Degrees of reversibility of ocean deoxygenation in an atmospheric carbon dioxide removal scenario
Bibliographic record
Abstract
Abstract Over the last century, increasing atmospheric carbon dioxide (CO2) concentrations, among other greenhouse gases, and resulting climate change have greatly impacted the ocean. Observed impacts include lower oxygen solubility and changes in ocean stratification, circulation and biological activity. To reduce the carbon burden in the atmosphere in the future and thereby mitigate anthropogenic climate change, carbon dioxide removal (CDR) techniques have been increasingly studied and tested. However, information on the impact of CDR on oceanic oxygen is still scarce. In the current study we explore dissolved oxygen responses from an idealized CDR implementation, with atmospheric CO2 ramp-up and ramp-down simulations following the CDR model intercomparison project protocol. We find that over the timescale of a few centuries, the degree of recovery of marine oxygen, after atmospheric CO2 has returned to pre-industrial levels, differs for different water depths. Oxygen concentrations strongly recover in the upper ocean, achieving a near reversibility within 97%–99% across models, and even overshoot pre-industrial levels at depths of 100–600 m. Conversely, oxygen responses show a long-lasting deoxygenation signal in the deep ocean, with a much smaller initial recovery signal by the end of the experiment. The main factor driving oxygen changes in the deep ocean is indicated by the apparent oxygen utilization, related to changes in circulation and ventilation, as inferred by the simulated age of deep water masses. According to our models and despite the effective recovery of oxygen in the upper ocean, the effects of time lags and hysteresis on deep ocean responses could lead to longstanding and deleterious impacts on redox-sensitive biogeochemical processes and on marine biota throughout the ocean.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".