Stochastic plume ensembles for an unified shallow-deep mass flux cumulus parameterization in the Community Earth System Model (CESM)
Bibliographic record
Abstract
Many biases in global climate models (GCMs) have been associated with the poor representation of unresolved variability due to organised convection in the tropics by the underlying cumulus parameterizations (CP). Many researchers have suggested that the quasi-equilibrium assumption (QEA) on which these CP’s are based is to blame. This is even more problematic with the recent and future increases in grid resolutions as the cloud large ensemble requirement for QEA breaks down. The stochastic multi-cloud model (SMCM) of Khouider et al. 2010 was proposed as a cheap alternative of overcoming this QEA dilemma by emulating the variability of the three cloud types which characterize tropical convection via a Markov jump birth-death process. The SMCM has proven to be very successful in terms of the simulation of the main modes of tropical variability when used as a simple alternative CP in a GCM. Here, we propose to incorporate the SMCM directly into the Zhang-McFarlane scheme (ZMS; Zhang and McFarlane 1995) to break the QEA dead end by using instead a stochastic plume ensemble and generalise the SMCM framework to cumulus schemes. The new stochastic ZMS (SZMS) uses a random number of plumes that are launched for each one of the three cloud types, shallow, congestus and deep, and that detrain at random levels, according to the SMCM. The new approach somehow combines the idea of Cohen and Craig (2006) of assuming a Poisson process for the number of plumes and that of Gentine et al. (2013) of prescribing a distribution of plume detrainment levels. Here we shall show the results of our experiment, for the single column version of the Community Earth System Model. Bibliography: Cohen BG, Craig GC. Fluctuations in an equilibrium convective ensemble. Part II: Numerical experiments. J. Atmos. Sci. 2006;63(8) Gentine P, Betts AK, Lintner BR, Findell KL, Van Heerwaarden CC, D’andrea F. A probabilistic bulk model of coupled mixed layer and convection. Part II: Shallow convection case. J. Atmos. Sci. 2013;70(6) Khouider B, Biello J, Majda AJ. A stochastic multicloud model for tropical convection. Comm. Math. Sci. 2010;8(1) Zhang GJ, McFarlane NA. Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos-ocean. 1995;33(3)
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".