Simulating the global atmospheric black carbon cycle: a revisit to the contribution of aircraft emissions
Bibliographic record
Abstract
Abstract. The black carbon (BC) burden of the upper troposphere and lowermost stratosphere (UTLS) is investigated with the general circulation model (GCM) ECHAM4. The special focus is the contribution of aircraft emissions to the UTLS BC loading. Previous studies on the role of aircraft emissions in the global BC cycle either neglect BC sources located at the Earth's surface or simplify the BC cycle by assuming pre-defined BC residence times. Here, the global BC cycle including emissions, transport, and removal is explicitly simulated. The BC emissions considered include surface sources as well as BC from aviation. This enables a consistent calculation of the relative contribution of aviation to the global atmospheric BC cycle. As a further extension to the previous studies, the aviation-induced perturbation of the UTLS BC particle number concentration is investigated. The uncertainties associated with the model predictions are evaluated by means of several sensitivity studies. Especially, the sensitivity of the results to different assumptions on the BC hygroscopic properties is analysed. The simulated UTLS BC concentrations are compared to in-situ observations. The simulations suggest that the large-scale contribution of aviation to the UTLS BC mass budget typically amounts to only a few percent, even in the most frequented flight regions. The aviation impact far away from these regions is negligible. The simulated aircraft contributions to the UTLS BC particle number concentration are much larger compared to the corresponding mass perturbations. The simulations suggest that aviation can cause large-scale increases in the UTLS BC particle number concentration of more than 30% in regions highly frequented by aircraft. The relative effect shows a pronounced annual variation with the largest relative aviation impact occurring during winter.
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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) | 0.001 | 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 | 0.001 |
| 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".