Articles | Volume 25, issue 12
https://doi.org/10.5194/acp-25-6273-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-6273-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jun 2025
Research article |
| 26 Jun 2025
| 26 Jun 2025
Representing improved tropospheric ozone distribution over the Northern Hemisphere by including lightning NOx emissions in CHIMERE
Sanhita Ghosh
CORRESPONDING AUTHOR
Laboratoire de Météorologie Dynamique, IPSL, École Polytechnique, Route de Saclay, Palaiseau, 91128, France
Arineh Cholakian
Laboratoire de Météorologie Dynamique, IPSL, École Polytechnique, Route de Saclay, Palaiseau, 91128, France
Sylvain Mailler
Laboratoire de Météorologie Dynamique, IPSL, École Polytechnique, Route de Saclay, Palaiseau, 91128, France
École des Ponts, Institut Polytechnique de Paris, Marne-la-Vallée, 77455, France
Laurent Menut
Laboratoire de Météorologie Dynamique, IPSL, École Polytechnique, Route de Saclay, Palaiseau, 91128, France
Related authors
No articles found.
Manuel Bettineschi, Arineh Cholakian, Victoria Sinclair, Katerina Sindelarova, Arnaud Patrick Praplan, Steven Job Thomas, Tuukka Petäjä, Federico Bianchi, and Giancarlo Ciarelli
EGUsphere, https://doi.org/10.5194/egusphere-2026-352, https://doi.org/10.5194/egusphere-2026-352, 2026
Short summary
Short summary
We studied how forests in Finland release natural gases that affect air quality and climate. Existing models strongly overestimated these emissions because they used overly simple forest descriptions. By adding detailed information on tree species, we greatly improved agreement with real measurements. This means more realistic estimates of particle formation in the air. Our results show that accurate forest data are essential for reliable climate and air quality predictions.
Bertrand Bessagnet, Narayan Thapa, Dikra Prasad Bajgai, Ravi Sahu, Arshini Saikia, Arineh Cholakian, Laurent Menut, Guillaume Siour, Tenzin Wangchuk, Monica Crippa, and Kamala Gurung
Atmos. Chem. Phys., 25, 18675–18696, https://doi.org/10.5194/acp-25-18675-2025, https://doi.org/10.5194/acp-25-18675-2025, 2025
Short summary
Short summary
This study highlights the use of numerical tools at very high resolution to support the Air Quality monitoring strategy in the Himalayan valley which suffer from Air Pollution. For the first time ever, a high resolution simulation is performed in Bhutan showing the high PM2.5 concentrations within the valleys and potential contaminations up to the glaciers enhancing climate related risks.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
Short summary
Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
Matthieu Vida, Gilles Foret, Guillaume Siour, Florian Couvidat, Olivier Favez, Gaelle Uzu, Arineh Cholakian, Sébastien Conil, Matthias Beekmann, and Jean-Luc Jaffrezo
Atmos. Chem. Phys., 24, 10601–10615, https://doi.org/10.5194/acp-24-10601-2024, https://doi.org/10.5194/acp-24-10601-2024, 2024
Short summary
Short summary
We simulate 2 years of atmospheric fungal spores over France and use observations of polyols and primary biogenic factors from positive matrix factorisation. The representation of emissions taking into account a proxy for vegetation surface and specific humidity enables us to reproduce very accurately the seasonal cycle of fungal spores. Furthermore, we estimate that fungal spores can account for 20 % of PM10 and 40 % of the organic fraction of PM10 over vegetated areas in summer.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
Short summary
Short summary
We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
Short summary
Short summary
A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
Short summary
Short summary
The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
Short summary
Short summary
We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
Gaëlle de Coëtlogon, Adrien Deroubaix, Cyrille Flamant, Laurent Menut, and Marco Gaetani
Atmos. Chem. Phys., 23, 15507–15521, https://doi.org/10.5194/acp-23-15507-2023, https://doi.org/10.5194/acp-23-15507-2023, 2023
Short summary
Short summary
Using a numerical atmospheric model, we found that cooling sea surface temperatures along the southern coast of West Africa in July cause the “little dry season”. This effect reduces humidity and pollutant transport inland, potentially enhancing West Africa's synoptic and seasonal forecasting.
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023, https://doi.org/10.5194/gmd-16-4265-2023, 2023
Short summary
Short summary
This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Laurent Menut, Arineh Cholakian, Guillaume Siour, Rémy Lapere, Romain Pennel, Sylvain Mailler, and Bertrand Bessagnet
Atmos. Chem. Phys., 23, 7281–7296, https://doi.org/10.5194/acp-23-7281-2023, https://doi.org/10.5194/acp-23-7281-2023, 2023
Short summary
Short summary
This study is about the wildfires occurring in France during the summer 2022. We study the forest fires that took place in the Landes during the summer of 2022. We show the direct impact of these fires on the air quality, especially downstream of the smoke plume towards the Paris region. We quantify the impact of these fires on the pollutants peak concentrations and the possible exceedance of thresholds.
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez García-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
Atmos. Chem. Phys., 23, 6487–6523, https://doi.org/10.5194/acp-23-6487-2023, https://doi.org/10.5194/acp-23-6487-2023, 2023
Short summary
Short summary
Desert dust modeling is important for understanding climate change, as dust regulates the atmosphere's greenhouse effect and radiation. This study formulates and proposes a more physical and realistic desert dust emission scheme for global and regional climate models. By considering more aeolian processes in our emission scheme, our simulations match better against dust observations than existing schemes. We believe this work is vital in improving dust representation in climate models.
Arineh Cholakian, Matthias Beekmann, Guillaume Siour, Isabelle Coll, Manuela Cirtog, Elena Ormeño, Pierre-Marie Flaud, Emilie Perraudin, and Eric Villenave
Atmos. Chem. Phys., 23, 3679–3706, https://doi.org/10.5194/acp-23-3679-2023, https://doi.org/10.5194/acp-23-3679-2023, 2023
Short summary
Short summary
This article revolves around the simulation of biogenic secondary organic aerosols in the Landes forest (southwestern France). Several sensitivity cases involving biogenic emission factors, land cover data, anthropogenic emissions, and physical or meteorological parameters were performed and each compared to measurements both in the forest canopy and around the forest. The chemistry behind the formation of these aerosols and their production and transport in the forest canopy is discussed.
Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel
Geosci. Model Dev., 16, 1119–1127, https://doi.org/10.5194/gmd-16-1119-2023, https://doi.org/10.5194/gmd-16-1119-2023, 2023
Short summary
Short summary
Large or even
giantparticles of mineral dust exist in the atmosphere but, so far, solving an non-linear equation was needed to calculate the speed at which they fall in the atmosphere. The model we present, AerSett v1.0 (AERosol SETTling version 1.0), provides a new and simple way of calculating their free-fall velocity in the atmosphere, which will be useful to anyone trying to understand and represent adequately the transport of giant dust particles by the wind.
Rémy Lapere, Nicolás Huneeus, Sylvain Mailler, Laurent Menut, and Florian Couvidat
Atmos. Chem. Phys., 23, 1749–1768, https://doi.org/10.5194/acp-23-1749-2023, https://doi.org/10.5194/acp-23-1749-2023, 2023
Short summary
Short summary
Glaciers in the Andes of central Chile are shrinking rapidly in response to global warming. This melting is accelerated by the deposition of opaque particles onto snow and ice. In this work, model simulations quantify typical deposition rates of soot on glaciers in summer and winter months and show that the contribution of emissions from Santiago is not as high as anticipated. Additionally, the combination of regional- and local-scale meteorology explains the seasonality in deposition.
Antoine Guion, Solène Turquety, Arineh Cholakian, Jan Polcher, Antoine Ehret, and Juliette Lathière
Atmos. Chem. Phys., 23, 1043–1071, https://doi.org/10.5194/acp-23-1043-2023, https://doi.org/10.5194/acp-23-1043-2023, 2023
Short summary
Short summary
At high concentrations, tropospheric ozone (O3) deteriorates air quality. Weather conditions are key to understanding the variability in O3 concentration, especially during extremes. We suggest that identifying the presence of combined heatwaves is essential to the study of droughts in canopy–troposphere interactions and O3 concentration. Even so, they are associated, on average, with an increase in O3, partly explained by an increase in precursor emissions and a decrease in dry deposition.
Mathieu Lachatre, Sylvain Mailler, Laurent Menut, Arineh Cholakian, Pasquale Sellitto, Guillaume Siour, Henda Guermazi, Giuseppe Salerno, and Salvatore Giammanco
Atmos. Chem. Phys., 22, 13861–13879, https://doi.org/10.5194/acp-22-13861-2022, https://doi.org/10.5194/acp-22-13861-2022, 2022
Short summary
Short summary
In this study, we have evaluated the predominance of various pathways of volcanic SO2 conversion to sulfates in the upper troposphere. We show that the main conversion pathway was gaseous oxidation by OH, although the liquid pathways were expected to be predominant. These results are interesting with respect to a better understanding of sulfate formation in the middle and upper troposphere and are an important component to help evaluate particulate matter radiative forcing.
Juan Cuesta, Lorenzo Costantino, Matthias Beekmann, Guillaume Siour, Laurent Menut, Bertrand Bessagnet, Tony C. Landi, Gaëlle Dufour, and Maxim Eremenko
Atmos. Chem. Phys., 22, 4471–4489, https://doi.org/10.5194/acp-22-4471-2022, https://doi.org/10.5194/acp-22-4471-2022, 2022
Short summary
Short summary
We present the first comprehensive study integrating satellite observations of near-surface ozone pollution, surface in situ measurements, and a chemistry-transport model for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020. It confirms the occurrence of a net enhancement of ozone in central Europe and a reduction elsewhere, except for some hotspots, linked with the reduction of precursor emissions from Europe and the Northern Hemisphere.
Adrien Deroubaix, Laurent Menut, Cyrille Flamant, Peter Knippertz, Andreas H. Fink, Anneke Batenburg, Joel Brito, Cyrielle Denjean, Cheikh Dione, Régis Dupuy, Valerian Hahn, Norbert Kalthoff, Fabienne Lohou, Alfons Schwarzenboeck, Guillaume Siour, Paolo Tuccella, and Christiane Voigt
Atmos. Chem. Phys., 22, 3251–3273, https://doi.org/10.5194/acp-22-3251-2022, https://doi.org/10.5194/acp-22-3251-2022, 2022
Short summary
Short summary
During the summer monsoon in West Africa, pollutants emitted in urbanized areas modify cloud cover and precipitation patterns. We analyze these patterns with the WRF-CHIMERE model, integrating the effects of aerosols on meteorology, based on the numerous observations provided by the Dynamics-Aerosol-Climate-Interactions campaign. This study adds evidence to recent findings that increased pollution levels in West Africa delay the breakup time of low-level clouds and reduce precipitation.
Laurent Menut, Bertrand Bessagnet, Régis Briant, Arineh Cholakian, Florian Couvidat, Sylvain Mailler, Romain Pennel, Guillaume Siour, Paolo Tuccella, Solène Turquety, and Myrto Valari
Geosci. Model Dev., 14, 6781–6811, https://doi.org/10.5194/gmd-14-6781-2021, https://doi.org/10.5194/gmd-14-6781-2021, 2021
Short summary
Short summary
The CHIMERE chemistry-transport model is presented in its new version, V2020r1. Many changes are proposed compared to the previous version. These include online modeling, new parameterizations for aerosols, new emissions schemes, a new parameter file format, the subgrid-scale variability of urban concentrations and new transport schemes.
Cited articles
Akimoto, H. and Tanimoto, H.: Rethinking of the adverse effects of NOx-control on the reduction of methane and tropospheric ozone–challenges toward a denitrified society, Atmos. Environ., 277, 119033, https://doi.org/10.1016/j.atmosenv.2022.119033, 2022. a, b
Albrecht, R. I., Goodman, S. J., Buechler, D. E., Blakeslee, R. J., and Christian, H. J.: Where Are the Lightning Hotspots on Earth?, B. Am. Meteorol. Soc., 97, 2051–2068, https://doi.org/10.1175/BAMS-D-14-00193.1, 2016. a
Alfaro, S. C. and Gomes, L.: Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distributions in source areas, J. Geophys. Res.-Atmos., 106, 18075–1808, https://doi.org/10.1029/2000JD900339, 2001. a
Allen, D., Pickering, K., Stenchikov, G., Thompson, A., and Kondo, Y.: A three-dimensional total odd nitrogen (NOy) simulation during SONEX using a stretched-grid chemical transport model, J. Geophys. Res.-Atmos., 105, 3851–3876, https://doi.org/10.1029/1999JD901029, 2000. a
Allen, D., Pickering, K., Duncan, B., and Damon, M.: Impact of lightning NO emissions on North American photochemistry as determined using the Global Modeling Initiative (GMI) model, J. Geophys. Res.-Atmos., 115, D22301, https://doi.org/10.1029/2010JD014062, 2010. a, b
Allen, D. J. and Pickering, K. E.: Evaluation of lightning flash rate parameterizations for use in a global chemical transport model, J. Geophys. Res.-Atmos,, 107, ACH 15-1–ACH 15-21, https://doi.org/10.1029/2002JD002066, 2002. a
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. a, b, c, d, e
Bessagnet, B., Menut, L., Curci, G., Hodzic, A., Guillaume, B., Liousse, C., Moukhtar, S., Pun, B., Seigneur, C., and Schulz, M.: Regional modeling of carbonaceous aerosols over Europe–focus on secondary organic aerosols, J. Atmos. Chem., 61, 175–202, https://doi.org/10.1007/s10874-009-9129-2, 2008. a
Blakeslee, R. J., Lang, T. J., Koshak, W. J., Buechler, D., Gatlin, P., Mach, D. M., Stano, G. T., Virts, K. S., Walker, T. D., Cecil, D. J., Ellett, W., Goodman, S. J., Harrison, S., Hawkins, D. L., Heumesser, M., Lin, H., Maskey, M., Schultz, C. J., Stewart, M., Bateman, M., Chanrion, O., and Christian, H.: Three Years of the Lightning Imaging Sensor Onboard the International Space Station: Expanded Global Coverage and Enhanced Applications, J. Geophys. Res.-Atmos., 125, e2020JD032918, https://doi.org/10.1029/2020JD032918, 2020. a, b, c
Boccippio, D. J.: Lightning Scaling Relations Revisited, J. Atmos. Sci., 59, 1086–1104, https://doi.org/10.1175/1520-0469(2002)059<1086:LSRR>2.0.CO;2, 2002. a
Boccippio, D. J., Cummins, K. L., Christian, H. J., and Goodman, S. J.: Combined satellite-and surface-based estimation of the intracloud–cloud-to-ground lightning ratio over the continental United States, Mon. Weather Rev., 129, 108–122, https://doi.org/10.1175/1520-0493(2001)129<0108:CSASBE>2.0.CO;2, 2001. a
Boersma, K. F., Eskes, H. J., Richter, A., De Smedt, I., Lorente, A., Beirle, S., van Geffen, J. H. G. M., Zara, M., Peters, E., Van Roozendael, M., Wagner, T., Maasakkers, J. D., van der A, R. J., Nightingale, J., De Rudder, A., Irie, H., Pinardi, G., Lambert, J.-C., and Compernolle, S. C.: Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project, Atmos. Meas. Tech., 11, 6651–6678, https://doi.org/10.5194/amt-11-6651-2018, 2018. a
Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J. P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M., Claas, J., Leitão, J., Richter, A., Zhou, Y., and Brunner, D.: An improved tropospheric NO2 column retrieval algorithm for the Ozone Monitoring Instrument, Atmos. Meas. Tech., 4, 1905–1928, https://doi.org/10.5194/amt-4-1905-2011, 2011 (data available at: https://www.temis.nl/airpollution/no2.php last access: 21 June 2025). a
Bucsela, E. J., Krotkov, N. A., Celarier, E. A., Lamsal, L. N., Swartz, W. H., Bhartia, P. K., Boersma, K. F., Veefkind, J. P., Gleason, J. F., and Pickering, K. E.: A new stratospheric and tropospheric NO2 retrieval algorithm for nadir-viewing satellite instruments: applications to OMI, Atmos. Meas. Tech., 6, 2607–2626, https://doi.org/10.5194/amt-6-2607-2013, 2013. a
Burkholder, J. B., Sander, S. P., Abbatt, J., Barker, J. R., Cappa, C., Crounse, J. D., Dibble, T. S., Huie, R. E., Kolb, C. E., Kurylo, M. J., Orkin, V. L., Percival, C. J., Wilmouth, D. M., and Wine, P. H.: Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies, Evaluation No. 19, Jet Propulsion Laboratory, Pasadena, http://jpldataeval.jpl.nasa.gov (last access: 20 June 2025), 2019. a
Butler, T., Lupascu, A., and Nalam, A.: Attribution of ground-level ozone to anthropogenic and natural sources of nitrogen oxides and reactive carbon in a global chemical transport model, Atmos. Chem. Phys., 20, 10707–10731, https://doi.org/10.5194/acp-20-10707-2020, 2020. a, b
Carey, L. D., Koshak, W., Peterson, H., and Mecikalski, R. M.: The kinematic and microphysical control of lightning rate, extent, and NO production, J. Geophys. Res.-Atmos., 121, 7975–7989, https://doi.org/10.1002/2015JD024703, 2016. a
Cecil, D.: LIS/OTD 0.5 Degree High Resolution Monthly Climatology (HRMC), NASA Global Hydrometeorology Resource Center Distributed Active Archive Center [data set], https://doi.org/10.5067/LIS/LIS-OTD/DATA303, 2025. a
Cecil, D. J., Buechler, D. E., and Blakeslee, R. J.: Gridded lightning climatology from TRMM-LIS and OTD: Dataset description, Atmos. Res., 135, 404–414, https://doi.org/10.1016/j.atmosres.2012.06.028, 2014. a
Cheng, P., Pour-Biazar, A., Wu, Y., Kuang, S., McNider, R. T., and Koshak, W. J.: Utility of Geostationary Lightning Mapper-derived lightning NO emission estimates in air quality modeling studies, Atmos. Chem. Phys., 24, 41–63, https://doi.org/10.5194/acp-24-41-2024, 2024. a
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric Aerosol Optical Thickness from the GOCART Model and Comparisons with Satellite and Sun Photometer Measurements, J. Atmos. Sci., 59, 461–483, https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2, 2002. a
Choi, Y., Wang, Y., Zeng, T., Martin, R. V., Kurosu, T. P., and Chance, K.: Evidence of lightning NOx and convective transport of pollutants in satellite observations over North America, Geophys. Res. Lett., 32, L02805, https://doi.org/10.1029/2004GL021436, 2005. a
Clark, S. K., Ward, D. S., and Mahowald, N. M.: Parameterization-based uncertainty in future lightning flash density, Geophys. Res. Lett., 44, 2893–2901, https://doi.org/10.1002/2017GL073017, 2017. a, b
CNEMC: Air quality data for China, China National Environmental Monitoring Centre, https://quotsoft.net/air/, last access: 24 June 2025. a
Cooper, M. J., Martin, R. V., Hammer, M. S., Levelt, P. F., Veefkind, P., Lamsal, L. N., Krotkov, N. A., Brook, J. R., and McLinden, C. A.: Global fine-scale changes in ambient NO2 during COVID-19 lockdowns, Nature, 601, 380–387, https://doi.org/10.1038/s41586-021-04229-0, 2022. a
Cummings, K. A., Pickering, K. E., Barth, M. C., Bela, M. M., Li, Y., Allen, D., Bruning, E., MacGorman, D. R., Ziegler, C. L., Biggerstaff, M. I., Fuchs, B., Davis, T., Carey, L., Mecikalski, R. M., and Finney, D. L.: Evaluation of lightning flash rate parameterizations in a cloud-resolved WRF-Chem simulation of the 29–30 May 2012 Oklahoma severe supercell system observed during DC3, J. Geophys. Res.-Atmos., 129, e2023JD039492, https://doi.org/10.1029/2023JD039492, 2024. a
Dahlmann, K., Grewe, V., Ponater, M., and Matthes, S.: Quantifying the contributions of individual NOx sources to the trend in ozone radiative forcing, Atmos. Environ., 45, 2860–2868, https://doi.org/10.1016/j.atmosenv.2011.02.071, 2011. a
Denier van der Gon, H., Gauss, M., Granier, C., Arellano, S., Benedictow, A., Darras, S., Dellaert, S., Guevara, M., Jalka- nen, J.-P., Krueger, K., Kuenen, J., Liaskoni, M., Liousse, C., Markova, J., Prieto Perez, A., Quack, B., Simpson, D., Sindelarova, K., and Soulie, A.: Documentation of CAMS emission inventory products, Copernicus Atmosphere Monitoring Service [data set], https://doi.org/10.24380/Q2SI-TI6I, 2023. a
Dufour, G., Hauglustaine, D., Zhang, Y., Eremenko, M., Cohen, Y., Gaudel, A., Siour, G., Lachatre, M., Bense, A., Bessagnet, B., Cuesta, J., Ziemke, J., Thouret, V., and Zheng, B.: Recent ozone trends in the Chinese free troposphere: role of the local emission reductions and meteorology, Atmos. Chem. Phys., 21, 16001–16025, https://doi.org/10.5194/acp-21-16001-2021, 2021. a
Dwyer, J. R. and Uman, M. A.: The physics of lightning, Phys. Rep., 534, 147–241, https://doi.org/10.1016/j.physrep.2013.09.004, 2014. a
EEA: Hourly air quality data for Europe, European Air Quality Portal, European Environmental Agency, https://aqportal.discomap.eea.europa.eu, last access: 24 June 2025. a
ECCC: Environment and Climate Change Canada, National Air Pollution Surveillance (NAPS) Program – Open Government Portal and the Canadian Air and Precipitation Monitoring Network, https://data-donnees.az.ec.gc.ca (last access: 24 June 2025), 2023. a
EPA: U.S. Environmental Protection Agency (U.S. EPA), Air data: Air quality data collected at outdoor monitors across the US, EPA, https://www.epa.gov/outdoor-air-quality-data (last access: 24 June 2025), 2022. a
Erdmann, F., Defer, E., Caumont, O., Blakeslee, R. J., Pédeboy, S., and Coquillat, S.: Concurrent satellite and ground-based lightning observations from the Optical Lightning Imaging Sensor (ISS-LIS), the low-frequency network Meteorage and the SAETTA Lightning Mapping Array (LMA) in the northwestern Mediterranean region, Atmos. Meas. Tech., 13, 853–875, https://doi.org/10.5194/amt-13-853-2020, 2020. a, b
Fehr, T., Höller, H., and Huntrieser, H.: Model study on production and transport of lightning-produced NOx in a EULINOX supercell storm, J. Geophys. Res.-Atmos., 109, D09102, https://doi.org/10.1029/2003JD003935, 2004. a
Finney, D. L., Doherty, R. M., Wild, O., Young, P. J., and Butler, A.: Response of lightning NOx emissions and ozone production to climate change: Insights from the Atmospheric Chemistry and Climate Model Intercomparison Project, Geophys. Res. Lett., 43, 5492–5500, https://doi.org/10.1002/2016GL068825, 2016b. a, b, c
Ghosh, A., Patra, P. K., Ishijima, K., Umezawa, T., Ito, A., Etheridge, D. M., Sugawara, S., Kawamura, K., Miller, J. B., Dlugokencky, E. J., Krummel, P. B., Fraser, P. J., Steele, L. P., Langenfelds, R. L., Trudinger, C. M., White, J. W. C., Vaughn, B., Saeki, T., Aoki, S., and Nakazawa, T.: Variations in global methane sources and sinks during 1910–2010, Atmos. Chem. Phys., 15, 2595–2612, https://doi.org/10.5194/acp-15-2595-2015, 2015. a
Ghosh, R., Pawar, S. D., Hazra, A., Wilkinson, J., Mudiar, D., Domkawale, M. A., Vani, K. G., and Gopalakrishnan, V.: Seasonal and Regional Distribution of Lightning Fraction Over Indian Subcontinent, Earth and Space Science, 10, e2022EA002728, https://doi.org/10.1029/2022EA002728, 2023. a, b
Goldenbaum, G. C. and Dickerson, R. R.: Nitric oxide production by lightning discharges, J. Geophys. Res.-Atmos., 98, 18333–18338, https://doi.org/10.1029/93JD01018, 1993. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012. a
Hasenkopf, C. A., Flasher, J. C., Veerman, O., and DeWitt, H. L.: OpenAQ: a platform to aggregate and freely share global air quality data, in: AGU Fall Meeting Abstracts, vol. 2015, San Francisco, 14–18 December 2015, A31D–0097, https://ui.adsabs.harvard.edu/abs/2015AGUFM.A31D0097H/abstract (last access: 20 June, 2025), 2015 (data available at: https://openaq.org, last access: 20 June 2025). a, b
Holle, R. L., Cummins, K. L., and Brooks, W. A.: Seasonal, Monthly, and Weekly Distributions of NLDN and GLD360 Cloud-to-Ground Lightning, Mon. Weather Rev., 144, 2855–2870, https://doi.org/10.1175/MWR-D-16-0051.1, 2016. a
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019. a
Jourdain, L., Kulawik, S. S., Worden, H. M., Pickering, K. E., Worden, J., and Thompson, A. M.: Lightning NOx emissions over the USA constrained by TES ozone observations and the GEOS-Chem model, Atmos. Chem. Phys., 10, 107–119, https://doi.org/10.5194/acp-10-107-2010, 2010. a
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012. a
Kang, D., Foley, K. M., Mathur, R., Roselle, S. J., Pickering, K. E., and Allen, D. J.: Simulating lightning NO production in CMAQv5.2: performance evaluations, Geosci. Model Dev., 12, 4409–4424, https://doi.org/10.5194/gmd-12-4409-2019, 2019. a
Kang, D., Mathur, R., Pouliot, G. A., Gilliam, R. C., and Wong, D. C.: Significant ground-level ozone attributed to lightning-induced nitrogen oxides during summertime over the Mountain West States, NPJ Climate and Atmospheric Science, 3, 6, https://doi.org/10.1038/s41612-020-0108-2, 2020. a, b
Koshak, W., Peterson, H., Biazar, A., Khan, M., and Wang, L.: The NASA Lightning Nitrogen Oxides Model (LNOM): Application to air quality modeling, Atmos. Res., 135-136, 363–369, https://doi.org/10.1016/j.atmosres.2012.12.015, 2014. a
Krider, E. P., Dawson, G. A., and Uman, M. A.: Peak power and energy dissipation in a single-stroke lightning flash, J. Geophys. Res., 73, 3335–3339, https://doi.org/10.1029/JB073i010p03335, 1968. a
Labrador, L. J., von Kuhlmann, R., and Lawrence, M. G.: The effects of lightning-produced NOx and its vertical distribution on atmospheric chemistry: sensitivity simulations with MATCH-MPIC, Atmos. Chem. Phys., 5, 1815–1834, https://doi.org/10.5194/acp-5-1815-2005, 2005. a, b, c
Lamsal, L. N., Krotkov, N. A., Vasilkov, A., Marchenko, S., Qin, W., Yang, E.-S., Fasnacht, Z., Joiner, J., Choi, S., Haffner, D., Swartz, W. H., Fisher, B., and Bucsela, E.: Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments, Atmos. Meas. Tech., 14, 455–479, https://doi.org/10.5194/amt-14-455-2021, 2021. a
Lang, T.: Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data V2, NASA Global Hydrometeorology Resource Center Distributed Active Archive Center [data set], https://doi.org/10.5067/LIS/ISSLIS/DATA111, 2025. a
Lelieveld, J. and Dentener, F. J.: What controls tropospheric ozone?, J. Geophys. Res.-Atmos., 105, 3531–3551, https://doi.org/10.1029/1999JD901011, 2000. a
Lelieveld, J., Gromov, S., Pozzer, A., and Taraborrelli, D.: Global tropospheric hydroxyl distribution, budget and reactivity, Atmos. Chem. Phys., 16, 12477–12493, https://doi.org/10.5194/acp-16-12477-2016, 2016. a, b, c, d
Levelt, P. F., Joiner, J., Tamminen, J., Veefkind, J. P., Bhartia, P. K., Stein Zweers, D. C., Duncan, B. N., Streets, D. G., Eskes, H., van der A, R., McLinden, C., Fioletov, V., Carn, S., de Laat, J., DeLand, M., Marchenko, S., McPeters, R., Ziemke, J., Fu, D., Liu, X., Pickering, K., Apituley, A., González Abad, G., Arola, A., Boersma, F., Chan Miller, C., Chance, K., de Graaf, M., Hakkarainen, J., Hassinen, S., Ialongo, I., Kleipool, Q., Krotkov, N., Li, C., Lamsal, L., Newman, P., Nowlan, C., Suleiman, R., Tilstra, L. G., Torres, O., Wang, H., and Wargan, K.: The Ozone Monitoring Instrument: overview of 14 years in space, Atmos. Chem. Phys., 18, 5699–5745, https://doi.org/10.5194/acp-18-5699-2018, 2018. a
Li, M., Mao, J., Chen, S., Bian, J., Bai, Z., Wang, X., Chen, W., and Yu, P.: Significant contribution of lightning NOx to summertime surface O3 on the Tibetan Plateau, Sci. Total Environ., 829, 154639, https://doi.org/10.1016/j.scitotenv.2022.154639, 2022. a
Liaskos, C. E., Allen, D. J., and Pickering, K. E.: Sensitivity of tropical tropospheric composition to lightning NOx production as determined by replay simulations with GEOS-5, J. Geophys. Res.-Atmos., 120, 8512–8534, https://doi.org/10.1002/2014JD022987, 2015. a, b
Luhar, A. K., Galbally, I. E., Woodhouse, M. T., and Abraham, N. L.: Assessing and improving cloud-height-based parameterisations of global lightning flash rate, and their impact on lightning-produced NOx and tropospheric composition in a chemistry–climate model, Atmos. Chem. Phys., 21, 7053–7082, https://doi.org/10.5194/acp-21-7053-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w
Luhar, A. K., Jones, A. C., and Wilkinson, J. M.: Quantifying the impact of global nitrate aerosol on tropospheric composition fields and its production from lightning NOx, Atmos. Chem. Phys., 24, 14005–14028, https://doi.org/10.5194/acp-24-14005-2024, 2024. a
Luo, C., Wang, Y., and Koshak, W. J.: Development of a self-consistent lightning NOx simulation in large-scale 3-D models, J. Geophys. Res.-Atmos., 122, 3141–3154, https://doi.org/10.1002/2016JD026225, 2017. a
Mailler, S., Menut, L., di Sarra, A. G., Becagli, S., Di Iorio, T., Bessagnet, B., Briant, R., Formenti, P., Doussin, J.-F., Gómez-Amo, J. L., Mallet, M., Rea, G., Siour, G., Sferlazzo, D. M., Traversi, R., Udisti, R., and Turquety, S.: On the radiative impact of aerosols on photolysis rates: comparison of simulations and observations in the Lampedusa island during the ChArMEx/ADRIMED campaign, Atmos. Chem. Phys., 16, 1219–1244, https://doi.org/10.5194/acp-16-1219-2016, 2016. a
Mao, J., Zhao, T., Keller, C. A., Wang, X., McFarland, P. J., Jenkins, J. M., and Brune, W. H.: Global Impact of Lightning-Produced Oxidants, Geophys. Res. Lett., 48, e2021GL095740, https://doi.org/10.1029/2021GL095740, 2021. a, b, c
Maseko, B., Feig, G., and Burger, R.: Estimating lightning NOx production over South Africa, S. Afr. J. Sci., 117, 1–11, https://doi.org/10.17159/sajs.2021/8035, 2021. a
Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N., Colette, A., Coll, I., Curci, G., Foret, G., Hodzic, A., Mailler, S., Meleux, F., Monge, J.-L., Pison, I., Siour, G., Turquety, S., Valari, M., Vautard, R., and Vivanco, M. G.: CHIMERE 2013: a model for regional atmospheric composition modelling, Geosci. Model Dev., 6, 981–1028, https://doi.org/10.5194/gmd-6-981-2013, 2013. a, b
Menut, L., Bessagnet, B., Mailler, S., Pennel, R., and Siour, G.: Impact of lightning NOx emissions on atmospheric composition and meteorology in Africa and Europe, Atmosphere, 11, 1128, https://doi.org/10.3390/atmos11101128, 2020a. a, b, c, d
Menut, L., Bessagnet, B., Siour, G., Mailler, S., Pennel, R., and Cholakian, A.: Impact of lockdown measures to combat Covid-19 on air quality over western Europe, Sci. Total Environ., 741, 140426, https://doi.org/10.1016/j.scitotenv.2020.140426, 2020b. a
Menut, L., Bessagnet, B., Briant, R., Cholakian, A., Couvidat, F., Mailler, S., Pennel, R., Siour, G., Tuccella, P., Turquety, S., and Valari, M.: The CHIMERE v2020r1 online chemistry-transport model, Geosci. Model Dev., 14, 6781–6811, https://doi.org/10.5194/gmd-14-6781-2021, 2021. a, b
Menut, L., Cholakian, A., Pennel, R., Siour, G., Mailler, S., Valari, M., Lugon, L., and Meurdesoif, Y.: The CHIMERE chemistry-transport model v2023r1, Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, 2024a. a, b
Menut, L., Cholakian, A., Pennel, R., Siour, G., Mailler, S., Valari, M., Lugon, L., and Meurdesoif, Y.: chimere_v2023r1 (2023r1), Zenodo [code], https://doi.org/10.5281/zenodo.10907951, 2024b. a
Michalon, N., Nassif, A., Saouri, T., Royer, J. F., and Pontikis, C. A.: Contribution to the climatological study of lightning, Geophys. Res. Lett., 26, 3097–3100, https://doi.org/10.1029/1999GL010837, 1999. a
Miyazaki, K., Eskes, H. J., Sudo, K., and Zhang, C.: Global lightning NOx production estimated by an assimilation of multiple satellite data sets, Atmos. Chem. Phys., 14, 3277–3305, https://doi.org/10.5194/acp-14-3277-2014, 2014. a, b
Monahan, E. C.: The Ocean as a Source for Atmospheric Particles, Springer Netherlands, 129–163, https://doi.org/10.1007/978-94-009-4738-2_6, 1986. a
Murray, L. T., Logan, J. A., and Jacob, D. J.: Interannual variability in tropical tropospheric ozone and OH: The role of lightning, J. Geophys. Res.-Atmos., 118, 11468–11480, https://doi.org/10.1002/jgrd.50857, 2013. a
Murray, L. T., Fiore, A. M., Shindell, D. T., Naik, V., and Horowitz, L. W.: Large uncertainties in global hydroxyl projections tied to fate of reactive nitrogen and carbon, P. Natl. Acad. Sci. USA, 118, e2115204118, https://doi.org/10.1073/pnas.2115204118, 2021. a, b
Naik, V., Voulgarakis, A., Fiore, A. M., Horowitz, L. W., Lamarque, J.-F., Lin, M., Prather, M. J., Young, P. J., Bergmann, D., Cameron-Smith, P. J., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R., Eyring, V., Faluvegi, G., Folberth, G. A., Josse, B., Lee, Y. H., MacKenzie, I. A., Nagashima, T., van Noije, T. P. C., Plummer, D. A., Righi, M., Rumbold, S. T., Skeie, R., Shindell, D. T., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S., and Zeng, G.: Preindustrial to present-day changes in tropospheric hydroxyl radical and methane lifetime from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), Atmos. Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, 2013. a, b, c, d, e, f
Nault, B. A., Laughner, J. L., Wooldridge, P. J., Crounse, J. D., Dibb, J., Diskin, G., Peischl, J., Podolske, J. R., Pollack, I. B., Ryerson, T. B., Scheuer, E., Wennberg, P. O., and Cohen, R. C.: Lightning NOx Emissions: Reconciling Measured and Modeled Estimates With Updated NO Chemistry, Geophys. Res. Lett., 44, 9479–9488, https://doi.org/10.1002/2017GL074436, 2017. a, b
Ott, L. E., Pickering, K. E., Stenchikov, G. L., Allen, D. J., DeCaria, A. J., Ridley, B., Lin, R.-F., Lang, S., and Tao, W.-K.: Production of lightning NO and its vertical distribution calculated from three-dimensional cloud-scale chemical transport model simulations, J. Geophys. Res.-Atmos., 115, D04301, https://doi.org/10.1029/2009JD011880, 2010. a, b, c, d, e
Pawar, S. D., Lal, D. M., and Murugavel, P.: Lightning characteristics over central India during Indian summer monsoon, Atmos. Res., 106, 44–49, https://doi.org/10.1016/j.atmosres.2011.11.007, 2012a. a
Pawar, V., Pawar, S. D., Beig, G., and Sahu, S. K.: Effect of lightning activity on surface NOx and O3 over a tropical station during premonsoon and monsoon seasons, J. Geophys. Res.-Atmos., 117, D05310, https://doi.org/10.1029/2011JD016930, 2012b. a
Pérez-Invernón, F. J., Gordillo-Vázquez, F. J., Huntrieser, H., Jöckel, P., and Bucsela, E. J.: Sensitivity of climate-chemistry model simulated atmospheric composition to lightning-produced NOx parameterizations based on lightning frequency, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3348, 2024. a
Price, C. and Rind, D.: What determines the cloud-to-ground lightning fraction in thunderstorms?, Geophys. Res. Lett., 20, 463–466, https://doi.org/10.1029/93GL00226, 1993. a, b, c, d
Price, C. and Rind, D.: Modeling global lightning distributions in a general circulation model, Mon. Weather Rev., 122, 1930–1939, https://doi.org/10.1175/1520-0493(1994)122<1930:MGLDIA>2.0.CO;2, 1994. a
Price, C., Penner, J., and Prather, M.: NOx from lightning: 2. Constraints from the global atmospheric electric circuit, J. Geophys. Res.-Atmos., 102, 5943–5951, https://doi.org/10.1029/96JD02551, 1997b. a, b
Pun, B. K. and Seigneur, C.: Investigative modeling of new pathways for secondary organic aerosol formation, Atmos. Chem. Phys., 7, 2199–2216, https://doi.org/10.5194/acp-7-2199-2007, 2007. a
Ren, Y., Xu, W., and Fu, J.: Characteristics of intracloud lightning to cloud-to-ground lightning ratio in thunderstorms over Eastern and Southern China, Atmos. Res., 300, 107231, https://doi.org/10.1016/j.atmosres.2024.107231, 2024. a
Stauffer, R. M. and Thompson, A. M.: SHADOZ – Southern Hemisphere Additional OZonesondes, NASA/Goddard Space Flight Center [data set], https://tropo.gsfc.nasa.gov/shadoz/index.html, last access: 21 November 2024. a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
Tost, H., Jöckel, P., and Lelieveld, J.: Lightning and convection parameterisations – uncertainties in global modelling, Atmos. Chem. Phys., 7, 4553–4568, https://doi.org/10.5194/acp-7-4553-2007, 2007. a
Troen, I. and Mahrt, L.: A simple model of the atmospheric boundary layer: Sensitivity to surface evaporation, Bound.-Lay. Meteorol., 37, 129–148, https://doi.org/10.1007/BF00122760, 1986. a
Uman, M. A.: The lightning discharge, Courier Corporation, ISBN 0-486-41463-9, 2001. a
van der A, R. J., Eskes, H. J., Boersma, K. F., van Noije, T. P. C., Van Roozendael, M., De Smedt, I., Peters, D. H. M. U., and Meijer, E. W.: Trends, seasonal variability and dominant NOx source derived from a ten year record of NO2 measured from space, J. Geophys. Res.-Atmos., 113, D04302, https://doi.org/10.1029/2007JD009021, 2008. a
van Leer, B.: Towards the ultimate conservative difference scheme: IV. A new approach to numerical convection, J. Comput. Phys., 23, 276–299, https://doi.org/10.1016/0021-9991(77)90095-X, 1977. a
Verma, S., Yadava, P. K., Lal, D. M., Mall, R. K., Kumar, H., and Payra, S.: Role of Lightning NOx in Ozone Formation: A Review, Pure Appl. Geophys., 178, 1425–1443, https://doi.org/10.1007/s00024-021-02710-5, 2021. a
Vonnegut, B.: Some facts and speculations concerning the origin and role of thunderstorm electricity, American Meteorological Society, 224–241, https://doi.org/10.1007/978-1-940033-56-3_11, 1963. a
Voulgarakis, A., Naik, V., Lamarque, J.-F., Shindell, D. T., Young, P. J., Prather, M. J., Wild, O., Field, R. D., Bergmann, D., Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R. M., Eyring, V., Faluvegi, G., Folberth, G. A., Horowitz, L. W., Josse, B., MacKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Stevenson, D. S., Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Analysis of present day and future OH and methane lifetime in the ACCMIP simulations, Atmos. Chem. Phys., 13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, 2013. a, b
Wesely, M. L.: Parameterization of Surface Resistances to Gaseous Dry Deposition in Regional-Scale Numerical Models, Atmos. Environ., 23, 1293–1304, https://doi.org/10.1016/0004-6981(89)90153-4, 1989. a
Wild, O., Zhu, X., and Prather, M. J.: Fast-J: Accurate Simulation of In- and Below-Cloud Photolysis in Tropospheric Chemical Models, J. Atmos. Chem., 37, 245–282, https://doi.org/10.1023/A:1006415919030, 2000. a
Williams, E. R.: Large-scale charge separation in thunderclouds, J. Geophys. Res.-Atmos., 90, 6013–6025, https://doi.org/10.1029/JD090iD04p06013, 1985. a
WMO/GAW Ozone Monitoring Community: OzoneSonde, World Meteorological Organization-Global Atmosphere Watch Program (WMO-GAW)/World Ozone and Ultraviolet Radiation Data Centre (WOUDC) [data set], https://doi.org/10.14287/10000008, 2023. a
Wu, Y., Pour-Biazar, A., Koshak, W. J., and Cheng, P.: LNOx emission model for air quality and climate studies using satellite lightning mapper observations, J. Geophys. Res.-Atmos., 128, e2022JD037406, https://doi.org/10.1029/2022JD037406, 2023. a, b, c
Xu, M., Qie, X., Zhao, C., Yuan, S., Li, J., Tao, Y., Shi, G., Pang, W., and Shi, L.: Distribution of lightning spatial modes and climatic causes in China, Atmospheric and Oceanic Science Letters, 16, 100338, https://doi.org/10.1016/j.aosl.2023.100338, 2023. a
Zhang, D., Cummins, K. L., Lang, T. J., Buechler, D., and Rudlosky, S.: Performance Evaluation of the Lightning Imaging Sensor on the International Space Station, J. Atmos. Ocean. Tech., 40, 1063–1082, https://doi.org/10.1175/JTECH-D-22-0120.1, 2023. a
Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle dry deposition scheme for an atmospheric aerosol module, Atmos. Environ., 35, 549–560, https://doi.org/10.1016/S1352-2310(00)00326-5, 2001. a
Zhao, C., Wang, Y., Choi, Y., and Zeng, T.: Summertime impact of convective transport and lightning NOx production over North America: modeling dependence on meteorological simulations, Atmos. Chem. Phys., 9, 4315–4327, https://doi.org/10.5194/acp-9-4315-2009, 2009. a
Zhao, Y., Saunois, M., Bousquet, P., Lin, X., Hegglin, M. I., Canadell, J. G., Jackson, R. B., and Zheng, B.: Reconciling the bottom-up and top-down estimates of the methane chemical sink using multiple observations, Atmos. Chem. Phys., 23, 789–807, https://doi.org/10.5194/acp-23-789-2023, 2023. a
Ziemke, J. R., Chandra, S., Duncan, B. N., Froidevaux, L., Bhartia, P. K., Levelt, P. F., and Waters, J. W.: Tropospheric ozone determined from Aura OMI and MLS: Evaluation of measurements and comparison with the Global Modeling Initiative's Chemical Transport Model, J. Geophys. Res.-Atmos., 111, D19303, https://doi.org/10.1029/2006JD007089, 2006. a
Ziemke, J. R., Labow, G. J., Kramarova, N. A., McPeters, R. D., Bhartia, P. K., Oman, L. D., Frith, S. M., and Haffner, D. P.: A global ozone profile climatology for satellite retrieval algorithms based on Aura MLS measurements and the MERRA-2 GMI simulation, Atmos. Meas. Tech., 14, 6407–6418, https://doi.org/10.5194/amt-14-6407-2021, 2021 (data available at: https://acd-ext.gsfc.nasa.gov/Data_services/cloud_slice/new_data.html, last access: 5 July 2024). a
Short summary
In this study, we evaluate the present state of modelling lightning flashes over the Northern Hemisphere, using the classical CTH (cloud-top height) scheme and the ICEFLUX scheme with the CHIMERE model. Our study provides a comprehensive 3D comparison of model outputs to assess the robustness and applicability of these schemes. An improvement in O3 distribution in the tropical free troposphere is observed due to inclusion of LNOx (nitrogen oxide emissions from lightning) in the model. Inclusion of LNOx also reduces the lifetime of trace gas CH4.
In this study, we evaluate the present state of modelling lightning flashes over the Northern...
Altmetrics
Final-revised paper
Preprint