Role of Atmospheric Variability in Driving the “Warm‐Arctic, Cold‐Continent” Pattern Over the North America Sector and Sea Ice Variability Over the Chukchi‐Bering Sea
Bibliographic record
Abstract
Abstract While the observed decline of sea ice over the Chukchi‐Bering Sea (CBS) has coincided with the “warm‐Arctic, cold‐continent” (WACC) pattern over the North America (NA) sector, there is a debate on the causes of the WACC pattern. Here we present a very similar WACC pattern over the NA sector on both interannual and subseasonal time scales. Lead‐lag regression analyses on the shorter time scale indicate that an anomalous anticyclonic circulation over Alaska/Yukon in conjunction with the downward surface turbulent heat flux and long‐wave radiation anomalies over CBS leads the formation of the WACC pattern by about 1–2 days, while the latter further leads CBS sea ice reduction by about 3 days. These results indicate that atmospheric variability may play an active role in driving both the WACC pattern over NA and CBS sea ice variability.
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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 | 0.000 | 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) | 0.001 | 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".