article · tub.dok (Hamburg University of Technology) · 2023

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

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

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Abstract

Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NOx)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NOx emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications.

Cite this paper

APA 7

Dietmüller, S., Matthes, S., Dahlmann, K., Yamashita, H., Simorgh, A., Soler, M., Linke, F., Lührs, B., Mendiguchia Meuser, M., Weder, C. M., Grewe, V., Yin, F., & Castino, F. (2023). A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0. tub.dok (Hamburg University of Technology). https://doi.org/10.15480/882.8681

MLA 9

Dietmüller, Simone, et al. “A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0.” tub.dok (Hamburg University of Technology), 2023, https://doi.org/10.15480/882.8681.

Chicago

Dietmüller, Simone, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, et al. 2023. “A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0.” tub.dok (Hamburg University of Technology). https://doi.org/10.15480/882.8681.

Harvard

Dietmüller, S. et al. (2023) ‘A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0’, tub.dok (Hamburg University of Technology). Available at: https://doi.org/10.15480/882.8681.

BibTeX

@article{dietmuller2023python,
  title = {{A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0}},
  author = {Dietmüller, Simone and Matthes, Sigrun and Dahlmann, Katrin and Yamashita, Hiroshi and Simorgh, Abolfazl and Soler, Manuel and Linke, Florian and Lührs, Benjamin and Mendiguchia Meuser, Maximilian and Weder, Christian Martin and Grewe, Volker and Yin, Feijia and Castino, Federica},
  journal = {tub.dok (Hamburg University of Technology)},
  year = {2023},
  doi = {10.15480/882.8681}
}

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