Stronger Southern Ocean Anthropogenic Carbon Uptake in Eddying Ocean Simulations
Bibliographic record
Abstract
The Southern Ocean plays a vital role in mitigating global warming through its uptake of anthropogenic carbon (Cant). However, this process is poorly constrained in Earth System Models (ESMs), leading to uncertainties in future climate projections. Because of its dynamic and strongly eddying nature, the Southern Ocean circulation is challenging to accurately simulate with today’s Earth System Models, which often have a resolution insufficient to resolve small-scale processes. Here we assess how the Southern Ocean Cant uptake is affected by the explicit simulation of mesoscale eddies - ring-like features of 10-100 km size strongly influencing the ocean circulation. To this end, we developed and ran a global ocean biogeochemistry model with eddy-rich resolution (0.1°) in the Southern Ocean, and compared it to a non-eddying companion simulation (0.5°). Our results show that explicitly simulating mesoscale eddies enhances the ocean Cant sink by 10%, thanks to an improved representation of the ocean circulation and water mass properties. Steeper density slopes in the eddying model facilitate the upward transport of deep waters, triggering mechanisms that enhance Cant uptake: lower surface Cant concentrations, elevated surface salinity, increased vertical mixing, and a higher chemical uptake capacity. These findings, consistent across an additional model family, help reconcile discrepancies between observations and ESMs, which often underestimate the Southern Ocean Cant uptake. This study emphasizes the need for adequate model resolution, or for an improved parameterization of mesoscale eddies, to accurately simulate the global carbon cycle and to reduce uncertainties in future climate projections informing climate policy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".