Introduction
North American wildfires are important sources of air pollutants, such as
ozone precursors carbon monoxide (CO), nitrogen oxides (NOx), and
volatile organic compounds (VOCs). Their emissions can strongly affect air
quality locally and, in the case of large fires, in areas thousands of
kilometers downwind in the United States and Canada (Wotawa and Trainer,
2000; Morris et al., 2006; Kang et al., 2014), over the mid-Atlantic (Val
Martin et al., 2006; Cook et al., 2007), and in Europe (Real et al.,
2007). Previous studies have projected increases in the area burned by North
American wildfire in the 21st century due mainly to warmer temperatures
(Flannigan et al., 2005; Balshi et al., 2009; Wotton et al., 2010; Price
et al., 2013; Boulanger et al., 2014), implying further degradation of air
quality by wildfire emissions in a changing climate. However, predicted
increases in future precipitation in Alaska and Canada
(Christensen et al., 2007) may have an opposing effect on
future wildfire activity, resulting in large uncertainties in fire
projections.
Wildfires in Canada and Alaska often have much larger size compared with
those in the contiguous United States (Stocks et al., 2002; Westerling et
al., 2003). Emissions from boreal wildfires can have significant effects on
air quality over the contiguous US (Sigler et al., 2003; Miller et al.,
2011; Kang et al., 2014). In the summer of 1995, transport of forest fire
emissions from northwestern Canada reached as far south as the central and
southern US, increasing CO concentrations as much as 200 ppb in that
region (Wotawa and Trainer, 2000). The same fires also enhanced ozone
in the central and southern US by 10–30 ppbv, most of which was associated
with NOx directly emitted by Canadian fires and the remainder with
the oxidation of wildfire CO by locally emitted NOx (McKeen et al.,
2002). The summer of 2004 was one of the most intense fire seasons on record
for Canada and Alaska (Turquety et al., 2007; Lavoue and Stocks, 2011).
An analysis of flight data over the northeastern US concluded that boreal
fire emissions during that summer contributed 10 % of the observed CO over
the northern United States (Warneke et al., 2006) and enhanced mean
summertime ozone there by 1–3 ppbv (Hudman et al.,
2009). Smoke plumes occasionally reached Houston that summer, increasing
ozone there as much as 30–90 ppbv between the surface and 3 km altitude and
likely contributing to violations of the 8-hour ozone air quality standard
(Morris et al., 2006).
Area burned in North America is influenced by fuel availability, weather,
ignition, and fire suppression practices. Many studies, however, have
suggested that meteorology is the single most important factor
(Hely et al., 2001). For example,
Gillett et al. (2004) found that changes in temperature
alone explain 59 % of the variance of the observed area burned in Canada
for 1920–1999. Regression studies using surface meteorological data and fire
indices also yield high R2 of 0.4–0.6 for area burned in boreal
ecoregions (Flannigan et al., 2005). In addition to the
surface weather conditions, the 500 hPa geopotential height is also found to
be important in predictions of area burned in boreal forests (Skinner et
al., 1999; Wendler et al., 2011), since this variable can indicate the
occurrence of blocking highs over the continent, which cause rapid fuel
drying (Fauria and Johnson, 2008).
Studies examining climate impacts on wildfire activity in North America have
projected increases in area burned over most boreal ecoregions in the
21st century. Flannigan and Van Wagner (1991) developed
linear regressions between area burned and fire indices. They applied these
regressions with the mean climate simulated by three general circulation
models (GCMs) and projected an increase of 40 % in Canadian area burned in
a doubled CO2 atmosphere, relative to present day.
Flannigan et al. (2005) improved the previous projection
with more complete meteorological station data, higher spatial resolution,
and a stepwise regression scheme with more potential regression factors.
Their results showed that area burned increases by 70–120 % in boreal
ecoregions by 2080 to -2100, a period with roughly tripled atmospheric CO2
concentrations in the scenario used. However, Balshi et al. (2009)
predicted that area burned in Alaska and Canada would double by 2050, a rate
more rapid than in the projections by Flannigan et al. (2005). The discrepancies among these studies arise in part from the
differences in the climate scenarios as well as the sensitivity of the
particular GCMs to increases in greenhouse gases.
In this study, we investigate the impact of changing climate on future
Alaskan and Canadian area burned and the consequences for ozone air quality
in North America by 2046–2065 under a moderately warming scenario. Wildfires
produce abundant ozone precursors, and many, but certainly not all,
observational studies of boreal fires suggest subsequent ozone generation
either locally or downwind (Jaffe and Wigder, 2012). We
build here on our earlier study (Yue et al., 2013), which projected
future area burned in the western US using stepwise regressions and the
simulated climate from an ensemble of climate models from the World Climate
Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3
(CMIP3) multi-model data set (Meehl et al., 2007a).
Yue et al. (2013) predicted that the warmer and drier summer climate over
the western US at the mid-century would increase area burned there by 60 %
and the consequent biomass burned by 77 %. Yue et al. (2013) further
calculated regional increases of 46–70 % in surface organic carbon aerosol
and 20–27 % in black carbon aerosol due to the increased fire emissions.
For this study, we focus on ozone air quality. We rely on the CMIP3 ensemble
of climate models to obtain confidence in projections of boreal area burned,
and we combine these results with those of Yue et al. (2013) for the
western US. Using the estimated fuel consumption and emission factors for
ozone precursors, we calculate future fire emissions over North America.
Finally, we quantify the impacts of those emissions on ozone mixing ratios
at the mid-century, using the GEOS-Chem chemical transport model (CTM) driven
by the Goddard Institute for Space Studies general circulation model 3 (GISS
GCM3).
Data and methods
Boreal ecoregions
We divide Alaskan and Canadian forests into 12 ecoregions (Fig. 1),
following the definitions of the Ecological Stratification Working Group
(1996) with modifications by Stocks et al. (2002) and
Flannigan et al. (2005). Area burned outside these
ecoregions is small. In northern Canada cold weather and the lack of fuel
continuity for the tundra and mountainous regions limits fire activity
(Stocks et al., 2002), while regulations restrict agricultural burning in
the southern part of central Canada.
Distribution of the 12 ecoregions used for this study. The black
triangle symbols indicate the GSOD meteorological data sites in Alaskan and
Canadian ecoregions.
We describe the 12 ecoregions as follows. Located in central Alaska, the
Alaska Boreal Interior consists mainly of plains and hills and is covered
with Arctic shrubs and open coniferous forest. The Taiga Cordillera in
western Canada has similar vegetation, although the higher elevation leads to
lower temperatures. Three western ecoregions, the Alaska Boreal Cordillera,
the Canadian Boreal Cordillera, and the Western Cordillera are located along
the Rocky Mountains. The high elevation causes abundant precipitation,
especially for the Western Cordillera, resulting in dense forests. In
contrast, the two central Canadian ecoregions, the Taiga and Boreal Plains,
are at lower altitudes and are characterized by tundra meadow and aspen
forest. The Western Taiga Shield is a plain in north central Canada
characterized by shrub and conifer forests. The Hudson Plain, to the south of
Hudson Bay, is dominated by wetlands. Stocks et al. (2002) defined the
Eastern Taiga Shield as covering most of northern Quebec. Here we redefine
this ecoregion so that it covers just the southwestern part, where
∼ 90 % of the area burned in the original ecoregion occurs. We
divide the Mixed Wood Shield, a large ecoregion in southeast Canada, into
eastern and western parts. Fire activity in these two subregions is
significantly different (Flannigan et al., 2005).
Fire data
We compile monthly 1∘ × 1∘ area burned from
1980 to 2009 based on interagency fire reports. For Alaska, we use incidence
reports managed by the National Wildfire Coordinating Group from the Fire
and Aviation Management Web Applications (FAMWEB, http://fam.nwcg.gov/fam-web/weatherfirecd/, downloaded on 5 June
2012). Five agencies, the US Forest Service (USFS), Bureau of Land
Management (BLM), Bureau of Indian Affairs (BIA), Fish and Wildlife Service
(FWS), and National Park Service (NPS), provide ∼ 5000 records
of fire incidence in Alaska between 1980 and 2009. Each record documents the
name, location (latitude and longitude), start and end time, ignition source
(lightning or human) and area burned of an individual fire. The minimum area
burned is 1 ha and the maximum is 2.5 × 105 ha for the Inowak
Fire, which began on 25 June 1997. Duplicates are expected because fires
burn in lands managed by different agencies (Kasischke et al., 2011). We
identify and delete duplicate records if two or more fires have same names
and areas, and occur within a distance of 50 km on the same day. Thus we
obtain a corrected subset and compare it with the annual fire report from
the National Interagency Coordination Center (NICC, http://www.nifc.gov/nicc/). NICC manages fire reports from federal
agencies, states, and private ownership, and so has more complete data sets
relative to FAMWEB. NICC, however, provides annual total area burned only
back to 1994. The correlation R between FAMWEB and NICC is 1.0 and the
differences are within 2 % for 1994–2009, giving us confidence in our
compilation of FAMWEB area burned.
For Canada, we use fire point data from the Canadian National Fire Database
(CNFDB, http://cwfis.cfs.nrcan.gc.ca/ha/nfdb), which is an extension of the
Large Fire Database (LFDB) summarized in Stocks et al. (2002). The
database provides over 210 000 records of forest fires during 1980–2009,
collected from provinces, territories, and Parks Canada. Each CNFDB record
includes the name, location, size, and time of one fire. The minimum area
burned is 0.1 ha and the maximum is 6.2 × 105 ha for a fire
that began on 12 July 1981. Duplicates in CNFDB are much fewer, possibly
because the redundant records were deleted when the data set was compiled
into a geographic information system. Although the total number of fires is
immense, only about 5 % are greater than 100 ha. These large fires account
for over 99 % in area burned in the data set, as was the case for the LFDB.
We aggregate both the FAMWEB and CNFDB report data onto 1∘ × 1∘ grids, based on the location of fires. Area burned
is assigned to the start month, as end dates are often uncertain
(Kasischke et al., 2011). The monthly gridded area burned is used to
derive fire emissions. To develop the fire models, we aggregate the fire
report data into boreal ecoregions across Alaska and the Canadian boreal
forest (Fig. 1) and then sum the area burned within each ecoregion for the
entire fire season (May–October) to reduce noise in the regression.
Meteorological data and fire weather indices
We use daily observations for 1978–2009 from the Global Surface Summary of
the Day data set (GSOD, http://www.ncdc.noaa.gov/). The length
of meteorological data is 2 years longer than that of fire data, because
the regressions employ terms that depend on the weather occurring up to 2
years before the area burned. The GSOD provides 18 daily surface
meteorological variables for over 2000 stations in Alaska and Canada. We
select 157 sites within the 12 ecoregions that provide observations for at
least two-thirds of the days during 1978–2009 (Fig. 1). We use daily mean
and maximum temperature, total precipitation, and wind speed and calculate
relative humidity using daily mean temperature and dew point temperature. We
also use the 500 hPa geopotential height from the North American Regional
Reanalysis (NARR, Mesinger et al., 2006). Both the site measurements and
the NARR reanalysis data are binned into ecoregions to derive monthly
averages.
The site observations are also used as input for the Canadian Fire Weather
Index system (CFWIS, Van Wagner, 1987). The CFWIS uses daily
temperature, relative humidity, wind speed, and total precipitation to
calculate three fuel moisture codes and four fire severity indices. The fuel
moisture codes indicate moisture levels for litter fuels (Fine Fuel Moisture
Code, FFMC), loosely compacted organic layers (Duff Moisture Code, DMC), and
deep organic layers (Drought Code, DC). The FFMC is combined with wind speed
to estimate the Initial Spread Index (ISI). The DMC and DC are used to
derive the Build-up Index (BUI) to indicate the availability of fuel. The
ISI and BUI are then combined to create the Fire Weather Index (FWI) and its
exponential form as the Daily Severity Rating (DSR). The CFWIS indices have
been widely used in fire-weather research over North America (Amiro et
al., 2004; Flannigan et al., 2005; Balshi et al., 2009; Spracklen et al.,
2009), and in our previous work (Yue et al., 2013).
Regression approach
We use total area burned during the fire season as the predictand, and we
assume that the influences of both topography and fuels on wildfire activity
are roughly uniform across each region. We calculate the means of five
meteorological variables (mean and maximum temperature, relative humidity,
precipitation, and 500 hPa geopotential height) over six different time
intervals (winter, spring, summer, autumn, annual, and fire season), making
30 meteorological predictors in all. The mean and maximum values of the
seven daily CFWIS indices during fire season are also included in the
regressions, making another 14 fire-index predictors. As a result, a total
of 44 terms are generated for the current year. As in Yue et al. (2013),
we also employ all these variables from the previous 2 years in the
regression, making 132 (44 × 3) potential terms for the regression.
We set up two criteria to select a factor as a predictor at each step.
First, the chosen factor must have the maximum contribution to the F value, a
metric for variance, of the predictand among the unselected factors. Second,
this factor must exhibit low correlation with those already selected, with p
value > 0.5. The first criterion produces a function with the
largest possible predictive capability, while the second helps increase the
stability of the function by introducing independent predictors
(Philippi, 1993). We cross validate all the regressions with the
leave-one-out approach following Littell et al. (2009). We calculate the
ratio of the predicted residual sum of squares (PRESS) root mean square
error (RMSE) to the standard deviation (SD) of area burned in each ecoregion
as an indicator of the leave-one-out prediction error. A robust regression
usually has an RMSE / SD ratio lower than 2 (Littell et al., 2009).
In Yue et al. (2013), we also developed a parameterization for area
burned in the western US. The parameterization was a function of
temperature, precipitation, and relative humidity. The same functional form
was applied throughout the domain, scaled by an ecoregion-dependent fire
potential coefficient. We find that the parameterization approach fails in
boreal forests, probably because the driving factors for wildfires vary
greatly over the vast boreal areas.
CMIP3 model data
We use daily output from 13 climate models in the CMIP3 archive
(Meehl et al., 2007a) for the fire projection
(Table S1 in the Supplement). The variables we select include daily mean and maximum
temperature, total precipitation, and surface wind speed. We calculate daily
relative humidity (RH) for the CMIP3 models using other archived meteorological variables. We also
use the monthly mean 500 hPa geopotential heights from all 13 GCMs. We use
the output from the 20C3M scenario for the prediction of area burned in the
present day (1981–1999). Simulations in the CMIP3 ensemble for the years
beyond 1999 (or in some cases 2000) are driven by a suite of future
greenhouse gas scenarios, making comparisons with observations difficult.
For the future atmosphere (2046–2064), we use the simulated climate under
the A1B scenario, which assumes a greater emphasis on non-fossil fuels,
improved energy efficiency, and reduced costs of energy supply. CO2
reaches 522 ppm by 2050 in this scenario (Solomon et al.,
2007), resulting in a moderate warming relative to other scenarios
(Meehl et al., 2007b). Over this relatively short time frame,
the A1B scenario is consistent with two moderate scenarios in the newer
Representative Concentration Pathways, RCP 4.5 and RCP6.0 (Moss et al.,
2010). We aggregate all of the climate simulations into ecoregions for the
projection. In order to reduce model bias, we scale the aggregated variables
of both present day and future from each GCM using the mean observations for
1980–2009 from the GSOD sites. The changes in area burned and meteorological
variables are examined with a Student t test and only those with p < 0.05 are considered as significant.
Fuel consumption
Fuel consumption is the amount of both live and dead biomass burned per unit
area. It depends on both fuel load and burning severity. In Yue et al. (2013), we estimated fuel load over the western US using the 1 km data set
from the USFS Fuel Characteristic Classification System (FCCS,
http://www.fs.fed.us/pnw/fera/fccs/, McKenzie et al., 2007). The FCCS
defines ∼ 300 types of fuel bed based on the distribution of
vegetation types from the Landscape Fire and Resource Management Planning
Tools (LANDFIRE, http://www.landfire.gov/). Each type of
fuel bed consists of seven basic fuel classes (i.e., light, medium, heavy
fuels, duff, grass, shrub, and canopy) each with a different load
(Ottmar et al., 2007). Here, for Canada, we use the 1 km fuel type map from the Canadian Fire Behavior Prediction (FBP) system,
which is derived from remote sensing and forest inventory data and includes
just 14 types (Nadeau et al., 2005). For Alaska, we use a fuel
map created by the USFS, which also follows the classification scheme of
Nadeau et al. (2005). However, the FBP system does not provide
fuel load, and so we follow Val Martin et al. (2012), who
matched the Canadian FBP fuel beds with their corresponding types in the FCCS
and in this way estimated the fuel load for both Canada and Alaska (see
their Table A1).
Burning severity indicates the fraction of fuel load burned by fires and
varies by moisture state. We follow the approach of Val
Martin et al. (2012), who used the USFS CONSUME model 3.0
(Ottmar, 2009) to calculate burning severity and the resulting
fuel consumption for a given fuel load. In this approach, the derived FBP
fuel loads are applied to CONSUME, yielding reference fuel consumption for
five moisture conditions: wet, moist, moderately dry, dry, and extra dry
(Val Martin et al., 2012). Here we use a newer model
version, CONSUME-python (https://code.google.com/p/python-consume/), which fixes some errors in
CONSUME 3.0. The updated reference fuel consumption for different FBP fuel
types and moisture states is given in Table S2. Our values for C3 (mature
jack or lodgepole pine) and C5 (red and white pine) fuel types are 40–65 %
lower than those in Val Martin et al. (2012), likely
because of errors in the calculation of duff fuel in CONSUME 3.0. We
aggregate the new 1 km fuel consumption map to 1∘ resolution to
match that of gridded area burned. Figure 2a shows fuel consumption for
moderately dry conditions. The figure shows heavy fuel consumption of
> 7 kg dry matter (DM) m-2 in the Taiga Plain and in the
Western and Eastern Mixed Wood Shield, where boreal spruce fuel types (C2)
dominate.
Fuel consumption over Alaska and Canada (a) for moderately
dry conditions and (b) weighted by the Drought Code (DC) and area
burned for 1980–2009. The average values are shown in brackets.
We rely on the DC index from the CFWIS in order to assign the moisture
condition and determine the monthly fuel consumption. This index is a good
indicator for fuel moisture content (Bourgeau-Chavez et al., 1999; Abbott
et al., 2007) and has been widely used to calculate fuel consumption
(e.g., de Groot et al., 2009; Kasischke and
Hoy, 2012). Higher DC values indicate greater dryness. Figure S1 in the Supplement shows the
monthly mean DC in boreal ecoregions for 1980–2009. The values of DC
increase gradually from May to September, as fuels become progressively
drier. The DC values in western ecoregions are usually higher than those in
eastern ones, probably because precipitation in the West (except for the
Pacific coast) is much lower relative to that in the East (not shown).
Figure S2 in the Supplement shows the cumulative probability of daily DC in all ecoregions
during the fire seasons of 1980–2009. This probability distribution differs
somewhat from the distributions in Amiro et al. (2004) who estimated DC for Canadian wildfires larger than 2 km2 in
different ecosystems during 1959–1999. Such fires typically occur in June to
August. In contrast, Fig. S2 shows the DC distribution over the entire
fire season, including days in September and October, when DC values are
usually very high. We relate burning severity to DC by defining four
arbitrary thresholds in the DC probability distribution: 85, 65,
35, and 15 %. The resulting moisture categories and their average DC
indices are as follows: extra dry (DC > 85 %, 774), dry
(65 % < DC ≤ 85 %, 590), moderately dry
(35 % < DC ≤ 65 %, 390), moist (15 % < DC ≤ 35 %, 196), and wet
(DC ≤ 15 %, 53). We then calculate the monthly fuel consumption in each
ecoregion by matching the DC in that month to these moisture categories and
choosing the appropriate fuel consumption (Table S2 in the Supplement). In this way, fuel
consumption varies yearly and seasonally. Amiro et al. (2004) found that the average DC for Canadian wildfires ranges from 210
to 372 depending on the ecoregion, and the cumulative probability of the DC
also varies with ecoregion. Here we have chosen to use a single distribution
for the North American boreal region to define the DC thresholds (Fig. S2). As a check, we also compare the fuel consumption derived in this way
with that which is calculated based on the ecoregion-specific DC thresholds (see
Table 4 and related discussion in Sect. 3.3).
We assume that the fuel load remains constant for both present day and
mid-century, based on the conclusion that changes in forest composition will
be a gradual process (Hanson and Weltzin, 2000). Fuel consumption per
unit area burned, however, does change in our approach since it depends on
the moisture state. We estimate fuel consumption for both present day and
mid-century based on the multi-model median DC in each ecoregion. As a
result, the modeled fuel consumption responds to trends in fuel moisture
conditions. Amiro et al. (2009) performed a similar
estimate of future boreal fuel consumption using modeled monthly mean values
of the DC and an empirical relationship derived by de
Groot et al. (2009) for forest floor fuel consumption in experimental fires
in Canada. However, this empirical relationship has predictive capability
only for fires set under experimental conditions, but not for wildfires
(de Groot et al., 2009), and we do not apply it here.
Estimate of gridded fire emissions
We calculate biomass burned as the product of area burned and fuel
consumption. The annual area burned estimated with regressions for each
ecoregion (Sect. 2.4) is first converted to monthly area burned using the
mean seasonality for each boreal ecoregion, on the basis of the observations
for 1980–2009. Large fires tend to burn in ecosystems with a history of
similarly large fires (Keane et al., 2008). Fuel availability, however,
limits reburning in the same location during the forest return interval,
which is typically ∼ 200 years for Canadian forests
(Ter-Mikaelian et al., 2009; de Groot et al., 2013). We assume a random
distribution of area burned within each ecosystem, to allow for these
tendencies.
We spatially allocate monthly area burned within each ecoregion to
1∘ × 1∘ as follows. In each 1∘ × 1∘ grid square we calculate the frequency of fires
larger than 1000 ha during 1980–2009; such fires account for ∼ 85 % of total area burned in Canada and Alaska over this time period.
Accordingly, we arbitrarily attribute 85 % of area burned within each
ecoregion to fires of 1000 ha in size, and we then allocate these large
fires among the 1∘ × 1∘ grid squares based on
the observed spatial probability of large fires (> 1000 ha),
which is the percentage of total large fires of the ecoregion located in a
specific grid box during this time frame. We then disaggregate the remaining
15 % of area burned into fires 10 ha in size, and randomly distribute
these fires across all grid boxes in the ecoregion. We apply this random
approach to calculate both present-day (1997–2001) and future (2047–2051)
biomass burned. Within each time frame, the effect of limited fuel
availability in the aftermath of a fire is taken into account by
reevaluating the spatial probability distribution of area burned at each
monthly time step. We scale the observed probabilities by the fraction
remaining unburned in each grid box, and then use this modified probability
distribution to allocate large fires for the remaining months. Using
sensitivity tests, we find that specifying different areas burned to the
large fires (100 or 10 000 ha rather than 1000 ha) yields < 1 %
changes in predicted biomass burned, suggesting that this approach is not
sensitive to the presumed fire size in the allocation procedure.
We take the emission factors for all ozone precursors except nitric oxide
(NO) from Andreae and Merlet (2001). For NO we average the values from
six studies of forest fires in the western US (Table S3 in the Supplement), yielding 2.2 g NOx kg DM-1. Based on the measurements by
Hegg et al. (1990), which showed that NO contributes
30 % of fire-induced NOx, this value is equivalent to 1.6 g NO kg
DM-1, consistent with the mean emission ratio of 1.4 g NO kg DM-1
derived from measurements from Alaskan fires (Nance et al., 1993; Goode
et al., 2000). Our NO emission factor is ∼ 50 % higher than
that derived by Alvarado et al. (2010) from aircraft measurements of
boreal fire plumes. They also found that 40 % of NOx emissions are
rapidly converted to peroxyacetyl nitrate (PAN) in fresh plumes. We use the
emission factor of 1.6 g NO kg DM-1 and neglect the rapid formation of
PAN for our simulations, recognizing that this likely leads to a small
overestimation of ozone formation immediately downwind of the fires. The
emission factors from Andreae and Merlet (2001) have recently been
updated by Akagi et al. (2011) and Urbanski (2014). As a check, we
compare the predicted fire emissions using all three sets of emission
factors (see Table S6 and related discussion in Sect. 3.3).
GEOS-Chem CTM and simulations
We simulate tropospheric ozone–NOx–VOC–aerosol chemistry using the
GEOS-Chem global 3-D model of tropospheric chemistry version 8.03.01, driven
by present-day and future simulated meteorological fields from the NASA/GISS
Model 3 with 4∘ × 5∘ resolution (Wu et al.,
2007; Wu et al., 2008b). Compared with finer resolution, 4∘ × 5∘ resolution does not induce a significant bias in
surface ozone and captures the major synoptic features over the United
States (Fiore et al., 2002, 2003), though it may
underestimate the average ozone level by 1–4 ppbv and predict fewer
pollution episodes (Wang et al., 2009; Zhang et al., 2011). The simulated
daily and monthly ozone concentrations from the GEOS-Chem model driven with
meteorological reanalyses have been widely validated with site-level,
aircraft, and satellite observations (Fiore et al., 2002; Wang et al.,
2009; Alvarado et al., 2010; Zhang et al., 2011). Monthly mean ozone
concentrations simulated with GISS meteorology have been evaluated by
comparison with climatological ozone-sonde data and reproduce values
throughout the troposphere usually to within 10 ppbv
(Wu et al., 2007). In addition, simulated daily ozone
with GISS meteorology reasonably reproduces the summertime temporal
variability of ozone concentrations as well as the pollution episodes in the
US (Wu et al., 2008b).
Anthropogenic emissions for ozone precursors, including NOx, CO, and
non-methane VOCs are as described in Table 1a of Wu et al. (2008b) and are summarized here for completeness and transparency.
Global emissions of NOx and CO are upscaled from the 1∘ × 1∘ Emissions Database for Global Atmospheric Research
(EDGAR) version 3 (Olivier and Berdowski, 2001).
Anthropogenic VOC emissions are derived from the Global Emission Inventory
Activity (GEIA) (Benkovitz et al., 1996). Over the North American domain,
these global emissions are replaced with the EPA National Emissions
Inventory (NEI) 2005 inventory (http://www.epa.gov/). All the anthropogenic
emissions are kept constant at the level of the year 2000 for both present-day and
future simulations, to isolate the effects of changes in biomass
burning emissions. However, natural emissions of these gases from
vegetation, soil, and lightning are computed locally based on the
meteorological variables within the model and allowed to change with
climate. Emissions of biogenic hydrocarbons are calculated with the Model of
Emissions of Gases and Aerosols from Nature (MEGAN), version 2.1
(Guenther et al., 2012). The lightning source of NOx is computed
locally in deep convection events using the scheme of Price and
Rind (1992), which relates number of flashes to convective cloud top
heights, together with the vertical NOx distribution from
Pickering et al. (1998). Stratosphere–troposphere
exchange (STE) is specified by the Synoz flux boundary condition
(McLinden et al., 2000) with a prescribed global annual
mean flux of 495 Tg ozone yr-1 for both present-day and future
simulations. Outside of North America, we use climatological biomass burning
emissions derived from the inventory described in Lobert
et al. (1999), with seasonality from Duncan et al. (2003) and placed into the boundary layer.
Summary of simulations in this study.
Simulations
Western US
Boreal fire
Climate
Emission
fire emissions
emissions
factors
FULL_PD
present-daya
present-day
present-day
AM2001c
FULL_A1B
futureb
future
future
AM2001
NOFIRE_PD
none
none
present-day
AM2001
NOFIRE_A1B
none
none
future
AM2001
WUS_FIRE
future
present-day
future
AM2001
BOREAL_FIRE
present-day
future
future
AM2001
CLIM_CHAN
present-day
present-day
future
AM2001
FULL_PD_EF
present-day
present-day
present-day
A2011d
FULL_A1B_EF
future
future
future
A2011
a Present-day denotes 1997–2001.
b Future denotes 2047–2051.
c Emission factors from Andreae and Merlet (2001) and NOx
emission factor from an ensemble of experiments (Table S3 in the Supplement).
d Emission factors from Akagi et al. (2011)
Over North America, we apply the biomass burning emissions predicted by our
method. For the western US, we use area burned predicted with regressions
from Yue et al. (2013). We update the fire emissions over southern
California with our improved fire scheme (Yue et al., 2014). For
Canada and Alaska, we use the fire emissions derived from calculated area
burned and the estimated fuel consumption. We do not change the emissions
over the eastern US, which are dominated by prescribed agricultural fires
(Liu, 2004). The GEOS-Chem model is not coupled with a plume
model, and as a result cannot simulate the impacts of plume rise. As in
Leung et al. (2007), we emit 20 % of emissions in each grid square to
the model levels between 3 and 5 km and leave the rest in the boundary
layer, as observations have shown that over 80 % of plumes from North
America fires are located in the boundary layer (Val
Martin et al., 2010). In calculating photolysis rates within the plume, the
model takes into account the attenuation of solar radiation by fire
aerosols. This calculation has some importance; in their model study,
Jiang et al. (2012) found that fire aerosols alone could
reduce ozone concentrations by up to 15 % close to the source due to the
light extinction.
Surface ozone concentrations in the 21st century will be influenced not
just by trends in wildfire emissions, but also by changes in atmospheric
transport, temperature, cloudiness, wet and dry deposition, and
natural/anthropogenic emissions. To isolate the changes due to biomass
burning emissions, we conduct an ensemble of 5-year simulations for present
day (1997–2001) and the mid-21st century (2047–2051) for a total of nine
sensitivity studies (Table 1). Two simulations, FULL_PD and
NOFIRE_PD, are carried out with present-day climate:
FULL_PD considers present-day fire emissions from both
western US and boreal forests, while NOFIRE_PD omits any
fire emissions in these regions. Five simulations are conducted with future
climate. In FULL_A1B, we additionally implement the projected
future fire emissions from western US and boreal forests, while
NOFIRE_A1B omits these emissions. Simulation
WUS_FIRE applies future fire emissions in western US but
the present-day emissions in boreal forests. In contrast,
BOREAL_FIRE uses present-day emissions in western US but
the future ones for boreal regions. The last simulation with future climate,
CLIM_CHAN, applies present-day fire emissions everywhere as
in FULL_PD. Finally, we perform another two sets of
simulations, one for present day (FULL_PD_EF)
and the other for mid-century (FULL_A1B_EF),
both of which use emission factors from Akagi et al. (2011), to estimate
the modeling uncertainties due to emission factors.
We examine the differences between FULL_PD and
NOFIRE_PD to quantify the impacts of wildfire emissions in
the present day, and the differences between FULL_A1B and
NOFIRE_A1B to quantify these impacts at the mid-century. We use
the differences between FULL_A1B and BOREAL_FIRE to isolate the impacts of increased fire emissions in western US
at the mid-century. The differences between FULL_A1B and
WUS_FIRE reveal the effects due to changes of fire emissions
in boreal forests, also at the mid-century. The differences between
CLIM_CHAN and FULL_PD represent the impacts
due solely to climate change on the simulated ozone concentrations. We
calculate the differences between FULL_PD_EF
and FULL_PD to quantify the present-day uncertainties due to
the emission factors, and the differences between FULL_A1B_EF and FULL_A1B to quantify these
uncertainties at the mid-century. Each model run was initialized with a 1-year
spin-up. Taken together, these seven cases yield insight into the influence of
changing wildfire activity on surface ozone concentrations across North
America, and the relative importance of local versus remote wildfires on
US and Canadian ozone air quality.
Results
Regressions and predictions of area burned at present day
Figure 3a shows observed, annual mean area burned for 1980–2009 averaged
over the boreal ecoregions. In Canada, the Western Mixed Wood Shield
exhibits the greatest area burned of nearly 7 × 105 ha yr-1.
In addition, large areas burned of ∼ 4 × 105 ha yr-1 and ∼ 3 × 105 ha yr-1
are observed in the Taiga Plain and the Western Taiga Shield. Most fires in
these very remote ecoregions are allowed to burn naturally, without
intervention. This practice, together with the hot summers typical of
continental interiors, leads to large area burned (Stocks et al., 2002).
The Western Cordillera shows the least area burned, at 0.4 × 105 ha yr-1, due to abundant rainfall as well as active fire
suppression (Stocks et al., 2002). Fires in Alaska are about 3 times
larger in the Alaska Boreal Interior than in the Alaska Boreal Cordillera,
because the summer in interior Alaska is warmer and drier relative to the
southern part, which is influenced by moisture from the Pacific
(Wendler et al., 2011). In each ecoregion, the top three
largest fire years account for 36–67 % the total area burned in 1980–2009,
with the largest fraction in the Alaska Boreal Cordillera (Fig. 4).
(a) Observed annual area burned and (b) fraction
of the variance in observed area burned explained by the regression in each
ecoregion for the period of 1980–2009 (R2). The ecoregions are: Alaska
Boreal Interior (ABI), Alaska Boreal Cordillera (ABC), Taiga Cordillera (TC),
Canadian Boreal Cordillera (CBC), Western Cordillera (WC), Taiga Plain (TP),
Boreal Plain (BP), Western Taiga Shield (WTS), Eastern Taiga Shield (ETS),
Hudson Plain (HP), Western Mixed Wood Shield (WS), and Eastern Mixed Wood
Shield (ES). Observations are compiled using fire reports from the Fire and
Aviation Management Web Applications (FAMWEB) for Alaska and those from the
Canadian National Fire Database (CNFD) for Canada.
Observed (red solid lines) and predicted (blue dashed lines) area
burned (105 ha) for 1980–2009 in boreal ecoregions. The area burned is
calculated using the regressions for the fire season (May–October) for each
ecoregion. Site-based meteorological observations from GSOD are used in the
prediction. The fraction of the variance in observed area burned explained by
the regression (R2) is shown on each panel.
Table 2 shows the regressions we developed between area burned and the suite
of meteorological variables and fire weather indices in each ecoregion.
These fits explain 34–75 % (p < 0.001) of the variance in area
burned (Fig. 3b). In most ecoregions, the regressions capture well the
interannual variations of area burned, although they usually underestimate
the values for extreme years (Fig. 4). For the top three large fire years
in each ecoregion, the predictions underestimate the total area burned by
22–57 %, with the worst match in the Hudson Plain. Such failure in
predicting extreme fires is a common weakness of fire models, no matter the
approach – e.g., regressions (Balshi et al., 2009; Spracklen et al.,
2009; Yue et al., 2013), parameterizations (Crevoisier et al., 2007;
Westerling et al., 2011), and dynamic global vegetation models
(DGVMs; Bachelet et al., 2005). The leave-one-out cross
validation shows RMSE / SD ratios between 0.53 and 1.1 in boreal ecoregions (Table 4), suggesting that the prediction error is usually smaller than the
variability of data. In a comparable study, Littell et al. (2009)
calculated cross-validated RMSE / SD ratios of 0.56–2.08 for area burned in
western US ecoregions during 1977–2003. Our prediction shows much lower
RMSE / SD ratios, indicating that the derived regressions (Table 4) are
reasonably robust for the future projections.
Regression fitsa for each aggregated ecoregion.
Ecoregion
Regressionsa
R2
RMSE/SDb
Alaska Boreal Interior
2.2 × 105 Tmax.SUM + 5.7 × 103 HGT.SUM(-1) – 8.1 × 104 ISImax(-1) - 3.5 × 107
60 %
0.66
Alaska Boreal Cordillera
5.8 × 103 HGT.SUM + 4.8 × 104 Tmax.AUT(-2) + 4.6 × 104T.SPR - 3.3 × 107
61 %
0.87
Taiga Cordillera
5.7 × 104 Tmax.ANN(-2) + 2.8 × 103 HGT.SUM – 1.5 × 107
36 %
0.98
Canadian Boreal Cordillera
7.6 × 103 HGT.SUM - 4.2 × 107
52 %
0.82
Western Cordillera
3.5 × 104 Tmax.SUM - 8.3 × 102 HGT.SPR + 6.4 × 102 DMCmax(-1) + 3.7 × 106
53 %
0.85
Taiga Plain
9.8 × 105 ISI - 5.9 × 105 Prec.FS(-1) - 1.5 × 106 Prec.Win - 4.7 × 103
75 %
0.53
Boreal Plain
8.8 × 104 DSRmax+ 5.1 × 104 RH.SUM(-2) + 2.1 × 104 FWImax(-1) - 4.0 × 106
52 %
0.86
Western Taiga Shield
1.9 × 105 ISImax+ 5.7 × 104 RH.AUT - 6.0 × 106
46 %
1.03
Eastern Taiga Shield
5.4 × 104 RH.WIN(-2) - 6.2 × 104 RH.ANN - 7.7 × 103 DMCmax(-2) + 1.2 × 106
38 %
1.10
Hudson Plain
2.4 × 103 HGT.SUM - 1.8 × 104 T.SPR - 1.6 × 104 Tmax.WIN(-1) - 1.4 × 107
34 %
1.03
Western Mixed Wood Shield
2.0 × 104 BUImax+ 8.3 × 103 HGT.SUM - 4.7 × 107
67 %
0.55
Eastern Mixed Wood Shield
-6.7 × 104 RH.SUM + 2.8 × 103 HGT.AUT(-1) - 1.0 × 107
43 %
0.81
a The values (-1) or (-2) after a predictor indicates that the
meteorological field is 1 or 2 years earlier than current area burned.
Variables are T (temperature), Tmax (maximum temperature), RH (relative
humidity), Prec (precipitation), HGT (geopotential height), and fire indexes
from CFWIS, such as Duff Moisture Code (DMC), Build-up Index (BUI), Initial
Spread Index (ISI), and Daily Severity Rating (DSR). Meteorological fields
are averaged for winter (WIN, DJF), spring (SPR, MAY), summer (SUM, JJA),
autumn (AUT, SON), fire season (FS, MJJASO), and the whole year (ANN). The
order of the terms indicates their contributions to the R2 in the
regression.
b Ratios between predicted residual sum of squares (PRESS) root mean
square error (RMSE) and standard deviation (SD) as an indicator of the
leave-one-out prediction error.
We find that meteorological variables for the current year are selected as
the first term in 10 of the 12 ecoregions, indicating that area burned
in the boreal forests is most related to current weather (Table 2). In
contrast, Westerling et al. (2003) suggested that
wildfire activity in shrub ecoregions in the western US is closely related
to meteorology in previous years, because the antecedent moisture levels can
control fuel growth. In boreal forests, however, fuel load is perennially
abundant, and so weather in the current year is more important here. Our
regressions show that the 500 hPa height is the dominant factor affecting
boreal fires, as it appears in eight regression fits and is selected as the
first term for three of them. Temperature, which highly correlates with
geopotential height (R > 0.85) in spring and summer, is selected as
the first term in three other ecoregions. Of the six ecoregions that have
either geopotential height or temperature as the first term, five are
located in Alaska and western Canada, suggesting that wildfire activity in
these areas is greatly influenced by temperature or by blocking highs that
lead to persistent hot and dry conditions. Since our regression method does
not permit correlation among the predictors, temperature and geopotential
height are not selected for the same season and year in any of the
ecoregions. Fire indices, which combine the impacts from temperature,
humidity, and wind speed, are the dominant predictors in the four central
Canadian ecoregions. In three of these four regions, moisture variables such
as relative humidity and precipitation are also selected. Our method yields
relative humidity as the leading term in the two eastern ecoregions,
indicating that the dryness of fuel is most important for wildfire activity
there.
Our results confirm that wildfires in Alaska and western Canada are related
to geopotential height anomalies, which are associated with the positive
phase of either the Pacific-North American (PNA) pattern or the Pacific
Decadal Oscillation (PDO; Fauria and Johnson, 2006, 2008). However, in
some of the central and eastern Canadian ecoregions (e.g., Taiga Plain and
Eastern Taiga Shield), such height anomalies are not selected as terms in
our regressions (Table 2). Although geopotential height may still influence
wildfire activity in those areas, this variable tends to correlate with fire
weather indices or moisture variables. We attempt to avoid collinearity in
our regressions, and so geopotential height may not be selected as a
predictor there.
We compared our results with those in Flannigan et al. (2005), who developed regressions in similar ecoregions. Relative to their
R2 of 0.56 and 0.60 in the Taiga Plain and the Western Mixed Wood
Shield, where large area burned is observed (Fig. 3a), our regressions
yield higher R2 of 0.75 and 0.67. This improvement may result from our
use of meteorological data with better spatial coverage or our inclusion of
terms dependent on the meteorology in previous years. However, our
regressions in the Western Taiga Shield, the Eastern Taiga Shield, and the
Hudson Plain explain 34–46 % of the variance in observed area burned, much
lower than the 64 % predicted in Flannigan et al. (2005),
which aggregated these three ecoregions into one. The larger domain in
Flannigan et al. (2005) apparently smoothed spikes in the
area burned data (Fig. 4) and as a result increased the R2 for
regressions (Spracklen et al., 2009). We treat the
three regions separately due to their very different ecologies.
We next calculate present-day (1983–1999) area burned by applying
present-day meteorological fields from the 13 GCMs to our regressions. We
start with 1983 since we need to apply factors from the previous 2 years
in the regressions. As Figure 5a shows, in eight ecoregions the median area
burned from the ensemble of GCMs matches the observations within ±15 %. However, the predicted area burned is overestimated by 54 % in the
Eastern Taiga Shield and underestimated by 30 % In the Taiga Plain. These
biases do not derive from the long-term mean model meteorology, since we
scale the simulated fields with means from observations. Instead, the biases
arise from our use of fire weather indices in the regressions, which depend
on the daily variability in meteorology. For example, in the Taiga Plain,
the predicted median ISI is lower than observed by 7 %. In the same
ecoregion, the site records show that more than 30 % of days have
precipitation less than 0.1 mm day-1 during fire seasons for 1980–2009.
However, the GCMs predict only 2–13 % days with < 0.1 mm day-1, even after scaling with the means from observations. In
contrast, they predict 55–65 % of days with rainfall of 0.1–1.0 mm day-1, much more than the 37 % from observations. The overprediction
of drizzle, a common problem in GCMs (Mearns et al., 1995),
results in lower ISI compared with observations. The same problem in modeled
precipitation also reduces the predicted DMCmax in the Eastern Taiga
Shield, leading to an overestimate in area burned when applied with a
negative coefficient. Flannigan et al. (2005) reported a
similar problem in their study, and they subtracted a constant from the GCM
precipitation to match the observed rainfall frequency. We do not follow
this approach because our predicted present-day median area burned agrees
reasonably well with that observed. The non-linear response of fire weather
indices to daily meteorology contributes to the uncertainty of predictions,
resulting in larger spread of ratios for those ecoregions whose regressions
depend on the fire indices (Table 2).
Projection of area burned at the mid-century
Figure 6 shows the changes in key meteorological variables at the mid-century
relative to present day, as predicted by the 13 GCMs. Temperatures across
all ecoregions show median increases of ∼ 2 ∘C
during the fire season, with all models predicting significant changes.
Meanwhile, precipitation rates increase by 0.05–0.23 mm day-1 in the
median, likely as a result of a poleward shift of mid-latitude storm tracks
and precipitation (Yin, 2005). However, these increases in
precipitation are significant for only four to eight GCMs, depending on the
ecoregion, and in some ecoregions some models project a drier climate by the
mid-century, reflecting the large uncertainty in model projections of
regional hydrology (Christensen et al., 2007). The 500 hPa
geopotential heights are predicted to rise by 2050, with median increases of
30–60 m (0.6–1 %) and these changes are significant for all GCMs.
We find that the wildfire response to these trends in meteorological
variables varies greatly by ecoregion, with large increases in area burned
by 2050 in Alaska and western Canada, but little or no change in area burned
elsewhere (Fig. 5b). The median area burned at the mid-century increases by
130–350 % in Alaska and the western Canadian ecoregions, relative to
present day (Figs. 5b, 7a and Table 3). The greatest increase in area
burned occurs in the Alaska Boreal Cordillera, where area burned at the
mid-century is more than 4 times that of the present day. These increases
in Alaska and western Canada are largely driven by changes in temperature
and/or geopotential height (Table S4 in the Supplement), and as a result are statistically
robust in 11 to 13 GCMs, depending on the ecoregion (Fig. 7b). The central
and southern Canadian ecoregions show more moderate and less robust
increases in area burned of 40–90 %, with only three to eight models projecting
significant changes. In these ecoregions, fire activity depends either on
hydrological variables (e.g., RH for the Eastern Mixed Wood Shield) or on fire
indices that combine effects from temperature and moisture (e.g., the fire
indices DSR and FWI in the Boreal Plain and the fire index BUI in the
Western Mixed Wood Shield; Table 2). As a result, the effects of increased
precipitation in these ecoregions may partly offset the effects of rising
temperatures on wildfires.
Observed and projected area burned in boreal ecoregions.
Ecoregions
Observeda
Present-day
Future
Ratioc
No. of
No. of
(1983–1999)
regressionb
regressionb
(future/
modelsd
modelse
(1983–1999)
(2048–2064)
present)
(p < 0.05)
(M ± 30 %)
Alaska Boreal Interior
2.1 ± 3
3.7 ± 2.9
9.7 ± 3.6
2.46
12
6
Alaska Boreal Cordillera
0.6 ± 1
1.1 ± 1.3
5.3 ± 1.7
4.85
13
10
Taiga Cordillera
0.9 ± 1.7
0.9 ± 0.8
3.3 ± 0.7
3.26
13
11
Canadian Boreal Cordillera
1.3 ± 1.3
1.7 ± 1.3
4.5 ± 1.4
2.64
13
13
Western Cordillera
0.2 ± 0.2
0.3 ± 0.4
0.8 ± 0.4
2.66
11
11
Taiga Plain
3.8 ± 4.6
2.5 ± 2.7
1.6 ± 1.9
0.48
5
5
Boreal Plain
2.4 ± 3.5
2.6 ± 2.7
4.7 ± 3.2
1.44
3
8
Western Taiga Shield
3.7 ± 7.1
4 ± 4.3
4.1 ± 3.7
0.96
0
9
Eastern Taiga Shield
1.9 ± 4.3
2 ± 1.2
1.6 ± 1.2
0.86
1
11
Hudson Plain
1 ± 1.6
0.9 ± 0.5
1 ± 0.5
1.2
2
9
Western Mixed Wood Shield
6.8 ± 7.4
7.3 ± 4.8
11.1 ± 5.1
1.65
8
9
Eastern Mixed Wood Shield
1.7 ± 1.8
1.8 ± 1.3
3.3 ± 1.6
1.91
8
8
a AB is area burned (105 ha yr-1). Results in each ecoregion
are shown as AB‾±σ. AB‾ is the
long-term average of the AB during fire season (May–October), and σ
is the standard deviation.
b Results in each ecoregion are the median values of AB‾
and σ predicted using the meteorological fields from 13 GCMs for the
A1B scenario.
c Results in each ecoregion represent the median value of the 13 ratios
of future AB to present-day AB, calculated with the GCM meteorology.
d Number out of 13 models that predict a significant (p < 0.05)
increase in AB in each ecoregion, as determined by the Student t test.
e Number out of 13 models that predict a ratio within ±30 % of
the median ratio.
(a) Ratios of modeled to observed area burned for
1983–1999 and (b) the ratios of mid-century (2048–2064) to the
present-day (1983–1999) area burned, as projected by an ensemble of GCMs.
The ecoregions are: Alaska Boreal Interior (ABI), Alaska Boreal Cordillera
(ABC), Taiga Cordillera (TC), Canadian Boreal Cordillera (CBC), Western
Cordillera (WC), Taiga Plain (TP), Boreal Plain (BP), Western Taiga Shield
(WTS), Eastern Taiga Shield (ETS), Hudson Plain (HP), Western Mixed Wood
Shield (WS), and Eastern Mixed Wood Shield (ES). Different symbols are used
for each model. The black bold lines indicate the median ratios. Note the
difference in scale between the two panels.
Calculated changes in (a) surface air temperature,
(b) precipitation, and (c) geopotential height at 500 hPa
during the fire season (May–October) in 2048–2064 relative to 1983–1999.
Results are from an ensemble of GCMs for the A1B scenario. The ecoregions
are: Alaska Boreal Interior (ABI), Alaska Boreal Cordillera (ABC), Taiga
Cordillera (TC), Canadian Boreal Cordillera (CBC), Western Cordillera (WC),
Taiga Plain (TP), Boreal Plain (BP), Western Taiga Shield (WTS), Eastern
Taiga Shield (ETS), Hudson Plain (HP), Western Mixed Wood Shield (WS), and
Eastern Mixed Wood Shield (ES). Different symbols are used for each model.
The black bold lines indicate the median changes.
(a) Median ratios of mid-century (2048–2064) to present-day
(1983–1999) area burned in each boreal ecoregions, as predicted by an
ensemble of GCMs and (b) the number of GCMs out of 13 total which
predict significant changes of the same sign as the median. The ecoregions
are: Alaska Boreal Interior (ABI), Alaska Boreal Cordillera (ABC), Taiga
Cordillera (TC), Canadian Boreal Cordillera (CBC), Western Cordillera (WC),
Taiga Plain (TP), Boreal Plain (BP), Western Taiga Shield (WTS), Eastern
Taiga Shield (ETS), Hudson Plain (HP), Western Mixed Wood Shield (WS), and
Eastern Mixed Wood Shield (ES).
In some of the most northern ecoregions within the Canadian interior, median
area burned decreases in the wetter climate of the mid-century. In the Taiga
Plain, the median area burned decreases by 50 % (Table 3, Fig. 7a)
despite the 1.7 ∘C increase in temperature (Fig. 6a). In the
Western Taiga Shield, where area burned is projected as a function of the
fire index ISI (positive relationship, Table 2) and relative humidity, the
median area burned shows a small, insignificant decrease in the future
atmosphere (Table 3, Fig. 7b), because the increases of rainfall
significantly reduce ISI there. In the Eastern Taiga Shield, where area
burned is a function of the fire index DMC (negative relationship, Table 2)
and relative humidity, the median area burned again shows an insignificant
decrease by mid-century (Table 3, Fig. 7b). DMC is related to both
temperature and precipitation. Here rising temperatures enhance DMC and
outweigh the effects of greater humidity (Table S4 in the Supplement).
Our projection of larger increases in Alaska and western Canadian ecoregions
is consistent with the observed trends for 1959–1999 in
Kasischke and Turetsky (2006) and with the projection by
Flannigan et al. (2005) for 2080 to 2100. However,
Flannigan et al. (2005) predicted area burned increases of
40–60 % in the Taiga Plain with 3 × CO2, where we project a
decrease of 50 % with ∼ 1.5 × CO2. The reasons
for this discrepancy are not clear. In our results, a median increase of 0.1 mm day-1 in summer precipitation drives the decrease in area burned in
the Taiga Plain, but Flannigan et al. (2005) did not report
their trend in modeled precipitation. In addition, our regression for the
Taiga Plain has ISI as the leading term, while the leading term in
Flannigan et al. (2005) is temperature. Based on the same
GCM meteorology as Flannigan et al. (2005) and using a
similar approach, Amiro et al. (2009) found a modest
increase of 10 % in area burned with 2 × CO2 for the Taiga
Plain, the lowest enhancement among all Canadian ecoregions for that study.
Estimate of future fire emissions
We first compare our derived fuel consumption with previous studies. Figure 8a shows the mean annual biomass burned for 1980–2009, calculated from
monthly areas burned and monthly fuel consumption (Sect. 2.6). Figure 2b
shows the mean fuel consumption per unit area during the fire season for
1980–2009. We find that the mean fuel consumption per unit area is
∼ 30 % less than that for moderately dry conditions for
which we assumed an average DC of 390 (Fig. 2). Most boreal area burned
occurs during the relatively moist months of June and July (Fig. S1 in the Supplement), when
the monthly average DC is usually less than 370
(Amiro et al., 2004). In the eastern ecoregions
(Hudson Plain, Eastern Taiga Shield, and Eastern Mixed Wood Shield), the
values for mean fuel consumption are as much as 50 % less than those for
moderately dry conditions due to high moisture content in fuel there (Fig. S1).
Biomass burning (BB) in Alaska and Canada in terms of dry matter
(DM) burned per year, calculated as the product of area burned and fuel
consumption. (a) shows values based on observations for 1980–2009,
(b) the predicted values for 1996–2001, and (c) the
projections for 2046–2051. The differences between mid-century and present
day (c–b) are shown in (d). Annual mean values summed over
the whole domain are shown in brackets. Units: Tg DM yr-1.
In Table 4 we compare our estimates for mean fuel consumption with those
from other studies, which were derived from forest inventories and field
measurements (French et al., 2000; Balshi et al., 2007), fuel-weather
models (Amiro et al., 2001; Amiro et al., 2009), and biogeochemical
models based on satellite observations (van der Werf et al., 2010). We
also compare our results with estimates based on wildfire incidents (Table S5 in the Supplement). In the Alaska Boreal Interior, our estimate of 5.5 kg DM m-2 is
within ∼ 10 % of those by Balshi et al. (2007) and van
der Werf et al. (2010), but is ∼ 25 % lower than that of
French et al. (2000). Turetsky et al. (2011) collected data from 178 sites in the Alaskan black spruce ecosystem
and estimated that average fuel consumption is 5.9 kg DM m-2 for
early-season fires (May–July) but increases to 12.3 kg DM m-2 for late-season
fires (after 31 July; Table S5). Based on our compilation of fuel
consumption (Table 2) and the calculated monthly DC values for Alaska
(Fig. S1), we find similar results of 6.1 kg DM m-2 for May–July and
14.6 kg DM m-2 for August–October for C2 fuel (boreal spruce). A recent
analysis by French et al. (2011) showed that different models of fuel
consumption provide very different results for a given fire, with a range of
2.7–12.2 kg DM m-2 for a major fire in Alaska in 2004 (Table S5). The
CONSUME model (v. 3.0) yielded 2.8–4.7 kg DM m-2 for moderate to very
dry conditions for that fire, while a field study estimated 5.2 kg DM m-2 (French et al., 2011).
Fuel consumptiona in boreal ecoregions, as reported by
recent studies.
Ecoregions
French et
Amiro
Amiro
Balshi
GFED3f
This studyg
al. (2000)b
et al. (2001)c
et al. (2009)d
et al. (2007)e
1980–2009
PD
A1B
Alaska Boreal Interior
7.5
N/A
N/A
4.9
5.2
5.5 (4.6)
5.4
5.6
Taiga Cordillera
N/A
3.1
N/A
N/A
2.7
3.8 (3.5)
3.6
3.7
Can. Boreal Cordillera
5.4
3.2
N/A
7.2
3.5
5.5 (4.7)
5.2
6.0
Western Cordillera
N/A
3.9
N/A
N/A
2.7
6.6 (5.9)
6.2
7.0
Taiga Plain
2.9
2.9
3.5
3.3
5.4
7.2 (6.6)
7.7
8.2
Boreal Plain
3.8
2.4
2.8
6.8
2.1
5.6 (5.0)
5.7
5.8
W. Taiga Shield
1.0
1.9
1.5
1.8
5.3
3.9 (3.9)
4.9
5.4
E. Taiga Shield
1.6
1.9
1.7
3.0
4.0
1.8 (2.2)
2.3
2.8
Hudson Plain
1.7
1.9
N/A
2.9
6.7
3.1 (4.1)
3.3
3.8
W. Mixed Wood Shield
2.1
2.5
3.0
5.7
4.9
6.4 (6.6)
6.4
6.9
E. Mixed Wood Shield
2.6
2.0
2.4
0.5
2.9
3.0 (4.1)
3.1
3.6
a Fuel consumption unit is kg DM m-2 burned. For some studies that
use units of kg C m-2 burned, we multiply their values by 2 g DM g-1 C. DM denotes dry matter.
b Values are averages of 1980–1994.
c Values are averages of 1959–1995.
d Values are estimated for forest floor fuel consumption in a GCM
1 × CO2 scenario.
e Values are averages of 1959–2002, estimated with the same burning
severity parameters as French et al. (2000) but with modeled vegetation and
soil carbon pool.
f GFED3: Global Fire Emission Database version 3 for 1997–2010.
g Results are the fuel consumption weighted by area burned and drought
code (DC) for 1980–2009, using the DC thresholds determined by a single
probability distribution for North America. As a comparison, the values
calculated with ecoregion-specific DC thresholds are shown in brackets. For
PD and A1B, values are calculated using predicted median DC for present day
(1996–2001) and mid-century (2046–2051) from the multi-model projection.
There is less consistency among different estimates of mean fuel consumption
in the Canadian ecoregions (Table 4). Our estimates fall in the range of
previous work for most ecoregions except for the Western Cordillera and the
Taiga Plain, where our values are ∼ 100 % higher than most
other estimates. These two ecoregions are located in western Canada,
where seasonal DC is usually high, indicating relatively dry conditions
(Fig. S1 in the Supplement). Our moisture categories derived from the single DC probability
distribution (Fig. S2 in the Supplement) may overestimate fuel dryness in the west. On the
other hand, our estimates show low fuel consumption in the eastern
ecoregions, such as Eastern Taiga Shield, Hudson Plain, and Eastern Mixed
Wood Shield, consistent with most of other studies. In a sensitivity test,
we derive fuel consumption with regional DC thresholds based on
ecoregion-specific probability distributions. This approach reduces western
fuel consumption by 8–16 %, but increases eastern values by 2–37 %
(Table 4). It also predicts lower Alaskan fuel consumption compared with
other studies. The boreal biomass burned calculated with this alternative
approach is about 156.2 Tg DM yr-1 for 1980–2009, almost identical to
that estimated using a single probability distribution to define the DC
thresholds (Fig. 8a).
We estimate fuel consumption at present day and mid-century with the median
DC values from the multi-model ensemble. The present-day values are close to
the ones based on observed meteorology (Table 4). By the mid-century, DC
values increase in the warming climate, indicating drying, and fuel
consumption increases by 2–22 %, depending on the ecoregion, with a 9 %
average enhancement. Using the random method described in Sect. 2.7, we
derive gridded area burned based on the projection with regressions. The
estimated biomass burned, averaged over 1997–2001 (Fig. 8b) correlates
with observations averaged over 1980–2009 (Fig. 8a) with R2= 0.5
for ∼ 1700 boreal grid squares, indicating that our prediction
captures the observed spatial pattern reasonably well. The total biomass
burned of 160.2 Tg DM yr-1 is just 2.5 % higher than that obtained
with the observed area burned.
Estimates of fire emissions depend on the emission factors. Using the same
biomass burned calculated with observed area burned, we calculate three
different sets of emissions using the factors from Andreae and Merlet
(2001) (except for NO, see Table S3 in the Supplement), Akagi et al. (2011), and
Urbanski (2014) (Table S6 in the Supplement). These emissions show similar magnitudes in CO
and NH3, but some differences in NOx and non-methane organic
compounds (NMOC). For example, NOx from Akagi et al. (2011) is
higher than that in Urbanski (2014) and in Table S3 by 30–50 %.
Meanwhile, NMOC from Andreae and Merlet (2001) is lower than
that in Akagi et al. (2011) and Urbanski (2014) by 20 %. In the following
simulations and analyses, we use emission factors from Andreae and
Merlet (2001) (except for NO from Table S3) and discuss the modeling
uncertainties due to the application of different emission factors.
Our value of biomass burned using the regression yields emissions of 0.27 Tg yr-1 for NO and 18.6 Tg yr-1 for CO in Alaska and Canada at the
present day. By the mid-century, we find that total biomass burned across the
boreal ecoregions increases by ∼ 90 % (Fig. 8c) due to the
∼ 70 % increase in area burned and the ∼ 10 % increase in average fuel consumption (Table 4). In Alaska, the
maximum increase of 36 Tg DM yr-1 (168 %) is predicted for the Alaska
Boreal Interior, where area burned by the 2050s increases by 146 % (Table 3). In Canada, the Western
Mixed Wood Shield has the highest increase of 29 Tg DM yr-1 (64 %). These changes in biomass burned result in
increases of 0.24 Tg yr-1 for NO emissions and 17.1 Tg yr-1 for CO
in boreal regions. Over the western US, the ∼ 80 %
enhancement in biomass burned yields an increase in NO emissions, from 0.03 Tg yr-1 in the present day to 0.05 Tg yr-1 in the future climate,
and an increase in CO emissions from 1.9 to 3.4 Tg yr-1.
Impacts of wildfire on ozone air quality
Daily maximum 8-hour average (MDA8) surface ozone is a metric used by the
US Environmental Protection Agency (EPA) to diagnose ozone air quality. In
this study, we use MDA8 ozone instead of daily mean ozone for all the
analyses and discussion. Figure 9a shows the simulated MDA8 surface ozone,
averaged over North American in summer (June-July-August, JJA). We focus on
the summer season, when fire activity peaks in both the US and Canada. The
figure shows mean MDA8 values of 40–75 ppbv across the US, with the
maximum in the east due to local anthropogenic emissions (Fiore et
al., 2002). The concentrations in Alaska and Canada range from 20 to 60 ppbv. However, for most regions north of 55∘ N, MDA8 is generally
less than 40 ppbv. As shown in Fig. 9b, we find that wildfire emissions in
these far northern areas contribute 1–10 ppbv to average JJA surface ozone
concentrations, with a mean contribution of 4 ppbv. These values are
considerably larger than the average 1 ppbv contribution of wildfires to
surface ozone that we calculate in the western US (Fig. 9b) because of
the much higher biomass burning emission in Alaska. In the eastern US,
wildfires make almost no contribution to mean surface ozone in summer.
(a) Simulated present-day MDA8 ozone at the surface in
summer (June–August). (b) shows the contribution to MDA8 summertime
ozone by wildfire emissions in the present day (FULL_PD – NOFIRE_PD), and
(c) shows the same contribution, but at the mid-century (FULL_A1B –
NOFIRE_A1B). (d) presents the change in the contribution of
wildfires to MDA8 ozone between the two periods (i.e., c–b).
Descriptions of the sensitivity simulations are given in Table 1. The color
scale saturates at both ends.
The increased fire emissions that we calculate at the mid-century result in
greater ozone pollution across North America (Fig. 9c). We find a maximum
JJA mean perturbation of 22 ppbv along the border between Alaska and Canada,
where the largest increase in future area burned is projected (Fig. 7a).
In central Canada, the future fire emissions contribute 6–9 ppbv to JJA mean
ozone concentrations. For the western US, the fire perturbation for
surface ozone is about 2 ppbv, with the largest values of 3–5 ppbv in the
Pacific-Northwest and Rocky Mountain forest ecoregions. Relative to the
present-day contribution, the fire perturbation at the mid-century enhances
JJA mean surface ozone by an additional 4.6 ppbv in Alaska, 2.8 ppbv in
Canada, and 0.7 ppbv in the western US (Fig. 9d), indicating a
degradation in air quality. Our estimate of future fire impacts depends on
the emission factors we adopted. Using emission factors from Akagi et al. (2011), we calculate larger fire-induced ozone enhancements at both present
day and the mid-century (Fig. S3 in the Supplement). As a result, simulations with emission
factors from Akagi et al. (2011) project ozone increases of 5.5 ppbv in
Alaska, 3.2 ppbv in Canada, and 0.9 ppbv in the western US by future
wildfire emissions. These enhancements are 14–23 % higher than our
previous estimates with emission factors from Andreae and Merlet
(2001) and Table S3.
A key question is to what extent boreal fires affect the more populated
regions of lower latitudes. In Fig. 10, we investigate the contributions
of climate, local and boreal wildfire emissions, and atmospheric transport
to JJA mean surface ozone concentrations in the central and western US.
Fig. 10a shows that all these effects together increase surface ozone in
the US by 1–4 ppbv at the mid-century but with large spatial variability.
The enhancement in central and southwestern states is mainly associated with
climate change (Fig. 10b), which increases temperature-driven soil
NOx emissions and air mass stagnation (Wu et al.,
2008b). In the northwestern coastal states, the impact of these effects is
offset by the reduced lifetimes of PAN and ozone in the warmer climate,
which diminish the impact of Asian emissions on surface ozone there
(Wu et al., 2008b). However, the calculated increase of
local wildfire emissions in these coastal states and across the northwest
enhances surface ozone by 1–2 ppbv at the mid-century (Fig. 10c). In the most
northern states, this increase is enhanced by another 0.5 ppbv due to
transport of pollutants from boreal wildfires (Fig. 10d).
(a) Simulated changes in MDA8 ozone at the surface in
summer (June–August) at the mid-century relative to the present day
(FULL_A1B – FULL_PD) over the western and central United States. The other
three panels show the contributions to the changes in (a) from
(b) climate change (CLIM_CHAN – FULL_PD), (c) changes in
fire emissions in the western US (FULL_A1B – BOREAL_FIRE), and
(d) changes in fire emissions in Alaska and Canada (FULL_A1B –
WUS_FIRE). Descriptions of the sensitivity simulations are given in
Table 1.
In Fig. 11 we examine the impact of wildfire emissions on the frequency of
ozone pollution episodes. In the northwestern US, where the impact of fire
emission is especially large (Fig. 10c), surface ozone above the 95th
percentile (i.e., on the five most polluted days in summer) increases by 2 ppbv
at the mid-century (Fig. 11a). Simulations without fire emissions show an
increase of 1 ppbv above the same percentile, indicating that the increased
wildfire emission alone contributes a 1 ppbv enhancement during ozone
pollution episodes in this region. The changes are more significant for
Alaska and Canada, where we predict large increases in fire activity (Fig. 9c). As Fig. 11b shows, climate change alone decreases ozone above the
95th percentile ozone by an average ∼ 3 ppbv in Alaska,
likely because of the effects of enhanced water vapor on background ozone
(Wu et al., 2008a). However, when changes in fire
emissions are included, the simulation predicts that ozone above the
95th percentile instead increases by 12 ppbv at the mid-century, suggesting
a positive change of 15 ppbv due to wildfire alone. Over high latitudes in
Canada, climate change decreases the 95th percentile ozone by 1 ppbv;
however, the inclusion of future fire perturbation enhances it by 4 ppbv
(Fig. 11c), indicating that the contribution from wildfire may be as great
as 5 ppbv.
Simulated cumulative probability distributions of MDA8 ozone at the
surface in summer (June–August) over (a) northwestern US
(> 40∘ N), (b) Alaska, and (c) Canada
(> 55∘ N) for different scenarios. Black shows the
present-day (1997–2001) climate without wildfire emissions; green shows
future (2047–2051) climate without wildfire emissions; blue indicates
present-day climate including the associated wildfire emissions; and red
indicates future climate including the associated wildfire emissions. Each
point represents the value in one grid square within each region for each day
during the five model summers (1997–2001 or 2047–2051).
Discussion and conclusions
We examined the effects of changing wildfire activity in a future climate on
June–August MDA8 ozone over the western US, Canada, and Alaska by the
mid-century. We built stepwise regressions between area burned and
meteorological variables in 12 boreal ecoregions. These regressions
explained 34–75 % of the variance in area burned for all ecoregions, with
500 hPa geopotential heights and temperatures the driving factors. With
these regressions and future meteorology from an ensemble of climate models,
we predicted that the median area burned increases by 150–390 % in Alaska
and the western Canadian ecoregions by the mid-century due to enhanced 500
hPa geopotential heights and temperatures. The area burned shows moderate
increases of 40–90 % in central and southern Canadian ecoregions, but
a 50 % decrease in the Taiga Plain, where most of the GCMs predict
increases in precipitation at the mid-century. Using the GEOS-Chem CTM, we found
that fire perturbation at the mid-century enhances summer mean daily maximum
8-hour surface ozone by 5 ppbv in Alaska, 3 ppbv in Canada, and 1 ppbv in
the western US. The changes in wildfire emissions have larger impacts on
pollution episodes, as ozone above the 95th percentile increases by 15 ppbv in
Alaska, 5 ppbv in Canada, and 1 ppbv in the northwestern US.
Our study represents the first time that multi-model meteorology has been
used to project future area burned in Alaskan and Canadian forest. The
individual models in our study predict changes in area burned of different
magnitudes or even of opposite sign, but the median values and the spread in
model results provide an estimate of both the sign and the uncertainty of
these projections. We find the projections are most robust over Alaska and
western Canada, where for almost all GCMs we calculate significant increases
in area burned (Fig. 7b; Table 3). For these regions, wildfire activity is
largely associated with blocking highs and the resulting hot, dry weather,
and both temperature and geopotential height show consistent and significant
increases here in all climate models (Fig. 6). However, for northern
Canada, where the control of blocking systems on area burned is weaker, we
projected a less robust decreasing trend in area burned, due to the
competing effects of hotter weather and wetter conditions. The multi-model
ensemble approach allows us to identify the most robust changes in the
future wildfire activity due to climate change, and as a result should be
more reliable than predictions using only 1–2 models, which can yield very
different projections, especially for northern Canada (e.g., Wotton et al., 2010).
Our approach neglects the impacts of topography, human activity, and fuel
changes on wildfire trends. The aggregation method used here for each
ecoregion may hide the spatial variation of both area burned and
meteorological variables and obscure their relationships (Balshi et al.,
2009; Meyn et al., 2010). Changes in fire domain and climate may lead to
changes in forest composition (DeSantis et al., 2011),
resulting in different fire severity and spread efficiency
(Thompson and Spies, 2009).
For our study, we assumed that fuel load remains constant for 50 years, but
we calculated a 9 % average increase in fuel consumption in boreal
regions. Our assumption of constant fuel load is justified at least for the
conterminous US since trends in heavy-fuel load in US forests are likely
to be gradual (Hanson and Weltzin, 2000). For boreal regions, recent
simulations with DGVMs show that large-scale forest dieback may occur in
coming decades, due to intense heat and drought (Heyder et al.,
2011). In addition, mountain pine beetle outbreaks are important
disturbances for both boreal and US forests, leading to changes in fuel
load and fuel moisture with climatic shifts (Fauria and Johnson, 2009;
Simard et al., 2011; Jenkins et al., 2014). We did not consider these
effects in this study.
Compared with previous studies, our estimate of fuel consumption shows
higher values over western Canada (Table 4), where the largest increase in
future area burned is predicted (Fig. 7a), suggesting that the boreal fire
emissions might be overestimated. However, our estimate of a 9 % increase
in fuel consumption may, in fact, be conservative. Some DGVM studies predict
30–40 % increases in burning severity for US Pacific-Northwest forest by
the end of the 21st century (Rogers et al.,
2011). Moreover, observations have suggested that large area burned
sometimes results in burning at greater soil depth than is typical
(Turetsky et al., 2011). Thus the projected increase in
fire areas may amplify future fuel consumption, leading to even larger
emissions than predicted in this study.
The emissions from boreal wildfires in our simulation show limited
contributions to ozone concentrations in downwind areas, but cause
significant local ozone enhancement in Alaska and Canada. However,
observations point to uncertainties in the relationship between wildfire
activity and ozone. First, the emission factors of ozone precursors are not
well constrained, especially for NOx. Sensitivity tests with emission
factors from Akagi et al. (2011) show 14–23 % higher fire-induced ozone
than that with emission factors from Andreae and Merlet (2001) and the
NOx emission factor derived from an ensemble of experiments (Table S3 in the Supplement).
Using aircraft data from boreal fires, Alvarado et al. (2010) determined
an emission factor of 1.1 g NO kg DM-1, lower than our value of 1.6 g NO kg DM-1
and much lower than the estimate of 3.0 g NO kg DM-1
for extratropical forest fires in Andreae and Merlet (2001).
Alvarado et al. (2010) found that 40 % of wildfire NOx is rapidly
converted to PAN and 20 % to HNO3, and their estimate of 1.1 g NO kg DM-1 for fresh emissions includes these two species. Second,
observations do not consistently reveal ozone enhancements during wildfire
events. Jaffe et al. (2008) found a significant correlation
between interannual variations of observed surface ozone and area burned in
the western US. Using the same ozone data set, however,
Zhang et al. (2014) did not find regional ozone
enhancements during wildfire events, when such enhancements would be
expected to be large. In their review, Jaffe and Wigder
(2012) reported that increased ozone is observed in most plumes, but with
huge variability in the enhancement ratio of ΔO3 / ΔCO
within the plume. Alvarado et al. (2010), on the other hand, found that
only 4 out of 22 plumes showed enhanced ozone. Such discrepancies in plume
data may be attributed to differences in plume age (Alvarado et al.,
2010), emissions of wildfire NOx and VOCs (Zhang et
al., 2014), or plume photochemistry (Verma et al., 2009; Jiang et al.,
2012). Third, the effect of long-range transport of wildfire PAN on ozone
downwind is not well known. Observations suggest that PAN forms rapidly in
fresh fire plumes and may enhance ozone downwind as it decomposes (Real
et al., 2007; Jaffe and Wigder, 2012). In their model study, Fischer et al. (2014) reported a large effect of fires on PAN in the high northern
latitudes but limited impacts over the downwind areas in the US. In any event,
our use of a moderately high NOx emission factor and omission of rapid PAN
formation within the plume may lead to an overestimate of fire-induced ozone
in local areas (Alvarado et al., 2010).
Uncertainties may also originate from limitations in the model
configuration. First, GEOS-Chem CTM does not allow feedbacks of fire
emissions to affect model meteorology or biogenic emissions. Second, we
estimated fire-induced O3 concentrations using monthly emissions, due
to the limits in the temporal resolution of predicted area burned. Such an
approach may have moderate impacts on the simulated O3; Marlier et al. (2014)
found < 1 ppb differences in surface O3 concentrations over North
America between simulations using daily and monthly fire emissions. The same
study also predicted < 10 % differences in the accumulated
exceedances for MDA8 O3 globally. Third, the projections were performed
at coarse spatial resolution of 4∘ × 5∘. As
shown in Zhang et al. (2011), however, mean MDA8 O3 in a nested grid
simulation (0.5∘ × 0.667∘) is only 1–2 ppbv
higher than that at 2∘ × 2.5∘ resolution in the
GEOS-Chem model. Fiore et al. (2002) reached a similar conclusion in
comparing simulations at 4∘ × 5∘ and
2∘ × 2.5∘. They found that the coarse model
resolution smoothed the regional maximum, resulting in a more conservative
estimate of the intensity of pollution episodes.
Given these limitations, our estimate with a multi-model ensemble
consistently shows that wildfire activity will likely increase in North
American boreal forest by the mid-century, especially in western Canada and
Alaska. Our study suggests that area burned could increase by 130–350 % in
these two regions, while in central and southern Canada, where most people
reside, area burned could increase 40–90 %. In north central Canada, the
competition between increased temperature and precipitation in the future
atmosphere results in uncertainty in the projections for area burned.
Overall, these trends in boreal wildfire activity may amplify the threat of
wildfires to Canadian residents, increase the expense of fire suppression,
and lead to more ozone pollution both locally and in the central and western
US. The regional perturbation of summer ozone by future wildfires can be as
high as 20 ppbv over boreal forests, suggesting large damage to the health
and carbon assimilation of the ecosystems (Pacifico et al., 2015). Using
a newly developed model of ozone vegetation damage (Yue and
Unger, 2014), we plan to explore the response of boreal ecosystems to
fire-induced ozone enhancements.