Evaluating carbon sequestration efficiency in an ocean circulation model by adjoint sensitivity analysis
Bibliographic record
Abstract
We demonstrate the application of the adjoint method to develop three‐dimensional maps of carbon sequestration efficiency and mean residence time in an ocean general circulation model. In contrast to perturbation sensitivity experiments, the adjoint approach provides a computationally efficient way to characterize both temporal and spatial variations of sequestration efficiency and residence time for a complete global model domain. Sequestration efficiency (the percentage of carbon injected at a continuous point source that remains in the ocean after an elapsed time), for injections at the base of the main thermocline (∼900 m), is initially lowest in the North Atlantic basin (except for regions of deep convection in the Labrador Sea) relative to the North Pacific. For injection periods of the order of a century or more, however, the model suggests that Pacific injection sites are generally less efficient for a constant rate injection source. The mean residence time (defined as the average period that impulsively injected carbon from a particular point source remains within the ocean) is also evaluated and mapped. This measure also suggests that Atlantic sequestration is more efficient in the long term. Our calculations draw out the dual role of convective mixing, both exposing shallow sequestration sources to the atmosphere and also, in the subpolar Atlantic and Labrador Sea, feeding carbon from shallow injection sources into the deep circulation away from the atmosphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".