On the Relationship between Winter Sea Ice and Summer Atmospheric Circulation over Eurasia
Bibliographic record
Abstract
Abstract Using NCEP–NCAR reanalysis and Japanese 25-yr Reanalysis (JRA-25) data, this paper investigates the association between winter sea ice concentration (SIC) in Baffin Bay southward to the eastern coast of Newfoundland, and the ensuing summer atmospheric circulation over the mid- to high latitudes of Eurasia. It is found that winter SIC anomalies are significantly correlated with the ensuing summer 500-hPa height anomalies that dynamically correspond to the Eurasian pattern of 850-hPa wind variability and significantly influence summer rainfall variability over northern Eurasia. Spring atmospheric circulation anomalies south of Newfoundland, associated with persistent winter–spring SIC and a horseshoe-like pattern of sea surface temperature (SST) anomalies in the North Atlantic, act as a bridge linking winter SIC and the ensuing summer atmospheric circulation anomalies over northern Eurasia. Indeed, this study only reveals the association based on observations and simple simulation experiments with SIC forcing. The more precise mechanism for this linkage needs to be addressed in future work using numerical simulations with SIC and SST as the external forcings. The results herein have the following implication: Winter SIC west of Greenland is a possible precursor for summer atmospheric circulation and rainfall anomalies over northern Eurasia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".