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Two different single particle mass spectrometers were operated in parallel at the Swiss High Alpine Research Station Jungfraujoch (JFJ, 3580 m a.s.l.) during the Cloud and Aerosol Characterization Experiment (CLACE 6) in February and March 2007. During mixed phase cloud events ice crystals from 5–20 micro m were separated from larger ice aggregates, non-activated, interstitial aerosol particles and supercooled droplets using an Ice-Counterflow Virtual Impactor (Ice-CVI). During one cloud period supercooled droplets were additionally sampled and analyzed by changing the Ice-CVI setup. The small ice particles and droplets were evaporated by injection into dry air inside the Ice-CVI. The resulting ice and droplet residues (IR and DR) were analyzed for size and composition by the two single particle mass spectrometers: a custom-built Single Particle Laser-Ablation Time-of-Flight Mass Spectrometer (SPLAT) and a commercial Aerosol Time-of-Flight Mass Spectrometer (ATOFMS, TSI Model 3800). During CLACE 6 the SPLAT instrument characterized 355 individual IR that produced a mass spectrum for at least one polarity and the ATOFMS measured 152 IR. The mass spectra were binned in classes, based on the combination of dominating substances, such as mineral dust, sulfate, potassium and elemental carbon or organic material. The derived chemical information from the ice residues is compared to the JFJ ambient aerosol that was sampled while the measurement station was out of clouds (several thousand particles analyzed by SPLAT and ATOFMS) and to the composition of the residues of supercooled cloud droplets (SPLAT: 162 cloud droplet residues analyzed, ATOFMS: 1094). The measurements showed that mineral dust was strongly enhanced in the ice particle residues. Close to all of the SPLAT spectra from ice residues did contain signatures from mineral compounds, albeit connected with varying amounts of soluble compounds. Similarly, close to all of the ATOFMS IR spectra show a mineral or metallic component. Pure sulfate and nitrate containing particles were depleted in the ice residues. Sulfate and nitrate was found to dominate the droplet residues (~90% of the particles). The results from the two different single particle mass spectrometers were generally in agreement. Differences in the results originate from several causes, such as the different wavelength of the desorption and ionisation lasers and different size-dependent particle detection efficiencies.
In situ single particle analysis of ice particle residuals (IPRs) and out-of-cloud aerosol particles was conducted by means of laser ablation mass spectrometry during the intensive INUIT-JFJ/CLACE campaign at the high alpine research station Jungfraujoch (3580 m a.s.l.) in January–February 2013. During the 4-week campaign more than 70 000 out-of-cloud aerosol particles and 595 IPRs were analyzed covering a particle size diameter range from 100 nm to 3 µm. The IPRs were sampled during 273 h while the station was covered by mixed-phase clouds at ambient temperatures between −27 and −6 °C. The identification of particle types is based on laboratory studies of different types of biological, mineral and anthropogenic aerosol particles. The outcome of these laboratory studies was characteristic marker peaks for each investigated particle type. These marker peaks were applied to the field data. In the sampled IPRs we identified a larger number fraction of primary aerosol particles, like soil dust (13 ± 5 %) and minerals (11 ± 5 %), in comparison to out-of-cloud aerosol particles (2.4 ± 0.4 and 0.4 ± 0.1 %, respectively). Additionally, anthropogenic aerosol particles, such as particles from industrial emissions and lead-containing particles, were found to be more abundant in the IPRs than in the out-of-cloud aerosol. In the out-of-cloud aerosol we identified a large fraction of aged particles (31 ± 5 %), including organic material and secondary inorganics, whereas this particle type was much less abundant (2.7 ± 1.3 %) in the IPRs. In a selected subset of the data where a direct comparison between out-of-cloud aerosol particles and IPRs in air masses with similar origin was possible, a pronounced enhancement of biological particles was found in the IPRs.
In-situ single particle analysis of ice particle residuals (IPR) and out-of-cloud aerosol particles was conducted by means of laser ablation mass spectrometry during the intensive INUIT-JFJ/CLACE campaign at the high alpine research station Jungfraujoch (3580 m a.s.l.) in January/February 2013. During the four week campaign more than 70000 out-of-cloud aerosol particles and 595 IPR were analyzed covering a particle size diameter range from 100 nm to 3 μm. The IPR were sampled during 273 hours while the station was covered by mixed-phase clouds at ambient temperatures between -27 °C and -6 °C. The identification of particle types is based on laboratory studies of different types of biological, mineral and anthropogenic aerosol particles. As outcome instrument specific marker peaks for the different investigated particle types were obtained and applied to the field data. The results show that the sampled IPR contain a larger relative amount of natural, primary aerosol, like soil dust (13 %) and minerals (11 %), in comparison to out-of-cloud aerosol particles (2 % and <1 %, respectively). Additionally, anthropogenic aerosol particles, like particles from industrial emissions and lead-containing particles, were found to be more abundant in the IPR than in the out-of-cloud aerosol. The out of-cloud aerosol contained a large fraction of aged particles (30 %, including organic material and secondary inorganics), whereas this particle type was much less abundant (3 %) in the IPR. In a selected subset of the data where a direct comparison between out-of-cloud aerosol particles and IPR in air masses with similar origin was possible, a pronounced enhancement of biological particles was found in the IPR.
In this study we show how size-resolved measurements of aerosol particles and cloud condensation nuclei (CCN) can be used to characterize the supersaturation of water vapor in a cloud. The method was developed and applied for the investigation of a cloud event during the ACRIDICON-Zugspitze campaign (17 September to 4 October 2012) at the high-alpine research station Schneefernerhaus (German Alps, 2650 m a.s.l.). Number size distributions of total and interstitial aerosol particles were measured with a scanning mobility particle sizer (SMPS), and size-resolved CCN efficiency spectra were recorded with a CCN counter system operated at different supersaturation levels.
During the evolution of a cloud, aerosol particles are exposed to different supersaturation levels. We outline and compare different estimates for the lower and upper bounds (Slow, Shigh) and the average value (Savg) of peak supersaturation encountered by the particles in the cloud. For the investigated cloud event, we derived Slow ≈ 0.19–0.25%, Shigh ≈ 0.90–1.64% and Savg ≈ 0.38–0.84%. Estimates of Slow, Shigh and Savg based on aerosol size distribution data require specific knowledge or assumptions of aerosol hygroscopicity, which are not required for the derivation of Slow and Savg from the size-resolved CCN efficiency spectra.
In this study we show how size-resolved measurements of aerosol particles and cloud condensation nuclei (CCN) can be used to characterize the supersaturation of water vapor in a cloud. The method was developed and applied during the ACRIDICON-Zugspitze campaign (17 September to 4 October 2012) at the high-Alpine research station Schneefernerhaus (German Alps, 2650 m a.s.l.). Number size distributions of total and interstitial aerosol particles were measured with a scanning mobility particle sizer (SMPS), and size-resolved CCN efficiency spectra were recorded with a CCN counter system operated at different supersaturation levels.
During the evolution of a cloud, aerosol particles are exposed to different supersaturation levels. We outline and compare different estimates for the lower and upper bounds (Slow, Shigh) and the average value (Savg) of peak supersaturation encountered by the particles in the cloud. A major advantage of the derivation of Slow and Savg from size-resolved CCN efficiency spectra is that it does not require the specific knowledge or assumptions about aerosol hygroscopicity that are needed to derive estimates of Slow, Shigh, and Savg from aerosol size distribution data. For the investigated cloud event, we derived Slow ≈ 0.07–0.25%, Shigh ≈ 0.86–1.31% and Savg ≈ 0.42–0.68%.
Size-resolved measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a full seasonal cycle (Mar 2014–Feb 2015). In a companion part 1 paper, we presented an in-depth CCN characterization based on annually as well as seasonally averaged time intervals and discuss different parametrization strategies to represent the Amazonian CCN cycling in modelling studies (M. Pöhlker et al., 2016b). The present part 2 study analyzes the aerosol and CCN variability in original time resolution and, thus, resolves aerosol advection and transformation for the following case studies, which represent the most characteristic states of the Amazonian atmosphere:
1. Near-pristine (NP) conditions, defined as the absence of detectable black carbon (< 0.01 µg m−3), showed their highest occurrence (up to 30 %) in the wet season (i.e., Mar–May). On average, the NP episodes are characterized by a bimodal aerosol size distribution (strong Aitken mode: DAit = 70 nm, NAit = ~ 200 cm−3 vs. weaker accumulation mode: Dacc = 170 nm, Nacc = ~ 60 cm−3), a mostly organic particle composition, and relatively low hygroscopicity levels (κAit = 0.12 vs. κacc = 0.18). The NP CCN efficiency spectrum shows that the CCN population is sensitive to changes in supersaturation (S) over a wide S range.
2. Long-range transport (LRT) conditions frequently mix Saharan dust, African combustion smoke, and sea spray aerosols into the Amazonian wet season atmosphere. The LRT episodes (i.e., Feb–Apr) are characterized by an accumulation mode dominated size distribution (DAit = 80 nm, NAit = 120 cm−3 vs. Dacc = 180 nm, Nacc = 300 cm−3), a clearly increased abundance of dust and salt compounds, and relatively high hygroscopicity levels (κAit = 0.18, κacc = 0.34). The LRT CCN efficiency spectrum shows that the CCN population is highly sensitive to changes in S in the low S regime.
3. Biomass burning (BB) conditions dominate the Amazonian dry season. A selected characteristic BB episode shows a very strong accumulation mode (DAit = 70 nm, NAit = ~ 140 cm−3 vs. Dacc = 170 nm, Nacc = ~ 3400 cm−3), particles with very high organic fractions (> 90 %), and correspondingly low hygroscopicity levels (κAit = 0.14, κacc = 0.17). The BB CCN efficiency spectrum shows that the CCN population is highly sensitive to changes in S in the low S regime.
4. Mixed pollution conditions show the superposition of African (i.e., volcanic) and Amazonian (i.e., biomass burning) aerosol emissions during the dry season. The African aerosols showed a broad monomodal distribution (D = 130 nm, N = ~ 1300 cm−3), with very high sulfate fractions (20 %), and correspondingly high hygroscopicity (κAit = 0.14, κacc = 0.22). This was superimposed by fresh smoke from nearby fires with one strong mode (D = 113 nm, Nacc = ~ 2800 cm−3), an organic-dominated aerosol, and sharply decreased hygroscopicity (κAit = 0.10, κacc = 0.20). These conditions underline the rapidly changing pollution regimes with clear impacts on the aerosol and CCN properties.
Overall, this study provides detailed insights into the CCN cycling in relation to aerosol-cloud interaction in the vulnerable and climate-relevant Amazon region. The detailed analysis of aerosol and CCN key properties and particularly the extracted CCN efficiency spectra with the associated fit parameters provide a basis for an in-depth analysis of aerosol-cloud interaction in the Amazon and beyond.
Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014–February 2015). The measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site.
The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172 nm at S = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit = 0.14 ± 0.03), higher values for the accumulation mode (κAcc = 0.22 ± 0.05), and an overall mean value of κmean = 0.17 ± 0.06, consistent with high fractions of organic aerosol.
The hygroscopicity parameter, κ, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.
For modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.
Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations as well as hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a one-year period and full seasonal cycle (March 2014 - February 2015). The presented measurements provide a climatology of CCN properties for a characteristic central Amazonian rain forest site.
The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The observed mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172 nm at S = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit = 0.14 ± 0.03), elevated values for the accumulation mode (κAcc = 0.22 ± 0.05), and an overall mean value of κmean = 0.17 ± 0.06, consistent with high fractions of organic aerosol.
The hygroscopicity parameter κ exhibits remarkably little temporal variability: no pronounced diurnal cycles, weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.
For modelling purposes, we compare different approaches of predicting CCN number concentration and present a novel parameterization, which allows accurate CCN predictions based on a small set of input data.
This paper presents results from the "INUIT-JFJ/CLACE 2013" field campaign at the high alpine research station Jungfraujoch in January/February 2013. The chemical composition of ice particle residuals (IPR) in a size diameter range of 200–900 nm was measured in orographic, convective and non-convective clouds with a single particle mass spectrometer (ALABAMA) under ambient conditions characterized by temperatures between −28 and −4 °C and wind speed from 0.1 to 21 km h−1. Additionally, background aerosol particles in cloud free air were investigated. The IPR were sampled from mixed-phase clouds with two inlets which selectively extract small ice crystals in-cloud, namely the Counterflow Virtual Impactor (Ice-CVI) and the Ice Selective Inlet (ISI). The IPR as well as the aerosol particles were classified into seven different particle types: (1) black carbon, (2) organic carbon, (3) black carbon internally mixed with organic carbon, (4) minerals, (5) one particle group (termed "BioMinSal") that may contain biological particles, minerals, or salts, (6) industrial metals, and (7) lead containing particles. For any sampled particle population it was determined by means of single particle mass spectrometer how many of the analyzed particles belonged to each of these categories. Accordingly, between 20 and 30% of the IPR and roughly 42% of the background particles contained organic carbon. The measured fractions of minerals in the IPR composition varied from 6 to 33%, while the values for the "BioMinSal" group were between 15 and 29%. Four percent to 31% of the IPR contained organic carbon mixed with black carbon. Both inlets delivered similar results of the chemical composition and of the particle size distribution, although lead was found only in the IPR sampled by the Ice-CVI. The results show that the ice particle residual composition varies substantially between different cloud events, which indicates the influence of different meteorological conditions, such as origin of the air masses, temperature and wind speed.