Using freezing spectra characteristics to identify ice-nucleating particle populations during the winter in the Alps

Abstract

Abstract. One of the least understood cloud processes is modulation of their microphysics by aerosols, specifically of cloud ice by ice-nucleating particles (INPs). To investigate INP impacts on cloud ice and subsequent precipitation formation, measurements in cloud environments are necessary but difficult given the logistical challenges associated with airborne measurements and separating interstitial aerosol from cloud residues. Additionally, determining the sources of INPs is important given the dependency of glaciation temperatures on the mineral or biological components and diversity of such INP populations. Here, we present results from a comparison of INP spectral characteristics in air, cloud rime, and fresh fallen snow at the High Altitude Research Station, Jungfraujoch. The goal of the study was twofold: (1) to assess variability in wintertime INP populations found in cloud based on wind and air mass direction during snowfall and (2) to evaluate possible INP sources between different sample types using a combination of cumulative INP (K(T)) and differential INP (k(T)) spectra. INP freezing temperatures and concentrations were consistently higher on average from the southeast as compared to the northwest for rime, snow, and especially aerosol samples, which is likely a result of air mass influence from predominantly boundary layer terrestrial and marine sources in southern Europe, the Mediterranean, and North Africa. For all three sample types combined, average onset freezing temperatures were −8.0 and −11.3 ∘C for southeasterly and northwesterly days, respectively, while K(T) were 3 to 20 times higher when winds arrived from the southeast. Southeasterly aerosol samples typically had a clear mode in the warm-temperature regime (i.e., ≥-15 ∘C) in the k(T) spectra – indicating a putative influence from biological sources – while the presence of a warm mode in the rime and snow varied. Evaluating K(T) concert with k(T) spectra exhibited variable modality and shape – depending on the types of INPs present – and may serve as a useful method for comparing different sampled substances and assessing the possible relative contributions of mixed mineral and biological versus only biological INP sample populations.

Spatial and temporal variability in the ice-nucleating ability of alpine snowmelt and extension to frozen cloud fraction

“…3). Consistent with the observed spread in triplicate freezing temperatures, the reported temperature uncertainty of DRINCZ is ±0.9 • C (David et al, 2019). Finally, background corrections for the freezing temperatures were not necessary, as all of the T 50 values were statistically above the water background of the instrument.…”

“…The DRoplet Ice Nuclei Counter Zurich (DRINCZ) technique used in this study, as well as the data processing and analysis are described in depth by David et al (2019). Additional information on the immersion freezing experimental details and on the frozen fraction (FF) analysis and can also be found in the Supplement.…”

“…A total of 88 snow samples were collected using sterile Teflon tubes in the winter of 2018 across 17 locations on 15 different days spanning an approximate area of 35 752 km 2 in the Swiss Alps. INPs in the snowmelt were measured in the immersion freezing mode using the DRoplet Ice Nuclei Counter Zurich (DRINCZ) (David et al, 2019). The ability of the snowmelt to freeze is reported as 96 freezing temperatures in one-dimensional boxplots.…”

Ice-nucleating particles impact the severity of precipitations in West Texas

“…Several previous studies have characterized the nINP in the precipitation samples from various locations (Creamean et al, 2019;Petters and Wright, 2015;Levin et al, 2019). Petters and Wright (2015) reported a wide during an atmospheric river event on the west coast of United States were studied.…”

Rc

“…Why did the authors not calculate differential freezing nucleus spectra? They present a very useful picture of the entire INP population (Vali 2019) and INPs could be qualitatively classified (warm mode and cold mode INP, see also Creamean et al 2019). Looking only at T_50 values might disguise the presence of a few INP at high temperatures.…”