This dataset provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types, daily or 3-hourly fields to scale the monthly emissions to higher temporal resolutions, and data for monthly biosphere fluxes. The data are at 0.25-degree latitude by 0.25-degree longitude spatial resolution and are available from June 1995 through 2016, depending on the dataset. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter less than 2.5 microns (PM2.5), total particulate matter (TPM), and sulfur dioxide (SO2) among others. These data are yearly totals by region, globally, and by fire source for each region.

This data set consists of 1 degree x 1 degree gridded monthly burned area, fuel loads, combustion completeness, and fire emissions of carbon (C), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), non-methane hydrocarbons (NMHC), molecular hydrogen (H2), nitrogen oxides (NOx), nitrous oxide (N2O), particulate matter (PM2.5), total particulate matter (TPM), total carbon (TC), organic carbon (OC), and black carbon (BC) for the time period January 1997 through December 2005. Emission estimates for the 2001-2005 period are also available with an 8-day time step. The data set was compiled using satellite data and the Carnegie-Ames-Stanford Approach (CASA) biogeochemical model. Burned area from 2001-2004 was derived from active fire and 500-m burned area data from MODIS (Giglio et al., 2006). ATSR (Along Track Scanning Radiometer) and VIRS (Visible and Infrared Scanner) satellite data were used to extend the burned area time series back to 1997 (Arino et al., 1999; Giglio et al., 2003; Van der Werf et al., 2004). Fuel loads and net flux from terrestrial ecosystems were estimated as the balance between net primary production, heterotrophic respiration, and biomass burning, using time varying inputs of precipitation, temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation. Tropical and boreal peatland emissions were also considered, using a global wetland cover map (Matthews and Fung, 1987) to modify surface and belowground fuel availability. The data set also includes monthly estimates of the C4 fraction of carbon emissions that can be used to construct the 13C isotope ratio (Randerson et al., 2005).The data files are in space delimited ASCII format. For each subject (e.g., burned area, fuel loads, combustion completeness, or individual fire emission species), all monthly files for the 9-year period are combined in one zipped file. Similarly, the emission estimates with an 8-day time step for the 2001-2005 period are combined in one zipped file by subject.Additional information about the methodology, data format, and parameters measured is found in the companion file: ftp://daac.ornl.gov/data/global_vegetation/fire_emission_v2/comp/global_fire_emissions_v2_1_readme.pdf. Version 2.1 Note: This data set is intended for use for large-scale modeling studies. It supersedes and replaces the Global Fire Emissions Database, Version 2 (GFEDv2) which was archived by the Oak Ridge National Laboratory Distributed Active Archive Center in 2006.

ABSTRACT: This data set provides monthly burned area, and monthly, and annual fire emissions data from July 1996 to February 2012. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter 2.5 micron (PM2p5), total particulate matter (TPM), and sulfur dioxide (SO2). The C4 fraction of carbon emissions is also provided. The annual C emissions estimates were derived by combining burned area data with a biogeochemical model, CASA-Global Fire Emissions Database (CASA-GFED), that estimates fuel loads and combustion completeness for each monthly time step. The fuel loads were based on satellite derived information on vegetation characteristics and productivity to estimate carbon input and carbon outputs through heterotrophic respiration, herbivory, and fires. Note that while most emissions estimates included data for 32 variables (trace gases, aerosols, and carbon), not all data are available for all years, and not all variables (emission species) are included in each data product. Additional information may be obtained from the Global Fire Data website: http://www.globalfiredata.org/index.html. Data products include: - 0.5 degree x 0.5 degree gridded monthly burned area data (ha) for 1996 to 2012 provided as text files and as GeoTIFF files for 1996 to 2012. - 3-Hourly emssions (fraction) for 2003 to 2010 in NetCDF (.nc) format. - Daily emssions (fraction) for 2003 to 2010, in NetCDF (.nc) format. - Monthly emissions for 32 variables from 1997 to 2011, in text and GeoTIFF format. - Monthly emissions for 31 variables from specific sources (grassland and savanna, woodland, deforestation & degradation, forest, agricultural waste burning, and peat fires), both as absolute and relative emissions. The time period is for 2007 to 2011, and the files are provided in text and GeoTIFF format. - Global emission totals of C and other species from all sources, and from each individual source (forest fires, peat fires, agricultural waste burning, etc). - Annual emissions of carbon and other trace gases for all countries, for the period 1997 to 2010, provided as text files. These files are for indicative use only; they are not suitable for official reporting due to large uncertainties and potential for missing key regional aspects in the global approach used. - Ancillary data for monthly biosphere fluxes. The CASA-GFED biosphere flux sources include Net Primary Production (NPP), Heterotrophic respiration (Rh), and fires (biomass burning). These files are for the time period 1997 to 2009 and are provided as text files and in GeoTIFF format. There are 12 compressed (*.zip) files with this data set. The data are in text, NetCDF (.nc), and GeoTIFF (.tiff) formats as described above.

The Global Fire Assimilation System (GFAS) assimilates fire radiative power (FRP) observations from satellite-based sensors to produce daily estimates of emissions from wildfires and biomass burning. FRP is a measure of the energy released by the fire and is therefore a measure of how much vegetation is burned.

The Global Fire Emissions Indicators, Country-Level Tabular Data: 1997-2015 contains country tabulations from 1997 to 2015 for the total area burned (hectares) and total carbon content (tons). The annual total area burned is for all fire types per country. There are two groups of total carbon content (TCC), annual totals for all six fire types per country and annual totals for each of six fire types per country which include Agricultural, Boreal, Tropical Deforestation, Peat, Savanna, and Temperate forest fires.

description: ABSTRACT: This data set consists of 1 degree x 1 degree gridded monthly burned area, fuel loads, combustion completeness, and fire emissions of carbon (C), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), non-methane hydrocarbons (NMHC), molecular hydrogen (H2), nitrogen oxides (NOx), nitrous oxide (N2O), particulate matter (PM2.5), total particulate matter (TPM), total carbon (TC), organic carbon (OC), and black carbon (BC) for the time period January 1997 through December 2005. Emission estimates for the 2001-2005 period are also available with an 8-day time step. The data set was compiled using satellite data and the Carnegie-Ames-Stanford Approach (CASA) biogeochemical model. Burned area from 2001-2004 was derived from active fire and 500-m burned area data from MODIS (Giglio et al., 2006). ATSR (Along Track Scanning Radiometer) and VIRS (Visible and Infrared Scanner) satellite data were used to extend the burned area time series back to 1997 (Arino et al., 1999; Giglio et al., 2003; Van der Werf et al., 2004). Fuel loads and net flux from terrestrial ecosystems were estimated as the balance between net primary production, heterotrophic respiration, and biomass burning, using time varying inputs of precipitation, temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation. Tropical and boreal peatland emissions were also considered, using a global wetland cover map (Matthews and Fung, 1987) to modify surface and belowground fuel availability. The data set also includes monthly estimates of the C4 fraction of carbon emissions that can be used to construct the 13C isotope ratio (Randerson et al., 2005).The data files are in space delimited ASCII format. For each subject (e.g., burned area, fuel loads, combustion completeness, or individual fire emission species), all monthly files for the 9-year period are combined in one zipped file. Similarly, the emission estimates with an 8-day time step for the 2001-2005 period are combined in one zipped file by subject.Additional information about the methodology, data format, and parameters measured is found in the companion file: ftp://daac.ornl.gov/data/global_vegetation/fire_emission_v2/comp/global_fire_emissions_v2_1_readme.pdf. Version 2.1 Note: This data set is intended for use for large-scale modeling studies. It supersedes and replaces the Global Fire Emissions Database, Version 2 (GFEDv2) which was archived by the Oak Ridge National Laboratory Distributed Active Archive Center in 2006.; abstract: ABSTRACT: This data set consists of 1 degree x 1 degree gridded monthly burned area, fuel loads, combustion completeness, and fire emissions of carbon (C), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), non-methane hydrocarbons (NMHC), molecular hydrogen (H2), nitrogen oxides (NOx), nitrous oxide (N2O), particulate matter (PM2.5), total particulate matter (TPM), total carbon (TC), organic carbon (OC), and black carbon (BC) for the time period January 1997 through December 2005. Emission estimates for the 2001-2005 period are also available with an 8-day time step. The data set was compiled using satellite data and the Carnegie-Ames-Stanford Approach (CASA) biogeochemical model. Burned area from 2001-2004 was derived from active fire and 500-m burned area data from MODIS (Giglio et al., 2006). ATSR (Along Track Scanning Radiometer) and VIRS (Visible and Infrared Scanner) satellite data were used to extend the burned area time series back to 1997 (Arino et al., 1999; Giglio et al., 2003; Van der Werf et al., 2004). Fuel loads and net flux from terrestrial ecosystems were estimated as the balance between net primary production, heterotrophic respiration, and biomass burning, using time varying inputs of precipitation, temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation. Tropical and boreal peatland emissions were also considered, using a global wetland cover map (Matthews and Fung, 1987) to modify surface and belowground fuel availability. The data set also includes monthly estimates of the C4 fraction of carbon emissions that can be used to construct the 13C isotope ratio (Randerson et al., 2005).The data files are in space delimited ASCII format. For each subject (e.g., burned area, fuel loads, combustion completeness, or individual fire emission species), all monthly files for the 9-year period are combined in one zipped file. Similarly, the emission estimates with an 8-day time step for the 2001-2005 period are combined in one zipped file by subject.Additional information about the methodology, data format, and parameters measured is found in the companion file: ftp://daac.ornl.gov/data/global_vegetation/fire_emission_v2/comp/global_fire_emissions_v2_1_readme.pdf. Version 2.1 Note: This data set is intended for use for large-scale modeling studies. It supersedes and replaces the Global Fire Emissions Database, Version 2 (GFEDv2) which was archived by the Oak Ridge National Laboratory Distributed Active Archive Center in 2006.

Emissions of atmospheric pollutants from biomass burning and vegetation fires are key drivers of the evolution of atmospheric composition, with a high degree of spatial and temporal variability, and an accurate representation of them in models is essential. The CAMS Global Fire Assimilation System (GFAS) utilises satellite observations of fire radiative power (FRP) to provide near-real-time information on the location, relative intensity and estimated emissions from biomass burning and vegetation fires. Emissions are estimated by (i) conversion of FRP observations to the dry matter (DM) consumed by the fire, and (ii) application of emission factors to DM for different biomes, based on field and laboratory studies in the scientific literature, to estimate the emissions. Emissions estimates for 40 pyrogenic species are available from GFAS, including aerosols, reactive gases and greenhouse gases, on a regular grid with a spatial resolution of 0.1 degrees longitude by 0.1 degrees latitude.

This dataset supports the State of Wildfires 2023-24 report under review at Earth System Science Data Discussions (Jones et al., under review, https://doi.org/10.5194/essd-2024-218). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2023-February 2024), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Jones et al. (2024).

Citation

Work utilising our regional summaries should cite both Jones et al. (2024, under review, ESSD) AND the primary reference for the variable(s) of interest as follows:

• Giglio et al. (2018) for MODIS MCD64A1 BA.
• van der Werf et al. (2017) for GFED4.1s fire C emissions.
• Kaiser er al. (2012) for GFAS fire C emissions.
• van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.
• Andela et al. (2019) for the Global Fire Atlas.

Input Data

Burned Area (BA)

• BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available at Giglio et al. (2021, https://lpdaac.usgs.gov/products/mcd64a1v061/).
  • Period: 2001-February 2024
  • Resolution: 500m

Fire Carbon (C) Emissions

• GFED4.1s fire C emissions data are extended from van der Werf and are available at https://globalfiredata.org/.
  • Period: 2003-February 2024
  • Resolution: 0.25 degree, daily

• GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available at https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation.
  • Period: 2003-February 2024
  • Resolution: 0.1 degree, daily

Global Fire Atlas (Individual Fire Atlas and Properties)

• Global Fire Atlas are extended from Andela et al. (2019) and are available at Andela and Jones (2024, https://doi.org/10.5281/zenodo.11400062, last access: 31 May 2024).
  
  • Period: 2002-February 2024
  • Driven by 500m MODIS BA data (collection 6.1)

Regional Analysis

We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer).

The statistics derived from cookie-cutting are listed below. Full details in Jones et al. (2024).

Regional Statistics and Anomalies

• Burned Area (BA)
  • Calculated regional totals for each fire season.
  • Relative and standardized anomalies from historical data (since 2001).
  • Ranking amongst all recorded fire seasons.
  • Onset, peak, and cessation based on monthly deviations from climatological means.

• Carbon Emissions
  • Calculated regional totals for each fire season.
  • Relative and standardized anomalies from historical data (since 2003).
  • Ranking amongst all recorded fire seasons.
  • Onset, peak, and cessation based on monthly deviations from climatological means.
  • Statistics available for GFAS, GFED, and their mean.

• Individual Fire Properties
  • Based on ignition point vectors from the Global Fire Atlas.
  • Calculated regional count.
  • Calculated regional maxima and 95th percentiles for each fire season.
  • Relative and standardized anomalies from historical data (since 2002).
  • Ranked anomalies among all recorded fire seasons.

The Global Fire Emissions Indicators, Grids: 1997-2015 contain a time-series of rasters from 1997 to 2015 for total area burned (hectares) and total carbon content (tons). The annual total area burned raster is the sum of monthly rasters, which are products of the Cell Area and Burn Fraction (fraction of the cell area burned in the month). There are two groups of total carbon content (TCC) rasters, annual totals for all fire types and annual totals for each of six fire types which include Agricultural, Boreal, Tropical Deforestation, Peat, Savanna, and Temperate forest fires. The annual TCC raster for all fire types is the sum of monthly carbon emission rasters. The annual TCC raster for each fire type is the product of Dry Matter, Burn Fraction, and Fire Type Contribution.

Anthropogenic carbon emissions induce rising temperatures and climate change. Fire carbon emissions contribute to the accumulation of atmospheric CO2 and affect climate change. It is crucial to accurately monitor the dynamics of global fire emissions for fire management and climate change mitigation. This study merged six commonly used satellite-based fire emission products (the GFED 4.1s, GFAS 1.2, QFED 2.5, FINN 2.5, FEER 1.0, and Xu et al. (2021)) using the three-corner hat method (TCH) (Premoli and Tavella, 1993) and produced a new global fire emission dataset, FiTCH. The FiTCH dataset provides global annual fire carbon emissions from 2001 to 2021 at the spatial resolution of 0.1 degree. The unit of carbon emissions is gigagram carbon (Gg C) per pixel. There are 21 bands in the stacked raster data, where each band indicates a year since 2001.

Note: The GFED 4.1s data were from the Global Fire Emissions Database (GFED) (van der Werf et al., 2017). The GFAS 1.2 data were from the Global Fire Assimilation System (GFAS) (Kaiser et al., 2012). The QFED 2.5 data were from the Quick Fire Emissions Dataset (QFED) (Koster et al., 2015). The FINN 2.5 data were from the Fire INventory from National Center for Atmospheric Research (NCAR) (FINN) (Wiedinmyer et al., 2023). The FEER 1.0 data were from the Fire Energetics and Emissions Research (FEER) (Ichoku and Ellison, 2014). The Xu et al. (2021) global fire emission data came from "Changes in global terrestrial live biomass over the 21st century" published in Science Advance.

References Ichoku, C., Ellison, L., 2014. Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements. Atmospheric Chemistry and Physics 14, 6643–6667. https://doi.org/10.5194/acp-14-6643-2014
Kaiser, J.W., Heil, A., Andreae, M.O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M.G., Suttie, M., van der Werf, G.R., 2012. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554. https://doi.org/10.5194/bg-9-527-2012 Koster, R.D., Darmenov, A.S., da Silva, A.M., 2015. The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4 (No. NASA/TM-2015-104606 /Vol. 38).
Premoli, A., Tavella, P., 1993. A revisited three-cornered hat method for estimating frequency standard instability. IEEE Transactions on Instrumentation and Measurement 42, 7–13. https://doi.org/10.1109/19.206671 van der Werf, G.R., Randerson, J.T., Giglio, L., Leeuwen, T.T. van, Chen, Y., Rogers, B.M., Mu, M., Marle, M.J.E. van, Morton, D.C., Collatz, G.J., Yokelson, R.J., Kasibhatla, P.S., 2017. Global fire emissions estimates during 1997–2016. Earth System Science Data 9, 697–720. https://doi.org/10.5194/essd-9-697-2017
Wiedinmyer, C., Kimura, Y., McDonald-Buller, E.C., Emmons, L.K., Buchholz, R.R., Tang, W., Seto, K., Joseph, M.B., Barsanti, K.C., Carlton, A.G., Yokelson, R., 2023. The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications. EGUsphere 1–45. https://doi.org/10.5194/egusphere-2023-124 Xu, L., Saatchi, S.S., Yang, Y., Yu, Y., Pongratz, J., Bloom, A.A., Bowman, K., Worden, J., Liu, J., Yin, Y., Domke, G., McRoberts, R.E., Woodall, C., Nabuurs, G.-J., de-Miguel, S., Keller, M., Harris, N., Maxwell, S., Schimel, D., 2021. Changes in global terrestrial live biomass over the 21st century. Science Advances 7, eabe9829. https://doi.org/10.1126/sciadv.abe9829

This dataset provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types, daily or 3-hourly fields to scale the monthly emissions to higher temporal resolutions, and data for monthly biosphere fluxes. The data are at 0.25-degree latitude by 0.25-degree longitude spatial resolution and are available from June 1995 through 2016, depending on the dataset. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter less than 2.5 microns (PM2.5), total particulate matter (TPM), and sulfur dioxide (SO2) among others. These data are yearly totals by region, globally, and by fire source for each region.

This project provided an integrated assessment of the effects of fires under different future climate and population scenarios on fine particulate matter mass (PM2.5) and ozone (O3) at global scale, with a particular focus on the United States. We employed the global Community Earth System Model (CESM) with the Representative Concentration Pathway (RCP) climate, anthropogenic emissions and land use, and the Shared Socioeconomic Pathways (SSP) population projections (i.e., RCP4.5/SSP1 and RCP8.5/SSP3). Within CESM, we used a complex-based fire parameterization to project future climate- and human-driven fire emissions, and considered landscape, deforestation, agricultural and peat fires. This data publication includes a) fire emissions for main fire species such as black carbon (BC), monoterpenes, carbon monoxide (CO), isoprene, Hydrogen cyanide (HCN), ammonia (NH3), nitric oxide (NO), organic carbon (OC), sulfur dioxide (SO2), etc.; b) area burned from landscape, agriculture, deforestation and peat fires; and c) air quality (PM2.5, BC, OC and O3). All data are at a global scale 0.9 x 1.25 horizontal resolution, with either monthly, daily or hourly resolution at decadal snapshots, i.e. 2000 Baseline (2000-2010), 2050 RCP45 and RCP85 (2040-2050) and 2100 RCP4.5 and RCP8.5 (2090-2100). All simulations were performed with CESM 1.2 with a fire module described in Li et al. (2012 and 2013).Data were created to investigate the effect of changing climate on future fire activity and its consequences for air pollution.Original metadata and publication date was 05/07/2018. Two of the data files included in this data publication were found to contain only zeros, so on 06/15/2018 this publication was updated to include the corrected netCDF files containing fire area burned for RCP45/SSP1 and RCP85/SSP3 scenarios. We discovered and corrected a file duplication error with these same fire area burned files on 06/10/2021. Some additional minor metadata updates were also made.

This product provides Daily average wildfire emissions (FIRE) andfuel wood burning emissions (FUEL) derived from the Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) model.The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.

Synthesis of fuel load and fuel consumption field measurements accompanying the publication:

"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"

Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5

1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands
2Department of Earth System Science, University of California, Irvine, CA 92697, USA
3Woodwell Climate Research Center, Falmouth, MA 02540, USA
4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

DOI: https://doi.org/10.5194/gmd-15-8411-2022

Units are g C / m2

We use MOPITT satellite retrievals of CO and a global atmospheric inversion system to estimate global monthly fire CO2 emissions at a horizontal resolution of 3.75° × 1.9°.

The global burned area declined by nearly one-quarter between 1998 and 2015. Drylands contain a large proportion of these global fires but there are important differences within the drylands, e.g., savannas and tropical dry forests (TDF). Savannas, a biome fire-prone and fire-adapted, have reduced the burned area, while the fire in the TDF is one of the most critical factors impacting biodiversity and carbon emissions. Moreover, under climate change scenarios TDF is expected to increase its current extent and raise the risk of fires. Despite regional and global scale effects, and the influence of this ecosystem on the global carbon cycle, little effort has been dedicated to studying the influence of climate (seasonality and extreme events) and socioeconomic conditions of fire regimen in TDF. Here we use the Global Fire Emissions Database and, climate and socioeconomic metrics to better understand long-term factors explaining the variation in burned area and biomass in TDF at the Pantropical scale. On average, fires affected 1.4% of the total TDF’ area (60,208 km2) and burned 24.4% (259.6 Tg) of the global burned biomass annually at Pantropical scales. Climate modulators largely influence local and regional fire regimes. Inter-annual variation in fire regime is shaped by El Niño and La Niña. During El Niño and the forthcoming year of La Niña, there is an increment in extension (35.2 and 10.3%) and carbon emissions (42.9 and 10.6%). Socioeconomic indicators such as land management and population were modulators of the size of both, burned area and carbon emissions. Moreover, fires may reduce the capability to reach the target of “half protected species” in the globe, i.e., high-severity fires are recorded in ecoregions classified as nature could reach half protected. These observations may contribute to improving fire management. Methods We used the Global Fire Emissions Database, Version 4.1 (GFED4s) (Randerson et al., 2018). GFED4s includes the monthly and daily fire burned above-ground biomass from 1997 to 2020, for all fire sizes including "small fires”. Burned area covers the period 1997-2016. The information has a spatial resolution of 0.25 degrees. We included small fires to fully capture fire dynamics across the TDF in the Pantropic. To ensure that we dominantly evaluated the TDF ecosystem, we crossed the GFED4s database to the realms defined by Dinerstein et al. (2017), similar to others (Zubkova et al., 2019). The realms are redefined and updated from their previous version of the terrestrial ecoregions of the world (Olson et al., 2001). For this study, we included the Tropical & Subtropical Dry Broadleaf Forests and the TDF in Mato Grosso, Brazil (Biudes et al., 2022), and excluded the woody savannas from our analysis, such as the savannas of Africa, Australia, and South America (Lehmann et al., 2011; Moncrieff et al., 2016). A total of 52 different ecoregions were included for six Pantropical regions: (i) Caribbean Islands, Central America, and Mexico (CEAM); (ii) Northern Hemisphere South America (including Colombia and Venezuela) (NHSA); (iii) Southern Hemisphere South America (including Bolivia, Brazil, Ecuador, and Peru) (SHSA); (iv) Southern Hemisphere Africa (including Angola, Madagascar, and Zambia) (SAHF); (v) Southeast Asia (including Cambodia, India, Laos, Myanmar, Sri Lanka, Thailand, and Vietnam) (SEAS); and (vi) Equatorial Asia (EQAS). To identify the main drivers of fires, we focused on characterizing the historical socio-ecological conditions (i.e climatic, biophysical, and socioeconomic drivers) in TDF regions for each grid-cell at 0.25° resolution. For this analysis, we used the most updated and improved biophysical and socio-economic data at the finest spatial resolution. All the selected databases have been used, either for modeling climate change or have been updated based on the most recent climatic modeling. Therefore, our results would be comparable to more recent and future studies. Also, all the spatial information has global coverage. Overall the spatial resolutions range from 90-m up to 10-km, and are interpolated to the common GFED4s grid-cell of 0.25 degrees. Climatic variables such as temperature, precipitation, and wind speed have been recognized as key drivers of moisture availability and fire propagation (Archibald et al., 2009). Some studies used weather conditions as drivers that modulate variations in ignition efficiency, fire spread rate, and fire size (Andela et al., 2017; van der Werf et al., 2017; Jiang et al., 2020), while others included eco-climatic zones (Chuvieco et al., 2008). Contrasting to previous studies, we used the “near current climate” (1970-2000) data from WorldClim version 2.1 released in 2020’ (Fick and Hijmans, 2017) with a spatial resolution of 1-km. We used the 19 current bioclimatic variables (Bio1 to Bio 19), mean solar radiation, water vapor pressure, and wind speed. All the bioclimatic variables are derived from the monthly temperature and rainfall values. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation), seasonality (e.g., annual range in temperature and precipitation), and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters) (Fick and Hijmans, 2017). Complementarily, from Trabucco and Zomer (2019) we used potential evapotranspiration, and Priestley-Taylor alpha coefficient (Lhomme, 1997). Precipitation and temperature alone have shown to be inadequate to measure the hydrological condition (Quan et al., 2013), we evaluated water deficiency in the TDF based on two different aridity indexes. The first index considers annual precipitation and temperature (Lang index). The second index further includes reference evapotranspiration (Trabucco and Zomer, 2019). Lang aridity index is the ratio of annual precipitation to the mean annual temperature (mm per °C) (Lang, 1920). This index suggests that the rise in temperature increases water deficiency and makes the air drier if not sufficiently recharged by precipitation and/or underground water (Quan et al., 2013). Trabucco and Zomers aridity index shows moisture availability/deficit for potential growth of reference vegetation excluding the impact of soil mediating water runoff events. Fuel availability relates to biomass favoring burning. NDVI has been used as a proxy for the conditions of vegetation, particularly for fuel availability (Jiang et al., 2020). However, we propose to use above-ground biomass (AGB) in live plants as a fuel load proxy, similar to others (Corona-Núñez et al., 2020; Tang et al., 2021). The AGB dataset refers to the epoch of the years 2000’s with a spatial resolution of 1-km (Avitabile et al., 2016). The AGB was transformed to above-ground carbon stocks (AGC) assuming a mean C concentration of 47.2% (Corona-Núñez et al., 2018). To estimate the potential biomass losses from fires we assumed that the total fuel load refers to the AGB. Socioeconomic factors have been shown to be important drivers of deforestation, forest degradation, and modulators of fire frequency and size. In this study, we evaluated population, richness, land accessibility, and land management as drivers of fires (Chuvieco et al., 2008; Archibald et al., 2009; Andela et al., 2017; Zubkova et al., 2019). We included population density (GPWv4) and gross domestic product (G-Econ v4) with a resolution of 30 arc seconds (CIESIN, 2018). We evaluated land accessibility by two means. Firstly, we used altitude. Altitude has been a significant driver to understand forest degradation and deforestation in the tropics (Mendoza-Ponce et al., 2018; Corona-Núñez et al., 2021). Altitude comes from a digital terrain model with a spatial resolution of 90-m from the SRTM (Shuttle Radar Topography Mission - V.2.1, NASA). Secondly, we used road density at a resolution of 5 arc-minutes (Meijer et al., 2018). Roads have been shown to be important drivers to explain burned areas (Archibald and Roy, 2009; Zubkova et al., 2019) and TDF degradation (Corona-Núñez et al., 2021). To integrate land-management practices we included the proportion of croplands, irrigated agriculture (Ramankutty et al., 2010a), and pastures (Ramankutty et al., 2010b) within 5 arc-minutes grid-cell, a similar approach undertaken by others (Chuvieco et al., 2008; Archibald et al., 2009; Andela et al., 2017; Zubkova et al., 2019). The global croplands, irrigated agriculture, and pastures data set represent the proportion of land areas used in the year 2000. This data was estimated from MODIS and SPOT sensors combined with inventory data (Ramankutty et al., 2008).

This dataset provides carbon monoxide and carbon dioxide flux from fires constrained by satellite observations.The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.

Satellites provide direct observations of fire activities (e.g., burned area, fire radiative power) (Andela et al., 2017; Giglio et al., 2006; Luo et al., 2024), but their temporal coverages are limited because most satellite data were available only after 1980s (Chuvieco et al., 2019). Fire modules in dynamic global vegetation models (DGVMs) are able to simulate long-term burned area and the interactions with vegetation dynamics based on climate conditions and soil properties (Sitch et al., 2015; Sitch et al., 2024), but the spatial resolution at the global scale is usually coarse due to the coarse resolution of the input meteorological forcing data, and most models failed in capturing the global trends of burned area (Andela et al., 2017; Hantson et al., 2020). The processes included and the parameterizations of fire processes are widely different across fire models, resulting in a large range of simulated burned area at both the regional and global scales (Hantson et al., 2020). Considering the limitations of satellite observations and fire models, a spatiotemporally consistent burned area dataset over the 20th century trained from present-day observations, is essential for fire modelling and can serve as publicly available benchmark for fire ecology and carbon cycle studies.

This study produced a global monthly 0.5°×0.5° burned area fraction (BAF) dataset from1901 to 2020 using machine learning models based on climate conditions, vegetation states, population density and land use data. We first divided the globe into 14 regions following the Global Fire Emission Dataset (GFED regions) (Giglio et al., 2006; van Der Werf et al., 2017) and conducted all steps described below in each GFED region individually. To better capture extreme fires, we first developed a classification model to distinguish grid cells with extreme and regular fires, using the 90th percentile of all burned area fractions within a region as the threshold to define extreme fires. We then trained separate regression models for grid cells categorized as having extreme or regular fires. The models were trained against the satellite-based burned area product (FireCCI51) during 2003-2020 excluding cropland fires, and then used to reconstruct the burned area from 1901 to 2020. In addition to validation against satellite observations that were not used for model training, we also compared our burned area predictions with charcoal records and other independent global and regional burned area datasets. In addition to the historical reconstructed burned area dataset based on the FireCCI51 mentioned above, we also produced two additional products of historical burned area with the same spatiotemporal resolution as the FireCCI51-based burned area reconstruction: 1) the GFED5-based data version, which is based on machine-learning models trained by the burned area from GFED5 which has much more fires than GFED4 (Chen et al., 2023) instead of FireCCI51, and 2) the FireCCI51-based data with burned area further calibrated using the relationship between statistic-based burned area (Mouillot and Field, 2005) and GDP (Bolt and Van Zanden, 2024) at the regional scale before 2000 (named as FireCCI51-GDP version).

This dataset can be used to benchmark historical simulations from fire modules in DGVMs, re-calculate historical fire emissions and estimate legacy effects of vegetation recovery after fires on terrestrial carbon sink. Though the temporal coverage of our product is long enough to support studies related to fire disturbance, carbon dynamics and climate change, more reliable explanatory data for model training and burned area data for validation would help further improve the accuracy of the reconstructed burned area product.

Description of variable names in netcdf files:
'lat' -- latitude of the center of grid cells
'lon' -- longitude of the center of grid cells
'ba' -- burned area fraction in 0.5°×0.5°grid cells
'ba_type' -- 0=no fire; 1=regular burned area; 2=extreme burned area. Note that regular & extreme BA is defined in each GFED region individually. Extreme BA refers to that burned area within grid cells surpassing the 90th percentile of all grid cells with burned area in each GFED region.

This dataset is currently for manuscript submission to the scientifc journal

The Fire INventory from NCAR (FINN) provides daily global fire emissions at high spatial resolution. The FINN model uses satellite detection of active fires (thermal anomalies) and the land cover type to determine the emission estimates. The emission estimates are based on two uses of satellite observations: (1) MODIS fire detection, (2) Both MODIS and VIIRS (Visible Infrared Imaging Radiometer Suite) active fire detection. Global daily emission estimates of key gases and aerosols as well as the speciation of the total VOC emissions for three common chemical mechanisms (MOZART-T1, SAPRC99 and GEOS-Chem) are included. Results are available in yearly files at 0.1 by 0.1 degree resolution, or in yearly text files of fire spots at 1 km resolution. The variables listed below are only partial of the variables in this dataset. For the complete list of variables, see detailed metadata [https://rda.ucar.edu/datasets/ds312.9/detailed_metadata/]

1. Annual_total_fire_NH3_emissions.xlsx, Annual total fire NH3 emission intensities in each gird (2.5° longitude ×2° latitude, units: kg N ha-1 yr-1) from 2015 to 2023.2. Annual_total_fire_NOx_emissions.xlsx, Annual total fire NOx emission intensities in each gird (2.5° longitude ×2° latitude, units: kg N ha-1 yr-1) from 2015 to 2023.3. Fire_emissions.xlsx, Monthly emissions of NH3 and NOx from boreal (50°–70°N) and tropical (40°S–20°N) fires, units: Tg N month-1.