DOI: 10.5281/zenodo.18104830
title: Multi-ensemble Biomass-burning Emissions Inventory (MBEI)_v1.0

description: MBEI: Multi-ensemble Biomass-burning Emissions Inventory (2003–2023)1. Dataset OverviewMBEI is a monthly, 0.1° global biomass-burning emissions ensemble for the period 2003–2023. It unifies two complementary pathways: a bottom-up (BU) line based on burned area and fuel load, and a top-down (TD) line based on satellite fire radiative power (FRP).All inputs are harmonized to 0.1° monthly grids, and emissions are provided for 11 species (BC, C, CH4, CO, CO2, N2O, NH3, NOx, OC, PM2.5, SO2) using land-cover- and region-specific emission factors.The dataset consists of individual sub-inventories (members) and ensemble statistical summaries to support uncertainty analysis.2. Ensemble Members (8 Sub-inventories)Each member name encodes four choices: Method (BU/TD), Active-fire Confidence (AC/HC), and the Key Dataset varied within that method.BU-AC-GB — Bottom-Up, All-Confidence, GlobBiomass: Uses all MODIS active fires and the GlobBiomass AGB map to drive fuel load.BU-AC-BC — Bottom-Up, All-Confidence, Biomass_cci: Identical to BU-AC-GB except AGB comes from Biomass_cci.BU-HC-GB — Bottom-Up, High-Confidence, GlobBiomass: Same as BU-AC-GB but restricted to medium-to-high confidence fire pixels (>30%).BU-HC-BC — Bottom-Up, High-Confidence, Biomass_cci: High-confidence fires with Biomass_cci AGB.TD-AC-LC30 — Top-Down, All-Confidence, 30-class: FRP-to-DM conversion uses a 30-class biome CR map.TD-AC-LC8 — Top-Down, All-Confidence, 8-class: Conversion uses the GFAS 8-class biome CR map.TD-HC-LC30 — Top-Down, High-Confidence, 30-class: As TD-AC-LC30 but limited to medium-to-high confidence fires.TD-HC-LC8 — Top-Down, High-Confidence, 8-class: As TD-AC-LC8 with the high-confidence filter.3. Ensemble Statistics FilesTo strictly quantify the uncertainty of global biomass burning emissions, we provide Ensemble Statistics files.These files are generated by spatially and temporally aligning the 8 sub-inventories onto a standardized 0.1° × 0.1° monthly grid. For each grid cell, the eight emission estimates are treated as a statistical ensemble. The files contain the following variables:Mean: Provides a robust emission estimate, mitigating potential biases inherent in individual input datasets or algorithms.Standard Deviation (Std): Represents the dispersion among different methods, effectively highlighting regions with significant algorithmic divergence.Maximum & Minimum: Define the upper and lower bounds of the uncertainty range.4.Datasets used in MBEI4.1 Active fire detection and fire radiative powerThe sourced active fire data were obtained from the MODIS Near-Real-Time active fire product (MCD14DL C6.1), provided by NASA's Fire Information for Resource Management System (FIRMS). This product provides fire detections from both the Terra and Aqua satellites based on the MOD14/MYD14 thermal anomalies algorithm (Giglio et al., 2006). Each active fire detection represents the center of a 1-km pixel flagged as containing one or more fires.For the period 2003–2023, we extracted daily fire locations, detection confidence, and FRP values. These 1-km daily data were then aggregated into monthly 0.1° grids, which form the primary input for both our top-down and bottom-up frameworks.4.2 Burning efficiency and emission factorTo assign region- and vegetation-specific BE and EF, we first utilized the annual 500-m MODIS Land Cover Type product (MCD12Q1 C6.1), adopting its International Geosphere-Biosphere Programme (IGBP) classification scheme. We then assigned a BE value to each of the 17 IGBP classes using coefficients derived from Mieville et al. (2010) and Shi et al. (2015).Emission factors were assigned by intersecting the MCD12Q1 land cover map with the 14 continental-scale regions defined by GFED (van der Werf et al., 2017). This process yielded a unique EF for each landcover region combination, allowing us to estimate emissions for 11 key atmospheric emission species as detailed in Table S4.4.3 Aboveground biomassTo quantify available fuel load for the bottom-up framework and assess related uncertainties, we employed two independent global AGB datasets. The GlobBiomass provides a global AGB map at 25-m spatial resolution for the baseline year 2010, generated by synergistically fusing multi-source data, including observations from spaceborne Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), and optical remote sensing, together with forest inventory data (Santoro, 2018). Biomass_cci, provided by the European Space Agency Climate Change Initiative (ESA CCI) project, contains global AGB maps at 100-m resolution for multiple years (2010, 2017, 2018, and annually for 2019–2021) (Mariani et al., 2016).4.4 Conversion factorIn the top-down method, satellite-derived FRE, which is the temporal integral of FRP, is converted into the mass of combusted dry matter. This conversion is performed using a biome-specific conversion factor (kg Dry Matter MJ⁻¹). To assess the uncertainty associated with this parameter, we implemented two distinct sets of conversion factors: one based on the 8 major biomes used in the GFAS (Kaiser et al., 2012), and another based on a more detailed 30-class biome map.4.5 Ancillary and validation dataTo derive a dynamic annual AGB time series for 2003–2022 from otherwise static AGB maps, we used the MODIS annual Net Primary Production product MYD17A3HGF v061, which provides global NPP at 500 m spatial resolution. We leveraged the empirically supported linear relationship between NPP and AGB to temporally extrapolate the baseline AGB maps and generate annual AGB maps.

Reference:

Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., and Kasibhatla, P.: Global estimation of burned area using MODIS active fire observations, Atmospheric Chemistry and Physics, 6, 957–974, https://doi.org/10.5194/acp-6-957-2006, 2006.

Mieville, A., Granier, C., Liousse, C., Guillaume, B., Mouillot, F., Lamarque, J.-F., Grégoire, J.-M., and Pétron, G.: Emissions of gases and particles from biomass burning during the 20th century using satellite data and an historical reconstruction, Atmospheric Environment, 44, 1469–1477, https://doi.org/10.1016/j.atmosenv.2010.01.011, 2010.

Shi, Y., Matsunaga, T., Saito, M., Yamaguchi, Y., and Chen, X.: Comparison of global inventories of CO2 emissions from biomass burning during 2002–2011 derived from multiple satellite products, Environmental Pollution, 206, 479–487, https://doi.org/10.1016/j.envpol.2015.08.009, 2015.

van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth System Science Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.

Santoro, M.: GlobBiomass - global datasets of forest biomass, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.894711, 2018.

Mariani, M., Fletcher, M.-S., Holz, A., and Nyman, P.: ENSO controls interannual fire activity in southeast australia, Geophysical Research Letters, 43, 10891–10900, https://doi.org/10.1002/2016GL070572, 2016.

Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: 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, 2012.