Sensitivity of the inorganic ocean carbon cycle to future climate warming in the UVic coupled model
Bibliographic record
Abstract
With increased anthropogenic CO2 emitted into the atmosphere, climate feedbacks could potentially reduce further uptake of carbon by the oceans. The most significant feedbacks acting on the system to reduce carbon sequestration by the oceans are reductions in the thermohaline circulation (THC) and increased sea surface temperatures (SSTs). Although changes in SSTs affect the solubility of atmospheric CO2 across the ocean‐atmosphere interface, changes to the THC lead to more fundamental modifications of the ocean circulation and hence transport and storage of carbon to the deep ocean. Using a coupled model of intermediate complexity which incorporates a carbon solubility pump, we project atmospheric CO2 levels under global warming scenarios. A transient weakening of the THC is found in most simulations and increased SSTs are found in all simulations. Although these positive feedbacks act on the carbon system to reduce oceanic uptake, the ocean has the capacity to take up 65–75% of the anthropogenic CO2 increase once the forcing is turned off. This reduces by about 5% for each 50‐year period that anthropogenic emissions are maintained at a stabilized and elevated atmospheric CO2 level, and converges to zero if the system is forced with stabilized levels well into the future. The effects of climate feedbacks on carbon uptake are also examined and we find that the ocean stores 7% more carbon when there are no climate feedbacks acting on the system. Sensitivity experiments are conducted with respect to the representation of ocean mixing and sea‐ice dynamics. The inclusion of the Gent‐McWilliams parametrization for mixing associated with mesoscale eddies leads to a further 6% increase in oceanic uptake, whereas the inclusion of sea‐ice dynamics only leads to a 2% variation in global uptake.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".