Articles | Volume 23, issue 20
https://doi.org/10.5194/acp-23-13061-2023
© Author(s) 2023. 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-23-13061-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Oct 2023
Research article |
| 17 Oct 2023
| 17 Oct 2023
Historical (1960–2014) lightning and LNOx trends and their controlling factors in a chemistry–climate model
Graduate School of Environment Studies, Nagoya University, Nagoya, 464-8601, Japan
Kengo Sudo
Graduate School of Environment Studies, Nagoya University, Nagoya, 464-8601, Japan
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, 237-0061, Japan
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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.
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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.
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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.
Altaratz, O., Kucienska, B., Kostinski, A., Raga, G. B., and Koren, I.: Global association of aerosol with flash density of intense lightning, Environ. Res. Lett., 12, 114037, https://doi.org/10.1088/1748-9326/aa922b, 2017.
Arfeuille, F., Luo, B. P., Heckendorn, P., Weisenstein, D., Sheng, J. X., Rozanov, E., Schraner, M., Brönnimann, S., Thomason, L. W., and Peter, T.: Modeling the stratospheric warming following the Mt. Pinatubo eruption: uncertainties in aerosol extinctions, Atmos. Chem. Phys., 13, 11221–11234, https://doi.org/10.5194/acp-13-11221-2013, 2013.
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.
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.
Boucher, O.: Atmospheric Aerosols, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-017-9649-1, 2015.
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.
Cecil, D. J., Buechler, D. E., and Blakeslee, R. J.: Gridded lightning climatology from TRMM-LIS and OTD: Dataset description, Atmos. Res., 135–136, 404–414, https://doi.org/10.1016/j.atmosres.2012.06.028, 2014.
Cerveny, R. S., Bessemoulin, P., Burt, C. C., Cooper, M. A., Cunjie, Z., Dewan, A., Finch, J., Holle, R. L., Kalkstein, L., Kruger, A., Lee, T., Martínez, R., Mohapatra, M., Pattanaik, D. R., Peterson, T. C., Sheridan, S., Trewin, B., Tait, A., and Wahab, M. M. A.: WMO Assessment of Weather and Climate Mortality Extremes: Lightning, Tropical Cyclones, Tornadoes, and Hail, Weather Clim. Soc., 9, 487–497, https://doi.org/10.1175/WCAS-D-16-0120.1, 2017.
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.
Cooper, M. A. and Holle, R. L.: Current Global Estimates of Lightning Fatalities and Injuries, in: Reducing Lightning Injuries Worldwide, edited by: Cooper, M. A. and Holle, R. L., Springer International Publishing, Cham, 65–73, https://doi.org/10.1007/978-3-319-77563-0_6, 2019.
Cooray, V., Rahman, M., and Rakov, V.: On the NOx production by laboratory electrical discharges and lightning, J. Atmos. Sol.-Terr. Phy., 71, 1877–1889, https://doi.org/10.1016/j.jastp.2009.07.009, 2009.
Danabasoglu, G.: NCAR CESM2-WACCM model output prepared for CMIP6 CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.10071, 2019.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
Del Genio, A. D., Yao, M.-S., and Jonas, J.: Will moist convection be stronger in a warmer climate?, Geophys. Res. Lett., 34, L16703, https://doi.org/10.1029/2007GL030525, 2007.
ESGF (Earth System Grid Federation): ESGF MetaGrid [data set], https://aims2.llnl.gov/search (last access: 1 February 2023), 2023.
Finney, D. L., Doherty, R. M., Wild, O., Huntrieser, H., Pumphrey, H. C., and Blyth, A. M.: Using cloud ice flux to parametrise large-scale lightning, Atmos. Chem. Phys., 14, 12665–12682, https://doi.org/10.5194/acp-14-12665-2014, 2014.
Finney, D. L., Doherty, R. M., Wild, O., and Abraham, N. L.: The impact of lightning on tropospheric ozone chemistry using a new global lightning parametrisation, Atmos. Chem. Phys., 16, 7507–7522, https://doi.org/10.5194/acp-16-7507-2016, 2016a.
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.
Finney, D. L., Doherty, R. M., Wild, O., Stevenson, D. S., MacKenzie, I. A., and Blyth, A. M.: A projected decrease in lightning under climate change, Nat. Clim. Change, 8, 210–213, https://doi.org/10.1038/s41558-018-0072-6, 2018.
Fujibe, F.: Long-term Change in Lightning Mortality and Its Relation to Annual Thunder Days in Japan, Journal of Natural Disaster Science, 38, 17–29, https://doi.org/10.2328/jnds.38.17, 2017.
Buechler, D. E., Wright, P. D., and Goodman, S. J.: Lightning/rainfall relationships during COHMEX, 16th Conf. on Severe Local Storms, Kananaskis Park, Alberta, Canada, 22–26 October 1990, 714, http://www.jstor.org/stable/26227919 (last access: 13 October 2023), 1990.
Goto, D., Nakajima, T., Dai, T., Takemura, T., Kajino, M., Matsui, H., Takami, A., Hatakeyama, S., Sugimoto, N., Shimizu, A., and Ohara, T.: An evaluation of simulated particulate sulfate over East Asia through global model intercomparison, J. Geophys. Res.-Atmos., 120, 6247–6270, https://doi.org/10.1002/2014JD021693, 2015.
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.
Ha, P. T. M., Matsuda, R., Kanaya, Y., Taketani, F., and Sudo, K.: Effects of heterogeneous reactions on tropospheric chemistry: a global simulation with the chemistry–climate model CHASER V4.0, Geosci. Model Dev., 14, 3813–3841, https://doi.org/10.5194/gmd-14-3813-2021, 2021.
He, Y., Hoque, M. S. H., and Sudo, K.: Introducing new lightning schemes into the CHASER (MIROC) chemistry climate model, Zenodo [code], https://doi.org/10.5281/zenodo.5835796, 2022a.
He, Y., Hoque, H. M. S., and Sudo, K.: Introducing new lightning schemes into the CHASER (MIROC) chemistry–climate model, Geosci. Model Dev., 15, 5627–5650, https://doi.org/10.5194/gmd-15-5627-2022, 2022b.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons, J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1, 2017.
Hui, J. and Hong, L.: Projected Changes in NOx Emissions from Lightning as a Result of 2000–2050 Climate Change, Atmospheric and Oceanic Science Letters, 6, 284–289, https://doi.org/10.3878/j.issn.1674-2834.13.0042, 2013.
Hussain, Md. M. and Mahmud, I.: pyMannKendall: a python package for non parametric Mann Kendall family of trend tests, Journal of Open Source Software, 4, 1556, https://doi.org/10.21105/joss.01556, 2019.
Ito, A. and Inatomi, M.: Water-use efficiency of the terrestrial biosphere: A model analysis focusing on interactions between the global carbon and water cycles, J. Hydrometeorol., 13, 681–694, https://doi.org/10.1175/JHM-D-10-05034.1, 2012.
Jensen, J. D., Thurman, J., and Vincent, A. L.: Lightning Injuries, in: StatPearls, StatPearls Publishing, Treasure Island (FL), https://www.ncbi.nlm.nih.gov/books/NBK431128/ (last access: Please provide your last access date.), 2022.
Kaufman, Y. J., Tanré, D., Holben, B. N., Mattoo, S., Remer, L. A., Eck, T. F., Vaughan, J., and Chatenet, B.: Aerosol Radiative Impact on Spectral Solar Flux at the Surface, Derived from Principal-Plane Sky Measurements, J. Atmos. Sci., 59, 635–646, https://doi.org/10.1175/1520-0469(2002)059<0635:ARIOSS>2.0.CO;2, 2002.
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C., García-Pando, C. P., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: GISS-E2.1: Configurations and Climatology, J. Adv. Model. Earth Sy., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020.
Koren, I., Kaufman, Y. J., Remer, L. A., and Martins, J. V.: Measurement of the Effect of Amazon Smoke on Inhibition of Cloud Formation, Science, 303, 1342–1345, https://doi.org/10.1126/science.1089424, 2004.
Koren, I., Martins, J. V., Remer, L. A., and Afargan, H.: Smoke Invigoration Versus Inhibition of Clouds over the Amazon, Science, 321, 946–949, https://doi.org/10.1126/science.1159185, 2008.
Krause, A., Kloster, S., Wilkenskjeld, S., and Paeth, H.: The sensitivity of global wildfires to simulated past, present, and future lightning frequency, J. Geophys. Res.-Biogeo., 119, 312–322, https://doi.org/10.1002/2013JG002502, 2014.
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.
Lal, D. M., Ghude, S. D., Mahakur, M., Waghmare, R. T., Tiwari, S., Srivastava, M. K., Meena, G. S., and Chate, D. M.: Relationship between aerosol and lightning over Indo-Gangetic Plain (IGP), India, Clim. Dynam., 50, 3865–3884, https://doi.org/10.1007/s00382-017-3851-2, 2018.
Li, Z., Guo, J., Ding, A., Liao, H., Liu, J., Sun, Y., Wang, T., Xue, H., Zhang, H., and Zhu, B.: Aerosol and boundary-layer interactions and impact on air quality, Natl. Sci. Rev., 4, 810–833, https://doi.org/10.1093/nsr/nwx117, 2017.
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., 120, 8512–8534, https://doi.org/10.1002/2014JD022987, 2015.
Liu, Y., Guha, A., Said, R., Williams, E., Lapierre, J., Stock, M., and Heckman, S.: Aerosol Effects on Lightning Characteristics: A Comparison of Polluted and Clean Regimes, Geophys. Res. Lett., 47, e2019GL086825, https://doi.org/10.1029/2019GL086825, 2020.
Lopez, P.: A lightning parameterization for the ECMWF integrated forecasting system, Mon. Weather Rev., 144, 3057–3075, https://doi.org/10.1175/MWR-D-16-0026.1, 2016.
Macias Fauria, M. and Johnson, E. A.: Large-scale climatic patterns control large lightning fire occurrence in Canada and Alaska forest regions, J. Geophys. Res.-Biogeo., 111, G04008, https://doi.org/10.1029/2006JG000181, 2006.
Mallick, C., Hazra, A., Saha, S. K., Chaudhari, H. S., Pokhrel, S., Konwar, M., Dutta, U., Mohan, G. M., and Vani, K. G.: Seasonal Predictability of Lightning Over the Global Hotspot Regions, Geophys. Res. Lett., 49, e2021GL096489, https://doi.org/10.1029/2021GL096489, 2022.
Manabe, S. and Wetherald, R. T.: The Effects of Doubling the CO2 Concentration on the climate of a General Circulation Model, J. Atmos. Sci., 32, 3–15, https://doi.org/10.1175/1520-0469(1975)032<0003:TEODTC>2.0.CO;2, 1975.
McCaul, E. W., Goodman, S. J., LaCasse, K. M., and Cecil, D. J.: Forecasting lightning threat using cloud-resolving model simulations, Weather Forecast., 24, 709–729, https://doi.org/10.1175/2008WAF2222152.1, 2009.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017.
Miller, R. L., Schmidt, G. A., Nazarenko, L. S., Tausnev, N., Bauer, S. E., DelGenio, A. D., Kelley, M., Lo, K. K., Ruedy, R., Shindell, D. T., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Chen, Y., Cheng, Y., Clune, T. L., Faluvegi, G., Hansen, J. E., Healy, R. J., Kiang, N. Y., Koch, D., Lacis, A. A., LeGrande, A. N., Lerner, J., Menon, S., Oinas, V., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Russell, G. L., Sato, M., Sun, S., Tsigaridis, K., Unger, N., Voulgarakis, A., Yao, M.-S., and Zhang, J.: CMIP5 historical simulations (1850–2012) with GISS ModelE2, J. Adv. Model. Earth Sy., 6, 441–478, https://doi.org/10.1002/2013MS000266, 2014.
Murray, L. T.: Lightning NOx and Impacts on Air Quality, Current Pollution Reports, 2, 115–133, https://doi.org/10.1007/s40726-016-0031-7, 2016.
NASA: Earthdata search :: GHRC Portal [data set], https://search.earthdata.nasa.gov/search?portal=ghrc (last access: 10 October 2023), 2023.
NCEI (National Centers for Environmental Information): Extended Reconstructed SST, National Centers for Environmental Information (NCEI) [data set], https://www.ncei.noaa.gov/products/extended-reconstructed-sst (last access: 27 March 2023), 2023.
NOAA National Centers for Environmental Information (NCEI), Climate at a Glance: Global Time Series, published June 2023, https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series/globe/land_ocean/3/8/1880-2020 (last access: 10 January 2023), 2022.
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 NOx and its vertical distribution calculated from three-dimensional cloud-scale chemical transport model simulations, J. Geophys. Res.-Atmos., 115, 4301, https://doi.org/10.1029/2009JD011880, 2010.
Pinto, O.: Lightning and climate: A review, in: 2013 International Symposium on Lightning Protection (XII SIPDA), 2013 International Symposium on Lightning Protection (XII SIPDA), 402–404, https://doi.org/10.1109/SIPDA.2013.6729250, 7–11 October 2013, Belo Horizonte, Brazil, 2013.
Price, C. and Rind, D.: A simple lightning parameterization for calculating global lightning distributions, J. Geophys. Res., 97, 9919–9933, https://doi.org/10.1029/92JD00719, 1992.
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.
Price, C. and Rind, D.: Possible implications of global climate change on global lightning distributions and frequencies, J. Geophys. Res., 99, 823–833, https://doi.org/10.1029/94jd00019, 1994.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002jd002670, 2003.
Ridley, B. A., Pickering, K. E., and Dye, J. E.: Comments on the parameterization of lightning-produced NO in global chemistry-transport models, Atmos. Environ., 39, 6184–6187, https://doi.org/10.1016/j.atmosenv.2005.06.054, 2005.
Romps, D. M.: Evaluating the Future of Lightning in Cloud-Resolving Models, Geophys. Res. Lett., 46, 14863–14871, https://doi.org/10.1029/2019GL085748, 2019.
Romps, D. M., Seeley, J. T., Vollaro, D., and Molinari, J.: Projected increase in lightning strikes in the united states due to global warming, Science, 346, 851–854, https://doi.org/10.1126/science.1259100, 2014.
Salmi, T., Määttä, A., Anttila, P., Ruoho-Airola, T., and Amnell, T.: Detecting Trends of Annual Values of Atmospheric Pollutants by the Mann–Kendall Test and Sen's Slope Estimates the Excel Template Application MAKESENS, Publications on Air Quality, 31, https://www.researchgate.net/publication/259356944_Detecting_Trends_of_Annual_Values_of_Atmospheric_Pollutants_by_the_Mann-Kendall_Test_and_Sen's_Solpe_Estimates_the_Excel_Template_Application_MAKESENS (last access: 13 October 2023), 2002.
Sato, M., Hansen, J. E., McCormick, M. P., and Pollack, J. B.: Stratospheric aerosol optical depths, 1850–1990, J. Geophys. Res.-Atmos., 98, 22987–22994, https://doi.org/10.1029/93JD02553, 1993.
Saunders, C. P. R., Keith, W. D., and Mitzeva, R. P.: The effect of liquid water on thunderstorm charging, J. Geophys. Res.-Atmos., 96, 11007–11017, https://doi.org/10.1029/91JD00970, 1991.
Schumann, U. and Huntrieser, H.: The global lightning-induced nitrogen oxides source, Atmos. Chem. Phys., 7, 3823–3907, https://doi.org/10.5194/acp-7-3823-2007, 2007.
Sekiguchi, M. and Nakajima, T.: A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model, J. Quant. Spectrosc. Ra., 109, 2779–2793, https://doi.org/10.1016/j.jqsrt.2008.07.013, 2008.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U. K. Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.
Shi, Z., Wang, H., Tan, Y., Li, L., and Li, C.: Influence of aerosols on lightning activities in central eastern parts of China, Atmos. Sci. Lett., 21, e957, https://doi.org/10.1002/asl.957, 2020.
Soden, B. J., Wetherald, R. T., Stenchikov, G. L., and Robock, A.: Global Cooling After the Eruption of Mount Pinatubo: A Test of Climate Feedback by Water Vapor, Science, 296, 727–730, https://doi.org/10.1126/science.296.5568.727, 2002.
Sudo, K. and Akimoto, H.: Global source attribution of tropospheric ozone: Long-range transport from various source regions, J. Geophys. Res.-Atmos., 112, D12302, https://doi.org/10.1029/2006JD007992, 2007.
Sudo, K., Takahashi, M., Kurokawa, J. I., and Akimoto, H.: CHASER: A global chemical model of the troposphere 1. Model description, J. Geophys. Res.-Atmos., 107, ACH 7-1–ACH 7-20, https://doi.org/10.1029/2001JD001113, 2002.
Takemura, T., Egashira, M., Matsuzawa, K., Ichijo, H., O'ishi, R., and Abe-Ouchi, A.: A simulation of the global distribution and radiative forcing of soil dust aerosols at the Last Glacial Maximum, Atmos. Chem. Phys., 9, 3061–3073, https://doi.org/10.5194/acp-9-3061-2009, 2009.
Tan, Y. B., Peng, L., Shi, Z., and Chen, H. R.: Lightning flash density in relation to aerosol over Nanjing (China), Atmos. Res., 174–175, 1–8, https://doi.org/10.1016/j.atmosres.2016.01.009, 2016.
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.6113, 2019.
Tost, H.: Chemistry–climate interactions of aerosol nitrate from lightning, Atmos. Chem. Phys., 17, 1125–1142, https://doi.org/10.5194/acp-17-1125-2017, 2017.
van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R.: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015), Geosci. Model Dev., 10, 3329–3357, https://doi.org/10.5194/gmd-10-3329-2017, 2017.
Veraverbeke, S., Finney, D., van der Werf, G., van Wees, D., Xu, W., and Jones, M.: Global attribution of anthropogenic and lightning fires, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1160, https://doi.org/10.5194/egusphere-egu22-1160, 2022.
Wang, Q., Li, Z., Guo, J., Zhao, C., and Cribb, M.: The climate impact of aerosols on the lightning flash rate: is it detectable from long-term measurements?, Atmos. Chem. Phys., 18, 12797–12816, https://doi.org/10.5194/acp-18-12797-2018, 2018.
Wang, Y., Wan, Q., Meng, W., Liao, F., Tan, H., and Zhang, R.: Long-term impacts of aerosols on precipitation and lightning over the Pearl River Delta megacity area in China, Atmos. Chem. Phys., 11, 12421–12436, https://doi.org/10.5194/acp-11-12421-2011, 2011.
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011.
Wild, O.: Modelling the global tropospheric ozone budget: exploring the variability in current models, Atmos. Chem. Phys., 7, 2643–2660, https://doi.org/10.5194/acp-7-2643-2007, 2007.
Williams, E. R.: Earle Williams, https://web.mit.edu/earlerw/www/index.html, last access: 19 December 2022.
Williams, E. R., Weber, M. E., and Orville, R. E.: The relationship between lightning type and convective state of thunderclouds, J. Geophys. Res.-Atmos., 94, 13213–13220, https://doi.org/10.1029/JD094iD11p13213, 1989.
Yang, X., Yao, Z., Li, Z., and Fan, T.: Heavy air pollution suppresses summer thunderstorms in central China, J. Atmos. Sol.-Terr. Phy., 95–96, 28–40, https://doi.org/10.1016/j.jastp.2012.12.023, 2013.
Yuan, T., Remer, L. A., Pickering, K. E., and Yu, H.: Observational evidence of aerosol enhancement of lightning activity and convective invigoration, Geophys. Res. Lett., 38, 4701, https://doi.org/10.1029/2010GL046052, 2011.
Zeng, G., Pyle, J. A., and Young, P. J.: Impact of climate change on tropospheric ozone and its global budgets, Atmos. Chem. Phys., 8, 369–387, https://doi.org/10.5194/acp-8-369-2008, 2008.
Zhao, P., Zhou, Y., Xiao, H., Liu, J., Gao, J., and Ge, F.: Total Lightning Flash Activity Response to Aerosol over China Area, Atmosphere, 8, 26, https://doi.org/10.3390/atmos8020026, 2017.
Zhao, P., Li, Z., Xiao, H., Wu, F., Zheng, Y., Cribb, M. C., Jin, X., and Zhou, Y.: Distinct aerosol effects on cloud-to-ground lightning in the plateau and basin regions of Sichuan, Southwest China, Atmos. Chem. Phys., 20, 13379–13397, https://doi.org/10.5194/acp-20-13379-2020, 2020.
Short summary
Lightning has big social impacts. Lightning-produced NOx (LNOx) plays a vital role in atmospheric chemistry and climate. Investigating past lightning and LNOx trends can provide essential indicators of all lightning-related phenomena. Simulations show almost flat global lightning and LNOx trends during 1960–2014. Past global warming enhances the trends positively, but increases in aerosol have the opposite effect. Moreover, global lightning decreased markedly after the Pinatubo eruption.
Lightning has big social impacts. Lightning-produced NOx (LNOx) plays a vital role in...
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