A New TKE‐Based Parameterization of Atmospheric Turbulence in the Canadian Global and Regional Climate Models
Bibliographic record
Abstract
Abstract A new semi‐empirical turbulence parameterization is presented. Key features of the scheme include representation of turbulent diffusivities in terms of the turbulent kinetic energy that is determined by solving a quasi‐equilibrium form of the equation representing the turbulent kinetic energy budget. The new parameterization is innovative in the treatment of turbulent transfer in stably stratified conditions and the representation of nonlocal contributions to the vertical transport of heat, moisture, and scalar prognostic variables in convectively active boundary layers. A key element in the modeling of turbulence in stably stratified conditions is the formulation of the turbulent Prandtl number based on the results of recently published theoretical, modeling, and observational studies of stratified turbulence in the atmospheric boundary layer. The new parameterization has been implemented in the CanAM4 single column model. Its performance in comparison with that of the operational CanAM4 turbulence parameterization is documented in terms of selected results from case studies for clear‐sky conditions based on meteorological observations from the KNMI‐mast at Cabauw, Netherlands, and the Second Dynamics and Chemistry of Marine Stratocumulus case study of stratocumulus‐topped marine boundary layers. The performance of the new and operational schemes is qualitatively similar in clear‐sky conditions in both convective and stable boundary layer regimes. However, they perform differently for the extended simulations for the Second Dynamics and Chemistry of Marine Stratocumulus case study. The new scheme maintains an elevated stratocumulus layer throughout a 30‐hr simulation, but peak liquid water contents are larger than large eddy simulations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".