Quantifying land carbon cycle feedbacks under negative CO <sub>2</sub> emissions
Bibliographic record
Abstract
Land and ocean carbon sinks play a major role in regulating atmospheric CO 2 concentration and climate. However, their future efficiency depends on feedbacks in response to changes in atmospheric CO 2 concentration and climate, namely the concentration–carbon and climate–carbon feedbacks. Since carbon dioxide removal (CDR) is a key mitigation measure in emission scenarios consistent with global temperature goals in the Paris Agreement, understanding carbon cycle feedbacks under negative CO 2 emissions is essential. This study investigates land carbon cycle feedbacks under positive and negative CO 2 emissions using an Earth system model of intermediate complexity (EMIC) driven with an idealized scenario of symmetric atmospheric CO 2 concentration increase (ramp-up) and decrease (ramp-down), run in three modes. Our results show that the magnitudes of carbon cycle feedbacks are generally smaller in the atmospheric CO 2 ramp-down phase than in the ramp-up phase, except for the ocean climate–carbon feedback, which is larger in the ramp-down phase. This is largely due to carbon cycle inertia: the carbon cycle response in the ramp-down phase is a combination of the committed response to the prior atmospheric CO 2 increase and the response to decreasing atmospheric CO 2 . To isolate carbon cycle feedbacks under decreasing atmospheric CO 2 and quantify these feedbacks more accurately, we propose a novel approach that uses zero emission simulations to quantify the committed carbon cycle response. We find that the magnitudes of the concentration–carbon and climate–carbon feedbacks under decreasing atmospheric CO 2 are larger in our novel approach than in the standard approach. Accurately quantifying carbon cycle feedbacks in scenarios with negative emissions is essential for determining the effectiveness of carbon dioxide removal in drawing down atmospheric CO 2 and mitigating warming.
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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) | 0.000 | 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 | 0.000 |
| 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".