Accounting for carbon cycle feedbacks in a comparison of the global warming effects of greenhouse gases
Bibliographic record
Abstract
Greenhouse gases other than CO2 make a significant contribution to human-induced climate change, and multi-gas mitigation strategies are cheaper to implement than those which limit CO2 emissions alone. Most practical multi-gas mitigation strategies require metrics to relate the climate warming effects of CO2 and other greenhouse gases. Global warming potential (GWP), defined as the ratio of time-integrated radiative forcing of a particular gas to that of CO2 following a unit mass emission, is the metric used in the Kyoto Protocol, and we define mean global temperature change potential (MGTP) as an equivalent metric of the temperature response. Here we show that carbon–climate feedbacks inflate the GWPs and MGTPs of methane and nitrous oxide by ~ 20% in coupled carbon–climate model simulations of the response to a pulse of 50 × 1990 emissions, due to a warming-induced release of CO2 from the land biosphere and ocean. The magnitude of this effect is expected to be dependent on the model, but it is not captured at all by the analytical models usually used to calculate metrics such as GWP. We argue that the omission of carbon cycle dynamics has led to a low bias of uncertain but potentially substantial magnitude in metrics of the global warming effect of other greenhouse gases, and we suggest that the carbon–climate feedback should be considered when greenhouse gas metrics are calculated and applied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".