Abstract
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.
Original language | English |
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Pages (from-to) | 40-55 |
Number of pages | 16 |
Journal | Transportation Research. Part D: Transport & Environment |
Volume | 46 |
DOIs | |
Publication status | Published - 2016 |
Keywords
- Uncertainties
- Climate impact
- Aviation
- Monte Carlo simulation
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Data and code underlying the publication "Alternative climate metrics to the Global Warming Potential are more suitable for assessing aviation non-CO2 effects"
Megill, L. (Creator), Deck, K. T. (Contributor) & Grewe, V. (Contributor), TU Delft - 4TU.ResearchData, 8 May 2024
DOI: 10.4121/344E24AD-B2F5-4ED9-8D49-6EFA2081D30C
Dataset/Software: Dataset