Comment on bg-2022-168
Bibliographic record
Abstract
Land and ocean carbon sinks play a major role in regulating atmospheric CO2 concentration and climate. However, their future efficiency depends on feedbacks in response to changes in atmospheric CO2 concentration and climate, namely the concentration-carbon and climate-carbon feedbacks. Since carbon dioxide removal is a key mitigation measure in emission scenarios consistent with global temperature goals in the Paris agreement, understanding carbon cycle feedbacks under negative CO2 emissions is essential. This study investigates land carbon cycle feedbacks under positive and negative CO2 emissions using an Earth system model driven with idealized scenarios of atmospheric CO2 increase and decrease, run in three modes. Our results show that the magnitude of carbon cycle feedbacks differs between the atmospheric CO2 ramp-up (positive emissions) and ramp-down (negative emissions) phases. These differences are likely largely due to climate system inertia: the response in the ramp-down phase represents the response to both the prior positive emissions and negative emissions. To isolate carbon cycle feedbacks under negative emissions and quantify these feedbacks more accurately, we propose a novel approach that uses zero emissions simulations to reduce this inertia. We find that the magnitudes of the concentration-carbon and climate-carbon feedbacks under negative emissions are larger in our novel approach than in the standard approach. This has two implications: using feedback parameters from the standard approach will (1) underestimate carbon release under negative emissions due to the concentration-carbon feedback, and (2) underestimate carbon gain due to the climate-carbon feedback. Given that the concentration-carbon feedback is the dominant feedback, quantifying carbon cycle feedbacks with the standard approach will result in the underestimation of carbon loss under negative emissions, thereby overestimating the effectiveness of negative emissions in drawing down CO2.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.038 | 0.020 |
| Insufficient payload (model declined to judge) | 0.045 | 0.047 |
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".