ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-1901-2017Global scale variability of the mineral dust long-wave refractive index:
a new dataset of in situ measurements for climate modeling and remote
sensingDi BiagioClaudiacldibiagio@gmail.comhttps://orcid.org/0000-0001-8273-6211FormentiPaolapaola.formenti@lisa.u-pec.frhttps://orcid.org/0000-0002-0372-1351BalkanskiYveshttps://orcid.org/0000-0001-8241-2858CaponiLorenzoCazaunauMathieuPanguiEdouardJournetEmilieNowakSophieCaquineauSandrineAndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925KandlerKonradSaeedThurayahttps://orcid.org/0000-0002-6540-4810PikethStuartSeibertDavidWilliamsEarleDoussinJean-Françoishttps://orcid.org/0000-0002-8042-7228Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), UMR7583, CNRS, Université Paris Est Créteil et Université Paris Diderot, Institut Pierre et Simon Laplace, Créteil, FranceLaboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, 91191, Gif sur Yvette, FranceDepartment of Physics & INFN, University of Genoa, Genoa, ItalyPlateforme RX UFR de chimie, Université Paris Diderot, Paris, FranceIRD-Sorbonne Universités (UPMC, Univ. Paris 06), CNRS-MNHN, LOCEAN Laboratory, IRD France-Nord, 93143 Bondy, FranceBiogeochemistry Department, Max Planck Institute for Chemistry, P.O. box 3060, 55020, Mainz, GermanyInstitut für Angewandte Geowissenschaften, Technische Universität Darmstadt, Schnittspahnstr. 9, 64287 Darmstadt, GermanyScience department, College of Basic Education, Public Authority for Applied Education and Training, Al-Ardeya, KuwaitClimatology Research Group, Unit for Environmental Science and Management, North-West University, Potchefstroom, South AfricaWalden University, Minneapolis, Minnesota, USAParsons Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USAGeology and Geophysics Department, King Saud University, Riyadh, Saudi ArabiaClaudia Di Biagio (cldibiagio@gmail.com) and Paola Formenti (paola.formenti@lisa.u-pec.fr)9February20171731901192912July201619October201611January201714January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/1901/2017/acp-17-1901-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/1901/2017/acp-17-1901-2017.pdf
Modeling the interaction of dust with long-wave (LW) radiation is still a
challenge because of the scarcity of information on the complex refractive
index of dust from different source regions. In particular, little is known
about the variability of the refractive index as a function of the dust
mineralogical composition, which depends on the specific emission source,
and its size distribution, which is modified during transport. As a
consequence, to date, climate models and remote sensing retrievals generally
use a spatially invariant and time-constant value for the dust LW refractive
index.
In this paper, the variability of the mineral dust LW refractive index as a
function of its mineralogical composition and size distribution is explored
by in situ measurements in a large smog chamber. Mineral dust aerosols were
generated from 19 natural soils from 8 regions: northern Africa, the Sahel, eastern Africa and the Middle
East, eastern Asia, North and South America, southern Africa, and Australia.
Soil samples were selected from a total of 137 available samples in order to
represent the diversity of sources from arid and semi-arid areas worldwide
and to account for the heterogeneity of the soil composition at the global
scale. Aerosol samples generated from soils were re-suspended in the
chamber, where their LW extinction spectra (3–15 µm), size
distribution, and mineralogical composition were measured. The generated
aerosol exhibits a realistic size distribution and mineralogy, including
both the sub- and super-micron fractions, and represents in typical
atmospheric proportions the main LW-active minerals, such as clays, quartz,
and calcite. The complex refractive index of the aerosol is obtained by an
optical inversion based upon the measured extinction spectrum and size
distribution.
Results from the present study show that the imaginary LW refractive index
(k) of dust varies greatly both in magnitude and spectral shape from sample
to sample, reflecting the differences in particle composition. In the 3–15 µm spectral range, k is between ∼ 0.001 and 0.92. The
strength of the dust absorption at ∼ 7 and 11.4 µm
depends on the amount of calcite within the samples, while the absorption
between 8 and 14 µm is determined by the relative abundance of quartz
and clays. The imaginary part (k) is observed to vary both from region to
region and for varying sources within the same region. Conversely,
for the real part (n), which is in the range 0.84–1.94, values are observed
to agree for all dust samples across most of the spectrum within the error
bars. This implies that while a constant n can be probably assumed for dust
from different sources, a varying k should be used both at the global and the regional scale. A linear relationship between the magnitude of the
imaginary refractive index at 7.0, 9.2, and 11.4 µm and the mass
concentration of calcite and quartz absorbing at these wavelengths was
found. We suggest that this may lead to predictive rules to estimate the LW
refractive index of dust in specific bands based on an assumed or predicted
mineralogical composition, or conversely, to estimate the dust composition
from measurements of the LW extinction at specific wavebands.
Based on the results of the present study, we recommend that climate models
and remote sensing instruments operating at infrared wavelengths, such as
IASI (infrared atmospheric sounder interferometer), use regionally dependent
refractive indices rather than generic values. Our observations also suggest
that the refractive index of dust in the LW does not change as a result of
the loss of coarse particles by gravitational settling, so that constant
values of n and k could be assumed close to sources and following transport.
The whole dataset of the dust complex refractive indices presented in this
paper is made available to the scientific community in the Supplement.
Introduction
Mineral dust is one of the most abundant aerosol species in the atmosphere
and contributes significantly to radiative perturbation, at both the
regional and the global scale (Miller et al., 2014). The direct radiative
effect of mineral dust acts at both short-wave (SW) and long-wave (LW)
wavelengths (Tegen and Lacis, 1996). This is due to the very large size
spectrum of these particles, which extends from hundreds of nanometers to
tenths of micrometers, and to their mineralogy, which includes minerals with
absorption bands at both SW and LW wavelengths (Sokolik et al., 1998;
Sokolik and Toon, 1999). The sub-micron dust fraction controls the
interaction in the SW, where scattering is the dominant process, while the
super-micron size fraction drives the LW interaction, dominated by
absorption (Sokolik and Toon, 1996, 1999). The SW and LW terms have
opposite effects at the surface, the top of the atmosphere (TOA), and within the
aerosol layer (Hsu et al., 2000; Slingo et al., 2006). The dust SW effect is
to cool the surface and at the TOA, and to warm the dust layer; conversely,
the dust LW effect induces a warming of the surface and TOA, and the cooling
of the atmospheric dust layer. The net effect of dust at the TOA is
generally a warming over bright surfaces (e.g., deserts; Yang et al., 2009)
and a cooling over dark surfaces (e.g., oceans; di Sarra et al., 2011).
The interaction of dust with LW radiation has important implications for
climate modeling and remote sensing. Many studies have shown the key role
of the LW effect in modulating the SW perturbation of dust not only close to
sources (Slingo et al., 2006), where the coarse size fraction is dominant
(Schütz and Jaenicke, 1974; Ryder et al., 2013a), but also after medium- and
long-range transport (di Sarra et al., 2011; Meloni et al., 2015), when the
larger particles (> 10 µm) were preferentially removed by
wet and dry deposition (Schütz et al., 1981; Maring et al., 2003; Osada
et al., 2014). Thus, the dust LW term has importance over the entire dust
life cycle, and has to be taken into account in order to evaluate the
radiative effect of dust particles on the climate system. Second, the
signature of the dust LW absorption modifies the TOA radiance spectrum,
which influences the retrieval of several climate parameters by satellite
remote sensing. Misinterpretations of the data may occur if the signal of
dust is not accurately taken into account within satellite inversion
algorithms (Sokolik, 2002; DeSouza-Machado et al., 2006; Maddy et al.,
2012). In addition, the dust LW signature obtained by spaceborne satellite
data in the 8–12 µm window region is used to estimate the
concentration fields and optical depth of dust (Klüser et al., 2011;
Clarisse et al., 2013; Vandenbussche et al., 2013; Capelle et al., 2014;
Cuesta et al., 2015), with potential important applications for climate and
air quality studies, health issues, and visibility.
Measured and retrieved quantities and their estimated
uncertainties. For further details refer to Sect. 2.
Parameter UncertaintyUncertainty calculationOptical LWTransmission 3–15 µm, T< 10 %Quadratic combination of noise (∼ 1 %) and standard deviation over 10 min (5–10 %)Extinction coefficient 3–15 µm, βext(λ)=-lnT(λ)x∼ 10 %Error propagation formula* considering uncertainties on the measured transmission T and the optical path x (∼ 2 %)Size distributionSMPS geometrical diameter (Dg), Dg=Dm/χ∼ 6 %Error propagation formula* considering the uncertainty on the estimated shape factor χ (∼ 6 %)SkyGrimm geometrical diameter (Dg)< 15.2 %Standard deviation of the Dg values obtained for different refractive index values used in the optical to geometrical conversionWELAS geometrical diameter (Dg)∼ 5–7 %The same as for the SkyGrimmdN/dlogDgCorr,WELAS=dN/dlogDg/1-LWELASDg∼ 20–70 %Error propagation formula* considering the dN/dlogDg SD over 10 min and the uncertainty on LWELAS (∼ 50 % at 2 µm, ∼ 10 % at 8 µm)dN/dlogDgfilter=dN/dlogDgCESAM×1-LfilterDg∼ 25–70 %Error propagation formula* considering the uncertainties on (dN/dlogDg)CESAM and Lfilter (∼ 55 % at 2 µm, ∼ 10 % at 12 µm)Mineral- ogical compositionClay mass (mClay=Mtotal-mQ-mF-mC-mD-mG))8–26 %Error propagation formula* considering the uncertainty on Mtotal (4–18 %) and that on mQ, mF, mC, mD, and mGQuartz mass (mQ=SQ/KQ)9 %Error propagation formula* considering the uncertainty on the DRX surface area SQ (∼ 2 %) and KQ (9.4 %)Feldspars mass (mF=SF/KF)14 % (orthose), 8 % (albite)The same as for the quartz, KF uncertainty 13.6 % (orthose) and 8.4 % (albite)Calcite mass (mC=SC/KC)11 %The same as for the quartz, KC uncertainty 10.6 %Dolomite mass (mD=SD/KD)10 %The same as for the quartz, KD uncertainty 9.4 %Gypsum mass (mG=SG/KG)18 %The same as for the quartz, KG uncertainty 17.9 %
*σf=∑i=1n∂f∂xiσxi2.
Currently, the magnitude and the spectral fingerprints of the dust signal in
the LW are still very uncertain. One of the factors contributing the highest
uncertainty is the poor knowledge regarding the dust spectral complex
refractive index (m=n-ik; Claquin et al., 1998; Liao and Seinfeld, 1998;
Sokolik et al., 1998; Highwood et al., 2003; Colarco et al., 2014). The dust
complex refractive index in the LW depends on the particle mineralogical
composition, in particular the relative proportion of quartz, clays
(kaolinite, illite, smectite, chlorite), and calcium-rich minerals (calcite,
dolomite), each exhibiting specific absorption features in the LW spectrum
(Sokolik et al., 1993, 1998). Because of the variability of the dust
composition resulting from the variability of composition of the source
soils (Jeong, 2008; Scheuvens et al., 2013; Formenti et al., 2014; Journet
et al., 2014), atmospheric dust produced from different regions of the world
is expected to have a varying complex refractive index. Additional
variability is expected to be introduced during transport due to the
progressive loss of coarse particles by gravitational settling and chemical
processing (particle mixing, heterogeneous reactions, water uptake), which
both change the composition of the particles (Pye et al., 1987; Usher et
al., 2003). As a consequence, the refractive index of dust is expected to
vary widely at the regional and global scale.
Several studies have recommended taking into account the variability of the
dust LW refractive index in order to correctly represent its effect in
climate models and satellite retrieval algorithms (Sokolik et al., 1998;
Claquin et al., 1999; Balkanski et al., 2007; Colarco et al., 2014; Capelle
et al., 2014; among others). However, to date this is precluded by the
limited body of observations available. Most past studies on the LW
refractive index have been performed on single synthetic minerals (see Table 1 in Otto et al., 2009). These data, however, are not adequate to represent
atmospheric dust because of the chemical differences between the reference
minerals and the minerals in the natural aerosol, and also because of the
difficulty of effectively evaluating the refractive index of the dust
aerosol based only on information on its single constituents (e.g.,
McConnell et al., 2010). On the other hand, very few studies have been
performed on natural aerosol samples. They include the estimates obtained
with the KBr pellet technique by Volz (1972, 1973), Fouquart (1987), and,
more recently, by Di Biagio et al. (2014a), on dust samples collected at a
few geographical locations (Germany, Barbados, Niger, and Algeria). Besides
hardly representing global dust sources, these datasets are also difficult
to extrapolate to atmospheric conditions as (i) they mostly refer to unknown
dust mineralogical composition and size distribution, and also (ii) are
obtained from analyses of field samples that might have experienced unknown
physico-chemical transformations. In addition, they have a rather coarse
spectral resolution, which is sometimes insufficient to resolve the main
dust spectral features.
As a consequence, climate models and satellite retrievals presently use a
spatially invariant and time-constant value for the dust LW refractive index
(e.g., Miller et al., 2014; Capelle et al., 2014), implicitly assuming a
uniform as well as transport- and processing-invariant dust composition.
Recently, novel data of the LW refractive index for dust from the Sahara, the
Sahel, and the Gobi deserts have been obtained from in situ measurements in a
large smog chamber (Di Biagio et al., 2014b; hereinafter DB14). These
measurements were performed in the realistic and dynamic environment of the
4.2 m3 CESAM (Chambre Expérimentale de Simulation Atmosphérique
Multiphasique, which translates as “multiphase atmospheric experimental
simulation chamber”; Wang et al., 2011), using a validated generation
mechanism to produce mineral dust from parent soils (Alfaro et al., 2004).
The mineralogical composition and size distribution of the particles were
measured along with the optical data, thus providing a link between particle
physico-chemical and optical properties.
Schematic configuration of the CESAM set up for the dust
experiments. The dust generation (vibrating plate, Büchner flask
containing the soil sample) and injection system is shown at the bottom on
the right side. The position of the SMPS, WELAS, and SkyGrimm used for
measuring the size distribution, FTIR spectrometer, SW optical instruments,
and filter sampling system are also indicated.
In this study, we review, optimize, and extend the approach of DB14 to
investigate the LW optical properties of mineral dust aerosols from 19
soils from major source regions worldwide, in order to (i) characterize the
dependence of the dust LW refractive index on the particle origin and
different mineralogical compositions, and (ii) investigate the variability
of the refractive index as a function of the change in size distribution
that may occur during medium- and long-range transport.
The paper is organized as follows: in Sect. 2 we describe the experimental
set-up, instrumentation and data analysis, while in Sect. 3 the algorithm to
retrieve the LW complex refractive index from observations is discussed.
Criteria for soil selection and their representativeness of the global dust
are discussed in Sect. 4. Results are presented in Sect. 5. At first, the
atmospheric representativeness in terms of mineralogy and size distribution
of the generated aerosols used in the experiments is evaluated (Sect. 5.1
and 5.2), then the extinction and complex refractive index spectra obtained
for the different source regions and at different aging times in the chamber
are presented in Sect. 5.3. The discussion of the results, a comparison with
the literature, and the main conclusions are given in Sects. 6 and 7.
Experimental set-up and instrumentation
The schematic configuration of the CESAM set-up for the dust
experiments is shown in Fig. 1. Prior to each experiment, the chamber was
evacuated and kept at a pressure of 3 × 10-4 hPa. Then, the
reactor was filled with a mixture of 80 % N2 (produced by evaporation
from a pressurized liquid nitrogen tank, Messer, purity > 99.995 %) and 20 % O2 (Linde, 5.0). The chamber was equipped with a
four-blade stainless steel fan to achieve homogeneous conditions within the
chamber volume (with a typical mixing time of approximately 1 min).
Mineral dust aerosols generated from parent soils were dispersed into the
chamber and left in suspension for a time period of 60–120 min, whilst
monitoring of the evolution of their physico-chemical and optical properties took place.
The LW spectrum of the dust aerosols was measured by means of an in situ
Fourier transform infrared (FTIR) spectrometer. Concurrently, the particle size distribution and the SW scattering,
absorption, and extinction coefficients were measured by several instruments
sampling aerosols from the chamber. They include a scanning mobility
particle sizer (SMPS) and WELAS and SkyGrimm optical particle counters for
the size distribution, a nephelometer (TSI Inc. model 3563), an
aethalometer (Magee Sci. model AE31), and two cavity-attenuated phase shift
extinction analyzers (CAPS PMeX by Aerodyne) for aerosol SW optical properties. Dust
samples were also collected on polycarbonate filters over the largest part
of each experiment (47 mm Nuclepore, Whatman, nominal pore size 0.4 µm) for an analysis of the particle mineralogical composition averaged over
the length of the experiment.
The inlets for sampling aerosols from the chamber (for size and SW optics
measurements and filter sampling) consisted of two parts: (1) a stainless
steel tube (∼ 20–40 cm length, 9.5 mm diameter) located inside
CESAM, which extracted air from the interior of the chamber, and (2) an
external connection from the chamber to the instruments. All external
connections were made using 0.64 cm conductive silicone tubing (TSI Inc.) to
minimize particle loss by electrostatic deposition. The sampling lines were
designed to be as straight and as short as possible, and their total length
varied between 40 and 120 cm. The possible losses as a function of particle
diameter were carefully estimated for each inlet and the related data
properly corrected (Sect. 2.3.2). To compensate for the air being extracted
from the chamber by the various instruments, a particle-free N2–O2
mixture was continuously injected into the chamber.
All experiments were conducted at ambient temperature and relative humidity
< 2 %. The chamber was manually cleaned between the different
experiments to avoid any carryover contaminations, as far as was possible.
Background concentrations of aerosols in the chamber varied between 0.5 and
2.0 µg m-3.
In the following paragraphs we describe the system for dust generation,
measurements of the dust LW spectrum, size distribution, and mineralogy, and
data analysis. A summary of the different measured and retrieved quantities
in this study and their estimated uncertainties is reported in Table 1.
Long-wave optical and size distribution data, acquired at different temporal
resolutions, are averaged over 10 min intervals. Uncertainties on the
average values are obtained as the standard deviation over the 10 min
intervals. A full description of the SW optical measurements and results is
out of the scope of the present study and will be provided in a forthcoming
paper (Di Biagio et al., 2017).
Dust aerosol generation
In order to mimic the natural emission process, dust aerosols were generated
by mechanical shaking of natural soil samples, as described in DB14. The
soils used in this study consist of the surface layer, which is subject to
wind erosion in nature (Pye et al., 1987). Prior to each experiment, the
soil samples were sieved to < 1000 µm and dried at 100 ∘C for about 1 h to remove any residual humidity. This processing
did not affect the mineral crystalline structure of the soil (Sertsu and
Sánchez, 1978).
About 15 g of soil sample was placed in a 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 then injected into the chamber by flushing it
with N2 at 10 L min-1 for about 10–15 min, whilst continuously
shaking the soil. Larger quantities of soil sample (60 g) mixed with pure
quartz (60 g) had been used in DB14 to maximize the concentrations of the
generated dust. The presence of the pure quartz grains increases the
efficiency of the shaking, allowing a rapid generation of high dust
concentrations. In that case it had been necessary, however, to pass the
aerosol flow through a stainless steel settling cylinder to prevent large
quartz grains from entering the chamber (DB14). For the present experiments,
the generation system was optimized, i.e., the mechanical system used to fix
the flask to the shaker was improved so that the soil shaking was more
powerful, and sufficient quantities of dust aerosols could be generated by
using a smaller amount of soil and without adding quartz to the soil sample.
In this way, the settling cylinder could be eliminated. No differences were
observed in the size distribution or mineralogy of the generated dust
between the two approaches.
LW optical measurements: FTIR extinction spectrum
The extinction spectrum of dust aerosols in the long wave was measured by
means of an in situ FTIR (Bruker® Tensor
37™) analytical system. The spectrometer is
equipped with a liquid-nitrogen-cooled mercury cadmium telluride (MCT)
detector and a globar source. The FTIR spectrometer measures between
wavelengths of 2.0 (5000 cm-1) and 16 µm (625 cm-1) at
2 cm-1 resolution (which corresponds to a resolution varying from about
0.0008 at 2.0 µm wavelength to 0.05 µm at
16 µm) by co-adding 158 scans over 2 min. The FTIR spectrometer is
interfaced with a multi-pass cell to achieve a total optical path length
(x) within the chamber of 192 ± 4 m. The FTIR reference spectrum
was acquired immediately before the dust injection. In some cases small
amounts of water vapor and CO2 entered CESAM during particle injection
and partly contaminated the dust spectra below 7 µm. This did not
influence the state of particles as the chamber remained very dry (relative
humidity < 2 %). Water vapor and CO2 absorption lines were
carefully subtracted using reference spectra. The measured spectra were then
interpolated at 0.02 µm wavelength resolution (which corresponds to
a resolution varying from about 0.8 at 625 cm-1 wavenumber to 50 at
5000 cm-1). Due to the excessive loss of energy in the FTIR-measured
transmission (T) from 2 to 3 and 15 to 16 µm, data were limited
to the 3–15 µm interval. In this spectral range the dust spectral
extinction coefficient βext was calculated as follows:
βext(λ)=-lnT(λ)x.
The uncertainty on βext was calculated with the error
propagation formula by considering the uncertainties arising from T noise
(∼ 1 %) and from the standard deviation of the 10 min
averages and of the path length x. We estimated it to be
∼ 10 %.
In the 3–15 µm range, the dust extinction measured by the FTIR spectrometer is due
to the sum of scattering and absorption. Scattering dominates below 6 µm, while absorption is dominant above 6 µm. The FTIR
multipass cell in the CESAM has been built following the White (1942) design (see Fig. 1). In this configuration, a significant fraction of
the light scattered by the dust enters the FTIR detector and is not measured
as extinction. This is because mineral dust is dominated by the super-micron
fraction, which scatters predominantly in the forward direction. As a
consequence, the FTIR signal in the presence of mineral dust will represent
only a fraction of dust scattering below 6 µm and almost exclusively
absorption above 6 µm. Figure S1 (Supplement), shows an
example of the angular distribution of scattered light (phase function) and
the scattering-to-absorption ratio calculated as a function of the
wavelength in the LW for one of the samples used in this study. The results
of the calculations confirm that above 6 µm the scattering signal
measured by the FTIR spectrometer accounts for less than 20 % of the total LW
extinction at the peak of the injection and less than 10 % after 120 min in the chamber. Consequently, we approximate Eq. (1) as follows:
βabs(λ)≈-lnT(λ)x(λ> 6µm).
Size distribution measurements
The particle number size distribution in the chamber was measured with
several instruments, based on different principles and operating in
different size ranges:
a scanning mobility particle sizer (SMPS; TSI, DMA Model 3080, CPC Model
3772; operated at 2.0/0.2 L min-1 sheath–aerosol flow rates; 135 s
resolution), measuring the dust electrical mobility diameters (Dm,
i.e., the diameter of a sphere with the same migration velocity in a
constant electric field as the particle of interest) in the range
0.019–0.882 µm. Given that dust particles have a density larger than
unity (assuming an effective density of 2.5 g cm-3), the cut point of
the impactor at the input of the SMPS shifts towards lower diameters. This
reduces the range of measured mobility diameters to ∼
0.019–0.50 µm. The SMPS was calibrated prior to the campaign with PSL
particles (Thermo Sci.) of 0.05, 0.1, and 0.5 µm nominal diameters;
a WELAS optical particle counter (Palas, model 2000; white light source
between 0.35 and 0.70 µm; flow rate 2 L min-1; 60 s
resolution), measuring the dust-sphere-equivalent optical diameters
(Dopt, i.e., the diameter of a sphere yielding on the same detector
geometry the same optical response as the particle of interest) in the range
0.58–40.7 µm. The WELAS was calibrated prior to the campaign with
Caldust 1100 (Palas) reference particles;
a SkyGrimm optical particle counter (Grimm Inc., model 1.129; 0.655 µm operating wavelength; flow rate 1.2 L min-1; 6 s resolution),
measuring the dust-sphere-equivalent optical diameters (Dopt) in the
range 0.25–32 µm. The SkyGrimm was calibrated after the campaign
against a “master” Grimm (model 1.109) just recalibrated at the factory.
The SMPS and the WELAS were installed at the bottom of the chamber, while
the SkyGrimm was installed at the top of the chamber on the same horizontal
plane as the FTIR spectrometer and at about 60 cm across the chamber from
the WELAS and the SMPS. As already discussed in DB14, measurements at the
top and bottom of the chamber were in very good agreement during the whole
duration of each experiment, which indicates a good homogeneity of the dust
aerosols in the chamber.
Corrections of SMPS, WELAS, and SkyGrimm data
Different corrections have to be applied to the instruments measuring the
particle size distribution. For the SMPS, corrections for particle loss by
diffusion in the instrument tubing and the contribution of multiple-charged
particles were performed using the SMPS software. The electrical mobility
diameter measured by the SMPS was converted to a geometrical diameter
(Dg) by taking into account the particle dynamic shape factor (χ), as Dg=Dm/χ. The shape factor χ, determined by
comparison with the SkyGrimm in the overlapping particle range
(∼ 0.25–0.50 µm), was found to be 1.75 ± 0.10.
This value is higher than those reported in the literature for mineral dust
(1.1–1.6; e.g., Davies, 1979; Kaaden et al., 2008). The uncertainty in
Dg was estimated with the error propagation formula and was
∼ 6 %.
For the WELAS, optical diameters were converted to sphere-equivalent
geometrical diameters (Dg) by taking into account the visible complex
refractive index. The Dopt to Dg diameter conversion was performed
based on the range of values reported in the literature for dust in the
visible range, i.e., 1.47–1.53 for the real part and 0.001–0.005 for the
imaginary part (Osborne et al., 2008; Otto et al., 2009; McConnell et al.,
2010; Kim et al., 2011; Klaver et al., 2011). Optical calculations were
computed over the spectral range of the WELAS 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. The spectrum of the WELAS lamp needed for
optical calculations was measured in the laboratory (Fig. S2). Dg was then set at the mean ± 1 standard deviation
of the values obtained for the different n and k. After calculations, the
WELAS Dg range became 0.65–73.0 µm with an associated
uncertainty of < 5 % for Dg < 10 µm and between
5 and 7 % at larger diameters. A very low counting efficiency was observed
for the WELAS below 1 µm; thus data in this size range were
discarded.
For the SkyGrimm, the Dopt to Dg diameter conversion was performed
with a procedure similar to that used for the WELAS. After calculations, the
Dg range for the SkyGrimm became 0.29–68.2 µm with an associated
uncertainty < 15.2 % at all diameters. The inter-calibration
between the SkyGrimm and the master instrument showed relatively good
agreement (< 20 % difference in particle number) at
Dg < 1 µm, but a large disagreement (up to 300 %
difference) at Dg > 1 µm. Based on inter-comparison
data, a recalibration curve was calculated for the SkyGrimm in the range
Dg < 1 µm, and the data for Dg > 1 µm were discarded.
The SkyGrimm particle concentration was also corrected for
the flow rate of the instrument, which during the experiment was observed to
vary between 0.7 and 1.2 L min-1 compared to its nominal value at 1.2 L min-1.
Correction for particle losses in sampling lines and determination of
the full dust size distribution at the input of each instrument
In order to compare and combine extractive measurements (size distribution,
filter sampling, and SW optics), particle losses due to aspiration and
transmission in the sampling lines were calculated using the particle loss
calculator (PLC) software (von der Weiden et al., 2009). Inputs to the
software include the geometry of the sampling line, the sampling flow rate,
the particle shape factor χ, and the particle density (set at 2.5 g cm-3 for dust).
Particle losses for the instruments measuring the number size distribution
(SMPS, WELAS, and SkyGrimm) were calculated. This allowed the reconstruction of the
dust size distribution suspended in the CESAM that corresponds to
the size distribution sensed by the FTIR spectrometer and that is needed for optical
calculations in the LW. Particle loss was found to be negligible at
Dg < 1 µm, reaching 50 % at Dg∼ 5 µm,
75 % at Dg∼ 6.3 µm, and 95 % at
Dg∼ 8 µm for the WELAS, the only instrument
considered in the super-micron range. Data for the WELAS were then corrected
as follows:
dN/dlogDgCorr,WELAS=dN/dlogDgWELAS/×1-LWELASDg,
where [dN/dlogDg]WELAS is the size measured by the WELAS and
LWELAS (Dg) is the calculated particle loss as a function of the
particle diameter. Data at Dg > 8 µm, for which the
loss is higher than 95 %, were excluded from the dataset due to their
large uncertainty. The uncertainty on LWELAS (Dg) was estimated
with a sensitivity study by varying the PLC software values of the input
parameters within their uncertainties. The LWELAS (Dg) uncertainty
varies between ∼ 50 % at 2 µm and ∼ 10 % at 8 µm. The total uncertainty in the WELAS-corrected size
distribution was estimated as the combination of the dN/dlogDg standard
deviation on the 10 min average and the LWELAS (Dg) uncertainty.
The full size distribution of dust aerosols within the CESAM, dN/dlogDgCESAM, was determined by combining SMPS and
SkyGrimm data with WELAS loss-corrected data: the SMPS was taken at
Dg < 0.3 µm, the SkyGrimm at Dg= 0.3–1.0 µm,
and the WELAS at Dg= 1.0–8.0 µm. Data were then interpolated in
steps of dlogDg= 0.05. An example of the size distributions measured
by the different instruments is shown in Fig. S3. Above 8 µm, where WELAS data were not available, the dust size
distribution was extrapolated by applying a single-mode lognormal fit. The
fit was set to reproduce the shape of the WELAS distribution between
Dg∼ 3–4 and 8 µm.
Particle losses in the filter sampling system (Lfilter (Dg)) were
calculated estimating the size-dependent particles losses that would be
experienced by an aerosol with the size distribution in CESAM reconstructed
from the previous calculations. Losses for the sampling filter were
negligible for Dg < 1 µm, and increased to 50 % at
Dg∼ 6.5 µm, 75 % at Dg∼ 9 µm, and 95 % at Dg∼ 12 µm. The loss
function, Lfilter (Dg), was used to estimate the dust size
distribution at the input of the filter sampling system as follows.
dN/dlogDgfilter=dN/dlogDgCESAM×1-LfilterDg
As a consequence of losses, the FTIR spectrometer and the filters sense particles over
different size ranges. Figure S4 illustrates this
point by showing a comparison between the calculated size distribution
within CESAM and that sampled on filters for one typical case. An
underestimation of the particle number on the sampling filter compared to
that measured in CESAM is observed above 10 µm diameter. While the
filter samples would underestimate the mass concentration in the chamber,
the relative proportions of the main minerals should be well represented. As
a matter of fact, at emission, where particles of diameters above 10 µm are most relevant, the mineralogical composition in the 10–20 µm
size class matches that of particles of diameters between 5 and 10 µm
(Kandler et al., 2009). When averaging, and also taking into account the
contribution of the mass of the 10–20 µm size class to the total,
differences in the relative proportions of minerals do not exceed 10 %.
Analysis of the mineralogical composition of the dust aerosol
The mineralogical composition of the aerosol particles collected on the
filters was determined by combining the following techniques: X-ray diffraction (XRD, Panalytical
model Empyrean diffractometer) to estimate the particles' 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; ±8–10 % uncertainty); and X-ray
absorption near-edge structure (XANES) to retrieve the content of iron
oxides (±15 % on the mass fraction) and their speciation between
hematite and goethite. Half of the Nuclepore filters were analyzed by XRD
and the other half by WD-XRF and XANES. Full details on the WD-XRF and XANES
measurements and data analysis are provided elsewhere (Caponi et al., 2017). Here we describe the XRD measurements.
XRD analysis was performed using a Panalytical model Empyrean diffractometer
with Ni-filtered CuKα radiation at 45 kV and 40 mA.
Samples were scanned from 5 to 60∘ (2θ) in steps of
0.026∘, with a time per step of 200 s. Samples were prepared and
analyzed according to the protocols of Caquineau et al. (1997) for low mass
loadings (load deposited on filter < 800 µg). Particles were
first extracted from the filter with ethanol, then concentrated by
centrifuging (25 000 rpm for 30 min), diluted with deionized water (pH ∼ 7.1), and finally deposited on a pure silicon slide.
For well crystallized minerals, such as quartz, calcite, dolomite, gypsum,
and feldspars (orthoclase, albite), a mass calibration was performed in
order to establish the relationship between the intensity of the diffraction
peak and the mass concentration in the aerosol samples, according to the
procedure described in Klaver et al. (2011). The calibration coefficients
Ki, representing the ratio between the total peak surface area in the
diffraction spectra (Si) and the mass mi of the ith-mineral,
are reported in Table S1 in the Supplement. The error in the
obtained mass of each mineral was estimated with the error propagation
formula taking into account the uncertainty in Si and the calibration
coefficients Ki. The obtained uncertainty is ±9 % for quartz,
±14 % for orthoclase, ±8 % for albite, ±11 % for
calcite, ±10 % for dolomite, and ±18 % for gypsum.
Conversely, the mass concentration of clays (kaolinite, illite, smectite,
palygorskite, chlorite), also detected in the samples, cannot be quantified
in absolute terms from the XRD spectra due to the absence of appropriate
calibration standards for these components (Formenti et al., 2014). Hence,
the total clay mass was estimated as the difference between the total dust
mass and the total mass of quartz, calcium-rich species, and feldspars,
estimated after XRD calibration, and iron oxides, estimated from XANES. The
mass of organic material was neglected in the calculation: its
contributions, however, should not exceed 3 % according to the literature
(Lepple and Brine, 1976). The total dust mass was calculated in two ways:
from the particle size distribution dN/dlogDgfilter (Msize,
by assuming a dust density of 2.5 g cm-3) and from the estimated elemental composition (Melemental, as
described in Caponi et al., 2017). Our results show that Msize
systematically overestimates Melemental. As a result, using Msize
or Melemental would result in different clay mass fractions. In the
absence of a way to assess whether Msize or Melemental is more
accurate, we decided to estimate the clay mass for each dust sample as the
mean ± maximum variability of the values obtained by using the two
mass estimates, Msize and Melemental. This approach should give a
reasonable approximation of the average clay content in the dust samples.
The error in the obtained clay mass varies in the range 14–100 %.
Subsequently, the mass fraction for each mineral was estimated as the ratio
of the mass of the mineral divided by the total mass of all minerals.
For the northern African and eastern Asian aerosols only, the mass
apportionment between the different clay species was based on literature
values of illite-to-kaolinite (I / K) and chlorite-to-kaolinite (Ch / I) mass
ratios (Scheuvens et al., 2013; Formenti et al., 2014). For the other
samples, only the total clay mass was estimated.
Retrieval of the LW complex refractive indices
An optical inversion procedure was applied to retrieve the LW complex
refractive index (m=n-ik) of the dust aerosols based on the simultaneous
measurements of the particle LW spectra and size. Starting from the number
size distribution, dN/dlogDgCESAM, the LW
absorption coefficient, βabs(λ), measured in CESAM can
be calculated as follows:
βabsλcalc=∑DgπDg24Qabsm,λ,DgdNdlogDgCESAMdlogDg,
where Qabs (m, λ, Dg) is the particle absorption
efficiency and πDg24dNdlogDgCESAM is the surface size distribution of the particles. As the
simplest approach, Qabs can be computed using Mie theory for
spherical particles.
Our retrieval algorithm consists of iteratively varying m in Eq. (5)
until (βabs(λ))calc matches the measured βabs(λ). However, as m is a complex number with two variables,
an additional condition is needed. According to electromagnetic theory, n
and k must satisfy the Kramers–Kronig (K–K) relationship (Bohren and
Huffmann, 1983):
nω-1=2πP∫0∞Ω⋅kΩΩ2-ω2⋅dΩ,
with ω the angular frequency of radiation (ω= 2πc/λ, (s-1)) and P the principal value of the Cauchy
integral. Equation (6) means that if k(λ) is known, then n(λ) can be calculated accordingly. Hence, the K–K relation is the additional
condition besides Eq. (5) to retrieve n and k. A direct calculation of the K–K
integral is, however, very difficult as it requires the knowledge of k over
an infinite wavelength range. A useful formulation, which permits one to
obtain the couple of n-k values that automatically satisfy the K–K
condition, is the one based on the Lorentz dispersion theory. In the Lorentz
formulation, n and k may be written as a function of the real (εr) and imaginary (εi) parts of the particle
dielectric function:
nω=12εrω2+εiω2+εrω1/2,kω=12εrω2+εiω2-εrω1/2.
In turn, εr(ω) and εi(ω) can be expressed as the sum of N Lorentzian harmonic oscillators:
εrω=ε∞+∑j=1NFjωj2-ω2ωj2-ω22+γj2ω2,εiω=∑j=1NFjγjωωj2-ω22+γj2ω2,
where ε∞=nvis2 is the real dielectric function
in the limit of visible wavelengths, nvis the real part of the
refractive index in the visible, and ωj, γj, and
Fj are the three parameters (eigenfrequency, damping factor, and
strength respectively) characterizing the jth oscillator.
In our algorithm we combined Eqs. (7a)–(7b) and (8a)–(8b) with Eq. (5) to retrieve
n-k values that allow both the reproduction of the measured βabs(λ) and the satisfaction of the K–K relationship. In practice, in
the iteration procedure only one of the two components of the refractive
index (in our case, k) was varied, while the other (n) was recalculated at
each step based on the values of the oscillator parameters (ωj,
γj, Fj) obtained from a best fit for k. In the
calculations, the initial value of k(λ) was set at k(λ)=λβabs(λ)/4π; then in the iteration
procedure, k(λ) was varied in steps of 0.001 without imposing any
constraint on its spectral shape. Initial values of the (ωj,
γj, Fj) parameters were set manually based on the initial
spectrum of k(λ). Between 6 and 10 oscillators were needed to model
the k(λ) spectrum for the different cases. The fit between
k(λ) and Eq. (7b) was performed using the Levenberg–Marquardt
technique. The iteration procedure was stopped when the condition:
(|βabs(λ))calc-βabs(λ)| < 1 % was met at all wavelengths.
Optical calculations were performed between 6 and 15 µm, within a
range where FTIR-measured scattering could be neglected (see Sect. 2.2). The
uncertainties caused by this choice are discussed in Sect. 3.1. Below 6 µm, k(λ) was
then fixed to the value obtained at 6 µm. Calculations were performed over 10 min intervals.
For each experiment and for each 10 min interval, the value of nvis to
use in Eq. (8a) was obtained from optical calculations using the
simultaneous measurements of the SW scattering and absorption coefficients
performed in CESAM (Di Biagio et al., 2017). For the various
aerosol samples considered here, the value of nvis varied between 1.47
and 1.52 with an uncertainty < 2 %. This approach is better than
the one used in DB14, where the value of nvis was manually adjusted for
successive trials. Specifically, in DB14, nvis was varied and set to
the value that allowed the best reproduction of the measured dust scattering signal
below 6 µm. As discussed in Sect. 2.2, however, only a fraction of the
total dust scattering is measured by the FTIR spectrometer. As a result, the nvis
values obtained in DB14 were considerably lower than the values generally
assumed for dust (nvis= 1.32–1.35 compared to 1.47–1.53 from the
literature; e.g., Osborne et al., 2008; McConnell et al., 2010), with a
possible resulting overall underestimation of n. Here, instead, the
nvis value was obtained based on additional SW optical measurements,
which ensured a more reliable estimate of the whole spectral n.
The validity of the proposed retrieval procedure was assessed by performing
a control experiment where ammonium sulfate aerosols were injected in the
chamber. Ammonium sulfate has been widely studied in the past and its
optical properties are well known (e.g., Toon et al., 1976; Flores et al.,
2009). The description and the results of the control experiment are
reported in Appendix Sect. A.
Caveats on the retrieval procedure for the LW refractive index
The procedure for the retrieval of the complex refractive index presented in
the previous section combines optical calculations, the Kramers–Kronig
relation, and the Lorentz dispersion theory, and was based on measurements
of spectral absorption and particle size distribution. The approach is quite
sensitive to the accuracy and representativeness of the measurements and
assumptions in the optical calculations. We now list the different points
that need to be addressed to ensure the accuracy of the retrieval procedure.
First, our optical calculations (Eq. 5) use Mie theory for spherical
particles. This is expected to introduce some degrees of uncertainties in
simulated LW spectra, especially near the resonant peaks (Legrand et al.,
2014). However, as discussed in Kalashnikova and Sokolik (2004), deviations
from spherical behavior are mostly due to the scattering component of
extinction since irregularly shaped particles have larger scattering
efficiencies than spheres. In contrast, particle absorption is much less
sensitive to particle shape. Given that our measured spectra are dominated
by absorption, we can therefore reasonably assume that Mie theory is well
suited to model our optical data. It also has to be pointed out that at
present almost all climate models use Mie theory to calculate dust optical
properties. So, with the aim of implementing our retrieved refractive
indices in model schemes, it is required that the same optical assumptions
are made in both cases, i.e., the optical theory used in models and that
used for refractive index retrieval.
Second, as discussed in Sect. 2.2, measured dust spectra at wavelengths
> 6 µm represent only dust absorption, with minimal
contribution from scattering. Dufresne et al. (2002) show that the
contribution of LW scattering from dust is quite important in the
atmosphere, especially under cloudy conditions. Therefore, the impact of
neglecting the scattering contribution has to be assessed. The retrieval
procedure used in this study is nearly independent of whether dust
extinction or absorption only is used. Indeed, the combination of Eq. (5)
with the Lorentz formulation in Eq. (7a) and (7b) ensures the retrieval of
n–k couples that are theoretically correct (fulfilling the K–K
relationship), and the specific quantity to reproduce by Eq. (5) – i.e.,
extinction or absorption – provides only a mathematical constraint on the
retrieval. Therefore, neglecting the scattering contribution to the LW
spectra has no influence on the estimates of the refractive index, and the
real and the imaginary parts obtained in this study represent both the
scattering and the absorption components of the dust extinction.
Third, our optical calculations are performed only at wavelengths
> 6 µm, while in the range 3–6 µm k(λ) is
fixed to the value obtained at 6 µm. We examine the accuracy of this
assumption. Given that, over the whole 3–6 µm range, dust is expected
to have a negligible absorption (k is close to zero; see Di Biagio et al.,
2014a), fixing k at the value at 6 µm is a reasonable approximation.
Concerning the impact of this assumption on the retrieval of n, it should be
pointed out that in the range 3–6 µm, where k is very low, the shape
of the n spectrum is determined only by the anchor point nvis, and the exact value of k is not relevant.
Uncertainty estimation
The uncertainty in the retrieved refractive index was estimated with a
sensitivity analysis. Towards this goal, n and k were also obtained by
using, as input to the retrieval algorithm, the measured βabs(λ) and size distribution ± their estimated uncertainties. The
differences between the so-obtained n and k and the n and k from the first
inversion were estimated. Then, we computed a quadratic combination of these
different factors to deduce the uncertainty in n and k.
The results of the sensitivity study indicated that the measurement
uncertainties on βabs(λ) (±10 %) and the size
distribution (absolute uncertainty on the number concentration, ±20–70 %, with values larger than 30 % found for diameters between about
0.5 and 2.0 µm) have an impact of ∼ 10–20 % on the
retrieval of n and k.
Summary of information on the soil samples used in this study.
Sample nameCollection CoordinatesGeographical zoneCountryDesert zoneTunisia33.02∘ N, 10.67∘ ENorthern AfricaTunisiaSahara desert (Maouna)Morocco31.97∘ N, 3.28∘ WNorthern AfricaMoroccoSahara desert (east of Ksar Sahli)Libya27.01∘ N, 14.50∘ ENorthern AfricaLibyaSahara desert (Sebha)Algeria23.95∘ N, 5.47∘ ENorthern AfricaAlgeriaSahara desert (Ti-n-Tekraouit)Mauritania20.16∘ N, 12.33∘ WNorthern AfricaMauritaniaSahara desert (east of Aouinet Nchir)Niger13.52∘ N, 2.63∘ ESahelNigerSahel (Banizoumbou)Mali17.62∘ N, 4.29∘ WSahelMaliSahel (Dar el Beida)Bodélé17.23∘ N, 19.03∘ ESahelChadBodélé depressionEthiopia7.50∘ N, 38.65∘ EEastern Africa andEthiopiaLake Shala National Parkthe Middle EastSaudi Arabia27.49∘ N, 41.98∘ EEastern Africa andSaudi ArabiaNefud desertthe Middle EastKuwait29.42∘ N, 47.69∘ EEastern Africa andKuwaitKuwaiti desertthe Middle EastGobi39.43∘ N, 105.67∘ EEastern AsiaChinaGobi desertTaklimakan41.83∘ N, 85.88∘ EEastern AsiaChinaTaklimakan desertArizona33.15∘ N, 112.08∘ WNorth AmericaArizonaSonoran desertAtacama23.72∘ S, 70.40∘ WSouth AmericaChileAtacama desertPatagonia50.26∘ S, 71.50∘ WSouth AmericaArgentinaPatagonian desertNamib-121.24∘ S, 14.99∘ ESouthern AfricaNamibiaNamib desert (area between theKuiseb and Ugab valleys)Namib-219.0∘ S, 13.0∘ ESouthern AfricaNamibiaNamib desert (Damaraland, rockyarea in north-western Namibia)Australia31.33∘ S, 140.33∘ EAustraliaAustraliaStrzelecki Desert
Location (red stars) of the soil and sediment samples used to
generate dust aerosols. The nine yellow rectangles depict the different
global dust source areas as defined in Ginoux et al. (2012): (1) northern
Africa, (2) the Sahel, (3) eastern Africa and Middle East,
(4) central Asia, (5) eastern Asia, (6) North America, (7) South America, (8) southern Africa, and (9) Australia.
Additionally, a sensitivity analysis was performed to test the dependence of
the retrieved LW refractive index on the accuracy of the shape of the size
distribution above 8 µm. As discussed in Sect. 2.3.2, the size
distribution, dN/dlogDgCESAM, used for the optical
calculations was measured between 0.1 and 8 µm based on SMPS,
SkyGrimm, and WELAS data. However, it was extrapolated to larger sizes by
applying a lognormal mode fit for particle diameters > 8 µm, where measurements were not available. The extrapolation was set to
reproduce the shape of the WELAS size distribution between
Dg∼ 3–4 and 8 µm. In the sensitivity study, n and
k were also obtained by using two different size distributions as input to
the retrieval algorithm, in which the extrapolation curve at
Dg > 8 µm was calculated by considering the WELAS data
± their estimated y uncertainties. The results of the sensitivity
study indicate that a change of the extrapolation curve between its minimum
and maximum may induce a variation of less than 10 % on the retrieved n
and k.
The total uncertainty in n and k, estimated as the quadratic combination of
these factors, was close to 20 %.
An additional source of uncertainty linked to the size distribution, which
however we do not quantify here, concerns the choice of performing a
single-mode extrapolation above 8 µm, which means neglecting the
possible presence of larger dust modes.
Selection of soil samples: representation of the dust mineralogical
variability at the global scale
A total of 19 soil samples were selected for experiments from a collection of 137
soils from various source areas worldwide. Their location is shown in Fig. 2. The main information on the provenance of the selected soils is
summarized in Table 2. Soils were grouped in the nine regions identified by
Ginoux et al. (2012): northern Africa, the Sahel, eastern Africa and Middle
East, central Asia, eastern Asia, North America, South America, southern
Africa, and Australia. The choice of which soils to analyze was made
according to two criteria: (1) soils had to represent all major arid and
semi-arid regions, as depicted by Ginoux et al. (2012) and (2) their
mineralogy should envelop the largest possible variability of the soil
mineralogical composition at the global scale.
A large set of soils were available for northern Africa, the Sahel, eastern
Africa and the Middle East, eastern Asia, and southern Africa. Here, the
selection was performed using as guidance the global database of Journet et al. (2014), reporting the composition of the clay (< 2 µm
diameter) and silt (< 60 µm diameter) fractions in terms of
12 different minerals. Amongst them, we analyzed the variability of the
minerals that are most abundant in dust as well as most optically relevant
to LW absorption, namely illite, kaolinite, calcite, and quartz in the clay
fraction, and calcite and quartz in the silt fraction. The comparison of the
clay and silt compositions of the soils extracted from the Journet database
with the available samples resulted in the selection of five samples for
northern Sahara, three for the Sahel, three for eastern Africa and the
Middle East, and two for eastern Asia and southern Africa, as listed in
Table 2. These soils constitute 15 of the 19 samples used in the
experiments. More information on these soils is provided in the following.
Box and whisker plots showing the variability of the soil
composition in the clay and silt fractions at the global scale, i.e., by
considering all data from the nine dust source areas identified in Fig. 2.
Data are from the soil mineralogical database by Journet et al. (2014). Dots
indicate specific mineralogical characteristics (illite-to-kaolinite mass
ratio (I / K), calcite and quartz contents, extracted from Journet et al., 2014) of
the soils used in the CESAM experiments, as listed in Table 2.
For northern Africa, we selected soils from the northern Sahara (Tunisia,
Morocco), which are richer in calcite and illite, central Sahara (Libya and Algeria),
which are enriched in kaolinite compared to illite and poor in calcite, and western
Sahara (Mauritania), which are richer in kaolinite. The three samples from the Sahel
are from Niger, Mali, and Chad (sediment from the Bodélé depression),
and are enriched in quartz compared to Saharan samples. The selected soils
from northern Africa and the Sahel represent important sources for medium-
and long-range dust transport towards the Mediterranean (Israelevich et al.,
2002) and the Atlantic Ocean (Prospero et al., 2002; Reid et al., 2003). In
particular, the Bodélé depression is one of the most active sources
at the global scale (Goudie and Middleton, 2001; Washington et al., 2003).
The three soils from eastern Africa and the Middle East are from Ethiopia,
Saudi Arabia, and Kuwait, which are important sources of dust in the Red and
Arabian seas (Prospero et al., 2002) and the North Indian Ocean (Leon
and Legrand, 2003). These three samples differ in their content of calcite,
quartz, and illite-to-kaolinite mass ratio (I / K).
For the second-largest global source of dust, eastern Asia, we considered
two samples representative of the Gobi and the Taklimakan deserts,
respectively. These soils differ in their content of calcite and quartz.
Unfortunately, no soils are available for central Asia, mostly due to the
difficulty of sampling these remote desert areas.
For southern Africa, we selected two soils from the Namib desert, one soil
from the area between the Kuiseb and Ugab valleys (Namib-1) and one soil
from the Damaraland rocky area (Namib-2), both sources of dust transported
towards the south-eastern Atlantic (Vickery et al., 2013). These two soils
present different compositions in term of calcite content and I / K ratio.
In contrast to Africa, the Middle East, and eastern Asia, a very limited
number of samples were available in the soil collection for North and South
America and Australia. Of the 19 soils used in our experiments, 4
were taken from these regions. These soils were collected in the Sonoran
Desert for North America, in the Atacama and Patagonian deserts for South
America, and in the Strzelecki desert for Australia. The Sonoran Desert is a
permanent source of dust in North America, the Atacama desert is the most
important source of dust in South America, whilst Patagonia emissions are
relevant for long-range transport towards Antarctica (Ginoux et al., 2012).
The Strzelecki desert is the seventh largest desert of Australia. No
mineralogical criteria were applied to these areas.
Mineralogy of the 19 generated aerosol samples considered in
this study. The mass apportionment between the different clay species
(illite, kaolinite, chlorite) is shown for northern African (Tunisia,
Morocco, Libya, Mauritania, Niger, Mali, Bodélé) and eastern Asian
(Gobi, Taklimakan) aerosols based on compiled literature values of the
illite-to-kaolinite (I / K) and chlorite-to-kaolinite (Ch / I) mass ratios
(Scheuvens et al., 2013; Formenti et al., 2014). For all other samples only
the total clay mass is reported.
A summary of the mineralogical composition of the 19 selected soils is
shown in Fig. 3 in comparison with the full range of variability obtained
considering the full data from the 9 different dust source areas. As
illustrated by this figure, the samples chosen for this study cover the
entire global variability of the soil compositions derived by Journet et al. (2014).
Surface size distributions in the CESAM at the peak of
dust injection for all cases analyzed in this study; the total measured dust
mass concentration and the percentage of the super-micron to sub-micron
number fraction at the peak are also reported in the legend.
The mineralogical composition measured for the 19 aerosol samples is
shown in Fig. 4. Data on the full mineralogy, also including the minimum and
the maximum of the estimated dust clay content, are provided in Table S2 in
the supporting material. The aerosol composition is dominated by clays
(∼ 46–92 % for the different samples), with variable
contents of quartz, calcite, dolomite, and feldspars. Identified clay
species are illite, kaolinite, smectite, palygorskite, and chlorite. Illite
and kaolinite are ubiquitous; smectite and palygorskite are detected in some
of the samples (Algeria, Ethiopia, Saudi Arabia, Kuwait, Arizona, and both
samples from Namibia); in contrast, chlorite is found only in the two
Chinese samples and in the Chilean samples. The estimated contribution of illite,
kaolinite, and chlorite to the total clay mass is shown in Fig. 4 for
northern Africa (Algerian sample excluded, given that smectite is also
detected in this sample) and eastern Asian aerosols. Quartz ranges from 3 to
42 % by mass in the samples, with the highest values measured for
Patagonia, Niger, Australia, Mali, and Bodélé dust. Calcite is less
than 23 %, with maxima observed for Tunisia, Morocco, and Gobi dusts.
Conversely, only minor traces of dolomite (< 3 %) are detected in
all the different samples. Finally, feldspars (orthoclase and albite)
represent less than 15 % of the dust composition.
The observations from the present study capture well the global tendencies
of the dust mineralogical compositions as observed in several studies based
on aerosol field observations, both from ground-based and airborne samples
(e.g., Sokolik and Toon, 1999; Caquineau et al., 2002; Shen et al., 2005;
Jeong, 2008; Kandler et al., 2009; Scheuvens et al., 2013; Formenti et al.,
2014). For instance, at the scale of northern Africa, we correctly reproduce
the geographical distribution of calcite, which is expected to be larger in
northern Saharan samples (Tunisia, Morocco), and very low or absent when
moving towards the southern part of the Sahara and the Sahel (Libya,
Algeria, Mauritania, Niger, Mali, and Bodélé). Similarly, we observe
an increase of the aerosol quartz content from the northern Sahara towards the
Sahel, which is well known at the regional scale of northern Africa (e.g.,
Caquineau et al., 2002). Also, we identify the presence of chlorite in the
eastern Asian samples (Gobi and Taklimakan), in agreement with field
observations in this region (Shen et al., 2005). A more direct comparison of
our data with field measurements of the dust mineralogical composition is
rather complicated due to possible differences linked to the size
distribution and representativeness of the specific sources between our data
and field measurements (Perlwitz et al., 2015). For the Niger sample
only, however, a semi-quantitative comparison can be performed against field
data of the dust mineralogy obtained for aerosols collected at Banizoumbou
during the AMMA (African Monsoon Multidisciplinary Analysis) campaign in
2006. The mineralogy for these samples was provided by Formenti et al. (2014). For a case of intense local erosion at Banizoumbou, they showed that
the aerosol is composed of 51 % (by volume) clays, 41 % quartz, and
3 % feldspars. Our Niger sample, generated from the soil collected at
Banizoumbou, is composed of 51 % (±5.1 %; by mass) clays, 37 %
(±3 %) quartz, and 6 % ( ± 0.8 %) feldspars, in very good
agreement with the field observations.
Atmospheric representativity: size distribution
The size distribution of the dust aerosols measured at the peak of the dust
injection in the chamber is shown in Fig. 5. We report in the plot the
normalized surface size distribution, defined as follows:
dSdlogDg(normalized)=1Stot⋅π4Dg2dN/dlogDgCESAM,
with Stot the total surface area. The surface size distribution is the
quantity that determines dust optical properties (see Eq. 5). The dust
surface size distributions present multimodal structures, where the relative
proportions of the different modes vary significantly between the samples.
The dust mass concentration at the peak of the injection estimated from size
distribution data varies between 2 and 310 mg m-3. These values are
comparable to what has been observed close to sources in proximity to dust
storms (Goudie and Middleton, 2006; Rajot et al., 2008; Kandler et al.,
2009; Marticorena et al., 2010). Given that the protocol used for soil
preparation and aerosol generation is always the same for the different
experiments, the observed differences in both the shape of the size
distribution and the mass concentration of the generated dust aerosols are
attributable to the specific characteristics of the soils, which may be more
or less prone to producing coarse-size particles.
Comparison of CESAM measurements at the peak of the injection with
dust size distributions from several airborne field campaigns in northern
Africa. The grey shaded area represents the range of sizes measured in CESAM
during experiments with the different northern African samples. Data from
field campaigns are: AMMA (Formenti et al., 2011), SAMUM-1 (Weinzierl et
al., 2009), and FENNEC (Ryder et al., 2013a). The shaded areas for each
dataset correspond to the range of variability observed for the campaigns
considered.
The comparison of the chamber data with observations of the dust size
distribution from several airborne campaigns in Africa is shown in Fig. 6.
This comparison suggests that the shape of the size distribution in the
chamber at the peak of the injection accurately mimics the dust distribution
in the atmosphere near sources.
Upper panel: surface size distribution measured at the peak of the
dust injection and at 30, 60, 90, and 120 min after injection for the
Algeria and Atacama aerosols. The dust mass concentration is also indicated
in the plot. Lower panel: fraction of particles remaining airborne in the
chamber as a function of time versus particle size calculated as
dNi(Dg)/dN0(Dg), where dNi(Dg) is the number
of particles measured by size class at time i (i corresponding to 30, 60, 90,
and 120 min after injection) and dN0(Dg) represents the
size-dependent particle number at the peak of the injection. Values are
compared to the estimate of Ryder et al. (2013b) for Saharan dust layers
aged 1–2 days after emission.
The time evolution of the normalized surface size distribution within CESAM
is shown in Fig. 7 for two examples taken from the Algeria and Atacama
experiments, while an example of the dust number and mass concentration
evolution over an entire experiment is illustrated in Fig. S5. The Algeria and Atacama samples were chosen as representative of
different geographic areas and different concentration levels in the
chamber. As shown in Fig. 7, the dust size distribution strongly changes
with time due to gravitational settling: the coarse mode above 5 µm
rapidly decreases, due to the larger fall speed at these sizes
(∼ 1 cm s-1 at 10 µm, compared to ∼ 0.01 cm s-1 at 1 µm; Seinfeld and Pandis, 2006), and the
relative importance of the fraction smaller than Dg= 5 µm
increases concurrently. In the chamber we are thus able to reproduce very
rapidly (in about 2 h) the size-selective gravitational settling, a process
that in the atmosphere may take about 1 to 5 days to occur (Maring et
al., 2003). In order to compare the dust gravitational settling in the
chamber with that observed in the atmosphere, the following analysis was
performed. For both Algeria and Atacama soils, the fraction of particles
remaining in suspension in the chamber as a function of time versus particle
size was calculated as dNi(Dg)/dN0(Dg), where
dNi(Dg) is the number of particles measured by size class at time
i (i corresponding to 30, 60, 90, and 120 min after injection) and
dN0(Dg) represents the size-dependent particle number at the peak
of the injection. The results of these calculations are shown in the lower
panels of Fig. 7, where they are compared to the fraction remaining airborne
after 1–2 days obtained in the field study by Ryder et al. (2013b) for
mineral dust transported out of northern Africa in the Saharan air layer
(Karyampudi et al., 1999), that is, at altitudes between 1.5 and 6 km above
sea level. The comparison indicates that the remaining particle fraction
observed 30 min after the peak of the injection is comparable to that
obtained by Ryder et al. (2013b) for particles between ∼ 0.4
and 3 µm for the Algeria case, and ∼ 0.4 and 8 µm for the Atacama case, but that the depletion is much faster for both
smaller and larger particles. This suggests, on the one hand, that the
number fraction of coarse particles in the chamber depends on the initial
size distribution, that is, on the nature of the soil itself. On the other
hand, it shows the limitation of the four-blade fan in providing a vertical
updraft sufficient to counterbalance the gravimetric deposition for
particles larger than about 8 µm. This point, however, is not
surprising since it is clear that in the laboratory it is not possible to
reproduce the wide range of dynamical processes that occur in the real
atmosphere, and so to obtain a faithful reproduction of dust gravitational
settling and the counteracting re-suspension mechanisms. Nonetheless, it
should be noted that the rate of removal is higher at the earlier stages of
the experiments than towards their end. The size-dependent particle
lifetime, defined as the value at which dN/dN0 is equal to 1/e (McMurry
and Rader, 1985), is relatively invariant for particles smaller than
Dg < ∼ 2 µm (> 60 min). This indicates
that no significant distortion of the particle size distribution occurs
after the most significant removal at the beginning of the experiment, and
that the fine-to-coarse proportions are modified with time in a manner
consistent with previous field observations on medium- to long-transport
(e.g., Maring et al., 2003; Rajot et al., 2008; Reid et al., 2008; Ryder et
al., 2013b; Denjean et al., 2016).
Dust extinction coefficient measured in the LW spectral range for
the 19 aerosol samples analyzed in this study. Data for each soil
refer to the peak of the dust injection in the chamber. Note that the
y scale is different for northern Africa–the Sahara compared to the other
cases. Main absorption bands by clays at 9.6 µm, quartz (Q) at 9.2
and 12.5–12.9 µm, kaolinite (K) at 10.9 µm, calcite (C) at 7.0
and 11.4 µm, and feldspars (F) at 8.7 µm are also indicated in
the spectra.
Dust LW extinction and complex refractive index spectra for the
different source regions
Figure 8 shows the dust LW spectral extinction coefficients measured at the
peak of the injection for the 19 aerosol samples. As discussed in
Sect. 2.2, the spectra in Fig. 8 show the contribution of dust scattering
below 6 µm, while the absorption spectrum only is measured above 6 µm. In this wavelength range, significant differences are observed
when comparing the samples, which in turn are linked to differences in their
mineralogical composition.
Figure 8 allows the identification of the spectral features of the minerals
presenting the strongest absorption bands, in particular in the 8–12 µm atmospheric window (Table 3). The most prominent absorption peak is found
around 9.6 µm for all samples, where clays have their Si–O stretch
resonance peak. The shape around the peak differs according to the relative
proportions of illite and kaolinite in the samples, as is illustrated with
the results for the Tunisia, Morocco, Ethiopia, Kuwait, Arizona, Patagonia,
Gobi, and Taklimakan samples (richer in illite) compared to the Libya,
Algeria, Mauritania, Niger, Bodélé, Saudi Arabia, and Australia
samples (richer in kaolinite). Aerosols rich in kaolinite also show a
secondary peak at ∼ 10.9 µm. The spectral signature of
quartz at 9.2 and 12.5–12.9 µm is ubiquitous, with a stronger
contribution in the Bodélé, Niger, Patagonia, and Australia samples.
Aerosols rich in calcite, such as the Tunisia, Morocco, Saudi Arabia,
Taklimakan, Arizona, Atacama, and Namib-1 samples show absorption bands at
∼ 7 and 11.4 µm. Conversely, these are not present in
the other samples and in particular in none of the samples from the Sahel.
Finally, the contribution of feldspars (albite) at 8.7 µm is clearly
detected only for the Namib-1 sample.
Position of LW absorption band peaks (6–15 µm) for the main
minerals composing dust. Montmorillonite is taken here as representative for
the smectite family. For feldspars literature data are available only for
albite.
Mineral speciesWavelength (µm)ReferenceIllite9.6Querry (1987)Kaolinite9.0, 9.6, 9.9, 10.9Glotch et al. (2007)Montmorillonite9.0, 9.6Glotch et al. (2007)Chlorite10.2Dorschner et al. (1978)Quartz9.2, 12.5–12.9Peterson and Weinman (1969)Calcite7.0, 11.4Long et al. (1993)Gypsum8.8Long et al. (1993)Albite8.7, 9.1, 9.6Laskina et al. (2012)
Extinction spectra measured at the peak of the dust injection and
at 30, 60, 90, and 120 min after injection for the Algeria and Atacama
aerosols.
The intensity of the absorption bands depend strongly on the particle size
distribution, in particular on the contribution of the aerosol super-micron
fraction, as well as on the total dust mass concentration. These, as
discussed in the previous section, are associated with the specific
characteristics of each of the soils used and their propensity for dust
emission. The highest values of dust absorption that can be seen in Fig. 8
for the 8–12 µm spectral region appear for the Bodélé aerosol
sample. In this particular sample, the super-micron particles represent
45 % of the total particle number at the peak of the injection, and this
sample showed the highest mass concentration in the chamber (310 mg m-3). Conversely, the lowest absorption is measured for the aerosols
from Mauritania, Mali, Kuwait, and Gobi, for which the super-micron particle
fraction and the mass concentrations are lower.
The intensity of the spectral extinction rapidly decreases after injection,
following the decrease of the super-micron particle number and mass
concentration. As an example, Fig. 9 shows the temporal evolution of the
measured extinction spectrum for the Algeria and Atacama aerosols. The
intensity of the absorption band at 9.6 µm is about halved after 30 min and reduced to ∼ 20–30 % and < 10 % of its
initial value after 60 and 90–120 min, respectively. Because of the
size-dependence of the mineralogical composition, notably the relative
proportions of quartz and calcite with respect to clays (Pye et al., 1987),
settling could also modify the spectral shape of the extinction spectrum.
This effect was investigated for two example cases, Algeria and Atacama, by
looking at the temporal evolution of the ratios of the measured extinction
coefficient in some specific mineral absorption bands. Changes would
indicate that the time variability of the mineralogical composition is
optically significant. For the Algeria case, we have considered the quartz
(12.5 µm) versus clay (9.6 µm) bands, and for the Atacama case
the calcite (∼ 7 µm) versus clay (9.6 µm) bands.
For both cases, the calculated ratios do not change significantly with time,
i.e., they agree within error bars: for Algeria, the quartz-to-clay ratio is
0.21 ± 0.03 at the peak of the injection and 0.25 ± 0.04 120 min
later; for Atacama, the calcite-to-clay ratio is 0.73 ± 0.10 and
0.67 ± 0.09 for the same times. Similar results were also obtained for
the other samples, with the exception of Saudi Arabia and Morocco, for which
we observed an increase of the calcite-to-clay ratio with time. The time
invariance of the quartz-to-clays and calcite-to-clays ratios observed for
the majority of the analyzed aerosol samples agrees with the observations of
the size-dependent dust mineralogical composition obtained by Kandler et al. (2009). These authors showed that in the super-micron diameter range up to
∼ 25 µm, i.e., in the range where dust is mostly
LW-active, the quartz-to-clay and calcite-to-clay ratios are approximately
constant with size. This would suggest that the loss of particles in this
size range should not modify the relative proportions of these minerals, and
thus their contributions to LW absorption. Nonetheless, the different
behavior observed for Saudi Arabia and Morocco would possibly indicate
differences in the size-dependence of the mineralogical composition compared
to the other samples.
Real (n) and imaginary (k) parts of the dust complex refractive
index 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 at ∼±20 %.
For each soil, the estimated real (n) and imaginary (k) parts of the complex
refractive index are shown in Fig. 10. The reported n and k correspond to
the mean of the 10 min values estimated between the peak of the injection
and 120 min later. This can be done because, for each soil, the time
variation of the complex refractive index is moderate. Standard deviations
are < 10 % for n and < 20 % for k. The data in Fig. 10
are reported by considering as error the absolute uncertainty in n and k,
previously estimated at ∼ 20 %. Figure 10 shows that the
dust refractive index widely varies both in magnitude and spectral shape
from sample to sample, following the variability of the measured extinction
spectra. The values for the real part n span the range 0.84–1.94, while
the imaginary part k is between ∼ 0.001 and 0.92. The
imaginary part, k, is observed to vary both from region to region, and also
within each region. The differences in k values obtained for different
sources within the same region are in most cases larger than the estimated k
uncertainties. For specific regions (northern Africa, South America), the
variability for k is of a similar order of magnitude to the variability at
the global scale. Conversely, the n values mostly agree within error for all
soils, both within a region and from one region to another. Exceptions are
observed only at wavelengths where strong signatures from specific minerals
are found in the n spectrum, as for example at 7 µm due to calcite
(Saudi Arabia and Gobi samples), or that of quartz at 9.2 µm
(Patagonia and Australia samples).
DiscussionPredicting the dust refractive index based on its mineralogical
composition
Our results show that the LW refractive index of mineral dust, having
different mineralogical compositions, varies considerably. Nevertheless, at
wavelengths where the absorption peaks due to different minerals do not
overlap, this variability can be predicted from the composition-resolved
mass concentrations. These considerations are illustrated in Fig. 11a, where
we relate the mean values of the dust k in the calcite, quartz, and clay
absorption bands between 7.0 and 11.4 µm to the percent mass fraction
of these minerals in the dust. Mean k values were calculated as averages
over the filter sampling times. For calcite and quartz (resonance peaks at
7.0, 9.2, and 11.4 µm), this relation is almost linear. These two
minerals are commonly large in grain size and well crystallized. Their
quantification by XRD is certain and they produce a strong and
well identified absorption peak in the LW. Nonetheless, there seems to be a
lower limit of the percent mass of calcite (around 5 %) that gives rise to
absorption at 7 µm, and therefore measurable k values. Conversely, at
11.4 µm, non-zero k values are obtained even in the absence of
calcite, due to the interference of the calcite peak and the clay resonance
bands. At this wavelength the correlation between k and the calcite mass
fraction is also very low.
Poorer or no correlation is found between k and the percent mass fraction in
the absorption bands of clays at 9.6 and 10.9 µm. This different
behavior is not unexpected. Clay minerals such as kaolinite, illite,
smectite, and chlorite are soil-weathering products containing aluminum and
silicon in a 1:1 or 1:2 ratio (tetrahedral or octahedral structure,
respectively). As a consequence, the position of their vibrational peaks is
very similar (Dorschner et al., 1978; Querry, 1987; Glotch et al., 2007). In
the atmosphere, these minerals undergo aging by gas and water vapor
adsorption (Usher et al., 2003; Schuttlefield et al., 2007). As a result of
the production conditions in the soils (weathering) and aging in the
atmosphere, their physical and chemical conditions (composition,
crystallinity, aggregation state) might differ from one soil to another, and
from that of mineralogical standards. That is the reason why XRD
measurements of clays in natural dust samples might be erroneous, and why we
prefer to estimate the clay fraction indirectly. Nonetheless, the indirect
estimate is also prone to error, and depends strongly on an independent
estimate of the total mass (which, in the presence of large particles, can be
problematic) as well as the correct quantification of the non-clay fraction.
This is likely reflected in the large scatter observed in Fig. 11a when
trying to relate the k value distribution to the corresponding percent mass
of clays. These considerations also affect the speciation of clays, and
explain the similar results obtained when separately plotting the spectral
k values against the estimated kaolinite or illite masses. The superposition
of the resonance bands of these two clays, as well as those of the
smectites, which in addition are often poorly crystallized and therefore
difficult to detect by XRD, as well as those in the quartz absorption band
at 9.2 µm, suggests that a more formal spectral deconvolution
procedure based on single mineral reference spectra is needed to understand
the shape and magnitude of the imaginary refractive index in this spectral
band.
Imaginary part of the complex refractive index (k) versus the
mineral content (in % mass) for the bands of calcite (7.0 and 11.4 µm), quartz (9.2 µm), and clays (9.6 and 10.9 µm). For
the band at 9.6 µm the plot is drawn separately for total clays, and
illite and kaolinite species. The linear fits are also reported for each
plot. Linear fits were performed with the FITEXY.PRO Interactive Data Language (IDL) routine taking into
account both x and y uncertainties in the data.
Same as Fig. 11a for the real part of the complex refractive
index (n).
Similarly to Fig. 11a, Fig. 11b shows the relationship between the mean
values of the dust refractive index versus the percent mass fraction of
calcite, quartz, and clays at 7.0, 9.2, 9.6, 10.9, and 11.4 µm for
the real part. The correlation between n and the mineral percent mass
fraction is found to be statistically significant only for the calcite band
at 7.0 µm, while for all other cases, very poor or no correlation is
found. The real refractive index of dust is also almost constant at all
bands (with the exception of that at 7.0 µm) regardless of the change
in particle composition.
Dust complex refractive index versus size distribution during
atmospheric transport
Quantifying the radiative impact of dust depends not only on the ability to
provide spatially resolved optical properties, but also on the accurate
representation of the possible changes of these properties during transport.
In the LW, this effect is amplified by the changes in the size distribution,
particularly the loss of coarse particles. Our experiments accurately
capture the overall features of the dust size distribution, including the
extent and modal position of the coarse-particle mode. However, the
depletion rate with time for coarse particles is higher than observed in the
atmosphere (e.g., Ryder et al., 2013b). The size distribution after 30 min still contains a significant, relatively invariant, but
not-predictable fraction of coarse particles. This calls for two
considerations: (1) the refractive indices obtained at the early stage of the
experiments (within 30 min after the dust injection) are representative
of dust at short to medium ranges of transport (∼ 1–2 days
after emission); (2) the refractive indices after 30 min of duration are
likely to represent long-range transported dust still containing coarse
particles in a fraction that will depend on the original soil. In our study,
the calculated refractive indices do not change with time in parallel with
the observed changes in the size distribution, thus suggesting that a
constant value can be assumed close to the source and following transport.
Still, further experiments taking into account only the fine fraction of the
aerosols will be needed to constrain the size-dependence of the refractive
index.
Comparison of results obtained in this study with
literature-compiled values of the dust refractive index in the LW.
Literature values are taken from Volz (1972) for rainout dust collected in
Germany, Volz (1973) for dust collected at Barbados, Fouquart (1987) for
Niger sand, Di Biagio et al. (2014a) for dust from Niger and Algeria, and the
OPAC database (Hess et al., 1998). The region in gray in the plot indicates
the full range of variability obtained in this study, and the dashed line is
the mean of n and k obtained for the different aerosol samples. The legend
in the top panel identifies the line styles used in the plot for the
literature data.
Comparison with the literature
In Fig. 12, we compare our results with estimates of the dust refractive
index reported in the literature. We consider data by Volz (1972, 1973) for
dust collected in Germany and at Barbados, Fouquart et al. (1987) for Niger
sand, and Di Biagio et al. (2014a) for dust from Algeria and Niger. We also
report data for dust as assumed in the OPAC database (Optical Properties of
Aerosols and Clouds; Hess et al., 1998; Koepke et al., 2015). These
literature data, in particular those of OPAC and Volz (1973), are the most
frequently used references in climate modeling and remote sensing
applications. Because of their limited regional span, the literature data
clearly cannot do justice to the full range of magnitude and of the spectral
variability of the LW complex refractive index that is presented in our
dataset. In particular, clearly none of the published data represent the
contribution of calcite at ∼ 7 µm. Some of the data
(Volz, 1973; Fouquart et al., 1987; OPAC) overestimate k above 11 µm,
where the 12.5–12.9 µm quartz absorption band is found. The best
correspondence, especially above 10 µm, is found with Di Biagio et al. (2014a). In the 8–12 µm atmospheric window, the agreement with
our estimated mean value is moderate, but the range of variability around
the mean and its spectral dependence are underrepresented. A shift towards
larger wavelengths is also observed for the main clay absorption peak at
∼ 9.6 µm for Volz (1973) and Di Biagio et al. (2014a),
which is possibly linked to the different method used in these studies to
retrieve the complex refractive index (pellet spectroscopy approach)
compared to our data. The agreement is even less satisfactory for the real
part of the refractive index (upper panel of Fig. 12), which is
overestimated in OPAC and Volz (1973) and underestimated in Fouquart et al. (1987). As discussed in Di Biagio et al. (2014a), differences for the real
part between the various studies come mostly from the different methods used
to estimate the dust refractive index. The methods used in the literature
most often do not fulfil the Kramers–Kronig relationship for the n–k
couples. The only dataset that fulfils the Kramers–Kronig relationship is
Fouquart et al. (1987), but that has the drawback of underestimating n as a
consequence of the low value of nvis (∼ 1) assumed in the
retrieval.
On average, the differences between our mean refractive index and the values
reported in the literature are large enough to have a significant effect on
radiative transfer. For example, at 10 µm the absolute difference
between our retrieved mean k and the k by OPAC and Volz (1973) is between
0.15 and 0.6. Highwood et al. (2003) have estimated that a change of about
0.3 in k at 10 µm, which corresponds to half of the difference we
have compared to Volz (1973), may result in up to 3 K change in the modeled
sky brightness temperature, the quantity measured by infrared remote
sensing. To give a comparison, the same order of brightness temperature
difference at 10 µm was found between clear sky and dusty conditions
for an optical depth of ∼ 1.5 at 0.55 µm. This example
illustrates the sensitivity of the brightness temperature to the differences
in the imaginary part of the refractive index that we find between our data
and those in the literature. Another example, of even more relevance for
climate applications, is provided by Di Biagio et al. (2014a), who have
shown that a 0.3 variation in k is sufficient to induce up to
∼ 15 % of change of the radiative forcing efficiency at 10 µm at the TOA.
Conclusions and perspectives
In this study we have presented a new set of laboratory in situ measurements
of the LW extinction spectra and complex refractive indices of mineral dust
aerosols from 19 natural soils from source regions in northern Africa,
the Sahel, Middle East, eastern Asia, North and South America, southern Africa,
and Australia. These sources are representative of the heterogeneity of the
dust composition at the global scale. Consequently, the envelope of
refractive index data obtained in this study can adequately represent the
full range of variability for dust as a function of the global variability of
its mineralogical composition. These data are expected to be widely
applicable for both radiative transfer modeling and remote sensing
applications.
The experiments described here were conducted in the realistic and dynamic
environment of the 4.2 m3 CESAM. Dust aerosols generated in the
chamber are characterized by a realistic size distribution, including both
the sub-micron and the super-micron fraction, and they have an
atmospherically representative mineralogical composition, including the main
LW active minerals, such as quartz, clays, and calcite. The complex
refractive index of dust at LW wavelengths is obtained following a rigorous
approach that permits the determination of n–k couples that satisfy the
Kramers–Kronig relation. Refractive index data from the present study are
much more reliable than those provided by DB14, given that a better estimate
of nvis was used in the retrieval algorithm. The average uncertainty in
the obtained n and k is ∼ 20 %.
The main results from this work can be summarized as follows.
The imaginary LW refractive index, k, of dust varies strongly both in
magnitude and spectral shape as a result of the variability of the particle
mineralogy related to the specific emission sources. The value of k is
observed to vary both from region to region, as well as within the same
region for varying sources. Conversely, for the real part n, values are
observed to agree within error for the most part of the spectrum for all
dust samples. This implies that while a constant n can be taken for dust
from different sources, a varying k should be used both at the global and at
the regional scale. The available literature data (Volz, 1972, 1973;
Fouquart et al., 1987; OPAC, Hess et al., 1998; Koepke et al., 2015) used
nowadays in climate models and satellite retrievals do not adequately
represent either the magnitude, or the spectral features and the variability
of the LW refractive index of mineral dust observed in our dataset. In
consequence, we recommend the use of source-specific extinction
spectra and/or imaginary refractive indices rather than generic values in models
and remote sensing applications.
We observe a linear relationship between the magnitude of the imaginary
LW refractive index and the mass concentration of specific minerals, i.e.,
quartz and calcite. This opens the possibility of providing predictive
relationships to estimate the LW refractive index of dust at specific bands
based on an assumed or predicted mineralogical composition, or conversely,
to estimate the dust composition (even partially) from measurements of LW
extinction at specific wavebands. This could have important implications for
the representation of LW optical properties of dust in climate models, which
have started to incorporate the representation of dust mineralogy in their
schemes (Scanza et al., 2015; Perlwitz et al., 2015). In addition, the
possibility to relate the mass of minerals to the absorption at specific
bands, such as for example the calcite band at ∼ 7 µm,
implies that the LW extinction spectra measured from space could be used to
distinguish between different dust sources.
The spectral shape of the dust extinction spectrum does not seem to
change significantly with time as a result of the loss of coarse particles
by gravitational settling. This suggests that, despite the dust coarse mode
being increasingly depleted, the relative proportions of minerals do not
change significantly with time or at least that their changes do not affect
the overall optical response of the dust samples. In consequence, the
retrieved LW refractive index (real and imaginary) does not change, and
therefore can be used to represent short- to medium-range transport
conditions. This finding supports the common practice in global models to
treat the dust LW refractive index as static during transport. This also
implies that to represent the dust LW refractive index versus mineralogy, models
just have to reproduce the dust composition at the source, without the
necessity of following its changes during transport, which could be a
challenge. This would considerably simplify the representation of dust
mineralogy in models.
The unique dataset presented in this study should be particularly useful for
improving the dust–climate interactions within regional and global models,
and to take into account the geographical variability of the dust LW
refractive index, which at present is not represented. This will allow
the obtainment of a more realistic representation of the dust LW effect and its
radiative forcing upon climate. To date, as shown in Boucher et al. (2013),
even the sign of the dust direct effect remains unknown. In this regard, we
estimate a lower dust absorption than most of the literature data (see k curves
in Fig. 12), and in particular than those of Volz (1973) and OPAC, which are
the reference data most frequently used in climate models. The integral of
the Volz and OPAC dust refractive indices (imaginary part) between 3 and 15 µm, for example, is about 15–20 % larger compared to the integral
obtained from our max k curve; an up to about 1 order of magnitude
overestimate is found when the integral of the Volz and OPAC k over the 3–15 µm range is compared to the integral of our min k curve. As a
consequence of this, we can conclude that the use of the Volz and OPAC data
may introduce a systematic bias in modeling dust radiative effects at LW
wavelengths.
The use of the data from the present study also will help to reduce
uncertainties in satellite retrievals, thus contributing to improving the
remote sensing capability over regions affected by dust (Clarisse et al.,
2013; Vandenbussche et al., 2013; Capelle et al., 2014; Cuesta et al.,
2015).
The work presented in this paper also opens various perspectives: first, as already pointed out, the results of the present study clearly
suggest that the LW refractive index of dust varies at the regional scale,
as can be observed in Fig. 10 for northern Africa, the Sahel, the Middle East,
eastern Asia, South America, and southern Africa. For some particular
regions, e.g., northern Africa and South America, the extent of this
variability is comparable to the variability obtained at the global scale.
The dust samples used in this study were chosen to cover the full
heterogeneity of the dust composition at the global scale. However, the
available samples do not necessarily explore the possible full variability
of the dust composition within each region. This regional variability needs
to be characterized further in order to better assess the influence of dust
on regional climate.
Second, the possibility of a more formal spectral deconvolution procedure,
based on single mineral reference spectra to understand the shape,
magnitude, and temporal variability of the refractive index in all different
spectral bands, must be investigated. This could strongly help in finding robust
relationships linking the dust refractive index to the particle mineralogy.
Third, further experimental efforts by increasing the lifetime and selecting
size classes will be needed to better verify the applicability of the
obtained refractive indices to long-range transport conditions. Also, the
experiments described here were done in conditions when dry deposition is
the only aging process. Other aging processes, such as heterogeneous
reactions, mixing with other aerosol types, or water uptake, have to be
investigated to evaluate their impact on the LW refractive index during
transport. For instance, some studies suggest a possible enhancement of dust
LW absorption over specific bands if water uptake occurs (Schuttlefield et
al., 2007) or if dust mixes with soot (Hansell et al., 2011).
Data availability
Complex refractive index data are provided in the Supplement.
CESAM data are available upon request to the contact authors.
Control experiment with ammonium sulfate particles
In order to validate the methodology applied in this study, a control
experiment was performed on ammonium sulfate aerosols. Particles were
generated from a 0.03 M solution of ammonium sulfate using a constant output
atomizer (TSI, model 3075). The aerosol flow passed through a diffusion
drier (TSI, model 3062), to be then injected in the CESAM at a flow
of 10 L min-1 for 10 min. At the peak of the injection the aerosol
concentration reached ∼ 160 µg m-3 and the size
distribution was mono-modal and centered at ∼ 0.06 µm.
The LW spectrum of ammonium sulfate measured in CESAM at the peak of the
injection is shown in Fig. A1 for the 2–15 µm range. Absorption bands
attributed to gas-phase water vapor and CO2 present in the chamber
during the experiments are indicated in the plot. The 2–15 µm
spectral region includes three of the four active vibrational modes of
ammonium sulfate: ν3(NH4+) (3230 cm-1 or
3.10 µm), ν4(NH4+) (1425 cm-1 or
7.02 µm; not identified in the plot due to its superposition with the
water vapor band), and ν3(SO2-4) (1117 cm-1 or 8.95 µm). The ν4(SO2-4) is
at 620 cm-1 (16.12 µm), and thus below the measurement range of the
FTIR spectrometer. The retrieval algorithm described in Sect. 3 was applied to estimate
the complex refractive index of ammonium sulfate aerosols. Calculations were
performed only in the 8–10 µm range where the ν3(SO2-4) band is found and where the contamination by water
vapor is minimal.
Left panel: long-wave spectrum of ammonium sulfate measured in
CESAM in the 2–15 µm range. The vibrational modes ν3(NH4+) (3230 cm-1 or 3.10 µm) and
ν3(SO2-4) (1117 cm-1 or 8.95 µm)
of ammonium sulfate are identified in the plot. Absorption bands attributed
to gas-phase water vapor and CO2 present in the chamber during
experiments are also indicated. The rectangle in the plot indicates the
spectral region where the retrieval of the complex refractive index was
performed. Right panel: real and imaginary parts of the refractive index
obtained by optical closure. The results are compared with the ammonium
sulfate optical constants from Toon et al. (1976).
The value of nvis to use as input to the algorithm
was set at 1.55, based on the analysis of simultaneous SW optical data (not
discussed here). The results of the calculations are shown in Fig. A1. The
comparison with the optical constants provided by Toon et al. (1976), also
shown in Fig. A1, is very satisfactory. A small bias is observed for our
retrieved n compared to the values by Toon et al. (1976). This can possibly
be linked to the method used in Toon et al. (1976) to retrieve the real part
of the refractive index, which is based on the measurement of the normal
incident reflectivity of a bulk sample instead of absorption data of aerosol
particles, as in our experiments. Overall, the results of the control
experiment indicate that the CESAM approach and the proposed retrieval
algorithm allow reproduction of the LW spectral signature of
the aerosols and estimating accurately their complex refractive index.
The Supplement related to this article is available online at doi:10.5194/acp-17-1901-2017-supplement.
Claudia Di Biagio, Paola Formenti, Yves Balkanski, and Jean-François Doussin designed the
experiments and discussed the results. Claudia Di Biagio realized the experiments
and performed the full data analysis with contributions by Paola Formenti, Lorenzo Caponi,
Mathieu Cazaunau, Edouard Pangui, Sandrine Caquineau, and Jean-François Doussin. Sophie Nowak
performed the XRD measurements. Meinrat O. Andreae, Konrad Kandler, Thuraya Saeed,
Stuart Piketh, David Seibert, and Earle Williams collected the soil samples used for
experiments. Emilie Journet participated in the selection of the soil samples
for experiments and contributed to the scientific discussion. Claudia Di Biagio,
Paola Formenti, and Yves Balkanski wrote the paper with comments from all
co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
This work has received funding from the European Union's Horizon 2020
research and innovation programme through the EUROCHAMP-2020 Infrastructure
Activity under grant agreement no. 730997. It was supported by the French
national programme LEFE/INSU, by the OSU-EFLUVE (Observatoire des Sciences de
l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie) through dedicated
research funding, by the CNRS-INSU by supporting CESAM as national facility,
and by the project of the TOSCA program of the CNES (Centre National des
Etudes Spatiales).
Claudia Di Biagio was supported by the CNRS via the Labex L-IPSL, which is
funded by the ANR (grant no. ANR-10-LABX-0018). Konrad Kandler received
support from the German Science Foundation DFG (KA 2280/2). Field sampling in
Saudi Arabia was supported by a grant from King Saud University. The authors
strongly thank S. Alfaro, B. Chatenet, M. Kardous, R. Losno, B. Marticorena,
J. L. Rajot, and G. Vargas, who participated in the collection of the soil
samples from Tunisia, Niger, Atacama, Patagonia, and the Gobi desert used in
this study, and S. Chevailler, G. Landrot, and E. Fonda for their
contribution in the WD-XRF and XANES analyses. The authors wish to
acknowledge J. L. Rajot and two anonymous reviewers for their helpful
comments.
Edited by: E. Gerasopoulos
Reviewed by: two anonymous referees
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