Bibliographic record
Abstract
Abstract. This study examines Quasi-Biennial Oscillation (QBO) teleconnections and their modulation by the El Niño-Southern Oscillation (ENSO), using a multi-model ensemble of the Atmospheric Processes And their Role in Climate (APARC) QBO initiative (QBOi) models. Some difficulties arise in examining observed QBO-ENSO teleconnections from distinguishing the QBO and ENSO influences outside of the QBO region, due to aliasing between the QBO and ENSO over the historical record. To separate the QBO and ENSO signals, simulations are conducted with annually-repeating prescribed sea-surface temperatures corresponding to idealized El Niño or La Nina conditions (QBOi EN and LN experiments, respectively). In the Arctic winter climate, higher frequencies of sudden stratospheric warmings (SSWs) are found in EN than LN. The frequency differences in SSW between QBO westerly (QBO-W) and QBO easterly (QBO-E) are indistinguishable, suggesting that the polar vortex responses to the QBO are much weaker than those to the ENSO in these models. The Asia-Pacific subtropical jet (APJ) shifts significantly equatorward during QBO-W compared to QBO-E in observations, while the APJ-shift is not robust across models, regardless of the ENSO phases. In the tropics, these experiments do not show a robust or coherent QBO influence on precipitation. The sign and spatial pattern of the precipitation response vary widely across models and experiments, indicating that any potential QBO signal is strongly modulated by the prevailing phases of the ENSO. The QBO teleconnection to the Walker circulation around boreal summer/autumn is investigated to identify the strongest signal in each model. It is found that the upper-level westerly and lower-level easterly anomalies in the equatorial troposphere over the Indian Ocean and Western Pacific are detected in the observations and most models in the La Nina year. Overall, the QBO can modulate the zonal circulation over the tropical Indian-Pacific oceans, with its impact varying depending on the ENSO phase.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.592 | 0.414 |
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".