Idealized Numerical Modelling of Internal Waves Through Density Staircases
Bibliographic record
Abstract
The Arctic Ocean contains a warm layer originating from the Atlantic Ocean below the pycnocline which has a thermohaline staircase structure that inhibits vertical mixing. If this heat were to rise to the surface, the rate of sea ice loss would increase dramatically. Wind stress and ice floes generate internal waves which can cause vertical mixing. As the ice cover in the Arctic continues to decline, it will be important to predict how these changing internal waves propagate through such stratification profiles. Here, we investigate how density staircases enhance or limit downward near-inertial wave propagation. We use direct numerical simulations to solve the Boussinesq equations of motion using spectral methods. We simulate the propagation of internal waves through a vertically stratified fluid which includes one or more steps (i.e., mixed layers). We find that we reproduce the results of laboratory experiments showing transmission and reflection of internal waves from one or two mixed layers. We then extend our parameter regime to simulate the propagation of internal waves through a more realistic stratification profile tending toward that of the Arctic pycnocline.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".