Landscape fires, predominantly found in the frequently burning global savannas, are a substantial source of greenhouse gases and aerosols. The impact of these fires on atmospheric composition is partially determined by the chemical breakup of the constituents of the fuel into individual emitted chemical species, which is described by emission factors (EFs). These EFs are known to be dependent on, amongst other things, the type of fuel consumed, the moisture content of the fuel, and the meteorological conditions during the fire, indicating that savanna EFs are temporally and spatially dynamic. Global emission inventories, however, rely on static biome-averaged EFs, which makes them ill-suited for the estimation of regional biomass burning (BB) emissions and for capturing the effects of shifts in fire regimes. In this study we explore the main drivers of EF variability within the savanna biome and assess which geospatial proxies can be used to estimate dynamic EFs for global emission inventories. We made over 4500 bag measurements of CO₂, CO, CH₄, and N₂O EFs using a UAS and also measured fuel parameters and fire-severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems under different seasonal conditions sampled over the course of six fire seasons between 2017 and 2022. We complemented our own data with EFs from 85 fires with locations and dates provided in the literature. Based on the locations, dates, and times of the fires we retrieved a variety of fuel, weather, and fire-severity proxies (i.e. possible predictors) using globally available satellite and reanalysis data. We then trained random forest (RF) regressors to estimate EFs for CO₂, CO, CH₄, and N₂O at a spatial resolution of 0.25^∘ and a monthly time step. Using these modelled EFs, we calculated their spatiotemporal impact on BB emission estimates over the 2002–2016 period using the Global Fire Emissions Database version 4 with small fires (GFED4s). We found that the most important field indicators for the EFs of CO₂, CO, and CH₄ were tree cover density, fuel moisture content, and the grass-to-litter ratio. The grass-to-litter ratio and the nitrogen-to-carbon ratio were important indicators for N₂O EFs. RF models using satellite observations performed well for the prediction of EF variability in the measured fires with out-of-sample correlation coefficients between 0.80 and 0.99, reducing the error between measured and modelled EFs by 60 %–85 % compared to using the static biome average. Using dynamic EFs, total global savanna emission estimates for 2002–2016 were 1.8 % higher for CO, while CO₂, CH₄, and N₂O emissions were, respectively, 0.2 %, 5 %, and 18 % lower compared to GFED4s. On a regional scale we found a spatial redistribution compared to GFED4s with higher CO, CH₄, and N₂O EFs in mesic regions and lower ones in xeric regions. Over the course of the fire season, drying resulted in gradually lower EFs of these species. Relatively speaking, the trend was stronger in open savannas than in woodlands, where towards the end of the fire season they increased again. Contrary to the minor impact on annual average savanna fire emissions, the model predicts localized deviations from static averages of the EFs of CO, CH₄, and N₂O exceeding 60 % under seasonal conditions.

Landscape fires are substantial sources of (greenhouse) gases and aerosols. Fires in savanna landscapes represent more than half of global fire carbon emissions. Quantifying emissions from fires relies on accurate burned area, fuel load and burning efficiency data. Of these, fuel load remains the source of the largest uncertainty. In this study, we used high spatial resolution images from an Unmanned Aircraft System (UAS) mounted multispectral camera, in combination with meteorological data from the ERA-5 land dataset, to model instantaneous pre-fire above-ground biomass. We constrained our model with ground measurements taken in two locations in savanna-dominated regions in Southern Africa, one low-rainfall region (660 mm year${}^{-1}$) in the North-West District (Ngamiland), Botswana, and one high-rainfall region (940 mm year${}^{-1}$) in Niassa Province (northern Mozambique). We found that for fine surface fuel classes (live grass and dead plant litter), the model was able to reproduce measured Above-Ground Biomass (AGB) (R${}^2$ of 0.91 and 0.77 for live grass and total fine fuel, respectively) across both low and high rainfall areas. The model was less successful in representing other classes, e.g., woody debris, but in the regions considered, these are less relevant to biomass burning and make smaller contributions to total AGB.

Tropical savannas and grasslands are the most frequently burned biome in the world, and fire constitutes an important part of the ecosystem. In this ecosystem it can have both rejuvenating and destructive effects, depending on several factors including fuel conditions, weather conditions, and time of year. For centuries humanity has used fire in these landscapes for hunting, land clearance, agriculture, and most recently carbon offsetting. Land managers in locations with a monsoonal climate and frequent fire regimes such as tropical savannas use prescribed burning as a management tool in the ‘early dry season’ (EDS) shortly after the last rains of the year. Fires at this time tend to be cooler, restricted to surface level and less severe, meaning they can be controlled more easily and tend to go out at night without external input. Commonly a specific, fixed date is used to indicate when this window of safe burning has expired, set based on experience of the local or regional authority. In this work, we have defined a method of determining when this window expires on the basis of active fire hotspot data from the twin MODIS instruments from 2001 through to 2021. By using the relationship between day and night-time active fire detections, we set a flexible date for the transition between the early and late dry seasons in fire-prone savannas globally in the five major tropical savanna regions - Northern & Southern hemisphere South America (NHSA & SHSA), Northern & Southern hemisphere Africa (NHAF & SHAF), and Australia (AUST). The variability across each region was high (lowest mean standard deviation annually was 24 days in NHAF and highest was 56 in AUST). The fraction of area burned in the late dry season ranged from 15% (SHSA) to as high as 85% (AUST) on average, with many parts of Africa and Australia especially showing a significant skew towards the late dry season. This suggests potential for implementation of prescribed burning programmes to increase the amount of desirable fire in the global savanna ecosystems.

Landscape fires emit climate-influencing greenhouse gases and aerosols. The vast majority of landscape fire emissions originate from tropical savannas, especially in Africa. During the fire season climatic conditions change, and fires burning later consume drier vegetation and occur in drier weather conditions than earlier fires. Previous studies have shown that it is possible to reduce emissions of some greenhouse gases (CH4 and N2O) by using “prescribed” fires, i.e. deliberate burning in the early dry season. In this study we examine the climate effect of (deliberately) changing fire regimes beyond CH4 and N2O, including aerosols and other short-lived species, CO2, and changes to surface albedo. We find that in general shifting burning earlier in a single fire season results in global negative climate forcing (cooling) of around −0.001 to −0.002 W m−2 (long-term) or −0.006 (short-term) W m−2, compared to less than −0.0005 W m−2 if only considering CH4 and N2O. Forcing from shifting burning later in contrast is negligible in the long term. CO2 emissions reduction through emission factor changes and burned area reduction is the largest contributing factor, though especially in the short term albedo effects are also substantial. Shifting fire activity towards the late fire season generally produces a positive climate forcing (warming) of a smaller magnitude. We find too that some localities within our study area have a potentially disproportionately large impact on our results, such that the efficacy of any fire regime change with respect to climate forcing must be carefully considered on a local scale.