Fluctuation of Mass Flux in a Cloud-Resolving Simulation with Interactive Radiation
Bibliographic record
Abstract
Abstract It was shown by Craig and Cohen that fluctuations of cumulus clouds under homogeneous large-scale forcing satisfy the Gibbs canonical ensemble in a strict radiative–convective equilibrium (RCE). In the limit of random noninteracting convective cells, an analytical expression for the distribution function of total mass flux over a region of given size was derived. The authors examine the consistency of the Gibbs canonical ensemble as a representation for the mass flux fluctuations when the large-scale forcing is time dependent. A cloud-resolving simulation (CRM) with interactive radiation, fixed imposed surface temperature, and diurnally varying solar forcing to mimic the diurnal cycle over the tropical ocean is used. As a necessary condition for the existence of a state of quasi-equilibrium, the time-scale separation between convective processes and forcing is studied. Detailed evaluation of time scales of convective adjustment and memory in a three-month run confirms the hypothesis of time-scale separation. The Craig and Cohen theory, in a varying range of heights between the cloud base up to the level of neutral buoyancy (LNB), is tested. It is shown that, although the theory is capable of reproducing the qualitative features of the variability, systematic deviations are detected. By quantifying the spatial distribution of the clouds, the authors suggest that deviations are associated with clustering effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".