A tale of two events: Arctic rain-on-snow meteorological drivers
Bibliographic record
Abstract
Abstract Arctic rain-on-snow (ROS) events can have significant impacts on Arctic wildlife and socio-economic systems. This study addresses the meteorology of two different Arctic ROS events. The first, occurring near Nuuk, Greenland, generated significant impacts, including slush avalanches. The second, less severe, event occurred within the community of Iqaluit, Nunavut, Canada. This research utilizes atmospheric reanalysis, automated surface observation station data and atmospheric soundings to determine the meteorological conditions driving these events and the differences between each case. In both cases, atmospheric blocking played a leading role in ROS initiation, with atmospheric rivers – narrow bands of high water vapor transport, typically originating from the tropics and subtropics – having both direct and indirect effects. Cyclone-induced low-level jets and resultant ‘warm noses’ of higher air temperatures and moisture transport were other key features in ROS generation. To our knowledge, our study is the first to visualize how the varying strength and manifestation of these coupled features contribute to differences in the severity of Arctic ROS events. The meteorological drivers identified here find support from other studies on Arctic ROS events and are similar to weather features associated with Arctic precipitation events of extreme magnitude.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".