Comparison of North Atlantic Oscillation‐related changes in the North Atlantic sea ice and associated surface quantities on different time scales
Bibliographic record
Abstract
Abstract By separating variations on different time scales, the present study reveals important differences in the North Atlantic Oscillation (NAO)‐related sea ice concentration (SIC), surface air temperature (SAT), and sea surface temperature (SST) patterns for trend, interdecadal, and interannual variations. The SIC has a prominent decreasing trend in the Greenland Sea and the Barents Sea, collocating with an increasing SAT trend and a weak increasing SST trend in the high‐latitude North Atlantic. The wind trends display a weak NAO signal. Corresponding to the positive interdecadal NAO phase, the SIC shows a decreasing trend in the Greenland and Barents Seas. The SAT change features a west negative‐east positive pattern along with positive anomalies extending to the Greenland Sea. The SST change is very weak in the Greenland Sea. Corresponding to the positive interannual NAO phase, the SIC change is opposite between the Greenland/Barents Seas and the Labrador Sea. The SAT change is characterized by a broad west–east pattern over the mid‐high latitudes. The SST change features an east–west dipole pattern in the mid‐latitude North Atlantic Ocean. In both interdecadal and interannual variations, NAO‐related meridional wind anomalies induce anomalous advection that contributes to the SAT change together with upward long‐wave radiation. The SIC and SAT changes are coupled closely through surface heat fluxes in all the three time scales. The present results suggest that it is necessary to distinguish time scales in studying the relationship among SIC, SAT, and SST variations.
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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 | 0.000 | 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".