The Buoyancy Reynolds Number Instability and Thermohaline Staircase Formation in the Polar Oceans
Bibliographic record
Abstract
The Arctic Ocean main thermocline may be characterized by a series of fine-scale thermohaline staircase structures that are present in a wide range of regions, the formation mechanism of which remains unclear. Recent analysis has led to the proposal of a theoretical model which suggested that these staircase structures form spontaneously in the salinity and temperature-stratified ocean when the turbulent intensity determined by the buoyancy Reynolds number Reb is sufficiently weak (Ma and Peltier (2021)). In the current work, we have designed a series of Reb controlled direct numerical simulations of turbulence in the Arctic Ocean thermocline to test the effectiveness of this theory. In these simulations, the staircases form naturally when Reb falls in the range predicted by the instability criterion that is the basis of the proposed theory. In the DNS analyses described we show that the exponential growth-rate of the layering mode of instability matches well with the prediction of (Ma and Peltier (2021)). The staircases formed in our simulations are further compared with the classical diffusive interface model initially proposed by (Linden and Schirtcliff (1978)), which argued that stable staircase structures can only form when the density ratio Rρ is smaller than the critical value Rρ^{cr}=τ^(-1/2). . We show that the staircase structures can stably persist in the model regardless of whether or not Rρ is satisfied because of the involvement of stratified turbulence in the interfaces of the staircase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".