A Python library for computing individual and merged non-CO2 algorithmic climate change functions
CLIMaCCF V1.0
Simone Dietmüller (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Sigrun Matthes (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Katrin Dahlmann (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Hiroshi Yamashita (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Abolfazl Simorgh (Carlos III University of Madrid)
Benjamin Lührs (Hamburg University of Technology, Deutsches Zentrum für Luft- und Raumfahrt (DLR))
V. Grewe (Deutsches Zentrum für Luft- und Raumfahrt (DLR), TU Delft - Aircraft Noise and Climate Effects)
F. Yin (TU Delft - Aircraft Noise and Climate Effects)
F. Castino (TU Delft - Aircraft Noise and Climate Effects)
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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.
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