Emissions of aviation include CO2, H2O, NOx and particles. While CO2 has a long atmospheric residence time and is uniformly distributed in the atmosphere, non-CO2 gases, particles and their products have short atmospheric residence times and are heterogeneously distributed. Their climate effects depend on chemical and meteorological background conditions during emission, which vary with geographic location, altitude, time, local insolation, actual weather, etc. This spatial and temporal variability can be utilized for aviation climate impact mitigation by identifying aircraft trajectories which avoid climate-sensitive regions. To determine the climate change contribution of individual emissions as function of 3-dimensional position, time and weather situation, contributions of local emissions to changes in O3, CH4, H2O and contrail-cirrus were computed by means of the ECHAM5/MESSy Atmospheric Chemistry model and four-dimensional climate change functions (CCFs) were derived thereof. Typical weather situations in the North Atlantic region were considered for winter and summer. For all non-CO2 species included in the study, we found distinct weather related differences with respect to their climate impact. Depending on the species, we found enhanced significance of the position of emission release in relation to high pressure systems, in relation to the jet stream, in relation to polar night and in relation to the tropopause altitude. The dominating parameters were found to be contrail-cirrus and total NOx. The results of this study represent a comprehensive basis for weather dependent flight trajectory optimization studies. Furthermore it constitutes the groundwork for the development of more generally applicable algorithmic CCFs. @en

Aviation contributes significantly to anthropogenic climate change, and one promising possibility for mitigation is eco-efficient flight planning by avoiding climate sensitive regions with only small changes in the aircraft trajectories. Climate sensitive regions result from strong spatial variation of the global climate impact of local non-CO2 emissions, which are expressed by so-called climate change functions. Previous research established high-fidelity climate change functions (CCFs) for aviation water vapour and NOx emissions, and contrail formation with a climate model as inputs for air traffic optimisation. The mitigation potential in this case study is promising but the climate change function simulations are too computationally intensive for real-time calculation and thus cannot be applied operationally. In this study we show for the first time that this problem can be overcome by formulating algorithmic approximations of the global climate impact. Here we approximate water vapour concentration changes from local aviation water vapour emissions, ozone changes from local NOx emissions and methane changes from local NOx emissions (i.e. algorithmic climate change functions; aCCFs) from instantaneous model weather data using regression analysis. Four candidate algorithms are formulated per chemical species and traded off. The final adjusted regression coefficients, indicating how well the aCCFs represent the CCFs, are 0.59, 0.42, and 0.17 for water vapour, ozone and methane. The results show that the meteorology at the time of emission largely controls the fate of the emitted species, where the quality of the aCCF degrades with increasing lifetime of the respective species.@en