Clouds at Arctic Atmospheric Observatories. Part II: Thermodynamic Phase Characteristics
Bibliographic record
Abstract
Abstract Cloud phase defines many cloud properties and determines the ways in which clouds interact with other aspects of the climate system. The occurrence fraction and characteristics of clouds distinguished by their phase are examined at three Arctic atmospheric observatories. Each observatory has the basic suite of instruments that are necessary to identify cloud phase, namely, cloud radar, depolarization lidar, microwave radiometer, and twice-daily radiosondes. At these observatories, ice clouds are more prevalent than mixed-phase clouds, which are more prevalent than liquid-only clouds. Cloud ice occurs 60%–70% of the time over a typical year, at heights up to 11 km. Liquid water occurs at temperatures above −40°C and is increasingly more likely as temperatures increase. Within the temperature range from −40° to −30°C, liquid water occurs in 3%–5% of the observed cloudiness. Liquid water is found higher in the atmosphere when accompanied by ice; there are few liquid-only clouds above 3 km, although liquid in mixed-phase clouds occurs at heights up to about 7–8 km. Regardless of temperature or height, liquid water occurs 56% of the time at Barrow, Alaska, and at a western Arctic Ocean site, but only 32% of the time at Eureka, Nunavut, Canada. This significant difference in liquid occurrence is due to a relatively dry lower troposphere during summer at Eureka in addition to warmer cloud temperatures with more persistent liquid water layers at the far western locations. The most persistent liquid clouds at these locations occur continuously for more than 70 h in the autumn and more than 30 h in the winter. Ice clouds persist for much longer than do liquid clouds at Eureka and occur more frequently in the winter season, leading to a total cloud occurrence annual cycle that is distinct from the other observatories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".