Seasonal evolution patterns of Arctic sea ice and their connection to tropical Pacific sea surface temperature anomalies
Bibliographic record
Abstract
Abstract This study applies the self-organizing map method to identify the primary modes of seasonal evolution in Arctic sea ice concentration anomalies from 1979 to 2023. The resulting patterns fall into two broad categories: negative sea ice anomalies, occurring more frequently after 2005, and positive anomalies, more common prior to 2005. Differences among the modes within each category are primarily driven by sea ice variability in the Bering Sea, Sea of Okhotsk, Labrador Sea, and Greenland Sea during boreal winter and spring. Warmer temperatures, increased longwave radiation, and southerly winds favor reduced sea ice, while cooler conditions and northerly winds support increased ice cover. These atmospheric anomalies are often linked to tropical Pacific sea surface temperature variability, which influences Arctic conditions via poleward-propagating Rossby wave trains triggered by changes in tropical convection. Our findings enhance the basis for seasonal, interannual, and decadal predictions of Arctic sea ice.
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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".