The Labile Nature of Ice Nucleation by Arizona Test Dust

Perkins, Russell; Gillette, Samantha; Hill, Thomas C. J.; DeMott, Paul J.

doi:10.1021/acsearthspacechem.9b00304

Cited by 46 publications

(70 citation statements)

References 63 publications

(139 reference statements)

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“…Thus, the number of publications reporting measurements of INP concentrations in precipitation has increased over the past decade. Numerable insights have been obtained in previous precipitationbased INP studies, including the efficient depletion of INPs relative to other aerosols of similar size in precipitating clouds (Stopelli et al, 2015); constraints on minimum enhancement factors for secondary ice formation processes (Petters and Wright, 2015); and the identification, characteristics and distribution of various INP populations (e.g., Christner et al, 2008;Hader et al, 2014;Stopelli et al, 2017). INP concentrations in precipitation have been used to estimate in-cloud concentrations, based on assumptions that the majority of particles (86 %) in precipitation originate from the cloud rather than the atmospheric column through which the hydrometeor descended (Wright et al, 2014).…”

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confidence: 99%

“…INPs have been sampled in clouds and precipitation for decades (e.g., Rogers et al, 1998;Vali, 1971Vali, , 1966 to measure abundances, probe their compositions and investigate the extent to which they impact the properties of clouds. There are several caveats to consider when inferring in-cloud INP concentrations or properties from precipitation samples (Petters and Wright, 2015), including sweep-out of additional INPs as the hydrometeor traverses the atmosphere below the cloud (Vali, 1974) and heterogeneous chemistry due to adsorption or absorption of gases (Hegg and Hobbs, 1982;Kulmala et al, 1997;Lim et al, 2010). However, assessing the composition of INPs in precipitation samples is more straightforward than cloud particles.…”

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confidence: 99%

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Best practices for precipitation sample storage for offline studies of ice nucleation in marine and coastal environments

et al. 2020

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Abstract. Ice-nucleating particles (INPs) are efficiently removed from clouds through precipitation, a convenience of nature for the study of these very rare particles that influence multiple climate-relevant cloud properties including ice crystal concentrations, size distributions and phase-partitioning processes. INPs suspended in precipitation can be used to estimate in-cloud INP concentrations and to infer their original composition. Offline droplet assays are commonly used to measure INP concentrations in precipitation samples. Heat and filtration treatments are also used to probe INP composition and size ranges. Many previous studies report storing samples prior to INP analyses, but little is known about the effects of storage on INP concentration or their sensitivity to treatments. Here, through a study of 15 precipitation samples collected at a coastal location in La Jolla, CA, USA, we found INP concentration changes up to > 1 order of magnitude caused by storage to concentrations of INPs with warm to moderate freezing temperatures (−7 to −19 ∘C). We compared four conditions: (1) storage at room temperature (+21–23 ∘C), (2) storage at +4 ∘C, (3) storage at −20 ∘C and (4) flash-freezing samples with liquid nitrogen prior to storage at −20 ∘C. Results demonstrate that storage can lead to both enhancements and losses of greater than 1 order of magnitude, with non-heat-labile INPs being generally less sensitive to storage regime, but significant losses of INPs smaller than 0.45 µm in all tested storage protocols. Correlations between total storage time (1–166 d) and changes in INP concentrations were weak across sampling protocols, with the exception of INPs with freezing temperatures ≥ −9 ∘C in samples stored at room temperature. We provide the following recommendations for preservation of precipitation samples from coastal or marine environments intended for INP analysis: that samples be stored at −20 ∘C to minimize storage artifacts, that changes due to storage are likely an additional uncertainty in INP concentrations, and that filtration treatments be applied only to fresh samples. At the freezing temperature −11 ∘C, average INP concentration losses of 51 %, 74 %, 16 % and 41 % were observed for untreated samples stored using the room temperature, +4, −20 ∘C, and flash-frozen protocols, respectively. Finally, the estimated uncertainties associated with the four storage protocols are provided for untreated, heat-treated and filtered samples for INPs between −9 and −17 ∘C.

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Best practices for precipitation sample storage for offline studies of ice nucleation in marine and coastal environments

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“…Freezing can be detected optically with manual visual inspection (e.g. Creamean et al, 2018;Hill et al, 2014), with software to detect freezing optically (e.g., Stopelli et al, 2014;David et al, 2019;Perkins et al, 2020;Gute and Abbatt, 2020), with pyroelectrics (e.g., Cook et al, 2020), or with infrared thermal detection (e.g., Zaragotas et al, 2016;Harrison et al, 2018). Each bench-top immersion freezing method has its advantages and disadvantages (Cziczo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard

Miller¹,

Brennan²,

Mignani³

et al. 2020

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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 of INPs' and INMs' 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 Nucleation Counter (FINC), a droplet freezing technique (DFT), for the quantification of INP and INM concentrations in the immersion freezing mode. FINC's design builds upon previous 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 −25.37 &pm; 0.15 ˚C with 5 µL, and has an instrument temperature uncertainty of &pm; 0.5 ˚C. We further conducted freezing control experiments to quantify the non-homogeneous 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 with two other drop-freezing instruments: ETH's DRoplet Ice Nuclei Counter Zurich (DRINCZ) and 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 show that commercial lignin has a consistent ice nucleating activity across product batches. Second, we demonstrate that aqueous lignin solutions exhibit good solution stability over time. Third, we compare its freezing temperature across different drop-freezing instruments, including on DRINCZ, LINDA, and on the Weizmann Institute's Supercooled Droplets Observation on a Microarray (WISDOM) and determine an empirical fit parameter for future drop freezing validations. With these findings, we aim to show that lignin can be used as a good immersion freezing standard in future technique intercomparisons in the field of atmospheric ice nucleation.

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“…Schnell (1977) reported significant losses in fog and seawater samples after storage at room temperature for short periods (6-11 hours). Several studies have reported on the lability of commercially available dust and biological IN entities in storage above 0 ºC or under freezing conditions, including Arizona Test Dust and SnoMax® (Perkins et al, 2020;Polen et al, 2016;Wex et al, 2015), and similar labilities could affect the INPs of similar composition in precipitation samples (Creamean et al, 2013;Martin et al, 2019). Considering the abundance of precipitation based INP https://doi.org/10.5194/amt-2020-183 Preprint.…”

Section: Introductionmentioning

confidence: 99%

“…Considering this and that INPs < 0.45 μm exhibit significant losses across all storage types, there is a substantial risk that filter-treatments on stored samples in this study would lead to a false conclusion: that the majority of INPs were > 0.45 μm.Previous studies on precipitation collected along the California coast have demonstrated the contribution of dust, marine and terrestrial bioparticles to INPs in precipitation(Levin et al, 2019; Martin et al, 2019). Considering that well-characterized IN-active dust and biological standards (Arizona Test Dust and Snomax®, respectively) are sensitive to storage conditions, it is possible that in situ dust or biological INPs contributed to the observed INP losses Perkins et al (2020). found that the INability of Arizona Test Dust is significantly degraded in most conditions, including aging in deionized water for 1 day, and results fromPolen et al (2016) show that the most efficient (i.e.…”

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Best practices for precipitation sample storage for offline studies of ice nucleation

Beall

Lucero

Hill

et al. 2020

Preprint

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Abstract. Ice nucleating particles (INPs) are efficiently removed from clouds through precipitation, a convenience of nature for the study of these very rare particles that influence multiple climate-relevant cloud properties including ice crystal concentrations, size distributions, and phase-partitioning processes. INPs suspended in precipitation can be used to estimate in-cloud INP concentrations and to infer their original composition. Offline droplet assays are commonly used to measure INP concentrations in precipitation samples. Heat and filtration treatments are also used to probe INP composition and size ranges. Many previous studies report storing samples prior to INP analyses, but little is known about the effects of storage on INP concentration or their sensitivity to treatments. Here, through a study of 15 precipitation samples collected at a coastal location in La Jolla, CA, USA, we found significant changes caused by storage to concentrations of INPs with warm to moderate freezing temperatures (−7 to −19 ºC). We compared four conditions: 1.) storage at room temperature (+21–23 ºC), 2.) storage at +4 ºC 3.) storage at −20 ºC, and 4.) flash freezing samples with liquid nitrogen prior to storage at −20 ºC. Results demonstrate that storage can lead to both enhancements and losses of greater than one order of magnitude, with non-heat-labile INPs being generally less sensitive to storage regime, but significant losses of INPs smaller than 0.45 μm in all tested storage protocols. No correlation was found between total storage time (1–166 days) and changes in INP concentration. We provide the following recommendations for preservation of precipitation samples from coastal environments intended for INP analysis: that samples be stored at −20 ºC to minimize storage artifacts, that changes due to storage are likely and an additional uncertainty in INP concentrations, and that filtration treatments be applied only to fresh samples. Average INP losses of 72 %, 42 %, 25 % and 32 % were observed for untreated samples stored using the room temperature, +4 ºC, −20 ºC, and flash frozen protocols, respectively. Finally, correction factors are provided so that INP measurements obtained from stored samples may be used to estimate concentrations in fresh samples.

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The Labile Nature of Ice Nucleation by Arizona Test Dust

Cited by 46 publications

References 63 publications

Best practices for precipitation sample storage for offline studies of ice nucleation in marine and coastal environments

Best practices for precipitation sample storage for offline studies of ice nucleation in marine and coastal environments

Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard

Best practices for precipitation sample storage for offline studies of ice nucleation

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