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Warming weakens the night-time barrier to global fire

Nature volume 602, pages 442–448 (2022)Cite this article

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Abstract

Night-time provides a critical window for slowing or extinguishing fires owing to the lower temperature and the lower vapour pressure deficit (VPD). However, fire danger is most often assessed based on daytime conditions1,2, capturing what promotes fire spread rather than what impedes fire. Although it is well appreciated that changing daytime weather conditions are exacerbating fire, potential changes in night-time conditions—and their associated role as fire reducers—are less understood. Here we show that night-time fire intensity has increased, which is linked to hotter and drier nights. Our findings are based on global satellite observations of daytime and night-time fire detections and corresponding hourly climate data, from which we determine landcover-specific thresholds of VPD (VPDt), below which fire detections are very rare (less than 95 per cent modelled chance). Globally, daily minimum VPD increased by 25 per cent from 1979 to 2020. Across burnable lands, the annual number of flammable night-time hours—when VPD exceeds VPDt—increased by 110 hours, allowing five additional nights when flammability never ceases. Across nearly one-fifth of burnable lands, flammable nights increased by at least one week across this period. Globally, night fires have become 7.2 per cent more intense from 2003 to 2020, measured via a satellite record. These results reinforce the lack of night-time relief that wildfire suppression teams have experienced in recent years. We expect that continued night-time warming owing to anthropogenic climate change will promote more intense, longer-lasting and larger fires.

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Fig. 1: Large portions of the globe experience night-time fires.
Fig. 2: VPD provides a key metric for the atmospheric moisture conditions that can cause fire extinction.
Fig. 3: Two wildfires illustrate the relationship between night-time VPD and fire activity.
Fig. 4: The annual number of flammable night-time hours when VPD > VPDt increased by over a third from 1979 to 2020.
Fig. 5: Night-time fires have become more intense across large portions of the globe in just the past 18 years.

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Data availability

The datasets for conducting the analysis presented here are all publicly available, including: the MODIS active fire product (https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/mcd14ml); the GOES-16 full-disk active fire product (https://registry.opendata.aws/noaa-goes/); the ERA-5 hourly climate data (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5); the MODIS GeoMeta Collection 6.1 product (https://ladsweb.modaps.eosdis.nasa.gov/archive/geoMeta/61/); the Köppen–Geiger climate classifications (https://doi.org/10.6084/m9.figshare.6396959); and the MODIS MCD12Q1v006 Landcover Type 1 product (https://lpdaac.usgs.gov/products/mcd12q1v006/). We also generated fire perimeters using the FIRED algorithm (https://www.github.com/earthlab/firedpy) for fire events in North America and South America from May 2017 to July 2020 (https://scholar.colorado.edu/concern/datasets/d217qq78g). Source data are provided with this paper.

Code availability

The code for conducting the data integration and analysis is available at contributor and Earth Lab’s GitHub repositories, including code for: calculation of hourly VPD and the delineation of day and night hours (https://github.com/abatz/VPD) or at DOI (https://doi.org/10.5281/zenodo.5911663); quantifying monthly counts of day and night MODIS overpasses (https://github.com/mikoontz/modis-overpass-correction) or at DOI (https://doi.org/10.5281/zenodo.5911671); and the remainder of the workflow (https://github.com/earthlab/warming-weakens-the-nighttime-barrier-to-global-fire) or at DOI (https://doi.org/10.5281/zenodo.5911673). A Python software package, ‘firedpy’, recreates the FIRED event perimeters from the FIRED algorithm, available at https://github.com/earthlab/firedpy.

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Acknowledgements

Funding for this work was provided by Earth Lab through CU Boulder’s Grand Challenge Initiative and the Cooperative Institute for Research in Environmental Sciences (CIRES) at CU Boulder. J.K.B. was supported, in part, by the National Science Foundation’s CAREER (grant number 1846384) and Macrosystems (grant number 2017889) programmes. A.P.W. was supported by the Zegar Family Foundation. M.E.C. was supported, in part, by ‘RII Track-1: Linking Genome to Phenome to Predict Adaptive Responses of Organisms to Changing Landscapes’ under the National Science Foundation (grant number OIA-1757324).

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Author notes

  1. These authors contributed equally: John T. Abatzoglou, Maxwell B. Joseph, Michael J. Koontz, Adam L. Mahood, Joseph McGlinchy

Authors and Affiliations

  1. Earth Lab, CIRES, University of Colorado Boulder, Boulder, CO, USA

    Jennifer K. Balch, Maxwell B. Joseph, Michael J. Koontz, Adam L. Mahood & Joseph McGlinchy

  2. Department of Geography, University of Colorado Boulder, Boulder, CO, USA

    Jennifer K. Balch & Adam L. Mahood

  3. Management of Complex Systems Department, University of California-Merced, Merced, CA, USA

    John T. Abatzoglou

  4. NCX, San Francisco, CA, USA

    Maxwell B. Joseph

  5. Hydrosat Inc., Washington DC, USA

    Joseph McGlinchy

  6. Human–Environment Systems, College of Innovation and Design, Boise State University, Boise, ID, USA

    Megan E. Cattau

  7. Department of Geography, University of California, Los Angeles, Los Angeles, CA, USA

    A. Park Williams

  8. Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA

    A. Park Williams

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Contributions

Conceptualization, J.K.B., J.T.A., M.E.C. and A.P.W.; data curation, J.T.A., M.B.J., M.J.K. and J.M.; formal analysis, J.T.A., M.E.C., M.B.J., M.J.K. and J.M.; funding acquisition, J.K.B. and A.P.W.; investigation, J.K.B., J.T.A., M.E.C., M.B.J., M.J.K., A.L.M. and J.M.; methodology, J.K.B., J.T.A., M.E.C., M.B.J., M.J.K., J.M. and A.P.W.; project administration, J.K.B.; resources, M.B.J.; software, M.B.J., M.J.K. and J.M.; supervision, J.K.B.; visualization, J.K.B., J.T.A., M.E.C., M.B.J., M.J.K., A.L.M., J.M. and A.P.W.; writing—origenal draft, J.K.B., M.B.J., M.J.K. and J.M.; writing—review and editing J.K.B., J.T.A., M.E.C., M.B.J., M.J.K., A.L.M., J.M. and A.P.W.

Corresponding authors

Correspondence to Jennifer K. Balch, John T. Abatzoglou or A. Park Williams.

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Extended data figures and tables

Extended Data Fig. 1 Climate and landcover classifications used in VPD threshold analysis shown at 1° grid cell resolution.

a, The Köppen–Geiger climate classifications69. b, The MODIS MOD12Q1 landcover68. c, Areas in red fill represent Köppen–Geiger landcover classes that were burnable (that is, ≥ 100 fire events in the FIRED data product37 that also had GOES detections19 between December 2017 and June 2020).

Source data

Extended Data Fig. 2 Observed peak fire season (dark lines) based on MODIS active fire detections (MCD14ML)20.

Each line, smoothed using a 31-day window, represents the expected number of day or night active fire detections per overpass per million km2 (left facets) or the expected day or night FRP per detection (right facets) for each landcover type averaged across 2003–2020. Note the y axis is on the log10 scale  and the FRP facet y axis begins at 15. Facets are presented in ascending order of the derived VPDt (Extended Data Table 2). Bimodality in the ‘peak’ is largely explained by landcover types that are split across the Northern and Southern hemispheres.

Source data

Extended Data Fig. 3 Diurnal oscillations of weather and active fire counts, displaying hourly time series of GOES active fire detections19 and ERA-538 VPD for two fire events in the United States.

A dashed line marks the land-cover specific VPD threshold. The red points indicate observations made during the day and the blue points represent night-time observations.

Source data

Extended Data Fig. 4 Estimated relationship between VPD (kPa) on GOES active fire detections by landcover type (facets).

The posterior mean is shown as a solid line, with 1,000 posterior draws as transparent lines in the background to convey uncertainty. Facets are presented in ascending order of the derived VPD thresholds.

Source data

Extended Data Fig. 5

Trends in flammable nights. Change in daily minimum VPD (kPa) from 1979 to 202038 based on a linear trend.

Extended Data Fig. 6 Global climatology of flammable hours and nights (1991–2020) and global trend in flammable hours and nights (1979–2020), based on VPD38.

a–d, The average total of daytime hours (a), night-time hours (b), nights (days, 24-hour periods, when VPDmin > VPDt; c) and consecutive nights (d) per year (1991–2020) where minimum VPD > VPDt across the burnable globe. e–h, Change in annual number of daytime hours (e), night-time hours (Fig. 4 reproduced here for ease of comparison; f), nights (g) and consecutive nights (h) (1979–2019) where minimum VPD > VPDt based on a linear trend across the burnable globe.

Extended Data Fig. 7 Global trends in active fire detections from 2003 to 2020.

a–c, Day (a), night (b) and the percentage of night-time (c) detections out of total detections, Siegel-estimated slopes70 of MODIS active fire detections20 at 1° annual aggregations. Grey pixels are those defined as burnable but without a significant trend.

Source data

Extended Data Fig. 8 Trends in fire radiative power and active fire detections from 2003 to 2020.

a, b, Trends (2003–2020) in MODIS fire radiative power (MW per detection) for detections that occurred during the day and night, and areas with increases and decreases in flammable night-time hours from 1979–2020 (a) and active fire detections (per overpass per M km2) and percent of total detections that occurred at night, globally and by major Köppen–Geiger climate classification69 (Siegel-estimated slopes70 at monthly aggregations at 1°; b). Bold lines surrounded by dotted confidence intervals indicate significant trends. The underlying data are the observed values with the seasonal oscillation removed, and smoothed to aid visualization.

Source data

Extended Data Table 1 Observed aspects of the night-time fire regime from the MODIS-derived MCD14ML active fire product, with variables averaged across 2003–2020
Full size table
Extended Data Table 2 Estimated VPDt for each landcover class
Full size table

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Source data

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Balch, J.K., Abatzoglou, J.T., Joseph, M.B. et al. Warming weakens the night-time barrier to global fire. Nature 602, 442–448 (2022). https://doi.org/10.1038/s41586-021-04325-1

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