ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-2507-2016Interpreting the ultraviolet aerosol index observed with the OMI satellite
instrument to understand absorption by organic aerosols: implications for
atmospheric oxidation and direct radiative effectsHammerMelanie S.melanie.hammer@dal.caMartinRandall V.van DonkelaarAaronBuchardVirginieTorresOmarRidleyDavid A.https://orcid.org/0000-0003-3890-0197SpurrRobert J. D.Department of Physics and Atmospheric Science, Dalhousie University,
Halifax, CanadaHarvard-Smithsonian Center for Astrophysics, Cambridge, MA, USANASA/Goddard Space Flight Center, Greenbelt, MD, USAGESTAR/Universities Space Research Association, Columbia, MD, USADepartment of Civil and Environmental Engineering, Massachusetts
Institute of Technology, Cambridge, MA, USART Solutions, Inc., 9 Channing Street, Cambridge, MA, USAMelanie S. Hammer (melanie.hammer@dal.ca)1March20161642507252314September201512October201525January201616February2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/2507/2016/acp-16-2507-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/2507/2016/acp-16-2507-2016.pdf
Satellite observations of the ultraviolet aerosol index (UVAI) are sensitive
to absorption of solar radiation by aerosols; this absorption affects
photolysis frequencies and radiative forcing. We develop a global simulation
of the UVAI using the 3-D chemical transport model GEOS-Chem coupled with
the Vector Linearized Discrete Ordinate Radiative Transfer model (VLIDORT).
The simulation is applied to interpret UVAI observations from the Ozone
Monitoring Instrument (OMI) for the year 2007. Simulated and observed values
are highly consistent in regions where mineral dust dominates the UVAI, but
a large negative bias (-0.32 to -0.97) exists between simulated and observed
values in biomass burning regions. We determine effective optical properties
for absorbing organic aerosol, known as brown carbon (BrC), and implement
them into GEOS-Chem to better represent observed UVAI values over biomass
burning regions. The inclusion of absorbing BrC decreases the mean bias
between simulated and OMI UVAI values from -0.57 to -0.09 over West Africa
in January, from -0.32 to +0.0002 over South Asia in April, from -0.97 to
-0.22 over southern Africa in July, and from -0.50 to +0.33 over South
America in September. The spectral dependence of absorption after including
BrC in the model is broadly consistent with reported observations for
biomass burning aerosol, with absorbing Ångström exponent (AAE) values
ranging from 2.9 in the ultraviolet (UV) to 1.3 across the UV–Near IR
spectrum. We assess the effect of the additional UV absorption by BrC on
atmospheric photochemistry by examining tropospheric hydroxyl radical (OH)
concentrations in GEOS-Chem. The inclusion of BrC decreases OH by up to
30 % over South America in September, up to 20 % over southern Africa in
July, and up to 15 % over other biomass burning regions. Global annual
mean OH concentrations in GEOS-Chem decrease due to the presence of
absorbing BrC, increasing the methyl chloroform lifetime from 5.62 to
5.68 years, thus reducing the bias against observed values. We calculate the
direct radiative effect (DRE) of BrC using GEOS-Chem coupled with the
radiative transfer model RRTMG (GC-RT). Treating organic aerosol as
containing more strongly absorbing BrC changes the global annual mean
all-sky top of atmosphere (TOA) DRE by +0.03 W m-2 and all-sky
surface DRE by -0.08 W m-2. Regional changes of up to +0.3 W m-2 at TOA and down to -1.5 W m-2 at the surface are found over major
biomass burning regions.
Introduction
Absorption of solar radiation by aerosols plays a major role in radiative
forcing and atmospheric photochemistry. Aerosol absorption has been
estimated to be the second largest source of radiative forcing after carbon
dioxide
(Bond et al., 2013; IPCC, 2014; Ramanathan and Carmichael, 2008), although
considerable uncertainty remains regarding the magnitude of the forcing
(Stier et al., 2007; Wang et al.,
2014). Absorption of ultraviolet (UV) radiation by aerosols decreases
photolysis frequencies, leading to a reduction in the concentrations of
atmospheric oxidants (Dickerson et al., 1997; Jacobson, 1998; Liao et al.,
2003; Martin et al., 2003). Many atmospheric chemistry models tend to
overestimate tropospheric hydroxyl radical (OH) concentrations compared to
observations (Mao et al., 2013; Naik et al., 2013). Accurately representing
aerosol absorption could help rectify the discrepancies between simulated and
observed OH concentrations, and offer constraints on radiative forcing.
The ultraviolet aerosol index (UVAI) is a method of detecting aerosol
absorption using satellite measurements. The UVAI is calculated by
separating the spectral contrast of radiances due to aerosol effects from
those due to Rayleigh scattering at two wavelengths in the near-UV region
(Herman et al., 1997; Torres et al., 1998, 2007). Two attributes of the UVAI method are (1) that aerosol optical
properties are more readily detected over surfaces with low reflectance such
as found in the UV (Torres et al., 2005), and (2) that the strong interaction
between aerosol absorption and molecular scattering in the near-UV increases
the sensitivity of UV-radiances to aerosol absorption (Torres et al., 1998).
These attributes enhance the ability of the UVAI to detect aerosol absorption
that affects UV photolysis and radiative forcing.
Traditionally, black carbon (BC) is treated as the predominant absorbing
carbonaceous aerosol, and all organic carbon is assumed to be primarily
scattering or weakly absorbing colorless aerosol. However, a growing number
of observations have found evidence of significant absorption by a subset of
organic carbon, known as “brown carbon” (BrC), which is strongest in the
ultraviolet and decreases into the visible and near-IR wavelength regions
(Bergstrom et al., 2007; Chen and Bond, 2010; Kirchstetter et al., 2004; Martins et
al., 2009; Yang et al., 2008; Zhong and Jang, 2014). The majority of BrC is
emitted to the atmosphere through low-temperature, incomplete combustion of
biomass and biofuel (Chen and Bond, 2010; Kirchstetter et al., 2004; Zhong
and Jang, 2014). There is evidence of a possible source from residential coal
burning (Bond, 2001), while the high-temperature environment associated with
other fossil fuel combustion is unfavorable to BrC formation
(Andreae and Gelencsér, 2006; Saleh et al., 2014). BrC has been observed to contribute significantly to the overall
absorption by biomass burning aerosol, especially in the UV
(Clarke et al., 2007; Corr et al., 2012; Kirchstetter and Thatcher, 2012). The UVAI
is sensitive to this absorption (Jethva and Torres, 2011).
A great deal of uncertainty exists regarding the fraction of total primary
organic carbon that is brown (BrC/POC). This uncertainty arises from the
variety of methods used to measure BrC absorption as well as the variable
nature of organic aerosols themselves (Lack and Langridge, 2013).
Filter-based measurements where the organic carbon is extracted from the
total biomass burning aerosol sample with the use of solvents range from
50 to 90 % (Chen and Bond, 2010; Kirchstetter et al., 2004). A broad range of
BrC/POC values have been used to simulate absorption by brown carbon. For
example, Feng et al. (2013) assume that 66 % of primary organic carbon from
biomass and biofuel emissions is brown, Wang et al. (2014) assume that 50 % of
POC from biomass and 25 % of POC from biofuel emissions is brown, while Lin
et al. (2014) assume that 100 % POC from biomass and biofuel emissions is
brown. Global observations of reflected solar radiation used in the UVAI
could offer a constraint on these different assumptions.
Several estimates of BrC absorption exist, but they all differ significantly.
The imaginary part of the refractive index (k) for BrC derived by
Kirchstetter et al. (2004) and Chen and Bond (2010) are often taken,
respectively, as the upper (k∼ 0.168 at 350 nm) and lower
(k∼ 0.074 at 350 nm) limits in modeling studies
(Arola et al., 2011; Feng et al., 2013; Lin et al., 2014). Different observations may
reflect different burn conditions (Saleh et al., 2014) as well as chemical
loss and evaporation of BrC (Forrister et al., 2015; Zhong and Jang, 2014).
Global observations are needed to infer the effective absorption across a
variety of conditions. Many studies have estimated the direct radiative
effect (DRE) and/or direct radiative forcing (DRF) by BrC. In Heald et
al. (2014) a clear distinction is made between the DRE, which is the
instantaneous imbalance of out-going longwave and incoming shortwave
radiation due to the presence of an atmospheric constituent, and the DRF,
which is the difference in DRE between present-day and preindustrial
conditions. Prior estimates of the change in all-sky top of atmosphere (TOA)
DRE from treating organic aerosol as containing BrC range from
+0.04 W m-2 to +0.25 W m-2 globally
(Chung et al., 2012; Feng et al., 2013), with estimates of regional seasonal DRE of
organic aerosol including absorbing BrC ranging from +0.5 to 1 W m-2
(Arola et al., 2015). Most studies estimate a
TOA DRF of between 0.07 and 0.57 W m-2 due to absorption by BrC
(Lin et al., 2014; Park et al., 2010; Wang et al., 2014). Following submission of
our paper, we learned of a submitted paper by
Jo et al. (2015) that developed a global simulation of BrC and applied it to investigate atmospheric photochemistry.
To our knowledge, this work and that by Jo et al. (2015) are the first two
chemical transport modeling studies that have considered the effect of
absorption by BrC on atmospheric photochemistry.
In this work we introduce BrC to the chemical transport model
GEOS-Chem and examine its effect on atmospheric absorption and
photochemistry, in particular in known biomass burning regions. To evaluate
aerosol absorption, Sect. 3 develops a simulation of the UVAI following
Buchard et al. (2015) using the Vector Linearized Discrete Radiative Transfer model (VLIDORT) coupled with aerosol
fields from GEOS-Chem. Section 4 compares the simulated UVAI values to
observations from the Ozone Monitoring Instrument (OMI). The change in
reflected solar radiation as observed by the UVAI tests the effective
representation of the absorption of UV radiation by BrC. Section 5 examines
the effect of the added BrC absorption on ozone photolysis frequencies and
tropospheric OH concentrations in the GEOS-Chem simulation. Section 6
calculates the DRE of absorbing brown carbon. Section 7 reports the
conclusions.
ObservationsOMI ultraviolet aerosol index
The OMI ultraviolet aerosol index is a method of detecting absorbing aerosols
from satellite measurements in the near-UV wavelength region. The UVAI was
first observed from the Nimbus-7 TOMS (Total Ozone Mapping Spectrometer)
(Herman et al., 1997; Torres et al., 1998) and is currently a product of the
OMI Near-UV algorithm (OMAERUV) (Torres et al., 2007). OMI flies on NASA's
Aura satellite and has been taking global daily measurements since 2004. The
OMAERUV algorithm uses the 354 and 388 nm radiances measured by OMI to
calculate the UVAI according to Torres et al. (1998, 2007):
UVAI=-100log10I354measI354calcR354∗,
where I354meas is the TOA at 354 nm as measured by OMI,
and I354calc is the radiance at 354 nm calculated for a
purely Rayleigh scattering atmosphere bounded by a Lambertian surface of
reflectance R354∗, which is known as the adjusted Lambert
equivalent reflectivity (LER)
(Dave, 1978). R354∗ is calculated by correcting the LER at 388 nm
(R388∗) for the spectral dependence of the surface reflectance
at 354 nm.
Positive UVAI values indicate absorbing aerosol. Negative values indicate
non-absorbing aerosol. Near-zero values indicate clouds, minimal aerosol, or
other non-aerosol related effects such as unaccounted for land surface albedo
wavelength dependence, ocean color effects or specular ocean reflection (i.e.,
sun glint). These second-order effects yield UVAI values ±0.5 within the
noise level (Torres et al., 2007). The OMAERUV product identifies clouds
using the measured scene reflectivity and the UVAI
(Torres et al., 2013). We reject cloudy conditions (quality
flag of 1) to focus on cloud-free conditions (quality flag 0).
In this work we use the OMI UVAI to evaluate simulated UVAI values, as
described in Sect. 3.
Absorption Ångström exponent (AAE)
We use observations of the absorption Ångström exponent (AAE) for biomass
burning aerosol to test our representation of the spectral dependence of
absorption. The AAE is the slope of aerosol absorption optical depth (AAOD)
versus wavelength (λ) in log-log space. Using the AAE, the AAOD can
be related to wavelength with the power-law relationship
AAOD=kλ-AAE,
where k is a constant. Aerosols with spectrally independent absorption
display an AAE of about 1, while aerosols with spectrally dependent
absorption have an AAE> 1. BC exhibits spectrally independent
absorption, and is often accepted as having an AAE close to 1
(Bergstrom et al., 2002;
Bond and Bergstrom, 2006). The observed AAE over the near-UV to near-IR
spectral regions can indicate aerosol type, with urban pollution aerosols
dominated by BC exhibiting an AAE near 1, biomass burning aerosols displaying
an AAE near 2, and desert dust having an AAE > 2
(Bergstrom
et al., 2007; Russell et al., 2010).
Several recent studies have attributed the spectrally dependent absorption
by biomass burning aerosols to the presence of BrC
(Clarke et al., 2007; Corr et al., 2012; Kirchstetter and Thatcher, 2012; Rizzo et
al., 2011; Zhong and Jang, 2014). Kirchstetter et al. (2004) measured over
the 300–1000 nm range an AAE of ∼ 2 for biomass burning aerosol and
an AAE of ∼ 1 for motor vehicle aerosol. They found that after
extracting the organic carbon from the samples using acetone, the AAE of the
biomass burning aerosol decreased to around 1, while the motor vehicle
aerosol AAE remained unchanged. They concluded that the spectral dependence
of absorption by biomass burning aerosol was due to BrC, while the absorption
by motor vehicle emissions was due to BC.
Absorption Ångström exponent (AAE) values for biomass burning
regions from the literature.
Wavelength (nm)AAE valueRegionReference350–400 nm 350–4002.5–3.0South AmericaJethva and Torres (2011)350–700 nm 360–7001.9Rural CaliforniaKirchstetter andThatcher (2012)450–550 nm 470–532 470–5321.9 1.4North-central Canada “Corr et al. (2012) “Mean ± SD*= 1.7 ± 0.35 400–700 nm 400–700 440–670 440–670 440–670 440–670 470–660 470–660 470–660 470–6601.5–1.9 1.8 1.3 1.4 1.6 1.7 1.3 1.5 2.1Laboratory Boreal Forest Southern Africa South America Amazon Arctic Arctic Outside Beijing North AmericaSchnaiter et al. (2005) Russell et al. (2010) “ “ “ Corr et al. (2012) “ Yang et al. (2009) Clarke et al. (2007)Mean ± SD = 1.6 ± 0.26 450–700 nm 470–6602.2North AmericaJ. Liu et al. (2015)300–1000 nm 325–1000 325–1685 330–1000 370–9501.1 1.5 2 1.5Southern Africa Southern Africa Southern Africa Outside BeijingBergstrom et al. (2007) “ Kirchstetter et al. (2004) Yang et al. (2009)Mean ± SD = 1.5 ± 0.37 400–1000 nm 440–870 440–870 440–870 440–870 440–1020 440–1020 440–1020 440–1020 450–9501.6 1.3 1.4 1.4 1.5 1.3 1.3 1.4 1.7Boreal Forest Southern Africa South America Amazon Boreal Forest Southern Africa South America Amazon AmazonRussell et al. (2010) “ “ “ “ “ “ “ Rizzo et al. (2011)Mean ± SD = 1.4 ± 0.14
* SD: standard deviation
Table 1 contains a summary of measured AAE values for biomass burning
aerosol. AAE values increase toward UV wavelengths as expected for BrC
absorption. Variability in the AAE at visible wavelengths may reflect
differences in burn conditions and fuel type. Observations in the
ultraviolet, such as the UVAI, offer an exciting opportunity to exploit the
large AAE of biomass burning aerosol at short wavelengths to assess the
global magnitude of BrC absorption (Jethva and Torres, 2011).
Simulated ultraviolet aerosol index
We simulate the UVAI following Buchard et al. (2015) using the VLIDORT model
(Spurr, 2006), which includes polarization effects
and uses the discrete ordinates method to solve the radiative transfer
equation. We supply VLIDORT with the OMI pixel viewing geometry to calculate
the UVAI for that pixel. The UVAI values are calculated from TOA radiances
computed by VLIDORT at 354 and 388 nm, the wavelengths used by the OMAERUV
product.
Following Buchard et al. (2015) we calculate simulated UVAI values as
UVAI=-100log10I354RAY+AERI354RAYR354∗,
where I354RAY+AER is the TOA radiance calculated with
VLIDORT at 354 nm assuming an atmosphere containing aerosol and Rayleigh
effects, and I354RAY is the TOA radiance calculated with
VLIDORT at 354 nm assuming a purely Rayleigh scattering atmosphere bounded
by a Lambertian surface of reflectance R354∗ (adjusted LER).
R354∗ is calculated by correcting the LER at 388 nm (R388∗) for wavelength dependence:
R354∗=R388∗-R388-R354,
where R388 and R354 are surface reflectance values from a
revisited TOMS-based climatology data set
(Torres et al., 2013).
R388∗ is calculated by relating TOA radiance to diffuse
reflectivity using the equation (Buchard et al., 2015)
R388∗=I388RAY+AER-I388RAYT388RAY+Sb388RAYI388RAY+AER-I388RAY,
where I388RAY+AER is the TOA radiance calculated with
VLIDORT at 388 nm assuming an atmosphere containing aerosol and Rayleigh
effects, I388RAY is the TOA radiance at 388 nm calculated
with VLIDORT for a purely Rayleigh scattering atmosphere bounded by a
Lambertian surface, T388RAY is the simulated transmittance
at 388 nm for a Rayleigh atmosphere, and Sb388RAY is
the spherical albedo of a Rayleigh atmosphere at 388 nm.
Imaginary part of the refractive index (k) values for the base case
with weakly absorbing “colorless” primary organic carbon.
For the calculation of TOA radiances, we provide VLIDORT with vertical
profiles of aerosol extinction, single-scattering albedo, and 32
Legendre-function expansion coefficients of the scattering phase function. We
assume all aerosol particles are spherical. We obtain these aerosol optical
properties using daily aerosol fields at satellite overpass time from
GEOS-Chem version 9-01-03 (http://geos-chem.org), a global three-dimensional chemical transport model driven by assimilated meteorological
data from the Goddard Earth Observation System (GEOS-5) of the NASA Global
Modeling and Assimilation Office (GMAO). Our simulation is conducted at a
spatial resolution of 2∘× 2.5∘ with 47 vertical
levels for the year 2007.
GEOS-Chem contains a detailed oxidant-aerosol chemical mechanism
(Bey et al., 2001; Park et al., 2004). The
aerosol simulation includes the sulfate–nitrate–ammonium system
(Fountoukis and Nenes, 2007; Park et al., 2004; Pye et al., 2009), primary carbonaceous aerosol
(Park et al., 2003), mineral dust (Fairlie et al., 2007), and sea salt
(Jaeglé et al., 2011). Aerosol optical properties are based on the Global Aerosol Data Set (GADS) (Koepke
et al., 1997) as implemented by Martin et al. (2003), with updates for
organics and secondary inorganics from aircraft observations (Drury et al.,
2010), and for mineral dust (Lee et al., 2009; Ridley et al., 2012). Aerosols
are treated as externally mixed. We treat the density of organic aerosol as
1.3 g cm-3 (Duplissy et al., 2011; Kuwata et al., 2012) and assume an
organic aerosol mass to organic carbon fraction of 2.1 (Aiken et al., 2008;
Canagaratna et al., 2015; Turpin and Lim, 2001).
Anthropogenic emissions are from the EDGAR v32-FT2000 global inventory for
2000 (Olivier et al., 2005) with emissions overwritten in areas with regional
inventories for the United States (NEI 2005), Mexico (BRAVO;
Kuhns et al., 2005), Europe (EMEP; http://www.emep.int/), and East Asia (Zhang
et al., 2009). Emissions are scaled to the year 2007 following emissions of
related CO2 sources as described in van Donkelaar et al. (2008). Global
biofuel emissions (Yevich and Logan, 2003), global anthropogenic emissions
for carbonaceous aerosols (Bond et al., 2007; Leibensperger et al., 2012),
and emissions from open fires for individual years from the GFED3 inventory
(Mu et al., 2011) are included.
We calculate UVAI values for two cases. The base case simulation treats the
aerosol optical properties as currently implemented in GEOS-Chem in which all
organic carbon aerosols are weakly absorbing and colorless as shown in
Table 2. The second case adds the more strongly absorbing BrC as described in
Sect. 4.2 below.
Comparison of simulated and OMI UVAIBase case simulation
Monthly mean ultraviolet aerosol index (UVAI) observations from
the OMI satellite instrument for 2007. White space indicates cloud or snow
contamination. Gray boxes outline regions examined in Tables 3 and 4.
Monthly mean UVAI values for 2007 simulated for OMI observing
conditions using the vector radiative transfer model VLIDORT coupled with
GEOS-Chem aerosol fields for the base case without brown carbon (BrC). White
indicates cloud or snow contamination.
Figure 1 shows the monthly mean OMI UVAI observations for January, April,
July, and September of 2007. Clear signals are apparent over regions
dominated by mineral dust and biomass burning (Herman et al., 1997; Torres et
al., 1998). Absorption over desert regions occurs for all four months, giving
UVAI values between 1 and 3, particularly over the Saharan, Iranian, and Thar
deserts. Aerosol absorption from biomass burning primarily follows the
seasonal cycle of agricultural burning (Duncan et al., 2003). In January,
absorption over West Africa yields UVAI values between 1 and 2.5. In April
aerosol absorption is visible over South Asia with UVAI values between 0.5
and 1. UVAI values of 1–1.7 occur over southern Africa in both July and
September, while UVAI values of up to 3 occur over South America in
September.
Figure 2 shows the monthly mean UVAI values for our base case simulation
without BrC, The simulation captures the major absorption features compared to
OMI over the desert regions, giving UVAI values of 1–3, however it fails to
capture the absorption by biomass burning aerosol, giving UVAI values between
-2 and 0 in all biomass burning regions for the four months. These negative
values indicate that the UVAI simulation is insensitive to the absorption by
BC and is dominated by the scattering from organic carbon aerosol. A
sensitivity test with doubled BC concentrations increased UVAI values by only
∼ 0.1. We also conducted a sensitivity test to determine whether the heights
of the biomass burning plumes could explain the underestimated absorption.
Raising the aerosol layer height to unrealistic altitudes (∼ 10 km
above the surface) increased the UVAI by only 0.1–0.3, which is insufficient
to account for the differences between the simulation and observations.
To further analyze the discrepancies between simulated and observed UVAI, we
choose four regions corresponding to the seasonal biomass burning outlined in
Fig. 1: West Africa (5∘ S–25∘ N,
40∘ W–20∘ E) in January, South Asia (5–35∘ N,
60–110∘ E) in April, southern Africa (0–30∘ S,
5∘ W–30∘ E) in July, and South America (0–40∘ S,
40–70∘ W) in September. Table 3 contains the correlation
coefficients (r) between the simulated and OMI UVAI as well as the mean
bias (simulated-OMI UVAI). The correlation between the OMI and simulated UVAI
is low (0.09–0.48) in all regions, with large mean biases of -0.32 to
-0.97.
Comparison of the simulated versus observed (OMI) UVAI values for
the biomass burning regions in the months examined. The base case
corresponds to a simulation without BrC, while case 2 corresponds to a
simulation including absorbing BrC.
Base case Case 2 with BrC RegiondncMonthMean OMI UVAIraMean BiasbrMean BiasWest Africa381January1.250.48-0.570.68-0.09South Asia280April0.340.46-0.320.66+0.0002Southern Africa184July0.660.09-0.970.63-0.22South America230September0.300.40-0.500.57+0.33
ar: Pearson correlation coefficient.
b Mean bias = simulated UVAI – observed UVAI. cn= number of GEOS-Chem grid boxes in region. d Regions are defined in Fig. 1.
The mean AOD values for each region from the GEOS-Chem (GC) base
case simulation, the MISR instrument, and the MODIS Terra satellite
instrument. The MODIS values are included for both the collection 6 Deep Blue and Dark Target algorithms.
Mean AOD Region*GCMISRMODIS Deep BlueMODIS Dark TargetWest Africa0.420.420.450.51South Asia0.320.320.300.37Southern Africa0.190.190.130.24South America0.310.360.390.57
* Regions are defined in Fig. 1.
Uncertainty in aerosol optical depth also cannot explain the UVAI bias.
Table 4 shows the simulated AOD compared with AOD retrieved from the MODIS
(Moderate Resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging
Spectroradiometer) satellite instruments. Overall the simulated values are
within the range of satellite-retrieved AOD values. The maximum difference in
simulated versus satellite AOD is found with the MODIS Dark Target algorithm
over South America in September. Matching the simulated AOD to the satellite
AOD changed the UVAI by less than 0.1.
We attempt to reconcile the differences between the simulated and OMI UVAI
in biomass burning regions by introducing absorbing BrC into GEOS-Chem, as
described below.
Treatment of brown carbon
Here we apply the OMI UVAI observations to estimate the effective absorption
by BrC. We exploit the fact that the TOA radiances used in the OMI UVAI
contain implicit information on the BrC from actual burn conditions, on the
BrC that remains after chemical loss or evaporation, on Br-SOA, and on the
BrC/POC fraction. We use the term effective to denote the implicit
dependence of the UVAI upon these multiple processes. Through sensitivity
simulations, we derive the effective k values for BrC given the assumed
BrC/POC fraction required to reproduce the observed absorption by the OMI
UVAI. This is accomplished by conducting the sensitivity simulations for
several cases of BrC/POC fraction, assuming the same fixed spectral
dependence for each case, and adjusting the magnitude of the effective k
values to match the OMI UVAI. We treat the relative spectral dependence of
k, log(Δk)/log(Δλ), as 3 for
wavelengths between 300 and 600 nm to represent the mean from laboratory and
field measurements of 3.2 ± 0.7 (Kirchstetter et al., 2004; Zhang et
al., 2013; Zhong and Jang, 2014). At wavelengths ≥ 600 nm we leave the
absorption properties of POC unchanged since BrC absorption decreases
significantly into the visible and near-IR
(Bergstrom et al., 2007; Chen and Bond, 2010; Yang et al., 2009).
Imaginary part of the refractive index (k) values inferred for
BrC as a function of wavelength and of the assumed fraction of primary
organic carbon that is brown (BrC/POC). The background spectrum represents
k values calculated using Eq. (6). The filled circles represent the k
values obtained from sensitivity simulations. An organic carbon density of
1.3 g cm-3 is assumed.
The filled circles in Fig. 3 show the effective k values of BrC derived
from seven sensitivity simulations that all achieve the same simulated UVAI.
Only the BrC/POC fraction varies between simulations. The choice of
simulated UVAI was selected to represent the global OMI UVAI over major
biomass burning regions. The imaginary part of the refractive index decreases
with increasing wavelength following an exponential relationship as
prescribed based on laboratory and field measurements, and decreases with
increasing BrC/POC fraction as required to reproduce the OMI UVAI. The
effective k values increase with decreasing BrC/POC fraction because as
BrC concentration decreases, BrC must be more absorbing to match the
absorption observed by OMI. Given the smoothly varying relationship between
k and BrC/POC we develop the following expression to represent this
relationship:
k=c⋅ρ⋅λ⋅35.4BrCPOC-1.25⋅exp-10.5λ;BrC/POC≥ 0.4,300nm≤λ≤600nm,
where λ is wavelength (µm), ρ is the density of
organic carbon (g µm-3), and c is a conversion constant
equal to 1.0×1012/4πµm2 g-1.
The background spectrum of Fig. 3 shows the k values calculated using
Eq. (6). This expression reproduces the full radiative transfer sensitivity
simulations with a root mean squared error (RMSE) of 0.004 and a coefficient
of determination (r2) of 0.99. Equation (6) does not apply for BrC/POC
fractions less than 0.4 since they do not reproduce the absorption observed
by OMI. We emphasize that multiple choices of k and BrC/POC will yield
the same TOA radiance and UVAI. The effects on tropospheric OH concentrations
and radiative forcing remain unaffected as BrC/POC and effective k vary
together, since the distribution of scattering and absorption remains the
same.
Imaginary part of the refractive index (k) values for brown carbon
(not total organic carbon) inferred for case 2. We include k values
associated with multiple densities (ρ), and multiple fractions of brown
carbon to primary organic carbon (BrC/POC). All four columns for case 2
yield the same absorption.
Case 2 with BrC (ρ= 1.3 g cm-3)Case 2 with BrC (ρ= 1.8 g cm-3)Wavelength (nm)BrC/POC = 0.50BrC/POC = 1.0BrC/POC = 0.50BrC/POC = 1.03000.110.0510.160.0713500.0770.0370.110.0514000.0520.0250.0730.0354500.0350.0140.0490.0195000.0230.0090.0320.0135500.0150.0070.0210.010
Table 5 contains our derived imaginary parts of the refractive index for
BrC/POC fractions of 0.5 and 1.0. Table 5 also contains effective k values
derived for an organic carbon density of 1.8 g cm-3 which has been
assumed in prior studies of BrC. The range of values for k covered by
varying the BrC/POC fraction encompasses the range of values for BrC found
in the literature
(Chen and Bond, 2010; Feng et al., 2013; Kirchstetter et al., 2004; Lin et al.,
2014; Sun et al., 2007; Wang et al., 2014; Zhang et al., 2013; Zhong and
Jang, 2014). The four columns with BrC yield identical wavelength-dependent
global distributions of scattering and absorption that in turn yield the
same UVAI, OH, and DRE.
The columns with effective k values for BrC/POC fraction of unity offer
the convenience of representing the effective absorption by BrC without
needing to assume an arbitrary BrC/POC fraction, or to introduce a separate
BrC tracer. The effective k values for unity BrC/POC fraction can be
thought of as the effective imaginary refractive index for an internal
mixture of BrC and colorless POC.
Evidence for the existence of brown secondary organic carbon (Br-SOA) also
exists. The majority of Br-SOA is from anthropogenic sources
(Jo et al., 2015; Liu et al., 2013; P. F. Liu et al., 2015; Zhang et al., 2013), while SOA formed
from biogenic carbon is largely non-absorbing
(Flores et al., 2014; P. F. Liu et al., 2015). On a global scale it is estimated that the
majority of SOA is formed from biogenic carbon
(Hallquist et al., 2009; Lack et al., 2004; Tsigaridis and Kanakidou, 2003). Therefore
we treat SOA as non-absorbing. We tested this approach in a sensitivity
study with the standard SOA mechanism in GEOS-Chem v9-01-03
(Henze and Seinfeld, 2006; Henze et al., 2008; Liao et al., 2007) by assigning 100 % anthropogenic SOA as
brown, and found that the change in UVAI was negligible (less than 0.1).
Alternative SOA implementations (e.g., as used in Jo et al., 2015) may have a
larger effect.
Simulation including brown carbon
Monthly mean UVAI values for 2007 simulated for OMI observing
conditions using the vector radiative transfer model VLIDORT coupled with
GEOS-Chem aerosol fields for case 2, which assumes the presence of absorbing
BrC aerosol. White indicates clouds or snow contamination.
Figure 4 shows the monthly mean UVAI values for the simulation including
BrC for the months of January, April, July, and September of 2007.
The simulated absorption features including BrC are much more consistent than
the base case simulation at reproducing the OMI UVAI over biomass burning
regions (Fig. 2). The simulated UVAI in the four biomass burning regions now
ranges from 0.5 to 3. As summarized in Table 3, the correlation coefficients
between the simulated and OMI UVAI for the four biomass burning regions now
range from 0.57 to 0.68, with mean biases of -0.22 to +0.33.
The simulated UVAI using global mean k values underestimates the OMI
observations for the West Africa and southern Africa regions, but
overestimates observations in the South American region. We tested how k
would need to change to explain the regional UVAI bias if k were the only
error source. We find that these regional biases could be eliminated by
changing k at 350 nm by +2 % over West Africa, by +10 % over
southern Africa, and by -30 % over South America. The presence of more
absorbing BrC over West and southern Africa where savannah fires dominate,
and less absorbing BrC over the South America region where forest fires
dominate, is consistent with work by Saleh et al. (2014) who found that the
absorptivity of BrC from biomass burning is greater for flaming fires
associated with burning grasslands than for smouldering fires associated with
burning forest.
The absorption in the West and southern Africa cases appears to be
concentrated closer to the source for the simulated values (Fig. 4) compared
to the OMI values (Fig. 1), which show an even distribution of UVAI values
away from the source. By contrast, the absorption in the South American
region appears to be distributed farther from the source in the simulation
than in the OMI observations. Evidence exists of atmospheric photochemical
loss and evaporation of BrC that causes it to become less absorbing
over a lifetime of less than a day (Forrister et al., 2015; Zhong and Jang,
2014). Representing these processes would improve the simulation in the South
American region but degrade the simulation in the West Africa and southern
Africa regions. Regional treatment of BrC loss processes may be warranted in
future work. The current implementation offers our best representation of the
effective BrC absorption at the global scale.
The absorption Ångström exponent (AAE) values for major biomass
burning regions and seasons obtained from the base case simulation without
BrC and the case 2 simulation including absorbing BrC.
Table 6 shows the calculated AAE values for biomass burning aerosol (i.e.,
black carbon + organic carbon aerosol) from our simulations for comparison
with the literature values in Table 1. Large biases are apparent for the base
case simulation without BrC. We evaluate the case 2 simulation including BrC
in detail. For the 350–400 nm wavelength region our mean AAE value of
2.8 ± 0.22 for the four biomass burning regions is within the
recommended values of 2.5–3.0 by Jethva and Torres (2011). In the
350–700 nm range our mean AAE of 2.2 ± 0.17 is close to the value of
1.9 from Kirchstetter and Thatcher (2012). The slight positive bias could
arise from the fact that Kirchstetter and Thatcher (2012) took their
absorption measurements from wood smoke emitted from houses in rural
California during the winter, which have different conditions than the
tropical open burning considered here. We obtain a mean AAE value of
1.7 ± 0.15 for the 300–1000 nm range, which falls within the
literature values of 1.1–2. For the 450–550 nm wavelength region, we
obtain a mean AAE value of 2.5 ± 0.14, which is biased high compared to
the values from Corr et al. (2012) extracted from an examination of biomass
burning plumes in North-Central Canada, where burn conditions differ from the
mostly tropical regions considered in our analysis. Over the 400–700 nm
region we obtain a mean AAE of 2.1 ± 0.15, falling within the range of
the literature values (1.3–2.1). In the 400–1000 nm region, we obtain a
mean AAE value of 1.3 ± 0.005, which is at the low end of the
literature values (1.3–1.7). The overall consistency between observed and
simulated AAE provides a measure of confidence in the spectral dependence of
aerosol optical properties from the UV to the IR. We now examine the
implications of this absorption for OH and DRE.
Analysis of the effect of absorption by BrC on OH concentrations in
GEOS-Chem
The strong absorption in the UV by BrC aerosol decreases photolysis
frequencies, which has implications for ozone photolysis and OH production.
Here we examine the effect of the added absorption by BrC on the O3→ O(1D) photolysis frequency, J(O(1D)), and tropospheric OH
concentrations.
Percent difference between OH concentrations in the lower
troposphere from a GEOS-Chem simulation including absorbing BrC minus a
simulation without BrC. The values are monthly means for January, April,
July, and September 2007.
Figure 5 shows the percent differences in lower-tropospheric OH
concentrations between the GEOS-Chem simulation including absorbing BrC versus the base case simulation. The most significant decreases
correspond with the major biomass burning regions. The addition of BrC
decreases OH concentrations by up to 30 % over the South American biomass
burning region in September and up to 20 % over the southern Africa biomass
burning region in July. OH concentrations decrease by up to 15 % over West
Africa in January and southern Africa in September, with decreases of up to
5 % over North America in July, South America in July, and South Asia in
January, April, and September. The spatial and seasonal pattern of
J(O1(D)) differences closely reproduces the changes in OH (r2=0.85) (not shown).
Methyl chloroform observations provide a valuable constraint on global OH
(Prather et al., 2012; Spivakovsky et al., 2000). The addition of BrC to the GEOS-Chem simulation
reduces global mean tropospheric OH concentrations. The reduction in global
mean OH concentrations increases the methyl chloroform lifetime to
tropospheric OH from 5.62 to 5.68 years. This change is noteworthy given the
buffered nature of OH. This change reduces the bias with observations giving
a methyl chloroform lifetime of 6.0 (+0.5, -0.4) years
(Prinn et al., 2005).
Radiative impact of brown carbon
We calculate the direct radiative effect (DRE) of including absorbing BrC
relative to that of the base case simulation. We use GEOS-Chem coupled with
the radiative transfer model RRTMG (Iacono et al.,
2008), a configuration known as GC-RT, that is described in
Heald et al. (2014). GC-RT calculates both
the longwave (LW) and shortwave (SW) instantaneous total radiative fluxes as
well as the flux differences due to a specific constituent of the atmosphere
(e.g., organic aerosol). The DRE is calculated by adding the LW and SW flux
differences determined through perturbation of the constituent of interest.
Our GC-RT simulations use version 10.1 of GEOS-Chem with the same aerosol
emissions described in Sect. 3 (e.g., GFED3 open fire emissions). We calculate
the DRE of absorption by BrC as the difference in the DRE of organic aerosol
when including BrC (case 2) minus the DRE of organic aerosol in the base
case. We focus on the DRE rather than the DRF to avoid ambiguity in
preindustrial BrC.
Annual mean all-sky DRE values for 2007 (W m-2). The top two
panels are the DRE values for organic aerosol from the case 2 simulation
including BrC at (a) the surface and (b) TOA. The bottom two panels are the
change in DRE values for absorption by BrC calculated as the difference
between the DRE values for organic aerosol from the case 2 simulation and
the base case simulation (without BrC) at (c) the surface and (d) TOA.
Figure 6 shows all-sky DRE values for 2007. The top two panels are the DRE
values for organic aerosol from the case 2 simulation including BrC. The
overall DRE of organic aerosol including BrC is negative, with the largest
effects over major biomass burning regions. The bottom two panels show the
DRE for absorption by BrC, calculated as the difference between the DRE of
organic aerosol for the case 2 simulation including BrC minus the base case
simulation. At the surface BrC absorption reduces the DRE by
-1.5 W m-2 over South America and southern Africa, and by -0.5 to
-0.25 W m-2 over South Asia, North America, West Africa, Australia,
and Europe. At TOA, BrC absorption increases the DRE by up to
0.3 W m-2 over South America and southern Africa, and by 0.05 to
0.15 W m-2 over broad regions. This overall cooling effect at the
surface and warming effect on the atmosphere are consistent with previous
work (e.g., Chen and Bond, 2010).
Global annual mean LW and SW flux differences and resulting DRE
values for 2007 at TOA and the surface. The values for organic aerosol are
shown for both the base case simulation without BrC and the case 2
simulation including absorbing brown carbon. The DRE values for BrC
absorption are calculated as the difference between the DRE of organic
aerosol from case 2 minus the base case.
Organic aerosol BrC absorptionBase caseCase 2Case 2 – Base caseTOA DRE, clear sky (W m-2)-0.33-0.31+0.02LW+0.0026+0.0028SW-0.33-0.31TOA DRE, all sky (W m-2)-0.24-0.21+0.03LW+0.0020+0.0021SW-0.24-0.21Surface DRE, clear sky (W m-2)-0.50-0.60-0.09LW+0.0061+0.0067SW-0.51-0.61Surface DRE, all sky (W m-2)-0.41-0.49-0.08LW+0.0051+0.0056SW-0.42-0.5
Table 7 contains LW and SW global annual mean flux differences as well as the
resulting DRE values for both organic aerosol and BrC absorption.
The values for organic aerosol are from the base case simulation assuming
weakly absorbing organic carbon and the case 2 simulation including BrC,
while the values for BrC absorption are calculated as their difference.
Absorption by BrC has a mean all-sky DRE at TOA of +0.03 W m-2 and
at the surface of -0.08 W m-2.
Our findings are at the lower end of the range of values from other studies
that estimate the DRE of BrC absorption. Feng et al. (2013) introduce
absorption by BrC based on the optical properties from Kirchstetter et
al. (2004) and Chen and Bond (2010) into a global model, and calculate an
all-sky TOA DRE for BrC absorption of +0.04 to +0.11 W m-2, and an
all-sky surface DRE for BrC absorption of -0.06 to -0.14 W m-2.
Chung et al. (2012) estimate BrC absorption by subtracting the absorption by
BC and desert dust from total aerosol AAE values from AERONET to calculate an
all-sky TOA DRE for organic aerosol when including BrC between -0.15 and
+0.12 W m-2 and an all-sky surface DRE between -1.50 and
-0.75 W m-2. Arola et al. (2015) use AERONET retrieved imaginary
parts of the refractive index for BrC at 440, 670, 870, and 1020 nm
to estimate over the Indo-Gangetic plain monthly all-sky TOA DRE values for
organic aerosol including BrC absorption up to +0.5 W m-2 in spring
and as low as -1 W m-2 in the winter.
Conclusions
We interpret OMI observations of the ultraviolet aerosol index (UVAI), which
provides a measure of absorbing aerosols, by developing a simulation of the
UVAI using the vector radiative transfer model VLIDORT coupled with GEOS-Chem
aerosol fields. The base case simulation without brown carbon (BrC) well
represents the observed UVAI in most of the world but significantly
underestimates the absorption in biomass burning regions. We apply the OMI
UVAI to estimate absorption by BrC. This approach exploits the strong
absorption by BrC at ultraviolet wavelengths and its effect on top of
atmosphere (TOA) radiance. We express the imaginary part of the refractive
index of BrC that is required to obtain near-identical TOA radiance values as
a function of the fraction of primary organic carbon that is brown. This
effective refractive index of BrC provides a measure of the degree of
browness needed to represent the complex processes (e.g., burn conditions,
photochemical loss) affecting global BrC and in turn the UVAI. This effective
refractive index of BrC eliminates the need to know the BrC/POC ratio to
model the radiative effects of BrC. Rather, an effective refractive index can
be chosen (Eq. 6) to represent an internal mixture of BrC and colorless POC.
The simulation including absorbing BrC is much more consistent than the base
case at reproducing the OMI UVAI over biomass burning regions. The mean bias
between simulated and OMI UVAI values is reduced from -0.57 to -0.09 over
West Africa in January, from -0.32 to +0.0002 over South Asia in April,
from -0.97 to -0.22 over southern Africa in July, and from -0.50 to
+0.33 over South America in September. The updated optical properties for
BrC result in AAE values for biomass burning aerosol ranging from 2.9 in the
UV to 1.3 across the UV–Near IR, which are broadly consistent with field
observations.
We apply this constraint on ultraviolet absorption to examine implications
for the O3→ O(1D) photolysis frequency. We find that the
inclusion of absorbing BrC into GEOS-Chem decreases J(O(1D)) and lower
tropospheric OH by up to 30 % over South America in September, up to 20 %
over southern Africa in July, up to 15 % over West Africa in January and
southern Africa in September, and up to 5 % elsewhere. The decrease in
global mean OH concentration in GEOS-Chem increases the methyl chloroform
lifetime to tropospheric OH from 5.62 to 5.68 years, reducing the bias with
estimates from observations of 6.0 (+0.5, -0.4) years.
We calculate the direct radiative effect (DRE) of BrC using GEOS-Chem coupled
with the radiative transfer model RRTMG (GC-RT). We obtain global annual mean
all-sky TOA DRE values for BrC absorption of +0.03 W m-2 and values
of -0.08 W m-2 at the surface. Regional changes of up to
+0.3 W m-2 at TOA and down to -1.5 W m-2 at the surface are
found over major biomass burning regions. Our results are within the range of
prior estimates of DRE for BrC absorption.
Ample opportunities exist to further develop the simulations of BrC and more
generally of the UVAI. These opportunities include explicitly accounting for
the range of processes affecting BrC such as burn conditions, photochemical
loss, secondary production, as well as regional treatment of BrC. An explicit
simulation of BrC (e.g., Jo et al., 2015) would facilitate these developments.
Interpretation of the long observational record of the UVAI from 1979 to the
present should offer constraints on trends in aerosol composition,
ultraviolet absorption, and radiative effects. The forthcoming TROPOMI
instrument and geostationary constellation (e.g., TEMPO, Sentinel-4, and GEMS)
will offer UVAI observations at 5–20 times higher spatial resolution, as
well as information on diurnal variation, both of which may offer additional
constraints on BrC evolution.
Acknowledgements
This work was supported by the Natural Science and Engineering Research
Council of Canada. Computational facilities were provided in part by the
Atlantic Computational Excellence Network consortium of Compute Canada. We
thank Farhan Khan for assistance during the early stages of this
work.Edited by: M. K. Dubey
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