Mean Cold Pool Size of Quasi-Equilibrium Convection. Part II: The Survival Competition Hypothesis
Bibliographic record
Abstract
Abstract The cold pool is a crucial component of tropical convection. However, what controls the mean cold pool size remains unclear. This two-paper series presents a theory of the mean cold pool radius in idealized quasi-equilibrium convection (Req). Part I derives an energy balance constraint between Req and the maximum potential radius of a cold pool (Rmax), showing that Req cannot reach Rmax. Cold pools must be densely packed and collide frequently. This Part II derives another constraint between Req and Rmax based on a cold pool survival competition hypothesis. A convective life cycle model with various candidate cold pool sizes is built. The type of cold pool producing the most intense next-generation cold pool is hypothesized to survive and set the spacing between convective towers. The size of the dominant cold pool type is determined by the trade-off between the mechanical lifting effect that favors a smaller cold pool, the thermodynamic forcing effect that favors a bigger cold pool, and the cloud radius feedback that also favors a bigger cold pool. Combining the energy balance and survival competition constraints, we obtain a solution for Req, which has an analytically tractable upper bound. The upper bound is set by the cold pool’s fractional entrainment rate and the free-tropospheric relative humidity: a lower fractional entrainment rate or a drier free troposphere raises the upper bound of Req. The Req predicted by the theory agrees with a set of large-eddy simulations with different rainwater evaporation rates.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".