Recent developments in gravity‐wave effects in climate models and the global distribution of gravity‐wave momentum flux from observations and models
Bibliographic record
Abstract
Abstract Recent observational and theoretical studies of the global properties of small‐scale atmospheric gravity waves have highlighted the global effects of these waves on the circulation from the surface to the middle atmosphere. The effects of gravity waves on the large‐scale circulation have long been treated via parametrizations in both climate and weather‐forecasting applications. In these parametrizations, key parameters describe the global distributions of gravity‐wave momentum flux, wavelengths and frequencies. Until recently, global observations could not define the required parameters because the waves are small in scale and intermittent in occurrence. Recent satellite and other global datasets with improved resolution, along with innovative analysis methods, are now providing constraints for the parametrizations that can improve the treatment of these waves in climate‐prediction models. Research using very‐high‐resolution global models has also recently demonstrated the capability to resolve gravity waves and their circulation effects, and when tested against observations these models show some very realistic properties. Here we review recent studies on gravity‐wave effects in stratosphere‐resolving climate models, recent observations and analysis methods that reveal global patterns in gravity‐wave momentum fluxes and results of very‐high‐resolution model studies, and we outline some future research requirements to improve the treatment of these waves in climate simulations. Copyright © 2010 Royal Meteorological Society and Crown in the right of Canada
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".