Comment on egusphere-2025-3007
Bibliographic record
Abstract
Abstract. Aviation-induced clouds, especially persistent contrails, contribute significantly to anthropogenic climate forcing, often surpassing the short-term impact of aviation CO2 emissions. These clouds form in ice-supersaturated regions, where they trap longwave radiation and warm the climate. On 25 November 2023, widespread ice-supersaturated layers over eastern Canada and the USA led to extensive contrail formation, confirmed by GOES-16 satellite imagery and ground-based photography. Atmospheric conditions were characterized using ceilometer data from Toronto Pearson Airport and radiosonde soundings. High-resolution simulations were conducted using the Global Environmental Multiscale (GEM) model with the Predicted Particle Properties (P3) microphysics scheme. The Contrail Avoidance Tool (CoAT), incorporating Schmidt-Appleman Criteria and a wake vortex model, simulated persistent contrail formation and properties. Sensitivity tests adjusting ice depositional growth rates evaluated their impact on ice supersaturation. Results indicate that the control (CNTL) simulation underestimated relative humidity over ice (RHi), a common limitation where moisture is depleted too rapidly. Reduced depositional growth rates improved RHi forecasts and contrail-forming regions. However, GEM-CoAT underestimated contrail depth and ice number concentration in very shallow high-RHi layers. CoAT simulations also revealed that SAC alone is insufficient, as wake vortex dynamics can induce adiabatic warming, leading to ice particle sublimation. Further analysis examined contrail formation for two aircraft types (A321 and B747). The B747 generated deeper wake vortices, enhancing adiabatic heating and reducing contrail ice number concentrations by 27 % in sensitivity simulations and 78 % in the CNTL simulations. Adjusting depositional growth rates allowed GEM-CoAT to accurately simulate contrail formation and persistence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.007 |
| Insufficient payload (model declined to judge) | 0.386 | 0.250 |
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".