Connection between winter Arctic sea ice and west Tibetan Plateau snow depth through the <scp>NAO</scp>
Bibliographic record
Abstract
Abstract The relationship between changes in Arctic sea ice and the mid‐latitude climate has been receiving increasing attention. As the highest and largest topography in Asia, the possible link between the west Tibetan Plateau snow depth (WTPSD) and the Arctic sea ice was investigated based on observational data and model simulations. The results indicate that a consistent variation between the WTPSD and the seesaw sea ice pattern in the Barents‐Nordic Sea and Labrador Sea (BLSIC) exists during boreal winter, and both the WTPSD and BLSIC are negatively correlated with the North Atlantic Oscillation (NAO) index. During the negative phases of the NAO, an anomalous Rossby wave train propagates from the North Atlantic to the north of the Arabian Sea, inducing cyclonic anomalies in the mid‐troposphere and enhancing water vapour transportation onto the TP, which is conducive to in situ snow accumulation. Furthermore, composite analysis and AGCM numerical experiments demonstrate that such an anomalous BLSIC pattern can in turn reinforce the negative phase of the NAO in strength, facilitating the propagation of the mid‐tropospheric Rossby waves to north of the Arabian Sea and hence an above normal WTPSD. Therefore, the variation in WTPSD is influenced by the direct effect of the NAO and additional feedback from BLSIC, which hints a potential cryospheric connection between the TP and the Arctic.
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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 | 0.000 | 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) | 0.002 | 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".