Introduction
The area burned by wildfire is increasing in recent decades in North
American boreal regions (Stocks et al., 2002; Kasischke and Turetsky, 2006).
Fire activity is closely related to weather conditions and large-scale
atmospheric oscillations (Gillett et al., 2004; Duffy et al., 2005) and is
projected to increase significantly in the future due to climatic changes
(Flannigan et al., 2005; Balshi et al., 2009; de Groot et al., 2013; Wang et
al., 2015). More area burned and the consequent fire emissions are
accelerating carbon loss in boreal North America (Bond-Lamberty et al., 2007;
Turetsky et al., 2011). Meanwhile, fire-induced air pollution, including
ozone (O3) and aerosols, is predicted to increase in boreal and downwind
regions by mid-century (Yue et al., 2013, 2015). Wildfire emissions have large
impacts on air quality (Wotawa and Trainer, 2000; Morris et al., 2006),
weather and climate conditions (Randerson et al., 2006; Zhao et al., 2014), and
public health (Zu et al., 2016; Liu et al., 2017). However, little is known
about how these pollutants affect ecosystem carbon assimilation and how this
impact will change with the increased wildfire activity in the future.
Surface O3 causes damages to photosynthesis through stomatal uptake
(Sitch et al., 2007). In the present climate state, fire-induced O3
enhancements are predicted to reduce net primary productivity (NPP) in the
Amazon forest by 230 Tg C yr-1 (1 Tg = 1012 g), a
magnitude comparable to the direct release of CO2 from fires in South
America (Pacifico et al., 2015). The aerosol effects are more uncertain
because both positive and negative feedbacks occur. Appearance of aerosols
increases diffuse light, which is beneficial for shaded leaves in the lower
canopy. Consequently, photosynthesis of the whole ecosystem will increase as
long as the total light availability is not compromised (Kanniah et
al., 2012). Rap et al. (2015) estimated that biomass burning aerosols
increase Amazon NPP by 78–156 Tg C yr-1, which offsets about half of
the damage caused by fire O3 (Pacifico et al., 2015). In contrast,
strong light attenuation associated with high aerosol loading may decrease
canopy photosynthesis (Cohan et al., 2002; Oliveira et al., 2007; Cirino et
al., 2014). Furthermore, the aerosol radiative effects indirectly influence
ecosystem productivity through concomitant meteorological perturbations that
are only beginning to be examined (Yue et al., 2017).
Future wildfire activity is projected to increase over boreal North America
but with large uncertainties (Flannigan et al., 2005; Tymstra et al., 2007;
Girardin and Mudelsee, 2008; Nitschke and Innes, 2008; Amiro et al., 2009;
Balshi et al., 2009; Bergeron et al., 2010; Wotton et al., 2010; de Groot et
al., 2013; Wang et al., 2016). For example, Amiro et al. (2009) predicted an
increase of 34 % in the area burned in Canada for a 2×CO2
scenario (2040–2060) relative to a 1×CO2 condition
(1975–1995), using the Canadian Fire Weather Index (CFWI) and output from
the Canadian Global Climate Model (CGCM) version 1. Balshi et al. (2009)
projected that area burned in boreal North America would double by the year
2045–2050 relative to 1991–2000, using the Multivariate Adaptive Regression
Splines (MARS) approach and meteorological output from CGCM version 2. The
increasing rate in Balshi et al. (2009) is higher than that in Amiro et
al. (2009), indicating substantial uncertainties in fire projections
originating from both fire models and simulated future climate. However, even
with the same fire models and climate change scenario, large uncertainties
(in both magnitude and signs) are found in the projection of area burned
among individual climate models (Moritz et al., 2012; Yue et al., 2013). The
multi-model ensemble approach has shown superior predictability over single
models in historical climate simulations (Flato et al., 2013) and near-term
climate predictions (Kirtman et al., 2014) and has been used as a standard
technique to assess changes of climate variables in the long-term projections
(Collins et al., 2013). Following this strategy, Yue et al. (2015) used
output from 13 climate models to drive fire regression models and predicted
an average increase of 66 % in boreal area burned at 2046–2065 relative
to 1981–2000 under the IPCC A1B scenario (Solomon et al., 2007). Yue et
al. (2015) further calculated that the wildfire emission increase by the
2050s would increase mean summertime surface O3 by 5 ppbv in Alaska and
3 ppbv in Canada. The study found regional maximum O3 enhancements as
high as 15 ppbv, suggesting the potential for possible vegetation damage and
land carbon loss due to the enhanced boreal fire-related air pollution.
Wildfire aerosols are also expected to increase significantly but not
predicted in Yue et al. (2015).
Illustration of atmospheric chemistry and physics as well as
biospheric processes investigated in the study. Carbonaceous aerosols from
fire plumes increase diffuse light and change temperature (T) and
precipitation (P), influencing vegetation photosynthesis. Ozone generated
photochemically from fire-emitted precursors (NOx, CO, and non-methane
volatile organic compounds, NMVOC) and associated biogenic volatile organic
compound (BVOC) changes causes direct damage to plant photosynthesis.
In this study, we quantify the impacts of O3 and aerosols emitted from
boreal wildfires on the land carbon uptake in North America in the present
climate state and in the future world at 2050, taking advantage of the
ensemble projection of future wildfire emissions by Yue et al. (2015). The
major chain we investigate includes (i) generation of aerosols and surface
ozone from wildfire emissions and (ii) impact of fire-emitted aerosols and
ozone on plant photosynthesis through physical and biogeochemical processes
(Fig. 1). We first analyze relationships between gross primary production
(GPP) and aerosol optical depth (AOD) at 550 nm over the boreal regions
based on observations. We then perform a suite of Earth system model
simulations using NASA GISS ModelE2 that embeds the Yale Interactive
Terrestrial Biosphere model (YIBs), a framework known as ModelE2-YIBs (Yue
and Unger, 2015). Future projections of wildfire emissions from Yue et
al. (2015) are applied as input to ModelE2-YIBs model to project fire-induced
O3 and aerosol concentrations in the 2010s and 2050s. The impacts of the
boreal fire O3 on forest photosynthesis are predicted using the
flux-based damage algorithm proposed by Sitch et al. (2007), which has been
fully evaluated against available O3 damage sensitivity measurements
globally and over North America (Yue and Unger, 2014; Yue et al., 2016,
2017). Fire aerosols induce perturbations to radiation, meteorology, and
hydrology, leading to multiple influences on the land carbon uptake.
Sensitivity experiments are performed using the YIBs model in offline mode to
isolate the contributions of changes in the individual meteorological
drivers.
Materials and methods
Observed GPP–AOD relationships
Following the approach by Strada et al. (2015), we investigate the GPP
sensitivity to diffuse radiation and AOD variability in boreal regions.
First, we identify study sites in Canada and Alaska from the AmeriFlux (AMF)
network (http://ameriflux.lbl.gov/). There are much fewer boreal sites
than those in temperate regions. We select AMF sites providing hourly (or
half-hourly) simultaneous measurements of GPP (non-gap-filled) and
photosynthetically active radiation (PAR, total and diffuse) for at least 3
consecutive years. Only two Canadian sites meet the criteria: Groundhog River
(CA-Gro; 82.2∘ W, 48.2∘ N), a mixed forest (MF), and Québec
Mature Boreal Forest Site (CA-Qfo; 73.4∘ W, 49.7∘ N), an
evergreen needleleaf forest (ENF). At the two selected sites, we calculate
the Pearson's correlation coefficients between half-hourly GPP and different
components of PAR. In total, we select 2432 and 3201 pairs of GPP and PAR
measurements at CA-Gro and CA-Qfo, respectively. We then apply instantaneous
Level 2 Collection 6 of AOD pixels at 3 km resolution retrieved by the
Moderate Resolution Imaging Spectroradiometer (MODIS,
https://ladsweb.nascom.nasa.gov/) onboard the Aqua and Terra satellites
(Levy et al., 2013). The MODIS 3 km AOD product has been fully validated
against ground-based sun photometers on both global (Remer et al., 2013) and
urban/suburban (Munchak et al., 2013) scales. Strada et al. (2015) used
ground-based AOD observations from the Aerosol Robotic Network (AERONET) near
AMF sites to validate the sampling technique of MODIS 3 km AOD product. They
found high correlations of 0.89–0.98 and regression slopes from 0.89 to 1.03
for daily AOD between AERONET and MODIS at four AMF sites. For this study,
the validation against ground-based AOD observations was not possible because
no AERONET stations exist near to the selected AMF sites.
Every day, MODIS satellite sensors pass a specific region between 10:00 and
14:00 local time (LT), leaving patchy signals around the AMF sites.
Most of MODIS AOD data at high latitudes are available only in boreal summer;
as a result, we narrow our explorations of the GPP–AOD relationships to the
noontime (10:00–14:00 LT) from June to August. The chosen noontime window
limits the contributions that confounding factors such as low solar angles
and high diffuse fraction may have on the amount of diffuse PAR and plant
productivity (Niyogi et al., 2004). For each summer day, we select
instantaneous MODIS 3 km AOD pixels that are (a) located within a distance
of 0.03∘ (about 3 km) from the targeted AMF site and
(b) “quasi-coincident” with AMF data, which are available each half-hour.
Because of the unavoidable temporal differences between MODIS overpass and
AMF data availability, we name this selection quasi-coincident. A cloud
mask applied to the MODIS retrieval procedure conveniently filters out cloudy
instants and should reduce the effect of clouds in the scattering process. We
calculate both the correlation and regression coefficients between
quasi-coincident GPP and AOD at the selected sites. Negative GPP is
considered as a missing value. To further reduce the influence of cloud
cover, we discard instants (both AMF and MODIS data) when precipitation is
nonzero. In total, we select 65 pairs of GPP and AOD at CA-Gro site and
another 59 pairs at CA-Qfo site. The GPP–AOD sampling pairs are much fewer
than GPP–PAR, because we select instants when both instantaneous AOD and GPP
data are available. In addition, AOD is screened for clear instants to
exclude the impacts of clouds.
Wildfire emissions
Wildfire emissions used in climate modeling are calculated as the product of
area burned, fuel consumption, and emission factors. To predict area burned,
we build stepwise regressions for area burned in 12 boreal ecoregions (Yue et
al., 2015). Observed area burned aggregated from interagency fire reports is
used as the predictand. Predictors are selected from 44 (5×6+7×2) variables including five meteorological parameters (mean and maximum
temperature, relative humidity, precipitation, and geopotential height at
500 hPa) of six different time intervals (winter, spring, summer, autumn,
fire season (May–October), and the whole year), as well as the mean and
maximum values of 7 fire indexes from the CFWI system during fire season. We
consider the impacts of antecedent factors on current fire activity by
including all above variables at the same year and those in the previous 2
years, making a total of 132 (44×3) factors. The final formats of
regression are different among ecoregions, depending on the selection of the
factors that contribute the maximum observed variance in predictands but
remain the minimum collinearity among predictors. These regression functions
are then driven with output from 13 Coupled Model Intercomparison Project
Phase 3 (CMIP3) climate models under the A1B scenario (Meehl et al., 2007) to
predict area burned at present day (1981–2000) and mid-century (2046–2065).
In the A1B scenario, CO2 concentration is projected to 532 ppm by the
year 2050, similar to the value of 541 ppm in the IPCC RCP8.5 scenario
(van Vuuren et al., 2011) archived for the Coupled Model Intercomparison
Project Phase 5 (CMIP5).
We derive a 1∘×1∘ gridded burned area based on the
prediction for each ecoregion following the approach by Yue et al. (2015).
Temporally, the annual area burned estimated with regressions is first
converted to monthly area burned using the mean seasonality for each boreal
ecoregion during 1980–2009. Spatially, large fires tend to burn in
ecosystems where historical fires are frequent because of favorable
conditions (Keane et al., 2008). In each 1∘×1∘ grid
square, we calculate the frequency of large fires (> 1000 ha) during
1980–2009; these fires account for about 85 % of total area burned in
boreal North America. We arbitrarily attribute 85 % of area burned within
each ecoregion to a number of fires with fixed size of 1000 ha. We then
allocate these large fires among the 1∘×1∘ grid cells
based on the observed spatial probability of large fires. For example, if one
grid box (named grid A) bears 1 % of large fires (> 1000 ha)
within an ecoregion at present day, the same grid will bear the same
possibility for large fires in the future. However, fuel
availability limits reburning and fire spread during the forest return
interval, suggesting that current burning will decrease the possibility of
future fires in the same location. To consider such impact, 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. For example, if present-day fires have consumed
20 % of the total area within grid A, then the possibility of
large fire will be 0.8 % (1 % × 0.8, instead of 1 %) for
this grid. Finally, we 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. With this method, we derive the gridded area
burned for boreal North America by eliminating reburning issues. Sensitivity
tests show that specifying different area 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.
Fuel consumption, the dry mass burned (DM) per fire area, is the product of fuel
load and burning severity. For fuel load in Alaska, we use 1 km inventory
from the US Forest Service (USFS) Fuel Characteristic Classification System
(FCCS; McKenzie et al., 2007). For fuel load in Canada, we use a 1 km fuel
type map from the Canadian Fire Behavior Prediction (FBP) system (Nadeau et
al., 2005), combined with fuel-bed definition from the FCCS. Burning
severity, the fraction of fuel load burned by fires, is calculated with the
USFS CONSUME model 3.0 following the approach described in Val Martin et
al. (2012). With both fuel load and burning severity, we derive fuel
consumption and further calculate biomass burned in boreal North America with
the predicted area burned. As in Amiro et al. (2009) and Yue et al. (2015),
we apply constant fuel load for both present day and mid-century because
opposite and uncertain factors influence future projections (Kurz et
al., 2008; Heyder et al., 2011; Friend et al., 2014; Knorr et al., 2016; Kim
et al., 2017). Instead, we consider changes in burning severity due to
perturbations in fuel moisture as indicated by CFWI (Yue et
al., 2015). On average, we estimate a 9 % increase in fuel consumption
over boreal North America by the mid-century, because higher temperature and
lower precipitation result in a future with drier fuel load (Flannigan et
al., 2016).
Fire emissions for a specific species are then estimated as the product
between biomass burned and the corresponding emission factor, which is
adopted from measurements by Andreae and Merlet (2001) except for NOx.
We use the average value of 1.6 g NO per kg DM from six
studies as NOx emission factor, because the number of 3.0 g NO per kg DM
reported in Andreae and Merlet (2001) is much higher than that of 1.1 g NO
per kg DM from field observations (Alvarado et al., 2010). Based on projected
area burned and observation-based fuel consumption and emission factors, we
derive fire emissions of NOx, carbon monoxide (CO), non-methane volatile
organic compounds (NMVOCs, alkenes, and alkanes), NH3, SO2, black carbon
(BC),
and organic carbon (OC) in the present day and mid-century.
Online simulations with ModelE2-YIBs climate model.*
Simulations
SST
[CO2]
Emissions
Fires
O3 effect
Aerosol effect
F10O3
2010s
2010s
2010s
2010s
Yes
No
F10AERO
2010s
2010s
2010s
2010s
No
Yes
F10CTRL
2010s
2010s
2010s
No
No
Yes
F50O3
2050s
2050s
2050s
2050s
Yes
No
F50AERO
2050s
2050s
2050s
2050s
No
Yes
F50CTRL
2050s
2050s
2050s
No
No
Yes
* Values of SST, [CO2], and emissions are adopted from
RCP8.5 scenario, with the average of 2006–2015 for the 2010s and that of
2046–2055 for the 2050s. For fire emissions, 2010 values are
predicted based on meteorology for 1981–2000 and 2050 values are for
2046–2065.
NASA ModelE2-YIBs model
The NASA ModelE2-YIBs is an interactive climate–carbon–chemistry model, which
couples the chemistry–climate model NASA ModelE2 (Schmidt et al., 2014) and
the YIBs vegetation model (Yue and Unger, 2015). NASA ModelE2 is a general
circulation model with horizontal resolution of 2∘×2.5∘ latitude by longitude and 40 vertical layers up to 0.1 hPa. It
dynamically simulates both the physical (emissions, transport, and
deposition) and chemical (production, conversion, and loss) processes of
gas-phase chemistry (NOx, HOx, Ox, CO, CH4, and NMVOCs),
aerosols (sulfate, nitrate, ammonium, BC, OC, dust, and sea salt), and their
interactions. In the model, oxidants influence the photochemical formation of
secondary aerosol species (e.g., sulfate, nitrate, and biogenic secondary
organic aerosol), in turn, aerosols alter photolysis rates and influence the
online gas-phase chemistry. Size-dependent optical parameters computed from
Mie scattering, including extinction coefficient, single scattering albedo,
and asymmetry parameters, are applied for each aerosol type (Schmidt et
al., 2014). The model also considers interactions between climate and
atmospheric components. Simulated climate affects formation, transport, and
deposition of atmospheric components, in turn, both O3 and aerosols
influence climate by altering radiation, temperature, precipitation, and
other climatic variables. Both observation-based evaluations and multi-model
intercomparisons indicate that ModelE2 demonstrates skill in simulating
climatology (Schmidt et al., 2014), soil moisture (Fig. S1 in the
Supplement), radiation (Wild et al., 2013), atmospheric composition (Shindell
et al., 2013b), and radiative effects (Shindell et al., 2013a).
YIBs is a process-based vegetation model that dynamically simulates changes
in leaf area index (LAI) through carbon assimilation, respiration, and
allocation for prescribed plant functional
types (PFTs). Coupled photosynthesis–stomatal conductance
is simulated with the Farquhar–Ball–Berry scheme (Farquhar et al., 1980;
Ball et al., 1987). Leaf-level photosynthesis is upscaled to canopy level by
separating diffuse and direct light for sunlit and shaded leaves (Spitters,
1986). Plant respiration considers thermal dependence as well as acclimation
to temperature (Atkin and Tjoelker, 2003). Soil respiration is calculated
based on the carbon flows among 12 biogeochemical pools (Schaefer et
al., 2008). Net carbon uptake is allocated among leaves, stems, and roots to
support leaf development and plant growth (Cox, 2001). The YIBs model has
been benchmarked against in situ GPP from 145 eddy covariance flux tower
sites and satellite retrievals of LAI and phenology (Yue and Unger, 2015). An
interactive flux-based O3 damage scheme proposed by Sitch et al. (2007)
is applied to quantify the photosynthetic responses to ambient O3 (Yue
and Unger, 2014). For this scheme, O3 damaging level is dependent on
excess O3 stomatal flux within leaves, which is a function of ambient
O3 concentration, boundary layer resistance, and stomatal resistance.
Reduction of photosynthesis is calculated on the basis of PFTs, each of which bears a range of low-to-high sensitivities to
O3 uptake.
Simulations
Using the NASA ModelE2-YIBs model, we perform six time-slice simulations, three
for present-day (2010s) and three for mid-century (2050s), with
atmosphere-only configuration to explore the impacts of fire emissions on NPP
in boreal North America (Table 1). Simulations F10CTRL and F50CTRL turn off
all fire emissions as well as O3 vegetation damage for the 2010s and
2050s, respectively. However, climatic feedbacks of aerosols from other
sources (both natural and anthropogenic) and related photosynthetic responses
are included. Simulations F10AERO and F50AERO consider the responses of plant
productivity to perturbations in radiation and meteorology caused by
aerosols, including emissions from wildfires and other sources, but do not
include any O3 vegetation damage. In contrast, simulations F10O3 and
F50O3 calculate offline O3 damage based on the simulated O3 from
all sources including fire emissions. For these simulations, reductions of
GPP are calculated twice with either low or high O3 sensitivity.
However, both of these GPP changes are not fed back into the model to
influence carbon allocation and tree growth. Plant respiration is changing in
response to meteorological perturbations due either to climate change or to
aerosol radiative effects. We assume no impact of O3 damage to plant
respiration and examine vegetation NPP, the net carbon uptake by biosphere,
for the current study. The difference between AERO and CTRL runs isolates the
impacts of fire aerosols on NPP, and the difference between O3 and CTRL runs
isolates O3 vegetation damage caused by fire and non-fire emission
sources.
All simulations are conducted for 20 years and outputs for the last 15 years
are used for analyses. The simulations apply sea surface temperatures (SSTs)
and sea ice distributions from previous NASA GISS experiments under the IPCC
RCP8.5 scenario (van Vuuren et al., 2011). Decadal average monthly-varying
SST and sea ice of 2006–2015 are used as boundary conditions for present-day
(2010s) runs while that of 2046–2055 are used for future (2050s) runs. In
the RCP8.5 scenario, global average SST increases by 0.62 ∘C while
sea ice area decreases by 13.8 % at the mid-century compared to the
present-day level. Decadal average well-mixed greenhouse gas concentrations
and anthropogenic emissions of short-lived species, both at present day and
mid-century, are adopted from the RCP8.5 scenario (Table 2). The enhancement
of CO2 will affect climate (through longwave absorption) and ecosystem
productivity (through CO2 fertilization) but not the fire activity and
related emissions directly. Natural emissions of soil and lightning NOx,
biogenic volatile organic compounds (BVOCs), dust, and sea salt are climate
sensitive and simulated interactively. The YIBs vegetation model cannot
simulates changes in PFT fractions. The RCP8.5 land cover change dataset
shows limited changes in land cover fractions between the 2010s and 2050s
(Oleson et al., 2010). For example, relative to the 2010s, a maximum gain of
5 % is predicted for grassland in the 2050s, resulting from a 1 %
loss in deciduous forest and another 1 % loss in needleleaf forest over
boreal North America. As a result, a land cover dataset derived from
satellite retrievals (Hansen et al., 2003) is applied as boundary conditions
for both the 2010s and 2050s.
Emissions from wildfires and non-fire sources over boreal North
America.
Species
Fire emissions
Non-fire emissions
(Tg yr-1)
(Tg yr-1)
2010s
2050s
2010s
2050s
NOxa
0.39
0.74
2.43
2.08
CO
15.7
28.8
5.9
4.0
SO2a
0.12
0.22
1.95
1.28
NH3
0.22
0.40
0.80
1.15
BC
0.08
0.16
0.03
0.01
OC
1.10
2.04
0.04
0.02
NMVOC
0.39
1.34
0.49
0.30
BVOCb
NA
NA
15.3
15.1
a Natural emissions are included for NOx
(lightning and soil) and SO2 (volcano).
b ModelE2-YIBs calculates BVOC emissions using
photosynthesis-dependent scheme implemented by Unger et al. (2013).
To evaluate the simulated GPP responses to changes in diffuse radiation, we
perform site-level simulations using the stand-alone YIBs model, which is
driven with observed hourly meteorology (including temperature, relative
humidity, surface pressure, wind speed, and soil moisture) and both diffuse
and direct PAR at sites CA-Gro and CA-Qfo. To isolate the impact of
individual aerosol-induced climatic perturbations on NPP, we perform 10
sensitivity experiments using the offline YIBs model driven with offline
meteorology simulated by ModelE2-YIBs model (Table 3). For example, the
offline run Y10_CTRL is driven with variables from the online simulation
of F10CTRL (Table 1). The run Y10_TAS adopts the same forcing as
Y10_CTRL except for temperature, which is simulated by the climate
simulation of F10AERO. In this case, we quantify the NPP responses to
individual and/or combined climate feedback (mainly in temperature,
radiation, and soil moisture) by fire aerosols. Each offline run is conducted
for 12 years and the last 10 years are used for analyses.
Observation datasets
We use observations to evaluated GPP, AOD, and O3 in boreal North
America simulated by ModelE2-YIBs. For GPP, we use a benchmark data product
upscaled from FLUXNET eddy covariance data using an ensemble of regression
trees (Jung et al., 2009). For AOD observations, we use satellite retrieval
at 550 nm from Terra MODIS Level 3 data product. For O3, gridded
datasets are not available. We use site-level observations from 81 US sites
at the Clean Air Status and Trends Network (CASTNET,
https://www.epa.gov/castnet) and 202 Canadian sites at the National Air
Pollution Surveillance (NAPS, http://www.ec.gc.ca/rnspa-naps/) program.
All datasets are averaged over the 2008–2012 period to represent present-day
climatological conditions. Gridded datasets are interpolated to the same
2∘×2.5∘ resolution as ModelE2-YIBs model.
Simulations with YIBs vegetation model driven by offline meteorology
from ModelE2-YIBs climate model.
Simulations
Base forcing
Temperature
PAR
Soil moisture
Y10_CTRL
F10CTRL
Y10_ALL
F10CTRL
F10AERO
F10AERO
F10AERO
Y10_TAS
F10CTRL
F10AERO
Y10_PAR
F10CTRL
F10AERO
Y10_SLM
F10CTRL
F10AERO
Y50_CTRL
F50CTRL
Y50_ALL
F50CTRL
F50AERO
F50AERO
F50AERO
Y50_TAS
F50CTRL
F50AERO
Y50_PAR
F50CTRL
F50AERO
Y50_SLM
F50CTRL
F50AERO
Relationships between (b, c) GPP and diffuse PAR and
(d, e) GPP and MODIS AOD at (a) two boreal sites: Groundhog
River (Gro) and Québec Mature Boreal Forest Site (Qfo). The two sites are
from the AmeriFlux network in Canada and are dominated by mixed forest (MF at
Gro) and evergreen needleleaf forest (ENF at Qfo) (Table 1). Data cover
summer days (June–August). AmeriFlux diffuse PAR and GPP (in
µmolm-2s-1) are half-hourly observations
(10:00–14:00 LT). Instantaneous MODIS Aqua and Terra 3 km AOD are selected
in a time span centered on AmeriFlux record time. For each plot: the red line
indicates the regression line and black lines depict the 1 σ
interval; the regression slope and correlation coefficient are both included
for each site (in bold if statistically significant at 95 % confidence
level). Blue dots in panels (b, c) show instants when MODIS Aqua and
Terra 3 km AODs overlap AmeriFlux data.
Results
Observed GPP–AOD relationships
Positive correlations between GPP and diffuse PAR are found at the two boreal
sites (Fig. 2b, c). The magnitude of diffuse PAR is similar for these sites,
possibly because they are located at similar latitudes (Fig. 2a). GPP values
at CA-Gro are generally higher than that at CA-Qfo, likely because deciduous
broadleaf forest (DBF) has higher photosynthetic rates. Consequently, the
slope of regression between GPP and PARdif is higher at CA-Gro
than that at CA-Qfo, suggesting that GPP of DBF (or MF) is more sensitive to
changes in diffuse PAR than that of ENF. We find almost zero correlation
between GPP and PARdir at the two sites (Table 4), indicating
that photosynthesis is in general light-saturated for sunlit leaves at these
sites during boreal summer noontime. As a result, modest reductions in direct
light by aerosols will not decrease GPP of the whole canopy.
With satellite-based AOD, we find positive correlations between GPP and AOD
at both sites (Fig. 2d, e). However, the slope of regression between GPP and
AOD is lower (and not significant) at CA-Gro compared with that at CA-Qfo,
opposite to the GPP–PARdif regressions. The cause of such
discrepancy might be related to the limitation of data availability. For the
same reason, the GPP–AOD correlation is insignificant at CA-Gro site. On
average, GPP sensitivity (denoted as mean ± range) is estimated as
3.5 ± 1.1 µmolm-2s-1 per unit AOD at lower
latitudes of boreal regions in the summer.
Pearson's correlation coefficients for GPP–PAR and GPP–AOD
relationships at AmeriFlux (AMF) sitesa.
Site
Periodb
Pearson's R
GPP–PAR
GPP–PARdir
GPP–PARdif
GPP–AOD
AOD-PARdif
AOD-PARdir
CA-Gro
2004–2013
0.19 (2432)
-0.01 (2432)
0.42 (2432)
0.15 (65)
0.60 (65)
-0.52 (65)
CA-Qfo
2003–2014
0.16 (3201)
-0.04 (3201)
0.45 (3201)
0.36 (59)
0.91 (34)
-0.80 (34)
a Both GPP and PAR (direct PARdir and
diffuse PARdif) data are adopted from site-level AMF
measurements. AOD data are adopted from instantaneous MODIS Aqua and Terra
3 km retrievals. Correlations are calculated for quasi-coincident AMF and
MODIS data over summer noontime (June–August, 10:00–14:00 LT). The
sampling number for each correlation is denoted in brackets. Significant (p<0.05) correlation coefficients are bolded. b For
CA-Gro site, diffuse PAR observations of 2005–2009 have been discarded
because of poor calibration, as documented on the AMF website.
Evaluation of simulated summer (a) GPP and (b) AOD
at 550 nm with (c, d) observations. Simulation results are from
F10AERO (Table 1). Each point on the (e, f) scatter plot represents
one grid square in boreal North America. The number of points (n),
correlation coefficient (r), and relative bias (b) for the evaluation are
presented on the plot.
Model evaluations
Simulated summer GPP shows high values in mid-western Canada (Alberta and
Saskatchewan) and the southeast (Ontario) (Fig. 3a). Forest GPP at high
latitudes is low because of the cool weather and light limitation there.
Simulated GPP reasonably captures the spatial distribution with a high
correlation coefficient of 0.77 (p≪0.01) and relatively small biases
within 20 % of the data product. Simulated AOD reproduces the observed
spatial pattern including the high values in boreal forests (Fig. 3b). In
contrast to the MODIS observations, predicted AOD is relatively uniform over
the west with a background value of ∼ 0.1. This discrepancy explains
the low correlation coefficient (R=0.25, p<0.01) between the model and
MODIS data. The simulation fails to capture the high values in the west,
possibly due to a climate model underestimation of biogenic secondary organic
aerosol, which may be an important contribution over the western boreal
forest. Simulated maximum daily 8 h average (MDA8) [O3] shows low
values in boreal North America and high values in the western and eastern
US (Fig. 4a). This pattern is consistent with surface observations
(Fig. 4b), but the model overestimates the measured surface O3 by
22 %. The Canadian measurement sites are located near the southern
boundary and as a result do not represent the average state over the vast
boreal region at higher latitudes.
Evaluation of simulated summer surface maximum daily 8 h average
[O3] with observations for 2008–2012. Observations are collected from
81 US sites at the Clean Air Status and Trends Network (CASTNET) and 202
Canadian sites at the National Air Pollution Surveillance (NAPS) program. The
number of points (n), correlation coefficient (r), and mean bias (b)
for the evaluation are presented on the plot. Values over Canada and Alaska
are denoted with blue points.
With the Sitch et al. (2007) scheme, the YIBs model simulates reasonable GPP
responses to [O3] in North America (Yue and Unger, 2014; Yue et
al., 2016). Generally, damage to GPP increases with the enhancement of
ambient [O3], but with varied sensitivities for different plant species
(see Fig. 6 of Yue and Unger, 2014). In response to the same level of
[O3], predicted O3 damages are higher for deciduous trees than
those
for needleleaf trees, consistent with observations from meta-analyses (Wittig
et al., 2007). The model also reproduces observed light responses of GPP to
diffuse radiation in boreal regions. With the site-level simulations, we
evaluate the modeled GPP–PARdif relationships at the hourly
(instead of half-hourly) time step during summer. For 1342 pairs of GPP and
PARdif at the site CA-Gro, the observed correlation coefficient
is 0.42 and regression slope is 0.011, while the results for the simulation
are 0.60 and 0.014, respectively. At the site CA-Qfo, the observations yield
a correlation coefficient of 0.46 and regression slope of 0.007 for 1777
pairs of GPP and PARdif. The simulated correlation is 0.61 and
the regression is 0.011 at the same site. The GPP sensitivity to
PARdif in the model is slightly higher than that of the available
observations, likely because the latter are affected by additional
non-meteorological abiotic factors. To remove the influences of compound
factors other than radiation, we follow the approach of Mercado et al. (2009)
to discriminate GPP responses to “diffuse” and “direct” components of PAR
at the two sites (Fig. 5). The model successfully reproduces the observed
GPP-to-PAR sensitivities. Increase in PAR boosts GPP, but the efficiency is
much higher for diffuse light than that for direct light, suggesting that
increase of diffuse radiation is a benefit for plant growth.
Observed (blue) and simulated (red) response of GPP to diffuse
(square) and direct (triangle) PAR at boreal sites (a) CA-Gro
(2004–2013) and (b) CA-Qfo (2004–2010). Observations and
simulations are split into “diffuse” and “direct” conditions when the
diffuse fraction is > 0.8 and < 0.2, respectively. Data points are then
averaged over PAR bins of 30 W m-2 with error bars indicating 1
standard deviation of GPP for each bin.
Changes in summer (a, b) [O3] and (c, d) AOD
at 550 nm induced by wildfire emissions in (a, c) the 2010s and
(b, d) the 2050s over boreal North America. Only significant changes
(p<0.05) are shown.
Simulated O3 damages to summer GPP in North America. Results
shown are from simulations with (a, b) low and (c, d) high
O3 sensitivities for (a, c) 2010 and (b, d) 2050.
Simulated [O3] includes contributions from both wildfire and non-fire
emissions. Results for 2010 are derived as
(F10O3/F10CTRL-1) ⋅ 100 %. Results for 2050 are derived as
(F50O3/F50CTRL-1) ⋅ 100 %.
Simulated summertime O3 stomatal fluxes in boreal North
America. Results shown are the (a, b) mean and
(c, d) excess flux at (a, c) 2010 and (b, d) 2050.
Simulated [O3] includes contributions from both wildfire and non-fire
emissions. Excess O3 stomatal flux is calculated as the difference
between the stomatal flux and a PFT-specific threshold as defined in Sitch et
al. (2007).
Changes in surface radiative fluxes induced by wildfire aerosols in
boreal North America. Results shown are for the changes in summertime
(June–August) (a, b) total, (c, d) direct, and (e, f) diffuse solar radiation at surface caused by aerosols from wildfire
emissions at (a, c, e) present day and (b, d, f)
mid-century. Significant changes (p<0.05) are marked with black dots.
Results for 2010 are calculated as F10AERO–F10CTRL. Results for 2050 are
calculated as F50AERO–F50CTRL.
Predicted (a, b) absorption of shortwave radiation and
(c, d) perturbations in vertical velocity by wildfire aerosols at
(a, c) present day and (b, d) mid-century. The absorption of
shortwave radiation is calculated as the differences of radiative
perturbations between top of atmosphere and surface. Vertical velocity is
calculated as the longitudinal average between 105 and 112.5∘ W (two
blue lines in panel a). Positive (negative) values indicate descending
(rising) motion. Results for the 2010s are calculated as F10AERO–F10CTRL.
Results for the 2050s are calculated as F50AERO–F50CTRL. Significant
changes (p<0.05) in panel (a, b) are indicated as black points.
Predicted changes in summertime (a, b) surface air
temperature, (c, d) precipitation, and (e, f) soil water
content at surface caused by aerosols from wildfire emissions at
(a, c, e) present day and (b, d, f) mid-century. Results for
temperature and precipitation are shown as absolute changes. Results for soil
water are shown as relative changes. Results for the 2010s are calculated as
F10AERO–F10CTRL. Results for the 2050s are calculated as
F50AERO–F50CTRL. Significant changes (p<0.05) are marked with black
dots.
Predicted percentage changes in summer NPP caused by wildfire
aerosols at (a) present day and (b) mid-century. Results for
the 2010s are calculated as (F10AERO/F10CTRL-1) ⋅ 100 %. Results
for the 2050s are calculated as (F50AERO/F50CTRL-1) ⋅ 100 %.
Significant changes (p<0.05) are marked with black dots.
Simulation of wildfire O3 and aerosols
During 1980–2009, wildfire is observed to burn 2.76×106 ha and
156.3 Tg DM every year over boreal North America. Similarly, the ensemble
prediction with fire regression models estimates present-day area burned of
2.88×106 ha yr-1 and biomass burned of
160.2 Tg DM yr-1 (Yue et al., 2015). By the mid-century, area burned
is projected to increase by 77 % (to 5.10×106 ha yr-1) in
boreal North America, mainly because of the higher temperature in future fire
seasons. Consequently, biomass burned increases by 93 % (to
308.6 Tg DM yr-1) because fuel consumption also increases by 9 %
on average in a drier climate (Yue et al., 2015). Enhanced fire emissions
increase concentrations of surface O3 and column AOD, especially over
Alaska and central Canada (Fig. 6). The maximum centers of air pollutants are
collocated for O3 and AOD but with disproportional magnitudes, suggesting
nonlinear conversion among fire emission species as well as the interactions
with natural emission sources (e.g., lightning/soil NOx and BVOC). On
average, wildfire emissions contribute 7.1 ± 3.1 %
(2.1 ± 0.9 ppbv) to surface O3 and 25.7 ± 2.4 %
(0.03 ± 0.003) to AOD in the summer over boreal North America in the
present day. By mid-century, these ratios increase significantly to
12.8 ± 2.8 % (4.2 ± 0.9 ppbv) for O3 and
36.7 ± 2.0 % (0.05 ± 0.003) for AOD.
Simulation of fire pollution impacts on NPP
Surface O3, including both fire and non-fire emissions (Table 2), causes
limited (1–2 %) damages to summer GPP in boreal North America (Fig. 7).
The most significant damage is predicted over eastern US, where observed
[O3] is high over vast forest ecosystems (Fig. 4). In the western US,
[O3] is also high but the O3-induced GPP reduction is trivial
because low stomatal conductance in the semiarid ecosystems limits O3
uptake there (Yue and Unger, 2014). Over boreal North America, dominant PFTs
are ENF (accounting for 44 % of total vegetation cover) and tundra
(treated as shrubland, accounting for 41 % of total vegetation cover).
Both species have shown relatively high O3 tolerance with a damaging
threshold of 40 ppbv as calculated with Sitch's scheme (Yue and Unger,
2014). For boreal regions, the mean [O3] of 28 ppbv (Fig. 4a) is much
lower than this damaging threshold, explaining why the excess O3
stomatal flux (the flux causing damages) is low there (Fig. 8). Statistics in
Yue et al. (2015) show that maximum daily 8 h average (MDA8) [O3] with
fire contributions can be higher than 40 ppbv in Alaska and Canada. However,
such episodes appear at the 95th percentile for present day and 90th percentile for
mid-century, suggesting that O3 vegetation damage is rare in boreal North
America and fire-induced O3 enhancement does not exacerbate such
damages. Therefore, we do not consider O3 damage effects further.
Fire aerosols cause significant perturbations in shortwave radiation at
surface (Fig. 9). The direct light is largely attenuated especially over
Alaska and central Canada, where fire aerosols are most abundant (Fig. 6). In
contrast, diffuse light widely increases due to particle scattering. In the
present day, the average reduction of 5.6 W m-2 in the direct light
component is in part offset by the enhancement of 2.6 W m-2 in the
diffuse light component, leading to a net reduction of 3.0 W m-2 in
solar radiation over boreal North America. By the mid-century, a stronger
reduction of 9.5 W m-2 in direct light is accompanied by an increase
of 4.0 W m-2 in diffuse light, resulting in a net reduction of
5.5 W m-2 in solar radiation. Fire-induced BC aerosols strongly absorb
solar radiation in the atmospheric column (Fig. 10a, b). On average, fire
aerosols absorb 1.5 W m-2 in the present day and 2.6 W m-2 by
the mid-century.
Atmospheric circulation patterns respond to the aerosol-induced radiative
perturbations (Fig. 10c, d). Surface radiative cooling and atmospheric
heating together increase air stability and induce anomalous subsidence. In
the present day, such descending motion is confined to 55–68∘ N,
accompanied by a rising motion at 52–55∘ N (Fig. 10c). As a result,
fire aerosols induce surface warming at higher latitudes but cooling at lower
latitudes in boreal regions (Fig. 11a). Meanwhile, precipitation is inhibited
by the subsidence in northwestern Canada but is promoted by the rising motion
in the southwest (Fig. 11c). By the mid-century, the range of subsidence
expands southward to 42∘ N (Fig. 10d) due to strengthened
atmospheric heating (Fig. 10b). The downward convection of warm air offsets
surface radiative cooling (Fig. 9b), leading to a significant warming in the
southwest (Fig. 11b). The expanded subsidence further inhibits precipitation
in vast domain of Canada (Fig. 11d). Soil moisture is closely related to
rainfall and as a result exhibits dipole changes (drier north and wetter
south) in the present day (Fig. 11e) but widespread reductions (Fig. 11f) by
the mid-century.
In response to the climatic effects of fire aerosols, boreal NPP shows
distinct changes between the present day and mid-century (Fig. 12). Such
changes in NPP are a consequence of changes in GPP and autotrophic
respiration (Fig. S2). Variations in plant respiration resemble those of GPP,
because higher photosynthesis leads to faster leaf and tissue development,
resulting larger maintenance and growth respiration. In the 2010s, forest NPP
increases by 5–15 % in Alaska and southern Canada but decreases by
5–10 % in northern and eastern Canada. This pattern of NPP changes
(ΔNPP) is connected to the climatic effects of aerosols, especially
changes in soil moisture (Fig. 11). The correlation between ΔNPP
(Fig. 12a) and changes in soil moisture (Fig. 11e) reaches R=0.56 (n=356), much higher than the values of R= -0.11 for temperature change
(Fig. 11a) and R=0.22 for precipitation change (Fig. 11c). On the
continental scale, the patchy responses of NPP offset each other. Since the
dominant fraction of carbon uptake occurs in southern Canada (Fig. 3a), where
positive NPP change is predicted (Fig. 12a), wildfire aerosols enhance the
total NPP by 72 Tg C yr-1 in the present day (Table 5). In contrast,
increased wildfire emissions in the 2050s inhibit precipitation (Fig. 11d)
and decrease soil moisture in boreal North America (Fig. 11f), leading to
widespread NPP reductions and a total NPP loss of 118 Tg C yr-1
(Fig. 12b, Table 5).
Changes in NPP (Tg C yr-1) caused by composite and individual
climatic effects of fire aerosols.
2010s
2050s
Onlinea
72
-118
Offline totalb
126
-97
Temperature
11
-22
Radiation
8
14
Soil moisture
104
-86
a Online results are calculated using the
ModelE2-YIBs model with F10AERO–F10CTRL for the 2010s and
F50AERO–F50CTRL for the 2050s. b Offline results are
calculated with the YIBs model driven with individual or combined changes in
temperature, radiation, and soil moisture.
Discussion
Roles of aerosol climatic feedback
The contrasting sign of NPP responses in the present day and mid-century are
closely related to the aerosol-induced surface climatic feedback. Sensitivity
experiments using offline YIBs model (Table 3) allowed assessment of the
impacts of individual changes in the major meteorological drivers, including
temperature, radiation (diffuse and direct), and soil moisture (Table 5). The
offline simulations driven with changes in all three variables yield
ΔNPP of 126 Tg C yr-1 for the 2010s and
-97 Tg C yr-1 for the 2050s. These values are different from the
online simulations, which predict ΔNPP of 72 Tg C yr-1 for the
2010s and -118 Tg C yr-1 for the 2050s. A lack of other aerosol-induced feedbacks in
the offline model, for example changes in relative humidity, surface
pressure, soil temperature, and turbulence momentum, may cause such
discrepancy between the online and offline simulations. Seasonal analyses
show that summertime ΔNPP is 99 Tg C at present day and -95 Tg C
at mid-century, dominating the NPP changes all through the year, because both
wildfire emissions and ecosystem photosynthesis maximize in boreal summer.
Observations show that aerosols can promote plant photosynthesis through
increasing diffuse radiation (Niyogi et al., 2004; Cirino et al., 2014;
Strada et al., 2015). Our analyses with ground data also show positive
correlations between GPP and PARdif (Fig. 2 and Table 4), and the
model reproduces observed GPP responses to perturbations in direct and
diffuse PAR (Fig. 5). Wildfire aerosols enhance diffuse radiation by
2.6 W m-2 (1.7 %) at present day and 4.0 W m-2 (2.3 %)
at mid-century in boreal North America (Fig. 9). With these changes, simulated
NPP increases by 8 Tg C yr-1 at the 2010s and 14 Tg C yr-1 at
the 2050s (Table 5). Near the two AMF sites (Fig. 2a), wildfires
increase local AOD by 0.03 (Fig. 6c). Meanwhile, we estimate that summer
average (00:00–24:00) GPP increases by 0.04 µmolm-2s-1
in the same region due to aerosol diffuse fertilization effects (DFE) based
on the results of Y10_PAR–Y10_CTRL. This change suggests a simulated
GPP sensitivity of 1.2 µmolm-2s-1 (22 %) per unit
AOD. Observed GPP sensitivity to AOD at the two sites are 2.3 (19 %) and
4.5 µmolm-2s-1 (58 %) per unit AOD
(Fig. 2d, e). The absolute value of GPP sensitivity from simulations is much
smaller than that of observations, because the former is for 24 h average
while the latter is only for noontime (10:00–14:00). The relative change of
22 % in YIBs model falls within the observed range of 19–58 %.
The estimated NPP changes of 8 Tg C yr-1 by the radiative effects of
boreal fire aerosols are much weaker than the enhancement of
78–156 Tg C yr-1 by fires in the Amazon basin (Rap et al., 2015). There
are at least two reasons for such a difference in the DFE between boreal and
Amazon fire aerosols. First, wildfire emissions and associated impacts on
radiation are much smaller in boreal regions. Wildfires in Alaska and Canada
directly emit 68 Tg C yr-1 at the 2010s, resulting in enhancement of
summer AOD by 35 % and diffuse radiation by 1.7 %. These boreal
emissions are much smaller than the ∼ 240 Tg C yr-1 in the Amazon
basin (van der Werf et al., 2010), where fires enhance regional PM2.5
concentrations by 85 % and diffuse radiation by 6.2 % in dry seasons
(Rap et al., 2015). Second, larger solar insolation at lower latitudes allows
stronger DFE for the same unit change of diffuse radiation. In our
prediction, most of the NPP changes occur at high latitudes in boreal regions
(Fig. 12), where total insolation is not so abundant as that in the tropical
areas. Consequently, the decline of direct radiation in boreal regions more
likely converts the light availability of sunlit leaves from light saturation
to light limitation, offsetting the benefit from enhanced diffuse radiation
for shaded leaves. For this study, we do not find GPP reduction by the
decline of direct light at the two AMF sites (Table 4), possibly
because these sites are located at middle latitudes (< 50∘ N). In
the future, more observations at higher latitudes (> 55∘ N) are
required to explore the sensitivity of GPP to AOD in light-limited
conditions.
Simulations have shown that absorbing aerosols can cause regional drought by
increasing air stability (Liu, 2005; Cook et al., 2009; Tosca et al., 2010).
Our results confirm this tendency but with a varied range of hydrological
responses depending on the magnitude of wildfire emissions (Figs. 11c–f).
Observations suggest that precipitation (and the associated soil moisture) is
the dominant driver of the changes in GPP over North America, especially for
the domain of cropland (Beer et al., 2010). Sensitivity experiments with
offline YIBs model show that changes in soil moisture account for 82.5 %
of ΔNPP at present day and 70.5 % of ΔNPP at mid-century
(Table 5). These results suggest that aerosol-induced changes in soil water
availability, instead of temperature and radiation, dominantly contribute to
the changes of boreal NPP, consistent with observational and experimental
results (Ma et al., 2012; Girardin et al., 2016; Chen et al., 2017).
Limitations and uncertainties
In this study, we examine the interactions among climate change, fire
activity, air pollution, and ecosystem productivity. To reduce the complexity
of the interactions, we focus on the most likely dominant feedback and thus
main chain of events: climate → fire → pollution → biosphere
(Fig. 1). However, our choice of feedback analysis does not mean that the
interplay of other processes is unimportant. For example, climate-induced
changes in vegetation cover and types can influence fire activity by alteration
of fuel load and air pollution by BVOC emissions (climate → biosphere
→ fire/pollution). In addition, other feedbacks may amplify ecosystem
responses but are not considered. For example, the drought caused by fire
aerosols in the mid-century (Fig. 11) may help increase fire activity (fire
→ pollution → climate → fire). Furthermore, we apply fixed SSTs
in the climate simulations because reliable ocean heat fluxes for the future
world were not available. Many previous studies have investigated regional
aerosol–climate feedbacks without ocean responses. For example, Cook et
al. (2009) found that dust–climate–vegetation feedback promotes drought in
US, with a climate model driven by prescribed SSTs. Similarly, Liu (2005)
found fire aerosols enhance regional drought using a regional climate model,
which even ignores the feedback between local climate and large-scale
circulation. While we do concede that our experimental design is not a
complete assessment of all known processes and feedbacks, within these
limitations, this study for the first time quantifies the indirect impacts of
wildfire on long-range ecosystem productivity under climate change.
We use the ensemble projected fire emissions from Yue et al. (2015). Area
burned is predicted based on the simulated meteorology from multiple climate
models. Such an approach may help reduce model uncertainties in climatic
responses to CO2 changes (Collins et al., 2013; Kirtman et al., 2014)
but cannot remove the possible biases in the selection of climate scenarios
and fire models. All predictions in Yue et al. (2015) are performed under the
IPCC A1B scenario. With two different scenarios, A2 of high emissions and B2
of low emissions, Balshi et al. (2009) showed that future area burned in
boreal North America increases at a similar rate until the 2050s, after which
area burned in A2 scenario increases much faster than that in B2 scenario. On
average, boreal area burned in Balshi et al. (2009) increases by
∼ 160 % during 2051–2060 compared with 2001–2010, which is much
higher than the change of 66 % in Yue et al. (2015). In contrast, Amiro
et al. (2009) predicted that boreal area burned at the 2 × CO2
scenario increases only by 34 % relative to the 1 × CO2
scenario. This ratio is only half of the estimate in Yue et al. (2015), which
compared results between periods with 1.44 × CO2 and
1 × CO2. The discrepancies among these studies are more likely
attributed to the differences in fire models. Although both Amiro et
al. (2009) and Yue et al. (2015) developed fire weather regressions in boreal
ecoregions, the former study did not include geopotential height at 500 hPa
and surface relative humidity as predictors, which make dominant
contributions to area burned changes in the latter study. In contrast, Balshi
et al. (2009) developed nonlinear regressions between area burned and climate
on a grid scale, which helps retain extreme values at both the temporal and
spatial domain. Compared to previous estimates, Yue et al. (2015) predicted
median increases in future fire emissions over boreal North America.
We apply constant land cover and fuel load for both present day and
mid-century, but we estimate an increase in fuel consumption due to changes
in fuel moisture. Future projection of boreal fuel load is highly uncertain
because of multiple contrasting influences. For example, using a dynamic
global vegetation model (DGVM) and an ensemble of climate change projections,
Heyder et al. (2011) predicted a large-scale dieback in boreal–temperate
forests due to increased heat and drought stress in the coming decades. In
contrast, projections using DGVMs show a widespread increase in vegetation
carbon under the global warming scenario with CO2 fertilization of
photosynthesis (Friend et al., 2014; Knorr et al., 2016). In addition,
compound factors such as greenhouse gas mitigation (Kim et al., 2017),
population change (Knorr et al., 2016), pine beetle outbreak (Kurz et
al., 2008), and fire management (Doerr and Santin, 2016) may exert varied
impacts on future vegetation and fuel load. Although we apply constant fuel
load, we consider changes of fuel moisture because warmer climate states tend
to dry fuel and increase fuel consumption (Flannigan et al., 2016). With
constant fuel load but climate-driven fuel moisture, we calculate a 9 %
increase in boreal fuel consumption by the mid-century (Yue et al., 2015).
Although such increment is higher than the prediction of 2–5 % by Amiro
et al. (2009) for a doubled-CO2 climate, the consumption-induced
uncertainty for fire emission is likely limited because changes in area
burned are much more profound.
Predicted surface [O3] is much higher than observations over boreal
North America (Fig. 4). This bias does not affect main conclusions of this
study, because predicted O3 causes limited damages to boreal GPP even
with the overestimated [O3] (Fig. 7). The result confirms that
fire-induced O3 vegetation damage is negligible in boreal North America.
For aerosols, the model captures reasonable spatial pattern of AOD but with a
background value of ∼ 0.1 outside fire-prone regions, where the
observed AOD is usually 0.1–0.2 (Fig. 3). This discrepancy may be related to
the insufficient representations of physical and chemical processes in the
model but may also result from the retrieval biases in MODIS data due to the
poor surface conditions (Liu et al., 2005) and small AOD variations (Vachon
et al., 2004) at high latitudes.
Simulated aerosol climatic effects depend on radiative and physical processes
implemented in the climate model. We find that present-day boreal fire
aerosols on average absorb 1.5 W m-2 in the atmosphere (Fig. 10),
which is much smaller than the value of 20.5 ± 9.3 W m-2 for
fires in equatorial Asia (Tosca et al., 2010). This is because boreal fires
enhance AOD only by 0.03 while tropical fires increase AOD by ∼ 0.4.
Previous modeling studies showed that fire plumes induce regional and
downwind drought through enhanced atmospheric stability (Feingold et
al., 2005; Tosca et al., 2010; Liu et al., 2014). Most of these results were
based on the direct and/or semidirect radiative effects of fire aerosols.
Inclusion of the indirect aerosol effect may further inhibit precipitation
and amplify drought but may also introduce additional uncertainties for the
simulations. The fire–drought interaction may promote fire activity,
especially in a warmer climate. Ignoring this interaction may underestimate
future area burned and the consequent emissions.
Implications
Inverse modeling studies have shown that the land ecosystems of boreal North
America are carbon neutral in the present day, with the estimated land-to-air
carbon flux from -270 ± 130 to 300 ± 500 Tg C yr-1
(Gurney et al., 2002; Rödenbeck et al., 2003; Baker et al., 2006;
Jacobson et al., 2007; Deng et al., 2014). Here, we reveal a missing land
carbon source due to future wildfire pollution, taking into account full
coupling among fire activity, climate change, air pollution, and the carbon
cycle. Fire pollution aerosol increases boreal NPP by 72 Tg C yr-1 in
the present day, comparable to the direct carbon loss of 68 Tg C yr-1
from wildfire CO2 emissions (product of biomass burned and CO2
emission factors). By mid-century, increasing fire emissions instead cause a
NPP reduction of 118 Tg C yr-1 due to the amplified drought. Although
NPP is not a direct indicator of the land carbon sink, reduction of NPP is
always accompanied with the decline of net ecosystem exchange and the
enhanced carbon loss. In combination with the enhanced carbon emission of
130 Tg C yr-1, future boreal wildfire presents an increasing threat
to the regional carbon balance and global warming mitigation. Furthermore,
the NPP reductions are mostly located in southern Canada, where cropland is
the dominant ecosystem, newly exposing the future wildfire-related air
pollution risk to food production.
Our analyses of fire pollution effects on boreal North American productivity
may not be representative for other boreal ecosystems and/or on the global
scale. There is substantial variability in plant species, topography, and
climatology across different boreal regions. Such differences indicate
distinct GPP sensitivities as well as fire characteristics. At lower
latitudes, where anthropogenic pollution emissions are more abundant, ambient
ozone concentrations may have exceeded damaging thresholds for most plant
species. In those regions, additional ozone from a fire plume may cause more
profound impacts on photosynthesis than our estimate for boreal North
America. For example, Amazonian fire is predicted to reduce forest NPP by
230 Tg C yr-1 through the generation of surface ozone (Pacifico et
al., 2015). Meanwhile, solar radiation is more abundant at lower latitudes,
indicating more efficient increases in photosynthesis through aerosol DFE
because the sunlit leaves receive saturated direct light in those regions. As
shown in Beer et al. (2010), partial correlations between GPP and solar
radiation are positive in boreal regions but negative over the
subtropics and tropics, suggesting that light extinction by fire aerosols has
contrasting impacts on plant photosynthesis in the high versus low latitudes.
Further simulations and analyses are required to understand the net impacts
of ozone and aerosols from biomass burning on the global carbon cycle.