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Updated algorithmic climate change functions (aCCF) V1.0A: Evaluation with the climate-response model AirClim V2.0.

Authors :
Matthes, Sigrun
Dietmüller, Simone
Dahlmann, Katrin
Frömming, Christine
Peter, Patrick
Hiroshi Yamashita
Grewe, Volker
Feijia Yin
Castino, Federica
Source :
Geoscientific Model Development Discussions. 11/10/2023, p1-28. 28p.
Publication Year :
2023

Abstract

Aviation aims to reduce its climate effect by exploiting the potential of identifying alternative climate optimized aircraft trajectories. Such climate-optimized trajectories require a dedicated meteorological service in order to inform on those regions of the atmosphere where aviation emissions have a large effect on climate, for example, by contrail formation or nitrogen-oxide (NOx)-induced ozone formation. With the algorithmic Climate Change Functions (aCCFs) prototypes of a mathematical formulation for the temporal and spatial climate effects of aviation emissions in the atmosphere is provided, which relies solely on numerical weather prediction at the time and location of emissions. Based on the recently published consistent set of aCCF-V1.0, we here introduce newly derived calibration factors for the individual non-CO2 effects of aviation (NOx, water vapour, contrail cirrus) and establish version V1.0A of aCCFs (aCCF-V1.0A). ACCF-V1.0A represents an updated formulation of aCCF while exploring the current level of scientific understanding of individual climate effects of aviation emissions by evaluating quantitative estimates of climate effects with the state-of-the-art climate-response model AirClim. Individual scaling factors (i.e. AirClim calibration factors) are provided for the respective non-CO2 effects comprising contrail cirrus, water vapour and NOx-induced climate effects on ozone and methane, resulting uniformly in lower estimates in aCCF-V1.0A for all species compared to the earlier version aCCF-V1.0. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19919611
Database :
Academic Search Index
Journal :
Geoscientific Model Development Discussions
Publication Type :
Academic Journal
Accession number :
173647679
Full Text :
https://doi.org/10.5194/gmd-2023-92