Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5373-2021
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

Related authors

Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls
Tabish Umar Ansari, Oliver Wild, Edmund Ryan, Ying Chen, Jie Li, and Zifa Wang
Atmos. Chem. Phys., 21, 4471–4485, https://doi.org/10.5194/acp-21-4471-2021,https://doi.org/10.5194/acp-21-4471-2021, 2021
Short summary
Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity
Oliver Wild, Apostolos Voulgarakis, Fiona O'Connor, Jean-François Lamarque, Edmund M. Ryan, and Lindsay Lee
Atmos. Chem. Phys., 20, 4047–4058, https://doi.org/10.5194/acp-20-4047-2020,https://doi.org/10.5194/acp-20-4047-2020, 2020
Short summary
Mitigation of PM2.5 and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period
Ying Chen, Oliver Wild, Edmund Ryan, Saroj Kumar Sahu, Douglas Lowe, Scott Archer-Nicholls, Yu Wang, Gordon McFiggans, Tabish Ansari, Vikas Singh, Ranjeet S. Sokhi, Alex Archibald, and Gufran Beig
Atmos. Chem. Phys., 20, 499–514, https://doi.org/10.5194/acp-20-499-2020,https://doi.org/10.5194/acp-20-499-2020, 2020
Short summary

Related subject area

Atmospheric sciences
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024,https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024,https://doi.org/10.5194/gmd-17-8773-2024, 2024
Short summary
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024,https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024,https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024,https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary

Cited articles

Baret, F., Weiss, M., Allard, D., Garrigue, S., Leroy, M., Jeanjean, H., Fernandes, R., Myneni, R., Privette, J., Morisette, J., and Bohbot, H.: VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products, Remote Sens. Environ., 76, 36–39, https://hal.inrae.fr/hal-03221068, last access: 16 August 2021. 
Bayarri, M. J., Walsh, D., Berger, J. O., Cafeo, J., Garcia-Donato, G., Liu, F., Palomo, J., Parthasarathy, R. J., Paulo, R., and Sacks, J.: Computer model validation with functional output, Ann. Statist., 35, 1874–1906, https://doi.org/10.1214/009053607000000163, 2007. 
Berg, B. A.: Introduction to Markov chain Monte Carlo simulations and their statistical analysis, in: Markov Chain Monte Carlo, edited by: Kendall, W. S., Liang, F., and Wang, J.-S., Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore, 7, 1–52, https://doi.org/10.1142/9789812700919_0001, 2005. 
Beven, K., and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. 
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. 
Download
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.