Introduction
Boreal vegetation covers about 17 % of the Earth's land surface but
contains more than 30 % of all terrestrial carbon stocks (Kasischke,
2000). This above-average carbon density reflects the large amount of soil
organic carbon being conserved thanks to the general cold and wet soil
conditions, especially in peat and carbon-rich frozen soils (Harden et al.,
1992; Jones and Yu, 2010; Tarnocai et al., 2009). Under stable environmental
conditions and disturbance regimes (such as fire, insect outbreak,
large-scale windthrow), the net carbon balance of boreal forest ecosystems is
expected to be close to zero over a time span longer than the disturbance
return interval (Kashian et al., 2006) and integrated on the scale of a small
region, as, over time and space, the post-disturbance carbon accumulation
compensates for the pulse of carbon release into the atmosphere at the time
of disturbance. However, in response to various anthropogenic perturbations
since preindustrial times, such as atmospheric CO2 increase, climate change and nitrogen
deposition, boreal ecosystems are estimated to have been a net carbon sink
for the past 2 decades (Kurz and Apps, 1999; McGuire et al., 2009; Pan et
al., 2011b), mainly because these forcings are suspected to have collectively
enhanced the vegetation production and carbon fixing. However, as climate
change continues, carbon stocks in boreal forest may become more vulnerable,
as indicated by (1) deceleration of “greening” over this biome as seen by
satellites (Xu et al., 2013), (2) locally observed decreased vegetation
productivity (Beck and Goetz, 2011), and (3) evidence for large
climate-related disturbances such as insect outbreaks (Kurz et al., 2008) and
catastrophic fires (Kasischke and Hoy, 2012) that cause CO2 losses to
the atmosphere.
Fire has always been a natural disturbance in boreal ecosystems (Anderson et
al., 2006), and it has multiple impacts on vegetation dynamics, carbon
cycling, soil processes, atmospheric chemistry and permafrost dynamics. Fire
plays an important role in the evolution of ecosystem species composition in
this region through complex fire–climate–vegetation feedbacks on different
timescales (Kelly et al., 2013; Schulze et al., 2012). The carbon balance of
boreal forests is modified immediately by fire through fire carbon emissions,
but fires also lead to successional post-fire carbon accumulation as the
ecosystem recovers – a long-term process of CO2 removal from the
atmosphere (Amiro et al., 2010; Goulden et al., 2011). Additionally, fires
impact soil carbon dynamics, primarily by direct combustion of the organic
layer at the soil surface but also through the creation and deposition of
recalcitrant charcoal (Santín et al., 2015). Furthermore, organic soil
carbon is also restored as post-fire vegetation carbon
recovers (Harden et al., 2012), though the extent of restoration may depend
on factors like post-fire vegetation type and regenerating forest stand
density (Kashian et al., 2006). Lastly, soil carbon dynamics are also changed
by altered soil temperature and moisture conditions after fire (Harden et
al., 2006).
Many factors contribute to the currently observed boreal carbon sink,
including the fertilization effect of increasing CO2 concentration
(Balshi et al., 2007), nitrogen deposition (DeLuca et al., 2008), forest
management (Kauppi et al., 2010), climate change (Wang et al., 2011), and the
balance between ecosystem (mainly forest) recovery from past disturbances
(Pan et al., 2011b) and emissions from current fires. However, the relative
contributions of these factors and their interactions are still poorly known,
although a large part of the carbon sink in boreal forests has been
attributed to forest recovering from past disturbance or degradation (Kauppi
et al., 2010; Pan et al., 2011a). Given the role of fire in driving the
demography and carbon balance of boreal forests, several studies used
biogeochemical models to examine the carbon balance of boreal ecosystems and
the related impacts from fires (Balshi et al., 2007; Hayes et al., 2011; Yuan
et al., 2012). These studies conducted simulations with fire and without fire
(or with a stationary fire regime) and examined the total-sum impacts of all
preceding fires on the boreal carbon balance for a particular target time
period. However, the immediate-source impacts of current fires through
emissions and the sink legacies by previous fires were not formally
separated. Consequently, the contributions of fires that occurred before the
current time (and associated post-fire vegetation recovery) to the current
carbon balance, i.e., the legacy sink effects of past fire, remained largely
unknown.
In the current study, we focus on the contributions of fires during different
past periods to the carbon balance in boreal ecosystems. Theoretically,
assuming stable environmental conditions, fires would have a close-to-zero
net effect on the vegetation carbon storage over the fire cycle as the
ecosystems are at a dynamic equilibrium state: fire emissions would be
compensated for by post-fire vegetation regrowth (Kashian et al., 2006; Odum,
1969), as illustrated by the black curve in Fig. 1a. In this case, the forest
net ecosystem production (NEP, which is photosynthesis minus respiration) may follow the classical temporal pattern, being negative
in young forest, peaking in intermediately aged forest and declining in old
forest. The temporal integration of NEP should be equal to the pulse of fire
emissions, as the carbon balance over the entire fire cycle is expected to be
zero.
Panel (a): the evolution of forest net ecosystem
productivity (NEP) with the time since disturbance after fire under
preindustrial conditions and as impacted by the CCN (climate, atmospheric
CO2, nitrogen deposition) perturbations. Under preindustrial conditions,
the net carbon balance over the fire cycle is close to zero and is a carbon
sink under CCN perturbations. Panel (b): the contemporary carbon
balance of a geographical point (with a total area of S) for the 2000–2009
decade is composed of three components: carbon fluxes from forest cohorts as
legacies of past decadal fires, fire carbon emissions within the 2000–2009
decade (with cumulative fire-disturbed area being ΔS), and carbon fluxes from undisturbed
mature forests (with area being S-ΔS). The nature (sink or source;
blue or red arrow) and size (the
width of arrows) of carbon balance of fire cohorts of different ages are
shown quantitatively in the figure. The mathematical symbols for the carbon
fluxes of the fire cohorts of the decades 2000–2009 and 1970–1979 and those
from undisturbed mature forests are indicated; the symbols are the same as in
Eq. (2) in the text. Note that, for clarity, the flux under preindustrial
conditions (fc (g,b)) and the additional flux caused by CCN
perturbations (Δfc (g,b)) are not separated for all (red
and blue) arrows that represent carbon fluxes.
However, when anthropogenic perturbations, especially those since
preindustrial times as a result of intensive use of fossil fuels, come into
play, this equilibrium state in which emissions are balanced by cumulative
NEP may be disturbed. Of the anthropogenic perturbations affecting the environment,
three prominent changes could exert a strong influence on the carbon dynamics
related to disturbances. Climate change, predominantly temperature rise,
could increase the growing-season length of Northern Hemisphere vegetation,
strengthening plant physiological activities such as photosynthesis
(Saxe et al., 2001). Atmospheric CO2 increase could further enhance vegetation productivity, directly as
a resource for photosynthesis but also indirectly by alleviating plant water stress (Franks et al.,
2013). Nitrogen availability is considered as one limiting factor for boreal
forest growth, and nitrogen deposition has been found to enhance vegetation productivity (Magnani et al.,
2007). These three factors are abbreviated as CCN (climate, CO2,
nitrogen) perturbations hereafter in this paper and are intended to
represent the perturbations that collectively enhance the growth of
vegetation regenerating after stand-replacing fires. As a result, the CCN
perturbations could cause the curve of forest NEP against
time since disturbance to shift toward higher carbon uptake, and the
integration of NEP over time would probably exceed the fire emission pulse,
making the vegetation a CO2 sink (Fig. 1b, blue curve). Note here that, as
fires are an agent leading to forest regeneration, the contributions of
fires to the carbon balance are closely related to post-fire forest
carbon dynamics and include the CCN perturbation effects that modify forest
carbon uptake.
Based on this understanding, past fires must have contributed to the current
boreal carbon balance through the enhanced post-fire forest regrowth as
a result of CCN
perturbations, termed the fire legacy carbon sink in this paper. The central
aim of our study is to develop a conceptual framework to quantify the decadal
contributions of past fires during 1850–2009 to the current carbon balance
(2000–2009) in the pan-boreal region (44–84∘ N). The tool used is
the global dynamic vegetation model ORCHIDEE (Organising Carbon and Hydrology
In Dynamic Ecosystems) with the prognostic fire module SPITFIRE (SPread and
InTensity of FIRE). Fire
occurrences are simulated in a prognostic way, with the dynamic vegetation
module being activated. Our objectives are (1) to compare the simulated
versus observed distribution of tree cover and tree groups, given fire
disturbance; (2) to separate the contribution of legacy sink of past fires
from emissions of current fires to the pan-boreal carbon balance and to
further quantify the relative sink contributions by fires in different
decades of the past. Being a preliminary effort, the different driving
factors influencing fire contributions (such as CCN) are not individually
separated; rather, their effects are included in the decadal fire
contributions.
Materials and methods
Model introduction
This study uses the process-based dynamic global vegetation model (DGVM)
ORCHIDEE (Krinner et al., 2005). The ORCHIDEE model has three sub-modules.
The SECHIBA sub-module simulates the fast exchange of water and energy
between the land and the atmosphere. The STOMATE sub-module simulates the
vegetation carbon cycle processes including photosynthesis, photosynthate
allocation, litter fall, litter and soil organic matter decomposition. The
third sub-module simulates vegetation dynamics. The equations of vegetation
dynamics are mainly taken from the LPJ (Lund–Potsdam–Jena) model (Sitch et al., 2003), with
modifications described by Krinner et al. (2005).
For this study, the prognostic fire module SPITFIRE as originally developed
by Thonicke et al. (2010) was incorporated into ORCHIDEE, from here on
referred to as ORCHIDEE–SPITFIRE. Global validation of simulated burned area
and fire carbon emissions were described by Yue et al. (2014) and Yue et
al. (2015). Notably, ORCHIDEE–SPITFIRE is able to capture the decadal
variations of burned area in boreal Russia when compared to the historical
reconstruction data by Mouillot and Field (2005) and the interannual
variations of burned area in boreal North America when compared with the fire
agency data. All fire processes are the same as described in Yue et
al. (2014), except that the human suppression of lightning-ignited fires is
introduced, as a function of human population density, following Li et
al. (2012):
Fs=0.99-0.98×e-0.025×Dp,
where, Dp is the population density (individuals per square kilometers), and
Fs a multiplicative coefficient applied to lightning ignitions to
account for human suppression at a given Dp. This corresponds to a
suppression fraction of 0.01 in sparsely inhabited regions and of 0.99 in
highly populated regions (i.e., Dp→+∞).
Within SPITFIRE, fire occurrence depends on vegetation and climate
conditions and has feedbacks on forest mortality through crown scorching
and cambial damage, which reduces forest stem density
(Thonicke et al., 2010). Thus, in ORCHIDEE–SPITFIRE,
vegetation dynamics are affected by both climatic factors, as simulated by
the dynamic vegetation module, and fire disturbances, as simulated by
SPITFIRE. In addition to the climatic limits that give the adaptation or
extinction for different tree vegetation types under specific climate and
climate variability conditions (Krinner et al., 2005; Sitch et al., 2003), fires further impact the tree–grassland
competition and the competition within woody vegetation types.
The ORCHIDEE–SPITFIRE used here includes the DGVM improvements made by Zhu et
al. (2015), which improved the simulation of northern vegetation
distribution. The improved DGVM processes include (1) tree mortality
dependence on growth efficiency, defined as the ratio of net annual biomass
increment to the preceding-year maximum leaf area index (LAI); (2) tree
mortality induced by winter extreme coldness for all tree plant functional
types (PFTs), except boreal deciduous needleleaf, and by spring frost in
broadleaf forests only; (3) the definition of the tree line limit as an
isotherm of a growing-season mean soil temperature of 6.7 ∘C. A
threshold of a mean monthly temperature of 22 ∘C is used to limit the
distribution of C4 grass, following Still et al. (2003). Maximum
carboxylation rates (Vc max, µmol m-2 s-1)
were adjusted based on the results of parameter optimization for ORCHIDEE
against flux tower measurements (Kuppel, 2012).
The conceptual framework
In this section we develop a conceptual framework which forms the basis of
our simulation protocol and allows us to separate legacy carbon sinks from
past fires for the carbon balance for the 2000–2009 decade from emissions by
current fires. This conceptual framework was inspired by the theoretical
attribution framework for the role of land-use change in carbon balance by
Gasser and Ciais (2013). The influence of CCN perturbations on the carbon
balance of regenerating forests as compared to a case without CCN is
introduced in Sect. 1. Further, one should note that CCN perturbations also
tend to increase carbon sinks in otherwise carbon-neutral old forests, i.e.,
land that is not disturbed by fires during the time of the CCN perturbation.
Likewise, as the CCN perturbation increases forest carbon stock, when forests
are burned, carbon emissions will also increase compared with the case
without CCN perturbation. Consequently, for the decade of 2000–2009, the
carbon balance of a grid cell is the sum of (1) fire emissions during
2000–2009, (2) legacy sinks caused
by fires that occurred since 1850 and are impacted by CCN to various degrees
(shown as the blue curve in Fig. 1a), and (3) source or sink of the tracts of
forests that have not burned since 1850 but are influenced by CCN (i.e.,
which are considered undisturbed mature ecosystems). The composition of the
carbon balance of 2000–2009 is illustrated in Fig. 1b.
The carbon balance of a geographical area covered by
a given biome (g, b) for the 2000–2009 decade, under the CCN
perturbation and taking into account decadal fire disturbances since 1850,
can be expressed as
FON(g,b)=fu∗(g,b)×[S(g,b)-ΔS(g,b)]+∑i=1850s2000s[fc(g,b)+Δfc(g,b)]×δSi,
where FON (g,b) is the total carbon balance of the area S(g,b), typically expressed in grams of carbon per year, with presence of fire, and all
lowercase f functions indicate the area-based carbon balance expressed as
grams of carbon per square meter per year for various cases: fu∗(g,b) is the
undisturbed land impacted by the CCN perturbation (thus not equal to zero);
fc (g,b) is the fire-generated cohort carbon flux density without
the CCN perturbation; and Δfc (g,b) is the deviation of carbon
flux from a cohort under steady environment conditions because of the CCN
perturbation (Fig. 1a blue curve). δSi represents the fire-disturbed land
cohorts within the ith decade, with i ranging from the 1850s (1850–1859)
to the 2000s (2000–2009); ΔS(g,b) is the sum of disturbed land areas
from fires of all decades since 1850. Note that, in Eq. (2), we separated the total
carbon flux into lands undisturbed and those disturbed by fire. Further, we
assume that fires also occurred before 1850, but their influence on the
2000–2009 carbon flux is included in the undisturbed land flux, given the
observed very small net ecosystem productivity in boreal forests older than
150 years (Goulden et al., 2011).
In studies using numerical biogeochemical models, Eq. (2) represents a case
in which fire-generated forest cohorts are explicitly simulated: the 2nd
part on the right-hand side of the equation gives the contributions of different
decadal fires to the carbon balance for the 2000–2009 decade. However, for models
that do not explicitly simulate forest cohorts (which is the case for the
version of ORCHIDEE used here), a workaround is possible by manually
suppressing fires in the model within a particular decade to allow
quantifying the contribution of fires from this decade by the difference
between the two simulations. Similar to Eq. (2), the carbon flux for the
2000–2009 decade if fires are suppressed in a particular decade D can be written as
FOFF,D(g,b)=fu∗(g,b)×[S(g,b)-ΔS(g,b)+δSD]+∑1850s≤i≤2000si≠D[fc(g,b)+Δfc(g,b)]×δSi,
where FOFF,D (g,b) is the carbon balance for the 2000–2009 decade but
with fires suppressed in the D decade and with the contribution by fires
of the D decade being simultaneously removed from the right-hand side of the
equation. Thus, the contribution by fires of the D decade is the difference
between FON (g,b) and FOFF,D (g,b):
ContD(g,b)=FON(g,b)-FOFF,D(g,b)=-fu∗(g,b)×δSD+fc(g,b)+Δfc(g,b)×δSD,
where ContD is the contribution of fires within the D decade to the
carbon balance of the 2000–2009 decade. In contrast with explicit cohort
simulation, this factorial approach quantifies the past-fire-generated cohort
contribution, taking as a baseline the carbon flux of otherwise undisturbed
land but as influenced by the CCN perturbation. Finally, one could vary D
from the 1850s to the 2000s to derive the contribution by fires within each
decade between 1850 and 2009. This conceptual framework remains valid when
integrating all the variables in Eqs. (2)–(4) over the geographical extent
and different vegetation types to attribute carbon fluxes on a regional
scale. Note that, in this framework, the effects of different factors of the
CCN perturbation are not individually separated, but rather their impact is
embedded as a whole in the fire contribution.
Simulation protocol and input data sets
Following the conceptual framework, we conducted factorial simulations to
quantify the decadal contributions of past “fire cohorts” to the simulated
carbon balance of 2000–2009. The carbon balance is defined as the net biome
production (NBP):
NBP=NPP-RH-EMI,
where NPP is net primary production (i.e., the net biomass accumulation by
plants after accounting for their own use), RH is the ecosystem heterotrophic
respiration, and EMI is carbon released by fire. A positive NBP indicates a
net carbon flux from the atmosphere to land, i.e., a land carbon sink. In the
following, we use the terms “carbon sink” and “NBP” interchangeably,
unless otherwise specified (e.g. if stated as a negative NBP, it is a
carbon source releasing carbon to the atmosphere).
We conducted a reference simulation (SIMfireON) from 1850 until
2011, accounting for climate change, atmospheric CO2 concentration
change and prognostically simulated fire disturbance. We then conducted a
series of other simulations (named SIMOFF), which branch off from
the SIMfireON simulation from the beginning year of each decade
between 1850 and 2009. In the SIMOFF simulations, the fire module
was switched off sequentially from the decade of the 1850s (1850–1859) to the 2000s
(2000–2009) and switched on afterwards, with all remaining parameter
settings and input data sets the same as in the reference simulation.
Following Eq. (4), the contribution by fires within a specific decade
to the carbon balance of each year for the time after this decade would be
quantified as the difference between the reference simulation and the decadal
SIMOFF simulation. In all simulations, the vegetation dynamics
module of ORCHIDEE was switched on to allow the vegetation distribution to
respond to climate variations and fire disturbances.
The spatial domain of our simulation covers the land pixels of
44–84∘ N at a 2∘ resolution. The land north of 84∘
was excluded as it is covered mainly by ice and snow. The model was forced by
the CRUNCEP climate data at a 2∘ resolution, regridded from its
original resolution of 0.5∘. The CRUNCEP consists of 6-hourly gridded
climate data generated by combining CRU TS 3.1 0.5∘ monthly climate
data and NCEP 6-hourly 2.5∘ reanalysis data (thus the name CRUNCEP).
Rainfall, cloudiness, relative humidity and temperature are from the CRU data
set and interpolated at a 6-hourly time step following the temporal
variability of NCEP. Pressure, longwave radiation, and wind speed are from
NCEP, reinterpolated on a 0.5∘ scale. The values for these variables for 1948 were also used for
the period before 1948. For more
details, see
http://dods.extra.cea.fr/store/p529viov/cruncep/V4_1901_2012/readme.htm.
A single global annual atmospheric CO2 concentration time series since
1850 was applied everywhere in the spatial domain of the model, which is a
combination of ice core and NOAA station measurements. The fire module needs
additional input data for lightning flashes and human population density.
Lightning flashes were retrieved from the High Resolution Monthly Climatology
of lightning flashes by the Lightning Imaging Sensor–Optical Transient
Detector (LIS/OTD) (http://gcmd.nasa.gov/records/GCMD_lohrmc.html). The
LIS/OTD data set provides annual mean flash rates over the period of
1995–2000 on a 0.5∘ scale with monthly time step, which was cycled
each year throughout the simulation. An annual historical population density
map was retrieved from the Netherlands Environmental Assessment Agency
(http://themasites.pbl.nl/tridion/en/themasites/hyde/download/index-2.html).
Both lightning and population density data sets were regridded at a
2∘ resolution before being fed into the model.
The reference simulation SIMfireON consists of a spin-up run from
bare soil and a transient run, with the fire module being activated. For the
spin-up, climate data for the period 1901–1930 were cycled and atmospheric
CO2 concentration (285 ppm) and population density were prescribed at
the 1850 level. The spin-up run lasted for 400 years but contained three
runs of soil-only processes each lasting 1000 years to speed up reaching
equilibrium for slow and passive soil carbon pools. We verified that the
average annual NBP during the last 30 years of the spin-up run was
-0.003 Pg C yr-1 (a negative value as the model recovers from fast
accumulation of soil carbon in the soil-only runs) and that no significant
trend exists for annual NBP, indicating that the model had approximately
reached an equilibrium state. The spin-up was followed by a transient
simulation for 1850–2011, in which transient climate data, atmospheric
CO2 concentration and population density data were used. For 1850–1900,
cycling climate data of 1901–1930 continue to be used.
As our focus is the carbon dynamics of natural vegetation in response to fires
within the boreal region, croplands were not simulated in the model. This is
acceptable given that land-use change during the 20th century in this region
was small (Hurtt et al., 2006). Cropland fractions within grid cells were prescribed according to a
current-day vegetation map (the IGBP-DIS 1 km global land-cover
map; Loveland et al., 2000), and fractions of natural vegetation (i.e., trees and grasses) were
simulated. Tundra in the high-arctic regions is simulated as C3 grassland.
Comparison of simulated forest distribution and fires to
observations
We compared the spatial distribution of three morphological and phenological
tree groups between the model simulation and MODIS land-cover data for the
year 2010: broadleaf (including evergreen and deciduous), evergreen
needleleaf and deciduous needleleaf trees, corresponding to the three boreal
tree PFTs in ORCHIDEE. The MCD12Q1 version 5 land-cover data (Friedl et al.,
2010) were used (http:glcf.umd.edu/data/lc, with a northern limit of
84∘ N). Fractions of the 17 different land-cover types in the IGBP
land classification scheme were calculated at a 2∘ resolution based on the 500 m
original resolution data. Further, the 2∘ land-cover fractions were
cross-walked to PFT fractions using the approach developed by Poulter et
al. (2011), in which the mixed tree–grass land-cover types such as
shrublands are assumed to be composed of different fractions of trees and
grasses (see Table 6 in Poulter et al., 2011, for more details). The
simulated maximum foliage projective cover for each of the three tree groups
was compared with the corresponding MODIS observation, with the sum of the
three groups being compared as tree cover.
Simulated burned area and fire carbon emissions were compared with
GFED3.1 burned area data (Giglio et al., 2010), and carbon
emission estimates were simulated by the CASA biosphere model
(van der Werf et al., 2010). Burned areas and fire carbon emissions from agricultural fires were excluded from GFED3.1
data before comparison because these fires are not included in the model.
Northern peatland fires were not simulated due to a lack of peatland PFT in
the model nor are they included in the GFED3.1 emission data.
Results
Simulated forest distribution
The simulated spatial extent of forest distribution is broadly similar to
that of MODIS land-cover data over the region north of 44∘ N for the year 2010, with the forest biome extending from eastern Canada northwestward
to Alaska in boreal North America, and to that in northern and northeastern
Europe, as well as most of Siberia (Fig. 2). The magnitude of foliage
projective tree cover between ORCHIDEE and MODIS land-cover data is generally
comparable, except at the southern and northern fringes of the study region
(mainly Asia and America), where tree cover is overestimated by approximately
30–50 % in ORCHIDEE (hatched areas in Fig. 2). When considering the
uncertainties in different observation data sets (by comparing different land-cover data sets of ESA-CCI, GLC2000 and VCF; see the Supplement for more
details on data sources and their treatment), the error in simulated tree
cover is less prominent (Supplement Fig. S1). The over- or underestimation of
tree cover by ORCHIDEE in central and northern Siberia disappears; however,
the overestimation of tree cover in southern Asian and North American boreal
forests remains. In central Alaska and western Canada, tree cover is also underestimated by 10–30 % of ground area.
Simulated (a) and MODIS-derived (b) foliage projective tree cover
in fraction of ground area. The MODIS tree cover data are derived by
cross-walking MOD12Q1 version 5 land-cover types to plant functional types
(PFTs) in ORCHIDEE using the methods developed by Poulter et al. (2011). Hatched areas show where the two data sets differ by > 30 % of ground area.
Spatial distribution of three different tree groups with the
coverage as a fraction of ground area for (1) broadleaf, (2)
evergreen needleleaf and (3) deciduous needleleaf by (a) ORCHIDEE
simulation and (b) MODIS land-cover data for year 2010. Hatched areas show
where the two data sets differ by > 30 % of ground area.
Mean annual burned fraction (in percent) by (a) ORCHIDEE
simulation and (b) GFED3.1 data for 1997–2009. Agricultural fires are not
modeled and were excluded from GFED3.1. Note the corresponding fire return
intervals (FRI, in years) for different burned fraction: 0–0.2 % for > 500 yr; 0.2–0.5 % for 200–500 yr; 0.5–1 %
for 100–200 yr; 1–2 % for 50–100 yr; 2–10 % for 10–50 yr, 10–50 % for 2–10 yr; these are used in Fig. 8.
Cumulative latitudinal distribution of (a) burned area and (b)
fire carbon emissions as given by the model simulation (solid line) and
GFED3.1 data (dashed line). Emissions from agricultural fires are excluded
from GFED3.1 data as they are not included in the model. Note that despite an
underestimation in annual burned area, simulated fire carbon emissions are
close to GFED3.1 data south of 52∘ N.
Figure 3 presents simulated and observed spatial distribution of three tree
groups: broadleaf (including evergreen and deciduous), evergreen needleleaf
and deciduous needleleaf. There is a widespread presence of broadleaf forest, but with generally low fractional cover, across the study region, which is reproduced fairly by ORCHIDEE (Fig. 3, panel 1a and b). Both MODIS land-cover data
and ORCHIDEE simulation indicate the dominance of evergreen needleleaf forest
in North America, in western Siberia, and in northern and eastern Europe
(Fig. 3, panel 2a and b). In contrast, MODIS data show that central and
eastern Siberia is dominated by deciduous needleleaf forests (Fig. 3, panel
3b). ORCHIDEE successfully captures this, but the spatial extent and
magnitude of tree cover are overestimated (Fig. 3, panel 3a). In addition,
ORCHIDEE also erroneously allocates more deciduous needleleaf forests in
Alaska and northwestern Canada than the MODIS data. We also extend the
comparison of different tree group extents by including more land-cover data
sets (see Figs. S2, S3 and S4). Again, when considering other land-cover maps
(ESA-CCI, GLC2000 and VCF), the model error is less than when using the MODIS
data set. Notably, both ESA-CCI and GLC2000 data sets indicate a larger
extent of deciduous needleleaf forest in eastern Siberia compared to MODIS,
resulting in much lower errors in the ORCHIDEE simulation (nevertheless, a model
overestimation of 20–50 % of ground
area persists in western Siberia).
Simulated burned area and fire carbon emissions
The spatial distribution of simulated mean annual burned fraction for
1997–2009 is compared with GFED3.1 data in Fig. 4, with non-modeled
agricultural fires being excluded from GFED data. The comparisons of
cumulative latitudinal distribution of burned area and fire carbon emissions
are shown in Fig. 5. Although spatial disagreements in burned area exist,
ORCHIDEE–SPITFIRE simulates an annual total burned area of
11.9 Mha yr-1 and fire carbon emissions of 0.20 Pg C yr-1,
which are close to GFED3.1 estimates giving an annual burned area of
16.9 Mha yr-1 and fire carbon emissions
of 0.20 Pg C yr-1. Spatially, burned area is underestimated within the
latitude band 44–54∘ N in Eurasia, concurrent with an
overestimation of tree cover in the same region (Figs. 2 and 3). On the other
hand, there is an overestimation of burned area in the regions north of
54∘ N covered by forest, shrubland and tundra according to the
MCD12Q1 land-cover map. Over North America, the spatial distribution of
simulated burned area is in fair agreement with the GFED3.1 data, with burned
area being dominated by the northwest-to-southeast boreal forest fires.
Panel (a): annual NBP (NEP minus fire emissions) from the
reference fireON simulation for 1850–2011. The terrestrial carbon sink
estimates for the 1990s and 2000s by other sources (Ciais et al., 2013) are
also presented for comparison. Panel (b): the fire effects on NBP by
switching off the fire module in a decadal sequence for 1850–2009, i.e., the
contributions of decadal fire cohorts (NBP by fireON minus that by decadal
fireOFF simulations according to Eq. 4). As the temporal patterns for
different decades are similar (i.e., fires are a carbon source term for the
decade when fire occurred and a sink term afterwards), curves for every other
decade since the 1850s are shown for clarity. The shaded rectangle indicates
the 2000–2009 decade, which is our quantification target period.
Decadal contributions of fire to the simulated carbon sink
The simulated annual NBP for 1850–2011 for the study region in
non-agricultural land and contributions of decadal fire cohorts to the carbon
balance after the fire occurrence are shown in Fig. 6. The annual carbon sink
of the reference simulation for 1990–2011 is 0.91 Pg C yr-1
(Fig. 6a), which falls within the range of forest-inventory-based estimates
(∼ 0.7 Pg C yr-1 by Pan et al., 2011b) and the mean value of the
terrestrial carbon cycle models (∼ 1.1 Pg C yr-1) as assessed by
IPCC AR5 (Ciais et al., 2013). Figure 6b shows how each decadal fire cohort
contributes to the NBP of the study domain. For example, the curve labeled
“1910s” shows the annual contribution of the cohort of the decade
1910–1919, which produced a net carbon source, followed by a long-term
carbon sink whose magnitude decreases with time. Note that for the decade of
2000–2009, all fires before this decade contribute as a carbon sink term
with varying sink sizes, whereas fires within the 2000–2009 decade
contribute as a source term.
Figure 7 shows the contributions of fires within each decade to the annual
NBP of the study region for 2000–2009. All decades before 2000 cause a fire
legacy sink, collectively having a total sink of 0.23 Pg C yr-1. These
legacy sinks are compensated for by a carbon source of 0.17 Pg C yr-1
from fires in 2000–2009, leaving a net fire effect of 0.06 Pg C yr-1.
This net sink fire effect represents only a very small fraction (6.3 %)
of the annual carbon sink of the reference simulation
(0.95 Pg C yr-1), indicating that most of this sink occurs in unburned
natural ecosystems for which the model produces enhanced carbon storage due
to climate warming (e.g., longer growing seasons) and the CO2
fertilization effect. The sink contributions of different decadal fire
cohorts (1850–1999) exhibit a general decaying trend as the cohort ages,
with the variations being affected by changes in climate, atmospheric
CO2 concentration and fire disturbance. Fires in the 4
decades prior to 2000–2009 (1960–1999, i.e., corresponding to a “cohort age” of 10–40 years)
collectively contribute 0.14 Pg C yr-1, accounting for 61 % of
total legacy sink effect. Fires in the past century (1900–1999) contribute
0.19 Pg C yr-1, or 83 %, of the total legacy sink.
Contributions of decadal “fire cohorts” of 1850–2009 to the
simulated carbon sink for 2000–2009. Fires within the 2000–2009 decade are a
carbon source term and all fires before this decade are sink terms. For
comparison, the carbon sink in the reference (fireON) simulation is
0.95 Pg C yr-1 for 2000–2009.
Share of contributions to the fire legacy carbon sink of the
2000–2009 decade from different fire groups characterized by increasing fire return
intervals. Only the decades contributing as a carbon sink term to the carbon balance of the 2000–2009 decade (i.e., 1850–1999) are included. Simulated mean
decadal burned area for each specific decade was used to partition the study
region into the six fire groups.
The whole study region can be classified into six fire groups according to
their different fire return intervals (FRIs, here quantified as the inverse
of burned fraction) as simulated by the model, with the shortest FRI of
2–10 years and the longest of more than 500 years. This classification was done
for each decade of 1850–1999 (i.e., decades having a carbon sink effect for
2000–2009), using a simulated mean decadal burned fraction, followed by
partitioning the decadal sink contribution into these fire groups. Figure 8
shows the relative contributions of each fire group by summing together the
partitioning results of all the decades. The fire group with an FRI of
10–50 years emerges as the biggest contributor, contributing a carbon sink of
0.1 Pg C yr-1 or 42.7 % of the total sink effect. Fires with
intermediate FRIs (50–200 years) contribute 0.06 Pg C yr-1
(26.1 % of the total sink effect), while very rare fires (with an FRI
> 500 years) or very frequent fires (with an FRI of 2–10 years)
contribute least to the total sink effect (collectively contributing 0.04 Pg
C yr-1 or 15.6 % of the total sink effect).
Discussion
We first describe in general the fire–climate–vegetation feedbacks in boreal
regions and the role of fires in the regional carbon balance to put our
findings in a more appropriate context (Sect. 4.1). Section 4.2 discusses some
general model performance issues, with Sect. 4.3 presenting more detailed
comparisons of our results with similar studies. Section 4.4 discusses
uncertainties and future perspectives.
Boreal fire–climate–vegetation feedbacks and fire contribution to the
regional carbon balance
In boreal regions the climate, vegetation dynamics and fire disturbances are
intrinsically linked with each other (Campbell and Flannigan, 2000). Given
the long time of exposure under insolation during summer days, fuels (e.g.,
litter on the ground) could get dry enough for fires to start if there are enough consecutive
days with little precipitation. In turn, plant traits adapt for fires, and fire
adaption is used as a strategy to maintain competitiveness by different tree
species (Wirth, 2005). For example, the gradual rising of black spruce
(Picea mariana) in place of Betula in Alaskan forests
during the Holocene has been aided by increased fire activities as a result
of climate warming since the last glacial maximum (Kelly et al., 2013), since
spruce trees keep their dead branches to promote fires and have serotinous
cones that geminate after fire, making them more competitive against
Betula under increasing fire disturbances.
Given a stable fire regime (fire return interval, fire severity, etc.),
spruce forests form stable self-replacement succession cycles: carbon stored
in fuels (litter and crown fuel) is released into atmosphere during fire;
young forest stand is regenerated, and surface organic litter and biomass
carbon stock are restored during forest growth until the next fire event (Harden et
al., 2012). At the early successional stage, deciduous broadleaf trees
(aspen, birch) often occur as pioneer species and are outcompeted at the late-successional stage due to their shade intolerance (Johnstone et al., 2010b). As
such, fire cycles are internally coupled with vegetation carbon dynamics
(and hydrological and energetic dynamics). As most carbon in boreal
ecosystems is stored in organic soil, which is the dominant source of fire
carbon emissions, fires have a comparatively big impact on the vegetation carbon
cycling (Turetsky et al., 2011). However, evidence shows that more intense fires could sustain the dominance of
broadleaf trees for a longer time and had the potential to alter the regional
vegetation composition (Johnstone et al., 2010a).
With growing atmospheric concentrations of greenhouse gases and anthropogenic
warming of the climate during past decades, there is increasing interest in examining boreal
ecosystems as a potential carbon sink and, especially, in how likely it is that increasing
fire activities would impact the carbon dynamics of this region. Research
foci include quantifying contemporary regional fire carbon emissions (French
et al., 2011), site-level post-fire carbon dynamics (Goulden et al., 2011),
and regional carbon balance analysis using large-scale biogeochemical models
(Balshi et al., 2007; Hayes et al., 2011). The large-scale biogeochemical
models have the particular advantage of evaluating the carbon balance on the
regional scale and separating the impacts of different environmental factors
such as climate, atmospheric CO2 and disturbances. Most modeling
studies examined the impacts of a changed fire regime or the collective impact
of past fires on the carbon balance for a target period. Bond-Lamberty et
al. (2007) found that the central Canadian boreal forest is a small carbon sink
(9.9 ± 11.8 g C m-2 yr-1) for 1958–2005, and, compared to a stable fire regime of the mid-20th century, fire
disturbances have reduced the sink by 8.5 g C m-2 yr-1. Balshi et
al. (2007) and Hayes et al. (2011) used additive biogeochemical model
simulations (i.e., simulations with and without fire) and quantified the
collective impact of past fires on the pan-boreal carbon balance for
different decades of the second half of 20th century, with fire contribution
varying from small source to sink effects (around 0.1 Pg C yr-1)
depending on different time periods.
Nevertheless, given increasing fire frequency during the second half of the
20th century in this region (Stocks et al., 2003) and the important post-fire
vegetation carbon dynamics linked with anthropogenic perturbations (such as
the CCN perturbations as introduced in Sect. 1), few studies have tried to
examine the potentially different impacts from fires occurring at different
times in the past and elucidate how the current pan-boreal carbon balance is
determined by past fire legacy sinks and current-day fire carbon emissions.
Using a factorial simulation protocol, we found that fires during 2000–2009
have a net source contribution of -0.17 Pg C yr-1 to the carbon
balance of the decade 2000–2009. However, this source effect is compensated
for by legacy sinks (in total 0.23 Pg C yr-1) in land recovering from
fires prior to the 2000s (1850–1999). These legacy sinks are
ameliorated by climate warming and CO2 fertilization. We further found
that more than 60 % of the sink effects are contributed by fires during
1960–1999. Our finding is unique in that it separates the effects of
previous fire legacy sinks and current-day fire emissions.
General model performance, simulated vegetation dynamics and burned
area
ORCHIDEE–SPITFIRE successfully captured the large-scale spatial pattern of
tree cover distribution and the distribution of broadleaf versus needleleaf
and evergreen versus deciduous forests in different continents, with the
presence of fire disturbances being prognostically simulated. The larger
spatial extent of deciduous needleleaf forests in Siberia and northern
regions of America in ORCHIDEE may be due to our DGVM parameterization according to which winter extreme
coldness leads to elevated mortality of all forests except deciduous
needleleaf ones; this expands their presence within the tree line limit as
represented by an isotherm of growing-season soil temperature (Zhu et al.,
2015).
Schulze et al. (2012) found that in a transitional zone (61–64∘ N,
90–107∘ E) in central Siberia, where the species Picea obovata and Abies sibirica (evergreen conifers) are natural
late-successional species, frequent surface fires are the major factor
explaining the dominance of Larix over the evergreen climax tree
species. Infrequent crown fires initiate new Larix cohorts, while
surface fires thin them and prevent evergreen needleleaf saplings from
reaching the canopy. Even though our model does not account explicitly for
these two different fire impacts, on a broad scale, the dominance of
evergreen coniferous forests in northern Europe and western Siberia coincides
with slightly lower fire frequencies (Figs. 3 and 4). This is consistent with
the observed pattern that more frequent fires in eastern Siberia are
associated with the dominance of Larix deciduous needleleaf trees.
For the majority of the pan-boreal region, ORCHIDEE–SPITFIRE simulates a fire
return interval of 10–200 years (Fig. 4, corresponding to burned fraction of
0.5–10 %), which is consistent with the evidence from various
observational data sets (Giglio et al., 2010; Stocks et al., 2003). The
simulated fire frequency (0.2–2 % yr-1) in Canada agrees with that
reported by Stocks et al. (2003) using the Canadian Large Fire Database. The
general spatial extent and magnitude of fires in northern Eurasia
(> 54∘ N) roughly agrees with GFED3.1 data, although
burned fractions in northern tundra and shrubland are overestimated. This
may be because tundra is treated as generic C3 grass in the model and thus
assigned a low fuel bulk density (Thonicke et al., 2010) that promotes fast
fire propagation. In reality tundra has a more dense growth form than
temperate grasslands and therefore has a much higher bulk density (Pfeiffer
et al., 2013). Fires are greatly underestimated by the model at the southern
edge of the study area in Eurasia, with a simulated burned fraction of
0.2–2 % compared to values of 1–30 % in GFED3.1 data. This
underestimation, especially in central Asian grasslands over Kazakhstan and
Mongolia, is accompanied by an overestimation of tree cover (Fig. 2). This
indicates that the role of fires in promoting grasslands over forests as
shown by other modeling studies (e.g., Bond et al., 2005; Poulter et al.,
2015) in these semiarid regions is underestimated in ORCHIDEE–SPITFIRE,
probably due to excessive tree sapling recruitment. Despite this, our
simulated boreal carbon sink for the 1990–1999 and 2000–2009 decades is comparable
with other independent approaches, with simulated fire carbon emissions being
close to GFED3.1 data. Therefore, though spatial model errors exist, we
believe that the quantified total carbon fluxes on the regional scale remain
valid.
Comparison of simulated fire impacts with other studies and fire
contributions linked with burned area and fire frequency
Balshi et al. (2007) and Hayes et al. (2011) used an additive simulation
protocol to examine fire impact on the carbon balance, i.e., the contribution
of fire to the carbon balance of a target decade (e.g., the 2000s) is given
by the difference between two simulations, with and without fires. Note that
this approach examines the total sum effect of all fires occurring before but
also within the target decade, i.e., equivalent to the effect of all fires of
1850–2009 and termed net fire effect in our analysis. Balshi et al. (2007)
further conducted parallel simulations with and without CO2
fertilization for all additive runs. They found that during 1996–2002, the
sum effect of fires in the pan-boreal region (north of 45∘ N)
increased the ecosystem carbon storage (ranging from 0.08 to 0.5 Pg C
yr-1) for all years except 2002, according to a simulation that includes
the CO2 fertilization effect. When the CO2 fertilization effect is
excluded, the role of fires is more varied, leading to a
close to zero sum fire effect for
the same period. We also found that the net fire effect during the 2000–2009
decade to be a carbon sink of 0.06 Pg C yr-1 (i.e., the equivalent of
the sum fire effect in Balshi et al., 2007), a value smaller than that
reported by Balshi et al. (2007). However, we noticed that in their study, the contribution
of fires varied greatly in magnitude from year to year, and it was sometimes
even 3 times higher than the sink term due to the CO2 fertilization
effect, which may indicate the great uncertainty in their results (Fig. 6 in
Balshi et al., 2007).
Hayes et al. (2011) also used the additive approach to find a net carbon sink
fire effect on the pan-boreal carbon balance for the decades of 1960–1969 to
1990–1999 with a similar magnitude to that in our study
(0.03–0.08 Pg C yr-1). They argue that fires have changed from a
carbon sink to source term for the 2000–2009 decade (ca.
-0.13 Pg C yr-1) due to increased fire activities (Fig. 3 in Hayes
et al., 2011), which is different from our conclusion. However, it should be
noted that their estimated pan-boreal carbon sink for 1997–2006
(0.04 Pg C yr-1) was much lower than those based on atmospheric
inversion or inventory approaches (Ciais et al., 2013). On the other hand,
their estimated fire carbon emissions (0.3 Pg C yr-1 for north of
45∘ N) are 50 % higher than GFED3.1 data. Thus, it is likely
that the biases in their estimated carbon fluxes (overestimation of emissions
and underestimation of carbon sink) could lead to an overestimation of the
carbon source effect by fires in the 2000–2009 decade. Finally, Yuan et
al. (2012) examined the effect of changes in fire regime on the carbon
balance of the Yukon River basin forests in Alaska from 1960 to 2006 by
comparing simulations with time-varying and fixed fire regimes. They found
that increased fires, compared with a stationary fire regime, have reduced
the total ecosystem carbon storage by 185 Tg C, or 4 Tg C yr-1.
Despite not using exactly the same simulation approach, we also found a net
carbon source fire effect of 1.5 Tg C yr-1 for the 2000–2009 decade
carbon balance for Alaska, similar to Yuan et al. (2012) but with a smaller
magnitude.
The sink contributions by different decadal “fire cohorts” show a general
decreasing trend in the past, with more than half of the total sink effect
contributed by the 4 decades before 2000 (1960–1999). This pattern may be
partly explained by the strong carbon uptake in the young to medium-aged
forests, as shown by site-level measurement (Goulden et al., 2011) and partly
reflected in the model (Fig. 6b). One may consider whether the sink magnitude
could be related to the amount of burned area, as suppressing of strong fire
may lead to strong recovery (and thus a strong legacy sink). As shown in
Fig. S5, the variation in decadal sink contribution magnitude does not echo
that of burned area exactly, despite the fact that the correlation does exist
(r=0.54, p<0.05). Thus, we suspect that the variation in decadal
fire legacy sinks may be related with both the known temporal pattern of
post-fire forest carbon uptake and the fire extent. The CCN perturbations
(represented in the model by applying transient climate forcing and
increasing atmospheric CO2) must also exert some control, but the full
separation of their impacts is beyond the scope here.
We also found the highest legacy sink is contributed by the fire group with a
fire return interval of 10–50 years (0.10 Pg C yr-1, or 43 % of
the total sink effect), followed by the 100–200-year fire group (0.04 Pg C yr-1) and 50–100 years (0.03 Pg C yr-1). In fact,
the highest contribution by 10–50-year fire group is related to its dominance in total burned area (58 % of the total burned area of all fire
groups) (Table S1 in Supplement). When examining the ratio of legacy sink
effect to burned area (somewhat like fire sink efficiency), the
100–200-year and 200–500-year fire groups emerge to have the highest
ratio (0.037 Pg C Mha-1). This ratio is reasonable as fires with this long return
interval often occur in forest (or tundra, but more rarely) that has a strong and
long-term recovery carbon uptake. The ratio of sink to burned area
decreases as the fire return interval increases, indicating more frequent fires
leading to weaker sink recovery, probably because increasing fire frequency
is associated with increasing grassland fraction (Yue et al., 2014), which has a
weaker sink recovery than forest. It is hard to conclude that more frequent
fires will necessarily lead to a stronger sink effect. However, in general, if the
same vegetation type could be maintained (e.g., forest regenerating after fire) rather than more intense fire leading to the replacement of
forest by grassland, then, combined with the CCN perturbations and the strong
carbon uptake of young to medium-aged forest, vegetation carbon uptake may increase with increasing fire frequency.
We highlight important contributions of past fire disturbances to the current
ecosystem carbon sink, thanks to post-fire vegetation recovery being enhanced
by CO2 fertilization and climate warming. These two factors, in spite of
their roles not having been entirely separated out in the current study, may also influence the occurrence of
fires and their emissions in the 2000–2009 decade, which partially
counteract the sink effects by previous fires. In the long term, change in
ecosystem structure and species will also affect fuel load and combustion
completeness and modify fire emissions as well. Therefore, the future role of
fires in the carbon balance of boreal regions remains rather uncertain and
depends on how the post-fire recovery sink and fire carbon emissions respond
to the changes in climate and atmospheric CO2 concentration.
Uncertainties and future perspective
As the version of ORCHIDEE used here does not include explicit forest stand
structure and successional dynamics (age classes) within grid cells, we are
unable to distinguish between the ecosystem effects of surface and crown
fires. Instead, simulated fire effects (e.g., fuel combustion completeness,
tree mortality) are applied to the whole grid cell in proportion to the
burned fraction, as is done in most other fire models (Kloster et al., 2010;
Li et al., 2012; Pfeiffer et al., 2013). Due to this inability to
characterize the sub-grid level fire regime, fires seldom lead to the complete
destruction of the whole forest stand and the re-establishment of a new cohort at
the grid cell level (because the burned fraction seldom approaches unity).
Instead, live biomass is removed in proportion to the simulated mortality
multiplied by the simulated burned fraction. As forest is never completely
killed, this approach may lead to a faster post-fire recovery in the model
compared with that after a crown fire in reality. Our finding that the legacy
sink peaked in the decade of 1990–1999 may be biased by this model behavior.
Due to lack of explicit forest structure and vertical profile, the model is
not able to simulate the thinning effects of surface fires. However, the
evolution of fire impacts the simulated NBP with time, since disturbance on
the regional scale (Fig. 6) generally resembles the temporal pattern of
post-fire forest NEP observed at site level (e.g., Fig. 1 in Amiro et al.,
2010), that is, a carbon source effect at the time of and for a few years
after fire occurrence, followed by long-term decaying sink effect.
Besides the uncertainties introduced by the model's inability to distinguish
crown fire versus surface fire, the underestimation of burned area in central
Asian grasslands and eastern Siberian boreal forests is another source of
uncertainty in our results. We expect the underestimation of grassland
burned area to make little impact on the estimated fire legacy sink effects,
as grasslands quickly recover from fires; thus, over a centennial timescale,
their fire legacy impact on NBP would be close to zero. The underestimation
of forest-fire-burned area in eastern Siberia, on the other hand, may lead
to an underestimation of the fire legacy sink effect, as it is clear that crown
fires create a long-term sink and surface fires also result in enhanced
forest growth due to a short-term increase in available resources
(Schulze et al., 2012).
However, it is difficult to quantify the uncertainties in our results by
comparing them with observational data. For one thing, as forest age is not
explicitly simulated within each grid cell, no forest age map could be
derived from our model simulation; this precludes evaluating our results
against inventory-based forest age maps. Despite the fact that a current-day
forest age map has been compiled for boreal North America (Pan et al., 2011a; Stinson et al., 2011), such maps are still
scarce for boreal Eurasia. Furthermore, the reconstruction of historical forest age dynamics will
need a hindcast of the current forest age map by combining it with known
disturbance histories. Geospatially explicit burned area data sets are
available for Alaska, the USA and Canada, starting from the 1950s (Kasischke
et al., 2010; Stocks et al., 2003); those for Russia are only available
starting satellite-based mapping of burned area (Giglio et al., 2013),
and existing reconstructed data were based on simple assumptions and are subject
to great uncertainties (Balshi et al., 2007; Mouillot and Field, 2005). To derive a better estimate of the role of fire
in the boreal carbon cycle, a two-pronged approach is required: collecting
historical fire data for the Eurasian boreal region and developing models further to include forest age groups in ORCHIDEE (Naudts et al.,
2014).