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.

Data sets

Sample ECCOH OH Parameterization (1.0) D. C. Anderson, M. B. Follette-Cook, S. A. Strode, J. M. Nicely, J. Liu, P. D. Ivatt, and B. N. Duncan https://doi.org/10.5281/zenodo.6604130

MERRA2 GMI NASA Goddard Space Flight Center https://acd-ext.gsfc.nasa.gov/Projects/GEOSCCM/MERRA2GMI/

CCMI-1 Data Archive CEDA Archive http://data.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1/output

Model code and software

Code for the development of.a parameterization of OH for CCMs D. C. Anderson, M. B. Follette-Cook, S. A. Strode, J. M. Nicely, J. Liu, P. D. Ivatt, and B. N. Duncan https://doi.org/10.5281/zenodo.6046037

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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.