Using the Stochastic Multicloud Model to Improve Tropical Convective Parameterization: A Paradigm Example
Bibliographic record
Abstract
Abstract Despite recent advances in supercomputing, current general circulation models (GCMs) poorly represent the variability associated with organized tropical convection. A stochastic multicloud convective parameterization based on three cloud types (congestus, deep, and stratiform), introduced recently by Khouider, Biello, and Majda in the context of a single column model, is used here to study flows above the equator without rotation effects. The stochastic model dramatically improves the variability of tropical convection compared to the conventional moderate- and coarse-resolution paradigm GCM parameterizations. This increase in variability comes from intermittent coherent structures such as synoptic and mesoscale convective systems, analogs of squall lines and convectively coupled waves seen in nature whose representation is improved by the stochastic parameterization. Furthermore, simulations with a sea surface temperature (SST) gradient yield realistic mean Walker cell circulation with plausible high variability. An additional feature of the present stochastic parameterization is a natural scaling of the model from moderate to coarse grids that preserves the variability and statistical structure of the coherent features. These results systematically illustrate, in a paradigm model, the benefits of using the stochastic multicloud framework to improve deterministic parameterizations with clear deficiencies.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".