Introduction
Predicting the occurrence and evolution of clouds at temperatures (T) below
273 K remains a challenge for global and regional climate models
. One source of uncertainty is the effect of certain
aerosol particles which influence the cold cloud microphysics by acting as
ice nucleating particles (INPs). Ice formation affects precipitation, cloud
lifetime, and radiative properties of these clouds and, thus, global climate . Mineral dust
particles have been known as efficient INPs at T ≤ 253 K for more
than 60 years (e.g., , and references given in
) and have been observed to nucleate ice in
the atmosphere in various regions worldwide . However, the molecular mechanisms and
particle properties triggering ice nucleation on atmospheric mineral dusts
are still the subject of ongoing research. Supercooled cloud droplets can
freeze homogeneously at temperatures below 235 K, without the aid of an INP
. At higher temperatures the surface of an INP
is required to overcome the energy barrier of freezing. Traditionally, four
pathways of ice nucleation are differentiated :
deposition nucleation, where ice forms on an INP directly from the vapor
phase;
condensation freezing, in which ice forms during the process of water
condensing on an INP;
immersion freezing, where an INP immersed in a supercooled cloud droplet
initiates freezing;
contact freezing, where the interaction of an INP with the surface of a
supercooled droplet either from the outside or inside of the droplet leads to
freezing.
Ice formation in clouds with top temperatures above 263 K is often observed
, but only very few aerosol particle types have been
identified to nucleate ice at these warm temperatures. These are mainly
biological particles, such as certain bacterial strains or macromolecules
. The ice nucleation
ability of soot at heterogeneous freezing
temperatures is still debated as contradicting results were observed,
spanning from hardly any ice nucleation ability at T> 236 K
to up to 3 % of soot particles active in the immersion
mode . Similarly, the reported freezing behavior of
secondary organic aerosol particles varies from inefficient to comparably
efficient . Aerosol particles from marine sources are believed to be
important INP at remote locations and are subject of current research
. Recently, the
K-feldspar microcline and the Na-feldspar albite, both minerals found in
atmospheric dust, have been identified to nucleate ice at temperatures up to
271 K .
For the implementation of ice nucleation into climate models, a simplistic
description of ice formation on different INP types is required. Existing
parameterizations for dust can be based on laboratory experiments using
commercially available dusts such as Arizona Test Dust (ATD) including mostly
pure clay mineral samples such as illite, kaolinite, and montmorillonite
, dust samples collected from
the surface , or on in situ measurements in the atmosphere
at locations often distant from major dust sources . One recent study by combines laboratory data of
two surface-collected dust samples with results from two flight campaigns
over the Pacific Ocean and the Caribbean Sea within dust layers that
underwent long-range transport from Asia and the Sahara, respectively. The
authors found relatively good agreement amongst the different samples. They
concluded that both a parameterization from as well as
one adapted from were applicable for predicting atmospheric
mineral dust INP concentrations.
For laboratory ice nucleation experiments, dust samples collected from the
surface typically have to be sieved or milled, which may break up larger
agglomerates and alter the size-dependent mineralogy .
This could significantly alter the ice nucleation ability of these dust
particles in laboratory experiments compared to their ambient ice nucleation
ability. It has been shown that milling of hematite or quartz particles leads
to an increase in ice nucleation efficiency compared to the unmilled samples
. It has been speculated that this is also
part of the reason for ATD, a commercially available dust sample that is
washed and milled after collection from a certain desert area in Arizona,
being more ice nucleation active than natural unprocessed dust samples
.
Due to their high abundance, for many decades the immersion freezing behavior
of atmospheric dust was attributed largely to clay minerals and ice
nucleation on relatively pure clay mineral samples was often studied in more
detail . Recently, showed
that compared to other minerals, feldspar particles are more efficient
immersion mode INPs at temperatures
above 245 K. The K-feldspars (microcline, orthoclase, and sanidine) were
found to be more ice nucleation active than the Na / Ca-feldspars albite,
anorthite, and other plagioclase feldspars . Amongst the K-feldspars microcline appears to be the most ice
nucleation active , even nucleating
ice at a temperature of 271 K . Feldspar is a highly
complex group of minerals and, depending on the source, mineralogically
similar samples can have different ice nucleation abilities
. Thus it remains an open question if and how feldspar is
affecting the ice nucleation behavior of dust in the atmosphere and if it is
causing ice nucleation in clouds at T> 263 K. A high variability in ice
nucleation activity was found for quartz, with some quartz samples being more
ice nucleation active in the immersion mode than clay minerals but always
less than the feldspars . It is
suspected that functional groups on the surface of feldspars and quartz are
responsible for their higher ice nucleation ability but it
is unknown where the high variability stems from. Quartz is commonly
(5–50 wt %) found in atmospherically transported Saharan dust samples
. A recent study
by investigated the ice nucleation ability of
surface-collected samples from eight different arid regions worldwide and
several single-mineral reference samples using differential scanning
calorimetry. The authors found at maximum a 6 K spread in freezing
temperatures of emulsion experiments amongst surface-collected samples from
different atmospheric dust source regions. They confirmed the exceptional
freezing ability of microcline but found only a minor fraction (4 wt %) in
one of the samples from the dust source regions studied. Their samples
contained quartz fractions between 1 and 26 wt %, K-feldspar fractions
between 0 and 10 wt %, and plagioclase fractions between 0 and 22 wt %.
It has been observed that the size distribution of dust changes during its
emission and transport compared to dust on the surface. This leads to
variations in the mineralogical composition of the dust , as the mineralogical composition is size
dependent due to differences in the hardness, cleavage, shape, and reactivity
of minerals. Hard minerals such as feldspar tend to be dominant in the large
grains whereas soft minerals are concentrated in the small size fraction
(e.g., clay minerals). Saltation and dust emission strength depend on
several factors and are nonlinear in dust particle size
. During
atmospheric transport, gravitational settling or wet deposition further
alters the size distribution. Additionally, minerals which act as cloud
condensation nuclei or INPs are
preferably lost.
Airborne dust particles smaller than 20 µm over the North Pacific
have been found to contain 10 to over 50 wt % clay minerals such as
illite, kaolinite, or smectite, 4–40 wt % quartz, and 4–75 wt %
plagioclase feldspar . found that dust
particles over Morocco consist of about 30 wt % clay minerals (illite,
kaolinite, chlorite), less than 5 wt % plagioclase but over 20 wt %
K-feldspar, less than 10 wt % quartz, and less than 10 wt % calcite in
the size range below about 20 µm geometric diameter. Other
identified minerals in the airborne dust were rutile, gypsum, dolomite,
hematite, or halite. Similar results were found by over
Israel. found a north–south gradient of the illite to
kaolinite ratio of soil samples in the Sahara with higher values in the
northern and western part of the Sahara and lower values in the southern and
central Sahara.
Non-mineral matter, which can become internally or externally mixed with the
mineral dust before or after emission, may affect the ice nucleating behavior
of the dust. Sulfuric acid or
secondary organic aerosol coating has been observed to
decrease the ice nucleating ability while exposure to ozone
or the presence of ammonium sulfate has been suggested to
improve it. Biological material can adsorb to mineral dust, enhancing its ice
nucleating ability .
In this study we investigate the immersion ice nucleation properties of 15
dust samples from nine different deserts around the world. Four of the
samples were collected directly from the air (Tenerife) or by deposition
after atmospheric transport (Crete, Egypt, Peloponnese) for subsequent
analysis in the laboratory without additional treatment such as sieving or
milling. Based on back trajectory analysis, the four airborne samples
originate from different parts of the Sahara. The ice nucleation ability of
these airborne dusts was compared to that of several samples collected in the
desert. The effect of sieving and milling on the ice nucleation behavior of
two surface-collected samples was investigated.
Immersion mode ice nucleation
measurements at temperatures between 235 and 250 K were conducted with the
combination of the Zurich Ice Nucleation Chamber, ZINC ,
and the Immersion Mode Cooling chAmber, IMCA . Particles of
four dust samples were collected on filters for subsequent offline analysis
with the Frankfurt Ice Deposition Freezing Experiment (FRIDGE) counter
operated in the droplet freezing mode as described by
and . This allowed examination of immersion freezing at
temperatures between 250 and 262 K, covering a wider range of heterogeneous
freezing temperatures than would otherwise be possible with IMCA-ZINC alone.
The aim of the current study, as well as a follow-up study on deposition/condensation
nucleation, is to investigate the link between ice nucleation and bulk
mineralogy of desert dust as it is found in the atmosphere and to compare it
to surface-collected samples. By using aeolian transported samples, the
particle size distribution and sample composition are as realistic as
possible. To our knowledge this is the first study to investigate ice
nucleation behavior of airborne desert dust in the laboratory, compare it
with surface-collected natural dust samples, and link it to the mineralogical
composition of these complex samples. With samples from nine different
deserts we present a data set covering most major global dust sources.
Methods
Dust sample origins and processing
Collection sites of the dust samples. Green squares/black stars
indicate sieved/milled samples which were collected directly from the
surface; pink circles indicate samples that were collected either directly
from the air or by deposition after transport from the Sahara. See text for
details on the collection methods and treatment after collection. The map was
adapted from and is based on data from Total Ozone
Mapping Spectrometer (TOMS) satellite data of the absorbing aerosol index
(AAI). Dark brown color indicates 21–31 days of AAI > 0.7,
corresponding to significant amounts of dust or smoke. Yellow indicates
7–21 days of AAI > 0.7. Arrows show typical dust transport
pathways in the atmosphere.
The immersion mode freezing behavior of a total of 15 different dust samples
was investigated. The collection sites are shown in Fig.
together with the major dust emission sources and common atmospheric
transport pathways. GPS coordinates of the collection sites are provided in
the Supplement. It can be seen from Fig. that the dust
samples stem from most of the major atmospheric dust sources. The Tenerife
sample was collected directly from the air over 4 days in August 2013 at
the Izaña observatory on Tenerife, Spain, using a custom-made large cyclone
(Advanced Cyclone Systems, S.A.: flow rate of 200 m3 h-1 and
D50 = 1.3 µm, the diameter at which the collection
efficiency is 50 %). After deposition on a roof and on solar panels, dust
samples were collected at the Aburdees observatory, Egypt, on 10 May 2010 and
in Crete and the Peloponnese in Greece in April 2014. The Crete sample was an
integrated sample over several dust events whereas the Peloponnese sample was
from one single dust event. Surface collection sites were (i) the Atacama
desert in Chile; (ii) a location approximately 70 km from Uluru in
Australia; (iii) the Great Basin in Nevada and (iv) the Mojave desert in
California, USA; (v) a Wadi in the Negev desert, approximately 5 km from Sde
Boker in Israel; (vi) dunes in the Sahara, close to Merzouga in Morocco;
(vii) dunes in the Arabian desert in Dubai; (viii) the Etosha pan in Namibia,
a dry salt pan; and (ix) the Taklamakan desert in China. The Israel sample
and the Etosha sample are from the same batch as those studied in
.
The surface-collected samples needed to be sieved to separate the grain sizes
larger than 32 µm from the remaining sample to avoid clogging of
the aerosol generation system used for the ice nucleation experiments.
Samples were sieved in a cascade of dry sieves with the smallest cutoff size
being at 32 µm diameter (Retsch Vibratory Sieve Shaker AS 200).
Typically only a few weight percent of the sample was in this size range. The
Australia and Morocco samples had no fraction in this size range and thus
were milled using a vibratory disc mill (Retsch, model RS1). For the Morocco
sample, particles of the lowest size bin (32 to 64 µm) were milled.
The Australia sample was first sieved with a coarse, millimeter-range sieve
to separate any large material, and the remaining sand was milled. For the
Atacama and Israel samples, both a milled and a sieved sample were compared
to investigate the effect of milling on ice nucleation. In case of the
Atacama sample, part of the unsieved sample was milled. The Israel sample was
first sieved and part of the sieved sample with d≤ 32 µm
was milled. The sub-32 µm size fraction of the other samples was
too small to investigate the milling effect. The composition of natural dust
samples is presumed to be heterogeneous, i.e., external and internal mixtures
of different minerals and potentially containing organic or biological
material . Additionally, they have probably undergone
natural aging processes due to the exposure to the atmosphere of the
surface-collected samples and actual atmospheric aging of the airborne
samples . This could physically or chemically alter the
surface of the dust particles, potentially changing the ice nucleation
properties compared to the pure mineral dust particles. Effects of washing or
heating of the samples, which could yield information on coating or mixing,
could not be investigated in this part of the study due to the small sample
size of the airborne samples.
Dust particle generation
The dust samples were dry dispersed into a 2.78 m3 stainless steel
aerosol reservoir tank using a Rotating Brush Generator
(RBG, Palas, model RBG 1000) with N2 (5.0) as carrier gas via a cyclone
that confined the dust size distribution to below
D50=2.5 µm. The maximum particle concentration in the tank
was about 1200 cm-3 and decreased steadily to about 300 cm-3 over
approximately 10 h. Before each experiment, the tank was cleaned by
repeatedly evacuating and purging it with N2 until the particle
concentration decreased to 30–90 cm-3. The total particle
concentration was monitored with a condensation particle counter (CPC; TSI
model 3772). The ice nucleating particle counters, the particle collection
for offline FRIDGE experiments and the instruments measuring the particles'
size distribution sampled directly from the tank. For the IMCA-ZINC
measurements, the particle concentration was diluted to about 60 cm-3 to
avoid coincidence effects in the detector which occur if more than one
particle is present in the laser beam of the detector .
Aerosol particle size distribution
The particle size distribution in the reservoir tank was monitored using a
scanning mobility particle sizer (SMPS; TSI, DMA model 3081, CPC model 3010)
for mobility diameters (dm) between 12.2 and 615 nm and an
Aerodynamic Particle Sizer (APS; TSI, model 3321) for aerodynamic diameters
(daer) between 0.5 and 20 µm. After converting the
mobility and aerodynamic diameter to volume equivalent diameter
(dve), the size distributions were merged. A shape factor of
χ=1.36 and a particle density of ρ=2.65 g cm-3 were assumed
for the conversion. These values lie in the range of natural dust samples
analyzed in earlier studies; e.g., for quartz, χ=1.10–1.36 and ρ=2.6 g cm-3 ,
and for illite NX, χ=1.49 and ρ=2.65 g cm-3 .
Assuming spherical particles, the area size distribution was calculated and
fitted with a bimodal lognormal distribution. The mean particle surface area
(Ave,w‾) was calculated from the resulting fit for
each sample (see Table ) as well as the corresponding surface
area-weighted mean diameter (dve,w‾). Over the course
of a single experiment the size distribution changed as larger particles
settle out of the volume faster than smaller ones. This effect was reduced by
a fan inside the aerosol tank leading to Ave,w‾
varying by 6 to 24 % over the course of an experiment for the different
samples, except for the Great Basin sample (64 %), which was coarser than
the other samples and settled out faster.
Overview of the dust size distribution parameters: the mean particle
surface area
per particle Ave,w‾ with relative error δ(Ave,w‾)
and the corresponding diameter of a particle with this surface area (dve,w‾) with the relative error δ(dve,w‾).
Sample
Collection site
Type
Ave,w‾ (µm2)
δ(Ave,w‾)
dve,w‾ (nm)
δ(dve,w‾)
number
1
Atacama
sieved
2.79
0.17
940
0.08
2
Atacama
milled
2.56
0.24
897
0.11
3
Australia
milled
2.14
0.09
824
0.05
4
Crete
airborne
3.04
0.08
983
0.04
5
Dubai
sieved
2.18
0.18
830
0.09
6
Egypt
airborne
3.26
0.12
1017
0.06
7
Etosha
sieved
2.08
0.07
813
0.03
8
Great Basin
sieved
16.4
0.64
2133
0.39
9
Israel
sieved
3.32
0.14
1024
0.07
10
Israel
milled
2.57
0.12
904
0.06
11
Mojave
sieved
2.99
0.14
973
0.07
12
Morocco
milled
2.81
0.13
960
0.07
13
Peloponnese
airborne
3.27
0.06
1020
0.03
14
Taklamakan
sieved
3.69
0.19
1079
0.09
15
Tenerife
airborne
3.55
0.12
1061
0.06
Schematic of size fractions used for XRD and ice nucleation.
Figure shows a
schematic of the different size fractions resulting from the different
collection methods and post-treatment (sieving/milling) of the samples used
for ice nucleation experiments in the tank and the mineralogical analysis.
Due to the small amount of sample, a mineralogical analysis of the identical
size fraction as in the tank (< 2.5 µm) was not possible.
Instead, we used the entire size fraction of the airborne samples, the
smallest size fraction of the sieved samples (< 32 µm), and the whole size distribution of
the milled samples after milling.
Mineralogy analysis
The quantitative mineralogical composition of the bulk dust samples was
investigated using the X-ray diffraction (XRD) Rietveld method
using a Bragg–Brentano diffractometer (Bruker AXS D8
Advance with CoKalpha radiation). The qualitative phase composition was
determined with the software DIFFRACplus (Bruker AXS). On the basis of the
peak positions and their relative intensities, the mineral phases were
identified in comparison to the PDF-2 data base (International Centre for
Diffraction Data). The quantitative composition was calculated by means of
Rietveld analysis of the XRD pattern (Rietveld program AutoQuan, GE SEIFERT;
).
The results and uncertainties for the mineralogy of each sample given from
the Rietveld refinement are provided in Tables and
. For the Egypt sample the mineralogical composition is
associated with a significantly higher uncertainty because the amount of
sample was small and the measured intensity of the diffracted X-rays very
low. In this case the grain statistics were poor and crystals more likely to
be arranged in a certain, preferred orientation instead of randomly, leading
to a potential overestimation of some mineral fractions. Similarly, the
milling of the Israel sample likely interfered with the preferred orientation
of the minor components in the sieved samples, leading to an observed
reduction of these mineral fractions (e.g., illite, dolomite, plagioclase) in
the milled compared to the sieved sample. The differentiation between the
microcline and orthoclase K-feldspar fraction was for some samples not
possible (i.e., Morocco and Australia) where both phases were likely present.
In case of a low K-feldspar content of a few wt % it was not possible to
determine if K-feldspar was present as microcline, orthoclase or sanidine or
a mixture of the different phases. Values are given for the K-feldspar with
the best Rietveld fit result. As Rietveld fit results for the various
Na-plagioclase feldspars (albite, oligoclase and andesine) were often
insignificantly different, they are summarized as Na-plagioclase. The
fractions of ankerite and dolomite are usually provided together because in
some cases (especially Morocco) it was not possible to differentiate between
them. Only for the Etosha sample are they provided separately because the
fractions were large enough to be distinguishable .
Mineralogical composition in wt % and uncertainty from the
Rietveld refinement (see text for details). Where microcline and orthoclase
are present in the same sample, their individual fraction could not be
distinguished reliably. Nevertheless, the best Rietveld fit results are given
for orthoclase and microcline individually in these cases. The Etosha sample
was taken from , who do not provide a Rietveld fit
uncertainty from AutoQuan but estimate a 15 % accuracy.
Mineral
Atacama
Atacama
Australia
Crete
Dubai
Egypt
Etosha
Great Basin
type
sieved
milled
milled
airborne
sieved
airborne
sieved
sieved
Ankerite
23
Biotite
2.8 ± 0.6
1.0 ± 0.5
Calcite
25.0 ± 0.6
37.2 ± 0.6
29.2 ± 1.2
29
12.9 ± 0.4
Chlorite
3.8 ± 0.6
14.0 ± 1.2
8.2 ± 1.3
2.1 ± 0.3
Cristobalite
12.7 ± 1.9
14.0 ± 1.7
Dolomite
3.1 ± 0.7
6.8 ± 0.5
8.1 ± 1.3
27
1.9 ± 0.3
Gypsum
3.7 ± 0.7
5.5 ± 0.9
2.8 ± 0.3
Halite
1.0 ± 0.2
4.4 ± 0.5
2.1 ± 0.1
Hematite
4.0 ± 0.5
3.2 ± 0.4
0.6 ± 0.1
0.9 ± 0.2
1.4 ± 0.2
0.9 ± 0.2
Hornblende
1.3 ± 0.7
1.8 ± 0.6
1.5 ± 0.4
1.8 ± 0.5
1.0 ± 0.5
Illite
10.0 ± 1.0
Kaolinite
12.4 ± 1.0
10.5 ± 1.5
17.7 ± 0.9
Microcline
3.9 ± 0.5
30.1 ± 0.8
Muscovite
4.2 ± 1.0
2.4 ± 0.6
9.0 ± 0.6
4.1 ± 0.5
7.6 ± 1.3
10
4.0 ± 0.5
Orthoclase
11.8 ± 0.9
22.3 ± 0.9
4.2 ± 0.5
5.1 ± 0.6
2.4 ± 0.4
3.5 ± 1.1
Palygorskite
4.5 ± 0.5
3.4 ± 0.5
Na-plagioclase
39.3 ± 1.6
43.2 ± 1.4
7.2 ± 0.4
9.5 ± 0.4
3.7 ± 0.3
Smectite
1
Quartz
16.7 ± 0.6
10.4 ± 0.4
91.3 ± 0.5
23.0 ± 0.5
13.3 ± 0.3
23.0 ± 1.1
1
20.1 ± 0.5
others
5.1 ± 0.4
9
0.7 ± 0.3
Mineralogical composition in wt % and uncertainty from the
Rietveld refinement using AutoQuan continued. Where microcline and orthoclase
are present in the same sample, their individual fraction could not be
distinguished.
Mineral
Israel
Israel
Mojave
Morocco
Peloponnese
Taklamakan
Tenerife
type
sieved
milled
sieved
milled
airborne
sieved
airborne
Biotite
Calcite
67.2 ± 1.2
81.0 ± 1.0
11.0 ± 0.5
6.0 ± 0.3
33.0 ± 0.6
14.6 ± 0.4
6.6 ± 0.3
Chlorite
7.5 ± 1.4
5.9 ± 1.0
2.7 ± 9.5
7.1 ± 0.9
1.6 ± 0.6
Cristobalite
Dolomite/ankerite
8.0 ± 0.4
1.3 ± 0.2
9.0 ± 0.5
1.4 ± 0.6
4.6 ± 0.5
4.6 ± 0.5
2.2 ± 0.3
Gypsum
1.2 ± 0.2
1.8 ± 0.5
2.4 ± 0.3
Halite
0.9 ± 0.2
0.4 ± 0.1
Hematite
0.5 ± 0.1
0.7 ± 0.2
2.7 ± 0.5
0.6 ± 0.2
0.6 ± 0.1
Hornblende
0.6 ± 0.3
1.5 ± 0.5
5.4 ± 0.5
Illite
4.2 ± 1.6
0.3 ± 0.2
8.0 ± 1.7
3.0 ± 0.7
12.5 ± 1.0
6.2 ± 0.8
Kaolinite
0.8 ± 0.6
0.3 ± 0.3
7.8 ± 0.7
15.6 ± 1.0
Microcline
1.7 ± 0.4
1.3 ± 0.4
3.8 ± 1.0
3.9 ± 0.5
Muscovite
1.1 ± 0.5
9.6 ± 0.7
1.8 ± 0.3
4.8 ± 0.8
8.0 ± 0.6
7.4 ± 0.6
Orthoclase
4.8 ± 0.4
2.2 ± 0.6
4.0 ± 0.5
5.3 ± 0.6
Palygorskite
2.2 ± 0.4
1.6 ± 0.4
5.3 ± 0.8
4.1 ± 0.4
Na-plagioclase
2.3 ± 0.5
0.7 ± 0.3
10.0 ± 0.8
8.8 ± 0.4
5.0 ± 0.4
19.3 ± 0.8
3.5 ± 0.4
Smectite
4.5 ± 0.6
6.4 ± 1.2
26.1 ± 2.6
31.8 ± 1.5
Quartz
7.3 ± 0.3
6.1 ± 0.2
12.8 ± 0.5
63.8 ± 1.2
17.9 ± 0.4
33.1 ± 0.7
14 ± 0.4
Others
0.2 ± 0.1
0.5 ± 0.1
Due to the broader size range of particles studied with XRD, the mineralogy
describes not only the particles that were studied with the ice
nucleation chambers but also the fraction between 2.5 and 32 µm.
This could lead to differences between the measured mineralogy and the actual
mineralogical composition of particles smaller than 2.5 µm due to
differences in the hardness and cleavage or fracture, i.e., the breaking
behavior, of different minerals. This is particularly true for softer
minerals such as calcite, which has a Mohs hardness of 3 (standard scale of
hardness between 1, talc, and 10, diamond), and clay minerals (2–2.5) in
contrast to feldspars (6) and quartz (7), as well as for minerals with a
higher cleavage such as gypsum and calcite (perfect cleavage) compared to
quartz (without cleavage; for information on mineral cleavage and hardness
see www.mindat.org or www.webmineral.com). Natural mechanical
weathering thus likely has enhanced the clay mineral and calcite content in
the smaller particle fraction whereas feldspars and quartz tend to be found
in the larger size fractions. For each filling of the reservoir tank a
similar volume of dust sample was used (≈ 0.2 cm3) and the dust
density is assumed to be comparable (about 2.65 g cm-3). This allows
us
to roughly approximate what fraction of particles was larger than
2.5 µm by the amount of dust sample left over in the
2.5 µm cutoff cyclone and the particle concentration reached in
the tank. Hardly any particles were left over in the cyclone and maximum
particle concentrations of 900–1200 cm-3 were reached by all milled
samples apart from the Morocco sample and by all airborne samples apart from
the Egypt sample. We suspect that the Egypt sample has a higher fraction of
large particles because it originated from local sources within Egypt and
thus the transport time was much shorter compared to the other airborne
samples leading to the size distribution being shifted to larger particles.
Of the sieved samples, the Dubai, Great Basin, Israel, Mojave, and Taklamakan
samples had a comparably high fraction of particles larger than
2.5 µm and particle concentrations of 400–970 cm-3 were
reached when filling the tank. For these samples the presented mineralogy may
not be fully representative for the particles < 2.5 µm
investigated for ice nucleation. In contrast, particles of the Etosha and
Atacama sieved samples were mainly smaller than 2.5 µm and thus
the mineralogy is representative of the small particle fraction. In summary,
the identified mineralogical composition is well representative for the
particle size fraction used for ice nucleation experiments on the Atacama
milled and sieved, sieved Etosha, Israel milled, milled Australia, and the
airborne Crete, Peloponnese, and Tenerife samples.
Immersion freezing experiments and data treatment
Immersion freezing experiments between 235 and 250 K were conducted by
extending ZINC with IMCA . ZINC is a
vertically oriented continuous flow diffusion chamber with
two flat parallel walls. The walls are ice coated before an experiment and
by applying a temperature gradient between the two walls at supercooled
temperatures supersaturation with respect to ice is established between the
walls. To ensure droplet activation of all sampled particles before freezing,
IMCA is installed upstream of ZINC. In IMCA a relative humidity of 120 %
with respect to water at a temperature of 303 K is provided by humidified
filter paper on the two parallel walls of the chamber. Under these
conditions, all particles activate such that each droplet contains a single
dust particle. The droplets are then cooled to the experimental temperature
before they enter ZINC. For the immersion freezing experiments the relative
humidity in ZINC is kept at water saturation. The IODE detector
measures the depolarization signal of a linearly
polarized laser beam by the particles. This allows differentiation between
spherical droplets, which nominally do not lead to a depolarization signal,
and the non-spherical ice crystals which depolarize the laser light. The
ratio of the detected ice crystal concentration (Ni) to the sum
of ice crystals and detected droplet concentration (Nd) is called
the frozen fraction (FF):
FF=NiNd+Ni.
IODE can distinguish the depolarization signal of droplets and ice crystals
between the limits of detection (LOD) of FF = 0.1 and FF = 0.9. Over
the course of about 3 h the temperature is stepwise ramped up. Each data
point represents 2000–3000 single detected particles.
Sample surface area distribution of four of the dust samples with
bimodal fits.
For independent offline
immersion freezing measurements between 250 and 263 K with FRIDGE, dust
particles were collected by filtration from the tank over 3.5 h using Teflon
membrane filters (Fluoropore PTFE, 47 mm, 0.2 µm, Merck
Millipore Ltd.). The particles were then extracted from the filters into
vials with 10 mL of deionized water for 10 minutes in an ultrasonic bath, and
150 drops of 0.5 µL each were randomly placed on a silicon plate on
the cold stage of FRIDGE using an Eppendorf pipette. At ambient pressure
conditions the temperature of the cold stage was then lowered by
1 K min-1 and the number of drops freezing as a function of
temperature is recorded with a CCD camera. This process is repeated several
times with fresh droplets until a minimum of 1000 droplets is exposed. The
INP concentration is given by
K′(T)=1Vdrop[ln(N0)-ln(N(T))]VwaterVair,
where K′(T) is the cumulative INP concentration, Vdrop is the
volume of a droplet, N0 the number of droplets sampled, N(T) the number
of frozen droplets, Vwater the volume of water used to wash off
the particles from the filter, and Vair the volume of air (N2
in the current study) sampled through the filter. The temperature uncertainty
is ±0.2 K and the uncertainty in FF typically ±30 % at
T≤ 260 K and decreases with lower temperatures.
Due to the small sample
amounts particularly of the airborne dust samples, generating monodisperse
particles for the ice nucleation measurements was not possible. Earlier
studies have shown that the probability of a particle to act as INP scales
with the surface area of the particle immersed in a droplet
. So-called ice-active sites
are assumed on the surface of an INP in the deterministic
concept . The probability of such a site to be present on
a particle increases with the surface area. To compare the FF measured in
IMCA from samples with different size distributions, the FF is normalized by
the mean aerosol particle surface area. This yields the ice-active surface
site density, ns:
ns=-ln(1-FF)Ave,w‾.
In the case of FRIDGE it is calculated as
ns=-ln(1-N(T)N0)At,drop,
with At,drop being the total aerosol surface area present in each droplet and given as
At,drop=N‾VairAve,w‾VwaterVdrop,
with the mean total aerosol concentration N‾ in the reservoir
tank during the time of the particle collection as measured by the CPC. The
assumption that active sites are uniformly distributed over individual
particle surfaces, and therefore that ns stays constant with
particle size, most likely has limitations for complex polymineral samples
such as desert dust particles. Therefore, the provided ns values
should not be treated as an exact parameter, valid at any particle size, but
rather a normalization method for the bulk natural dust samples
< 2.5 µm which we investigated.
Frozen fraction of non-Saharan samples (panels a and
b) and samples originating in the Sahara (panel c). Data
were binned into 1 K-intervals. Lines are best sigmoidal fits. Squares are
surface-collected and sieved; stars are surface-collected and milled; circles
are airborne samples. The light gray area is the homogeneous freezing regime
derived from classical nucleation theory and the
two dark gray rectangles show the upper and lower detection limits of IODE.
Ice-active surface site density of non-Saharan (panels d and
e) and Saharan samples (f). Lines are best exponential fits
to Eq. (). The fit parameters are given in
Table .
Results and discussion
Dust size distribution
Most of the size distributions of the different dust samples in the tank were
bimodal. Figure shows exemplary SMPS and APS surface area
distribution data of four samples together with the bimodal fit. Since one
mode was detected in each instrument's size range, the shape factor χ
was optimized to give the best overlap of the two size distributions. For any
shape factor within realistic limits for atmospheric dusts
(1.1 ≤ χ ≤ 1.6, ) the two modes
remained distinguishable. They are likely related to the high inhomogeneity
of the samples with respect to hardness and fracture. Two airborne (Crete and
Egypt), one surface-collected sieved, and one surface-collected milled (both
Israel) samples are shown. The Crete sample has a third small mode at
dve=50 nm. Since the smaller aerosol particles contribute only
little to the average surface area, the distribution was also bi-modally
fitted. The mean particle surface area values are given in
Table together with the relative error
δ(Ave,w‾) resulting from a change in
distribution during the course of the experiment. All samples peak in number
concentration between dve = 200 and 400 nm and a mean
particle surface area of
Ave,w‾ = 2–3.7 µm2 corresponding
to a diameter of dve,w‾ = 800–1100 nm. Only the
Great Basin sample differs strongly because of the presence of predominantly
large particles leading to a high mean particle surface area
(Ave,w‾ = 16.4 µm2,
dve,w‾ = 2133 nm). The relative error
δ(Ave,w‾) is 64 % in the case of the Great
Basin sample because two refills were necessary during the course of the
experiment. For all other samples δ(Ave,w‾) is
less than 24 %.
Ice nucleation of desert dust
The plots on the left side of Fig. show the FF as a
function of temperature between 235 and 253 K, separately for non-Saharan
and the Saharan samples. The majority of the non-Saharan FF curves
(Fig. a and b) behave similarly, with two samples being
distinctly different: the Australia sample shows significantly higher FF
values at all temperatures, whereas the milled Israel sample falls clearly
below the other FF curves for all T> 237 K. Of the intermediately
active samples, the Taklamakan and Great Basin samples are at the upper end
whereas the Dubai sample shows the second lowest FF values. The remaining
samples are mostly not significantly different, with FF values lying within
each others error bars.
The five Saharan samples in Fig. c cover a comparable range
of FF to the non-Saharan ones at any temperature. The only surface-collected
Saharan sample (Morocco) has higher FF values compared to the airborne
Saharan samples. All samples were fit with sigmoidal curves. None of the
samples shows a stepwise FF owing to the polydisperse size distribution of
the particles. Due to the heterogeneous particle composition, a partial
step-like activation spectrum could be expected with decreasing temperature
if the single mineral components were externally mixed and not present within
one particle. Since ice nucleation activity is also dependent on the surface
area of each particle , larger
particles will activate at higher temperatures than smaller ones, smoothing
out the potential step function of different minerals.
ns was calculated from the FF using
Ave,w‾ in Eq. () to account for
differences in the size distributions which may impact the ice nucleation
behavior. The results are shown in the plots on the right side of
Fig. . The error bars in ns are derived by
error propagation from the error in FF and
δ(Ave,w‾) and are dominated by the error in FF.
Data points outside of 0.1 <FF< 0.9 and in the homogeneous
freezing regime are omitted. The ns of the
Australia sample remains
the highest of all samples and that of the Israel milled sample one of the
lowest. The ns of the Great Basin sample, which has one of the
highest FFs, is amongst the lowest due to its coarse particle sizes. Like
their FF, the range of ns of the Saharan samples is comparable to
those of the non-Saharan ones (Fig. f). Among the Saharan
samples, the ns of the Tenerife sample is similar to that of
Crete, whereas the Egypt sample is higher for T> 238 K. The
ns of the surface-collected and milled Morocco sample is
distinctly higher at all temperatures.
(a) Ice-active surface site density fits for all dust
samples. The fit parameters are given in Table . Solid lines
indicate surface-collected and sieved samples, dashed lines
surface-collected, and milled samples and dotted lines are airborne samples.
(b) Full temperature range measurements are taken by IMCA and
FRIDGE. Colors and markers are the same as in Fig. . IMCA
data are binned into 1 K-intervals. Error bars are drawn for every 10th data
point. A parameterization for desert dust from is shown
as a thick dashed line. Parameterizations for kaolinite ,
illite , and K-feldspar as well as
the range of data for quartz are given as
areas representing the range between ns,BET, as provided in the
literature, and the corresponding ns,geo. See text for details on
the calculations of ns,geo.
To compare
all dust samples, the ns in m-2 was fitted using the
exponential function:
ns=exp(-a(T-273.15K)+b),
with the fit parameters a and b, which are given in Table
for each sample. The resulting fit lines from all samples are shown in
Fig. a. Overall the Australia sample is by far the most ice nucleation-active sample. The Israel milled, Great Basin, and
Peloponnese samples show a low ice nucleation activity. For comparison, the
ns fits for K-feldspar from , for kaolinite
KGb-1b from , for Illite NX from , data
for quartz from and , and the
ns parameterization curve from are shown. The
K-feldspar, kaolinite, illite, and quartz curves and data points were provided
as ns,BET, i.e., the surface area of the particles was measured
with the Brunauer–Emmett–Teller (BET) nitrogen adsorption method
. This method yields typically a higher surface area than
that based on volume equivalent diameter. The literature ns,BET
was converted to ns using a conversion factor of 3.5 in case of
K-feldspar as given in the Supplement of . For illite, we
followed , using a specific surface area (SSA) of
104.2 m2 g-1 and a ratio of total surface area
to total mass of 6.54 m2 g-1 . Similarly, for
kaolinite we used SSA=11.8 m2 g-1, a density of
2.63 g cm-3, and a mean mass-weighted diameter of 674 nm
, yielding a correction factor of 3.49. For the same
ns,BET values, the three quartz samples from
were active over a range of 10 K. No SSA values were provided and therefore we
used also a conversion factor of 3.5 given that feldspar is somewhat similar
to quartz. This comes with a very high uncertainty, as the size distribution
and particle shape of quartz are likely to differ from the K-feldspar of
. The K-feldspar, kaolinite, illite, and quartz
ns areas cover the range from the ns,BET as provided
in the literature and the calculated ns to show the uncertainty
inherent to the conversion. It can be seen that all desert dust samples fall
between the K-feldspar and the clay mineral and quartz fits at all
temperatures.
Similarly to our study the parameterization from was
based on the ns(T) of three polydisperse surface-collected dust
samples from China, Egypt, and the Canary Islands and one sample collected
after deposition in Israel. For T< 250 K the parameterization falls in
the lower end of the range of ns observed for our broader
collection of global surface-collected and airborne dust samples (a factor of
3 to 4 below the average ns(T) of all measured curves; not
shown). Given that the measurements were conducted with different
instruments, which can lead to a systematic offset of up to 3 orders of
magnitude in terms of ns , and the
polydisperse size distribution of the dust samples, the agreement is
considered reasonable. The maximum difference in temperature between the
parameterization from and the average of all
ns curves from this study (not shown) is less than 3 K, while
the spread in ns curves across all samples in our study is up to
10 K. The parameterization has a slope close to that of all airborne
samples, whereas most of the surface-collected samples show a more moderate
slope, i.e., a lower temperature dependence. This can be seen as an indication
of active sites, which activate at warmer temperatures, being more frequent
in the surface-collected samples compared to the airborne samples.
The high
temperature measurements from FRIDGE for four samples are shown in
Fig. b together with those from IMCA and parameterizations.
Again, the desert dusts fall between extrapolations of the clay mineral and
K-feldspar fits. The parameterization from predicts 1 to
2 orders of magnitude higher ns than measured by FRIDGE. Only for the Atacama milled sample does the parameterization show about 30 % higher values than the measurements at T = 251–256 K. While at T< 250 K the
ns values of the four samples mostly overlap within error bars,
at T> 250 K the Atacama milled sample has about 1 order of magnitude
higher ns than the other three samples. This shows that the fits
of ns(T) are not constant over the whole temperature spectrum and
are only valid for the given range. It indicates that the Atacama milled
sample contains active sites at these temperatures which are missing in the
other samples.
(a) Median and minimum to maximum ns range of
airborne and surface-collected samples. (b) Comparison of ice-active
surface site density of milled and sieved samples.
Figure a shows the median and minimum to maximum
ns(T) range of the airborne and surface-collected samples. This
illustrates that the ns range of the airborne samples falls in
the lower half of the ns range of the surface-collected samples
or even below. It shows that for immersion-mode ice nucleation,
surface-collected dust samples are not representative for airborne dust
samples, which all stem from North Africa, the world's largest source of
atmospheric dust. This might be caused by a non-representative surface-dust
collection, e.g., soil rather than dust is collected which has a different
size distribution and composition, or dust from a location where threshold
wind velocities for dust lifting are not reached. Another cause could be that
atmospheric processes taking place during or after particle lofting may alter
the particle surface and decrease the ice nucleation ability which has been
suggested to occur in the field and laboratory
. The potential effects of
mineralogy on the ice nucleation activity at different temperatures is
investigated in the following section.
Role of mineralogy
Various earlier studies have shown that the ice nucleation activity expressed
by ns varies by several orders of magnitude between different
types of minerals. By analyzing the bulk mineralogy we investigate whether the
dust's mineralogical composition explains the observed ice nucleation
activity. Tables and show the results of the
mineralogical analysis of the dust samples. The distinct composition of the
Australia sample is striking, consisting almost entirely of quartz
(91.3 wt %) and K-feldspars (4.2 wt % orthoclase, 3.9 wt %
microcline), which are highly ice-active minerals in the immersion mode
. The Morocco sample also has a high quartz
content (63.8 wt %), followed by the Taklamakan sample (33.1 wt %),
both being two of the most ice-active samples of this study. The remaining
samples have a quartz content of 23 wt % or less. Another obvious
difference is the high feldspar content of both Atacama samples (milled:
22.3 wt % orthoclase, 43.2 wt % Na-plagioclase; sieved: 11.8 wt %
orthoclase, 39.3 wt % Na-plagioclase). The milled Atacama sample shows the
highest ns of the four investigated samples in FRIDGE at
T> 250 K (Fig. b), close to the K-feldspar parameterization
by at 260 <T< 262 K and also higher activities
than the sieved sample at T> 240 K (Fig. b). The
Israel samples have a distinctly high calcite content (milled: 81 wt %;
sieved: 67.2 wt %), followed by Dubai (37.2 wt %) and Peloponnese
(33 wt %). Calcite has been found to be a weakly ice-active mineral in the
immersion mode and also in the condensation
mode . The Etosha sample consists of about
one quarter each of calcite (29 wt %), dolomite (27 wt %), and ankerite
(23 wt %) with 10 wt % muscovite and no significant fraction of clay
minerals, feldspars, or quartz (1 wt % smectite and 1 wt % quartz were
identified). This is surprising as the Etosha sample is one the most ice nucleation-active samples at T< 242 K. The ice nucleation ability of
the mica muscovite is debated as some studies have found hardly any ice
nucleation activity at heterogeneous freezing temperatures
while others found
significant ice nucleation ability at T< 243 K . found little ice nucleation activity of
a reference dolomite sample. Thus, the high ice nucleation activity at
T< 242 K of the Etosha sample is not explainable by the known ice
nucleation ability of its mineral components. To our knowledge, no study so
far has investigated the ice nucleation behavior of pure ankerite.
Overview of the dust ns fit parameters a and b, the
resulting R2, and the number of data points in each fit, N.
Sample
Collection site
Type
a (K-1)
b
R2
N
number
1
Atacama
sieved
0.513
9.39
0.91
36
2
Atacama
milled
0.363
14.50
0.96
50
3
Australia
milled
0.274
18.93
0.89
16
4
Crete
airborne
0.545
7.32
0.98
30
5
Dubai
sieved
0.391
13.04
0.96
35
6
Egypt
airborne
0.390
13.22
0.96
41
7
Etosha
sieved
0.289
17.09
0.93
33
8
Great Basin
sieved
0.286
15.47
0.93
35
9
Israel
sieved
0.477
10.11
0.91
39
10
Israel
milled
0.777
-1.43
0.95
13
11
Mojave
sieved
0.317
15.62
0.96
46
12
Morocco
milled
0.257
18.55
0.95
45
13
Peloponnese
airborne
0.535
6.84
0.95
17
14
Taklamakan
sieved
0.355
15.00
0.93
21
15
Tenerife
airborne
0.455
10.16
0.97
14
The remaining samples are more complex mixtures of quartz, feldspars, clay
minerals, micas, and other minerals. We find less than 4 wt % microcline in
the sieved and milled Israel and the airborne Tenerife samples. In the
samples from Australia and Morocco both K-feldspars orthoclase and microcline
seem to be present. The surface-collected Great Basin sample contained
30 wt % microcline in the bulk sample but likely much less in the size
fraction < 2.5 µm. The Saharan samples show a great variety
with the Tenerife sample having the highest content of clay minerals (illite
+ kaolinite + smectite + palygorskite: 57.7 wt %) of all dusts,
similar to the findings of . The other airborne Saharan
samples (Egypt, Peloponnese, and Crete) consist to about 50 wt % of quartz
and calcite, likely due to different source regions within the Sahara. The
mineral fraction of the main minerals in soil samples is not homogeneous
throughout the Sahara . Based on air mass back trajectory
calculations the Tenerife sample originated in Northern Mauritania or
Morocco, whereas the Egypt sample stemmed from local sources in Egypt and the
Peloponnese and Crete samples from Northern Saharan sources in Algeria,
Tunisia, and Libya. found that the mineral composition of
the size fraction < 5 µm of surface-collected samples in the
Sahara was very similar throughout the Sahara and concluded that this size
fraction is already well mixed within the desert. The main differences were a
higher calcite and palygorskite content in the Northern Sahara, which is
consistent with our findings for the Egypt, Peloponnese, and Crete samples in
the case of calcite. We find comparable amounts of palygorskite in the Crete,
Peloponnese, and Tenerife samples (4.5–5.3 wt %) but no palygorskite in
the Egypt and Morocco samples. The differences with regard to mineralogical
composition and ice nucleation ability found between the Morocco (higher
quartz content) and Tenerife (higher clay mineral content) samples shows
that, even if the source region of an airborne sample can be roughly
localized, the mineralogy of surface-based and airborne samples can differ:
surface-collected samples are not necessarily representative of the dust
aerosol. This is supported by the fact that the Morocco sample consisted
mostly of dust grains larger than 32 µm, which sediment quickly
before being transported long distances in the atmosphere.
In the following we investigate if the different ns values of the
dust samples can be attributed to their mineralogy. For this, we compare the
fraction of single minerals to the ns of our dust samples at five
different temperatures, using the most prominent minerals which have been
found to have ns values in a range which is measurable with
IMCA-ZINC . Those are quartz, illite, kaolinite, and calcite.
Additionally, we compare the samples' ns to the following sums of
minerals: K-feldspars (microcline plus orthoclase); all feldspars
(K-feldspars plus Na-plagioclase feldspars); sum of all feldspars plus
quartz; sum of all feldspars plus quartz plus illite and/or kaolinite. We do
not differentiate between the different K-feldspar polymorphs because even
the same type of feldspar can vary in ns as shown by
due to the complex structure of feldspars. For each
temperature, all samples which showed a FF between 0.1 and 0.9 were used for
the correlations. The Etosha sample was excluded from the correlations with
feldspars, quartz, and clays, as it does not contain any significant amount of
these minerals. However, it was included for the comparison with calcite.
Table shows the Pearson correlation coefficients (R) between
the mineral fractions and the ns at five temperatures. Only a few
correlations are statistically significant, owing to the low number of
samples. Nevertheless, the overview of the correlation coefficients gives an
idea of the effect of certain minerals on ns. The related scatter
plots are given in Fig. 1 of the Supplement.
Overview of the Pearson correlation coefficients of the sum of
selected mineral fractions and ln(ns) at different temperatures.
The names of the included sample numbers can be found in
Tables and . At 253 K only four samples were
measured. At T≤ 245 K only samples with FF between 0.1 and 0.9 were
included for the correlations. The Etosha sample (7) was only included in the
correlation with calcite because it does not contain feldspars, illite, or
kaolinite and only traces of quartz. An asterisk indicates that the
correlation was significant at the 0.05 level.
ln(ns) at
253 K
245 K
243 K
240 K
238 K
Number of samples
3 (4)
7
11 (12)
13 (14)
13 (14)
Samples included
2,6,(7),14
2,3,6,8,11,12,14
1,2,3,5,6,(7),8,9,11,12,13,14
1,2,4,5,6,(7),8,9,10,11,12,13,14,15
K-feldspar
0.97
-0.42
-0.13
0.05
-0.14
Feldspars
0.87
-0.29
-0.12
0.37
0.30
Quartz
-0.96
0.89∗
0.91∗
0.52∗
0.45
Illite
-0.11
-0.39
-0.14
-0.16
Kaolinite
-0.35
-0.66
-0.47
-0.55
-0.61∗
Feldspars + quartz
0.66
0.78∗
0.77∗
0.65∗
0.54
Feldspars + quartz + illite
0.66
0.80∗
0.73∗
0.62∗
0.51
Feldspars + quartz + kaolinite
0.74
0.65
0.69∗
0.50
0.37
Feldspars + quartz + illite + kaolinite
0.74
0.68
0.64∗
0.47
0.34
Calcite
-0.81
-0.58
-0.49
-0.45
-0.36
At 253 K only three samples (Atacama milled, Egypt, and Taklamakan) are
available for comparison. For these, the K-feldspar content leads to a very
high correlation (R = 0.97) and adding the Na-plagioclase to the
feldspar sum reduces the R value to 0.87. At lower temperatures, no
correlation is found between the K-feldspar content and ns. At
245 K, the ns correlates best (R = 0.89) with the quartz
alone and adding feldspar, illite, or kaolinite leads to a lower R value
(0.65–0.80). This is in agreement with earlier studies showing the
comparable high immersion mode ice nucleation activity of some quartz samples
at this temperature . Interestingly, this
behavior stays the same at 243 K (R = 0.91). At 240 and 238 K the
quartz correlation is not as good and adding feldspars to the quartz improves
the correlation (from R = 0.52 and 0.45 to 0.65 and 0.54,
respectively). Note that the Australia sample, which has the highest quartz
content, is excluded at 240 and 238 K from the correlation analysis because
FF was > 0.9. Illite alone shows a weak negative correlation with
ns at any of the investigated temperatures and leads to reduced
or constant R values when added to the quartz plus feldspar sum. Also
calcite and kaolinite are negatively correlated with ns at all
presented temperatures. This means that a higher ice nucleation activity in
one sample can be attributed at 253 K to the K-feldspar content present in
this sample whereas at temperatures between 238 and 245 K it is attributed to
the sum of feldspar and quartz content present. A high clay mineral content, in contrast, is associated with lower ice nucleation activity. To
exclude a bias from varying numbers of samples at different temperatures, we
have repeated the correlations at 245, 243, 240, and 238 K for the Atacama
milled, Egypt, and Taklamakan samples only. The corresponding scatter plots
are provided as Fig. 2 of the Supplement and confirm the observations that
quartz plus feldspar yield the best correlations at T< 245 K.
These results need to be treated carefully, because the mineralogy is derived
from the full size range up to 32 µm and may be different to the
studied size fraction (< 2.5 µm). Therefore, we do the same
analysis exclusively for the samples for which the size fraction larger than
2.5 µm was small and the mineralogical composition determined by
XRD can be assumed to be representative for particles
< 2.5 µm. These samples are the Atacama milled and sieved
samples, the sieved Etosha, milled Israel and Australia, and the airborne
Crete, Peloponnese, and Tenerife samples. From these samples only the ice
nucleation ability of the Atacama milled and the Etosha sample was measured
with FRIDGE at 253 K and only two of the samples show measurable
ns at 245 K in the IMCA data, and thus those temperatures are
excluded. The results for 243, 240, and 238 K are given in
Table . The correlation with illite and kaolinite is still
negative at any temperature despite the fact that some of the samples in this
subset contain a comparably large amount of these clay minerals (Tenerife:
21.8 wt %; Peloponnese: 20.2 wt %). At 243 K quartz shows the highest
R value (0.91) for the selected samples. At 240 K and 238 K the sum of
all feldspars (R = 0.95 and R = 0.88) and the sum of all
feldspars plus quartz (R = 0.97 and R = 0.90) show the highest
correlation with ns. This is different to when all samples were
included, where quartz exclusively led to the highest R values. The
difference stems mainly from the exclusion of the Morocco, Taklamakan, Egypt,
and Great Basin samples, which all had a high quartz content.
Overview of the Pearson correlation coefficients of the sum of
ice-active minerals and ln(ns) at different temperatures of the
samples where the mineralogy is representative for the size fraction smaller
than 2.5 µm. The names of the included sample numbers can be
found in Tables and . Only samples with FF
between 0.1 and 0.9 were included for the correlations. The Etosha sample (7)
was only included in the correlation with calcite because it does not contain
feldspars, illite, or kaolinite and only traces of quartz. An asterisk
indicates if the correlation was significant at the 0.05 level.
ln(ns) at
243 K
240 K
238 K
Number of samples
4 (5)
6 (7)
6 (7)
Samples included
1,2,3,(7),13
1,2,4,(7),10,13,15
K-feldspar
0.04
0.89∗
0.79
All feldspars
-0.30
0.95∗
0.88∗
Quartz
0.91
0.09
0.12
Illite
-0.80
-0.05
-0.17
Kaolinite
-0.56
-0.40
-0.36
Feldspars + quartz
0.87
0.97∗
0.90∗
Feldspars + quartz + illite
0.84
0.93∗
0.84∗
Feldspars + quartz + kaolinite
0.90
0.95∗
0.88∗
Feldspars + quartz + illite + kaolinite
0.87
0.90∗
0.82∗
Calcite
-0.45
-0.64
-0.61
It should be noted that XRD is a bulk analysis of the mineralogical
composition whereas ice nucleation is a process sensitive to the particle's
surface, including cracks, crevices, or pores .
Despite this difference in sensitivity, a high correlation between bulk
mineralogy and ice nucleation activity is found in this study. As XRD does
not allow any inference of the mixing state of different minerals it is not
known, particularly for the small particles, if each particle contains some
amount of quartz and/or feldspar or if pure calcite or clay mineral particles
exist. However, our results indicate that feldspar or quartz present in the
bulk dust will dominate its freezing behavior down to 238 K. While the
correlations do not exclude an influence of non-mineral material, the results
suggest that potential coatings or mixing of the particles only play a
secondary role for the immersion freezing ability at the studied temperatures
of the mineral dusts. The majority of the results can be explained by the
mineralogy, as also observed by . This may be due to a
significant dilution of coating material in the droplets forming on each dust
particle and may have a more prominent role in deposition nucleation.
Measurements in the condensation mode, which is the subject of Part 2 of this
study, suggest that the ice nucleation activity of this sample is in large
part related to organic or biological material mixed with the dust.
Effect of milling
Two of the dust samples have undergone two different treatments (sieving and
milling) to compare the effect of milling on ns of a polymineral
sample. The Israel sample was first sieved and then part of the
d≤ 32 µm fraction was milled. For the Atacama sample, the
original sample containing particles of all sizes was split: one part was
sieved and one part milled. The resulting ns curves are shown in
Fig. b. Interestingly, the two sieved samples are very
similar in ns. The milling of the Atacama sample led to a
slightly higher ice nucleation efficiency at T> 240 K compared to the
sieved sample. Contrastingly, for the Israel sample milling led to a decrease
in ns at all studied temperatures above 237 K. The latter could
be related to the high calcite content of the Israel dust. Calcite is a
rather soft mineral with a Mohs hardness of 3 and a perfect cleavage, and
during the milling process it could be ground to a smaller grain size faster
than compared to harder minerals such as quartz (Mohs hardness of 7) or
feldspar (6). Thus the size fraction
dve≤ 2.5 µm (the D50 cutoff of the
particle generation system used) could be enriched in calcite. Calcite has
been found to be ice-active only very close to the homogeneous freezing
regime and negatively correlated with
ns at all presented temperatures in this study
(Tables and ). Similarly, the slightly higher
ns at T> 240 K of the milled Atacama sample is likely due
to more feldspar being present in the dve> 32 µm
fraction compared to the sieved sample, which then got milled into sizes
dve< 2.5 µm. It can be seen from
Tables and that the K-feldspar (orthoclase)
content is higher in the Atacama milled sample (22 wt %) than in the
sieved one (12 wt %). The milled sample mostly consisted of particles
smaller than 2.5 µm, whereas the sieved one had a large fraction
of particles larger than 2.5 µm. Thus, there was likely more
orthoclase content in the milled Atacama sample particles smaller than
2.5 µm compared to those in the sieved sample, leading to higher
ns at warmer temperatures. Effects such as an increase in surface
irregularities, defect density, and functional groups due to the milling as
reported by other authors are not excluded
but were not investigated in this study. An increase in defect density and
surface irregularities has been shown to increase the ice nucleation activity
of monomineral or single compound samples. As milling reduced the ice
nucleation activity of the Israel sample, we conclude for this specific
sample that any morphology effect is small in comparison to the change in
mineralogical composition of the analyzed size range caused by the milling.
This emphasizes the importance of mineralogy for the surface sensitive ice
nucleation process.
Conclusions
The ice nucleation ability in the immersion mode of 15 natural
desert dust samples was quantified by the frozen fraction and ice-active
surface site density and compared with the bulk dust mineralogy. A diverse
mineralogical composition was found for the different desert dust samples
which can be related to variable ice nucleation abilities. The comparison
showed that at temperatures above 250 K the highest ns is
related to the highest K-feldspar content in the sample confirming earlier
findings on the superior ice nucleation ability of K-feldspars compared to
other minerals. Microcline was found in one airborne sample (4 wt %) and
in surface-collected samples from four different locations. We could not
confirm a superior role of microcline over orthoclase, in part because a
differentiation of the two minerals was often difficult and partly because
their content was too low in the size range investigated for ice nucleation
to cause an effect detectable with the IMCA-ZINC experiment. A conclusion on
the atmospheric relevancy of microcline is therefore not possible because
even in low amounts – of a few percent – it could nucleate ice and glaciate
clouds at temperatures warmer than 253 K.
At temperatures below 250 K, the ice nucleation ability was mainly
attributed to the quartz content of the samples, as well as to the sum of all
feldspars (K-feldspars and Na-plagioclase feldspars). Keeping in mind that
quartz is ubiquitous in atmospheric desert dust, this suggests that quartz
plays a more prominent role for atmospheric ice nucleation than previously
thought. The clay mineral (illite and kaolinite) and calcite content of the
dust samples negatively correlated with ns at all studied
temperatures, suggesting a minor importance of these minerals for the ice
nucleation activity of natural dust samples in the immersion mode, especially
if quartz or feldspar are present. suggested that the
global mineral dust INP concentration down to a temperature of about 240 K
is dominated by feldspar. At temperatures between the homogeneous freezing
limit and 240 K, where quartz is an active INP, it dominates the total INP
concentration as it is much more abundant than feldspar. Our experiments on
natural dust confirm this suggestion.
The variation in mineralogy with particle size leads to variations in
ns. The most ice nucleation-active mineral feldspar is less
common in the smallest dust size fractions. Quartz is found in all size
ranges but more common in the larger dust size fractions. The less ice
nucleation-active clay minerals and calcite dominate the small size fraction.
As the size distribution of dust changes during atmospheric transport, a
resulting change in mineralogy will have implications for the atmospheric
relevance of certain mineral components. Three of the four airborne samples
(Crete, Peloponnese, and Tenerife) had the highest clay mineral content and
were amongst the least ice nucleation-active samples. For all desert dust
samples we found a high correlation of the ice nucleation activity of
particles smaller than 2.5 µm with the quartz content of the dust
samples. This shows that despite the dominance of the clay minerals in the
small size fraction, quartz is an important atmospheric INP component and
also found in the particle size fraction with the longest atmospheric
residence time.
Milling of dust samples in the laboratory or by natural mechanical weathering
processes can lead to more surface inhomogeneities and also to an enrichment
of more ice nucleation-active minerals, such as quartz, in sizes relevant for
atmospheric ice nucleation. This potentially explains the higher ice
nucleation activity of Arizona test dust compared to desert dust samples
found in earlier studies . The observed
differences between the surface-collected and the airborne Saharan dust
samples suggest that surface-collected dust may not be representative of
atmospheric dust transported over long distances. Thus, more ice nucleation
studies on airborne, transported dust also from deserts other than the Sahara
are crucial to quantify the ability of atmospheric dust to nucleate ice.
Furthermore, if airborne dust is generally found to be less ice
nucleation active than surface-collected dust and does not show ice
nucleation activity at temperatures above 263 K, it cannot explain first ice
formation in clouds with top temperatures warmer than 263 K. For that other
INP such as biological particles or different mechanisms such as seeder –
feeder processes would need to be investigated. In our study the ice
nucleation activity of only one sample (Etosha) was not explainable by the
known ice nucleation activity of its mineral components. The role of adsorbed
organic material for the ice nucleation activity of the dust samples will be
investigated in Part 2 of this paper series.
The applicability of the parameterization by to describe
an average ice nucleation behavior of desert dust was confirmed by a global
set of dust samples. However, the variation between the different samples in
temperature was up to 10 K. To more adequately describe immersion freezing
by desert dust in the atmosphere, mineralogy sensitive emission and transport
schemes would be desirable. We suggest that K-feldspar for temperatures above
250 K and for lower temperatures additionally Na-plagioclase feldspars
and quartz emissions and transport should be quantified.
Since this is complex and computationally expensive to implement, more
studies quantifying the ice nucleation ability of dust as it is found in the
atmosphere may circumvent this complexity.