Verifying and improving the estimation of NO_x-O₃ effects of aviation using Uncertainty Quantification (UQ) techniques

Reducing anthropogenic climate change is a significant challenge requiring a global response to prevent tipping points in the climate system, such as the disintegration of ice sheets, and thawing of permafrost, among others. The rapidly growing air transport sector, which carried 4.5 billion passengers in 2019, is projected to emit nearly 2 Gt CO2 by 2050—about 2.6 times the emissions in 2021. Decarbonising aviation is challenging due to its reliance on fossil fuels, and while technological, operational, and regulatory measures have reduced fuel consumption, they are insufficient to mitigate aviation’s overall climate impact. The non-CO2 effects are significant, accounting for about two-thirds of aviation’s warming impact in terms of Effective Radiative Forcing (ERF). These effects include contrails, contrail-induced cirrus clouds, nitrogen oxides (NOx ), and water vapour emissions, collectively contributing to approximately 4% of anthropogenic forcing since the pre-industrial era. Given their spatio-temporal variability, climate-optimised flight planning canmitigate these impacts by avoiding sensitive regions, but this faces several challenges. These include the inherent chaos of weather, low scientific understanding of non-CO2 effects, and the large computational expense of calculating sensitive regions using climate change functions (CCFs).

To address these issues, this thesis first analyses algorithmic climate change functions (aCCFs), a simple surrogate model obtained by regressing the CCFs against local atmospheric variables. The aCCFs are computationally inexpensive to run since they only use few meteorological inputs to estimate climate impact, enabling real-time flight trajectory optimisation on arbitrary days. However, aCCFs are applicable only in parts of the Northern Hemisphere and require thorough verification before implementation. The focus is narrowed down on local aviation NOx effects on climate change, which largely causes warming via short-term increase in tropospheric ozone (O3) and is characterised by large variability. This necessitates a detailed investigation of NOx-O3 effects in isolation and its mitigation, which is a previously unexplored area. After verifying the O3 aCCFs through complex climate-chemistry model simulations, it is concluded that while it enables a reasonable first estimate, there are a few discrepancies.

TheO3 aCCFs are replaced by using a more comprehensive dataset comprising global NOx-O3 impacts, identifying additional physical variables that influence this impact, and using this information to train stochastic surrogates based on homoscedastic and heteroscedastic Gaussian processes. These models provide mean and uncertainty estimates for the climate impact of NOx on O3, for the first time. The heteroscedastic model more accurately reproduces the data distribution and its ease of use in predicting the climate impact of individual flights is demonstrated. Defined as probabilistic aCCFs (paCCFs), these models demonstrate superior accuracy over aCCFs, provide valuable insights for aviation’s non-CO2 effects, and offer broader implications for climateoptimised flight planning. The thesis concludes with limitations and recommendations to furthermitigate aviation’s environmental impact.