The ice nucleating ability of macromolecules in immersion freezing decreases in the presence of salts: Implications for freezing point depression calculations
Bibliographic record
Abstract
Cloud formation represents a large uncertainty in current climate predictions. In particular, ice in mixed-phase clouds requires the presence of ice nucleating particles (INPs) or ice nucleating macromolecules (INMs). An influential population of INPs has been proposed to be organic sea spray aerosols in otherwise pristine ocean air. However, the interactions between INMs present in sea water and their freezing behavior under atmospheric immersion freezing conditions warrants further research to constrain the role of sea spray aerosols on cloud formation. Indeed, salt is known to lower the freezing temperature of water, through a process called freezing point depression (FPD). Yet, current FPD corrections are solely based on the salt content and assume that the INMs’ ice nucleation abilities are identical with and without salt. Thus, we measured the effect of salt content on the ice nucleating ability of INMs, known to be associated with marine phytoplankton, in immersion freezing experiments in the Freezing Ice Nuclei Counter (FINC) (Miller et al., AMTD, 2020). We measured eight INMs, namely taurine, isethionate, xylose, mannitol, dextran, laminarin, and xanthan as INMs in pure water at temperatures relevant for mixed-phase clouds (e.g. 50% activated fraction at temperatures above –23 °C at 10 mM concentration). Subsequently, INMs were analyzed in artificial sea water containing 36 g salt L-1. Most INMs, except laminarin and xanthan, showed a loss of ice activity in artificial sea water compared to pure water, even after FPD correction. Based on our results, we hypothesize sea salt has an inhibitory effect on the ice activity of INMs. This effect influences our understanding of how INMs nucleate ice as well as challenges our use of FPD correction and subsequent extrapolation to ice activity under mixed-phase cloud conditions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".