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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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).
Codes used to analyse the data are available from https://github.com/screbec/Overwintering-fires or https://doi.org/10.5281/zenodo.4549321.
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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).
The authors declare no competing interests.
Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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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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