A parameter space for evaluating oceanic convection regimes
Bibliographic record
Abstract
Oceanic convection, parameterized through vertical mixing schemes, is still not well captured by ocean general circulation models. A preliminary step necessary to improve these schemes is to evaluate and compare how the models behave for different forcing regimes. Literature often proposes single-case comparison (either on a specific location or a specific time or with a specific metrics). The goal of our work is to propose a more systematic framework allowing evaluations and comparisons over a larger range of forcing regimes. For doing so, we define a parameter space which has been derived thanks to a theoretical 1D model of the mixed-layer depth (MLD) evolution. This parameter space is formed by two dimensionless numbers : λs which describes the relative contribution of the buoyancy flux and the wind in the surface layer, and the Richardson number Rh which characterizes the stability of the water column at the mixed layer base. In this presentation, I will highlight the key features of this parameter space and I will illustrate its physical robustness with an ensemble of 1D simulations. These simulations were conducted by applying a 10 years JRA55-do 1.4.0 atmospheric forcing within a 1D standalone code making use of the NEMO Turbulent Kinetic Energy + Enhanced Vertical Diffusivity (TKE + EVD) scheme. Then, I will present a test case to study the impact of the horizontal resolution on the convection regimes for a TKE + EVD scheme in 1D, 1°, 1/12° and 1/60° realistic NEMO simulations. I will define convective regimes by sorting the values according to the normalized evolution of the mixed layer depth dt MLD / MLD and I will show that these regimes are almost kept in the parameter space between 1D and 1° but become generally less convective / more restratifiying when increasing the resolution, highlighting the restratification processes by lateral fluxes. Moreover, I will show that the dynamics in the Mediterranean is much more affected by the increase of resolution than the Labrador sea, suggesting that it involves more intense lateral restratification processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".