Air traffic simulation in chemistry-climate model EMAC 2.41: AirTraf 1.0

Abstract

Mobility is becoming more and more important to society and hence air transportation is expected to grow further over the next decades. Reducing anthropogenic climate impact from aviation emissions and building a climate-friendly air transportation system are required for a sustainable development of commercial aviation. A climate optimized routing, which avoids climate-sensitive regions by re-routing horizontally and vertically, is an important measure for climate impact reduction. The idea includes a number of different routing strategies (routing options) and shows a great potential for the reduction. To evaluate this, the impact of not only CO2 but also non-CO2 emissions must be considered. CO2 is a long-lived gas, while non-CO2 emissions are short-lived and are inhomogeneously distributed. This study introduces AirTraf (version 1.0) that performs global air traffic simulations, including effects of local weather conditions on the emissions. AirTraf was developed as a new submodel of the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model. Air traffic information comprises Eurocontrol's Base of Aircraft Data (BADA Revision 3.9) and International Civil Aviation Organization (ICAO) engine performance data. Fuel use and emissions are calculated by the total energy model based on the BADA methodology and Deutsches Zentrum für Luft- und Raumfahrt (DLR) fuel flow method. The flight trajectory optimization is performed by a genetic algorithm (GA) with respect to a selected routing option. In the model development phase, benchmark tests were performed for the great circle and flight time routing options. The first test showed that the great circle calculations were accurate to −0.004 %, compared to those calculated by the Movable Type script. The second test showed that the optimal solution found by the algorithm sufficiently converged to the theoretical true-optimal solution. The difference in flight time between the two solutions is less than 0.01 %. The dependence of the optimal solutions on the initial set of solutions (called population) was analyzed and the influence was small (around 0.01 %). The trade-off between the accuracy of GA optimizations and computational costs is clarified and the appropriate population and generation (one iteration of GA) sizing is discussed. The results showed that a large reduction in the number of function evaluations of around 90 % can be achieved with only a small decrease in the accuracy of less than 0.1 %. Finally, AirTraf simulations are demonstrated with the great circle and the flight time routing options for a typical winter day. The 103 trans-Atlantic flight plans were used, assuming an Airbus A330-301 aircraft. The results confirmed that AirTraf simulates the air traffic properly for the two routing options. In addition, the GA successfully found the time-optimal flight trajectories for the 103 airport pairs, taking local weather conditions into account. The consistency check for the AirTraf simulations confirmed that calculated flight time, fuel consumption, NOx emission index and aircraft weights show good agreement with reference data.