Laboratory studies of immersion and deposition mode ice nucleation of ozone aged mineral dust particles
Bibliographic record
Abstract
Abstract. Ice nucleation in the atmosphere is central to the understanding the microphysical properties of mixed-phase and cirrus clouds. Ambient conditions such as temperature (T) and relative humidity (RH), as well as aerosol properties such as chemical composition and mixing state play an important role in predicting ice formation in the troposphere. Previous field studies have reported the absence of sulfate and organic compounds on mineral dust ice crystal residuals sampled at mountain top stations or aircraft based measurements despite the long-range transport mineral dust is subjected to. We present laboratory studies of ice nucleation for immersion and deposition mode on ozone aged mineral dust particles for 233 < T < 263 K. Heterogeneous ice nucleation of untreated kaolinite (Ka) and Arizona Test Dust (ATD) particles is compared to corresponding aged particles that are subjected to ozone concentrations of 0.4–4.3 ppmv in a stainless steel aerosol tank. The portable ice nucleation counter (PINC) and immersion chamber combined with the Zurich ice nucleation chamber (IMCA-ZINC) are used to conduct deposition and immersion mode measurements, respectively. Ice active fractions as well as ice active surface site densities (ns) are reported and observed to increase as a function of decreasing temperature. We present first results that demonstrate enhancement of the ice nucleation ability of aged mineral dust particles in both the deposition and immersion mode due to ageing. We also present the first results to show a suppression of heterogeneous ice nucleation activity without the condensation of a coating of (in)organic material. In immersion mode, low ozone exposed Ka particles showed enhanced ice activity requiring a median freezing temperature of 1.5 K warmer than that of untreated Ka, whereas high ozone exposed ATD particles showed suppressed ice nucleation requiring a median freezing temperature of 3 K colder than that of untreated ATD. In deposition mode, low exposure Ka had ice active fractions of an order of magnitude higher than untreated Ka, whereas high ozone exposed ATD had ice active fractions up to a factor of 4 lower than untreated ATD. From our results, we derive and present parameterizations in terms of ns(T) that can be used in models to predict ice nuclei concentrations based on available aerosol surface area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".