Abstract
Abstract. Aerosol–cloud interactions, including the ice nucleation of supercooled liquid water droplets caused by ice-nucleating particles (INPs) and macromolecules (INMs), are a source of uncertainty in predicting future climate. Because INPs and INMs have spatial and temporal heterogeneity in source, number, and composition, predicting their concentration and distribution is a challenge requiring apt analytical instrumentation. Here, we present the development of our drop Freezing Ice Nuclei Counter (FINC) for the estimation of INP and INM concentrations in the immersion freezing mode. FINC's design builds upon previous droplet freezing techniques (DFTs) and uses an ethanol bath to cool sample aliquots while detecting freezing using a camera. Specifically, FINC uses 288 sample wells of 5–60 µL volume, has a limit of detection of −25.4 ± 0.2 ∘C with 5 µL, and has an instrument temperature uncertainty of ± 0.5 ∘C. We further conducted freezing control experiments to quantify the nonhomogeneous behavior of our developed DFT, including the consideration of eight different sources of contamination. As part of the validation of FINC, an intercomparison campaign was conducted using an NX-illite suspension and an ambient aerosol sample from two other drop freezing instruments: ETH's DRoplet Ice Nuclei Counter Zurich (DRINCZ) and the University of Basel's LED-based Ice Nucleation Detection Apparatus (LINDA). We also tabulated an exhaustive list of peer-reviewed DFTs, to which we added our characterized and validated FINC. In addition, we propose herein the use of a water-soluble biopolymer, lignin, as a suitable ice-nucleating standard. An ideal INM standard should be inexpensive, accessible, reproducible, unaffected by sample preparation, and consistent across techniques. First, we compared lignin's freezing temperature across different drop freezing instruments, including on DRINCZ and LINDA, and then determined an empirical fit parameter for future drop freezing validations. Subsequently, we showed that commercial lignin has consistent ice-nucleating activity across product batches and demonstrated that the ice-nucleating ability of aqueous lignin solutions is stable over time. With these findings, we present lignin as a good immersion freezing standard for future DFT intercomparisons in the research field of atmospheric ice nucleation.
Highlights
Aerosol–cloud interactions are a source of uncertainty in predicting future radiative forcing (IPCC, 2013)
As part of our intercomparison study with lignin, we show results with two other drop freezing instruments: ETH Zurich’s DRoplet Ice Nuclei Counter Zurich (DRINCZ; David et al, 2019) and the University of Basel’s LED-based Ice Nucleation Detection Apparatus (LINDA; Stopelli et al, 2014)
We describe our home-built drop Freezing Ice Nuclei Counter (FINC) to measure the ice-nucleating ability of Ice Nuclei Counter particles (INPs) and ice-nucleating macromolecules (INMs) in the immersion freezing mode
Summary
Aerosol–cloud interactions are a source of uncertainty in predicting future radiative forcing (IPCC, 2013). We compiled a summary of multidrop bench-top immersion freezing instruments used for atmospheric ice nucleation measurements published between 2000 and 2020 (Table 1) Included in this summary is a brief description of the operation of each instrument, the water background with the reported protocol, the average drop size, and the average number of droplets per experiment. Advantageous qualities include large operating temperature ranges, low background freezing temperatures, and a high number of drops per experiment As these types of instruments are not yet commercial, we built our own drop Freezing Ice Nuclei Counter (FINC) using a cooling bath and an optical detection method. We conclude by recommending commercial lignin as a standard to validate DFTs based on a detailed analysis of lignin’s reproducibility and stable IN activity
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