Mesoscale simulation of surface fluxes and boundary layer clouds associated with a Beaufort Sea polynya
Bibliographic record
Abstract
Measurements with the Canadian Convair‐580 aircraft over a large polynya in the Beaufort Sea provided detailed observations of turbulent heat fluxes and cloud properties during the First ISCCP Regional Experiment (FIRE) Arctic Cloud Experiment (FIRE.ACE). On 25 April 1998, cold air advection resulted in strong surface heat fluxes over the polynya and in the formation, despite the low temperatures (−19°C), of mixed‐phase clouds at the top of the Arctic boundary layer. The Canadian Mesoscale Compressible Community model (MC2) has been used to simulate this case at 2‐km resolution, with a detailed treatment of surface processes and the actual observed structure of the large polynya. The evolution of the Arctic boundary layer, together with most of the cloud features, compares favorably with in situ aircraft observations. The sensitivity of the Arctic boundary layer clouds to various surface and cloud microphysical processes has been examined. Aircraft observations and model simulations confirm that the generation of clouds associated with polynyas depends critically on the air‐sea temperature contrast controlling the magnitude of the heat fluxes. The crucial role of leads and polynyas for cloud formation is highlighted in a sensitivity run with surface evaporation turned off. Though it does not affect significantly the structure of the Arctic boundary layer, evaporation from the open waters provides the small moisture excess needed to trigger the generation of low‐level clouds when cold air advects over the polynya. Sensitivity runs with two cloud microphysical schemes revealed the importance of the turbulent transport of moisture in producing supercooled liquid clouds in this case. It is also found that these unusual boundary layer clouds particular to the Arctic conditions can be reasonably well reproduced with a cloud microphysical scheme of intermediate complexity that accounts for mixed phases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".