The greenhouse gas climate commitment and reversibility of peak warking from historical emissions
Bibliographic record
Abstract
The warming caused by past CO2 emissions is known to persist for centuries to millennia, even in the absence of additional future emissions. Other non-CO2 greenhouse gas emission have caused additional historical warming, though the persistence of this non-CO2 warming varies among gases owing to their different atmospheric lifetimes. Under deep mitigation scenarios or in an idealized scenario of zero future greenhouse gas emissions, the past warming from shorter-lived non-CO2 gases has been shown to be considerably more reversible than that caused by CO2 emissions. Here we use an intermediate-complexity global climate model coupled to an atmospheric chemistry module to quantify the warming commitment and its reversibility for individual and groups of non-CO2 greenhouse gases. We show that warming caused by gases with short atmospheric lifetimes will decrease by more than half its peak value within 30 years following zeroed emissions at present day, with more 80 percent of peak temperature reversed by the end of this century. Despite the fast response of atmospheric temperature to the elimination of non-CO2 emissions, the ocean responds much more slowly: past ocean warming does not reverse, but rather continues for several centuries after zero emissions. Further consequences are shown for the land carbon pool, which decreases as an approximately linear function of historical non-CO2 greenhouse gas induced warming. Given that CO2 and non-CO2 greenhouse gas emissions share common emission sources, we also explore a set of scenarios where sets of emissions are zeroed according to two broad source categories: (1) fossil fuel combustion, and (2) land-use and agriculture. Using these additional mode runs, we investigate the temperature change that is avoided if all CO2 and non-CO2 greenhouse gas emissions from a particular source abruptly stops while others are allowed to continue. These results indicate the possibility of land-use change and agriculture activities continuing under deep mitigation scenarios and ambitious climate targets, without leading to exceedance of global climate targets. Though we analyze unlikely scenarios, our work provides baselines from which more realistic mitigation scenarios can be assessed. The reversibility of peak temperature caused by historical non-CO2 gases is a relevant measure for policy frameworks seeking to limit global warming to ambitious targets, such as the 1.5 oC target adopted by the Paris Agreement
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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 | 0.001 |
| 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".