Robustness of climate-optimized trajectories and mitigation Potential: flying atm4e
S Matthes (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Benjamin Lührs (Universität Hamburg)
Katrin Dahlmann (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
F. Linke (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Volker Grewe (TU Delft - Aircraft Noise and Climate Effects, Deutsches Zentrum für Luft- und Raumfahrt (DLR))
F. Yin (TU Delft - Aircraft Noise and Climate Effects)
K.P. Shine (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
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Abstract
Aviation can reduce its climate impact by controlling its CO2-emission and non-CO2 effects, e.g. aviation-induced contrail-cirrus and ozone caused by nitrogen oxide emissions. One option is the implementation of operational measures which aim to avoid those atmospheric regions that are in particular sensitive to non-CO2 aviation effects, e.g. where persistent contrails form. Quantitative estimates of mitigation potentials of such climate-optimized aircraft trajectories are required, when working towards sustainable aviation. Results are presented from a comprehensive modelling approach which is working towards identifying such climate-optimized aircraft trajectories. The overall concept relies on a multi-dimensional environmental change function concept, which is capable of providing environmental impact information to air traffic management (ATM) and which in principal could include the noise and air quality impacts. A one-day case study with a weather situation containing regions with high contrail impacts for European air traffic estimated an overall climate impact reduction of about 30% for an increase of costs of 0.5%, relying on best estimate for climate impact information. The climate impact reduction and mitigation potential varies strongly with individual routes. By using a range of different climate metrics, the robustness of proposed mitigation trajectories is assessed. Sustainable ATM needs to integrate comprehensive environmental impacts and associated forecast uncertainties into route optimisation in order to identify robust eco-efficient trajectories.