Non-CO2 forcing changes will likely decrease the remaining carbon budget for 1.5°C
Bibliographic record
Abstract
Estimates of the 1.5°C carbon budget vary widely among recent studies. One key contribution to this range is the non-CO2 climate forcing scenario uncertainty. Based on a partitioning of historical non-CO2 forcing, we show that there is currently a net negative non-CO2 forcing from fossil fuel combustion (FFC) mainly due to the co-emission of aerosols, and a net positive non-CO2 climate forcing from land-use change (LUC) and agricultural activities. We then perform a set of future simulations in which we prescribed a 1.5°C temperature stabilization trajectory, and diagnosed the resulting 1.5°C carbon budgets. Using the results of our historical partitioning, we prescribed changing non-CO2 forcing scenarios that are consistent with our model’s simulated decrease in FFC CO2 emissions. We compared the diagnosed carbon budgets from these idealized scenarios to those resulting from the default RCP scenario non-CO2 forcing, as well as from a scenario in which we assumed proportionality between future CO2 and non-CO2 forcing. We find a large range of carbon budget estimates across scenarios, with the largest budget emerging from the scenario with assumed proportionality of CO2 and non-CO2 forcing. Furthermore, our adjusted-RCP scenarios, in which the non-CO2 forcing is consistent with model-diagnosed FFC CO2 emissions, produced carbon budgets that are smaller than the corresponding default RCP scenarios. Our results suggest that ambitious mitigation scenarios will likely be characterized by an increasing contribution of non-CO2 forcing, and that an assumption of continued proportionality between CO2 and non-CO2 forcing would lead to an overestimate of the remaining carbon budget required to avoid low-temperature targets. Maintaining such proportionality under ambitious fossil fuel mitigation would require mitigation of non-CO2 emissions from agriculture and other non-FFC sources at a rate that is substantially faster than is found in the standard RCP scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".