Increase in the skewness of extratropical vertical velocities with climate warming: fully nonlinear simulations versus moist baroclinic instability
Bibliographic record
Abstract
The distribution of vertical velocities in the extratropical troposphere is skewed such that upward motions are faster than downward motions. This skewness is important for the intensity distribution of precipitation and for the effective static stability experienced by moist eddies. We show here that the skewness of the vertical velocity increases in magnitude as the climate warms in simulations with an idealized general circulation model (GCM), except in very warm climates. That the skewness increases with warming is consistent with studies of moist baroclinic instability which suggest that the area of updraughts should contract as the stratification approaches moist neutrality in warm climates. However, the increase in skewness with warming is much weaker in the fully nonlinear simulations as compared to what is found for unstable modes of moist baroclinic instability in the same GCM. Nonlinear equilibration to a macroturbulent state leads to a reduction in skewness in warm climates. Therefore, while the unstable modes may be relevant for some cases of cyclogenesis, they overestimate the effect of warming on the skewness of the overall distribution of the vertical velocity. Remarkably, the most unstable mode transitions from a quasi‐periodic wave to an isolated diabatic Rossby vortex at sufficiently high temperatures, with possible implications for fast‐growing disturbances in warm climates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".