Ice‐supersaturated regions and subvisible cirrus in the northern midlatitude upper troposphere
Bibliographic record
Abstract
Humidity and temperature data from the Measurement of Ozone by Airbus in‐service Aircraft (MOZAIC) project have been used to produce maps of probability for ice supersaturation in two 50 hPa thick layers centered around 200 and 250 hPa. As the MOZAIC data cover only international air routes, the resulting maps cover mainly the northern midlatitudes. The data of ice supersaturation have then been correlated with data of frequency of occurrence of subvisible cirrus from the Stratospheric Aerosol and Gas Experiment (SAGE II) satellite instrument. The correlation analysis provided strong indications that subvisible cirrus (SVC) is associated to ice‐supersaturated regions (ISSRs), although processes are possible that can decouple SVC from ISSRs. A first trial to derive a global picture of ice supersaturation near the tropopause was performed using a measure of cirrus fractional coverage constructed from meteorological analyses of European Centre for Medium‐Range Weather Forecasts and to correlate this with the supersaturation data. The correlation was only moderate (although significant), leading to the tentative conjecture that regions of frequent ice supersaturation are to be expected over the Indonesian archipelago, over the Amazonas basin, and over the northern Pacific between Japan and Canada. A final correlation analysis between the meteorological analysis data and the SVC data indicated that the formation of SVC is generally thermodynamically controlled, with the exception of the northern midlatitude SVC. The composition of the aerosol at the northern midlatitude tropopause is probably variable due to industrial emissions and air traffic. Hence the freezing properties of these particles may become important, which results in a weaker thermodynamic control of SVC formation in the northern midlatitudes.
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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) | 0.000 | 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".