Disequilibrium and soft tissue pump contributions to glacial CO2 drawdown
Bibliographic record
Abstract
A more ‘efficient’ biological pump is thought to have played a key role in glacial CO2 drawdown, with two principal mechanisms invoked to stem the CO2 ‘leak’ from the modern Southern Ocean. The first sees a strengthened soft tissue pump, associated with a reduction in the ocean’s ‘preformed’ nutrient inventory. The second sees an increase in air-sea CO2 disequilibrium (termed the disequilibrium pump). We use an Earth System Model (CM2Mc) to show the tracers radiocarbon (Δ14C) and oxygen (O2) exhibit distinct sensitivities to these two pumps: Δ14C is more sensitive to disequilibrium pump changes, whereas O2 is more sensitive to soft tissue pump changes, as expected from the processes underlying the relationships. We apply these pump-specific tracer stoichiometries to available deep ocean Δ14C and O2 proxy data from the LGM. Despite the sparsity of O2 data, the results show a consistent increase in soft tissue and disequilibrium DIC within the deep ocean of ~135 µmol/kg, with broadly comparable contributions from the two pumps. Our results imply both a reduction in air-sea gas exchange, likely linked to expansion and/or increased isolation of Antarctic Bottom Waters (AABW), as well as a slowdown of deep ocean overturning and diminished Southern Ocean upwelling. Notably, current ocean models struggle to simulate both of these changes simultaneously under glacial forcings. The combined changes in the soft tissue and disequilibrium DIC are of sufficient magnitude to explain the glacial reduction in atmospheric CO2 once a whole ocean alkalinity increase is accounted for.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".