Carbon dioxide emissions from wild and anthropogenic fires return the carbon
absorbed by plants to the atmosphere, and decrease the sequestration of
carbon by land ecosystems. Future climate warming will likely increase the
frequency of fire-triggering drought, so that the future terrestrial carbon
uptake will depend on how fires respond to altered climate variation. In this
study, we modelled the role of fires in the global terrestrial carbon balance
for 1901–2012, using the ORCHIDEE global vegetation model equipped with the
SPITFIRE model. We conducted two simulations with and without the fire module
being activated, using a static land cover. The simulated global fire carbon
emissions for 1997–2009 are 2.1 Pg C yr
Vegetation fires contribute significantly to the interannual variability
(IAV) of atmospheric
The estimation of global carbon emissions from fires was pioneered by Seiler
and Crutzen (1980), who used available literature data of field experiments
to assess important fire parameters like area burned, fuel load and the
combustion completeness. More recently, large-scale spatially explicit
estimation of fire carbon emissions has been aided by satellite-derived
burned area and active fire counts (Giglio et al., 2010; Roy et al., 2008;
Tansey et al., 2008), as well as vegetation models in which burned area is
either prescribed (Randerson et al., 2012; van der Werf et al., 2006, 2010)
or simulated with a prognostic fire model (Kloster et al., 2010; Li et al.,
2013; Prentice et al., 2011; Thonicke et al., 2010). Several recent estimates
have converged to give annual fire carbon emissions of
Just as vegetation can be classified into biomes according to its climatic,
morphological and physiological features, so fires occurring under different
climate and vegetation patterns have distinctive features that allow them to
be characterized by
In a companion study (Yue et al., 2014), we incorporated the SPITFIRE prognostic fire model into the ORCHIDEE global vegetation model, and evaluated the modelled burned area and fire regimes during the twentieth century using multiple observation data sets. In the present study, fire carbon emissions are simulated for 1901–2012, and the role of fires in the terrestrial carbon balance is investigated in relation to different climatic drivers and fire pyromes. Here we address what difference fires have made in the global terrestrial carbon balance, and how this difference is driven by large-scale climate variations, with a special focus on the naturally occurring vegetation fires. More specifically, the objectives of this study are the following: (a) to benchmark the ORCHIDEE–SPITFIRE model in terms of simulated carbon emissions against GFED3.1 data, in order to identify model strengths and weaknesses. (b) To investigate the role of fires in the terrestrial carbon balance for 1901–2012 and the climatic factors driving its magnitude and temporal variation. This objective is tackled by conducting two simulations with and without fire occurrence. (c) To examine the characteristics of different fire regimes (as defined in Archibald et al., 2013) in terms of the role of fires in the terrestrial carbon balance. We hypothesize that more frequent and larger fires will have greater carbon consumption rates than infrequent and smaller ones, and consequently the fire-induced carbon uptake reduction is larger in the former type of fire.
ORCHIDEE is a global dynamic vegetation model that simulates the exchange of energy, water and carbon between the atmosphere and the land surface. It is the land surface model of the IPSL-CM5 Earth system model (Dufresne et al., 2013; Krinner et al., 2005). The processes and equations of the SPITFIRE fire model (Thonicke et al., 2010) were implemented in ORCHIDEE, with some modifications being described in Yue et al. (2014). There, the model was evaluated against different satellite observations for simulated burned areas and fire regimes.
The SPITFIRE module simulates burned area and fire consequences (e.g.
emissions, plant mortality) in a mostly mechanistic way. The central
underlying engine is the Rothermel fire spread model (Rothermel, 1972; Pyne
et al., 1996; Wilson, 1982), which links fire spread rate to fuel state,
weather conditions and fire physics. Weather and fuel moisture conditions
determine the time that a fire persists, which, combined with fire spread
rate, yields an estimate of mean fire size. Ignition sources are scaled into
fire numbers depending on weather conditions, with sources from both
lightning and human activities being included. The daily burned area is thus
derived as the product of fire number and mean fire size. Anthropogenic
ignitions are estimated as a function of population density with the maximum
ignition being obtained at ca. 16
Fire carbon emissions follow a classical paradigm (Seiler and Crutzen, 1980) as the product of daily burned area, fuel load, and combustion completeness. Dead litter on the ground and live biomass from grasses and trees are available for burning. For live grass biomass and dead litter, combustion completeness is calculated as a function of fuel moisture state following the approach of Peterson and Ryan (1986). Tree crown live biomass consumption is simulated to depend on fire intensity and fire scorching height. Two factors are considered concerning fire-caused tree mortality: damage to tree crown because of crown scorching; and cambial damage linked with fire persistence time and tree bark resistance to fire. We refer the reader to Yue et al. (2014) and Thonicke et al. (2010) for a more detailed description of the fire module.
The simulation of combustion completeness (CC) for surface dead fuel was
modified compared to the original scheme as presented by Thonicke et
al. (2010). In SPITFIRE, the calculation of surface fuel CC follows Peterson
and Ryan (1986), which allows CC to increase with decreasing fuel wetness and
level out when the fuel wetness drops below some threshold (see Fig. 1 in Yue
et al., 2014). During the model testing, it was found that simulated CCs were
much higher than the recently compiled field observations for different
biomes (van Leeuwen et al., 2014). We thus adjusted the maximum CC for fuel
classes of 100 (with original maximum CC as 1.0) and 1000
As shown by Yue et al. (2014), the mean annual burned area on non-crop lands
for 2001–2006 was simulated to be 346
The simulated global gross primary productivity (GPP) by ORCHIDEE (version 1.9.6)
as driven by CRUNCEP climate forcing data is 205 Pg C yr
The default ORCHIDEE plant functional types (PFTs, excluding bare land) were
grouped into five biomes: boreal forest, temperate forest, tropical forest,
grassland and agricultural land. The spatial extent of each biome was
determined as the area where a corresponding ORCHIDEE PFT occupies more than
90 % of a grid cell in the 0.5
To evaluate the role of fires in the global terrestrial carbon balance, two
parallel simulations were conducted: fireON and fireOFF, with SPITFIRE being
switched on or off, respectively. In both simulations, the dynamic vegetation
module of ORCHIDEE was de-activated, and a current-day vegetation
distribution map (converted into the 13-PFT map in ORCHIDEE based on the IGBP
1
Agricultural fires are not simulated in the model for two reasons. First, the
timing of agricultural burning is strongly constrained by the sowing and
harvest dates (Magi et al., 2012). An enhanced crop phenology module is under
development for ORCHIDEE and this will allow precise agricultural fire
seasons to be included in the future. Second, agricultural fires are normally
under strict human control and the spread and size of fires are limited by
field size; they are thus very different from wildfires and warrant a special
modelling approach. Carbon emissions from tropical and boreal peat fires are
not explicitly simulated, although the model does simulate some burned
fractions in tropical regions where deforestation fires dominate, because the
model could capture the “climate window” when the climate is relatively dry
and deforestation fires are possible. Thus, even though the model does not
explicitly simulate deforestation fires using a land-cover-change approach,
it does capture some fire activities in the region dominated by deforestation
fires, and simulates them like natural wildfires. Figure S1 in the Supplement
compares simulated and GFED3.1 emissions for the tropical region of
20
Both fireON and fireOFF simulations followed the same protocol, which
comprised three steps. For both simulations, the model was first run for
200 years (including a 3000-year soil-only spin-up to speed up the
equilibrium of slow and passive soil carbon pools) starting from bare ground
without fire, with atmospheric
The fireOFF simulation follows the same first spin-up, second spin-up and
transient steps as the fireON simulation, except that the fire model is
switched off throughout all simulations. The climate data used for 1901–2012
are 6-hourly CRUNCEP data
(
For the fireON simulation, after the second spin-up, there is a global
carbon sink of 0.19 Pg C yr
We define NEP, the net ecosystem production, as
The GFED3.1 fire carbon emissions from the CASA biosphere model forced by GFED3.1 burned area data were used to evaluate simulated fire carbon emissions (van der Werf et al., 2010). Much work has been done to calibrate the CASA model against observations, e.g. in terms of productivity and NPP allocation (van der Werf et al., 2006, 2010). Carbon emissions from six different fire types are identified in GFED3.1 data, namely forest fire, grassland fire, woodland fire, agricultural fire, deforestation and peatland fire. For convenience of description, emission sources of the former three types of fire are tentatively referred to as natural sources (that ORCHIDEE–SPITFIRE simulates explicitly), and those of the latter three types as anthropogenic sources (that ORCHIDEE does not explicitly include, although it is able to capture part of the deforestation fire emissions as explained in Sect. 2.3). Note that the grouping of different emission sources in GFED3.1 data does not necessarily reflect the exact nature of different fire types. For example, peat fires in tropics are mainly due to intentional drainage followed by burning to remove a (logged) forest (thus anthropogenic, e.g. Marlier et al., 2015), while in northern high-latitude regions, peatland fires might be due to drought (thus natural, e.g. Turetsky et al., 2011).
Not all anthropogenic carbon emissions (mainly from fossil fuel consumption,
cement production and deforestation) into the atmosphere remain there, and
some of them are absorbed by the terrestrial ecosystem (land sink) and the
ocean (ocean sink). The so-called residual carbon sink in land ecosystems can
be obtained by subtracting the annual
The fire variability at global and regional scales is known to relate to the
ENSO mode of climate variability (Kitzberger et al., 2001; Prentice et al.,
2011; van der Werf et al., 2004), mainly affecting the tropics but with
global teleconnections (Kiladis and Diaz, 1989). The Southern Oscillation
Index (SOI,
Finally, the fire pyrome distribution map of Archibald et al. (2013) was used to relate the influence of fires on NBP to different fire pyromes (Fig. S2). Five fire pyromes were identified by using a Bayesian clustering algorithm with information on key characteristics of fire regimes – size, frequency, intensity, season and extent. The five pyromes are FIL (frequent–intense–large), FCS (frequent–cool–small), RIL (rare–intense–large) (RIL), RCS (rare–cool–small) and ICS (intermediate–cool–small). Frequent fires (FIL and FCS) are characterized by large annual burned fractions in areas with a relatively long fire season. Australia has large, intense fires (FIL pyrome), whereas in Africa, smaller less intense fires (FCS pyrome) dominate. Rare fires (RIL and RCS pyromes) are found in areas with a short fire season, dominating in temperate and boreal regions (see Table 1 and Fig. 2 in Archibald et al., 2013, and the descriptions for more information).
Annual GPP as a function of annual precipitation according to Jung et al. (2011) (dashed bar); model simulation before (black bar) and after calibration (grey bar).
Simulated mean annual burned fraction (%) for 1997–2009 for
Annual global fire carbon emissions for 1997–2009 simulated by ORCHIDEE (blue), and from the GFED3.1 data. Carbon emissions from natural sources (forest fire, grassland fire, and woodland fire) are shown as the black solid line. Carbon emissions from agricultural fire, deforestation fire and peat fire (which are not explicitly simulated in ORCHIDEE) are shown as shaded areas stacked on top of GFED3.1 natural source fire carbon emissions.
Annual fire carbon emissions simulated by ORCHIDEE and from the GFED3.1 data for 1997–2009 for the 14 different GFED regions. The 14 GFED regions are BONA: boreal North America; TENA: temperate North America; CEAM: Central America; NHSA: Northern Hemisphere South America; SHSA: Southern Hemisphere South America; EURO: Europe; MIDE: Middle East; NHAF: Northern Hemisphere Africa; SHAF: Southern Hemisphere Africa; BOAS: boreal Asia; CEAS: central Asia; SEAS: Southeast Asia; EQAS: equatorial Asia; and AUST: Australia and New Zealand. Refer to Fig. S5 for their distributions.
The calibration of carboxylation rates significantly improved the
model–observation agreement in terms of the distribution of GPP as a
function of annual precipitation (Fig. 1). The calibrated model is also able
to capture the productivity decrease when annual precipitation exceeds
3000
The simulated global burned area for 2001–2006 is 239
The simulated mean annual global fire carbon emissions for 1997–2009 are
2.1 Pg C yr
The interannual variability of fire carbon emissions is known to be partially decoupled from that of burned area (van der Werf et al., 2006), mainly because emission variability is driven by forest fires with higher fuel consumption, whereas burned area variability is driven by savanna fires with relatively large burned fraction but low fuel consumption. At the global scale, the IAV of fire carbon emissions is simulated to be closely related to that of burned area (Fig. S4, giving a correlation coefficient of 0.88 over 1997–2009 – all data detrended). In contrast, the correlation coefficient between GFED3.1 natural source emissions and burned area is 0.52 over the same period (0.04 when emissions from both natural and anthropogenic sources are included), i.e. smaller than ORCHIDEE–SPITFIRE. Thus the IAV of carbon emissions is more strongly coupled to that of burned area in ORCHIDEE than in GFED3.1, because emissions are dominated by burning of litter (from grassland, savanna and forest) and are less driven by forest fires that involve a large amount of live biomass burning.
Comparison of simulated and GFED3.1 fire carbon emissions, burned
area and total fuel consumption (TFC, including consumption of surface dead
litter or organic soil, and live biomass) for different regions averaged over
1997–2009. The locations of the GFED regions are mapped in Fig. S5, the
abbreviations expanded in the caption to Fig. 4. The last three columns
provide a qualitative indication of the error in simulated carbon emissions
and its attribution to those of burned area and TFC. To obtain the
qualitative error information, the ratio of the simulated value to GFED3.1 is
compared to the coefficient of variation (CV) of the corresponding GFED3.1
value as follows:
Annual fire carbon emissions simulated by ORCHIDEE–SPITFIRE are compared
with GFED3.1 data for 1997–2009 for different regions in Fig. 4 (see figure
caption for expansion of GFED region abbreviations and Fig. S5 for region
distribution). The three regions with the most frequent fires, Northern
Hemisphere Africa (NHAF), Southern Hemisphere Africa (SHAF) and Australia
(AUST), have total fire emissions of 1.17 Pg C yr
The GFED3.1 data have very low emissions in temperate North America (TENA),
the Middle East (MIDE), central Asia (CEAS) and Europe (EURO) (50 Tg C
yr
The three regions where the model underestimates carbon emissions are boreal
Asia (BOAS), Southeast Asia (SEAS) and equatorial Asia (EQAS), with simulated
emissions of 103 Tg C yr
Fuel consumption (g C per m
Simulated fuel consumption (g C per
Figure 6 shows carbon emissions per grid cell area (g C per m
By looking at the latitudinal distribution of burned area and emission, the
systematic error in ORCHIDEE's estimated emissions can be clearly related to
that in burned areas (Fig. 7). The underestimation of burned area in tropical
and subtropical regions (30
Table 1 compares mean annual simulated and GFED3.1 emissions for 1997–2009 for different regions. The model bias of emissions is qualitatively attributed to those of burned area and fuel consumption. Table S2 further compares NPP and fire combustion completeness between the model and the GFED3.1 data (where NPP is from the CASA biosphere model, with all GFED3.1 data in Table S2 obtained from Table 4 in van der Werf et al., 2010). For all regions (except NHAF and AUST) where emissions are overestimated by the model (TENA, CEAM, NHSA, SHSA, EURO, MIDE, CEAS), there is a coincident overestimation in burned area, which sometimes overrides the underestimated fuel consumption in regions such as CEAM. Regions where emissions are underestimated also show underestimated burned area (with the exception of BOAS), some of them also having underestimated fuel consumption (EQAS).
The simulated NPP regional averages are in general agreement with those from the CASA model reported by van der Werf et al. (2010) (Table S2), indicating that the simulated fuel load might be comparable to GFED3.1 data, and that systematic errors in fuel consumption might be dominated by errors in the combustion completeness of different fuels. On the one hand, simulated combustion completeness for litter agrees well with the values used in GFED3.1, but on the other hand, combustion completeness for the litter and above-ground live biomass combined is much higher in ORCHIDEE than GFED3.1 over BOAS, BONA, MIDE, NHAF, SHAF and AUST, and much lower over EQAS. This might reflect a higher or lower simulated combustion completeness of tree live biomass, which needs further investigation. The higher simulated combustion completeness for litter and live biomass combined in NHAF, SHAF and AUST contributes to the higher fuel consumptions in these regions, given the fact that simulated NPP is rather similar to GFED3.1 over these regions (except for NHAF, where the simulated NPP is 40 % higher than GFED3.1 and combustion completeness is 2.6 times higher). A recent comparison among different fuel load products by Pettinari et al. (2015) also indicates that our simulated fuel loads in savannas and shrublands are higher than their fuel-model-based data, consistent with the higher NPP in Africa and Australia (Table S2). At the same time, one should also keep in mind that GFED3.1 is not completely an observation data set, but is another model calculation of fire emissions. Given the availability of the comprehensive fuel combustion field data recently compiled by van Leeuwen et al. (2014), more careful calibration and validation of the simulated combustion completeness for different fuel types could be performed in the future.
Mean annual carbon emissions (g C m
The latitudinal distribution of
Finally, the combustion completeness (CC) values used for the 100 and 1000
The fire carbon emissions as percentage (%) of net primary production (NPP) for 2003–2012.
Different components of global carbon fluxes for fireON and fireOFF
simulations. The carbon fluxes are
Figure 8 shows the percentage of NPP emitted by fire over the last decade
(2003–2012). Regions with frequent burning show a higher fraction of NPP
being returned to the atmosphere by fire. Yet, heterotrophic respiration
remains the dominant pathway for returning NPP to the atmosphere, accounting
for 85.7 % of the global NPP (91.1 % when agricultural harvest is
included, the CH term in Eq. 1). Fire carbon emissions account for 3.4 %
of NPP, with the remaining 5.2 % of NPP being accumulated in the
biosphere as a carbon sink (NBP) (as mentioned in Sect. 2.3, the remaining
positive NBP of 0.19 Pg C yr
The different components of global carbon fluxes for the fireON and fireOFF
simulations are shown in Fig. 9. Net primary production (NPP) for fireON and
fireOFF is very similar (NPP is 6 Tg C yr
The carbon sink in fireOFF is greater than that in fireON (Fig. 9c). This is
because fire emissions (1.91 Pg C yr
The small fire-induced carbon sink reduction obtained in this study, when
only natural wildfires are modelled and with static vegetation cover, implies
that if carbon stocks in the fuel (dominated by litter or organic soil except
in cases of peat and deforestation fires) were not consumed in fires, they
would have been decomposed and have contributed to the heterotrophic
respiration. This suggests a fire respiration partial compensation in the
model; i.e. fire carbon emissions are somewhat analogous to heterotrophic
respiration, and when fires are extreme their emissions would far exceed
their role of respiration compensation, causing a larger net reduction in
carbon sink compared to a world without fire. The sink reduction variability
is closely correlated with fire emission anomalies during 1901–2012 (with a
correlation coefficient of 0.71, Fig. 9d). Fire carbon emissions show an
acceleration of 1.8 Tg C yr
Our simulated cumulative land carbon sink (NBP) for 1959–2012 is 109.6 Pg C (with 80.8 Pg C stored in live biomass and 28.8 Pg C in litter and soil), which is close to the cumulative residual sink of 105.9 Pg C (Le Quéré et al., 2013). The cumulative land sink in fireOFF is 127.2 Pg C, suggesting a cumulative sink reduction of 17.6 Pg C by fire since 1959. The correlation coefficient between detrended time series of NBP by the fireON simulation and the residual sink is 0.59, indicating that the model is moderately successful at capturing the IAV of the carbon sink by the terrestrial ecosystem.
Prentice et al. (2011) pointed out that fire emissions account for one-third
and one-fifth of the IAV of the 1997–2005 global carbon balance as indicated
by atmospheric inversions, when emissions were from the GFED3.1 data and
simulated by the LPX vegetation model, respectively. In our study, fire
carbon emissions explained 20 % of the IAV of simulated NBP (which is the
We selected 10 “high fire years” as the 10 years with the highest global
fire-induced sink reduction (SR
The Pearson correlation coefficient (
The fire-induced sink reduction (left vertical axis,
The opposite of SR
As we did not include agricultural fires, deforestation fires and peat fires
in our simulation, the analysis of fire-induced sink reduction related to
climate variations presented here mainly represents a scenario of naturally
occurring fires. Globally, the 1997–1998 fire emissions anomaly is
underestimated in the model, principally related to the fact that the
anthropogenic peatland and deforestation burning in tropical Asia and America
(Field et al., 2009; Page et al., 2002; van der Werf et al., 2004, 2010) are
not included. The underestimated IAV in fire carbon emissions by the model
might lead to underestimated temporal variability in SR
Despite the fact that systematic bias exists for simulated burned area, as
global total fire carbon emissions are constrained with the GFED3.1 estimate,
the estimated long-term average SR
The suggested “respiration partial compensation” by fires (i.e. larger sink
reduction with more extreme fires), and the strong relevance of
SR
Characteristics of different fire pyromes (defined as by Archibald
et al., 2013) in terms of the role of fires in the terrestrial carbon
balance.
Li et al. (2014) investigated the role of fires in the terrestrial carbon
cycle using the CLM4.5 model and a similar modelling approach (fire-on versus
fire-off simulations, with prescribed historical land cover and a
de-activated dynamic vegetation module). They found that fires reduced the
terrestrial carbon sink by on average 1.0 Pg C yr
Li et al. (2014) estimated that fire reduced global NPP by 1.9 Pg C
yr
Lastly, our study shares two prominent uncertainties in quantifying the role of fires in the terrestrial carbon cycle with those discussed by Li et al. (2014). Firstly, the vegetation dynamics module was switched off in our simulation, and this might limit the terrestrial carbon sink by land ecosystems in a world without fire. Previous studies have pointed out that if all fires were suppressed, tree cover would expand in regions where current grassland or woodland ecosystems are maintained by fires (Bond et al., 2005; Staver et al., 2011), and that the expanded forest coverage would increase land carbon stock (Bond et al., 2005). Secondly, because ORCHIDEE was not coupled to an atmosphere model, the atmospheric concentration changes for various gases released by fire, or a complete fire–vegetation–climate feedback, as discussed in the Introduction, were not included.
We compared fire fuel consumption, the fraction of NPP returned via fire emissions and its temporal variation, and carbon sink efficiencies (SE) for fireOFF and fireON simulations for the five pyromes defined by Archibald et al. (2013) (see Sect. 2.5). The temporal variation for the fraction of NPP lost to fire emissions is examined as the coefficient of variation during 1901–2012, which is the standard deviation divided by the mean.
According to model simulation, frequent–intense–large (FIL) and
frequent–cool–small (FCS) fires have higher fuel consumption than
infrequent rare–intense–large (RIL) and rare–cool–small (RCS) fires
(Fig. 11), fuel consumption being highest in the FCS pyrome (1.2 kg C
m
It is reasonable to find that frequent fires have higher fuel consumption
than small cool ICS and RCS fires, because the latter are generally
human-controlled burning with limited fuel load (Archibald et al., 2013).
However, intuitively, the rare–intense–large (RIL) fires are expected to
have at least comparable, if not larger, fuel consumption than the FIL and
FCS pyromes, since their spatial extent covers the North American boreal
forest biome where large amounts of soil (and biomass) carbon stocks are
exposed to burning. Our model simulation does show a high amount of fire fuel
consumption in North American boreal forests: 1–5 kg C m
We also find that the carbon sink efficiencies for infrequent-fire pyromes
are higher than frequent ones for both fireON and fireOFF simulations,
probably because more forests are located in infrequent-fire pyromes (Table 1
in Archibald et al., 2013). The sink efficiency reduction
(SE
In this study, we used the ORCHIDEE land surface model with the recently
integrated SPITFIRE model to estimate the role of fires in the terrestrial
carbon balance for the twentieth century. The simulated global fire carbon
emissions for 1997–2009 are 2.1 Pg C yr
Fires reduced the terrestrial carbon uptake by an average of 0.32 Pg C
yr
We thank S. Archibald for providing the fire pyrome distribution map.
Moreover, we thank the two anonymous reviewers for providing valuable
comments which improved the quality of the manuscript. Funding for this work
was provided by the ESA firecci project (