Arctic Ocean mixing maps inferred from pan-Arctic observations
Bibliographic record
Abstract
Quantifying ocean mixing rates in the Arctic Ocean is critical to our ability to predict upwards oceanic heat flux, freshwater distribution, and circulation. However, direct ocean mixing measurements in the Arctic are sparse and cannot characterize the high spatiotemporal variability typical of ocean mixing. Further, latitude, ice, and stratification make the Arctic Ocean mixing environment unique, with all of double-diffusive (DD), internal wave (IW)-driven and non-turbulent mixing processes playing a role. In this work, we use year-round temperature and salinity data from Ice-Tethered Profilers (ITPs), as well as an archived record of ship-based measurements, to construct highly-resolved, pan-Arctic maps characterizing the relative prevalence of DD, IW-driven and non-turbulent mixing mechanisms based on thermohaline staircase identification and estimations of turbulence intensity. We next quantify pan-Arctic maps of estimates of average effective vertical diffusivity inferred from these observations that account for all of DD, IW-driven, and non-turbulent mixing processes. Finally, focusing on the water column segment directly above the Atlantic Water (AW) temperature maximum, we use this mixing regime characterization and regime-specific estimates of effective diffusivity to compute estimates of the pan-Arctic distributions of average vertical heat and buoyancy flux from the AW layer. We find that estimates of effective vertical diffusivities are highly variable in both space and time. Although variability in diffusivity reflects both variations in the prevalence of the various mixing processes and variability in the strength of IW-driven mixing, the prevalence of the mixing mechanisms (predominantly DD and non-turbulent in the basins vs. IW-driven on the shelf) sets the dominant large-scale spatial patterns and the notable shelf-basin contrast. Estimated heat fluxes out of the AW layer also exhibit distinct regional patterns set by mixing mechanism prevalence and regional patterns in the vertical temperature gradient. Buoyancy fluxes from DD mixing compete with the destabilizing effects of IW-driven mixing in the basins, a competition that may be an important control on stratification in the Arctic Ocean interior. These results are significant as they show that mixing mechanism prevalence is an important consideration in computing robust estimates of average effective diffusivity. They further suggest that the sensitivity of mixing rates to changing environmental conditions may have important regional dependencies owing to differing prevalence of the various mixing processes.
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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) | 0.000 | 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".