Articles | Volume 15, issue 16
Geosci. Model Dev., 15, 6341–6358, 2022
https://doi.org/10.5194/gmd-15-6341-2022
Geosci. Model Dev., 15, 6341–6358, 2022
https://doi.org/10.5194/gmd-15-6341-2022
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 et al.

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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.