Abstract. 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.

Abstract. Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre. In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone 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). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. 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 effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

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.

<p>While efforts have been made to curb CO<sub>2</sub> emissions from aviation, the more uncertain non-CO<sub>2</sub> effects that contribute about two-thirds to the warming in terms of radiative forcing (RF), still require attention. The most important non-CO<sub>2</sub> effects include persistent line-shaped contrails, contrail-induced cirrus clouds and nitrogen oxide (NO<sub>x</sub>) emissions that alter the ozone (O<sub>3</sub>) and methane (CH<sub>4</sub>) concentrations, both of which are greenhouse gases, and the emission of water vapour (H<sub>2</sub>O). The climate impact of these non-CO<sub>2</sub> 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, [1]), 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 O<sub>3</sub> aCCFs, which provide a reasonable first estimate for the short-term impact of aviation NO<sub>x</sub> on O<sub>3</sub> warming using temperature and geopotential as features, can be vastly improved. 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 :</p><p>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,</p><p>2. Following an objective approach to selecting atmospheric variables (feature selection) and considering the importance of local as well as non-local effects,</p><p>3. Regressing the iRF against selected atmospheric variables using supervised machine learning techniques such as homoscedastic and heteroscedastic Gaussian process regression.</p><p>We present a new surrogate model that predicts iRF of aviation NO<sub>x</sub>-O<sub>3</sub> effects on a regular basis with confidence levels, which not only improves our scientific understanding of NO<sub>x</sub>-O<sub>3</sub> effects, but also increases the potential of global climate-optimised flight planning.</p><p><strong>References</strong></p><p>[1] van Manen, J.; Grewe, V. Algorithmic climate change functions for the use in eco-efficient flight planning. <em>Transp. Res. Part D Transp. Environ.</em> <strong>2019</strong>, <em>67</em>, 388–405.</p>

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