Multicloud Models for Organized Tropical Convection: Enhanced Congestus Heating
Bibliographic record
Abstract
Abstract Despite the recent advances in supercomputing, the current general circulation models (GCMs) poorly represent the large-scale variability associated with tropical convection. Multicloud model convective parameterizations based on three cloud types (congestus, deep, and stratiform), introduced recently by the authors, have been revealed to be very useful in representing key features of organized convection and convectively coupled waves. Here a new systematic version of the multicloud models is developed with separate upper- and lower-troposphere basis functions for the congestus and stratiform clouds. It naturally leads to a new convective closure for the multicloud models enhancing the congestus heating in order to better pinpoint the congestus preconditioning and moistening mechanisms. The models are studied here for flows above the equator without rotation effects. First, the new model results consist of the usual synoptic-scale convectively coupled moist gravity wave packets moving at 15–20 m s−1 but, in addition, these packets have planetary-scale envelopes moving in the opposite direction at about 6 m s−1 and have many of the self-similar features of convectively coupled waves, reminiscent of the Madden–Julian oscillation. Second, when a warm pool forcing is imposed, dry regions of roughly 250 km in extent form “convective barriers” surrounding the warm pool region where only congestus heating survives. Deep convection and moist gravity waves are thus confined within the warm pool region. Finally, linear analysis reveals that, for sufficiently dry mean states, in addition to the inherent synoptic-scale moist gravity waves, the new model supports a planetary (wavenumber 1) standing congestus mode that provides, within its congestus active phase, a region where moist gravity waves evolve and propagate, which results in a Walker-like circulation over a uniform SST background.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".