Large surface radiative forcing from surface-based ice crystal events measured in the High Arctic at Eureka
Bibliographic record
Abstract
Abstract. Ice crystals, also known as diamond dust, are suspended in the boundary layer air under clear sky conditions during most of the Arctic winter in Northern Canada. Occasionally ice crystal events can produce significantly thick layers with optical depths in excess of 2.0 even in the absence of liquid water clouds. Four case studies of high optical depth ice crystal events at Eureka in the Nunavut Territory of Canada during the winter of 2006–2007 are presented. They show that the measured ice crystal surface infrared downward radiative forcing ranged from 8 to 36 W m−2 in the wavelength band from 5.6 to 20 μm for visible optical depths ranging from 0.2 to 1.7. MODIS infrared and visible images and the operational radiosonde wind profile were used to show that these high optical depth events were caused by surface snow being blown off 600 to 800 m high mountain ridges about 20 to 30 km North-West of Eureka and advected by the winds towards Eureka as they settled towards the ground within the highly stable boundary layer. This work presents the first study that demonstrates the important role that surrounding topography plays in determining the occurrence of high optical depth ice crystal events and points to a new source of boundary layer ice crystal events distinct from the classical diamond dust phenomenon.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".