Algorithmic climate change functions for the use in eco-efficient flight planning. (February 2019)

• Record Type: Journal Article
• Authors: van Manen, J.; Grewe, V.
• Is Part Of: Transportation research. Volume 67(2019)
• Page Start: 388
• Page End: 405
• Publication Date: 2019-02
• Subjects: Aviation -- Climate impact -- Meteorology -- Algorithm -- Regression analysis; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2018.12.016
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: The global climate impact of local aviation emissions are approximated based on instantaneous weather data. This enables climate-optimised and eco-efficient aircraft routings avoiding climate-sensitive regions. We formulated approximations for aviation NOX and water vapour emissions depending on basic meteorological parameters. 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.