Update of Climate Change Functions and comparison with algorithmic Climate Change Functions
Authors: Frömming Christine , Matthes Sigrun , Dietmüller Simone , Peter Patrick , Grewe Volker , Dahlmann Katrin , Jöckel Patrick 
Published: March 2024

Strategic planning of climate-optimal flight trajectories is one option to potentially reduce the climate impact of non-CO2 aviation emissions. Such a measure builds upon detailed knowledge of climate response to aviation emissions at specific locations. So-called climate change functions (CCFs) were calculated by means of a Lagrangian approach within the atmospheric chemistry climate model system EMAC (ECHAM5/MESSy Atmospheric Chemistry Model) to provide this information. The CCFs contain temporally and spatially resolved information on the climate effect of standardized non-CO2 aviation emissions, such as water vapour, nitrogen oxides, and effects of contrail cirrus. The initial CCFs were calculated for three specific summer and five winter weather situations covering the Northern Atlantic flight corridor (Fr&#246;mming et al., 2021).The present study (Fr&#246;mming et al., in prep) describes updates over previous CCF calculations. These include the geographical expansion of the CCF domain from the Northern Atlantic towards EU and USA, the calculation of CCFs for a weather situation in spring, a higher spatial resolution for contrail CCFs, employing nudged climate model simulations enabling the comparison with observations, a consistent methodology for instantaneous to adjusted radiative forcing conversion, a more sophisticated choice of future emission scenario and the inclusion of efficacies.As the calculation of CCFs demands very high computational effort, they cannot be used for operational eco-efficient flight planning. For that reason, more generally applicable algorithmic Climate Change Functions (aCCFs) were derived (van Manen and Grewe, 2019; Yin et al., 2023) based on statistics of weather-related similarities within the CCFs. The aCCFs require only a small number of local meteorological parameters taken from numerical weather forecast models and represent a fast methodology to predict the specific climate impact per unit emission for a certain location, altitude and time.Since the new CCFs are located partially outside the initial aCCF domain and time, an independent comparison of CCFs and aCCFs is performed. Results indicate that, depending on species, particular attention is required, when aCCFs - developed for winter and summer - are transferred to other seasons, e.g. spring, when midlatitudes might be influenced by polar airmasses. Further studies expanding the spatial and temporal domains of CCFs appear necessary.References:Fr&#246;mming, C., Grewe, V., Brinkop, S., J&#246;ckel, P., Haslerud, A. S., Rosanka, S., Van Manen, J., and Matthes, S.: Influence of weather situation on non-CO2 aviation climate effects: The REACT4C climate change functions, ACP, 21, 9151 &#8211; 9172, 2021.van Manen, J. and Grewe, V.: Algorithmic climate change functions for the use in eco-efficient flight planning, Transportation Research Part D: Transp. Env., 67, 388&#8211;405, 2019.Yin, F., Grewe, V., Castino, F., Rao, P., Matthes, S., Dahlmann, K., Dietm&#252;ller, S., Fr&#246;mming, C., Yamashita, H., Peter, P., 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, GMD, 16, 3313&#8211;3334, 2023.Fr&#246;mming, C., Matthes, S., Dietm&#252;ller, S., Peter, P., Grewe, V., Dahlmann, K., J&#246;ckel., P., Geographical extension and refinement of Climate Change Functions: AIRTRAC.Vxy (included in EMAC-MESSy d2.52), GMD, in prep.