Algorithmic climate change functions for the use in eco-efficient flight planning

== Abstract ==

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.

== Original document ==

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* [https://doi.org/10.1016/j.trd.2018.12.016 https://doi.org/10.1016/j.trd.2018.12.016] under the license cc-by-nc-nd

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* [http://dx.doi.org/10.1016/j.trd.2018.12.016 http://dx.doi.org/10.1016/j.trd.2018.12.016],
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* [https://www.sciencedirect.com/science/article/pii/S1361920917309781 https://www.sciencedirect.com/science/article/pii/S1361920917309781],
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: [https://academic.microsoft.com/#/detail/2906034511 https://academic.microsoft.com/#/detail/2906034511]