The Modulation of Stationary Waves, and Their Response to Climate Change, by Parameterized Orographic Drag
Bibliographic record
Abstract
Abstract The parameterization of orographic drag processes in atmospheric models remains uncertain because of a lack of observational and theoretical constraints on their formulation and free parameters. While previous studies have demonstrated that parameterized orographic drag acting near the surface has a significant impact on the atmospheric circulation, this work follows a more systematic approach to investigate its impacts on the large-scale circulation and the circulation response to climate change. A set of experiments with a comprehensive atmospheric general circulation model is used to ascertain the range of climatological circulations that may arise from parameter uncertainty. It is found that the Northern Hemisphere (NH) wintertime stationary wave field is strongly damped over the North Pacific (NP) and amplified over the North Atlantic (NA) as a result of increased low-level parameterized orographic drag, both of which are shown to be conducive to higher-latitude westerlies. A comparison with the stationary wave field presented in other studies suggests that the too-zonal NA jet and equatorward NP jet biases that are prevalent in climate models may be at least partly due to their representation of orographic drag. The amplitude of the stationary wave response to climate change across the experiments is shown to scale with the magnitude of low-level parameterized orographic drag through its influence on the present-day climatological stationary wave amplitudes over different sectors of the NH, which is consistent with linear stationary wave theory. This work highlights the importance of fidelity in a model’s basic state for regional climate change projections.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".