Bibliographic record
Abstract
Abstract. Quasi-stationary waves (QSWs), atmospheric Rossby waves with near constant phase that persist on subseasonal timescales, are not distributed homogeneously across the globe, even at a given latitude. The climatological QSW amplitude has a distinct spatial pattern, with clear zonal asymmetries, particularly in the Northern Hemisphere; those asymmetries must be impacted by stationary forcings such as land, topography, and sea surface temperatures (SSTs). To investigate the effects of stationary forcings on QSW characteristics, including their duration and spatial distribution, eight simulations were conducted using CAM6 with prescribed SSTs. These simulations range from realistic, semi-realistic (with some stationary forcings matching reality) to fully idealized (with idealized forcings added in aquaplanet simulations). The control simulation was validated against ERA5 reanalysis data. Stationary forcings tend to extend the duration of QSWs and move the zonal-mean amplitude of QSW northward in the midlatitudes. The stationary forcings also strongly impact the zonal asymmetric distribution of QSWs. QSWs are primarily influenced by both the local stationary wavenumber Ks, which depends on jet speed and its second-order meridional gradient, and by the strength of transient eddies. In some cases, QSW strength is also associated with the strength of the stationary waves. When the timescale of the QSWs is changed (e.g. from 15–30 days to >30 days), the relative contributions from different mechanisms changes, but stationary wavenumber Ks and transient eddies strength are important in all time scales for experiments with realistic land.
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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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.521 | 0.357 |
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".