Reconstructed Global Monthly Burned Area Maps from 1901 to 2020

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 .