Investigating periods and regions with large mitigation potential using algorithmic climate change functions

Planning climate-optimized aircraft trajectories requires temporally and spatially resolved quantitative estimates of the climate effects of aviation emissions. Algorithmic climate change functions (aCCFs) are applied, which efficiently assesses the climate effects of CO₂ and individual non-CO₂ effects (i.e. nitrogen oxide (NO_x) induced ozone, methane and PMO, water vapour, and contrail-cirrus) using meteorological input data at the time and location of the emission. A consistent set of initial prototype aCCFs (aCCF-V1.0) has recently been made available (Yin et al., 2023), and an updated formulation of this aCCFs calibrated towards the climate response model AirClim has been developed (aCCF-V1.0A, Matthes et al., 2023).

Utilizing the recently published open source Python Library CLIMaCCF (Dietmüller et al., 2023), we calculate aCCFs for individual and merged non-CO₂ climate effects for a variety of different summer and winter weather patterns over the North Atlantic flight corridor as well as over the European airspace. The calculations are based on meteorological data from the ERA5 reanalysis dataset. Through a detailed analysis of these aCCFs, we identify meteorological conditions with large non-CO₂ climate effects and demonstrate the influence of these identified weather patterns on the mitigation potential.

We use ensemble members to systematically characterize the uncertainties arising from the limited predictability of weather forecasts for the weather patterns identified above. This allows to access the robustness of the climate effect estimates (and their mitigation potential). Moreover, we investigate the sensitivity of using different physical climate metrics and efficacy parameters and thus provide further insight to uncertainties linked to climate science.

We further investigate the dependence of aCCF patterns to differently resolved meteorological input data (temporal, spatial, and vertical variation). Based on this analysis, we provide recommendations regarding the level of complexity for such an advanced MET service. Additionally, sensitivity studies using meteorological data from different data products (i.e. archived historical forecast) are shown.

Acknowledgement: The current study has been supported by the following projects: CICONIA, which has received funding from the European Union under grant agreement no. 101114613, CONCERTO, which has received funding from the European Union under grant agreement no. 101114785 and the BMWK LuFo project D-KULT 20M2111A.