Causality in the Winter Interaction Between Extratropical Storm Tracks, Atmospheric Circulation, and Arctic Sea Ice Loss
Bibliographic record
Abstract
Abstract Global warming is accelerating the decline of Arctic sea ice, with wide‐ranging impacts on the Earth's climate system. Using ERA5 data from 1980 to 2023, we investigated the relationship between winter extratropical storm tracks, atmospheric circulation patterns, and sea ice area (SIA) in three key Arctic regions. We classified the winters into two categories: atmosphere‐driven winters (ADWs), when atmospheric circulation influences sea ice, and ice‐driven winters (IDWs), when sea ice influences atmospheric circulation. This classification was based on the sign of SIA and surface turbulent heat flux anomalies in the Barents‐Kara Sea (BKS), Baffin Bay, Davis Strait, and Labrador Sea (BDL), and Chukchi‐Bering Seas (CBS). Our findings show that in IDWs, reduced SIA has a minor effect on extratropical storm tracks. However, we observed significant midtropospheric cooling over northeastern Asia, aligning with the effects of reduced ice in the BKS during IDWs. This emphasizes the importance of considering the entire tropospheric temperature profile to capture the impact of sea ice loss. In contrast, during ADWs, the BKS and CBS regions experience amplified surface warming and SIA loss due to storm‐induced intrusion of warm and moist air, with sea ice loss in the BKS contributing to strengthening Ural blocking. Although cyclone‐induced heat and moisture intrusion is prevalent, we found no significant trend in track density or mean intensity of positive V extrema in the North Atlantic sector of the Arctic, suggesting that changes in atmospheric circulation are unlikely to be the driver of recent sea ice loss in the BKS.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".