Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 15
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.
https://doi.org/10.5194/gmd-16-4405-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 15
Development and technical paper
 | 
02 Aug 2023
Development and technical paper |  | 02 Aug 2023

A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0

Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino

Supplement

https://doi.org/10.5194/gmd-16-4405-2023-supplement

Data sets

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.bd0915c6

Model code and software

CLIMaCCF Python Library Simone Dietmüller https://doi.org/10.5281/zenodo.7074582

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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.
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