the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Predicting the climate impact of aviation for en-route emissions: The algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53
- 1Delft University of Technology, Faculty of Aerospace Engineering, 2629HS, Delft, the Netherlands
- 2Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, 82234 Wessling, Germany
- 3University of Reading, Department of Meteorology, RG6 6AH Reading, United Kingdom
- 4Deutsches Zentrum für Luft- und Raumfahrt, Institut für Lufttransportsysteme, 21079 Hamburg, Germany
- 1Delft University of Technology, Faculty of Aerospace Engineering, 2629HS, Delft, the Netherlands
- 2Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, 82234 Wessling, Germany
- 3University of Reading, Department of Meteorology, RG6 6AH Reading, United Kingdom
- 4Deutsches Zentrum für Luft- und Raumfahrt, Institut für Lufttransportsysteme, 21079 Hamburg, Germany
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.
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Feijia Yin et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2022-220', Anonymous Referee #1, 14 Oct 2022
This study presents the ACCF 1.0 to describe the climate impact of aviation emissions. ACCF 1.0 takes the atmospheric conditions as input to calculate the climate impact, mainly through the average temperature response over 20 years (ATR20). The emissions include , (via and ), vapour, and contrail-cirrus. The study is valuable as it provides an integrated model to assess the environmental impact of the non-CO2 emissions. I have a few comments below:
1.The ACCF 1.0 model is based on the aCCF, which is proposed by the earlier project REACT4C2 for researching the climate change caused by emissions. ACCF works as a sub-model of the global atmospheric-chemistry model EMAC. What new features/functions are developed should be discussed.
2.The application simulation of existing trajectory is conducted to show the climatology impact. They also used the calculation model to optimize the trajectory, from which they draw the conclusion that climate-optimized trajectories considering non-CO2 effects fly lower altitudes to reduce the impact of the total NOx, H2O, and contrails. The scalability of the tool for large-scale problems should be discussed.
3.However, in their scenarios, the CO2-related environmental impact is considered to be lower than the non-CO2 impact, which may limit the possible subsequent applications of the ACCF. Maybe discuss futural models which can provide a comprehensive assessment of climate impact caused by aviation emissions.
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RC2: 'Comment on gmd-2022-220', Anonymous Referee #2, 11 Nov 2022
The paper deals with an interesting approach to the determination of the climate impact on air traffic. The abstract lacks motivation, results and applicability. The first sentence of the abstract has no content. Already in the abstract, there are numerous unexplained abbreviations.
In the introduction, the motivation is based on a 4-year-old prediction. This should be made acute. The state of the art is completely missing. Instead, we find a paragraph with far too many self-citations, which summarises preliminary views of the authorship.
The work is based on Climate costs functions CCF, which is not comprehensibly derived in any of the sources mentioned. The errors of the CCF are not discussed. The transferability to other time periods is very questionable and is not discussed. The scientific amount of Figure 1 to the paper is not made clear. Equations 1 and 2 were copied from Manen and Grewe and should be properly cited. The constant factor 0.0151 K/W/m2 in line 244 should be critically questioned and its error should be critically discussed. The sole distinction between day and night is not sufficient in the context of the Contrail RF and ignores cooling effects during sunrise and sunset. The extreme heterogeneity of the contrail CCFs in Figure 6 supports the assumption that the developed CCFs are extremely weather-dependent and thus not applicable to other time periods. Please explain why the effectiveness in line 285 is not included in the CCF and derive the uncertainty of the effectiveness. In Figure 9, your definition of a cost-optimal and a climate-optimal trajectory is absolutely necessary to understand the procedure. The dents and ripples in the optimised trajectories in Figures 10 and 11 should definitely be explained and critically questioned. All results and assumptions should have been critically questioned and discussed in the conclusions at the latest. An error analysis of such a strongly empirically driven model is absolutely necessary.
Feijia Yin et al.
Feijia Yin et al.
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