Space & time variability of pan-Arctic estimates of internal wave-driven dissipation, mixing, and heat fluxes inferred from the Ice-Tethered Profiler network
Bibliographic record
Abstract
Quantifying mixing rates in the Arctic Ocean is critical to our ability to predict heat flux, freshwater distribution, and circulation. However, turbulence measurements in the Arctic are sparse, and cannot characterize the high spatiotemporal variability typical of ocean mixing. Using year-round temperature and salinity data from Ice-Tethered Profiler (ITP) instruments between 2004 and 2018, we apply a finescale parameterization to obtain pan-Arctic estimates of turbulent dissipation and mixing rates at unprecedented space-time resolution. Building on previous work that used ITP data to identify double-diffusive staircases and analyze the associated convective mixing, we apply the finescale parameterization only where these step-like thermohaline structures are not present and mixing is expected to be internal wave-dominated. We find that the inferred wave-driven dissipation and mixing rates are generally low, but highly variable in both space and time, displaying significant regional differences between the shelves and central basins, as well as a small seasonal cycle. We detect no statistically significant interannual trend in mixing rate estimates over the period examined, with the exception of a small increase in the Canada Basin immediately below the mixed layer. The joint consideration of turbulent dissipation rates and stratification imply varied Arctic Ocean mixing regimes, which are most often not appropriately characterized as isotropic turbulence. Where justified, we infer turbulent heat fluxes out of the Atlantic Water layer that are mostly small, but also exhibit a distinct regional dependence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".