Asymmetry in the climate-carbon cycle response to positive and negative CO2 emissions
Bibliographic record
Abstract
The majority of emissions scenarios that limit warming to 2°C, and nearly all emission scenarios that do not exceed 1.5°C warming by the year 2100 require negative CO2 emissions. Negative emission technologies (NETs) in these scenarios are required to offset emissions from sectors that are difficult or costly to decarbonize and to generate global ‘net negative’ emissions, allowing to compensate for earlier emissions and to recover a carbon budget after overshoot. It is commonly assumed that the carbon cycle and climate response to a negative CO2emission is equal in magnitude and opposite in sign to the response to an equivalent positive CO2 emission, i.e. that the climate-carbon cycle response is symmetric. This assumption, however, has not been tested for a range of emissions. Here we explore the symmetry in the climate-carbon cycle response by forcing an Earth system model with positive and negative CO2emission pulses of varying magnitude and applied from different climate states. Our results suggest that an emission of CO2into the atmosphere is more effective at raising atmospheric CO2than a CO2removal is at lowering atmospheric CO2, indicating that the carbon cycleresponse is asymmetric, particularly for emissions/removals > 100 GtC. The surface air temperature response, on the other hand, is largely symmetric. Our findings suggest that the emission and subsequent removal of a given amount of CO2 would not result in the same atmospheric CO2concentration as if the emission were avoided. Furthermore, our results imply using simple models used to estimate negative emission requirements may result in underestimating the amount of negative emissions needed to attain a given CO2concentration target.
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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 | 0.003 |
| 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) | 0.006 | 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".