Wildfires pose a
significant risk to human livelihoods and are a substantial health hazard due
to emissions of toxic smoke. Previous studies have shown that climate change,
increasing atmospheric CO
Wildland fires – or in short wildfires – are burning events that occur
in natural or semi-natural landscapes such as (managed or unmanaged)
forests, shrublands, or grazing lands including savannahs. They are a major
natural hazard (Bowman et al., 2009) and an important source of air
pollutants (Langmann, 2009), which can impact air pollution thousands of
kilometres downwind (Lee et al., 2005). Wildfires also play an important role
in several atmospheric chemistry–climate feedback mechanisms (Fiore
et al., 2012). Emissions of fine aerosol particles, i.e. particulate matter
up to an aerodynamic diameter of 2.5
Climate warming has already led to more frequent hot and dry weather
in many parts of the globe, increasing the probability of wildfires
(Flannigan et al., 2009), and this is expected to continue into the
future. Studies based on calculated fire severity indices under
climate change argue for large increases in burned area (Flannigan
et al., 2005 for Canada; Amatulli et al., 2013, for southern Europe)
and resultant pollutant emissions (Spracklen et al., 2009; Yue
et al., 2013, for the western US), with some regional exceptions of
declining emissions due to increased precipitation (Yue et al., 2015,
for a few subregions in northern Canada). However, a long-term
increase in the length of the fire season or in weather conditions
conducive to wildfires does not necessarily lead to increases in
burned area (Doerr and Santin, 2016). This is because on longer timescales vegetation responds not only to climate change but also
directly to rising atmospheric
Further factors that have so far received less attention are growth and changes
in human population size and distribution. Contrary to common perception,
higher population density tends to be associated with lower wildfire risks
when measured by burned area (Archibald et al., 2009, 2010; Lehsten
et al., 2010; Knorr et al., 2014; Bistinas et al., 2014), even though higher
population density in rural and remote regions tends to lead to more but on
average smaller fires (Archibald et al., 2009, 2010). This can be explained
by the concept of the ignition-saturated fire regime, which is reached at
very low levels of population density. Above this level, human impact
manifests itself less in enhancing burned area (by igniting fires), but more
by creating barriers to and suppressing fire spread, thus reducing area
burned (Guyette et al., 2002). Indeed, coupled vegetation–fire models that
include the effects of changing human population size and spatial
distribution suggest a reduced rate of increase of fire activity during the
21st century compared to simulations that do not account for demographic changes
(Kloster et al., 2010). Some studies even showed a decline in burned area
(Knorr et al., 2016b) or emissions (Knorr et al., 2016a) for moderate levels
of climate change, when combined with slow urbanization and high population
growth. These results are backed by observational evidence of a long-term
declining trend in past fire activity or emissions from wildfires (Marlon
et al., 2008; Wang et al., 2010; van der Werf et al., 2013), and more recent
negative trends in northern Africa that have been related to the expansion of
cropland (also resulting from increasing population density; Andela and van
der Werf, 2014). Furthermore, the impacts of wildfires on
human society are also largely determined by population growth and
spatial distribution (Knorr et al., 2016b). It is therefore important to
consider not only climate and
The overarching research question addressed in this paper is whether
socio-economic developments influencing both greenhouse gas emissions
as well as human population size and spatial distribution might impact
wildfire emissions enough to make a difference for meeting the WHO air
quality target, provided anthropogenic
We use the LPJ-GUESS global dynamic vegetation model (Smith et al., 2001; Ahlström et al., 2012) coupled to the global semi-empirical fire model SIMFIRE (Knorr et al., 2014), with details given by Knorr et al. (2016a). In short, LPJ-GUESS is a patch-scale dynamic vegetation model that represents age cohorts of perennial vegetation and computes vegetation establishment and growth, allocation of carbon pools in living plants, and turnover of carbon in plant litter and soils. SIMFIRE provides burned area to LPJ-GUESS on an annual basis, which then evokes plant mortality according to a probability dependent on plant functional type (PFT). Specified fractions of plant litter and live leaf biomass are burnt and emitted into the air in a fire, while the remaining biomass of the killed vegetation is transferred to the litter pool of LPJ-GUESS (see Knorr et al., 2012). Population data needed to drive SIMFIRE are based on gridded data from HYDE 3.1 (Klein-Goldewijk et al., 2010) up to 2005 and are then rescaled using per-country relative growth in rural and urban population, retaining the urban masks of the HYDE data. Grid cells with more than 50 % past or future cropland area in either the RCP6.0 or 4.5 land use scenarios (Hurtt et al., 2011; see Sect. 2.2) were excluded from the calculations (see Knorr et al., 2016a, b, for details).
In order to compute emissions of chemical species, we use the emission
factors of the Global Fire Emissions Database version 4 (GFED4; van der Werf
et al., 2010, based mainly on Akagi et al., 2011; see
Climate simulations were driven by output from an ensemble of eight
global climate models from the Climate Model Intercomparison Project 5
(CMIP5; Taylor et al., 2012) for two climate scenarios based on the
Representative Concentration Pathways (van Vuuren et al., 2011) RCP4.5
with a moderate and RCP8.5 with a high degree of climate
change. Simulations for 1901 to 2100 are carried out on a global
equal-area grid with
In addition to the emissions simulated by LPJ-GUESS–SIMFIRE, we also
use the GFED4.1s observation-based emission fields for wildfires (van
der Werf et al., 2010, updated using Randerson et al., 2012, and Giglio
et al., 2013) aggregated to
For wildfires, we use the sum of boreal forest fires, temperate forest fires, and savannah fires from GFED4.1s. Agricultural waste burning from GFED4.1s has been excluded from the calculations. Instead, we use anthropogenic emissions that include agricultural burning from the ECLIPSE data set (Granier et al., 2011). Deforestation fires (caused by deforestation activities) and peat fires (fires occurring in forested or non-forested peatlands; see van der Werf et al., 2010) were found to be minor sources globally despite their regional importance mainly in South East Asia (Fig. S1 and Table S2 in the Supplement).
In contrast to Knorr et al. (2016c), in which changes in the spatial
distribution of population within a country did not affect predicted wildfire
emissions, here we also account for demographic effects on the grid-cell
scale. To do so, we combine a scalar accounting for climate and vegetation
effects,
The computed seasonal cycle of anthropogenic emissions of CO,
The configuration of CAM-Chem is identical to the one used in the recent
Chemistry-Climate Model Initiative simulations discussed in Tilmes
et al. (2016) under REF-C1 (specified sea-surface temperatures and sea-ice
distribution), except for using a higher horizontal resolution of
0.9
The current representation of aerosols in CAM-Chem has been extensively tested and compared with observations. In particular, Lamarque et al. (2012) provide a comparison of present-day observations of the IMPROVE network over the conterminous US, indicating an overall good representation of the probability density function for all species considered in the present study. In addition, Shindell et al. (2013) have shown that CAM-Chem indicated the lowest bias of the models when compared to observed aerosol optical depth. Finally, CAM-Chem results have been used in several health impact studies (West et al., 2013; Silva et al., 2013, 2016) and is usually very consistent with other chemistry–climate models used for those analyses. Four simulations are carried out for a period of 25 months each, and a mean annual cycle is computed from months 2 to 25. While there is some residual inter-annual variability in the 2 meteorological years simulated by the model, this signal is small compared to the impact associated with the changes in emissions (not shown).
Scenarios and scenario differences considered in the analysis.
Our analysis focuses on two time windows, current conditions and 2090. To assess
the relevance of wildfire
For anthropogenic emissions, we use the ECLIPSE-GAINS-v4a data (Amann
et al., 2011) developed as part of the ECLIPSE project (Granier
et al., 2011; Klimont et al., 2013; Stohl et al., 2015). This data set
provides two scenarios: current legislation (CLE) and maximum
feasible reductions (MFRs) on top of business-as-usual projections
until 2050 from the Energy Technology Projections study, which is considered roughly
equivalent to RCP6.0, from the
International Energy Agency (IEA, 2012). MFR corresponds to a policy- and technology-driven abatement scenario, implementing all currently known
technologies at a reasonable cost, with the aim, among others, to
lower
To facilitate the regional aspects of our analysis, we use a global map of nine major world regions (see Fig. S4 in the Supplement). Of these, three belong to the high-income group of countries of the SSP scenarios (see Jiang and O'Neill, 2015): high-income Europe, Australia and New Zealand, and North America. Countries of Europe belonging to the middle-income group were assigned to the region eastern Europe and central Asia, which also includes Russia. High-income countries of the Middle East (Israel and oil-rich states of the Persian Gulf) or East Asia (Japan and South Korea), which only account for a very small fraction of wildfire emissions in their respective regions, have been excluded from the analysis.
In this study, we address the question of whether there is a risk that
the combined direct (as estimated by changing population patterns) and
indirect impacts (through climate change) of human activities on
wildfire pollutant emissions will compromise meeting the WHO guideline
value of 10
Dominant
In order for wildfire emissions to be relevant for atmospheric pollution
levels, at least two conditions should be met: (1) they need to be of
a similar or greater magnitude compared to anthropogenic sources and (2)
they should be in or close to populated areas. Therefore, we first compare
emission levels of both types of sources for the current and future time
slices. Figure 1 shows areas where wildfire emissions currently exceed those
from anthropogenic sources (in either red or light blue; shown in all
panels). At first, these regions appear to lie in relatively remote
areas of wildfire-prone regions (see Fig. S5 in the Supplement, e.g. the
boreal forest zones of Canada and Alaska, eastern Siberia, the western US, the
Brazilian interior, and in Africa away from the main population centres of
Nigeria, Ethiopia, and Kenya). A breakdown of emissions by population density
shown for the nine world regions (see Fig. S4 in the Supplement) shows that
current (or future) anthropogenic emissions display a universal increase with
increasing population density (solid blue lines in Fig. 2). Wildfire
emissions also often increase with increasing population density and only
for North America do per-area emissions decrease with increasing population
density across the entire range of population densities (dashed blue lines in
Fig. 2, i.e. current wildfire emissions). Most of the remaining regions have
peaked distributions with the highest per-area emissions in some intermediate
population density category, and it is not the most remote and sparsely
inhabited areas that have the highest emissions. Therefore, despite
a dominance of anthropogenic emissions in the more densely populated areas,
wildfire emissions are often found to be an important pollution source in
regions with intermediate population density, meaning that they approach or
even exceed anthropogenic sources for population densities in the range from 10 to
100
Predicted per-area annual
For the simulated future, we either expect anthropogenic emissions to surpass
those from wildfires in many regions (mainly in Africa, light blue areas in
Fig. 1a and b) for the CLE scenario or the reverse for the MFR scenario,
where wildfire emissions surpass anthropogenic sources in a wide range of
regions (yellow areas in Fig. 1c, d: South America, Central America, Africa,
eastern and southern Europe, central Asia, South East Asia, and southern
China). This dominant role of the anthropogenic emission scenario is largely
independent of the wildfire emission scenario (i.e. the difference between
Fig. 1a and b or between Fig. 1b and d is much smaller than the difference
between Fig. 1a and b on the one hand and Fig. 1c and d on the other hand). The
strongest impact of the wildfire emission scenario is found for sub-Saharan
Africa for the CLE scenario, in which SSP3/RCP4.5 shows many more regions newly
dominated by anthropogenic emissions than SSP5/RCP8.5 (Fig. 1a vs. Fig. 1b).
This is due to declining wildfire emissions in that scenario (see Fig. S2a in
the Supplement). The scenario in which wildfire emissions are most likely to
become a relevant source of
The MFR scenario assumes a decline in anthropogenic emissions by
approximately 1 order of magnitude in areas with at least
10 people
Simulated changes in
Predicted annual
In contrast to the current situation with a steady increase in
pollutant concentrations with population density (dark blue lines in
Fig. 3), concentrations for 2090 peak at an intermediate level of
population density in most regions (red lines: sub-Saharan Africa,
Latin America and the Caribbean, eastern Europe–Russia–central Asia,
Australia and New Zealand, and high-income Europe), reflecting the
increased importance of wildfires for the spatial distribution of
pollutant concentrations. This change over time is most pronounced for
sub-Saharan Africa, where emissions from wildfires far outweigh
anthropogenic
The effect of the anthropogenic emission scenario can be seen by comparing the MFR and CLE scenarios for the case of the high-wildfire-emission scenario (red and green lines in Fig. 3; see Table 1). CLE/SSP5/RCP8.5 compared to MFR/SSP5/RCP8.5 leads to large reductions in concentrations in pollutant concentrations in all regions by 2090, in particular for the most densely populated categories. Compared to this large effect of the anthropogenic emission scenario, the effect of the wildfire scenario (difference between high- and low-wildfire-emission scenarios, red and light blue lines in Fig. 3) is relatively small and only visible in sub-Saharan Africa, South and South East Asia, and developing East Asia.
Simulated spatial patterns of
Simulated annual-mean anthropogenic and
wildfire
Simulated
Further analysis hereafter focuses on monthly mean
In most regions of the world, decisive and effective reductions of
anthropogenic air pollutant emissions will likely be able to limit
pollutant levels below the WHO threshold of 10
Previous simulations with chemistry–climate models using RCP emission
projections have already shown a strong future downward trend in East
and South Asia, driven by reduced anthropogenic emissions, but no
notable trend in Africa (Fiore et al., 2012). Knorr et al. (2016a)
have shown a general picture of climate-driven fire emission
increases, both for RCP4.5 and 8.5 scenarios, that may be overridden
by demographic changes only in sub-Saharan Africa. This simulated
future decline in Africa is in agreement with observations of
currently already declining burned area that has been linked to
demographic trends of increasing rural population for the northern
part of sub-Saharan Africa (Andela and van der Werf, 2014). In the
present study, southern China is identified as a new area of possible
high human exposure to wildfire-generated air pollution under
a scenario of rapid urbanization and population decline in rural
areas. While forest fires in China may have received comparatively
little attention, they can still be substantial, with over
670 000
While any additional emission source of
In our simulations, air pollutant concentrations follow similar but
more dispersed patterns compared to emissions, with the highest levels in
densely populated areas and the lowest levels in sparsely populated areas. In the
future, in the case of strong reduction in anthropogenic sources, this
pattern is predicted to shift to one in which the highest pollution
levels are found in regions of intermediate population density for
most regions (sub-Saharan Africa, Latin America and the Caribbean, and
eastern Europe–Russia–central Asia), resembling more closely the pattern of
wildfire emissions. This means that also due to their geographical
distribution, wildfires pose a smaller risk to humans than
anthropogenic emissions. Nevertheless, in our simulations under strong
emission reduction from anthropogenic sources, the future trajectory
of wildfire emissions has a discernible impact on air pollution in
certain regions (sub-Saharan Africa, South and South East Asia, and
developing East Asia) and is particularly relevant if we consider
seasonal maxima in pollution levels. Even though the WHO
recommendations are based on annual mean concentrations of
It also needs to be taken into account that
the
Significant health impacts are therefore very likely, even if this limit is exceeded only on a seasonal basis. For certain regions, it will be of critical importance whether future air quality policy objectives will converge to the current WHO guidelines, in which case fire management will become increasingly important. If anthropogenic emissions are aggressively curtailed (MFR scenario), wildfires in sub-Saharan Africa are predicted to decline less than anthropogenic sources, and in parts of South East Asia, southern China, and central South America climate change may even lead to new areas with wildfire emission levels relevant for air quality and the associated health impacts. In many boreal regions wildfires will also increase to levels at which they become pollution sources with relevant health impacts. Because past efforts aimed at a lasting reduction in wildfire activity have largely failed (Donovan et al., 2007; Doerr and Santin, 2016), it is questionable whether it is even possible to devise policy measures aimed at bringing down wildfire emissions to avoid adverse health effects.
LPJ-GUESS–SIMFIRE only simulate wildfires. The predictions presented in this study therefore leave out the possibility of significant increases or decreases in deforestation or peat fire sources. Peat fires can be associated with considerable emissions (Page et al., 2002; Kajii et al., 2002), and forest conversion into cropland or pasture is often accompanied by burning (van der Werf et al., 2010). Both are of minor importance for air pollution except for South East Asia (see Fig. S1 and Table S2 in the Supplement), mainly in Indonesia (Field et al., 2009), where they are the dominant pollution source and occur even in more densely populated areas. In other regions, including Russia, peat fires are of minor importance. Whether or not future land-use change will lead to an increase or a decrease in deforestation is unknown. Based on integrated-assessment model realizations of the four RCPs, Hurtt et al. (2011) projected little increase or even a decline in future crop and pasture areas. However, in studies that assessed future land-use change from a broader perspective, a much larger range of crop and pasture changes emerged (Eitelberg et al., 2015; Prestele et al., 2016), which makes the relative change in deforestation vs. wildfires highly uncertain. In the present analysis, declining wildfire emissions are only predicted for sub-Saharan Africa, where it appears to be related to conversion of savannah to cropland (Andela and van der Werf, 2014).
Apart from the issue of dry vs. wet aerosols and the omission of some
components that are difficult to model, there are additional limitations of
this study. We expect that results will be affected by the presence of
natural aerosols, such as mineral dust and sea salt, which –
depending on location and time of year – could be significant
fractions of the
Nevertheless, to our knowledge, this is the first global-scale air
quality study to consider changes in both climate and demographic
drivers of air pollutant emissions from wildfires. Future work should
aim at using general circulation models with realistic plume heights
for a series of dedicated present and future time slices, combining
observed plume height information, fire radiative energy data (for
their finer temporal resolution), satellite-derived burned area (for
better spatial coverage), projected emission changes from coupled
dynamic vegetation–fire models (as the present study), and improved
demographic scenarios accounting for changes in urban population
density. Such studies would then also simulate the temporal statistics
of pollution events on a daily timescale. Wildfire episodes can
elevate
Globally, wildfire emissions are unlikely to thwart aggressive
measures to reduce anthropogenic pollutant emissions enough to stay
under the ambitious 10 In a number of regions, wildfire emissions will remain or could rise
above critical thresholds relevant for health policy, in particular
when pollution levels during the fire season are considered. So far,
there is no generally accepted method for wildfire management that has
been shown to lead to lasting reductions in fire activity or
emissions. Demographic changes appear to be the main drivers of the expected
changes in wildfire emissions in sub-Saharan Africa. For a scenario of
high population growth and slow urbanization, anthropogenic sources
could surpass air pollutant emissions from wildfires in most populated
areas. Exposure of humans to
RCP historical and scenario CO
W. Knorr conceptualized the study, carried out the analysis, and wrote the first draft of the paper. J. F. Lamarque performed the CAM-Chem simulations. All authors contributed to discussions and writing.
The authors declare that they have no conflict of interest.
This work was supported by EU contracts 265148 (Pan-European Gas–AeroSOls–climate interaction Study, PEGASOS) and 603542 (Land-use change: assessing the net climate forcing, and options for climate change mitigation and adaptation, LUC4C) as well as BECC (Biodiversity and Ecosystem services in a Changing Climate) funded by the Swedish Government. The National Center for Atmospheric Research is sponsored by the National Science Foundation. The article processing charges for this open-access publication were covered by a research centre of the Helmholtz Association. Edited by: Dominick Spracklen Reviewed by: three anonymous referees