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Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here, we fuse multiple streams of remote sensing data to create a 24 year (1997–2020) dataset of monthly burned area as a component of the fifth version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and adjust for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in croplands, peatlands, and deforestation regions is estimated from MODIS active fire detections. Along-Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. The global annual burned area during 2001–2020 is estimated to be 774 ± 63 Mhayr-1 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 %yr-1 , a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by a decrease in fires in savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit significant long-term trends. The GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from activity-based and atmospheric-based approaches, and improve our understanding of global fire impacts on the carbon cycle and climate system. The GFED5 global burned area product is freely accessible at 10.5281/zenodo.7668423 (Chen et al., 2023). [ABSTRACT FROM AUTHOR]

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 CO 2 , CO, CH 4 , and N 2 O 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 CO 2 , CO, CH 4 , and N 2 O 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 CO 2 , CO, and CH 4 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 N 2 O 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 CO 2 , CH 4 , and N 2 O 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, CH 4 , and N 2 O 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, CH 4 , and N 2 O exceeding 60 % under seasonal conditions. [ABSTRACT FROM AUTHOR]

Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here we fuse multiple streams of remote sensing data to create a 24-year (1997–2020) dataset of monthly burned area as a component of the 5th version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and correct for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in cropland, peatland, and deforestation regions is estimated from MODIS active fire detections. Along Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. Global annual burned area is estimated to be 774 ± 63 Mha yr−1 during 2001–2020 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 % yr−1, a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by decreases in fire within savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit long-term trends. GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with burned area products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from top-down and bottom-up approaches, and improve understanding of global fire impacts on the carbon cycle and climate system. [ABSTRACT FROM AUTHOR]

In fire emission models, the spatial resolution of both the modelling framework and the satellite data used to quantify burned area can have considerable impact on emission estimates. Consideration of this sensitivity is especially important in areas with heterogeneous land cover and fire regimes and when constraining model output with field measurements. We developed a global fire emissions model with a spatial resolution of 500 m using MODerate resolution Imaging Spectroradiometer (MODIS) data. To accommodate this spatial resolution, our model is based on a simplified version of the Global Fire Emissions Database (GFED) modelling framework. Tree mortality as a result of fire, i.e. fire-related forest loss, was modelled based on the overlap between 30 m forest loss data and MODIS burned area and active fire detections. Using this new 500 m model, we calculated global average carbon emissions from fire of 2.1±0.2 (±1σ interannual variability, IAV) Pg C yr -1 during 2002–2020. Fire-related forest loss accounted for 2.6±0.7 % (uncertainty range =1.9 %–3.3 %) of global burned area and 24±6 % (uncertainty range =16 %–31 %) of emissions, indicating that fuel consumption in forest fires is an order of magnitude higher than the global average. Emissions from the combustion of soil organic carbon (SOC) in the boreal region and tropical peatlands accounted for 13±4 % of global emissions. Our global fire emissions estimate was higher than the 1.5 Pg C yr -1 from GFED4 and similar to 2.1 Pg C yr -1 from GFED4s. Even though GFED4s included more burned area by accounting for small fires undetected by the MODIS burned area mapping algorithm, our emissions were similar to GFED4s due to higher average fuel consumption. The global difference in fuel consumption could mainly be explained by higher SOC emissions from the boreal region as constrained by additional measurements. The higher resolution of the 500 m model also contributed to the difference by improving the simulation of landscape heterogeneity and reducing the scale mismatch in comparing field measurements to model grid cell averages during model calibration. Furthermore, the fire-related forest loss algorithm introduced in our model led to more accurate and widespread estimation of high-fuel-consumption burned area. Recent advances in burned area detection at resolutions of 30 m and finer show a substantial amount of burned area that remains undetected with 500 m sensors, suggesting that global carbon emissions from fire are likely higher than our 500 m estimates. The ability to model fire emissions at 500 m resolution provides a framework for further improvements with the development of new satellite-based estimates of fuels, burned area, and fire behaviour, for use in the next generation of GFED. [ABSTRACT FROM AUTHOR]

Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact the climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from ground or aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO 2), methane (CH 4), and nitrous oxide (N 2 O) as well as PM 2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and with airborne aerosol sensors. In this study, we address the main challenges associated with this approach: (1) the degree to which a limited number of samples is representative for the integral smoke plume and (2) the performance of the lightweight aerosol sensors. While aerosol measurements can be made continuously in a UAS set-up thanks to the lightweight analysers, the representativeness of our Tedlar bag filling approach was tested during prescribed burning experiments in the Kruger National Park, South Africa. We compared fire-averaged EFs from UAS-sampled bags for savanna fires with integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R2 ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors with high-fidelity equipment to empirically determine specific calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, although the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multi-wavelength aethalometer (AE33) (linear R2 of 0.95 at λ=880 nm) and the wavelength corrected multi-angle absorption photometer (MAAP, R2 of 0.83 measuring at λ=637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2±5.1 m 2 g -1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS set-up can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood. [ABSTRACT FROM AUTHOR]

Landscape fires are a significant contributor to atmospheric burdens of greenhouse gases and aerosols. Although many studies have looked at biomass burning products and their fate in the atmosphere, estimating and tracing atmospheric pollution from landscape fires based on atmospheric measurements are challenging due to the large variability in fuel composition and burning conditions. Stable carbon isotopes in biomass burning (BB) emissions can be used to trace the contribution of C3 plants (e.g. trees or shrubs) and C4 plants (e.g. savanna grasses) to various combustion products. However, there are still many uncertainties regarding changes in isotopic composition (also known as fractionation) of the emitted carbon compared to the burnt fuel during the pyrolysis and combustion processes. To study BB isotope fractionation, we performed a series of laboratory fire experiments in which we burned pure C3 and C4 plants as well as mixtures of the two. Using isotope ratio mass spectrometry (IRMS), we measured stable carbon isotope signatures in the pre-fire fuels and post-fire residual char, as well as in the CO2 , CO, CH4 , organic carbon (OC), and elemental carbon (EC) emissions, which together constitute over 98 % of the post-fire carbon. Our laboratory tests indicated substantial isotopic fractionation in combustion products compared to the fuel, which varied between the measured fire products. CO2 , EC, and residual char were the most reliable tracers of the fuel 13C signature. CO in particular showed a distinct dependence on burning conditions; flaming emissions were enriched in 13C compared to smouldering combustion emissions. For CH4 and OC, the fractionation was the other way round for C3 emissions (13C -enriched) and C4 emissions (13C -depleted). This indicates that while it is possible to distinguish between fires that were dominated by either C3 or C4 fuels using these tracers, it is more complicated to quantify their relative contribution to a mixed-fuel fire based on the δ13C signature of emissions. Besides laboratory experiments, we sampled gases and carbonaceous aerosols from prescribed fires in the Niassa Special Reserve (NSR) in Mozambique, using an unmanned aerial system (UAS)-mounted sampling set-up. We also provided a range of C3:C4 contributions to the fuel and measured the fuel isotopic signatures. While both OC and EC were useful tracers of the C3 -to- C4 fuel ratio in mixed fires in the lab, we found particularly OC to be depleted compared to the calculated fuel signal in the field experiments. This suggests that either our fuel measurements were incomprehensive and underestimated the C3:C4 ratio in the field or other processes caused this depletion. Although additional field measurements are needed, our results indicate that C3 -vs.- C4 source ratio estimation is possible with most BB products, albeit with varying uncertainty ranges. [ABSTRACT FROM AUTHOR]

Landscape fires are a significant contributor to atmospheric burdens of greenhouse gases and aerosols. Although many studies have looked at biomass burning products and their fate in the atmosphere, estimating and tracing atmospheric pollution from landscape fires based on atmospheric measurements is challenging due to the large variability in fuel composition and burning conditions. Stable carbon isotopes in biomass burning (BB) emissions can be used to trace the contribution of C3 plants (e.g., trees or shrubs) and C4 plants (e.g. savanna grasses) to various combustion products. However, there are still many uncertainties regarding changes in isotopic composition (also known as fractionation) of the emitted carbon compared to the burnt fuel during the pyrolysis and combustion processes. To study BB isotope fractionation, we performed a series of laboratory fire experiments in which we burned pure C3 and C4 plants as well as mixtures of the two. Using isotope ratio mass spectrometry (IRMS), we measured stable carbon isotope signatures in the pre-fire fuels and post-fire residual char, as well as in the CO2, CO, CH4, organic carbon (OC), and elemental carbon (EC) emissions, which together constitute over 98% of the post-fire carbon. Our laboratory tests indicated substantial isotopic fractionation in combustion products compared to the fuel, which varied between the measured fire products. CO2, EC and residual char were the most reliable tracers of the fuel 13C signature. CO in particular showed a distinct dependence on burning conditions; flaming emissions were enriched in 13C compared to smouldering combustion emissions. For CH4 and OC, the fractionation was opposite for C3 emissions (13C-enriched) and C4 emissions (13C-depleted). This indicates that while it is possible to distinguish between fires that were dominated by either C3 or C4 fuels using these tracers, it is more complicated to quantify their relative contribution to a mixed-fuel- fire based on the δ[sup 13]C signature of emissions. Besides laboratory experiments, we sampled gases and carbonaceous aerosols from prescribed fires in the Niassa special Reserve (NSR) in Mozambique, using an unmanned aerial system (UAS)-mounted sampling set-up. We also provide a range of C3:C4 contributions to the fuel and measured the fuel isotopic signatures. While both OC and EC were useful tracers of the C3 to C4 fuel ratio in mixed fires in the lab, we found particularly OC to be depleted compared to the calculated fuel signal in the field experiments. This suggests that either our fuel measurements were incomprehensive and underestimated the C3:C4 ratio in the field, or that other processes caused this depletion. Although additional field measurements are needed, our results indicate that C3 vs C4 source ratio estimation is possible with most BB products, albeit with varying uncertainty ranges. [ABSTRACT FROM AUTHOR]

Landscape fires, often referred to as biomass burning (BB), emit substantial amounts of (greenhouse) gases and aerosols into the atmosphere each year. Frequently burning savannas, mostly in Africa, Australia, and South America are responsible for over 60 % of total BB carbon emissions. Compared to many other sources of emissions, fires have a strong seasonality. Previous research has identified the mitigation potential of prescribed fires in savanna ecosystems; by burning cured fuels early in the dry season when landscape conditions still provide moist buffers against fire spread, fires are in general smaller, patchier, and less intense. While it is widely accepted that burned area (BA) and the total carbon consumed are lower when fires are ignited early in the dry season, little is known about the intraseasonal variability of emission factors (EFs). This is important because potentially, higher EFs in the early dry season (EDS) could offset some of the carbon benefits of EDS burning. Also, a better understanding of EF intraseasonal variability may improve large-scale BB assessments, which to date rely on temporally static EFs. We used a sampling system mounted on an unmanned aerial vehicle (UAV) to sample BB smoke in the Estação Ecológica Serra Geral do Tocantins in the Brazilian states of Tocantins and Bahia. The protected area contains all major Cerrado vegetation types found in Brazil, and EDS burning has been implemented since 2014. Over 800 smoke samples were collected and analysed during the EDS of 2018 and late dry season (LDS) of 2017 and 2018. The samples were analysed using cavity ring-down spectroscopy, and the carbon balance method was used to estimate CO 2 , CO, CH 4 , and N 2 O EFs. Observed EF averages and standard deviations were 1651 (±50) g kg -1 for CO 2 , 57.9 (±28.2) g kg -1 for CO, 0.97 (±0.82) g kg -1 for CH 4 , and 0.096 (±0.174) g kg -1 for N 2 O. Averaged over all measured fire prone Cerrado types, the modified combustion efficiency (MCE) was slightly higher in the LDS (0.961 versus 0.956), and the CO and CH 4 were 10 % and 2.3 % lower in the LDS compared to the EDS. However, these differences were not statistically significant using a two-tailed t test with unequal variance at a 90 % significance level. The seasonal effect was larger in more wood-dominated vegetation types. N 2 O EFs showed a more complex seasonal dependency, with opposite intraseasonal trends for savannas that were dominated by grasses versus those with abundant shrubs. We found that the N 2 O EF for the open Cerrado was less than half the EF suggested by literature compilations for savannas. This may indicate a substantial overestimation of the contribution of fires in the N 2 O budget. Overall, our data imply that in this region, seasonal variability in greenhouse gas emission factors may offset only a small fraction of the carbon mitigation gains in fire abatement programmes. [ABSTRACT FROM AUTHOR]

The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NO x) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties in these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage of filling this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide (XNO2 and XCO) from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the ΔXNO2 / ΔXCO ratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g., high-temperature flaming vs. low-temperature smoldering combustion) and burning practice (e.g., the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise ratio of the retrieved ΔXNO2 / ΔXCO signals were quantified with WRF-Chem experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper Northern Hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in ΔXCO relative to ΔXNO2 than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights into regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs. [ABSTRACT FROM AUTHOR]

Landscape fires, often referred to as biomass burning (BB), emit substantial amounts of (greenhouse) gases and aerosols into the atmosphere each year. Frequently burning savannas, mostly in Africa, Australia, and South America are responsible for over 60 % of total BB carbon emissions. Compared to many other sources of emissions, fires have a strong seasonality. Previous research has identified the mitigation potential of prescribed fires in savanna ecosystems; by burning early in the dry season when the vegetation has not fully cured, fires are in general patchier and burn less intense. While it is widely accepted that burned area and the total carbon consumed is lower when fires are ignited early in the dry season, little is known about the seasonality of emission factors (EF) of greenhouse gases. This is important because potentially, higher EFs in the early dry season (EDS) could offset some of the carbon benefits of EDS burning. Also, a better understanding of EF seasonality may improve large-scale BB assessments, which to date rely on temporally-static EFs. We used a sampling system mounted on an unmanned aerial vehicle (UAV) and cavity ring-down spectroscopy to estimate CO2, CO, CH4, and N2O EFs in the Estação Ecológica Serra Geral do Tocantins in the Brazilian states of Tocantins and Bahia. The protected area contains all major Cerrado vegetation types found in Brazil, and EDS burning was implemented on a large scale since 2014. We collected and analyzed over 800 smoke samples during the EDS and late dry season (LDS). Averaged over all measurements, the modified combustion efficiency (MCE) was slightly higher in the LDS (0.976 vs. 0.972) and the CH4 and CO EFs were 13 % and 15 % lower in the LDS compared to the EDS. This seasonal effect was larger in more wood-dominated vegetation types. N2O EFs showed a more complex seasonal dependency, with opposite seasonal trends for savannas that were dominated by grasses versus those with abundant shrubs. We found that the N2O EF for the open cerrado was less than half of those reported so far in the BB literature for savannas. This may indicate a substantial overestimation of the contribution of fires in the N2O budget. Overall, our data implies that in this region, seasonal variability in greenhouse gas emission factors may offset only a small fraction of the carbon mitigation gains in fire abatement programs. [ABSTRACT FROM AUTHOR]

The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NOx) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties on these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage to fill this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide (XNO2 and XCO)from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the ΔXNO2/ΔXCO ratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g. high temperature flaming vs. low temperature smoldering combustion) and burning practice (e.g. the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise of the retrieved ΔXNO2/ΔXCO signals were quantified with WRF-CHEM experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper northern hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in ΔXCO relative to ΔXNO2 than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights on regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs. [ABSTRACT FROM AUTHOR]

Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded datasets produced for use by the modeling community, particularly for the Coupled Model Intercomparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios was coordinated to produce consistent data over 1750–2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-methane volatile organic compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work. [ABSTRACT FROM AUTHOR]

Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. We developed a model using MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500 m spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050, 0.125, 0.250 ∘). We estimated fire emissions of 0.68 Pg C yr -1 at 500 m resolution and 0.82 Pg C yr -1 at 0.25 ∘ resolution; a difference of 24 %. At 0.25 ∘ resolution, our model results were relatively similar to GFED4, which also runs at 0.25 ∘ resolution, whereas our 500 m estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 Pg C yr -1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse-resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500 m and 0.25 ∘ resolution simulations, besides those stemming from representation errors in the calibration process, namely (1) biome misclassification leading to errors in parameterization, (2) errors due to the averaging of input data and the associated reduction in variability, and (3) a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations of up to a factor 4 and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse-resolution models and suggest the need for increased spatial resolution in global fire emission models. [ABSTRACT FROM AUTHOR]

Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded data sets produced for use by the modelling community, particularly for the Coupled Model Inter-comparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios were coordinated to produce consistent data over 1750-2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-Methane Volatile Organic Compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work. [ABSTRACT FROM AUTHOR]

Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. In this paper, we developed a model using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500-meter spatial resolution (native MODIS burned area) for the 2002-2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050°, 0.125°, 0.250°). We estimated fire emissions of 0.68 PgC yr-1 at 500-meter resolution and 0.82 PgC yr-1 at 0.25° resolution; a difference of 24 %. At 0.25° resolution, our model results were relatively similar to GFED4, which also runs at 0.25° resolution, whereas our 500-meter estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 PgC yr-1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500-meter and 0.25° resolution simulations, besides those stemming from representation errors in the calibration process, namely: 1. biome misclassification leading to errors in parameterization, 2. errors due to the averaging of input data and the associated reduction in variability, and 3. a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations up to a factor 4, and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse resolution models and suggest the need for increased spatial resolution in global fire emission models. [ABSTRACT FROM AUTHOR]

Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and 10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system. [ABSTRACT FROM AUTHOR]

Fire emissions are critical for carbon and nutrient cycles, climate, and air quality. Dynamic Global Vegetation Models (DGVMs) with interactive fire modeling provide important estimates for long-term and large-scale changes of fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700–2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table from field and laboratory studies over various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. Evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products, and can capture the high emissions over the tropical savannas, low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most of the models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Historically, all models show only a weak trend in global fire emissions before ~ 1850s, consistent with multi-source merged historical reconstructions. The long-term trends among DGVMs are quite different for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land-use and land-cover change (LULCC). Our study provides a basic dataset for developing regional and global multi-source merged historical reconstructions and merging methods, and analyzing historical changes of fire emissions and their uncertainties as well as their role in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH3) is a precursor to fine particulate matter formation and contributes to nitrogen (N) deposition, with potential implications for the health of humans and ecosystems. Agricultural soils and animal excreta are the primary source of atmospheric NH3, but natural soils can also be an important emitter. In regions with distinct dry and wet seasons such as the Sahel, the start of the rainy season triggers a pulse of biogeochemical activity in surface soils known as the Birch effect, which is often accompanied by emissions of microbially produced gases such as carbon dioxide and nitric oxide. Field and lab studies have sometimes, but not always, observed pulses of NH3 after the wetting of dry soils; however, the potential regional importance of these emissions remains poorly constrained. Here we use satellite retrievals of atmospheric NH3 using the Infrared Atmospheric Sounding Interferometer (IASI) regridded at 0.25° resolution, in combination with satellite-based observations of precipitation, surface soil moisture, and nitrogen dioxide concentrations, to reveal substantial precipitationinduced pulses of NH3 across the Sahel at the onset of the rainy season in 2008. The highest concentrations of NH3 occur in pulses during March and April when NH3 biomass burning emissions estimated for the region are low. For the region of the Sahel spanning 10 to 16° N and 0 to 30° E, changes in NH3 concentrations are weakly but significantly correlated with changes in soil moisture during the period from mid-March through April when the peak NH3 concentrations occur (r = 0:28, p = 0:02). The correlation is also present when evaluated on an individual pixel basis during April (r = 0:16, p < 0:001). Average emissions for the entire Sahel from a simple box model are estimated to be between 2 and 6 mgNH3 m-2 d-1 during peaks of the observed pulses, depending on the assumed effective NH3 lifetime. These early season pulses are consistent with surface observations of monthly concentrations, which show an uptick in NH3 concentration at the start of the rainy season for sites in the Sahel. The NH3 concentrations in April are also correlated with increasing tropospheric NO2 concentrations observed by the Ozone Monitoring Instrument (r = 0:78, p < 0:0001), which have previously been attributed to the Birch effect. Box model results suggest that pulses occurring over a 35-day period in March and April are responsible for roughly one-fifth of annual emissions of NH3-N from the Sahel. We conclude that precipitation early in the rainy season is responsible for substantial NH3 emissions in the Sahel, likely representing the largest instantaneous fluxes of gas-phase N from the region during the year. [ABSTRACT FROM AUTHOR]

Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles, and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or subdaily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions and fire size, duration, daily expansion, fire line length, speed, and direction of spread. Here we present the underlying methodology and Global Fire Atlas results for 2003-2016 derived from daily moderate resolution (500 m) Collection 6 MCD64A1 burned area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in the boreal regions and natural landscapes in the humid tropics suggest that fire-season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multi-day fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest, and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through ww.globalfiredata.org/ and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation-fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system. [ABSTRACT FROM AUTHOR]

Over the past decades, fires have burned annually in Indonesia, yet the strength of the fire season is for a large part modulated by the El Niño Southern Oscillation (ENSO). The two most recent very strong El Niño years were 2015 and 1997. Both years involved high incidences of fire in Indonesia. At present, there is no consistent satellite data stream spanning the full 19-year record, thereby complicating a comparison between these two fire seasons.We have investigated how various fire and precipitation datasets can be merged to better compare the fire dynamics in 1997 and 2015 as well as in intermediary years. We combined nighttime active fire detections from the Along Track Scanning Radiometer (ATSR) World Fire Atlas (WFA) available from 1997 until 2012 and the nighttime subset of the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor from 2001 until now. For the overlapping period, MODIS detected about 4 times more fires than ATSR, but this ratio varied spatially. Although the reasons behind this spatial variability remain unclear, the coefficient of determination for the overlapping period was high (R2 D 0:97, based on monthly data) and allowed for a consistent time series. We then constructed a rainfall time series based on the Global Precipitation Climatology Project (GPCP, 1997-2015) and the Tropical Rainfall Measurement Mission Project (TRMM, 1998-2015). Relations between antecedent rainfall and fire activity were not uniform in Indonesia. In southern Sumatra and Kalimantan, we found that 120 days of rainfall accumulation had the highest coefficient of determination with annual fire intensity. In northern Sumatra, this period was only 30 days. Thresholds of 200 and 305mm average rainfall accumulation before each active fire were identified to generate a highincidence fire year in southern Sumatra and southern Kalimantan, respectively. The number of active fires detected in 1997 was 2.2 times higher than in 2015. Assuming the ratio between nighttime and total active fires did not change, the 1997 season was thus about twice as severe as the one in 2015. Although large, the difference is smaller than found in fire emission estimates from the Global Fire Emissions Database (GFED). Besides different rainfall amounts and patterns, the two-fold difference between 1997 and 2015 may be attributed to a weaker El Niño and neutral Indian Ocean Dipole (IOD) conditions in the later year. The fraction of fires burning in peatlands was higher in 2015 compared to 1997 (61 and 45 %, respectively). Finally, we found that the nonlinearity between rainfall and fire in Indonesia stems from longer periods without rain in extremely dry years. [ABSTRACT FROM AUTHOR]

Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data have shown the overall global extent of fires. Our knowledge of historic fire emissions has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently, there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emission estimates back in time based on satellite data starting in 1997, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage. According to our approach, global biomass burning emissions were relatively constant, with 10-year averages varying between 1.8 and 2.3 PgC yr-1. Carbon emissions increased only slightly over the full time period and peaked during the 1990s after which they decreased gradually. There is substantial uncertainty in these estimates, and patterns varied depending on choices regarding data representation, especially on regional scales. The observed pattern in fire carbon emissions is for a large part driven by African fires, which accounted for 58% of global fire carbon emissions. African fire emissions declined since about 1950 due to conversion of savanna to cropland, and this decrease is partially compensated for by increasing emissions in deforestation zones of South America and Asia. These global fire emission estimates are mostly suited for global analyses and will be used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. [ABSTRACT FROM AUTHOR]

Abstract. Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED) and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the Carnegie–Ames–Stanford Approach (CASA) biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1)new burned area estimates with contributions from small fires, (2)a revised fuel consumption parameterization optimized using field observations, (3)modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4)fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25°) and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were 2.2 × 1015grams of carbon per year (Pg Cyr−1) during 1997–2016, with a maximum in 1997 (3.0 Pg C yr−1) and minimum in 2013 (1.8 Pg C yr−1). These estimates were 11% higher than our previous estimates (GFED3) during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37%), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (−19%) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emission factors. If small fire burned area was excluded (GFED4 without the s for small fires), average emissions were 1.5 Pg C yr−1. The addition of small fires had the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. [ABSTRACT FROM AUTHOR]

As formaldehyde (HCHO) is a high-yield product in the oxidation of most volatile organic compounds (VOCs) emitted by fires, vegetation, and anthropogenic activities, satellite observations of HCHO are well-suited to inform us on the spatial and temporal variability of the underlying VOC sources. The long record of space-based HCHO column observations from the Ozone Monitoring Instrument (OMI) is used to infer emission flux estimates from pyrogenic and biogenic volatile organic compounds (VOCs) on the global scale over 2005-2013. This is realized through the method of source inverse modeling, which consists in the optimization of emissions in a chemistry-transport model (CTM) in order to minimize the discrepancy between the observed and modeled HCHO columns. The top-down fluxes are derived in the global CTM IMAGESv2 by an iterative minimization algorithm based on the full adjoint of IMAGESv2, starting from a priori emission estimates provided by the newly released GFED4s (Global Fire Emission Database, version 4s) inventory for fires, and by the MEGAN-MOHYCAN inventory for isoprene emissions. The top-down fluxes are compared to two independent inventories for fire (GFAS and FINNv1.5) and isoprene emissions (MEGAN-MACC and GUESS-ES). The inversion indicates a moderate decrease (ca. 20%) in the average annual global fire and isoprene emissions, from 2028 Tg C in the a priori to 1653 Tg C for burned biomass, and from 343 to 272 Tg for isoprene fluxes. Those estimates are acknowledged to depend on the accuracy of formaldehyde data, as well as on the assumed fire emission factors and the oxidation mechanisms leading to HCHO production. Strongly decreased top-down fire fluxes (30-50%) are inferred in the peak fire season in Africa and during years with strong a priori fluxes associated with forest fires in Amazonia (in 2005, 2007, and 2010), bushfires in Australia (in 2006 and 2011), and peat burning in Indonesia (in 2006 and 2009), whereas generally increased fluxes are suggested in Indochina and during the 2007 fires in southern Europe. Moreover, changes in fire seasonal patterns are suggested; e.g., the seasonal amplitude is reduced over southeast Asia. In Africa, the inversion indicates increased fluxes due to agricultural fires and decreased maxima when natural fires are dominant. The top-down fire emissions are much better correlated with MODIS fire counts than the a priori inventory in regions with small and agricultural fires, indicating that the OMI-based inversion is well-suited to assess the associated emissions. Regarding biogenic sources, significant reductions in isoprene fluxes are inferred in tropical ecosystems (30-40%), suggesting overestimated basal emission rates in those areas in the bottom-up inventory, whereas strongly positive isoprene emission updates are derived over semiarid and desert areas, especially in southern Africa and Australia. This finding suggests that the parameterization of the soil moisture stress used in MEGAN greatly exaggerates the flux reduction due to drought in those regions. The isoprene emission trends over 2005-2013 are often enhanced after optimization, with positive top-down trends in Siberia (4.2%year-1) and eastern Europe (3.9%year-1), likely reflecting forest expansion and warming temperatures, and negative trends in Amazonia (-2.1%year-1), south China (-1%year-1), the United States (-3.7%year-1), and western Europe (-3.3%year-1), which are generally corroborated by independent studies, yet their interpretation warrants further investigation. [ABSTRACT FROM AUTHOR]

Landscape fires occur on a large scale in (sub)tropical savannas and grasslands, affecting ecosystem dynamics, regional air quality and concentrations of atmospheric trace gasses. Fuel consumption per unit of area burned is an important but poorly constrained parameter in fire emission modelling. We combined satellite-derived burned area with fire radiative power (FRP) data to derive fuel consumption estimates for land cover types with low tree cover in South America, Sub-Saharan Africa, and Australia. We developed a new approach to estimate fuel consumption, based on FRP data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) in combination with MODIS burned-area estimates. The fuel consumption estimates based on the geostationary and polar-orbiting instruments showed good agreement in terms of spatial patterns. We used field measurements of fuel consumption to constrain our results, but the large variation in fuel consumption in both space and time complicated this comparison and absolute fuel consumption estimates remained more uncertain. Spatial patterns in fuel consumption could be partly explained by vegetation productivity and fire return periods. In South America, most fires occurred in savannas with relatively long fire return periods, resulting in comparatively high fuel consumption as opposed to the more frequently burning savannas in Sub- Saharan Africa. Strikingly, we found the infrequently burning interior of Australia to have higher fuel consumption than the more productive but frequently burning savannas in northern Australia. Vegetation type also played an important role in explaining the distribution of fuel consumption, by affecting both fuel build-up rates and fire return periods. Hummock grasslands, which were responsible for a large share of Australian biomass burning, showed larger fuel build-up rates than equally productive grasslands in Africa, although this effect might have been partially driven by the presence of grazers in Africa or differences in landscape management. Finally, land management in the form of deforestation and agriculture also considerably affected fuel consumption regionally. We conclude that combining FRP and burned-area estimates, calibrated against field measurements, is a promising approach in deriving quantitative estimates of fuel consumption. Satellite-derived fuel consumption estimates may both challenge our current understanding of spatiotemporal fuel consumption dynamics and serve as reference datasets to improve biogeochemical modelling approaches. Future field studies especially designed to validate satellite-based products, or airborne remote sensing, may further improve confidence in the absolute fuel consumption estimates which are quickly becoming the weakest link in fire emission estimates. [ABSTRACT FROM AUTHOR]