Introduction
Background
Primary ice formation by atmospheric ice-nucleating particles (INPs)
markedly influences the formation and life cycle of mixed-phase clouds and
very often also initiates precipitation formation. Therefore, ice-containing
clouds play a significant role in the energy balance of the climate system
and the hydrological cycle on Earth (Chapter 7 of IPCC 2013; Boucher et al., 2013).
Currently, quantitative predictions for the impact of these clouds on the
Earth's radiative budget and thereby the climate are highly uncertain. This
uncertainty arises primarily from a lack of fundamental understanding of ice
microphysical processes, the representation of these processes in cloud
models and knowledge of the abundance of INPs (Hoose and Möhler, 2012; Murray et al., 2012). In
particular, yearly emission rates of soil dust are 1000 to 4000 teragrams,
accounting for a major proportion of both the dust component and the total
particle loading in the atmosphere (Boucher et al., 2013). The resulting radiative
forcing directly exerted by mineral dust is estimated to range from -0.3 to
+0.1 W m-2. Therefore, dust slightly contributes to the direct
cooling effect of aerosols. However, our understanding of the influence of
the dust burden upon overall climate forcing, including its secondary effect
on cloud albedo, remains highly uncertain, in part due to the absence of
accurate INP representations in atmospheric models. Thus, the effective
radiative forcing effect of airborne dust on current climate predictions
remains unresolved.
A small subset of all particles acts as INPs across a range of subzero
temperatures, triggering ice formation in clouds via the process of
heterogeneous ice nucleation. Previous laboratory experiments have taken
diverse approaches in an attempt to mimic ice nucleation and freezing
processes. These heterogeneous ice formation processes include deposition
nucleation, immersion freezing, condensation freezing, and contact freezing (Vali, 1985),
inside-out contact freezing (i.e., freezing of an immersed INP in contact
with the droplet surface from the inside; Durant and Shaw, 2005; Fornea et al., 2009) and surface
condensation freezing (i.e., freezing of supercooled water or residual
aqueous solution trapped on particle surfaces, e.g., by the inverse Kelvin
effect; Christenson, 2013; Hiranuma et al., 2014a; Marcolli, 2014; Welti et al., 2014; Wex et al., 2014). Without INPs, pure cloud
water droplets or solution within particles can be supercooled to below -37 ∘C before freezing (Koop et al., 2000; Murray et al., 2010; Rosenfeld and Woodley, 2000).
Among the various modes of atmospheric ice nucleation, immersion freezing is
one of the most important mechanisms for primary ice formation, accounting
for 85 % of ice formation in clouds that contain supercooled droplets
(Hoose et al., 2010). Furthermore, many of the previous experimental studies have
investigated heterogeneous ice nucleation at conditions where water is
supercooled before freezing (e.g., Murray et al., 2012). However, the relative
importance of the particles' physicochemical properties (i.e., size,
composition, solubility, hygroscopicity, cloud condensation nuclei (CCN) activity,
ice nucleation (IN) active sites, surface charge and/or crystallographic
structure) for immersion freezing is not yet well known (e.g., Hiranuma et al., 2013, 2014b; Murray et al., 2012). Hence, more in-depth investigations and understanding of
heterogeneous ice nucleation processes in supercooled clouds (as well as
mixed-phase clouds) is of particular importance.
State of the art of IN measurement techniques
The concept of condensation nuclei contributing to ice formation was first
introduced by Alfred Wegener in 1911 (Wegener, 1911). Since then, various
instruments and methods have been developed to investigate the composition
of atmospherically relevant INPs as well as their abundance; for example,
the rapid expansion cloud-simulation chamber (RECC) was first introduced as
a detector of ionizing particles. Such instruments have been used in many
ice nucleation studies since the 1940s (e.g., Cwilong, 1947; Fournier d'Albe, 1949; Palmer, 1949;
Bigg, 1957; Kline and Brier, 1961). Supersaturated conditions with respect to water and ice, as
a function of temperature, are created in the RECC vessel by a rapid
pressure drop caused by mechanical expansion and concomitant cooling.
Subsequently, water vapor in the supersaturated air can either deposit or
condense on sample particles, leading to the formation of water droplets
and/or ice.
A different type of instrument widely used to measure abundance and
efficiency of INPs is the continuous flow diffusion chamber (CFDC). The need
for portable instruments capable of obtaining continuous measurements for
aircraft applications emerged in discussions during the 1970s was a main
driver of CFDC development. In CFDCs, particles are sampled into a region
between two ice-coated concentric cylinders (or dual parallel plates)
maintained at different temperatures, which generates a region of ice
supersaturation between ice-coated walls. As the particles experience ice
supersaturation conditions for a few seconds, INPs can be activated and
diffusively grow to supermicron ice crystals. Typically, these large ice
crystals can be detected and counted by an optical particle counter (OPC)
downstream of the instrument while the chamber temperature and humidity
conditions are continuously recorded. Since its first appearance in the
1980s with horizontal parallel plates (Hussain and Saunders, 1984; Tomlinson and Fukuta, 1985), several new designs
and operational principles have been introduced (e.g., vertically oriented
cylinders; Rogers et al., 1988, horizontally oriented parallel plates; Kanji and Abbatt, 2009,
vertically oriented parallel plates; Stetzer et al., 2008; Chou et al., 2011; Friedman et al., 2011). An
alternative configuration is the continuous flow mixing chamber (e.g., Fast
Ice Nucleus Chamber or FINCH; Bundke et al., 2008). The operation principle of this type
of chamber does not involve water vapor diffusion from the ice walls, as in
CFDC, but water vapor is available for ice growth from the humidified air
within the chamber flow. This leads to an upper limit on INP concentrations
that are observable with this methodology (DeMott et al., 2011). A flow tube (e.g.,
Leipzig Aerosol Cloud Interaction Simulator or LACIS, Hartmann et al., 2011) has also been
developed in which a humidified stream containing aerosol particles is first
cooled to activate droplets on the particles, which upon further cooling may
then freeze.
In addition to chamber techniques, the mode-specific conditions for
heterogeneous ice nucleation of a known INP placed on a substrate surface
have been studied using optical microscope techniques. For example, by
immersing ice nuclei in water droplets placed on a hydrophobic substrate
surface and collecting a series of images at controlled cooling rates, the
change in reflectivity and opacity following ice formation can be
characterized, and the associated freezing conditions can be identified
(e.g., Knopf and Alpert, 2013; Murray et al., 2011). More recently, other optical microscopy techniques
coupled with a unique method of encapsulating particles into droplets
followed by cooling (Iannone et al., 2011) or using the hydrophobic squalene/water emulsion
(Wright and Petters, 2013) were introduced to the community. Using a similar approach,
substrate-supported cooling studies have been applied to determine the
freezing temperature in the contact mode (e.g., Fornea et al., 2009; Niehaus et al., 2014), or of
deposition nucleation (e.g., Kanji and Abbatt, 2006; Bingemer et al., 2012; Dymarska et al., 2006). The
microscopy-coupled substrate-supported freezing devices are advantageous for visualizing the consequences of specific ice nucleation modes in controlled
and simulated environments. In some studies, immersion freezing of
microliter scale droplet volumes was analyzed at temperatures (Ts) higher
than -10 ∘C with a sensitivity of INP concentration as good as
∼ 10-5 L-1 (Ardon-Dreyer et al., 2011).
The freezing temperature of INPs either immersed in or in contact with
levitated supercooled water droplets suspended in the air can also be
determined by the change in light scattering with a charge-coupled device
(CCD) camera using an electrodynamic balance (EDB; Hoffmann et al., 2013), an acoustic
levitator (Diehl et al., 2014) or in a vertical wind tunnel (Szakáll et al., 2009). The advantage of
these methods is the ability to provide, via high-resolution images,
substrate-free information for statistically representative ice nucleation
processes on a single droplet basis. This advantage is shared with all of
the above-mentioned chamber and flow tube devices.
Undoubtedly, these enormous efforts to develop numerous IN measurement
techniques have advanced our basic knowledge of atmospheric ice formation.
As a consequence, the atmospheric science community will continue to pursue
investigations of IN to unravel their associated effects on climate.
Accordingly, exploring the sensitivities, uncertainties and biases of
various experimental techniques (e.g., methods for particle generation, size
segregation, size estimation, ice detection and any other notable
experimental procedures) in nucleating ice on particles of known
physicochemical properties is crucial in order to compile comparative INP
data of multiple and complex measurement techniques from various research
institutions. The information obtained from one technique guides other
measurement techniques (DeMott et al., 2011; Riechers et al., 2013). A better understanding of the
sensitivity of multiple techniques and the role of associated experimental
parameters upon INP measurements will also help in transferring the
laboratory-based measurements of INPs of various atmospheric constituents to
their reliable parameterizations in models of atmospheric processes.
Since the 1960s, four international workshops have been organized to compare
the performance of IN measuring instruments that were emerging or available
at the time (DeMott et al., 2011). In particular, effort was made during
the fourth international ice nucleation workshop in 2007 (ICIS-2007) to
assemble a total of nine laboratory and field IN instruments at the AIDA
(Aerosol Interaction and Dynamics in the Atmosphere) facility and compare
them using identical test dust samples (e.g., Arizona Test Dust, or ATD, and
Saharan dust) over similar thermodynamic conditions. State-of-the-art
knowledge was obtained from each workshop activity, and such measurement
understanding was further incorporated to develop the next generation of IN
instruments.
Objectives
The major aim of this study, and concurrent studies within the framework of
the INUIT (Ice Nuclei Research Unit) project, is to investigate the
immersion freezing behavior of reference particles (e.g., Snomax for
bacterial IN processes and potassium-rich feldspar, K-feldspar, for mineral
dust IN processes). In this work, we distributed illite NX samples from the
same batch [with the exceptions of the samples used for Leeds-NIPI, ZINC and
IMCA-ZINC (acronyms are defined in the Supplement Sect. S4);
Broadley et al., 2012; Welti et al., 2009] among the INUIT project and associated partners. With a
total of 17 different IN measuring instruments, we intercompared IN
data from each instrument in order to obtain a comprehensive data set for
evaluating immersion freezing properties of illite NX particles. The data set
captures the functional dependence of various experimental parameter
variables, such as particle concentration, particle size, droplet size,
temperature, cooling rate and nucleation time, on the immersion freezing
properties of illite NX particles. Further, some instruments used test
samples suspended in water prior to experiments, while others used
dry-dispersed particles. The basic experimental methods and parameterization
approaches used to interpret the overall results and perform the
intercomparison are discussed.
Results of freezing efficiencies at specific temperatures are presented
using the ice nucleation active surface-site density (ns)
parameterization (e.g., Connolly et al., 2009; Niemand et al., 2012; Hoose and Möhler, 2012) developed on the basis of
suggestions by DeMott et al. (1995). For instance, Niemand et al. (2012) showed that the singular
parameterization approach of immersion freezing (i.e., freezing along water
saturation conditions while cooling) of various desert dust particles
derived from AIDA experiments converge upon one representative fit as a
function of temperature, which is valid across a temperature range from -12
to -36 ∘C. The time-independent ns parameterization has also
been used in describing INP activation by several different constituents of
clay minerals, e.g., microcline and kaolinite, using the cold stage droplet
freezing technique (Atkinson et al., 2013; Murray et al., 2010, 2011). Hence, comparison of IN
efficiencies can be readily performed for multiple types of instruments
using ns parameterizations. Moreover, such time-independent and
surface-area-scaled ns formulations can be further adapted to
comprehensively assess ice nucleation in a wide range of atmospherically
relevant temperatures and relative humidities with respect to ice
(RHice), as was recently presented in Hiranuma et al. (2014a). The ns parameterization
for both immersion freezing and deposition nucleation can be directly
implemented in cloud, weather and climate models to calculate the
temperature-dependent abundance of INPs as a function of the aerosol surface
area concentration.
Methods
Illite NX characterization
In this study, we have chosen illite NX (Arginotec, NX Nanopowder) as a
surrogate for natural desert dusts. This choice of an illite-rich material
is based on a comparison of its mineralogical composition to that of desert
dusts, which are also rich in illite but are also mixed with a range of
other minerals (Broadley et al., 2012). The present work gives an overview of laboratory
experiments for immersion freezing of particles of illite NX, used as a
surrogate for atmospheric desert dust particles. Illite NX bulk powder was
previously characterized for its physicochemical properties, such as
mineralogy and specific surface area (SSA or θ for brevity). It was
observed that illite NX samples contained more than 74 weight percent
(wt %) illite (Broadley et al., 2012; Friedrich et al., 2008) along with other components [kaolinite,
quartz, calcite and feldspars (most likely orthoclase/sanidine), see Sect. 3.1 for more detail] which is similar to the X-ray diffraction (XRD) data
specified by the manufacturer. These test particles typically have
aggregates of many nanometer-sized grains, yielding an order of magnitude
greater SSA (104.2 m2 g-1; Broadley et al., 2012). The aspherical and elongated
nature of illite NX particles (aspect ratio up to ∼ 4.8;
Veghte and Freedman, 2014) emphasizes the importance of considering its irregular shape. The
manufacturer reports the particle density, after mechanical granulation, as
2.65 g cm-3.
To determine the purity of our sample, and to compare this with previous
observations, the dust mineralogy of a bulk illite NX sample was
characterized using XRD (Waseda et al., 2011) prior to distribution. In addition,
complementary energy dispersive X-ray (EDX) spectroscopy analysis was
performed to characterize the elemental composition of individual particles.
The illite NX particles were sampled directly from the AIDA chamber using a
47 mm Nuclepore® filter (Whatman, 0.2 µm
pore-size, filter Cat. No. 111106) and used in the EDX analysis.
The N2-adsorption-based SSA (or BET surface, Brunauer et al., 1938) of the illite NX
sample was also measured. BET is a gas adsorption technique where the
quantity of various gases required to form a monolayer over the entire
available surface of dry particles, including internal surfaces, is measured
(Gregg and Sing, 1982; Bickmore et al., 2002). From the knowledge of the size of a molecule on the
surface, it is possible to determine the total surface area (Stotal). In
this work, BET surface areas were determined using two different gas
adsorbates: N2 and H2O (resulting in θN2 and
θH2O), with the latter being the surface area exposed to water.
BET measurements with H2O were limited to 28 % relative humidity with
respect to water (RHw) to correctly account for a monolayer of H2O
(Quantachrome Instruments, 2013).
The effect of particle processing, such as removal of hydrophilic ions by
water, in a water suspension was examined by ion chromatography (IC). The
influence of dust washing and discharge of soluble materials on IN
propensity has been previously proposed (Welti et al., 2014). More specifically, the
authors postulated two different scenarios at different temperatures based
on their observations. At temperatures below ∼ -38 ∘C, the washed dust component may have enhanced water
condensation below water saturation, and a formed liquid layer presumably
may have stabilized the subcritical ice embryo entrapped inside the liquid.
The authors proposed this capillary condensation process as a part of
condensation freezing or homogeneous nucleation based on the previous
observation (Christenson, 2013) and the theoretical framework (Marcolli, 2014). Above
∼ -38 ∘C, on the other hand, heterogeneous
nucleation might have been suppressed because the liquid layer derived from
the deliquescence of soluble impurities from individual particles may have
diminished accessibility of water vapor to active sites (e.g., localized
surface features such as cracks and edges), originally proposed by Koehler et al. (2010),
preventing the ice embryo formation. In this study, suspended samples were
prepared by stirring illite NX powders (0.1 g in 10 mL of 18.2 MΩ cm
nanopure water) over 3 weeks. IC (Dionex DX-500 IC System equipped with
Dionex CD20 Conductivity Detector) was used to determine the concentrations
of washed out cations (K+, Ca2+ and Mg2+) as a function of
time. A weak solution of sulfuric acid [5 mL H2SO4 (96 wt %)
diluted in 2 L of Nanopure water] was used as the eluent. The measurements
were conducted in three series: every 5 to 10 s (seconds) within the first
2 min (minutes) (ultra-short time series, USTS), then every 10 min within the
first hour after immersion (short time series, STS) followed by a long time
series (LTS) with cation concentration measurements conducted every 2 days
thereafter for a 3-week period.
Particle size distribution
Size distributions and the Stotal (in m2 cm-3) of
both suspended and dry-dispersed illite NX particles were characterized using
four size measurement techniques (i.e., aerosol size spectrometers and light
scattering instruments). In particular, the dynamic light scattering (DLS)
size of suspended illite NX particles (0.05 to 1 mg bulk illite NX sample in
1 mL of double-distilled water) was determined using the
StabiSizer® (Microtrac Europe GmbH, PMX
200CS) over the range of 0.0008 to 6.5 µm hydrodynamic diameter. A
more detailed description of this instrument and its application for studying
the size of particles in suspension are addressed in Hiranuma et
al. (2014b), and only a brief discussion is given here. The DLS measurements
were carried out with negligible contribution of multiple scattering due to
the utilized 180∘ backscattering mode. The hydrodynamic diameter,
which was comparable to the volume equivalent diameter, is determined using a
refractive index of 1.55 to 1.58 for illite and of 1.333 for water, and a
viscosity of water of 1.002 and 0.797 mPa s at 20 and 30 ∘C,
respectively. From this metric, the surface area was calculated assuming
spherical particles.
Size distributions of dry polydisperse illite NX particles were measured at
AIDA controlled expansion cloud-simulation chamber (CECC) and Meteorological Research Institute (MRI) dynamic
CECC (DCECC) prior to the expansion experiments. For AIDA-CECC,
de-agglomerated illite NX particles from a rotating brush disperser (PALAS,
RGB 1000) were passed through a series of inertial cyclone impactor stages
(D50 ∼ 1 and 5 µm) and introduced to the 84 m3
volume AIDA vessel. Subsequently, a scanning mobility particle sizer (SMPS,
TSI Inc., Model 3081 differential mobility analyzer, DMA, and Model 3010
condensation particle counter, CPC) and an aerodynamic particle sizer (APS,
TSI Inc., Model 3321) were used to measure particle size distributions over
the range of 0.01 to 15.4 µm volume equivalent diameter. The assumption
of particle sphericity, a dynamic shape factor (DSF or χ in equations)
of 1.49 ± 0.12 (average of 10 measurements ± standard
deviation) and a particle density of 2.65 g cm-3 were used to obtain
the geometric-based (volume equivalent) diameter from an APS (Hiranuma et al., 2014b). At
MRI-DCECC, a combination of an SMPS (TSI Inc., Model 3936) and a welas® optical particle counter (welas-OPC, PALAS, Sensor series 2500) was used to
acquire a size distribution for the size range of 0.01 to 47.2 µm
volume equivalent diameter directly from the 1.4 m3 volume vessel. The
same disperser type was used at both chambers for particle generation, and
the upstream cyclone impactors (D50 ∼ 1 and 2.5 µm)
were similarly deployed to filter out any larger particles and safeguard
against injecting these particles into the vessel. We note that a linear
correction factor of ∼ 2 was applied to convert the optical
diameter measured by the welas-OPC to the APS-inferred volume equivalent
diameter in several studies (Wagner et al., 2011; Hiranuma et al., 2014a).
The particle number size distribution of dry particles in the 0.3–10 µm diameter range was also measured by a TSI 3330 optical particle sizer
(OPS, TSI Inc.; TSI-OPS hereafter). For particle generation, the illite NX
sample was dispersed using a magnetic stirrer in a 100 mL glass vessel that
was purged with 200 mL min-1 of dry particle-free compressed laboratory
air, and then diluted further in two stages by approximately 1 : 100 with dry
air. Subsequently, the backward scattering intensity of scattered light from
a particle illuminated by a laser (λ= 660 nm) was measured. The
instrument estimated the particle size distribution, assuming spherical
particles, using Mie theory. As a result, the reported size is a volume
equivalent spherical diameter. Additionally, these dry-dispersed particles
were used for the immersion mode experiments of FRIDGE as described in the
supplementary methods.
Ice nucleation measurements
The ice nucleation measurement techniques contributing to this collaborative
effort are listed in Table 1. Descriptions of each measurement technique and
their acronyms are available in Sect. S4.
Briefly, four CFDC-type instruments, one continuous flow mixing chamber, two
cloud simulation chambers, one diffusion cell, two levitators, one vertical
wind tunnel, one laminar flow tube and five cold stage-type systems were
employed in the intercomparison. As seen in Table 1, measurement techniques
with the first seven instruments (i.e., ID 1 to 7) and the immersion mode
measurements of FRIDGE (ID 12) examined droplets produced from bulk illite
NX samples in suspension, while the rest used dry-dispersed illite NX
powder, sometimes followed by size selection with a DMA. Methods working
with suspensions and those using dry particles employed different ways to
determine the particle surface area, and the influence of these differences
on the determination of ns was investigated. For instance, CSU-IS was
used to investigate the freezing activity of both bulk suspension and
size-segregated particles in suspension. Two cloud expansion chambers,
AIDA-CECC and MRI-DCECC, examined both polydisperse and size-selected dry
illite NX particles. LACIS and IMCA-ZINC measured immersion freezing of
droplets, where each droplet contained a single particle, and examined
differently sized dry particles. The role of IN modes upon the estimation of
ns was also examined across various temperature ranges. The EDB-based
method was used to measure the contact and immersion mode efficiencies of
size segregated dry illite NX particles around -30 ∘C. Immersion
freezing results from IMCA-ZINC were compared to previously reported ZINC
data (Welti et al., 2009) at temperatures below -31 ∘C and to PINC data for
temperatures below -26 ∘C. In the present study, we derived
ZINC's ns values from the results reported in Welti et al. (2009). Specifically, ice
formation above 105 % RHw up to the water drop survival line was used
to calculate ns based on given illite NX particle sizes. We note that the
latent heat of condensation has minimal impact on droplet temperature, such
that RHw > 105 % maintains a water supersaturating
condition for droplet freezing.
FRIDGE investigated ice nucleation of both dry-dispersed particles on a
substrate at fixed temperatures (-25 ∘C < T < -18 ∘C) with increasing humidity (“default” deposition mode
nucleation) as well as immersed particles. In the case of immersion freezing
experiments with suspended samples, the cell temperature was lowered by 1 ∘C min-1.
The range of mass concentrations of the bulk illite NX sample in suspension
varied from 3.1 × 10-6 wt % (CSU-IS) to 2.6 wt % (M-WT). For
dry-dispersed particle measurements, particle concentrations varied from
∼ 10 cm-3 (AIDA) up to ∼ 9000 cm-3
(MRI-DCECC). Experiments with M-AL, M-WT, EDB, and IMCA-ZINC were performed
on a single drop basis. The shortest residence time of roughly 1.6 s was
used for the laminar flow tube, LACIS, and the slowest cooling rate of
0.3 ∘C min-1 (time-average cooling rate over an expansion,
which translates to the equivalent updraft rate of ∼ 0.5 m s-1) was used in AIDA-CECC. Altogether, immersion freezing was examined
across the temperature range from ∼ -10 to ∼ -38 ∘C, and over a varied range of cooling rates, nucleation times
and particle concentrations (summarized in publically accessible data base
available at http://imk-aaf-s1.imk-aaf.kit.edu/inuit/).
Summary of INUIT measurement techniques and instruments. All
acronyms are available in Sect. S4. Note “poly” and “mono”
denote polydisperse and quasi-monodisperse size-selected particle
distributions, respectively.
ID
Instrument
Description
Portable ?
Reference
Investigable T range
Ice detected T range for this study
1
BINARY∗
Cold stage-supported droplet assay
No
Budke and Koop (2015)
-25 ∘C < T < ∼ 0 ∘C
-24 ∘C < T < -15 ∘C
2
CSU-IS
Immersion mode ice spectrometer
Yes
Hill et al. (2014)
-30 ∘C < T < ∼ 0 ∘C
poly: -25 ∘C < T < -11 ∘C
mono: -26 ∘C < T < -20 ∘C
3
Leeds-NIPI
Nucleation by immersed particles
No
O'Sullivan et al. (2014)
-36 ∘C < T < ∼ 0 ∘C
-21 ∘C < T < -11 ∘C
instrument
4
M-AL∗
Acoustic droplet levitator
No
Diehl et al. (2014)
-30 ∘C < T < ∼ 0 ∘C
-25 ∘C < T < -15 ∘C
5
M-WT∗
Vertical wind tunnel
No
Szakáll et al. (2009);
-30 ∘C < T < ∼ 0 ∘C
-21 ∘C < T < -19 ∘C
Diehl et al. (2011)
6
NC State-CS
Cold stage-supported droplet assay
No
Wright and Petters (2013)
-40 ∘C < T < ∼ 0 ∘C
-34 ∘C < T < -14 ∘C
7
CU-RMCS
Cold stage-supported droplet assay
No
Schill and Tolbert (2013)
-40 ∘C < T < -20 ∘C
-32 ∘C < T < -23 ∘C
8
AIDA∗
CECC
No
Möhler et al. (2003)
-100 ∘C < T < -5 ∘C
poly: -35 ∘C < T < -27 ∘C
Hiranuma et al. (2014a, b)
mono: -34 ∘C < T < -28 ∘C
9
CSU-CFDC
Cylindrical plates CFDC
Yes
Tobo et al. (2013)
-34 ∘C < T < -9 ∘C
-29 ∘C < T < -22 ∘C
10
EDB∗
Electrodynamic balance levitator
No
Hoffmann et al. (2013)
-40 ∘C < T < -1 ∘C
imm.a: -31 ∘C < T < -28 ∘C
contactb: -34 ∘C < T < -27 ∘C
11
FINCH∗
Continuous flow mixing chamber
Yes
Bundke et al. (2008)
-60 ∘C < T < -2 ∘C
-27 ∘C < T < -22 ∘C
12
FRIDGE∗
Substrate-supported diffusion and
Yes
Bingemer et al. (2012)
-25 ∘C < T < -8 ∘C
defaultc: -25 ∘C < T < -18 ∘C
condensation/immersion cell
imm.d: -25 ∘C < T < -18 ∘C
13
LACIS∗
Laminar flow tube
No
Hartmann et al. (2011);
-40 ∘C < T < -5 ∘C
-37 ∘C < T < -31 ∘C
Wex et al. (2014)
14
MRI-DCECC
Dynamic CECC
No
Tajiri et al. (2013)
-100 ∘C < T < ∼ 0 ∘C
poly: -26 ∘C < T < -21 ∘C
mono: -29 ∘C < T < -21 ∘C
15
PINC
Parallel plates CFDC
Yes
Chou et al. (2011);
-40 ∘C < T < -9 ∘C
-35 ∘C < T < -26 ∘C
Kanji et al. (2013)
16
PNNL-CIC
Parallel plates CFDC
Yes
Friedman et al. (2011)
-55 ∘C < T < -15 ∘C
-35 ∘C < T < -27 ∘C
17
IMCA-ZINC
Parallel plates CFDC
No
Lüönd et al. (2010)
-65 ∘C < T < -5 ∘C
imm.e: -36 ∘C < T < -31 ∘C
Stetzer et al. (2008);
ZINCf: -33 ∘C < T < -32 ∘C
Welti et al. (2009)
∗ Instruments of INUIT project partners, a immersion freezing, b
contact freezing, c default deposition nucleation, d immersion freezing
with suspended particles, e immersion freezing with IMCA, f ZINC
alone.
Ice nucleation parameterization
We now describe a method to parameterize surface area-scaled immersion
freezing activities using the size equivalent ice nucleation active
surface-site density based on geometric size (ns,geo; Connolly et al., 2009;
Niemand et al., 2012; Hoose and Möhler, 2012). In short, this surface-site density approach approximates
ice crystal formation observed in an experiment as a function of
temperature, thus not accounting for time dependence. Accordingly,
ns,geo can be expressed by
ns,geoT=-ln1-Nice(T)Ntotal1Sve,
in which Nice is the number concentration of formed ice crystals
(cm-3), Ntotal is the total number concentration of particles prior
to any freezing event (cm-3), and Sve is the volume equivalent
surface area of an individual particle (m2). As demonstrated in
Niemand et al. (2012), if the activated ice fraction is small (< 0.1), the Taylor
series approximation can be applied to Eq. (1). Assuming a uniform
distribution of ns,geo over a given Stotal and a size
independency of ns,geo, we can approximate ns,geo as
ns,geoT≈Nice(T)NtotalSve=Nice(T)Stotal.
In addition, the IN efficiency can be related to the BET-SSA to estimate
BET-inferred ice nucleation surface-site density, ns,BET. A
description of the procedures used to estimate both ns metrics is given
in Hiranuma et al. (2014b). The advantage of using ns,geo is its
applicability to both measurements and modeling activities due to the
assumption of particle sphericity. Conversely, ns,geo cannot be
directly obtained through suspension experiments because the size
distribution of a suspended sample for each experiment is not available;
therefore, Stotal is determined from BET and the sample mass suspended
in water.
In order to convert ns,geo values of all dry-dispersed particle
measurements into ns,BET, the geometric size-based ice-nucleating
mass, nm,geo (g-1), is first calculated from the IN active
surface using either the surface-to-mass conversion factor (in m2 g-1) of 6/Dveρ (size-selected case) or
Stotal/Mtotal (polydisperse case) by
nm,geoT=Nice(T)NtotalMve=6Dveρns,geoT≈StotalMtotalns,geoT,
where Mve is the mass of a spherical particle of volume-equivalent
diameter (g), Dve is the volume equivalent midpoint diameter of
particles (m), ρ is the particle density of illite NX (2.65 × 106 g m-3), and Mtotal is
the total particle mass concentration (g cm-3). We note that the DLS size distribution-derived
Stotal/Mtotal (i.e., DLS-SSA) is 6.54 m2 g-1 and use for
the measurements with suspended particles. We also note that the conversion
factor ranges from 11.3 to 2.26 m2 g-1 for size-selected
particle diameters from 200 to 1000 nm, respectively, where these sizes
denote the range of particle diameters used in the size-selected cases in
the present study. Therefore, ice-nucleating mass can be scaled to the
BET-SSA (θ, 124.4 m2 g-1) to derive ns,BET as
ns,BETT=nm,geo(T)θ≈nm,sus(T)θ=αMveθ,
in which nm,sus is the IN active mass for suspension measurements,
α represents the ice activated fraction (= Nice/Ntotal), which is the direct measurement of suspension
experiments and some of the dry-dispersed particle methods. With an
assumption of a uniform BET-SSA, the resulting ns,BET may be
representative of measurements with suspended samples because minimal
corrections (only α and θ) are involved when compared to
that with dry-dispersed particles. Owing to internal surface area and
surface roughness, BET-SSA may be greater than DLS-SSA (O'Sullivan et al., 2014).
Alternatively, we can also convert ice-nucleating mass derived from
suspension measurements, nm,sus, to ns,geo using
DLS-SSA to provide a reasonable comparison to dry-dispersed particle
measurements. However, this process requires one more step than when using
ns,BET (with an additional assumption of constant size
distribution for all suspensions) and two more steps than when using
nm. For our intercomparison study, we used both ns,BET and
ns,geo. Because fewer conversion factors are involved,
ns,BET may be best suited for suspension measurements, and
ns,geo may be best suited for dry-dispersed particle measurements
(Eq. 3 to 4 or vice versa).
The usage of DLS-SSA for the calculation of Stotal/Mtotal of
suspension measurements appears to be reasonable, as this leads to
ns,geo for suspension measurements nearly equivalent to
ns,geo for dry-dispersed particles. When
Stotal/Mtotal is derived based on TSI-OPS measurements, a value of
0.49 m2 g-1 is obtained, which is smaller by a factor of about 13 compared to DLS-SSA. This difference may be mainly due to the fact
that dry-dispersed particles are typically prone to agglomeration (discussed
below, i.e., Sect. 3.1) compared to the measurements with suspended
particles. The presence of fewer agglomerates in suspended particles is
shown in Fig. 1 of Hiranuma et al. (2014b). Since the size distribution of a suspended
sample for each experiment was not measured, DLS-SSA was used for the data
evaluation for suspension measurements throughout this study.
Results
Illite NX characterization
XRD results from the present and previous studies (Friedrich et al., 2008; Broadley et al., 2012) of the
major minerals in bulk samples of illite NX are presented in Table 2. The
results show that the bulk illite NX powder is composed of various minerals:
illite, kaolinite, quartz, calcite and feldspar, but the relative mass of
these minerals for this study differs from previous studies. For example,
our measurement shows that the illite NX sample is composed of
∼ 69 wt % illite mineral, whereas others report a larger
amount of illite from 74 to 86 wt %. Similarly, we observed a somewhat
different content of other minerals compared to previous studies as listed
in Table 2 (see also the Supplement Fig. S1). We note that the fractional
values in compositional fingerprints may deviate even within the same batch,
as all three XRD measurements deviated from the manufacturer's data (Table 2). Furthermore, our XRD result indicates that the illite NX sample contains
a smaller quartz fraction (3 %) than illite IMt1 from the Clay Minerals
Society (10 to 15 % quartz according to the official XRF data and 20 %
based on our own measurements).
EDX spectra of representative illite NX particles. (a) Typical
illite, (b) calcite-rich mineral, (c) titanium-oxide-rich mineral,
and (d) lead-rich mineral. Scanning electron microscopy images of characterized
particles are shown in subpanels. A schematic representation of the illite's
crystal structure (silicon in yellow, aluminum in black, oxygen in red and
potassium in purple) is also shown.
X-ray diffraction analyses of the bulk composition of illite NX
powder.
Weight Percentage (wt %)
Mineral
This
Manufacturer
Broadley
Friedrich
study
Data
et al. (2012)
et al. (2008)∗
Illite
69
86
74
76
Kaolinite
10
10
7
5
Quartz
3
4
7
< 1
Calcite/Carbonate
3
N/A
2
2
Feldspar
14
N/A
10
4
(Orthoclase/Sanidine)
∗ Friedrich et al. (2008) noted 11 wt % additional impurities, including
phlogopite (7.8 wt %), anhydrite (1.4 wt %), plagioclase (1.1 wt %),
and apatite (0.7 wt %).
To complement bulk XRD analysis, the abundances of 13 elements (Pt, K, C, Ca,
O, Fe, Mg, Al, Si, P, S, Pb and Ti), which are commonly identified in
illite-rich samples, were measured by EDX spectroscopy on a single particle
basis. Four representative EDX spectra are presented in Fig. 1. The presence
of Fe and Mg is typical and characteristic for illite NX particles. The
observed large amounts of Si and Al are due to the presence of layered
aluminosilicate structures (i.e., layer of SiO2 and Al2O3).
The observed dominant platinum (Pt) signals in all spectra originate from the
sputter coating conducted prior to EDX analyses. Figure 1a shows the typical
illite spectrum, which is similar to the one previously published in
Welton (1984). Illite-rich minerals, which included impurities of calcite,
TiO2 and Pb-P, were located by the brightness difference in the
backscattered electron detector micrograph images. The results are shown in
Fig. 1b, c and d (inclusion of calcite, TiO2 and Pb-P, respectively).
However, the EDX technique is not automated to detect these impurities
present within the illite NX particles because of their very small weight
fraction. Therefore, the possible effect of these observed impurities in
illite NX upon the ice nucleation activity cannot be evaluated on the basis
of its bulk analysis of the chemical composition. Nonetheless, detection of
non-illite mineral components may reflect the complexities of natural dust
particles, which typically contain multiple sites with differing nucleation
abilities. Thus, illite-rich clay minerals can be used as reference material
to mimic the ice nucleation activity of physically and chemically complex
natural dusts (Murray et al., 2012).
The measured BET-SSA are 124.4 and 123.7 m2 g-1 with N2 and
H2O vapor, respectively, as the adsorbing gas on illite NX particle
surfaces. The similar BET surface areas for both N2 and H2O vapor
gas adsorption suggest that the formation of a few monolayers of H2O
does not alter the surface morphology or the mineralogical phase of illite
NX particles. For comparison, our measurements of θN2 for
illite NX particles agreed with previously reported data within 20 %
(104.2 m2 g-1; Broadley et al., 2012). Since illite NX particles have
significant internal surface area, BET-derived surface areas can be expected
to be larger than those derived from the laser diffraction technique.
Supporting this notion, an SEM (scanning electron microscopy) image of an illite NX particle from Broadley et al. (2012)
shows how micron-sized particles are made up of many nanometer-sized grains.
Surface area distributions of (a) suspended and (b–d) dry illite
NX particles. Hydrodynamic size-based surface area distributions are
measured in suspension using DLS. The average ( ± standard error) of
five measurements with different concentrations of suspended illite NX
powder (0.05, 0.1, 0.25, 0.5 and 1 mg mL-1) is presented in (a). Volume
equivalent diameter-based dry-dispersed particle surface area distributions
measured in the AIDA chamber (mean of 10 measurements ± standard
error) and MRI-DCECC (two individual measurements) are shown in (b) and (c),
respectively. Panel (d) shows optical diameter-based particle surface area
distributions measured by a TSI-OPS used for the FRIDGE immersion mode
experiments. Dotted lines represent log-normal fittings, and corresponding mode
diameters are (a) 0.32 µm, (b) 0.36 µm, (c) 0.62 µm and
(d) 4.75 µm. The width-parameters of log-normal fittings are (a) 0.55,
(b) 0.65, (c) 0.95 and (d) 1.10.
Normalized surface area distributions to the total surface area
concentration measured by four different techniques are shown in Fig. 2.
According to the manufacturer, 95 % (by mass) of the dry and mechanically
de-agglomerated illite NX particles have a diameter smaller than 650 nm
(i.e., D95). This mass-based particle size is substantially smaller than
that of another type of Arginotec illite (Arginotec, SE-illite, D95= 5 µm). Interestingly, all mass size distributions measured in this
study (not shown here) indicate a substantial mass fraction above 650 nm
which is, in all cases, larger than 5 % (18, 24, 77 and
99.9 % for DLS, AIDA, MRI-DCECC and TSI-OPS for the FRIDGE immersion
experiments, respectively), indicating the presence of agglomerates in the
aerosol and suspension phases prepared for the IN experiments. The surface
area distribution of the DLS hydrodynamic diameter-based measurement (Fig. 2a) agreed well with in situ measurements from the AIDA chamber (Fig. 2b),
suggesting the size distributions of dry illite NX particles during AIDA
experiments were similar to those of suspension measurements. This
observation is consistent with results presented in Hiranuma et al. (2014b). Briefly, the
authors found agreement between the DLS-based hydrodynamic diameter and the
AIDA-derived volume equivalent diameter of hematite particles. As opposed to
the AIDA observation, the wider distributions and the shift in the mode
diameters in the MRI-DCECC measurements towards a larger size (0.62 µm,
Fig. 2c) when compared to Fig. 2a and b may indicate a higher degree of
particle agglomeration as a result of different degrees of pulverization
during the particle generation processes or particle coagulation at the high
aerosol number concentration used for these measurements. A more pronounced
agglomeration effect was observed by the TSI-OPS measurements (Fig. 2d),
such that a surface area distribution of supermicron-sized particles was
obtained. Thus, different types of dry particle dispersion methods can
contribute to varying degrees of agglomeration and the observed differences
in surface area distributions. Though all size segregating instruments used
in the present study are well calibrated, we cannot rule out the effect of
measurement techniques themselves on the observed differences in particle
size distribution. In Sect. 4.4 we discuss whether agglomeration has an
effect on the IN activity.
The cation release by illite NX in the aqueous suspension was measured with
IC as a function of time. The suspension was kept mechanically agitated for
3 weeks. The following cations were identified in the samples: K+,
Ca2+ and Mg2+. As seen in Fig. 3, IC data clearly demonstrates
that roughly all cations were released into the aqueous environment by
illite NX almost instantaneously. The concentration of the cations increased
rapidly and reached equilibrium within the first 2 min after immersion of
sample into water. Of all the cations measured, only Ca2+ exhibited a
slow concentration raise on the longer time scales.
Immersion freezing measurements and intercomparisons
All ice nucleation spectra with ns,BET(T) and
ns,geo(T) are shown in Figs. 4 and 5, respectively. A similar figure
with nm(T) is also shown in Fig. S2. Furthermore, we
compare the ns data from 17 instruments to 4 literature
results. Specifically, IN spectra reference curves of previously reported
illite NX particles (Broadley et al., 2012, hereafter B12), microcline particles (Atkinson et al., 2013,
hereafter A13), ATD and desert dusts (Niemand et al., 2012, hereafter N12) are also
expressed as both ns,BET(T) and ns,geo(T). The conversion
between ns,geo(T) and ns,BET(T) was performed according to
(Eqs. 3 and 4). The ns(T) (m-2 as a function of ∘C)
fits from the reference literature are
ns,BETA13=104×exp(-1.038T+273.150+275.260),ns,BETB12=104×exp[6.530×104+-8.215×102×T+273.150+3.447×T+273.1502+-4.822×10-3×T+273.1503],ns,geoN12(ATD)=exp(-0.380T+13.918),ns,geoN12(Dust)=exp-0.517T+8.934.
For microcline (K-feldspar), the ns,geo to ns,BET conversion was performed using a laser diffraction-based surface-to-mass
conversion factor of 0.89 m2 g-1 and an N2 BET-SSA of 3.2 m2 g-1 (Atkinson et al., 2013). For ATD and natural dust, we used a
surface-to-mass conversion factor of 3.6 m2 g-1, assuming a
monodisperse particle size at the log-normal fit mode diameter of 0.64 µm (Niemand et al., 2012) and the measured N2 BET-SSA of 34.4 m2 g-1
(this study). We note that the ATD parameterization is valid only for -26.7 ∘C < T < -17.7 ∘C. In addition, we also
present 14, 0.14 and 0.0014 % scaled A13 nscurves to see if K-feldspar
(microcline) can be used as a scaling factor to determine the ns(T) of
illite NX.
Evolution of the cation concentration in aqueous suspension of
0.1 g illite in 10 mL deionized water with time. The scaling of the time-axis
is different for three different subsections of the time series (USTS, STS
and LTS).
Intercomparison of 17 instruments using
ns,BET. Black or red cross markers are interpolated ns(T) used
for T-binned averaging. Note that M-AL and M-WT results are presented in (d).
In (k), FRIDGE results of default (solid square) and imm.mode (open diamond)
measurements are presented. Both ZINC (solid square) and IMCA-ZINC (open
diamond) data are shown in (p). Reference immersion freezing ns(T)
spectra for illite NX (B12; Broadley et al., 2012), K-feldspar (A13; Atkinson et al., 2013), ATD and
desert dusts (Dust) (N12; Niemand et al., 2012) are also shown (See Sect. 3.2).
Geometric size-based ice nucleation active surface-site density,
ns,geo, of 17 measurement techniques. Black or red cross
markers are interpolated ns(T) used for T-binned averaging. Note that M-AL
and M-WT results are presented in (d). In (k), FRIDGE results of default
(solid square) and imm.mode (open diamond) are presented. Both ZINC (solid
square) and IMCA-ZINC (open diamond) data are shown in (p). Reference
immersion freezing ns(T) spectra are provided as in Fig. 4.
We do not attempt to completely discuss the immersion freezing activity of
illite NX particles measured by each measurement technique. Instead, brief
remarks regarding each method are summarized below. The detailed discussion
of the methods intercomparison follows in Sect. 3.3.
BINARY
This recently developed microliter droplet assay
technique demonstrated its capability of measuring immersion freezing of
clay minerals in the temperature range of -15 to -24 ∘C. Similar
to most of the other suspension-based techniques, BINARY identified a steep
ns(T) increase, which started just below -20 ∘C. The BINARY
ns(T) spectrum was derived by compiling measurements with varied illite
NX mass concentrations over 2 orders of magnitude (0.1 to 10 mg mL-1,
see the supplementary methods). Immersion freezing efficiency of illite NX
particles collapsed into a single ns(T) spectrum, i.e., IN efficiency does
not depend on suspended particle mass for the concentration range studied
here. This observation is a check for consistency and it implies that ice
nucleation is indeed triggered by suspended illite NX particles, and neither
by impurities contained in the water used for dilution nor at the glass
surface supporting the droplets. If IN efficiency did depend on suspended
particle mass, different ns(T) spectra would result from the various
illite NX concentrations, which are shifted by the respective dilution
factor.
CSU-IS
This new immersion freezing device was used to
investigate the freezing activity of both bulk suspension and
size-segregated particles in suspension. A new approach was employed for
size-selected measurements, wherein 500 nm mobility diameter size-selected
particles were collected on a Nuclepore filter and then rinsed from it for
the immersion freezing measurements. The results suggest size independence
of ns within the experimental uncertainties (a combination of binomial
sampling error and the uncertainty of conversion of aerodynamic particle
diameter to mass) for the range of examined size (500 nm vs. bulk) and mass
concentrations of bulk illite NX powder in suspensions from 3.1 × 10-6
to 0.5 wt %, for non-size-segregated particles, and 2.2 × 10-5 to 4.4 × 10-4 wt % for size-segregated particles.
Leeds-NIPI
This suite of cold stage instruments has the
capacity to operate using droplets with volumes in the microliter to
picoliter range. This enables high resolution immersion freezing analysis
for a wide range of temperatures from higher (-22 ∘C < T < -11 ∘C) to
lower temperatures (-37 ∘C < T < -26 ∘C). The highest freezing temperatures are attained with the
largest droplets, which contain the largest surface area of illite NX.
Combined with the previous parameterization reported in Broadley et al. (2012), the
Leeds-NIPI data follows the overall ns(T) spectrum defined by the bulk of
the instruments. This suggests that immersion freezing efficiency, inferred
by ns(T), of illite NX particles is dependent on neither droplet volume
nor mass of illite NX particles in suspension (i.e., wt % 0.1 or 1 %);
instead the freezing efficiency only depends on the surface area per
droplet. Together with CSU-IS, these two instruments provided data points
for temperature as high as ∼ -11 ∘C, estimating a
similar lower-limit of ns,BET values of ∼ 10 m-2.
M-AL and M-WT
Both methods examine individual drops that are
freely suspended without any contact with walls or substrates. In M-WT drops
are floated at their terminal velocities in a laminar air stream, in which
conditions of ventilation and heat transfer are similar to those of droplets
falling through the atmosphere. Both M-AL and M-WT techniques analyzed the
freezing efficiency of drops containing polydisperse illite NX particles in
the temperature range between -14 and -26 ∘C. The ns values
agree reasonably well with substrate-supported suspension experiments (with
the exception of FRIDGE experiments), implying that the surface making
contact with the substrate has a negligible effect on immersion freezing for
our experimental conditions.
NC State-CS
Extensive experimental conditions were realized by NC State-CS (Wright and
Petters, 2013; Hader et al., 2014). Unique aspects of this instrument are
the sampling of drops within a squalene oil matrix that allows for
experiments using cooling rates as slow as 0.01 K min-1 and an
automated freeze detection algorithm that allows for the rapid processing of
more than 1000 possible drops per experiment to improve sample statistics.
Drops containing ∼ 0.0001 to 1.0 wt % of the illite NX test sample
were studied at a cooling rate of 1 K min-1 to find the immersion
freezing ability. A total of nine immersion mode freezing experiments,
spanning a range of drop volumes from ∼ 400 picoliter to 150 nanoliter,
were performed. Using this instrument a wide range of temperatures was
investigated (-34 ∘C < T < -14 ∘C) yielding ns(T) values ranging
from 102 to 1010 m-2. The data from the nine individual runs collapsed into a
single ns(T) spectrum suggesting that the mass loading of dust in
the drop did not affect the measurements for the wt % values
investigated. At the high T end (T > -20 ∘C), the
data are in reasonable quantitative agreement with the CSU-IS measurements.
At the low T end (T < -20 ∘C), the data are in
agreement with the B12 reference spectrum.
CU-RMCS
The University of Colorado (CU)-RMCS examined the
freezing abilities of droplets containing 1.0 wt % illite NX. CU-RMCS
detected the warmest immersion freezing of illite NX particles at about -23 ∘C under the experimental conditions used in the present work
(see the Supplement for further details). Results for -32 ∘C < T < -23 ∘C are from six different
experiments using four different droplet size bins: 10–20, 20–60, 60–120, and 120–200 µm (lateral diameter). These
droplet sizes correspond to a variation in droplet volume from
∼ 0.3 picoliter to 2.5 nanoliter.
AIDA
The AIDA cloud simulation chamber generates
atmospherically relevant droplet sizes (several µm in diameter, varying
with cooling rates), and therefore closely simulates mixed-phase cloud
conditions. Ice-nucleating efficiencies of both polydisperse and
quasi-monodisperse illite NX particles were investigated in this study.
ns of DMA size-selected illite NX particles (200, 300 and 500 nm mobility
diameter) agreed well with that of the polydisperse population for immersion
freezing experiments, within previously reported uncertainties (T ± 0.3
∘C and ns ± 35 %; Steinke et al., 2011). Thus, a negligible size
dependency of ns for “submicron” dry illite NX particles for temperatures
below -27 ∘C was found. Previously, Hiranuma et al. (2014a) demonstrated the
size independence of the ns value using two different sizes of submicron
hematite particles (200 and 1000 nm volume equivalent diameter) based on
AIDA deposition mode nucleation experiments. Such a similarity might remain
true for the immersion mode freezing of mineral dust particles that are
smaller than 1 µm diameter.
CSU-CFDC
This CFDC provided data for condensation/immersion freezing at around
-21.2, -25.1 and -29.7 ∘C (a total of eight data points with
two, two and four points at around each temperature, respectively), which
extends to a warmer region than the AIDA measurements. As demonstrated in
DeMott et al. (2015), higher RHw values were required for full
expression of immersion freezing in CSU-CFDC. The use of 105 %
RHw in the CFDC has been shown to underestimate INP activity for natural dusts by up to a factor of 3, but is a necessary compromise. Comparably, the CSU-CFDC
results agreed well with the AIDA measurements within a factor of 3 in
ns,geo estimation (AIDA ns > CSU-CFDC ns; DeMott et al., 2015). All the
CFDC measurements were conducted with 500 nm mobility diameter size-selected
particles, as discussed in the supplementary methods.
EDB
With EDB, both the contact and immersion mode
freezing efficiencies of illite NX particles were investigated. The contact
nucleation mode ns were clearly higher than the immersion mode
ns (by more than 1 order of magnitude in terms of ns,geo, Fig. 5i). This was in part due to the fact that immersion freezing experiments
were conducted only when illite NX particles were not frozen via contact
nucleation but remained immersed in a supercooled droplet in the EDB cell
(see the Supplement).
FINCH
The immersion freezing results from FINCH showed the
highest ns values in the -22 to -27 ∘C temperature range out
of all of the other instrument results. All the FINCH measurements were
conducted with 500 nm mobility diameter size-selected particles. Two
possible reasons for high ns values when compared to the other
measurements are: (1) an overestimation of nsdue to excess Nice
and/or underestimated Stotal or (2) a large temperature-uncertainty. It
is noteworthy that the total INP concentration was kept below 140 L-1
in order to avoid saturation limitation due to a high number of growing ice
crystals (DeMott et al., 2011). A constant total concentration of particles continuously
passing through the chamber was maintained at 1.07 ± 0.17 cm-3
(average ± standard deviation).
FRIDGE
FRIDGE data, which cover both measurements of dry and
immersed particles with the same instrument but with different sample
processing, lie within the upper edge of the bulk of other ns data
points. There are a few important implications from the FRIDGE results.
First, on average, the measurements with dry particles in the “default”
setting showed more than an order of magnitude higher ns in comparison to
the immersed particles in FRIDGE experiments (both ns,BET and
ns,geo, Figs. 4 and 5) at -25 ∘C < T < -18 ∘C. For instance, FRIDGE experiments in the pure immersion mode
showed much lower ns than that with the default setting (i.e., combined
deposition and immersion mode), but agreed with other immersion data sets.
Second, a sudden increase in ns(T) was found for the measurements with
immersed particles at ∼ -20 ∘C, suggesting a
dominant activation around -20 ∘C. This transition is a unique
behavior only found with the FRIDGE's IN detecting sensitivity. A
temperature shift (i.e., shifting the data ∼ 7 ∘C
lower) results in FRIDGE data overlapping with the bulk of other data and
may offset discrepancies. However, other mechanistic interpretations (e.g.,
contribution of agglomeration) are also plausible causes of this
discrepancy. More detailed discussions of the role of agglomerates upon
ns and sample processing are available in Sects. 4.4 and 4.5.
LACIS
With the shortest instrument residence time
(∼ 1.6 s), LACIS measured immersion mode freezing of illite NX
particles for three different mobility diameters (300, 500 and 700 nm) from
-31 ∘C down to the homogeneous freezing temperature. Similar to
AIDA results, a size independence of ns of submicron illite NX particles
was observed within defined experimental uncertainties (see the
supplementary methods). Further, without any data corrections, the results
of LACIS reasonably agreed with AIDA measurements. Furthermore, though there
is no overlapping temperature range for LACIS and CSU-CFDC in the present
study, consistency between data from LACIS and CSU-CFDC for other clay
minerals (i.e., different kaolinite samples) has been described previously
(Wex et al., 2014). The results from both instruments agreed well with each other
from a data evaluation based on ns, and this agreement was even improved
when the different residence times in LACIS and the CSU-CFDC were accounted
for (i.e., when nucleation rate coefficients were compared). Furthermore, a
size independence of the immersion mode freezing was seen for
Fluka-kaolinite particles with mobility diameters of 300 and 700 nm in Wex et al. (2014), and for illite NX particles when comparing particles with mobility
diameters of 500 nm to bulk material (Augustin-Bauditz et al., 2014).
MRI-DCECC
Comparison between polydisperse and size-selected
(300 nm mobility diameter) measurements in this cloud simulation chamber
demonstrated the size independency of ns for submicron illite NX
particles for slightly higher temperatures (up to -21 ∘C) than
AIDA results. Interestingly, MRI-DCECC data exhibited at least an order of
magnitude higher ns values than most other suspension measurements. We
note that only negligible freezing events were detected above -21 ∘C even with a ∼ 9000 cm-3 number
concentration of polydisperse illite NX particles in part due to the
detection limit of the welas® optical counter of Nice= 0.1 cm-3.
PINC
PINC provided data for immersion freezing at around
-25.4, -30.2 and -34.6 ∘C (a total of nine data points with one,
four and four points at around each temperature, respectively). The
estimated nsvalues are in agreement with other measurements for the
test range of -35 ∘C < T < -25 ∘C after
applying a residence time correction of about a factor of 3. The data
are for ice nucleation onto 500 and 1000 nm mobility diameter illite NX
particles; therefore, an OPC threshold size of 2 µm for ice detection
is used. The impactor used for sampling particles into PINC was
characterized for size-resolved particle losses and was found to have a
cutoff (D50) of 725 nm mobility diameter. As such, when determining
ns,geo the particles losses (25 to 60 %, see the Supplement for more details) were taken into account for calculating activated
fractions. We note that ns,geo increased after correcting the data for particle losses, resulting in agreement between the data from PINC and data from LACIS, AIDA
and UC-RMCS in the temperature range from -25 to -35 ∘C.
PNNL-CIC
The IN efficiency of illite NX particles in the
immersion mode in the temperature range of -35 ∘C < T < -27 ∘C was observed to increase at lower temperatures. Estimated
ns values were somewhat higher in this temperature range when compared to
those from most of the other measurements. Data were obtained at conditions
where PNNL-CIC was operated at 105 % RHw at three different
temperatures. Dust particles greater than ∼ 1 µm (50 %
cut size) were removed before they were size-selected and transported to the
PNNL-CIC. The OPC detection threshold was set ≥ 3 µm; see the
Supplement for more details.
IMCA-ZINC
Coupled with IMCA, ZINC showed reasonable agreement
with AIDA and PNNL-CIC. This reproducibility verified the performance of the
IMCA-ZINC combination, which was not tested during ICIS-2007 (DeMott et al., 2011),
perhaps due to the similarity in the experimental conditions (i.e., particle
generation) to the other two methods. We also note that the residence time
in ZINC is about a factor of 3 longer than that in PINC. The IMCA-ZINC
measurements in comparison to the measurements with ZINC alone (i.e., a
combination of deposition nucleation, contact freezing, condensation freezing, surface
condensation freezing and immersion freezing) is discussed in Sect. 4.5 in more
detail.
Overall, as described above (Sects. 3.2.1 to 3.2.6), suspension experiments
with cold stage devices and levitation techniques provide IN measurements
under more controlled (with respect to droplet size, concentration and mass
of particles) conditions and a wider temperature range (up to -11 ∘C) than comparable dry-dispersed particle experiments. The
resulting nsvalues from these suspension experiments are also
independent of the total number of droplets and suspended dust particle
mass.
The estimated nsvalues of dry test particles below
-25.5 ∘C are in reasonable agreement with a previous study
(Broadley et al., 2012) at temperatures below -25 ∘C.
Furthermore, the strong temperature dependence and size independence of
ns may suggest a uniform distribution of freezing sites over the
total surface of illite NX particles in the immersion mode in this
temperature range. Specifically, AIDA and MRI-DCECC have shown
size-independent ns values for submicron dry-dispersed
particles. Overall, compared to suspension measurements, dry-dispersed
particle measurements showed higher ns values. For example,
FINCH is the only instrument which showed higher ns values than
the parameterization by Niemand et al. (2012) for ATD. Likewise, AIDA
results indicated slightly higher ns values than CSU-CFDC's
results. The lower ns of CSU-CFDC may be a consequence of
underestimation of Nice, possibly due to its constrained
RHw (at 105 %) and/or the disturbance of aerosol lamina
between two plates in a CFDC (DeMott et al., 2015).
Intercomparisons based on the slope parameter of ns(T)
spectra
A compilation of 17 ns spectra from 17 instruments in a
temperature range between -10.1 and -37.5 ∘C is presented in Fig. 6. For both the geometric area-based and the BET area-based ns, the
differences among measurements can be more than 1 order of magnitude at
any given temperature. Diversity is especially pronounced for several orders
of magnitude in ns at -27 ∘C ≤T≤ -18 ∘C,
where the results from suspension measurements and a majority of dry
measurements coexist (see the investigated T range for each technique in
Table 1). Another notable feature of this specific temperature range in Fig. 6 is the coincidence of the steepest slope in the spectrum (i.e.,
the absolute value of Δlog(ns)/ΔT or |Δlog(ns)/ΔT|
in log (m-2) ∘C-1, hereafter denoted as Δlog(ns)/ΔT) when compared
to other temperature ranges. For instance, ns increases sharply at
temperatures colder than -18 ∘C to be nearly parallel to the A13
parameterization down to -27 ∘C, where it starts leveling off and
is eventually overlapping with the N12 parameterization at the low
temperature segment.
Immersion freezing ns(T) spectra of illite NX particles from 17 instruments calculated as a function of the BET (a) and geometric
(b) surface areas. Reference immersion freezing ns(T) spectra are
provided as in Figs. 4 and 5. Dry-dispersed particle (red markers) and
suspension (blue markers) results for ns,BETand ns,geo are shown in (c) and (d), respectively, to highlight the difference
between dry particle and suspension subsets.
Correspondingly, the overall trend of the spectrum is traced by the
measurements from NC State-CS alone (Fig. 4e). Moreover, the slopes of the
spectrum for three sub-segments (-34 ∘C < T < -27 ∘C, -27 ∘C < T < -20 ∘C, and
-20 ∘C < T < -14 ∘C) can be calculated
from interpolated data and compared to N12 and A13 parameterizations. As
expected, the steepest slope in the spectrum (= 0.66) of the NC State-CS
data was found in the -27 ∘C < T < -20 ∘C range, which was similar to that of the A13 parameterization (0.45 for T > -25 ∘C). However, smaller slope values are found for the other
two segments (0.18 for T < -27 ∘C and 0.29 for T
> -20 ∘C), which are comparable to the
temperature-independent N12 slopes (0.17 for ATD and 0.22 for Dust) and the
B12 slope (0.25 for -35 ∘C < T < -27 ∘C), suggesting that a dominant fraction of INP contained in our test dust
becomes ice active in immersion freezing at -27 ∘C < T < -20 ∘C. In addition, FRIDGE immersion mode measurements also show
a sharp decrease in Δlog(ns)/ΔT (from 0.59 to 0.25, Figs. 4k and 5k) for the measurements with immersed particles at ∼ -20 ∘C. Similar observations are made by most of the other
suspension measurement techniques. In short, most suspension methods capture
the steepest segment of the ns(T) spectral slopes (Δlog(ns)/ΔT) at -27 ∘C < T < -20 ∘C, where the slope is nearly parallel to the A13
parameterization. One exception is CU-RMCS (Fig. 4f). The highest possible
freezing temperature investigated by this experimental system was about -23 ∘C with ∼ 2.5 nanoliter droplets containing 1.0 wt % illite NX (see the supplementary methods). Hence, CU-RMCS did not
capture the transition in Δlog(ns,BET)/ΔT at around -20 ∘C, but the steep slope of the spectrum (= 0.36) validated the
high density of IN active sites below -23 ∘C. The error in
temperature for this technique is always ±0.5 ∘C, based on
freezing experiments without any foreign substances in supercooled drops
(i.e., homogeneous freezing experiments).
Similarly, dry-dispersed particle measurements also exhibit scattered data
for their measured temperature ranges. Both agreements and equally important
disagreements were observed. First, the agreements are summarized. AIDA data
show that the values of Δlog(ns,geo)/ΔT (= 0.22,
Fig. 5g) are identical for both polydisperse and size-selected measurements,
perhaps suggesting a uniform distribution of active sites over the available
Stotal of illite NX in this study. Similarly, IMCA-ZINC's Δlog(ns,geo)/ΔT (= 0.24, Fig. 5p) derived from 200, 400
and 800 nm mobility diameters is virtually identical to the slope estimated
from AIDA measurements. PINC estimated Δlog(ns,geo)/ΔT (= 0.26, Fig. 5n) values are in reasonable
agreement with AIDA and IMCA-ZINC and N12 parameterizations at temperatures
below -25 ∘C. From the CSU-CFDC results, Δlog(ns,geo)/ΔT derived from interpolated data is 0.40 (Fig. 5h). Considering the AIDA and CSU-CFDC data, the ns(T) spectrum depicts
similar trends (i.e., ns or temperature deviation around -27 ∘C) compared to those seen in the NC State-CS results (Fig. 5e) and is also
parallel to the A13 curve (slope = 0.45) down to temperatures around -27 ∘C and is parallel to the N12 Dust curve (slope = 0.22) for the
lower temperature segment. LACIS measurements show that Δlog(ns,geo)/ΔT (= 0.19, Fig. 5l) is also in agreement
with that from AIDA, verifying a deteriorated freezing ability of illite NX
particles in the investigated temperature range. EDB was used to examine
both the contact and immersion freezing modes. Nonetheless, the slopes of
the spectra for both modes (0.11 for immersion mode freezing and 0.16 for
contact mode freezing, Fig. 5i) are similar to the N12 ATD curve
(slope = 0.17). From the fact that the value of Δlog(ns,geo)/ΔT of FINCH (= 0.27, Fig. 5j) above -27 ∘C is similar to that of the N12 dust parameterization (whereas
this relationship would be expected below -27 ∘C), we suspect
that a temperature uncertainty may be the main cause of the observed
deviation of its data from others. Lastly, at -35 ∘C < T < -27 ∘C, PNNL-CIC's Δlog(ns,geo)/ΔT
(= 0.19, Fig. 5o) agreed well with that of the N12 dust parameterization in the
same temperature range.
Next, the disagreements between dry-dispersed particle and suspension
measurements are discussed. Specifically, the MRI-DCECC results show lower
values of Δlog(ns,geo)/ΔT (= 0.29) up to -21 ∘C as compared to the suspension measurements. Additionally, in
the temperature range from -29 ∘C < T < -21 ∘C, the MRI-DCECC data show higher values of ns than those
observed in suspension measurements. This relatively constant Δlog(ns)/ΔT value along with higher nsvalues through the range
contrasts with the observed sharp transition in Δlog(ns)/ΔT in suspension measurements. We note that MRI-DCECC
experiments may have been carried out in the presence of a high degree of
agglomeration (Fig. 2c and d). Hence, particle processing (i.e., drying and
suspension) may not be the only factor causing this difference and other
contributions cannot be ruled out (see Sect. 4).
To conclude, the results from suspension and dry measurements suggest
evidence that the ns of illite NX particles derived from immersion
freezing is independent of or only weakly dependent on droplet size, mass
percent of illite NX sample in suspension and droplets, particle size of the
tested illite NX and cooling rate during freezing in the range of conditions
probed; see the Supplement for more detailed information
regarding experimental conditions for each instrument. Overall, the
sample processing (i.e., dry vs. suspension sample) may have an effect on
the immersion freezing efficiency of illite clays. A more detailed
discussion will follow in Sect. 4 below.
Discussion
For detailed comparison of methodologies, the immersion freezing properties
of illite NX particles in a wide range of temperatures is further discussed
by comparing ns(T) spectra from all 17 instruments (Sect. 4.1).
Specifically, we present T-binned average data (i.e., 1 ∘C bins
for -37 ∘C < T < -11 ∘C). A moving
average (where original data points are finer than 1 ∘C) or a
Piecewise Cubic Hermite Interpolating Polynomial function (where original
data points are coarser than 1 ∘C) was used for data
interpolation. All data from the 17 instruments, as shown in Figs. 4
and 5, were interpolated.
We also discuss potential reasons for the diversity observed from
intercomparisons of dry and suspension measurement techniques. Both
systematic errors (Sect. 4.2) and mechanistic uncertainties (Sects. 4.3 to
4.6) are qualitatively evaluated to understand the measurement uncertainties
of such techniques. Some factors may introduce diversity in ns, whereas
others may shift activation temperatures horizontally to match the
ns values from other instruments, perhaps biasing the overall accuracy
and precision of instruments. Here we address the relative importance of
those factors with respect to their effect on the estimation of ns.
List of the Gumbel cumulative distribution fit parameters to the
ns,BET and ns,geo for T-binned ensemble data set fitted in
the linear space [All (lin)], ensemble data set fitted in the log space [All
(log)], ensemble maximum values (Allmax), ensemble minimum values
(Allmin), suspension subset fitted in the linear space [Sus (lin)],
suspension subset fitted in the log space [Sus (log)], dry-dispersed
particle subset fitted in the linear space [Dry (lin)] and dry-dispersed
particle subset fitted in the log space [Dry (log)]. Note that Allmax
and Allmin are fitted in the linear space. The correlation
coefficient, r, for each fit is also shown. T is in ∘C.
Fitted data set
Fitted T range
Fit parameters [ns,BET(T) =
exp(a ⋅ exp(-exp(b ⋅ (T+c)))+d)]
a
b
c
d
r
All (lin)∗
-37 ∘C < T < -11 ∘C
23.82
0.16
17.49
1.39
0.60
All (log)∗
-37 ∘C < T < -11 ∘C
22.00
0.16
20.07
3.00
0.80
Allmax∗
-37 ∘C < T < -11 ∘C
24.72
0.15
17.27
1.56
0.63
Allmin∗
-37 ∘C < T < -11 ∘C
21.86
0.16
22.73
2.70
0.94
Sus (lin)
-34 ∘C < T < -11 ∘C
24.38
0.14
19.61
1.89
0.99
Sus (log)
-34 ∘C < T < -11 ∘C
24.28
0.14
21.19
2.70
0.99
Dry (lin)∗
-37 ∘C < T < -18 ∘C
27.35
0.07
16.48
3.19
0.59
Dry (log)∗
-37 ∘C < T < -18 ∘C
26.22
0.07
16.27
3.31
0.72
Fitted data set
Fitted T range
Fit Parameters [ns,geo(T) =
exp(a⋅ exp(-exp(b⋅(T+c)))+d)]
a
b
c
d
r
All (lin)∗
-37 ∘C < T < -11 ∘C
25.75
0.13
17.17
3.34
0.73
All (log)∗
-37 ∘C < T < -11 ∘C
22.93
0.16
20.31
5.72
0.80
Allmax∗
-37 ∘C < T < -11 ∘C
25.72
0.15
16.39
3.52
0.75
Allmin∗
-37 ∘C < T < -11 ∘C
22.16
0.16
22.13
5.64
0.98
Sus (lin)
-34 ∘C < T < -11 ∘C
22.72
0.16
19.52
5.50
1.00
Sus (log)
-34 ∘C < T < -11 ∘C
22.64
0.16
20.93
5.92
0.98
Dry (lin)∗
-37 ∘C < T < -18 ∘C
29.38
0.05
16.49
7.19
0.64
Dry (log)∗
-37 ∘C < T < -18 ∘C
27.92
0.05
13.25
6.32
0.83
∗ To derive the fits that are representative for immersion mode
freezing, we excluded EDB (contact) and ZINC data.
The ns parameterization, based on the BET (a) and geometric
(b) surface areas, as a function of temperature (T). The multiple exponential
distribution fit in the linear space (T-binned Lin. Avg.) is expressed as
ns,BET(T)= exp(23.82 × exp(-exp(0.16 × (T+
17.49))) + 1.39) or ns,geo(T)= exp(25.75 × exp(-exp(0.13 × (T+ 17.17))) + 3.34). The same fit in the log
space (T-binned Log. Avg.) is expressed as ns,BET(T)= exp(22.00 × exp(-exp(0.16 × (T+ 20.07))) + 3.00) or
ns,geo(T)= exp(22.93 × exp(-exp(0.16 × (T+ 20.31))) + 5.72). Note that ns and T are in m-2 and ∘C,
respectively. The maximum deviation between maxima and minima in horizontal
axis (in T, ∘C) and vertical axis [in
log(ns,max/ns,min)] corresponds to HorMax-Min and
VerMax-Min, respectively. All fit parameters are shown in Table 3.
Dry vs. suspension ns(T) data
The multiple exponential distribution fits (also known as the Gumbel
cumulative distribution function) for T-binned-ns(T) data are shown in Fig. 7. The
fits for T-binned maxima and minima ns from 17 measurement
techniques are presented as pink shaded areas. All fits presented in this
figure are derived using parameters shown in Table 3. As can be inferred
from the table, a higher correlation coefficient (r) was found when
intercomparing the suspension measurements as compared with intercomparing
the dry-dispersed methods, suggesting reasonable agreement and consistency
for the results from immersion freezing studies with suspensions.
Interestingly, a higher r for ns,geo than ns,BET was
found for dry-dispersed particle measurements as compared to the suspension
measurements. The use of more conversion factors to estimate
ns,BET (i.e., from Eqs. 3 and 4) may introduce uncertainties
and discrepancies between these measurement techniques. It is also
noteworthy that the T-binned ensemble maximum and minimum values are largely
influenced by dry-dispersed particle and suspension results, respectively,
implying the previously discussed discrepancy between these two techniques.
It is observed that the largest deviation between the maxima and minima in
the horizontal and vertical axes, corresponding to HorMax-Min and
VerMax-Min, respectively, shown in Fig. 7, is similar for both
ns,BET (Fig. 7a) and ns,geo (Fig. 7b). Nevertheless,
ns,BET is representative of measurements with suspended samples
because fewer corrections and assumptions are involved for its estimation
when compared to that with dry-dispersed particles. Hence,
ns,BET may be a good proxy for comparing IN efficiencies of
dust particles from various instruments. We also report the absolute values
of Δlog(ns)/ΔT for four T-segregated segments based on
T-binned Lin. Avg. (multiple exponential distribution fit to the T-binned
average data in the linear space), T-binned Max. (fit to the T-binned maxima
in the linear space) and T-binned Min. (fit to the T-binned minima in the
linear space) in Fig. 7 (i.e., T1 to T4). The slopes are comparable
to the slope of the A13 parameterization in the T1 to T3 segments (-11
to -27 ∘C), while the slope in the T4 segment is similar to
those of the N12 parameterizations. These results are consistent with the
results described in Sect. 3.3. Further, VerMax-Min for roughly 3 orders of magnitude with respect to ns is observed in a temperature
region around ∼ -20 ∘C for both
ns,BET(T) and ns,geo(T) spectra. Such high nsvariability
was expected due to the contribution from MRI-DCECC, FINCH and FRIDGE
measurements, which may have influenced the overall fit in that temperature
range. Likewise, our HorMax-Min shows that the 17 measurements
are in reasonable agreement within 7.8 ∘C (-36.8,
-33.0, -29.0 ∘C (min, log fit, max)) at ns,BET of 5.2 × 109 m-2
and 7.5 ∘C (-36.7, -32.8, -29.2 ∘C (min, log fit, max))
at ns,geo of 1.5 × 1011 m-2.
T-binned ns,BET(T) and ns,geo(T) spectra are presented in
Fig. 8a and b, respectively. In this figure, panels i, ii and iii show T-binned
data averaged in the linear space of all 17 instruments, all
suspension type measurements, and all measurements that involved dry
particles, respectively, while panel iv shows a comparison between
suspension and dry-particle measurements. We note that the data from “EDB
(contact)” and “ZINC” (Welti et al., 2009) were not used for generating T-binned data
since our focus was on immersion mode freezing. We also note that the
ns results from nine IN measurement techniques provide ns data at -23 and -24 ∘C, where we find an abrupt increase in
Δlog(ns)/ΔT and ns deviations. Investigated T ranges
for each instrument are listed in Table 1.
T-binned ns,geo (a) and ns,BET (b).
T-binned data (i.e., average in the linear space with 1 ∘C bins
for -37 ∘C < T < -11 ∘C) of
ns(T) spectra are presented for (i) All interpolated data set (All), (ii)
suspension measurements (Sus), (iii) dry-dispersed particle measurements
(Dry), and (iv) comparison between Sus and Dry. Red sticks represent maxima
(positive direction) and minima (negative direction) and black sticks
represent ±standard error. Literature results (B12, A13, and N12) are
also shown.
As described in Sect. 3.2, suspension measurements possess sensitivity at
high temperatures (up to -11 ∘C), indicating that their ability
to control the concentration or dilution of suspension over a wide range is
of great advantage in detecting rare INPs. Moreover, suspension experiments
with small picoliter or nanoliter droplets allow measurements right down to
the homogeneous freezing limit (∼ -37 ∘C;
Koop et al., 2000). In turn, suspension methods with microliter droplets may run into
“background problems” at temperatures below about -20 to -25 ∘C for samples that do not contain many IN active at these
temperatures, because then impurities contained in the water may trigger
freezing. Conversely, dry aerosol methods lack sensitivity for detecting
rare IN at high temperatures because of their low sample volume. These dry
particle measurements are in general good for low temperature measurements,
where the number of particles nucleating ice increases and instruments have
higher ice detection efficiencies. For temperatures below -27 ∘C,
our T-binned fits exhibit a reasonable agreement with the suspension
experiments reported by Broadley et al. (2012). Furthermore, dry-dispersed particle
measurements show higher ns values when compared to suspension
measurements above about -27 ∘C (Fig. 8iv). We will discuss
possible explanations for the observed diversity of data from different
techniques in detail below.
In addition, T-binned ns,BET(T) and ns,geo(T) spectra
averaged in the log space are presented in Fig. S3. Similarly, we also
present T-binned ratios of the individual measurements to the log fit of the
data [All (log), Sus (log) or Dry (log) from Table 3] across the temperature
range covered for all the measurement techniques (-37 ∘C < T < -11 ∘C) in Figs. S4–S8.
These figures provide intercomparisons of the ns deviations across the
various techniques employed in this study.
Limitations of instrument types
Groups participating in this study used different experimental setups to
measure immersion freezing efficiencies of illite NX test samples. As a
consequence, various experimental procedures, such as particle generation,
particle size-segregation, Stotal estimation, ice crystal detection or
counting, ice crystal detection size limits for OPCs or CCDs, and particle
loss at the inlet and/or in the chamber can potentially yield substantial
systematic uncertainties in the estimation of ns. Below we qualitatively
discuss potential errors and limitations involved in each instrument-type
(cold stage, levitator, CECC and CFDC).
Limitations of substrate-supported optical microscopy and cold stage
experimental setups may come from inhomogeneous cooling of the substrate and
the surrounding media, the effects of RH changes surrounding the drops for
non-substrate-supported cold stage setups, potential contamination during
sample preparation and measurements (e.g., particle processing in a solvent)
and/or uncontrollable heat transfer between the cold plate surface and the
particle substrate (e.g., FRIDGE).
Levitator techniques require extensive pre-characterization of
physicochemical properties. Furthermore, since the overall system
characterization is more complex and labor intensive, only specific subsets
(i.e., suspended samples or reference particles) can be examined using this
method.
The development of AIDA-CECC allows the simulation of atmospherically
representative cloud parcel formation and evolution (Möhler et al., 2003). Therefore, it
is an advantage of CECC that the parameterization derived from its
experiments can be most readily extended to atmospheric conditions (Niemand et al.,
2012). Development of large (up to 84 m3, i.e., AIDA) and/or
temperature-controlled dynamic cloud simulation chambers (e.g., MRI-DCECC;
Tajiri et al., 2013, a design which follows from DeMott and Rogers, 1990) enabled the exploration of
heterogeneous ice nucleation properties of typical particulate samples in a
wide range of particle concentrations, temperatures (-100 ∘C < T < 0 ∘C), cooling rates and nucleation times.
However, the utilization of such an instrument to correctly measure the
totality of INPs with a reasonable detection sensitivity (< 0.1 L-1), both in the lab and field settings, has not yet been realized due
to CECC's limitations. These limitations include ice losses by settling
(e.g., DeMott and Rogers, 1990) over the relatively long expansion periods in the confined
vessel and internal turbulence during the expansion leading to
heterogeneously supersaturated water vapor and temperature fields. These
artifacts can bias IN measurements.
CFDCs are the most widely used technique to measure INPs in the atmosphere,
but their inability to quantify INPs at high temperatures is an issue that
exists due to the physical principals of operation, the limited sample
volume (typically 1 to 2 L min-1) and background frost formation in the
chamber over periods of operation. Based on the operational equations in
Rogers (1988), the warmest operating temperature of a CFDC is approximately -6.5 ∘C, controlled
by the fact that the warmest wall cannot exceed 0 ∘C. Low sample volumes necessitate integration over longer sample
periods and result in a general lower detection limit of 0.2 L-1 of
sampled air, absent any particle pre-concentration (Prenni et al., 2009). According to
Tobo et al. (2013), the highest temperature that can be achieved in a CFDC is -9 ∘C. Above this threshold, temperature and ice saturation
conditions cannot be maintained in the chamber. Rogers et al. (2001) and other papers
since have identified measurement issues due to frost emanating from the
walls of the chamber when the dew point temperature of the sample air is not
effectively controlled, although this appears to be an operational issue
that can be mitigated if monitored properly, and will be most obtrusive for
atmospheric sampling scenarios.
Stochastic nature of freezing and time dependence
The longstanding discussion of the stochastic theory (i.e., the freezing
process is time-dependent) vs. the deterministic approximation (i.e.,
freezing occurs at specific temperature and humidity conditions) of
heterogeneous freezing has introduced another complication towards complete
understanding of heterogeneous ice nucleation in the atmosphere (Vali, 2014).
Many studies have attempted to characterize ice nucleation based on the
classical nucleation theory (CNT), which incorporates a nucleation rate
(Murray et al., 2012; Kashchiev, 2000; Mullin, 2001). In this treatment, the ice nucleation process is
always of a stochastic nature (i.e., time-dependent; Bigg, 1953; Vali, 1994, 2014).
According to the nucleation rate approach, the heterogeneous ice nucleation
rate is strongly sensitive to INP size and the kinetic activation energy of
the ice embryo on the nucleating site/surface at a specific temperature
(Khvorostyanov and Curry, 2000; Fletcher, 1962). A few variants of the CNT-based approaches have been
developed over the past few decades. These approaches assume uniform surface
characteristics and only one ice nucleation probability (i.e., a single
contact angle), nominally categorized as the single component nucleation
rate approach (e.g., Bigg, 1953). Several recent studies have applied a
probability density function (PDF) of contact angles and active sites over
the INP surface in CNT, or in other words described a distribution of
nucleation efficiencies, bridging the gap between the stochastic theory and
the deterministic treatment (Marcolli et al., 2007; Lüönd et al., 2010; Kulkarni et al., 2012; Niedemeier et al., 2011; Wright and Petters., 2013;
Broadley et al., 2012).
The deterministic or time-independent singular approximation has been
developed as an alternative option to quantitatively understand atmospheric
ice nucleation. The concept was first developed by Levine (1950), while the term
“active sites” per surface area was introduced by Fletcher (1969). More recently,
Connolly et al. (2009) introduced the ns density parameterization (see Sect. 2.4). This
specific approach neglects the time dependence of freezing, and assumes that
a characteristic condition (e.g., temperature) must be met to nucleate ice.
The semi-deterministic forms of the singular approach have a cooling rate
dependence incorporated (Vali, 2008; Herbert et al., 2014). Predicting ice nucleation from a
singular perspective does not require a vast knowledge of particle-specific
parameters (e.g., surface composition, structures, surface tension and
solubility) that are particular to each ice nucleus and, therefore, enables
ice nucleation parameterization to be relatively simple and efficient
compared to the CNT-based approaches (Murray et al., 2011).
The assumption that the time dependence of the freezing of droplets is of
secondary importance when compared to temperature dependence is supported by
a recent modeling sensitivity study that shows that common INPs are
substantially more sensitive to temperature than to time (Ervens and Feingold, 2013).
Furthermore, while Broadley et al. (2012) shows that freezing by illite NX is
time-dependent through isothermal experiments, the shift in freezing
temperature on changing cooling rates by an order of magnitude is less than
0.6 ∘C, which is within the experimental uncertainty. A similar
observation of weak time dependence of immersion freezing for various types
of suspended samples, inferred by comparing the results with varied cooling
rates from 0.01 to 1 ∘C min-1, is reported by Wright et al. (2013).
In the context of dry-dispersed measurements, the sensitivity of the ice
nucleation to a possible time dependence, and the respective influence on ns,
was examined to further discern its importance and uncertainty.
Specifically, a contact angle distribution was fitted to the LACIS
measurements and was used, together with the soccer ball model (SBM;
Niedermeier et al., 2011, 2014), to simulate frozen fractions for different residence
times varying over 4 orders of magnitude (i.e., 1, 10, 100 and 1000 s
residence time). These frozen fractions were then used to calculate
ns, shown as lines in Fig. 9. More specifically, frozen fractions for
500 nm diameter illite NX particles were calculated based on SBM to obtain
ns(T) spectra. To accomplish this, a contact angle distribution was used
which was derived based on LACIS data for the illite NX particles as shown
in this work, resulting in values of 1.90 rad for the mean and 0.27 rad for
the width of the contact angle distribution. Frozen fractions were obtained
for ice nucleation residence times of 1, 10, 100 and 1000 s. An increase in
the residence time by a factor of 10 resulted in a shift of approximately 1 ∘C towards higher freezing temperatures. This is similar to the
results found in a previous study by Welti et al. (2012) for measurements of kaolinite-rich clay minerals. Indeed, ns,geo data obtained from AIDA agree within the measurement uncertainty with LACIS data without
accounting for time dependence. These results suggest that time dependence
of immersion freezing for illite NX particles can be neglected as a factor
in the comparisons shown in Figs. 4, 5 and 6. They also imply that the
immersion freezing nature of illite NX is only slightly dependent on cooling
rate across a wider range of temperatures (as compared to a -26 to -37 ∘C range as shown in Broadley et al., 2012), regardless of the sample
preparation process.
Soccer ball model analysis for time dependency of immersion
freezing of illite NX particles. Comparison to LACIS measurements in
ns,geo space is also shown. Error bars represent experimental
uncertainties (T ± 0.3 ∘C and ns ± 28 %). The
subpanel shows a section of T (-31 to -38 ∘C) and
ns,geo (1.2 × 1010 to 5.1 × 1011 m-2) space without
error bars. A shift in the residence time from 1s to 10 s shifts ns (as
well as nm, not shown) towards higher temperatures by about 1 ∘C.
Potential effect of agglomerates
As seen in the particle surface area distributions (Fig. 2) and
agglomerated-fractions based on a relative comparison to D95, aggregates
are rather persistent and dominant for most of the dry-dispersed particle
measurements. Since dry aggregates can have large “supermicron” sizes, they
may have different IN propensities and efficiencies (Wheeler et al., 2014) as compared to
the smaller sizes investigated in the present study (i.e., up to 1000 nm
from PINC). Further, the degree of agglomeration may conceivably affect the
surface area exposed to liquid water when suspended in supercooled droplets.
Hence, an overall quantification of the effect of agglomerates is difficult.
Moreover, the degree of agglomeration seems to vary from experiment to
experiment, introducing diversity on the estimation of Stotal of
particles and ns for dry-dispersed particle measurements. For instance, a
combination of several methods for particle dispersion and subsequent
particle size selection was employed for particle generation from illite NX
samples. Further, most of the dry dispersion techniques used upstream
impactors to filter out large agglomerated particles and avoid counting
these large particles as INPs. The different types of dispersion methods,
impactors and size segregating instruments used in the present work are
listed in the Supplement Table S1. These different aerosol generation
processes may have caused different degrees of agglomeration. This may in
part explain why ns measurements obtained using dry dispersion techniques
deviated from those using suspension measurements. Further quantification of
the influences of different methods for particle dispersion,
size-segregation and particle impaction/filtration on the estimation of
Stotal and ns is an important topic for future works.
In contrast, in suspension experiments, illite NX samples were directly
suspended in water. Despite no pre-treatments (e.g., pre-impaction or size
segregation), suspended particles appeared adequately de-agglomerated (Fig.
2a). Though the number of immersed particles can vary from droplet to
droplet and the random placement of particles in the drop may have an effect
on the ns values, the ns spectra from suspension measurements are in
reasonable agreement with slight deviations even over a wide range of wt %
of illite NX samples (Figs. 6, 8, S4–S8). Thus, the influence
of the random placement of particles in the drop and agglomeration on the
ns estimation for suspension measurements seems small. To support this,
Wright and Petters (2013) and Hader et al. (2014) simulated the role of a statistical distribution in
drops. The authors demonstrated that the random component due to drop
placement seemed to be small relative to the statistical variation due to
nucleation probability. Hence, assuming the degree of agglomeration or
flocculation is similar in all suspension samples, the degree of
agglomeration and the random placement of particles in the drop may lead to
less pronounced deviations in ns when compared to dry-dispersed
measurements.
Nucleation mode dependence
While all suspension methods only measured immersion mode freezing of the
illite NX particles, a contribution of other nucleation or freezing modes
cannot be ruled out for dry-dispersed particle measurements. Hence, we now
discuss inferences in the present experiments regarding the mode dependency
of the ice nucleation ability of illite NX particles. Figure 10a and b show
the comparison of ns derived from the two different operation types of
FRIDGE measurements. For instance, `default mode' considers deposition mode
nucleation and immersion mode freezing of dry particles in which RHw is
scanned upwards and `imm.mode' counts immersion freezing of suspended
particles in which the particles are first washed into droplets and then
placed on the substrate. With these two different operational modes, FRIDGE
investigated the ice nucleation ability of both dry and droplet suspended
particles deposited on a substrate (see the supplementary methods). FRIDGE
scans RHice and RHw (low to high) at a constant temperature.
During such scans an abrupt increase in an activated ice fraction near water
saturation as well as the highest Nice is typically observed. We
consider ice crystals formed at the highest RHw (near 100 % RHw)
as a measure of immersion Nice from dry-dispersed particle measurements
in this study. Some default runs of FRIDGE show much higher
ns,BET values compared to the immersion mode runs. This difference
may be a consequence of the different IN efficiencies of nucleation modes
(deposition + immersion vs. immersion alone) in the examined temperature
range (-25 ∘C < T < -18 ∘C), the
different sample preparation processes (dry or suspended sample), effects of
agglomeration or a combination of the three. We note that a major difference
between the two measurement setups is the pressure within the instrument.
For instance, default conditions involve processing at a few hPa of water
vapor while the immersion measurements are conducted at atmospheric
pressure. In addition, corrective post-analysis of droplet/ice separation
was taken into account in this study, so that errors from counting large
droplets as ice crystals were successfully removed. Interestingly, our
comparison suggests that ns values derived from the FRIDGE default mode
seem similar to those from MRI-DCECC, in which experiments were carried out
with a high degree of particle agglomeration (Fig. 2c).
Examination of mode dependency of heterogeneous ice nucleation of
illite NX particles. A comparison of FRIDGE (default) and FRIDGE (imm.mode)
in ns,BET and ns,geo are shown in (a) and (b),
respectively. (c) and (d) show a comparison between EDB (contact), EDB
(imm.), ZINC, IMCA-ZINC, and PNNL-CIC data in ns,BET and
ns,geo, respectively.
Some other variations on applied methods suggest nucleation mode effects on
the IN efficiency of illite NX particles at lower temperatures (Fig. 10c and
d). For instance, the comparison between ZINC and IMCA-ZINC show about an
order of magnitude diversity in ns,BET beyond experimental
uncertainties at -33 ∘C, suggesting a mode-dependent IN
efficiency of clay minerals at this temperature. This observation is
consistent with a statement that the immersion freezing parameterization
from CNT may not reliably predict the activated fraction observed at
RHw > 100 % as observed from condensation freezing (Welti et al.,
2014). However, this is in contrast to observations indicated by PNNL-CIC
below -25 ∘C and to results presented in Wex et al. (2014), where
ns,geo obtained from kaolinite measurements made with LACIS and the
CSU-CFDC (at 104 % > RHw > 106 % for the
latter) agreed well. When a freezing point depression is taken into account,
even data obtained with the CSU-CFDC for water-vapor-sub-saturated
conditions is in agreement with data obtained from both LACIS and CSU-CFDC
at water-vapor super-saturated conditions. Concerning data presented here,
PNNL-CIC and IMCA-ZINC measure condensation/immersion and purely immersion
mode freezing efficiency of particles, respectively, and are in reasonable
agreement within experimental uncertainties (Fig. 10c and d). Thus, the
observed inconsistencies between methods should be subject to further
methodological improvements to provide accurate data as a basis for model
parameterization. Similar heterogeneous ice nucleation mode-dependent
observations were made by our EDB experiments. We observed that ns values
derived from contact freezing experiments were higher than those derived
from immersion experiments (Fig. 10c and d). As described in the
supplementary methods, immersion mode experiments were performed for the
droplets, which were not activated via contact freezing.
Effect of mineralogical properties: which component of illite NX
nucleates ice?
Atkinson et al. (2013) suggested that the mass fraction of K-feldspar in a sample can be
used as a scaling factor to estimate the ns values of other K-feldspar
containing dust and soil samples. O'Sullivan et al. (2014) showed that this scaling rule
could be used as an approximate predictor for the ns of soil samples once
the biological ice-nucleating particles were deactivated. However,
inspection of Fig. 6 reveals that the line based on 14 % feldspar
(assuming all microcline) significantly over predicts the ns values for
illite NX. There are a number of reasons why this might be.
The K-feldspar sample used by Atkinson et al. (2013) was the British Chemical Standard
Chemical Reference Material (BCS-CRM) number 376/1 and X-ray diffraction
analysis shows that the crystal structure is consistent with that of
microcline. Microcline is one possible form of a K-feldspar and, as
discussed above, other feldspars are sanidine and orthoclase, which have
distinct crystal structures. The ice nucleation abilities of sanidine and
orthoclase are not yet published, but given that they have different crystal
structures, they may have different nucleating abilities. Unfortunately, the
X-ray diffraction analysis of illite NX is unable to identify the
K-feldspar(s) present in illite NX, although the mineralogical analysis
conducted as part of this study concluded that there was no detectable
microcline in illite NX. Hence, one explanation for the K-feldspar scaling
rule not working for illite NX is that there is only a trace of the strongly
ice active microcline present in illite NX. For suspension measurements,
only the 0.0014 % microcline parameterization reproduces the slope and
magnitude of the illite NX data in Fig. 6, but this quantity of microcline
is well below the detection limit of the X-ray diffraction technique.
Perhaps, in the case of illite NX, it may not be the feldspar which triggers
nucleation, but instead it could be another mineral present in this sample.
For example, Atkinson et al. (2013) found that a quartz sample nucleated ice more
efficiently than the clay minerals, but less efficiently than the feldspar
samples they used. At about -28 ∘C, they reported an ns of
∼ 1010 m-2. The X-ray analysis in this study
revealed the presence of 3 % quartz, hence we would predict an ns of
3 × 108 m-2, which is consistent with the illite NX data. Finally,
an alternative explanation is that the surfaces of K-feldspars are
chemically altered in illite NX. The surfaces of feldspars are known to
transform to an amorphous silicate which can then recrystallize as a clay if
exposed to an acidic environment. Wex et al. (2014) suggested that it was the acid
processing of K-feldspar which deactivated Fluka-kaolinite. It is feasible
that the surfaces of feldspar grains in illite NX have at some point become
deactivated. More quantitative investigations of the acid processing of both
reference and atmospherically relevant materials, and of acid processing's influence on
their respective immersion mode ice nucleation efficiencies, are needed.
Recently, re-partitioning of soluble components of both swelling and
non-swelling clay minerals and their effect on cloud condensation nucleation
activity was reported (Sullivan et al., 2010; Kumar et al., 2011; Garimella et al., 2014). To address a potential
importance of this effect on the ice-nucleating activity of illite NX in the
wet dispersion experiments, we have measured the concentration of cations
released by the illite NX sample placed into deionized water as a function
of time, as described in Sect. 3.1 (i.e., Fig. 3).
It is instructive to compare the quantity of cations released by illite NX
into an aqueous environment with the value of the cation exchange capacity
(CEC) for illite, which is known to be 25 to 40 cmol kg-1(Meunier and Velde, 2004). CEC is
defined as the amount of cations retained by all the negative charges in
100 g of clay immersed in water at pH 7 (e.g., see Meunier, 2005). Per this definition,
CEC describes the total quantity of exchangeable cations, including
interlayer cations which are in fact not accessible for substitution in
non-swelling clays. The molar fraction of external cations, located on the
basal planes of the crystals and on the crystal edges is roughly evaluated
for illites as 20 % of the total CEC, yielding 5 to 8 cmol kg-1 (Wilson, 2013). Remarkably, the total amount of all cations (K+,
Mg2+ and Ca2+) released within the first hour by illite NX, if
recalculated with account for cation valence and for the actual mass of
illite in the aqueous suspension (0.1 g), gives the number 7.5 cmol kg-1, which corresponds nicely with the upper bound of the external CEC
(8 cmol kg-1). Furthermore, Grim (1953) has shown that the CEC of illite
increases with decreasing size of the clay particle size, with the upper
bound (∼ 40 cmol kg-1) being characteristic for illite
with a particle size below 100 nm. This is again consistent with the very
small size of particles in illite NX.
These findings have two potential implications for the measurements of
illite NX ice-nucleating efficiency obtained with different instruments.
First, in the methods where dry illite NX particles are activated to
droplets prior to cooling, the concentration of cations released into the
water surrounding the particles is still far from the equilibrium and is a
function of the residence time (e.g., ∼ 2–3 s for LACIS,
∼ 4 s for PINC, ∼ 12 s for PNNL-CIC, and over
the range of several tens of seconds to a few minutes for AIDA depending on
initial chamber T and RH). At the same time, the amount of external cations
retained on the surface of illite particles determines the charge
properties, such as charge distribution landscape and zero charge point. A
potential importance of the surface charge of hematite particles for their
IN activity was suggested recently in Hiranuma et al. (2014b). These considerations,
however speculative, might shed some light on the observed scattering of
experimentally measured values of ns. Second, for the freezing
measurements where the illite-rich sample was suspended in water prior to
cooling, all accessible external cations were already released into the
aqueous environment. In these cases the concentration of cations in the
droplets is a function of mass concentration of illite in suspension. To
access high freezing temperatures, high concentrations of illite are needed
in the droplet assay techniques, resulting in the possibility that not all
cations are released into solution due to the inhibition of the ion exchange
process. Again, this would change the surface charge distribution and
potentially affect the ice-nucleating efficiency of illite particles. If wet
particle generation (dispersion of aqueous suspension by means of a
pressurized air atomizer) is used, the redistribution of cations between
suspended particles may be an issue, as suggested by Garimella et al. (2014) for the case
of CCN experiments. Further studies of samples without modification or
ageing after dry dispersion or wet suspension are needed to get a better
idea of the method intercomparison.
Conclusions
The framework of the present work is designed to advance the existing state
of knowledge regarding IN measurement techniques. After ICIS-2007, there has
been an increase in new instrument development, especially off-line,
substrate-supported cold stage techniques, and modifications of existing
online techniques. Concepts to formulate area-scaled IN efficiency with
ns parameters have also since been introduced to the community. These
improvements are comprehensively evaluated in this work.
The partners of the INUIT group and external partners have for the first
time identified and shared a reference mineral dust sample (illite NX) in
order to obtain a comprehensive data set for evaluating immersion freezing
properties of atmospherically relevant particles across a wide range of
particle concentrations, temperatures, cooling rates and nucleation times.
Illite NX samples were extensively characterized for their physicochemical
properties before they were distributed to INUIT partners and collaborators.
Both bulk and single particle elemental composition analyses were conducted
by XRD and EDX analyses, respectively.
A total of 17 IN measurement techniques were intercompared based on
their immersion freezing measurements. Our intercomparison exercise
provided unique results that would not have been achieved by individual
investigators in isolation. Both consistencies and discrepancies among the
instruments have been identified. Our results suggest that the immersion
freezing efficiency (i.e., ns) of illite-rich clay minerals is
relatively independent of droplet size, mass percent of illite NX sample in
droplets for the methods examining suspensions, physical size of illite NX
particles for the methods examining dry-dispersed particles and cooling rate
during freezing within typical experimental uncertainties, verifying the
premise of the ns concept (i.e., size independency for submicron illite
NX particles, strong temperature dependency and weak time dependency of
immersion freezing for illite-rich clay mineral particles).
Furthermore, comparisons of the suspension subsets against the dry-dispersed
particle techniques were performed. Dry samples alone showed higher
ns values compared to the pre-suspended samples above -27 ∘C. A possible explanation for this deviation (i.e.,
ns from dry-dispersed methods > ns from suspension
methods) may be the surface modification of the illite NX particles (e.g.,
due to ion dissolution effects in the aqueous suspension).
Comparisons of the absolute values of Δlog(ns)/ΔT as an
ice activation parameter suggest that the predominant freezing sites of
illite NX particles exist in a temperature range between -20
and -27 ∘C for suspension experiments. In comparison to previous
measurements, our synergetic work, which covers a wide temperature range,
shows a similar result to the Broadley parameterization (B12), and our
overall fit for the low temperature region below -27 ∘C also
agrees with the Niemand parameterization (N12).
Overall accuracy and precision of the IN measurement techniques was examined
by evaluating T-binned (i.e., 1 ∘C bins) ns(T) data derived from
all 17 instruments for the temperature range from -11
to -37 ∘C. Our analysis revealed that discrepancies among
measurements were within about 8 ∘C in terms of temperature and
up to 3 orders of magnitude with respect to ns. This diversity is
much larger than the individual uncertainties of each instrument, suggesting
that all instruments may be reasonably precise but it is still difficult to
find overall accuracy of current IN measurement techniques, at least while
using illite NX as the standard and allowing partners to investigate it
independently. In addition, two different ns metrics, ns,geo and ns,BET, were compared, and we found that ns,BET is
a better proxy for suspension-based IN measurements, while
ns,geo is better for dry-dispersed particle measurements.
Other than the intercomparison aspects described above, several important
implications were inferred from our study and enhanced our basic knowledge
of immersion freezing. First, the existence of only a comparably small
contribution of time dependence to the intercomparison was reconciled by
the SBM simulation. Specifically, a change of the residence time, from 1 to
10 s, shifts ns values towards higher temperatures by only about 1 ∘C. Second, several nucleation modes and their contribution to
nucleation efficiency were also evaluated. A comparison among EDB, ZINC and
IMCA-ZINC below -25 ∘C implied some mode dependencies. Likewise,
a mode dependency was also pronounced based on FRIDGE results at
temperatures above -25 ∘C. Third, immersion freezing experiments
were performed with both polydisperse and size-selected illite NX particles
for the AIDA-CECC, MRI-DCECC and CSU-IS measurements, and size independence
of ns for immersion freezing of submicron illite NX particles (DMA
size-selected 200, 300 and 500 nm diameter) was also demonstrated. Finally,
our observations show that temperature is the major variable influencing the
immersion freezing of illite NX particles, as the ns values in general
increase while temperature decreases. In addition, our results of
ns and absolute values of Δlog(ns)/ΔT
distributions across a wide range of temperatures imply that clay minerals
may contain various freezing activation energies, and the immersion freezing
nature of clay minerals (e.g., illite NX) in a wide range of temperatures
cannot be fitted by simple exponential functions but are governed by a
hybrid of multi-exponential functions (a combination of scaled A13 and N12
parameterizations).
Though we shared identical test samples with each other, it is still
difficult to compare ns results because sample preparation techniques and
measurement methods (e.g., particle dispersion and size distribution
characterization) differ from group to group, which can result in different
degrees of agglomeration or different nucleation modes. Therefore, a
continued investigation to obtain further insights into consistencies or
diversity of IN measurement techniques from an experimental perspective is
important to explore freezing conditions for specific compositions and more
atmospherically relevant particles (e.g., soil dusts and long range
transported weathered dusts). In parallel, an empirically constrained model
including parameterizations of immersion freezing that correctly and
efficiently represent particle-specific experimental data is also in high
demand for overall predictions of current and future climate. We
demonstrated that the ns formulation offers a simplified expression
for quantitatively parameterizing immersion freezing. Further developments
of more simplified (efficient but accurate) descriptions, constrained by
more accurate IN counting techniques, of governing atmospheric IN processes
are needed.