Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Future increases in Arctic lightning and fire risk for permafrost carbon


Lightning is an indicator and a driver of climate change. Here, using satellite observations of lightning flash rate and ERA5 reanalysis, we find that the spatial pattern of summer lightning over northern circumpolar regions exhibits a strong positive relationship with the product of convective available potential energy (CAPE) and precipitation. Applying this relationship to Climate Model Intercomparison Project Phase 5 climate projections for a high-emissions scenario (RCP8.5) shows an increase in CAPE (86 ± 22%) and precipitation (17 ± 2%) in areas underlain by permafrost, causing summer lightning to increase by 112 ± 38% by the end of the century (2081–2100). Future flash rates at the northern treeline are comparable to current levels 480 km to the south in boreal forests. We hypothesize that lightning increases may induce a fire–vegetation feedback whereby more burning in Arctic tundra expedites the northward migration of boreal trees, with the potential to accelerate the positive feedback associated with permafrost soil carbon release.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Contemporary lightning flash rates across high-northern-latitude regions are positively correlated with the product of CAPE and Precip.
Fig. 2: Lightning flash rates in high-northern-latitude terrestrial ecosystems are projected to increase by 93 ± 27%, with a larger increase (112 ± 38%) in areas underlain by permafrost.
Fig. 3: By the end of the twenty-first century, projected lightning flash rates over Arctic tundra are similar to levels now detected over boreal forests south of the treeline.
Fig. 4: Future increases in lightning flash rate may initiate a feedback that amplifies the impacts of climate change in high-northern-latitude terrestrial ecosystems.

Data availability

The LIS/OTD lightning data products are from the NASA Global Hydrology Resource Center website (, the CMIP5 meteorological data are from the Earth System Grid Federation ( and the burned-area data are from the Global Fire Emissions Database (, the Alaska Interagency Coordination Center ( and the Canadian Wildland Fire Information System ( Other data supporting the findings of this study are available within the paper and its supplementary information files. Source data are provided with this paper.

Code availability

The code used for the lightning and burned area analysis is available from the corresponding author upon request.


  1. 1.

    Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593 (2014).

    Google Scholar 

  2. 2.

    Mishra, U. et al. Empirical estimates to reduce modeling uncertainties of soil organic carbon in permafrost regions: a review of recent progress and remaining challenges. Environ. Res. Lett. (2013).

  3. 3.

    Scharlemann, J. P. W., Tanner, E. V. J., Hiederer, R. & Kapos, V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manage. 5, 81–91 (2014).

    CAS  Google Scholar 

  4. 4.

    Pan, Y. D. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

    CAS  Google Scholar 

  5. 5.

    Hugelius, G. et al. The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3–13 (2013).

    Google Scholar 

  6. 6.

    Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

    CAS  Google Scholar 

  7. 7.

    Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).

    CAS  Google Scholar 

  8. 8.

    McGuire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523–555 (2009).

    Google Scholar 

  9. 9.

    Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth Sci. Rev. 172, 75–86 (2017).

    CAS  Google Scholar 

  10. 10.

    Koven, C. D., Lawrence, D. M. & Riley, W. J. Permafrost carbon−climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. Proc. Natl Acad. Sci. USA 112, 3752–3757 (2015).

    CAS  Google Scholar 

  11. 11.

    Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J. & Slater, A. G. Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions. Environ. Res. Lett. 10, 094011 (2015).

    Google Scholar 

  12. 12.

    McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115, 3882–3887 (2018).

    Google Scholar 

  13. 13.

    Mekonnen, Z. A., Riley, W. J. & Grant, R. F. 21st century tundra shrubification could enhance net carbon uptake of North America Arctic tundra under an RCP8.5 climate trajectory. Environ. Res. Lett. (2018).

  14. 14.

    Schädel, C. et al. Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils. Nat. Clim. Change 6, 950–953 (2016).

    Google Scholar 

  15. 15.

    Turetsky, M. R. et al. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat. Geosci. 4, 27–31 (2011).

    CAS  Google Scholar 

  16. 16.

    Veraverbeke, S., Rogers, B. M. & Randerson, J. T. Daily burned area and carbon emissions from boreal fires in Alaska. Biogeosciences 12, 3579–3601 (2015).

    CAS  Google Scholar 

  17. 17.

    Hu, F. S. et al. Arctic tundra fires: natural variability and responses to climate change. Front. Ecol. Environ. 13, 369–377 (2015).

    Google Scholar 

  18. 18.

    Rogers, B. M., Balch, J. K., Goetz, S. J., Lehmann, C. E. R. & Turetsky, M. Focus on changing fire regimes: interactions with climate, ecosystems, and society. Environ. Res. Lett. (2020).

  19. 19.

    Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).

    CAS  Google Scholar 

  20. 20.

    Mack, M. C. et al. Carbon loss from an unprecedented Arctic tundra wildfire. Nature 475, 489–492 (2011).

    CAS  Google Scholar 

  21. 21.

    Harsch, M. A., Hulme, P. E., McGlone, M. S. & Duncan, R. P. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett. 12, 1040–1049 (2009).

    Google Scholar 

  22. 22.

    Rocha, A. V. et al. The footprint of Alaskan tundra fires during the past half-century: implications for surface properties and radiative forcing. Environ. Res. Lett. 7, 044039 (2012).

    Google Scholar 

  23. 23.

    Chambers, S. D., Beringer, J., Randerson, J. T. & Chapin, F. S. III Fire effects on net radiation and energy partitioning: contrasting responses of tundra and boreal forest ecosystems. J. Geophys. Res. Atmos. (2005).

  24. 24.

    Genet, H. et al. Modeling the effects of fire severity and climate warming on active layer thickness and soil carbon storage of black spruce forests across the landscape in interior Alaska. Environ. Res. Lett. 8, 045016 (2013).

    CAS  Google Scholar 

  25. 25.

    Mekonnen, Z. A., Riley, W. J., Randerson, J. T., Grant, R. F. & Rogers, B. M. Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire. Nat. Plants 5, 952–958 (2019).

    Google Scholar 

  26. 26.

    Johnstone, J. F., Hollingworth, T. N., Chapin, F. S. III & Mack, M. C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Change Biol. 16, 1281–1295 (2010).

    Google Scholar 

  27. 27.

    Christian, H. J. et al. Global frequency and distribution of lightning as observed from space by the Optical Transient Detector. J. Geophys. Res. Atmos. (2003).

  28. 28.

    Cecil, D. J., Buechler, D. E. & Blakeslee, R. J. Gridded lightning climatology from TRMM-LIS and OTD: dataset description. Atmos. Res. 135, 404–414 (2014).

    Google Scholar 

  29. 29.

    Veraverbeke, S. et al. Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Change 7, 529–534 (2017).

    Google Scholar 

  30. 30.

    Krause, A., Kloster, S., Wilkenskjeld, S. & Paeth, H. The sensitivity of global wildfires to simulated past, present, and future lightning frequency. J. Geophys. Res. Biogeosci. 119, 312–322 (2014).

    Google Scholar 

  31. 31.

    Price, C. Lightning applications in weather and climate research. Surv. Geophys. 34, 755–767 (2013).

    Google Scholar 

  32. 32.

    Williams, E. R. Lightning and climate: a review. Atmos. Res. 76, 272–287 (2005).

    Google Scholar 

  33. 33.

    Albrecht, R. I., Goodman, S. J., Buechler, D. E., Blakeslee, R. J. & Christian, H. J. Where are the lightning hotspots on Earth? Bull. Am. Meteorol. Soc. 97, 2051–2068 (2016).

    Google Scholar 

  34. 34.

    Price, C. & Rind, D. Possible implications of global climate change on global lightning distributions and frequencies. J. Geophys. Res. Atmos. 99, 10823–10831 (1994).

    Google Scholar 

  35. 35.

    Jayaratne, E. R. & Kuleshov, Y. The relationship between lightning activity and surface wet bulb temperature and its variation with latitude in Australia. Meteorol. Atmos. Phys. 91, 17–24 (2006).

    Google Scholar 

  36. 36.

    Romps, D. M., Seeley, J. T., Vollaro, D. & Molinari, J. Projected increase in lightning strikes in the United States due to global warming. Science 346, 851–854 (2014).

    CAS  Google Scholar 

  37. 37.

    Romps, D. M. Evaluating the future of lightning in cloud-resolving models. Geophys. Res. Lett. 46, 14863–14871 (2019).

    Google Scholar 

  38. 38.

    Finney, D. L. et al. A projected decrease in lightning under climate change. Nat. Clim. Change 8, 210–213 (2018).

    Google Scholar 

  39. 39.

    Bieniek, P. A. et al. Lightning variability in dynamically downscaled simulations of Alaska’s present and future summer climate. J. Appl. Meteorol. Climatol. 59, 1139–1152 (2020).

    Google Scholar 

  40. 40.

    Price, C. & Rind, D. A simple lightning parameterization for calculating global lightning distributions. J. Geophys. Res. Atmos. 97, 9919–9933 (1992).

    Google Scholar 

  41. 41.

    Reeve, N. & Toumi, R. Lightning activity as an indicator of climate change. Q. J. R. Meteorol. Soc. 125, 893–903 (1999).

    Google Scholar 

  42. 42.

    Petersen, W. A. & Rutledge, S. A. On the relationship between cloud-to-ground lightning and convective rainfall. J. Geophys. Res. Atmos. 103, 14025–14040 (1998).

    Google Scholar 

  43. 43.

    Allen, D. J. & 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 (2002).

    Google Scholar 

  44. 44.

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).

  45. 45.

    Price, C. Global surface temperatures and the atmospheric electrical circuit. Geophys. Res. Lett. 20, 1363–1366 (1993).

    Google Scholar 

  46. 46.

    Michalon, N., Nassif, A., Saouri, T., Royer, J. F. & Pontikis, C. A. Contribution to the climatological study of lightning. Geophys. Res. Lett. 26, 3097–3100 (1999).

    Google Scholar 

  47. 47.

    Peterson, D., Wang, J., Ichoku, C. & Remer, L. A. Effects of lightning and other meteorological factors on fire activity in the North American boreal forest: implications for fire weather forecasting. Atmos. Chem. Phys. 10, 6873–6888 (2010).

    CAS  Google Scholar 

  48. 48.

    Kasischke, E. S., Williams, D. & Barry, D. Analysis of the patterns of large fires in the boreal forest region of Alaska. Int. J. Wildland Fire 11, 131–144 (2002).

    Google Scholar 

  49. 49.

    Stocks, B. J. et al. Large forest fires in Canada, 1959–1997. J. Geophys. Res. Atmos. (2002).

  50. 50.

    Rogers, B. M., Soja, A. J., Goulden, M. L. & Randerson, J. T. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).

    CAS  Google Scholar 

  51. 51.

    McGuire, A. D., Chapin, F. S., Walsh, J. E. & Wirth, C. Integrated regional changes in Arctic climate feedbacks: implications for the global climate system. Annu. Rev. Environ. Resour. 31, 61–91 (2006).

    Google Scholar 

  52. 52.

    Euskirchen, E. S., McGuire, A. D., Chapin, F. S. III, Yi, S. & Thompson, C. C. Changes in vegetation in northern Alaska under scenarios of climate change, 2003–2100: implications for climate feedbacks. Ecol. Appl. 19, 1022–1043 (2009).

    CAS  Google Scholar 

  53. 53.

    Higuera, P. E. et al. Frequent fires in ancient shrub tundra: implications of paleorecords for Arctic environmental change. PLoS ONE 3, e0001744 (2008).

    Google Scholar 

  54. 54.

    Trugman, A. et al. Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone. J. Adv. Model. Earth Syst. 8, 1180–1209 (2016).

    Google Scholar 

  55. 55.

    Bret-Harte, M. S. et al. The response of Arctic vegetation and soils following an unusually severe tundra fire. Phil. Trans. R. Soc. B (2013).

  56. 56.

    Turner, M. G. Disturbance and landscape dynamics in a changing world. Ecology 91, 2833–2849 (2010).

    Google Scholar 

  57. 57.

    Dissing, D. & Verbyla, D. L. Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation. Can. J. For. Res. 33, 770–782 (2003).

    Google Scholar 

  58. 58.

    Sedano, F. & Randerson, J. T. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences 11, 3739–3755 (2014).

    Google Scholar 

  59. 59.

    Yi, S. H., Woo, M. K. & Arain, M. A. Impacts of peat and vegetation on permafrost degradation under climate warming. Geophys. Res. Lett. 34, L16504 (2007).

    Google Scholar 

  60. 60.

    Jones, B. M. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci. Rep. (2015).

  61. 61.

    Brown, D. R. N. et al. Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing. Remote Sens. (2016).

  62. 62.

    Walker, G. A world melting from the top down. Nature 446, 718–721 (2007).

    CAS  Google Scholar 

  63. 63.

    Bonfils, C. J. W. et al. On the influence of shrub height and expansion on northern high latitude climate. Environ. Res. Lett. (2012).

  64. 64.

    McConnell, J. R. et al. 20th-century industrial black carbon emissions altered Arctic climate forcing. Science 317, 1381–1384 (2007).

    CAS  Google Scholar 

  65. 65.

    Swann, A. L., Fung, I. Y., Levis, S., Bonan, G. B. & Doney, S. C. Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect. Proc. Natl Acad. Sci. USA 107, 1295–1300 (2010).

    CAS  Google Scholar 

  66. 66.

    Keuper, F. et al. Carbon loss from northern circumpolar permafrost soils amplified by rhizosphere priming. Nat. Geosci. 13, 560–565 (2020).

    CAS  Google Scholar 

  67. 67.

    Bonan, G. B. & Doney, S. C. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models. Science (2018).

  68. 68.

    Magi, B. I. Global lightning parameterization from CMIP5 climate model output. J. Atmos. Ocean. Technol. 32, 434–452 (2015).

    Google Scholar 

  69. 69.

    Kloster, S. & Lasslop, G. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Glob. Planet. Change 150, 58–69 (2017).

    Google Scholar 

  70. 70.

    Orville, R. E., Huffines, G. R., Burrows, W. R. & Cummins, K. L. The North American Lightning Detection Network (NALDN)—analysis of flash data: 2001–09. Mon. Weather Rev. 139, 1305–1322 (2011).

    Google Scholar 

  71. 71.

    Virts, K. S., Wallace, J. M., Hutchins, M. L. & Holzworth, R. H. Highlights of a new ground-based, hourly global lightning climatology. Bull. Am. Meteorol. Soc. 94, 1381–1391 (2013).

    Google Scholar 

  72. 72.

    Pohjola, H. & Makela, A. The comparison of GLD360 and EUCLID lightning location systems in Europe. Atmos. Res. 123, 117–128 (2013).

    Google Scholar 

  73. 73.

    Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2013).

  74. 74.

    Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464, 1334–1337 (2010).

    CAS  Google Scholar 

  75. 75.

    Chapin, F. S. et al. Role of land-surface changes in Arctic summer warming. Science 310, 657–660 (2005).

    CAS  Google Scholar 

  76. 76.

    Foley, J. A. Tipping points in the tundra. Science 310, 627–628 (2005).

    CAS  Google Scholar 

  77. 77.

    Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).

    Google Scholar 

  78. 78.

    Mach, D. M. et al. Performance assessment of the Optical Transient Detector and Lightning Imaging Sensor. J. Geophys. Res. 112, D09210 (2007).

    Google Scholar 

  79. 79.

    Mackerras, D., Darveniza, M., Orville, R. E., Williams, E. R. & Goodman, S. J. Global lightning: total, cloud and ground flash estimates. J. Geophys. Res. Atmos. 103, 19791–19809 (1998).

    Google Scholar 

  80. 80.

    Farukh, M. A. & Hayasaka, H. Active forest fire occurrences in severe lightning years in Alaska. J. Nat. Disaster Sci. 33, 71–84 (2012).

    Google Scholar 

  81. 81.

    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Google Scholar 

  82. 82.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  83. 83.

    Seeley, J. T. & Romps, D. M. The effect of global warming on severe thunderstorms in the United States. J. Clim. 28, 2443–2458 (2015).

    Google Scholar 

  84. 84.

    van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).

    Google Scholar 

  85. 85.

    Chronis, T. G. et al. Global lightning activity from the ENSO perspective. Geophys. Res. Lett. 35, L19804 (2008).

    Google Scholar 

  86. 86.

    Satori, G., Williams, E. & Lemperger, I. Variability of global lightning activity on the ENSO time scale. Atmos. Res. 91, 500–507 (2009).

    Google Scholar 

  87. 87.

    Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M. & Morton, D. C. Global burned area and biomass burning emissions from small fires. J. Geophys. Res. Biogeosci. 117, G04012 (2012).

    Google Scholar 

  88. 88.

    van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).

    Google Scholar 

  89. 89.

    Walker, D. A. et al. The circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).

    Google Scholar 

Download references


This work was supported by the US Department of Energy (DOE) Office of Science Biological and Environmental Research RUBISCO Science Focus Area (with funding to J.T.R, Z.A.M. and W.J.R.), the NGEE-Arctic project (with funding to Z.A.M. and W.J.R.) and NASA’s Interdisciplinary Science (IDS) and Carbon Monitoring System (CMS) programmes (with grants to J.T.R and Y.C.). D.M.R. was supported by the US DOE Atmospheric System Research (ASR), an Office of Science, Office of Biological and Environmental Research programme. Lawrence Berkeley National Laboratory is operated for the DOE by the University of California under contract no. DE-AC02-05CH11231. We thank the World Climate Research Programme Working Group on Coupled Modeling, responsible for CMIP, and we thank the climate modelling groups for generating their model outputs and making them available.

Author information




Y.C. and J.T.R. designed the study. Y.C. led the research and performed the analyses described in the main text and supporting information. D.M.R. and J.T.S. derived the lightning index (CAPE × Precip) from meteorological variables archived from historical and RCP8.5 ESM simulations contributed to CMIP5. S.V. contributed to the development of the lightning model and to the conceptual model of a lightning-initiated dynamic vegetation feedback. W.J.R. and Z.A.M. contributed to the conceptual model of the dynamic vegetation feedback and to the discussion of the implications of a changing lightning regime for terrestrial ecosystems. All authors contributed to the writing and review of the manuscript.

Corresponding author

Correspondence to Yang Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Circumpolar observations of present-day lightning flash rate, tree cover fraction, permafrost soil carbon, and burned area.

a, lightning flash rate (# km−2 mo−1, from OTD, averaged over May-August of 1996-1999); b, tree cover fraction (%, based on MODIS observations in 2012); c, permafrost soil carbon at depth of 0-100 cm (kg C m−2, from NCSCDv2); and d, burned area (% yr−1, from GFED4s, averaged over 1996-2016) in high northern latitude regions (north of 55°N).

Source data

Extended Data Fig. 2 Circumpolar observations of meteorological parameters.

Maps of a, CAPE (J kg−1); b, precipitation (Precip, mm day−1); c, CAPE × Precip (W m−2); and d, surface air temperature (°C) in high northern latitude regions of 55°N, representing the mean of summers (May-August) during 1996-1999. All meteorological parameters are from the ERA5 global reanalysis dataset.

Source data

Extended Data Fig. 3 Scatter plot relationships and probability distribution functions for meteorological variables known to be important for lightning flash rate prediction.

The surface air temperature (T) is in °C, CAPE is in J kg−1, precipitation (Precip) is in mm day−1, and CAPE × Precip is in 10−3 W m−2. Each point represents a different spatial location (at a 1°×1° resolution) north of 55°N. Diagonal panels show the spatial probability distribution for each variable, created by taking the mean at each point during summers (May-August) of 1996-1999. All meteorological parameters are from the ERA5 global reanalysis dataset.

Source data

Extended Data Fig. 4 Future (2081-2100) to the present day (1986-2005) ratios of CAPE, precipitation, and lightning flash rate in Arctic tundra for different CMIP5 models.

The mean flash rate values calculated from 5 regression formula (see Supplementary Table 2) are shown for each CMIP5 model.

Source data

Extended Data Fig. 5 Sources of uncertainty for estimated lightning flash rates, burned area and carbon emissions.

The flash rates were averaged over the Arctic tundra region. The burned area and carbon emissions were averaged over the Arctic tundra region within 500 km of northern treeline as indicated in Fig. 3. Left panels show uncertainties due to the use of CAPE × Precipitation from the use of multiple CMIP5 model simulations. Right panels show uncertainties related to the use of statistical models relating flash rate and CAPE × Precipitation. SV and DV represents future burned area estimations using the ‘static vegetation’ and ‘dynamic vegetation’ approaches (see Methods and Supplementary Table 3 for detail). Note the burned area and carbon emissions have same data distribution but with different units and scales.

Source data

Extended Data Fig. 6 Present day and future estimates of total precipitation (TP) and convective precipitation (CP) as a function of distance from northern treeline.

a, TP and CP percent changes from present day to the future. b, TP and CP values for the present day and future. c, The fraction of TP change that is due to CP change. The orange shade indicates the Arctic tundra region 0-500 km north of treeline. All data are based on the ensemble means of 15 CMIP5 model simulations over 1986-2005 and 2081-2100.

Source data

Extended Data Fig. 7 Distributions of surface temperature, CAPE × Precip, and the ice water path as a function of distance north of treeline.

a, surface temperature (T2m), b, the product of CAPE and precipitation (CAPE × Precip) and c, the ice water path (IWP) were calculated during summers for a contemporary period (1986-2005) and a future period (2081-2100). The percent changes are relative to the mean values during the contemporary period. The orange shade indicates the Arctic tundra region 0-500 km north of treeline. All data are based on the ensemble means of 15 CMIP5 model simulations over 1986-2005 and 2081-2100.

Source data

Extended Data Fig. 8 Contemporary lightning and wildfire properties as a function of distance from northern treeline.

a, The ratio of burned area (BA) to lightning flash rate (FR). Black dashed line represents parameterized step function (‘static vegetation’). Purple dashed line shows the shifted step function used for future burned area estimation (‘dynamic vegetation’). b, Fire number and mean fire size in Alaska, as reported by Alaska Interagency Coordination Center for the period of 2000 to 2016. c, Fire number and mean fire size in Canada, as reported by Canadian Wildland Fire Information System during the same period. The orange shade denotes the Arctic tundra region that may be vulnerable to future changes in lightning, burned area, and vegetation dynamics (0-500 km north of treeline).

Source data

Extended Data Fig. 9 Vapor pressure deficit (VPD) and the difference between precipitation and evapotranspiration (P-E) as a function of distance from northern treeline.

The VPD and P-E values are derived from the ensemble mean meteorology of 15 CMIP5 model simulations over 1986-2005 and 2081-2100.

Source data

Extended Data Fig. 10 Comparison of lightning flash rates measured by satellite and a surface network in Alaska during summers of 1996-1999.

a, Flash rate (# deg−1 mo−1) recorded by the Optical Transient Detector (OTD), b, Flash rate (# deg−1 mo−1) from the Alaskan Lightning Detection Network (ALDN), c, Spatial correlation between the flash rates from OTD and ALDN. Each dot represents the mean summer value in a 1°×1° grid cell in Alaska.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–3.

Source data

Source Data Fig. 1

Numerical data used to generate graphs in Fig. 1.

Source Data Fig. 2

Numerical data used to generate graphs in Fig. 2.

Source Data Fig. 3

Numerical data used to generate graphs in Fig. 3.

Source Data Extended Data Fig. 1

Numerical data used to generate graphs in Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Numerical data used to generate graphs in Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Numerical data used to generate graphs in Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Numerical data used to generate graphs in Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Numerical data used to generate graphs in Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Numerical data used to generate graphs in Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Numerical data used to generate graphs in Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Numerical data used to generate graphs in Extended Data Fig. 8.

Source Data Extended Data Fig. 9

Numerical data used to generate graphs in Extended Data Fig. 9.

Source Data Extended Data Fig. 10

Numerical data used to generate graphs in Extended Data Fig. 10.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Romps, D.M., Seeley, J.T. et al. Future increases in Arctic lightning and fire risk for permafrost carbon. Nat. Clim. Chang. 11, 404–410 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing