Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 16
Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 16
Model description paper
 | 
17 Aug 2022
Model description paper |  | 17 Aug 2022

A machine learning methodology for the generation of a parameterization of the hydroxyl radical

Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan

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Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
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