In the atmosphere, cloud droplets can remain in a supercooled liquid phase at temperatures as low as -40 °C. Above this temperature, cloud droplets freeze via heterogeneous ice nucleation whereby a rare and poorly understood subset of atmospheric particles catalyze the ice phase transition. As the phase state of clouds is critical in determining their radiative properties and lifetime, deficiencies in our understanding of heterogeneous ice nucleation poses a large uncertainty on our efforts to predict human induced global climate change. Experimental challenges in properly simulating particle-induced freezing processes under atmospherically relevant conditions have largely contributed to the absence of a well-established model and parameterizations that accurately predict heterogeneous ice nucleation. Conversely, the sparsity of reliable measurement techniques available struggle to be interpreted by a single consistent theoretical or empirical framework, which results in layers of uncertainty when attempting to extrapolate useful information regarding ice nucleation for use in atmospheric cloud models. In this dissertation a new framework for describing heterogeneous ice nucleation is developed. Starting from classical nucleation theory, the surface of an ice nucleating particle is treated as a continuum of heterogeneous ice nucleating activity and a particle specific distribution of this activity g is derived. It is hypothesized that an individual particle species exhibits a critical surface area. Above this critical area the ice nucleating activity of a particle species can be described by one g distribution, 𝑔, while below it 𝑔 expresses itself expresses externally resulting in particle to particle variability in ice nucleating activity. The framework is supported by cold plate droplet freezing measurements for dust and biological particles in which the total surface area of particle material available is varied. Freezing spectra above a certain surface area are shown to be successfully fitted with 𝑔 while a process of random sampling from 𝑔 can predict the freezing behavior below the identified critical surface area threshold. The framework is then extended to account for droplets composed of multiple particle species and successfully applied to predict the freezing spectra of a mixed proxy for an atmospheric dust-biological particle system. The contact freezing mode of ice nucleation, whereby a particle induces freezing upon collision with a droplet, is thought to be more efficient than particle initiated immersion freezing from within the droplet bulk. However, it has been a decades’ long challenge to accurately measure this ice nucleation mode, since it necessitates reliably measuring the rate at which particles hit a droplet surface combined with direct determination of freezing onset. In an effort to remedy this longstanding deficiency a temperature controlled chilled aerosol optical tweezers capable of stably isolating water droplets in air at subzero temperatures has been designed and implemented. The new temperature controlled system retains the powerful capabilities of traditional aerosol optical tweezers: retrieval of a cavity enhanced Raman spectrum which could be used to accurately determine the size and refractive index of a trapped droplet. With these capabilities, it is estimated that the design can achieve ice supersaturation conditions at the droplet surface. It was also found that a KCl aqueous droplet simultaneously cooling and evaporating exhibited a significantly higher measured refractive index at its surface than when it was held at a steady state temperature. This implies the potential of a “salting out” process. Sensitivity of the cavity enhanced Raman spectrum as well as the visual image of a trapped droplet to dust particle collisions is shown, an important step in measuring collision frequencies of dust particles with a trapped droplet. These results may pave the way for future experiments of the exceptionally poorly understood contact freezing mode of ice nucleation.
| Identifer | oai:union.ndltd.org:cmu.edu/oai:repository.cmu.edu:dissertations-1832 |
| Date | 01 February 2017 |
| Creators | Beydoun, Hassan |
| Publisher | Research Showcase @ CMU |
| Source Sets | Carnegie Mellon University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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