Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5373–5391, 2021
https://doi.org/10.5194/gmd-14-5373-2021
Geosci. Model Dev., 14, 5373–5391, 2021
https://doi.org/10.5194/gmd-14-5373-2021
Model experiment description paper
01 Sep 2021
Model experiment description paper | 01 Sep 2021

Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide

Edmund Ryan and Oliver Wild

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Short summary
Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. In this study, we estimate some of the model parameters using machine learning and statistics. Our findings identify the level of error and spatial coverage in the O2 and CO data that are needed to achieve good parameter estimates. We also highlight the benefits of using multiple constraints to calibrate atmospheric chemistry models.