Asymmetry in Regional Land Carbon Cycle Feedbacks under CO2 Emissions and Removals
Bibliographic record
Abstract
Carbon cycle feedbacks regulate the CO2 concentration in the atmosphere, with higher atmospheric CO2 levels resulting in increased uptake, and higher temperatures resulting in reduced CO2 uptake globally. Under positive emissions, the magnitude and sign of these feedbacks vary regionally. Achieving the Paris climate goals requires the use of carbon dioxide removal to reach net-zero, then enter a net-negative emissions phase, where CO2 removal exceeds CO2 emissions. The magnitude of global carbon cycle feedbacks is expected to differ under emissions and removals due to nonlinearities and state dependence of the climate-carbon cycle response. However, the magnitude of this difference (asymmetry) is poorly understood, both globally and on a regional scale. This study uses an Earth system model to investigate the regional asymmetry in land carbon cycle feedbacks under CO2 emissions and removals. To this end, two symmetric concentration-driven simulations are initialized from a state at equilibrium with twice the preindustrial CO2 concentration, with CO2 concentration increasing by 280 ppm in the “emissions” run and decreasing by an equivalent amount in the “removals” run. Each simulation is run in fully coupled, biogeochemically coupled and radiatively coupled modes to allow separate quantification of carbon cycle feedbacks. We use the Boer & Arora (2010) framework, which utilizes a carbon budget equation to compute local contributions to the global carbon cycle feedbacks, then compare these contributions under emissions and removals to determine their asymmetry. Understanding regional asymmetry in land carbon cycle feedbacks is key for determining regions likely to play a significant role in enhancing or counteracting carbon dioxide removal efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".