Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

Abstract. Aerosols from biomass burning (BB) emissions are poorly constrained in global and regional models, resulting in a high level of uncertainty in understanding their impacts. In this study, we compared six BB aerosol emission datasets for 2008 globally as well as in 14 regions. The six BB emission datasets are (1) GFED3.1 (Global Fire Emissions Database version 3.1), (2) GFED4s (GFED version 4 with small fires), (3) FINN1.5 (FIre INventory from NCAR version 1.5), (4) GFAS1.2 (Global Fire Assimilation System version 1.2), (5) FEER1.0 (Fire Energetics and Emissions Research version 1.0), and (6) QFED2.4 (Quick Fire Emissions Dataset version 2.4). The global total emission amounts from these six BB emission datasets differed by a factor of 3.8, ranging from 13.76 to 51.93 Tg for organic carbon and from 1.65 to 5.54 Tg for black carbon. In most of the regions, QFED2.4 and FEER1.0, which are based on satellite observations of fire radiative power (FRP) and constrained by aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), yielded higher BB aerosol emissions than the rest by a factor of 2–4. By comparison, the BB aerosol emissions estimated from GFED4s and GFED3.1, which are based on satellite burned-area data, without AOD constraints, were at the low end of the range. In order to examine the sensitivity of model-simulated AOD to the different BB emission datasets, we ingested these six BB emission datasets separately into the same global model, the NASA Goddard Earth Observing System (GEOS) model, and compared the simulated AOD with observed AOD from the AErosol RObotic NETwork (AERONET) and the Multiangle Imaging SpectroRadiometer (MISR) in the 14 regions during 2008. In Southern Hemisphere Africa (SHAF) and South America (SHSA), where aerosols tend to be clearly dominated by smoke in September, the simulated AOD values were underestimated in almost all experiments compared to MISR, except for the QFED2.4 run in SHSA. The model-simulated AOD values based on FEER1.0 and QFED2.4 were the closest to the corresponding AERONET data, being, respectively, about 73 % and 100 % of the AERONET observed AOD at Alta Floresta in SHSA and about 49 % and 46 % at Mongu in SHAF. The simulated AOD based on the other four BB emission datasets accounted for only ∼50 % of the AERONET AOD at Alta Floresta and ∼20 % at Mongu. Overall, during the biomass burning peak seasons, at most of the selected AERONET sites in each region, the AOD values simulated with QFED2.4 were the highest and closest to AERONET and MISR observations, followed closely by FEER1.0. However, the QFED2.4 run tends to overestimate AOD in the region of SHSA, and the QFED2.4 BB emission dataset is tuned with the GEOS model. In contrast, the FEER1.0 BB emission dataset is derived in a more model-independent fashion and is more physically based since its emission coefficients are independently derived at each grid box. Therefore, we recommend the FEER1.0 BB emission dataset for aerosol-focused hindcast experiments in the two biomass-burning-dominated regions in the Southern Hemisphere, SHAF, and SHSA (as well as in other regions but with lower confidence). The differences between these six BB emission datasets are attributable to the approaches and input data used to derive BB emissions, such as whether AOD from satellite observations is used as a constraint, whether the approaches to parameterize the fire activities are based on burned area, FRP, or active fire count, and which set of emission factors is chosen.

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Six Global Biomass Burning Emission Datasets: Inter-comparison and Application in one Global Aerosol Model

10.5194/acp-2019-475 ◽

2019 ◽

Author(s):

Xiaohua Pan ◽

Charles Ichoku ◽

Mian Chin ◽

Huisheng Bian ◽

Anton Darmenov ◽

...

Keyword(s):

Biomass Burning ◽

Satellite Observations ◽

Burned Area ◽

Aerosol Model ◽

Fire Emissions ◽

Earth Observing System ◽

Active Fire ◽

Aerosol Emission ◽

Moderate Resolution Imaging Spectroradiometer ◽

High Level

Abstract. Aerosols from biomass burning (BB) emissions are poorly constrained in global and regional models, resulting in a high level of uncertainty in understanding their impacts. In this study, we compared six BB aerosol emission datasets for 2008 globally as well as in 14 sub-regions. The six BB emission datasets are: (1) GFED3.1 (Global Fire Emissions Database version 3.1); (2) GFED4s (Global Fire Emissions Database version 4 with small fires); (3) FINN1.5 (Fire INventory from NCAR version 1.5); (4) GFAS1.2 (Global Fire Assimilation System version 1.2); (5) FEER1.0 (Fire Energetics and Emissions Research version 1.0), and (6) QFED2.4 (Quick Fire Emissions Dataset version 2.4). Although biomass burning emissions of aerosols from these six BB emission datasets showed similar spatial distributions, their global total emission amounts differed by a factor of 3–4, ranging from 13.76 to 51.93 Tg for organic carbon and from 1.65 to 5.54 Tg for black carbon. In most regions, QFED2.4 and FEER1.0, which are based on the satellite observations of fire radiative power (FRP) and utilize the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), yielded higher BB emissions than the rest by a factor of 2–4. In comparison, the BB emission from GFED4s and GFED3.1, which are based on satellite retrieval of burned area and no AOD constraints, were at the low end of the range. In order to examine the sensitivity of model simulated AOD to the different BB emission datasets, we ingested these six BB emission datasets separately into the same global model, the NASA Goddard Earth Observing System (GEOS) model, and compared the simulated AOD with observed AOD from the AErosol RObotic NETwork (AERONET) and MODIS in 14 sub-regions during 2008. In Southern hemisphere Africa (SHAF) and South America (SHSA), where aerosols tend to be clearly dominated by smoke in September, the simulated AOD were underestimated in all experiments. More specifically, the model-simulated AOD based on FEER1.0 and QFED2.4 were the closest to the corresponding AERONET data, being about 73 % and 100 % of the AERONET observed AOD at Alta-Floresta in SHSA, 49 % and 46 % at Mongu in SHAF, respectively. The simulated AOD based on the other four BB emission datasets accounted for only ~ 50 % of the AERONET AOD at Alta Floresta and ~ 20 % of at Mongu. Overall, during the biomass burning peak seasons, at most of the selected AERONET sites in each region, the AOD simulated with QFED2.4 were the highest and closest to AERONET and MODIS observations, followed closely by FEER1.0. The differences between these six BB emission datasets are attributable to the approaches and input data used to derive BB emissions, such as whether AOD from satellite observations is used as a constraint, whether the approaches to parameterize the fire activities are based on burned area, FRP, or active fire count, and which set of emission factors is chosen.

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On the use of ATSR fire count data to estimate the seasonal and interannual variability of vegetation fire emissions

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-2-1159-2002 ◽

2002 ◽

Vol 2 (4) ◽

pp. 1159-1179 ◽

Cited By ~ 8

Author(s):

M. G. Schultz

Keyword(s):

Interannual Variability ◽

Biomass Burning ◽

Seasonal Pattern ◽

Burned Area ◽

Time Resolved ◽

Fire Emissions ◽

Seasonal And Interannual Variability ◽

Active Fire ◽

Time Period ◽

Along Track

Abstract. Biomass burning has long been recognised as an important source of trace gases and aerosols in the atmosphere. The burning of vegetation has a repeating seasonal pattern, but the intensity of burning and the exact localisation of fires vary considerably from year to year. Recent studies have demonstrated the high interannual variability of the emissions that are associated with biomass burning. In this paper we present a methodology using active fire counts from the Along-Track Scanning Radiometer (ATSR) sensor on board the ERS-2 satellite to estimate the seasonal and interannual variability of global biomass burning emissions in the time period 1996--2000. From the ATSR data, we compute relative scaling factors of burning intensity for each month, which are then applied to a standard inventory for carbon monoxide emissions from biomass burning. The new, time-resolved inventory is evaluated using the few existing multi-year burned area observations on continental scales.

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On the use of ATSR fire count data to estimate the seasonal and interannual variability of vegetation fire emissions

Atmospheric Chemistry and Physics ◽

10.5194/acp-2-387-2002 ◽

2002 ◽

Vol 2 (5) ◽

pp. 387-395 ◽

Cited By ~ 81

Author(s):

M. G. Schultz

Keyword(s):

Interannual Variability ◽

Biomass Burning ◽

Seasonal Pattern ◽

Burned Area ◽

Time Resolved ◽

Fire Emissions ◽

Seasonal And Interannual Variability ◽

Active Fire ◽

Time Period ◽

Along Track

Abstract. Biomass burning has long been recognised as an important source of trace gases and aerosols in the atmosphere. The burning of vegetation has a repeating seasonal pattern, but the intensity of burning and the exact localisation of fires vary considerably from year to year. Recent studies have demonstrated the high interannual variability of the emissions that are associated with biomass burning. In this paper I present a methodology using active fire counts from the Along-Track Scanning Radiometer (ATSR) sensor on board the ERS-2 satellite to estimate the seasonal and interannual variability of global biomass burning emissions in the time period 1996--2000. From the ATSR data, I compute relative scaling factors of burning intensity for each month, which are then applied to a standard inventory for carbon monoxide emissions from biomass burning. The new, time-resolved inventory is evaluated using the few existing multi-year burned area observations on continental scales.

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APIFLAME v2.0 trace gas and aerosol emissions from biomass burning: application to Portugal during the summer of 2016 and evaluation against satellite observations of CO (IASI) and AOD (MODIS)

10.5194/gmd-2019-210 ◽

2019 ◽

Author(s):

Solène Turquety ◽

Laurent Menut ◽

Guillaume Siour ◽

Sylvain Mailler ◽

Juliette Hadji-Lazaro ◽

...

Keyword(s):

Biomass Burning ◽

Western Europe ◽

Transport Model ◽

Regional Scale ◽

Temporal Variations ◽

Satellite Observations ◽

Burned Area ◽

Time Step ◽

Fire Emissions ◽

Area Product

Abstract. Biomass burning emissions are a major source of trace gases and aerosols. Wildfires being highly variable in time and space, calculating emissions requires a numerical tool able to estimate fluxes at the kilometer scale and with an hourly time-step. Here, the APIFLAME model version 2.0 is presented. It is structured to be modular in terms of input databases and processing methods. The main evolution compared to the version v1.0 is the possibility to merge burned area and fire radiative power (FRP) satellite observations to modulate the temporal variations of fire emissions and to integrate small fires that may not be detected in the burned area product. Accounting for possible missed detection due to small fires results in an increase ranging from ∼ 5 % in Africa and Australia to ∼ 30 % in North America, on average over the 2013–2017 time period based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) collection 6 fire products. An illustration for the case of south-western Europe during the summer of 2016, marked by large wildfires in Portugal, is presented. Emissions calculated using different possible configurations of APIFLAME show a dispersion of 75% on average over the domain during the largest wildfires (8–14/08/2016), which can be considered as an estimate of uncertainty on emissions (excluding the uncertainty on emission factors). Corresponding enhancements of aerosols and carbon monoxide (CO) simulated with the regional chemistry transport model CHIMERE are consistent with observations (good timing for the beginning and end of the events, &pm; 1 day for the timing of the peak values) but tend to be overestimated compared to observations at surface stations. On the contrary, vertically integrated concentrations tend to be underestimated compared to satellite observations of total column CO by the Infrared Atmospheric Sounding Interferometer (IASI) instrument and aerosol optical depth (AOD) by MODIS, which allow regional scale evaluation. This underestimate is lower close to the fire region (5 % to 40 % for AOD depending on the configuration, and 8–18 % for total CO) but rapidly increases downwind. For all comparisons, better agreement is achieved when emissions are injected higher into the free troposphere using a vertical profile as estimated from observations of aerosol plume height by the MISR satellite instrument (injection up to 4 km). The overestimate compared to surface sites and underestimate compared to satellite observations point to uncertainties not only on emissions (total mass and daily variability) but also on their injection profile and on the modelling of the transport of these dense plumes.

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APIFLAME v2.0 biomass burning emissions model: impact of refined input parameters on atmospheric concentration in Portugal in summer 2016

Geoscientific Model Development ◽

10.5194/gmd-13-2981-2020 ◽

2020 ◽

Vol 13 (7) ◽

pp. 2981-3009 ◽

Cited By ~ 1

Author(s):

Solène Turquety ◽

Laurent Menut ◽

Guillaume Siour ◽

Sylvain Mailler ◽

Juliette Hadji-Lazaro ◽

...

Keyword(s):

Biomass Burning ◽

Transport Model ◽

Temporal Variations ◽

Satellite Observations ◽

Burned Area ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Atmospheric Concentration ◽

Time Step ◽

Fire Emissions

Abstract. Biomass burning emissions are a major source of trace gases and aerosols. Wildfires being highly variable in time and space, calculating emissions requires a numerical tool able to estimate fluxes at the kilometer scale and with an hourly time step. Here, the APIFLAME model version 2.0 is presented. It is structured to be modular in terms of input databases and processing methods. The main evolution compared to version 1.0 is the possibility of merging burned area and fire radiative power (FRP) satellite observations to modulate the temporal variations of fire emissions and to integrate small fires that may not be detected in the burned area product. Accounting for possible missed detection due to small fire results in an increase in burned area ranging from ∼5 % in Africa and Australia to ∼30 % in North America on average over the 2013–2017 time period based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) Collection 6 fire products. An illustration for the case of southwestern Europe during the summer of 2016, marked by large wildfires in Portugal, is presented. Emissions calculated using different possible configurations of APIFLAME show a dispersion of 80 % on average over the domain during the largest wildfires (8–14 August 2016), which can be considered as an estimate of uncertainty of emissions. The main sources of uncertainty studied, by order of importance, are the emission factors, the calculation of the burned area, and the vegetation attribution. The aerosol (PM10) and carbon monoxide (CO) concentrations simulated with the CHIMERE regional chemistry transport model (CTM) are consistent with observations (good timing for the beginning and end of the events, ±1 d for the timing of the peak values) but tend to be overestimated compared to observations at surface stations. On the contrary, vertically integrated concentrations tend to be underestimated compared to satellite observations of total column CO by the Infrared Atmospheric Sounding Interferometer (IASI) instrument and aerosol optical depth (AOD) by MODIS. This underestimate is lower close to the fire region (5 %–40 % for AOD depending on the configuration and 8 %–18 % for total CO) but rapidly increases downwind. For all comparisons, better agreement is achieved when emissions are injected higher into the free troposphere using a vertical profile as estimated from observations of aerosol plume height by the Multi-angle Imaging SpectroRadiometer (MISR) satellite instrument (injection up to 4 km). Comparisons of aerosol layer heights to observations by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) show that some parts of the plume may still be transported at too low an altitude. The comparisons of the different CTM simulations to observations point to uncertainties not only on emissions (total mass and daily variability) but also on the simulation of their transport with the CTM and mixing with other sources. Considering the uncertainty of the emission injection profile and of the modeling of the transport of these dense plumes, it is difficult to fully validate emissions through comparisons between model simulations and atmospheric observations.

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Identifying required model structures to predict global fire activity from satellite and climate data

10.5194/gmd-2016-301 ◽

2016 ◽

Cited By ~ 3

Author(s):

Matthias Forkel ◽

Wouter Dorigo ◽

Gitta Lasslop ◽

Irene Teubner ◽

Emilio Chuvieco ◽

...

Keyword(s):

Satellite Observations ◽

Burned Area ◽

Human Society ◽

Fire Emissions ◽

Fire Models ◽

Fire Activity ◽

Process Oriented ◽

Density And Biomass ◽

Global Vegetation ◽

Model Structures

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

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Estimates of biomass burning emissions in tropical Asia based on satellite-derived data

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-2335-2010 ◽

2010 ◽

Vol 10 (5) ◽

pp. 2335-2351 ◽

Cited By ~ 78

Author(s):

D. Chang ◽

Y. Song

Keyword(s):

Biomass Burning ◽

Atmospheric Chemistry ◽

Crop Residue ◽

Fire Season ◽

Burned Area ◽

Published Data ◽

Tropical Asia ◽

Fire Emissions ◽

Burned Areas ◽

In Fire

Abstract. Biomass burning in tropical Asia emits large amounts of trace gases and particulate matter into the atmosphere, which has significant implications for atmospheric chemistry and climatic change. In this study, emissions from open biomass burning over tropical Asia were evaluated during seven fire years from 2000 to 2006 (1 March 2000–31 February 2007). The size of the burned areas was estimated from newly published 1-km L3JRC and 500-m MODIS burned area products (MCD45A1). Available fuel loads and emission factors were assigned to each vegetation type in a GlobCover characterisation map, and fuel moisture content was taken into account when calculating combustion factors. Over the whole period, both burned areas and fire emissions showed clear spatial and seasonal variations. The size of the L3JRC burned areas ranged from 36 031 km2 in fire year 2005 to 52 303 km2 in 2001, and the MCD45A1 burned areas ranged from 54 790 km2 in fire year 2001 to 148 967 km2 in 2004. Comparisons of L3JRC and MCD45A1 burned areas using ground-based measurements and other satellite data were made in several major burning regions, and the results suggest that MCD45A1 generally performed better than L3JRC, although with a certain degree of underestimation in forest areas. The average annual L3JRC-based emissions were 123 (102–152), 12 (9–15), 1.0 (0.7–1.3), 1.9 (1.4–2.6), 0.11 (0.09–0.12), 0.89 (0.63–1.21), 0.043 (0.036–0.053), 0.021 (0.021–0.023), 0.41 (0.34–0.52), 3.4 (2.6–4.3), and 3.6 (2.8–4.7) Tg yr−1 for CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10, respectively, whereas MCD45A1-based emissions were 122 (108–144), 9.3 (7.7–11.7), 0.63 (0.46–0.86), 1.1 (0.8–1.6), 0.11 (0.10–0.13), 0.54 (0.38–0.76), 0.043 (0.038–0.051), 0.033 (0.032–0.037), 0.39 (0.34–0.47), 3.0 (2.6–3.7), and 3.3 (2.8–4.0) Tg yr−1. Forest burning was identified as the major source of the fire emissions due to its high carbon density. Although agricultural burning was the second highest contributor, it is possible that some crop residue combustion was missed by satellite observations. This possibility is supported by comparisons with previously published data, and this result may be due to the small size of the field crop residue burning. Fire emissions were mainly concentrated in Indonesia, India, Myanmar, and Cambodia. Furthermore, the peak in the size of the burned area was generally found in the early fire season, whereas the maximum fire emissions often occurred in the late fire season.

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Global fire emissions estimates during 1997–2015

10.5194/essd-2016-62 ◽

2017 ◽

Cited By ~ 10

Author(s):

Guido R. van der Werf ◽

James T. Randerson ◽

Louis Giglio ◽

Thijs T. van Leeuwen ◽

Yang Chen ◽

...

Keyword(s):

North America ◽

Fuel Consumption ◽

Fire Severity ◽

Emission Factors ◽

Trace Gas ◽

Burned Area ◽

Biogeochemical Model ◽

Fire Dynamics ◽

Fire Emissions ◽

Aerosol Emissions

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 1997–2015. 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 x 1015 grams carbon per year (Pg C yr-1) during 1997–2015, 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 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. Our improved 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. GFED data is available from http://www.globalfiredata.org.

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Interannual variability of global biomass burning emissions from 1997 to 2004

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-6-3175-2006 ◽

2006 ◽

Vol 6 (2) ◽

pp. 3175-3226 ◽

Cited By ~ 54

Author(s):

G. R. van der Werf ◽

J. T. Randerson ◽

L. Giglio ◽

G. J. Collatz ◽

P. S. Kasibhatla ◽

...

Keyword(s):

Interannual Variability ◽

Biomass Burning ◽

Satellite Data ◽

Net Primary Production ◽

Terrestrial Ecosystems ◽

Total Carbon ◽

Burned Area ◽

Biogeochemical Model ◽

Fuel Wood ◽

Fire Emissions

Abstract. Biomass burning represents an important source of atmospheric aerosols and greenhouse gases, yet little is known about its interannual variability or the underlying mechanisms regulating this variability at continental to global scales. Here we investigated fire emissions during the 8 year period from 1997 to 2004 using satellite data and the CASA biogeochemical model. Burned area from 2001–2004 was derived using newly available active fire and 500 m burned area datasets from MODIS following the approach described by Giglio et al. (2005). ATSR and VIRS satellite data were used to extend the burned area time series back in time through 1997. In our analysis we estimated fuel loads, including peatland fuels, and the net flux from terrestrial ecosystems as the balance between net primary production (NPP), heterotrophic respiration (Rh), and biomass burning, using time varying inputs of precipitation (PPT), temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation (fAPAR). For the 1997–2004 period, we found that on average approximately 58 Pg C year−1 was fixed by plants, and approximately 95% of this was returned back to the atmosphere via Rh. Another 4%, or 2.5 Pg C year−1 was emitted by biomass burning; the remainder consisted of losses from fuel wood collection and subsequent burning. At a global scale, burned area and total fire emissions were largely decoupled from year to year. Total carbon emissions tracked burning in forested areas (including deforestation fires in the tropics), whereas burned area was largely controlled by savanna fires that responded to different environmental and human factors. Biomass burning emissions showed large interannual variability with a range of more than 1 Pg C year−1, with a maximum in 1998 (3.2 Pg C year−1) and a minimum in 2000 (2.0 Pg C year−1).

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Global fire emissions estimates during 1997–2016

Earth System Science Data ◽

10.5194/essd-9-697-2017 ◽

2017 ◽

Vol 9 (2) ◽

pp. 697-720 ◽

Cited By ~ 376

Author(s):

Guido R. van der Werf ◽

James T. Randerson ◽

Louis Giglio ◽

Thijs T. van Leeuwen ◽

Yang Chen ◽

...

Keyword(s):

North America ◽

Fuel Consumption ◽

Fire Severity ◽

Emission Factors ◽

Trace Gas ◽

Burned Area ◽

Biogeochemical Model ◽

Fire Dynamics ◽

Fire Emissions ◽

Aerosol Emissions

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  ×  1015 grams of carbon per year (Pg C yr−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. GFED data are available from http://www.globalfiredata.org.

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