Temperature and concentration feedbacks in the carbon cycle
Bibliographic record
Abstract
Feedback processes in the carbon budget are investigated in a manner that parallels the treatment of feedback processes in the energy budget. The analysis is applied to simulations with the CCCma earth system model CanESM1 using a range of emission scenarios. For the atmosphere there is a positive “carbon‐temperature” feedback which acts to increase CO2 flux to the atmosphere as temperatures warm. There is also a negative “carbon‐concentration” feedback which acts to remove CO2 from the atmosphere via enhanced uptake of CO2 by the land and ocean as CO2 concentration increases. While the positive feedback associated with temperature change is reasonably linear and consistent as temperature increases, the feedback associated with CO2 concentration is not. The negative carbon‐concentration feedback weakens with increasing CO2 concentration thereby enhancing atmospheric CO2 and accelerating global warming. The behaviour of the inferred carbon‐concentration feedback is different for different emission scenarios implying a dependence on state variables other than CO2 concentration. The carbon‐concentration feedback behaviour inferred for a particular scenario may not, therefore, be used to infer system behaviour for other scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".