Boreal Winter Extratropical Weather Regime Changes During 1979–2019 and Their Weather Impacts and Possible Linkages to Sea‐Ice in the Nordic Seas
Bibliographic record
Abstract
Abstract Previous studies have suggested possible connections between the decreasing Arctic sea‐ice and long‐duration (>5 days, LD) cold weather events in Eurasia and North America. Here we document the occurrences of weather regimes in winter by their durations, based on the empirical orthogonal function analyses of the daily geopotential height fields at 500 hPa (z500) for the months of November–March 1979–2019. Significant changes in the occurrence frequency and persistence of Ural ridge (UR) and weak stratospheric polar vortex (PV) were found between winters following high and low autumn sea‐ice covers (SIC) in the Barents and Kara seas. It is shown that a strengthening of the UR is accompanied with a weakening of the PV, and a weak PV favors Greenland ridge (GR). Cold spells in East Asia persist for 5 more days after an LDUR. Cold spells from Canada to the U.S. occur 2–5 days after an LD Ural trough (UT) and are associated with a z500 anomaly dipole centered over Alaska (+) and Hudson Bay (−). Cold spells in the eastern U.S. occur 1–4 days after an LDGR due to circulations resembling the Pacific‐North America pattern. Increased occurrences of UR in winter are associated with a decreased eastward propagation of synoptic waves from the North Atlantic to Japan and the North Pacific.
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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 | 0.000 |
| 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".