Report on the SPARC QBO Workshop: The QBO and its Global Influence - Past, Present and Future
Bibliographic record
Abstract
There is no known atmospheric phenomenon with a longer horizon of predictability than the quasibiennial oscillation (QBO) of tropical stratospheric circulation. With a mean period of about 28 months, the QBO phase can routinely be predicted at least a year in advance. This predictability arises from internal atmospheric dynamics, rather than from external forcings with long timescales, and it offers the tantalizing prospect of improved predictions for any phenomena influenced by the QBO. Observed QBO teleconnections include an apparent QBO influence on the stratospheric winter polar vortices in both hemispheres, the Madden-Julian Oscillation (MJO), and the North-Atlantic Oscillation (NAO). Yet the degree to which such teleconnections are real, robust, and sufficiently strong to provide useful predictive skill remains an important topic of research. Utilizing and understanding these linkages will require atmospheric models that adequately represent both the QBO and the mechanisms by which it influences other aspects of the general circulation, such as tropical deep convection. The 2016 QBO workshop in Oxford aimed to explore these themes, and to build on the outcomes of the first QBO workshop, held in March 2015 in Victoria, BC, Canada (as reported in SPARC Newsletter No. 45). This earlier workshop was the kick-off meeting of the SPARC QBOi (QBO Initiative) activity, and its key outcome was to plan a series of coordinated Atmosphere General Circulation Model (AGCM) experiments (the “phase-one” QBOi experiments). These experiments provide a multi-model dataset that can be used to investigate the aforementioned themes. While the focus of the Victoria meeting was primarily on the QBO itself, the Oxford workshop has broadened the scope of the QBOi activity to encompass QBO impacts. Its primary outcome is a planned set of core papers analysing the phaseone QBOi experiments,
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.027 |
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".