Classification of Polar-Night Jet Oscillations and Their Relationship to Fast and Slow Variations in a Global Mechanistic Circulation Model of the Stratosphere and Troposphere
Bibliographic record
Abstract
Abstract Polar-night jet oscillation (PJO), which is a low-frequency intraseasonal oscillatory variation in the winter stratosphere, is analyzed statistically with a 14 000-yr-long dataset obtained with an idealized global mechanistic circulation model of the stratosphere and troposphere. After performing an empirical orthogonal function (EOF) analysis on the low-pass-filtered time series of the northern polar temperature, 10 647 PJO events are identified and classified into four groups. About 80% of them are two groups of warm events while the rest are two groups of cold events, which are newly identified variations with opposite sign from the warm events by the same EOF analysis. All of them show slow downward propagations of a positive or negative temperature anomaly, with a relatively short or long lifetime. Composite analysis with such a large number of samples shows that each group has its own typical relationship to unfiltered relatively fast variations in the polar stratosphere known as stratospheric sudden warming and polar vortex intensification and to the slow variation in the troposphere known as the Arctic Oscillation. Statistically significant evidence of the downward dynamical influence of PJO on the surface is obtained for a group of warm events with a longer lifetime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".