Predicting Upwelling due to Down-Fjord Winds
Bibliographic record
Abstract
Abstract Down-fjord winds have been implicated in upwelling and exchange of water in fjords. In one temperate fjord, strong winds oxygenated and cooled water deeper than 100 m. Deep temperature minima accompanied by oxygen maxima are a common feature in fjords, yet do not have a ready prediction. Here, it is shown that the dominant process driving the upwelling is wind-driven transport divergence at the head of the fjord, as opposed to local convection due to cooling or mechanical mixing. Previous work has focused on two-layer approximations, but those fail to differentiate the depth of upwelling if the bottom layer is continuously stratified. Simulations with a constant stratification show that the depth that the densest water comes from is proportional to , where L is the length of the fjord or the horizontal scale of the wind, is the wind stress, and N2 is the buoyancy profile. The time scale of the upwelling is similarly scaled as . These scalings apply over a wide range of forcings and geometry, so long as the depth scale does not approach the depth of the fjord. A simple procedure can be used to get a similar scaling if N(z) is not constant. If the wind is allowed to relax, the simulations come back to rest after a vigorous seiche, subducting the upwelled water. Despite strong turbulence, the net exchange with water outside the fjord is found to be small, and oxygen concentrations are mostly modified by air–sea gas exchange rather than diapycnal mixing. Significance Statement Upwelling in fjords due to down-fjord winds has the potential to ventilate deep water and to drive exchange with water outside the fjord. Here, we offer a simple method to predict the depth and time scale of the upwelling based on the wind strength, the initial stratification, and the length of the fjord. Vertical mixing has only a minor impact on the vertical distribution of properties in the fjord due to the wind events, and much of the transport into the fjord is reversible in the absence of other mixing sources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".