Weather patterns characterizing eco-efficient aircraft trajectories

Aviation emissions perturb the atmosphere through CO₂ and non-CO₂ effects, which include, for instance, the radiative effects of contrail cirrus, H₂O emissions, and NO_x induced changes on atmospheric ozone and methane concentrations. The life-time of these non-CO₂ perturbations is in the order of hours to days or weeks, and thus the resulting climate impact is highly dependent on the background atmospheric conditions, which vary with time, location, and altitude of emission. Mitigation strategies to reduce aviation climate impact could exploit this dependency on weather conditions, e.g., optimizing aircraft trajectories to minimize their climate impact. In particular, previous research shows the potential of “eco-efficient” trajectories, which lead to significant climate impact reductions at limited cost increases [1]. However, a strategy to identify days with a high mitigation potential is currently missing. For this purpose, we investigate in our study the correlation between days characterized by the identification of anomalously high numbers of eco-efficient trajectories, and the atmospheric conditions simulated on those days, e.g., considering the strength and location of the jet stream.

We use the ECHAM/MESSy Atmospheric Chemistry (EMAC) model, a numerical chemistry and climate simulation system that includes sub-models describing tropospheric and middle atmosphere processes and their interaction with oceans, land and human influences [2]. Our simulations include the submodels ACCF [3], using prototype algorithmic Climate Change Functions (aCCFs) to estimate the climate effects of aviation emissions, and AIRTRAF [4], optimizing aircraft trajectories under the atmospheric conditions simulated by EMAC. The total Average Temperature Response in 20 years (ATR20) of NO_x, contrails, CO₂, and H₂O from each flight is computed using the aCCFs [5]. We conduct 1-year simulations optimizing 100 European flights per day. Our results show that 20% of the flights are responsible for about 70% of the total climate impact reduction, and that these flights are not homogeneously distributed over the simulated days: a strong daily variability is found, due to the aCCFs gradients variability under different atmospheric conditions.

Acknowledgment: FlyATM4E has received funding from the SESAR Joint Undertaking under grant agreements No. 891317 under European Union's Horizon 2020 research and innovation program. ClimOp has received funding from European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 875503.

[1] Matthes, S., et al.: Climate-optimized trajectories and robust mitigation potential: Flying atm4e, Aerospace, 7, 1–15, https://doi.org/10.3390/aerospace7110156, 2020.

[2] Jöckel, P., et al.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geoscientific Model Development, 3, 717–752, https://doi.org/10.5194/gmd-3-717-2010, 2010.

[3] Yin, F., et al.: Predicting the climate impact of aviation for en-route emissions: The algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-220, in review, 2022.

[4] Yamashita, H., et al.: Newly developed aircraft routing options for air traffic simulation in the chemistry–climate model EMAC 2.53: AirTraf 2.0, Geoscientific Model Development, 13, 4869–4890, https://doi.org/10.5194/gmd-13-4869-2020, 2020.

[5] Dietmüller, S., et al.: A python library for computing individual and merged non-CO₂ algorithmic climate change functions: CLIMaCCF V1.0, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-203, in review, 2022.