Ice nucleating particles at a coastal marine boundary layer site: correlations with aerosol type and meteorological conditions
Bibliographic record
Abstract
Abstract. Information on what aerosol particle types are the major sources of ice nucleating particles (INPs) in the atmosphere is needed for climate predictions. To determine which aerosol particles are the major sources of immersion-mode INPs at a coastal site in Western Canada, we investigated correlations between INP number concentrations and both concentrations of different atmospheric particles and meteorological conditions. We show that INP number concentrations are strongly correlated with the number concentrations of fluorescent bioparticles between −15 and −25 °C, and that the size distribution of INPs is most consistent with the size distribution of fluorescent bioparticles. We conclude that biological particles were likely the major source of ice nuclei at freezing temperatures between −15 and −25 °C at this site for the time period studied. At −30 °C, INP number concentrations are also well correlated with number concentrations of the total aerosol particles ≥ 0.5 μm, suggesting that non-biological particles may have an important contribution to the population of INPs active at this temperature. As we found that black carbon particles were unlikely to be a major source of ice nuclei during this study, these non-biological INPs may include mineral dust. Furthermore, correlations involving chemical tracers of marine aerosols and marine biological activity, sodium and methanesulfonic acid, indicate that the majority of INPs measured at the coastal site likely originated from terrestrial rather than marine sources. Finally, six existing empirical parameterizations of ice nucleation were tested to determine if they accurately predict the measured INP number concentrations. We found that none of the parameterizations selected are capable of predicting INP number concentrations with high accuracy over the entire temperature range investigated. This finding illustrates that additional measurements are needed to improve parameterizations of INPs and their subsequent climatic impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".