Atmospheric forcing of the Beaufort Sea ice gyre: Surface pressure climatology and sea ice motion
Bibliographic record
Abstract
The Beaufort Gyre (BG) typically rotates anticyclonically and exerts an important control on Arctic Sea ice dynamics. Previous studies have shown reversals in the BG to rotate cyclonically during summer months and, in recent decades, throughout the annual cycle. In this investigation, we explore the synoptic climatology of atmospheric forcing and its relationship to sea ice motion and BG reversals. A catalog of daily synoptic weather types is generated for the Beaufort Sea Region covering the period 1979 to 2006 using NCEP/NCAR reanalysis mean sea level pressure data, principle components, and k‐means cluster analyses. Mean synoptic type frequency, persistence, and duration values are calculated for each synoptic type and contrasted between the summer and winter seasons. Daily synoptic types are linked to changes in sea ice vorticity by using correlation analysis on lagged sea ice vorticity data. Lag correlations are found between synoptic types and sea ice vorticity smoothed over a 12‐week running mean and show that cyclonic types, which promote southerly or easterly atmospheric circulation over the southern Beaufort Sea, commonly precede summer reversals. Furthermore, significant seasonal within‐type variability in sea ice vorticity is detected within the synoptic types illustrating the importance of seasonal variability on these processes.
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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 | 0.000 | 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".