Reliable prediction of aviation’s environmental impact, including the effect of nitrogen oxides on ozone, is vital for effective mitigation against its contribution to global warming. Estimating this climate impact however, in terms of the short-term ozone instantaneous radiative forcing, requires computationally-expensive chemistry-climate model simulations that limit practical applications such as climate-optimised planning. Existing surrogates neglect the large uncertainties in their predictions due to unknown environmental conditions and missing features. Relative to these surrogates, we propose a high-accuracy probabilistic surrogate that not only provides mean predictions but also quantifies heteroscedastic uncertainties in climate impact estimates. Our model is trained on one of the most comprehensive chemistry-climate model datasets for aviation-induced nitrogen oxide impacts on ozone. Leveraging feature selection techniques, we identify essential predictors that are readily available from weather forecasts to facilitate the implementation therein. We show that our surrogate model is more accurate than homoscedastic models and easily outperforms existing linear surrogates. We then predict the climate impact of a frequently-flown flight in the European Union, and discuss limitations of our approach.

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.

The Modular Earth Submodel System (MESSy) provides an interface to couple submodels to a base model via a modular flexible data management facility. This paper presents the newly developed MESSy submodel, ACCF version 1.0 (ACCF 1.0), based on algorithmic Climate Change Functions version 1.0 (aCCFs 1.0), which describes the climate impact of aviation emissions. The ACCF 1.0 is coupled via the second version of the standard MESSy infrastructure. ACCF 1.0 takes the simulated atmospheric conditions at the location of emission as input to calculate the climate impact (in terms of average temperature response over 20 years (ATR20)) of aviation emissions, including CO2 and non-CO2 impacts, such as from NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail-cirrus. The online calculated ATR20 value per emitted mass fuel burn or flown-kilometer using ACCF 1.0 in the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model is presented. We perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by the ACCF 1.0 to previous studies. Secondly, we evaluate the reduction of NOx-induced O3 effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effect is considered.

While efforts have been made to curb CO2 emissions from aviation, the more uncertain non-CO2 effects that contribute about two-thirds to the warming in terms of radiative forcing (RF), still require attention. The most important non-CO2 effects include persistent line-shaped contrails, contrail-induced cirrus clouds and nitrogen oxide (NOx) emissions that alter the ozone (O3) and methane (CH4) concentrations, both of which are greenhouse gases, and the emission of water vapour (H2O). The climate impact of these non-CO2 effects depends on emission location and prevailing weather situation; thus, it can potentially be reduced by advantageous re-routing of flights using Climate Change Functions (CCFs), which are a measure for the climate effect of a locally confined aviation emission. CCFs are calculated using a modelling chain starting from the instantaneous RF (iRF) measured at the tropopause that results from aviation emissions. However, the iRF is a product of computationally intensive chemistry-climate model (EMAC) simulations and is currently restricted to a limited number of days and only to the North Atlantic Flight Corridor. This makes it impossible to run EMAC on an operational basis for global flight planning. A step in this direction lead to a surrogate model called algorithmic Climate Change Functions (aCCFs), derived by regressing CCFs (training data) against 2 or 3 local atmospheric variables at the time of emission (features) with simple regression techniques and are applicable only in parts of the Northern hemisphere. It was found that in the specific case of O3 aCCFs, which provide a reasonable first estimate for the short-term impact of aviation NOx on O3 warming using temperature and geopotential as features, can be vastly improved [1]. There is aleatoric uncertainty in the full-order model (EMAC), stemming from unknown sources (missing features) and randomness in the known features, which can introduce heteroscedasticity in the data. Deterministic surrogates (e.g. aCCFs) only predict point estimates of the conditional average, thereby providing an incomplete picture of the stochastic response. Thus, the goal of this research is to build a new surrogate model for iRF, which is achieved by : 1. Expanding the geographical coverage of iRF (training data) by running EMAC simulations in more regions (North & South America, Eurasia, Africa and Australasia) at multiple cruise flight altitudes, 2. Following an objective approach to selecting atmospheric variables (feature selection) and considering the importance of local as well as non-local effects, 3. Regressing the iRF against selected atmospheric variables using supervised machine learning techniques such as homoscedastic and heteroscedastic Gaussian process regression. We present a new surrogate model that predicts iRF of aviation NOx-O3 effects on a regular basis with confidence levels, which not only improves our scientific understanding of NOx-O3 effects, but also increases the potential of global climate-optimised flight planning.

Aviation contributes to 3.5% of anthropogenic climate change in terms of Effective Radiative Forcing (ERF) and 5% in terms of temperature change. Aviation climate impact is expected to increase rapidly due to the growth of air transport sector in most regions of the world and the effects of the COVID-19 pandemic are expected to only have a temporary effect on this growth. While efforts have been made to curb CO2 emissions, non-CO2 effects that are at least equally significant according to recent research, require more attention. The EU Horizon 2020 project ClimOp considers a comprehensive approach to tackling the climate impact of aviation using novel operational measures. One such measure is climate-optimised flight planning, where small deviations can be made in aircraft trajectories to minimise their overall climate impact. Algorithmic Climate Change Functions (aCCFs) are used to estimate the climate impact of local non-CO2 effects such as nitrogen oxide (NOx) emissions (via ozone (O3) formation and methane (CH4) depletion), aviation water vapour (H2O) and contrails using weather variables directly as inputs. By using these functions in an air traffic optimisation module, climate sensitive regions are detected and avoided leading to climate-optimised trajectories. Here, we focus specifically on evaluating the effectiveness of reducing the aviation NOx induced climate impact via O3 formation, using only O3 aCCFs for the optimisation strategy. This is achieved using the chemistry climate model EMAC (ECHAM5/MESSy) and various submodels. A summer and winter day, characterised by high spatial variability of O3 aCCFs are selected, following which, air traffic over the European airspace is optimised with respect to climate as well as operating cost. The air traffic is laterally and vertically optimised separately to enable an evaluation of the horizontal and vertical pattern of O3 aCCFs. It is shown that despite the significant impact of the synoptic situation on the transport of emitted NOx, the climate-optimised flights lead to lower O3 Radiative Forcing (RF) compared to the cost-optimised flights. The study finds that while O3 aCCFs can reduce the climate impact, there are certain discrepancies in the prediction of O3 impact from aviation NOx emissions, as seen for the selected summer day. Although the aCCFs concept is a rough simplification in predicting future pathways of emissions and subsequent climate impact, we could show that it enables a reasonable first estimate. Further research is required to better describe the aCCFs allowing an improved estimate in O3-RF reduction for optimisation approaches.

One possibility to reduce the climate impact of aviation is the avoidance of climate-sensitive regions, which is synonymous with climate-optimised flight planning. Those regions can be identified by algorithmic Climate Change Functions (aCCFs) for nitrogen oxides (NOx), water vapour (H2O) as well as contrail cirrus, which provide a measure of climate effects associated with corresponding emissions. In this study, we evaluate the effectiveness of reducing the aviation-induced climate impact via ozone (O3) formation (resulting from NOx emissions), when solely using O3 aCCFs for the aircraft trajectory optimisation strategy. The effectiveness of such a strategy and the associated potential mitigation of climate effects is explored by using the chemistry–climate model EMAC (ECHAM5/MESSy) with various submodels. A summer and winter day, characterised by a large spatial variability of the O3 aCCFs, are selected. A one-day air traffic simulation is performed in the European airspace on those selected days to obtain both cost-optimised and climate-optimised aircraft trajectories, which more specifically minimised a NOx-induced climate effect of O3 (O3 aCCFs). The air traffic is laterally and vertically re-routed separately to enable an evaluation of the influences of the horizontal and vertical pattern of O3 aCCFs. The resulting aviation NOx emissions are then released in an atmospheric chemistry–climate simulation to simulate the contribution of these NOx emissions to atmospheric O3 and the resulting O3 change. Within this study, we use O3-RF as a proxy for climate impact. The results confirm that the climate-optimised flights lead to lower O3-RF compared to the cost-optimised flights, although the aCCFs cannot reproduce all aspects of the significant impact of the synoptic situation on the transport of emitted NOx. Overall, the climate impact is higher for the selected summer day than for the selected winter day. Lateral re-routing shows a greater potential to reduce climate impact compared to vertical re-routing for the chosen flight altitude. We find that while applying the O3 aCCFs in trajectory optimisation can reduce the climate impact, there are certain discrepancies in the prediction of O3 impact from aviation NOx emissions, as seen for the summer day. Although the O3 aCCFs concept is a rough simplification in estimating the climate impact of a local NOx emission, it enables a reasonable first estimate. Further research is required to better describe the O3 aCCFs allowing an improved estimate in the Average Temperature Response (ATR) of O3 from aviation NOx emissions. A general improvement in the scientific understanding of non-CO2 aviation effects could make climate-optimised flight planning practically feasible

The aviation industry is an essential contributor to total anthropogenic climate change, and the ever-growing demand for air transport requires serious attention. While efforts have been made to curb CO2 emissions, non-CO2 effects that are even more significant according to recent research have not been given enough attention. The EU Horizon 2020 project ClimOp steps in to follow a more holistic approach to tackling the climate impact of aviation using novel operational measures. One such measure is climate-optimised flight planning, where small deviations can be made in aircraft trajectories to minimise their overall climate impact. Algorithmic Climate Change Functions (aCCFs) are used to estimate the climate impact of local non-CO2 effects such as Nitrogen oxide (NOx) emissions (via Ozone formation and Methane depletion), aviation water vapour, and contrails by using meteorological inputs directly. By plugging these functions directly into an aircraft trajectory optimization module, climate sensitive regions are detected and avoided leading to climateoptimised trajectories. While a preliminary verification conducted specifically for NOx-ozone aCCFs showed positive signs, this research entails a more detailed verification procedure, which provides a deeper insight into its capability in predicting the impact of aviation NOx emissions on atmospheric changes in ozone. Air traffic simulation is performed concerning a subset of one-day European flights on the specifically chosen days characterized by high variability of NOx-ozone aCCFs. The air traffic on these days is optimized for cost and climate. A subsequent chemistry simulation captures the NOx effects from these re-routing procedures, and the climate impact of both scenarios can thereby be directly compared. It is expected that the results will confirm the effectiveness of NOx-ozone aCCFs in producing climate-friendly trajectories.

The aviation industry is an essential contributor to total anthropogenic climate change, and the ever-growing demand for air transport requires serious attention. While efforts have been made to curb CO2 emissions, non-CO2 effects that are even more significant according to recent research have not been given enough attention. The EU Horizon 2020 project ClimOp steps in to follow a more holistic approach to tackling the climate impact of aviation using novel operational measures. One such measure is climate-optimised flight planning, where small deviations can be made in aircraft trajectories to minimise their overall climate impact. In order to achieve this, algorithmic Climate Change Functions (aCCFs) are used to estimate the climate impact of local non-CO2 effects such as Nitrogen oxide (NOx) emissions (via Ozone formation and Methane depletion), aviation water vapour, and contrails by using meteorological inputs directly. By plugging these functions directly into an aircraft trajectory optimisation module, climate sensitive regions are detected and avoided leading to climate optimised trajectories. While a preliminary verification conducted specifically for NOx-ozone aCCFs showed positive signs, this research entails a more detailed verification procedure, which provides a deeper insight into its capability in predicting the impact of aviation NOx emissions on atmospheric changes in ozone. Air traffic simulation is performed concerning a subset of one-day European flights on the specifically chosen days characterised by high variability of NOx-ozone aCCFs. The air traffic on these days is optimised for cost and climate. A subsequent chemistry simulation captures the NOx effects from these re-routing procedures, and the climate impact of both scenarios can thereby be directly compared. It is expected that the results will confirm the effectiveness of NOx-ozone aCCFs in producing climate-friendly trajectories.