Introduction
Carbonaceous aerosols, including both black carbon (BC) and organic aerosols
(OA), are among the largest sources of uncertainty in the estimate of the
global direct radiative effect (DRE) and forcing (DRF) of aerosols. BC is the
principle light-absorbing aerosol in the atmosphere, whereas OA is generally
considered as “white carbon” which scatters light without corresponding
absorption. However, a fraction of OA is also found to efficiently absorb
light, predominately at near-UV wavelengths (Kirchstetter et al., 2004;
Hecobian et al., 2010; Arola et al., 2011). This absorbing OA, referred to as
brown carbon (BrC), has primarily been associated with biofuel or biomass
combustions (Andrea et al., 2006; Ramanathan et al., 2007; Washenfelder et
al., 2015). These BrC emissions are typically mixed with co-emitted BC and
non-absorbing OA, challenging the measurement community's ability to evaluate
the optical properties of ambient BrC. Additional sources of BrC, including
the photo-oxidation of volatile organic compounds (VOCs) and aqueous-phase
chemistry in cloud droplets, typically produce less absorbing BrC, with
properties that are even more uncertain (Graber and Rudich,
2006; Ervens et al., 2011; Wang et al., 2014; Laskin et al., 2015). A few
studies have attempted to simulate BrC in global models and estimate its DRE
(Park et al., 2010; Feng et al., 2013; Lin et al., 2014; Wang et al., 2014;
Jo et al., 2016; Saleh et al., 2015). These estimates range
from +0.03 to +0.6 Wm-2, corresponding to up to 40 % of the
total absorption of carbonaceous aerosol across studies. Due to our poor
understanding of the sources, optical properties, chemistry, and mixing state
of BrC, the uncertainty surrounding the global absorption from BrC remains
high.
Most modeling studies follow a similar approach to simulating BrC: some
fraction of OA is assumed to be BrC and assigned different optical
properties from non-absorbing OA. The assumed optical properties of BrC are
based on laboratory measurement of organics extracted in water, acetone,
methanol, or other organic solvents. However, these properties are not well
constrained by laboratory studies. First, measured absorption properties
differ significantly among studies (Wang et al., 2014). Even within a study,
different combustion conditions (e.g., burning temperature) can also lead to
up to a factor of 2 difference in absorption properties (e.g., the imaginary
part of the refractive index, or the mass absorption coefficient) (Chen and Bond,
2010). Previous modeling studies have typically used either the lower or
higher bound from laboratory studies to estimate the minimum or maximum
absorption properties of BrC (Feng et al., 2013; Lin et al., 2014). In
addition, it is unclear what fraction of the OA is BrC and how this differs
with source and ambient combustion conditions (Pokhrel et al., 2017). In
laboratory studies organics are not always fully soluble; typically,
40–90 % of the total material can be extracted, depending on the solvent
(e.g., ∼ 40 % can be extracted in water and more than 90 % can
be extracted in methanol; Chen and Bond, 2010). The absorption properties of
the insoluble fraction are unknown. Thus previous modeling studies have
applied a range of assumptions: Lin et al. (2014) assumed all primary organic
aerosols (POA) from biofuel and biomass emissions and all secondary organic
aerosols (SOA) from biogenic and anthropogenic emissions to be BrC; Feng et
al. (2013) assumed 66 % of biofuel/biomass POA to be BrC; Wang et
al. (2014) assumed 25 % of biomass burning and 50 % of biofuel POA as
well as aromatic SOA were brown. There is tenuous scientific support for
these assumptions. In addition, extrapolating laboratory experiments to
real-world combustion sources may also lead to large uncertainties.
Recent studies show that the BrC absorption from biofuel or biomass sources
is likely affected by combustion efficiency (Chen and Bond, 2010; Saleh et
al., 2014; Pokhrel et al., 2016, 2017). A number of modeling studies have
attempted to connect BrC absorption to emission properties, by using the
modified combustion efficiency (MCE, a function of CO / CO2) (Jo et
al., 2016) or the BC / OA ratio (Park et al., 2010; Saleh et al., 2015).
These approaches should better represent the temporal and spatial variability
of BrC emissions and properties; however, in practice these parameterizations
are difficult to apply in models given the lack of information regarding burn
conditions in emissions inventories. The variability of quantities such as
BC / OA or MCE in these inventories reflects differences in fuel types
(and the associated emission factors), not burn conditions. Therefore, these
studies fail to describe the variation in emissions of BrC within a given
fuel type.
Both of these methods focus primarily on the sources of BrC; however,
chemical transformation and the mixing state of BrC also play an important
role in controlling BrC absorption. In laboratory studies, the absorption of
BrC is found to both increase during the formation or chemical aging of
certain types of OA and decrease during oxidation or photolysis (Zhong and
Jang, 2011; Flores et al., 2014; Lee et al., 2014; Liu et al., 2016). Field
studies provide evidence that BrC may be formed in clouds or during
convective transport, due to aqueous-phase chemistry or condensation
(Gilardoni et al., 2016; Zhang et al., 2017). Observations also indicate that
biomass burning BrC absorption decreases with photochemical aging with a
lifetime of ∼ 1 day (Forrister et al., 2015; X. Wang et al., 2016). It
is likely that the absorption and DRE of BrC would change significantly if
these chemical processes were included in models. For the mixing state, the
key question is whether BrC is internally or externally mixed with BC.
Previous studies typically assume that BrC is externally mixed with BC (Liu
et al., 2013). When considering BC only, the internal mixing is widely
idealized as a core-shell morphology (Jacobson, 2001; Bond and Bergstrom,
2006). When coated by other materials, typically inorganic and non-absorbing
OA, the absorption of BC will be enhanced by the lensing effect (Jacobson,
2001; Bond et al., 2006). However, if BrC coats BC, Mie calculations show a
lower absorption enhancement for BC. At the same time, the absorption of BrC
itself will decrease since there is less externally mixed BrC left in the
atmosphere. As a result, the mixing state of BrC will affect the absorption
of not only the BrC but also the BC. This influence is sensitive to the
absorption properties of BrC, which are highly uncertain, as we have
discussed, and also the proportion of externally/internally mixed BrC, which
to the best of our knowledge, has not been measured in the atmosphere. Saleh
et al. (2015) investigate this influence and conclude that for a single
particle with fixed size (BC = 150 nm and BrC = 200 nm) the global
mean absorption DRE of BrC decreases by 45 % when assuming complete
internal mixing (compared to complete external mixing). In this case, BrC is
assumed to not absorb light when serving as the coating material on BC cores.
This assumption is challenging to test given current analytical measurement
capabilities. If BrC shells absorb light, there would be a higher absorption
enhancement for BC when BrC coats BC.
Observational constraints on BrC are scarce, thus making it a challenge to
test and improve models based on observational evidence. Although the
absorption of aerosols is widely measured in the form of absorption aerosol
optical depth (AAOD) by satellite or ground-based measurement, these
observations include the absorption of both BrC and other aerosols (primarily
BC). In our previous work (X. Wang et al., 2016), we presented a method to
distinguish the absorption contributions of BrC and BC. However, the method
can only be used for multiple-wavelength absorption observations with two
wavelengths longer than 600 nm and at least one in the near-UV. Such
measurements are currently limited and exhibit large uncertainties. In
addition, absorption measurement at very low wavelengths where BrC dominates
absorption would also help constrain the abundance and properties of BrC;
however, these wavelengths are not available for current remote sensing
observations. Recently, during two aircraft campaigns (DC3 and SEAC4RS;
see details in Sect. 2), BrC absorption was directly measured. This provides
an opportunity to test the model assumptions. However, properties of BrC,
including absorption, chemical transformation, and mixing state, are still
challenging to evaluate because of the uncertainty surrounding the simulation
of OA mass. Models fail to reproduce the observed magnitude and variation of
OA mass concentrations (Heald et al., 2011; Spracklen et al., 2011;
Tsigaridis et al., 2014). Thus, it can be challenging to untangle whether any
discrepancy between modeled and observed BrC absorption should be attributed
to BrC properties or OA mass concentrations. Furthermore, uncertainties
surrounding the simulation of BC (Koch et al., 2009; Bond et al., 2013; Wang
et al., 2014) may also impact a combustion-based approach (MCE or
BC / OA) to simulating BrC.
Given the above context, it is highly challenging to develop and test an
accurate model simulation of BrC. A reasonable approach is to test the
simplest assumptions for BrC modeling. In this study, we develop a model
simulation of BrC, test it against BrC absorption measurements from two
aircraft campaigns in the United States (SEAC4RS and DC3), and optimize it to match
these observational constraints. To the best of our knowledge, this is the first study to
compare simulated BrC absorption and its vertical variation with direct,
continuous aircraft measurements. We explore how assumptions for BrC sources,
processing, and properties impact the comparisons with these observational
constraints and estimate the resulting global direct radiative effect of BrC
under these conditions.
Aircraft observations
In this study, we compare our model results to the DC-8 airborne measurements
from two campaigns: DC3 and SEAC4RS. The DC3 (Deep Convective Clouds and
Chemistry) campaign was conducted from May 18 to 22 June in 2012, over the
central and southeastern United States (Barth et al., 2015). The SEAC4RS (Studies
of Emissions, Atmospheric Composition, Clouds and Climate Coupling by
Regional Surveys) campaign occurred in a similar region during 6 August to
23 September in 2013 (Toon et al., 2016). Flight tracks are shown in Fig. 1.
BrC absorption and related aerosol measurements of interest were made by the
same instruments during these two campaigns.
The flight tracks during the DC3 (black) and
SEAC4RS (red) campaigns in 2012 and 2013, respectively. The blue tracks indicate
the SEAC4RS data influenced by Rim fires on 26–27 August. The green box
indicates the region of focus for our analysis (see Sect. 4 for details).
The OA absorption (hence the BrC) was directly measured from liquid extracts
of aerosol samples. The samples with aerodynamic diameter smaller than
4.1 µm were collected on Teflon™ filters every 5 min. Water
extracts were transferred to an LWCC–TOC system (Liquid Waveguide Capillary
Cell coupled to a Total Organic Carbon analyzer). The absorption spectra of
the extracts were measured in the 200 to 800 nm wavelength range; these
measurements are referred to as H2O_Abs. The detection limit and
uncertainty of H2O_Abs is 0.031 Mm-1 and 20 % respectively,
at 365 nm. The insoluble fraction of the samples was sequentially extracted
in methanol following the same method as water extracts. This part of the
absorption is referred to as MeOH_Abs and has a detection limit and
uncertainty of 0.11 Mm-1 and 37 % at 365 nm. The total absorption
of OA is determined by summing H2O_Abs and MeOH_Abs as reported at
365 nm. A multiplication factor of 2 is applied here to convert the solution
absorption to aerosol absorption, reflecting the enhanced absorption by
aerosols in the Mie regime versus molecules in the liquid extract (Zhang et
al., 2017). This factor corresponds to the measured size distribution at
three sites (Liu et al., 2013); this size distribution is similar to that assumed
in the model (discussed in Sect. 3). An important assumption here is that
essentially all of the BrC can be extracted in water and methanol, which is
supported by laboratory experiments (Chen and Bond, 2010). Further details on
these measurements can be found in Liu et al. (2015).
In addition to OA absorption, the mass concentrations of aerosols and gases
were measured throughout the two campaigns. Submicron OA (and inorganic
aerosols) were measured by a high-resolution time-of-flight Aerodyne Aerosol
Mass Spectrometer (AMS, DeCarlo et al., 2006) with an estimated uncertainty
of 38 %. The transmission of particles through the AMS aerodynamic lens
is ∼ 100 % on the range 50–550 nm and then declines up to above
1 µm and is referred to approximately as PM1 (Dunlea et al.,
2009). BC accumulation-mode mass concentrations were measured with a Single
Particle Soot Photometer (SP2, Schwarz et al., 2008) with an estimated
uncertainty of 30 %; these measurements were made off a different inlet
and sampling line with good transport efficiency only up to 3 µm
total particle diameter (50 % transport efficiency at 3 µm).
Carbon monoxide (CO) and acetonitrile (CH3CN) were measured with a diode
laser spectrometer and proton-transfer reaction mass
spectrometry (PTR-MS) with uncertainties of
2 and 20 %, respectively. Details on all of these measurements, as well
as other aerosol and gas measurements made during the campaigns, can be found
in Barth et al. (2015) and Toon et al. (2016).
Model description
The GEOS-Chem model with RRTMG
We use the global chemical transport model GEOS-Chem (Bey et al., 2001)
coupled with the rapid radiative transfer model for GCMs (RRTMG, Iacono et
al., 2008) in this study. Our simulations use the GEOS-FP assimilated
meteorology from the Goddard Earth Observing System (GEOS) at the NASA Global
Modeling and Assimilation office. The global simulations use v10-1 of
GEOS-Chem with a horizontal resolution of 2∘ × 2.5∘
and 47 vertical levels. When comparing with aircraft measurements, we perform
nested simulations over North America (10–60∘ N,
130–60∘ W) at 0.25∘ × 0.3125∘ horizontal
resolution. RRTMG is a radiative transfer model which calculates both
longwave and shortwave atmospheric radiative fluxes. This calculation is
coupled to GEOS-Chem and conducted every 3 h. Details of the implementation
of RRTMG in GEOS-Chem are available in Heald et al. (2014).
The simulation of POA and BC mass is based on the standard GEOS-Chem
simulation with modifications described in Wang et al. (2014). The model
assumes 50 % of anthropogenic and 30 % of emitted biomass burning
organic carbon (OC) is hydrophobic and the remaining is hydrophilic.
Hydrophobic OC converts to hydrophilic OC with an e-folding time of 1.15
days, equal to an aging rate of ∼ 10-5 s-1. The POA is
inferred from simulated primary OC by applying an OA / OC mass ratio of
2.1 (Turpin and Lim, 2001; Aiken et al., 2008; Canagaratna et al., 2015).
This represents average atmospheric OA / OC composition. Freshly emitted
POA is less oxidized (with an OA / OC range of 1.34–1.65; Canagaratna et
al., 2015); however, aging occurs quickly in the atmosphere, in particular for
biomass burning OA (Cubison et al., 2011; Forrister et al., 2015). The
simulation of BC includes a source-specific treatment. For the fossil fuel
BC, we assume 80 % are emitted as hydrophobic and convert to hydrophilic
with an aging rate related to SO2 and OH levels in the atmosphere:
k=∝SO2OH+b,
where α= 2 × 10-22 cm6 molec-2 s-1
and b= 5.8 × 10-7 s-1 (Liu et al., 2011; Wang et al.,
2014). The biofuel and biomass burning BC is assumed to be emitted as
70 % hydrophilic and 30 % hydrophobic with an aging e-folding time
from hydrophobic to hydrophilic of 4 h (note that throughout our analysis
“biomass burning” refers to open burning and does not include biofuel). The
details of the BC scheme and evaluation against BC mass concentrations can be
found in Wang et al. (2014). Our simulation of SOA is from the standard
GEOS-Chem simulation, which is based on reversible partitioning of
semivolatile products of aromatic and biogenic VOC oxidation (Pye and
Seinfield, 2010; Pye et al., 2010).
The global anthropogenic emissions of BC and POA follow the Bond et
al. (2007) emission inventory (8.7 TgC yr-1 for POA and
4.4 TgC yr-1 for BC, globally). For the North America region, The EPA
National Emission Inventory for 2011 (EPA/NEI11) is used. We also implement
the annual scaling factors from the EPA's air pollutant emissions trends data
(https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data)
and a 17 % decrease for SO2 and a 30 % decrease for BC,
suggested by Kim et al. (2015), who conducted an analysis of the aerosols
during SEAC4RS. The resulting anthropogenic POA and BC emissions from
the contiguous United States total 0.58 and 0.26 TgC yr-1 for 2013.
Since the EPA/NEI11 inventory does not separate fossil and biofuel emissions,
we apply the fossil / biofuel emission ratios from the Bond et al. (2007)
emission inventory and the seasonal cycle of residential emission from Park
et al. (2003) to separate these two emission types. This produces an annually
averaged fossil / biofuel emission ratio of 8 for BC and 1.2 for OC in the United States. The
biomass burning emissions of BC and POA follow the year-specific daily mean
GFED4s (Global Fire Emissions Database with small fires) inventory (van der
Werf et al., 2010; Giglio et al., 2013), contributing 0.1 TgC yr-1 of
BC and 1.42 TgC yr-1 of POA in the United States in 2012 and 0.15 TgC yr-1 of BC
and 2.24 TgC yr-1 of POA in the United States in 2013. The biogenic VOC emissions are
simulated online based on the MEGAN2.1 (Model of Emissions of Gases and
Aerosols from Nature) scheme (Guenther et al., 2012). The anthropogenic VOC
emissions are based on the combination of REanalysis of the TROpospheric
chemical composition (RETRO) global emission inventory (Pulles et al., 2007)
and EPA/NEI11 inventory for the United States.
Treatment of BrC optical properties
Most previous BrC modeling studies assume some fraction of OA to be BrC and
assign it different optical properties from non-absorbing OA. Unlike this
approach, we assign absorption properties for all OA, thus convolving two
unknowns into a single assumption. This simplifies our analysis, given that
the total absorption from OA equals the absorption from BrC:
AbsBrC=AbsOA=MACOA⋅MassOA=MACBrC⋅MassBrC=MACBrC⋅f⋅MassOA.
MAC is the mass absorption coefficient. f is the fraction of OA mass that
is BrC. MACOA is the optical property typically measured in
laboratory studies, which includes information on both MACBrC and
the contribution of BrC to OA (f). Here, to determine MACOA, we
take the OA properties from the Global Aerosol Data Set (GADS) database
(Kopke et al., 1997) with updates from Drury et al. (2010), except for the
imaginary part of the refractive index (k).
Little evidence to the contrary, we assume that fossil fuel POA is not
absorbing. To date, there are no field observations that indicate POA
associated with fossil fuels is light-absorbing (Laskin et al., 2015) except
measurements in Beijing (Yan et al., 2017). For biofuel and biomass burning
POA, we use the experimental results from Saleh et al. (2014) to parameterize
the imaginary part of the refractive index. In that study, k is related to
the BC / OA mass ratio from biofuel and biomass emissions:
w=0.21BCOA+0.07k550=0.016lgBCOA+0.04,
where w refers to the wavelength dependence of k, and k550 is the
imaginary part of the refractive index at wavelength of 550 nm. For other
wavelengths (λ), i can be calculated as
k=k550(550/λ)w.
The BC / OA emission ratio is associated with both combustion fuel type
and burning conditions. In the GFED4s emission inventory, the BC / OA
emission ratio ranges from ∼ 0 to 0.23 for biofuel (however the
majority of points range between 0.06 and 0.16, the 10th and 90th percentiles) and 0.03 to
0.06 for biomass burning. These ranges are not large because the variability
in burn conditions, which is likely to dominate the variability in
BC / OA emission ratio, is not represented. We use global average
BC / OA emission ratios in the model for each source: 0.12 for biofuel
and 0.05 for biomass burning. This simple assumption reflects the average
burning conditions globally but not for specific fires. The assumed
BC / OA ratio is further used to derive the wavelength-dependent k. The
size distribution of OA is assumed to be log-normal, with a geometric median
diameter (GMD) of 180 nm and standard deviation (δ) of 1.6. The
density of OA is assumed to be 1.3 g cm-3. Based on these values, the
MAC of OA at 365 nm is calculated to be 1.19 m2 g-1 for biofuel
OA and 1.28 m2 g-1 for biomass burning OA. The OA AAE (absorption
Ångström exponent) of the 300 and 600 nm wavelengths pair is 2.6 for
biofuel OA and 3.1 for biomass burning OA. These assumptions will be
evaluated in the following comparisons with observations. We choose this
approach for our model simulation of BrC because relationships between the
absorption of OA and the BC / OA ratio have been confirmed by field
measurements (X. Wang et al., 2016; Gilardoni et al., 2016).
For SOA, we assume that only aromatic SOA absorbs light since experiments
show most light-absorbing SOA is related to aromatic carbonyls (Jaoui et
al., 2008; Desyaterik et al., 2013) and since absorption from biogenic SOA in the field (in
the same region and years studied here) has been found to be negligible
compared to even mild biomass burning influence (Washenfelder et al., 2015).
We specify the absorption properties of aromatic SOA based on our earlier
study (Wang et al., 2014); these are among the highest values from laboratory
experiments (MAC = 1.46 m2 g-1 at 365 nm; Zhang et al.,
2013). We note that the model does not include biomass burning SOA.
All of the above assumptions, for both POA and SOA, are the initial
properties for our model simulations. The goal of this study is to
investigate whether these assumptions are consistent with the absorption
properties in the real atmosphere. To distinguish with other simulations
described below, we call this the “Base” simulation.
Chemical aging of biomass burning BrC
In the Base simulation, we assume that the absorption properties of organic
aerosol are fixed. To investigate the influence of chemical aging, we perform
additional simulations with assumptions derived from our previous study
(X. Wang et al., 2016). In that study, we found that the BrC absorption of
biomass burning plumes observed at T3 site of the Green Ocean Amazon campaign
(GoAmazon2014/5) exhibited a ∼ 1-day photochemical lifetime (in
sunlight). This photochemical lifetime is qualitatively consistent with the
study of Forrister et al. (2015), who investigate the Rim fires during
SEAC4RS. To the best of our knowledge, these two field studies are the
only ones to investigate the change in BrC absorption during chemical aging.
To include this aging effect in the model, we assume that the absorption of
OA decreases at a rate related to OH:
AbsBrC,t+Δt=AbsBrC,t⋅exp-OH⋅Δt5×105,
where AbsBrC,t and AbsBrC,t+Δt are the
absorption of BrC at time t and t+Δt (in days), and [OH] is the
concentration of OH in molec cm-3. As both of these studies found that
the absorption did not decrease beyond some minimum threshold, we do not
allow absorption to drop below a specified minimum (1/4 of the starting
point). We add this scheme to the Base simulation described above to conduct
a model simulation with aging (Base_Age).
Mixing of BC and OA
As discussed in Sect. 1, BC and OA are likely to be internally mixed in a
form reasonably well modeled with core-shell morphology (China et al., 2015).
This morphology enhances the absorption of BC through lensing, and this
enhancement depends upon the absorption properties of the shell material
(including BrC). However, this is challenging to represent accurately, given
uncertainties in the coating thickness and composition. Furthermore,
considering the low BC / OA emission ratio from biomass burning and
biofuel, together with the typical coating thickness (Moffet and Prather,
2009; Schwarz et al., 2008; Perring et al., 2017), the majority of OA from
these sources is generally externally mixed with BC. Indeed multiple field
studies have reported that BC is only present in a few percent of the biomass
burning particles and that the large majority of the emitted particles do not
contain BC (Kondo et al., 2011; Perring et al., 2017). Therefore, we treat BC
and OA as externally mixed in our simulation. We apply a constant absorption
enhancement for BC (1.1 for fossil BC, 1.5 for biofuel/biomass burning BC),
as described in Wang et al. (2014), based on a series of laboratory and field
observations , regardless of whether the coating shell absorbs light or not.
As a result, this value likely represents some average state which includes
the influence of BC–OA internal mixing. Our assumption of externally mixed
OA with an associated absorption enhancement for BC may overestimate OA
absorption since the OA which coats BC is double-counted; however, given that
the majority of the OA is likely externally mixed, this overestimate in
absorption is modest and likely negligible for air masses influenced
predominantly by biofuel and biomass burning sources.
The median vertical profile of (a) sulfate,
(b) BC, (c) OA mass concentration, and (d) OA
absorption, shown in 1 km bins, from the DC-8 aircraft measurement during
the DC3 campaign in the region shown in Fig. 1. Observations (black) are
compared to the Base simulation (red) and source-specific contributions to
that simulation, as well as to the optimized Modified_Age simulation (red
dashed). Error bars show the 25th and 75th percentiles of measurements in
each vertical bin. Gray points show the original measurements (1 min
averaged values for a, b, and c, 5 min averaged
values for d). The ranges of x-axes are set to emphasize the
vertical profile, so several data points higher than the maximum values of
x-axes are not shown. Details regarding the model simulations of Base and
Modified_Age can be found in Sects. 3.2 and 4.1.
Comparing simulated BrC to aircraft observations
In this section, we evaluate our assumptions for BrC by comparing the
GEOS-Chem nested model simulations with aircraft observations from the DC3
and SEAC4RS campaigns. The region included in the analysis is the
central and southeastern United States, which is shown in Fig. 1. We focus on this subset
of the measurements because (1) aircraft measurements from both DC3 and
SEAC4RS cover this region; and (2) the emissions inventories for this
region have been evaluated by a series of SEAC4RS studies (resulting
modifications described in Sect. 3.1). Dry aerosol absorption is used when
comparing the model with observations since hygroscopic growth is not considered
in the measurements. Before evaluating the simulation of OA absorption, we
first need to explore the fidelity of the simulation of OA mass.
DC3 campaign
We first compare the Base simulation to observations. Figure 2 compares the
median vertical profile of modeled sulfate, BC, and OA mass concentrations
with the DC-8 aircraft measurements during the DC3 campaign. Our simulation
reproduces the median vertical distribution of observed sulfate and BC but
underestimates OA by about a factor of 2 at low altitudes
(< 3 km). To investigate the source of this bias, we show all the
observed 1 min averaged data points together with model results as a
“points-to-points” plot in Fig. 3. The model reproduces the BC observations
(normalized mean bias (NMB) of -5 %) except for some occasional
peaks, which are challenging to capture given the limitations of model
temporal and spatial resolution. We note here that the model skill in
capturing this variability improves in the nested grid (R= 0.54) compared
to the global 2∘ × 2.5∘ grid (R= 0.48), with
little change in NMB. In comparison, the bias in the simulation of OA is much
larger, with an overall NMB of -45 %. The unbiased simulation of
sulfate and BC suggests that the model generally captures the transport,
deposition, and primary emissions (fossil, biofuel, and biomass burning) of
aerosols. Therefore, the underestimate of OA is more likely associated with
biased emission factors for POA and/or an underestimate of SOA. A key
question is whether this bias is associated with absorbing or non-absorbing
sources of OA.
Points-to-points comparison between 1 min averaged observed (black)
and simulated (red) (a) BC and (b) OA made aboard the DC-8
aircraft during the DC3 campaign in the region shown in Fig. 1. The simulated
total mass concentrations (red) as well as mass concentrations associated
with biomass burning only (green) are from the Base simulation. The observed
concentrations of acetonitrile are also shown (c). The blue dashed
lines separate different flights. The green shading indicates two biomass
burning dominated periods (BP1 and BP2, discussed in Sect. 4.1).
According to the emission inventories used here, biofuel contributes very
little OA in this region (< 3 % of the total POA source during
the campaign). This is consistent with a negligible demand for heating during
spring and summer in the southeastern United States. We therefore conclude
that it is highly unlikely that the substantial OA underestimation identified
in Figs. 2c and 3b is associated with biofuel sources. To investigate whether
an underestimate in fire emissions contributes to the bias, we also show the
measured acetonitrile (CH3CN) concentrations in Fig. 3c. Acetonitrile is
a tracer for biomass burning and biofuel emissions (Andreae and Merlet,
2001). We calculate the hourly correlations between CH3CN and OA, and BC
and OA to help to identify whether the OA during plumes are associated with
fires. When CH3CN peaks, OA peaks, BC peaks, and high CH3CN–OA
correlation are observed, we can be confident that biomass burning dominates
the sources of OA. We observe two such periods (BP1 and BP2), which are shown
with green shading in Fig. 3. BP1 is a period with a series of CH3CN
peaks measured in the central United States. The correlations between
CH3CN and OA are continuously high (R2= 0.5–0.9) throughout the
period. Both modeled BC and OA are dominated by biomass burning and enhanced
during BP1 but underestimate the measurements. This suggests that the model does not capture the strength of
these plumes. The simulated mass of BC from fires needs to increase by
130 % to match observations. This bias could be associated with transport
(including excessive dilution) as well as inaccuracies in the amount or
intensity of burning in the emissions inventory or in the emission factor for
fire sources of BC. In contrast, the mass of biomass burning OA needs to
increase by 210 % to match the observations; this is ∼ 80 %
more than for BC. BP2 is a 1 h period dominated by a biomass burning plume
observed in the southeastern United States, with very high CH3CN–OA
correlation (R2= 0.84). The model is able to represent the BC
concentrations during this period quite well (< 10 %
underestimate); though if we attribute this entire bias to the biomass
burning source, it implies a 36 % increase in that source. Similarly, the
mass of biomass burning OA needs to increase by 145 %, which is also
∼ 80 % more than for BC (similar to BP1). Since the influence from
transport and errors in fuel burned should be very similar for BC and OA, the
higher bias in simulated OA suggests that either the biomass burning
BC / OA emission ratio is overestimated or that biomass burning
constitutes a large source of SOA which has been neglected in the model. If
all of the 80 % difference is due to the overestimate of the BC / OA
emission ratio, the BC / OA emission ratio would need to be reduced to
0.027 to meet the observations; this value is lower than the emission ratio
for any fire type in the GFED4s emission inventory. It is therefore unlikely
that this difference can be attributed entirely to an overestimate of the
BC / OA emission ratio. A number of studies have explored the formation
of SOA in biomass burning plumes. Yee et al. (2013) conduct photo-oxidation
experiments in their chamber, and find that the formation of SOA from
oxidation of phenol, guaiacol, and syringe can be larger than 25 % of the
co-emitted biomass burning POA. Ortega et al. (2013) investigate the biomass
burning smoke from fuels combusted during the FLAME-3 study and find that the
net increase in mass due to biomass burning SOA is 42 ± 36 % of
the biomass burning POA. However, compared to the laboratory studies,
aircraft field measurement show much less SOA formation. Cubison et
al. (2011), Jolleys et al. (2012), and Shrivastava et al. (2017) have
reviewed all aircraft field studies of SOA formation in BB plumes and have
found that the increase in total OA from SOA production is most often
undetectable, with a smaller fraction of the cases showing increases or
decreases with aging, which are a small fraction of the initial POA. Some
studies have included simple biomass burning SOA schemes in their models.
Hodzic and Jimenez (2011) assume a simplified biomass burning SOA scheme in
the CHIMERE model (VOC is oxidized by OH with a constant rate); their
simulation estimates that biomass burning SOA contributes 11 % of the
total SOA in Mexico City. Kim et al. (2015) used the same scheme for the
SEAC4RS period and concluded that biomass burning SOA contributed
1 % of the OA in this region, comparable to 10 % of the biomass
burning POA. Therefore it is unlikely that the majority of the 80 % bias
can be attributed to SOA formation from fires. The small bias in the
simulation of BC during BP2 suggests that the emission factor for BC from
biomass burning is not substantially biased. Rather, it is likely that the
bias in BC (particularly in BP1) results from an underrepresentation of total
emissions from these fires. In both BP1 and BP2, after adjusting both BC and
OA mass concentrations upwards to eliminate this bias, we still need to
increase OA by an additional 80 % to account for the underestimate of
either the POA emission factor or biomass burning SOA. This 80 %
represents the upper limit on missing OA associated with biomass burning,
given that other sources likely contribute to background concentrations.
Based on the above analysis, we first increase the biomass burning OA mass by 210
and 145 % during BP1 and BP2, respectively, to fix the model bias
associated with these specific fire plumes. We then increase the biomass
burning OA mass by 80 % for all the remaining data (including background
biomass burning OA), likely to account for the bias from the POA emission
factor and any missing biomass burning SOA. This modified simulation of OA
mass (referred to as FixBB) reflects the highest possible biomass burning
contribution. In Fig. 2c, this modified model (same as Modified_Age,
described at the end of this section) still underestimates the vertical
profile of OA mass. This underestimate can be observed as underestimated OA
peaks in Fig. 3b but not in the corresponding BC concentrations in Fig. 3a,
and it is therefore unlikely to be related to combustion sources (fossil fuel,
biofuel, or biomass burning). This suggests that the remaining underestimate
of OA is related to anthropogenic and/or biogenic secondary sources, which
are not a source of BC. This is consistent with previous work which suggests
a general underestimate of SOA in the GEOS-Chem simulation (Heald et al.,
2011). Furthermore, OA absorption is not enhanced during these peaks,
suggesting that this SOA is not strongly absorbing and these biases are not
relevant to our analysis of absorption which follows.
Figure 2d compares the simulated median vertical profile of OA absorption
with measurements from the DC3 campaign. Note that this vertical profile may
not be entirely representative since there are very few data points
(< 10) available at some altitudes. It is also important to note
that the OA particle size may differ between model and observations. The size
distribution assumed in the model is for fine-mode particles and would not
include the absorption from coarse-mode OA (> 1 µm
diameter). The biomass burning source contributes ∼ 90 % of the
total absorption from OA in our simulation. The model underestimates the OA
absorption at both high and low altitudes. At altitudes above 10 km, some of
the observations show abnormally high OA absorption considering the
correspondingly low submicron OA mass. The model fails to capture these high
values even by applying the highest absorption properties from laboratory
studies for OA. Zhang et al. (2017) analyzed the inflow and outflow of OA
absorption during DC3 and conclude that the high absorption aloft may relate
to coarse-mode OA or OA formation during convective transport. A number of
studies also suggest that aqueous-phase chemistry in cloud droplets at high
altitudes can produce absorbing OA (Ervens et al., 2011; Desyaterik et al.,
2013). These sources of OA are not included in our simulation, and given the
limited observational constraints provided by this dataset, we do not
consider these data further in our study, but we agree with Zhang et al. (2017)
that further investigation of high-altitude BrC is needed. The absorption
enhancement in both observations and the model around 4.5 km is related to
biomass burning; this is confirmed by elevated observed acetonitrile
concentrations at this altitude. The high concentrations at this altitude are
influenced by the fire plumes during BP1.
To investigate the absorption properties of biomass burning OA, we select the
data during BP1 as we are confident that nearly all of the OA absorption is
related to biomass burning in this period. Figure 4 compares OA absorption
from simulated biomass burning OA with measurements during BP1. The Base
modeled biomass burning OA absorption is moderately correlated (R= 0.56)
with the observations but overestimates them by ∼ 10 %. This
overestimation increases to more than a factor of 2 after the
underrepresentation of fire OA is corrected (FixBB). This suggests that the
model assumption for MACOA is too high if no whitening process is
included. Given this overestimate, we perform an additional simulation which
includes photochemical “whitening” of BrC (described in Sect. 3.3,
FixBB_Age); the results are shown as green points in Fig. 4. By applying the
aging scheme, the correlation between modeled and observed absorption
increases (R= 0.60), and the model is brought into much better agreement
with observations (NMB = -4 %). We note that if the 80 %
increase in biomass burning sourced OA included in FixBB is attributed solely
to an overestimate of the BC / OA ratio (which we previously note seems
unrealistic), this would imply an 8 % decrease in the MAC following the
Saleh et al. (2014) parameterization used here. This suggests that our
initial assumption of MACOA is not significantly biased by the
potential overestimate of the global BC / OA ratio used in our
simulation. We further note that the decrease in MACOA required
to match observations exceeds the contribution of SOA from biomass burning,
and therefore the model cannot be brought into agreement with observations by
assuming non-absorbing biomass burning SOA.
Correlation between observed and modeled OA absorption
during the BP1 interval (see Fig. 3) of the DC3 campaign. The 1-to-1 line
is shown as a dotted black line; the best-fit lines are shown as solid
lines. NMB: normalized mean bias between the simulation and observations.
Details of the model simulations can be found in Sect. 4.1.
Figure 4 suggests that our initial assumption of MACOA for
biomass burning only needs to increase by 4 % when including the
whitening process (decrease by 71 % if not considering aging) to match
the observations in BP1. At 365 nm, the best MACOA to represent
the measurements is 1.33 m2 g-1 with aging (this is the MAC for
freshly emitted OA) and 0.37 m2 g-1 without aging. The
0.37 m2 g-1 value is around the lower end of previous
experimental studies, whereas 1.33 m2 g-1 falls close to the
median of previous experimental studies (Kirchstetter et al., 2004; Chen and
Bond, 2010; Liu et al., 2013; Zhong and Jang, 2011; Zhang et al., 2013). This
supports the idea that OA from fresh BB emissions exhibits similar absorption
properties as observed in laboratory studies but that including an aging
process is important for simulating OA absorption in the ambient atmosphere.
Given that there are only three OA absorption measurements available during
BP2, we cannot repeat this analysis for BP2. For the other periods not
dominated by biomass burning, the correlation between modeled and observed OA
absorption is very low (R < 0.1). OA absorption is typically
lower during these periods and represents a mix of biomass burning and
biofuel influences, the combination of which reproduces the magnitude of
observed OA absorption.
The median vertical profile of (a) sulfate,
(b) BC, (c) OA mass concentration, and OA
absorption (d), shown in 1 km bins, from the DC-8 aircraft
measurement during the SEAC4RS campaign in the region shown in Fig. 1.
Observations (black) are compared to the Base simulation (red) and
source-specific contributions to that simulation, as well as to the optimized
Modified_Age simulation (red dashed). Error bars show the 25th and 75th
percentiles of measurements in each vertical bin. Gray points show the
original measurement data points (1 min averaged values for a,
b, and c, 5 min averaged values for d). The ranges
of x-axes are set to emphasize the vertical profile, so several data
points higher than the maximum values of x-axes are not shown. Details of
model simulations of Base and Modified_Age can be found in Sects. 3.2 and
4.1.
After applying a series of new model assumptions, which include increasing
fire OA mass, decreasing the biomass burning MACOA, and adding an
aging scheme, we conduct a new simulation (Modified_Age). The simulated
vertical profile of OA absorption in this simulation is now able to capture
the measurements. Since the observational constraints on MACOA
and the aging scheme only affect absorption but not aerosol mass, the
simulated OA concentrations in FixBB, FixBB_Age, and Modified_Age are the
same.
SEAC4RS campaign
The SEAC4RS campaign offers us the opportunity to test our updated
simulation developed based on DC3 measurements with a new dataset. Figures 5
and 6 show the vertical profiles and points-to-points plot for DC-8 aircraft
measurements during SEAC4RS. Similar to DC3, our model generally
captures the median vertical profile of sulfate (Fig. 5a) and BC (Fig. 5b).
During SEAC4RS, biogenic SOA constitutes a much larger source of OA in
the model (compared to DC3). Consistent with DC3, the Base simulated OA
absorption captures the observations at low altitudes but is too low at high
altitudes. The observed absorption at high altitudes is much lower than
observed in DC3. Zhang et al. (2017) suggest that this is because
measurements during SEAC4RS are less influenced by convection than DC3;
thus there may be less secondary formation of BrC during convective
transport. Note that there are very few data points available at altitudes
above 4 km. The Base model underestimates the OA mass observations, but with
a much lower bias (∼ 50 %) than seen during DC3. Similarly, the
model bias for BC is modest (NMB of -30 %). There is therefore
weaker evidence for missing or underestimated fire activity in the GFED4s
inventory during SEAC4RS. Furthermore, Fig. 6c shows that there are no
coincident peaks, with both elevated CH3CN and CO correlated to each other.
In our selected region, during SEAC4RS, there is no clear period which is
dominated by biomass burning. The Rim fires occurred on 26–27 August 2013.
During this period, CO is underestimated by more than 400 % in the model,
which indicates that the model fails to reproduce the fire plumes from the
Rim fires. However, as shown in Fig. 1, these measurements are located around
the northwestern United States and are not included in our analysis.
Points-to-points comparison between observed and modeled
(a) BC and (b) OA made aboard the DC-8 aircraft during the
SEAC4RS campaign in the region shown in Fig. 1. The modeled total
mass concentrations (red) as well as mass concentrations associated with
biomass burning only (green) are from the Base simulation. The observed
concentrations of acetonitrile and CO are also shown (c). The blue
dashed lines separate different flights.
When applying the same modified model assumptions constrained from DC3
(Modified_Age simulation), the model simulation of observed OA
concentrations (Fig. 5c) improves. When only considering altitudes below 4km
(where there are sufficient measurement data points), OA absorption is very
similar between Base and Modified_Age simulations, which are both able to capture the
observed values. However, the mean bias in OA mass concentrations between the
model and observations decreases from -42 to -28 % when moving from
the Base to the Modified_Age simulation. This confirms that the
modifications applied based on the DC3 campaign in 2012 are generally
appropriate for this region.
Washenfelder et al. (2015) analyzed measurements of OA absorption at a
surface site within the study region (central Alabama) and during a similar
time period (June 2013) of SEAC4RS. They found that most of the OA
absorption was associated with biomass burning with little contribution from
biogenic SOA, consistent with our analysis of the aircraft data. They suggest
a biomass burning MACOA of 1.35 m2 g-1, which is very close to
ours. However, as their site was rarely affected by biomass burning
(∼ 6 % of all OA), the identification of biomass burning OA
absorption properties from this site is challenging and may not be regionally
representative.
Recommendations for OA absorption properties
Although the assumption of a relationship between BrC absorption and the
BC / OA ratio, which is applied in our simulations, has been observed in
several studies, including both laboratory (Saleh et al., 2014; Pokhrel et
al., 2017) and field measurements (X. Wang et al., 2016; Gilardoni et al.,
2016), the specific relationship (e.g., slope) differs among these studies.
Based on the above analysis, our assumed MAC for fresh biomass burning OA at
365 nm (based on Saleh et al., 2014) needs to be increased by 4 % to
reproduce the observations from DC3 and SEAC4RS (when including an aging
scheme). As discussed in Sect. 4.1, this suggests that the absorption
properties of freshly emitted OA are very similar to those from laboratory
experiments. As a result, we retain the absorption wavelength dependence
based on Saleh et al. (2014) but increase the MACOA by 4 % in
the model. Our recommended MACOA for biomass burning is therefore
1.33 m2 g-1 at 365 nm, 0.77 m2 g-1 at 440 nm, and
0.35 m2 g-1 at 550 nm, with the suggested aging scheme described
in Sect. 3.3. We assume that biomass burning SOA is equally absorbing as
primary OA from biomass burning. All of these numbers can be translated to
the form of MACBrC if the contribution of BrC to OA is known or
specified. For example, the MACOA of 1.33 m2 g-1 is
equivalent to a MACBrC of 2.66 m2 g-1 with BrC
contribution of 50 % to total OA. The years 2012 and 2013 were not
exceptionally low or high fire years in the United States. During DC3 and
SEAC4RS, fires in the United States (e.g., 12 586 fires in June 2012,
data from www.globalfiredata.org) were somewhat more frequent than the
last 10-year average (e.g., average 9831 fires in June). The difference
between 2012 and 2013 and 10-year average emissions in our research region
during the measurement period is not large (22 and 34 % higher in 2012
and 2013 respectively, compared to the 10-year average). This suggests that
our conclusions based on the constraints from these two campaigns can be
generalized to other biomass burning seasons in the United States. Given that
the measurements only constrain absorption at one wavelength, we cannot
evaluate the model-assumed AAE. Previous field and laboratory studies show a
large range of BrC AAE of 2 to 9 (Laskin et al., 2015). Our model assumption
(3.1 for biomass burning OA at 300 and 600 nm wavelength pair) is around the
lower end of this range.
The spring and summer in the southeastern United States are not substantially impacted by
biofuel emissions; therefore, the measurements during the DC3 and SEAC4RS
campaigns are not suitable for evaluating the absorption from biofuel OA.
Given that we see no model bias when biofuel influence exceeds the biomass
burning influence (typically during low absorption background OA periods), we
retain our assumptions in Sect. 3.2 for biofuel OA. Therefore, our
recommended value for biofuel MACOA is 1.19 m2 g-1 at
365 nm, 0.76 m2 g-1 at 440 nm, and 0.39 m2 g-1 at
550 nm. We assume that there is no whitening of biofuel OA with aging
given that, to date, there is no field evidence to support this. These
assumptions require further testing against measurements with significant
biofuel influence.
During both DC3 and SEAC4RS, anthropogenic SOA contributes very little
absorption in the model (∼ 4 % in DC3 and < 1 % in
SEAC4RS) despite the fact that we apply upper-limit assumptions
regarding the absorption properties of SOA. In our analysis of DC3, there
remain several underestimated OA mass peaks even after increasing biomass
burning OA mass. These peaks are likely due to secondary biogenic or
anthropogenic sources. During SEAC4RS, there are also some peaks with
substantial simulated biogenic SOA; observed absorption is not elevated
during these peaks. Therefore, we conclude that the absorption from biogenic
and anthropogenic SOA is negligible in the southeastern United States, consistent with SOAS
results (Washenfelder et al., 2015). This may not be true in other regions.
Using the above model configuration (Modified_Age simulation), the model is
able to reproduce the vertical profile of OA absorption during DC3 and
SEAC4RS at altitudes below 10 km that include more than 340 data
points. Our optimized MACOA is comparable with previous BrC model
studies. Feng et al. (2013) assume that 66 % of the OA from biofuel and
biomass burning is BrC. They applied two different sets of assumptions for
the absorption properties of BrC: a moderately absorbing BrC with
MAC = 0.63 m2 g-1 at 450 nm, and a strongly absorbing BrC
with MAC = 1.6 m2 g-1 at 450 nm. These numbers are 0.41 and
1.06 m2 g-1 when transferring MACBrC to
MACOA, the median of which (0.74 m2 g-1) is similar
to our assumptions at 450 nm. Jo et al. (2016) assume different BrC to OA
contributions for different biomass burning and biofuel fuels, resulting in a
range of MACOA of 0.65–5.01 m2 g-1 at 365 nm. The
assumptions of Saleh et al. (2015) and Q. Wang et al. (2016) are also based
on the laboratory results from Saleh et al. (2014) but use the modeled
BC / OA mass concentration ratio instead of emitted BC / OA ratio.
The modeled BC / OA mass concentration ratio reflects the mixed
contribution of various sources and the effect of differential removal during
transport; it is not equivalent to the initial emission properties of
carbonaceous aerosol. They assume that all OA from biofuel and biomass
burning is BrC and apply a MAC = 2.5 m2 g-1 for biofuel OA
and 3.1 m2 g-1 for biomass burning OA at 550 nm. These values
are much higher than ours and higher than any of the previous experimental
studies. Although some of these modeling studies compare their simulated
total AAOD with observations, none evaluates their assumptions with direct
BrC absorption measurements.
Simulating the whitening process of BrC as we do in our Modified_Age
simulation comes at a computational cost of adding extra species or tracking
absorption in a model, something which may not be practical for all 3-D
models. If we neglect the whitening of BrC with aging in our simulation, we
must further reduce the MACOA for biomass burning to match the
observational constraints. In this case, we estimate an average
MACOA for biomass burning of 0.37 m2 g-1 at 365 nm,
0.23 m2 g-1 at 440 nm, and 0.10 m2 g-1 at 550 nm; we
call this the Modified_Simple simulation. Another approach to simplify the
whitening process for models may be to apply constant whitening factors with
altitude to the simulated absorption; the consistency of these factors may
require additional observational support.
Global implications
The model assumptions applied in Modified_Age and Modified_Simple are
constrained and tested against conditions influenced by US fires observed
during DC3 and SEAC4RS. We assume that such constraints are
generalizable though the combustion conditions may differ in other regions.
We test this assumption in Sect. 5.1 by comparing our global simulation with
AAOD observations outside of the United States. In this analysis, we conduct
Modified_Age and Modified_Simple simulations with a horizontal resolution
of 2∘ × 2.5∘, using 2014 meteorology and 10-year
averaged biomass burning emissions (2005–2014).
Surface absorption and AAOD of BrC
We use the results from Modified_Age to conduct the analysis in this
section. Both of the Modified_Age and Modified_Simple simulations are
optimized to meet the observational constraints and therefore exhibit very
similar average surface absorption. We note that the Modified_Age scheme
creates a somewhat sharper contrast in absorption from land to ocean (as
aging during transport whitens the BrC) than the Modified_Simple scheme;
however, the suite of observations currently available (and discussed here)
would not capture this gradient and therefore cannot be used to discriminate
between these two schemes.
Global distribution of simulated BrC dry absorption
contribution to total dry aerosol absorption at 370 nm at the surface for
2014. Results are from the Modified_Age simulation. The
circles show the retrieved results from multiple-wavelength absorption
measurements at 8 surface sites (see Sect. 5.1 for details).
Global distribution of simulated 2014 annual
mean (a) BrC AAOD, and (b) contribution of BrC AAOD to
total AAOD at 440 nm. Results are from the Modified_Age simulation.
Figure 7 shows the global distribution of surface OA dry absorption
contributions (i.e., BrC absorption contribution) to total absorption from
aerosols at 370 nm. The modeled contribution ranges from 5 to 72 %
globally, with an average value of 32 %. The circles in Fig. 7 show the
BrC absorption contributions from observations at eight surface sites. These
data are derived from multiple years' multiple-wavelength absorption
measurements from Aethalometers (AE, Magee Scientific,
http://www.mageesci.com), using a BC–BrC absorption separation method.
Details of this methodology and the specific datasets can be found in X. Wang
et al. (2016). Although the model assumptions were optimized based on
measurements in the United States, the model is able to represent the BrC
absorption contribution at sites in Europe. However, the model shows much
higher BrC absorption contributions than observations in other fire-rich
regions (e.g., Amazon and Siberia). The simulated contributions reflect
10-year averaged values over all seasons, but the measurements over these
eight sites are not continuous and usually cover several months in a year.
Therefore, this is not an exact comparison between the model and these
measurements.
Figure 8 shows the global distribution of simulated column BrC AAOD and the
contribution of BrC AAOD to total AAOD at 440 nm. The BrC AAOD at 440 nm
ranges from ∼ 10-5 to 0.05, with a global mean of 0.002. BC still
dominates the total AAOD in most regions. The contribution of BrC AAOD to
total AAOD at 440 nm ranges from 15 to 70 %, with a global mean of
46 %. The Aerosol Robotic Network (AERONET) provides a worldwide
measurement network of AAOD at four wavelengths (440, 675, 870, and
1020 nm). However, several shortcomings limit its use for constraining
modeled BrC AOOD, which include the uncertainties in AERONET retrievals,
possible inconsistencies between assumptions in the retrieval scheme and our
model, poor data availability, no data at low wavelengths where BrC dominates
absorption, and the influence from dust. Details of the processing and the
uncertainty issues surrounding AERONET AAOD are discussed in X. Wang et
al. (2016). When assuming only BC absorbs light, the modeled AAOD has a
moderate correlation with AERONET AAOD at 440 nm (R= 0.54). This
correlation is smaller than that at 675 nm (R= 0.59), where OA
contributes nearly no absorption. After including the absorption from BrC,
the correlation of AAOD at 440 nm increases to R= 0.60; this increase in
model skill qualitatively supports our description of BrC absorption.
These comparisons suggest that our simulation, optimized based on
observations in the United States, is not substantially biased in other
regions of the world. However, we emphasize that we have developed a simple
approach to modeling BrC, and more observations are needed to refine this
simulation for other regions where sources and optical properties may
differ.
Estimating the direct radiative effect (DRE) of BrC
Figure 9a and c show the DRE of total OA from the Base and Modified_Age
simulations. The global mean value of all-sky DRE is -0.290 and
-0.344 Wm-2 in Base and Modified_Age, at the top of the atmosphere. This
number is -0.392 Wm-2 when assuming OA does not absorb light.
Therefore the global mean absorption DRE from OA (BrC) is estimated to be
+0.102 and +0.048 Wm-2 in Base and Modified_Age. In the
Modified_Age simulation, biofuel and biomass burning sources contribute 60
and 40 % respectively to the global absorption DRE of BrC. The absorption
DRE of BrC from our best (Modified_Age) simulation (+0.048 Wm-2) is
about 30 % of the DRE from BC (+0.17 Wm-2). The aging process
significantly impacts our estimate of the absorption DRE; the global
absorption DRE is 43 % higher when using the same optical assumptions but
excluding the aging scheme in Modified_Age. We also find that the global
mean absorption DRE is very similar using the Modified_Simple scheme
(+0.049 Wm-2) or the Modified_Age scheme, both of which are
observationally constrained.
The global annual mean OA DRE (a, c) and BrC absorption
DRE (b, d) at the top of the atmosphere (TOA) in 2014 from Base (a, b) and Modified_Age (c, d) simulations. Numbers indicate the
global mean value in Wm-2.
The DRE could be underestimated due to two reasons: First, we attribute all
the mass bias of OA to biomass burning OA during fire plumes without
considering other sources. This may overestimate the contribution of biomass
burning in our analysis, thereby underestimating the MAC of biomass burning
OA when constrained by absorption observations in fire plumes. Using a
potentially underestimated MAC globally could result in an underestimate of
the global DRE. Second, we neglect some very high OA absorption at high
altitudes (> 10 km). Zhang et al. (2017) suggest that this
contributes a local DRE of 0.65 ± 0.34 Wm2. However it is unclear
how important this convectively formed BrC is globally; therefore we neglect
it here, implying that our estimate of absorption DRE of BrC is a lower
estimate.
In contrast, the DRE could also be overestimated for two reasons. First, if
we assume that biofuel BrC is subject to the same aging process as biomass
burning BrC, the global absorption DRE would be 37 % lower than our
estimate. Second, we assume that BrC is completely externally mixed with
other aerosols. This will overestimate the absorption since the BrC coated on
BC is also counted (via an absorption enhancement factor). Assuming the shell
thickness is ∼ 60 % of the core radius (observed in field
measurements; Cross et al., 2010; Shiraiwa et al., 2010), and all coated
material is BrC for biofuel/biomass burning related BC, the absorption DRE of
BrC will be ∼ 15 % lower. This effect is likely even smaller given
that BrC may contribute little to the coating material compared to
non-absorbing OA and nonorganic aerosols.
Our estimate of BrC DRE is near the lower bound of previous studies, which
have not been evaluated against direct measurements of BrC absorption. Saleh
et al. (2015) apply BrC absorption properties based on the modeled
BC / OA ratio and estimate the absorption DRE from BrC to be +0.12 to
+0.22 Wm-2. Jo et al. (2016) use the modified combustion efficiency
(MCE, a function of CO / CO2) to determine BrC absorption and
estimate the absorption BrC DRE to be +0.11 Wm-2. These values are
similar to the absorption DRE of BrC estimated from our Base simulation,
prior to optimization against observations. Hammer et al. (2016) estimate an
absorption BrC DRE of +0.03 Wm-2, which is the lowest value from
previous studies, and lower than our estimate. We note that their model is
constrained by satellite observations of the ultraviolet aerosol index
(UVAI), which is not specific to BrC; furthermore, uncertainties in the UVAI product
are not well understood. More widespread direct measurements of BrC
absorption may offer opportunities to evaluate the UVAI product.
In this study, we do not estimate the DRF, which is the difference between
pre-industrial and present-day DRE, given the challenges in identifying the
anthropogenic fraction of biomass burning emissions. Several previous studies
report the absorption DRF for BrC: +0.04 to +0.11 Wm-2 by Feng et
al. (2013) and +0.22 to +0.57 Wm-2 by Lin et al. (2014).
Conclusions
We use the GEOS-Chem model coupled with the RRTMG model to investigate the mass
optical properties and direct radiative effect of brown carbon (BrC). Our
model assumptions for the optical properties of BrC are based on the
laboratory study of Saleh et al. (2014) and constrained by the aircraft
measurements from the DC3 campaign in the United States. These assumptions are further
tested against observations made during the SEAC4RS campaigns.
Our model captures the magnitude and vertical distribution of sulfate and BC
mass concentrations during both DC3 and SEAC4RS. However, the model
underestimates the OA mass concentrations in both campaigns. By analyzing the
fire plumes in the observations, we find the biomass burning OA is likely to
be underestimated by 80 % due to the bias in OA emission factors and/or
missing biomass burning related SOA. After fixing the OA mass from biomass
burning, our model is able to represent the variation of OA absorption in
fire plumes but substantially overestimates the magnitude. Applying an aging
scheme where OA photochemically whitens further increases the correlation
between modeled and observed absorption and decreases the model bias. These
comparisons suggest fire emissions are characterized by an MACOA
of 1.33 m2 g-1 at 365 nm, 0.77 m2 g-1 at 440 nm, and
0.35 m2 g-1 at 550 nm, which decreases with aging. The optical
properties for biofuel emissions are not well constrained by these datasets,
and we retain our original assumptions based on Saleh et al. (2014) with
biofuel MACOA of 1.19 m2 g-1 at 365 nm,
0.76 m2 g-1 at 440 nm, and 0.39 m2 g-1 at 550 nm.
Using these assumptions, we estimate a global mean top-of-the-atmosphere DRE
of -0.344 Wm-2 for OA and an absorption DRE of +0.048 Wm-2
for BrC in all-sky conditions. These properties and the resulting estimated DRE
are lower than values from most previous modeling studies; however, none of
these studies have been constrained by or evaluated against direct BrC
absorption measurements.
Although the model can reproduce the aircraft observations from DC3 and
SEAC4RS when using the above model configuration, further studies,
especially global, direct measurements, are necessary to build a credible
simulation of BrC. First, current emission inventories do not provide enough
information to accurately apply combustion condition based BrC absorption
properties. Emission measurements representative of varying burning
conditions as well as different fuel types are needed. Second, we extend the
model assumptions constrained from regional observations (mainland United States) to a
global simulation. It is not clear whether BrC properties are consistent
worldwide. Third, more studies are required to investigate the contribution
of biomass burning SOA and its absorptivity. Fourth, the whitening scheme
needs to be further evaluated by future measurements. Previous near-source
direct observations of BrC absorption have been limited by low temporal
resolution and/or the absence of accompanying measurements of other species
(Liu et al., 2013; Washenfelder et al., 2015; Zhang et al., 2013) and have
therefore not been able to provide the much needed constraints on the
photochemical aging state or transport time. Fifth, the absorption
assumptions for biofuel OA must be more thoroughly evaluated. It is also not
clear whether the whitening process also affects the absorption of biofuel
OA. Last, our simulations do not include OA absorption from fossil fuels.
Fossil OA has only been identified as light-absorbing in Beijing (Yan et al.,
2017). However, applying this assumption for fossil OA worldwide in the
model would substantially increase background OA absorption, leading to a
considerable model overestimate of OA absorption observed during aircraft
campaigns (Sect. 4) and OA absorption contributions from surface sites
(Sect. 5.1). Therefore, to further constrain the global impacts of BrC,
additional field measurements representative of various source influences
(fossil, biofuel OA, SOA) are required.