Transport patterns of global aviation NOx and their short-term O3 radiative forcing – a machine learning approach

Maruhashi, Jin; Grewe, Volker; Frömming, Christine; Jöckel, Patrick; Dedoussi, Irene C.

doi:https://doi.org/10.5194/acp-22-14253-2022

Articles | Volume 22, issue 21

Atmos. Chem. Phys., 22, 14253–14282, 2022

https://doi.org/10.5194/acp-22-14253-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Atmos. Chem. Phys., 22, 14253–14282, 2022

https://doi.org/10.5194/acp-22-14253-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 21

Research article

08 Nov 2022

Research article | 08 Nov 2022

Transport patterns of global aviation NO_x and their short-term O₃ radiative forcing – a machine learning approach

Jin Maruhashi et al.

Data sets

Supplementary Figures of "Transport Patterns of Global Aviation NOx and their Short-term O3 Radiative Forcing – A Machine Learning Approach" Jin Maruhashi, Volker Grewe, Christine Frömming, Patrick Jöckel, and Irene C. Dedoussi https://doi.org/10.4121/20338212

Supplementary Data for "Transport Patterns of Global Aviation NOx and their Short-term O3 Radiative Forcing – A Machine Learning Approach" Jin Maruhashi, Volker Grewe, Christine Frömming, Patrick Jöckel, and Irene C. Dedoussi https://doi.org/10.4121/16886977

Short summary

Aviation NO_x emissions lead to the formation of ozone in the atmosphere in the short term, which has a climate warming effect. This study uses global-scale simulations to characterize the transport patterns between NO_x emissions at an altitude of ~ 10.4 km and the resulting ozone. Results show a strong spatial and temporal dependence of NO_x in disturbing atmospheric O3 concentrations, with the location that is most impacted in terms of warming not necessarily coinciding with the emission region.