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

Ryan, Edmund; Wild, Oliver

doi:https://doi.org/10.5194/gmd-14-5373-2021

Articles | Volume 14, issue 9

https://doi.org/10.5194/gmd-14-5373-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-5373-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 9

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

Supplement

https://doi.org/10.5194/gmd-14-5373-2021-supplement

Data sets

Global Sensitivity Analysis of Tropospheric Ozone and OH: Budgets from three global chemistry-climate models Oliver Wild https://catalogue.ceda.ac.uk/uuid/d5afa10e50b44229b079c7c5a036e660

Model code and software

Data and R code for "Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide" Edmund Ryan https://doi.org/10.5281/zenodo.4537614

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.