Turbulent heat fluxes over leads and polynyas, and their effects on arctic clouds during FIRE.ACE: Aircraft observations for April 1998
Bibliographic record
Abstract
In this study, aircraft observations obtained during the First International Satellite Cloud Climatology Project @ISCCP) Regional Experiment‐Arctic Cloud Experiment (FIRE.ACE) were used to calculate latent and sensible heat fluxes over leads and polynyas. The purpose of this study is to analyse turbulent heat fluxes related to ocean surface characteristics, and study their effect on Arctic cloud formation. Aircraft passes were made over the leads and polynyas at an altitude of about 100 m. The measurements of a Land Resources Satellite System (LANDSAT) simulator, an airborne PRT‐5 infra‐red radiometer, and a lidar at 1.064 μm wavelength were used to specify ocean surface characteristics. Air temperature, vertical air velocity, and water vapour density measurements were used in the flux calculations. Cloud microphysical parameters, e.g., droplet concentration, ice crystal concentration, and water content were obtained using optical and hot wire probes. The results indicated that a 3‐km lead generated a sensible heat flux of 56 W m−2 and a latent heat flux of 14 W m−2, whereas over the ice they were about ‐20 W m−2 and ‐13 W m−2, respectively. Turbulent fluxes from leads and polynyas were found to be highly variable because of various surface and environmental conditions. Temperature at the ocean water surface reached 3°C on 8 April 1998 and this high surface temperature could also be related to steam fog or thin cloud. Clouds tended to form over the leads and polynyas or in the downwind region as cold air moved from north to south, resulting in a temperature difference of 15°–20°C. The effective radius and droplet concentrations were calculated to be less than 8 μm and 90 cm−3, respectively, in such clouds. The effective values were found to be significantly less than those (∼10 μm) of mid‐latitude clouds over the ocean.
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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 | 0.000 | 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".