform clouds after 12 h of modelling time. The x axis represents
the innermost horizontal grid cells divided by 100. The black contour lines
represent the simulated clouds. The black line corresponds to a LWC of 0.01 g m-3 and the white line to 0.1 g m-3. The area
framed by the white line includes LWC above 0.1 g m-3. The initial
background concentration of methane sulfonate is at about 30 ng m-3 and
that of sulfate at 1 µg m3.
Also, the concentration of sulfate (see Fig. 10b) is
enhanced in the grid cells at the left cloud edge but because of different
reasons. At the left cloud edge, the lower photolysis rates increase the
SO2 oxidation into sulfate by HOX and H2O2. Hence, a stronger
HOX-related (especially HOI) reactive SO2 uptake on the aerosols is
modelled. The reaction of HOBr results in the formation of bromide. In
addition to the uptake of HBr, this increases the bromide concentration in
cloud droplets by up to 1 order of magnitude compared to the ground-level
concentration before the left cloud edge (see Fig. S12). The uptake is
highest under the optically thickest modelled clouds, resulting in the
highest modelled sulfate concentrations. Therefore, the modelled spatial
concentration is contrary to that of DMSO.
Conclusion and outlook
Reduced multiphase chemistry mechanisms of DMS and reactive halogen
compounds are developed through the reduction of the near-explicit
multiphase chemistry mechanisms CAPRAM-DM1.0 and CAPRAM-HM3.0. Simulations
that compare the reduced with the original mechanisms revealed that the
reduced mechanisms are able to reproduce the concentrations and time
evolutions of main air pollutants as well as key reactive halogen compounds.
Additionally, CPU time in the box model simulations is reduced by 16 %,
5 %, and 6 %, depending on the model scenario. Afterwards, the reduced
mechanisms are implemented into the chemistry transport model COSMO-MUSCAT.
This process is evaluated by idealized 2D simulations of an atmospheric
pristine ocean environment. It was proven that the reduced marine multiphase
chemistry mechanism can represent marine aerosol chemistry and linked
halogen activation as it matches measured field concentrations, e.g. HCl and BrO.
Following that implementation, 2D simulations of a pristine ocean scenario
are carried out, investigating the effect of stable (stratiform cloud) and
more unstable meteorological conditions (convective clouds) on multiphase
DMS oxidation. The simulations reveal that clouds have both strong direct
and indirect photochemical effects on the oxidation and vertical
distribution of DMS in the marine atmosphere. Firstly, locally high updraught
velocities in the unstable scenario result in fast transport of DMS from the
marine boundary layer into the free troposphere. Hence, transport and
further oxidation of DMS can be an important source of SO2 within the
free troposphere, particularly in the Southern Ocean region that is less
affected by anthropogenic pollution. Secondly, clouds enhance the formation
of MSA via the DMS addition channel. The formed DMSO is effectively consumed
by cloud droplets, where it is rapidly oxidized into MSA. Thirdly, the
shading of clouds has a high impact on the photolysis of dihalogens that are
the main contributor to Cl and Br atom activation. Thus, a much lower
oxidation of DMS into DMSO occurs below stratiform clouds. In contrast, the
lower HOX photolysis induces stronger sulfate formation. The results
indicate that clouds strongly affect the oxidation of DMS directly because
of enhanced aqueous-phase oxidation into MSA and indirectly by suppressing
the DMSO formation due to lower halogen atom activation. In total, a strong
possible effect on the atmospheric oxidation capacity within the MBL of the
pristine ocean is assumed. The important effect of wet scavenging by clouds
was not investigated as COSMO-MUSCAT(5.04e) did not implement
it in detail but represented it using a first-order scavenging rate. Future
studies aim to implement a more precise scheme. Since the clouds modelled in
this study are not known to precipitate, the propagated error should be small.
Overall, the 2D simulations demonstrate the capability of COSMO-MUSCAT to
now cover the multiphase chemistry in marine-influenced atmospheric
environments. This allows for deeper investigations of multiphase chemistry
in a wide range of temporal and spatial resolutions together with transport
and microphysical processes in the future. In a next step, the mechanism
will be applied in simulations with COSMO-MUSCAT for modelling measurement
campaigns at the Cape Verde Atmospheric Observatory (Carpenter et al.,
2010), supporting the interpretation of the measurement data and enabling
further model and/or mechanism evaluation. Finally, the reduced mechanism is
designed in such a way that new findings in DMS or halogen chemistry can
easily be implemented, e.g. improved understanding of the multiphase
chemistry of the unimolecular H-shift of CH3SCH2O2.
Code and data availability
The code for the COSMO model is available according to the Software License
Agreement by Deutscher Wetterdienst (German Weather Service, http://cosmo-model.org, last access: 12 November 2019). The source code of MUSCAT and SPACCIM, external
parameters, and applied mechanisms are archived on a local Git server and
can be obtained by request through Ralf Wolke (wolke@tropos.de). Access to
the model code used in the paper has been granted to the editor.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-2587-2020-supplement.
Author contributions
EHH, AT, and RW did the model development on SPACCIM. EHH, AT, and HH
designed the SPACCIM modelling work. EHH performed the SPACCIM simulations.
EHH, AT, and HH analysed the SPACCIM model results. EHH, RS, and RW did the
model development on COSMO-MUSCAT. EHH, RS, and RW designed the COSMO-MUSCAT
modelling work. EHH performed the COSMO-MUSCAT simulations. EHH analysed the
COSMO-MUSCAT model results. EHH, RS, AT, and HH wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Erik H. Hoffmann thanks the PhD scholarship programme of the German Federal
Environmental Foundation (Deutsche Bundesstiftung Umwelt, DBU, AZ: 2016/424)
for its financial support. This work has received funding from the European
Union's Horizon 2020 research and innovation programme through the
EUROCHAMP-2020 Infrastructure Activity under grant agreement no. 730997.
Financial support
This research has been supported by the European Commission (EUROCHAMP-2020 (grant no. 730997)).
This work was also supported by the EU Marie Skłodowska-Curie Actions
(grant no. 690958-MARSU-RISE-2015).
Review statement
This paper was edited by Andrea Stenke and reviewed by two anonymous referees.
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