Extratropical teleconnections in an ensemble of models nudged towards the observed equatorial QBO
Bibliographic record
Abstract
The Quasi-Biennial Oscillation (QBO) is the leading natural model of interannual variability of the zonal mean wind in the equatorial stratosphere, consisting of alternating regions of easterly and westerly zonal wind that descend through the equatorial stratosphere with a mean period of approximately 28 months. Its dominant influence on the dynamical structure of the equatorial stratosphere raises the prospect of teleconnections to the extratropical atmosphere. For example, the QBO has been linked to variability in the Northern Hemisphere winter stratospheric polar vortex, the timing and frequency of sudden stratospheric warmings, the phase of the North Atlantic Oscillation, and the modulation of tropospheric mid-latitude waves in the Pacific region. However, the reproduction of these extratropical teleconnections in free-running models relies upon on a quantitatively realistic internally-generated QBO, and the ability of the model dynamics to respond to this QBO. To isolate the dynamical response, a new experiment protocol, defined by the Atmospheric Processes and their Role in Climate (APARC) Quasi-Biennial Oscillation initiative (QBOi), describes how the observed equatorial stratospheric zonal winds can be imposed in model experiments. This allows the dynamical response across different models with similar and realistic QBOs to be analysed. Using a multi-model ensemble generated by QBOi modelling centres, we present an assessment of the extratropical teleconnections in comparison with observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".