Patterns of Asian Winter Climate Variability and Links to Arctic Sea Ice
Bibliographic record
Abstract
Abstract This paper describes two dominant patterns of Asian winter climate variability: the Siberian high (SH) pattern and the Asia–Arctic (AA) pattern. The former depicts atmospheric variability closely associated with the intensity of the Siberian high, and the latter characterizes the teleconnection pattern of atmospheric variability between Asia and the Arctic, which is distinct from the Arctic Oscillation (AO). The AA pattern plays more important roles in regulating winter precipitation and the 850-hPa meridional wind component over East Asia than the SH pattern, which controls surface air temperature variability over East Asia. In the Arctic Ocean and its marginal seas, sea ice loss in both autumn and winter could bring the positive phase of the SH pattern or cause the negative phase of the AA pattern. The latter corresponds to a weakened East Asian winter monsoon (EAWM) and enhanced winter precipitation in the midlatitudes of the Asian continent and East Asia. For the SH pattern, sea ice loss in the prior autumn emerges in the Siberian marginal seas, and winter loss mainly occurs in the Barents Sea, Labrador Sea, and Davis Strait. For the AA pattern, sea ice loss in the prior autumn is observed in the Barents–Kara Seas, the western Laptev Sea, and the Beaufort Sea, and winter loss only occurs in some areas of the Barents Sea, the Labrador Sea, and Davis Strait. Simulation experiments with observed sea ice forcing also support that Arctic sea ice loss may favor frequent occurrence of the negative phase of the AA pattern. The results also imply that the relationship between Arctic sea ice loss and winter atmospheric variability over East Asia is unstable, which is a challenge for predicting the EAWM based on Arctic sea ice loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".