Denitrification under glacial and interglacial conditions: A physical approach
Bibliographic record
Abstract
Recent analysis of sedimentary δ15N records over the last 200,000 years has shown an expansion of water column denitrification zones during warmer periods and a shrinkage of these oxygen‐poor regions during glacial periods. Two different mechanisms could be responsible for driving the changes in the denitrification records: variations in local productivity leading to a change in remineralization rates and/or changes in the ocean circulation and ocean temperature resulting in variations of the physical supply of dissolved oxygen. Here we focus on the “supply” mechanism by using a fully coupled atmosphere‐ocean‐sea ice‐land surface scheme model (the University of Victoria Earth System Climate Model) to examine oxygen supply under varying physical conditions while maintaining an invariant biological oxygen utilization scheme. We show that circulation changes can be the cause for the observed changes in dissolved oxygen in the glacial ocean. Lower temperatures and enhanced formation of Antarctic Intermediate Water and North Pacific Intermediate Water during glacial periods increase the physical supply of oxygen and therefore decrease water column denitrification. In our Last Glacial Maximum simulations the change in water characteristics in the eastern tropical Pacific is important enough to reduce denitrification by 46% to 65% compared to present‐day conditions, depending on the wind fields used as boundary conditions. The consequences of our findings could be substantial for the near future. With a warming climate, denitrification zones could expand, leading to changes in the biological pump and the flux of N2O into the atmosphere.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".