Articles | Volume 21, issue 23
https://doi.org/10.5194/acp-21-17529-2021
© Author(s) 2021. 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-21-17529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Lightning-ignited wildfires and long continuing current lightning in the Mediterranean Basin: preferential meteorological conditions
Francisco J. Pérez-Invernón
CORRESPONDING AUTHOR
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Heidi Huntrieser
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Sergio Soler
Instituto de Astrofísica de Andalucía, CSIC, Glorieta de la Astronomía s/n, 18008 Granada, Spain
Francisco J. Gordillo-Vázquez
Instituto de Astrofísica de Andalucía, CSIC, Glorieta de la Astronomía s/n, 18008 Granada, Spain
Nicolau Pineda
Meteorological Service of Catalonia, Carrer Berlín 38–46, 08029 Barcelona, Spain
Lightning Research Group, Technical University of Catalonia, Campus de Terrassa, Edifici TR1, Carrer Colom 1, Terrassa, 08222 Barcelona, Spain
Javier Navarro-González
Image Processing Laboratory, University of Valencia, Valencia, Spain
Víctor Reglero
Image Processing Laboratory, University of Valencia, Valencia, Spain
Joan Montanyà
Lightning Research Group, Technical University of Catalonia, Campus de Terrassa, Edifici TR1, Carrer Colom 1, Terrassa, 08222 Barcelona, Spain
Oscar van der Velde
Lightning Research Group, Technical University of Catalonia, Campus de Terrassa, Edifici TR1, Carrer Colom 1, Terrassa, 08222 Barcelona, Spain
Nikos Koutsias
Department of Environmental Engineering, University of Patras, G. Seferi 2, Agrinio GR-30100, Greece
Related authors
Francisco J. Pérez-Invernón, Francisco J. Gordillo-Vázquez, Heidi Huntrieser, Patrick Jöckel, and Eric J. Bucsela
Atmos. Chem. Phys., 25, 5557–5575, https://doi.org/10.5194/acp-25-5557-2025, https://doi.org/10.5194/acp-25-5557-2025, 2025
Short summary
Short summary
Lightning plays a significant role in tropospheric chemistry by producing substantial amounts of nitrogen oxides. According to recent estimates, thunderstorms that produce a higher lightning frequency rate also produce less nitrogen oxide per flash. We implemented the dependency of nitrogen oxide production per flash on lightning flash frequency in a chemical atmospheric model.
Sergio Soler, Francisco J. Gordillo-Vázquez, Francisco J. Pérez-Invernón, Patrick Jöckel, Torsten Neubert, Olivier Chanrion, Victor Reglero, and Nikolai Østgaard
Atmos. Chem. Phys., 24, 10225–10243, https://doi.org/10.5194/acp-24-10225-2024, https://doi.org/10.5194/acp-24-10225-2024, 2024
Short summary
Short summary
Sudden local ozone (O3) enhancements have been reported in different regions of the world since the 1970s. While the hot channel of lightning strokes directly produce significant amounts of nitrogen oxide, no direct emission of O3 is expected. Corona discharges in convective active regions could explain local O3 increases, which remains unexplained. We present the first mathematical functions that relate the global annual frequency of in-cloud coronas with four sets of meteorological variables.
Francisco J. Pérez-Invernón, Francisco J. Gordillo-Vázquez, Alejandro Malagón-Romero, and Patrick Jöckel
Atmos. Chem. Phys., 24, 3577–3592, https://doi.org/10.5194/acp-24-3577-2024, https://doi.org/10.5194/acp-24-3577-2024, 2024
Short summary
Short summary
Sprites are electrical discharges that occur in the upper atmosphere. Recent modelling and observational data suggest that they may have a measurable impact on atmospheric chemistry. We incorporate both the occurrence rate of sprites and their production of chemical species into a chemistry–climate model. While our results indicate that sprites have a minimal global influence on atmospheric chemistry, they underscore their noteworthy importance at a regional scale.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
Short summary
Short summary
This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Francisco J. Pérez-Invernón, Heidi Huntrieser, Thilo Erbertseder, Diego Loyola, Pieter Valks, Song Liu, Dale J. Allen, Kenneth E. Pickering, Eric J. Bucsela, Patrick Jöckel, Jos van Geffen, Henk Eskes, Sergio Soler, Francisco J. Gordillo-Vázquez, and Jeff Lapierre
Atmos. Meas. Tech., 15, 3329–3351, https://doi.org/10.5194/amt-15-3329-2022, https://doi.org/10.5194/amt-15-3329-2022, 2022
Short summary
Short summary
Lightning, one of the major sources of nitrogen oxides in the atmosphere, contributes to the tropospheric concentration of ozone and to the oxidizing capacity of the atmosphere. In this work, we contribute to improving the estimation of lightning-produced nitrogen oxides in the Ebro Valley and the Pyrenees by using two different TROPOMI products and comparing the results.
Francisco J. Pérez-Invernón, Heidi Huntrieser, Patrick Jöckel, and Francisco J. Gordillo-Vázquez
Geosci. Model Dev., 15, 1545–1565, https://doi.org/10.5194/gmd-15-1545-2022, https://doi.org/10.5194/gmd-15-1545-2022, 2022
Short summary
Short summary
This study reports the first parameterization of long-continuing-current lightning in a climate model. Long-continuing-current lightning is proposed to be the main precursor of lightning-ignited wildfires and sprites, a type of transient luminous event taking place in the mesosphere. This parameterization can significantly contribute to improving the implementation of wildfires in climate models.
Alejandro Luque, Francisco José Gordillo-Vázquez, Dongshuai Li, Alejandro Malagón-Romero, Francisco Javier Pérez-Invernón, Anthony Schmalzried, Sergio Soler, Olivier Chanrion, Matthias Heumesser, Torsten Neubert, Víctor Reglero, and Nikolai Østgaard
Geosci. Model Dev., 13, 5549–5566, https://doi.org/10.5194/gmd-13-5549-2020, https://doi.org/10.5194/gmd-13-5549-2020, 2020
Short summary
Short summary
Lightning flashes are often recorded from space-based platforms. Besides being valuable inputs for weather forecasting, these observations also enable research into fundamental questions regarding lightning physics. To exploit them, it is essential to understand how light propagates from a lightning flash to a space-based observation instrument. Here, we present an open-source software tool to model this process that extends on previous work and overcomes some of the existing limitations.
Francisco J. Pérez-Invernón, Francisco J. Gordillo-Vázquez, Heidi Huntrieser, Patrick Jöckel, and Eric J. Bucsela
Atmos. Chem. Phys., 25, 5557–5575, https://doi.org/10.5194/acp-25-5557-2025, https://doi.org/10.5194/acp-25-5557-2025, 2025
Short summary
Short summary
Lightning plays a significant role in tropospheric chemistry by producing substantial amounts of nitrogen oxides. According to recent estimates, thunderstorms that produce a higher lightning frequency rate also produce less nitrogen oxide per flash. We implemented the dependency of nitrogen oxide production per flash on lightning flash frequency in a chemical atmospheric model.
Eric Förster, Heidi Huntrieser, Michael Lichtenstern, Falk Pätzold, Lutz Bretschneider, Andreas Schlerf, Sven Bollmann, Astrid Lampert, Jarosław Nęcki, Paweł Jagoda, Justyna Swolkień, Dominika Pasternak, Robert A. Field, and Anke Roiger
EGUsphere, https://doi.org/10.5194/egusphere-2025-1010, https://doi.org/10.5194/egusphere-2025-1010, 2025
Short summary
Short summary
We introduce a helicopter-borne mass balance approach, utilizing the HELiPOD platform, to accurately quantify methane (CH₄) emissions from coal mining activities. The comparison of our top-down mass flux estimates (up to 3000 kg h-1) against those from bottom-up in-mine CH4 safety sensors revealed very good agreement. This approach also has a great potential in quantifying emission source strengths (down to 20 kg h-1) from a wide range of other CH4 emitters (e.g. landfills, oil & gas industry).
Sergio Soler, Francisco J. Gordillo-Vázquez, Francisco J. Pérez-Invernón, Patrick Jöckel, Torsten Neubert, Olivier Chanrion, Victor Reglero, and Nikolai Østgaard
Atmos. Chem. Phys., 24, 10225–10243, https://doi.org/10.5194/acp-24-10225-2024, https://doi.org/10.5194/acp-24-10225-2024, 2024
Short summary
Short summary
Sudden local ozone (O3) enhancements have been reported in different regions of the world since the 1970s. While the hot channel of lightning strokes directly produce significant amounts of nitrogen oxide, no direct emission of O3 is expected. Corona discharges in convective active regions could explain local O3 increases, which remains unexplained. We present the first mathematical functions that relate the global annual frequency of in-cloud coronas with four sets of meteorological variables.
Francisco J. Pérez-Invernón, Francisco J. Gordillo-Vázquez, Alejandro Malagón-Romero, and Patrick Jöckel
Atmos. Chem. Phys., 24, 3577–3592, https://doi.org/10.5194/acp-24-3577-2024, https://doi.org/10.5194/acp-24-3577-2024, 2024
Short summary
Short summary
Sprites are electrical discharges that occur in the upper atmosphere. Recent modelling and observational data suggest that they may have a measurable impact on atmospheric chemistry. We incorporate both the occurrence rate of sprites and their production of chemical species into a chemistry–climate model. While our results indicate that sprites have a minimal global influence on atmospheric chemistry, they underscore their noteworthy importance at a regional scale.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
Short summary
Short summary
This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Francisco J. Pérez-Invernón, Heidi Huntrieser, Thilo Erbertseder, Diego Loyola, Pieter Valks, Song Liu, Dale J. Allen, Kenneth E. Pickering, Eric J. Bucsela, Patrick Jöckel, Jos van Geffen, Henk Eskes, Sergio Soler, Francisco J. Gordillo-Vázquez, and Jeff Lapierre
Atmos. Meas. Tech., 15, 3329–3351, https://doi.org/10.5194/amt-15-3329-2022, https://doi.org/10.5194/amt-15-3329-2022, 2022
Short summary
Short summary
Lightning, one of the major sources of nitrogen oxides in the atmosphere, contributes to the tropospheric concentration of ozone and to the oxidizing capacity of the atmosphere. In this work, we contribute to improving the estimation of lightning-produced nitrogen oxides in the Ebro Valley and the Pyrenees by using two different TROPOMI products and comparing the results.
Nicolau Pineda, Juan Carlos Peña, Xavier Soler, Montse Aran, and Núria Pérez-Zanón
Adv. Sci. Res., 19, 39–49, https://doi.org/10.5194/asr-19-39-2022, https://doi.org/10.5194/asr-19-39-2022, 2022
Short summary
Short summary
Wildfire origins can be related to human activity or to natural phenomena, like lightning. Under favourable environmental conditions, lightning ignitions can develop into a fire. In the present study, we analyse the kind of weather that favours wildfires ignited by lightning in Catalonia. We have found that most fires occur under three types of weather. These results help to improve our understanding of lightning fires and are of great assistance to wildfire management agencies.
M. Dolores Andrés Hernández, Andreas Hilboll, Helmut Ziereis, Eric Förster, Ovid O. Krüger, Katharina Kaiser, Johannes Schneider, Francesca Barnaba, Mihalis Vrekoussis, Jörg Schmidt, Heidi Huntrieser, Anne-Marlene Blechschmidt, Midhun George, Vladyslav Nenakhov, Theresa Harlass, Bruna A. Holanda, Jennifer Wolf, Lisa Eirenschmalz, Marc Krebsbach, Mira L. Pöhlker, Anna B. Kalisz Hedegaard, Linlu Mei, Klaus Pfeilsticker, Yangzhuoran Liu, Ralf Koppmann, Hans Schlager, Birger Bohn, Ulrich Schumann, Andreas Richter, Benjamin Schreiner, Daniel Sauer, Robert Baumann, Mariano Mertens, Patrick Jöckel, Markus Kilian, Greta Stratmann, Christopher Pöhlker, Monica Campanelli, Marco Pandolfi, Michael Sicard, José L. Gómez-Amo, Manuel Pujadas, Katja Bigge, Flora Kluge, Anja Schwarz, Nikos Daskalakis, David Walter, Andreas Zahn, Ulrich Pöschl, Harald Bönisch, Stephan Borrmann, Ulrich Platt, and John P. Burrows
Atmos. Chem. Phys., 22, 5877–5924, https://doi.org/10.5194/acp-22-5877-2022, https://doi.org/10.5194/acp-22-5877-2022, 2022
Short summary
Short summary
EMeRGe provides a unique set of in situ and remote sensing airborne measurements of trace gases and aerosol particles along selected flight routes in the lower troposphere over Europe. The interpretation uses also complementary collocated ground-based and satellite measurements. The collected data help to improve the current understanding of the complex spatial distribution of trace gases and aerosol particles resulting from mixing, transport, and transformation of pollution plumes over Europe.
Francisco J. Pérez-Invernón, Heidi Huntrieser, Patrick Jöckel, and Francisco J. Gordillo-Vázquez
Geosci. Model Dev., 15, 1545–1565, https://doi.org/10.5194/gmd-15-1545-2022, https://doi.org/10.5194/gmd-15-1545-2022, 2022
Short summary
Short summary
This study reports the first parameterization of long-continuing-current lightning in a climate model. Long-continuing-current lightning is proposed to be the main precursor of lightning-ignited wildfires and sprites, a type of transient luminous event taking place in the mesosphere. This parameterization can significantly contribute to improving the implementation of wildfires in climate models.
Alejandro Luque, Francisco José Gordillo-Vázquez, Dongshuai Li, Alejandro Malagón-Romero, Francisco Javier Pérez-Invernón, Anthony Schmalzried, Sergio Soler, Olivier Chanrion, Matthias Heumesser, Torsten Neubert, Víctor Reglero, and Nikolai Østgaard
Geosci. Model Dev., 13, 5549–5566, https://doi.org/10.5194/gmd-13-5549-2020, https://doi.org/10.5194/gmd-13-5549-2020, 2020
Short summary
Short summary
Lightning flashes are often recorded from space-based platforms. Besides being valuable inputs for weather forecasting, these observations also enable research into fundamental questions regarding lightning physics. To exploit them, it is essential to understand how light propagates from a lightning flash to a space-based observation instrument. Here, we present an open-source software tool to model this process that extends on previous work and overcomes some of the existing limitations.
Cited articles
Abreu, D., Chandan, D., Holzworth, R. H., and Strong, K.: A performance assessment of the World Wide Lightning Location Network (WWLLN) via comparison with the Canadian Lightning Detection Network (CLDN), Atmos. Meas. Tech., 3, 1143–1153, https://doi.org/10.5194/amt-3-1143-2010, 2010. a
Adachi, T., Cummer, S. A., Li, J., Takahashi, Y., Hsu, R.-R., Su, H.-T., Chen, A. B., Mende, S. B., and Frey, H. U.: Estimating lightning current moment waveforms from satellite optical measurements, Geophys. Res. Lett., 36, L18808, https://doi.org/10.1029/2009GL039911, 2009. a, b
Agencia Estatal de Meteorologica (AEMET): Homepage, available at: http://www.aemet.es/ca/eltiempo/observacion/radar, last access: 12 February 2021. a
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, 2002. a
Altaratz, O., Koren, I., Yair, Y., and Price, C.: Lightning response to smoke from Amazonian fires, Geophys. Res. Lett., 37, L07801, https://doi.org/10.1029/2010GL042679, 2010. a
Amatulli, G., Peréz-Cabello, F., and de la Riva, J.: Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty, Ecol. Model., 200, 321–333, 2007. a
Badia, A., Saurí, D., Cerdan, R., and Llurdés, J.-C.: Causality and management of forest fires in Mediterranean environments: an example from Catalonia, Glob. Environ. Change Part B: Environ. Hazards, 4, 23–32, 2002. a
Baranovskiy, N. and Yankovich, E.: GIS-Technologies and Mathematical Simulation to Predict Lightning-caused Forest Fire Danger, Radio Electronics, Computer Science, Control, 1839, 2–15, 2018. a
Bitzer, P. M. and Christian, H. J.: Timing uncertainty of the lightning imaging sensor, J. Atmos. Ocean. Tech., 32, 453–460, 2015. 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.: Lightning Imaging Sensor (LIS) for the International Space Station (ISS): mission description and science goals, in: International Conference on Atmospheric Electricity (ICAE 2014), Norman, OK, USA, 16–20 June 2014, No. M14-3658, 2014. 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, Earth Space Sci. Open Archive, 35812, 83, 2020. a, b
Blakeslee, R. J.: Non-Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data, [indicate subset used], NASA Global Hydrology Resource Center DAAC [data set], Huntsville, Alabama, U.S.A., https://doi.org/10.5067/LIS/ISSLIS/DATA110, 2021. a
Camia, A., Durrant Houston, T., and San-Miguel-Ayanz, J.: The European fire database: development, structure and implementation, in: Proceedings of the VI International conference on forest fire research, Coimbra, Portugal, 15 November 2010, edited by: Viegas, D. X., A20, 2010. 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, 2014. a
Christian, H. J., Blakeslee, R. J., Boccippio, D. J., Boeck, W. L., Buechler, D. E., Driscoll, K. T., Goodman, S. J., Hall, J. M., Koshak, J. M., Mach, D. M., and Stewart, M. F.: Global frequency and distribution of lightning as observed from space by the Optical Transient Detector, J. Geophys. Res., 108, ACL 4-1, https://doi.org/10.1029/2002JD002347, 2003. a
Conedera, M., Cesti, G., Pezzatti, G., Zumbrunnen, T., and Spinedi, F.: Lightning-induced fires in the Alpine region: An increasing problem, Forest Ecol. Manag., 234, S68, 2006. a
Coughlan, R., Di Giuseppe, F., Vitolo, C., Barnard, C., Lopez, P., and Drusch, M.: Using machine learning to predict fire-ignition occurrences from lightning forecasts, Meteorol. Appl., 28, e1973, 2021. a
Cummins, K. L. and Murphy, M. J.: An overview of lightning locating systems: History, techniques, and data uses, with an in-depth look at the US NLDN, IEEE T. Electromagn. C., 51, 499–518, 2009. a
Deierling, W., Latham, J., Petersen, W. A., Ellis, S. M., and Christian, H. J.: On the relationship of thunderstorm ice hydrometeor characteristics and total lightning measurements, Atmos. Res., 76, 114–126, https://doi.org/10.1016/j.atmosres.2004.11.023, 2005. a
Dowden, R. L., Brundell, J. B., and Rodger, C. J.: VLF lightning location by time of group arrival (TOGA) at multiple sites, J. Atmos. Sol.-Terr. Phy., 64, 817–830, 2002. a
Earth Networks: Homepage, available at: https://www.earthnetworks.com/, last access: 12 February 2021. a
Emersic, C., Heinselman, P., MacGorman, D. R., and Bruning, E.: Lightning activity in a hail-producing storm observed with phased-array radar, Mon. Weather Rev., 139, 1809–1825, 2011. a
EUMETSAT: Cloud Top Height – MSG – 0 degree, EUMETSAT [data set], available at: https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:CTH,
last access: 12 February 2021. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007. a
Fernandes, S.: Incêndios Florestais em Portugal Continental fora do “período crítico”. Contributos para o seu conhecimento, Master's thesis, Departamento de Geografia e Turismo, NICIF, CEGOT e RISCOS, Faculdade de Letras, Universidade de Coimbra, Portugal, https://doi.org/10.14195/1647-7723_25-1_15, 2015. a
Fernandes, W. A., Pinto, I. R., Pinto Jr, O., Longo, K. M., and Freitas, S. R.: New findings about the influence of smoke from fires on the cloud-to-ground lightning characteristics in the Amazon region, Geophys. Res. Lett., 33, L20810, https://doi.org/10.1029/2006GL027744, 2006. a
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. a, b
Flannigan, M. and Wotton, B.: Lightning-ignited forest fires in northwestern Ontario, Can. J. Forest Res., 21, 277–287, 1991. a
Fuquay, D. M.: A model for predicting lightning fire ignition in wildland fuels, vol. 217, Intermountain Forest and Range Experiment Station, Forest Service, US, 1979. a
Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., and Lampin, C.: A review of the main driving factors of forest fire ignition over Europe, Environ. Manage., 51, 651–662, 2013. a
García-Ortega, E., Trobajo, M. T., López, L., and Sánchez, J. L.: Synoptic patterns associated with wildfires caused by lightning in Castile and Leon, Spain, Nat. Hazards Earth Syst. Sci., 11, 851–863, https://doi.org/10.5194/nhess-11-851-2011, 2011. a
Gora, E. M., Bitzer, P. M., Burchfield, J. C., Schnitzer, S. A., and Yanoviak, S. P.: Effects of lightning on trees: A predictive model based on in situ electrical resistivity, Ecol. Evol., 7, 8523–8534, 2017. a
Gordillo-Vázquez, F. J., Pérez-Invernón, F. J., Huntrieser, H., and Smith, A. K.: Comparison of Six Lightning Parameterizations in CAM5 and the Impact on Global Atmospheric Chemistry, Earth Space Sci., 6, 2317–2346, https://doi.org/10.1029/2019EA000873, 2019. a
Granström, A.: Fire management for biodiversity in the European boreal forest, Scand. J. Forest Res., 16, 62–69, 2001. a
Granström, A. and Niklasson, M.: Potentials and limitations for human control over historic fire regimes in the boreal forest, Philos. T. R. Soc. B, 363, 2351–2356, 2008. a
Grewe, V., Brunner, D., Dameris, M., Grenfell, J., Hein, R., Shindell, D., and Staehelin, J.: Origin and variability of upper tropospheric nitrogen oxides and ozone at northern mid-latitudes, Atmos. Environ., 35, 3421–3433, 2001. a
Gutiérrez Núñez, J. M., Guerrero Peralta, I., and Momblona, D.: Radar meteorológico y red de rayos, https://doi.org/10.31978/014-18-009-X.08, 2018. a, b
Hall, B. L. and Brown, T. J.: Climatology of positive polarity flashes and multiplicity and their relation to natural wildfire ignitions, in: Preprints, International Lightning Detection Conference, Tucson, Arizona, USA, 24–25 April 2006. a
Hardy, C. C., Bunnell, D. L., Menakis, J., Schmidt, K., Long, D., Simmerman, D., and Johnston, C.: Coarse-scale spatial data for wildland fire and fuel management, USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, Missoula, Montana, USA, 1999. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018a. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018b. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020. a
Huntrieser, H., Lichtenstern, M., Scheibe, M., Aufmhoff, H., Schlager, H., Pucik, T., Minikin, A., Weinzierl, B., Heimerl, K., Pollack, I. B., Peischl, J., Ryerson, T. B., Weinheimer, A. J., Honomichl, S., Ridley, B. A., Biggerstaff, M. I., Betten, D. P., Hair, J. W., Butler, C. F., Schwartz, M. J., and Barth, M. C.: Injection of lightning-produced NOx, water vapor, wildfire emissions, and stratospheric air to the UT/LS as observed from DC3 measurements, J. Geophys. Res.-Atmos., 121, 6638–6668, 2016. a
Hutchins, M., Holzworth, R., Brundell, J., and Rodger, C.: Relative detection efficiency of the world wide lightning location network, Radio Sci., 47, 1–9, 2012a. a
Hutchins, M. L., Holzworth, R. H., Rodger, C. J., and Brundell, J. B.: Far-field power of lightning strokes as measured by the World Wide Lightning Location Network, J. Atmos. Ocean. Tech., 29, 1102–1110, 2012b. a
Instituto da Conservação da Natureza e das Florestas (ICNF): Incêndios Rurais, ICNF [data set], available at: http://www2.icnf.pt/portal/florestas/dfci/inc, last access: 12 February 2021. a
Kochtubajda, B., Flannigan, M., Gyakum, J., Stewart, R., Logan, K., and Nguyen, T.-V.: Lightning and fires in the Northwest Territories and responses to future climate change, Arctic, 59, 211–221, 2006. a
Komarek, E.: The natural history of lightning, in: Proceedings of the Tall Timbers fire ecology conference, Tallahassee, Florida, 9–10 April 1964,
vol. 3, 139–183, 1964. a
Krawchuk, M., Cumming, S., Flannigan, M. D., and Wein, R.: Biotic and abiotic regulation of lightning fire initiation in the mixedwood boreal forest, Ecology, 87, 458–468, 2006. a
Kruskal, W. H. and Wallis, W. A.: Use of ranks in one-criterion variance analysis, J. Am. Stat. Assoc., 47, 583–621, 1952. a
Lang, T. J. and Rutledge, S. A.: Cloud-to-ground lightning downwind of the 2002 Hayman forest fire in Colorado, Geophys. Res. Lett., 33, L03804, https://doi.org/10.1029/2005GL024608, 2006. a
Lang, T. J., Rutledge, S. A., Dolan, B., Krehbiel, P., Rison, W., and Lindsey, D. T.: Lightning in wildfire smoke plumes observed in Colorado during summer 2012, Mon. Weather Rev., 142, 489–507, 2014. a
Lapierre, J. L., Laughner, J. L., Geddes, J. A., Koshak, W. J., Cohen, R. C., and Pusede, S. E.: Observing US regional variability in lightning NO2 production rates, J. Geophys. Res.-Atmos., 125, e2019JD031362, 2020. a
Larjavaara, M., Kuuluvainen, T., and Rita, H.: Spatial distribution of lightning-ignited forest fires in Finland, Forest Ecol. Manag., 208, 177–188, 2005a. a
Latham, D. and Williams, E.: Lightning and forest fires, in: Forest Fires, Elsevier, San Diego, https://doi.org/10.1016/B978-012386660-8/50013-1, 375–418, 2001. a, b
Latham, D. J. and Schlieter, J. A.: Ignition probabilities of wildland fuels based on simulated lightning discharges, vol. 411, US Department of Agriculture, Forest Service, Intermountain Research Station, Missoula, 1989. a
Liu, C. and Heckman, S.: The application of total lightning detection and cell tracking for severe weather prediction, in: 91st American Meteorological Society Annual Meeting, Seattle, WA, 23–27 January 2011, 1–10, 2011. a
López-Santalla, A. and López-Garcia, M.: Los Incendios Forestales en España. Decenio 2006–2015, Madrid, 003-19-031-5, 2019. a
Lynn, B. H., Yair, Y., Price, C., Kelman, G., and Clark, A. J.: Predicting Cloud-to-Ground and Intracloud Lightning in Weather Forecast Models, Weather Forecast., 27, 1470–1488, https://doi.org/10.1175/WAF-D-11-00144.1, 2012. a
Lyons, W. A., Nelson, T. E., Williams, E. R., Cramer, J. A., and Turner, T. R.: Enhanced Positive Cloud-to-Ground Lightning in Thunderstorms Ingesting Smoke from Fires, Science, 282, 77, https://doi.org/10.1126/science.282.5386.77, 1998. a
Mach, D. M., Christian, H. J., Blakeslee, R. J., Boccippio, D. J., Goodman, S. J., and Boeck, W. L.: Performance assessment of the optical transient detector and lightning imaging sensor, J. Geophys. Res.-Atmos., 112, D09210, https://doi.org/10.1029/2006JD007787, 2007. a, b, c
Mallick, S., Rakov, V., Hill, J., Ngin, T., Gamerota, W., Pilkey, J., Jordan, D., Uman, M., Heckman, S., Sloop, C. D., and Liu, C.: Performance characteristics of the ENTLN evaluated using rocket-triggered lightning data, Electr. Pow. Syst. Res., 118, 15–28, 2015. a
McEachron, K. and Hagenguth, J.: Effect of lightning on thin metal surfaces, IEEE T. Commun., 61, 559–564, 1942. a
Müller, M. M., Vacik, H., Diendorfer, G., Arpaci, A., Formayer, H., and Gossow, H.: Analysis of lightning-induced forest fires in Austria, Theor. Appl. Climatol., 111, 183–193, 2013. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to 1980, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2021. a
Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C., and Koshak, W. J.: Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data, J. Geophys. Res.-Atmos., 117, D20307, https://doi.org/10.1029/2012JD017934, 2012. a
Nauslar, N. J.: A forecast procedure for dry thunderstorms, Order No. 1484044, ProQuest Dissertations & Theses Global, 839910451, available at: https://www.proquest.com/dissertations-theses/forecast-procedure-dry-thunderstorms/docview/839910451/se-2?accountid=10336 (last access: 12 February 2021), 2010. a, b, c, d, e, f, g, h
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., and Pereira, J. M.: Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest, Forest Ecol. Manag., 275, 117–129, 2012. a
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F., Laloyaux, P., Tan, D. G., Peubey, C., Thépaut, J.-N., Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.: ERA-20C: An atmospheric reanalysis of the twentieth century, J. Climate, 29, 4083–4097, 2016. a
Pyne, S. J., Andrews, P. L., Laven, R. D., and Cheney, N.: Introduction to wildland fire, Forestry, 71, 82–82, 1998. a
Reineking, B., Weibel, P., Conedera, M., and Bugmann, H.: Environmental determinants of lightning-v. human-induced forest fire ignitions differ in a temperate mountain region of Switzerland, Int. J. Wildland Fire, 19, 541–557, 2010. a
Rodger, C. J., Brundell, J. B., and Dowden, R. L.: Location accuracy of VLF World-Wide Lightning Location (WWLL) network: Post-algorithm upgrade, Ann. Geophys., 23, 277–290, https://doi.org/10.5194/angeo-23-277-2005, 2005. a
Rolstad, J., Blanck, Y.-l., and Storaunet, K. O.: Fire history in a western Fennoscandian boreal forest as influenced by human land use and climate, Ecol Monogr., 87, 219–245, 2017. a
Rosenfeld, D., Fromm, M., Trentmann, J., Luderer, G., Andreae, M. O., and Servranckx, R.: The Chisholm firestorm: observed microstructure, precipitation and lightning activity of a pyro-cumulonimbus, Atmos. Chem. Phys., 7, 645–659, https://doi.org/10.5194/acp-7-645-2007, 2007. a
Rossow, W. B.: International Satellite Cloud Climatology Project (ISCCP) documentation of new cloud datasets, WMO/TD737, World Climate Research Programme (ICSU and WMO), World Climate Research Programme, Geneva, Switzerland, 115, 1996. a
Said, R. K., Cohen, M. B., and Inan, U. S.: Highly intense lightning over the oceans: Estimated peak currents from global GLD360 observations, J. Geophys. Res.-Atmos., 118, 6905–6915, 2013. a
San José, R., Pérez, J. L., González, R. M., Pecci, J., and Palacios, M.: Analysis of fire behaviour simulations over Spain with WRF-FIRE, Int. J. Environ. Pollut., 55, 148–156, 2014. a
San-Miguel-Ayanz, J., Moreno, J. M., and Camia, A.: Analysis of large fires in European Mediterranean landscapes: lessons learned and perspectives, Forest Ecol. Manag., 294, 11–22, 2013. a
Schultz, M. G., Heil, A., Hoelzemann, J. J., Spessa, A., Thonicke, K., Goldammer, J. G., Held, A. C., Pereira, J. M., and van Het Bolscher, M.: Global wildland fire emissions from 1960 to 2000, Global Biogeochem. Cy., 22, GB2002, https://doi.org/10.1029/2007GB003031, 2008. a
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. a
Soriano, L. R., De Pablo, F., and Díez, E. G.: Relationship between convective precipitation and cloud-to-ground lightning in the Iberian Peninsula, Mon. Weather Rev., 129, 2998–3003, 2001. a
Stocks, B. J., Mason, J. A., Todd, J. B., Bosch, E. M., Wotton, B. M., Amiro, B. D., Flannigan, M. D., Hirsch, K. G., Logan, K. A., Martell, D. L., and Skinner, W. R.: Large forest fires in Canada, 1959–1997, J. Geophys. Res.-Atmos., 107, FFR-5, 2002. a
Stuhlmann, R., Rodriguez, A., Tjemkes, S., Grandell, J., Arriaga, A., Bézy, J.-L., Aminou, D., and Bensi, P.: Plans for EUMETSAT's Third Generation Meteosat geostationary satellite programme, Adv. Space Res., 36, 975–981, 2005. a
Takahashi, T.: Riming electrification as a charge generation mechanism in thunderstorms, J. Atmos. Sci., 35, 1536–1548, 1978. a
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols on convective clouds and precipitation, Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369, 2012. 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
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010.
a
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017. a
Veraverbeke, S., Rogers, B. M., Goulden, M. L., Jandt, R. R., Miller, C. E., Wiggins, E. B., and Randerson, J. T.: Lightning as a major driver of recent large fire years in North American boreal forests, Nat. Clim. Change, 7, 529, 2017. a
World Wide Lightning Location Network (WWLLN): Homepage, available at: http://wwlln.net/, last access: 12 February 2021. a
Wotton, B. and Martell, D. L.: A lightning fire occurrence model for Ontario, Can. J. Forest Res., 35, 1389–1401, 2005. a
Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., and Llasat, M. d. C.: Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields, J. Geophys. Res., 115, D04205, https://doi.org/10.1029/2008JD010868, 2010. a
Zhu, Y., Rakov, V. A., Tran, M. D., Stock, M. G., Heckman, S., Liu, C., Sloop, C. D., Jordan, D. M., Uman, M. A., Caicedo, J. A., Kotovsky, D. A., Wilkes, R. A., Carvalho, F. L., Ngin, T., Gamerota, W. R., Pilkey, J. T., and Hare, B. M.: Evaluation of ENTLN performance characteristics based on the ground truth natural and rocket-triggered lightning data acquired in Florida, J. Geophys. Res.-Atmos., 122, 9858–9866, 2017. a
Short summary
Lightning-ignited fires tend to occur in remote areas and can spread significantly before suppression. Long continuing current (LCC) lightning, preferably taking place in dry thunderstorms, is believed to be the main precursor of lightning-ignited fires. We analyze fire databases of lightning-ignited fires in the Mediterranean basin and report the shared meteorological conditions of fire- and LCC-lightning-producing thunderstorms. These results can be useful to improve fire forecasting methods.
Lightning-ignited fires tend to occur in remote areas and can spread significantly before...
Altmetrics
Final-revised paper
Preprint