A method to diagnose sources of annular mode time scales
Bibliographic record
Abstract
[1] Time scales derived from annular mode (AM) variability provide dynamical insight into stratosphere-troposphere coupling and are linked to the strength of AM responses to climate forcings. AM time scales reflect decorrelation times of geopotential height in the stratosphere and troposphere. But geopotential height involves a vertical integral via the hypsometric equation, and this makes ambiguous some aspects of the dependence of the time scales on vertical level. In this study, a method for decomposing AM variability into contributions from surface pressure and from temperature is presented that is based on a linearization of the hypsometric equation. The decomposition is then used to interpret stratosphere-troposphere coupling events and the seasonal variation of AM time scales in reanalysis products and in two versions of a general circulation model that have distinctly different stratospheric representation. Surface pressure variations best account for tropospheric AM variability and stratospheric temperature variations best account for stratospheric AM variability during coupling events. But AM time scales are not so readily separated because they involve strong coupling between the surface pressure and stratospheric temperature variations: the pressure-temperature cross-correlation functions are small in magnitude but highly persistent and thus provide significant sources of AM persistence. These empirical results provide a basis for further theoretical analysis on the origins of zonal mean stratosphere-troposphere coupling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.002 |
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".