The Eddy-Diffusivity Mass-Flux parameterization: improved representation of convective mixing, global evaluations and implications for Ocean Heat Content
Bibliographic record
Abstract
The Eddy-Diffusivity Mass-Flux (EDMF) parameterization (Giordani et al., 2020) offers a new, coherent way to simultaneously parameterize local (diffusivity) and non-local (convective thermal) vertical mixing. This second component parametrizes sub-grid-scale convective plumes propagating through the water column which, through energy conservation, can propagate counter to the stratification gradient. The EDMF scheme is assessed in a 13-year global ¼° coupled NEMO4.2-SI3 simulation, forced by ERA5 atmospheric reanalysis. Its performance in representing observed ocean temperatures is compared to that of a twin simulation using the commonly applied Enhanced Vertical Diffusivity (EVD) parameterization.The EDMF simulation shows globally reduced temperature biases relative to in-situ observations (0–700 m) compared to the EVD simulation, with similar RMSD (Root Mean Square Deviation) values between the two. By better representing tropical night-time shallow convection, EDMF reduces the cold bias typically observed in EVD simulations within the tropical ocean. We show that the horizontal scales (convective areas), penetration depths and vertical velocities of the simulated plumes agree with measurements of deep convective plumes in the Labrador Sea, and with diurnal convection in the equatorial Pacific Ocean. Additionally, first estimates of convection's contribution to Ocean Heat Content are proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".