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Overwintering fires in boreal forests

Abstract

Forest fires are usually viewed within the context of a single fire season, in which weather conditions and fuel supply can combine to create conditions favourable for fire ignition—usually by lightning or human activity—and spread1,2,3. But some fires exhibit ‘overwintering’ behaviour, in which they smoulder through the non-fire season and flare up in the subsequent spring4,5. In boreal (northern) forests, deep organic soils favourable for smouldering6, along with accelerated climate warming7, may present unusually favourable conditions for overwintering. However, the extent of overwintering in boreal forests and the underlying factors influencing this behaviour remain unclear. Here we show that overwintering fires in boreal forests are associated with hot summers generating large fire years and deep burning into organic soils, conditions that have become more frequent in our study areas in recent decades. Our results are based on an algorithm with which we detect overwintering fires in Alaska, USA, and the Northwest Territories, Canada, using field and remote sensing datasets. Between 2002 and 2018, overwintering fires were responsible for 0.8 per cent of the total burned area; however, in one year this amounted to 38 per cent. The spatiotemporal predictability of overwintering fires could be used by fire management agencies to facilitate early detection, which may result in reduced carbon emissions and firefighting costs.

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Fig. 1: Landsat 8 false-colour time series of a 2015 fire in Alaska that generated an overwintering fire in 2016.
Fig. 2: Temporal drivers of overwintering fires and their long-term trends.
Fig. 3: Burn depth and overwintering.
Fig. 4: Occurrence of overwintering flare-ups.

Data availability

The location and timing of ignition of the overwintering fires used in this study are given in the Supplementary Information. Daily burned area, emissions and ignitions data for Alaska and the Northwest Territories are archived at the Oak Ridge National Laboratory Distributed Active Archive Center for biogeochemical dynamics (https://doi.org/10.3334/ORNLDAAC/1812). Lightning data are available from the Alaska Interagency Coordination Center (https://fire.ak.blm.gov/predsvcs/maps.php) and from Environment and Climate Change Canada. Infrastructure data are available for Alaska from the Department of Natural Resources (https://catalog.data.gov/dataset/alaska-infrastructure-1-63360), and for the Northwest Territories from Statistics Canada (https://www150.statcan.gc.ca/n1/en/catalogue/92-500-X) and the Government of Yukon (https://hub.arcgis.com/datasets/322b6cf3fa1444c289a1d611a4778ead_42/data). MODSCAG snow fraction data are freely available from the JPL Snow Data Server (http://snow.jpl.nasa.gov/portal/). All climate data used in this study are available from the North America Regional Reanalysis (https://psl.noaa.gov/data/gridded/data.narr.html). All data used for the analysis of spatial drivers are freely available, including the ArcticDEM (https://doi.org/10.7910/DVN/OHHUKH), the Northern Circumpolar Soil Carbon Database v2 (https://doi.org/10.5879/ECDS/00000002) and the Fuel Characteristic Classification System (https://www.landfire.gov/fccs.php).

Code availability

Codes used to analyse the data are available from https://github.com/screbec/Overwintering-fires or https://doi.org/10.5281/zenodo.4549321.

References

  1. 1.

    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).

    ADS  Google Scholar 

  2. 2.

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

    ADS  Google Scholar 

  3. 3.

    Calef, M. P., McGuire, A. D. & Chapin, F. S. Human influences on wildfire in Alaska from 1988 through 2005: an analysis of the spatial patterns of human impacts. Earth Interact. 12, 1–17 (2008).

    ADS  Google Scholar 

  4. 4.

    McCarty, J. L., Smith, T. E. L. & Turetsky, M. R. Arctic fires re-emerging. Nat. Geosci. 13, 658–660 (2020).

    ADS  CAS  Google Scholar 

  5. 5.

    Irannezhad, M., Liu, J., Ahmadi, B. & Chen, D. The dangers of Arctic zombie wildfires. Science 369, 1171 (2020).

    ADS  Google Scholar 

  6. 6.

    Rein, G. in Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science (ed. Belcher, C. M.) 15–34 (Wiley-Blackwell, 2013).

  7. 7.

    Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Overland, J. E., Wang, M., Walsh, J. E. & Stroeve, J. C. Future Arctic climate changes: adaptation and mitigation time scales. Earth’s Future 2, 68–74 (2014).

    ADS  Google Scholar 

  9. 9.

    Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, GB2023 (2009).

    ADS  Google Scholar 

  10. 10.

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

    ADS  CAS  Google Scholar 

  11. 11.

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

    ADS  CAS  Google Scholar 

  12. 12.

    Walker, X. J. et al. Soil organic layer combustion in boreal black spruce and jack pine stands of the Northwest Territories, Canada. Int. J. Wildl. Fire 27, 125–134 (2018).

    Google Scholar 

  13. 13.

    Turetsky, M. R. et al. Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci. 8, 11–14 (2015).

    ADS  CAS  Google Scholar 

  14. 14.

    Flannigan, M. D. et al. Fuel moisture sensitivity to temperature and precipitation: climate change implications. Clim. Change 134, 59–71 (2016).

    ADS  CAS  Google Scholar 

  15. 15.

    Coops, N. C., Hermosilla, T., Wulder, M. A., White, J. C. & Bolton, D. K. A thirty year, fine-scale, characterization of area burned in Canadian forests shows evidence of regionally increasing trends in the last decade. PLoS One 13, e0197218 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    USDA Forest Service, USFS-USDI and NASF. Large Fire Cost Reduction Action Plan https://www.fs.usda.gov/sites/default/files/media_wysiwyg/5100_largefirecostreductionaction_mar_03.pdf (2003).

  17. 17.

    Podur, J. & Wotton, M. Will climate change overwhelm fire management capacity? Ecol. Modell. 221, 1301–1309 (2010).

    Google Scholar 

  18. 18.

    Tymstra, C., Stocks, B. J., Cai, X. & Flannigan, M. D. Wildfire management in Canada: review, challenges and opportunities. Prog. Disaster Sci. 5, 100045 (2020); erratum 8, 100045 (2020).

    Google Scholar 

  19. 19.

    Stocks, B. J. et al. Large forest fires in Canada, 1959–1997. J. Geophys. Res. 107, https://doi.org/10.1029/2001JD000484 (2002).

  20. 20.

    Wiggins, E. B. et al. Evidence for a larger contribution of smoldering combustion to boreal forest fire emissions from tower observations in Alaska. Atmos. Chem. Phys. https://doi.org/10.5194/acp-2019-1067 (in the press).

  21. 21.

    Rein, G., Garcia, J., Simeoni, A., Tihay, V. & Ferrat, L. Smouldering natural fires: comparison of burning dynamics in boreal peat and Mediterranean humus. WIT Trans. Ecol. Environ. 119, 183–192 (2008).

    Google Scholar 

  22. 22.

    Baber, C. & McMaster, R. 2019 Alaska Statewide Annual Operating Plan. https://fire.ak.blm.gov/administration/asma.php (Alaska Statewide Master Agreement, 2019).

  23. 23.

    Alaska Interagency Coordination Center. 2010 Alaska fire statistics. https://www.frames.gov/catalog/12055 (Wildland Fire Summary and Statistics Annual Report, 2010).

  24. 24.

    Alaska Division of Forestry. State Forestry monitoring hot spots that overwintered from Deshka Landing Fire. https://akfireinfo.com/2020/04/10/state-forestry-monitoring-hot-spots-that-overwintered-from-deshka-landing-fire/ (2020).

  25. 25.

    Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).

    ADS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Kasischke, E. S., Rupp, T. S. & Verbyla, D. L. in Alaska’s Changing Boreal Forest (eds Chapin, F. S. III, Oswood, M. et al.) 285–301 (Oxford Univ. Press, 2006).

  27. 27.

    Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase western U.S. forest wildfire activity. Science 313, 940–943 (2006).

    ADS  CAS  Google Scholar 

  28. 28.

    Painter, T. H. et al. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens. Environ. 113, 868–879 (2009).

    ADS  Google Scholar 

  29. 29.

    Scholten, R. C., Jandt, R. R., Miller, E. A., Rogers, B. M. & Veraverbeke, S. ABoVE: Ignitions, burned area and emissions of fires in AK, YT, and NWT, 2001–2018. https://doi.org/10.3334/ORNLDAAC/1812 (2020).

  30. 30.

    Xiao, J. & Zhuang, Q. Drought effects on large fire activity in Canadian and Alaskan forests. Environ. Res. Lett. 2, 044003 (2007).

    ADS  Google Scholar 

  31. 31.

    Flannigan, M. D. et al. Global wildland fire season severity in the 21st century. For. Ecol. Manage. 294, 54–61 (2013).

    Google Scholar 

  32. 32.

    Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Adams, W. H. The Role of Fire in the Alaska Taiga. An Unsolved Problem (Bureau of Land Management, State Office, Anchorage, AK, 1974); preprint at https://scholarworks.alaska.edu/handle/11122/6675 (2016).

  34. 34.

    Certini, G. Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10 (2005).

    ADS  PubMed  Google Scholar 

  35. 35.

    Kane, E. S., Kasischke, E. S., Valentine, D. W., Turetsky, M. R. & McGuire, A. D. Topographic influences on wildfire consumption of soil organic carbon in interior Alaska: implications for black carbon accumulation. J. Geophys. Res. Biogeosci. 112, 1–11 (2007).

    Google Scholar 

  36. 36.

    Hoy, E. E., Turetsky, M. R. & Kasischke, E. S. More frequent burning increases vulnerability of Alaskan boreal black spruce forests. Environ. Res. Lett. 11, 095001 (2016).

    ADS  Google Scholar 

  37. 37.

    Miyanishi, K. & Johnson, E. A. Process and patterns of duff consumption in the mixedwood boreal forest. Can. J. For. Res. 32, 1285–1295 (2002).

    Google Scholar 

  38. 38.

    Kasischke, E. S. & Turetsky, M. R. Recent changes in the fire regime across the North American boreal region — spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33, https://doi.org/10.1029/2006GL025677 (2006).

  39. 39.

    Johnstone, J. F. et al. Factors shaping alternate successional trajectories in burned black spruce forests of Alaska. Ecosphere 11, https://doi.org/10.1002/ecs2.3129 (2020).

  40. 40.

    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 

  41. 41.

    Andreae, M. O. & Merlet, P. Emission of trace gases and aerosols from biomass burning. Glob. Biogeochem. Cycles 15, 955–966 (2001).

    ADS  CAS  Google Scholar 

  42. 42.

    Dean, J. F. et al. Methane feedbacks to the global climate system in a warmer world. Rev. Geophys. 56, 207–250 (2018).

    ADS  Google Scholar 

  43. 43.

    Beaudoin, A., Bernier, P. Y., Villemaire, P., Guindon, L. & Guo, X. J. Tracking forest attributes across Canada between 2001 and 2011 using a k nearest neighbors mapping approach applied to MODIS imagery. Can. J. For. Res. 48, 85–93 (2018).

    Google Scholar 

  44. 44.

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

    ADS  CAS  Google Scholar 

  45. 45.

    Kasischke, E. S. et al. Quantifying burned area for North American forests: implications for direct reduction of carbon stocks. J. Geophys. Res. Biogeosci. 116, 1–17 (2011).

    Google Scholar 

  46. 46.

    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 

  47. 47.

    Burrows, W. R. & Kochtubajda, B. A decade of cloud-to-ground lightning in Canada: 1999-2008. Part 1: flash density and occurrence. Atmos.-Ocean 48, 177–194 (2010).

    Google Scholar 

  48. 48.

    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).

    ADS  Google Scholar 

  49. 49.

    Kochtubajda, B. et al. Exceptional cloud-to-ground lightning during an unusually warm summer in Yukon, Canada. J. Geophys. Res. Atmos. 116, https://doi.org/10.1029/2011JD016080 (2011).

  50. 50.

    Kochtubajda, B., Stewart, R. & Tropea, B. Lightning and weather associated with the extreme 2014 wildfire season in Canada’s Northwest Territories. In Proceedings of the 24th International Lightning Detection Conference 1–4 (VAISALA, 2016).

  51. 51.

    Dowdy, A. J. & Mills, G. A. Atmospheric and fuel moisture characteristics associated with lightning-attributed fires. J. Appl. Meteorol. Climatol. 51, 2025–2037 (2012).

    ADS  Google Scholar 

  52. 52.

    Larjavaara, M., Pennanen, J. & Tuomi, T. J. Lightning that ignites forest fires in Finland. Agric. For. Meteorol. 132, 171–180 (2005).

    ADS  Google Scholar 

  53. 53.

    Duncan, B. W., Adrian, F. W. & Stolen, E. D. Isolating the lightning ignition regime from a contemporary background fire regime in east-central Florida, USA. Can. J. For. Res. 40, 286–297 (2010).

    Google Scholar 

  54. 54.

    Veraverbeke, S. et al. Mapping the daily progression of large wildland fires using MODIS active fire data. Int. J. Wildl. Fire 23, 655–667 (2014).

    Google Scholar 

  55. 55.

    Statistics Canada. Road Network File 2010. https://www150.statcan.gc.ca/n1/en/catalogue/92-500-X (2016).

  56. 56.

    Government of Yukon. Corporate Spatial Warehouse. ftp://ftp.geomaticsyukon.ca/GeoYukon/Transportation/Roads_1M/ (2018).

  57. 57.

    Rittger, K., Painter, T. H. & Dozier, J. Assessment of methods for mapping snow cover from MODIS. Adv. Water Resour. 51, 367–380 (2013).

    ADS  Google Scholar 

  58. 58.

    Gallant, A. L., Binnian, E. F., Omernik, J. M. & Shasby, M. B. Ecoregions of Alaska (Professional Paper 1567, USGS, 1995).

  59. 59.

    Canadian Council on Ecological Areas (CCEA). Canada ecozones. https://ccea-ccae.org/ecozones-downloads/ (2016).

  60. 60.

    Mesinger, F. et al. North American regional reanalysis. Bull. Am. Meteorol. Soc. 87, 343–360 (2006).

    ADS  Google Scholar 

  61. 61.

    Van Wagner, C. E. Development and Structure of the Canadian Fire Weather Index System. Forestry Technical Report Vol. 35 (Canadian Forestry Service Headquarters, Ottawa, 1987).

  62. 62.

    York, A. D. & Jandt, R. R. Opportunities to Apply Remote Sensing in Boreal/Arctic Wildfire Management & Science: A Workshop Report www.frames.gov/catalog/57849 (University of Alaska, Fairbanks, 2019).

  63. 63.

    Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).

    ADS  Google Scholar 

  64. 64.

    Welch, B. L. The significance of the difference between two means when the population variances are unequal. Biometrika 29, 350–362 (1938).

    MATH  Google Scholar 

  65. 65.

    Welch, B. L. The generalization of ‘Student’s’ problem when several different population variances are involved. Biometrika 34, 28–35 (1947).

    MathSciNet  CAS  MATH  Google Scholar 

  66. 66.

    Morin, P. et al. ArcticDEM; a publically available, high resolution elevation model of the Arctic. Geophys. Res. Abstr. 18, EGU2016-8396 (2016).

    Google Scholar 

  67. 67.

    Porter, C. et al. ArcticDEM. https://doi.org/10.7910/DVN/OHHUKH (Harvard Dataverse, 2018).

  68. 68.

    Dai, C., Durand, M., Howat, I. M., Altenau, E. H. & Pavelsky, T. M. Estimating river surface elevation from arcticDEM. Geophys. Res. Lett. 45, 3107–3114 (2018).

    ADS  Google Scholar 

  69. 69.

    Hansen, M. C. et al. Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact. 7, 1–15 (2003).

    Google Scholar 

  70. 70.

    Pettinari, M. L. & Chuvieco, E. Generation of a global fuel data set using the fuel characteristic classification system. Biogeosciences 13, 2061–2076 (2016).

    ADS  Google Scholar 

  71. 71.

    Ottmar, R. D., Sandberg, D. V., Riccardi, C. L. & Prichard, S. J. An overview of the fuel characteristic classification system — quantifying, classifying, and creating fuelbeds for resource planning. Can. J. For. Res. 37, 2383–2393 (2007).

    Google Scholar 

  72. 72.

    Riccardi, C. L. et al. The fuelbed: a key element of the fuel characteristic classification system. Can. J. For. Res. 37, 2394–2412 (2007).

    Google Scholar 

  73. 73.

    Beaudoin, A., Bernier, P. Y., Villemaire, P., Guindon, L. & Guo, X. Species Composition, Forest Properties and Land Cover Types Across Canada’s Forests at 250m Resolution for 2001 and 2011. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 (Natural Resources Canada, Canadian Forest Service, Laurentian Forest Centre, 2017).

  74. 74.

    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).

    ADS  Google Scholar 

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Acknowledgements

We would like to thank C. Van Der Horn and G. Branson (Alaska Interagency Coordination Center), and M. Coyle (Forest Management Division, Department of Environment and Natural Resources, Government of the Northwest Territories), for providing ground truth data on overwintering fires. We wish to thank Environment and Climate Change Canada for their generous permission to use Canadian Lightning Detection Network data, and the Bureau of Land Management, Alaska Fire Service, for providing cost information. We thank NASA JPL’s Snow Data Center for making their MODSCAG data available. This work was funded by the Netherlands Organization for Scientific Research (NWO) through a Vidi grant (Fires Pushing Trees North) awarded to S.V. B.M.R. acknowledges support from the National Aeronautics and Space Administration (NASA) Arctic-Boreal Vulnerability Experiment (ABoVE; NNX15AU56A).

Author information

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Authors

Contributions

S.V. and R.C.S. designed the research; R.C.S. performed the analysis with input from S.V.; B.M.R. contributed to the interpretation of cost data; R.R.J. and E.A.M. contributed to the interpretation of field data; R.C.S. drafted the manuscript; and all authors participated in manuscript editing.

Corresponding author

Correspondence to Rebecca C. Scholten.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Aerial view of the Seven Mile Slough fire in Alaska on 9 May 2011.

Smouldering hotspots (labelled with ‘a’) had overwintered and burned in the organic soil layer below the spruces of an unburned island. Green tree crowns of the fallen trees (labelled ‘b’) in the original unburned island (perimeter approximated in black) suggest that tree roots were damaged by subsurface burning. (Photograph by E.A.M.).

Extended Data Fig. 2 Workflow used to detect large overwintering fires.

First, ignition locations, dates and causes according to official fire databases were extracted. In four steps, the algorithm filters these ignitions by date, distance to an old burn scar, and co-occurrence of lightning strikes and infrastructure elements. Small overwintering fires that were not detected by satellite products were used to derive thresholds for the algorithm.

Extended Data Fig. 3 Spatiotemporal characteristics of small overwintering fires.

Overwintering fires emerge earlier after the seasonal snow melt (a) and closer to a fire scar from the year before (b) than other fires. ‘Other fires’ refer to all fires not classified as overwintering in official fire databases. Day since regional snow melt was taken from government sources.

Extended Data Fig. 4 Lag times and distance to roads.

Shown are histograms of lag time between lightning strikes and ignition detections (a), and distance to road for ignitions by humans (b). Human and lightning ignitions were characterized on the basis of the Alaskan Wildland Fire Maps (Alaska, AK) and the Canadian National Fire Database (Northwest Territories, NWT). The black lines indicate the thresholds used to eliminate potential overwintering fires due to spatial proximity to infrastructure and spatiotemporal proximity to lightning strikes.

Extended Data Fig. 5 Average and extreme temperature trends for interior Alaska and the Northwest Territories.

a, b, Average of the daily maximum temperature of the summer months May–September; c, d, its 90th percentile. e, f, Number of hot days surpassing the 90th percentile. Panels a, c, e show data for interior Alaska, and panels b, d, f for the taiga plains and the taiga shield of the Northwest Territories.

Extended Data Fig. 6 Scatter plots and Spearman correlations (ρ) of summer temperature, burned area and overwintering flare-ups.

a, b, Daily mean maximum temperature between May and September (MJJAS; Tmean) and annual burned area. c, d, Previous year’s burned area and the number of overwintering flare-ups. e, f, MJJAS maximum temperature and the number of overwintering flare-ups. Panels a, c, e show data for Alaska, and panels b, d, f for the Northwest Territories.

Extended Data Fig. 7 Scatter plots and Spearman correlations of temperature extremes and burned area, overwintering flare-ups and burn depth.

a, b, Number of MJJAS hot days (days with a maximum temperature hotter than the 1979–2020 90th percentile, T90) and burned area. c, d, Number of MJJAS hot days and overwintering ignitions. e, f, 90th percentile of MJJAS temperature (Tmax90) and average burn depth. Panels a, c, e show data for Alaska, and panels b, d, f for the Northwest Territories.

Extended Data Table 1 Correlation of meteorology and the number of overwintering flare-ups
Extended Data Table 2 Comparison of burn scars with and without overwintering fires
Extended Data Table 3 Effect of spring meteorology on the size of overwintering fires

Supplementary information

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Scholten, R.C., Jandt, R., Miller, E.A. et al. Overwintering fires in boreal forests. Nature 593, 399–404 (2021). https://doi.org/10.1038/s41586-021-03437-y

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