Response of the Ocean Natural Carbon Storage to Projected Twenty-First-Century Climate Change
Bibliographic record
Abstract
Abstract The separate impacts of wind stress, buoyancy fluxes, and CO2 solubility on the oceanic storage of natural carbon are assessed in an ensemble of twentieth- to twenty-first-century simulations, using a coupled atmosphere–ocean–carbon cycle model. Time-varying perturbations for surface wind stress, temperature, and salinity are calculated from the difference between climate change and preindustrial control simulations, and are imposed on the ocean in separate simulations. The response of the natural carbon storage to each perturbation is assessed with novel prognostic biogeochemical tracers, which can explicitly decompose dissolved inorganic carbon into biological, preformed, equilibrium, and disequilibrium components. Strong responses of these components to changes in buoyancy and winds are seen at high latitudes, reflecting the critical role of intermediate and deep waters. Overall, circulation-driven changes in carbon storage are mainly due to changes in buoyancy fluxes, with wind-driven changes playing an opposite but smaller role. Results suggest that climate-driven perturbations to the ocean natural carbon cycle will contribute 20 Pg C to the reduction of the ocean accumulated total carbon uptake over the period 1860–2100. This reflects a strong compensation between a buildup of remineralized organic matter associated with reduced deep-water formation (+96 Pg C) and a decrease of preformed carbon (−116 Pg C). The latter is due to a warming-induced decrease in CO2 solubility (−52 Pg C) and a circulation-induced decrease in disequilibrium carbon storage (−64 Pg C). Climate change gives rise to a large spatial redistribution of ocean carbon, with increasing concentrations at high latitudes and stronger vertical gradients at low latitudes.
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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) | 0.000 | 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 | 0.001 | 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".