A Simple Multicloud Parameterization for Convectively Coupled Tropical Waves. Part II: Nonlinear Simulations
Bibliographic record
Abstract
Abstract Observations in the Tropics point to the important role of three cloud types, congestus, stratiform, and deep convective clouds, besides ubiquitous shallow boundary layer clouds for both the climatology and large-scale organized anomalies such as convectively coupled Kelvin waves, two-day waves, and the Madden–Julian oscillation. Recently, the authors have developed a systematic model convective parameterization highlighting the dynamic role of the three cloud types through two baroclinic modes of vertical structure: a deep convective heating mode and a second mode with lower troposphere heating and cooling corresponding respectively to congestus and stratiform clouds. The model includes both a systematic moisture equation where the lower troposphere moisture increases through detrainment of shallow cumulus clouds, evaporation of stratiform rain, and moisture convergence and decreases through deep convective precipitation and also a nonlinear switch that favors either deep or congestus convection depending on whether the lower middle troposphere is moist or dry. Here these model convective parameterizations are applied to a 40 000-km periodic equatorial ring without rotation, with a background sea surface temperature (SST) gradient and realistic radiative cooling mimicking a tropical warm pool. Both the emerging “Walker cell” climatology and the convectively coupled wave fluctuations are analyzed here while various parameters in the model are varied. The model exhibits weak congestus moisture coupled waves outside the warm pool in a turbulent bath that intermittently amplify in the warm pool generating convectively coupled moist gravity wave trains propagating at speeds ranging from 15 to 20 m s−1 over the warm pool, while retaining a classical Walker cell in the mean climatology. The envelope of the deep convective events in these convectively coupled wave trains often exhibits large-scale organization with a slower propagation speed of 3–5 m s−1 over the warm pool and adjacent region. Occasional much rarer intermittent deep convection also occurs outside the warm pool. The realistic parameter regimes in the multicloud model are identified as those with linearized growth rates for large scale instabilities roughly in the range of 0.5 K day−1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".