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Aviation is an important contributor to the global economy, satisfying society's mobility needs. It contributes to climate change through CO 2 and non-CO 2 effects, including contrail-cirrus and ozone formation. There is currently significant interest in policies, regulations and research aiming to reduce aviation's climate impact. Here we model the effect of these measures on global warming and perform a bottom-up analysis of potential technical improvements, challenging the assumptions of the targets for the sector with a number of scenarios up to 2100. We show that although the emissions targets for aviation are in line with the overall goals of the Paris Agreement, there is a high likelihood that the climate impact of aviation will not meet these goals. Our assessment includes feasible technological advancements and the availability of sustainable aviation fuels. This conclusion is robust for several COVID-19 recovery scenarios, including changes in travel behaviour.

Climate impact of anthropogenic activities is more and more of public concern. But while CO2 emissions are accounted in emissions trading and mitigation plans, emissions of non-CO2 components contributing to climate change receive much less attention. One of the anthropogenic emission sectors, where non-CO2 effects play an important part, is aviation. Hence, for a quantitative estimate of total aviation climate impact, assessments need to comprise both CO2 and non-CO2 effects (e.g., water vapor, nitrogen dioxide, and contrails), instead of calculating and providing only CO2 impacts. However, while a calculation of CO2 effects relies directly on fuel consumption, for non-CO2 effects detailed information on aircraft trajectory, engine emissions, and ambient atmospheric conditions are required. As often such comprehensive information is not available for all aircraft movements, a simplified calculation method is required to calculate non-CO2 impacts. In our study, we introduce a simple calculation method which allows quantifying climate assessment relying on mission parameters, involving distance and geographic flight region. We present a systematic analysis of simulated climate impact from more than 1000 city pairs with an Airbus A330-200 aircraft depending on the flight distance and flight region to derive simplified but still realistic representation of the non-CO2 climate effects. These new formulas much better represent the climate impact of non-CO2 effects compared to a constant CO2 multiplier. The mean square error decrease from 1.18 for a constant factor down to 0.24 for distance dependent factors and can be reduced even further to 0.19 for a distance and latitude dependent factor. [ABSTRACT FROM AUTHOR]

Air traffic has an increasing influence on climate; therefore identifying mitigation options to reduce the climate impact of aviation becomes more and more important. Aviation influences climate through several climate agents, which show different dependencies on the magnitude and location of emission and the spatial and temporal impacts. Even counteracting effects can occur. Therefore, it is important to analyse all effects with high accuracy to identify mitigation potentials. However, the uncertainties in calculating the climate impact of aviation are partly large (up to a factor of about 2). In this study, we present a methodology, based on a Monte Carlo simulation of an updated non-linear climate-chemistry response model AirClim, to integrate above mentioned uncertainties in the climate assessment of mitigation options. Since mitigation options often represent small changes in emissions, we concentrate on a more generalised approach and use exemplarily different normalised global air traffic inventories to test the methodology. These inventories are identical in total emissions but differ in the spatial emission distribution. We show that using the Monte Carlo simulation and analysing relative differences between scenarios lead to a reliable assessment of mitigation potentials. In a use case we show that the presented methodology can be used to analyse even small differences between scenarios with mean flight altitude variations. [ABSTRACT FROM AUTHOR]