Investigation of mid-latitude contrail formation regions in EMAC in comparison to in-situ observations from aircrafts

Aviation has long been associated with environmental problems such as pollution, noise and climate change. While CO2 emissions are at the forefront of public debate, non-CO2 emissions from aviation can have similar impacts on climate as carbon dioxide from aviation, such as contrails, nitrogen oxides, or cloud cover caused by aviation. Previous studies investigated the influence of different weather situations on the climate impact of aviation, identified climate-sensitive regions, and created data products such as 4-dimensional climate change functions (CCFs) that enable air traffic management (ATM) to plan for climate optimized trajectories. However, the applicability and potential implementation of climate change functions for climate-optimized flight planning is only possible if the CCFs and the corresponding mitigation potential are validated [1,2].

The research presented here is part of the European Horizon 2020 project ClimOP and focuses on a further validation of the modular global chemistry climate model EMAC to analyze whether the model is able to represent real-world situations. For the comparison, we focus on contrail parameter as contrails have the largest uncertainties among all CCFs [2,3]. To investigate which vertical resolution in EMAC is suitable to study key atmospheric parameters for contrail formation, we compared different model setups. Furthermore, results from nudged simulation are compared with measurement data from the DLR ML-CIRRUS campaign.

The study shows a systematic cold and dry/wet bias between EMAC and aircraft measurements that differs with a nudging approach. These variations have implications for relative humidity, ice supersaturated regions, and potential contrail coverage in the model and must also be considered when using CCFs. Thus, this study represents a first step toward the applicability and potential implementation of climate change functions for climate-optimized flight planning.

The project leading to this study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875503 (ClimOP). High performance supercomputing resources were used from the German CARA Cluster in Dresden.

References:
[1] Matthes S. et al., Aerospace 7, 156 (2020).
[2] Frömming C. et al. Atmos. Chem. Phys., 21, 9151–9172 (2021).
[3] Peter P. et al. (2021) EASN Conference Sep 2021 (2021).