Linking upstream cold, continental air to the intensity of marine cold air outbreaks along the western boundary currents
Bibliographic record
Abstract
The thermal contrast between cold land masses and warm oceans is an important contributor to extratropical circulation. During boreal winter, intense radiative cooling leads to the formation of extremely cold continental air masses at high latitudes over the land masses of Siberia and Canada. As these regions are located upstream of the Northern Hemisphere storm tracks, maritime cold air outbreaks (CAOs) originating from such regions may induce severe air-sea interaction and affect low-level baroclinicity, altering storm track activity. We refer to these two regions as the “Boreal cold air reservoirs” (BCARs) for the North Pacific and Atlantic storm tracks.Starting from a case study of the January ’23 record-breaking cold air outbreak over eastern Russia, China and Japan, we revisit the connection between storm track activity and the strength of surface-based cooling over the upstream continents. Here, we link the CAO intensity to the presence and characteristics of upstream cold continental air using a backward trajectory analysis. Using backward trajectories initiated from 167 CAO events in the Japan Sea and 192 CAO events along the North American east coast, we then proceed to systematically link the CAO intensity to the presence and the characteristics of upstream, cold continental air. We show that the CAO intensity -measured by the associated surface sensible heat fluxes- scale with an increasing contribution of low-level, cold continental air to the total CAO air mass. The intensity of latent heat fluxes, on the other hand, scales with respect to the magnitude of the dry intrusion air stream in the extratropical cyclone usually associated with the CAO.
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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) | 0.000 | 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".