The role of ocean transport in the uptake of anthropogenic CO <sub>2</sub>
Bibliographic record
Abstract
Abstract. We compare modeled oceanic carbon uptake in response to pulse CO2 emissions using a suite of global ocean models and Earth system models. In response to a CO2 pulse emission of 590 Pg C (corresponding to an instantaneous doubling of atmospheric CO2 from 278 to 556 ppm), the fraction of CO2 emitted that is absorbed by the ocean is: 37±8%, 56±10%, and 81±4% (model mean ±2σ ) in year 30, 100, and 1000 after the emission pulse, respectively. Modeled oceanic uptake of pulse CO2 on timescales from decades to about a century is strongly correlated with simulated present-day uptake of chlorofluorocarbons (CFCs) and CO2 across all models, while the amount of pulse CO2 absorbed by the ocean from a century to a millennium is strongly correlated with modeled radiocarbon in the deep Southern and Pacific Ocean. However, restricting the analysis to models that are capable of reproducing observations within uncertainty, the correlation is generally much weaker. The rates of surface-to-deep ocean transport are determined for individual models from the instantaneous doubling CO2 simulations, and they are used to calculate oceanic CO2 uptake in response to pulse CO2 emissions of different sizes pulses of 1000 and 5000 Pg C. These results are compared with simulated oceanic uptake of CO2 by a number of models simulations with the coupling of climate-ocean carbon cycle and without it. This comparison demonstrates that the impact of different ocean transport rates across models on oceanic uptake of anthropogenic CO2 is of similar magnitude as that of climate-carbon cycle feedbacks in a single model, emphasizing the important role of ocean transport in the uptake of anthropogenic CO2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".