Submitted as: development and technical paper
17 Oct 2022
Submitted as: development and technical paper | 17 Oct 2022
Status: this preprint is currently under review for the journal GMD.

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

Simone Dietmüller1, Sigrun Matthes1, Katrin Dahlmann1, Hiroshi Yamashita1, Abolfazl Simorgh2, Manuel Soler2, Florian Linke3,4, Benjamin Lührs3, Maximilian Mendiguchia Meuser3,4, Christian Weder3, Volker Grewe5,1, Feijia Yin5, and Federica Castino5 Simone Dietmüller et al.
  • 1Deutsches Zentrum für Luft und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 2Department of Aerospace Engineering, Universidad Carlos III de Madrid, Spain
  • 3Hamburg University of Technology (TUHH), Hamburg, Germany
  • 4Deutsches Zentrum für Luft und Raumfahrt, Air Space Transportation Systems, Hamburg, Germany
  • 5Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands

Abstract. Aviation aims to reduce its climate impact by adopting trajectories, that avoid those regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions can be used, which provide spatially and temporally resolved information on aviation’s climate impact in terms of future near-surface temperature change. These alogorithmic climate change functions can be calculated with meteorological input data obtained from e.g. numerical weather prediction models. We here present an open-source Python Library, an easy to use and flexible tool which efficiently calculates both the individual algorithmic climate change functions of water vapour, nitrogen oxide (NOx) induced ozone and methane, and contrail-cirrus and also the merged non-CO2 algorithmic climate change functions that combine all individual contributions. These merged aCCFs can be only constructed with the technical specification of aircraft/engine parameters, i.e., NOx emission indices and flown distance per kg burnt fuel. These aircraft/engine specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft/engine classes (i.e. regional, single-aisle, wide-body). Moreover, CLIMaCCF allows by a user-friendly configuration setting to choose between a set of different 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 by a series of example applications.

Simone Dietmüller et al.

Status: open (until 08 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Simone Dietmüller et al.

Model code and software

CLIMaCCF Python Library Simone Dietmüller, Abolfazl Simorgh, Hiroshi Yamashita, Manuel Soler, Sigrun Matthes

Simone Dietmüller et al.


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Short summary
Climate-optimized aircraft trajectories avoid atmospheric regions with large climate impact due to aviation emission. This requires spatially and temporally resolved information on aviation's climate impact. We propose to use algorithmic climate change functions (aCCF) for CO2 and non-CO2 effects (i.e. ozone, methane, water vapour, contrail-cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft specific parameters and climate metric. Technically this is done with a Python Library.