The Role of the Central Asian Mountains on the Midwinter Suppression of North Pacific Storminess
Bibliographic record
Abstract
Abstract The role of the central Asian mountains on North Pacific storminess is examined using an atmospheric general circulation model by varying the height and the areas of the mountains. A series of model integrations show that the presence of the central Asian mountains suppresses the North Pacific storminess by 20%–30% during boreal winter. Their impact on storminess is found to be small during other seasons. The mountains amplify stationary waves and effectively weaken the high-frequency transient eddy kinetic energy in boreal winter. Two main causes of the reduced storminess are diagnosed. First, the decrease in storminess appears to be associated with a weakening of downstream eddy development. The mountains disorganize the zonal coherency of wave packets and refract them more equatorward. As the zonal traveling distance of wave packets gets substantially shorter, downstream eddy development gets weaker. Second, the central Asian mountains suppress the global baroclinic energy conversion. The decreased baroclinic energy conversion, particularly over the eastern Eurasian continent, decreases the number of eddy disturbances entering into the western North Pacific. The “barotropic governor” does not appear to explain the reduced storminess in our model simulations.
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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 | 0.000 | 0.000 |
| 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".