Modelled black carbon radiative forcing and atmospheric lifetime in AeroCom Phase II constrained by aircraft observations
Bibliographic record
Abstract
Abstract. Atmospheric black carbon (BC) absorbs solar radiation, and exacerbates global warming through exerting positive radiative forcing (RF). However, the contribution of BC to ongoing changes in global climate is under debate. Anthropogenic BC emissions, and the resulting distribution of BC concentration, are highly uncertain. In particular, long-range transport and processes affecting BC atmospheric lifetime are poorly understood. Here we discuss whether recent assessments may have overestimated present-day BC radiative forcing in remote regions. We compare vertical profiles of BC concentration from four recent aircraft measurement campaigns to simulations by 13 aerosol models participating in the AeroCom Phase II intercomparison. An atmospheric lifetime of BC of less than 5 days is shown to be essential for reproducing observations in remote ocean regions, in line with other recent studies. Adjusting model results to measurements in remote regions, and at high altitudes, leads to a 25% reduction in AeroCom Phase II median direct BC forcing, from fossil fuel and biofuel burning, over the industrial era. The sensitivity of modelled forcing to BC vertical profile and lifetime highlights an urgent need for further flight campaigns, close to sources and in remote regions, to provide improved quantification of BC effects for use in climate policy.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".