Preprints
https://doi.org/10.5194/gmd-2021-39
https://doi.org/10.5194/gmd-2021-39

Submitted as: model experiment description paper 25 Feb 2021

Submitted as: model experiment description paper | 25 Feb 2021

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

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

Edmund Ryan1,a and Oliver Wild1 Edmund Ryan and Oliver Wild
  • 1Lancaster Environment Centre, Lancaster University, UK
  • anow at: School of Mathematics, University of Manchester, UK

Abstract. Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. To ensure that these models represent the atmosphere adequately it is important to compare their outputs with measurements. However, ground based measurements of atmospheric composition are typically sparsely distributed and representative of much smaller spatial scales than those resolved in models, and thus direct comparison incurs uncertainty. In this study, we investigate the feasibility of using observations of one or more atmospheric constituents to estimate parameters in chemistry transport models and to explore how these estimates and their uncertainties depend upon representation errors and the level of spatial coverage of the measurements. We apply Gaussian process emulation to explore the model parameter space and use monthly averaged ground-level concentrations of ozone (O3) and carbon monoxide (CO) from across Europe and the US. Using synthetic observations we find that the estimates of parameters with greatest influence on O3 and CO are unbiased, and the associated parameter uncertainties are low even at low spatial coverage or with high representation error. Using reanalysis data, we find that estimates of the most influential parameter – corresponding to the dry deposition process – are closer to its expected value using both O3 and CO data than using O3 alone. This is remarkable because it shows that while CO is largely unaffected by dry deposition, the additional constraints it provides are valuable for achieving unbiased estimates of the dry deposition parameter. In summary, these findings identify the level of spatial representation error and coverage needed to achieve good parameter estimates and highlight the benefits of using multiple constraints to calibrate atmospheric chemistry models.

Edmund Ryan and Oliver Wild

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

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

Edmund Ryan and Oliver Wild

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://zenodo.org/record/4537614

Edmund Ryan and Oliver Wild

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