Control of the oxygen to ocean heat content ratio during deep convection events
Bibliographic record
Abstract
Earth System Models project a decline of dissolved oxygen in the oceans under warming climate. Observational studies suggest that the ratio of O 2 inventory to ocean heat content (O 2 -OHC) is several fold larger than can be explained by solubility alone, but the ratio remains poorly understood. In this work, models of different complexity are used to understand the factors controlling the O 2 -OHC ratio during deep convection, with a focus on the Labrador Sea, a site of deep water formation in the North Atlantic Ocean. A simple one-dimensional convective adjustment model suggests two limit case scenarios. When the near-surface oxygen level is dominated by the entrainment of subsurface water, surface buoyancy forcing, air-sea gas exchange coefficient and vertical structure of sea water together affect the O 2 -OHC ratio. In contrast, vertical gradients of temperature and oxygen become important when the surface oxygen flux dominates. The former describes the O 2 -OHC ratio of individual convective event in agreement with model simulations of deep convection. The latter captures the O 2 -OHC ratio of interannual variability, where the pre-conditioning of interior ocean gradients dominates. The relative vertical gradients of temperature and oxygen, which in turn depend on the lateral transport and regional biological productivity, control the year-to-year variations of O 2 -OHC ratio. These theoretical predictions are tested against the output of a three-dimensional regional circulation and biogeochemistry model which captures the observed large-scale distribution of the O 2 -OHC ratio, and agrees broadly with the prediction by the simpler model.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".