The representation of volatile organic compound (VOC) deposition and
oxidation mechanisms in the context of secondary organic aerosol (SOA)
formation are developed in the United Kingdom Chemistry and Aerosol (UKCA)
chemistry–climate model. Impacts of these developments on both the global SOA
budget and model agreement with observations are quantified. Firstly, global
model simulations were performed with varying VOC dry deposition and wet
deposition fluxes. Including VOC dry deposition reduces the global
annual-total SOA production rate by 2 %–32 %, with the range
reflecting uncertainties in surface resistances. Including VOC wet deposition
reduces the global annual-total SOA production rate by 15 % and is
relatively insensitive to changes in effective Henry's law coefficients. Without precursor deposition, simulated SOA concentrations are lower than observed
with a normalised mean bias (NMB) of
Aerosols are detrimental to human health (WHO, 2013) and are linked to climate change (Forster and Ramaswamy, 2007). The development of air quality and climate management plans are hindered by the challenges in representing aerosol within models. Secondary organic aerosol (SOA) is formed in the atmosphere from a variety of hydrocarbons. Gas-phase production of SOA occurs by condensation of volatile organic compound (VOC) oxidation products (Odum et al., 1996, 1997) and from semi-volatile and intermediate-volatility organic compounds (S/IVOCs) (Donahue et al., 2006, 2011). Additionally, SOA formation can take place within the aqueous phase of cloud and aerosol liquid water (McNeill, 2015; Ervens, 2015). The treatment of hydrocarbon physicochemical processes within SOA schemes varies sizably across global chemistry–climate and chemical transport models, and this is reflected in both an uncertain global SOA budget and poor model agreement with observations (Tsigaridis et al., 2014).
The diversity in model treatment of SOA formation is partially due to the myriad of unique organic molecules in the atmosphere, a small fraction of which have been measured (Goldstein and Galbally, 2007). In the simplest of schemes, production of SOA is calculated as a function of emissions, hence SOA is “emitted” as opposed to being formed in the atmosphere (Tsigaridis et al., 2014). In cases where gas-phase oxidation of SOA precursors is treated, several simplifications are commonly made. For example, biogenic VOCs, such as isoprene and monoterpenes, are known to have multi-generational oxidation mechanisms, but the mechanisms are often reduced to less than two reaction steps when implemented in global models (Chung and Seinfeld, 2002; Heald et al., 2011; Scott et al., 2014, 2015). Similarly, multi-generational oxidation mechanisms of aromatic compounds are often represented by less than two reaction steps (Tsigaridis and Kanakidou, 2003; Heald et al., 2011). Gas-phase oxidation schemes can also be simplified by grouping organic compounds together (i.e. “lumping”). In some schemes, organic compounds are lumped according to emissions types, anthropogenic or biomass burning (Spracklen et al., 2011; Hodzic et al., 2016), whereas in others they are grouped according to volatility (Donahue et al., 2006, 2011). By lumping organic species together, chemical ageing can be accounted for, even if the exact mechanism is not known. However, in grouping species together, molecular information is lost and therefore it is challenging to select the appropriate reaction coefficients and SOA yields from laboratory studies (Kelly et al., 2018). In more complex SOA schemes, gas-phase oxidation is treated explicitly (Lin et al., 2012, 2014;Khan et al., 2017), but this method is limited to SOA precursors with relatively well-known oxidation mechanisms.
The sources and physicochemical processes of hydrocarbons included within SOA
schemes also vary between models. Examples of model diversity include the
inclusion of SOA formation within the aqueous phase (Lin et al., 2014) and
from S/IVOCs (Pye and Seinfeld, 2010), as well as SOA being treated as
semi-volatile as opposed to non-volatile (Shrivastava et al., 2015). The
treatment of dry (Bessagnet et al., 2010) and wet deposition (Knote et
al., 2015) of SOA precursors is an another aspect of SOA which varies from
model to model. Recent field and modelling studies have provided evidence
that several known SOA precursors are susceptible to deposition. For example,
explicit modelling of the oxidation of terpene and aromatic VOCs has
identified extremely soluble products, with effective Henry's constants
(
Vegetation is estimated to release around 1000 Tg (C) of VOCs into the
atmosphere annually (Guenther et al., 2006, 2012). Estimates of the global
annual-total SOA production rate from biogenic VOCs range from 27.6 to
97.5 Tg (SOA) a
The first studies to quantify the SOA yields from aromatic compounds (Odum et
al., 1997, 1996) provide yields that are not high enough to account for the
concentrations of aromatic SOA observed in field studies (Tsigaridis and
Kanakidou, 2003; Hoyle et al., 2007). For instance, early estimates of SOA
yields from aromatic compounds, which were conducted in relatively high
nitrogen oxide (
The exact mechanism describing aromatic oxidation is not yet fully
understood, despite considerable progress to date (Kautzman et al., 2010; Li
et al., 2016, 2017b; Al-Naiema and Stone, 2017; Schwantes et al., 2017). As
aromatic oxidation is initiated by the hydroxyl radical (OH), the influence
of
The peroxy radical reaction intermediate, together with competitive NO and
The objective of this study is to further develop the SOA scheme within a
chemistry–climate model, the United Kingdom Chemistry and Aerosol (UKCA)
model. Firstly, the model is updated to include the wet and dry deposition of
SOA precursors. Secondly, the mechanism describing SOA formation from
anthropogenic and biomass burning VOCs is updated to account for the
influence of
In this section, the model is briefly described. This begins with a brief
description of the default configuration, followed by the model developments
made in this study. The chemistry–climate model used in this study is the
United Kingdom Chemistry and Aerosol (UKCA) model (Morgenstern et al., 2009;
Mann et al., 2010; O'Connor et al., 2014) which is coupled to the Global
Atmosphere 4.0 (GA4.0) configuration (Walters et al., 2014)
of the Hadley Centre Global Environmental Model (Hewitt et al., 2011)
version 3 (HadGEM3). The atmosphere-only configuration with prescribed sea
surface temperature and sea ice fields based on 1995–2004 reanalyses data
(Reynolds et al., 2007) was used. The model was run at a horizontal
resolution of N96 (1.875
The United Kingdom Chemistry and Aerosol (UKCA) model used in this study
combines the “TropIsop” tropospheric chemistry scheme from O'Connor et
al. (2014) with the stratospheric chemistry scheme from Morgenstern et
al. (2009). There are 75 species with 285 reactions. This includes odd oxygen
(
Within UKCA, wet deposition of gases is calculated as a first-order process
as a function of precipitation, following Walton et al. (1988). For a
detailed description of the wet deposition within UKCA, see O'Connor et
al. (2014). Within each grid box, the scavenging rate,
Dry deposition refers to the transfer of chemical species from the atmosphere
to the surface in the absence of precipitation. Dry deposition of gas-phase
species within UKCA has also been described in detail before (O'Connor et
al., 2014) so is only described briefly here. The dry deposition velocity
(
The quasi-laminar resistance term,
The aerosol component of UKCA is the 2-moment modal version of the Global
Model of Aerosol Processes (GLOMAP-mode; Mann et al., 2010). Both aerosol
mass and number are transported in seven internally mixed log-normal modes
(four soluble and three insoluble). Aerosol components considered are
sulfate (
The emissions used in this study are all monthly-varying decadal-average values centred on the year 2000. Anthropogenic and biomass burning gas-phase
emissions are prescribed following Lamarque et al. (2010). Biogenic emissions
of isoprene, monoterpene, and methanol (
In this section, the current treatment of SOA in the UKCA model is first
described, followed by descriptions of new treatments of precursor deposition
and oxidation mechanisms. Within the model, SOA is treated by a coupling
between the UKCA gas-phase chemistry and GLOMAP-mode. Emitted parent
hydrocarbon gases undergo a single-step oxidation, forming a secondary
organic gas (SOG) which condenses, forming SOA. This is shown in Eq. (8),
In this study, SOA production is considered from gas-to-particle partitioning
of VOC oxidation products. S/IVOC emissions are not considered and aqueous-phase SOA production is not included. These include monoterpene, isoprene,
VOC
Kinetic parameters used to calculate rate coefficient (Eq. 1) for
both existing and new SOA precursors, taken from Atkinson and Arey (2003).
Note, VOC
Precursors of SOA include the emitted parent hydrocarbons (monoterpene,
isoprene, VOC
Surface resistances for SOA precursors over the nine different surface types in the model. “Low” represents surface resistances of ROOH, which are taken field studies (Hall et al., 1999; Nguyen et al., 2015). “High” represents surface resistances of CO.
As discussed in Sect. 2.4, initially VOC
Formation of lower volatility vapours from toluene photooxidation, as described in Ng et al. (2007).
Figure 1 shows a mechanistic description of SOA production from toluene,
accounting for the influence of
In this study, 10 simulations were performed to explore the influence of
hydrocarbon deposition and oxidation mechanisms on SOA, and are described in
Table 3. The duration of all simulations is 2 years, spanning from 1999 to
2000. The first year was discarded as spin-up, and analysis was performed on
the second year – 2000. Firstly, a control simulation was conducted, where
the oxidation of all parent hydrocarbons (isoprene, monoterpene,
VOC
Simulations conducted in this study. Surface resistances, Low and
High, are shown in Table 2. For both surface resistances and
Next, the influence of VOC oxidation mechanisms on SOA was explored by
modifying the mechanistic description of SOA production from anthropogenic
and biomass burning VOCs. As discussed in Sect. 1, oxidation mechanisms
within SOA schemes vary substantially. Therefore, in this section, where
necessary, changes to VOC
This section describes the observations used to test the effects of variations in hydrocarbon physicochemical processes on model performance. To make direct comparisons, and provide a consistent method for evaluating model performance, a suite of observations were chosen which are identical to those used in previous studies involving the UKCA model (Kelly et al., 2018).
The aerosol mass spectrometer (AMS) allows on-line detection of submicron
non-refractory aerosol (Jayne et al., 2000; Canagaratna et al., 2007). This
method was used to measure OA concentrations for all observations utilised in
this study. Uncertainties associated with this method are estimated to be
between 30 % and 50 % (Bahreini et al., 2009). All observations used
in this study can be accessed on the AMS global network website
(
Surface OA observations from the AMS network, originally compiled by Zhang et al. (2007), span the time period 2000–2010. The 37 observed surface measurement locations are shown in Fig. 2 and coloured according to the environment sampled: urban, urban downwind, or remote. With the exceptions of Manaus (Brazil; Martin et al., 2010) and Welgegund (South Africa; Tiitta et al., 2014), all surface OA spectra were analysed further using factor analysis, classifying OA as either oxygenated OA (OOA) or hydrocarbon-like OA (HOA). Here, measured OOA is assumed comparable to modelled SOA, and measured HOA is assumed comparable to POA. For each observation, the corresponding model-grid box was selected. Also, observations were compared to the simulated monthly-mean from the year 2000.
Global map showing the 40 surface AMS observations, originally compiled by Zhang et al. (2007) and classified as urban (red triangles), urban downwind (blue squares), or remote (green circles). Of the surface observations, 37 have been classified as hydrocarbon-like OA and oxygenated OA. Observations from 10 aircraft campaigns, originally compiled by Heald et al. (2011), are also shown (light-blue diamonds); these remain as total OA.
Observed OA concentrations from several aircraft campaigns were also used.
Observation data from these aircraft campaigns, which were originally
compiled by Heald et al. (2011), can also be accessed on the AMS global
network website (
In this section, the influence of VOC deposition (Sect. 2.4.1) on simulated SOA is quantified. Next, the influence of VOC deposition on model agreement with observations is evaluated.
When precursor deposition is neglected from the model, the simulated global
annual-total SOA production rate is 75 Tg (SOA) a
Wet removal also has a substantial impact on SOA. For example, under the
assumption of an effective Henry's coefficient of
Generally, global- (Hodzic et al., 2016) and regional-scale modelling studies (Bessagnet et al., 2010; Knote et al., 2015) suggest that dry deposition of precursor dominates over wet deposition. Therefore, for subsequent
simulations, where both dry and wet removal were included in the model
(DryH_WetL), surface resistances corresponding to Dry_High, which had
the largest impact on global SOA production, were used, along with
Prior to including deposition of SOA precursors, biogenic VOCs account for
57 % of the global annual-total SOA production rate, with
VOC
Figure 3 shows the sensitivity of annual-average surface SOA concentrations
to precursor deposition. The spatial distribution of SOA closely reflects the
location of biogenic, anthropogenic, and biomass burning emissions, as noted
previously (Kelly et al., 2018). Over India, extremely high anthropogenic
emissions combine with moderate biogenic emissions to result in
annual-average surface SOA concentrations reaching up to
17
Annual-average surface SOA concentrations for
Over India and tropical forest regions of South America and Africa, including
VOC dry deposition reduces annual-average surface SOA concentrations by 1.5
to 5
Until now, the impacts of precursor deposition on SOA concentrations have only been quantified over Europe (Bessagnet et al., 2010) and North America (Knote et al., 2015), both of which use regional-scale models and treat SOA as semi-volatile. Note, Bessagnet et al. (2010) treat SOA formation by a single-step oxidation of parent VOC followed by reversible condensation into the aerosol phase. Knote et al. (2015) treat SOA formation using the volatility basis set (VBS). The sensitivity of SOA to precursor dry removal is in broad agreement with Bessagnet et al. (2010), who estimate that precursor dry deposition reduces July-average surface SOA concentrations by 20 %–40 % over Europe compared to 25 %–35 % for the same period in our study. Also, Knote et al. (2015) estimate that precursor dry deposition reduces annual-average surface SOA concentrations by 46 % over North America, compared to up to 20 %–35 % in our study. The modelled sensitivity of SOA concentrations to wet deposition in this study is in relatively good agreement with Knote et al. (2015), who estimate a 10 % reduction in annual-average surface SOA concentrations over North America when precursor wet deposition is included, which agrees with the 5 %–15 % reduction found here.
When dry and wet removal of VOC precursors are both included, SOA
concentrations are substantially lower. However, as noted before, the effects
of these removal processes do not add linearly. Inclusion of both dry and wet
deposition of SOA precursors reduces annual-average surface SOA
concentrations by 25 %–40 % over most continental regions (Fig. 3d),
with maximum reductions of 5
The lifetime of SOA precursors with respect to both oxidation and deposition is small. Hence, SOA precursors undergo very little transport before removal. Therefore, dry and wet deposition rates of VOCs are largest over terrestrial environments, where they are released. Across these simulations where the deposition of SOA precursors is altered, the global-average annual-average SOA lifetime varies from 4.3 to 4.7 d (not shown).
In this section, the influence of SOA precursor deposition on model agreement with observations is quantified. First, simulated SOA and OA concentrations are evaluated against surface observations in the Northern Hemisphere (NH) and Southern Hemisphere (SH), respectively. Next, vertical profiles of simulated OA concentrations are compared against aircraft observations.
Figure 4 shows SOA concentrations for the simulations described in Table 2
compared to observed surface SOA concentrations across the NH mid-latitudes,
which are shown in Fig. 2. Observed SOA concentrations are in the form of
averages over the campaign period (which ranges from a few days to 1 year),
and span from 2000 to 2010. These observed concentrations are then matched to
the grid box which they fall in, with the simulated monthly averages being
selected for the year 2000. Hence, there is a mismatch in terms of the
measurement year and the simulated year. When deposition of SOA precursors is
omitted from the model, simulated SOA concentrations are substantially lower
than observed, with a normalised mean bias (NMB) of
Simulated versus observed surface SOA concentrations
(
The model negative bias with respect to observed SOA concentrations is common among global models (Tsigaridis et al., 2014). For several modelling studies, the negative bias is primarily attributed to either underestimated reaction yields, underestimated emissions, and/or missing emission sources. Hodzic et al. (2016) partially attributes the model negative bias with respect to observations to laboratory-derived SOA yields which do not account for wall losses. Other studies highlight VOC emission uncertainties such as underestimates in inventories (Li et al., 2017a) or the absence of semi- and intermediate-volatility organic compounds (S/IVOCs) which can contribute to SOA (Pye and Seinfeld, 2010), both of which are not included in this study.
Inclusion of precursor deposition further reduces model agreement with
observations. As discussed in Sect. 4.1, including VOC dry deposition reduces
the global annual-total SOA production rate by 32 %
(24 Tg (SOA) a
Observed and simulated OA are shown in Fig. 5 for two sites in the tropics and SH, over Manaus (Brazil) and Welgegund (South Africa). Without precursor deposition, simulated SOA is overestimated compared to observed OA over Manaus (Brazil) (Fig. 5a), but underestimated over Welgegund (South Africa) (Fig. 5b). Therefore, inclusion of precursor deposition improves model performance over Manaus (Brazil) (Fig. 5a) but not over Welgegund (South Africa) (Fig. 5b). However, the scarcity of observations in the tropics and the SH result in difficulty in drawing robust conclusions on the influence of precursor deposition on model agreement with observations in this region.
Simulated and observed OA surface concentrations
(
Figure 6 shows the simulated OA vertical profiles against the AMS aircraft
measurements. Without precursor deposition, model negative biases are again
evident and are largest in polluted and biomass-burning-influenced regions in
the NH. For example, over Europe (ADIENT, ADRIEX, and EUCAARI) and North
America (ARCTAS-A, ARCTAS-B and ARCTAS-CARB), OA concentrations are
underestimated by 71 % (ARCTAS-CARB; Fig. 6j) to 97 % (ARCTAS-B;
Fig. 6h) when considering all altitudes. When VOC precursors of SOA do not
undergo deposition, over western Africa, simulated OA concentrations are in
good agreement between 0 and 3 km (Fig. 6k). However, above 3 km, model and
simulated OA concentrations begin to deviate, with observed OA increasing
with altitude, but modelled OA decreasing with altitude (Fig. 6k). When
considering all altitudes of the AMMA campaign, modelled and measured OA
concentrations are in fairly good agreement with a NMB of
Mean vertical profile of OA (
Over North America and Europe, including precursor deposition slightly worsens the model negative bias. When both precursor dry and wet deposition are included, the model underestimates observed OA concentrations by 75 % (ARCTAS-CARB; Fig. 6j) to 98 % (ARCTAS-B; Fig. 6h). Over West Africa, when VOC precursors of SOA undergo deposition, the model underestimates observed OA concentrations by 61 % (Fig. 6k).
Compared to other environments, in remote regions, model agreement with
observations is relatively good, and the inclusion of precursor deposition
results in both improvements and degradations in model biases in simulated OA
compared to observations. Without SOA precursor deposition, simulated OA
levels in VOCALS and ITOP-UK, similar to the pollution and biomass burning
influenced regions, are much lower compared to observed OA (NMB
Overall, the inclusion of precursor deposition influences model agreement with observations somewhat. In particular, inclusion of precursor deposition worsens model negative biases with respect to observations in the NH mid-latitudes. However, differences between simulated OA concentrations from these simulations is substantially less than the difference between simulated and observed OA. These results highlight that variations in VOC deposition contribute to considerable uncertainty in both the global SOA budget and have some impact on model agreement with observations.
In this section, the sensitivity of SOA to hydrocarbon oxidation mechanisms is quantified. Here, oxidation mechanisms for anthropogenic and biomass burning VOCs are modified as described in Sect. 2.4.2. To begin with, the influence of anthropogenic and biomass burning VOC oxidation mechanisms on simulated SOA is explored. Next, the impact on model agreement with observations is evaluated. In all simulations, deposition of SOA precursors is included (Table 3), emissions of all SOA precursors are held constant, and the mechanistic description describing the oxidation of biogenic SOA precursors (monoterpene and isoprene) is held fixed, following Eq. (8).
Global annual-total reaction fluxes and total SOA production rate
from anthropogenic and biomass burning hydrocarbons for the simulations
described in Table 3. The global annual-total VOC
Firstly, the single-step oxidation mechanism of VOC
The combination of a single-step oxidation mechanism and the assumption of a
relatively reactive parent hydrocarbon results in rapid production of SOA.
Figure 8 shows the spatial distributions of annual-total surface
VOC
Global distributions of
Also, as shown in Eq. (8), oxidation of the parent VOC results in immediate
production of the condensing species, SOG. Hence, not only do parent VOCs
undergo rapid oxidation, but the product of this reaction is in the form of
condensable organic vapours. Therefore, this combination of high parent VOC
reactivity with few reaction steps results in extremely localised SOA
production from anthropogenic and biomass burning emissions. This is in
contrast to other global modelling studies, which predict more regionally
distributed SOA production (Pye and Seinfeld, 2010; Tsimpidi et al., 2016).
Differences in the geographical extent to which SOA production occurs may be
attributed to precursor reactivity and the number of reaction intermediates.
For example, here, the parent hydrocarbon is a VOC, with a rate constant of
To summarise, the combination of fast reactivity and a single-step oxidation mechanism favours extremely localised SOA production, with parent VOCs undergoing rapid oxidation and subsequent condensation close to source.
In the following subsections, SOA formation mechanisms are altered,
including introducing a reaction intermediate, accounting for the influence
of oxidants on SOA yields, and reducing the chemical reactivity of the parent
VOC (Eq. 9; Sect. 2.4.2). This begins with an evaluation of the mechanism
with the reaction intermediate and with reactivity based on naphthalene
(Multi_nap) and how this mechanism compares to the single-step oxidation
mechanism with reactivity based on
Production of SOA from anthropogenic and biomass burning hydrocarbons is
modified in the following subsections to follow the mechanism of Eq. (9)
which include the reaction intermediate. Naphthalene, the most reactive
aromatic VOC considered in this study, is first selected (Sect. 2.4.2), with
identical reaction yields applied to both
The initial reaction of VOC
Global distribution of the absolute differences in annual-total
vertically integrated VOC
The response of regional VOC oxidation rates to a
Oxidation of the parent VOC forms a new reaction intermediate, the peroxy
radical
For the peroxy radical, chemical removal (top row;
Chemical removal of the peroxy radical via the two oxidative pathways is an
important factor in governing the strength of SOA production, as discussed
later in Sect. 5.1.3 to 5.1.5.
Global distributions of annual-average
The substantial preference for
Differences in reactivity of
The ratio,
For this mechanism with parent VOC reactivity based on naphthalene
(Multi_nap), the initial oxidation and subsequent reaction of the
intermediate were discussed in Sect. 5.1.1 and 5.1.2, respectively. In this
section, the production of SOA from this mechanism is examined. In this
oxidation scheme, identical reaction yields of 13 % are applied for both
the
The difference in annual-average surface SOA concentrations for the
mechanisms with the reaction intermediate relative to the mechanisms without
the reaction intermediate with reactivity based on
Difference in annual-average surface SOA concentrations, expressed
as absolute concentrations (
To summarise, moving from a mechanism with no reaction intermediate and with
the reactivity of
In this section, the effects of accounting for the difference in volatility
between
Accounting for differences in volatility between
The relative spatial homogeneity of
As discussed in Sect. 2.4, VOC
Firstly, consider how reactivity affects SOA production among the oxidation
mechanisms which include the reaction intermediate (Multi_nap_yield,
Multi_tol_yield, and Multi_benz_yield). Reducing the chemical
reactivity of VOC
Secondly, consider the net effects of using aromatic oxidation to describe
SOA production from VOC
The spatial distribution of SOA is also influenced by these changes in
VOC
The spatial pattern simulated in the oxidation mechanism with the reaction intermediate and with reactivity based on benzene is in greater agreement with the more regionally distributed SOA concentrations simulated in models based on S/IVOC sources (Pye and Seinfeld, 2010; Tsimpidi et al., 2016).
In this section, the influence of anthropogenic and biomass burning
hydrocarbon oxidation mechanisms on model agreement with observations is
quantified. Reduced parent hydrocarbon reactivity combined with accounting
for the different SOA yield pathways of the peroxy radical affects model
agreement with observations. Figure 13 shows simulated versus observed
surface SOA concentrations for the NH from the simulations described in
Table 3. In the oxidation mechanisms which include the reaction intermediate,
using naphthalene and toluene, the annual-total SOA production rate increased
relative to the single-step fast oxidation pathway. This increase was due to
the difference in volatility between products of the peroxy radical oxidation
pathways, despite the reduction in parent hydrocarbon reactivity. Therefore,
simulated SOA concentrations are in closer agreement to observations
(Multi_nap_yield; NMB
Simulated versus observed SOA concentrations (
For the aircraft campaigns, mechanisms of anthropogenic and biomass burning
oxidation have a limited influence on model agreement with observations. For
the campaigns in remote regions, VOCALS (Fig. 6a), TROMPEX (Fig. 6b), and OP3
(Fig. 6c), and over western Africa (AMMA; Fig. 6k), introduction of the
reaction intermediate combined with a reduction in reactivity (cf.
DryH_WetL and Multi_nap) has no effect on the NMB. However, when accounting
for the high-yield
In this study, the description of both deposition and oxidation for SOA precursors was developed in a global chemistry–climate model. Several model integrations were conducted and the treatments of deposition and oxidation mechanisms of SOA precursors were varied. Subsequent effects on the global SOA budget were quantified and simulated OA was evaluated against a suite of surface and aircraft campaigns spanning both the Southern Hemisphere and Northern Hemisphere.
Within UKCA, SOA formation is considered from VOCs – monoterpene, isoprene,
a lumped anthropogenic VOC (VOC
Production of SOA from aromatic compounds, which are typically emitted from
anthropogenic and biomass burning activities, has been partially elucidated
by environmental chamber studies. Briefly, parent aromatic hydrocarbons are
oxidised by the hydroxyl radical (OH) to form a reaction intermediate, the
peroxy radical (
The influence of VOC oxidation mechanisms on the global SOA budget was also
examined. For the anthropogenic and biomass burning sources of SOA
(VOC
Overall, the effects of using aromatic oxidation to describe SOA formation
from anthropogenic and biomass burning compounds versus using a single-step
mechanism with reactivity based on
These variations in oxidation mechanisms can either improve or worsen model
agreement with observations, depending on the chemical reactivity of the
parent VOC. In the absence of the reaction intermediate, and with reactivity
based on
In this study, observed
These results highlight that the global SOA budget is highly sensitive to
hydrocarbon physicochemical processes. For example, the global annual-total
SOA production rate has varied from 47 to 75 Tg (SOA) a
Despite the limitations of this study, such as the lack of chemical complexity and geographical coverage of observations, it is apparent that SOA precursor deposition and oxidation contribute considerably towards uncertainties in both the global SOA budget and model agreement with observations. These results highlight the need for greater insight into the physicochemical processes of gas-phase hydrocarbons related to SOA production together with a greater density of observations.
The model used in this study is the Global Atmosphere
4.0 (GA4.0) configuration of the HadGEM3 climate model with interactive
chemistry and aerosols from UKCA, both of which are based on the UK Met
Office's Unified Model (UM). Due to intellectual property right restrictions,
we cannot provide either the source code or documentation papers for the UM.
The Met Office Unified Model is available for use under licence. A number of
research organisations and national meteorological services use the UM in
collaboration with the Met Office to undertake basic atmospheric process
research, produce forecasts, develop the UM code, and build and evaluate Earth
system models. For further information on how to apply for a licence, see
JMK developed, ran, and analysed the model simulations in this study, all under the supervision of RMD and FMO'C. JMK, RMD, and FMO'C wrote the paper. GWM provided guidance on model development. HC and DL provided observational data from an aircraft campaign.
The authors declare that they have no conflict of interest.
This work is supported by Natural Environment Research Council (NERC; NE/L008947/1) and the Met Office through a CASE award. The development of UKCA and Fiona M. O'Connor are supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). Fiona M. O'Connor also acknowledges additional funding received from the Horizon 2020 European Union's Framework Programme for Research and Innovation “Coordinated Research in Earth Systems and Climate: Experiments, Knowledge, Dissemination and Outreach (CRESCENDO)” project under grant agreement no. 641816. Computer resources provided by the Met Office, the MONSooN supercomputer facility, were used for the UKCA simulations reported here. The MONSooN system is a collaborative facility supplied under the Joint Weather and Climate Research Programme (JWCRP), which is a strategic partnership between the Met Office and NERC. ERA-Interim data used in this study were provided by the European Centre for Medium Range Weather Forecasts (ECMWF).
This paper was edited by Jason Williams and reviewed by three anonymous referees.