Chemical transport models have historically struggled to
accurately simulate the magnitude and variability of observed organic
aerosol (OA), with previous studies demonstrating that models significantly
underestimate observed concentrations in the troposphere. In this study, we
explore two different model OA schemes within the standard GEOS-Chem
chemical transport model and evaluate the simulations against a suite of 15
globally distributed airborne campaigns from 2008 to 2017, primarily in the
spring and summer seasons. These include the ATom, KORUS-AQ, GoAmazon,
FRAPPE, SEAC4RS, SENEX, DC3, CalNex, OP3, EUCAARI, ARCTAS and ARCPAC
campaigns and provide broad coverage over a diverse set of
atmospheric composition regimes – anthropogenic, biogenic, pyrogenic and
remote. The schemes include significant differences in their treatment of
the primary and secondary components of OA – a “simple scheme” that models
primary OA (POA) as non-volatile and takes a fixed-yield approach to
secondary OA (SOA) formation and a “complex scheme” that simulates POA as
semi-volatile and uses a more sophisticated volatility basis set approach
for non-isoprene SOA, with an explicit aqueous uptake mechanism to model
isoprene SOA. Despite these substantial differences, both the simple and
complex schemes perform comparably across the aggregate dataset in their
ability to capture the observed variability (with an R2 of 0.41 and
0.44, respectively). The simple scheme displays greater skill in minimizing
the overall model bias (with a normalized mean bias of 0.04 compared to 0.30 for the
complex scheme). Across both schemes, the model skill in reproducing
observed OA is superior to previous model evaluations and approaches the
fidelity of the sulfate simulation within the GEOS-Chem model. However,
there are significant differences in model performance across different
chemical source regimes, classified here into seven categories.
Higher-resolution nested regional simulations indicate that model resolution
is an important factor in capturing variability in highly localized
campaigns, while also demonstrating the importance of well-constrained
emissions inventories and local meteorology, particularly over Asia. Our
analysis suggests that a semi-volatile treatment of POA is superior to a
non-volatile treatment. It is also likely that the complex scheme
parameterization overestimates biogenic SOA at the global scale. While this
study identifies factors within the SOA schemes that likely contribute to OA
model bias (such as a strong dependency of the bias in the complex scheme on
relative humidity and sulfate concentrations), comparisons with the skill of
the sulfate aerosol scheme in GEOS-Chem indicate the importance of other
drivers of bias, such as emissions, transport and deposition, that are
exogenous to the OA chemical scheme.
Introduction
Aerosols in the atmosphere have significant climate impacts through
radiative scattering and cloud formation (IPCC, 2013; Ramanathan et al., 2001).
Exposure to these particles is also detrimental to human health and is
associated with over 4 million premature deaths per year worldwide (Pope and
Dockery, 2006; Cohen et al., 2017). Organic aerosol (OA) often accounts for
a large portion of the total fine aerosol burden (Jimenez
et al., 2009), a fraction that has been increasing over time, particularly
in regions where sulfur dioxide controls have reduced anthropogenic sources
of sulfate (Marais et al., 2017).
Characterizing aerosol impacts on air quality and climate thus requires a
comprehensive understanding of the life cycle of organic aerosol in the
atmosphere.
Organic aerosol that is emitted directly into the atmosphere from
anthropogenic or natural sources is called primary organic aerosol (POA). A
significant fraction of primary organic emissions has been shown to be
semi-volatile, partitioning between the gas and particle phase depending on
the ambient temperature and background organic aerosol concentration (Grieshop
et al., 2009; Lipsky and Robinson, 2006; Robinson et al., 2007; Shrivastava
et al., 2006). As these compounds are dispersed through the atmosphere, they
are oxidized (in both the gas and particle phase) and typically form lower-volatility products. In addition to the primary component, organic aerosol
is also generated dynamically in the atmosphere from volatile organic
compound (VOC) and intermediate-volatility organic compound (IVOC)
precursors that are both anthropogenic (e.g., benzene, toluene, xylene) and
biogenic (e.g., isoprene, monoterpenes, sesquiterpenes). These gas-phase
precursors undergo multi-phase, multigenerational oxidation processes that
result in a complex array of semi-volatile species that partition into
organic aerosol under conducive conditions. This class of aerosol products
is called secondary organic aerosol (SOA). Both POA and SOA are important
drivers of climate and air quality, often influencing regions far removed
from their original source locations (Kanakidou et al.,
2005).
Primary organic aerosol has traditionally been modeled as non-volatile (e.g., Chung and Seinfeld, 2002), but recent studies have
incorporated a semi-volatile treatment that allows the aerosol species to
dynamically partition between the condensed phase and gas phase, while
simultaneously undergoing gas-phase oxidation to form organic compounds of
lower volatility (Donahue
et al., 2006; Robinson et al., 2007; Huffman et al., 2009; Pye and Seinfeld,
2010). There has been a similar evolution in the methods to model the
formation and chemical processing of SOA in the atmosphere. Initial global
modeling efforts often simulated SOA as a species that is directly formed
upon the emission of various precursors, based on a fixed yield from laboratory
or field studies (Chin
et al., 2002; Kim et al., 2015; Pandis et al., 1992; Park et al., 2003).
Many earth system models continue to use this simple approach (Tsigaridis et
al., 2014). The two-product absorptive reversible partitioning scheme was
then developed to account for the semi-volatile nature of SOA using lumped
oxidation products from precursor VOCs (Odum et al., 1996;
Pankow, 1994). Advances in computational resources have enabled recent
studies to more effectively capture the volatility distribution of organics
using a volatility basis set (VBS) of volatility-resolved semi-volatile
surrogates that absorptively partition based on dry ambient OA
concentrations (Donahue
et al., 2006; Pye et al., 2010). There have also been more explicit chemical
treatments of organic aerosol formation, such as those involving the
implementation of a master chemical mechanism coupled with equilibrium
absorptive partitioning and reactive surface uptake mechanisms (Li
et al., 2015; Xia et al., 2008) or the explicit description of irreversible
aqueous OA formation pathways (Fisher
et al., 2016; Lin et al., 2012; Marais et al., 2016).
The wide range of VOC precursors, the complexities of the various formation
pathways and the limited laboratory constraints on these processes make
accurately modeling OA concentrations highly challenging. Previous model
studies have identified large underestimates in the simulated OA when
compared to observations (e.g.,
Heald et al., 2011; Volkamer et al., 2006). Over the past decade, the
treatment of organic aerosol in chemical transport models has grown in
complexity with models showing improved regional skill at simulating OA over
areas like the southeast US (Marais
et al., 2016; Budisulistiorini et al., 2017). However, studies that have
evaluated OA model simulations against globally distributed measurements
have demonstrated a consistent model inability to capture the magnitude and
variability of observed OA concentrations (Heald
et al., 2011; Tsigaridis et al., 2014). In particular, the evaluation by
Heald et al. (2011) that used a two-product OA scheme revealed significant
deficiencies in model skill and suggested that the GEOS-Chem model
underestimated both the sources and sinks of OA at the global scale. The
complex nature of OA formation and loss mechanisms in the atmosphere has
thus made it difficult to constrain global models using a bottom-up
approach, particularly given the uncertainties inherent in the various
emission inventories and chemical mechanisms. Here, we use a top-down
approach, leveraging a suite of 15 aircraft campaigns to evaluate the two
different organic aerosol schemes implemented within the standard GEOS-Chem
chemical transport model in order to assess their relative strengths and
weaknesses over a wide range of chemical and spatial regimes.
Model description
We use the chemical transport model GEOS-Chem (http://www.geos-chem.org, last access: 5 January 2019) to simulate organic aerosol mass concentrations along
the flight tracks of a suite of airborne campaigns described in Sect. 3. In
order to contrast the different approaches to modeling organic aerosol and
its precursors in the atmosphere, we perform a series of simulations from
2008 to 2017 using two distinct model schemes that vary based on their
treatment of organic aerosol (see Sect. 2.1 and Table S1 in the Supplement).
These simulations were performed with the GEOS-Chem model version 12.1.1
(10.5281/zenodo.2249246; International GEOS-Chem User Community, 2018) with a horizontal
resolution of 2∘×2.5∘ and 47 vertical hybrid-sigma
levels that extend from the surface to the lower stratosphere. A series of
nested simulations, over North America and Asia, were performed at a higher
spatial resolution of 0.5∘×0.625∘ using boundary
conditions from the 2∘×2.5∘ global run. The model is
driven by the MERRA-2 assimilated meteorological product from the NASA
Global Modeling and Assimilation Office (GMAO) with a transport time step of
10 min as recommended by Philip et al. (2016). The model
includes a coupled treatment of HOx–NOx–VOC–O3 chemistry (Mao et al.,
2013; Travis et al., 2016; Chan Miller et al., 2017) with integrated Cl–Br–I
chemistry (Sherwen et al., 2016) and uses a bulk aerosol
scheme with fixed lognormal modes (Martin et al.,
2003). GEOS-Chem simulates sulfate aerosol (Park, 2004),
sea salt (Jaeglé et al., 2011), black carbon (Park et al., 2003) and mineral dust (Fairlie et al., 2007;
Ridley et al., 2012). Ammonium and nitrate thermodynamics are described
using the ISORROPIA II model (Fountoukis and Nenes,
2007). Deposition losses are dictated by aerosol and gas dry deposition to
surfaces based on a resistor-in-series scheme (Wesely, 1989;
Zhang et al., 2001) and wet deposition from scavenging by rainfall and moist
convective cloud updrafts (Amos
et al., 2012; Jacob et al., 2000; Liu et al., 2001). More details on the
deposition schemes are provided in the Supplement.
Organic aerosol simulations
This study evaluates the two standard organic aerosol schemes within the
GEOS-Chem model. The complex scheme represents a more detailed, recently
updated treatment of organic aerosol in the atmosphere based on numerous
laboratory studies and an explicit chemical mechanism for the oxidation of
isoprene. The simple scheme is designed to serve as a computationally
efficient alternative for approximating tropospheric OA concentrations
without attempting to model the formation and fate of the various aerosol
species mechanistically and without explicit thermodynamic partitioning. We
note that the simple scheme was developed independently from the complex
scheme and should not be regarded as a reduced version of the complex
scheme. These schemes are described below and are graphically illustrated in
Fig. 1.
A graphical overview of the two organic aerosol model schemes in
GEOS-Chem. TERP denotes monoterpenes and sesquiterpenes. Pyrogenic VOCs
(FVOCs) denote the various volatile and semi-volatile organic compounds
emitted from fires, while anthropogenic VOCs (AVOCs) are comprised of benzene,
toluene, xylene and various intermediate-volatility organic compounds that
are modeled using naphthalene as a proxy. OPOA∗ is sometimes classified as
secondary organic aerosol from SVOCs. MPOA∗∗ denotes lumped
marine POA consisting of both fresh (M-EPOA) and oxidized (M-OPOA)
components. Species in orange contribute to OA; images modified from https://openclipart.org/ (last access: 11 January 2018) and https://publicdomainvectors.org/ (last access: 5 December 2019). See Sect. 2.1 in the text for
details.
The simple scheme treats all organic aerosol as non-volatile. The POA
consists of a hydrophobic “emitted” component (EPOA) with an assumed organic-mass-to-organic-carbon (OM : OC) ratio of 1.4 and a hydrophilic “oxygenated”
component (OPOA) with an assumed OM : OC ratio of 2.1. Of the organic carbon
emitted from primary sources, 50 % is assumed to be hydrophilic (OPOA) to
simulate the near-field oxidation of EPOA. The atmospheric aging of EPOA is
modeled by its conversion to hydrophilic aerosol (OPOA) with an atmospheric
lifetime of 1.15 d, with no explicit dependence on local oxidant levels (Chin
et al., 2002; Cooke et al., 1999). The EPOA and OPOA species are represented
within the GEOS-Chem model using the variable names OCPO and OCPI,
respectively. In addition, GEOS-Chem includes an online emission
parameterization for submicron non-volatile marine primary organic aerosol
(MPOA) as described in Gantt et al. (2015). The marine POA is emitted as hydrophobic (M-EPOA) and is aged in the
atmosphere by its conversion to hydrophilic aerosol (M-OPOA), with the same
1.15 d lifetime. For the purpose of this study, the hydrophobic and
hydrophilic components have been lumped together under the MPOA moniker.
The simple scheme uses a lumped SOA product (SOAS) with a molecular weight
of 150 g mol-1 and an SOA precursor (SOAP) that is emitted from
biogenic, pyrogenic and anthropogenic sources with fixed OA yields. For biogenic SOA, a 3 % yield
from isoprene (Kim et al.,
2015) and 10 % yield from both monoterpenes and sesquiterpenes (Chin et al., 2002) is assumed.
SOA precursor emissions from combustion sources are estimated using CO
emissions as a proxy, with a 1.3 % scaled co-emission of SOAP from fire
and biofuel CO and a 6.9 % SOAP co-emission from fossil fuel CO (Cubison
et al., 2011; Hayes et al., 2015; Kim et al., 2015). For biogenic SOA from
isoprene, monoterpenes and sesquiterpenes, 50 % is emitted directly as SOAS
to account for the near-field formation of secondary organic aerosol. The
SOAP converts to SOAS based on a first-order rate constant with a lifetime
of 1 d as it is transported through the troposphere (Fig. 1).
For the purpose of this study, the default simple scheme in GEOS-Chem was
modified to individually simulate 14 OA lumped model tracers from
anthropogenic, biogenic, marine and pyrogenic sources. These consisted of six
POA tracers, four SOA tracers and four SOA precursor tracers, allowing for the
independent adjustment of parameters such as emission rates, yields,
chemical lifetimes and deposition rates, enabling a robust testing of
various sensitivities and OA source attributions.
The complex scheme, based primarily on Pye et al. (2010) and Marais et al. (2016), is graphically described in Fig. 1. The primary organics are treated
as semi-volatile and allowed to reversibly partition between the aerosol (EPOA)
and gas (EPOG) phase using a two-product reversible partitioning model while
simultaneously undergoing oxidation with OH in the gas phase to form
oxidized primary organic gases (OPOGs) that, in turn, reversibly partition
to oxidized primary organic aerosols (OPOAs). Primary semi-volatile organic
vapors that are oxidized to form lower-volatility products are sometimes
classified as secondary organic aerosol (Murphy et al., 2014). However, for the
purpose of this study, we define SOA as being formed exclusively from
volatile precursors, while classifying the OA resulting from the oxidation
of primary organic compounds as OPOA, in order to be consistent with
previous model studies using GEOS-Chem (Pye et al., 2010). Model EPOG
emissions are based on the EPOA inventories used in the simple scheme and
have been scaled up by 27 % to account for semi-volatile organic matter
emitted in the gas phase (Pye
et al., 2010; Schauer et al., 2001). As in the simple scheme, the EPOA and
OPOA are assumed to have an OM : OC ratio of 1.4 and 2.1, respectively. The
complex scheme also includes the non-volatile MPOA simulation as described
above.
SOA formation from anthropogenic, pyrogenic and select biogenic precursors
is based on the VBS outlined in Pye et al. (2010) that oxidizes gas-phase
SOA precursors (with oxidants – OH, O3) to form alkyl peroxy (RO2)
radicals that react with either HO2 or NO. The SOA formed from these
second-generation products depends on the NOx regime – with high and
low NOx yields and partitioning coefficients based on experimental fits
from laboratory studies. The resulting products are classified into two
categories based on the origins of their precursors, anthropogenic SOA
(ASOA; formed from the oxidation of light aromatic compounds) and terpene
SOA (TSOA; formed from the oxidation of monoterpenes and sesquiterpenes),
that dynamically partition between the aerosol and gas phases based on their
saturation vapor pressures and ambient aerosol concentrations. Aerosol
formed from intermediate-volatility organic compounds (IVOCs) is modeled
using naphthalene as a proxy that, when oxidized, contributes to the ASOA
lumped product. A comprehensive overview of the VBS scheme can be found in
Pye et al. (2010).
The complex scheme builds on this VBS framework by incorporating aerosol
formed irreversibly from the aqueous-phase reactive uptake of isoprene (Marais et al., 2016) and the
formation of organo-nitrates (Org-Nit) from both isoprene and monoterpene
precursors (denoted in Fig. 1) based on work by Fisher et
al. (2016). These mechanisms replace the “pure-VBS” treatment of isoprene
SOA (ISOA) and organic nitrates (formed from the oxidation of isoprene and
monoterpenes by NO3) from Pye et al. (2010). The total organic aerosol
loadings in the complex scheme are thus comprised of the EPOA, OPOA, ASOA
and TSOA species in addition to the various products resulting from the
isoprene and monoterpene organo-nitrate oxidation pathways (organic nitrates
from isoprene and monoterpene precursors, aerosol-phase glyoxal,
methylglyoxal, isoprene epoxydiols – IEPOX, C4 epoxides, organo-nitrate
hydrolysis products, second-generation hydroxy-nitrates, and low-volatility
non-IEPOX products of isoprene hydroxy hydroperoxide oxidation), lumped here
as ISOA and Org-Nit. ISOA and Org-Nit are generated exclusively through the
aqueous uptake pathway and do not include any “nonaqueous” OA. The model
does not explicitly consider cloud processing of SOA. More information on
the treatment of OA in the complex scheme can be found in the Supplement.
In order to conduct a comparison with a VBS treatment of isoprene SOA (as
described in Pye et al., 2010), an analysis was also conducted with the
isoprene SOA forming exclusively through the VBS (referred to here as pure
VBS).
Emissions
Global annual mean emissions of key species for a single simulation year
(2013) are shown in Table 1. The corresponding emissions (and atmospheric
sources) for OA species are shown in Table 2. Year-specific pyrogenic
emissions are simulated at a 3 h resolution from the GFED4s satellite-derived global fire emissions database (van
der Werf et al., 2017). Global anthropogenic emissions follow the Community
Emissions Data System (CEDS) inventory (Hoesly et al., 2018).
Anthropogenic IVOC emissions are estimated using naphthalene as a proxy (see
the Supplement for more information), which is assumed to have the same spatial
distribution as benzene and is scaled from the CEDS inventory using the same
approach as Pye and Seinfeld (2010). These emissions are overwritten with
regional inventories when available, such as the National Emissions
Inventory (NEI 2011) for the US (as described by Travis et al., 2016), the
Big Bend Regional Aerosol and Visibility Observational (BRAVO) inventory for
Mexico (Kuhns et al., 2005), the Criteria Air Contaminants
(CAC) inventory for Canada (https://www.canada.ca/en/environment-climate-change.html, last access: 5 December 2019), the European
Monitoring and Evaluation Programme (EMEP) inventory for Europe (http://www.emep.int/, last access: 5 December 2019), the Diffuse and Inefficient Combustion Emissions
(DICE) inventory for Africa (Marais and
Wiedinmyer, 2016) and the MIX inventory for Asian emissions (Li et al., 2017). In addition to the anthropogenic and
pyrogenic inventories listed above, nitrogen oxides are also emitted from
lightning (Murray et al., 2012; Ott et al.,
2010), soil (Hudman
et al., 2012) and ship (Holmes et
al., 2014) sources. Biogenic emissions for isoprene and terpene species in
GEOS-Chem are based on the coupled ecosystem emissions model MEGAN (Model of
Emissions of Gases and Aerosols from Nature) v2.1 (Guenther et al., 2012). All emissions are
managed via the Harvard–NASA Emissions Component (HEMCO) module (Keller et al., 2014).
We note that given the interannual variability in emissions, particularly
from fires, the emissions for years other than 2013 may differ somewhat from
the values shown in Tables 1 and 2.
Global annual mean emissions of SOA precursors and relevant species
used in the GEOS-Chem simulation for the year 2013.
Species Annual globalemissions (Tg yr-1)Total aromatics 25.6Anthropogenic23.5Pyrogenic2.1IVOCs 5.43Isoprene 385.3Terpenes 153.6Total CO 891.2Anthropogenic593.0Pyrogenic298.2Total NOx111.7Anthropogenic70.7Pyrogenic12.1Lightning12.7Soil and fertilizer16.2
Annual mean simulated global source, burden, lifetime (against
physical deposition), and wet and dry deposition rates for the individual OA
species averaged over 2013 for the complex and simple schemes.
Complex Simple SourceBurdenLifetimeDry dep.Wet dep.SourceBurdenLifetimeDry dep.Wet dep.(Tg yr -1)(Tg)(d)(Tg yr-1)(Tg yr-1)(Tg yr-1)(Tg)(d)(Tg yr-1)(Tg yr-1)Total POA 87.3c1.466.114.772.673.8c0.924.613.260.6Emitted POA55.4d0.1111.52.11.421.80.067.81.41.4Marine EPOA7.00.024.30.90.87.00.024.30.90.8Oxygenated POAa74.1c1.276.310.263.961.3c0.784.69.451.9Marine OPOAa8.0c0.062.81.56.58.0c0.062.81.56.5Total SOA 62.9c0.915.37.355.671.71.025.29.462.2Anthropogenic SOA4.6c0.107.90.64.041.00.635.66.234.8Terpene SOA13.1c0.195.31.511.615.20.184.31.613.6Isoprene SOA22.2c0.315.12.319.911.60.154.71.110.4Organic nitrates23.0c0.314.92.920.1–––––Pyrogenic SOA–––––3.90.065.70.53.4Total OOAb145.0c2.245.619.0126.0140.9c1.864.820.3120.6Total OA 150.1c2.375.821.9128.2145.3c1.944.922.6122.8
a SVOCs from primary sources that are oxidized in the atmosphere, sometimes classified as SOA. b OOA (oxygenated organic aerosol) = OPOA + M-OPOA + SOA.c Calculated based on a steady-state assumption with depositional losses in order to account for atmospheric formation. The total POA source is not the direct sum of the individualPOA sources since a significant fraction of EPOA and MPOA forms OPOA and M-OPOA, respectively. See Sect. 2.2 for emissions totals and more information. d Primary organic emissions in the complex scheme are in the gas phase (EPOG), while primary organic emissions in the simple scheme are in the form of non-volatile particulate. An OM : OCratio of 1.4 is assumed for the EPOG and EPOA species, while an OM : OC ratio of 2.1 is assumed for the OPOA species.
In the simple scheme, 50 % of the primary OA is emitted as EPOA and 50 %
is emitted as OPOA to approximate the near-field aging of EPOA. Total OC
emissions are 31.2 Tg C. Given the OC : OM ratios of 1.4 and 2.1 assumed for
EPOA and OPOA, respectively, total POA emissions in the simple scheme are
21.8 Tg EPOA and 32.8 Tg OPOA for a total annual POA emission of 54.6 Tg. We
note that OPOA emissions in the simple scheme are a subset of the sources
listed in Table 2 since they do not include atmospheric formation through
the oxidative aging of EPOA. In the complex scheme, all POA is emitted as
gas-phase EPOG after scaling the same inventory used in the simple scheme by
27 % to account for the extra gas-phase material. Total primary emissions
in the complex scheme are thus exclusively from EPOG gas-phase emissions and
amount to 55.4 Tg yr-1. Both schemes emit an additional 7.0 Tg yr-1 of OA from marine sources. The simple scheme also directly emits
71.7 Tg yr-1 of SOA (in the form of SOAS and SOAP), over half of
which comes from anthropogenic sources. The total OA source (POA + SOA;
includes direct emissions and atmospheric formation) in both the complex and
simple schemes (150.1 and 145.3 Tg yr-1, respectively;
Table 2) is greater than the ensemble median OA source of around 100 Tg yr-1 calculated by Tsigaridis
et al. (2014) across a set of various global models.
Model evaluation
Two primary metrics have been used through this study to evaluate model
performance compared to ambient observations (see Sect. 3) – the
coefficient of determination (R2) and the normalized mean bias (NMB).
The coefficient of determination is defined by the regression fit using Eq. (1) and can be interpreted as the proportion of the variance in the
observational data that is accurately captured by the model. The normalized
mean bias is calculated using Eq. (2). A positive NMB indicates that the
model is biased high on average and vice versa.
1CoefficientofdeterminationR2=1-ResidualsumofsquaresTotalsumofsquares2NormalizedmeanbiasNMB=∑1n(model-observation)∑1n(observation)
Description of observations
For the purposes of evaluating the GEOS-Chem model, we focus on airborne
data that provide regional 3-D sampling and reduce the challenges
associated with model representation error at single sites. We further
define a set of observations that make use of a single measurement
technique, are publicly accessible and do not extend beyond the last
decade. The resulting observations are from 15 aircraft campaigns conducted
between 2008 and 2017 and cover a wide range of geographic locations and
chemical regimes. Table 3 provides a brief overview of the various campaigns
included here, and Fig. 2 shows the spatial extent of the individual flight
tracks. Aerosol concentrations were measured using aerosol mass
spectrometers (AMSs) (Jayne
et al., 2000; Canagaratna et al., 2007) with small variations in the
instrumentation and aircraft inlet configurations between the different
campaigns (as referenced in Table 3). The AMS measures submicron
non-refractory dry aerosol mass and is estimated to have an uncertainty of
34 %–38 %, depending on the species (Bahreini et al., 2009). All
concentration measurements in this study have been converted to standard
conditions of temperature and pressure (STP: 273 K, 1 atm;
µg sm-3). In addition to organic aerosol mass loadings,
concentrations of other species, such as nitrogen oxides, carbon monoxide,
isoprene and sulfate, are used in this study to validate chemical regimes
(see Sect. 4.2). Table S2 provides an overview of the instrumentation and
associated primary investigators for the organic aerosol and trace gas
observations. Environmental and meteorological measurements such as
temperature and relative humidity are also used in the analysis.
Aircraft measurements of organic aerosol used in this analysis. The
statistical metrics for OA provided here (mean, median, standard
deviation) are based on filtered data for each campaign (as discussed in the
text; units: µg m-3).
CampaignDates (UTC, mm/dd)RegionAbbreviationMeasurementMean, median,techniqueSDARCPAC (Brock et al., 2011)2008 spring (03/29–04/24)Arctic, North America–C-ToF-AMS1.9, 0.9, 2.1ARCTAS (Jacob et al., 2010)2008 spring (04/01–04/20)Arctic, North AmericaARCTAS-SPHR-ToF-AMS0.7, 0.4, 0.9ARCTAS (Jacob et al., 2010)2008 summer (06/18–07/13)Arctic, North AmericaARCTAS-SUHR-ToF-AMS3.2, 0.9, 5.1EUCAARI (Morgan et al., 2010)2008 spring (05/06–05/22)Northwest Europe–C-ToF-AMS2.5, 2.4, 2.0OP3 (Hewitt et al., 2010)2008 summer (07/10–07/20)Borneo–C-ToF-AMS0.4, 0.1, 0.5CalNex (Ryerson et al., 2013)2010 spring and summer (04/30–06/22)Southwest US–C-ToF-AMS1.3, 0.8, 1.4DC3 (Barth et al., 2014)2012 spring and summer (05/18–06/23)Central US–HR-ToF-AMS2.5, 1.4, 2.4SENEX (Warneke et al., 2016)2013 summer (06/03–07/10)Southeast US–C-ToF-AMS5.3, 4.7, 3.7SEAC4RS (Toon et al., 2016)2013 summer and fall (08/06–09/24)Southeast, west US–HR-ToF-AMS3.2, 0.6, 4.6GoAmazon (Shilling et al., 2018)2014 wet season (02/22–03/23)AmazonGOAMA-WHR-ToF-AMS1.0, 0.9, 0.6FRAPPE (Dingle et al., 2016)2014 summer (07/26–08/19)Central US–C-ToF-mAMS2.7, 2.5, 1.4GoAmazon (Shilling et al., 2018)2014 dry season (09/06–10/04)AmazonGOAMA-DHR-ToF-AMS4.6, 4.6, 1.8KORUS-AQ (Nault et al., 2018)2016 spring and summer (05/03–06/10)South KoreaKORUSHR-ToF-AMS4.8, 2.4, 5.5ATom2016 summer (07/29–08/20)Remote oceanATOM1-WHR-ToF-AMS0.1, 0.1, 0.2(Wofsy et al., 2018)North AmericaATOM1-L0.5, 0.2, 0.8ATom2017 spring (01/26–02/21)Remote oceanATOM2-WHR-ToF-AMS0.1, 0.1, 0.1(Wofsy et al., 2018)North AmericaATOM2-L0.1, 0.1, 0.1Aggregate2.4, 0.7, 3.6
Location of flight tracks for the airborne field campaigns.
Observations are gridded to the GEOS-Chem model resolution of 2∘×2.5∘ (or alternatively to 0.5∘×0.625∘ for
comparisons with nested simulations) and are averaged over the model
time step of 10 min in cases in which multiple observations were conducted
within the span of a single time step (see the Supplement for more details on model
sampling). In order to limit the impact of localized plumes, in particular
from fires, we filter the observations to remove concentrations over the
97th percentile for each campaign, eliminating measurements that can
often exceed 500 µg sm-3. This enables a more fair comparison
with the model by disregarding the impact of sub-grid features that cannot
be reproduced by an Eulerian model (Rastigejev et al.,
2010). Following the averaging process, we obtain a merged dataset of over
25 000 unique points, with a broad spatial extent (Fig. 2) covering a
variety of chemical regimes representing anthropogenic, pyrogenic, biogenic
and remote environments. Despite the large temporal range of the
observational dataset, most of the campaigns analyzed in this study were
conducted during the spring and summer seasons, limiting the ability to
perform a seasonal analysis.
Based on the proximity to emission sources and exposure to long-range
pollutants, there is significant variation in the observed mean, medians and
standard deviations across the different campaigns (Table 3, Fig. S1). The
campaigns are also influenced by different OA sources depending on their
sampling region. The EUCAARI campaign over western Europe (Morgan et al., 2010), KORUS-AQ over the Korean Peninsula (Nault et al., 2018), CalNex over California (Ryerson
et al., 2013), and DC3 (Barth et
al., 2014) and FRAPPE over the central US (Dingle et
al., 2016) sample over regions that are heavily influenced by anthropogenic
emissions. In contrast, the GoAmazon campaigns during the wet and dry
seasons (Martin
et al., 2016; Shilling et al., 2018) over the Manaus region in the Amazon
and the OP3 campaign (Hewitt
et al., 2010) over equatorial forests in southeast Asia are heavily
influenced by biogenic emissions, although the GoAmazon campaign in the dry
season is also strongly influenced by biomass burning. Additionally, data
from both seasons of the GoAmazon campaign are influenced by anthropogenic
urban outflow from Manaus (Shilling et
al., 2018). Campaigns like SENEX (Warneke
et al., 2016) and SEAC4RS (Toon et al., 2016)
that conducted measurements over the southeast US are influenced by both
anthropogenic and biogenic emissions, while the ARCPAC campaign (Brock
et al., 2011) during the spring and the ARCTAS (Jacob
et al., 2010) campaign during the spring and summer over the northern
latitudes are strongly influenced by pyrogenic emissions from forest fires
(particularly during the summer) and aged anthropogenic and biogenic
emissions over the Arctic region. The KORUS-AQ campaign also includes a
short deployment over California. However, for the purpose of this study, we
restrict observations from this campaign to those over the Korean Peninsula.
Lastly, the dataset includes measurements from the ATom-1 and ATom-2
campaigns (Wofsy
et al., 2018). We divide the ATom campaigns into two datasets using a
land mask in order to separate the observations of remote, well-mixed air
masses over the Atlantic and the Pacific from near-source measurements over
North America.
The percentage contribution of organic aerosol by mass to the
total observed non-refractory mass concentrations measured by the AMS;
organized by campaign. This includes aerosol mass from organic aerosol,
sulfate, nitrate and ammonium. Campaigns are broadly organized based largely
on model-characterized source influence. However, as noted in the text, this
characterization is often not indicative of the true sampling profile. For
instance, the GoAmazon campaigns sampled heavily from fire and anthropogenic
sources in addition to being strongly influenced by biogenic sources.
Figure 3 demonstrates that organic aerosol accounts for a significant
portion (52 % on average) of the total non-refractory aerosol mass
loadings measured by AMS across all of the campaigns. The GoAmazon
measurements during the dry season have the highest contribution of OA to
the total submicron aerosol loading (77 %), while the ARCTAS campaign
during the spring has the lowest OA contribution of any campaign (31 %).
Results and discussionSimulated OA budget
Figure 4 shows the global annual mean simulated surface OA concentrations
and global annual mean burdens using the simple and complex schemes for the
year 2013 (burden numbers are provided in Table 2). The complex scheme
simulates a larger annual mean OA burden than the simple scheme (2.37 Tg
compared to 1.94 Tg). This is largely due to the scaled emissions of the
primary organic gases in the complex scheme (greater by a factor of 27 %)
as well as the semi-volatile treatment of the EPOA/EPOG and OPOA/OPOG
species, which substantially extends their tropospheric residence time due to
the longer lifetime of the gas-phase component in the boundary layer. As a
result, the complex scheme simulates a larger POA burden (EPOA + OPOA + MPOA) of 1.46 Tg, compared to 0.92 Tg POA in the simple scheme. The majority
(91.4 %) of the POA in the complex scheme consists of oxidized POA and
oxidized MPOA (M-OPOA) that, given its aged and chemically processed nature,
is often indistinguishable from secondary organic aerosol with typical AMS
measurements (Jimenez
et al., 2009). Consequently, 94.7 % of the global OA burden in the complex
scheme is oxygenated organic aerosol (OOA = OPOA + M-OPOA + SOA; Table 2). Similarly, 91.7 % of the total POA burden and 96.1 % of the total OA
burden are oxygenated in the simple scheme.
Global map of simulated OA surface concentrations in 2013 for the
(a) complex and (b) simple schemes; panel (c) illustrates the difference in
OA surface loadings between the complex and simple schemes. Panel (d)
displays the total global burden for the individual OA species from both
schemes averaged over 2013. Refer to Sect. 3 for details on model sampling
and averaging.
Both the complex and simple schemes simulate comparable global SOA burdens
(0.91 and 1.02 Tg, respectively). However, the complex scheme produces
more isoprene-derived SOA (ISOA) and biogenic organo-nitrates (Org-Nit) than
the simple scheme (Fig. 4d), particularly over areas with elevated isoprene
and anthropogenic sulfate concentrations (such as the southeast US and
southeast Asia) since the ISOA formation is acid-catalyzed. The explicit
aqueous uptake mechanism for the isoprene-derived SOA products also results
in substantially larger global isoprene SOA burdens (0.31 Tg) when compared
to the pure-VBS treatment of isoprene-derived SOA that simulates an
annually averaged ISOA burden of 0.12 Tg. This is consistent with other
comparisons that have shown that the VBS treatment in GEOS-Chem underpredicts
observed ISOA concentrations compared to the complex treatment (Jo et al., 2019). Despite
the different treatments, both the complex and simple schemes have similar
terpene-derived SOA (TSOA) burdens at 0.19 and 0.18 Tg, respectively
(Table 2).
Anthropogenic SOA (ASOA) is a particularly important global OA source in the
simple scheme, accounting for almost a third of the total OA burden. The
simple scheme, with its near-field formation of SOA proportional to
anthropogenic CO emissions, simulates a substantially larger ASOA burden
than the complex scheme (0.63 vs. 0.10 Tg; Table 2), particularly over
industrialized regions in Asia (Fig. 4c). Previous studies that have
constrained global SOA burdens using observed mass loadings have proposed a
missing model SOA source over anthropogenic regions (Spracklen et al., 2011), as have recent
regional studies (Schroder
et al., 2018; Shah et al., 2019). The simple scheme appears to capture a
greater fraction of this missing burden. However, we note that ASOA yields
in the simple scheme are based on a lumped parameterization over the Los
Angeles basin (Hayes et al., 2015)
and might not be representative of global yields across different chemical
regimes. The global ASOA burden of 0.63 Tg is 4 times greater than the ASOA
burden proposed by Spracklen et al. (2011), but it is well within the
“anthropogenically controlled” SOA burden proposed by the same study. This
suggests that the simple parametrization in its current form might
unintentionally represent some anthropogenically controlled biogenic SOA.
Additionally, while the simple scheme includes separate SOA yield parameters
for fossil fuel and biofuel combustion, the emissions inventories used in
this study do not always explicitly differentiate between the two sources.
As a consequence, biofuel is often lumped together with fossil fuel CO,
potentially leading to an overestimate in ASOA yields from biofuel
emissions.
Pye and Seinfeld (2010) performed a similar analysis of tropospheric OA
burdens using a semi-volatile POA treatment and a pure-VBS treatment of
SOA (i.e., all SOA treated in the VBS, including isoprene) with the GEOS-Chem
model (v8.01.04). Their model simulated 0.03 Tg EPOA, 0.81 Tg OPOA and 0.80 Tg SOA compared to 0.11 Tg EPOA, 1.27 Tg OPOA and 0.91 Tg SOA for the
complex scheme and 0.06 Tg EPOA, 0.78 Tg OPOA and 1.02 Tg SOA for the simple
scheme in this study. When compared to an analysis of organic aerosol
loadings from 31 different chemical transport and general circulation models (Tsigaridis
et al., 2014), the primary OA burden from the complex scheme (EPOA + MPOA + OPOA) is substantially higher than most of the models surveyed, while
the SOA burden falls below the mean but above the median of the
distribution. The simple scheme, with a much smaller POA burden, is
approximately on par with the Tsigaridis et al. (2014) ensemble mean. The
simple SOA burden is roughly equivalent to the Tsigaridis et al. (2014) model mean
(but significantly greater than the median) for global SOA loadings.
Aerosol lifetimes are calculated using the ratio between the mass burden and
the physical loss rates due to dry and wet deposition (Table 2). POA in the
complex scheme has an average lifetime to physical loss of 6.1 d (τEPOA∼ 11.5 d, τOPOA∼6.3 d, τMPOA∼3.0 d) in the atmosphere, while SOA
has a lifetime of 5.3 d on average (τASOA∼7.9 d, τTSOA∼ 5.3 d, τISOA∼5.1 d, τORG-NIT∼4.9 d). POA
in the simple scheme has an average global lifetime of 4.6 d (τEPOA∼7.8 d, τOPOA∼4.6 d, τMPOA∼3.0 d), while the parameterized
SOA species have an average lifetime of 5.2 d (τASOA∼5.6 d, τTSOA∼4.3 d, τISOA∼4.7 d). POA lifetimes in both the complex and
simple schemes are similar to the simulated POA lifetimes from Tsigaridis et
al. (2014), who calculated an ensemble mean POA lifetime of approximately 5 d. SOA lifetimes from this study are lower than the ensemble mean of 8 d calculated by Tsigaridis et al. (2014). The range in aerosol lifetimes
can be attributed to several different factors. The hydrophobic nature of
EPOA leads to longer lifetimes against wet deposition since the particles
are unaffected by rainout. The spatial distribution of the different aerosol
types also plays an important role in determining their lifetimes, with
species emitted over marine–tropical regions experiencing a higher
likelihood of being deposited via wet deposition than aerosol over drier
regions. Surface land types also affect dry deposition velocities, impacting
aerosol lifetimes. In addition, there is a marked difference in lifetimes
between the semi-volatile species in the complex scheme and non-volatile
species in the simple scheme. Due to the temperature-dependent partitioning,
the semi-volatile aerosol species are often in the gas phase in the warmer
parts of the troposphere and are advected to higher altitudes before they
partition to aerosol. The non-volatile species do not simulate this process
and are more likely to be deposited before they can be transported to higher
altitudes.
Flight tracks colored by regime type (top). The bar plots (bottom)
compare observed mean values for various species across the different
regimes. Mean values for OA are in units of micrograms per standard meter cubed (µg sm-3). Mean values
of isoprene, nitrogen oxides and carbon monoxide are in units of parts per
billion (ppb). The regimes are as follows – anthropogenic (A), pyrogenic
(F), biogenic (B), anthropogenic + pyrogenic (AF), anthropogenic + biogenic (AB), mixed (AFB) and remote–marine (R). Refer to Sect. 3 for
details on model sampling and averaging. See Fig. S2 for altitude-differentiated maps.
Regime analysis
We use the observations from the 15 field campaigns described in Sect. 3 as
a single coherent dataset. Given the wide range of chemical regimes sampled
by the various field campaigns, a method for classifying the observations is
needed to better inform the model–measurement comparisons. While the
chemical composition of the observed OA can provide some insight into source
types or aging, a comprehensive classification is not possible using only
the observations, requiring that we rely on the model for such a
segmentation. In this analysis, we use the relative dominance of the
different OA species within the GEOS-Chem simple scheme simulation to
classify the measurements into different regimes (described in Table S3 in the
Supplement). The sorting algorithm weights the relative
importance of the three OA source types – anthropogenic (A), biogenic (B)
and pyrogenic (F) – based on their relative contribution by mass to the total
OA loading in the model. Any data point with a source contribution greater
than 70 % of the total organic mass loading is categorized as being
dominated by that source (such as A for anthropogenic). Although this
threshold limit is somewhat arbitrary, an analysis of different threshold
values between 60 % and 80 % shows that the resulting classifications are
not particularly sensitive to changes within this range. Data points without
a single dominant source but with two large sources, contributing greater
than 85 % of the total OA mass, are classified into a second type of
regime category (such as AB for anthropogenic–biogenic), and points without
any dominant OA source types are classified into the mixed regime category
(AFB). Points with an aggregate OA mass concentration below 0.2 µg sm-3 across the three source types are classified as “remote–marine”.
Points for which MPOA contributes over 50 % of the mass are also categorized
under the remote–marine regime.
While we expect these model-based categories to adequately reflect source
influences (i.e., biogenic emissions over the Amazon vs. anthropogenic
emissions over Asia), the relative mass contributions simulated by the model
are subject to large uncertainties in OA formation and lifetime. As noted in
Sect. 3, sampling conditions over the regions can vary significantly from
the model treatment (such as the sampling of the Manaus anthropogenic plume
or biomass burning plumes during the “biogenic” GoAmazon campaign). Due to
the coarse model resolution, the regime segmentation described above is
incapable of accurately categorizing some of these data points. We therefore
compare the relative concentrations of observed NOx, CO and isoprene to
independently validate the segmentation approach. For instance, mean
observed NOx values over the anthropogenic regime approach 1 ppb
compared to 0.36 ppb over the AB regime and 0.17 ppb over the Biogenic
regime, consistent with the expected chemical signature over these regions.
Similarly, averaged isoprene observations over the biogenic regime are over
20 times greater than average measurements over the anthropogenic regime.
Median concentrations over anthropogenic regions are markedly lower than
those over other sources. Fire-influenced regions display the highest
variability, consistent with the expected source profile. Table S1 provides
an overview of the observational datasets used for this validation. An
overview of the resulting segmentation, validation and regime categories is
provided in Table S2. Figure 5 provides a spatial representation of the
regime categorization for all the flight data. We note that a large
proportion of the observations from the GoAmazon and OP3 campaigns are
densely colocated over the Amazon and Borneo and are thus difficult to
discern in the figure. We also note that the “remote” points over the
southeast US represent observations in the upper troposphere and are
plotted over points in the lower troposphere, making them difficult to
distinguish. Figure S2 provides a spatial characterization of the different
regimes differentiated by altitude for further clarity. While the regime
analysis provides useful insight into the primary sources of OA over the
region, the classifications are intended to be broad and do not, for
instance, distinguish between fresh and aged aerosol contributions from the
same source. For example, a number of points over the northern Atlantic and
Pacific oceans are classified as anthropogenic because they are
composed of a minimum of 70 % anthropogenic OA from continental sources
and are high enough in concentration to not be classified as “remote”.
Evaluation of model simulations against airborne measurements
Here we evaluate the two model schemes against the suite of airborne
observations described in Sect. 3. Despite the substantial differences
described in Sect. 2.1, both schemes reproduce the broad distribution (Fig. 6a) of OA observations. While the schemes exhibit slight offsets in their
peaks near the lower end of the distribution, there is no evidence of a
large systematic skew compared to observations, suggesting that there is not
an obvious mode of formation or loss of OA that the model fails to capture.
Differences between the two model distributions are also relatively small,
and both exhibit fairly comparable skill. The simple scheme is less biased
than the complex scheme on average, with median OA values of 0.81 and 0.86 µg sm-3, respectively, compared to the
observational median of 0.68 µg sm-3. An analysis of the
model–observation distributions for the individual campaigns (see Fig. 7)
demonstrates that both model schemes appear to overestimate OA mass at the
low and high ends of the distribution for several campaigns (as seen in the
case of KORUS, GOAMA-W and OP3), while underestimating organic aerosol
loadings in the middle of the distribution, suggesting a potential
mischaracterization of aerosol sources and lifetimes over these regions.
This might also be the result of the coarse model resolution in regions with
a high spatial variance in source strengths. Both model schemes
underestimate the lowest concentrations and overestimate the highest
concentrations over the ocean (ATOM1-W and ATOM2-W). However, Fig. 6a
suggests that these are not pervasive issues with the OA simulation at the
global scale. We note, however, that this could be due to an averaging
effect. Figure 6b shows the same comparison for sulfate as a benchmark for
a species that is generally well simulated by the GEOS-Chem model (Fisher
et al., 2011; Heald et al., 2011; Kim et al., 2015). While the comparison
suggests that there continues to be further scope for improvement within the
OA chemical schemes, the model simulations are approaching the skill of the
sulfate simulation both in terms of bias (the sulfate simulation normalized
mean bias of 0.20 is similar to the model OA bias outlined above) and
captured variability (with an R2 of 0.62 for the model sulfate scheme
relative to the observations compared to an R2 of 0.41 and 0.44 for
the simple and complex OA schemes, respectively). This suggests the potential
importance of other drivers of variability common to both sulfate and
organic aerosol, such as emissions and transport, in controlling aerosol
concentrations.
(a) Distribution plots of OA mass concentrations for the complex
scheme (dark green), simple scheme (light green) and AMS observations
(black). The x axis has been transformed using a square-root function.
Vertical lines represent median values for the different distributions. (b) Distribution plots of sulfate mass concentrations for the model (red) and
AMS observations (black). Refer to Sect. 3 for details on model sampling and
averaging.
Superimposed distributions from the complex (dark green) and
simple (light green) schemes with the observations in black for the
different campaigns. Vertical lines represent median values for the
different distributions.
Mean vertical profiles (in kilometers) comparing the observed
(black) and simulated (colored) OA mass concentrations classified into the
different regimes. The dashed lines represent the uncertainty in the
observed OA mass loadings. The profiles are binned at 200 m intervals. For
the simple scheme, A-POA represents anthropogenic POA and F-POA represents
pyrogenic POA. Refer to the text for other OA categories and details on model
sampling.
Figure 8 shows that both the complex and simple schemes exhibit substantial
skill in capturing the vertical OA profile across the aggregate dataset,
with a vertical R2 of 0.97 and 0.95 across the complex and simple
schemes, respectively. Despite significant differences in the treatment of OA
formation and atmospheric processing (and thus the source of simulated OA),
both schemes appear to have similar skill in reproducing the observed
vertical profile across the individual regimes, with the exception of the
remote regime (driven largely by ATOM1-W and ATOM2-W) for which both schemes
struggle somewhat to reproduce the variability in the observed vertical
profile (Fig. S3). This result is not surprising given the low
concentrations and the potential for uncertainties in transport and chemical
processing to be exacerbated in the remote regime. Overall, the schemes
display similar skill at capturing the vertical variability across the
different regimes, highlighting that much of this variability is likely
driven by the prescribed transport and vertical mixing and is independent of
the OA chemical scheme.
When compared in aggregate, the simple scheme is less biased in the lower
troposphere, while the complex scheme is less biased in the upper
troposphere (Figs. 8, S3). This could be due to the partitioning
mechanism in the complex scheme that is able to model semi-volatile OPOA and
SOA with greater sophistication using the VBS framework. There are also
various regime-specific differences in model performance. For instance, the
complex scheme significantly overestimates OA in the lower troposphere over
fire-influenced regions, likely due to the 27 % increase in primary OA
emissions to account for the dynamic partitioning between gas- and aerosol-phase POA. However, both the complex and simple schemes underestimate OA
loadings in the mid-troposphere over these same regions. This bias may be
due to fire injection from large fires into the free troposphere,
particularly over boreal regions (Turquety et al., 2007), that is not
captured by the model (all emissions from fires are assumed to be in the
boundary layer). This shortcoming is also evident over regions influenced by
both anthropogenic and fire emissions (AF Regime). Figure 8 also
demonstrates that lower-tropospheric concentrations cannot be reproduced
over oceans without the inclusion of a marine source of POA, although the
comparisons suggest that the marine POA source may be a factor of
∼2 too high. While the model appears to capture the vertical
profile of OA in anthropogenic regions reasonably well (Fig. 8), there are
regional differences (Fig. 9), with large model underestimates of OA in the
lower troposphere over California (CalNex), the central US (DC3) and Europe
(EUCAARI) as well as large overestimates over Korea and parts of the Pacific
influenced by outflow from Asia (Figs. 9, S4). These differences are
consistent across both the simple and complex schemes, highlighting the
importance of accurate anthropogenic emission inventories. The overestimate
in the Asian outflow region might specifically point to the importance of
constraining Asian IVOC emissions, given that recent studies have suggested
that SOA from IVOCs accounts for a major fraction of the total OA burden
across China (Zhao et al., 2016). In regions
influenced by both anthropogenic and biogenic emissions (AB regime) the
complex scheme is less biased than the simple scheme, which underestimates
the observed concentrations. This difference in bias is likely due to the
more sophisticated treatment of isoprene-derived SOA yields (through the
aqueous uptake and organic nitrate formation mechanisms) in the complex
scheme. The NOx-dependent yields of isoprene- and terpene-derived SOA in
the complex scheme might also be a source of increased model skill, given
that organic nitrates and oxidized isoprene products account for a dominant
fraction of the total modeled OA in the complex scheme over these regions.
The relative skill of the complex scheme is unsurprising given that the vast
majority of the AB regime is over the southeast US, for which the complex
scheme was developed and validated. However, the model skill over the AB
regime may be fortuitous, given that recent studies have demonstrated that a
significant fraction of the observed OA over the southeast US is generated
from monoterpene precursors rather than isoprene (Xu et al., 2018;
Zhang et al., 2018). This potentially suggests that monoterpene SOA yields
over the southeast US are low in the model. This may also contribute to the
underestimate of OA observed during EUCAARI, which is influenced by the
forests of northern Europe (Figs. 9, S4). Recent work has also
demonstrated that organo-nitrates contribute a significant fraction of the
total OA mass over certain parts of Europe (Kiendler-Scharr
et al., 2016), potentially indicating a model underestimate in
organo-nitrate formation over the region. In contrast to its skill over the
US, the complex scheme displays a large positive bias over biogenic (B)
regions (such as the Amazon), primarily driven by an overestimate in terpene
SOA, potentially suggesting that the scheme may not accurately capture
global biogenic SOA burdens and needs to be better constrained. The
overestimate of OA in both schemes in the boundary layer over the Amazon and
Borneo is accompanied by an underestimate in the upper troposphere (Fig. 9), potentially indicating overly rapid model SOA formation or a failure to
capture vertical mixing in the region.
Mean vertical profiles (in kilometers) comparing the observed
(black) and simulated (colored) OA mass concentrations across the different
campaigns. The dashed lines represent the uncertainty in the observed OA
mass loadings. The profiles are binned at 200 m intervals. For the simple
scheme, A-POA represents anthropogenic POA and F-POA represents pyrogenic
POA. Refer to the text for other OA categories and details on model sampling.
We note that while the observations used in this study have a large spatial
range, they are temporally limited and might not be representative of the
mean state. Atypical meteorological conditions during the different
campaigns may contribute significantly to the model–observation bias. For
example, the EUCAARI campaign was characterized by a westward flow across
Germany and the southern UK (Morgan et al., 2010), capped by a
strong inversion that limited vertical mixing. Similarly, differences in
sampling priorities might impact the chemical composition of the
observations in a manner that deviates from climatology. For instance, the
GoAmazon campaign was partially oriented toward sampling anthropogenic
outflow from the city of Manaus (Shilling et al., 2018), impacting the
OA measurements in a manner that the model is ill-equipped to reproduce.
However, despite the various gaps in model fidelity, this analysis suggests
that both schemes are relatively skilled at capturing the observed magnitude
and vertical variability across the different regimes. A previous comparison
of observed vertical profiles by Heald et al. (2011) concluded that the
two-product SOA with non-volatile POA model used in earlier versions of
GEOS-Chem required additional sinks and sources in order to match
observations, suggesting the need for photochemical sinks from photolysis
and fragmentation pathways. Figure 8 indicates no obvious need for large
additional sinks for either scheme in aggregate, although specific regions
may benefit from a more sophisticated treatment of SOA formation and loss.
Statistical evaluation of the OA model skill for the complex
(dark green) and simple (light green) scheme against observations shown as
(a) the coefficients of determination (R2) and (b) the normalized mean
bias (NMB) across the segmented regimes. A positive normalized mean bias
indicates that the model overpredicts OA loadings.
Statistical evaluation of the model skill against observations
shown as (a) the coefficients of determination (R2) and (b) the
normalized mean bias (NMB) across the individual field campaigns. The
complex (dark green) and simple (light green) OA schemes are compared to the
sulfate simulation (red).
An analysis of the coefficients of determination (R2) and the
normalized mean biases (NMB) across the different regimes (Fig. 10) and
campaigns (Fig. 11) indicates that the complex scheme marginally outperforms
the simple scheme across the aggregate dataset in its ability to reproduce
the observed OA variability (with an R2 of 0.44 compared to an R2 of 0.41 for the simple scheme), with small differences in performance over
the different regimes. The simple scheme is more skilled at minimizing bias
over the aggregate dataset and most source regimes, but it is biased low over
the AB and AFB regimes. Figure S4 provides spatial context for the
model–measurement comparisons discussed here. The result that both the
complex and simple schemes slightly overestimate OA in the aggregate
dataset is distinct from the conclusion drawn by Heald et al. (2011), who
demonstrated a consistent model underestimate of OA over most regions. In
this study, median modeled concentrations are within 1 µg sm-3 of
the observations for 14 out of the 17 datasets analyzed with both schemes.
Figure S5 provides distributions of the ratio and bias between the observed
and modeled organic aerosol concentrations for both model schemes across
the different campaigns.
When compared to the simple scheme, the complex scheme does a superior job
at minimizing the bias over much of the US. However, there continues to be
an underestimate in OA loadings in both schemes (Figs. 9, S4). The bias
is likely driven by a variety of factors that need to be explored on a
regional basis. For instance, a previous model analysis of FRAPPE
observations over Colorado suggested that an underestimate of anthropogenic
emissions from the oil and gas sector contributed to an underestimate of
ASOA in the region (Bahreini
et al., 2018). Both schemes overestimate OA loadings in the northern
latitudes (over parts of Alaska and Canada), likely due to an overestimate
in POA from fires (Figs. 9, 11, S3, S4). The complex scheme is
also biased high over the Amazon rainforest due to the large mass loadings
of terpene SOA and various isoprene- and monoterpene-derived organo-nitrates.
Conversely, the simple scheme assumes an identical SOA yield from both
monoterpenes and sesquiterpenes, likely degrading its skill. Both schemes
are biased low over Europe but high over the Korean Peninsula, which are both
anthropogenically influenced regions, potentially due to the different
regional inventories (EMEP and MIX) used by the model. Both schemes
overestimate the OA concentrations observed during the winter ATom-2
deployment (Figs. 9, 11) driven largely by an overestimate in
anthropogenic OA, particularly in the North Pacific (Fig. S4); a similar
bias is not apparent in the summertime ATom-1 deployment, suggesting a
potential seasonal overestimate in anthropogenic emissions in Asia that may
warrant further study. In comparison to the complex scheme, simulations
conducted using a pure-VBS treatment of SOA were significantly less
skilled at capturing OA variability and minimizing model bias over the
aggregate dataset, demonstrating the value of an explicit description of
isoprene SOA over the nonmechanistic VBS treatment.
Exploring the model–measurement differences in OA
There are many factors that contribute to the model performance over
individual campaigns or regions, and investigating the specific drivers of
regional differences is not the goal of this work. However, here we explore
general features of the model–measurement comparisons to identify issues
that may inform the development of future model OA schemes.
There is a large spread in the model–observation bias both within and across
the individual campaigns. A comparison of OA metrics (such as R2 and
NMB) with the corresponding model sulfate simulations for the same campaigns
demonstrates a similar variance (Fig. 11). This suggests that the lack of
model skill over certain campaigns could be due to physical processes, such
as transport and deposition, that impact both OA and sulfate species and are
independent of the chemical scheme utilized.
A comparison of the simulated (GEOS-Chem) coefficient of
variation (CV, the ratio of the standard deviation to the mean) for the complex
(dark green) and simple (light green) OA schemes against the observed CV for
each airborne campaign. The one-to-one line is shown as a dashed black line.
A comparison between the simulated and observed coefficients of variation
(CV; defined as the ratio of the standard deviation to the mean) for the
different campaigns indicates that both the complex and simple schemes are
relatively skilled at capturing the range of observations within the
individual campaigns, with the CV from the simple scheme and the complex
scheme both showing a high degree of correlation when compared to the
observed CV (R2 of 0.7; Fig. 12). The CV provides a measure of
statistical dispersion. Figure 12 highlights how localized campaigns such as
GoAmazon and FRAPPE have low CVs. Both schemes demonstrate a lack of ability
to accurately capture intra-campaign variability (described above by the
campaign R2 in Fig. 11). The coarse model resolution and the resulting
inability to resolve sub-grid concentration and emission gradients is likely
an important barrier to model skill, particularly across more localized
campaigns (with low CVs) with smaller dynamic ranges and/or spatial extents,
like OP3, KORUS-AQ and FRAPPE. To explore this, additional simulations (not
shown) were conducted using a nested 0.5∘×0.625∘ grid
with the simple scheme (while maintaining all other model parameters) over
North America for the FRAPPE campaign and over Asia for the KORUS-AQ and
OP3 campaigns. The nested simulations performed significantly better at
capturing the observed variability in OA for FRAPPE (with a change in
R2 from 0.19 to 0.34). However, the nested KORUS-AQ simulations
resulted in a decrease in model skill, with a change in R2 from 0.37 to
0.25. This result suggests that uncertainties in emission inventories and
meteorology over Asia may degrade higher-resolution comparisons, consistent
with recent work demonstrating deficiencies in emission inventories in the
region (Goldberg et al., 2019). The nested simulations also
did nothing to improve model fidelity for the OP3 campaign over Borneo (with
a change in R2 from 0.49 to 0.48). Biogenic emissions and chemical
conditions are likely relatively uniform over this region, and therefore a
higher-resolution simulation does not lead to a distinct improvement in the
simulation.
To compare the underlying source signatures for the ambient OA
concentrations over different regimes, we analyze the relationship between
OA and CO concentrations across both the model schemes and the observational
dataset (Fig. S6). Generally, the model underestimates the observed OA : CO
slope but captures the relative difference in OA : CO slopes observed in
different environments. The two schemes are broadly consistent, and the
model skill in reproducing this relationship is not notably better or worse
over most regimes or environments, providing little insight into model
scheme deficiencies. However, there is a notable difference between the
observed and modeled OA : CO slope over the anthropogenic regime (though it
is not consistent over all regions), potentially warranting further
exploration of regional anthropogenic OA yields within the simple scheme.
A comparison of model–observation OA bias and observed (a, c) relative humidity and (b, d) sulfate mass concentrations for the complex (a, b – dark green) and simple (c, d – light green) OA schemes across
the aggregate dataset (observations are binned by intervals of 1 % for RH
and 0.1 µg sm-3 for sulfate). The best-fit line is shown in black.
Model bias is also evaluated as a function of a suite of observed parameters
(relative humidity, temperature NOx, sulfate, isoprene, CO) to identify
any salient relationships (Fig. S7). We find that the model–observation bias
in the complex scheme displays a robust positive correlation with the
observed relative humidity and sulfate concentrations (Fig. 13). This
suggests that the aqueous uptake of isoprene oxidation products in the
complex scheme is overestimated in conditions of high humidity and high
acidity and that further work is needed to constrain this formation pathway
under a range of ambient environmental conditions. It also suggests that
large additional pathways of aqueous SOA formation are unlikely to be
missing from the model. In-cloud processing of SOA is not explicitly
considered in the complex scheme, with Marais et al. (2016) estimating that
the pathway accounts for a minor fraction of the total SOA. However, studies
have suggested that cloud chemistry can significantly impact SOA
concentrations during certain cloud-cycling events (Brégonzio-Rozier
et al., 2016; Giorio et al., 2017), indicating the need for more research to
constrain the regional relevance of such systems.
The simple and complex schemes differ significantly in their treatment of
primary organic aerosol. The simple scheme simulates POA using two
non-volatile primary species, while the complex scheme uses two semi-volatile
primary species that partition between the gas and aerosol phase. This is an
important difference because aerosol partitioning in the semi-volatile
species is sensitive to ambient temperature and organic aerosol
concentration, influencing concentrations far away from the original source.
Given the differences in POA treatment, an analysis of model skill (in terms
of its ability to minimize bias and capture observational variability) was
conducted by considering the effects of combining EPOA and OPOA loadings
from the complex scheme with SOA loadings from the simple scheme (and vice
versa). With an R2 of 0.46 and an NMB of 0.03, this model configuration
(complex scheme POA with simple scheme SOA) outperformed both the simple and
complex schemes over the aggregate dataset in its ability to capture the
observed variability and minimize observational bias, supporting the need to
explicitly model the semi-volatile nature of POA (Fig. S8). We note that
this analysis assumes a parameterized enthalpy of vaporization of 50 kJ mol-1 to estimate saturation vapor pressures for semi-volatile
partitioning in the complex scheme, an assumption that needs to be more
rigorously examined in field and modeling studies.
Based on the results from the simple scheme, an offline analysis was
conducted to optimize the various model parameters by running a
multivariate linear regression in combination with a gradient descent
optimizer that used a weighted cost function to maximize the coefficient of
determination and minimize the normalized mean bias. This was done across
multiple parameter classes (such as emission rates and yields) in order to
ascertain a set of optimized model parameters. The optimized parameters
improved the model coefficient of determination by only up to 5 % in most
cases. This is perhaps unsurprising given that this simplistic analysis
assumes that simulated OA concentrations are linearly correlated with
changes in emissions and yields, an assumption that is not truly
representative of the model treatment, which includes nonlinear effects such
as wet deposition loss. More work is required to optimize these parameter
classes using an online analysis.
We also incorporated a rudimentary NOx and sulfate dependency into the
biogenic SOA yields for the simple scheme using offline monthly averaged
NOx and sulfate concentrations from a full-chemistry GEOS-Chem
simulation for the year 2013. Isoprene-derived SOA was modeled as having a
negative NOx dependency, ranging from a 3 % yield in low-NOx
conditions to a 2.25 % yield at high NOx. Monoterpene SOA was also
modeled as having a negative NOx dependency – ranging from a 10 %
yield under low-NOx conditions to a 7.5 % yield under high-NOx
conditions. Sesquiterpene SOA yields were simulated as having a positive NOx
dependence, ranging from 10 % under low-NOx conditions to 20 % under
high-NOx conditions. These yields were determined based on an analysis of
relevant literature (e.g., Kroll et al.,
2006; Ng et al., 2007) coupled with various offline optimizations from this
study. ISOA was also modeled as having a positive SO4 dependence (from a yield of 1.5 % in clean conditions to a high of
4.5 % in extremely polluted conditions with high sulfate) based on
previous work (Marais et al.,
2016) that demonstrated the importance of the acid-catalyzed SOA formation
pathway for isoprene.
The NOx-dependent parameterization did not meaningfully improve model skill.
However, the sulfate parametrization improved model performance by a few
percentage points, bringing the aggregate R2 to within 0.01 of the
complex scheme and demonstrating the potential to further improve model
performance. The analysis also points to the limitations of the simple
scheme in its current form. For instance, OA yields have also been shown to
be highly variable by region and source, particularly in the case of fires (Jolleys et al., 2014), a facet that is
not currently captured within the simple scheme. Chemical processing
lifetimes are also highly dependent on the ambient regime, with
observational studies finding that OA in urban environments (e.g.,
Jimenez et al., 2009) is often oxidized at timescales that are significantly
faster than the 1.15 d assumed in the simple scheme. Our rudimentary
optimization of the simple scheme with a sulfate dependency demonstrates the
potential to further improve model performance, although additional work is
needed to conduct a more rigorous optimization of the various model
parameters.
Conclusions
In this study, we use a suite of observations that represent a variety of
spatial and chemical regimes to undertake a comprehensive evaluation of the
two standard organic aerosol schemes in the GEOS-Chem chemical transport
model, both with very different treatments of OA. The simple scheme, which
uses non-volatile tracers to model primary organic aerosol, simulates a
total annual POA burden that is approximately two-thirds of the comparable
burden simulated by the complex scheme that treats POA as semi-volatile.
While the total SOA burdens are similar, the simple scheme simulates an
anthropogenic SOA burden that is over 6 times greater than the complex
scheme. Conversely, the complex scheme simulates a global burden of biogenic
SOA that is roughly 2.5 times greater than the comparable burden in the
simple scheme, largely due to higher isoprene SOA and organo-nitrate mass
loadings. Due to the lack of well-differentiated fossil fuel and biofuel
emissions, the simple parameterization likely overestimates ASOA from
biofuel sources. We note that the simple ASOA parameterization as applied in
this study might also capture some “anthropogenically controlled” SOA formed
from biogenic VOC precursors, potentially accounting for some of the
disparities noted above. More work is needed to constrain these yields
across different chemical regimes at a global scale.
Despite the substantial difference in the complexity of these OA schemes and
the relative magnitudes of their sources, differences in their ability to
capture observed airborne OA concentrations from around the world are
modest. The simple scheme appears to slightly outperform the more
sophisticated complex scheme in terms of its ability to minimize bias over
the aggregate dataset, while the complex scheme is slightly more skilled in
its ability to capture the observed variability. When compared spatially to
the simple scheme, the complex treatment is less biased over the southeast
US and certain regions in North America and Europe, while displaying reduced
skill in pyrogenic regimes over the northern latitudes and biogenic regimes
in the Amazon, where it produces large overestimates. When comparing
vertical profiles, both schemes overestimate OA loadings in the lower
troposphere. However, the complex scheme is more skilled at capturing the
mid-tropospheric burden, likely due to the more sophisticated semi-volatile
treatment of primary OA. Both schemes underestimate mid-tropospheric OA
loadings over fire-influenced campaigns, pointing to the potential
importance of fire injection into the free troposphere in those regions,
which was not modeled in this study. The overestimate of OA in the tropical
boundary layer and the underestimate aloft similarly indicate model failure
to capture the chemical lifetimes of biogenic SOA formation or point to
deficiencies in its ability to capture vertical mixing in these regions. Our
analysis of nested simulations over North America and Asia also points to
the importance of constraining regional emissions and local meteorology over
Asia in order to improve model fidelity. As a result of our analysis, we
recommend that (1) POA be modeled as semi-volatile, (2) fire POA emissions
not be scaled up by 27 % in the complex scheme and (3) marine POA be
included in the simulation of marine-influenced regions. Further
explorations of the fire injection heights of aerosols (e.g.,
Zhu et al., 2018) and anthropogenic emissions of OA
precursors, particularly in Asia, are needed. However, despite these
deficiencies, both model schemes generally capture the magnitude of the
observed OA. This is particularly true given the 38 % uncertainty
associated with the AMS OA observations; 33 % of the modeled data points
fall within this observed uncertainty, demonstrating significant progress
since the first airborne analysis of OA simulated in the GEOS-Chem model,
which revealed biases of up to an order of magnitude (Heald et al., 2005).
The surprising result that both the simple and complex schemes perform
comparably across the aggregate dataset challenges our expectations that a
more complex and mechanistic description of OA should outperform a highly
parameterized scheme. This may suggest that accurately capturing the source
influence (i.e., emissions of OA and its precursors) is a more crucial
limitation on current model skill than the specific details pertaining to OA
formation. Alternatively, it may suggest that substantial
deficiencies remain in our understanding of the mechanistic formation of OA, as
represented in the complex scheme (for example, associated with the
oxidation of aromatics). The VBS oxidation of monoterpenes and
sesquiterpenes in the complex scheme uses NOx-dependent yields to
determine the formation of second-generation oxidation products. However,
these yields are uncertain and recent studies have suggested the importance
of accounting for interactions between multigenerational oxidation products
when determining these yields, demonstrating that such interactions can
significantly depress SOA formation under ambient conditions (McFiggans et al., 2019). Recent work has also
demonstrated the importance of RO2 autoxidation pathways in the
formation of SOA (e.g., Crounse et al.,
2013; D'Ambro et al., 2017; Pye et al., 2019). A more sophisticated,
explicit treatment that accounts for these oxidation product interactions
under different chemical regimes could thus improve model fidelity (but with
an associated computational cost). Additionally, the underlying mechanisms
(and related uptake coefficients) behind the treatment of isoprene in the
complex scheme were developed and validated primarily using data from
campaigns over the southeast US; more work is needed to constrain these
coefficients under different chemical regimes outside this region. Finally,
the lack of model fidelity could also indicate the importance of better
constraining the physical processes inherent to both schemes, such as
transport and deposition, or point to the salience of photochemical loss,
atmospheric aging and fragmentation loss, which are not represented in either
scheme (Heald
et al., 2011; Hodzic et al., 2016). In addition to these factors, the
observational comparison with model sulfate suggests that the large drivers
of unexplained model variability might be exogenous to the OA chemical
scheme.
At the global scale, the computational advantages and relative skill of the
simple scheme make it an attractive tool. Our analysis demonstrates that
this computational benefit is accompanied by a relatively limited decline in
model skill. However, caution should be exercised when applying such a
scheme that fails to incorporate the mechanistic responses necessary to
ensure predictive skill (e.g., for climate studies). There is thus a need to
improve upon both the simple parameterized approach as well as the more
sophisticated mechanistic scheme in order to further our understanding of
organic aerosol in the atmosphere.
This study highlights the critical need to develop new methods to translate
experimental studies on the formation and fate of OA into global models in
order to identify the key processes that are required to reproduce observed
atmospheric OA concentrations. The study also indicates the importance of
additional observational constraints to benchmark and improve model
fidelity. The AMS observations offer a rich mass-differentiated dataset that
could be further leveraged using factor ratios and clustering analyses to
inform future model evaluations. Standardized reporting of AMS data during
future campaigns could enable further model evaluation using a more
comprehensive range of the instrument's capabilities. In addition,
observations of organic aerosol would be particularly useful in understudied
regions such as India, China, central Asia and Africa. Recent campaigns over
these regions (such as the 2016 DACCIWA, 2018 ORACLES and 2016 SWAAMI
campaigns) could also be leveraged to study the relevant chemistry. Due to
the relative paucity of airborne AMS observations, this study does not
include an analysis of seasonal trends. Additional aircraft campaigns over
the fall and winter seasons (such as the 2015 WINTER campaign over the
northeastern US) could enable a more comprehensive intra-annual analysis,
which could provide insight into seasonal sources. There is also a need for
more field observations at a regional scale, as opposed to localized
sampling, in order to better constrain and improve the treatment of organic
aerosol in large-scale regional and global models. Finally, this analysis,
while a comprehensive model evaluation of OA, is limited to two schemes
within one model and does not include any surface constraints. An ongoing,
meticulous evaluation of new OA model schemes against globally distributed
datasets is thus paramount to advancing simulations of the air quality and climate impacts of aerosols.
Code and data availability
The GEOS-Chem model code is available at
http://acmg.seas.harvard.edu/geos/ (last access: 5 January 2019; International GEOS-Chem User Community, 2018).
Observational data for ARCTAS
(https://doi.org/10.5067/SUBORBITAL/ARCTAS2008/DATA001; Chen, 2020), ATom (10.3334/ORNLDAAC/1581; Wofsy et al., 2018), DC3
(10.5067/AIRCRAFT/DC3/DC8/AEROSOL-TRACEGAS; DC3 Science Team, 2012), FRAPPE
(https://www-air.larc.nasa.gov/cgi-bin/ArcView/discover-aq.co-2014?C130=1; FRAPPE Science Team, 2014), KORUS-AQ
(10.5067/SUBORBITAL/KORUSAQ/DATA01; Chen, 2018) and SEAC4RS
(https://doi.org/10.5067/AIRCRAFT/SEAC4RS/AEROSOL-TRACEGAS-CLOUD; SEAC4RS Science Team, 2014) can be obtained from the NASA LaRC data
archive. GoAmazon data are accessible through the Atmospheric Radiation
Measurement (ARM) user facility
(https://www.arm.gov/research/campaigns/amf2014goamazon; GoAmazon Science Team, 2014). Data for the OP3
(http://data.ceda.ac.uk/badc/op3; OP3 Science Team, 2008) and
EUCAARI
(http://data.ceda.ac.uk/badc/appraise/data/adient; EUCAARI Science Team, 2008) campaigns are archived at the Centre for
Environmental Data Analysis (CEDA). ARCPAC
(https://esrl.noaa.gov/csd/groups/csd7/measurements/2008ARCPAC/P3/DataDownload/; ARCPAC Science Team, 2008), CalNex
(https://esrl.noaa.gov/csd/groups/csd7/measurements/2010calnex/P3/DataDownload/; CalNex Science Team, 2010) and SENEX
(https://esrl.noaa.gov/csd/groups/csd7/measurements/2013senex/P3/DataDownload/; SENEX Science Team, 2013) data are available via the NOAA ESRL data
archive. GoAmazon data were obtained from the Atmospheric Radiation Measurement (ARM)
user facility (10.5439/1346559; Martin et al., 2015), a U.S. Department of Energy (DOE) Office of Science user
facility managed by the Office of Biological and Environmental
Research.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-2637-2020-supplement.
Author contributions
CLH designed the study. SJP modified the code, performed the simulations and
led the analysis. JRP, SCF and EAM contributed to the GEOS-Chem organic
aerosol simulation. JLJ, PCJ, BAN, AMM, HC, JES, RB, JHD and KV provided organic
aerosol measurements used in the analysis. SJP and CLH wrote the paper with
input from the coauthors and the acknowledged individuals listed below.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
This paper has not been reviewed by the EPA, and thus no endorsement should be inferred.
Acknowledgements
The authors would like to acknowledge Katherine R. Travis, Jesse H. Kroll,
Jeffrey L. Collett Jr. and Taehyoung Lee for useful discussions and inputs.
We also acknowledge the following investigators, who provided measurements of
NOx, CO and isoprene: Andrew J. Weinheimer, Armin Wisthaler, Bruce C. Daube, Carsten Warneke, Chelsea R. Thompson, David J. Knapp, Denise D. Montzka, Donald R. Blake, Eric A. Kort, Eric C. Apel, Frank M. Flocke, Glen Sachse, Glenn S. Diskin, Ilana B. Pollack, Jeffrey Peischl, John E. Shilling, John S. Holloway, Joost A. de Gouw, Lisa Kaser, Markus Müller,
Martin Graus, Philipp Eichler, Rebecca S. Hornbrook, Roisin Commane, Sally E. Pusede, Stephen R. Springston, Steven C. Wofsy, Teresa L. Campos, Thomas B. Ryerson, Tomas Mikoviny. Roya Bahreini was supported by the Colorado Department of
Public Health and Environment. The art for Fig. 1 was obtained and modified
from public domain images.
Financial support
This work was supported by the National Science Foundation (AGS-1564495). John E. Shilling was supported by the U.S. Department of Energy Office of Biological and Environmental Research as part of the ARM and ASR programs. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. Jeffrey R. Pierce was supported by the U.S. NSF (AGS-1559607) and the NOAA 31 (NA17OAR430001). Aerosol measurements from the EUCAARI and OP3 campaigns was collected under NERC grants NE/D013690/1, NE/D004624/1 and NE/E01108X/1. The CU-Boulder group was supported by NASA (NNX15AH33, NNX15AT96G, and 80NSSC18K0630), EPA STAR (83587701-0), and DOE (DE-SC0016559). PTR-MS measurements during DC3 and KORUS-AQ were supported by the Austrian Federal Ministry for Transport, Innovation and Technology through the Austrian Space Applications Programme (ASAP) of the Austrian Research Promotion Agency (FFG).
Review statement
This paper was edited by Neil M. Donahue and reviewed by two anonymous referees.
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