Modelling of contrail climate effects with the AviTeam and the CoCiP model

Currently, contrails and the induced contrail cirrus are estimated to have the strongest contribution to global warming from the aviation sector. However, the uncertainty is still large, primarily because the characteristics of contrails are strongly dependent on the atmospheric conditions at cruise altitude. Therefore, to accurately estimate the climate forcing effects of contrails, both atmospheric data and aircraft activity data must be of high quality and resolution.

The recent deployment of ADS-B transponders on aircraft has provided a new open source of aircraft activity data. The AviTeam, developed by Klenner et al. (2022), uses the ADS-B data to produce spatially and temporarily explicit emission inventories. In our work (Indo, 2024), we couple the AviTeam with the Contrail Cirrus Prediction (CoCiP) model (Schumann, 2012) to simulate the evolution of contrails from a sample of domestic flights in Norway in 2019. The results show the expected pattern of seasonal variability, with the winter (December, January and February) and autumn (September, October and November) months accounting for 81% of the annual total contrail energy forcing. Similarly, the hours between 18:00 and 06:00 were responsible for 93% of the total daily contrail energy forcings. Additionally, it is found that 2% of the flights were responsible for 80% of the annual total contrail energy forcing. Comparing our results with the literature, we also deduce that short-haul flights have significantly less contrail energy forcing per km than long-haul flights, the latter of which typically cruise in higher altitudes and are more likely to fly at night. Thus, our work reveals the specific characteristics of contrails produced by domestic flights in Norway, and also underlines the value of geospatially and temporarily explicit emissions modelling tools such as the AviTeam for modelling contrail climate forcings. Our findings also suggest that flight scheduling could be used as a tool to mitigate contrail climate forcing effects.

 

References

Klenner, J., Muri, H., and Strømman, A. H. (2022). High-resolution modeling ofaviation emissions in Norway. Transportation Research Part D: Transport and Environment, 109:103379. https://www.sciencedirect.com/science/article/pii/S1361920922002073.

Indo, K. (2024). Modelling of contrail climate effects with the AviTeam and the CoCiP model [master’s thesis]. https://hdl.handle.net/11250/3155585.

Schumann, U. (2012). A contrail cirrus prediction model. Geoscientific Model Development, 5(3):543–580. https://gmd.copernicus.org/articles/5/543/2012/.