Quantifying the transient climate response to carbon dioxide removal
Bibliographic record
Abstract
As anthropogenic greenhouse emissions continue to rise, limiting warming to 1.5°C has become elusive. Emissions pathways seeking to return to 1.5°C after overshoot will therefore require net negative emissions. A crucial question in this context is how much CO2 needs to be removed from the atmosphere to achieve a given amount of cooling (say 0.1°C). Studies seeking to answer this question often resort to the Transient Climate Response to Emissions (TCRE), a measure of the warming effect of cumulative CO2 emissions, neglecting that the climate may respond asymmetrically to CO2 emissions and removals. This contribution draws on CDRMIP pulse CO2 removal simulations to quantify the temperature response to CO2 emissions and removals in a range of Earth system models of full and intermediate complexity. We find that the temperature response to an equivalent amount of CO2 emissions and removals differs in magnitude, with the sign of this difference being model dependent. We investigate the cause for these inter-model differences by quantifying the contribution of carbon cycle and physical climate response differences to the overall temperature asymmetry. Establishing a robust metric of the transient climate response to CO2 removal is key to our understanding of how climate will respond to net negative emissions and to quantifying the amount of removal needed to restore a given temperature 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".