Sensitivity of Idealized Moist Baroclinic Waves to Environmental Temperature and Moisture Content
Bibliographic record
Abstract
Idealized simulations are used to examine the sensitivity of moist baroclinic wave growth to environmental temperature and moisture content. With relative humidity held fixed, the surface temperature at 45°N, denoted T0, is varied from 275 to 290 K. As T0 increases, the atmospheric moisture content, moist instability, and moist available potential energy also increase. For the chosen initial configuration, moist waves develop larger eddy kinetic energy K e than corresponding dry waves, but enhanced diabatic heating at larger T0 does not further increase K e. This finding is linked to a warm-frontal cyclonic potential vorticity (PV) anomaly that strengthens and shifts downstream at larger T0 owing to increased diabatic heating along the frontal cloud band. This eastward shift feeds back negatively on the parent cyclone by increasing the downstream export of mechanical energy aloft and degrading the phasing between dry baroclinic vertical motion and buoyancy within the warm sector. The latter suppresses the conversion from eddy potential energy to K e [ C( P e, K e)], offsetting a direct enhancement of C( P e, K e) by diabatic heating. Compared to their dry counterparts, isolated moist waves (initiated by a single finite-amplitude PV anomaly) display a similar sensitivity to T0, while periodic wave trains (initiated by multiple such anomalies) exhibit a stronger negative relationship. The latter stems from anticyclonic diabatic PV anomalies aloft that originate along the warm front and recirculate through the system to interact with the upper-level trough. This interaction leads to a horizontal forward wave tilt that enhances the conversion of wave K e into zonal-mean kinetic energy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".