Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3313-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3313-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Jun 2023
Model description paper |
| 13 Jun 2023
| 13 Jun 2023
Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53
Faculty of Aerospace Engineering, Delft University of Technology,
2629HS, Delft, the Netherlands
Volker Grewe
Faculty of Aerospace Engineering, Delft University of Technology,
2629HS, Delft, the Netherlands
Institut für
Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Wessling, Germany
Federica Castino
Faculty of Aerospace Engineering, Delft University of Technology,
2629HS, Delft, the Netherlands
Pratik Rao
Faculty of Aerospace Engineering, Delft University of Technology,
2629HS, Delft, the Netherlands
Sigrun Matthes
Institut für
Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Wessling, Germany
Katrin Dahlmann
Institut für
Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Wessling, Germany
Simone Dietmüller
Institut für
Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Wessling, Germany
Christine Frömming
Institut für
Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Wessling, Germany
Hiroshi Yamashita
Institut für
Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Wessling, Germany
Patrick Peter
Institut für
Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Wessling, Germany
Emma Klingaman
Department of Meteorology, University of Reading, Reading, RG6 6ET,
UK
now at: Institute for Environmental Analytics, University of Reading, Reading RG6 6BX, UK
Keith P. Shine
Department of Meteorology, University of Reading, Reading, RG6 6ET,
UK
Benjamin Lührs
Institut für
Lufttransportsysteme, Deutsches Zentrum für Luft- und Raumfahrt, 21079 Hamburg, Germany
Florian Linke
Institut für
Lufttransportsysteme, Deutsches Zentrum für Luft- und Raumfahrt, 21079 Hamburg, Germany
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Cited
17 citations as recorded by crossref.
- Biofuels in Aviation: Exploring the Impact of Sustainable Aviation Fuels in Aircraft Engines R. Khujamberdiev & H. Cho 10.3390/en17112650
- In Praise of Computation C. Sunstein & L. Reisch 10.1007/s10640-025-00958-2
- Feasibility of contrail avoidance in a commercial flight planning system: an operational analysis A. Martin Frias et al. 10.1088/2634-4505/ad310c
- Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0 F. Castino et al. 10.5194/gmd-17-4031-2024
- Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment D. Sperber & K. Gierens 10.5194/acp-23-15609-2023
- Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53 F. Yin et al. 10.5194/gmd-16-3313-2023
- A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0 S. Dietmüller et al. 10.5194/gmd-16-4405-2023
- Concept of robust climate-friendly flight planning under multiple climate impact estimates A. Simorgh et al. 10.1016/j.trd.2024.104215
- Robust 4D climate-optimal aircraft trajectory planning under weather-induced uncertainties: Free-routing airspace A. Simorgh et al. 10.1016/j.trd.2024.104196
- Analytical formulations for nitrogen oxides emissions estimation of a hydrogen fueled Synergetic Air-Breathing Rocket Engine (SABRE) in air-breathing mode R. Fusaro et al. 10.1016/j.actaastro.2024.11.061
- Linear Contrails Detection, Tracking and Matching with Aircraft Using Geostationary Satellite and Air Traffic Data R. Chevallier et al. 10.3390/aerospace10070578
- Blended-Wing-Body Regional Aircraft Optimization with High-Fidelity Aerodynamics and Critical Design Requirements A. Gray & D. Zingg 10.2514/1.C037876
- Network-level aircraft trajectory planning via multi-agent deep reinforcement learning: Balancing climate considerations and operational manageability F. Baneshi et al. 10.1016/j.eswa.2025.126604
- Comparison of Actual and Time-Optimized Flight Trajectories in the Context of the In-Service Aircraft for a Global Observing System (IAGOS) Programme O. Boucher et al. 10.3390/aerospace10090744
- Climate-optimized flight planning can effectively reduce the environmental footprint of aviation in Europe at low operational costs A. Simorgh & M. Soler 10.1038/s43247-025-02031-8
- Case Study for Testing the Validity of NOx-Ozone Algorithmic Climate Change Functions for Optimising Flight Trajectories P. Rao et al. 10.3390/aerospace9050231
- Influence of weather situation on non-CO2 aviation climate effects: the REACT4C climate change functions C. Frömming et al. 10.5194/acp-21-9151-2021
15 citations as recorded by crossref.
- Biofuels in Aviation: Exploring the Impact of Sustainable Aviation Fuels in Aircraft Engines R. Khujamberdiev & H. Cho 10.3390/en17112650
- In Praise of Computation C. Sunstein & L. Reisch 10.1007/s10640-025-00958-2
- Feasibility of contrail avoidance in a commercial flight planning system: an operational analysis A. Martin Frias et al. 10.1088/2634-4505/ad310c
- Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0 F. Castino et al. 10.5194/gmd-17-4031-2024
- Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment D. Sperber & K. Gierens 10.5194/acp-23-15609-2023
- Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53 F. Yin et al. 10.5194/gmd-16-3313-2023
- A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0 S. Dietmüller et al. 10.5194/gmd-16-4405-2023
- Concept of robust climate-friendly flight planning under multiple climate impact estimates A. Simorgh et al. 10.1016/j.trd.2024.104215
- Robust 4D climate-optimal aircraft trajectory planning under weather-induced uncertainties: Free-routing airspace A. Simorgh et al. 10.1016/j.trd.2024.104196
- Analytical formulations for nitrogen oxides emissions estimation of a hydrogen fueled Synergetic Air-Breathing Rocket Engine (SABRE) in air-breathing mode R. Fusaro et al. 10.1016/j.actaastro.2024.11.061
- Linear Contrails Detection, Tracking and Matching with Aircraft Using Geostationary Satellite and Air Traffic Data R. Chevallier et al. 10.3390/aerospace10070578
- Blended-Wing-Body Regional Aircraft Optimization with High-Fidelity Aerodynamics and Critical Design Requirements A. Gray & D. Zingg 10.2514/1.C037876
- Network-level aircraft trajectory planning via multi-agent deep reinforcement learning: Balancing climate considerations and operational manageability F. Baneshi et al. 10.1016/j.eswa.2025.126604
- Comparison of Actual and Time-Optimized Flight Trajectories in the Context of the In-Service Aircraft for a Global Observing System (IAGOS) Programme O. Boucher et al. 10.3390/aerospace10090744
- Climate-optimized flight planning can effectively reduce the environmental footprint of aviation in Europe at low operational costs A. Simorgh & M. Soler 10.1038/s43247-025-02031-8
2 citations as recorded by crossref.
- Case Study for Testing the Validity of NOx-Ozone Algorithmic Climate Change Functions for Optimising Flight Trajectories P. Rao et al. 10.3390/aerospace9050231
- Influence of weather situation on non-CO2 aviation climate effects: the REACT4C climate change functions C. Frömming et al. 10.5194/acp-21-9151-2021
Latest update: 22 Feb 2025
Short summary
This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0...
