In the present work, three different techniques to separate ice-nucleating
particles (INPs) as well as ice particle residuals (IPRs) from non-ice-active
particles are compared. The Ice Selective Inlet (ISI) and the Ice Counterflow
Virtual Impactor (Ice-CVI) sample ice particles from mixed-phase clouds and
allow after evaporation in the instrument for the analysis of the residuals.
The Fast Ice Nucleus Chamber (FINCH) coupled with the Ice Nuclei Pumped
Counterflow Virtual Impactor (IN-PCVI) provides ice-activating conditions to
aerosol particles and extracts the activated particles for analysis. The
instruments were run during a joint field campaign which took place in
January and February 2013 at the High Alpine Research Station Jungfraujoch
(Switzerland). INPs and IPRs were analyzed offline by scanning electron
microscopy and energy-dispersive X-ray microanalysis to determine their size,
chemical composition and mixing state. Online analysis of the size and
chemical composition of INP activated in FINCH was performed by laser
ablation mass spectrometry. With all three INP/IPR separation techniques high
abundances (median 20–70 %) of instrumental contamination artifacts were
observed (ISI: Si-O spheres, probably calibration aerosol; Ice-CVI: Al-O
particles; FINCH
The impact of clouds – and in particular cloud–aerosol interactions – on earth's radiation balance is still one of the most uncertain aspects in our understanding of the climate system (Flato et al., 2013). The understanding of tropospheric cloud ice formation processes is crucial for predicting precipitation and cloud radiative properties. Aerosol–cloud interactions play a key role in determining cloud properties like phase, size distribution and colloidal stability of the cloud elements, as well as the lifetime, dimensions and precipitating efficiency of a cloud. Though there have been advances during the last decades, in particular for aerosol–cloud interactions, the level of scientific understanding is still classified as “very low” to “low” (Flato et al., 2013). A considerable uncertainty of the response of aerosol and cloud processes to changes in aerosol properties still arises from the lack of fundamental understanding of the interaction of aerosol particles with the cloud ice phase (Lohmann and Feichter, 2005). Although large attention was given to field studies in the last decade (e.g., Richardson et al., 2007; Prenni et al., 2009a, c; Santachiara et al., 2010; Ardon-Dryer et al., 2011; Conen et al., 2012; Ardon-Dryer and Levin, 2014), these measurements cover only limited geographic regions as well as a limited time. Thus, additional field work is certainly needed.
Many ice nucleation experiments were performed under laboratory conditions (e.g., Hoose and Möhler, 2012, and references therein), and provided valuable knowledge on ice-nucleating particle (INP) properties of pure components and artificially generated mixtures. Mineral dust and biological particles are regarded in general as efficient INP, while experiments disagreed on the INP abilities of soot and organics (Hoose and Möhler, 2012). Sea salt and sulfate are often not considered as INP (Pruppacher and Klett, 1997). However, this conclusion is challenged by several authors (Abbatt et al., 2006; Schill and Tolbert, 2014). Furthermore, it was shown recently in laboratory work for NaCl particles that a partial efflorescence under suitable conditions might lead to ice activation (Wise et al., 2012). The situation is even more complex in the ambient atmosphere, where particles are often present as a complex mixture of different compounds. In addition, the particles may be modified by heterogeneous processes, which may change their ice nucleation ability. In laboratory experiments, these effects are currently addressed for single substances (Hoose and Möhler, 2012; Wex et al., 2014), but the level of atmospheric mixing complexity is not yet realized. Though mixing state was regarded by previous investigations (Knopf et al., 2010, 2014; Ebert et al., 2011; Hiranuma et al., 2013), the data basis is still sparse and further field work is needed.
During the last decade, several techniques emerged which are capable of
distinguishing INPs or ice particle residuals (IPRs) for subsequent chemical
analysis. Particles are usually exposed to thermodynamic conditions favoring
ice nucleation, either in the airborne state or on a substrate. Examples for
these techniques are the Fast Ice Nucleus Chamber (FINCH)
(Bundke et al., 2008) in combination with the Ice Nuclei Pumped Counterflow Virtual
Impactor (IN-PCVI)
(Schenk et al., 2014), the Continuous Flow
Diffusion Chamber (CFDC) in combination with the laboratory counterflow
virtual impactor (LCVI) (Cziczo et al., 2003)
and the Frankfurt Ice Nuclei Deposition Freezing Experiment (FRIDGE)
(Bundke et al., 2008; Klein et al., 2010). While in FINCH
Techniques and operation principles used for ice-nucleating particle (INP) and ice particle residual (IPR) differentiation.
In the present work, three state-of-the-art techniques for INP/IPR sampling
– ISI, Ice-CVI and FINCH
In January/February 2013, a field campaign of INUIT (Ice Nuclei Research
Unit) was performed at the High Alpine Research Station Jungfraujoch in
Switzerland (JFJ; 3580 m a.s.l., 46.55
INPs and IPRs were sampled by three different techniques. INPs were detected by
the FINCH
FINCH
The ice crystals are then separated by the PCVI from the non-activated
particles and from the small supercooled droplets
(Schenk et al., 2014). As the PCVI input
flow must be identical to the FINCH output flow, the counterflow must be
continuously adjusted to achieve this requirement. This adjustment leads to
variable cutoff diameters between 4.5 and 8
The freezing temperature of FINCH during the campaign was slightly varied
around
From the mixed-phase clouds prevailing at JFJ, IPRs were collected by the
Ice-CVI (Mertes et al., 2007). It consists of a
series of different modules that allow sampling of small ice particles
by a simultaneous pre-segregation of all other cloud constituents. The
vertical, omnidirectional inlet already reduces the sampling of ice crystals
larger than 50
The novel ISI (Kupiszewski et al., 2014) was designed to extract small ice crystals from mixed-phase clouds, simultaneously counting, sizing and imaging the hydrometeors contained in the cloud with the use of WELAS 2500 sensors (Pallas GmbH, Karlsruhe, Germany) and a Particle Phase Discriminator (PPD-2K; Kupiszewski et al., 2014). The core of the ISI is a droplet evaporation unit with ice-covered inner walls, removing droplets using the Bergeron–Findeisen process, while transmitting the ice crystals. In the final stage of the ISI, a pumped counterflow virtual impactor removes interstitials and cloud condensation nuclei released in the droplet evaporation unit from the sample flow, thus ensuring only ice crystals are transmitted. The extracted ice crystals are subsequently sublimated, releasing the IPRs which are transferred into the laboratory for further on- and offline characterization of their physical and chemical properties.
A total of 36 samples (18 from FINCH, 13 from Ice-CVI, 5 from ISI) were acquired during the field campaign. All samples were analyzed by SEM (FEI Quanta 200 FEG, FEI, Eindhoven, the Netherlands) and EDX (EDAX, Tilburg, the Netherlands). The particles of the different samples were manually characterized with respect to their chemical composition, size, morphology, internal mixing state and stability under electron bombardment. Particle size was determined as the average geometrical diameter (equivalent projected area diameter) from the electron images.
Based on chemical composition, morphology, mixing state and beam stability, 18 particle groups were defined and combined into 11 particle classes. Table 2 lists the particle groups, particle classes and classification criteria for the manual analysis.
Pb-bearing particles were classified according to the presence of Pb only (i.e., as soon as Pb could be detected). They might be homogeneous Pb-rich particles or particles containing Pb-rich inclusions. In the latter case, the main matrix particles can be carbonaceous, soot, sulfate, sea salt, silicate, metal oxide, a droplet or belong to the “other” class. Droplets are identified by their typical morphology of larger residual particles centered in a halo of small residuals, originating from the splashing of the droplet at impaction. The center of the residual can consist of unstable material (e.g., sulfate) or stable sea salt, silicate, metal oxide, Ca-rich particles, or mixtures thereof. The halo particles are usually unstable under electron bombardment. Particles which could not be classified into one of the classes mentioned above are summarized in the particle class “other”. This particle class contains for example Zn-rich and Mg-rich particles as well as Sn-, Ba-, Bi- and Br-bearing particles with a total abundance of usually less than 1 %.
Classification criteria for particle classes and particle groups. Common features for certain particle types not used for classification are given in parentheses.
Due to the difference in sample substrate composition between TEM grids and elemental boron, in particular for the detection of carbonaceous particles and thin carbonaceous coatings, systematic deviations may occur with a potential bias towards better detection of these particles on boron.
LA-MS was carried out with ALABAMA (Aircraft-based Laser Ablation Aerosol Mass Spectrometer), which was originally developed for aircraft operation (Brands et al., 2011) but was also used in several ground-based measurement campaigns. It provides the chemical composition of single aerosol particles in an aerodynamic particle size range between 150 and 1500 nm, including refractory compounds such as metals, dust, and soot. It was used during the INUIT-JFJ campaign for the analysis of background aerosol particles and IPR (Schmidt et al., 2015). A total of 1809 IPR mass spectra were collected: 1663 with the Ice-CVI (104 operation hours) and 146 with ISI (32 operation hours).
Confidence intervals (after Clopper and Pearson, 1934) given in this manuscript and in the Supplement were calculated with R version 3.0.3 (R core team, 2014). For data displayed in figures, the confidence intervals are given in the Supplement.
The JFJ station is located in a saddle between the mountains Jungfrau and Mönch, which is oriented WSW–NNE. This topography results in a channeling of the atmospheric flow leading to a near-binary distribution of wind directions as either NW or SSE. The atmospheric conditions during the campaign are illustrated in Fig. 1. Hourly 5-day backward trajectories for the JFJ station were calculated with the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model based on GDAS (Global Data Assimilation System) data (Draxler and Rolph, 2014).
Atmospheric and FINCH operating conditions and INP/IPR sampling periods in February 2013. Times are given in UTC. Particle number concentrations were taken from the World Data Centre for Aerosols home page (WDCA, 2014). Temperature and wind direction were provided by the Jungfraujoch station operated by International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat. Cloud presence was detected by measuring the liquid water content using a Particulate Volume Monitor (PVM-100, Gerber Scientific, Reston, VA, USA) and a Cloud Droplet Probe (Droplet Measurement Technologies, Boulder, CO, USA). Sampling phases for SEM are marked by wide, intensely colored bars; sampling phases for MS are shown as narrower, pale-colored bars. “A” marks a period used for case comparison.
At the top of Fig. 1, a period (labeled A) with
comparatively homogeneous atmospheric conditions is marked. Trajectories for
this period can be found in the Supplement (Fig. S1). It was
chosen for instrumental comparison based on individual samples. Homogeneity
was determined from meteorology, particle concentrations and changes in air
mass origin. Period A (2 February, 13:00–18:00 UTC) can be described as
follows. During the last day before arrival, air masses travel approximately
along the Rhine Valley at altitudes between 1.5 and 2.5 km. Two days
before arrival, the air mass backward trajectories cross the North Sea and
the United Kingdom in the same altitude range. For the rest of the
trajectory length, the air masses were over the northern Atlantic Ocean, in
the region of Iceland. Wind, temperature and in-cloud conditions were very
stable during this period. While the JFJ is usually in the free troposphere
during the winter months (Collaud Coen et al.,
2011), abrupt increases in particle concentrations may indicate a rise in
the atmospheric boundary layer height to the station altitude, which leads
to a local influence. This effect is visible as a sudden increase in
particle concentration in the middle of this period. The samples were
collected before (FINCH
Secondary electron images and energy-dispersive X-ray spectra of instrumental contamination artifact particles. Characteristic X-ray peaks of elements are labeled. Elements contained in the sample substrate are given in parentheses.
The sampling instruments yielded different types of artifact particles indicated by their clear non-atmospheric origin. They consisted either of compounds used for manufacturing the instruments (e.g., aluminum, stainless steel) or had the same composition and morphology as calibration aerosol (e.g., Si-O spheres). Therefore, they were removed from further analysis. Figure 2 shows secondary electron images of the most common contamination artifact particles and their energy-dispersive X-ray spectra. The relative abundance of the dominating artifact particles for each instrument is shown in Fig. 3 as box plots.
With all three sampling techniques, small amounts of Fe-Cr particles are observed as an artifact. They may derive from internal abrasion of the instrument or tubing. In addition, for the samples collected on boron substrates, Cu-rich particles are present, which are most likely fragments from the embedding material of the boron substrates (an epoxy resin containing copper chips for increased conductivity).
In the ISI samples, mainly Si-O spheres with a size of approx. 1
In the FINCH
Box plots of the different instrumental contamination artifact particles for each sampling technique. Shown are minimum, lower quartile, median, upper quartile, and maximum.
In the Ice-CVI samples, Al-O particles – probably aluminium oxides/hydroxides – occur as artifacts. The relative abundance of these Al-O particles varied in the range of 0–94 % by number. If we consider a particle breakup during impaction as indicated by their small size in relation to the nominal impactor cutoff size, the relative number abundance might be lower for airborne particles. As all Al-O particles are classified as artifacts in the present paper, potentially occurring atmospheric aluminium oxides/hydroxide particles in the Ice-CVI would be overlooked. However, it can be safely assumed that this potential error is minor, as no Al-O particles with the characteristic morphology (Fig. 2) were identified with the other two sampling instruments. The abundance of other artifact particles in the Ice-CVI sample is small (range of 0–8 %).
Lead-bearing particles are frequently found in the Ice-CVI samples, but also
to a much lesser extent in FINCH
In summary, it must be concluded that the abundance of contamination artifacts in the separated INP and IPR is generally large and cannot be neglected. Thus, the INP/IPR concentrations must be corrected to obtain accurate results. It is highly recommended that measurements of INP/IPR concentrations are always accompanied by chemical and morphological single-particle characterization in order to avoid large systematic errors caused by contamination artifacts.
During the field campaign 5 ISI, 18 FINCH
Relative number abundance (integrated over all samples) of different particle groups as function of sampling technique and particle size. The total number of analyzed particles is shown above the bars. For confidence intervals see Tables S2 and S3 in the Supplement.
Silicates are the main group of INPs/IPRs, independent of sampling technique and size range (with the exception of submicron particles encountered in ISI). Ca-rich particles are predominantly found in the supermicron range with all three sampling techniques, in contrast to soot and sulfate particles, which occur mainly in the submicron range. Metal oxides are present in both size ranges with a tendency to the submicron range while sea-salt particles tend to be in the supermicron range. However, if the low number of analyzed particles and the resulting statistical uncertainty are considered, the observed differences between the techniques are regarded only as a trend. In addition, the three instruments could not be operated strictly in parallel and thus sampled different time periods. In particular, ISI samples were taken only at the end of the field campaign.
The main differences in composition trends between the three sampling methods are the high content of carbonaceous particles measured downstream of the ISI and the high content of Pb-bearing particles obtained by Ice-CVI. The high concentration of carbonaceous particles in the ISI samples may result from different air masses being sampled at the end of the field campaign, when the ISI was operated. During this time, higher black carbon concentrations were measured than during the earlier periods (WDCA, 2014). The Pb-bearing particles are discussed later in Sect. 3.4 in more detail.
If the 11 particle classes are grouped into four simplified components –
particles of potential terrigenous origin (i.e., silicates and Ca-rich
particles), C-dominated particles (carbonaceous, soot),
metal-oxides-dominated and soluble particles (sulfate, droplets, sea salt)
– the terrigenous particles are the main component with relative abundances
of 32 % (ISI), 51 % (FINCH
Relative number abundance of different particle groups among INP/IPR
for 2 February determined by FINCH
The composition of the INP/IPR-samples varies between different cloud events
as well as between the INP/IPR sampling techniques. The heterogeneity of the
INP/IPR composition is illustrated with the example of 2 February
(Fig. 5), where relatively stable atmospheric
conditions prevailed. During this period, two samples were taken between
17:40–18:10 (Ice-CVI) and 14:50–17:11 UTC (FINCH
In addition to the clearly identifiable instrumental contamination artifacts, potential INP/IPR sampling artifacts may occur. We define potential sampling artifacts as particles, which pass the selection mechanisms similar to INPs/IPRs, while being questionable to act as INP/IPR (e.g., Pruppacher and Klett, 1997). The potential sampling artifacts include sea-salt particles, sulfate particles and particles which impact on the sampling substrates as droplets. As we cannot exclude that these particles are INPs/IPRs, we do not exclude them from further analysis in contrast to the instrumental contamination artifacts.
Secondary electron images of droplets with their typical morphology of a halo around a residual.
Droplets are characterized by their morphology of a residual with a halo (Fig. 6). While in principle the heating and drying line should lead to total evaporation of particle-bound water, obviously some particles were still in liquid state during impaction sampling. As we cannot distinguish incompletely dried IPRs from supercooled droplets, which were falsely identified as INP/IPR, we consider droplets as potential INP/IPR sampling artifacts. Sulfate particles were preferentially found in the submicron size range, while sea-salt particles have a tendency to be of larger size. Droplets, however, occur rather uniformly in both size ranges.
The relative number abundances of the three potential sampling artifacts (droplets, (non-droplet) sulfate and sea salt) are shown as box plots (in Fig. 7), separately for each INP/IPR sampling instrument. All potential INP/IPR sampling artifacts are observed for all three techniques, and their relative abundances are on comparable levels of 0–10 % for each particle type. However, the Ice-CVI, in particular, extracted a higher number of sea-salt particles as IPR. For single measurements, the abundance of these potential sampling artifacts can reach up to 40 %.
To allow for the display of a size distribution
(Fig. 8), we combined the classes into generalized
components of INP/IPR to achieve higher particle counts for each particle
size interval. Instrumental contamination artifacts and Pb-bearing particles
are excluded in this presentation. Note that size distributions obtained
with the different techniques cannot be compared directly due to different
inlet and transmission efficiencies. However, all three methods yield a
maximum of between 0.3 and 0.5
Box plots of impacted droplets, (non-droplet) sulfate and sea-salt
abundance for ISI, FINCH
A significant fraction of the INPs/IPRs consists of particles with coatings or
inclusions (see groups in Fig. 4). The relative
abundance of internally mixed particles for each particle type is summarized
in Table 3. It is apparent that mainly silicate
particles and to a lesser extent metal oxides are internally mixed. Mixing
partners are mostly sulfate and carbonaceous matter, but also sea salt, if
present in the total aerosol. The other particle types are less frequently
internally mixed. Regarding differences between the sampling techniques, in
particular INPs measured by FINCH
Average of all single sample size distributions of major INP/IPR
components for ISI, FINCH
In previous IPR measurements at the JFJ station (Cziczo et al., 2009b;
Ebert et al., 2011), Pb-bearing particles were found in high abundance with
the Ice-CVI. For comparison with the previous work
(Fig. 9), we have selected the Pb-bearing particles
from the total INPs/IPRs and determined their mixing partner. For
comparability, the particles were classified in the same way as for the CLACE
5 campaign (Ebert et al., 2011). Pb-bearing particles are
only found with Ice-CVI and FINCH
Number fraction (%) of internally mixed particles in each particle class (95 % confidence interval in parentheses).
Comparison of the composition/mixing state of Pb-bearing particles from INUIT (present contribution) and CLACE 5 (Ebert et al., 2011) from the Ice-CVI. Note that classification criteria and nomenclature of Ebert et al. (2011) were used for this graph. For confidence intervals see Table S5 in the Supplement.
Silicates were identified as common INP/IPR in laboratory experiments as well as in field experiments (Hoose and Möhler, 2012; Murray et al., 2012). Also, in our field campaign silicates are the most abundant INP/IPR components. Ca-rich particles – e.g., carbonates like calcite – are not frequently regarded as INPs (e.g., Murray et al., 2012). However, according to laboratory experiments calcite can act as an INP (Zimmermann et al., 2008). Therefore, the Ca-rich particles are regarded as INP/IPR. Metal oxides are also commonly observed as IPR in field experiments (Chen et al., 1998; DeMott et al., 2003). Similar to our study, Fe-rich particles are usually the main group within the metal oxides. In addition, Al-, Ti-, Zn-, Cr-, and Ca-rich particles were found in the present investigation and by Chen et al. (1998).
Based on field experiments and laboratory studies, Pb-bearing particles are in general regarded as good ice nuclei (for a detailed discussion refer to Cziczo et al., 2009b). In the present study, lead is found in two forms: as Pb-rich inclusions in other particles (major abundance) and as homogeneous Pb-rich particles (minor abundance). The minor fraction of homogeneous Pb-rich particles is regarded as an instrumental artifact (see discussion above), but due to its low abundance of less than 10 % (equaling about 10 particles), it is neglected from further discussion.
The ice nucleation ability of soot and carbonaceous particles is discussed controversially in the previous literature. While an enrichment of black carbon in IPRs was observed in field experiments (Cozic et al., 2008), there are also other findings where organic-rich particles preferentially remain unfrozen (Cziczo et al., 2004). It has to be mentioned, however, that carbon-rich particles are often named ambiguously depending on the technique used for analysis (see also Murray et al., 2012; Petzold et al., 2013). Thus, discrepancies may arise from the fact that different types of carbonaceous material (e.g., nanocrystalline graphite, organic material) are compared. Laboratory experiments show that the ice-forming activity of soot is influenced by size, surface area and the concentration of the surface chemical groups that can form hydrogen bonds with water molecules (Gorbunov et al., 2001; Koehler et al., 2009). According to the latter, the ice-forming activity of soot is close to that of metal oxides. In summary, we conclude that soot and carbonaceous particles observed in our samples were active as INP.
Also, for secondary aerosol particles the ice nucleation ability is discussed controversially. As in the case of soot and carbonaceous matter, secondary aerosol particles are found in field measurements of INP (Abbatt et al., 2006; Prenni et al., 2009b) and in laboratory experiments under cirrus cloud conditions (Hoose and Möhler, 2012). In contrast, Cziczo et al. (2004) report from a field study that organic-rich particles (internally mixed particles of sulfates and organic species) preferentially remain unfrozen. Based on our data, where secondary material is present in many INP/IPR samples, we consider these particles to be INPs/IPRs.
Sea salt as INP/IPR was described for field studies by Cziczo and Froyd (2014) and Targino et al. (2006). While crystalline salts were found in a laboratory study to be able to act as INPs under upper-tropospheric conditions (Zuberi et al., 2001), there has been a need of clarifying the process by which a hygroscopic and soluble material should act as IN. However, recently, Wise et al. (2012) explained this behavior by fractional crystallization of the solute component under decreasing temperatures. Based on these findings, we consider sea salt as potential sampling artifacts.
Similar to sea salt, no agreement exists on the ice nucleation ability of
sulfate particles. Sulfates may act as INP in cirrus clouds in the upper
troposphere and lower stratosphere, both in immersion and deposition modes
(Abbatt et al., 2006, and references therein; Hoose and Möhler,
2012). Sulfates acting as INP are found in a field study in increasing
abundance with decreasing temperature under cirrus conditions
(
As explained in the methods section, contamination artifact particles were removed from the further analysis, while potential sampling artifacts are included in the data.
If all INP/IPR particles of the three sampling methods are summed up, the following averaged INP/IPR composition of the whole field campaign is obtained: 52 % terrigenous particles (38 % silicates, 9 % metal oxides, 5 % Ca-rich particles), 14 % C-rich particles (12 % carbonaceous particles, 2 % soot), 1 % secondary particles, 11 % sulfate, 11 % droplets, 4 % sea salt, 5 % Pb-bearing particles, and 2 % other particles.
A compilation of INP/IPR composition encountered in mixed-phase clouds is shown in Table 4. In general, the results of the present study are in good agreement with the findings of previous work. Silicates are the most abundant component of INP/IPR with a relative number abundance varying between 40 and 71 %. The second most abundant component is carbonaceous material (16–43 %), followed by salts (sea salt, sulfates, droplets) with a relative number abundance between 5 and 27 %. The high abundance of coated particles observed in the present study is in good agreement with Targino et al. (2006), who observed sulfur coatings for all groups indicating ageing and in-cloud processing.
An overview of IPR compositions found during 13 field campaigns of cirrus clouds is given by Cziczo and Froyd (2014). Also, here the main particle types are mineral dust, metals, BC/soot, sea salt, sulfate, and biomass burning.
A relatively high abundance of Pb-bearing particles, in particular internally
mixed ones, seems to be characteristic for IPR at the JFJ station. They were
found in previous work (Cziczo et al., 2009b; Ebert et al., 2011) and
during the present field campaign. However, the fraction of Pb-bearing
particles in the whole INUIT campaign is 1 % for FINCH
Feldspar minerals and in particular K-feldspars (e.g., microcline) were
discussed as efficient INPs (Atkinson et al., 2013; Yakobi-Hancock et al.,
2013). Despite the fact that we did not determine the mineralogical phase of
the silicate particles, we can show by SEM-EDX that they have low potassium
contents (K
A significant fraction of the INPs/IPRs occurs as internal mixtures (Table 3). This fraction is similar to previous literature data. Chen et al. (1998) reported that a fraction of 25 % of the INPs were a mixture of sulfates and elements indicative of insoluble particles. The same relative abundance of mixtures of metal oxides/dust with either carbonaceous components or salts/sulfates was reported by Prenni et al. (2009a). For the JFJ station, a slightly lower fraction of internally mixed particles was found during the CLACE 5/6 campaigns: 9–15 % by Ebert et al. (2011) and up to 15 % by Kamphus et al. (2010).
Average INP/IPR composition encountered in mixed-phase clouds for several field experiments.
Especially notable is the observed difference between silicates and Ca-rich
particles. While silicates are usually internally mixed, the Ca-rich
particles do not have a detectable coating. This may indicate that for
silicates a coating is less effective in reducing their IN ability than for
Ca-rich particles, pointing to a more pronounced processing (e.g.,
destruction of the surface structure) of the latter. However, the influence
of coatings on the ice nucleation ability of silicates is discussed
controversially. In field experiments, coatings on silicates and metal oxides
are commonly observed (Chen et al., 1998; Targino et al., 2006; Prenni et
al., 2009a). In laboratory experiments, conflicting results are obtained.
While Cziczo et al. (2009a) as well as
Hoose and Möhler (2012) reported a deactivation of the
ice nuclei due to coatings, Sullivan et al. (2010)
found that coatings do not always effect the ice nucleation ability. In
contrast, Archuleta et al. (2005) and
Zuberi et al. (2002) discuss mineral dust as an efficient
nucleus for ice in NH
A reasonable agreement between the different sampling techniques is obtained for the major particle classes observed among the INP/IPR. However, the variation in INP/IPR composition due to meteorological conditions in connection with the non-parallel sampling introduces a systematic error. The non-parallel sampling could not be avoided during the present field campaign, as the sampling techniques were not yet in a state allowing for synchronized operation and the available flow from the INP/IPR samplers was insufficient for a sampling for SEM and operation of LA-MS in parallel. Consequently, INP/IPR composition snapshots from different time periods needed to be integrated for comparison of the INP/IPR composition.
The reasons for the different instrumental contamination artifacts were identified. Thus, these artifacts can be avoided in future by removing their sources (e.g., replacement/sealing of contaminating surfaces, thoroughly purging). The relative abundance of potential sampling artifacts is in general low (median < 5 %), except for sea-salt particles sampled by the Ice-CVI with a median of 10 % (Fig. 7).
Despite the frequent non-parallel sampling, the major INP/IPR classes found
by all three techniques include silicates, Ca-rich particles, carbonaceous
material, and metal oxides. In addition, soot was observed as a minor
component in the fine fraction (< 1
In contrast, in the fine fraction a considerably higher relative abundance of carbonaceous material was found by ISI and a higher relative abundance of silicates and silicate mixtures by Ice-CVI. These differences are most likely caused by the non-parallel sampling. It must be emphasized again that samples from the ISI were only obtained during the last week of the field campaign (Fig. 1).
The results of the offline SEM-EDX analysis of the collected INP/IPR particles can be compared to the findings of online LA-MS. Unfortunately, both techniques could not be run in parallel because of the limited available sample flow that could be provided by the sampling systems. Due to the low INP/IPR concentrations, it was necessary to integrate all available data, which may lead to systematic errors due to significant variations in the IPR chemical composition as a function of changing air masses and meteorological conditions. Furthermore, for a comparison between SEM-EDX and LA-MS a more general particle classification scheme, combining the detailed SEM-EDX classes, was necessary.
Comparison of particle class relative number abundance determined by SEM-EDX and LA-MS for IPRs sampled by ISI and Ice-CVI. To allow for a comparison of the two different analytical approaches of SEM-EDX and LA-MS, classes were combined accordingly. For confidence intervals see Table S6 in the Supplement.
The average particle class number abundance, derived by SEM-EDX – separately for all IPRs from the ISI and Ice-CVI – is compared in Fig. 10 to the results of the LA-MS (Schmidt et al., 2015). The most obvious difference between the two analysis techniques is the presence of 10–18 % of secondary particles (mostly mixtures of sulfates/nitrates and/or organics), pure sulfates and droplets (residuals of volatile species like nitrates and organics) in SEM-EDX. These classes are completely absent in LA-MS. This difference can be explained by the fact that due to technical issues anions were not measured by LA-MS during the present campaign. Without the detection of anions, sulfate and nitrate cannot be identified by LA-MS, such that these particles are classified according to their dominant cations and are assigned to one of the listed particle groups.
For the other classes, a fair agreement of the results is obtained. First,
the sum of mineral dust, sea salt, carbonaceous material and soot (red and
green colors in Fig. 10) contributes 70–90 % to
the IPR. Second, metal oxides (based on SEM-EDX: mainly iron oxides) occur
at an abundance of 5–10 %. Third, Ice-CVI samples contain Pb-rich
particles (5–10 %), while these particles are absent in the ISI. These
results do not change considerably if, for SEM-EDX, the particles outside the
LA-MS size range (> 1.5
However, pronounced discrepancies exist between SEM-EDX and LA-MS data, in particular for Ice-CVI. For this sampling technique, a lower abundance of carbonaceous material is found by SEM-EDX as is a higher abundance of silicates. This quantitative comparison of compositional data from both analysis techniques is hampered by the different approach in particle characterization. The particle classification with SEM-EDX relies on the characteristic X-ray signals, which can be used to quantify the chemical composition of a particle. Our classification scheme uses mainly the major elements (i.e., a relative contribution larger than 10 at. %, excluding oxygen) detected inside a particle to assign it to the respective group. Minor elements (less than approximately 10 at. %) are mostly neglected in particle classification. Trace elements (less than 0.5 at. %) cannot be measured at all. In contrast, single particle LA-MS relies on ionized compounds, so ionization efficiency plays a major role. Thus, strong signals often originate from the atoms or molecules, which can be ionized best in LA-MS, but are not necessarily a major component of the particle. While LA-MS works usually well for externally mixed particles, problems can arise for the classification of internally mixed particles. In our particular case, it cannot be excluded that, for example, a silicate particle with a thin organic coating is classified as silicate in SEM-EDX (based on Si as major element) but as a carbonaceous particle in LA-MS (based on a strong signal of ionized carbonaceous matter). This example clearly demonstrates the need for further systematical comparison between these two analytical techniques.
For the first time, the chemical composition of individual INPs/IPRs collected
by three techniques – ISI, FINCH
For all three INP/IPR separation techniques, different contamination artifacts and potential sampling artifacts were identified. These artifacts are easily detectable by the chemical and morphological analysis. In contrast, the counting or size distribution techniques would consider these contamination and sampling artifacts as real INP/IPR and, consequently, overestimate the INP/IPR concentration. Thus, the present work provides information suitable for correction of counting techniques, for the contamination artifacts as well as for sampling artifacts. While, for the former, correction is necessary, interpretation of the latter might change with further knowledge regarding the INP/IPR abilities of soluble compounds.
Deeper data investigations reveal that beyond the agreement in the maximum of the INP/IPR size distribution, there are considerable differences between the instruments pointing to different efficiencies in INP activation and IPR separation. This is particularly obvious when we consider the large difference in internally mixed-particle abundance. While a part of these discrepancies might be explained by atmospheric variability in connection with non-parallel sampling (an issue which is expected to be overcome in future experiments by increased stability in instrument operation), they also indicate lack in understanding regarding the chemical selectivity of the different INP/IPR-discriminating techniques.
Finally, a few statements regarding limitations of the investigated
techniques as well as recommendations for future work on INP/IPR can be
made.
Measurements of INP/IPR concentrations should be always accompanied by
characterization of the INP/IPR chemistry to readily identify strong
contributions of instrumental artifacts. Although different techniques are
in principal possible, scanning electron microscopy with high-resolution
instruments has proven to be especially suited for this purpose. More work is needed to clarify the ice nucleation ability of sea salt and
sulfates in mixed-phase clouds. More emphasis should be placed on the particle mixing state in the
atmosphere. Due to its complexity, laboratory tests on the performance of
the different INP/IPR sampling techniques may lead to overconfidence in the
results of field measurements. Substantial work is still necessary to develop the here-presented
approaches of INP/IPR sampling to robust routine techniques.
We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft within the research group Ice Nuclei Research Unit – INUIT (FOR 1525). We thank Emanuel Hammer and Gary Lloyd for providing the liquid water content data and MeteoSwiss/EMPA for the meteorological data.
We also thank Stephan Günnel (Institut für Troposphärenforschung, Leipzig, Germany) for his help in setting up the Ice-CVI on the platform of the Sphinx Laboratory at the Jungfraujoch Research Station. In addition, we thank the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG) for the opportunity to perform experiments at the Jungfraujoch station.
Finally, we gratefully recognize the thorough reading and the many helpful comments of our reviewers. Edited by: B. Ervens