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Enhanced dust emission following large wildfires due to vegetation disturbance

Abstract

Large wildfires reduce vegetation cover and soil moisture, leaving the temporally degraded landscapes an emergent source of dust emission. However, the global extent of post-fire dust events and their influencing factors remain unexplored. Using satellite measurements of active fires, aerosol abundance, vegetation cover and soil moisture from 2003 to 2020, here we show that 54% of the examined ~150,000 global large wildfires are followed by enhanced dust emission, producing substantial dust loadings for days to weeks over normally dust-free regions. The occurrence and duration of post-fire dust emission is controlled primarily by the extent of precedent wildfires and resultant vegetation anomalies and modulated secondarily by pre-fire drought conditions. The intensifying wildfires and drying soils during the studying period have made post-fire dust events one day longer, especially over extratropical forests and grasslands. With the predicted intensification of regional wildfires and concurrent droughts in the upcoming decades, our results indicate a future enhancement of sequential fire and dust extremes and their societal and ecological impacts.

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Fig. 1: Extreme dust activity associated with vegetation disturbances caused by the 2019–2020 Australian bushfires.
Fig. 2: Global distribution of post-fire dust events.
Fig. 3: Severity of post-fire dust events regulated by the extent of precedent wildfires and vegetation disturbance.
Fig. 4: Observed evolution of the occurrence and duration of post-fire dust events during 2003–2020.

Data availability

The datasets for conducting the analysis presented here are all publicly available, including the MODIS Collection 6 Active Fire Detections (MCD14ML) acquired from NASA Fire Information for Research Management System (https://earthdata.nasa.gov/firms); the MODIS Deep Blue aerosol products acquired from the Level-1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC) (https://ladsweb.modaps.eosdis.nasa.gov/); the MISR aerosol products acquired from the NASA Langley Research Center Atmospheric Science Data Center (https://l0dup05.larc.nasa.gov/cgi-bin/MISR/main.cgi); the AERONET coarse-mode aerosol optical depth data downloaded from https://aeronet.gsfc.nasa.gov; the ESA CCI soil moisture data downloaded from https://www.esa-soilmoisture-cci.org/node/238; the ERA5 hourly climate data provided by ECMWF (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5); the MODIS MCD12Q1v006 Landcover Type 1 product (https://lpdaac.usgs.gov/products/mcd12q1v006/); and the MODIS L3 EVI (MOD13C1 and MYD13C1) from DAAC (https://lpdaac.usgs.gov/products/mod13c1v006/). We generate a list of all identified dust-emission cases following large fires available at https://doi.org/10.6084/m9.figshare.20648055 (ref. 77).

Code availability

The code to carry out the current analyses is available from the corresponding author on request.

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Acknowledgements

This research is supported by Peking University School of Physics (Y.Y.), NASA grant number NNH19ZDA001N-HMA (P.G.), and the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission (P.G.). We thank J. Dunne for helpful comments on the early version of this paper. We thank the MODIS, MISR, AERONET and CALIOP teams for providing data. We thank the Earth Surface Mineral Dust Source Investigation (EMIT) team, J. Lin and J. Mao for useful discussions. Computation is supported by High-performance Computing Platform of Peking University.

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Y.Y. conceived the study, analysed the data and wrote the manuscript with contribution from P.G. P.G. retrieved MODIS DOD and FOD data from MODIS Deep Blue aerosol products.

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Correspondence to Yan Yu.

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Nature Geoscience thanks Junran Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Dust activity following wildfires during the 2010 and 2012 burning season in the western United States, as analyzed in Wagenbrenner et al.15,16.

Time series of number of active fires (orange bars, referring to the left y-axis), enhanced vegetation index (EVI, green line, referring to rightmost y-axis), and daily maximum dust optical depth (DOD, black dots, referring to the inter right y-axis) within ±0.05° of a. 43°40’N, 112°35’W in 2010 (Wagenbrenner et al.15) and b. 42.40°N, 117.90°W in 2012 (Wagenbrenner et al.16). The light green and light grey shadings represent the long-term 10th to 90th percentiles in EVI and DOD, respectively. The large and small black dots represent time series of DOD; large dots indicate situations with relatively small amount of biomass burning aerosols, represented by below-average coincident fine-mode optical depth (FOD). The blue square or purple triangle indicate dates when soil moisture is below long-term 10th percentile or daily maximum wind speed is above long-term 90th percentile. Based on in situ measurements of dust fluxes and particulate matter concentrations (PM10), Wagenbrenner et al.15 identified several major wind erosion events, with the largest wind erosion event in early September, in several months following the July 2010 Jefferson Fire in southeastern Idaho, United States. Based on a modeling framework, Wagenbrenner et al. 16 further quantified dust emission during August 5-6, 2012 from the burnt scars left by wildfires in western United States sagebrush landscapes in July 2012.

Extended Data Fig. 2 Aerosol vertical distribution during the 2019-2020 Australian bushfire season, detected by the space-borne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO).

Four nighttime overpasses (shown in panel a with their color matching the text of date) on b-d November 5, 2019, e-g December 22, 2019, h-j January 4, 2020, and k-m January 22, 2020 are shown here, including b, e, h, k total backscatter (m − 1 sr−1), indicating the abundance of aerosols, c, f, i, l depolarization ratio, indicating the non-sphericity of aerosol particles, and d, g, j, m aerosol type determined by CALIOP operational retrieval algorithm. The typical depolarization ratio of dust particles is between 0.2 to 0.3. The red triangles in a represent locations within the dashed box where major wildfire (weekly active fire counts exceeding 20) occurred during November 2019 within the dashed box.

Extended Data Fig. 3 Long-term vegetation and dust states.

a. Minimum monthly average Enhanced Vegetation Index (EVI) during 2003 to 2020. b. 95th percentile of monthly mean Dust Optical Depth (DOD) during 2003 to 2020.

Extended Data Fig. 4 Temporal evolution of dust optical depth (DOD) and dust-favoring environmental conditions during and after burning.

The probability distribution of a. DOD, b. EVI, c. soil moisture, and d. maximum wind speed during the burning period and 12 weeks afterwards. The probability distribution is represented by the frequency (%) of each variable falling below the long-term 2.5th percentile, between the 2.5th–5th, 5th–10th, 10th–25th, 25th–50th, 50th–75th, 75th–90th, 90th–95th, 95th–97.5th percentiles, and above the 97.5th percentile.

Extended Data Fig. 5 Duration of post-fire dust events regulated by the severity of proceeding wildfires and soil moisture and wind conditions.

Scatterplot of a. minimum soil moisture (m3 m−3) and b. upper decile wind speed (m s−2) during the 60 days after major fires as a function of number of precedent fires. The color of each dot represents the duration (days) of the corresponding dust event. The boxes in a indicate the 5th, 25th, 50th, 75th, and 95th percentiles of duration of post-fire dust (days, referring to the right y-axis) events with precedent fires ranging between 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, 81–90, 91–100, and above 100 during the burning week.

Extended Data Fig. 6 Month of peak wildfire and post-fire dust activity during 2003–2020.

a. Month of maximum active fire counts. Only locations with more than 18 fires detected during 2003-2020 are shown in color. b. Month of highest occurrence of post-fire dust events. c–g Joint probability distribution function (PDF, %) of the peak month of fire and post-fire dust emission.

Extended Data Fig. 7 Duration of post-fire dust events regulated by the severity of proceeding wildfires and vegetation conditions for different land cover types.

Scatterplot of minimum EVI (referring to the left y-axis) during the 60 days after major fires as a function of proceeding number of fires during the burning week, for a. forest, b. shrubland, c. savannah, d. grassland, and e. cropland ecoregions. The color of each dot represents the duration (days) of the corresponding dust event. The boxes indicate the 10th, 25th, 50th, 75th, and 90th percentiles of the duration of post-fire dust events (days, referring to the right y-axis) with proceeding fires ranging between 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, 81–90, 91–100, and above 100 during the burning week.

Extended Data Fig. 8 Probability distribution of dust optical depth (DOD) as a function of concurrent soil dryness, with and without fires.

The probability distribution is represented by the frequency (%) of monthly mean DOD falling below the long-term 2.5th percentile, between the 2.5th–5th, 5th–10th, 10th–25th, 25th–50th, 50th–75th, 75th–90th, 90th–95th, 95th–97.5th percentiles, and above the 97.5th percentile. Four background conditions are analyzed here, including dry soils (monthly mean soil moisture below the 25th percentile) but no fires during the month, wet soils (monthly mean soil moisture above the 75th percentile) but no fires during the month, dry soils with more than 20 fires during the month, and wet soils with more than 20 fires during the month.

Extended Data Fig. 9 Occurrence and intensity of post-fire dust events, grouped by pre-fire drought condition during 2003–2020.

a. Occurrence of post-fire dust events with pre-fire dry condition (when soil moisture during the week before the occurrence of fires is below long-term 25th percentile) indicated by the size of dots, with color representing the dominant land cover type. b. Maximum Dust Optical Depth (DOD, color of dots) and mean duration (days, size of dots) of post-fire dust events with pre-fire dry condition. c. Occurrence of post-fire dust events with pre-fire wet condition (when soil moisture during the week before the occurrence of fires is above long-term 75th percentile) indicated by the size of dots, with color representing the dominant land cover type. d. Maximum DOD (color of dots) and mean duration (days, size of dots) of post-fire dust events with pre-fire wet condition.

Extended Data Fig. 10 Extreme dust activity associated with vegetation disturbances during the 2020 western North American extreme fire season.

a. Time series of number of active fires within ±0.05° of 38.9°N, 122.3°W (location indicated in panel b) (orange bars, referring to the left y-axis), enhanced vegetation index (EVI, green line, referring to rightmost y-axis), and daily maximum dust optical depth (DOD, black dots, referring to the inter right y-axis). The light green and light grey shadings represent the long-term 10th to 90th percentiles in EVI and DOD, respectively. The large or small dot represent DOD when dust or fine-mode particles are dominant, indicated by coincident fine-mode optical depth (FOD) below or above long-term median. The blue square or purple triangle indicate dates when soil moisture is below long-term 10th percentile or daily maximum wind speed is above long-term 90th percentile. Satellite image from NASA Earth Observatory.

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Yu, Y., Ginoux, P. Enhanced dust emission following large wildfires due to vegetation disturbance. Nat. Geosci. 15, 878–884 (2022). https://doi.org/10.1038/s41561-022-01046-6

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