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

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
Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range
Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, and Claudia Plant
Geosci. Model Dev., 16, 251–270, https://doi.org/10.5194/gmd-16-251-2023,https://doi.org/10.5194/gmd-16-251-2023, 2023
Short summary
ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application
Peter J. M. Bosman and Maarten C. Krol
Geosci. Model Dev., 16, 47–74, https://doi.org/10.5194/gmd-16-47-2023,https://doi.org/10.5194/gmd-16-47-2023, 2023
Short summary
Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023,https://doi.org/10.5194/gmd-16-1-2023, 2023
Short summary
The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022,https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
A method for transporting cloud-resolving model variance in a multiscale modeling framework
Walter Hannah and Kyle Pressel
Geosci. Model Dev., 15, 8999–9013, https://doi.org/10.5194/gmd-15-8999-2022,https://doi.org/10.5194/gmd-15-8999-2022, 2022
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.