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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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.
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.
Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).
Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).
Easterling, D. R. et al. Maximum and minimum temperature trends for the globe. Science 277, 364–367 (1997).
Peterson, T. C. & Vose, R. S. An overview of the Global Historical Climatology Network temperature database. Bull. Am. Meteorol. Soc. 78, 2837–2850 (1997).
Goens, D. W. Forecast Guidelines for Fire Weather and Forecasters—How Nighttime Humidity Affects Wildland Fuels Technical Memorandum NWS WR-205 (NOAA, 1989).
Byram, G. M. & Nelson Jr., R. M. An Analysis of the Drying Process in Forest Fuel Material General Technical Report SRS-200 (US Department of Agriculture Forest Service, Southern Research Station, 2015).
Seager, R. et al. Climatology, variability, and trends in the US vapor pressure deficit, an important fire-related meteorological quantity. J. Appl. Meteorol. Climatol. 54, 1121–1141 (2015).
Krawchuk, M. A., Moritz, M. A., Parisien, M. A., Van Dorn, J. & Hayhoe, K. Global pyrogeography: the current and future distribution of wildfire. PLoS ONE 4, e5102 (2009).
Forkel, M. et al. Recent global and regional trends in burned area and their compensating environmental controls. Environ. Res. Commun. 1, 051005 (2019).
Ray, D., Nepstad, D. & Moutinho, P. Micrometeorological and canopy controls of fire susceptibility in a forested Amazon landscape. Ecol. Appl. 15, 1664–1678 (2005).
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).
Abatzoglou, J. T. & Williams, A. P. The impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).
Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future 7, 892–910 (2019).
Rothermel, R. C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels Research Paper INT-115 (US Department of Agriculture Forest Service, Intermountain Forest and Range Experiment Station, 1972).
Nolan, R. H. et al. Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data. Remote Sens. Environ. 174, 100–108 (2016).
Viney, N. & Catchpole, E. Estimating fuel moisture response times from field observations. Int. J. Wildland Fire 1, 211–214 (1991).
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).
Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331 (2019).
Schmidt, C. C. & Prins, E. M. GOES wildfire ABBA applications in the Western Hemisphere. In 2nd International Wildland Fire Ecology and Fire Management Congress and 5th Symposium on Fire and Forest Meteorology (Citeseer, 2003).
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).
Cahoon, D. R., Stocks, B. J., Levine, J. S., Cofer, W. R. & O’Neill, K. P. Seasonal distribution of African savanna fires. Nature 359, 812–815 (1992).
Giglio, L., Csiszar, I. & Justice, C. O. Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. J. Geophys. Res. Biogeosci. 111, G02016 (2006).
Giglio, L. Characterization of the tropical diurnal fire cycle using VIIRS and MODIS observations. Remote Sens. Environ. 108, 407–421 (2007).
Amraoui, M., DaCamara, C. C. & Pereira, J. M. C. Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery. Remote Sens. Environ. 114, 1038–1052 (2010).
Andela, N., Kaiser, J. W., van der Werf, G. R. & Wooster, M. J. New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations. Atmos. Chem. Phys. 15, 8831–8846 (2015).
Roberts, G., Wooster, M. J. & Lagoudakis, E. Annual and diurnal African biomass burning temporal dynamics. Biogeosciences 6, 849–866 (2009).
Williamson, G. J. et al. Measurement of inter-and intra-annual variability of landscape fire activity at a continental scale: the Australian case. Environ. Res. Lett. 11, 035003 (2016).
Balch, J. K. et al. The susceptibility of southeastern Amazon forests to fire: insights from a large-scale burn experiment. BioScience 65, 893–905 (2015).
Gill, A. M., Hoare, J. R. L. & Cheney, N. P. In Fire in the Tropical Biota: Ecosystem Processes and Global Challenges (ed. Goldammer, J. G.) 159–178 (Springer, 1990); https://doi.org/10.1007/978-3-642-75395-4_10
Daniels, J. Night-flying by fire agencies in California gains interest as state’s wildfire threat grows. CNBC https://www.cnbc.com/2018/08/09/night-flying-by-fire-agencies-in-california-gains-more-interest.html (2018).
Pyne, S. J. Fire in America: A Cultural History of Wildland and Rural Fire (Univ. Washingon Press, 1997).
St. Denis, L. A., Mietkiewicz, N. P., Short, K. C., Buckland, M. & Balch, J. K. All-hazards dataset mined from the US National Incident Management System 1999–2014. Sci. Data 7, 64 (2020).
Alencar, A. et al. Desmatamento na Amazônia: indo além da "emergência crônica" Vol. 90 (Ipam Belém, 2004).
McCarty, J., Justice, C. & Korontzi, S. Agricultural burning in the southeastern United States detected by MODIS. Remote Sens. Environ. 108, 151–162 (2007).
Kolden, C. A. We’re not doing enough prescribed fire in the western United States to mitigate wildfire risk. Fire 2, 30 (2019).
Huffman, M. R. The many elements of traditional fire knowledge: synthesis, classification, and aids to cross-cultural problem solving in fire-dependent systems around the world. Ecol. Soc. 18, art3 (2013).
Balch, J. K. et al. FIRED (Fire Events Delineation): an open, flexible algorithm and database of US fire events derived from the MODIS burned area product (2001–2019). Remote Sens. 12, 3498 (2020).
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Noble, I., Gill, A. & Bary, G. McArthur’s fire-danger meters expressed as equations. Aust. J. Ecol. 5, 201–203 (1980).
Van Wagner, C. E. et al. Development and Structure of the Canadian Forest Fire Weather Index System Technical Report (Canadian Forestry Service, 1987).
Wooster, M. J., Roberts, G., Perry, G. L. W. & Kaufman, Y. J. Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. J. Geophys. Res. Atmos. 110, D24311 (2005).
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).
Libonati, R. et al. Twenty-first century droughts have not increasingly exacerbated fire season severity in the Brazilian Amazon. Sci. Rep. 11, 4400 (2021).
Alencar, A., Nepstad, D. & Diaz, M. C. V. Forest understory fire in the Brazilian Amazon in ENSO and non-ENSO years: area burned and committed carbon emissions. Earth Interact. 10, 1–17 (2006).
Balch, J. K. et al. Switching on the big burn of 2017. Fire 1, 17 (2018).
Higuera, P. E. & Abatzoglou, J. T. Record-setting climate enabled the extraordinary 2020 fire season in the western United States. Glob. Change Biol. 27, 1–2 (2021).
Chiodi, A. M., Potter, B. E. & Larkin, N. K. Multi‐decadal change in western US nighttime vapor pressure deficit. Geophys. Res. Lett. 48, e2021GL092830 (2021).
Joseph, M. B. et al. Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima. Ecol. Appl. 29, 1266–1281 (2019).
Perkins, S. E., Alexander, L. V. & Nairn, J. R. Increasing frequency, intensity and duration of observed global heatwaves and warm spells. Geophys. Res. Lett. 39, L20714 (2012).
Stone, D. A. & Weaver, A. J. Daily maximum and minimum temperature trends in a climate model. Geophys. Res. Lett. 29, 70-1–70-4 (2002).
Kolden, C. A., Lutz, J. A., Key, C. H., Kane, J. T. & van Wagtendonk, J. W. Mapped versus actual burned area within wildfire perimeters: characterizing the unburned. For. Ecol. Manage. 286, 38–47 (2012).
Krawchuk, M. A. et al. Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America. Ecosphere 7, e01632 (2016).
Koontz, M. J., North, M. P., Werner, C. M., Fick, S. E. & Latimer, A. M. Local forest structure variability increases resilience to wildfire in dry western U.S. coniferous forests. Ecol. Lett. 23, 483–494 (2020).
Saide, P. E. et al. Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion. Geophys. Res. Lett. 42, 3609–3618 (2015).
Korontzi, S., McCarty, J., Loboda, T., Kumar, S. & Justice, C. Global distribution of agricultural fires in croplands from 3 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data. Glob. Biogeochem. Cycles 20, GB2021 (2006).
Schmit, T. J. et al. A closer look at the ABI on the GOES-R series. Bull. Am. Meteorol. Soc. 98, 681–698 (2017).
Schmidt, C. In The GOES-R Series (eds Goodman, S. J. et al.) 145–163 (Elsevier, 2020).
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).
Hawbaker, T. J. et al. Mapping burned areas using dense time-series of Landsat data. Remote Sens. Environ. 198, 504–522 (2017).
Schroeder, W. et al. Early characterization of the active fire detection products derived from the next generation NPOESS/VIIRS and GOES-R/ABI instruments. In 2010 IEEE International Geoscience and Remote Sensing Symposium 2683–2686 https://doi.org/10.1109/IGARSS.2010.5650863 (IEEE, 2010).
Schroeder, W. et al. Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data. Remote Sens. Environ. 112, 2711–2726 (2008).
Hall, J. V., Zhang, R., Schroeder, W., Huang, C. & Giglio, L. Validation of GOES-16 ABI and MSG SEVIRI active fire products. Int. J. Appl. Earth Obs. Geoinf. 83, 101928 (2019).
Li, F., Zhang, X., Kondragunta, S., Schmidt, C. C. & Holmes, C. D. A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records. Remote Sens. Environ. 237, 111600 (2020).
Li, F., Zhang, X., Roy, D. P. & Kondragunta, S. Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States. Atmos. Environ. 211, 274–287 (2019).
Jordahl, K. GeoPandas: Python tools for geographic data; https://github.com/geopandas/geopandas (2020).
Gillies, S. et al. Rasterio: Geospatial raster I/O for Python programmers; https://github.com/rasterio/rasterio (2020).
Lyapustin, A. & Wang, Y. MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) data user’s guide; https://modis-land.gsfc.nasa.gov/pdf/MCD19_UserGuide_final_Feb-6-2018.pdf (2018).
Sulla-Menashe, D. & Friedl, M. A. User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) roduct; https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf (2018).
Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).
Siegel, A. F. Robust regression using repeated medians. Biometrika 69, 242–244 (1982).
Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. STL: a seasonal-trend decomposition. J. Stat. 6, 3–73 (1990).
Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).
Wood, S. N., Pya, N. & Säfken, B. Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).
Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
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).
The authors declare no competing interests.
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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).
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.
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.
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.
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.
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.
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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