A Review of the Factors Influencing Arctic Mixed‐Phase Clouds: Progress and Outlook
Why this work is in the frame
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Bibliographic record
Abstract
Mixed-phase clouds are ubiquitous in the Arctic and play a critical role in Earth's energy budget at the surface and top-of-the-atmosphere. These clouds typically occupy the lower and mid-level troposphere and are composed of purely supercooled liquid droplets or mixtures of supercooled liquid water droplets and ice crystals. Here, we review progress in our understanding of the factors that control the formation and dissipation of Arctic mixed-phase clouds, including the thermodynamic structure of the lower troposphere, warm and moist air intrusions into the Arctic, large-scale subsidence, and aerosol particles. We then provide a brief survey of numerous Arctic field campaigns that targeted local cloud-controlling factors and follow this with specific examples of how the Arctic Cloud Observations Using airborne measurements during polar Day (ACLOUD)/ Physical feedback of Arctic PBL, Sea ice, Cloud And AerosoL (PASCAL) and Airborne measurements of radiative and turbulent FLUXes of energy and momentum in the Arctic boundary layer (AFLUX) field campaigns that took place in the vicinity of Svalbard in 2019 were able to advance our understanding on this topic to demonstrate the value of field campaigns. Finally, we conclude with a discussion of the outlook of future research in the study of Arctic cloud-controlling factors and provide several recommendations for the observational and modeling community to advance our understanding of the role of Arctic mixed-phase clouds in a rapidly changing climate.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it