Microphysics of Arctic Stratiform Boundary-layer Clouds during ARCSIX
Bibliographic record
Abstract
Abstract. Clouds have a major impact on rapidly decreasing sea ice in the Arctic, yet much is still unknown how cloud microphysics influences cloud development. In situ and remote data were collected by the NASA P-3 and SPEC Inc. Learjet research aircraft in Arctic stratiform boundary-layer clouds over the oceans and sea ice bordering northern Greenland between 25 May and 15 August 2024 during the ARCSIX project. Both aircraft carried a suite of nearly identical state-of-the-art microphysical sensors. Additionally, the P-3 was equipped with aerosol and remote-sensing instrumentation and the Learjet was equipped with a zenith/nadir Ka-band radar. The total length of clouds examined remotely and in-situ by the two aircraft totaled 12,417 km, with 6,266 km of in-situ measurements. Mixed-phase clouds were sampled during 60.5 % of time in cloud, and all-liquid clouds were measured 39.5 % of the time. Cloud- top temperatures were ≥ - 9 °C during 90 % of the stratiform boundary-layer cloud investigations. Single-layer mixedphase clouds sampled with cloud-top temperatures ≥ - 4 °C often contained concentrations of ice particles more than five orders of magnitude higher than measured concentrations of ice-nucleating particles. Despite the high ice concentrations, microphysical conditions supporting secondary ice production were not always present. In contrast, in some clouds where environmental conditions met commonly accepted criteria for secondary ice production, ice particle concentrations were closer to what is expected from primary nucleation. The quality of measurements was unprecedented, but results from our preliminary analyzes raise more questions about primary and secondary nucleation mechanisms than they answer.
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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 | 0.001 | 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) | 0.001 | 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".