Introduction
Wildfires are responsible for approximately 70 % of the global biomass
burned annually (van der Werf et al., 2010). Emissions from
wildfires in the form of trace gases and aerosols can have a considerable
impact on the radiative balance of the atmosphere (Langmann et al., 2009) and
also constitute a large source of atmospheric pollutants (Kasischke and
Penner, 2004). At the same time, wildland fires are an important component of
terrestrial ecosystems (Bowman et al., 2009) and the Earth system
(Arneth et al., 2010). Fires respond to changes in climate, vegetation composition
and human activities (Krawchuk et al., 2009; Pechony and Shindell, 2010;
Kloster et al., 2012; Moritz et al., 2012), with some model simulations
showing a positive impact of climate change on emissions during the 21st
century, but a negative, albeit smaller, impact due to changes in land use
and increased fire suppression (Kloster et al., 2012).
Empirical studies designed at isolating the effect of human population
density – here used as an aggregate value representing human interference at
the landscape scale – have generally shown that higher population density
per se leads to a decrease in the annual area burned (Archibald et al.,
2008; Knorr et al., 2014; Bistinas et al., 2014), even though there is a
common perception that wildfire activity peaks at intermediate levels of
population density. This apparent paradox was shown to be the result of
co-variations between population density and other factors such as fuel load
or flammability – if these co-variations are taken into account, the view of
a negative impact is consistent with the observed peak (Bistinas et al.,
2014).
The main future drivers of changing wildfire have potentially opposing
effects on emissions – temperature (increasing), CO2 via productivity
(increasing), CO2 via woody thickening
(Wigley et al., 2010;
Buitenwerf et al., 2012;
decreasing) and human population density (decreasing emissions). Sociodemographic change, interacting with other economic and
technological factors, may also lead to climate change – e.g. slow
population growth combined with a conventional development pathway of high
fossil fuel dependence would result in high CO2 emissions and large
temperature increases. Moreover, the same population growth but with
different urbanisation trends could also lead to different levels of spatial
population distributions and concentrations, and consequently different
results concerning wildfire emissions. Therefore, it is important to first
assess the impact of each factor individually before arriving at conclusions
concerning aggregate effects. Another important point of consideration is
that if climate forcing is based on a model with low climate sensitivity to
CO2 change (i.e. relatively small change in global mean temperature
simulated for a given rise in atmospheric CO2), CO2 effects might
dominate over climate effects. The reverse applies to climate models with a
high climate sensitivity. We therefore use an ensemble of climate models
instead of only one or two, consider a wide range of future scenarios of
population density change, and differentiate between the effects of changes
in not only population sizes within a country, but also population spatial
distribution via urbanisation.
While previous studies have focused on the task of predicting future
wildfire emissions and have at most considered impacts of population changes
separately to those of climate and CO2, here we partition the projected
changes into the following drivers: climate via changes in burned area,
climate via changes in fuel load, CO2 via changes in burned area,
CO2 via changes in fuel load, and population density considering both
the effects of population growth and urbanisation. The goal is a better
understanding of the underlying processes of wildfire emission changes,
which should help establishing the necessary links between climate policy
(emissions), climate science (climate sensitivity), demography, air
pollution and atmospheric chemistry, as well as wildfire management.
Methods
Models and driving data
We use the coupled fire–vegetation model LPJ–GUESS–SIMFIRE (Knorr et al.,
2014) to simulate establishment, growth and mortality of
natural vegetation, fuel load, burned area and wildfire emissions under
changing climate, CO2 and human population density. LPJ–GUESS (Smith et
al., 2001) is a global dynamic vegetation model that simulates potential
vegetation as a mixture of user-defined plant functional types (PFTs) which
compete with each other in so-called patches. Each PFT is characterized by a
set of traits, such as leaf longevity and phenology, growth form and
bioclimatic limits to establishment and survival. In these simulations, we
use five patches per grid cell, and within each patch, LPJ–GUESS simulates
several age cohorts. In “cohort mode”, which is used here, all individuals
of a given age cohort would be identical.
When a fire occurs, individuals of woody PFTs within each patch are selected at
random to be killed or to survive according to the PFT's fire resistance (Knorr
et al., 2012). Grass PFTs have no individuals and therefore we only adjust the
biomass of each these PFTs.
We use PFTs designed
for global simulations as given by Ahlström et al. (2012).
Fire impacts on vegetation are simulated at monthly intervals as described by
Knorr et al. (2012). SIMFIRE predicts annual fractional burned area, A (the
fraction of each grid cell burned per year)
using the following equation:
A(y)=a(B)FbNmax(y)cexp(-ep);
here, y is the fire year defined as in Knorr et al. (2012) in such a way
that it never “cuts” the fire season
in two, B is the biome type, F is
annual potential fraction of absorbed photosynthetically active radiation
(FAPAR), an approximation of vegetation fractional cover easily observed from
satellites and here used as a measure of fuel continuity (Knorr et al.,
2014), Nmax is the annual maximum Nesterov Index based on daily
diurnal temperature mean, Tm, range, Tr and
precipitation, P, and p is human population density. The Nesterov index used
is given by Thonicke et al. (2010) as the cumulative sum of Tm×(Tr+ 4 K) over all consecutive days with
equal or less than
3 mm rainfall. a(B), b, c and e are global parameters derived by
the optimisation of SIMFIRE
against observed burned area from GFED3
(Giglio et al., 2010) on a spatial grid and for the entire globe (Table 2,
“GFED3”, “all population densities” of Knorr et al., 2014). To derive
monthly burned area, we use the average diurnal cycle of burned area derived
from GFED3 for 2001–2010 using a variable spatial averaging radius around
each grid cell which is at least 250 km but has a total burned area over the
period of 10 000 km2. Information on biome type is passed from
LPJ–GUESS to SIMFIRE, where biome type is a discrete number ranging from one
to eight, using FAPAR of woody and herbaceous vegetation and of vegetation of at least 2 m in height as well as geographic latitude as information. F in Eq. (1)
is a bias corrected value derived from LPJ–GUESS-simulated FAPAR,
Fs, via
F=0.42Fs-0.15Fs2.
In LPJ–GUESS, woody thickening effects emissions in two ways: when
the fraction of shrubs increases, the area belonging to the biome “shrubland”
increases relative to the area of the biome “savannah and grassland”. Because
a(B) of Eq. (1) for the former is approximately half of the value for the
latter (Knorr et al., 2014), an increase in the fraction of shrubland
immediately leads to a decrease in burned area. The second effect results from
the fact that in a fire, 100 % of live and dead leaves of grasses burn, while
for woody vegetation, 100 % of dead leaves but only between 46 and 59 % of live
leaves (depending on fire resistance), 20 % of dead wood and no live wood burn in a
fire (Knorr et al., 2012). As a result, the fraction of net primary productivity
emitted in a fire tends to decrease with woody encroachment. The measure used to
document woody thickening in LPJ–GUESS is the maximum seasonal leaf area index
(LAI) assigned the woody individuals of a grid cell divided by the total grid
cell LAI.
LPJ–GUESS–SIMFIRE, in the following denoted “LPJ–GUESS”, is driven by output
from Earth system model (ESM) simulations from the CMIP5 project (Taylor et
al., 2012) in a way mostly following Ahlström et al. (2012), where
climate output of monthly mean temperature, precipitation and downward
shortwave radiation is bias corrected using the mean observed climate for the
period 1961–1990, and atmospheric CO2 levels used by LPJ–GUESS are
taken from the RCP (Representative Concentration Pathway) scenarios as prescribed for CMIP5 (Meinshausen et al.,
2011). In variance to the cited work, we use CRU TS3.10 (Harris et al., 2014)
as climate observations, and we predict monthly mean diurnal temperature
range and number of wet days per month based on linear regressions against
mean temperature and precipitation, respectively. Simulations are carried out
on an equal-area pseudo-1∘ grid, which has a grid spacing of
1∘ × 1∘ at the equator and a wider E–W spacing
towards the poles in order to conserve the average grid cell area
across latitude bands.
We use global historical gridded values of human population density from HYDE
(Klein-Goldewijk et al., 2010) for simulations up to 2005. For future
scenarios, no gridded data are available, but we use instead per-country
values of total population and percentage of urban population. In order to
generate gridded population density after 2005, we use separate urban and
rural population density from HYDE for the year 2005 and re-scale both by the
relative growth of each in each country. After this procedure, we multiply
the population density of all grid cells representing each country by a
constant factor such that the growth of the total population of the given
country relative to the 2005 HYDE data matches that of the per-country total
population scenario used.
Scenarios
We run simulations for two climate change scenarios from the Representative
Concentration Pathways (RCPs). Of these, RCP4.5 represents an approximate
radiative forcing scenario typical of the majority of stabilisation scenarios
included in the Fourth Assessment of Report of the International Panel on
Climate Change. The other, RCP8.5, is a typical case of high emissions
resulting from a lack of enforced stabilisation of greenhouse gases, leading
to high levels of climate change (van Vuuren et al., 2011). In this study, we
will consider both scenarios separately as two alternative futures without
any assignment of relative probabilities.
Climatic trends simulated for the 20th century as well as for RCP4.5 and
RCP8.5 are shown in Table 1 for different regions, for the eight-ESM ensemble
mean and range. (For definition of regions see Sect. 2.4 and Fig. 4.) There
is a spatially rather uniform warming trend of around 0.5 ∘C
during the 20th century roughly in accordance with observations (Harris et
al., 2014), with inter-model differences larger than differences between
regions. Precipitation declines slightly during the same period, most
strongly for the already dry Middle East, with generally rather large inter-model
differences, in particular for Africa, Oceania and in South Asia. Temperature
change under the RCP4.5 scenario towards the end of the 21st century is
around +2.5 ∘C for most regions, except for higher values for the
two regions comprising most of the Arctic (North America, north Asia), while
precipitation overall increases, albeit with considerable declines for
Oceania and the Middle East on average, and for South America and Africa for the
their respective ensemble minima. For RCP8.5, global mean temperature change
reaches as high as +5 ∘C, with North America, north Asia and
the Middle East exceeding this value. Precipitation changes are similar to
RCP4.5, but with both the inter-model ranges and the inter-region differences
considerably amplified. (For example, there is an almost 40 % decline for Oceania for
the ensemble minimum.)
Simulated changes in climate by region.
Absolute change in annual-mean temperature [K]1
Region
historical2
RCP4.53
RCP8.53
North America
0.62
(0.03, 1.18)
3.15
(1.88, 4.90)
5.70
(3.78, 7.97)
Europe
0.50
(-0.20, 1.00)
2.56
(1.77, 3.83)
4.53
(3.46, 6.26)
North Asia
0.51
(0.07, 0.98)
3.25
(2.13, 4.81)
5.69
(3.91, 7.63)
Middle East
0.50
(0.09, 0.86)
2.71
(1.82, 3.78)
5.05
(3.68, 6.33)
South America
0.43
(0.07, 0.78)
2.36
(1.65, 3.19)
4.34
(2.83, 5.39)
Africa
0.47
(0.08, 0.72)
2.54
(1.77, 3.34)
4.67
(3.48, 5.87)
South Asia
0.37
(0.01, 0.65)
2.28
(1.60, 3.06)
4.07
(2.95, 5.09)
Oceania
0.44
(0.17, 0.74)
2.18
(1.35, 2.83)
4.16
(2.83, 5.35)
Globe
0.50
(0.08, 0.83)
2.77
(1.83, 3.89)
5.01
(3.49, 6.48)
Relative change in mean annual precipitation3
North America
-0.5 %
(-1.8 %, 1.6 %)
4.6 %
(-2.1 %, 7.6 %)
5.3 %
(-5.7 %, 10.8 %)
Europe
-1.0 %
(-4.5 %, 1.5 %)
1.9 %
(-3.0 %, 10.7 %)
0.6 %
(-5.6 %, 13.1 %)
North Asia
-0.8 %
(-3.3 %, 1.0 %)
9.4 %
(5.8 %, 15.1 %)
13.8 %
(8.2 %, 19.7 %)
Middle East
-6.4 %
(-11.8 %, 0.9 %)
-6.0 %
(-17.0 %, 5.7 %)
-10.7 %
(-28.3 %, 0.0 %)
South America
-2.5 %
(-6.8 %, -0.9 %)
-0.7 %
(-8.8 %, 11.7 %)
-1.3 %
(-10.6 %, 14.3 %)
Africa
-2.7 %
(-9.3 %, 0.1 %)
1.4 %
(-6.3 %, 5.0 %)
2.7 %
(-5.0 %, 9.6 %)
South Asia
-1.2 %
(-6.0 %, 1.8 %)
8.3 %
(4.9 %, 12.8 %)
14.5 %
(9.0 %, 22.3 %)
Oceania
-1.5 %
(-7.2 %, 2.7 %)
-1.9 %
(-27.2 %, 6.6 %)
-6.7 %
(-38.3 %, 11.8 %)
Globe
-1.8 %
(-3.2 %, 0.1 %)
3.3 %
(-1.1 %, 5.6 %)
4.7 %
(0.8 %, 7.6 %)
1 Mean across eight-ESM ensemble, ensemble minimum and maximum in parentheses.
2 Changes from the periods 1901–1930 to 1971–2000.
3 Changes from the periods 1971–2000 to 2071–2100.
For population scenarios, we use marker scenarios of the Shared Socioeconomic
Pathways (SSPs; O'Neill et al., 2012; Jiang, 2014).
We consider a total of five scenarios: SSP2 scenario with medium
population growth and central urbanisation, two extreme scenarios with either
high population growth and slow urbanisation (SSP3) or low population growth
with fast urbanisation (SSP5) and two further scenarios in which the medium
population growth (SSP2) is combined with either slow (SSP3) or fast (SSP5)
urbanisation. Fur the purpose of analysis, we will consider these five
scenarios equally plausible, keeping in mind, however, that this is mainly a
working hypothesis.
Simulations
We combine output from eight ESMs with two different emissions pathways, one
based on RCP4.5 and one on RCP8.5, all run with the medium population and
central urbanisation scenario of SSP2. These 16 simulations are repeated six
times using the other 4 population and urbanisation scenarios, 1
simulation each where population is held constant at 2000 levels, and 1 simulation where both population and atmospheric CO2 levels are held
constant at 2000 levels, giving 8 × 2 × 7 = 112
simulations. To these we add two more sets of six simulations each with a
different parameterisation of SIMFIRE, comprising runs using the SSP2
demographic scenario, fixed population, and fixed population and CO2 and
output from MPI-ESM-LR based on either RCP4.5 or RCP8.5. The first
alternative SIMFIRE parameterisation is derived from a global optimisation
against MCD45 burned area (Roy et al., 2008) according to Knorr et al. (2014,
Table 2, “MCD45”, “all population densities”), and the other assumes a
slight increase in burned area with increasing population density if p is
less than 0.1 inhabitants per km2, where Eq. (1) is replaced by
A(y)=(0.81+1.9p)a(B)FbNmax(y)cexp(-ep),
based on results presented by Knorr et al. (2014).
Temporal average of global wildfire emissions in PgC yr-1 by time period, scenario and ESM9.
Period
RCP
Population
Urban-
ESM
MPI-ESM-LR1
CCSM42
CSIRO-Mk3.63
EC-EARTH4
CNRM-CM55
GISS-E2-R6
IPSL-CM5A-MR7
HADGEM2-ES8
growth
isation
Ensemble
1901–1930
–
Historical
Historical
1.43
1.44
1.42
1.46
1.42
1.43
1.42
1.44
1.39
1971–2000
1.28
1.32
1.27
1.28
1.29
1.29
1.25
1.28
1.27
2071–2100
4.5
low
fast
1.31
1.36
1.31
1.27
1.31
1.29
1.27
1.33
1.36
intermediate
fast
1.27
1.32
1.27
1.23
1.26
1.26
1.23
1.29
1.32
intermediate
central
1.22
1.26
1.22
1.17
1.20
1.20
1.18
1.23
1.27
intermediate
slow
1.17
1.21
1.16
1.13
1.15
1.15
1.13
1.18
1.21
high
slow
1.11
1.15
1.11
1.07
1.09
1.09
1.07
1.12
1.16
8.5
low
fast
1.43
1.52
1.45
1.41
1.38
1.41
1.37
1.42
1.50
intermediate
fast
1.39
1.47
1.41
1.38
1.34
1.36
1.33
1.38
1.46
intermediate
central
1.33
1.41
1.36
1.32
1.29
1.30
1.28
1.33
1.40
intermediate
slow
1.28
1.35
1.31
1.26
1.24
1.25
1.23
1.27
1.35
high
slow
1.22
1.29
1.24
1.19
1.18
1.19
1.18
1.22
1.28
1 Max Planck Institute for Meteorology;
2 National Centre for Atmospheric Research;
3 Commonwealth Scientific and Industrial Research Organisation in collaboration with Queensland CSIRO Climate Change Centre of Excellence;
4 EC-EARTH consortium;
5 Centre National de Recherches Météorologiques/Centre Européen de Recherche et Formation Avancée en Calcul Scientifique;
6 NASA Goddard Institute for Space Studies;
7 Institut Pierre-Simon Laplace;
8 Met Office Hadley Centre;
9 Emissions larger than during 1971–2000 (italics) are shown in bold.
Analytical framework
Since the present analysis only considers wildfires, we exclude all grid
cells that contain more than 50 % of cropland at any time during
1901–2100 in either the RCP6.0 or 8.5 land use scenarios (Hurtt et al.,
2011). The threshold of 50 % is the same as used during the SIMFIRE
optimisation. A time-invariant crop mask is used in order to avoid
introducing time trends in the results through temporal variations of the
crop mask.
We therefore only consider the indirect of effect of cropland expansion via the
empirically derived burned area–population density relationship of SIMFIRE, not
the direct displacement of wildlands. This indirect effect can be considerable
and arises from the fact that cropland expansion tends to be accompanied by
higher population density, a denser road network, and a decrease in burned area
in the areas that have not been converted to croplands (Andela and van der
Werf, 2014).
The changes in emissions may be caused by climate change alone, by changes in
atmospheric CO2, or by changes in population density. Emissions are
determined by the product of burned area, the amount of fuel present, and the
fraction of fuel combusted in a fire. Climate
affects
burned area directly by
changing fire risk via Nmax, while climate and CO2
affect
burned area indirectly by changing the vegetation type, which affects a(B),
or vegetation cover, which affects F in Eq. (1). Fuel load is also affected
by vegetation productivity which is driven by both climate and CO2, and
by litter decay rates, which depend on temperature and precipitation (Smith
et al., 2001). The combusted fraction of fuel mainly depends on the presence
of grasses vs. trees (Knorr et al., 2012). Finally, population density
affects emissions through burned area via Eq. (1).
In order to assess the
effect
of different driving factors on changing
emissions, we employ the following analytical framework:
ET2=ET1+ΔE,ET2p2=ET1p2+ΔEp2,ET2cp2=ET1cp2+ΔEpc2,
with
ΔE=ΔEclim+ΔECO2+ΔEpop,ΔEp2=ΔEclim+ΔECO2,ΔEcp2=ΔEclim,
where subscript T1 denotes the temporal average over the initial
reference period (either 1901–1930 or 1971–2000), and T2 over the
subsequent reference period (1971–2000 or 2071–2100), E are wildfire
emissions, ΔE the change in the temporal average of emissions between
the two reference periods, and the subscripts “clim”, “CO2” and
“pop” denote the effects of changing climate, CO2 and human population
density, respectively.
The superscripts p2 are for the simulations with population density
fixed at year 2000 levels, and cp2 for the simulations with both
CO2 and population fixed at 2000 levels.
We choose the year 2000 as a reference year for fixed input variables in the
middle of the simulation period in order to minimise deviations from the values
of the transient runs.
The climate effect in the context of this study is therefore defined as the
change in emissions between two time periods of a transient simulation with
variable climate but fixed population density and atmospheric CO2, the CO2
effect as the additional change in emissions when CO2 is also varied in time,
and the population effect as the additional effect when population density also
becomes time variant. The computed effects are not expressions of model
sensitivity to small perturbations, but rather arise from a series of specific
scenarios. We choose this order of scenarios for historical reasons: we first
include the effect studied most (e.g. Krawchuk et al., 2009; Moritz et al.,
2012), then the effect that is usually included as soon as a dynamic vegetation
model is used (Scholze et al., 2006), and at last the effect that is the
focus of the current study. If we were to add the population effect first –
by including simulations where population changes in time but CO2 is kept
constant – the results would be somewhat different, and the difference could be
expressed as interaction terms following Stein and Alpert (1993). However, this
method is usually applied to time slice experiments (e.g. Claussen et al., 2001; Martin Calvo and Prentice, 2015), and its application to transient
simulations is less straightforward, still
depends
on finite perturbations,
and would require a large number of additional simulations, which is why we restricted ourselves here to the setup described by Eqs. (4) and (5).
Fire emissions in this study are computed as the product of burned area and area-specific fuel combustion.
Therefore, we can further subdivide the CO2 effect on emissions
between those that work via changing burned area (ΔECO2b.a.)
and those via changing
combustible
fuel load as the remainder
(ΔECO2c.f.l.=ΔECO2-ΔECO2b.a.).
We derive the former in a first-order forward projection using emissions
per area burned of the previous time step:
ΔECO2b.a.=ΔBCO2(ET1/BT1),
where BT1 is the temporal average of burned area during reference period
T1, and ΔBCO2 the change in burned area due to CO2
changes, which we approximate in an analogous way to ΔECO2 as
ΔBCO2=BT2p2-BT1p2-(BT2cp2-BT1cp2).
An analogous formulation is used in order
to
discern climate impacts due to
burned area from those due to changes in fuel load and its degree of
combustion:
ΔEclimb.a.=ΔBclim(ET1/BT1),
with
ΔBclim=BT2cp2-BT1cp2.
We analyse the main driving factors of emissions changes using
Eqs. (5–9)
for selected large regions, aggregated from the standard GFED (Global Fire Emissions Database) regions (Giglio et
al., 2010):
North America (GFED Boreal and Temperate North America, Central
America),
South America (GFED Northern- and Southern-Hemisphere South America),
Europe (same as GFED),
Middle East (same as GFED),
Africa (GFED Northern- and Southern-Hemisphere Africa),
North Asia (GFED Boreal and Central Asia),
South Asia (GFED Southeast and equatorial Asia),
Oceania (GFED Australia and New Zealand).
For a probabilistic analysis of changes in emissions, we follow previous work
by Scholze et al. (2006), who counted ensemble members driven by differing
climate models where the change of the temporal average between two reference
periods was more than 1 standard deviation of the interannual variability
of the first reference period. The authors found a general pattern of
increasing
fractional burned area
in arid regions, and a
decline at high latitudes and some tropical regions. Here, we apply the
method to emissions and use 2 standard deviations instead in order to
ensure that the change is highly significant.
Results
Global emission trends
Global simulated emissions taking into account changes in all factors,
climate, CO2 and population, decline continuously between about 1930 and
2020 for all members of the ESM ensemble (Fig. 1). Thereafter, emissions
approximately stabilize, albeit with a very slight upward trend during
2080–2100 for the moderate greenhouse gas concentrations and climate change
scenario RCP4.5 and the central demographic scenario (Fig. 1a). However,
different demographic scenarios lead to considerable variations in simulated
emissions: while emissions continue to decline until 2100 under high
population growth and slow urbanisation (SSP3), the trend of declining
emissions is reversed from around 2010 and
emissions
will resume current
levels by the end of the 21st century under low population growth and
fast urbanisation (SSP5) when taking the ESM ensemble mean. In general,
higher population growth drives emissions downward (comparing SSP3 to SSP5),
while faster urbanisation contributes to higher wildfire emissions
(comparing SSP2 population with fast and slow urbanisation). By the end of
the century, different demographic trends generate
approximately
0.2 PgC
(petagrams of carbon)
per year difference (ranging from around 1.1 to 1.3 PgC yr-1) under the
climate change RCP4.5. Overall, the range of future emissions spanned by the
eight ESMs, but using a single, central population scenario, is less than
half of the range spanned by all
ESMs
and population scenarios combined.
None of the simulations for the late 21st century reach
the levels again that are found for
the beginning of the 20th century (Table 2). Only 9
out of 40 simulations show global average emissions during 2071–2100 higher
than during 1971–2000, seven out of which are for low population growth and
fast urbanisation, and one for intermediate population growth and fast
urbanisation.
Simulated global wildfire emissions 1900 to 2100. Shaded areas are
for the range of ensemble members either across all ESMs using only the
central population scenario SSP2, or across ESMs and all population
scenarios. Lines show ensemble averages for specific population scenarios.
(a) RCP4.5 greenhouse gas concentrations and climate change;
(b) RCP8.5.
Under RCP 8.5, with high greenhouse gas concentrations and climate change,
global wildfire emissions start to rise again after 2020 even for the
central demographic scenarios (SSP2) and by the end of the 21st century
reach levels only slightly below those of the beginning of the 20th
century (Fig. 1b). According to this climate change scenario, the world is
currently in a temporary minimum of wildfire emissions, independent of
demographic scenario or ESM simulation. The population scenario rather
determines when emissions are predicted to rise again and how fast emissions
increase. For a scenario of high population growth and slow urbanisation
(SSP3), emissions rise again after ca. 2070 and reach about 1.2 PgC yr-1 by
the end of the century, while under the fast urbanisation scenarios (SSP5
and SSP2 population with fast urbanisation), they already start rising
around 2020. Under RCP8.5, different demographic trends result in different
wildfire emissions ranging from 1.2 to 1.5 PgC yr-1. Overall, for 28 out of 40
simulations average emissions during 2071–2100 are higher than during
1971–2000, and for three out of the eight simulations with low population
growth and fast urbanisation they are even higher than for 1901–1930 (Table 2).
Simulations with atmospheric CO2 and population held constant at 2000
levels reveal the impact of climate change on simulated wildfire emissions
(Fig. 2a). The climate impact is here shown as the difference in emissions
against the average during 1971–2000
(1.28 PgC yr-1, see Table 2). There is a
modest positive climate impact on global emissions for RCP8.5, which reaches
close to 10 % towards the end of the 21st century for the ESM
ensemble mean, with a range between close to 0 and +20 %. For the past,
there is no discernable impact of climate change. For RCP4.5, the impact is
very small and peaks around 2050 for the ensemble mean, but with a range
skewed slightly towards increased emissions.
Effects of different factors on global emissions for historical
change (until 2005) and two future climate change scenarios (RCP4.5 and
RCP8.5). (a) Effect of climate change, (b) effect of
changing atmospheric CO2, (c) effect of changing human
population density. All simulations are for the central SSP2 population
scenario. Solid lines for ESM ensemble means and shaded areas for the range
across eight ESM simulations each.
The CO2 impact is computed as the difference between two simulations
with fixed population density, the one with variable climate and CO2
minus the one with variable climate but fixed CO2
(Eq. 5).
The resulting emissions differences (Fig. 2b) remain negative throughout the
historical period until 2005 because the fixed-CO2 simulations start out
with considerably higher CO2 levels than the variable-CO2 ones,
leading to higher productivity (CO2 fertilisation, see Hickler et al.,
2008; Ahlström et al., 2012), higher fuel load and therefore higher
emissions. For RCP8.5, the global CO2 impact on emissions is about the
same as the climate impact, but for RCP4.5 it is much larger. The magnitude
of the CO2 effect itself is climate dependent, which can be seen by the
inter-ensemble range, which is caused solely by differences in climate (all
ensemble members use the same atmospheric CO2 scenarios for a given
RCP). There is also a small interannual variability caused mainly by climate
fluctuations, since interannual variations in atmospheric CO2 are small
until 2005 and absent from the scenarios (Meinshausen et al., 2011). As for
climate, there is no discernable CO2 impact on past emission changes.
Finally, the demographic impact is simulated by the difference between
simulations with time varying climate, CO2 and population, and the
corresponding simulations where population is fixed, but the other two vary
with time
(Eq. 5).
As one would expect, the results for the two RCPs are
indistinguishable, with a small climate-related ensemble range and a small
amount of interannual variability caused by climate fluctuations (Fig. 2c).
The simulated demographic impact for the central population scenario is
towards declining emissions mainly driven by population growth. After 2050,
the effect declines rapidly, and there is a very slight positive trend after
ca. 2090 which is due to the levelling off of projected population growth (SSP2)
and continuing urbanisation. As can be seen by comparing simulated emissions
between the central (SSP2) and the remaining population scenarios (Fig. 1a),
the demographic impact varies considerably between scenarios,
with a continuing negative impact until 2100 for the scenario with high
population growth with slow urbanisation (SSP3), but a positive impact of
the demographic change on global emission trends from about 2040 for low
population growth with fast urbanisation (SSP5).
Impact of changing fire model parameterisation on the simulated
climate, CO2 and population effects on emissions. Standard
parameterisation of SIMFIRE optimised against GFED3 burned area, optimisation
against MCD45 burned area, and simulation assuming an increasing effect of
population density on burned area between 0 and 0.1 inhabitants km-2.
(a) RCP4.5. (b) RCP8.5.
Regional wildfire emissions during 1901–1930 for eight regions and
global and regional changes, average 1971–2000 minus average 1901–1930, for
ensemble mean (white/coloured bars) and range across ensemble comprising
eight ESMs (error bars), in TgC yr-1. The change in emissions is
further subdivided into climate effect due to changes in burned area or
changes in combusted fuel per burned area, effect of atmospheric CO2
change due to changed burned area or fuel combustion, and population effect.
Results for the set of sensitivity tests where the parameterisation of
SIMFIRE was modified are shown in Fig. 3 for the climate, CO2 and
demographic impacts separately. Note that in this case, simulations are
performed with only one ESM (MPI-ESM-LR). The climate impact on emissions is
again small for RCP4.5, but discernably positive for RCP8.5 after ca. 2020.
The climate impact is hardly affected by changing the SIMFIRE
parameterisation. The CO2 effect is similar to the ensemble mean
(Fig. 2b), but with a marked decline after ca. 2080 for RCP8.5. In this case,
SIMFIRE optimised against MCD45 burned area shows less of a positive trend
after 2020 as a result of CO2 changes than the standard formulation
and a more pronounced negative effect after 2080. Also, the simulated
historical and future demographic impacts are slightly less for MCD45 than
for the standard version. The SIMFIRE version with an initial increase in
burned area with population density (Eq. 3) has only a very small impact on
simulated global emissions.
As previous figure, but for average emissions during 1971–2000 and
changes as 2071–2100 minus 1971–2000 averages, both differentiated between
RCP4.5 and RCP8.5 climate scenarios. In this case, the ensemble is across 8 ESMs times 5 population scenarios.
The recent estimate from the GFED4.0s data set puts the average global
wildfire emissions at 1.5 PgC yr-1 (released May 2015, 1997–2014 average of
savannah, boreal and temperate forest fires combined, against 2.2 PgC yr-1 for
all biomass burning, van der Werf et al., 2010, updated using Randerson et
al., 2012 and Giglio et al., 2013), slightly higher than simulated here
(Table 2). During the 20th century, global emissions decrease by around
150 TgC yr-1, a little more than 10 %. The main driving factor of this
decrease is growing population, while climate and CO2 changes have only
a very small impact on emissions, as already discussed with Fig. 2. Further
analysis of these driving factors (Fig. 4), however, reveals that this small
impact is due to compensating action on either burned area
(Eqs. 6 and 8)
or combustible fuel load (the remainder). Globally, climate had a small positive
and CO2 a slightly smaller negative effect on emissions via burned area.
At the same time, climate had a negative and CO2 a positive impact on
combustible fuel load. For the 21st century (Fig. 5), this
constellation is predicted to continue, with a somewhat larger demographic
impact that is negative across all ensemble members. The overall effect on
emissions, however, is small and of uncertain sign (ensemble range including
both positive and negative changes). This is because the climate impact and
even more both CO2 effects, acting in opposite directions, increase
several fold compared to the situation during the 20th century.
Driving factors of regional emission changes
By the beginning of the 20th century, the main wildfire emitting
region is clearly Africa (Fig. 4), followed by South America, north Asia and
Oceania. Emission changes towards the end of the 20th century are
mainly due to changes in population density in all regions except for
Europe, North America and Oceania, where population growth rates are
significantly lower. For Europe, climate change has led to an increase in
burned area, but an about analogous decrease in fuel load, such that the
overall climate effect is small and uncertain. The result for North America
is similar, while there is a larger but still uncertain positive CO2
effect on fuel load, similar to Oceania and South America. For Oceania the
population effect is by far the smallest and the only one uncertain in sign
(judging by the ensemble range).
The climate effect via fuel load is negative in all regions, while the
climate effect via burned area is almost always positive, except for the
Middle East where it is negative but with a large ensemble range spanning
both positive and negative, and South Asia, where it is close to zero. We
find a negative CO2 effect via burned area in the tropics (Africa, South
America), but a positive effect in the arid sub-tropics and temperate zones
(Middle East, north Asia). The positive climate effect can be explained by
regional changes in Nmax (Table 3, cf. Eq. 1), which are always
positive, small for changes during the 20th century, but reaching up to
over 100 % for Europe
between
the periods 1971–2000 to 2071–2100 under the
RCP8.5 climate change scenario. The highest increases are for the northern
regions, and the smallest for regions with large deserts, like Africa and
the Middle East, but starting from a high base. However, climate change can also
affect burned area indirectly through vegetation change by changing B or
F in Eq. (1), for which a good indicator is the fraction of the total leaf
area index that is attributed to grasses (“grass fraction”, Table 3). This is
because a(B) for grassland and savannahs is about 1 order of magnitude
larger than a(B) for woody biomes (Knorr et al., 2014). There is a general
increase in the fraction of woody biomes at the expense of grass vegetation across
all except the hyper-arid Middle East region. Here, the grass fraction is by
far the highest, and the climate is too dry to support the expansion of shrubs.
Changes in climatic and vegetation fire risk1.
Mean annual-maximum Nesterov index
Region
1901–1930
1971–2000
RCP4.52
RCP8.52
North America
153
(143, 165)
160
(148, 170)
204
(178, 236)
250
(211, 327)
Europe
80
(73, 93)
83
(77, 87)
120
(94, 152)
166
(103, 228)
North Asia
146
(142, 154)
149
(144, 155)
188
(163, 220)
227
(185, 292)
Middle East
2878
(2731, 3184)
2923
(2831, 3169)
3201
(2962, 3443)
3401
(3060, 3776)
South America
240
(223, 254)
248
(233, 272)
298
(258, 338)
348
(265, 432)
Africa
1461
(1379, 1491)
1481
(1434, 1530)
1618
(1519, 1728)
1719
(1566, 1898)
South Asia
288
(272, 314)
296
(276, 318)
332
(300, 368)
368
(312, 449)
Oceania
570
(509, 605)
586
(535, 625)
671
(553, 851)
795
(598, 1085)
Globe
726
(700, 765)
740
(715, 773)
827
(767, 878)
903
(817, 1007)
Grass fraction
North America
30 %
(28 %, 31 %)
28 %
(27 %, 29 %)
22 %
(20 %, 23 %)
20 %
(19 %, 22 %)
Europe
14 %
(13 %, 15 %)
12 %
(11 %, 13 %)
10 %
(9 %, 12 %)
11 %
(9 %, 12 %)
North Asia
36 %
(34 %, 37 %)
33 %
(33 %, 34 %)
21 %
(17 %, 23 %)
16 %
(13 %, 18 %)
Middle East
75 %
(74 %, 76 %)
76 %
(75 %, 77 %)
77 %
(76 %, 79 %)
76 %
(75 %, 78 %)
South America
26 %
(25 %, 28 %)
23 %
(23 %, 24 %)
16 %
(15 %, 16 %)
13 %
(12 %, 14 %)
Africa
57 %
(56 %, 59 %)
53 %
(53 %, 54 %)
40 %
(39 %, 42 %)
34 %
(32 %, 36 %)
South Asia
26 %
(25 %, 27 %)
23 %
(23 %, 24 %)
17 %
(16 %, 18 %)
15 %
(14 %, 15 %)
Oceania
82 %
(79 %, 85 %)
81 %
(79 %, 83 %)
76 %
(74 %, 81 %)
69 %
(65 %, 76 %)
Globe
43 %
(43 %, 44 %)
41 %
(41 %, 41 %)
33 %
(32 %, 34 %)
29 %
(28 %, 31 %)
1 Mean across eight-ESM ensemble, ensemble minimum and maximum in
parentheses.
2 Temporal average for 2071–2100.
For 1971–2000, simulated wildfire emissions are markedly lower than for the
beginning of the 20th century for Africa, South America, South Asia and
the Middle East (Fig. 5). Of these regions, only Africa is predicted to continue
to decline for the entire ensemble range for both RCPs. The main drivers are
population growth and CO2 impact on burned area, partly compensated by
increased fuel load. For South America, South Asia and Oceania the pattern
is similar, except with a much smaller demographic impact, resulting in an
overall change of uncertain direction.
All northern regions (North America, Europe and north Asia) are predicted to
increase emissions across the entire ensemble. In all of these, climate
impacts wildfire emissions positively, but with large uncertainties due to
diverging effects of climate on burned area (increasing) and fuel load
(decreasing).
All of these have a slight
positive climate impact, but with large uncertainties, where climate change
strongly increases burned area compensated largely by a decrease in fuel
load. Since precipitation is predicted to increase in these regions (Table 1),
the climate effect is mainly due to increasing temperatures and
Nmax (Tables 1, 3). For North America and north Asia there is a clear
positive effect of CO2 on fuel load which appears to be the main reason
for tilting the balance towards emission increases. However, population
change plays a rather small role, with a large ensemble range for Europe and
north Asia making the sign of the impact uncertain given their slower
population growth. For North America, the demographic impact is small, but
universally slightly negative. An exception is the region Middle East, which
has a large positive CO2 effect via burned area (cf. Fig. 4).
Ensemble–mean combustible fuel load in kgC m-2
and change due to climate and CO2 effects.
(a) Average emissions 1971–2000; (b) change from
1971–2000 to 2071–2100 for RCP8.5 due to climate effect; (c) same
as (b) but due to CO2 effect. Grey areas have no fire or are
excluded as dominated by agriculture. Combustible fuel load is the amount of
carbon potentially emitted if a fire occurs.
Overall, there is a marked shift in emissions towards the extra-tropics: for 1971–2000, the tropics have 700 TgC yr-1 emissions vs. 580 for the
extra-tropics (ensemble mean), and for 2071–2100 the split ranges between 420
tropics vs. 680 extra-tropics for RCP4.5, high population growth/slow
urbanisation and 600 tropics vs. 720 extra-tropics for RCP8.5, low
population growth/fast urbanisation. As the regional analysis shows, this
change is mainly the result of expanding population in Africa. However,
there is also a much stronger negative climate effect on fuel load at high
compared to low latitudes (Fig. 6), which to some degree slows down the
shift of emissions to the north. This
contrasts with a generally positive CO2 effect across most of the
globe, but with about the same magnitude for tropical and extra-tropical
vegetated areas.
At high latitudes, combustible fuel load is
generally much higher than at low latitudes, implying that this is compensated
for by a much smaller burned area, leading to overall lower emissions in
this region.
Probabilistic forecast of future emission changes
For simulated emissions during the 20th century, we find that a
majority of ensemble members show significant increases (i.e. by more than
2 standard deviations) for northern boreal regions and the Tibetan
plateau, and decreases for some scattered regions in Europe and China, but
in general, changes are small compared to interannual variability (Fig. 7a).
For the 21st century, most simulations for both RCP4.5 (Fig. 7b)
and RCP8.5 (Fig. 7c) predict a significant decrease in emissions in Africa,
mainly north of the equator, and to a lesser degree and mostly for RCP8.5
for north Australian savannahs. The main regions for which a significant
increase in fire emissions is predicted are the boreal-forest/tundra
transition zones, Europe and China as well as arid regions in central Australia,
southern Africa and Central Asia. For the arid regions, however, the
increase is much more pronounced for RCP8.5 than for RCP4.5.
Fraction of ensemble members with either a significant decrease or
increase in wildfire emissions (positive or negative change by more than 2 standard deviations of the interannual variability of the initial period).
Agricultural areas and areas with ensemble median emissions less than
10 % of global median during 2071–2100 were excluded. (a) Changes
from 1901–1930 to 1971–2000; (b) changes from 1971–2000 to 2071–2100 for RCP4.5; (c) as (b) but for
RCP8.5.
As previous figure, but for emissions changes due to single driving
factors. (a, b) climate effect, (c, d) CO2 effect,
(e) population effect; (a, c) RCP4.5, (b, d) RCP8.
These changes in fire emissions during the 21st century relative to
current variability can also be analysed by driving factor
(Eqs. 4 and 5). The analysis reveals that increases in emissions in the boreal/tundra
transitional zone are mostly due to climate change, except for the more
continental and arid north-eastern Siberia. For the rest of the globe, the
climate effect has a surprisingly small impact, being confined to narrow
bands of arid regions in southern Africa, Australia and the Arabian
Peninsula. Climate change also leads to a significant decrease in emissions
in northern Africa and the Middle East (Fig. 8a–b, cf. Fig. 5). For RCP4.5,
CO2 has only a small positive impact on emissions, mainly for Central
Asia, and a negative impact for African, South American and North Australian
tropical savannahs. For RCP8.5, the CO2 effect has a much bigger
impact globally on the relative change of emissions, leading to increased
emissions in large regions including Mexico, southern South America, most of the southern half of
Australia and north-eastern Siberia and all
African, Arabian and Central Asian semi-deserts. The negative effect is also much more
pronounced and comprises most tropical savannahs (Fig. 8c–d). This creates
opposing effects for the large zone covering North Africa, Arabia and Central
Asia, with climate change leading to a decrease in plant productivity and
fuel load (hence lower emissions) against CO2 change leading to CO2
fertilisation (hence higher emissions).
For the moister and in general much
more highly emitting savannahs (van der Werf et al., 2010), the dominant
effect comes from CO2 change and is negative, due to shrub encroachment.
This creates an interesting situation for Australia: in the very north,
higher CO2 leads to shrub encroachment, leading to lower emissions
(Figs. 7 and 8); in a central zone across the continent, climate change is the leading
driver of increased emissions, but for most of the southern half, CO2
change leads to enhanced water-use efficiency of the already woody vegetation
(Morgan et al., 2007) causing the opposite effect compared to the north.
The same pattern is repeated for southern Africa, but with a stronger positive
climate effect in the central zone. The demographic effect (Fig. 8e) leads to
a significant increase in wildfire emissions in central and Eastern Europe as
well as East Asia due to its projected declining population, but a decrease
mainly in African savannahs but also Turkey and Afghanistan/southern Central
Asia given their projected large increases in population.
Discussion
In this study, we find that wildfire emissions declined by likely more than
10 % during the course of the 20th century, in agreement with ice
core measurements of the isotopic signature of carbon monoxide (Wang et al.,
2010). A decline in global wildfire activity since the late 19th century
was also suggested by Marlon et al. (2008) based on charcoal
records,
even though issues remain concerning the magnitude of the decline, and whether there have also been periods of increasing emissions
(van der Werf et al., 2013).
In the present simulations, the decline is caused overwhelmingly by
increasing population density, in agreement with the results of Knorr et al. (2014)
who used SIMFIRE alone to simulate burned area, without coupling to
LPJ–GUESS, driven by the same historical population data. According to the
present study, population effects dominated because a positive effect of climate change
on burned area was compensated by a negative effect on fuel load, and a negative
effect of CO2 increase on burned area was compensated by a positive effect on
fuel load. This broad general pattern, found for the main active wildfire
regions, is predicted to continue throughout the 21st century, albeit
with much stronger climate and CO2 effects, while the negative
population effect on emissions continues to have about the same magnitude.
This dominant pattern of opposing climate and CO2 effects, and
opposing effects via burned area and fuel load, calls for a mechanistic
explanation. A positive impact of climate change on burned area or numbers of
fires is what is commonly expected (Krawchuck et al., 2009; Pechony and
Shindell, 2010) and it was found for all regions in agreement with simulated
changes in fire risk (Nmax in Eq. 1). The exception is the Middle East
region during the 20th century, with a negative climate impact on burned
area, which is likely caused by a decline in fuel continuity which suppresses
the spread of fires (reduced F in Eq. 1). A negative climate impact on
fuel load is consistent with the widely expected positive climate-carbon
cycle feedback (Friedlingstein et al., 2006), whereby rising temperatures
increase soil and litter respiration rates, releasing CO2 from the
terrestrial biosphere. The faster decomposition of litter under warmer
conditions, incorporated into LPJ–GUESS (Smith et al., 2001), leads to a
reduction in fuel available for combustion (Knorr et al., 2012). Since
combustion by fire is nothing more than a shortcut for litter decomposition,
higher temperatures simply shift the balance between the two processes
towards microbial decomposition. However, the opposite climate effect could
also be expected, where warming leads to increased productivity in boreal,
temperature-limited ecosystems, leading to increased fuel production (Pausas
and Ribeiro, 2013). For the present study, at least, this situation does not
play a global role and is only found for scattered regions of north-eastern
Canada and northern Russia (Fig. 6b).
A positive effect of CO2 on fuel load, which is found to be active
almost everywhere across the globe, is fully consistent with the notion of
CO2 fertilisation of the terrestrial biosphere (Long et al., 1996;
Körner, 2000), whereby higher atmospheric CO2 concentrations increase
the rate of carboxylation, increasing net primary production and thus fuel
load (Hickler et al., 2008). However, we also find a negative impact of
rising CO2 on wildfire emissions for all tropical savannah ecosystems,
which outweighs the positive impact through increasing fuel load and is
caused by an increase in the dominance of woody biomes at the expense of grass
vegetation. This phenomenon of shrub encroachment, or woody thickening, in
tropical savannahs has been repeatedly observed in field studies (Wigley et
al., 2010; Bond and Midgley, 2012) and frequently attributed to CO2
enrichment of the atmosphere (Morgan et al., 2007; Buitenwerf et al., 2012).
This link is less observed for arid savannahs (Bond and Midgley, 2012),
consistent with the finding here that in the most arid regions, no decrease
in the grass fraction is predicted.
On a global scale, according to the present simulations, the level of future
wildfire emissions is highly uncertain for a scenario of moderate greenhouse
gas increases (RCP4.5), with the ensemble mean showing slightly lower
emissions towards the end of the 21st as opposed to the end of
the 20th century. For a high, business-as-usual scenario of greenhouse
gas forcing (RCP8.5), the ensemble mean points towards an increase across
the same time span, but with a range including both positive and negative
changes. There is also a general trend towards increases during the second
half of this century. The slight bias towards increased emissions is the
result of a combination of increased fire risk due to warming, and increased
fuel load due to CO2 fertilisation, but with population growth, woody
thickening and faster litter decomposition all counteracting. We therefore
find that climatic impacts on fire risk are only one of many, often opposing
factors that might lead to increased wildfire emissions in the future.
The future demographic dynamics can lead to a wide range of future wildfire
emissions. In addition to its indirect impact on wildfire emissions through
interactions with economic and technological changes contributing to GHGs
emissions and climate change, changes in population size and spatial
distribution play a direct and important role for fire prevalence, as an
ignition source but predominantly as fire suppressors. While fertility
decline is occurring in almost all global regions, the population momentum will
continue to drive global population size upward for at least some years and
likely contribute to continuously declining wildfire frequencies. The
uncertainty of future population dynamics, however, leads to a wide range
of population trends and causes large variations in simulated wildfire
emissions. Moreover, the same changes in population sizes can result in
rather different emissions due to variations in spatial population
distribution, particularly through different urbanisation patterns. While
the whole world is expected to be further urbanised, variations in speed and
patterns of urbanisation across regions and over time can lead to
significantly different wildfire patterns.
Simulated emissions presented here generally agree with similar results with
a coupled fire–vegetation–biogeochemical model by Kloster et al. (2012),
insofar as climate only starts to impact on fire during the course of the
21st century (but not before); they also agree that changes in population density
generally lead to lower emissions. The difference is that in the present
study, climate has a much smaller impact on emissions, ranging between 0 and
+20 % for RCP8.5 and few percent at most for RCP4.5. A similar study
reporting simulations of increasing fire emissions for Europe (Migliavacca et
al., 2013a) reports an increase for Europe of about 15 TgCyr -1 until the late
21st century, when measured for the same reference period as here,
which is within the ensemble range found in this study. Even though they used
the same Community Land Model, their fire parameterisation (Migliavacca et
al., 2013b) differed from the one used by Kloster et al. (2012).
Our results also differ partly from that by Lasslop and Kloster (2015), who
simulated increased combustible fuel load (emission per burned area) during the
20th century, but in their study, wood thickening did not counteract the increase by
reducing burned area. As a result, emissions increased by approximately
40 %
over that period, with about half of the increase due to increasing burned area.
The difference between the present study and the one by
Kloster et al. (2012) and Lasslop and Kloster (2015)
might be due to the pronounced negative effect of temperature
change on fuel load, and of CO2 on burned area, found here. Another
important difference is that
their study included deforestation fires, and
employed the more common approach of representing the impact of population
density by a combination of number of ignitions times an explicit function of
fire suppression, the combination of which leads to a small decrease in
emissions during the 21st century.
This approach, based on Venevsky et al. (2002), always leads
to an increase in burned area if ignitions increase, all else being equal.
Kloster et al. (2012) simulate no decline
during the 20th century, neither due to changing population density, nor
land use.
Our study, by contrast, uses a semi-empirical approach with a functional form
of the relationship between burned area and population density derived by the optimisation against observed burned area and simulates the historical
decline that is suggested on the basis of ice core and charcoal records.
The implicit assumption here is that for most of the world, except for
areas where population density is very low, the fire regime is ignition
saturated (Guyette et al. 2002), in contradiction to the approach by Venevsky et
al. (2002). This means that above a threshold of typically 0.1 inhabitants per
km2, burned area becomes independent of human population density (cf. Knorr
et al., 2014). However, if we assume some increase in burned area with
population density below the threshold, the results change only little (Fig. 3).
Therefore we argue for universal ignition saturation as a reasonable
approximation at the scales considered in the present study. We also expect
possible future increases in lightning activity (Romps et al., 2014) to have
only a marginal effect on burned area and thus on emissions.
An important outcome of this study is that it predicts
a large shift in fire emissions from the tropics towards the extra-tropics, driven by two
coinciding effects, causing a secular decline in emissions in African
savannahs and grasslands: CO2 increases, driving
woody thickening,
in turn
make the vegetation less flammable (Bond and Midgley, 2012), and
population growth leads to decreased burned area (Archibald et al., 2008).
The impact of this shift on the global budget of carbon emissions from
wildfires is so large because these regions currently have by far the largest
emissions worldwide (van der Werf et al., 2010). In agreement with observed
evidence (Bond and Midgley, 2012), the negative CO2 effect on emissions
via burned area is limited to the semi-humid tropics, and
does not play a role
either
in the most arid regions, nor at higher latitudes. It is also not
simulated for South Asia, where most of the potential semi-humid grasslands
and savannahs have long been converted to agriculture. For the mostly arid Middle East region, we find that a strong positive CO2 effect via burned
area is the larger contributor to emission change during the 20th
century, and the biggest during the 21st. This leads to a marked
increase in emissions for RCP8.5, outcompeting negative impacts of growing
population and climate change on fuel load and driven by a marked decline in
precipitation (Table 1), while during the 20th century, there is a
marked negative impact of climate change on burned area. Here, CO2
fertilisation leads to denser vegetation, increasing fuel continuity (higher
F in Eq. 1), thus leading to higher burned area, while decreasing
precipitation results in a lower F. To a lesser extent this is simulated
for north Asia, which also contains large, highly arid regions, but with a
positive ensemble–mean climate effect on burned area. For both regions,
however, the ensemble spread is very large, making the projections highly
uncertain.
For Australia, we find an interesting zonal pattern of changing effects from
the northern savannahs to the arid southern coast. In the very north, woody
thickening due to higher CO2 leads to decreased emissions through
decreased burned area, with negligible climate effects. This is followed by
a central zone where both climate and CO2 change lead to increased
emissions, and a third zone comprising the southern half of the Australian
interior, where CO2 fertilisation leads to increased emissions via
higher productivity. Population change plays almost no role for changing
emissions in this region. As a result, the north is predicted to decrease
significantly in emissions, while for the central zone where climate and
CO2 effects overlap, and for the south there is no clear signal in the
prediction. A similar tri-zonal pattern is also predicted for southern
Africa stretching from the Miombo woodlands across the Kalahari to the Cape
region.
This zonal differentiation resembles the results by Kelley and Harrison (2014),
who simulated a reduction in burned area in north Australia due to CO2 driven
woody thickening, but an increase in burned area in the Australian interior due
to enhanced fuel continuity with denser vegetation caused by CO2
fertilisation.
In these simulations, we have implicitly assumed that management practices
follow developments characterized by population density, but do not themselves
adapt to climate or CO2 driven changes in vegetation or fire regime. There is
indeed evidence of considerable encroachment of shrub vegetation across all land
use types (Wigley et al. 2010), despite the efforts of herders to decrease shrub
cover and increase the available amount of grazing (Bond and Midgley, 2012).