Cleaning up our water: reducing interferences from non-homogeneous freezing of 
pure water in droplet freezing assays of ice nucleating particles

Polen, Michael R; Brubaker, Thomas A.; Somers, Joshua; Sullivan, R. C.

doi:10.5194/amt-2018-134

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…The results are displayed in Figure A2. The droplets started to freeze at around −20 • C, and all droplets froze at approximately −30 • C. This temperature range is higher than those detected by other cold-stage based instruments [35,36]. Such discrepancy might be caused by the different substrate materials or water impurities.…”

Section: Distilled Water Experimentsmentioning

confidence: 73%

“…The volume of droplets could affect the frozen temperature of pure water as well. Polen et al [36] summarized the freezing of water in different conditions (i.e., supporting substrates, water sources, droplet sizes, etc.). Stopelli, et al [18] reported the median freezing temperature of water at −15 • C with a droplet volume of 400 µL.…”

Section: Distilled Water Experimentsmentioning

confidence: 99%

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Development, Characterization, and Validation of a Cold Stage-Based Ice Nucleation Array (PKU-INA)

Chen¹,

Pei²,

Wang³

et al. 2018

Atmosphere

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A drop-freeze array (PeKing University Ice Nucleation Array, PKU-INA) was developed based on the cold-stage method to investigate heterogeneous ice nucleation properties of atmospheric particles in the immersion freezing mode from −30 to 0 °C. The instrumental details as well as characterization and performance evaluation are described in this paper. A careful temperature calibration protocol was developed in our work. The uncertainties in the reported temperatures were found to be less than 0.4 °C at various cooling rates after calibration. We also measured the ice nucleation activities of droplets containing different mass concentrations of illite NX, and the results obtained in our work show good agreement with those reported previously using other instruments with similar principles. Overall, we show that our newly developed PKU-INA is a robust and reliable instrument for investigation of heterogeneous ice nucleation in the immersion freezing mode.

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Section: Distilled Water Experimentsmentioning

confidence: 73%

Section: Distilled Water Experimentsmentioning

confidence: 99%

Development, Characterization, and Validation of a Cold Stage-Based Ice Nucleation Array (PKU-INA)

Chen¹,

Pei²,

Wang³

et al. 2018

Atmosphere

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“…We discuss INP concentrations in the context 35 of the overall dataset and for comparison with other published datasets. Although different arguments on omitting the first two wells exist Polen et al, 2018), we argue that trimming enhances representativeness, reproducibility and confidence in the INP concentrations. Thus, due to contamination potentially observed in the freezing of the first couple of wells at higher temperatures, the first two wells to freeze were omitted for calculating INP concentrations.…”

Section: Immersion Freezing Experiments and Data Analysis With Drinczmentioning

confidence: 67%

“…This visualization allows for the clear comparison of many samples side by side for every sample measured in this study and using all 96 data points without trimming ( Figure S2). Polen et al (2018) similarly uses boxplots to show results of several measurements for instrument development purposes and we are extending this approach to our field collected data. 10…”

Section: Immersion Freezing Experiments and Data Analysis With Drinczmentioning

confidence: 99%

Ice nucleating particles measured in Swiss alpine snow samples are spatially, temporarily and chemically heterogeneous

Brennan

David

Borduas-Dedekind

2019

Preprint

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Ice nucleating particles (INPs) produce ice from supercooled water droplets through heterogeneous 10 freezing in the atmosphere. Since the concentration of ice crystals affects the radiative properties of clouds as well as precipitation, constraining the liquid water to ice ratio could help reduce aerosol-cloud interaction uncertainties. INPs have been collected at the Jungfraujoch research station (at 3500 m a.s.l.) in central Switzerland; yet spatially diverse data on INP occurrence in the Swiss Alps are scarce and remain uncharacterized. We address this scarcity through our Swiss Alpine snow sample study which 15 took place during the winter of 2018. We collected a total of 88 fallen snow samples across the Alps at different locations, altitudes, terrains, times since last snowfall and depths. The INP concentrations were measured using the homebuilt DRoplet Ice Nuclei Counter Zurich (DRINCZ) and were then compared to spatial, meteorological and physiochemical parameters. We also extend an alternative way of displaying frozen fraction (FF) versus temperature data through visualizing freezing temperatures as a 20boxplot to field collected samples. This plotting method displays the freezing temperature in one dimension, instead of the former two dimensions of FF vs temperature, allowing a condensed display of freezing temperature measurements. In the collected snow samples, large variability in INP occurrence was found, even for samples collected 10 m apart on a plain and 1 m apart in depth. Furthermore, undiluted samples had INP concentrations ranging between 1 and 100 INP ml -1 of snow water over a 25 temperature range of −5 to −19 C. From this field-collected data set, we parameterize the INP concentrations per milliliter of meltwater as a function of temperature with the following equation * ( ) = − . − . , comparing well with previously reported precipitation data presented in Petters and Wright, 2015. 30 When assuming a cloud water content of 0.4 g m -3 and a critical INP concentration for glaciation of 10 m -3 , the majority of the snow precipitated from clouds with glaciation temperatures between −5 and −20 °C. Based on the observed variability in INP concentrations, we conclude that studies conducted at the high-altitude research station Jungfraujoch are representative for INP measurements in the Swiss Alps. Furthermore, the INP concentration precipitation estimates allow us to extrapolate the 35 concentrations to a cloud frozen fraction. Indeed, this approach for estimating the liquid water to ice ratio in mixed phase clouds compares well with aircraft measurements, ground-based lidar and satellite retrievals of cloud frozen fractions. In all, the generated parameterization for INP concentrations in meltwater could help estimate cloud glaciation temperatures.

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“…where is the dilution factor of the initial sample. However, in some cases dilution alone cannot be used to observe the total 73 number of ( ) due to the presence of impurities that act as INPs in the water used for dilution (Polen et al, 2018). 74 Therefore, it is necessary to use different bulk techniques that measure aliquots with volumes that span several orders of 75 magnitude, typically microliter to picoliter volumes (Harrison et 2018).…”

mentioning

confidence: 99%

Development of the DRoplet Ice Nuclei Counter Zürich (DRINCZ): Validation and application to field collected snow samples

David¹,

Cascajo-Castresana²,

Brennan³

et al. 2019

Preprint

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Abstract. Ice formation in the atmosphere is important for regulating cloud lifetime, Earth's radiative balance and initiating precipitation. Due to the difference in the saturation vapor pressure over ice and water, in mixed-phase clouds (MPCs), ice will grow at the expense of supercooled cloud droplets. As such, MPCs, which contain both supercooled liquid and ice, are particularly susceptible to ice formation. However, measuring and quantifying the concentration of ice nucleating particles (INPs) responsible for ice formation at temperatures associated with MPCs is challenging due to their very low concentrations in the atmosphere (~ 1 in 105 at − 30 °C). Atmospheric INP concentrations vary over several orders of magnitude at a single temperature and strongly increase as temperature approaches the homogeneous freezing threshold of water. To further quantify the INP concentration in nature and perform systematic laboratory studies to increase the understanding of the properties responsible for ice nucleation, a new drop freezing instrument, the DRoplet Ice Nuclei Counter Zurich (DRINCZ) is developed. The instrument is based on the design of previous drop freezing assays and uses a USB camera to automatically detect freezing in a 96-well tray cooled in an ethanol chilled bath with an automated analysis procedure. Based on an in-depth characterization of DRINCZ, we develop a new method for quantifying and correcting temperature biases across drop freezing assays. DRINCZ is further validated performing NX illite experiments, which compare well with the literature. The temperature uncertainty in DRINCZ was determined to be ± 0.9 ˚C. Furthermore, we demonstrate the applicability of DRINCZ by measuring and analyzing field collected snow samples during an evolving synoptic situation in the Austrian Alps. The field samples fall within previously observed ranges for cumulative INP concentrations and show a dependence on air mass origin and upstream precipitation amount.

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Cleaning up our water: reducing interferences from non-homogeneous freezing of pure water in droplet freezing assays of ice nucleating particles

Cited by 4 publications

References 10 publications

Development, Characterization, and Validation of a Cold Stage-Based Ice Nucleation Array (PKU-INA)

Development, Characterization, and Validation of a Cold Stage-Based Ice Nucleation Array (PKU-INA)

Ice nucleating particles measured in Swiss alpine snow samples are spatially, temporarily and chemically heterogeneous

Development of the DRoplet Ice Nuclei Counter Zürich (DRINCZ): Validation and application to field collected snow samples

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