ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-697-2017Global fire emissions estimates during 1997–2016van der WerfGuido R.guido.vander.werf@vu.nlRandersonJames T.GiglioLouisvan LeeuwenThijs T.ChenYanghttps://orcid.org/0000-0002-0993-7081RogersBrendan M.MuMingquanhttps://orcid.org/0000-0002-9498-7920van MarleMargreet J. E.https://orcid.org/0000-0001-7473-5550MortonDouglas C.CollatzG. JamesYokelsonRobert J.https://orcid.org/0000-0002-8415-6808KasibhatlaPrasad S.https://orcid.org/0000-0003-3562-3737Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the NetherlandsDepartment of Earth System Science, University of California, Irvine,
CA 92697, USADepartment of Geographical Sciences, University of Maryland, MD
20742, USASRON Netherlands Institute for Space Research, 3584 CA Utrecht, the
NetherlandsWoods Hole Research Center, Falmouth, MA 02540, USABiospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USADepartment of Chemistry, University of Montana, Missoula, MT 59812,
USANicholas School of the Environment, Duke University, Durham, NC
27708, USAnow at: VanderSat BV, 2011 VK, Haarlem, the Netherlandsnow at: Deltares, 2629 HV, Delft, the NetherlandsGuido R. van der Werf (guido.vander.werf@vu.nl)12September2017926977209December201612January20176July201718July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://essd.copernicus.org/articles/9/697/2017/essd-9-697-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/697/2017/essd-9-697-2017.pdf
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.
Introduction
Fires have occurred naturally since the rise of vascular plants on land over
400 million years ago (Scott and Glasspool, 2006), shaping biomes and
influencing climate through modulation of the carbon cycle and emissions of
greenhouse gases and aerosols (Edwards et al.,
2010; Langmann et al., 2009; van Langevelde et al., 2003). During the
Anthropocene, humans have become an increasingly important driver of fire
occurrence (Bowman et al., 2011). Human activity has enhanced
fire activity in locations such as deforestation zones, while fire
suppression and conversion of fire-prone landscapes such as savannas to
agriculture in Africa, or of fire-maintained open lands to closed-canopy
forests in the eastern US has generally decreased fire activity
(Andela and van der Werf, 2014; Bowman et
al., 2009; Nowacki and Abrams, 2008). To study how climate influences fires
at the global scale and, in turn, how fires influence the carbon cycle, air
quality, and climate we have developed the Global Fire Emissions Database
(GFED).
The scientific community has used past releases of GFED for over a decade.
GFED has been used by atmospheric and biogeochemical modeling groups as an
input dataset to study the impact of fires on biogeochemical cycles
(Chen et al., 2010; Schwietzke et al., 2016), atmospheric
chemistry (Aouizerats et al., 2015; Castellanos et al., 2014), and
human health (Johnston et al., 2012; Marlier et al., 2013), in
assessment reports of the Intergovernmental Panel on Climate Change (IPCC)
to estimate the role of fire and deforestation in biogeochemical cycles
(Ciais et al., 2013), in the National Oceanic and Atmospheric
Administration (NOAA's) CarbonTracker system
(Peters et al., 2007), and in annual updates of
the Global Carbon Project (Le Queré et al., 2015). GFED also serves
as a benchmark for optimizing fire modules in dynamic global vegetation and
Earth system models (Hantson et al., 2016), and for fire emissions estimates
derived from fire radiative power (FRP), including the Global Fire
Assimilation System (Kaiser et al., 2012). Finally, burned area from GFED
has provided a means for building early warning systems of fire season
severity (Chen et al., 2016).
The first version of GFED was released in 2004 and has since undergone
several revisions as improved burned area estimates became available. GFED2
was released after Giglio et al. (2006) improved on the mapping of burned
area from active fire data. GFED3 was released when this conversion was no
longer necessary because almost all burned area in the Moderate Resolution
Imaging Spectroradiometer (MODIS) era had been mapped (Giglio et al.,
2010), and the current version follows further improvements in the burned
area algorithm (Giglio et al., 2013). Satellite burned area is the
most important input dataset regulating the spatial and temporal pattern of
emissions following the Seiler and Crutzen (1980) approach, and is
complemented in GFED by a biogeochemical modeling framework that provides
estimates of biomass in various carbon “pools” including leaves, grasses,
stems, coarse woody debris, and litter. These pools are combusted to
different degrees during a fire depending on pool-specific parameters and
environmental conditions that influence fuel moisture and the simulated burn
depth in organic soils of boreal forests and peatlands.
Over the past decade, a parallel line of research has made considerable
progress in estimating emissions using satellite observations of FRP. When continuous observations are available or the FRP
diurnal cycle can be modeled, FRP can be integrated over time, yielding fire
radiative energy (FRE). FRE is directly related to fire emissions
(Wooster, 2002), and approaches using FRP observations can provide
emissions estimates in near-real time (Darmenov and da Silva, 2015;
Kaiser et al., 2012). Despite progress (Ichoku and Ellison, 2014;
Schroeder et al., 2014a), there is still substantial uncertainty and some of
these FRE approaches apply a scaling factor to match GFED. Comparisons
between the “classical” burned area approach and the FRP approach, or
approaches based on active fire detections in general, have indicated there
is considerable variability in the amount of burned area associated with an
individual active fire detection, and thus the two approaches do not always
align (Giglio et al., 2006; Randerson et al., 2012). In general,
direct mapping of burned area excels when fires are large, but has
difficulty in detecting smaller fires, for example, in croplands and in
other areas where many fires have a size below the 21 ha of an individual
500 m MODIS pixel. Combining both burned area and active fire data,
Randerson et al. (2012) provided evidence that the total area burned by
these relatively small fires could be substantial at the global scale.
Therefore, emission estimates based solely on active fires, including the
Fire INventory from NCAR (Wiedinmyer et al., 2011), may better capture
spatial and temporal variability in regions with many small fires than
emission estimates based solely on burned area (Reddington et al., 2016).
However, approaches based solely on active fires often do not account for
spatial and temporal variability in the amount of burned area per active
fire detection or variability in fuel consumption within biomes.
In this paper we describe the emissions estimates associated with the GFED4
burned area product from Giglio et al. (2013), with or without
additional burned area from small fires based on a revised version of the
Randerson et al. (2012) small-fire estimation approach. The main focus
of our analysis will be on the model version that includes small fires
(GFED4s), while the emissions estimates based on burned area without small
fires will be referred to as GFED4. We also used a recent meta-analysis
(van Leeuwen et al., 2014) to constrain our modeled estimates of fuel
consumption. Fuel consumption is the amount of biomass, coarse and fine
litter, and soil organic matter consumed per unit area burned and is the
product of fuel load and combustion completeness. Besides these two main
improvements over earlier versions, we made a number of additional
modifications including updated input datasets, the use of satellite-derived
estimates of parameters governing fuel consumption and tree mortality in the
boreal region (Rogers et al., 2015), and application of a new emission
factor methodology that separates temperate and boreal forest ecosystems
(Akagi et al., 2011). In Sect. 2 we provide more detail on these input
datasets, followed by a description of the modeling framework in Sect. 3.
Results are given in Sect. 4 followed by a discussion in Sect. 5 that
includes a description of the main differences with GFED3 and an assessment
of the primary sources of uncertainty in estimating fire emissions. In the
conclusions (Sect. 6) we summarize the main points of our analysis and
describe several important directions for future work.
Input datasets
Our version of the Carnegie–Ames–Stanford Approach
(CASA) model described in Sect. 3 requires input datasets
on vegetation characteristics, meteorology, and fire parameters. Most of
these datasets are somewhat different from those used in previous versions
of GFED, in part from a need for shorter latency in our updates. We
re-gridded all of the input datasets to 0.25∘ spatial resolution
and a monthly temporal resolution. We took additional steps to create
estimates of fire dynamics on daily and 3-hourly time steps.
Vegetation characteristics
In CASA, the fraction of absorbed photosynthetically active radiation
(fAPAR) is used to estimate net primary production (NPP), fractional tree
cover (FTC) is used in the allocation of NPP between living carbon pools,
and land cover (LC) is used to set turnover rates for stems and leaves,
applying emission factors, and for categorizing fire carbon emissions into
various fire types.
We calculated fAPAR based on the Global Inventory Modeling and Mapping
Studies (GIMMS) normalized difference vegetation index (NDVI) version 3g
(Pinzon and Tucker, 2014) and relations established by Los et
al. (2000). This dataset is derived from the Advanced Very High Resolution
Radiometer (AVHRR) sensor flying on board several satellites. We capped
fAPAR at 0.95, corresponding to an NDVI value of 0.9. Data were not
available for several remote islands, including Hawaii and Fiji, and we do
not report emissions for these locations.
FTC was derived by aggregating the annual MODIS MOD44B vegetation continuous
fields (250 m, V051; Hansen et al., 2005) to 0.25∘. In order to
provide consistency over the full time period, we used the last year
available (2013) and increased FTC in prior years using the fire-driven
deforestation rates. These fire-driven deforestation rates were based on the
amount of burned area within tropical forests at an annual time step. We
used land cover maps from the annual MODIS MCD12C1 land cover type product
and University of Maryland (UMD) classification scheme (Friedl et
al., 2010). The climate modeling grid (CMG, 0.05∘) dataset was
resampled to 0.25∘ based on the most abundant land cover type.
This dataset was available for 2001–2012; data from 2001 were applied to
earlier years in the time series, and 2012 land cover data were used for
years after 2012.
Meteorological datasets
We now use air temperature (t2m), soil moisture (swvl), and solar radiation
(ssrd) from the ERA-Interim dataset (Dee et al., 2011) produced by
the European Centre for Medium-Range Weather Forecasts (ECMWF). We
calculated the monthly mean for all datasets and regridded the
0.75∘ dataset to our 0.25∘ resolution without
interpolation.
These datasets are somewhat different from inputs for earlier GFED versions
but are now internally consistent. Interannual and seasonal variability was
relatively similar to datasets previously used in GFED, and these variations
have the largest impact on our calculations. The use of soil moisture is
new; previously, we used a bucket model based on rainfall and potential
evaporation to calculate the wetness of soils, a key input dataset for
calculating heterotrophic respiration (Rh) rates and combustion
completeness (see Sect. 3). Soil moisture is now transformed to a soil
moisture index (SMI) based on soil-type-specific permanent wilting point
(PWP) and field capacity (FC) values as described in http://www.ecmwf.int/en/forecasts/documentation-and-support/evolution-ifs/cycles/change-soil-hydrology-scheme-ifs-cycle
and is capped at 1. This was done for all four different soil layers (0–7,
8–28, 29–100, 101–255 cm). The SMI for the 0–7 cm layer replaced the scalar
used previously for combustion completeness. The average SMI of the top two
layers was used to down-regulate NPP in herbaceous vegetation in the light
use efficiency model when moisture was limiting, whereas the average of the
top four layers was used for NPP in woody vegetation. The average SMI for
the upper two layers was also used to represent the influence of soil
moisture on the abiotic scalar regulating rates of Rh. Finally, the
average SMI of all layers was used in the allocation of assimilated carbon
to above- and belowground pools (see Sect. 3).
Fire processes
We derived burned area (both mapped burned area and active fire detections
scaled to burned area) and metrics that can be used to assess fire-induced
tree mortality and combustion completeness from satellite. Our burned area
time series is based on MODIS data for the August 2000 onwards period (the
“MODIS era”) and based on other sensors before that period. In Sect. 2.3.1
we briefly describe the MODIS burned area data for which a more detailed
description is described in Giglio et al. (2013). In Sect. 2.3.2 we then
explain how the small fire burned area estimates for the MODIS era were
derived based on Randerson et al. (2012). This is the GFED4s burned area
time series and complemented with other sensors to compute the full
1997–2016 time period dataset (Sect. 2.3.3).
Burned area from MODIS
For the MODIS era we used the MODIS Collection 5.1 MCD64A1 burned area
product (Giglio et al., 2013). Compared with Collection 5 and
earlier versions of the MCD64A1, the Collection 5.1 product reduces the
unintentional removal of small burns and eliminates some systematic omission
errors (Giglio et al., 2013). The MCD64A1 product maps daily burned
area at 500 m spatial resolution; these data are then aggregated to a
0.25∘ grid (both monthly and daily) to produce the MODIS-era GFED4
burned area product (Fig. 1a).
Average burned area over 2003–2016 from (a) MODIS surface
reflectance imagery (MCD64A1) and (b) small fire burned area. Panel
(c) shows the small fire percentage of total burned area.
Small fire burned area during the MODIS era
In the MODIS era, we combined 500 m burned area (see above), 1 km thermal
anomalies (active fires) from Terra and Aqua MODIS, and 500 m surface
reflectance observations to statistically estimate burned area associated
with small fires, BAsf, in each 0.25∘ grid cell (i), month
(t), and aggregated vegetation type (v):
BAsfi,t,v=FCouti,t,v×αr,s,v,y×γr,s,v,y,
where FCout is the number of active fire pixels outside of the perimeter
of the MCD64A1 burned area, α is a ratio of burned area to active
fires within MCD64A1 burned areas, and γ is a correction factor
derived by comparing difference normalized burned area (dNBR) of active
fires observed outside (dNBRout) and inside (dNBRin) of MCD64A1 burned
areas with unburned control areas (dNBRcontrol; see Eq. 4 of Randerson
et al., 2012). α and γ scalars were estimated each year
(y), as a function of region (r), seasonal interval (s), and aggregated
vegetation type (v). Our method was similar to that described in
Randerson et al. (2012), but with several important modifications to
each of the three factors on the right-hand side of Eq. (1) as described below.
First, we used the MCD64A1 product from Collection 5.1, replacing Collection
5 that was used in Randerson et al. (2012). Second, instead of using a
single source of level 3 composited thermal anomaly/fire product from Terra
(MOD14A1), here we used individual active fire detections from both Terra
and Aqua. Third, to improve geolocation accuracies, we used the MODIS fire
location product (MCD14ML) instead of the gridded composite fire product
(MOD14A1). To further reduce geolocation uncertainties, we only retained
active fire detections with small or moderate scan angles (equal to or less
than 0.5 radians). This threshold was somewhat arbitrary and future research
is required to identify how a balance between sample size and area of view
is best achieved. Even with the above adjustments to improve
georegistration, some remaining resampling error was introduced in the
process of projecting the variable-size MODIS fire pixels onto the 500 m
sinusoidal grid on which the MCD64A1 burned area product is generated. To
partially correct this known bias, we applied region-specific factors ranging from 0.88 in Africa north of the Equator to 1.12 for temperate and boreal Asia. These correction factors, which were derived using a rigorous
model of the sample-dependent MODIS pixel shape and size, partially
compensated for the simplified, fixed 1 km radius initially used to
determine whether an active fire pixel was co-located (inside) or outside of
the MCD64A1 burn area pixels. Finally, to estimate dNBR for active
fires inside of MCD64A1 burned area, we only used active fire detections for
which each of the four overlapping 500 m pixels were classified as burned. This
was a stricter criterion than in Randerson et al. (2012) that increases
dNBRin and its separation from dNBRout and other areas used as
controls (Fig. 2).
The distribution of difference normalized burn ratio (dNBR) for
active fires detected within burned areas from MCD64A1 (red), outside of
burned areas (orange), and for control areas (blue) within Northern
Hemisphere Africa (NHAF) and Central Asia (CEAS). The distributions,
generated using observations in 2001–2012, were constructed during the peak
fire month for each region. The improved approach (see Sect. 2.3.2 for
details) compressed the distributions in unburned control areas and increased
the separation between the three categories.
It was not possible to apply the same constraint in the calculation of
dNBRout, so this adjustment usually had the effect of lowering
γ. We note that dNBRout in particular is strongly affected by
resampling error; thus, the individual γ correction factors are in
turn also influenced by resampling error. The net effect is to limit the
range of values that may be attained by γ, in a sense leaving an
“imprint” of resampling error on the resulting small fire burned area
estimates. This imprint is an unavoidable outcome of using relatively coarse
1 km and 500 m gridded time series data to track small, sub-pixel fires. At
the same time, we raised the filtering standard for control pixels (Eq. 4 of
Randerson et al., 2012) so that pixels within a 1 km buffer area of
active fire detections by either Terra or Aqua MODIS were excluded in the
calculation of dNBR for non-burning areas (dNBRcontrol). During the
regional aggregation of dNBR, we excluded 500 m pixels that were marked as
“water” by MODIS land cover type product (MCD12Q1).
During the time both Terra and Aqua fire detections were available (January 2003–December 2016), we calculated BAsf separately for Terra (MOD) and
Aqua (MYD). BAsf was then estimated as the arithmetic mean of the two
estimates. A climatological ratio of BAsf-MYD/ BAsf-MOD was used to
estimate BAsf-MYD during periods when Aqua MODIS observations were
not available (August 2000–December 2002). The final GFED4s burned area
during the MODIS era was the sum of GFED4 burned area (Sect. 2.3.1; Fig. 1a)
and burned area from small fires (BAsf, Fig. 1b). As expected, burned
area from small fires is more prevalent in areas with extensive agriculture
and in other human-dominated landscapes (Fig. 1c).
Estimating burned area prior to the MODIS era (1997–2000) for
GFED4s
For the pre-MODIS era, we used monthly active fire data from the Visible and
Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission
(TRMM) or the Along Track Scanning Radiometers (ATSR) on board multiple
platforms to estimate burned area. Two steps of optimization were used to
derive total burned area, starting with the GFED4s product described above.
The first step was to develop a relationship between aggregated active fires
(from VIRS or ATSR) and burned area during the MODIS era in each GFED
region, with the aim of using this relationship to estimate regional burned
area during 1997–2000. The second step involved distributing the aggregated
burned area within each region to individual 0.25∘ grid cells.
Map of the 14 regions used in this study, after Giglio et al. (2006)
and van der Werf et al. (2006).
To calculate the regional sum of BA during the pre-MODIS era, we first
performed regression analyses between ATSR or VIRS active fires and the
regional sum of GFED4s burned area during the MODIS era. We developed linear
regression models for each GFED region (Fig. 3), for each month, and for
each of the five aggregated vegetation classes (see Randerson et al., 2012, for a description of the vegetation classes). When ATSR and VIRS
active fire data were both available (January 1998–July 2000), the highest
performing regression from these two datasets was used to estimate the
burned area in each region. Among the 14 continental-scale regions, we used
VIRS data in Africa, Southeast Asia, Equatorial Asia, and Australia and ATSR
data in all other regions (Fig. 4). Prior to 1998, when VIRS data were not
available, regressions based on ATSR were used. If the ATSR or VIRS active
fires for any given month were outside the dynamic range of active fires
during the MODIS era, we instead used linear regression derived from all of
the monthly data during the MODIS era for that region.
Regional time series (1997–2016) of GFED4s monthly burned area. The
different colors indicate the contribution from each of the different data
sources and methodologies (ATSR, TRMM-VIRS, 500 m MCD64A1, and small fires)
used to produce the entire dataset.
After quantifying the sum of burned area within each region, we distributed
it among 0.25∘ grid cells using the following approach. While
active fires from ATSR or VIRS provide some indication about the temporal
dynamics of fire in a region, the active fire approach tends to
underestimate burning in savannas and other areas with herbaceous fuels. To
assess how well active fires captured regional spatial patterns, we
estimated the spatial correlation between active fires and burned area in
each GFED region during the MODIS era. Higher correlations from these
analyses indicated better agreement between the spatial distribution of
ATSR/VIRS active fires and GFED4s burned area. Since we found the
correlation coefficients varied seasonally, a mean monthly (m) set of spatial
correlation coefficients (SC) was derived to determine the level of
representation of burned area by ATSR/VIRS active fires. The spatial
distribution function of burning was based on a linear combination of
climatological distribution of burned area (cl) and the distribution of active
fires (FC):
BApre-MODISi,t=BArsr,t×SDFFCr,i,t×SCr,m+SDFclr,i,t×(1-SCr,m),
where SDFFC and SDFcl are unitless spatial distribution functions that
each sum to 1 in each GFED region and were derived from active fire
detections or the monthly climatology of burned area during the MODIS era
from GFED4s, and BArs is the regional (r) sum of burned area for that
month and region derived from the regressions between GFED4s and ATSR or
VIRS active fires described above. In temperate and high-latitude regions,
where the spatial correlation between active fires and burned area is
relatively high, the equation primarily uses information from the pre-MODIS
active fires to assign the spatial distribution of burned area. In regions
where the spatial correlation between active fires and burned area is
relatively low, the equation relies more on the climatological burned area
pattern from the MODIS era. For consistency with the previous step, the
source of the active fires for generating the SDF was the same as active
fires used to generate the regional sum of burned area in each region. The
contribution of ATSR, VIRS, MCD64A1, and BAsf to the total burned area is
shown in Fig. 4 for the GFED4s time series.
Combustion completeness and fire-induced mortality in boreal
forests
Despite relatively similar environmental conditions and vegetation
attributes, the boreal regions in North America and Eurasia exhibit
significantly different patterns of fire severity (Wooster and Zhang,
2004). This was shown to primarily be a function of divergent plant traits
for the dominant tree species in each continent (Rogers et al., 2015).
Species in North America tend to promote crown fires with higher levels of
combustion completeness of the canopy and tree mortality compared to
lower-severity surface fires in Eurasia. As with other global fire models,
GFED3 did not capture these differences due to biome-wide parameterizations.
To address the large-scale differences in boreal fire effects, we integrated
satellite-based metrics of severity from Rogers et al. (2015) including
immediate tree mortality and an index of vegetation destruction. These were
initially calculated at 1 km and 500 m resolutions, respectively, and
aggregated to 1∘ , but here rescaled to our 0.25∘ grid
without interpolation. Vegetation destruction was derived from three
MODIS-based metrics that provide information on immediate fire-induced
losses of green vegetation, reduction in canopy and soil water, and
landscape charring. These included dNBR, decreases in NDVI, and increases in
summer land surface temperature (LST). The original vegetation destruction
product used LST from Aqua and was available from 2003 to 2012. We extended it
here to 2001 and 2002 using multiple linear regression relationships based
on Terra LST, dNBR, and changes in NDVI at 1∘ (r2=0.95
for North America, 0.96 for northwest Eurasia, 0.95 for northeast Eurasia,
and 0.91 for southern Eurasia). Immediate tree mortality was based on
decreases in tree cover and increases in spring albedo 1 year after a
fire, and was provided for fires between 2001 and 2009. For both products,
grid-cell-specific averages were used in years not covered, and grid cells
without valid values were assigned regional burned-area-weighted means. On
average, vegetation destruction was 36 % lower and fire-induced tree
mortality was 42 % lower in boreal Eurasia compared to boreal North
America. More details on model integration are given in Sect. 3.1, and more
information on these products can be found in Rogers et al. (2015).
Modeling framework and modifications
GFED is based on the CASA model, which was
developed in the early 1990s to simulate the terrestrial carbon cycle using
satellite data (Potter et al., 1993; Field et al., 1995;
Randerson et al., 1996). In previous work we adjusted the model to account
for fires (van der Werf et al., 2003, 2004); further
revisions were implemented in GFED2 (van der Werf et al., 2006) and
GFED3, including modifications to estimate the contribution of different
fire categories including agricultural waste burning, boreal forest fires,
deforestation fires, peatland fires, and savanna fires (van der Werf et
al., 2010). Below we describe the model in general (Sect. 3.1), followed by a more
detailed explanation of the changes we made in this version (Sect. 3.2–3.5).
CASA-GFED framework
When CASA was developed it computed carbon fluxes as the difference between
NPP and Rh. Both are still calculated for each month and each
0.25∘ grid cell. NPP is based on a light use efficiency model
(Field et al., 1995) and is distributed over various live biomass
“pools” (leaves, stems, roots) according to satellite-derived fractional
tree cover maps. In forests we allocate NPP to all three live biomass pools,
and in grasslands to leaves and roots, accounting for variability in
allocation due to gradients in mean annual precipitation as in GFED3. The
carbon in these pools is subsequently delivered to nine litter pools at the
surface and in the soil with turnover rates set for each pool depending on
moisture conditions and temperature.
The turnover rates of the wood pool in GFED4 (the modeling framework used to
derive both GFED4 and GFED4s emissions) were adjusted at the biome level to
match observed aboveground biomass (Avitabile et al., 2016;
Santoro et al., 2015). Wood turnover now varies between 40 years for
deciduous broadleaf forest and 65 years for deciduous needleleaf forest,
with turnover times for evergreen forest in between those values: 52 years
for evergreen needleleaf and 55 for evergreen broadleaf (Fig. 5). Similarly,
turnover times of slowly decomposing soil pools were adjusted in GFED4 to
better match measured values reported for 0–30 and 30–100 cm
(Batjes, 2016).
Comparison of modeled standing biomass with the compilation from
Avitabile et al. (2016) and Santoro et al. (2015). Bins with
fewer than 100 grid cells are excluded.
In GFED1 we added fire, herbivory, and grazing as additional carbon loss
pathways besides Rh. Fires transfer carbon to the atmosphere and
between the different pools depending on the burned fraction of the grid
cell, combustion completeness, fire-induced mortality rates, and information
on whether belowground carbon pools are susceptible to fire or not.
Combustion completeness (CC) is treated similarly in GFED4 as in our
previous work with set minimum and maximum values; see Table 1 in van
der Werf et al. (2010). We scaled CC using the soil moisture index (SMI) of
the top 7 cm such that the 5th and 95th percentiles
corresponded with the minimum and maximum values. Fire-induced tree
mortality was set to 2 % for low tree cover regions (mainly savannas and
agriculture) and 50 % for forests in general but modified in tropical
forests based on fire persistence as in GFED3, and in boreal regions
according to satellite derived proxy datasets (Sect. 2.3.4). More
specifically, in boreal forests we used the satellite-derived instantaneous
tree mortality to represent fire-induced tree mortality. In addition, we did
not use the CC scaling by SMI for the aboveground wood in the boreal region
but used the satellite-derived vegetation destruction scalar for this. The
combustion completeness of the wood pool ranged between the set minimum and
maximum values (0.2 and 0.4, respectively), and linearly depended on the
vegetation destruction scalar instead of SMI.
Burned area, fuel load, and emissions for a hypothetical grid cell
where 50 % of the area burns in month 2 and 50 % in month 3, and assuming
a combustion completeness of 100 %. “Previous” refers to our previous
work in GFED3 and before where no adjustments were made in the conversion of
burned area to the fraction of fuel load that is combusted; “modified”
refers to the current approach (GFED4 and GFED4s), where we treat the burned
fraction as the fraction of the total remaining fuel in the grid cell that is
combusted using Eq. (3).
Emission factors for different fire types, in g specie per kg dry
matter burned. Emission factors for other species, uncertainties, and source
information is provided in
http://www.geo.vu.nl/~gwerf/GFED/GFED4/ancill/GFED4_Emission_Factors.xlsx.
Dry matter carbon content (DMCC) was derived from the carbonaceous species
and used to convert carbon to dry matter.
SpecieSavannaBorealTemperateTropicalPeatAgricultureMean emissionsforestforestforest(Tg yr-1)CO21686148916471643170315857320CO631278893210102357CH41.945.963.365.0720.85.8216.1NMHC3.48.48.41.71.79.917.8H21.72.032.033.363.362.599.31NOx (as NO)3.900.901.922.551.003.1114.60N2O0.200.410.160.200.200.100.93PM2.57.215.312.99.19.16.336.6TPM8.517.617.613.013.012.446.6TPC (OC+BC)3.0010.1010.105.246.063.0518.4OC2.629.609.604.716.022.3016.6BC0.370.500.500.520.040.751.86SO20.481.101.100.400.400.402.32NH30.522.720.841.331.332.174.22DMCC (%)48.8346.5048.9449.1857.0148.04–Modifying the burned fraction to account for sub-grid-scale
heterogeneity in fuels
In our previous model setup, fires lowered the fuel load in each grid cell
depending on burned area, combustion completeness, and fire-induced
mortality rates. This was done uniformly in the grid cell, not accounting for
the fact that fires only lower fuel in the fraction of the grid cell that
actually burned. This may have led to an underestimation of emissions in
frequently burning regions, especially towards the end of the fire season.
For example, in a grassland grid cell that burns in two consecutive months,
each with 0.5 burned fraction, modeled fuel loads in the second month are
half those of the first month if combustion completeness is set at 100 %
(Fig. 6). In reality, the fuel load in that grid cell in the second month
should be similar to that in the first month for the part that had not
burned, and depleted for the part that had burned. To compensate for this
effect we now calculate the modified burned fraction of the grid cell as
MBF(it)=BA(i,t)A(i)/1-∑t-4t-1BA(i,t)A(i),
where MBF is the modified fraction of the grid cell that burns, BA is the burned
area, and A is the area of the grid cell at location (i). In our hypothetical
example from above MBF now becomes 1 in the second month according to Eq. (3),
thus generating similar emissions in the 2 months that each burn the same
area (Fig. 6). When cumulative burned area over a fire season exceeds the
grid cell area this approach yields negative values towards the end of the
season; if this occurs these values are replaced by the burned area divided
by the grid cell area. Because we only take into account the burned area
from the actual month and the three preceding months, grid cells with two
burning seasons are probably not impacted because they are usually separated
in time by more than 3–4 months. Our approach does not influence the burned
area datasets but only the way it is used in the conversion of burned area
to emissions.
Comparison of monthly (top panels), and disaggregated daily (middle)
and 3-hourly (bottom) emissions from GFED3 (left-hand side) and GFED4s
(right-hand side) for an example grid cell in South America (11.75∘ S,
51.75∘ W).
Fuel consumption optimization
Emissions are derived from the multiplication of burned area and fuel
consumption per unit burned area, the latter being the product of fuel loads
per unit area and combustion completeness. Van Leeuwen et al. (2014)
summarized the peer-reviewed literature on fuel consumption rates consisting
of 76 studies and covering 121 unique measurement locations. In addition to
the fuel consumption measurement, we also included the fuel load
measurements mostly in savannas from Scholes et al. (2011) and assumed
a combustion completeness of 0.9 for these fuel measurements to calculate
fuel consumption. This latter set of 95 measurements were mostly confined to
South Africa, Botswana, and Zambia.
We used these two compilations to adjust the turnover rates of herbaceous
leaf and surface litter pools where the largest discrepancies between the
model and measurements were found. Uncertainties in the comparison stem from
comparing different time period (most measurements were made before our
study period) and from comparing local measurements with model estimates for
0.25∘ grid cells. Fuel consumption rates are highly variable, not
only between biomes but also within biomes and between separate fuel
classes. The overall spatial representativeness of the fuel consumption
field measurements is reasonable for most fire-prone regions. However,
several important regions from a fire emissions perspective – including
Southeast Asia and Central Africa – are under-represented. For this study we
used version 1 of the fuel consumption database available from http://www.geo.vu.nl/~gwerf/FC/.
Emission factors
Emission factors are used to convert dry matter burned into emissions of
trace gases and aerosols. These were assigned in GFED3 based on the
compilation of Andreae and Merlet (2001) with annual updates. A new
compilation was developed by Akagi et al. (2011), who considered a subset
of the available literature focusing on measurements of smoke that had
cooled to ambient temperature but had not undergone photochemical processes.
In addition to this approach that may better match the requirements from the
atmospheric community, Akagi et al. (2011) reported mean values for more
biome categories. The most important change in that regard from the GFED
perspective is the partitioning of the extratropical forest category into
temperate and boreal forests. We compiled a subset of the available species
that are most frequently used in large-scale chemistry transport models and
filled missing values using those of Andreae and Merlet (2001) with
annual updates (see Table 1). Updates to the Akagi et al. (2011) database can
be found at http://bai.acom.ucar.edu/Data/fire/ and will be
incorporated into future GFED versions.
Redistributing monthly emissions on daily and 3-hourly
timescales
We made several improvements to the approach described by Mu et al. (2011) for redistributing monthly emissions to daily and 3-hourly time steps
in each 0.25∘ grid cell. This set of higher temporal resolution
emissions was created only for the period of 2003 to the present because of
increased MODIS active fire data availability after the launch of Aqua.
To estimate the daily distribution of emissions, we used two sources of
information: active fires from MCD14ML and the day of burning reported in
the MCD64A1 burned area product. In tropical regions between 25∘ N
and 25∘ S, we weighted the information content from these two
sources equally in grid cells for which both data streams were available. In
GFED3, the day of burning was not available for use as a constraint on daily
variability. In the extra-tropics (poleward of 25∘ N and
25∘ S) we solely used active fires to distribute the daily pattern
of emissions. In these regions, gaps between successive overpasses of Aqua
and Terra are smaller, and active fires have been shown to be moderately
effective in capturing daily variations in fire spread rates
(Veraverbeke et al., 2014). We removed persistent active fire locations
associated with volcanoes, gas flaring, and many other non-fire sources,
using a more recent static hotspot database (Randerson et al., 2012). A
simple 3-day center mean smoothing filter was applied in tropical regions to
adjust for gaps in MODIS coverage, following Mu et al. (2011).
We created a climatological diurnal cycle of burning in each region and for
different aggregated vegetation types to redistribute daily emissions on a
3 h time step. The approach is similar to the one described in Mu et
al. (2011), and uses active fire data derived from full hemispheric scans of
GOES-11 (west) and GOES-12 (east) observations during 2007–2009 with version
6.0 of the WF_ABBA algorithm (Prins et al., 1998;
Reid et al., 2009). Here, we used an improved land cover type product from
Friedl et al. (2010), MCD12C1 version 5.1, during 2007–2009 to create
diurnal cycles of emissions for three aggregated vegetation classes within
continental-scale regions in the western hemisphere. These diurnal cycles
were then applied in other regions using the same mapping strategy as
described in Mu et al. (2011). An example of the redistribution of
emissions using this approach for daily and hourly emissions is shown in
Fig. 7, showing relatively comparable results as in GFED3.
GFED4s burned fraction (a), fuel consumption (b),
and emissions (c) averaged over 1997–2016.
GFED4s annual fire carbon emissions for various regions and
sources.
Monthly emissions from GFED4 (red) and GFED4s (grey).
Results
Over the 1997–2016 period, fire emissions according to GFED4s are on
average 2.2 Pg C yr-1 with substantial interannual variability. In
Sect. 4.1 we discuss the spatial pattern of burned area and the resulting
emissions, and in Sect. 4.2 the temporal patterns. We then discuss the modeled
fuel consumption (Sect. 4.3) and the greenhouse gas forcing of fires in Sect. 4.4. We
also explain the main differences between GFED4s and GFED3 as well as
differences in emissions between GFED4s and GFED4, with the latter derived
from the same modeling framework but using the burned area dataset without
small fires (i.e., with burned area from GFED4) (Sect. 4.5).
Spatial patterns
1997–2016 area-averaged fire return time, biomass and fuel
quantities, combustion completeness, and fuel consumption. Region
abbreviations are explained in Fig. 3.
Aboveground carbon Combustion completeness Fuel consumption (g C m-2)(–) (g C m-2 burned) RegionAreaFire returnStandingStandingSurfaceStandingStandingSurfaceStandingSurfaceBelowgroundAllEmissions(Mkm2)time (yr)biomass*fuel*litterbiomass*fuel*litter(Tg C yr-1)BONA11.2392.613059587380.310.430.564104091269208959TENA7.8270.816036785750.170.390.62265359663018CEAM2.777.73465106710370.140.440.5747159231109438regular82.031986689530.070.340.56225533075925deforestation1482.48291829125450.590.590.6549141642590714613NHSA3.053.825566025020.100.430.602593021157232SHSA14.753.627119787780.190.520.65513508381059291regular57.123555076750.090.410.652054360642165deforestation864.68112811223310.640.640.69516116026197382126EURO7.0501.610734085790.150.400.6716438615518MIDE11.9861.4337611340.100.580.753510101362NHAF14.77.910871441980.090.650.75931481242451regular7.910741301960.080.650.75851460231430deforestation4486.58354835415980.560.560.624705987571626320SHAF9.84.818161513080.050.650.74992291330669regular4.818031333060.050.670.74892280317641deforestation1512.95836583611380.560.560.623248704392434428BOAS15.2158.312336066900.160.320.611944216991314126TEAS18.079.55222012440.150.390.70791702026961SEAS6.643.326647896700.130.420.6033440416754115regular44.825215786280.080.360.60207376058386deforestation1243.36617661718410.580.580.6338571168463548929EQAS2.7103.17151512317840.350.480.572476101230466534173regular158.35511239615020.120.270.516427621930333458deforestation295.510 21210 21223120.580.580.6458991479512712 505116AUST7.913.76171091620.120.660.79721270200116Global148.827.414772573110.100.550.70141219383982160regular27.614221922960.070.530.71102210263381817deforestation3335.48087808720390.600.600.664841134114997681343
* Fuel is the fraction of the biomass that can be combusted, i.e.
biomass multiplied by fire-induced mortality rates.
The spatial patterns of emissions and burned area are similar but because
fuel consumption is, in general, inversely related to fire frequency (Table 2), emissions are less spatially variable than burned area (Fig. 8). About
84 % of global carbon emissions have an origin in the tropics between
23.5∘ N and 23.5∘ S (1830 Tg C yr-1), and 62 %
come from tropical savannas (1341 Tg C yr-1), underscoring the
importance of fire as a driver of biogeochemical cycles and ecosystem
processes in tropical ecosystems.
The relative importance of different regions or continents varies depending
on whether one is considering burned area, carbon emissions, or trace gas
emissions. For example, while Equatorial Asia (mostly Indonesia) is
responsible for only 0.6 % of global burned area, the region accounts for
8 % of carbon emissions and 23 % of CH4 emissions from global
fire activity. Boreal forests offer a similar, although less extreme,
example: 2.5 % of global burned area, 9 % of global fire carbon
emissions, and 15 % of global fire CH4 emissions. This difference is
due to the large variability in fire behavior and fuel consumption in
forested regions with high fuel loads, especially when fires consume organic
soils. The larger contribution of coarse fuels and smoldering stages of
combustion in organic soils also contributes to higher emission factors for
reduced species such as CO and CH4. More information on the relative
contribution of the different regions is provided in Tables 2 and 3 for fire
carbon emissions and in Table 1 for mean annual emissions of individual
trace gases and aerosols. More time series information on individual trace
gases and aerosols can be found at http://www.geo.vu.nl/~gwerf/GFED/GFED4/tables/.
Carbon emissions estimates and the contribution of different fire
categories over the 1997–2016 study period. Region abbreviations are
described in Fig. 3.
RegionCarbon emissions (Tg C yr-1)CV (%)Contribution of different fire categories to total carbon emissions (%) MeanMinimumMaximumSavannaBorealTemperateTropicalPeatAgricultureforestforestforestBONA5912128530.386.54.20.07.31.7TENA1811312833.20.046.40.00.020.3CEAM38151779245.50.01.936.70.015.9NHSA3213603671.10.00.023.00.05.9SHSA2911045614449.30.01.845.70.03.2EURO84194329.00.212.20.00.058.6MIDE2132435.80.03.40.00.060.8NHAF4513596451688.30.00.05.20.06.5SHAF669583774792.40.00.14.80.02.7BOAS12645280512.079.52.50.01.714.3TEAS6136852329.811.412.72.40.043.6SEAS115661772853.40.07.131.30.08.3EQAS17318111013911.20.00.043.742.82.2AUST116421903586.30.09.92.30.01.5Global2160177330321565.37.42.315.13.76.3Temporal dynamics
Forest fires are the primary driver of interannual variability in fire
emissions (Fig. 9, Table 3). In the tropics, much of this variability is
linked with sea surface temperatures, including large-scale climate modes
such as El Niño, which alter fire risk in tropical forests (Chen et
al., 2016). El Niño years including 1997–1998, 2002, and 2015 have
relatively large contributions from tropical forests. Peat burning in
Equatorial Asia contribute substantially to anomalously high emissions 1997
and 2015, in part due to the human-ignited fires that burn in drained
peatlands during prolonged drought periods associated with El Niño
(Field et al., 2016; van der Werf et
al., 2008). Most of the interannual variability in emissions originates from
regions outside of Africa, which is shown in the top right panel in Fig. 9.
August and September are usually the months with highest emissions,
coinciding with the main austral fire season (Fig. 10). This dominance of
the Southern Hemisphere is because Southern Hemisphere Africa has higher
emissions than Northern Hemisphere Africa (especially during the latter part
of our time period) and the deforestation regions south of the equator are
larger and more active than those north of the equator. Finally, it
coincides with the burning season in the temperate and boreal Northern
Hemisphere summer, which produces far more emissions than these eco-regions
in the Southern Hemisphere summer. The inclusion of small fires does not
influence these dynamics (Fig. 10), while the modified conversion of burned
area to burned fraction of fuel causes a slight delay in the peak fire
season, mostly in Africa (Fig. 11).
Fuel consumption
Modeled and measured (van Leeuwen et al., 2014) fuel consumption agree
reasonably when aggregated to biome levels (Fig. 12). Fuel consumption in
savannas and other regions with herbaceous fuels is lower in GFED4 (both
with and without small fires) than in GFED3 because of increases in the
turnover rates of herbaceous leaf and surface litter pools. As a
consequence, fuel consumption in GFED4 in savannas has decreased 30 %
compared to GFED3. Compared with the fuel consumption database from van
Leeuwen et al. (2014), GFED4 predicts estimates that are, on average, 14 % higher than the fuel consumption measured in the collocated grid cells.
GFED4 also shows a somewhat lower range than the observations.
Fuel consumption in tropical forests is substantially higher (45 %) than
measured. However, measured fuel consumption typically does not account for
repeated burning during the deforestation process, which can lead to
complete combustion over a full fire season following multiple fires
(van der Werf et al., 2009; Yokelson et al., 2007). In temperate
forests, GFED4 average fuel consumption is 33 % below the measured values,
while in boreal forests the model is 39 % higher. The discrepancy in
temperate forests can be traced back to one very high measurement in
Tasmania that is not reproduced in the collocated grid cell in GFED4; the
medians are in close agreement. Pinpointing the reasons for the disagreement
in boreal regions is less straightforward; the range, mean, and medians for
the modeled values exceed the measured ones. One potential reason might be
related to the relatively large number of experimental burns in the database
of van Leeuwen et al. (2014) for this biome, which in general occur under
conditions less favorable for large fires to prevent them from growing out
of control. For the state of Alaska, GFED4 estimates of fuel consumption are
similar to estimates from the Alaska Large Fire Database that rely solely on
fuel consumption observations from uncontrolled wildfires (Veraverbeke et
al., 2015). The satellite-derived maps of tree mortality and combustion
completeness led to an increase in fuel consumption in North America. On
average, fuel consumption there is now 38 % higher than in boreal Asia
for grid cells north of 55∘ N and with more than 20 % tree
cover. For all other biomes the number of fuel consumption measurements is
probably too small for a fair comparison.
Greenhouse gas forcing of fires and potential for mitigation
Fires emit the greenhouse gases CO2, CH4, and N2O and also modify
the climate by emitting precursors of aerosols and ozone, aerosols, and
changing surface properties such as albedo in often complex ways
(Randerson et al., 2006; Ward et al., 2012). Average total annual
greenhouse gas emissions according to GFED4s were 7.3 Pg CO2, 16 Tg CH4, and 0.9 Tg N2O. Note that in this section we refer to C
emissions in CO2 mass units rather than the C mass units used in the
rest of the paper. Using a 100-year time horizon and based on global warming
potentials of 34 for CH4 and 298 for N2O (Myhre et al., 2013),
this translates to 8.1 Pg CO2 equivalent annually, or 23 % of global
fossil fuel CO2 emissions in 2014 (Boden et al., 2017; Le
Queré et al., 2015).
However, fire emissions are not generally a net CO2 source to the
atmosphere, and may be better viewed as “fast respiration”, because
regrowing vegetation in many burned areas will sequester a roughly
equivalent amount of atmospheric CO2 during post-fire stages of
ecosystem recovery over a period of years to decades (Landry and
Matthews, 2016). In general, only fires that are not balanced by regrowth
are a net CO2 source. The most obvious fire types in this category are
fires used in the deforestation process or those that burn drained
peatlands. CO2 emissions from these two fire types are estimated here
to be 0.4 Pg C or 1.3 Pg CO2 per year. Including CH4 and N2O
of all fire types, the contribution of fires to the greenhouse gas budget is
2.1 Pg CO2 equivalent annually or 6 % of global fossil fuel CO2
emissions in 2014 (Boden et al., 2017). Another category of fire emissions
that may add to the build-up of atmospheric CO2 are those that increase
over time, for example increasing burned area or combustion completeness in
boreal regions related to climate change. Our time series is too short and
our modeling framework is too incomplete to capture the exact magnitude of
emissions from a changing boreal fire regime.
Monthly GFED4s fire carbon emissions for Northern Hemisphere Africa
(a) and Southern Hemisphere Africa (b) based on straight
conversion of burned area to burned fraction (“previous”) and with the new
parameterization according to Eq. (3) (“modified”).
Savanna fire season management has been proposed as a climate mitigation
instrument (Russell-Smith et al., 2013). By burning early in the season
instead of late, fires are in general more patchy, release fewer emissions,
and prevent large late-season fires. According to GFED4s, total annual
tropical savanna fire emissions averaged 4.9 Pg CO2, 6 Tg CH4, and
0.6 Tg N2O. In this case, only CH4 and N2O emissions are
relevant and combined account for 0.3 Pg CO2 equivalent of annual
emissions. Experiments with early burning in Australia have shown a
potential reduction of up to 50 % (Walsh et al., 2014), but it is not
known to what extent it is possible to use this approach in other regions,
what the side effects will be, and whether some of the mitigation will be
offset by higher CH4 emission factors because early season fires may
occur when fuels have had less time to cure. In Australia the latter is
probably not the case (Meyer et al., 2012), but whether this is found in
other regions remains to be investigated.
Differences between GFED4s, GFED4, and GFED3
Measured and modeled fuel consumption for various biomes showing the
range (whiskers), mean (colored dots and diamonds), median (open dots and
diamonds), and 25th and 75th percentiles (boxes) for those biomes with more
than 10 measurements. Comparison is based on the meta-analysis of van Leeuwen
et al. (2014) and collocated 0.25∘ grid cells. The time periods of
measurement and model do not necessarily overlap. “n” indicates the
number of measurements for each biome. Note the logarithmic scale.
Relative differences in burned area, fuel consumption per unit
burned area, carbon emissions, and carbon monoxide (CO) emissions between
GFED4 (black) and GFED4s (grey) compared to GFED3 for 14 basis regions
explained in Fig. 3 and the globe averaged over 1997 to 2011.
In general, small fire burned area (GFED4s) and the modified
burned-area-to-burned-fraction conversion (GFED4 and GFED4s) cause emissions
to increase, while the optimization of fuel consumption causes emissions to
decrease as compared with earlier versions of GFED. On a global scale, these
modifications yield a modest net increase in fire carbon emissions in GFED4s
as compared with GFED3 (11 % for the overlapping 1997–2011 time period).
However, the effects of the three main adjustments vary spatially; on a
regional scale the differences are larger (Fig. 13). The relative effect of
the small fire burned area is largest in temperate and subtropical regions
where agricultural waste burning and shifting cultivation are important
drivers of fire activity. The more than doubling of burned area in Central
America and Northern Hemisphere South America compared to GFED3 reflects
differences in both GFED4 burned area and the inclusion of small fires (Fig. 13). Burned area in Temperate North America and Europe also increases by
about a factor of 2, and most of this difference is due to small fire burned
area.
Our modifications to herbaceous fuel turnover rates cause fuel consumption
per unit area (per m2 of burned area) to decrease, whether or not small
fire burned area is included, in all regions except Central Asia, where
consumption increased by approximately 20 to 30 % (Fig. 13). Estimates of
fuel consumption per unit area are similar in GFED4 and GFED4s, indicating
that fuel loads in areas burned by small fires are not substantially
different from those in nearby mapped burned areas (or that our relatively
coarse modeling setup cannot resolve finer-scale landscape differences).
The exception is Central Asia, where small fire burned area causes a relative
increase in burned area in forested regions. In Central America and
Equatorial Asia, in contrast, small fire burned area occurs predominantly in
areas with relatively low fuel loads.
The modified burned-area-to-burned-fraction parameterization causes an
increase of 5 % in carbon emissions (not shown). The new parameterization
only influences grid cells that burn for more than 1 month in a season,
and has a larger effect in grid cells that have a high burn fraction.
Regions with frequent savanna fires therefore have the highest sensitivity,
with emissions in Northern Hemisphere Africa, Southern Hemisphere Africa,
and Australia increasing by 9, 8, and 6 %, respectively. In other
regions, the differences are smaller than 2 %. In addition to the
increase in emissions in frequently burning savannas, the new
parameterization also changes the temporal dynamics (Fig. 11); early season
emissions are lower because less fuel remains from the previous growing
season, and late-season emissions are higher because the parameterization
has the effect of increasing grid-cell level fuel consumption later in the
fire season.
Without small fire burned area, the impact of decreasing fuel consumption
and a minor reduction in burned area (2 % globally) yields a total carbon
emissions estimate of 1.5 Pg C yr-1 in GFED4, a 23 % reduction
compared to GFED3 during 1997–2011. Although globally GFED4 emissions are
lower than GFED3, in some regions both burned area and emissions increase,
mostly in temperate regions (Fig. 13). Using the new set of emission factors
that separate extratropical forests into boreal forest and temperate forest
components generates a larger increase in CO emissions in boreal regions
than expected from the change in carbon emissions alone (Fig. 14).
Latitudinal distribution of carbon monoxide (CO) emissions for
GFED3, GFED4, and GFED4s.
Discussion
We have calculated global carbon emissions from fires by using a
biogeochemical model to combine satellite fire observations with estimates
of fuel consumption that respond to variations in environmental conditions.
In a subsequent step, we have used a higher-resolution set of emission
factors to convert carbon emissions into emissions of trace gases and
aerosols. Since the publication of GFED3 in 2010, burned area algorithms
have been improved considerably (Giglio et al., 2013), and now include a
preliminary estimate of the impact of small fires (Randerson et
al., 2012). In parallel, the fuel consumption database created by van
Leeuwen et al. (2014) has enabled the development of an improved
parameterization of herbaceous vegetation turnover in grassland and savanna
ecosystems, and validation of our modeled values in several other biomes.
New emission factor measurements and a more systematic assessment of the
available data has led to a more consistent set of emission that better
resolve extratropical forest biomes (Akagi et al., 2011). Together, all
of the elements required to calculate emissions following the Seiler and
Crutzen (1980) paradigm have seen substantial improvements. Our new emission
estimates are therefore more reliable than previous estimates because they
account for updated information on key components of the fire emissions
equation, but uncertainties remain substantial and are difficult to
quantify.
The addition of small fire burned area is a key improvement in GFED4s
compared to earlier versions, for example, and the modifications we describe
in this paper have improved our estimates compared to Randerson et al. (2012). However, the actual magnitude of small fire burned area is difficult
to quantify on global scales because it requires a large sample of burned
area measurements from sensors with a higher spatial resolution than MODIS.
To date, Landsat estimates of burned area have been produced for various
regions and purposes including the validation of coarser resolution data
(Padilla et al., 2014, 2015; Roy and
Boschetti, 2009; Silva et al., 2005) but a publicly available and
global-scale database of Landsat burned area is needed to better validate
ongoing efforts to produce reliable burned area estimates from coarser
resolution satellite imagery. In addition, new missions such as the Visible
Infrared Imager Radiometer Suite (VIIRS) and Landsat-8 also increase the
number of active fires detected compared to MODIS (Schroeder et al.,
2014b).
A somewhat similar story exists with respect to validating fuel consumption.
The fuel consumption database from van Leeuwen et al. (2014) has enabled
a more systematic validation but the number of studies is limited,
relatively few measurements were made during our study period, and it is
questionable to what degree the local measurements are representative for
the 0.25∘ grid cell averages reported here. Thus, our estimates
are likely to remain most useful for large-scale studies. Although recent
regional studies have shown that our global modeling framework is indeed
capable of generating reliable large-scale emissions in Alaska and the
tropics, these studies also show that GFED may have problems capturing finer-scale dynamics (Andela et al., 2016; Veraverbeke et al., 2015). While
improved satellite missions and combining various data streams may help in
improving the fuel consumption parameterization in models, systematic
field-based assessments of fuel consumption along gradients of productivity
and other factors influencing variability in fuel consumption within biomes
are a necessary step in further improving bottom-up fire emission estimates.
New satellite estimates of biomass may be helpful in this regard (for
example the Global Ecosystem Dynamics Investigation (GEDI) mission),
particularly in deforestation and temperate forest and shrubland regions,
where aboveground living biomass comprises a large component of fuel
consumption.
Given the large uncertainties in bottom-up emission estimates in the past,
top-down constraints have often been used to pinpoint discrepancies between
modeled and measured atmospheric abundances of trace gases or aerosols.
Carbon monoxide (CO) was most often used (Arellano et al., 2004;
Hooghiemstra et al., 2011; Huijnen et al., 2016) because fires are a major
source of CO, its lifetime is relatively long, and column CO is measured
from several satellite sensors. More recent work also includes other species
such as formaldehyde, NO2, and aerosol optical depth (Bauwens
et al., 2016; Mebust et al., 2011; Petrenko et al., 2012). While providing
additional information on strengths and weaknesses of inventories such as
GFED, for example potentially missing late-season fires (Castellanos et
al., 2014), the results of these studies are often contradicting (van
Leeuwen et al., 2013), potentially due to the use of different atmospheric
models and sources of observations. We would therefore respectfully argue
that uncertainties in bottom-up and top-down approaches are overlapping. For
example, carbon emissions from Indonesia during the 2015 high fire year
according to GFED4s were almost 400 TgC (Fig. 9,
http://www.geo.vu.nl/~gwerf/GFED/GFED4/tables/GFED4.1s_C.txt). Two inversion
studies using Measurement of Pollution in the Troposphere (MOPITT) CO
measurements derived either 100 Tg higher (Yin et al., 2016) or 100 Tg
lower (Huijnen et al., 2016). Part of the difference can be attributed to
the use of higher CO emission factors in the latter study, which thus
requires less carbon burned to match atmospheric observations, but part is
also due to differences in model setup and analysis design. The use of
different top-down constraints (e.g. Infrared Atmospheric Sounding
Interferometer (IASI) versus MOPITT) could lead to additional discrepancies,
although studies employing column CO2 from the Orbiting Carbon
Observatory-2 (OCO-2) may omit some of the issues related to uncertainty in
emission factors. Heymann et al. (2017) provided evidence for lower
estimates than found in GFED4s in Indonesia for 2015 based on OCO-2 data.
Studies focusing on aerosol optical depth (AOD) do not give conflicting results but indicate that
bottom-up estimates are roughly a factor 3 too low (Johnston
et al., 2012; Kaiser et al., 2012; Petrenko et al., 2012; Tosca et al.,
2013). While some studies have therefore boosted bottom-up emissions or
created new inventories with much higher emissions to get AOD values more in
line with observations (Liousse et al., 2010), this may jeopardize the
reasonable agreement between bottom-up and top-down estimates found for most
trace gases. To date, the disagreement between measured and modeled AOD has most often been linked to bottom-up emissions, but AOD calculation in
models are uncertain as well. For example, increasing the hygroscopicity
reduced the offset in tropical regions (Reddington et al., 2016). Besides
exploring the factors that are used to estimate AOD in models such as the
hygroscopicity, combining multiple species in inversion studies and better
emission factors are needed to resolve one of the most important questions
in biomass burning emissions research.
Most of the emission factors (EFs) used in these top-down approaches are
based on midday sampling during peak fire emission rates. The EFs measured
under these somewhat restricted circumstances are still highly variable with
a coefficient of variation about the mean of about 40 % on average
(Akagi et al., 2011). The diurnal or longer-term variation in EFs should
be larger but has not been explicitly well-measured yet (Saide et al.,
2015). The EFs of many species have rarely been measured in the field for
important fire types such as wildfires (Akagi et al., 2011) and for some
compound classes with perhaps the most important missing species being the
semi-volatile precursors to organic aerosol, which are difficult to measure
even in lab experiments (Gilman et al., 2015). A related area of
uncertainty is the temporal evolution of emissions within the fire plume.
Only a few field studies have measured how organic aerosol (OA) levels
change with time. In one an increase in OA by a factor of about 2.5 was
observed (Yokelson et al., 2009), while in another study OA decreased by
about 20 % (Akagi et al., 2012). Understanding what controls secondary
OA levels is critical to guide the proper use of AOD in inversions and to
understand health and climate impacts.
Additional small errors also occur. In straightforward application of the
carbon mass balance method the carbon content of the fuel that is actually
volatilized is based on a few carbon content measurements of fuel
subsamples. EFs are proportional to the carbon content used. This can
theoretically cause an overestimation of the EFs by about 4 % if charcoal
yields are important (Surawski et al., 2016). On the other hand,
uncertainty in what ecosystem components actually burn means that the high
carbon components can burn preferentially leading to underestimated EFs if
based on average fuel C content (Santin et al., 2015). In general
these small uncertainties may tend to cancel out. EFs may also be systematically
overestimated by 1–3 % because many carbon-containing species cannot yet
be measured (Akagi et al., 2011).
For GFED3, we performed a Monte Carlo simulation to estimate carbon
emissions uncertainties based on assumed uncertainties of key input data
including burned area and best-guess estimates of various model parameters.
We now refrain from estimating formal uncertainties because of difficulties
in assessing the uncertainties in the various layers. For example, the
burned area in many regions where small fires seem to be important now by
far exceeds the range of uncertainty reported for GFED3 burned area. Given
the level of agreement between our burned area estimates and more refined
regional estimates (Randerson et al., 2012), and between our modeled
biome-average fuel consumption estimates and those measured in the field, a
best-guess uncertainty assessment at regional scales could be a 1σ
of about 50 % in general but higher in areas where small fire burned area
is important or where there is significant fuel consumption in organic
soils.
Lowering and/or better quantifying this uncertainty involves a thorough
assessment of the burned area estimates and especially those from small
fires, using more direct satellite observations of fire severity and fuel
consumption based on FRP data, and new field data on fuel consumption and
emission factors along critical gradients such as productivity and grazing
intensity. Increasing the spatial resolution of our modeling framework could
lower the impact of spatial heterogeneity in fire parameters and make for
easier comparisons with or validation using ground-based data. Better
understanding and modeling diurnal cycles may be equally important in
addressing how variable, for example, the relative importance of flaming and
smoldering combustion is. Finally, with new missions such as Suomi-NPP and
the various Sentinel satellites now collecting data, an emphasis on merging various
time series would help in lengthening the time series over which we have
consistent data to over 20 years.
GFED data are freely available at http://www.globalfiredata.org. The site provides documentation, related publications, updates, and online analysis tools to compute emissions for custom regions and
countries.
Conclusions
We have revised the Global Fire Emissions Database using new observations of
burned area including those from smaller fires as well as several other new
data streams. In addition we have modified the fuel consumption
parameterization in our model to better match observations. Global average
fire emissions were estimated to be 2.2 Pg C yr-1 over 1997–2016 with
substantial interannual variability. This is an 11 % increase compared to
our previous work (GFED3), and in regions where small fires are relatively
important such as temperate cropland regions the increase could be as large
as 100 %. Net greenhouse gas emissions from all fires were on average 6 % of
global 2014 fossil fuel CO2 emissions, consisting of 0.4 Pg C yr-1 emissions from deforestation and tropical peat fires, which are a
net CO2 source to the atmosphere just like fossil fuel emissions, and
16 Tg CH4 and 0.9 Tg N2O yr-1 from all fire types using a
100-year horizon to convert the warming potential of these greenhouse gases
to CO2 equivalents.
Over the past several years, uncertainties in all of the data layers used to
calculate emissions (burned area, fuel consumption, and emission factors)
have been reduced from new algorithms and data availability. While
biome-level fuel consumption rates are now more in line with observations
than in our previous work, uncertainties are still substantial at higher
resolutions as indicated by regional studies. In addition, the small fire
burned area approach carries substantial uncertainties and is known to be
impacted by resampling error. Merging information from the long-term MODIS
era with newer instruments could reduce some of these uncertainties, but
carefully designed and interdisciplinary field campaigns measuring fuel
consumption, fire dynamics, and emission factors along gradients and
throughout fire seasons are equally necessary to further improve biomass
burning estimates.
The authors declare that they have no conflict of interest.
Acknowledgements
This research was supported by the European Research Council (ERC) grant
number 280061, the Gordon and Betty Moore Foundation (GBMF3269), NASA
(NNX14AP45G), the U.S. National Science Foundation, and the EU H2020
Monitoring Atmospheric Chemistry and Climate (MACC-III)
project.Edited by: Vinayak Sinha
Reviewed by: two anonymous referees
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