The impact of aerosol emissions on the 1.5 °C pathways
Bibliographic record
Abstract
To assess the impact of anthropogenic aerosol emission reduction on limiting global temperature increase to 1.5 °C or 2 °C above pre-industrial levels, two climate modeling approaches have been used (MAGICC6, and a combination of ECHAM-HAMMOZ and the UVic ESCM), with two aerosol control pathways under two greenhouse gas (GHG) reduction scenarios. We found that aerosol emission reductions associated with CO 2 co-emissions had a significant warming effect during the first half of the century and that the near-term warming is dependent on the pace of aerosol emission reduction. The modeling results show that these aerosol emission reductions account for about 0.5 °C warming relative to 2015, on top of the 1 °C above pre-industrial levels that were already reached in 2015. We found also that the decreases in aerosol emissions lead to different decreases in the magnitude of the aerosol radiative forcing in the two models. By 2100, the aerosol forcing is projected by ECHAM–UVic to diminish in magnitude by 0.96 W m −2 and by MAGICC6 by 0.76 W m −2 relative to 2000. Despite this discrepancy, the climate responses in terms of temperature are similar. Aggressive aerosol control due to air quality legislation affects the peak temperature, which is 0.2 °C–0.3 °C above the 1.5 °C limit even within the most ambitious CO 2 /GHG reduction scenario. At the end of the century, the temperature differences between aerosol reduction scenarios in the context of ambitious CO 2 mitigation are negligible.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".