Climate Assessment Platform of Different Aircraft Routing Strategies in the Chemistry-Climate Model EMAC 2.41: AirTraf 1.0

Abstract

Aviation contributes to anthropogenic climate impact through various emissions. Mobility becomes more and more important to society and hence air transportation is expected to grow further over the next decades. Reducing the 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 approach 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 and stable gas, while non-CO2 emissions are short-lived and vary regionally. This study introduces AirTraf (version 1.0) for climate impact evaluations that performs global air traffic simulations on long time scales, 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 were calculated by the total energy model based on the BADA methodology and DLR fuel flow method. The flight trajectory optimization was performed by a Genetic Algorithm (GA) with respect to routing options. In the model development phase, two benchmark tests were performed for great circle and flight time routing options. The first test showed that the great circle calculations were accurate to within ±0.05 %, compared to those calculated by other published code. The second test showed that the optimal solution 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 optimal solutions on initial populations was analyzed. We found that the influence was small (around 0.01 %). The trade-off between the accuracy of GA optimizations and the number of function evaluations is clarified and the appropriate population and generation sizing is discussed. The results showed that a large reduction in number of function evaluations of around 90 % can be achieved with only a small decrease in the accuracy of less than 0.1 %. Finally, one-day AirTraf simulations are demonstrated with the great circle and the flight time routing options for a specific winter day. 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 options. In addition, the GA successfully found the time-optimal flight trajectories for all airport pairs, reflecting local weather conditions. The consistency check for the one-day AirTraf simulations verified that calculated flight time, fuel consumption, NOx emission index and aircraft weights are comparable to reference data.