Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4405-2023
© 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-16-4405-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Aug 2023
Development and technical paper |
| 02 Aug 2023
| 02 Aug 2023
A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0
Simone Dietmüller
CORRESPONDING AUTHOR
Deutsches Zentrum für Luft und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Sigrun Matthes
Deutsches Zentrum für Luft und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Katrin Dahlmann
Deutsches Zentrum für Luft und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Hiroshi Yamashita
Deutsches Zentrum für Luft und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Abolfazl Simorgh
Department of Aerospace Engineering, Universidad Carlos III de Madrid, Spain
Manuel Soler
Department of Aerospace Engineering, Universidad Carlos III de Madrid, Spain
Florian Linke
Deutsches Zentrum für Luft und Raumfahrt, Air Space Transportation Systems, Hamburg, Germany
Institute of Air Transport Systems, Hamburg University of Technology (TUHH), Hamburg, Germany
Benjamin Lührs
Deutsches Zentrum für Luft und Raumfahrt, Air Space Transportation Systems, Hamburg, Germany
Maximilian M. Meuser
Deutsches Zentrum für Luft und Raumfahrt, Air Space Transportation Systems, Hamburg, Germany
Institute of Air Transport Systems, Hamburg University of Technology (TUHH), Hamburg, Germany
Christian Weder
Deutsches Zentrum für Luft und Raumfahrt, Air Space Transportation Systems, Hamburg, Germany
Volker Grewe
Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Deutsches Zentrum für Luft und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Feijia Yin
Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Federica Castino
Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
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Cited
18 citations as recorded by crossref.
- Feasibility of integrating multiple climate impact estimation models to enhance confidence in environmentally-friendly aircraft trajectory optimization A. Simorgh & M. Soler
- Ground-based contrail observations: comparisons with reanalysis weather data and contrail model simulations J. Low et al.
- Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment D. Sperber & K. Gierens
- Integrating Non-CO2 climate impact considerations in air traffic management: Opportunities and challenges F. Baneshi et al.
- Robust 4D climate-optimal aircraft trajectory planning under weather-induced uncertainties: Free-routing airspace A. Simorgh et al.
- Predicting ice supersaturation for contrail avoidance: ensemble forecasting using ICON with two-moment ice microphysics M. Hanst et al.
- Influence of temperature and humidity on contrail formation regions in the general circulation model EMAC: a spring case study P. Peter et al.
- Climate-optimized flight planning can effectively reduce the environmental footprint of aviation in Europe at low operational costs A. Simorgh & M. Soler
- Kinematic properties of regions that can involve persistent contrails over the North Atlantic and Europe during April and May 2024 S. Hofer & K. Gierens
- Balancing fuel efficiency and environmental impact: 4D trajectory optimization through Fast Marching Tree for transatlantic flights considering contrails C. Demouge et al.
- Integrating realistic 3D aircraft trajectory optimization with climate impact metrics for sustainable aviation R. Chevallier et al.
- Feasibility of contrail avoidance in a commercial flight planning system: an operational analysis A. Martin Frias et al.
- Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0 F. Castino et al.
- Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0 Z. Engberg et al.
- Pathways for including non-carbon dioxide aviation climate effects in the European Emission Trading System V. Grewe et al.
- Modeling and verifying ice supersaturated regions in the ARPEGE model for persistent contrail forecast S. Arriolabengoa et al.
- Integrating aviation’s non-CO2 effects into EU ETS: Impact of CO2e accounting on operational and technological climate mitigation measures Z. Zengerling et al.
- Concept of robust climate-friendly flight planning under multiple climate impact estimates A. Simorgh et al.
18 citations as recorded by crossref.
- Feasibility of integrating multiple climate impact estimation models to enhance confidence in environmentally-friendly aircraft trajectory optimization A. Simorgh & M. Soler
- Ground-based contrail observations: comparisons with reanalysis weather data and contrail model simulations J. Low et al.
- Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment D. Sperber & K. Gierens
- Integrating Non-CO2 climate impact considerations in air traffic management: Opportunities and challenges F. Baneshi et al.
- Robust 4D climate-optimal aircraft trajectory planning under weather-induced uncertainties: Free-routing airspace A. Simorgh et al.
- Predicting ice supersaturation for contrail avoidance: ensemble forecasting using ICON with two-moment ice microphysics M. Hanst et al.
- Influence of temperature and humidity on contrail formation regions in the general circulation model EMAC: a spring case study P. Peter et al.
- Climate-optimized flight planning can effectively reduce the environmental footprint of aviation in Europe at low operational costs A. Simorgh & M. Soler
- Kinematic properties of regions that can involve persistent contrails over the North Atlantic and Europe during April and May 2024 S. Hofer & K. Gierens
- Balancing fuel efficiency and environmental impact: 4D trajectory optimization through Fast Marching Tree for transatlantic flights considering contrails C. Demouge et al.
- Integrating realistic 3D aircraft trajectory optimization with climate impact metrics for sustainable aviation R. Chevallier et al.
- Feasibility of contrail avoidance in a commercial flight planning system: an operational analysis A. Martin Frias et al.
- Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0 F. Castino et al.
- Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0 Z. Engberg et al.
- Pathways for including non-carbon dioxide aviation climate effects in the European Emission Trading System V. Grewe et al.
- Modeling and verifying ice supersaturated regions in the ARPEGE model for persistent contrail forecast S. Arriolabengoa et al.
- Integrating aviation’s non-CO2 effects into EU ETS: Impact of CO2e accounting on operational and technological climate mitigation measures Z. Zengerling et al.
- Concept of robust climate-friendly flight planning under multiple climate impact estimates A. Simorgh et al.
Saved (final revised paper)
Latest update: 16 May 2026
Short summary
Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact...
