Abstract

Abstract. A number of new measurement methods for ice nucleating particles (INPs) have been introduced in recent years, and it is important to address how these methods compare. Laboratory comparisons of instruments sampling major INP types are common, but few comparisons have occurred for ambient aerosol measurements exploring the utility, consistency and complementarity of different methods to cover the large dynamic range of INP concentrations that exists in the atmosphere. In this study, we assess the comparability of four offline immersion freezing measurement methods (Colorado State University ice spectrometer, IS; North Carolina State University cold stage, CS; National Institute for Polar Research Cryogenic Refrigerator Applied to Freezing Test, CRAFT; University of British Columbia micro-orifice uniform deposit impactor–droplet freezing technique, MOUDI-DFT) and an online method (continuous flow diffusion chamber, CFDC) used in a manner deemed to promote/maximize immersion freezing, for the detection of INPs in ambient aerosols at different locations and in different sampling scenarios. We also investigated the comparability of different aerosol collection methods used with offline immersion freezing instruments. Excellent agreement between all methods could be obtained for several cases of co-sampling with perfect temporal overlap. Even for sampling periods that were not fully equivalent, the deviations between atmospheric INP number concentrations measured with different methods were mostly less than 1 order of magnitude. In some cases, however, the deviations were larger and not explicable without sampling and measurement artifacts. Overall, the immersion freezing methods seem to effectively capture INPs that activate as single particles in the modestly supercooled temperature regime (> −20 °C), although more comparisons are needed in this temperature regime that is difficult to access with online methods. Relative to the CFDC method, three immersion freezing methods that disperse particles into a bulk liquid (IS, CS, CRAFT) exhibit a positive bias in measured INP number concentrations below −20 °C, increasing with decreasing temperature. This bias was present but much less pronounced for a method that condenses separate water droplets onto limited numbers of particles prior to cooling and freezing (MOUDI-DFT). Potential reasons for the observed differences are discussed, and further investigations proposed to elucidate the role of all factors involved.

Highlights

  • Heterogeneous ice nucleation by atmospheric aerosols impacts the microphysical composition, radiative properties and precipitation processes in clouds colder than 0 ◦C

  • DeMott et al (2015) showed in laboratory studies that operational relative humidity (RH) up to 109 % might be required for full expression of freezing in the continuous flow diffusion chamber (CFDC); 105 % is the value that has been consistently used in field studies so that liquid droplets do not survive through the evaporation region and are not counted as false-positive INPs

  • Considering the capture efficiencies vs. size noted in Sect. 2.2, the lack of significant difference in ice spectrometer (IS) nINPs(T ) measured with the filters of 0.2 and 3 μm pore sizes implies that most INPs were likely large enough to be captured effectively

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Summary

Introduction

Heterogeneous ice nucleation by atmospheric aerosols impacts the microphysical composition, radiative properties and precipitation processes in clouds colder than 0 ◦C. To quantify the initial stage of ice nucleation in the atmosphere, multiple sampling techniques are being used in field studies (Hader et al, 2014; Mason et al, 2015; DeMott et al, 2015; Stopelli et al, 2015; Boose et al, 2016; Schrod et al, 2016, 2017). Since these various measurements are being used as bases for developing numerical model parameterizations for different emission sources, their comparability should be assessed. INP number concentration can increase up to 10 orders of magnitude as temperatures cool from −5 to −35 ◦C (DeMott et al, 2015, 2016; Hiranuma et al, 2015; Murray et al, 2012; Petters and Wright, 2015), and there can be up to 2–3 orders of magnitude of temporal and spatial variability at a single temperature by any given method (DeMott et al, 2010; Petters and Wright, 2015)

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