The Influence of Tides on Anisotropic Eddy Diffusivities in the Labrador Sea Using a Lagrangian Framework
Bibliographic record
Abstract
In ocean models the evolution of a tracer is generally assumed to be caused by two different processes; advection by the mean flow and diffusion. Diffusion parameterizes all non-resolved processes and the rate of diffusion is given by the diffusivity. This diffusivity is often assumed to be isotropic or even constant, however due to different processes, it can be greatly enhanced or suppressed in certain directions, giving rise to anisotropies. We investigated the influence of the tides on these diffusivities in the horizontal plane. Using a simple kinematic model we studied the interaction of different components of the velocity fields and how this affects the effective diffusivity. These concepts were then applied to a more realistic study in the Labrador Sea where we calculated diffusivities from Lagrangian model simulations, with and without tides, as well as from GPS drifter observations.\nWe showed that the interaction of the tides with eddies or a shear flow can have a large influence on the diffusivities, greatly enhancing it in the direction of the tidal excursion. This effect depends on the length and velocity scales of the eddies and the tides, as well as on the angle the tides make with the eddy field. A shear flow perpendicular to the tidal flow can reduce this effect of the tides, suppressing the mixing in the cross-flow direction. The Lagrangian model simulations in the Labrador Sea indeed showed largely increased and anisotropic diffusivities due to the tides. The same effect of the tides was however not observed in the diffusivities calculated from drifter observations, possibly due to a lack of data in areas with large tidal currents. Furthermore, the diffusivities from the observational data were in general larger, mainly in the areas with large mean currents. This might be the effect of shear dispersion and subgrid-scale processes. In general this research shows that the tides can have a large influence on the diffusivity when the tidal currents are strong enough. This effect is however hard to observe from drifter data, since these areas are often not very well sampled. This implies that ocean models would perform better in tidal regions and in regions with high shear gradients if anisotropic diffusivities were included.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".