Regulation of atmospheric CO<sub>2</sub> by deep‐sea sediments in an Earth system model
Bibliographic record
Abstract
We have extended the GENIE‐1 Earth system model to include a representation of sedimentary stratigraphy and the preservation of biogenic carbonates delivered to the ocean floor. This has enabled us to take a novel approach in diagnosing modern marine carbon cycling: assimilating observation of the calcium carbonate (CaCO 3 ) content of deep‐sea sediments with an ensemble Kalman filter. The resulting calibrated model predicts a mean surface sediment content (32.5 wt%) close to the observed value (34.8 wt%), and a global burial rate of CaCO 3 in deep sea sediments of 0.121 PgC yr −1 , in line with recent budget estimates of 0.10−0.14 PgC yr −1 . We employ the GENIE‐1 model in quantifying the multimillennial‐scale fate of fossil fuel CO 2 emitted to the atmosphere. In the absence of any interaction between ocean and sediments, an equilibrium partitioning of CO 2 is reached within ∼1000 years of emissions ceasing, with 34% (645 ppm) remaining in the atmosphere out of a total fossil fuel burn of 4173 PgC. An additional 12% of CO 2 emissions (223 ppm) are sequestered as bicarbonate ions (HCO 3 − ) by reaction with deep‐sea carbonates (“seafloor CaCO 3 neutralization”) on a timescale of ∼1.7 ka. Excess of carbonate weathering on land over deep‐sea burial results in a further net transformation of 14% of CO 2 emissions (261 ppm) into HCO 3 − (“terrestrial CaCO 3 neutralization”) on a timescale of ∼8.3 ka. We have also assessed the importance of a changing climate in modulating the stabilization of atmospheric CO 2 through ocean‐sediment interaction. Increased ocean stratification suppresses particulate organic carbon export, which in turn enhances seafloor CaCO 3 preservation. The resulting reduction in the sequestration of fossil fuel CO 2 represents a new positive feedback on millennial‐scale climate change.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".