ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-1565-2016Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol speciesSchusterG. L.gregory.l.schuster@nasa.govDubovikO.https://orcid.org/0000-0003-3482-6460ArolaA.NASA Langley Research Center, Hampton, VA, USALaboratoire d'Optique Atmosphérique, Université de Lille-1, CNRS, Villeneuve d'Ascq, FranceFinnish Meteorological Institute, Kuopio, FinlandG. L. Schuster (gregory.l.schuster@nasa.gov)11February20161631565158524March201512May20157December201522December2015This 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/16/1565/2016/acp-16-1565-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/1565/2016/acp-16-1565-2016.pdf
We describe a method of using the Aerosol Robotic Network (AERONET)
size distributions and complex refractive indices to retrieve the
relative proportion of carbonaceous aerosols and free iron
minerals (hematite and goethite). We assume that soot carbon has a spectrally flat
refractive index and enhanced imaginary indices at the
440 nm wavelength are caused by brown carbon or hematite.
Carbonaceous aerosols can be separated from dust in imaginary
refractive index space because 95 % of biomass burning aerosols
have imaginary indices greater than 0.0042 at the
675–1020 nm wavelengths, and 95 % of dust has imaginary
refractive indices of less than 0.0042 at those wavelengths.
However, mixtures of these two types of particles can not be
unambiguously partitioned on the basis of optical properties alone,
so we also separate these particles by size. Regional and seasonal
results are consistent with expectations. Monthly climatologies of
fine mode soot carbon are less than 1.0 % by volume for West
Africa and the Middle East, but the southern African and South
American biomass burning sites have peak values of 3.0 and 1.7 %.
Monthly averaged fine mode brown carbon volume fractions have a peak
value of 5.8 % for West Africa, 2.1 % for the Middle East,
3.7 % for southern Africa, and 5.7 % for South America.
Monthly climatologies of free iron volume fractions show little
seasonal variability, and range from about 1.1 to 1.7 % for
coarse mode aerosols in all four study regions. Finally, our
sensitivity study indicates that the soot carbon retrieval is not
sensitive to the component refractive indices or densities assumed
for carbonaceous and free iron aerosols, and the retrieval differs by only
15.4 % when these parameters are altered from our chosen
baseline values.
The total uncertainty of retrieving soot carbon mass is ∼50 %
(when uncertainty in the AERONET product and mixing state is included in the analysis).
Introduction
Soot carbon (sC) is a byproduct of combustion that is composed of
aggregated graphite spheres . It is often called
light absorbing carbon (LAC), black carbon (BC), refractory black
carbon (rBC), or elemental carbon (EC) in the scientific literature
(depending upon the measurement technique), but we prefer the
carbonaceous aerosol definitions nicely presented by
. Thus, the term sC presented here
is equivalent to LAC in and
BC or rBC in .
Loosely, the term EC refers to sC that is obtained by
thermal techniques, and the term BC refers to sC that is obtained
by optical techniques; the term BC is also used generically
by the modeling community.
We use the term brown carbon (BrC) for absorbing organic matter
to distinguish these particles from organic matter that does not absorb
significantly at visible wavelengths
.
However, both BrC and organic carbon (OC) are composed of many organic species,
and the BrC detected in the atmosphere also contains some non-absorbing organic particulates as well
(i.e., the scientific community has probably not isolated “pure” BrC).
Atmospheric warming caused by sC is highly uncertain. The most recent
Intergovernmental Panel on Climate Change IPCC;
estimate of the sC direct radiative forcing for fossil fuel and
biofuel sC is +0.4Wm-2 (with an uncertainty range of
+0.05 to +0.8Wm-2); this estimate is based upon the
AeroCom Phase II model simulations of and the scaled
absorption aerosol optical depth (AAOD) findings of .
The direct effect does not capture all forcing mechanisms, however,
and estimate that the industrial-era climate forcing
associated with sC is RFsC=+1.1Wm-2, with an uncertainty range of +0.17 to
+2.1Wm-2 when all forcing mechanisms are included.
Soot carbon radiative forcing is second only to CO2 radiative
forcing as a contributor to global warming
. Finally, although CO2
radiative forcing is larger than sC radiative forcing
RFCO2=1.8±0.19Wm-2,
per, the range of uncertainty for sC forcing is much
larger than the range of uncertainty for CO2 forcing.
The cooling associated with all aerosols in the IPCC fifth assessment
report is -0.35±0.5Wm-2; thus, soot carbon
warming from the direct effect alone reduces aerosol cooling
by about 53 %.
This has prompted suggestions that reducing sC could be
a viable method of mitigating global warming in the short term
. Others have noted that non-absorbing aerosols
that are co-emitted with sC make this difficult in practice
.
Soot carbon warming is uncertain because modeled sC concentrations and the
associated absorption are unconstrained by in situ measurements. The
difficulty is the lack of available sC data. The Interagency
Monitoring of Protected Visual Environments (IMPROVE) network provides
routine EC measurements at the surface in the
United States, but mainly in remote areas . Some
surface measurements are also available in other parts of the world
, but global coverage of sC concentration and
absorption is not available from satellite data.
Modeled sC concentrations are too high by a factor of 1.6 on average
over North America when compared to long-term in situ measurements at
the surface , and they are too high by a factor of 5
to 7.9 when compared to aircraft measurements during field campaigns
over the United States, Canada, and the Pacific Ocean
. Modeled AAOD, however, is
lower than the Aerosol Robotic Network (AERONET) and Ozone Monitoring
Instrument (OMI) products by a factor of 0.69 and 0.85 over North
America and a factor of 2–4 lower than AERONET
worldwide . Thus, the link between sC emissions, sC
concentrations, and AAOD is not straightforward, and the low bias in
modeled AAOD over North American source regions can not be simply
associated with errors in the sC emission inventories alone.
One problem is that sC is not the only absorbing aerosol in the
atmosphere, as BrC and free iron (hematite and goethite) in dust are significant absorbers
at ultraviolet through mid-visible wavelengths
. Separating the absorption
associated with sC from the absorption associated with these other
aerosols is not trivial; this is especially true for absorption at the
550 nm wavelength favored by many studies, since all of these
aerosol species absorb at that wavelength.
Thus, there is a need for determining the relative proportions of sC,
BrC, and free iron in atmospheric aerosols.
Knowledge of both sC mixing ratios and AAOD are important for
constraining how sC is transported, removed, and mixed with other
aerosols in the global models.
AERONET provides aerosol size distributions and complex
refractive index at four wavelengths (440, 675, 870, and 1020 nm) at hundreds of
surface sites throughout the world .
This information can be used to retrieve the relative proportions of
carbonaceous aerosols and free iron minerals.
retrieved sC from the aerosol complex refractive index provided in the
AERONET database by assuming that all aerosol absorption is associated
with sC. used the spectral variability of the
imaginary refractive index in the AERONET database to retrieve BrC.
Similarly, used AERONET complex refractive indices to
retrieve sC, BrC, and dust over Beijing. added
single-scatter albedo as an additional constraint to these
refractive index approaches. used the spectral
variability of the imaginary index to retrieve hematite concentrations
at dust sites.
Other methods of retrieving sC from AERONET data include the absorption
Ångström exponent (AAE) approach and the
approach. However, the AAE approach assumes that all
absorbing aerosols are externally mixed (which is inconsistent with the
AERONET retrieval algorithm), and it does not necessarily maintain a link to the
measured radiances. The approach computes the AAOD for the
coarse mode by using a climatological value for dust and subtracting the dust
AAOD from the total AAOD to infer the sC AAOD (BrC absorption is omitted
from the computation). Since the coarse mode refractive index is different
from the AERONET-retrieved value, this approach does not necessarily maintain
a link to the radiance field, either.
Here, we present a method of deriving
column concentrations of sC mass and mixing ratios that are consistent
with the AERONET AAODs, size distributions, and refractive indices.
The method is an improvement over our previous method because it uses
different mixtures of aerosol species for the fine mode than for the
coarse mode. That is, we assume that the fine mode is dominated by
carbonaceous absorbing aerosols and that the coarse mode is dominated
by the free iron commonly found in mineral aerosols.
Description of the AERONET product
AERONET is a network of several hundred sun and sky scanning radiometers
located at surface sites throughout the world and
http://aeronet.gsfc.nasa.gov. The radiometers
have a narrow field of view (1.2∘) and typically provide direct sun
measurements in eight narrow spectral bands (nominally centered at 0.34,
0.38, 0.44, 0.50, 0.675, 0.87, 0.94, and 1.02 µm). The
measurements are processed to provide aerosol optical depth (AOD) products in
the AERONET database. The direct sun measurements are corrected for
NO2 and O3 absorption using monthly climatologies from
satellite records. The ESA's Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) climatology is used for the NO2
correction, and NASA's Total Ozone Mapping Spectrometer (TOMS) is used for
the O3 correction
(http://aeronet.gsfc.nasa.gov/new_web/Documents/version2_table.pdf).
Corrections for CO2, CH4, Rayleigh scattering, water vapor absorption,
and the Earth–sun distance are also included in the AERONET products. Cloud
screening is accomplished with the procedure. Instruments
are calibrated on a 6–12 month rotation, and changes in the calibration
coefficients are linearly interpolated between calibrations. Additional
instrument details may be found in and at
http://aeronet.gsfc.nasa.gov/new_web/system_descriptions.html.
The AERONET instruments also measure sky radiances at four wavelengths (0.44,
0.675, 0.87, and 1.02 µm). One component of the sky radiance measurements
is the almucantar scan
, which forms the
basis of the size distribution and absorption retrieval products in the
AERONET database . This scan
occurs at a constant viewing zenith angle that is equal to the solar zenith
angle (θ∘), and it includes 56 azimuth angles that vary from -180 to
180∘ (with respect to the solar azimuth angle). The use of positive
and negative azimuth angles results in many redundant scattering angles, and
this redundancy is used with symmetry arguments to filter measurements that
are affected by clouds and inhomogeneous aerosol plumes. Pairs of
measurements with identical scattering angles must agree to within 20 % to be
used in a retrieval, and at least 14 angular pairs at specific angular ranges
must survive for a Level 2 retrieval . The minimum required
solar zenith angle is 50∘ for the Level 2 products, which assures a
scattering angle measurement range of at least 0 to 100∘.
Additionally, AOD is required to be greater than 0.4 at the 0.440 µm
wavelength for the Level 2 absorption products (i.e., complex refractive
index, single-scatter albedo, AAOD). Additional screening
implemented for Level 2 retrievals may be found at
http://aeronet.gsfc.nasa.gov/new_web/Documents/Quality_Control_Checklist.pdf.
Throughout this paper we consider only Level 2 AERONET retrievals.
Instruments manufactured prior to 2004 utilize dual detectors in order to cover the wide dynamic range of the sun and sky measurements.
The measurement protocol for these systems includes a consistency check at ±6∘ azimuth, where both detectors observe the sky.
Sky radiances reported by the two sensors at this angle are required to agree to within ±5 % for the Level 2 inversions .
Instruments manufactured since 2004 utilize a single detector for both the sun and sky measurements and therefore do not need this consistency check.
The microphysical model used for the advanced retrieval products is a
homogeneous internal mixture of spheres and spheroids, which are dispersed
throughout a uniform aerosol layer
. The size distribution and
refractive index are adjusted in a forward radiation model to produce a
statistically optimized solution, and the final solution accounts for
measurement accuracy as well as a priori constraints
. The retrieval provides the aerosol
volume size distribution (dV/dlnr) for 22 radii between 0.05 and 15 µm
(integrated over the atmospheric column) and the complex refractive index at
the four scanning wavelengths. The size distributions can have any shape
(i.e., lognormal distributions are not a requirement), but a priori
smoothness constraints are applied to avoid unrealistically sharp
oscillations, and concentrations are forced to asymptote to small values at
the extreme sizes .
The AERONET algorithm also assumes that all particles in the atmosphere have the
same complex refractive index (regardless of size), which is equivalent to assuming that
all particles have identical composition (and all aerosol species are internally mixed).
This assumption is necessary to achieve a unique solution, and it forces the absorption
to be spread over all retrieved particle sizes, even if the absorption really occurs in only
the smallest particles. The repercussions of this assumption are discussed in .
Once an optimal size distribution and refractive index are found, they
are used to compute the AAOD, AAE, single-scatter albedo (ω∘),
and other optical parameters reported in the AERONET database.
Thus, all of the almucantar retrieval products are mathematically linked by Mie theory and T-matrix theory, and we can not claim that one of these parameters is more robust than another.
The AERONET product is a “package” in this sense – taken together, all of the products provide a consistent set of parameters that produce the measured radiance field.
The advantage of computing aerosol optical properties in this way is that the
retrievals are constrained to realistic values. For instance, the almucantar
scan provides radiances over a limited range of scattering angles (θ),
with the maximum scattering angle being twice the solar zenith angle;
consequently, a significant range of scattering angles are unobserved. Thus,
the inferred size distributions and refractive indices provide the correct
phase functions and radiances where measurements are available (i.e., θ≤2×θ∘), and viable phase functions at angles where they
are not available (θ>2×θ∘). The resulting radiances
and irradiances computed from the AERONET microphysical models compare well
with independent measurements . Finally, since
single-scatter albedo is computed from Mie or T-matrix theory, ω∘≤1 for all of the retrievals in the AERONET database, and the
non-physical value of ω∘>1 that is sometimes observed with
extinction and nephelometer measurements never occurs.
The microphysical model affects how we can interpret the data, especially the internal mixture assumption.
For instance, we can not conclude that low AAEs in urban regions are attributed to external mixing of sC
as in,
because all aerosol species are always internally mixed in the AERONET retrieval model.
Additionally, we can not assume that the component sum of the AAODs for all absorbing species equals the total AAOD, because component absorption efficiencies are not additive with internal mixtures;
this is because a non-absorbing host aerosol increases the absorption cross section for all of the embedded absorbing particles
(e.g., the absorption of a particle with sC embedded in a non-absorbing host is greater than the absorption of the isolated sC particle).
We discuss this extensively in .
Likewise, we can not assign a refractive index to the coarse aerosol mode that is different from the retrieved value as in without the risk of severing the link to the radiation field.
The link to the radiance field is very important,
as it forces AERONET to produce viable solutions that are
the strength of the AERONET product.
Consequently, the use of reasonable
model assumptions that are
not consistent with the original
retrieval can produce results that are inconsistent with the radiance
measurements (even if the new assumptions are more realistic than the AERONET assumptions).
This lack of consistency with the AERONET
retrieval model is a potential source of error in the approach that may or may not be significant when dust is mixed with biomass burning aerosols.
All of the AERONET retrieval products are affected by the errors associated
with choosing simplified particle shapes to represent the myriad of particles
in the atmosphere (although this is not the case for AOD, which is directly
measured). The retrieval would undoubtedly produce different results if chain
aggregates, hexagonal columns, and particles with rough surfaces were chosen
as the particle shapes in the forward component of the retrieval model.
Although these shapes might be more realistic than spheres and spheroids for
atmospheric aerosols, the scientific community has not created computer codes
that are capable of providing the single-scatter optical properties for size
distributions of these particles. Consequently, biases in the retrieval
products that are caused by the simplified shape assumption have not been
quantified. (Simplified shapes may also be an issue for some in situ
measurements that require Mie theory for calibration; e.g., optical particle
sizers and nephelometer truncation corrections.)
The size distribution accuracy expected for fine mode dominated aerosols is 15–25 percent for radii between 0.1 and 7 µm, 25–100 % otherwise.
The complex refractive index is allowed to vary with wavelength in the microphysical model, but it remains constant with respect to particle size.
The real refractive index is expected to be accurate to 0.04 and the imaginary refractive index accurate to 30–50 %
for aerosol optical thicknesses greater than 0.4 at the 0.440 µm wavelength.
The uncertainties are higher for coarse-mode-dominated aerosols such as dust and optically thin aerosols;
an accuracy assessment of the AERONET retrievals can be found in .
Although there have been many attempts to validate AERONET retrievals with
independent measurements, few would argue that the products derived from the
AERONET almucantar scans have been robustly validated. This is because the
AERONET Level 2 quality-control restrictions require clear skies, solar
zenith angles θ∘≥50∘, and AOD(440) greater than 0.4;
numerous occurrences of these conditions are difficult to achieve during a
6–8 week aircraft field mission. Consequently, no study thus far has provided
a statistically robust comparison that quantifies the biases associated with
the AERONET retrieval products.
A thorough review of the AERONET validation studies is beyond the scope of
this paper, but in the following paragraphs we note some important concepts
that must be kept in mind when comparing AERONET products to in situ
measurements. An ideal comparison of aircraft in situ measurements with
AERONET retrievals would include many tight spirals above AERONET sites
during almucantar scans in conditions that are appropriate for Level 2
retrievals. The aircraft spirals should also cover all portions of the
atmospheric column where aerosols are present and be numerous enough for a
robust statistical analysis.
probably provides the most complete comparison of
AERONET single-scatter albedo products to simultaneous in situ measurements obtained on board aircraft, but that study is still limited to eight comparisons.
We
refer readers who are interested in additional validation studies to
, , , , , and .
Additionally, Holben et al. compiled a nice summary of validation
efforts that were available up until 2011, which can be found at
http://aeronet.gsfc.nasa.gov/new_web/Documents/DRAGON_White_Paper_A_system_of_experiment.pdf.
AERONET provides products that represent column-integrated values, whereas in
situ instruments sample a small volume of the atmosphere. One can mount in
situ instruments on board aircraft to measure aerosol properties throughout
the atmospheric column, but this still requires integration of the measured
profiles over the entire column. Integration of profiles is a relatively
straightforward process for extrinsic parameters like size distributions and
AOD, because we can sum measurements that are obtained from multiple layers
of known geometric thickness. Column integration of the single-scatter albedo
requires a little more thought, but it can be accomplished by computing AOD and
AAOD prior to computing the column ω∘. There really is not a
viable method of obtaining column-integrated values for intrinsic parameters
like refractive index with in situ measurements at the present time, though.
We as a community should resist the temptation to compute column averages by
weighting in situ measurements with extinction profiles, as the fine and
coarse mode aerosols have significantly different extinction efficiencies.
Aerosol water content is another difficulty that confounds comparisons of
AERONET data with in situ measurements. AERONET instruments observe the
entire atmospheric column at ambient relative humidity, but in situ
measurements onboard aircraft are necessarily dried. Since relative humidity
can vary substantially in vertical profiles of aerosol measurements, it is
important to correct the dried measurements for the swelling of hygroscopic
aerosols . This is typically accomplished for aerosol
scattering coefficients by obtaining tandem measurements at low and high
relative humidities and then fitting empirical curves through the
measurements at these two reference humidities. However, aerosol scattering
increases exponentially with respect to relative humidity, so the empirical
fits are not precise.
Absorption measurements that rely upon filter techniques do not fare well for
aqueous-phase aerosols, so measurements of aerosol absorption on board
aircraft do not generally include tandem instruments. Thus, single-scatter
albedo measurements that are determined by using absorption can only be
partially corrected for humidification (i.e., the scattering or extinction
component is corrected, but the absorbing component is not). This is not an
issue when all of the absorbing aerosols are hydrophobic (like dust and
externally mixed soot carbon), but absorbing aerosols that are internally
mixed with hygroscopic aerosols can pose a problem (e.g., sC coated with
water-soluble organics).
At a minimum, any attempt to validate AERONET with aircraft profiles should
include a column AOD comparison. The AERONET AODs are robust measurements
based upon the extinction law, and they do not require the radiative transfer
modeling that is used for the almucantar products. Since AOD is an extrinsic
aerosol property and is obtained at ambient relative humidity, it enables a
“check” for the aircraft profiles which improves confidence that (1) all
atmospheric layers with significant aerosol mass have been adequately sampled
by the aircraft instruments and (2) the in situ aerosol humidification
corrections are reasonable. Thus, data from aircraft profiles that are unable
to produce the correct column AOD should be omitted from analyses attempting
to validate the AERONET retrieval products.
Imaginary refractive index of several absorbing aerosols.
Bon06 is soot carbon ; Che81 is hematite
; Gil92 is hematite ; Hsu85 is
hematite ; Bed93 is goethite ;
Sun07 is brown carbon ; Kir04 is brown carbon
; Che10 is brown carbon ;
Dust is the range of AERONET dust climatologies over Africa and the
Middle East.
Method
There are four absorbing aerosols species that are commonly found in
the atmosphere: sC, BrC, hematite, and
goethite. Soot carbon and brown carbon are produced by the same
combustion sources, and they generally coexist in aerosol layers. Hematite
and goethite are different forms of “free” iron,
and they typically appear together as well . Our task is to separate the various
contributions of these absorbing aerosol species.
Our approach utilizes imaginary refractive index, which we present for
the common absorbing aerosols in Fig. . Note
that the BrC imaginary refractive index is substantial at UV
wavelengths, decreases dramatically as wavelength increases throughout
the visible, and absorbs negligibly at wavelengths longer than about
0.7 µm.
Dust particles containing hematite also have a strong spectral
signature, with the greatest absorption occurring at the UV and blue
wavelengths. It is generally assumed that the hematite imaginary index is spectrally flat
in the near infrared region (∼0.7–1 µm), but
measurements are sparse at those wavelengths. Nonetheless, the
AERONET climatologies shown in Fig.
corroborate a flat spectral signature at the 0.67–1.02 µm
wavelengths for absorbing dust, as does , ,
and .
Since both BrC and hematite have strong spectral dependencies for the
imaginary index, mixtures of carbonaceous aerosols and dust can not be
unambiguously partitioned on the basis of the imaginary index alone.
Fortunately, we can separate these particles by size, since
carbonaceous particles are dominated by fine mode particles and dust
is dominated by coarse mode particles; thus, our retrieval initially
populates the fine mode with BrC and the coarse mode with dust.
Unfortunately, the AERONET imaginary indices over regions like West
Africa often indicate greater spectral dependencies than is observed
in biomass burning aerosols (requiring BrC/sC mass
ratios greater than 15.2 in the fine mode). Likewise, the
675–1020 nm imaginary indices in the AERONET database are
often greater than is observed in pure dust and would require
free iron fractions greater than 10 % in the coarse mode
(if no other absorbers were present). We solve this difficulty by
populating some of the fine mode with free iron and some of the
coarse mode with carbonaceous aerosols (which is qualitatively consistent with what is found in nature).
The foundation of the retrieval is presented in
Sect. , which demonstrates the range of imaginary
refractive indices that we can reasonably expect to observe for dust
or carbonaceous aerosols. Next, we use AERONET data to illustrate how
the imaginary refractive indices of dust and carbonaceous aerosols
differ (Sect. ). Finally, the results of
Sect. and are applied in
Sect. to retrieve the component volume fractions of
sC, BrC, and free iron.
Schematic illustrating carbonaceous aerosol retrieval (sC and
BrC) for the fine mode and free iron retrieval (goethite and
hematite) for the coarse mode. AERONET provides refractive indices
for uniform particles, and the retrieval uses different components
for each mode to find a mixture that matches the AERONET refractive
indices.
Aerosol layers with two absorbing species
If we know that we are observing “pure” dust or “pure” biomass
burning aerosols, we can use the imaginary refractive indices shown in
Fig. to retrieve the relative fractions of
hematite and goethite in the dust or sC and BrC in the biomass
burning aerosols. Later, we will discuss a technique for retrieving
absorbing aerosol species in more complex aerosol mixtures. The basic
procedure is presented in , which we briefly
review here.
A schematic of our approach is illustrated in Fig. .
The operational AERONET product assumes that all particles have the
same homogeneous refractive index, which implies that all particles are internally
mixed as in Fig. 2b of. Thus, our task is to determine an internal mixture of aerosols
that produce the AERONET refractive indices at all available
wavelengths. The complex refractive index of an aerosol mixture that
contains a non-absorbing host aerosol (i.e., khost=0) and two absorbing inclusions can
be expressed as a function (F) of the volume fraction of the
inclusions (fi), the complex refractive index of the inclusions
(mi=ni+iki), and the real
refractive index of the host
aerosol (nhost):
mmix(λj)=F(f1,f2,m1(λj),m2(λj),nhost(λj)),
where λj represents wavelength of interest. In practice,
Eq. () is implemented for each aerosol mode using the
Maxwell Garnett effective medium approximation (EMA), the Bruggeman EMA, or
volume averaging . Volume averaging provides
the simplest method for determining mmix (i.e., mmix=∑ifimi), but the Maxwell Garnett and Bruggeman EMAs are determined
from the complex dielectric constants of the host and inclusions; therefore they require additional equations for conversion to the refractive index
see. The Maxwell Garnett and Bruggeman EMAs differ by
less than 5 % , so we use the Maxwell Garnett EMA
because of its superior computational speed. We also note that the Maxwell
Garnet EMA accurately represents the mass absorption cross section of sC
particles with a collapsed aggregate morphology when compared to discrete
dipole approximation computations; the Maxwell Garnett EMA also performs better than the
core-shell model for such particles .
If the inclusion refractive indices (mi) are known, we can compute
the inclusion fractions and the host refractive index that “best”
matches the AERONET refractive indices (mrtr) by iterating
fi and nhost until a minimum χ2 value is achieved
:
χ2=∑j=14(nrtr(λj)-nmix(λj))2nrtr(λj)+(krtr(λj)-kmix(λj))2krtr(λj)→0.
The solution is unique because the co-emitted absorbing aerosols that
we consider (sC and BrC or hematite and goethite) have different
spectral signatures, as shown in Fig. . Thus,
the sC mixing ratio in the fine mode is determined by the
670–1020 nm wavelengths (since BrC exhibits little or no
absorption in this spectral region). Likewise, a unique combination of
hematite and goethite exists that provides the “best” match to the
AERONET-retrieved refractive indices for the coarse mode.
Theoretical imaginary refractive index space occupied by dust and carbonaceous aerosols. Shaded area indicates imaginary refractive indices at the
AERONET wavelengths that are possible with mixtures of 0–3 %
free iron by volume in the form of hematite and/or goethite
(as denoted by the labeled orange isolines). The bottom border of the
shaded area represents a 0 % hematite isoline, and the top
border represents a 0 % goethite isoline. The x axis is an
average for the 670, 870, and 1020 nm wavelengths (krnir). Solid
black line presents spectrally invariant refractive index, as
expected for soot carbon (sC). Solid grey lines indicate
BrC / sC mass mixing ratios of 1, 5, 10, and 20 when
no absorbing dust is present.
Note that the host species is generally a mixture of components
itself, with an unknown refractive index. Thus, we allow the real
refractive index nhost to be a free parameter that is
retrieved unlike inwhere we assumed that the host was
water. We assume that all absorption is caused by sC and
BrC or hematite and goethite, and the host species is
non-absorbing (i.e., khost(λ)=0). We also assume that
the real refractive index of the host is spectrally flat
(dnhost/dλ=0). Finally, the real
refractive index of BrC is not well characterized, so we assume that
it has the same refractive index as the host aerosol (i.e.,
nBrC=nhost).
Refractive indices used to test sensitivity of this retrieval to
component aerosol optical properties. The subscript “rnir” refers to the red
and near infrared wavelengths, and krnir is the average k for the 670–1020 nm wavelengths.
a water soluble organic carbon; b water insoluble organic carbon. c As tabulated by .
Imaginary refractive index space for dust and carbonaceous aerosols
We can use the Maxwell Garnett effective medium approximation
to determine the theoretical range of imaginary refractive
indices expected for atmospheric dust that contains only hematite and
goethite as absorbing species (i.e., no carbonaceous aerosols), which we
present as the shaded area in Fig. . In this case,
f1=fh is the volume fraction of hematite and f2=fg
is the volume fraction of goethite. We use a real refractive index of 1.5 for
the host aerosol, for the hematite refractive indices,
for the goethite refractive indices (see
Table ), and assume that all other minerals present have
negligible absorption throughout the UV/near-infrared range of wavelengths. (We discuss
the repercussions of using different refractive indices in the
Sect. .) Field measurements indicate that free iron
mass concentrations are typically less than about 5 %
. Hematite and goethite have densities that are
much higher than common clay minerals (4.28–5.26 gcm-3 for goethite
and hematite; 2–3 gcm-3 for illite, montmorillonite, and kaolinite), so
this corresponds to a maximum of about 3 % free iron by volume. Thus, we
present the range of computed refractive indices associated with up to
3 % free iron (by volume) as the shaded area in
Fig. . The bottom border of the shaded area
represents an fh=0 isoline and the top border represents an
fg=0 isoline. Finally, the numbers along the top of the shaded
area indicate the total percentage of free iron (i.e., hematite plus goethite)
on the vertical isolines (we also include an additional isoline outside of
the shaded area for 5 % free iron).
AERONET sites used in this study.
West Africa (waf): Agoufou, Banizoumbou, IER_Cinzana, DMN_Maine_Soroa,Ouagadougou, Djougou, Saada, Capo_Verde, Dahkla, Dakar,Ilorin, Quarzazate, Santa_Cruz_Tenerife, Tamanrasset_INM,Tamanrasset_TMP.Middle East (mea): Solar_Village, Nes_Ziona, SEDE_BOKER, Dhabi, Hamim.South Africa (saf): Mongu, Skukuza.South America (sam): Alta_Floresta, Cuiabá, CUIABA-MIRANDA, Abracos_Hill,Balbina, Belterra, SANTA_CRUZ.
In order to complete our description of two co-emitted aerosol
absorbers in k(440) vs. krnir space, we similarly compute
refractive indices for mixtures of carbonaceous aerosols. In this
case, sC and BrC are the only absorbing species and f1=fsC, f2=fBrC. We use a spectrally invariant
imaginary index of msC=1.95+0.79i for soot carbon
and the measurements for BrC
(see Fig. ). The solid black line in
Fig. is a 1:1 line, and it represents the
spectrally invariant refractive index of soot carbon. The grey lines
above the black line in Fig. denote the
contribution of brown carbon, with BrC/sC mass
mixing ratios ranging from 1 to 20 assuming that sC has
a density of 1.8 gcm-3 and BrC has a density of
1.2 gcm-3, as in.
AERONET Level 2.0 imaginary refractive indices
(color code
corresponds to the absorption Ångström exponent). Top panel: data
over the Middle East, filtered to retain only dust (i.e., require
fine volume fractions less than 0.05 and depolarizations greater
than 0.2 at the 532 nm wavelength). Vertical magenta line
denotes the median. Bottom panel: South American biomass burning
sites for August and September. The vertical dashed line at
krnir=0.0042 separates 95 % of the two data sets. See
Table for a listing of AERONET sites used in
these regions.
Box plots for the Level 2.0 imaginary refractive indices
averaged over the 670–1020 nm wavelengths at the AERONET
sites listed in Table . Circles represent
medians; box edges are the 25th and 75th percentiles; whisker ends
are the 5th and 95th percentiles. Grey bars indicate the number of
data points contributing to each box plot. The Middle Eastern (mea) and
West African (waf) sites are filtered for dust using two different
methods. Biomass burning sites are considered only during the peak
of the biomass burning seasons (August–September for South America,
or sam; July–September for South Africa, or saf). The dashed blue
line illustrates that krnir=0.0042 separates at least
95 % of the dust and biomass imaginary refractive indices.
Separation of dust and carbonaceous aerosols
The previous section provides a theoretical range of values expected for both
dust and carbonaceous aerosols in the imaginary refractive index space of
Fig. . Note that there is substantial overlap of
the imaginary refractive indices computed for dust (shaded area) and
carbonaceous aerosols (grey and black isolines) in Fig. . Thus, additional constraints are needed to
separate dust from carbonaceous aerosols, which we discuss in this section.
We begin by assessing “pure” dust over the Middle Eastern
sites listed in Table , and plot AERONET Level 2
retrievals in Fig. a. We define “pure” dust as
retrievals with fine mode volume fractions (fvf) less than or equal to 0.05
and lidar depolarization ratios (δp) greater than or equal to 0.2 at the
0.532 µm wavelength . Note that all
of the retrievals in Fig. a lie in the shaded
region, indicating that these AERONET retrievals are consistent with
the free iron fractions found in the literature and the refractive
indices that we used to compute the shaded region.
Similarly, we plot the AERONET retrievals from the South American sites
during the peak of the biomass burning season in
Fig. b. Note that these aerosols tend to have much
higher imaginary indices at the red and near-infrared wavelengths
(krnir) than the dust aerosols of
Fig. a, and most of the retrievals do not
occupy the shaded area. This is because biomass burning aerosols are
generally more absorbing than dust at 0.670–1.020 µm.
Also note that our retrieved BrC/sC mass ratio for
biomass burning aerosols is always less than 15.2, which is an
important constraint for retrieving mixtures of dust with carbonaceous
aerosols (described in Sect. ).
There is a clear separation between the dust and biomass burning
aerosols in Fig. – the carbonaceous aerosols are
much more absorbing at the red and near-infrared wavelengths than the
dust aerosols. Thus, we explore this phenomenon as a means of
separating dust from carbonaceous aerosols using the box plots of
Fig. . The biomass burning sites in
Fig. are considered only during the peak of the
biomass burning seasons in order to minimize possible contamination by
dust (August–September for South America, July–September for South
Africa). We have used two different techniques to minimize
carbonaceous aerosol contamination of dust. The middle pair of boxes
in Fig. are restricted to retrievals with fine
volume fraction less than 0.05 and lidar depolarization ratios greater
than 0.2 at the 0.532 µm wavelength as in
Fig. a and. Some readers may
desire a stricter constraint, so we also limit the retrievals to AE≤0 for the first pair of boxes in Fig. .
This is a stringent requirement that demands high coarse mode
concentrations and allows very little pollution or biomass
burning in the data set ; unfortunately, this
restriction reduces the size of our data set to ∼100 points (see
the right axis of Fig. ). Both restrictions for
dust result in similar medians, but the
restriction has larger upper limits than the AE≤0 restriction
(compare the leftmost pair of box plots to the middle pair of
box plots). Neither technique results in any overlap of the whiskers
at the dust regions with the whiskers at the biomass burning regions
in Fig. .
Thus, there is less than 5 % overlap between any of the dust
distributions with either of the biomass burning distributions, and
the horizontal dashed line at krnir=0.0042 separates at
least 95 % of the dust and biomass burning aerosols. Finally, we
note that krnir=0.0042 corresponds to a volume-averaged
free iron content of about 3.4 % when using the
refractive indices for hematite and the refractive
indices for goethite; this can also be inferred by the close proximity
of the krnir=0.0042 line to the 3 % free iron
isoline in Fig. . Note that 3.4 % free iron by
volume is roughly equivalent to 6.8 % free iron by mass, since
the density of free iron is roughly twice that of other minerals
. Thus, this is consistent with the maximum
6.5 % free iron content measured by .
Aerosol layers with up to four absorbing species
We outlined the imaginary refractive index space occupied by dust and
carbonaceous aerosols in Sect. . The absorption for
both of these aerosol types can be described with two absorbing
aerosol species (hematite and goethite for dust, organic and black
carbon for carbonaceous aerosols). However, mixtures of dust with
biomass burning require us to include four absorbers in the retrieval,
which we discuss in this section.
Flowchart showing absorbing aerosol retrieval
process. Initially, all carbonaceous species are assumed to occupy
the fine mode and all iron oxide (hematite, goethite) is assumed to
occupy the coarse mode. If BrC/sC>15.2 would
be required for a two-component mixture, then hematite is used instead
of BrC to characterize the spectral dependence of the refractive
index in the fine mode (branch A). Likewise, if the retrieved
absorption at the red and near-infrared wavelengths is above
a threshold of krnir=0.0042, then the hematite and
goethite fractions are fixed at the values we retrieved for “pure”
African dust, and some carbonaceous aerosol is assumed to occupy the
coarse mode (branch D).
Our basic approach is outlined in Fig. . Since
carbonaceous aerosols are mainly found in the fine mode
(radii ≲ 0.6µm), we initially assume that all
sC and BrC are located in that mode. Likewise, free iron is mainly
internally mixed with other minerals, and are the dominant absorbers
in coarse mode dust
, so we initially
assume that all hematite and goethite are located in the coarse mode.
Unfortunately, we can not maintain all carbon in the fine mode and all
free iron in the coarse mode for all aerosol retrievals; this is
because AERONET provides a single refractive index for particles in
both the fine and coarse modes, and sometimes the retrieved refractive
indices can not be achieved with reasonable concentrations of
a two-absorber mixture.
This conundrum is illustrated in Fig. , where we present
all level 2.0 AERONET retrievals at the West African sites in k440
vs. krnir space.
Aerosols at these sites include
substantial concentrations of dust throughout the year, as well as
seasonal biomass burning . The points above the
uppermost grey line in Fig. would require
BrC/sC mass ratios higher than 20 for the fine
mode with our scheme (if we did not make an adjustment). However, the
BrC/sC ratio is never greater than 15.2 for the
South American biomass burning aerosols in Fig. b
(this is also true for the South African biomass burning sites of
Table ). Hence, the retrievals with
BrC/sC>20 in Fig. are actually
dominated by dust, so including extreme values of BrC in the fine mode
to obtain the correct spectral dependence for the imaginary index is
not realistic.
All level 2.0 retrievals for the West African AERONET sites
of Table , including mixtures of biomass
burning and dust. Many of the points are located above the uppermost
grey line, which would require BrC/sC ratios
greater than 15.2 for a mixture containing only carbonaceous
aerosols. Likewise, retrievals to the right of the shaded area
require iron oxide volume mixing ratios greater than 3 % if no
carbonaceous aerosol is included.
Thus, we use hematite instead of BrC to represent the spectral
dependence of the fine mode when the BrC/sC ratio
exceeds 15.2; this is diagrammed in the left flowchart of
Fig. . This is qualitatively consistent with observations, as
iron-rich dust is known to exist in the fine mode as well as the
coarse mode when such dust is present
. This branch of the code is
necessary for 12 % of the retrievals in West Africa and 14 %
of the retrievals in the Middle East, but it is never called for the
retrievals in South America or southern Africa.
Likewise, AERONET retrievals with refractive indices to the right of
the shaded area in Fig. likely contain biomass burning
aerosols (as in Fig. b). Attempting to model the
coarse mode without carbonaceous aerosols will require free iron
concentrations exceeding 5 or even 10 % by volume for that
mode (since these data fall to the right of the 5 % free iron
oxide isolines in Fig. ). This is unrealistic, so we
separate carbonaceous and free iron aerosol absorption at
krnir=0.0042, as inferred from the box plots of
Fig. . That is, we fix the free iron volume
concentration at 1 % for the coarse mode when krnir>0.0042 (based upon the retrieved median for pure dust at the West
African and Middle Eastern sites),
as diagrammed in the right flowchart of
Fig. . Then the remainder of the absorption for that
mode is accounted for with sC and BrC. This branch of the code is
necessary for 24 % of the retrievals in West Africa, 17 % of
the retrievals in the Middle East, and 96–98 % of the retrievals
at the biomass burning sites in South America and southern Africa.
Although carbonaceous aerosols are sometimes observed attached to
coarse mode dust , it is also likely that
the AERONET product redistributes some fine mode absorption to the
coarse mode .
Our choice of 1 % free iron in branch D of Fig. is based
upon climatology for pure dust at the West African and Middle Eastern sites. We
retrieve median hematite and goethite fractions of 0.39 and 0.62 % for pure
dust in West Africa, and 0.34 and 0.58 % for pure dust in the Middle East,
0.38 and 0.62 % when both these regions are combined. This corresponds to
k440/674/870/1020=0.0036/0.0014/0.0013/0.0013 for pure dust in the
combined data set, which is also consistent with .
Column mass concentrations of sC (top row), BrC (middle row),
and iron oxide (bottom row) retrieved from Level 2.0 AERONET data
for January (left column) and August (right column). Minimum number
of retrievals is 25 for each site.
Upper panels: monthly averaged column concentrations of
retrieved sC, BrC, and free iron in the fine and coarse modes at the
West African and southern African sites (fine mode iron is negligible
at these scales). Note the difference in y axis scales. Lower
panels: monthly averaged volume fractions at the same sites. Extra
dashed maroon line for West Africa indicates monthly averages for coarse mode iron,
but computed without using retrievals that require branch D of Fig. . Error
bars represent the SD of the means. Number of retrievals per month
shown along upper axes; only months with more than 20 retrievals are
shown in plots.
Results
We present climatological maps of the retrieved column mass of sC,
BrC, and free iron in Fig. . We begin our discussion
with the right column of Fig. , which presents our
retrieved concentrations for the month of August. This is near the
peak of the biomass burning season at the southern hemispheric sites,
and the retrieval indicates higher sC concentrations in those regions
than in the rest of the world. The retrieval also indicates elevated
concentrations of BrC in those regions (right middle panel),
consistent with our expectations for biomass burning regions.
Likewise, the urban regions of North America, Europe, and Asia
indicate intermediate concentrations of sC and little BrC. Finally,
we note that the West African sites indicate little or no carbonaceous
aerosols during the month of August, consistent with the lack of
biomass burning in that region at that time. Note the elevated iron
concentrations in West Africa, however, as well as the high
concentrations of iron in the Middle East, India, and parts of Europe
(bottom right panel).
The month of January tells a different story (left column of maps in
Fig. ). The West African sites have high concentrations
of sC and BrC during this month, reflecting the strong winter biomass
burning signal that occurs there. The dust signal also remains at
those sites, however, as indicated by the large iron
concentrations shown in the lower left map. Note that the Saudi Solar
Village site on the Arabian Peninsula also indicates high iron
concentrations in both January and August, but the carbonaceous
aerosol signal does not exist there in either month. Taken together,
maps of these two regions (West Africa and the Middle East) indicate
that the retrieval is able to discriminate dust aerosols from mixtures
of dust and carbonaceous aerosols.
The monthly averaged absorbing aerosol concentrations for the two
African regions are shown in the upper panels of
Fig. , broken down by species for each mode (see
Table for locations). Here, we see that the
West African dust sites are dominated by free iron, whereas the
biomass burning sites of southern Africa are dominated by carbonaceous
aerosols. We also present the seasonal climatology of the component
mixing ratios for these sites in the lower panels of
Fig. . We show volume fractions this time (instead
of column concentrations) to illustrate the intrinsic properties of
the retrieval. Note that even though both African regions exhibit
similar mixing ratios of free iron throughout the year (as shown in
the lower panels of Fig. ), the West African region
has much higher free iron concentrations than the southern African
region (as shown in the upper panels of Fig. ). This
is because the West African sites have a much stronger coarse mode
than the southern African sites.
Maximum monthly climatological averages for
absorbing components of the fine (f) or coarse (c) mode, in percent by volume. The AERONET
sites are listed in Table and the regions correspond to
West Africa (WAF), Middle East (MEA), southern Africa (SAF), and South America (SAM).
The lower left panel of Fig. indicates that the volume
fraction of free iron remains relatively constant in West Africa throughout
the year (1.4–1.7 %, as shown by the solid maroon line). However, the
signal is somewhat stabilized because we use climatological mixing ratios for
iron whenever the retrieval is contaminated by carbonaceous aerosols, which
constitutes 24 % of the retrievals in West Africa (i.e., “branch D” of
Fig. ). Hence, we also show the monthly averages that are
obtained by omitting the contaminated retrievals (dashed maroon line in the
lower left panel). Note that both monthly averages produce nearly identical
free iron mixing ratios in the absence of biomass burning aerosols during the
summer months. Monthly averaged free iron mixing ratios obtained during the
winter biomass burning season increase when we omit retrievals that require
branch D of Fig. , though, peaking at 2.3 % in December. This
is because omitting retrievals that require branch D results in a heavier
weighting of the retrievals with krnir greater than the median value of
pure dust (i.e., krnir≳0.0013 and krnir<0.0042). Thus,
omitting the branch D retrievals causes the monthly iron volume fractions to
increase during the biomass burning season and more closely mimic the
monthly carbonaceous aerosol signals. Hence, it is likely that some of the
remaining retrievals are still contaminated with carbonaceous aerosols during
the biomass burning season. Finally, we note that branch D is called for 98 %
of the retrievals in southern Africa, so the solid maroon line in the lower
right panel indicates very little seasonality because it is dominated by
climatology.
The carbonaceous signals of Fig.
show a very strong seasonal variability,
with sC and BrC peaking at 1.0 and 5.8 % for the fine mode during
the West African biomass burning season.
We see a similar pattern in southern Africa, with fine mode peaks of 3.0 % for sC and 3.7 % for BrC.
The peak climatological volume mixing ratios for all four regions of
Table are listed in Table .
One can discern that BrC‾/sC‾
volume ratio is always less than 2 at the South African sites of
Fig. , and it is sometimes less than 1; indeed, the
median BrC/sC mass ratio is 0.7 during the peak of
the biomass burning season. This is much lower than the organic/soot
carbon mass ratios measured using in situ techniques, which typically
range from 3 to 12 or more e.g.,. However, in situ measurements are
typically reported for measurements obtained close to sources and
during fire events, but monthly averages represent periods between
burns as well as the burn events; this could lower the
BrC/sC ratios.
Additionally, in situ measurements typically account for all OC, whereas our retrieval is responsive only to organic
species that have significant absorption at 0.440 µm. That
is, BrC is part of OC , so BrC concentrations
are always less than OC concentrations, and BrC/sC<OC/sC. Alternatively, a greater proportion of OC
could be encompassed by using a lower imaginary refractive index for
BrC; for instance, the median retrieved BrC/sC
volume ratio increases from 1.5 to 2.7 for the South American biomass
burning season when is used for the BrC refractive
index instead of . However, the maximum
BrC/sC value also increases from 15.2 to 27.5, and
the latter value is outside of the range outlined by
or .
Volume fraction of absorbing species
associated with each absorber, as a function of AAE and fine volume fraction. Data located mainly on the left
are
from the West African sites; data on the right correspond to the
biomass burning season at the South American sites.
Recently, some authors have attempted to use AAE to speciate
carbonaceous aerosols and dust , so we
present AAE vs. fvf for the West African and
South American sites in Fig. . The color code
in each of the panels represents the volume fraction of one of the
absorbing aerosol species with respect to the total volume of all
absorbing aerosol species. The fraction of sC is presented in the top
panel, BrC in the middle panel, and free iron in the lower panel.
Data on the left side of the panels correspond to low fvf, and
therefore they are dominated by dust at the West African sites. Data on
the right are dominated by biomass burning at the South American sites.
It is immediately obvious that the AAE parameter is sensitive to the
relative proportions of sC and BrC when carbonaceous aerosols dominate
the retrievals, as shown by the strong color gradients on the right
side of the upper two panels. This is because of the strong spectral
dependence of kBrC and the wavelength-independent
refractive index of sC shown in Fig. . The
strong spectral dependence of kBrC enhances the absorption
at the shortest wavelengths, which increases AAE. Soot carbon has
a wavelength-independent refractive index ,
so the AAE of pure sC is smaller than the AAE of pure BrC for
particles of the same size. Thus, both the AAE and our retrieved BrC
are sensitive to the spectral dependence of the retrieved imaginary
index, and the color gradient for carbonaceous aerosols in
Fig. reflects this.
However, Fig. also indicates that AAE is not
useful for discriminating between dust and carbonaceous aerosols. For
instance, a retrieved AAE∼1.2 can not discriminate
between the dust aerosols with fvf<0.1 on the left side
of Fig. and the carbonaceous aerosols with
fvf>0.7 on the right side of
Fig. . This is because the AAE of pure dust
has a large range of possible values (from less than 0 to greater than
3), depending upon the relative fractions of hematite and goethite.
Thus, the AAE of dust is not well separated from the AAE
of carbonaceous aerosols along the vertical axis in
Fig. . This is discussed further in
.
Maximum and median BrC volume fractions, and BrC/sC
mass ratios retrieved for fine mode aerosols at the biomass burning sites during
the peak of the burning seasons (August–September for South America, July–September
for southern Africa), using different BrC refractive indices. The refractive index used for sC is msC=1.95+0.79i.
Maximums Medians fBrCBrCsCfBrCBrCsCSouth America a0.1838.00.0140.80.34315.20.0281.5a0.65827.50.0512.7b1.00047.70.1166.5Southern Africa a0.1115.80.0120.40.23011.80.0230.7a0.41421.20.0431.3b0.84245.20.1053.2
a Upper limit; b lower limit.
Sensitivity to component refractive indices
The refractive indices that we use for this retrieval are outlined in
the upper portion of Table ; refractive index
uncertainty in any one of these components has an affect on all of the
absorbing components to some extent, which we assess in this section.
We assess this uncertainty by repeatedly retrieving sC, BrC, and
hematite climatologies using all of the refractive index sources
listed in Table , and then we compare the results to our
baseline retrieval. In order to stress the algorithm, we seek
locations with significant concentrations of both dust and
carbonaceous aerosols; thus, we use the month of January at the West
African sites of Table . Dust is always present
at those sites, and January is near the peak of the biomass burning
season at those locations. We explain our baseline refractive index
choices in Sect. ; the sensitivity studies for sC,
BrC, and free iron are presented in Sect. and
.
Rationale for our choice of baseline refractive indices
did an extensive review of sC refractive indices; they
concluded that the available data were consistent with a constant
refractive index at visible wavelengths. They also hypothesized that
“strongly absorbing carbon with a single refractive index exists, and
that some of the variation in reported refractive indices results from
void fractions in the material.” They recommended using k=0.63–0.79 for sC at visible wavelengths, favoring the larger extreme
for highly graphitized carbon. The spectrally invariant value of
ksC=0.79 was later adopted in . Thus, we
use msC=1.95+0.79i at all wavelengths as our baseline
complex refractive index for sC.
Reported values for the imaginary index of BrC varies by more than an
order of magnitude at λ=0.440µm, as shown in
Fig. and Table . This is
because BrC is a generic term for many absorbing organic carbon
particles, and it does not represent a single aerosol species per se.
The refractive index of BrC depends upon combustion conditions and the
fuel source, and it therefore can vary substantially between the various
AERONET sites. We require that our BrC retrieval produces reasonable
BrC fractions and BrC/sC ratios for all cases. So,
for instance, Table indicates that using the lower
limit of can result in fBrC as high as 1.0
(i.e., 100 % brown carbon), which is clearly not acceptable.
Fortunately, the refractive index of all BrC is negligible at the
670–1020 nm wavelengths (see Fig. ),
so BrC refractive index does not have a substantial impact on the
retrieved sC mixing ratio (as shown in Sect. ).
We use for our baseline refractive index of
BrC because it provides reasonable maximum and median fBrC
and BrC/sC ratios at the biomass burning sites.
Hematite also exhibits a large range of reported refractive indices,
some of which are noted in Table . Hematite and
goethite are the two dominant absorbers in mineral dust, and most of
the spectral variability of dust absorption at visible wavelengths is
associated with hematite. Consequently, we must choose hematite
refractive indices
for our retrieval that have enough spectral variability to
describe all of the AERONET retrievals. That is,
k440/krnir is often greater than 5 in the AERONET
database, so we need to choose hematite and goethite optical
properties that can accomplish this spectral variability. Since the
only available goethite measurement has a positive spectral dependence
i.e., dk/dλ>0, per,
the hematite source that we choose must also indicate
k440/krnir>5.
Additionally, our choice of mineral refractive indices must produce
retrieved hematite and goethite fractions that are consistent with
other work. In situ measurements indicate that hematite and goethite
constitute 2.8–6.5 % of mineral dust by mass
.
This corresponds to approximately 1.4–3.25 % by volume, since the
density of free iron is much greater than other common minerals
4.28 gcm-3 for goethite and
5.25 gcm-3 for hematite, as opposed to
2.65 gcm-3 for illite, kaolinite, quartz, and
calcite;, Additionally, the hematite fraction of
free iron ranges from 17 to 61 %
.
We implemented the retrieval at the West African sites of
Table , considering only “pure” dust (i.e.,
fine volume fractions less than 0.05 and depolarizations greater than
0.2), and present maximum and median results in
Table . Here, we see that all of our candidate
refractive indices for hematite produce median free iron and hematite
fractions that are consistent with the in situ measurements, but only
the refractive indices produce a maximum free iron
fraction that is less than 3.5 % by volume. Thus, we choose
for our baseline retrieval, but we note that we have
not scoured the literature for the most suitable hematite refractive
index. The sensitivity of the results to our choice of refractive
indices is discussed in the following two sections.
Volume fractions of hematite, goethite, and free iron for “pure” dust,
and the volume percentage of iron associated with hematite (%H=Vhem/(Vhem+Vgoe)×100) at the West African
sites, using different refractive indices for hematite.
Relative bias associated with component refractive index
uncertainty, utilizing 18 combinations of the refractive indices
listed in Table . Each group of bars utilizes either
the lowest (L) or highest (H) sC refractive index recommended by
. Groups of bars are also labeled according to the
four BrC refractive index sources in Table ; color
code denotes hematite refractive index source. Baseline components
are denoted by the diamond. Note that the sC bias associated with
refractive index uncertainty is always less than 15 %. See
Table for refractive index citations.
Sensitivity of retrieved sC to other absorbers
The sC results are shown in Fig. , which presents
the relative mean biases with respect to the baseline value (i.e.,
sC‾/sC‾base). The
baseline retrieval is designated by the diamond in
Fig. ; it utilizes BrC refractive indices from
, hematite refractive indices from
, and sC refractive indices from . We
have included an additional six refractive indices for sC, BrC, and
hematite (as listed in the lower portion of Table ) and
tested 18 combinations of these component refractive indices. All of
the retrievals utilize for the refractive index of
goethite, since that is the only source available for this mineral.
There are six groups of bars in Fig. . Each
group of bars utilizes the same sC and BrC refractive indices but
different hematite refractive indices. Thus, the small variability of
the bars within each group indicates that the choice of hematite
refractive index (as indicated by color) has very little effect on the
sC retrieval (≲ 1 %). There are two reasons for
this: (1) the retrieval initially assumes that sC and hematite are
located in different modes (i.e., fine vs. coarse), so the absorption
interference associated with these species is minimized; (2) the sC
retrieval is mainly determined by the red and near-infrared
wavelengths, and hematite has a much lower imaginary refractive index
than sC at those wavelengths.
The two leftmost groups of bars in Fig. utilize
the same BrC refractive indices i.e.,, so
the dominant difference between these two groups is caused by the
different sC refractive indices used for the retrievals. Groups of
bars labeled “L” utilize msC=1.75+0.63i, which is the
low extreme from ; groups labeled “H” utilize
msC=1.95+0.79i, the highest recommended value of
. Thus, the full range of sC refractive indices
recommended by produces a sC retrieval uncertainty of
≤14.2 %.
We can assess the effect of kBrC variability on the sC
retrieval by observing all of the groups labeled “H” in
Fig. . The retrievals in these four groups
consistently use ksC=0.79, and the color code
corresponds to the hematite refractive index. Consequently, the only
difference between like-colored bars in the “H” groups is the BrC
refractive index. Thus, the dark blue bars indicate that using
water soluble organic carbon for the BrC
refractive index produces the maximal relative bias of 5.8 % below
our baseline retrievals.
Some authors expected this value to be substantially higher
e.g.,, but BrC does not appreciably absorb radiation at
red or near-infrared wavelengths
. Hence, it is actually
quite easy to separate the effect of sC from BrC using refractive
indices at the 670–1020 nm wavelengths, since sC absorption
is more than 2 orders of magnitude greater than BrC absorption in this
spectral region (as shown in Fig. ).
Overall, Fig. indicates that relative bias in
the sC retrieval ranges from -5.8 to +14.2 % at the
African dust sites during the biomass burning season. The largest
uncertainty is driven by the uncertainty in the sC refractive index.
Uncertainty in the hematite refractive index has very little effect on
the sC retrieval (<1 %), and uncertainty in the BrC refractive
index alters the retrieval by less than 5.8 %. We also did
a similar sensitivity study for the month of August at the biomass
burning sites of South America (per Table 2) to determine performance
when carbonaceous aerosols are the dominant particles, and we found that
both BrC and hematite refractive index uncertainties alter the sC
retrieval by less than 1 % (not shown).
Same as Fig. , except y axis is BrC
(top panel) and free iron (bottom panel) relative biases.
We are now in a position to estimate the uncertainty associated with
retrieving sC mass from the AERONET products. Recall that the estimated
uncertainty for the imaginary refractive index in the Level 2 AERONET
products is 50 % , and sC is linearly related to the
imaginary index for internal aerosol mixtures when sC is the only absorber
present ; thus, the sC uncertainty associated with the
AERONET retrieval is also 50 %. The measured sC densities are well known
1.8gcm-3±6 %, per, and we
estimated a maximum 14.2 % uncertainty associated with the sC, BrC, and free
iron refractive indices above. Since the AERONET products are based upon
radiances and optical depth measurements and do not depend upon the component
densities or refractive indices, these are independent uncertainties that can
be added in quadrature . Thus, the RMS uncertainty in
retrieving sC mass that is associated with the AERONET refractive indices, sC
density, and the component refractive indices of sC, hematite, and BrC is
502+62+14.22=52%.
Sensitivity of retrieved BrC and free iron to other
absorbers
We can apply a similar analysis to BrC and hematite using
Fig. . The top panel indicates that sC and hematite
refractive indices have a small effect on the BrC bias (because all of the
bars for a given BrC have similar magnitudes). However, the bias can be as
low as 50 % usingwater insoluble OC for BrC or as high
as 440 % usingwater soluble OC for BrC. This is
because the range of imaginary refractive indices for BrC varies by more than
an order of magnitude at the 0.440 µm wavelength (e.g., see
Fig. ), and this ambiguity propagates through the
retrieval. Biases in our BrC retrieval could also occur whenever NO2
optical depths differ significantly from climatological values, but we do not
attempt to quantify that here.
The lower panel of Fig. tells a similar story for
iron. That is, all of the like-colored bars are similar in magnitude,
indicating that the sC and BrC refractive indices have a relatively
small effect on the amount of iron retrieved. However, the retrieval
changes by 83 % if we use the refractive
indices as tabulated by.
It is doubtful that the
various forms of hematite truly exhibit the large range of refractive
indices found in the literature, though, and the remote sensing
community could benefit from future work that narrows the range of
plausible refractive indices for hematite.
Since coarse mode iron is internally mixed with other minerals , we estimate the
uncertainty associated with the free iron retrieval to be 502+832=97 %.
Uncertainty associated with incongruous retrieval assumptions
Other possible errors associated with the AERONET product have already been
described in Sect. . Some of these errors occur when
conditions are not congruous with the retrieval assumptions, and these errors
are very difficult to quantify. For instance, we stated earlier that the
AERONET retrieval model assumes internal homogenous mixing for all aerosol
particles; this could produce a bias in our sC retrievals if a significant
portion of atmospheric sC is externally mixed. used a
single-particle soot photometer (SP2) to determine the mixing state of sC
and found that internal mixing occurred for 70 % of the sC particles in
fresh biomass burning plumes and 46 % of the background sC, but internal
mixing only occurred for 9 % of fresh urban emissions. Hence, although the
internal mixing assumption required for the AERONET product might be
reasonable in biomass burning regions, that assumption is less reliable in
urban regions. Another unquantifiable error is the effect of using spheres
and spheroids to approximate real particle shapes.
These are just two examples of errors that are known to exist but are
extremely difficult to quantify in a realistic fashion. Uncertainty
associated with ill-suited retrieval assumptions are endemic to all
retrievals, though, and such errors escape the analysis provided by
uncertainty propagation. However, in situ measurements are not immune to
incongruous assumptions, either. Since quantifying the effect of
inappropriate assumptions is not feasible, it is important to be mindful of
the a priori assumptions when appraising the merit of any retrieval or
measurement. Thus, we caution readers not to blindly interpret sC uncertainty
estimates, both here and elsewhere in the literature.
Conclusions
We present a method of distinguishing the relative concentrations soot
carbon (sC), brown carbon, and free iron (hematite and goethite) aerosol species in the atmosphere.
Our approach determines a mixture of absorbing and
scattering aerosols that is consistent with the complex refractive
indices provided by each AERONET retrieval. The method initially
assumes that all carbonaceous aerosols are located in the fine aerosol
mode, and all free iron is located in the coarse aerosol mode.
However, the retrieval allows some carbonaceous aerosols to populate
the coarse mode and some hematite to populate the fine mode (if this
is necessary to reproduce the AERONET refractive indices). The
solution for sC is unique because it is the only fine mode aerosol species with
significant absorption at the red and near-infrared wavelengths. The
solution for brown carbon and free iron is more ambiguous than the
soot carbon retrieval, but the result for these other absorbers could
be improved with better characterization of the refractive indices for
those components.
The results show sensible regional and seasonal variability of the
component aerosols, with the highest proportion of carbonaceous
aerosols occurring at the seasonal biomass burning sites. The free iron mixing ratios and hematite/goethite ratios are also consistent
with the values published in the scientific literature.
We also present a sensitivity study, which indicates a ∼50 %
uncertainty in retrieved sC concentration that is mainly associated
with the uncertainty of the refractive index in the AERONET products. Finally, since
our mixtures maintain the AERONET internal homogeneous mixture assumption and are
constrained by the AERONET refractive indices, our approach maintains
a link to the measured radiance fields.
Acknowledgements
This material was supported by the National Aeronautics and Space
Administration under the NASA Glory Science Team, issued through the
Science Mission Directorate, Earth Science Division. Oleg Dubovik
was supported by the Labex CaPPA project involving several research
institutions in Nord-Pas-de-Calais, France. Antti Arola acknowledges support from
the Academy of Finland (through the project number 264242).
We enjoyed informative discussions with Brent Holben and Tom Eck about the AERONET products,
and we appreciate the
efforts of the 29 AERONET and PHOTONS (Service d'Observation from
LOA/USTL/CNRS) principal investigators and the entire AERONET and
PHOTONS teams for obtaining, processing, documenting, and
disseminating their respective data sets.
We also appreciate the thorough efforts of the four expert reviewers, as their
input greatly improved the final version of this paper. Finally, we acknowledge the efforts of Phillip Stier for overseeing the review process.
Edited by: P. Stier
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