Nonlinearity of Ocean Carbon Cycle Feedbacks in CMIP5 Earth System Models
Bibliographic record
Abstract
Abstract Carbon cycle feedbacks are usually categorized into carbon–concentration and carbon–climate feedbacks, which arise owing to increasing atmospheric CO2 concentration and changing physical climate. Both feedbacks are often assumed to operate independently: that is, the total feedback can be expressed as the sum of two independent carbon fluxes that are functions of atmospheric CO2 and climate change, respectively. For phase 5 of the Coupled Model Intercomparison Project (CMIP5), radiatively and biogeochemically coupled simulations have been undertaken to better understand carbon cycle feedback processes. Results show that the sum of total ocean carbon uptake in the radiatively and biogeochemically coupled experiments is consistently larger by 19–58 petagrams of carbon (Pg C) than the uptake found in the fully coupled model runs. This nonlinearity is small compared to the total ocean carbon uptake (533–676 Pg C), but it is of the same order as the carbon–climate feedback. The weakening of ocean circulation and mixing with climate change makes the largest contribution to the nonlinear carbon cycle response since carbon transport to depth is suppressed in the fully relative to the biogeochemically coupled simulations, while the radiatively coupled experiment mainly measures the loss of near-surface carbon owing to warming of the ocean. Sea ice retreat and seawater carbon chemistry contribute less to the simulated nonlinearity. The authors’ results indicate that estimates of the ocean carbon–climate feedback derived from “warming only” (radiatively coupled) simulations may underestimate the reduction of ocean carbon uptake in a warm climate high CO2 world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".