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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-39', Anonymous Referee #1, 26 Mar 2021
    • AC1: 'Author Response to RC1', Edmund Ryan, 01 Jun 2021
  • RC2: 'Comment on gmd-2021-39', Kai-Lan Chang, 30 Mar 2021
    • AC2: 'Reply on RC2', Edmund Ryan, 01 Jun 2021
    • AC3: 'Reply on RC2', Edmund Ryan, 01 Jun 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Edmund Ryan on behalf of the Authors (05 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (08 Jul 2021) by Augustin Colette
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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.