The optimization of aircraft trajectories involves balancing operating costs and climate impact, which are often conflicting objectives. To achieve compromised optimal solutions, higher-level information such as preferences of decision-makers must be taken into account. This paper introduces the SolFinder 1.0 module, a decision-making tool designed to identify eco-efficient aircraft trajectories, which allow for the reduction of the flight's climate impact with limited cost penalties compared to cost-optimal solutions. SolFinder 1.0 offers flexible decision-making options that allow users to select trade-offs between different objective functions, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel, which optimizes trajectories under atmospheric conditions simulated by the ECHAM/MESSy Atmospheric Chemistry model. This paper focuses on the ability of the module to identify eco-efficient trajectories while solving a bi-objective optimization problem that minimizes climate impact and operating costs. SolFinder 1.0 enables users to explore trajectory properties at varying locations of the Pareto fronts without prior knowledge of the problem results and to identify solutions that limit the cost of reducing the climate impact of a single flight.@en

Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NOx)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NOx emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications.@en

The climate impact of non-CO2 emissions, which are responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation-induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate-optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic optimization computationally fast. An open-source Python library called ROOST (V1.0) is developed based on the aircraft trajectory optimization technique. The effectiveness of our proposed strategy to plan robust climate-optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with a large contrail climate impact and a scenario with no formation of persistent contrails. It is shown that, for a nighttime flight from Frankfurt to Kyiv, a 55ĝ€¯% reduction in climate impact can be achieved at the expense of a 4ĝ€¯% increase in the operating cost.

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The strong growth rate of the aviation industry in recent years has created significant challenges in terms of environmental impact. Air traffic contributes to climate change through the emission of carbon dioxide (CO₂) and other non-CO₂ effects, and the associated climate impact is expected to soar further. The mitigation of CO₂ contributions to the net climate impact can be achieved using novel propulsion, jet fuels, and continuous improvements of aircraft efficiency, whose solutions lack in immediacy. On the other hand, the climate impact associated with non-CO₂ emissions, being responsible for two-thirds of aviation radiative forcing, varies highly with geographic location, altitude, and time of the emission. Consequently, these effects can be reduced by planning proper climate-aware trajectories. To investigate these possibilities, this paper presents a survey on operational strategies proposed in the literature to mitigate aviation’s climate impact. These approaches are classified based on their methodology, climate metrics, reliability, and applicability. Drawing upon this analysis, future lines of research on this topic are delineated.

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Air traffic contributes to global warming through CO2 and non-CO2 effects, including the impact of NOx emissions on atmospheric ozone and methane, formation of contrails, and changes in the amount of stratospheric water vapour. The climate impact of non-CO2 effects is highly dependent on the background atmospheric conditions at the time and location of emission. Therefore, there is the potential of mitigating the climate impact of aviation by optimizing the aircraft trajectories. In the present paper, we focus on the properties of alternative trajectories which have the potential to minimize the climate impact of NOx emissions, under a multitude of weather patterns. This study aims at enhancing the understanding of the relation between NOx-climate impacts and routing strategies, by employing the European Center Hamburg general circulation model (ECHAM) and the Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model. To this end, we conduct 1-year simulations with the air traffic submodel AirTraf 2.0, coupled to the EMAC model. We optimize 85 European flights, considering the atmospheric conditions at the time and location of the flight, to calculate the expected climate impact from the emitted species through a set of prototype algorithmic Climate Change Functions (aCCFs). The mean flight altitudes of NOx-climate optimal trajectories showed seasonal and latitudinal dependencies. We found that the potential of reducing ozone effects from aviation NOx is subjected to a strong seasonal cycle, reaching a minimum in summer. @en