Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5627-2022
https://doi.org/10.5194/gmd-15-5627-2022
Development and technical paper
 | 
21 Jul 2022
Development and technical paper |  | 21 Jul 2022

Introducing new lightning schemes into the CHASER (MIROC) chemistry–climate model

Yanfeng He, Hossain Mohammed Syedul Hoque, and Kengo Sudo

Related authors

Historical (1960–2014) lightning and LNOx trends and their controlling factors in a chemistry–climate model
Yanfeng He and Kengo Sudo
Atmos. Chem. Phys., 23, 13061–13085, https://doi.org/10.5194/acp-23-13061-2023,https://doi.org/10.5194/acp-23-13061-2023, 2023
Short summary

Related subject area

Climate and Earth system modeling
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025,https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025,https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
A Fortran–Python interface for integrating machine learning parameterization into earth system models
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025,https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025,https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025,https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary

Cited articles

Allen, D. J., Pickering, K. E., Bucsela, E., Krotkov, N., and Holzworth, R.: Lightning NOx Production in the Tropics as Determined Using OMI NO2 Retrievals and WWLLN Stroke Data, J. Geophys. Res.-Atmos., 124, 13498–13518, https://doi.org/10.1029/2018JD029824, 2019. 
Banerjee, A., Archibald, A. T., Maycock, A. C., Telford, P., Abraham, N. L., Yang, X., Braesicke, P., and Pyle, J. A.: Lightning NOx, a key chemistry–climate interaction: impacts of future climate change and consequences for tropospheric oxidising capacity, Atmos. Chem. Phys., 14, 9871–9881, https://doi.org/10.5194/acp-14-9871-2014, 2014. 
Betz, H. D., Schumann, U., and Laroche, P.: Lightning: Principles, instruments and applications: Review of modern lightning research, Springer Netherlands, 1–641, https://doi.org/10.1007/978-1-4020-9079-0, 2009. 
Boccippio, D. J., Koshak, W. J., and Blakeslee, R. J.: Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part I: Predicted Diurnal Variability, J. Atmos. Ocean. Tech., 19, 1318–1332, https://doi.org/10.1175/1520-0426(2002)019<1318:PAOTOT>2.0.CO;2, 2002. 
Bucsela, E. J., Pickering, K. E., Allen, D. J., Holzworth, R. H., and Krotkov, N. A.: Midlatitude Lightning NOx Production Efficiency Inferred From OMI and WWLLN Data, J. Geophys. Res.-Atmos., 124, 13475–13497, https://doi.org/10.1029/2019JD030561, 2019. 
Download
Short summary
Lightning-produced NOx (LNOx) is a major source of NOx. Hence, it is crucial to improve the prediction accuracy of lightning and LNOx in chemical climate models. By modifying existing lightning schemes and testing them in the chemical climate model CHASER, we improved the prediction accuracy of lightning in CHASER. Different lightning schemes respond very differently under global warming, which indicates further research is needed considering the reproducibility of long-term trends of lightning.
Share