Stronger Southern Ocean carbon uptake in high-resolution ocean biogeochemistry simulations
Bibliographic record
Abstract
It is increasingly recognized that the way Southern Ocean mesoscale eddies are represented in ocean models influences air-sea CO2 fluxes and their response to climate change. In this study, we assess the Southern Ocean carbon uptake since the 1960s in a hierarchy of global ocean biogeochemistry models (GOBMs) based on the NEMO-MOPS and FESOM-REcoM models. The horizontal resolutions of the GOBMs range from 1° and 0.5° resolutions (“eddy-parameterized”) to 0.25° and 0.1° resolutions (“eddy-rich”, where eddies are explicitly represented). We find that the “eddy-rich” models have steeper density surfaces across the ACC with respect to “eddy-parameterized” models, in better agreement with observations. A larger amount of deep waters low in anthropogenic carbon (Cant) is thereby transported to the surface, leading to a 10% higher Cant uptake and storage. Natural CO2 (Cnat), which integrated over the whole Southern Ocean is directed into the ocean, shows a somewhat higher ingassing in the “eddy-rich” models. As a result, the net CO2 uptake is about 14% higher in the “eddy-rich” with respect to the “eddy-parameterized” models. Trends over the 1958-2018 period reveal a gradual wind-driven reduction of Cnat uptake in all configurations, but this trend is about 40% weaker in the 0.1° model with respect to the lower resolution models. At the same time, the upward trend in the residual meridional overturning circulation (MOC) is weaker in the 0.1° model, supporting the hypothesis of a more pronounced “eddy-compensation” of the wind-driven Cnat trends. Our study suggests that GOBMs using standard eddy parameterizations may underestimate the net and anthropogenic CO2 uptake by about 10%, and emphasizes the importance of adequately simulating mesoscale eddies for better constraining the Southern Ocean carbon uptake in changing climate conditions.
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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".