Sensitivities of atmospheric ozone to supersonic emissions above the transatlantic flight corridor
Bibliographic record
Abstract
The rapid growth of the global aviation market has spurred commercial interest in the redevelopment of a civil supersonic aviation market. The emissions of these aircraft are expected to have an adverse impact on climate, as well as changing the composition of the ozone layer [1,2,3,4]. There is however still considerable uncertainty about the scale of future civil supersonic adoption, as well as future emissions, due to the rapid development of the technology and potential changes in regulations.Evaluating the impacts of the wide range of future adoption scenarios is computationally demanding, but atmospheric sensitivities might be used to assist the evaluation. Here, we use the GEOS-Chem global chemistry transport model to evaluate the impact of supersonic fuel burn perturbations above the transatlantic flight corridor on global ozone in a modern atmosphere over a period of 10 years. Variations of this scenario are evaluated to assess global ozone sensitivities to the emission of NOx, SOx, H2O, CO, and hydrocarbons across multiple altitudes between 17.2 and 21.4 km, as well as the cross-sensitivities between the emissions of NOx, SOx, and H2O.From the sensitivities it is found that changes in global ozone columns are primarily driven by NOx emissions in this emission region, followed by SOx and H2O, with marginal contributions from CO and hydrocarbon emissions. The impact of these emissions is found to depend strongly on altitude, with higher emission altitudes increasing ozone depletion from NOx, SOx, and H2O, emissions. For kerosene-based emissions above the transatlantic flight corridor, the effect of cross-sensitivities between the emitted species is estimated to be up to two orders of magnitude smaller than direct responses to emission species. This difference implies that the effect of cross-sensitivities on ozone may be neglected in predictive models at a small cost in accuracy, simplifying future development efforts. Considering this application, future work will first need to apply this method to global emission networks where the effect of cross-sensitivities might differ from the region presented here. References:[1] Matthes, S., Lee, D. S., …, Terrenoire, E., Review: The Effects of Supersonic Aviation on Ozone and Climate, Aerospace, 9(1), 41, (2022).[2] Eastham, S. D., Fritz, T.,  …, Barrett, S. R. H., Impacts of a near-future supersonic aircraft fleet on atmospheric composition and climate. Environmental Science: Atmospheres. doi:10.1039/d1ea00081k, (2022).[3] Zhang, J., Wuebbles, D., Kinnison, D., & Baughcum, S. L., Stratospheric Ozone and Climate Forcing Sensitivity to Cruise Altitudes for Fleets of Potential Supersonic Transport Aircraft. Journal of Geophysical Research: Atmospheres, 126(16), (2021).[4] Grewe, V., Stenke, A., ..., Pascuillo, E., Climate impact of supersonic air traffic: an approach to optimize a potential future supersonic fleet – results from the EU-project SCENIC. Atmospheric Chemistry and Physics, 7(19), 5129-5145, (2007).
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".