The optical properties of airborne mineral dust depend on its mineralogy,
size distribution, and shape, and they might vary between different source
regions. To date, large differences in refractive index values found in the
literature have not been fully explained. In this paper we present a new
dataset of complex refractive indices (m=n-ik) and single-scattering albedos
(SSAs) for 19 mineral dust aerosols over the 370–950 nm range in dry
conditions. Dust aerosols were generated from natural parent soils from
eight source regions (northern Africa, Sahel, Middle East, eastern Asia,
North and South America, southern Africa, and Australia). They were selected
to represent the global-scale variability of the dust mineralogy. Dust was
resuspended into a 4.2 m3 smog chamber where its spectral shortwave
scattering (βsca) and absorption (βabs)
coefficients, number size distribution, and bulk composition were measured.
The complex refractive index was estimated by Mie calculations combining
optical and size data, while the spectral SSA was directly retrieved from
βsca and βabs measurements. Dust is assumed to be
spherical in the whole data treatment, which introduces a potential source
of uncertainty. Our results show that the imaginary part of the refractive
index (k) and the SSA vary widely from sample to sample, with values for k in
the range 0.0011 to 0.0088 at 370 nm, 0.0006 to 0.0048 at 520 nm, and 0.0003
to 0.0021 at 950 nm, as well as values for SSA in the range 0.70 to 0.96 at 370 nm,
0.85 to 0.98 at 520 nm, and 0.95 to 0.99 at 950 nm. In contrast, the real
part of the refractive index (n) is mostly source (and wavelength)
independent, with an average value between 1.48 and 1.55. The
sample-to-sample variability in our dataset of k and SSA is mostly related to
differences in the dust iron content. In particular, a
wavelength-dependent linear relationship is found between the magnitude of
k and SSA and the mass concentrations of both iron oxide and total elemental
iron, with iron oxide better correlated than total elemental iron with both
k and SSA. The value of k was found to be independent of size. When the iron
oxide content exceeds 3 %, the SSA linearly decreases with an increasing
fraction of coarse particles at short wavelengths (< 600 nm).
Compared to the literature, our values for the real part of the refractive
index and SSA are in line with past results, while we found lower values of
k compared to most of the literature values currently used in climate models.
We recommend that source-dependent values of the SW spectral refractive
index and SSA be used in models and remote sensing retrievals instead of
generic values. In particular, the close relationships found between k or SSA
and the iron content in dust enable the establishment of predictive rules for
spectrally resolved SW absorption based on particle composition.
Introduction
With teragram amounts of annual emissions, a residence time of about 1–2 weeks in the atmosphere, and a planetary-scale transport, mineral dust
aerosols are a global phenomenon (Uno et al., 2009; Ginoux et al., 2012)
and contribute significantly to the global and regional aerosol loading
(Ridley et al., 2016) and direct radiative effect (Miller et al., 2014).
However, large uncertainties still persist on the magnitude and overall sign
of the dust direct radiative effect (Boucher et al., 2013; Highwood and
Ryder, 2014; Kok et al., 2017). One of the major sources of this uncertainty
is our insufficient knowledge of dust absorption properties in the
shortwave (SW) and longwave (LW) spectral ranges (e.g., Balkanski et al.,
2007; Samset et al., 2018), given that mineral dust contains large particles
and a variety of minerals absorbing over both spectral regions (e.g., iron
oxides, clays, quartz, and calcium-rich species; Sokolik and Toon, 1999;
Lafon et al., 2006; Di Biagio et al., 2014a, b). Global- and regional-scale
mapping of dust absorption remains limited and more information is required
(Samset et al., 2018).
Aerosol absorption is represented by both the imaginary part (k) of the
complex refractive index (m=n-ik) of its constituent material and by the
single-scattering albedo (SSA; i.e., the ratio of the scattering to
extinction coefficient) of the particle population, as well as by the mass
absorption efficiency (MAE; m2 g-1), i.e., the aerosol
absorption coefficient per unit mass concentration.
In the shortwave spectral range, absorption by dust accounts for up to
∼10 %–20 % of its total extinction. Dust absorption is
highest in the ultraviolet–visible (UV–Vis) and almost nil towards the near infrared (IR) (Cattrall et al.,
2003; Redmond et al., 2010) due to the combined contribution of large
particles in the size distribution and the dust mineralogy, notably the
presence of iron oxides (Karickhoff and Bailey, 1973; Lafon et al., 2006;
Derimian et al., 2008; Moosmüller et al., 2012; Formenti et al., 2014a, b; Engelbrecht et al., 2016; Caponi et al., 2017). The mineralogy of
airborne mineral dust varies according to that of the parent soils (Nickovic
et al., 2012; Journet et al., 2014). Consequently, dust aerosols of
different origins should be more or less absorbing in the SW and have
a different imaginary spectral refractive index and SSA. Field and laboratory
measurements, including ground-based and spaceborne remote sensing, show
that k varies at a regional scale by almost 2 orders of magnitude
(0.0001–0.008 at 550 nm) with corresponding SSAs between 0.80 and 0.99 at
550 nm (Volz, 1972; Patterson et al., 1977; Shettle and Fenn, 1979; Dubovik
et al., 2002; Haywood et al., 2003; Sinyuk et al., 2003; Linke et al., 2006;
Osborne et al., 2008; Müller et al., 2009; Otto et al., 2009; Petzold et
al., 2009; Schladitz et al., 2009; McConnell et al., 2010; Formenti et al.,
2011; Wagner et al., 2012; Ryder et al., 2013a; Engelbrecht et al., 2016;
Rocha-Lima et al., 2018). Despite some variability being instrumental or
analytical (differences in the sampled size fraction or in the method used
to retrieve optical parameters), geographic differences persist when the
same measurement approach and retrieval method are applied, e.g., in AERONET
inversions, supporting the dependence of dust k and SSA on its origin
(Dubovik et al., 2002; Koven and Fung, 2006; Su and Toon, 2011). In
contrast, the real part (n) of the dust refractive index, mostly related to
particle scattering, is less variable, with values between 1.47 and 1.56 at 550 nm (e.g., Volz, 1972; Patterson et al., 1977; Balkanski et al., 2007;
Petzold et al., 2009).
Differences in k or SSA caused by the spatial variability of the iron content
may affect the sign of the dust radiative effect (heating vs. cooling) (Liao
and Seinfeld, 1998; Claquin et al., 1999; Miller et al., 2014) and its
global and regional implications (Myhre and Stordal, 2001; Colarco et al.,
2014; Das et al., 2015; Jin et al., 2016; Bangalath and Stenchikov, 2016;
Strong et al., 2018). The direct radiative effect of dust has a strong
impact on the western African monsoon (Yoshioka et al., 2007; Konaré et
al., 2008) and the Indian summer monsoon (Vinoj et al., 2014; Das et al.,
2015; Jin et al., 2016). However, there is no consensus on whether dust
increases or decreases precipitation over these regions (Solmon et al.,
2008; Jin et al., 2016; Strong et al., 2018). As an example, Solmon et al. (2008) indicate that dust reduces precipitation over most of the Sahelian
region but increases it over the northern Sahel–southern Sahara. This
pattern is, however, very sensitive to the dust absorbing properties, and a
decrease of a few percent in dust absorption may even cancel out the increase
in precipitation over the Sahel. Similarly, Jin et al. (2016) show that by
varying k from zero to 0.008 at 600 nm (i.e., the highest value currently
used in models) the dust effect on the Indian summer monsoon may shift from
negative (reduction of precipitation) to positive (increase in
precipitation) values.
In spite of this sensitivity, present climate models adopt a
globally constant spectral complex refractive index (and SSA) for dust and
hence still implicitly assume the same dust mineralogical composition at the
global scale. This is mainly due to the lack of a globally consistent
dataset providing information on the geographical variability of the dust
scattering and absorption properties (e.g., Samset et al., 2018). Reference
values for the refractive index are usually taken from Volz (1972),
Patterson et al. (1977), D'Almeida et al. (1991), Shettle and Fenn (1979),
Sokolik et al. (1993), Sinyuk et al. (2003), or OPAC (Optical Properties of
Aerosols and Clouds; Hess et al., 1998; Koepke et al., 2015). A
parameterization of the spectrally resolved dust refractive index as a
function of the mineralogical composition of the particles is desirable to
replace the globally constant values in current climate models, in
particular for those models that started to incorporate the representation
of dust mineralogy into their schemes (Scanza et al., 2015; Perlwitz et al.,
2015a, b).
Improving our knowledge of the spectral SW refractive index of mineral dust
and its relation to particle composition (henceforth origin) is also key for
the detection of dust aerosols in the atmosphere and the quantification of
its mass loading, as well as total or absorption spectral optical depth from active
and passive remote sensing (e.g., Ridley et al., 2016). As an example, the
retrieval of the dust SSA and optical depth over bright desert surfaces with
the MODIS (Moderate Imaging Resolution Spectroradiometer) Deep Blue
algorithm (Hsu et al., 2004) applies the critical surface reflectance method
(Kaufman, 1987) to retrieve dust properties from measured top-of-atmosphere
(TOA) spectral reflectance. This algorithm depends critically on a priori
information on the spectral refractive index (Kaufman et al., 2001; Yoshida
et al., 2013). Similarly, active remote sensing techniques (lidar, light
detection and ranging) require knowledge of the
extinction-to-backscatter ratio (the lidar ratio), which is also a strong
function of the complex index of the refraction or SSA of the aerosol particles
(e.g., Gasteiger et al., 2011; Shin et al., 2018). Gasteiger et al. (2011)
have shown that a 5 % change in the SSA at 532 nm can modify by up
to 20 % the lidar ratio of dust, which means a 20 % change in the
estimated profile of the dust extinction coefficient and retrieved optical
depth from lidar measurements.
In this paper we address these issues by reporting a new laboratory
investigation of the shortwave refractive index and SSA of dust from various
source regions worldwide in the framework of the RED-DUST project (Di
Biagio et al., 2017a; hereafter DB17; Caponi et al., 2017; hereafter C17).
Dust optical properties at discrete wavelengths between 370 and 950 nm are
derived in conjunction with the particle elemental and mineralogical
composition, including total elemental iron and iron oxides. We investigate
the relationship of k and SSA to the iron content to provide a
parameterization of the dust absorption as a function of its mineralogy,
which can be applied to climate models. The dependence of dust absorption on
the particle coarse size fraction is also investigated to evaluate the
change in dust absorption with atmospheric transport time.
Experimental setup and instrumentation
As previously described in DB17 and C17, all experiments discussed here
were conducted in the 4.2 m3 stainless-steel CESAM chamber (French
acronym for Experimental Multiphasic Atmospheric Simulation Chamber) (Wang
et al., 2011). Mineral dust aerosols were generated through the mechanical shaking of
parent soils using about 15 g of soil sample (first sieved to < 1000 µm and then dried at 100 ∘C) placed in a 1 L Büchner
flask and shaken for about 30 min at 100 Hz by means of a sieve shaker
(Retsch AS200). The dust suspension in the flask was injected into the
chamber by flushing with N2 at 10 L min-1 for about 10–15 min.
After injection in the chamber, the largest fraction of the dust aerosol
(> 1.5 µm diameter) remained in suspension for
approximately 60 to 120 min thanks to a four-blade stainless-steel fan
located at the bottom of the chamber, which also ensured homogeneous
conditions within the chamber volume. The submicron dust fraction instead
remained constant with time during the experiments (see Sect. 4.1.1). The
evolution of the physicochemical and optical properties of the suspended
dust was measured by different instruments connected to the chamber. The
spectral particle volume dry scattering (βsca) and absorption
(βabs) coefficients were respectively measured by a
three-wavelength nephelometer (TSI Inc. model 3563, operating at 450, 550, and
700 nm; 2 L min-1 flow rate, 2 s time resolution) and a seven-wavelength
aethalometer (Magee Sci. model AE31, operating at 370, 470, 520, 590, 660,
880, and 950 nm; 2 L min-1 flow rate, 2 min time resolution). The size
distribution of aerosols was measured by means of a scanning mobility
particle sizer (SMPS, TSI, DMA model 3080, CPC model 3772; mobility diameter
range 0.019–0.882 µm; 2.0 and 0.2 L min-1 sheath–aerosol flow
rates, 135 s time resolution), a WELAS optical particle counter (OPC)
(PALAS, model 2000, white light source between 0.35 and 0.70 µm;
optical-equivalent diameter range 0.58–40.7 µm; 2 L min-1 flow
rate, 1 min time resolution), and a SkyGrimm OPC (Grimm Inc., model 1.129,
0.655 µm operating wavelength; optical-equivalent diameter range
0.25–32 µm; 1.2 L min-1 flow rate, 6 s time resolution). Aerosol
elemental and mineralogical composition, including iron oxides, was derived
from the analysis of dust samples collected on polycarbonate filters (47 mm
diameter Nuclepore, Whatman, nominal pore size 0.4 µm) mounted in a
custom-made stainless-steel sample holder (operated at 6 L min-1) for
most of the duration of each experiment.
All instruments (size, SW optics, filters) sampled air from the chamber. To
equalize the airflow extracted by the different instruments, a particle-free
N2/O2 mixture airflow was continuously injected into the chamber.
Inlets for all extractive measurements consisted of a stainless-steel tube
located inside the CESAM chamber and an external connection of silicone tubing (TSI
Inc.) from the chamber to the instruments for a total length varying
between 0.4 and 1.2 m. As detailed in DB17 and shown in Fig. S1 in the
Supplement, the transmission efficiency due to aspiration and transmission
in the sampling lines as a function of particle diameter was estimated to
calculate the effective dust fraction sensed by each instrument, taking into
account the sampling flow rate, tubing diameter, tubing geometry, and
particle shape and density. For the nephelometer and the aethalometer, the
length of the sampling line from the intake point in the chamber to the
instrument entrance was about 1.2 m, which resulted in a 50 % cutoff of
the transmission efficiency at 3.9 µm particle geometric diameter and
100 % cutoff at 10 µm. For the filter sampling system, the length
of the sampling line of about 0.5 m resulted in a 50 % (100 %) cutoff at
6.5 µm (15 µm) particle geometric diameter (or 50 % cutoff at
10.6 µm aerodynamic diameter as indicated in C17; therefore,
compositional analyses refer to the PM10.6 size fraction). For the
WELAS, the only OPC considered for size distribution in the coarse fraction
(see Sect. 2.2), the 50 % (100 %) cutoff was reached for particles of 5 µm (8 µm) diameter.
Flowchart illustrating the procedure for data treatment and
retrieval of the physical and chemical (size, composition) as well as spectral optical
properties (single-scattering albedo (SSA) and complex refractive index) of
mineral dust aerosols. In red we mention the different corrections performed
and the values adopted in the calculations. The αs symbol in the scheme indicates the SAE450–550 and the SAE550–700 Ångström exponents used to extrapolate the nephelometer data at the aethalometer wavelengths.
All experiments were conducted at ambient temperature and relative humidity
< 2 %. In addition to overnight evacuation, the chamber was
manually cleaned between experiments to avoid contamination from remaining
dust. Background concentrations of aerosols in the chamber were less than
2.0 µg m-3 (that is, 102 to 105 times smaller than the
concentration of dust aerosols in suspension in the chamber during
experiments)
A flowchart of the procedure used to treat and combine optical, size, and
compositional data, as well as the algorithm for SSA and complex refractive index
retrieval, is shown in Fig. 1. Full details on the data treatment for size
distribution measurements and filter compositional data are provided in DB17
and C17, and in the following we only mention the main points of interest
for the present paper. Full details on the data treatment of the SW optical
data are provided in Sects. 2.1 and 3.
Measured and retrieved quantities and their estimated relative
uncertainties. For further details, refer to Sect. 2, as well DB17 and C17.
Parameter Time resolutionRelative uncertaintyUncertainty calculationCommentsOptical SWScattering co-efficient at 450,550, and 700 nm, βsca(λ)10 min data5 %–12 %Quadratic combination of photon counting and gas calibration uncertainty (5 %), angular corrections uncertainty (< 5 %) and standard deviation (SD) over 10 min intervals (2 %–10 %).The uncertainty on βsca(λ) usually decreases with increasing dust residence time in the chamber as a result of the reduction of the coarse component.Absorption co- efficient at 370,470, 520, 590,660, 880, and950 nm, βabs(λ)10 min data22 %–30 % at370 nm 23 %–87 % at950 nmError propagation formula* on Eq. (2), considering the uncertainties on βATT (λ) from 10 min fitting procedure (error propagation formula* on Eq. (1), -20 %), and uncertainties on α(λ) (1 %), βsca (λ) (5 %–12 %), Cref (10 %), and R (1 %–10 %).Extinction coef-ficient, βext(λ)=βsca(λ)+βabs(λ)10 min data-25 %Sum of βsca(λ) and βabs(λ) uncertainties.Single-scat-tering albedo,SSA (λ)=βsca(λ)/(βsca(λ)+βabs(λ))10 min data9 %–12 %Error propagation formula* considering single uncertainties on βsca(λ) and βabs(λ).Single-scat- tering albedo,SSA (λ)=(1+1/m(λ))-1Experiment averaged1 %–12 % at 370 nm 1 %–3 % at 950 nmError propagation formula* on Eq. (6) considering the uncertainty on m(λ), i.e., the slope of the linear fit between βsca(λ) and βabs(λ) over the whole duration of each experiment.Complex re- fractive index (n-ik)10 min data< 5 % for n < 50 % for kDeviations of the values of n and k retrieved in the sensitivity study (see Sect. 3.2) with respect to those obtained in the first inversions were assumed to correspond to the 1 standard deviation uncertainty to 10 min retrieved values.Complex re- fractive index(n-ik)Experiment averaged< 8 % for n 13 %–75 % for kQuadratic combination of the SD of n and k over the experiment and the deviation on the experiment-averaged values between those obtained from central inversions and inversions using input data ± their uncertainty.Size distributionSMPS geomet- rical diameter(Dg), Dg=Dm/χ–∼6 %Error propagation formula* considering the uncertainty on the estimated shape factor χ (∼6 %)The electrical mobility to geometrical diameter conversion was performed by assuming for dust a dynamic shape factor of 1.75±0.10, as determined by SMPS–SkyGrimm comparison in their overlapping range (see DB17).
Continued.
Parameter Time resolutionRelative uncertaintyUncertainty calculationCommentsSkyGrimm geometrical diameter (Dg)–< 15.2 %SD of the Dg values obtained for different refractive indices values used in the optical to geometrical conversion.The conversion of optical to geometrical diameters for the SkyGrimm and the WELAS was performed by taking into account the visible complex refractive index of dust aerosols. Optical calculations were computed at the SkyGrimm operating wavelength (0.655 µm) and over the spectral range of the WELAS (0.35 to 0.7 µm) using Mie theory for spherical particles by fixing n at 1.47, 1.50, and 1.53 and by varying k in steps of 0.001 between 0.001 and 0.005. Then Dg is set at the mean ±1 SD of the values obtained for the different values of n and k (see DB17). The refractive index is assumed to be constant with particle size and wavelength independent.WELAS geometrical diameter(Dg)–< 7 %The same as for the SkyGrimm.(dN/dlogD)SWoptics10 min data∼20 %–90 %Error propagation formula* considering the dN/dlogDg SD over 10 min and the uncertainty on particle loss function along sampling tubes L(Dg) (∼50 % at 2 µm, ∼10 % at 8 µm).The uncertainty of L(Dg) was estimated with a sensitivity study by varying the values of the input parameters to the Particle Loss Calculator software (von der Weiden et al., 2009).Deff,fine10 min data< 5 %Deviation obtained by repeating the calculations by using the size distribution ± its uncertainty.Deff,coarse10 min data5 %–40 %Mineralogical compositionElemental ironmass concentra- tion (MCFe%)Experiment averaged 10 %Uncertainties calculated asdiscussed in DB17 and C17.Iron oxide mass concentration (MCFe-ox%)Experiment averaged15 %Goethite massconcentration (MCGoet%)Experiment averaged< 10 %Hematite massconcentration (MCHem%)Experiment averaged< 10 %
*σf=∑i=1n∂f∂xiσxi2.
The optical and size datasets were acquired at different temporal
resolutions and then averaged over compatible 10 min intervals, whereas the
compositional data represent the experiment integral. The SSA and complex
refractive index data were retrieved both at 10 min resolution and as
experiment averages to relate them to both size and compositional data.
Table 1 summarizes the uncertainties on the measured and derived parameters
described in the following.
The aerosol scattering coefficients (βsca) at 450, 550, and 700 nm were measured by the nephelometer at angles between 7 and
170∘ and needed to be corrected for the restricted field of view of
the instrument (truncation correction) to retrieve βsca at
0–180∘. The truncation correction factor
(Ctrunc), i.e., the ratio of the βsca at 0–180 and 7–170∘, was estimated by Mie
calculations for homogeneous spherical particles using the size distribution
measured simultaneously behind SW inlets (see Sect. 2.2). In the
calculations, the real part of the complex refractive index of dust was
assumed to be wavelength independent and fixed at a value of 1.53, while the
imaginary part was set to 0.003 at 450 and 550 nm and to 0.001 at 700 nm,
according to preexisting information (Sinyuk et al., 2003; Schladitz et
al., 2009; Formenti et al., 2011; Rocha-Lima et al., 2018). For the
different dust samples, Ctrunc ranged between 1.2 and 1.7 and decreased
with wavelength and the dust residence time in the chamber, following the
relative importance of the coarse component in the dust population. The
uncertainty on Ctrunc, calculated by repeating the optical calculations
by using the size distribution of dust within its error bars as input to the
optical code, is less than ±5 % at all wavelengths. In order to
assess the consistency of the derived truncation correction, we made a
sensitivity study in which we recalculated Ctrunc by varying the
refractive index used as input to the Mie calculations in the range of n and
k values obtained in this study (i.e., values at 10 % and 90 %
as reported in Table 5 for the whole dataset; that is, n between
1.49 and 1.54 and k between 0.001 and 0.006 at 450, 550, and 700 nm). The
results of this sensitivity study indicate that, for a fixed dust size
distribution, the truncation correction Ctrunc varies less than 1 %
for n between 1.49 and 1.54 and < 5 % for k between 0.001 and
0.006, so it is quite insensitive to the exact assumed n and k
values.
Once corrected for truncation, the spectral βsca was
extrapolated at the aethalometer wavelengths. With this aim, the scattering
Ångström exponents, SAE450–550 and SAE550–700, were
calculated as the linear fit of βsca vs. λ at 450–550 nm
and 550–700 nm, respectively. The SAE450–550 and SAE550–700
coefficients were used to extrapolate βsca at wavelengths
respectively lower and higher than 550 nm. Extrapolated βsca
values were used to derive an average SAE of dust for the entire
investigated spectral range.
Aerosol absorption coefficient
The aerosol absorption coefficient (βabs) at 370, 470, 520, 590,
660, 880, and 950 nm was retrieved from aethalometer measurements. The
aethalometer measures the attenuation (ATT) through an aerosol-laden quartz
filter, related to the spectral attenuation coefficient (βATT)
as
βATTλ=ΔATTλΔtAV,
where A is the area of the aerosol collection spot (0.5±0.1) cm2 and V the air sample flow rate (0.002 m3 min-1). The
slope ΔATTλΔt is the linear fit of the measured attenuation as a function of
time calculated over 10 min intervals. The spectral attenuation coefficient
was converted into an absorption coefficient βabs following the
formula by Collaud Coen et al. (2010):
βabsλ=βATTλ-α(λ)βsca(λ)CrefR(λ).
The α(λ)βsca(λ) term accounts for the
fraction of the measured attenuation due to side and backward scattering and
not to light absorption. The Collaud Coen correction scheme has been
recently shown to yield quite accurate values of the absorption coefficients
and absorption Ångström exponents from aethalometer data (Saturno et
al., 2017). The value of α(λ) was calculated with the formula
by Arnott et al. (2005) and varied between 0.002 and 0.02 (< ±1 % from formal error propagation on the Arnott formula), while βsca(λ) is the scattering coefficient from the nephelometer
extrapolated to the aethalometer wavelengths. The Cref term accounts for
multiple scattering by the filter fibers, aerosol laden or not. Its spectral
value, obtained by the linear extrapolation of Cref at 450 and 660 nm
estimated for mineral dust by Di Biagio et al. (2017b), varied between 4.30
at 370 nm and 3.32 at 950 nm. We assume for the extrapolated Cref an
uncertainty of ±10 % as estimated in Di Biagio et al. (2017b). The
correction factor, R, accounts for the decrease in the aethalometer
sensitivity with the increase in the aerosol filter loading. The value of
R depends on the absorptivity properties of the sampled aerosol and can be
calculated as a function of the particle SSA. In this study, we calculated
R by estimating a first-guess SSA∗ as the ratio of the
nephelometer-corrected βsca and βext obtained as the
sum of βsca and the βabs non-corrected for filter
loading effect. The R was estimated by using the Collaud Coen et al. (2010)
formulation. For the range of estimated SSA∗ (about 0.60 to 0.99),
R varied between 0.5 and 1.0 (±1 %–10 %).
The absorption Ångström exponent (AAE) was calculated as the power-law
fit of βabs versus λ.
Due to an instrumental problem, aethalometer data were not always available,
with a typical 30 min interruption usually 10 to 30 min after the
beginning of experiments.
Size distribution
The aerosol number size distribution was obtained from SMPS, WELAS, and
SkyGrimm measurements over different diameter ranges. The measured
electrical mobility and optical equivalent diameters from the SMPS and the
OPCs were first converted into geometrical diameters (Dg) as described
in DB17 and summarized in Table 1. The OPC conversion assumes a dust
complex refractive index that in our study was set in the range 1.47–1.53
for n and 0.001–0.005 for k for both the SkyGrimm and the WELAS (following
DB17; for more details see Table 1). After conversion, the estimated
Dg range was 0.01–0.50 µm for the SMPS, 0.65–73.0 µm for
the WELAS, and 0.29–68.2 µm for the SkyGrimm. Due to a calibration
issue, data for the SkyGrimm in the range Dg > 1 µm were discarded so that the WELAS is the only instrument
considered in the super-micron range. A very low counting efficiency was
observed for the WELAS below 1 µm, and data in this size range were
also discarded.
The SMPS, WELAS, and SkyGrimm data were combined, as detailed in DB17, to
obtain the full size distribution of the dust aerosols suspended in the
CESAM chamber, (dN/dlogDg)CESAM, and the size distribution behind SW optical
instruments inlets, (dN/dlogDg)SWoptics, after taking into account particle
losses along sampling lines (see Fig. S1). As
previously discussed, due to the particle losses in the sampling line from
the chamber to the nephelometer and the aethalometer, the
(dN/dlogDg)SWoptics size distribution is cut at 10 µm, so no particles
above this diameter reach the SW instruments.
The measured size distributions, (dN/dlogDg)CESAM and
(dN/dlogDg)SWoptics, were used to estimate the mass concentration of
aerosols and their effective diameter (Deff) in the CESAM chamber and
behind the SW instrument inlets as
3mass concentration=∫π6Dg3dNdlogDgρ⋅dlogDg,4Deff=∫Dg3dNdlogDgdlogDg∫Dg2dNdlogDgdlogDg.
The effective dust density ρ in Eq. (3) was set at 2.5 g cm-3, a
value that is approximately in the middle of the range of desert dust
densities reported in the literature, i.e., 2.1–2.75 g cm-3 (Maring et
al., 2000; Iwasaka et al., 2003; Reid et al., 2003). The effective diameter
was evaluated separately for the fine and coarse fractions of dust by
integrating Eq. (4) for diameters ≤1µm (Deff,fine) and
> 1 µm (Deff,coarse), respectively. For
Deff,coarse the upper limit of the calculation is 10 µm when
calculated from (dN/dlogDg)SWoptics, i.e., measured behind the SW inlets.
The dust size distribution, (dN/dlogD)SWoptics, measured at each 10 min time step
for each sample was fitted with a sum of five lognormal functions to smooth
data inhomogeneities linked to the different instrument operating
principles and artifacts. Fitting was performed using the
Levenberg–Marquardt algorithm. For each mode, the parameters of the
lognormal functions, i.e., the total number concentration (Ni), the
geometric median diameter (Dg,i), and the geometric standard deviation
of the distribution (σi), were retrieved. The uncertainties in
the retrieved parameters were estimated by repeating the fit using size data
within their uncertainties. The resulting parameters of the fits at the peak
of the injection in the chamber are reported in Table S1, and an example of
size fitting is shown in Fig. S2.
The procedure described here to estimate (dN/dlogDg)CESAM and
(dN/dlogDg)SWoptics implies that assumptions are made on the values of n and
k to correct OPC data, and this may introduce a circularity in the estimates
of the refractive index of dust that use (dN/dlogDg)SWoptics as input in
optical calculations (see Sect. 3.2). In order to analyze the dependence of
the results on this assumption, we made a sensitivity calculation by varying
the values of n and k used for OPC corrections within the range of values
retrieved in this study (10 % and 90 % in Table 4, i.e.,
1.49–1.54 for n and 0.001–0.006 for k). We concluded that changing n and k in
this range has a very low impact on the retrieved number size distribution
behind the SW inlets (dN/dlogDg)SWoptics compared to the original
assumptions made in our calculations (< 5 % changes in the
retrieved size number distribution at the different diameters between the
original correction and the correction by varying n and k). This is due to the
fact that when changing Dg due to changes in the n and k in the OPC
correction, the loss function also modifies to values corresponding to the
new Dg. Given that the loss function increases–decreases for
increasing–decreasing Dg, the combined changes in Dg and the loss
function compensate so that the net number concentration behind the SW
inlets varies less than a few percent. These results therefore suggest that
the procedure to estimate the complex refractive index of dust is nearly
independent of the assumed OPC correction.
Other sources of uncertainties are linked to the spherical assumption to
perform the optical to geometrical diameter conversion (discussed in Sect. 3.3) as well as those due to Mie resonance oscillations of the calculated
scattering intensities. Concerning Mie resonances, a sensitivity study was
performed varying the size resolution of our calculations (high–low diameter
resolution in the calculations to have a better–worse reproduction of Mie
resonance oscillations) and show that Mie resonances impact the optical to
geometrical diameter correction by less than 1 %.
Dust elemental and mineralogical composition and iron content
The elemental and mineralogical composition of the dust aerosols in the
PM10.6 size fraction was estimated by combining different techniques:
X-ray diffraction (XRD; Panalytical model Empyrean diffractometer) to
estimate the particle mineralogical composition in terms of clays, quartz,
calcite, dolomite, gypsum, and feldspars; wavelength dispersive X-ray
fluorescence (WD-XRF; Panalytical PW-2404 spectrometer) to determine the
dust elemental composition (Na, Mg, Al, Si, P, K, Ca, Ti, Fe); and X-ray
absorption near-edge structure (XANES) to retrieve the content of iron
oxides and their speciation between hematite and goethite. The dust mass
collected on Nuclepore filters during the experiments varied between 0.3 and
6 mg m-3 as calculated from elemental concentrations according to Lide (1992).
Full details on the XRD, WD-XRF, and XANES measurements and data analysis
are provided in DB17 and C17. In this study, we discuss the dust elemental
iron mass concentration, MCFe%, i.e., the percent mass of elemental
iron with respect to the total dust mass concentration, and the iron oxides
mass concentration, MCFe-ox%, i.e., the percent mass fraction of iron
oxides with respect to the total dust mass concentration, estimated as the
sum of goethite (MCGoet%) and hematite (MCHem%) species.
Strategy for data analysisCalculation of the spectral extinction coefficient and SSA from
scattering and absorption coefficients
The spectral scattering and absorption coefficients, βsca(λ) and βabs(λ), measured by the nephelometer
and the aethalometer were used to estimate 10 min averages of the spectral
extinction coefficient, βext(λ), at the 7λ of
the aethalometer between 370 and 950 nm as
βext(λ)=βabs(λ)+βsca(λ).
The extinction Ångström exponent (EAE) was calculated as the
power-law fit of βext versus λ.
The spectral single-scattering albedo of dust at 10 min resolution
(SSA10min) was retrieved as
SSA10minλ=βsca(λ)βext(λ).
The experiment-averaged SSA (λ) was calculated for each soil type
based on the following formula (Moosmüller et al., 2012):
SSAλ=1+1mλ-1,
where m(λ) represents the slope of the linear fit between the 10 min
averages of βsca(λ) and βabs(λ)
measured along the whole duration of each experiment. An example of βsca(λ) versus βabs(λ) fitting to retrieve
the spectral SSA is shown in Fig. S3. The correlation
coefficient R2 of the βsca(λ) versus βabs(λ) fit usually ranges between 0.97 and 1 at all
wavelengths. As will be discussed later in the paper, the single-scattering
albedo of dust depends on the particle coarse size fraction, and during our
experiments SSA10min was not derived continuously for the different
samples due to the aethalometer measurement interruptions. The application
of Eq. (7) avoids any bias in the calculated averaged SSA for different
soils due to size effects. For two of the analyzed samples (Tunisia and
Namib-2), however, the linear fitting procedure was not applicable due to
the fact that only two absorption measurements and one absorption measurement, respectively, from
the aethalometer were available just after the peak of the injection, with
no data afterwards. Average SSA data for Tunisia were thus estimated as the
mean of the two available SSA10min data points, while the single
SSA10min measurement at the peak of the injection was reported for
Namib-2. This difference in time sampling should be kept in mind when
comparing data for these two samples to the rest of the dataset.
Retrieval of the spectral complex refractive index
An optical calculation was performed to estimate the complex refractive
index (m=n-ik) of dust aerosols based on optical and size data. The retrieval
algorithm consisted of recalculating the spectral scattering βsca(λ) and absorption βabs(λ) coefficients
measured at each 10 min interval by using the fitted
(dN/dlogD)SWoptics size distribution as input and by varying the real and
imaginary parts of the complex refractive index in the calculations until
the best agreement between measurements and calculations was found. At each
wavelength the root mean square deviation (RMSD) was calculated as
RMSD(λ)=βsca,measured(λ)-βsca,calculated(λ)(n,k)βsca,calculated(λ)(n,k)2+βabs,measured(λ)-βabs,calculated(λ)(n,k)βabs,calculated(λ)(n,k)2‾.
The RMSD was minimized at each wavelength to obtain n–k pairs that most
closely reproduce the measured scattering and absorption coefficients.
Optical calculations were performed at the seven wavelengths of the aethalometer
between 370 and 950 nm using Mie theory. In the calculations, the real part
of the refractive index was varied in the range 1.40–1.60 at steps of 0.01,
while the imaginary part was varied in the range 0.0001–0.050 at steps of
0.0001. For each sample, this resulted in 10 500 computations per wavelength
and per 10 min time step. The uncertainty on the real and imaginary parts of
the refractive index was estimated with a sensitivity study. For this
purpose, the values of n and k were also obtained by using as input the
observed βsca(λ), βabs(λ), and
(dN/dlogD)SWoptics, plus or minus 1 SD on their measurement.
The deviations of the values of n and k retrieved in the sensitivity study
with respect to those obtained in the first inversions were assumed to
correspond to the 1 SD uncertainty of 10 min retrieved
values.
Experiment-averaged values of the spectral n and k were estimated as the
average of single n and k values retrieved at 10 min steps (indicated
as n10min and k10min). In fact, differently from the SSA,
the refractive index did not seem to depend on the particle coarse size
fraction (Sect. 4.5).
A control experiment was performed with submicron ammonium sulfate aerosols
(see DB17 and Fig. S4) with the aim of validating the proposed
methodology to estimate the aerosol complex refractive index for a
nonabsorbing aerosol type. For ammonium sulfate particles with a
mono-modal size distribution centered at 0.06 µm, as measured with
the SMPS, the retrieved real part of the refractive index was 1.56 (±0.01) in the 450–700 nm wavelength range, as expected from the literature (Toon
et al., 1976; Flores et al., 2009; Denjean et al., 2014).
Assumptions on the retrieval of SSA and complex refractive index
The approach used to retrieve the SSA and the complex refractive index of
dust and the accuracy of the results depend on the accuracy of the input
data and the assumptions in the optical calculations. We discuss here two
points of the applied procedure, in part already mentioned in the previous
paragraphs.
The size distribution from OPCs and also the scattering coefficient from
the nephelometer used as input to the n and k retrieval procedure and SSA
calculation depend more or less directly on the dust refractive index. These
instruments, in fact, need to be corrected for instrumental artifacts, and
these corrections require a priori knowledge of the n and k, which in our
approach were set to fixed values (1.47–1.53 for n and 0.001–0.005 for k for
OPC optical to geometrical diameter conversion; 1.53 for n and
0.001–0.003 for k for nephelometer truncation correction). This choice may in
principle introduce a certain degree of uncertainty and circularity into the
derived n, k, and SSA for dust. Nonetheless, we note that the range of
refractive index values used to correct OPCs and nephelometer data falls in
the range of variability of the refractive index values obtained in this
study (see Sect. 4.3), which suggests that the values used for the
corrections are appropriate. Additionally, as previously discussed, both the
size distribution (dN/dlogDg)SWoptics and the scattering coefficient are not
very sensitive to the assumptions about n and k used for the calculations
(less than 5 % changes in both the number size distribution behind SW
inlets and the scattering coefficient from changing n and k within the range
of estimated values in this study) which further demonstrates the robustness
of the proposed approach.
The retrieval procedure for n and k, as well as the calculations for OPC
optical-to-geometrical diameter and the nephelometer truncation correction,
simplifies the nonspherical heterogeneous dust aerosols (e.g., Chou et al.,
2008; Okada et al., 2001; Nousiainen and Kandler, 2015) into homogeneous
spherical particles that can be represented by Mie theory. In the present
study, we decided not to use a more advanced shape-representing theory for
three main reasons. First, the spherical model has been shown to produce
only moderate errors when computing angular-integrated quantities
(Mishchenko et al., 1995; Otto et al., 2009; Sorribas et al., 2015) such as
those we calculate in this study to retrieve the OPC and truncation
corrections and for n and k retrieval. For instance, Sorribas et al. (2015)
showed that using a spheroidal model has a limited effect on the truncation
correction. These authors estimated that using a spheroidal model permits a 4 % to 13 % improvement in the agreement between the modeled and measured spectral
scattering coefficient at 450–700 nm but only for super-micron particles.
Conversely, for submicron dust the spherical approximation is better suited
than the spheroidal model to reproduce the scattering coefficients by the
nephelometer. The study by Mogili et al. (2007) also found an excellent
agreement between measured shortwave extinction spectra and those calculated
from Mie theory simulations for dust minerals, supporting the use of Mie
theory for dust optical modeling. On the other side, other studies point to
the need for a nonspherical assumption to improve the modeling of dust
optical properties (e.g., Otto et al., 2009). Second, we used Mie theory for
the sake of comparison with the large majority of previous field and
laboratory data published so far, which have used calculations with the
spherical approximation. Third, the shape distribution and morphology of the
dust samples were not measured during experiments. Improper assumptions on
the particle shape and morphology may induce even larger errors than using
Mie theory, in particular for super-micron aerosols (Kalashnikova and
Sokolik, 2004; Nousiainen and Kandler, 2015). It should be pointed out,
however, that dust is usually assumed to be spherical in global climate
models (e.g., Myhre and Stordal, 2001; Balkanski et al., 2007; Jin et al.,
2016), and different studies still show contradictory results on the true
impact of dust nonsphericity on radiative fluxes and heating rates from
global model simulations (Mishchenko et al., 1995; Yi et al., 2011;
Räisänen et al., 2012; Colarco et al., 2014). On the other hand,
shape effects can be important for the retrieval of aerosol properties from
remote sensing techniques using spectral, angular, and polarized reflectance
measurements (e.g., Feng et al., 2009). In synthesis, accounting for shape
effects is still controversial for dust modeling and also a complex issue
beyond the scope of this paper. Thus, while we acknowledge the potential
uncertainties induced by spherical assumptions in our study, we do not
quantify the overall impact of this assumption on our results here.
Summary of information on the soil samples and sediments used in
this study.
Geographical areaSampleCoordinatesDesert areaNorthern Africa –Tunisia33.02∘ N, 10.67∘ EMaounaSaharaMorocco31.97∘ N, 3.28∘ WEast of Ksar SahliLibya27.01∘ N, 14.50∘ ESebhaAlgeria23.95∘ N, 5.47∘ ETi-n-TekraouitMauritania20.16∘ N, 12.33∘ WEast of Aouinet NchirSahelNiger13.52∘ N, 2.63∘ EBanizoumbouMali17.62∘ N, 4.29∘ WDar el BeidaBodélé17.23∘ N, 19.03∘ EBodélé depressionEastern AfricaEthiopia7.50∘ N, 38.65∘ ELake Shala National Parkand theSaudi Arabia27.49∘ N, 41.98∘ ENefudMiddle EastKuwait29.42∘ N, 47.69∘ EKuwaitiEastern AsiaGobi39.43∘ N, 105.67∘ EGobiTaklimakan41.83∘ N, 85.88∘ ETaklimakanNorth AmericaArizona33.15∘ N, 112.08∘ WSonoranSouth AmericaAtacama23.72∘ S, 70.40∘ WAtacamaPatagonia50.26∘ S, 71.50∘ WPatagoniaSouthern AfricaNamib-121.24∘ S, 14.99∘ ENamibNamib-219.00∘ S, 13.00∘ ENamibAustraliaAustralia31.33∘ S, 140.33∘ EStrzeleckiResults
A total of 19 soil samples from different desert areas in northern Africa, Sahel,
eastern Africa and the Middle East, eastern Asia, North
America, South America, southern Africa, and Australia were selected for
experiments from a collection of 137 soil samples from source areas
worldwide. The main information on the provenance of these soils is provided
in Table 2. The 19 selected soils, the same as analyzed in DB17,
represent the major dust source regions depicted in Ginoux et al. (2012).
Amongst the database of 137 samples from all the world regions that
constitute significant dust emitters, the range in mineralogical composition obtained for the chosen samples represents the largest variability in iron oxide contents that
can be found worldwide. This is illustrated in Fig. 2 where we represent the
variability of hematite and goethite content in the 19 selected soils
and compare it with the range of variability of the global desert soils from
the database of Journet et al. (2014).
Box-and-whisker plot showing the full variability of hematite and
goethite mass fractions in the soils for the clay-sized (< 2 µm diameter) and silt-sized (< 60 µm diameter) fractions as
retrieved from the global soil mineralogical database by Journet et al. (2014). The box-and-whisker plot includes data for the nine desert source
areas depicted in Ginoux et al. (2012) and DB17 (northern Africa, Sahel,
eastern Africa and the Middle East, central Asia, eastern Asia, North
America, South America, southern Africa, and Australia). Dots indicate
hematite and goethite content in clay-sized and silt-sized soils (always
from Journet et al.) extracted in correspondence to the geographical
coordinates at which the 19 soils used in the CESAM experiments were
collected. The Journet et al. database assumes that the iron oxides in the
silt fraction consist only of goethite.
Physical and chemical properties of analyzed dust samplesDust mass concentration and size distribution
Figure 3 shows a typical example of a time series of aerosol mass
concentration and effective fine and coarse diameters measured inside the
CESAM chamber and behind the SW instruments inlets during the experiments,
as well as the corresponding βsca and βabs at 370 nm. The figure shows the rapid increase in the mass concentration within
CESAM during dust injection in the chamber and its subsequent decrease
during the experiments due to both size-selective gravitational settling,
occurring mostly within the first 30 min of the experiments, and dilution by
sampling. The scattering and absorption coefficients of dust decrease with
time after injection in tandem with the decrease in the mass concentration
and the size-dependent depletion in the chamber. The dust mass concentration
inside CESAM at the peak of the injection is between 2 (Mali)
and 310 mg m-3 (Bodélé) and falls to values between 0.9 (Mali) and 20 mg m-3 (Bodélé) behind the SW
instruments inlets. These values are comparable to those measured close to
sources during dust storms (Rajot et al., 2008; Kander et al., 2009). After
2 h, the dust mass concentration has decreased to values of 0.2 to 2.5 mg m-3 (inside CESAM) and 0.1 to 1.9 mg m-3 (behind the SW
inlets), as after medium- to long-range dust transport in the real
atmosphere (Weinzierl et al., 2011; Denjean et al., 2016b). This indicates
that in a 2 h experiment in CESAM it is possible to reproduce the
temporal changes in the dust mass load observed in the real atmosphere from
emission to medium- to long-range transport.
As the mass concentration, the effective diameter of the coarse fraction,
Deff,coarse, also rapidly decreases with time due the progressive
deposition of the coarsest particles in the chamber. For the various soils,
Deff,coarse varies in the range of 4–8 µm (peak of injection) to
3–4 µm (after 2 h) inside the CESAM chamber and in the range of
3–4 µm (peak of injection) to 2–3 µm (after 2 h) behind
the SW inlets. In contrast, Deff,fine remains quite constant during the
experiments, with a value between 0.6 and 0.7 µm for all soils. The
values of Deff,coarse obtained in this study inside the
CESAM chamber are in line with those measured close to African sources (4–12 µm; Rajot et al., 2008; Weinzierl et al., 2009; Ryder et al., 2013a)
and for dust transported across the Mediterranean (5–8 µm; Denjean et
al., 2016a). Conversely, the values of Deff,coarse behind the SW
instrument inlets are mostly in agreement with those reported for dust
transported at Cape Verde and across the Atlantic Ocean (∼3µm;
Maring et al., 2003; Müller et al., 2011; Denjean et al., 2016b). Our
values of Deff,fine are higher compared to values reported by
Denjean et al. (2016a) for dust aerosols transported over the Mediterranean
(0.2 to 0.5 µm), reflecting the fact that we analyze pure dust,
whereas these authors often encountered dust externally mixed with pollution
particles.
The comparison of Deff,coarse values suggests that while the size
distribution in CESAM is mostly representative of dust close to sources (see
DB17), the size measured behind the SW instrument inlets is mostly
representative of transport conditions. Figure 4 illustrates this point by
showing the volume size distributions of the generated dust aerosols at the
peak of injection seen by the SW optical instruments compared to the
average size of dust measured in CESAM (DB17) and field observations close
to sources (e.g., Niger) and after long-range transport (Cape Verde,
Suriname, Puerto Rico, and Barbados). The size distribution of dust inside
CESAM includes a coarse mode up to 50 µm and reproduces field
observations close to sources well, as shown in comparison to the Niger case. Due
to particle losses along tubes, particles above 10 µm diameter are
not seen by the SW instruments. The overall shape of the dust size
distribution sensed by the SW instruments is comparable to that measured
after atmospheric long-range transport, even if the fraction of particles
above 3.9 µm diameter, which is at the 50 % cutoff of the
transmission efficiency for the SW optical instruments, is significantly
underrepresented compared to observations (i.e., Betzer et al., 1988;
Formenti et al., 2001; Maring et al., 2003; Ryder et al., 2013b, 2018; Jeong
et al., 2014; Denjean et al., 2016b). It should be keep in mind that field data are also often affected by inlet restrictions so that they cannot
measure the whole coarse dust fraction (see Table 1 in Ryder et al., 2018).
The lowest cutoffs for field data shown in Fig. 4 are for the NAMMA and PRIDE
datasets and correspond to upper size limits at 5 and 10 µm in
diameter, respectively. These values being above our cutoff of 3.9 µm means that the comparison with our size dataset is meaningful within
the range of our measurements. Only the data from AER-D did not
suffer from significant inlet restrictions, thus leading to the observation
of giant dust particles up to tens of microns in the Saharan Air Layer off
the coasts of western Africa.
(a) Time series of the aerosol mass concentration (cross
symbols) and effective fine (Deff,fine, open dots) and coarse diameter
(Deff,coarse, open squares) measured inside the CESAM chamber (red
symbols) and at the input of the SW instruments (black symbols) for one
experiment (Morocco dust). (b) Time series of the scattering
βsca and absorption βabs coefficients at 370 nm for
the same experiment. Mass concentrations are reported as 6 s data, while
all other quantities are 10 min averages.
Chemical characterization of the dust aerosols in the
PM10.6 size fraction. Column 3 shows MCFe%, the fractional mass of
elemental iron with respect to the total dust mass concentration (±10 % relative uncertainty), and column 4 reports MCFe-ox%, the mass
fraction of iron oxides with respect to the total dust mass concentration
(±15 % relative uncertainty) and its speciation in hematite
MCHem% and goethite MCGoeth% (< ±10 %
relative uncertainty). The iron oxide measurements were not made on the
Taklimakan sample. Mean values and standard deviations (SDs) based on single-sample data are reported for the full dataset.
Geographical areaSampleMCFe%MCFe-ox%MCHem%MCGoet%Northern Africa –Tunisia4.12.21.21.1SaharaMorocco3.61.40.41.0Libya5.23.10.92.2Algeria6.62.71.41.4Mauritania8.13.33.30.0SahelNiger6.15.82.33.5Mali6.63.72.01.7Bodélé4.10.70.70.0Eastern AfricaEthiopia6.82.02.00.0and theSaudi Arabia3.82.61.80.8Middle EastKuwait5.01.51.50.0Eastern AsiaGobi4.80.90.90.0Taklimakan5.8–––North AmericaArizona5.31.51.50.0South AmericaAtacama4.71.61.60.0Patagonia5.11.50.90.6Southern AfricaNamib-12.41.10.80.3Namib-210.64.84.80.0AustraliaAustralia7.23.63.60.0Full dataset mean (SD)5.6 (1.9)2.4 (1.4)1.8 (1.1)0.7 (1.0)
Comparison of dust size distributions sensed by the SW optical
instruments (i.e., behind the SW instrument inlet
(dV/dlogDg)SWoptics), with field data for long-range-transported dust. The
thick black line represents the mean value of
(dV/dlogDg)SWoptics at the peak of the dust injection in CESAM for
experiments with the different samples. The grey shaded area indicates the
range of (dV/dlogDg)SWoptics for all samples. The dotted black line shows
the average of the dust size distribution at the peak of the injection
inside the CESAM chamber from DB17. Field data are from the following: Formenti et al. (2001) (CLAIRE campaign in Suriname, South America), Maring et al. (2003),
and Denjean et al. (2016b) (PRIDE and DUST-ATTACK campaigns in Puerto Rico,
Caraibes); Müller et al. (2011), Chen et al. (2011), and Ryder et al. (2018) (SAMUM2, NAMMA, and AER-D campaigns in Cape Verde, eastern Atlantic);
and Weinzierl et al. (2017) (SALTRACE campaign, data from Barbados). For
comparison, data taken close to the source in Niger from Formenti et al. (2011) during the AMMA campaign are also shown. SAL stands for Saharan Air
Layer. All data are reported as volume size distributions normalized at the
maximum. The different acronyms are spelled out as follows. AER-D: AERosol Properties – Dust; AMMA: African Monsoon Multidisciplinary Analysis; CLARE: Cooperative LBA Airborne Regional Experiment; DUST-ATTACK: Dust Aging and TransporT from Africa to the Caribbean; NAMMA: NASA African Monsoon Multidisciplinary Analysis; PRIDE: Puerto Rico Dust Experiment; SALTRACE: Saharan Aerosol Long–range Transport and Aerosol–Cloud–Interaction Experiment; SAMUM: Saharan Mineral Dust Experiment.
Iron and iron oxide dust content
Elemental iron includes iron in the form of iron oxides and hydroxides,
i.e., hematite and goethite (the so-called free iron, mostly controlling SW
absorption), as well as the iron incorporated in the crystal structure of silicates
and aluminosilicates (illite, smectite), which does not substantially
contribute to SW absorption (Karickhoff and Bailey, 1973; Lafon et al.,
2004). The mass concentrations of these components (total iron oxides,
hematite, goethite, and total elemental iron) for the different analyzed
samples are reported in Table 3. There is a considerable variability in the
iron and iron oxide content for our samples. Total iron in the dust samples
is in the range from 2.4 % (Namib-1) to 10.6 % (Namib-2). Iron oxides
account for 11 % to 62 % of the iron mass (calculated following C17, not
reported in Table 3), whereas the percent of iron oxides to the total dust
mass varies between 0.7 % (Bodélé Depression) and 5.8 % (Niger).
These data are in the range of values reported in the literature (Reid et
al., 2003; Scheuvens et al., 2013; Formenti et al., 2011, 2014a). For the
samples from the Sahara and the Sahel, goethite is the dominant iron oxide
species, in agreement with Lafon et al. (2006) and Formenti et al. (2014a, b). Elsewhere, hematite dominates over goethite, as reported by some
studies (Arimoto et al., 2002; Shen et al., 2006; Lu et al., 2011).
Spectral extinction coefficient, absorption coefficient, SSA, and
real (n) and imaginary (k) parts of the refractive index at the peak of the
dust injection in the chamber and after 30 and 90 min for Morocco and
Algeria dust samples. Data are reported at the seven aethalometer
wavelengths (370, 470, 520, 590, 660, 880, and 950 nm) as 10 min averages.
In the top panels we report the extinction calculated as the sum of
scattering and absorption coefficients. For the sake of clarity error bars
are not shown for SSA, n, and k data.
Spectral- and time-dependent dust extinction and absorption
coefficients, complex refractive index, and SSA
Figure 5 illustrates a typical spectral- and time-dependent set of measured
optical properties. The spectral extinction coefficient, absorption
coefficient, SSA, and real and imaginary parts of the complex refractive
index obtained at 10 min resolution for the Morocco and Algeria samples are
shown at the peak of the dust injection in CESAM and 30 and 90 min after the
peak. Figure 5 shows that absorption decreases with wavelength but not
extinction. The SSA increases from 370 to 590 nm, while it is almost constant
between 590 and 950 nm. The imaginary part of the refractive index decreases
with λ following the decrease in βabs. The real part of
the refractive index does not depend on wavelength.
The extinction and absorption coefficients decrease in absolute value with
time, as already shown in Fig. 3. Their spectral dependence remains quite
constant with time but varies from soil to soil. The experiment-averaged
absorption, scattering, and extinction Ångström exponents in the
370–950 nm spectral range, representing the spectral variation of the
absorption, scattering, and extinction coefficients, vary between values
of 1.5 and 2.4 (AAE), -0.4 and 0.4 (SAE), and -0.2 and +0.5 (EAE) for the
different samples. These values are in line with those previously reported
by Moosmüller et al. (2012) and C17 for dust from various locations. The
retrieved n and k also show negligible changes in their spectral shape with
time, and their magnitude remains approximately constant. In contrast, the
SSA increases with time, in particular below the 600 nm wavelength, and its
spectral shape changes. This is mostly due to the decrease in the coarse
size fraction with residence time in the chamber, as will be analyzed in
Sect. 4.5. Similarly to the absorption, scattering, and extinction
coefficients, the spectral shape of k and SSA is somewhat different between
the various samples, with the sharpest spectral variations observed for the
most absorbing samples and a less pronounced spectral variation for the less
absorbing ones, as is evident, for example, by comparing the SSA data for
Morocco and Algeria in Fig. 5.
Real (n) and imaginary (k) parts of the refractive index estimated
for the 19 analyzed dust samples and mean values calculated for the
eight regions and for the full dataset. Data for single soils are reported
as experiment-averaged values, and their uncertainty is calculated as
indicated in Table 1. Mean values and standard deviations (SDs) at each wavelength
based on single-sample data are reported for the eight regions and the full
dataset. The median and 10 % and 90 % percentile are also
reported for the full dataset. For North America and Australia, for which
only one dust sample was analyzed, the reported data correspond to the
single sample available from these regions. For the real part, the average
over the whole shortwave range (nSW) is indicated. Mean (± SD), median, and 10 % and 90 % values are indicated in bold font. The region acronyms are as follows. NAF–S: northern Africa–Sahara, SAH: Sahel, EAF–ME: eastern Africa and the Middle East, EA: eastern Asia, NAM: North America, SAM: South America, SAF: southern Africa, AUS: Australia.
SamplenSWσnSWkσkregion037–037–0.37 µm0.47 µm0.52 µm0.59 µm0.66 µm0.88 µm0.95 µm0.37 µm0.47 µm0.52 µm0.59 µm0.66 µm0.88 µm0.95 µm0.950.95µmµmTunisia1.510.060.00450.00350.00260.00180.00150.00130.00120.00300.00260.00180.00120.00100.00080.0007Morocco1.490.030.00230.00160.00120.00080.00070.00060.00070.00060.00040.00030.00020.00020.00020.0002Libya1.50.040.00290.00190.00140.00070.00060.00070.00070.00060.00040.00020.00010.00020.00020.0002Algeria1.520.040.00250.00160.00120.00070.00050.00060.00060.00100.00060.00040.00030.00030.00030.0003Mauritania1.50.030.00430.00330.00260.00140.00130.00100.00100.00100.00090.00080.00030.00030.00040.0003NAF–S1.510.030.00330.00240.00180.00110.00090.00080.00080.00100.00100.00070.00050.00040.00030.0003Niger1.510.040.00880.00610.00480.00340.00310.00280.00210.00430.00310.00230.00180.00150.00100.0013Mali1.520.050.00480.00380.00300.00230.00240.00210.00210.00080.00060.00040.00030.00030.00030.0003Bodélé1.490.030.00110.00070.00060.00040.00040.00030.00030.00060.00040.00030.00020.00020.00010.0001SAH1.510.030.00490.00350.00280.00200.00200.00170.00150.00380.00270.00210.00150.00140.00130.0011Ethiopia1.550.060.00260.00200.00160.00130.00110.00070.00060.00090.00080.00070.00050.00040.00020.0002Saudi Arabia1.540.060.00280.00210.00150.00070.00060.00060.00060.00060.00050.00040.00020.00010.00010.0001Kuwait1.500.040.00160.00100.00080.00060.00050.00050.00040.00050.00030.00030.00020.00020.00030.0002EAF–ME1.530.050.00230.00170.00130.00090.00070.00060.00050.00070.00060.00050.00040.00030.00010.0001Gobi1.480.050.00410.00250.00180.00120.00110.00120.00120.00170.00090.00060.00040.00040.00050.0005Taklimakan1.540.070.00180.00120.00090.00060.00050.00050.00050.00080.00050.00040.00020.00020.00020.0002EA1.510.050.00300.00190.00140.00090.00080.00080.00090.00160.00090.00060.00050.00050.00050.0005Arizona1.510.050.00110.00090.00070.00050.00050.00050.00040.00050.00040.00030.00020.00020.00020.0002NAM1.510.050.00110.00090.00070.00050.00050.00050.00040.00050.00040.00030.00020.00020.00020.0002Atacama1.540.070.00160.00150.00120.00080.00060.00060.00060.00050.00040.00030.00020.00020.00020.0002Patagonia1.530.070.00240.00160.00110.00090.00060.00070.00060.00080.00050.00030.00030.00030.00030.0002SAM1.540.060.00200.00150.00110.00080.00060.00070.00060.00060.00010.00010.00010.00000.00010.0000Namib-11.530.060.00120.00090.00060.00040.00030.00040.00040.00060.00040.00030.00020.00010.00020.0001Namib-21.550.070.00720.00540.00440.00250.00180.00140.00140.00270.00190.00160.00090.00070.00060.0006SAF1.540.060.00420.00310.00250.00140.00110.00090.00090.00420.00320.00270.00150.00100.00070.0007Australia1.540.060.00580.00420.00330.00170.00130.00130.00120.00220.00110.00100.00060.00060.00040.0003AUS1.540.060.00580.00420.00330.00170.00130.00130.00120.00220.00110.00100.00060.00060.00040.0003Mean1.520.040.00330.00240.00180.00120.00100.00090.00090.00210.00160.00130.00080.00070.00060.0005Median1.520.00260.00190.00140.00080.00060.00070.000610 %1.490.00120.00090.00070.00050.00040.00040.000490 %1.540.00610.00440.00350.00230.00190.00150.0015Spectral complex refractive index and SSA for the different source
regions and comparison to literature data
Figures 6 and 7 show the experiment-averaged n, k, and SSA between 370 and 950 nm for the 19 aerosol samples analyzed in this study. Data for n, k, and
SSA and their uncertainties are reported in Tables 4 and 5 for each sample
together with the average values for each of the eight different source
regions and for the full dataset. Figures 6 and 7 show that there are
significant differences, both in magnitude and spectral shape, between the
imaginary refractive index and SSA for the different samples. The highest
values of k (0.0048–0.0088 at 370 nm and 0.0012–0.0021 at 950 nm) and lowest
values of SSA (0.70–0.75 at 370 nm and 0.95–0.97 at 950 nm) are obtained for
the Niger, Mali, Namib-2, and Australia samples, which also show the highest
values of both the iron oxide content between 3.6 % and 5.8 % and
hematite content between 2.0 % and 4.8 %. The lowest values (k is
0.0011–0.0012 at 370 and 0.0003–0.0004 at 950 nm, and SSA is in the range
0.91–0.96 at 370 nm and 0.97–0.99 at 950 nm) are obtained for the
Bodélé, Namib-1, and Arizona samples, which have iron oxide contents
between 0.7 % and 1.5 %. Both k and SSA vary from region to region, with
the largest absorptions (highest k, lowest SSA) for the Sahel and Australia
and the lowest absorption (lowest k, highest SSA) in North and South America
and the Middle East; k and SSA values also vary within the same region, as
illustrated for the Sahelian and southern African samples. The real part of
the refractive index, on the other hand, is not only almost
wavelength independent, as anticipated, but also relatively invariant from
sample to sample. Its average over the 370–950 nm spectral range is between
1.48 (Gobi) and 1.55 (Ethiopia and Namib-2).
As in Table 4 for the single-scattering albedo (SSA) data. Mean, median, and 10 % and 90 % values are indicated in bold font.
The full envelope of n, k, and SSA obtained for the entire set of analyzed
samples is shown in Fig. 8. The real refractive index is relatively
invariant, while the spectral k varies by up to an order of magnitude
(0.001–0.009 at 370 nm and 0.0003–0.002 at 950 nm). The SSA changes
accordingly for the different dust samples at the different wavelengths
(30 % change at 370 nm corresponding to values between 0.70 and 0.96 and a 4 %
change at 950 nm for values within 0.95–0.99). The population mean is 1.52
for n (as spectral average) and varies in the range 0.0033–0.0009 for k and
0.85–0.98 for the SSA between 370 and 950 nm (0.0016 and 0.94 as spectral
averages for k and SSA) (Fig. 8 and Tables 4 and 5).
Real (n) and imaginary (k) parts of the dust complex refractive
index at seven wavelengths between 370 and 950 nm obtained for the 19
aerosol samples analyzed in this study. Data correspond to the time average
of the 10 min values obtained between the peak of the injection and 120 min
later. The error bar corresponds to the absolute uncertainty in n and k,
estimated to be < 8 % for n and between 13 and 75 % for k.
Single-scattering albedo (SSA) at seven wavelengths between 370
and 950 nm obtained for the 19 aerosol samples analyzed in this study. Data
correspond for each sample (with the exception of Tunisia and Namib-2; see
Sect. 3.1) to the fit of the 10 min values of βsca versus
βabs, and the uncertainty is between 1 % and 12 % at 370 nm
and between 1 % and 3 % at 950 nm.
The comparison between the full envelope of n, k, and SSA in this study with
literature data is also shown in Fig. 8. Literature values considered for
comparison include estimates from ground-based, aircraft, and satellite
observations, laboratory studies, AERONET inversions, and estimates from
mixing rules based on the dust mineralogical composition. Given that the
sample selection in our experiments fully envelopes the global variability
of the mineralogy of natural dust, we could expect that our dataset would also
fully envelope the global-scale variability of the dust absorption and
scattering properties in the SW. When comparing with available literature
data we found that our n and SSA datasets encompass the range of
values indicated in the literature very well, with only a few outlier points. In
contrast, for the imaginary refractive index the reported range of
variability from the literature is significantly larger than that found in
our study, with our range of k being mostly at the lower bound of previous
results. Nonetheless, our range of k values fully envelopes the ensemble of
remote sensing and field campaign data on airborne dust from the previous
literature reported in Fig. 8a. The global average spectral values for k in
our study (thick black line) perfectly match the Dubovik et al. (2002)
dataset from a synthesis of AERONET observations from various locations
worldwide. Likewise, our k average is also very close to the dataset by
Balkanski et al. (2007) estimated from mineralogical composition assuming
1.5 % (by volume) hematite in dust, a value shown to allow for a
reconciliation of climate modeling and satellite observations of the dust
direct SW radiative effect. By comparison, the average dust hematite content
for the ensemble of our analyzed samples is 1.8 % (in mass), close to the
1.5 % value proposed by Balkanski et al. (2007).
Comparison of the results obtained in this study with
literature-compiled values of the (a) dust real and imaginary parts of the
refractive index (n, k) and (b) single-scattering albedo (SSA) in the SW
spectral range. The regions in grey indicate the full range of variability
obtained in this study, and the thick black lines are the means of n, k, and
SSA obtained for the different aerosol samples. Literature values include
estimates from ground-based and aircraft observations during field
campaigns, laboratory studies, AERONET inversions, and estimates from dust
mineralogical composition. Data are in some cases for the full dust size
distribution, while in others only the fine fraction below about 2 µm
is represented (identified with *). The main provenance of the dust and datasets from the literature is provided
in the following: Volz et al. (1972) data are for rainout dust collected in
Germany; Patterson et al. (1977) data are for Saharan dust; Hess et al. (1998) data are
from the OPAC database; Colarco et al. (2002) data are for dust from Dakar,
SAL, and Tenerife; Dubovik et al. (2002) include data from the Bahrain–Persian
Gulf and Solar Village–Saudi Arabia AERONET stations; Haywood et al. (2003)
include dust from Mauritania; Sinyuk et al. (2003) data are from Cape Verde,
Dakar, and Burkina Faso; Clarke et al. (2004) include Asian dust offshore of
China, Japan, and Korea; Linke et al. (2006)-A includes dust from Cairo; Linke et al. (2006)-B includes dust from Morocco; Balkanski et al. (2007) calculated data from the
mineralogical composition assuming a 1.5 % hematite mass fraction in dust;
Todd et al. (2007) data are from Bodélé; Osborne et al. (2008) data are from
Niger; Otto et al. (2009), Petzold et al. (2009), Schladitz et al. (2009),
and Müller et al. (2009, 2011) include dust originated mostly in Morocco;
McConnell et al. (2008, 2010) include dust from Niger–Senegal; Chen et al. (2011)
include dust from the western Sahara; Formenti et al. (2011) in the k plot represents an
average of airborne observations for the AMMA campaign in Niger, while for
the SSA plot, Formenti et al. (2011)-A represents observations in the Saharan
Air Layer, Formenti et al. (2011)-B data are from Bodélé–Sudan, and Formenti et al. (2011)-C represents a Sahelian uplift
episode; Johnson and Osborne (2011) include dust from the western Sahara; Moosmüller
et al. (2012) analyzed samples from the Middle East, Mali, and Spain, and here we
report the average of their obtained values; Wagner et al. (2012) obtained
k values for several samples from Burkina Faso, Cairo, and the SAMUM campaign,
and here we report the values for the maximum of their spectral k (Burkina
Faso) and the minimum (Cairo); Ryder et al. (2013a) include dust from the western
Sahara and Mauritania, and we report in both the k and SSA plots the average of
their observations; Engelbrecht et al. (2016) analyzed many dust samples
from all over the world, and here we report their estimated minimum and maximum
of the dust SSA (A) from California and (B) from the Etosha Pan in
Namibia; Stegmann and Yang (2017) modeled the refractive index of dust
based on assumed mineralogical compositions typical for the northern and
southern Sahara and western and eastern Asia dust, and here we report the
average of their results for both n and k. Uncertainties in the field
observations have been omitted for the sake of clarity. The legend
identifies the line styles used in the plots.
The different acronyms are spelled out as follows (see also the caption of Fig. 4). AERONET: Aerosol Robotic Network; OPAC: Optical Properties of Aerosols and Clouds; SHADE: Saharan Dust Experiment; BODEX: the Bodélé Dust Experiment; DABEX: Dust and Biomass Experiment; SAMUM1 and SAMUM2 refer to the two SAMUM campaigns in Morocco and Cape Verde, respectively, SAMUM: Saharan Mineral Dust Experiment; DODO: Dust Outflow and Deposition to the Ocean; ACE-Asia: Asian Pacific Regional Aerosol Characterization Experiment; GERBILS: Geostationary Earth Radiation Budget Intercomparison of Longwave and Shortwave radiation.
Looking at Fig. 8, the datasets that show the largest values, which also
fall outside our estimated range of k over the entire considered wavelength
range, are the ones by Wagner et al. (2012) obtained from laboratory chamber experiments, especially deviating
below the 600 nm wavelength from our range of k, and the ones by Volz (1972), Patterson et al. (1977), and Hess et al. (1998; i.e., the OPAC 3.1 version database, which is the same k dataset
used in the new OPAC 4.0 version; Koepke et al., 2015). The Volz (1972), Patterson et al. (1977), and OPAC datasets are amongst the most commonly used references for the dust imaginary
refractive index in many climate models. The reasons for these
discrepancies in the k values are difficult to assess, since they could be
related to both instrumental and analytical aspects. In the studies by Volz (1972) and Patterson et al. (1977), for instance, the complex refractive
index was obtained by transmittance and diffuse reflectance on pellet
samples, a technique that requires the dust to be pressed in a matrix of
nonabsorbing material. In this case a discrepancy arises from the different
optical behavior between dust compressed in a pellet and the airborne
particles. Moreover, Volz (1972) and Patterson et al. (1977) analyze dust
aerosols collected after mid- to long-range transport, thus after the dust
has possibly been mixed with absorbing species.
For the case of Wagner et al. (2012) the imaginary refractive index was
retrieved from laboratory chamber experiments on suspended dust, as in our
study. Nonetheless, their approach differs in various aspects from the one
applied here and this can lead to the observed differences in the retrieved
k. First, the aerosol generation technique is different between the two works,
and this possibly leads to particles with different physicochemical
features compared to our study. In Wagner et al. (2012) the dust aerosol was
generated by a rotating brush disperser using only the 20–75 µm
sieved fraction of the soils. This system acts to disaggregate the finest
particles of the soil by passing it through a nozzle. Then the largest
aerosol grains were removed by a cyclone system (50 % cutoff at 1.2 µm aerodynamic diameter) so that only the submicron size fraction
was measured. We show in Sect. 4.5 that k is independent of size for the
range of investigated effective coarse diameters between 2 and 4 µm,
but the range of sizes analyzed in Wagner et al. (2012) is significantly
lower than in our study and a size effect cannot be excluded. In fact, the
relationship between dust absorption and iron content may vary depending on
the considered size fraction (see C17) due to the fact that iron-bearing
minerals are more concentrated in the clay fraction (< 2.0 µm) of the dust (Kandler et al., 2009). Moreover, generating dust in a
different way may lead to differences in the chemical and mineralogical
size-dependent composition of the sample, therefore contributing to the
observed differences. The impact of this, however, is difficult to evaluate.
Another difference concerns the choice of the optical theory to retrieve k
(T matrix in Wagner et al. instead of Mie theory as used in our work). This
can contribute to the observed differences, even if in a limited way (Mogili
et al., 2007; Sorribas et al., 2015). Third, in their retrieval Wagner et
al. fixed the real refractive index to a wavelength-independent value of
1.53 (as done in several other field and laboratory studies in Fig. 8), and
this assumption can bias high–low the retrieved k if the actual n is
higher–lower than the assumed 1.53 value. So, in summary, while multiple
factors could contribute to the discrepancy, it remains difficult to
assess which source of discrepancy is dominant.
Results of the linear fit between k, SSA, and the mass
concentration of iron oxides (MCFe-ox%), hematite (MCHem%),
goethite (MCGoeth%), and elemental iron (MCFe%) in dust.
Column 1 indicates the wavelength; (a±σa) indicates the
retrieved slope and its estimated uncertainty; (b±σb)
indicates the retrieved intercept and its estimated uncertainty; R2
denotes the correlation coefficient; and χred2 is the reduced
chi square of the fit.
The sample-to-sample variability of the imaginary part of the refractive
index k and the SSA observed in Figs. 6 and 7 is related to the dust
composition by investigating the dependence on the particle iron content. In
Fig. 9 we show the experiment-averaged k and SSA at 370, 520, and 950 nm
versus the mass concentration of iron oxides (hematite+goethite,
MCFe-ox%), hematite (MCHem%), goethite (MCGoeth%), and
total elemental iron (MCFe%) measured for the different dust samples
in this study. The data are linearly fitted to relate k and SSA to
MCFe-ox%, MCHem%, MCGoeth%, and MCFe%. The
results of the fits at all wavelengths between 370 and 950 nm are reported
in Table 6, together with statistical indicators of the goodness of fit
(correlation coefficient, R2, and reduced chi square, χred2, i.e., the obtained chi square divided by the number of
degrees of freedom). There is an excellent correlation between MCFe-ox% and both k and SSA
at the different wavelengths (R2> 0.75).
A weaker correlation is found when relating k and SSA to MCHem% and
MCFe% (R2 between 0.40 and 0.74 for k and between 0.49 and 0.78
for SSA), as well as MCGoeth% (R2 between 0.17 and 0.62). The better
correlation of k and SSA with MCFe-ox% compared to MCFe% is
expected since dust optical properties in the visible wavelengths are mostly
sensitive to the fraction of iron oxides, rather than to iron incorporated
into the crystal structure of silicates (Karickhoff and Bailey, 1973; Lafon
et al., 2006; Moosmüller et al., 2012; Klaver et al., 2011; Engelbrecht
et al., 2016; C17). The quantities that most robustly satisfy a linear
relationship are k and MCFe-ox%, as indicated by the reduced chi
square χred2 that is around 1 at all different wavelengths.
The χred2 increases to values also larger than 2 in the
other cases, indicating the poorer robustness of the fit in these cases.
Experiment-averaged imaginary part of the refractive index (k, top panels) and single-scattering albedo (SSA, bottom panels) at 370, 520, and
950 nm versus the mass concentration of iron oxides (MCFe-ox%),
hematite (MCHem%), goethite (MCGoeth%), and elemental iron
(MCFe%) measured for the different dust samples analyzed in this
study. The calculated linear fit regression lines are shown, together with
the correlation coefficients of the fits (R2). The legend indicates the
line styles used in the plots. Data for the Taklimakan sample were excluded
from the k and SSA plots versus MCFe-ox%, MCHem%, and
MCGoeth% due to the absence of iron oxide data for this sample.
We also investigated the dependence of the spectral k and SSA on the mass
concentration of other minerals, such as clays, calcite, quartz, and
feldspars, as well as on the mass concentration of different elements. We
found that there is no statistically significant correlation between k or SSA
and the mass concentration of any of these compounds (not shown), with
R2 values between 0.002 and 0.46 at the different wavelengths for all
cases.
These results therefore clearly show that iron, particularly in the form of
iron oxides (hematite+goethite), is the main driver of dust shortwave
absorption. Measuring only the hematite mass fraction to estimate the dust
absorption, as is sometimes done, is therefore not sufficient.
Imaginary refractive index and SSA versus dust coarse size fraction
The dependence of the spectral k and SSA on the dust coarse fraction is
investigated by relating it to the Deff,coarse calculated from the size
distribution data behind the SW instruments inlets. The k10min and
SSA10min at 370, 520, and 950 nm versus Deff,coarse are shown in
Fig. 10 for all experimental data, which we separated into three classes
based on their iron oxide content (MCFe-ox%≤1.5%, 1.5 % < MCFe-ox% < 3 %, MCFe-ox%≥3%).
Figure 10 shows that even if the correlation is not very strong
(R2 < 0.54), there is a clearly decreasing tendency for the
SSA10min with increasing Deff,coarse, particularly at 370 and 520 nm for strongly absorbing samples with iron oxide content larger than 3 %.
The SSA10min is mostly independent of changes in Deff,coarse at
950 nm. Conversely, k10min has a very poor correlation with
Deff,coarse (R2 < 0.35) and thus does not depend on size.
Similar results were also obtained for the real part (not shown).
The 10 min averaged imaginary refractive index (k10min, a, b, c) and single-scattering albedo (SSA10min, d, e, f) at 370,
520, and 950 nm versus effective coarse diameter (Deff,coarse) estimated
at the input of the SW instruments. Data were classified into three classes
based on the iron oxide content of the dust samples. The linear fit curves
and the correlation coefficients for the linear regression fits for each
dataset are also reported. The legend identifies the line styles used in the
plots.
These results confirm previous observations (Sokolik and Toon, 1999;
McConnell et al., 2008, 2010; Ryder et al., 2013a, b) that the
refractive index is independent of size. This suggests that size-dependent
mineralogical composition is not sufficient to affect k (in the limit of our
measurement and retrieval procedure precision). It is worth mentioning that
only a few past studies evidenced a dependence of k on the size distribution of
dust aerosols (i.e., Kandler et al., 2009, 2011 Otto et al., 2009); this
may be because the refractive index was retrieved in these studies from
mixing rules based on the estimated size-dependent mineralogical
composition.
Differently from k, the SSA increases as the coarse dust size fraction
decreases. This is due to the fact that absorption efficiency for a single
particle (Qabs) increases with particle diameter, while the scattering
efficiency (Qsca) decreases. Ryder et al. (2013a) also showed that the
dependence of SSA on size is linear but important only when the coarse
fraction is high (if particles larger than about 3 µm in diameter are
present); otherwise, the SSA depends mainly on composition, also in agreement
with more recent field observations by Ryder et al. (2018).
Summary
In this paper we presented new measurements of the spectral SW complex
refractive index (m=n-ik) and single-scattering albedo (SSA) for 19
mineral dust aerosols generated in the laboratory from natural soil samples
from major desert dust source areas in northern Africa, the Sahel, Middle
East, eastern Asia, North and South America, southern Africa, and Australia. These were selected to represent the heterogeneity of dust composition at the
global scale, in particular the range of iron oxide concentrations. The
envelope of refractive indices and SSA data obtained in this study can thus
be taken as representative of the variability of global dust aerosols.
The experiments described here were conducted in the 4.2 m3 CESAM chamber,
a dynamic environment in which dust aerosols are generated and maintained in
suspension for several hours while monitoring the evolution of their
physical, chemical, and optical properties. The generated dust aerosols are
characterized by a realistic size distribution, including both the
submicron and the super-micron fraction, and they have an atmospherically
representative mass concentration and composition, including iron oxides and
elemental iron content.
Some other laboratory studies have been performed in the past to investigate
the shortwave SSA of dust from different sources worldwide and its
dependence on composition (Linke et al., 2006; Moosmüller et al., 2012;
Engelbrecht et al., 2016). Conversely, for the refractive index there is
to our knowledge only one other chamber study (Wagner et al., 2012) that
retrieved the imaginary part k between 305 and 955 nm for dust aerosols from
a limited number of source areas in Africa (Burkina Faso, Egypt, and
Morocco). As a matter of fact, our work provides the first consistent
simulation chamber study of the complex refractive index of global dust.
The results of the present study can be summarized as follows.
The spectral k and SSA retrieved in this study vary from sample to sample
within the same region but also from one region to another. For k, values vary
from 0.0011 to 0.0088 at 370 nm, 0.0006 to 0.0048 at 520 nm, and
0.0003 to 0.002 at 950 nm. For SSA, values vary from 0.70 to 0.96 at 370 nm,
0.85 to 0.98 at 520 nm, and from 0.95 to 0.99 at 950 nm. In contrast, n is
wavelength independent and almost uniform for the different sources, with
values between 1.48 and 1.55. Values for n and SSA fall within the range of
published literature estimates, while for k we obtain a much narrower range
of variability than the ensemble of literature results, as illustrated in
Fig. 8. In particular, we found lower values of k compared to most of the
literature values currently used in climate models, such as Volz et al. (1972), Patterson et al. (1977), and the OPAC database (Hess et al., 1998;
Koepke et al., 2015). In their study, Miller et al. (2014) state that the
values of Dubovik et al. (2002) from AERONET, Patterson et al. (1977) for
far-traveled dust, and OPAC probably encompass global solar absorption by
dust. In contrast, our results indicate that dust absorption is lower than
previously thought, and its average is close to the values reported by
Dubovik et al. (2002) from AERONET observations and Balkanski et al. (2007)
for dust with a 1.5 % volume fraction of hematite. Our range of
variability of an order of magnitude for k and between 4 % and 30 % for
the spectral SSA is actually large enough to change the sign of the global
dust direct effect at the TOA (Miller et al., 2004), as well as its regional
implications (e.g., Solmon et al., 2008; Jin et al., 2016), and has to be
taken into account in climate modeling.
The documented changes in k and SSA also impact remote sensing retrievals. To
give an example, following Gasteiger et al. (2011), our observed variability
of about 10 % for the SSA at 532 nm would translate to about 40 %
variability in the retrieved extinction profiles and optical depths from
lidar observations for dust from varying sources.
The sample-to-sample variability observed in this study is mostly related to
the iron oxide and elemental iron content in dust. At each investigated
wavelength the magnitude of k and SSA is linearly correlated with the mass
concentration of total iron oxides, hematite, goethite, and total elemental
iron. Small variations of these compounds translate into large variations of
k and SSA.
We also investigated the dependence of k and SSA on the size distribution of
dust. While k is independent of size (suggesting that a constant value can be
used along transport), below 600 nm the SSA linearly decreases for
increasing Deff,coarse for strongly absorbing samples with more than
3 % iron oxide content. The investigated range of Deff,coarse is
within about 2 and 4 µm and thus comparable to values obtained along
a transport path over the Atlantic Ocean for dust during about 2 to 6 d
following emission (Denjean et al., 2016a).
The observations of points (3) and (4) suggest that while it is sufficient to
know the content of iron oxide (or elemental iron) in dust to predict its
spectral k, which means that only one tracer is needed in models to
parameterize its regional and global variability, for the spectral SSA both
composition and size distribution are required.
Concluding remarks
Based on our results, we recommend that dust simulations, as well as remote
sensing retrievals, use source-dependent values of the spectral SW
refractive index and SSA instead of generic values. We propose, as a first
step, a set of regionally averaged n, k, and SSA values to represent dust from
each of the eight regions analyzed here as well as a global average value
from the ensemble of our data (Tables 4 and 5). Furthermore, the
relationships found between k, SSA, and the iron oxides or elemental iron
content in dust create an opportunity to establish predictive rules to
estimate the spectrally resolved SW absorption of dust based on composition.
We recommend the use of iron oxide content rather than iron content as it is
better correlated with k and SSA. The relationship found in this study
nonetheless refers to the bulk composition of the dust aerosols and to a
size range typical of 2 to 6 d of transport in the atmosphere. As
demonstrated in C17 for the mass extinction efficiency, the relationships
linking dust absorption to iron content vary as a function of the
analyzed size fraction due to the fact that iron-bearing minerals are more
concentrated in the clay fraction (< 2.0 µm) than in the
coarsest fraction of the dust (Kandler et al., 2009; C17). Further
investigation should therefore evaluate the dependence of
the spectral k and SSA versus iron content as a function of the size
distribution of the particles, in particular extending to a wider range of
Deff,coarse compared to the one investigated in the present study. This
will allow for the determination of whether the k and SSA versus iron relationship changes or
not in different phases of the aerosol lifetime and therefore whether it is valid close to
source areas (when the coarsest fraction is dominant, i.e.,
Deff,coarse up to 15 µm; Ryder et al., 2013b) and in long-range
transport conditions (when most of the coarse particle fraction above a few
micrometers has settled out, i.e., Deff,coarse of 2–3 µm or lower;
Denjean et al., 2016b).
We point out, however, that the use of mineralogy to estimate k and SSA based
on linear relationships, as obtained in our study, requires the
model-predicted dust composition to accurately reflect that of natural
atmospheric aerosols. For this aim, realistic soil mineralogy databases and
accurate modeling of the soil to aerosol size fractionation need to be
developed in model schemes. In this sense we mention the EMIT project (Earth
Surface Mineral Dust Source Investigation) as a potential near-future source
of high-resolution surface mineralogy data for arid and semiarid regions
based on imaging spectroscopy satellite data (Green et al., 2018). Also, a
realistic representation of the size distribution, in particular the coarse
mode fraction of dust and its retention during atmospheric transport, should
be provided in models given its importance in affecting the SSA, as shown in
this study and previously reported in other papers (Ryder et al., 2013a,
b, 2018).
Our study focuses on the dust spectral optical properties between 370 and
950 nm. Further work is required to extend the range of spectral refractive
index and SSA data to wavelengths lower than 370 nm or higher than 950 nm
given that these data are often required in global circulation models and
numerical weather prediction models.
We do not provide any quantification of the uncertainty associated with the
assumption of spherical particles in our study, even if we acknowledge the
potential role of nonsphericity in affecting our data treatment and
results. Additional work is foreseen to better investigate the shape of our
generated dust and the impact of nonsphericity on retrieved spectral
refractive indices and SSA.
Finally, this study had the objective to investigate the variability of
dust SW optical properties at the global scale linked to the global
variability of dust composition. It is noteworthy that observations over
southern Africa and the Sahel from the present study indicate that the k and
SSA variability over these regions is comparable to the one obtained for the
global scale. For other regions, such as North America and Australia, only
one sample was analyzed, with no information on the regional-scale
variability of k and SSA. Additionally, for some of the analyzed areas, such
as the Bodélé depression, even local-scale variability (on the order
of a few kilometers) may be of relevance given the documented local-scale changes in
the particle mineralogy and iron content (Bristow et al., 2010). More
efforts should therefore be devoted to better characterizing the variability
of dust spectral optical properties at the regional and subregional scale
with the aim of better assessing the dust impact on the climate of different
areas of the world.
Code availability
The following IDL routines were used in the analysis: mpfitexy.pro (available at http://purl.org/mike/mpfitexy, last access: 16 December 2019) was used to linearly fit data, taking into account uncertainties on both x and y (Williams et al., 2010). The MPFITEXY routine depends on the MPFIT package (Markwardt, 2009); mie_single.pro (available at http://www.atm.ox.ac.uk/code/mie/mie_single.html; McGarragh et al., 2019) was used for optical calculations using Mie theory; and mpcurvefit.pro (available at http://cow.physics.wisc.edu/~craigm/idl/idl.html; Markwardt, 2019) was used for size lognormal fitting.
Data availability
Complex refractive index and single-scattering albedo data for the different
analyzed samples are provided in Tables 4 and 5 and will be compiled
together with aerosol properties from other studies within the Library of
Advanced Data Products (LADP) of the EUROCHAMP data center
(https://data.eurochamp.org/data-access/optical-properties/, Di Biagio et al., 2019a, b). The CESAM data used in this study are
immediately available upon request to the contact author and will also soon
be made available through the Database of Atmospheric Simulation Chamber
Studies (DASCS) of the EUROCHAMP data center (https://data.eurochamp.org/data-access/chamber-experiments/, Di Biagio et al., 2019c).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-15503-2019-supplement.
Author contributions
CDB, PF, YB, and JFD designed the
experiments and discussed the results. CDB performed the
experiments and performed the full data analysis with contributions by PF, LC, MC, EP, and JFD. The soil
samples used for the experiments were collected by MOA, KK, TS, SP, DS, and EW. EJ participated in
the selection of the soil samples for experiments. SN performed the
XRD measurements. CDB and PF wrote the paper with
comments from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Simulation chambers as tools in atmospheric research (AMT/ACP/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The RED-DUST project was supported by the French national program
LEFE/INSU and by the OSU-EFLUVE (Observatoire des Sciences de
l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie) through
dedicated research funding. The authors acknowledge the CNRS-INSU for
supporting the CESAM chamber as a national facility and the AERIS data center
(https://www.aeris-data.fr, last access: 16 December 2019) for distributing and curing the data
produced by the CESAM chamber through the hosting of the EUROCHAMP
data center.
This work has received funding from the European Union's Horizon 2020
research and innovation program through the EUROCHAMP-2020 Infrastructure
Activity under grant agreement no. 730997. Claudia Di Biagio was supported by the
CNRS via the Labex L-IPSL, funded by the ANR (grant no. ANR-10-LABX-0018).
Konrad Kandler is funded by the Deutsche Forschungsgemeinschaft (DFG, German
Research Foundation; 264907654, 416816480; KA 2280). Field sampling in
Saudi Arabia was supported by a grant from King Saud University. The authors
thank the LISA staff, who participated in the collection of the soil samples
from Tunisia, Niger, Atacama, Patagonia, and the Gobi desert used in this
study, and Sandrine Caquineau (LOCEAN), Servanne Chevaillier (LISA), and Gautier Landrot
(synchrotron SOLEIL) for their contribution to the XRD, WD-XRF, and XANES
analyses. Claudia Di Biagio thanks Patrick G. Stegmann for providing the corrected refractive
index data shown in Fig. 8. The authors also wish to acknowledge Claire Ryder
and Carlos Pérez Garcia-Pando for providing valuable comments that helped to
increase the readability and quality of the paper.
Financial support
This research has been supported by Horizon 2020 (grant no. EUROCHAMP-2020 (730997)), the Agence Nationale de la Recherche (grant no. ANR-10-LABX-0018), and the Deutsche Forschungsgemeinschaft (grant nos. 264907654; 416816480 (KA 2280)).
Review statement
This paper was edited by Patrick Chuang and reviewed by Claire Ryder and Carlos Pérez García-Pando.
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