The spatiotemporal dependency of aviation-induced non-CO₂ climate effects can be incorporated into flight planning tools to generate climate-friendly flight plans. However, estimating climate impact is challenging and associated with high uncertainty. To ensure the effectiveness of such an operational measure, sources that induce uncertainty need to be identified and considered when planning climate-aware trajectories. The mismatch between different assessments of climate impact is an important indicator of uncertainty. This study introduces a concept aimed at planning robust climate-optimized aircraft trajectories under multiple climate impact estimates. The objective is to generate climate-optimal trajectories that achieve mitigation potential consistent with all available assessments. Case studies show that, even when there is a significant discrepancy between input models in specific regions, the proposed approach can effectively generate trajectories to mitigate the climate impact with a high level of confidence.

The non-CO₂ climate impact of aviation strongly relies on the atmospheric conditions at the time and location of emissions. Therefore, it is possible to mitigate their associated climate impact by planning trajectories to re-route airspace areas with significant climate effects. Identifying such climate-sensitive regions requires specific weather variables. Inevitably uncertain weather forecasts can lead to inefficient aircraft trajectories if not accounted for within flight planning. The current study addresses the problem of generating robust climate-friendly flight plans under meteorological uncertainty characterized using the ensemble prediction system. We introduce a framework based on the concept of robust tracking optimal control theory to formulate and solve the proposed flight planning problem. Meteorological uncertainty effects on aircraft performance variables are captured using the formulated ensemble aircraft dynamical model and controlled by penalizing the performance index variance. Case studies show that the proposed approach can generate climate-optimized trajectories with minimal sensitivity to weather uncertainty.