Aerosols from fire emissions can potentially have large impact on clouds and
radiation. However, fire aerosol sources are often intermittent, and their
effect on weather and climate is difficult to quantify. Here we investigated
the short-term effective radiative forcing of fire aerosols using the global
aerosol–climate model Community Atmosphere Model version 5 (CAM5). Different
from previous studies, we used nudged hindcast ensembles to quantify the
forcing uncertainty due to the chaotic response to small perturbations in the
atmosphere state. Daily mean emissions from three fire inventories were used
to consider the uncertainty in emission strength and injection heights. The
simulated aerosol optical depth (AOD) and mass concentrations were evaluated
against in situ measurements and reanalysis data. Overall, the results show
the model has reasonably good predicting skills. Short (10-day) nudged
ensemble simulations were then performed with and without fire emissions to
estimate the effective radiative forcing. Results show fire aerosols have
large effects on both liquid and ice clouds over the two selected regions in
April 2009. Ensemble mean results show strong negative shortwave cloud
radiative effect (SCRE) over almost the entirety of southern Mexico, with a 10-day
regional mean value of
Natural and human-induced fires play an important role in the Earth system. Aerosol and gas emissions from biomass burning can change the atmospheric composition and potentially affect the weather and climate. Over 30 % of the global total emission of black carbon (BC) comes from open burning of forests, grasslands, and agricultural residues (Bond et al., 2013). For organic aerosols, substantial increases of concentrations dominated by organic carbon enhancements are observed in regions with biomass burning events (Zeng and Wang, 2011; Lin et al., 2013; Brito et al., 2014; Reddington et al., 2014). As a result, biomass burning emissions have a large impact on the global and regional mean aerosol optical depth (AOD; Jacobson, 2014).
Through interactions with radiation and cloud, fire aerosols can
significantly affect Earth's long-term energy budget. Previous studies have
investigated the global and regional radiative forcing of fire aerosols using
long climatological simulations or satellite retrievals. For example, Ward et
al. (2012) investigated the radiative forcing of global fires in
preindustrial, present-day, and future periods. For the present-day
condition, they estimated a direct aerosol effect (or radiative forcing
through aerosol–radiation interactions as defined in the IPCC Fifth
Assessment Report (AR5), RFari; see Sect. 2.4) of
On short timescales, fire aerosols have even larger radiative impacts.
Observed maximum daily direct aerosol radiative effects can reach
Previous modeling studies on the short-term fire aerosol effects mainly
focused on aerosol direct effects (e.g., Keil and Haywood, 2003; Chen et
al., 2014; Kolusu et al., 2015), and only a couple of studies have
investigated the indirect effects of fire aerosols (Lu and Sokolik, 2013). In
addition, to estimate the aerosol indirect effect, long simulations
(multi-year,
In this study, we performed month-long and 10-day nudged Community Atmosphere Model version 5 (CAM5) simulations to
investigate the effects of fire aerosols on radiation and cloud processes on
short timescales (less than 2 weeks). Horizontal winds were nudged
towards 6-hourly reanalysis to constrain the large-scale circulation and to
allow for more accurate model evaluations against observations. We also used
daily mean emissions from three fire inventories to consider the uncertainty
in emission strength and injection heights. Even for short simulations,
small perturbations of meteorological states might have large impact on the
local aerosol and cloud properties, thus bringing uncertainty to the aerosol
forcing estimate. Therefore, in our simulations, we also employed very weak
temperature nudging (
The rest of the paper is organized as follows. Section 2 describes the model and data used in this study. It also introduces how the ensembles are generated in the short nudged simulations and explains how the fire aerosol forcing is estimated. Results and discussions are presented in Sect. 3, and conclusions are summarized in Sect. 4.
In this study, we used the CAM version 5.3 with the finite-volume dynamical
core at 1.9
Three fire emission inventories were used in this study. Two of them are
widely used bottom-up inventories – Global Fire Emissions Database
version 3.1 (GFED v3.1; van der Werf et al., 2010;
To drive CAM5 simulations, fire emission data were regridded to the model resolution and distributed vertically. For the GFED v3.1 and QFED v2.4 emission data we adopted the same injection heights (from surface to 6 km) as used in the standard CAM5 model, while for GFEDv4.1s in this study the injection heights were estimated using a fire plume model and scaled to a 6-hourly interval.
The fire emission inventories were first analyzed to select appropriate time
periods and regions for our study before being used to drive model
simulations. Figure 1 shows the multi-year mean biomass burning emissions
from GFED v4.1 over North America. The emission manifests significant
seasonality with large dry-matter consumption during March to April and June
to September. The summer and autumn burning covers the Pacific Northwest and part
of Canada and is mainly associated with forest fires, while the spring
burning occurs in more densely populated regions like Mexico and the central and
eastern United States with a large contribution of agricultural fires in
croplands (Korontzi et al., 2006; Magi et al., 2012). Similar features are
also captured in GFED v3.1 and QFED v2.4 with differences in the magnitude.
We chose to analyze the simulated fire aerosol effect in April, the peak
month of spring burning, when there are extreme fire activities over Mexico
(10–25
Fire-emitted BC from different emission inventories in April 2009 is shown is Fig. 2. Although GFED v4.1s includes the contributions of small fires (Randerson et al., 2012), the emitted BC in GFED v4.1 shows no substantial increase compared to GFED v3.1 during the selected period. Only an increase by 1.75 is seen over southern Mexico. In the central US, the BC emission is even slightly weaker in GFED v4.1. QFED v2.4 shows a much larger BC emission than the GFED inventories. Monthly mean values of emitted BC in QFED v2.4 are larger than those in GFED v4.1s by a factor of 11.4 in the central US and a factor of 3.3 in southern Mexico.
Spatial distributions of multi-year monthly mean
biomass-burning-consumed dry matter over North America during 2003–2014 from
GFEDv4.1. Boxes denote selected regions: central US (35–45
Spatial distributions of monthly mean BC emissions from three emission inventories in April 2009. IMPROVE data sites are shown as asterisks for sites near the source region and as dots for sites in the region downwind of the fire source.
List of CAM5 simulations.
Two groups of simulations were conducted (Table 1) using the same greenhouse
gas concentrations, sea surface conditions, and anthropogenic emissions of
aerosols and precursors. Each group includes four simulations, performed
either without fire emission or with daily fire emissions from one of the
three fire emission inventories introduced in Sect. 2.2. The emitted species
include BC, OC, and SO
Simulations in group A are month-long single-member nudged simulations. These simulations were performed to provide longer time series for model evaluation and generate initial condition files for simulations in group B. They started in 1 January 2009 and were integrated for 4 months with 3-month spin-up. Initial condition files were generated on 1 April at 00:00 UTC for simulations in group B.
Simulations in group B are 10-day ensemble simulations. Unlike the traditional way of perturbing initial conditions, in this study we constructed the ensembles by implementing a very weak temperature nudging and perturbing the nudging timescale. This is because under the influence of horizontal-wind nudging, ensemble differences generated by perturbing initial conditions would fade away during the integration. In contrast, our method can consider the influence of small temperature perturbations during the entire simulation period, as nudging is applied at every time step. On the other hand, the large-scale circulation patterns simulated in the different ensemble members are very similar (not shown), so the noises caused by the chaotic system can be constrained and the effective fire aerosol forcing signal can be easily identified.
Each ensemble in group B includes 10 members. The only difference between the members is the relaxation timescale of temperature, which varies from 10 to 11 days at an interval of 0.1 day. All simulations started on 1 April 2009 and were integrated for 10 days. For each simulation (e.g., E_QF), the initial condition was generated by combining the meteorological fields from initial condition outputs in the S_NF simulation with aerosol and precursor concentrations from initial condition outputs in the single-member simulation forced by the corresponding fire emission (S_QF).
The IPCC AR5 provides a more useful characterization of aerosol forcing by allowing for rapid tropospheric adjustments (Boucher et al., 2013) compared to the original definition of aerosol forcing. It quantifies aerosol radiative effects in terms of effective radiative forcing from aerosol–radiation interactions (ERFari) and effective radiative forcing from aerosol–cloud interactions (ERFaci). ERFari refers to the combined effect of instantaneous radiative forcing from direct scattering and absorption of sunlight (aerosol direct effect) and related subsequent rapid adjustments of atmospheric state variables and cloudiness (aerosol semi-direct effect). ERFaci refers to the indirect forcing resulting from aerosol-induced changes in cloud albedo (first albedo effect) and subsequent changes in cloud lifetime as rapid adjustments (second aerosol indirect effect) via microphysical interactions.
To allow for a straightforward comparison with previous studies in the literature, we followed the IPCC concept of including rapid adjustments (effective aerosol radiative forcing) but continued to decompose the aerosol effect in the conventional terms as aerosol DRE, aerosol cloud radiative effect (CRE) and surface albedo effect. Note that, as the nudging timescale determines the degree to which model physics are constrained (Kooperman et al., 2012), the use of a 6 h relaxation timescale for horizontal-wind nudging means only very fast adjustments are considered in the simulations.
Similar to Jiang et al. (2016), our calculations are based on the work of
Ghan et al. (2012) and Ghan (2013). The fire aerosol DRE, CRE, and surface albedo
effect are defined as fire-induced changes in aerosol forcing, cloud forcing,
and surface albedo forcing, respectively, and are calculated as the difference
of each item between simulations with and without fire emissions (denoted by
In this study, we used two sets of AOD reanalysis and the Aerosol Robotic
Network (AERONET) data (Holben et al., 1998) to evaluate the modeled AOD. The
two AOD reanalysis datasets are the Naval Research Laboratory (NRL)
reanalysis (Rubin et al., 2016) and the Monitoring Atmospheric Composition
and Climate (MACC) reanalysis (Eskes et al., 2015). Both are generated by
assimilating AOD retrievals from MODIS (Zhang et al., 2008; Benedetti et
al., 2009) with forecast fields. The NRL reanalysis provides 6-hourly AOD at
1
In addition, the simulated BC and primary organic matter (POM)
concentrations were compared with observations from the Interagency
Monitoring of Protected Visual Environments (IMPROVE) (Malm et al., 2004).
IMPROVE aerosol data are only available over the central US. A total of
15 sites were selected and marked in Fig. 2, which include the sites
west of 94
In this part, the model performance is first evaluated based on the simulations in group A. Next, we present the simulated short-term effective fire aerosol forcing on 10-day and daily timescales based on the results from group B simulations. We will demonstrate the importance of using ensemble simulations in estimating the short-term aerosol effective forcing and give a quantitative estimate of how many ensemble members are needed for the case selected in this study.
Time series of daily regional mean AOD in April 2009 in simulations and reanalysis data. Numbers in parentheses denote time correlation coefficient (TCC) and root mean square error (RMSE) between each simulation in group A and reanalysis data (left: NRL; right: MACC). Individual lines indicate group A simulations. Shaded areas (very narrow) in slightly darker colors during 1–10 April illustrate maximum and minimum values of daily mean AOD among ensemble members in group B simulations. For the single-member simulation and the ensemble simulation driven by same fire emission, the shaded area and the solid line almost overlap, given the barely indistinguishable AOD between ensemble members and the corresponding group A simulation.
Model-simulated AODs are evaluated against the NRL and MACC reanalysis data (Fig. 3). The simulated temporal variation of regional mean AOD over the central US is consistent with that in the reanalysis, but the magnitudes of simulated AOD are lower (Fig. 3). A better agreement is found between the model and the NRL data, despite the horizontal winds in the simulation being nudged towards a reanalysis that is very similar to the data used to derive MACC. Temporal correlation coefficients (TCCs) between the modeled AOD and the NRL reanalysis are 0.87 and 0.82 for S_QF and S_GF4 simulations, respectively, but are lower (0.67 and 0.78) between the modeled AOD and the MACC reanalysis data. The corresponding root mean square errors (RMSEs) rise from 0.13 (S_QF) and 0.1 (S_GF4) to 0.23 and 0.21. Generally, AOD is underestimated by a factor of 2–4 in all simulations compared to the reanalysis, especially in simulations with GFED emissions. Previous studies have found the underestimation of AOD in simulations with GFED emissions and suggested the need to scale up GFED emissions by a factor of 1–3 to match the observed AOD (Tosca et al., 2013). This is consistent with the large negative bias in the simulations S_GF3 and S_GF4. However, a much larger scaling factor might be needed in this case. Simulated AODs in these two simulations are almost indistinguishable due to the small difference in the total fire emission in the region.
Over Mexico, different simulations produce similar temporal variations in AOD, but the magnitude is smaller in the GFED simulations. Fire-aerosol-induced AOD increase accounts for 8.1 % (S_GF3), 11.2 % (S_GF4), and 48.8 % (S_QF) of the background AOD (Table S2 in the Supplement). Large discrepancies are found between model results and reanalysis data during 17–20 April. An increase of AOD is captured by both reanalysis datasets, while model results display a decrease of AOD compared to earlier days in the simulation period. Note that the two sets of reanalysis data also have some differences occasionally. For example, during 10–12 April, NRL data display an increase of AOD, while MACC data show the opposite. These discrepancies may partly result from the large internal variability in this tropical region, where the simulated atmosphere state and its influence on aerosol transport are more likely to disagree between the model and the reanalysis. Generally speaking, the model forced with different fire emissions is capable of capturing daily variation of AOD in both regions, especially during 1–10 April. This period was selected for further investigation of the short-term fire aerosol effect.
Model-simulated AODs are also evaluated against AERONET retrievals (Fig. 4).
At CART site (36
Time series of hourly regional mean AOD in April 2009 from group A simulations, reanalysis data and AERONET retrievals at AERONET sites. Numbers in parentheses denote TCC (left) and RMSE (right) between each simulation and AERONET AOD.
Evaluation of simulated BC
Spatial distributions of 10-day-average (1–10 April) ensemble mean AOD differences between simulations with (E_GF3, E_GF4, and E_QF) and without (E_NF) fire emission.
Spatial distributions of 10-day-average (1–10 April) ensemble mean
fire aerosol shortwave direct radiative effect (SDRE) and shortwave cloud
radiative effect (SCRE) (W m
The model is further evaluated against the IMPROVE data for BC and POM mass concentrations (Fig. 5). In the downwind region, the simulated mass concentrations in the S_QF simulation lie within a factor of 2 of the observed values at most sites. However, the magnitude is generally underestimated in simulations with the GFED emissions (S_GF3 and S_GF4), especially in S_GF3. BC and POM concentrations in the downwind regions are affected by transport of aerosols from southern Mexico (Fig. S3). A larger amount of fire emission in southern Mexico would result in a higher BC (POM) concentration in the downwind region. This explains the slightly higher concentrations in the S_GF4 simulation than in S_GF3, as BC and POM emissions over southern Mexico are higher in GFED v4.1 due to the inclusion of small fires (Randerson et al., 2012). The good agreement between S_QF and observations suggests that the QFED data have a reasonable total emission rate. However, in the source region, the S_QF simulation displays a large positive bias with a large majority of the values falling out of the a-factor-of-2 band. Given the reasonable total emission rate in QFED and a good agreement of AOD with AERONET retrievals at CART site, this might result from the discrepancies in the vertical distribution of the fire emissions. Fire-emitted BC and POM in simulations S_QF and S_GF3 reach maximum values in the lowest level and decrease sharply to the next level, while low-level fire emissions in S_GF4 are distributed in a more uniform way (Fig. S4). The fact that the sampling was done on the lowest model level at most sites to compare with the IMPROVE data explains the strong overestimation in S_QF. Although the same impact from vertical distribution of the fire emission also appears in the S_GF3 simulation, it is partly offset by its negative bias in the total emission rate.
Given the good model performance during 1–10 April, we proceed to analyze the short-term effects of fire aerosols during this period with nudged ensemble simulations. We define “fire AOD” as the AOD difference between the simulations with and without fire emissions.
Figure 6 shows the spatial distributions of 10-day-average ensemble mean fire AOD. For reference, the total AOD in the simulation without fire emissions is shown in Fig. S2. During the period, regional mean AOD increases by 6.4 % (E_GF3), 6.4 % (E_GF4), and 70.2 % (E_QF) in the central US and 10.4 % (E_GF3), 13.3 % (E_GF4), and 49.6 % (E_QF) in southern Mexico when fire emissions are included. In E_QF, high fire AOD covers almost the entire selected region and extends further north. Maximum values of fire AOD stay above 0.2 around the Yucatán Peninsula. Over the central US, significant fire AOD ranging between 0.04 and 0.1 appears in the southwest part of the selected region. Apart from the significant AOD difference in selected regions, large fire AOD also appears near the eastern coast as a result of local fire emission and the eastward transport of fire aerosols from both regions. Overall, the modeled fire AOD is much smaller in simulations with GFED emissions.
As described in Sect. 2.4, the fire aerosol radiative effect can be decomposed
into three items: fire aerosol DRE, fire aerosol CRE, and fire
aerosol surface albedo effect (Table S3). Figure 7 shows the spatial
distributions of the shortwave direct radiative effect (SDRE) and shortwave cloud radiative
effect (SCRE). They are major contributors to the total fire aerosol forcing
in the selected regions. For reference, total aerosol forcing and total
shortwave cloud forcing in the simulation without fire emissions are shown in
Fig. S2. The spatial distribution of SDRE and SCRE are similar for the three
cases but have different magnitudes and statistical significant regions for
simulations with QFED and GFED fire emissions. In the central US, fire
aerosol SDRE is negligible in GFED-forced simulations due to small fire AOD.
Although the fire AOD is larger in the E_QF simulation, the compensation
between the warming effect of fire BC and the cooling effect of fire POM still
results in a weak forcing of about
In the following analysis, we will focus on the results from the E_QF
simulation. Both SDRE and SCRE spread outside the two selected regions and
extend eastward, reaching coastal regions. A stronger fire aerosol effect is
seen in the southern Mexico region. Strong SDRE appears over the Yucatán
Peninsula, where fire AOD peaks (Fig. 6). Regional mean 10-day averages of SDRE
and SCRE reach
Difference of 10-day-average (1–10 April) ensemble mean between
simulations E_NF and E_QF:
Probability distributions of 10-day-average (1–10 April)
10-day-average (1–10 April) regional mean
Spatial distributions of ensemble mean fire aerosol
Spatial distributions of fire BC SDRE and fire POM SDRE
(W m
Time series of daily regional mean total
Time series of daily ensemble mean fire aerosol
Root mean square error (RMSE) of the ensemble mean of the regional
mean fire aerosol SCRE during 1–10 April over southern Mexico in simulations
with different total numbers of ensemble members (
To find out the causes of the fire aerosol SCRE, fire-aerosol-induced changes in cloud properties are analyzed. Given the largely insignificant change in cloud fraction (Fig. 8), the negative fire aerosol SCRE in both regions is mainly associated with increases in liquid water path (LWP) and droplet number concentration (CDNC). The increased CDNC due to an increase of cloud condensation nuclei (CCN) from fire aerosols (Fig. 8) leads to smaller droplet sizes, which in turn increase cloud albedo by enhancing backscattering (Twomey, 1977) and further affect LWP by decreasing precipitation efficiency and allowing more liquid water to accumulate (Albrecht, 1989; Ghan et al., 2012). These changes in warm-cloud properties demonstrate important contributions of both aerosol first and second indirect effects to the negative SCRE. Over southern Mexico, although changes of CDNC and LWP are of comparable magnitudes between the Gulf of Mexico and the land region (Fig. 8), relative changes of both quantities are much larger over the Gulf of Mexico (Fig. S5) due to the smaller magnitudes of background CDNC and LWP over the region (Fig. S6), which tend to lead to a more sensitive response of SCRE. That is why the maximum SCRE over southern Mexico is centered over the Gulf of Mexico. Changes in ice water path (IWP) and ice crystal number concentration (ICNC) can also significantly affect SCRE, albeit with an opposite sign and mostly in the central US. The decreased IWP and ICNC, which are possibly caused by fire-aerosol-induced changes in the circulation (Ten Hoeve et al., 2012) and reduced coarse-mode dust aerosol concentrations (Fig. S7), are responsible for the positive SCRE and the negative longwave cloud radiative effect (Table S3) in the northern part of the central US. In the southern part of the central US, the reduction of IWP and ICNC also results in a positive SCRE, which partly offsets the negative SCRE resulting from changes in warm-cloud properties. This explains the weaker total negative SCRE in this region than in the southern Mexico region despite the more substantial increase in CDNC and LWP here. In the northeast of the extended coastal regions, a more significant change of LWP comparable to that in the central US appears, while a more significant change of CDNC comparable to that in southern Mexico occurs in the southwest. The combined effect leads to the total fire aerosol effect in the extended regions.
The ensemble method provides another effective way to distinguish the fire
aerosol radiative effect by comparing the radiative forcing distribution of
ensemble members between simulations with and without fire emission. A
significant difference in the distribution of total aerosol (cloud) forcing
indicates a significant fire aerosol direct (cloud) effect. As shown in
Fig. 9, a shift towards stronger magnitude occurs to the total aerosol
forcing when fire aerosols are considered. The E_QF simulation has a
larger percentage of grid cells with SDRE below
Figure 10 illustrates ensemble behavior of 10-day-average regional mean total
aerosol and cloud forcing from all simulations as well as resulted fire
aerosol SDRE and SCRE. The GFED-forced simulations not only resemble in
ensemble mean but also have small differences in ensemble member
distribution. Although members in the E_QF simulation capture stronger
aerosol forcing, and thus stronger fire aerosol SDRE than those in E_GF3
and E_GF4, the ensemble spread (as indicated by the maximum and minimum
values) in the three simulations is similar. Moreover, the E_QF simulation
yields a smaller spread of SCRE than the GFED-forced
simulations despite a stronger ensemble mean SCRE.
In each fire simulation, ensemble mean fire aerosol SCRE has a much larger
magnitude than SDRE, as does the corresponding ensemble spread. Taking
results from the E_QF simulation as an example, the ensemble spread of
SCRE reaches 0.47 W m
The fire aerosol effect is also investigated for individual days. The spatial
distributions of SDRE and SCRE on 7 April are shown in Fig. 11, when
relatively high fire emissions appear in both regions. Negative fire aerosol
SDRE appears in the central US biomass burning region, indicating the
dominant role of POM scattering. Fire aerosol SDRE over southern Mexico shows
the contrast of a warming effect in land region and a cooling effect in the
adjacent ocean despite similar aerosol loading in the two regions. However,
they do have nearly equal clear-sky BC absorption and POM scattering
(Fig. 12). Difference in low-level cloud distributions between the two
regions leads to different signs of the simulated all-sky SDRE. Over land,
when clouds appear under elevated aerosol layers, more solar radiation is
reflected back to space, and this leads to amplified BC absorption and more
positive direct aerosol forcing (Keil and Haywood, 2003; Zhang et al., 2016;
Jiang et al., 2016). In contrast, neither absorption nor scattering changes
significantly from clear-sky to all-sky conditions over adjacent areas over
the ocean, since the small cloud fraction is small. The same enhanced
absorption of above-cloud aerosols is also found over the west Atlantic
Ocean. Fire aerosols produce remarkably negative SCRE up to
Figure 13 shows the daily variation of the regional mean total (direct)
aerosol forcing and cloud forcing. Both the ensemble mean and spread are
investigated here. The total aerosol and cloud forcing exhibit considerable diversity across
ensemble members within each simulation even though the simulated AOD is
nearly indistinguishable (Fig. 3). Taking results from the E_QF simulation
as an example, maximum values of difference between members exceed
0.4 W m
Fire aerosol sources are often intermittent and height-dependent, and there is a need to estimate the short-term effective aerosol forcing. Although nudging helps to constrain large-scale features, the simulated cloud properties (e.g., cloud fraction and LWP) and their response to aerosol changes can still be sensitive to small perturbations in the atmospheric state. Therefore, for investigating the short-term aerosol effect, a single simulation might not be sufficient to tell whether the aerosol effect is significant. The use of ensembles provides an effective way to estimate the uncertainty. Previous investigations of the short-term fire aerosol effect are mainly based on single-member simulations (Wu et al., 2011; Sena et al., 2013; Kolusu et al., 2015). While this might be less a problem for SDRE, one should be more careful when investigating the aerosol indirect effect and conduct ensemble simulations to see whether the estimated fire aerosol effects are robust.
In this study, we investigated the short-term effect of fire aerosols on cloud and radiation using CAM5 simulations. Month-long single-member simulations and 10-day ensemble simulations were conducted in April 2009. In order to help extract signals on short timescales, we used nudging to constrain horizontal winds in all simulations. Our investigation focused on southern Mexico, where there were constant intensive fire activities, and the central US, with occasionally large fires. Apart from the local effect, fire emissions from the two regions are shown to affect downwind coastal regions through transport.
Modeled AOD and mass concentrations (BC and POM) were evaluated against observations. In general, all simulations with fire emissions reproduce the observed temporal variation of daily mean AOD well, although the simulated magnitude is smaller. The model performance is better when QFEDv2.4 is used, which has larger fire emissions. Modeled regional mean AOD values in simulations using two versions of GFED fire emission data are barely distinguishable, despite the inclusion of small fires and changed injection heights in GFEDv4.1 used in this study. Both simulate about a factor-of-1.5-smaller AOD than that in the simulation using the QFED fire emissions. At sites in the downwind region, the modeled BC and POM mass concentrations in the simulation with QFEDv2.4 emission (S_QF) agree well with the IMPROVE data. In contrast, simulations with the other two fire emission datasets (S_GF3 and S_GF4) have a low bias. The simulated AOD in the source region in S_QF also agrees well with the AERONET data (CART site). If there is no large compensating error in the model, QFEDv2.4 seems more reasonable in terms of the total (vertically integrated) emission rate. On the other hand, S_QF strongly overestimates BC and POM concentrations in the source region. Considering that the source-region AOD and the downwind surface mass concentrations are well simulated, the overestimation suggests the actual emission peak might appear at higher levels than the height-dependent injection rates applied in the S_QF simulation.
Based on the evaluation, we chose the first 10 days as the simulation period and focused on the simulation with QFEDv2.4 fire emission in our nudged-ensemble simulations. In our method, the nudged ensembles are generated by adding a very weak temperature nudging along with horizontal-wind nudging and perturbing the nudging timescale of temperature gently. In this way, small temperature perturbations are added to the simulation at each time step, while the large-scale circulation features are very similar between individual members. We first investigated the 10-day-mean effective fire aerosol forcing. Decomposition of total aerosol radiative forcing shows that fire aerosol effects in the two selected regions are dominated by the SCRE. All fire simulations show similar spatial distribution of SDRE and SCRE, albeit with different magnitudes and statistically significant regions. The similarity in the spatial distribution is expected since the three emission datasets differ mainly in the emission magnitude and not much in spatial distribution in the focus regions of this study. Fire aerosol effects in simulations with GFED emissions (E_GF3 and E_GF4) are weaker than with QFEDv2.4 emissions (E_QF) by a factor of 1.5 for SCRE and a factor of more than 4 for SDRE. Overall, the difference in simulated AOD and fire aerosol indirect radiative effects between simulations is smaller than the difference between fire emissions, consistent with the findings in the sub-Saharan African biomass burning region (F. Zhang et al., 2014).
Fire aerosols produce a negative direct effect of
We also investigated fire aerosol effects on a daily timescale, where the variation in the simulated fire aerosol effect can be large among the ensemble members. The large ensemble spread of total aerosol and cloud forcing indicates large uncertainties in estimating daily fire aerosol effects, despite similar AOD across ensemble members. Further investigations show that the simulated ensemble mean and spread with fewer than seven members differs considerably from those with more members. Our results suggest that, for short-term simulations of aerosol and cloud processes, even small perturbations might result in large difference across members despite constrained large-scale features. In order to obtain a robust estimate of the effective fire aerosol forcing during a short period, it is important to conduct ensemble simulations with sufficient ensemble members.
All model results are available from the corresponding author upon request.
The authors declare that they have no conflict of interest.
We thank two anonymous reviewers for their careful reviews and suggestions that helped to greatly improve the analyses and discussion presented in this paper. This study was supported by the US Department of Energy (DOE)'s Office of Science as part of the Regional and Global Climate Modeling Program (NSF-DOE-USDA EaSM2). The work was also supported by the National Natural Science Foundation of China (NSFC) under grant nos. 41621005 and 41330420, the National Key Basic Research Program (973 Program) of China under grant no. 2010CB428504, and the Jiangsu Collaborative Innovation Center of Climate. The Pacific Northwest National Laboratory (PNNL) is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. Computations were performed using resources of the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory and PNNL Institutional computing. Edited by: Yves Balkanski Reviewed by: two anonymous referees