Lessening of non-CO₂ aviation impacts on climate is often accompanied by an increase of fuel consumption and emissions. Planning mitigation actions requires balancing the desired climate benefit against inevitable simultaneous climate damage. Due to uncertainty, statistical methods are needed to estimate the failure risk of concrete mitigation measures. This paper presents the required mathematics and assumptions, and points to research needs and alternative risk-aware strategies.

Aviation non-CO₂ climate effects could be reduced through rerouting using Climate Change Functions (CCFs) and its surrogate aCCFs. This study expands CCFs regionally and seasonally and enables a comparison with aCCFs. ACCFs simplify complex processes but reproduce magnitudes and most gradients, though they underestimate variability compared to detailed CCF simulations and limitations to certain altitudes were revealed. The present study promotes future development of refined and extended aCCFs.

This randomized control trial provides evidence for the efficacy of airline-led contrail avoidance to reduce aviation's climate impact. By integrating ML forecasts into standard flight planning, dispatchers routed flights to avoid warming contrails. Satellite validation showed an 11.6 % overall reduction in contrails, increasing to 62 % for flights strictly following the optimized paths, all with no significant fuel penalty.

It can be difficult to know if a given aircraft made a contrail. Existing methods that can be applied to any aircraft either use numerical weather data or satellite observations. In this work we create a new method by combining weather data and observations together. By comparison to in-situ measurements we show that the new method is superior to previous methods.