Atmospheric forcing on the drift of Arctic sea ice in 1989–2009
Bibliographic record
Abstract
Inter‐annual variations in Arctic sea ice drift speed (Vi) in 1989–2009 were analyzed on the basis of buoy data and atmospheric circulation indices. In the circumpolar and eastern Arctic and the Fram Strait, the annual mean Vi was best explained by the sea level pressure (SLP) difference across the Arctic Ocean along meridians 270°E and 90°E, called as the Central Arctic Index (CAI). In general, Vi was more strongly related to CAI than to the Dipole Anomaly (DA). This was because CAI is calculated across the Transpolar Drift Stream (TDS), whereas the pressure patterns affecting DA sometimes move far from TDS. CAI also has the benefit of being a simple index, insensitive to the calculation method applied, whereas DA, as the second mode of a principal component analysis, is sensitive both to the time period and area of calculations. In summer, the circulation index most important for the circumpolar mean Vi was the SLP gradient across the Fram Strait. In the Canadian Basin in winter, the Arctic Oscillation index was most important. Circulation indices explained 48% of the variance of the annual mean Viin the circumpolar Arctic, 38% in the eastern Arctic, and 25% in the Canadian Basin. The local air‐ice momentum flux (τ) was always better than the 10 m wind speed in explaining Vi, but τ outperformed the circulation indices only in the Fram Strait. Atmospheric forcing did not explain the increasing trend in Vi in the period 1989–2009.
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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 | 0.001 |
| 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".