Toward a Stochastic Relaxation for the Quasi‐Equilibrium Theory of Cumulus Parameterization: Multicloud Instability, Multiple Equilibria, and Chaotic Dynamics
Bibliographic record
Abstract
Abstract The representation of clouds and organized tropical convection remains one of the biggest sources of uncertainties in climate and long‐term weather prediction models. Some of the most common cumulus parameterization schemes, namely, mass‐flux schemes, rely on the quasi‐equilibrium (QE) closure, which assumes that convection consumes the large‐scale instability and restores large‐scale equilibrium instantaneously. However, the QE hypothesis has been challenged both conceptually and in practice. Subsequently, the QE assumption was relaxed, and instead, prognostic equations for the cloud work function (CWF) and the cumulus kinetic energy (CKE) were derived and used. It was shown that even if the CWF kernel serves to damp the CWF, the prognostic system exhibits damped oscillations on a timescale of a few hours, giving parameterized‐cumulus‐clouds enough memory to interact with each other, with the environment, and with stratiform anvils in particular. Herein, we show that when cloud‐cloud interactions are reintroduced into the CWF‐CKE equations, the coupled system becomes unstable. Moreover, we couple the CWF‐CKE prognostic equations to dynamical equations for the cloud area fractions, based on the mean field limit of a stochastic multicloud model. Qualitative analysis and numerical simulations show that the CKE‐CWF‐cloud area fraction equations exhibit interesting dynamics including multiple equilibria, limit cycles, and chaotic behavior both when the large‐scale forcing is held fixed and when it oscillates with various frequencies. This is representative of cumulus convection variability, and its capability to transition between various regimes of organization at multiple scales and regimes of scattered convection, in an intermittent and chaotic fashion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".