The dependence of ice microphysics on aerosol concentration in arctic mixed‐phase stratus clouds during ISDAC and M‐PACE
Bibliographic record
Abstract
Cloud and aerosol data acquired by the National Research Council of Canada (NRC) Convair‐580 aircraft in, above, and below single‐layer arctic stratocumulus cloud during the Indirect and Semi‐Direct Aerosol Campaign (ISDAC) in April 2008 were used to test three aerosol indirect effects hypothesized to act in mixed‐phase clouds: the riming indirect effect, the glaciation indirect effect, and the thermodynamic indirect effect. The data showed a correlation of R = 0.78 between liquid drop number concentration, N liq inside cloud and ambient aerosol number concentration N PCASP below cloud. This, combined with increasing liquid water content LWC with height above cloud base and the nearly constant vertical profile of N liq , suggested that liquid drops nucleated from aerosol at cloud base. No evidence of a riming indirect effect was observed, but a strong correlation of R = 0.69 between ice crystal number concentration N i and N PCASP above cloud was noted. Increases in ice nuclei (IN) concentration with N PCASP above cloud for 2 flight dates combined with the subadiabatic LWC profiles suggest possible mixing of IN from cloud top consistent with the glaciation indirect effect. The lower N ice and lower effective radius r el for the more polluted ISDAC cases compared to data collected in cleaner single‐layer stratocumulus conditions during the Mixed‐Phase Arctic Cloud Experiment is consistent with the operation of the thermodynamic indirect effect. However, more data in a wider variety of meteorological and surface conditions, with greater variations in aerosol forcing, are required to identify the dominant aerosol forcing mechanisms in mixed‐phase arctic clouds.
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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 | 0.000 | 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".