Introduction
High concentrations of aerosols in China have been reported in recent years
(e.g., Zhang et al., 2008, 2012), which are largely attributed to the
increases in emissions due to rapid economic development. In addition,
studies have shown that meteorological parameters are important factors in
driving the interannual variations of aerosols in China (e.g., Jeong and
Park, 2013; Mu and Liao, 2014; Yang et al., 2015). For example, Mu and Liao
(2014) reported that meteorological parameters, e.g., precipitation, wind
direction, wind speed, and boundary layer condition, significantly
influenced the variations of emissions (biomass burning emissions),
transport, and deposition of aerosols.
China is located in the East Asian monsoon (EAM) domain. In a strong (weak)
summer monsoon year, China experiences strong (weak) southerlies, large
rainfall in northern (southern) China, and a deficit of rainfall in the
middle and lower reaches of the Yangtze River (northern China) (Zhu et al.,
2012). A strong winter monsoon is characterized by a stronger Siberian High
and Aleutian Low (Chen et al., 2000), and China thus experiences stronger
northerlies, more active cold surge, lower surface temperature, and excess
snowfall (Jhun and Lee, 2004). The EAM has been reported to influence the
interannual variations of aerosols in China via changes in monsoon
circulation, precipitation, vertical convection, etc. (e.g., Liu et al.,
2010; Zhang et al., 2010a, b; Yan et al., 2011; Zhu et al., 2012). The
observed weakening EAM in recent years is also considered to contribute to
the increase in aerosols in eastern Asia (e.g., Chang et al., 2000; Ding et
al., 2008; Wang et al., 2009; Zhou et al., 2015).
Studies have reported that the strength of the East Asian summer monsoon
(EASM) negatively influences the interannual variations of aerosols in
eastern China. Tan et al. (2015) showed that both the MODIS aerosol mass
concentration and fine-mode fraction in eastern China were high during weak
monsoon years but low during active monsoon years for 2003–2013. By using
the National Centers for Environmental Prediction, National Center for
Atmospheric Research (NCEP/NCAR) reanalysis data and surface observations,
Zhang et al. (2016) reported that the frequency of occurrence of
cyclone-related weather patterns decreased in the weak EASM years, which
significantly degraded the air quality in northern China for 1980–2013.
Modeling studies also reported that the strength of the EASM influenced
simulated aerosol concentrations and optical depths over eastern Asia (Zhang
et al., 2010a, b; Yan et al., 2011; Zhu et al., 2012). For example, Zhu et
al. (2012), using a global chemical transport model (GEOS-Chem), found that
simulated summer surface PM2.5 (particulate matter with a diameter of
2.5 µm or less) concentrations averaged over eastern China
(110–125∘ E, 20–45∘ N) were ∼ 18 % higher in
the 5 weakest summer monsoon years than in the 5 strongest monsoon years for
1986–2006.
Similarly, negative correlations have been found between the strength of the
East Asian winter monsoon (EAWM) and changes of air quality in eastern
China. By analyzing the observed visibility and meteorological parameters
from surface stations, studies have shown that the weak EAWM is related to
the decrease in cold wave occurrence and surface wind speed, and therefore
partially accounts for the decrease in winter visibility and the increase in
number of haze days and the severe haze pollution events in China since the 1960s
(Wang et al., 2014; Qu et al., 2015; Yin et al., 2015; Zhang et al., 2016).
By further analyzing the reanalysis data, e.g., NCEP/NCAR and European
Centre for Medium-Range Weather Forecasts (ECMWF), Li et al. (2015) showed
that the stronger (weaker) EAWM was correlated with less (more)
wintertime fog–haze days. The weak EAWM results in a reduction of wind
speed and a decline in the frequency of northerly winds, which leads to an
increase in the number of haze days and occurrences of severe haze events
(Chen and Wang, 2015; Zhou et al., 2015).
Black carbon (BC) as a chemically inert species is a good tracer to
investigate the impact of the meteorological parameters and the EAM on the
interannual variations of aerosols. BC is an important short-lived aerosol;
the reduction of BC emissions is identified as a near-term approach to
efficiently benefit human health, air quality, and climate change (Ramanathan
and Xu, 2010; Shindell et al., 2012; Bond et al., 2013; IPCC, 2013; Smith and
Mizrahi, 2013). BC emissions in China have dramatically increased in the last
several decades, which contributes about 25 % of the global total
emissions (Cooke et al., 1999; Bond et al., 2004; Lu et al., 2011; Qin and
Xie, 2012; Wang et al., 2012). Observed annual mean surface BC concentrations
are typically about 2–5 µg m-3 at rural sites (Zhang et al.,
2008). Simulated annual direct radiative forcing (DRF) due to BC at the top
of the atmosphere (TOA) is in the range of 0.58–1.46 W m-2 in China,
reported by previous modeling studies (summarized in Li et al., 2016). Mao et
al. (2016), using the GEOS-Chem model, showed that annual mean BC DRF
averaged over China increased by 0.35 W m-2 (51 %) between 2010
and 1980.
The changes in BC concentrations in China are coupled with the changes in
monsoon (e.g., Menon et al., 2002; Lau et al., 2006). Studies in the past
decades were generally focused on the impacts of BC on the Asian monsoon
(Menon et al., 2002; Lau et al., 2006; Meehl et al., 2008; Bollasina et al.,
2011). Studies also showed that the climate effect of increasing BC could
partially explain the north drought–south flooding precipitation pattern in
China in recent decades (e.g., Menon et al., 2002; Gu et al., 2010).
Conversely, the EAM could influence the spatial and vertical distributions of
BC concentrations and further the radiative forcing and climate effect of BC.
Zhu et al. (2012) showed that simulated summer surface BC concentrations
averaged over northern China (110–125∘ E, 28–45∘ N) were
∼ 11 % higher in the 5 weakest monsoon years than in the 5
strongest monsoon years for 1986–2006. However, to our knowledge, few
studies have systematically quantified the impact of the EAM (especially the
EAWM) on the variations in concentrations and DRF of BC in China.
The goal of the present study is to improve our understanding of the impacts
of the EAM on the interannual variations in surface concentrations, vertical
distributions, and DRF of BC in eastern China for 1986–2006. We aim to
examine the mechanisms through which the EASM and EAWM influence the
variations of BC. We describe the GEOS-Chem model and numerical simulations
in Sect. 2. Section 3 shows simulated impacts of the EASM on interannual
variations of June–July–August (JJA) BC in eastern China and examines the
influence mechanisms. Section 4 presents the impacts of the EAWM on
interannual variations of December–January–February (DJF) BC and the
relevant mechanisms. Summary and conclusions are given in Sect. 5.
Methods
GEOS-Chem model and numerical experiments
The GEOS-Chem model is driven by assimilated meteorology from the Goddard
Earth Observing System (GEOS) of the NASA Global Modeling and Assimilation
Office (GMAO; Bey et al., 2001). Here we use GEOS-Chem version 9-01-03
(available at http://geos-chem.org) driven by the GEOS-4 and the Modern Era
Retrospective-analysis for Research and Applications (MERRA) meteorological
fields (Rienecker et al., 2011), with 6 h temporal resolution (3 h for
surface variables and mixing depths), 2∘ (latitude) × 2.5∘ (longitude) horizontal resolution, and 30 (GEOS-4)
or 47 (MERRA) vertical layers from the surface to 0.01 hPa. The GEOS-Chem
simulation of carbonaceous aerosols has been reported on previously by Park et
al. (2003). Emitted from primary sources, 80 % of BC is assumed to
be hydrophobic, and hydrophobic aerosols become hydrophilic with an
e-folding time of 1.2 days (Cooke et al., 1999; Chin et al., 2002; Park et
al., 2003). BC in the model is assumed to be externally mixed with other
aerosol species.
Tracer advection is computed every 15 min with a flux-form
semi-Lagrangian method (Lin and Rood, 1996). Tracer moist convection is
computed using GEOS convective, entrainment, and detrainment mass fluxes as
described by Allen et al. (1996a, b). The deep convection scheme of GEOS-4
is based on Zhang and McFarlane (1995), and the shallow convection treatment
follows Hack (1994). MERRA convection is parameterized using the relaxed
Arakawa–Schubert scheme (Arakawa and Schubert, 1974; Moorthi and Suarez,
1992). Simulation of aerosol wet and dry deposition follows Liu et al. (2001) and is updated by Wang et al. (2011). Wet deposition includes
contributions from scavenging in convective updrafts, rainout from
convective anvils, and rainout and washout from large-scale precipitation.
Dry deposition of aerosols uses a resistance-in-series model (Walcek et al.,
1986) dependent on local surface type and meteorological conditions.
The anthropogenic emissions of BC, including both global fossil fuel and biofuel
emissions, are from Bond et al. (2007) and are updated in Asia
(60–150∘ E, 10∘ S–55∘ N) with
the Regional Emission inventory in Asia (REAS, available at
http://www.jamstec.go.jp/frsgc/research/d4/emission.htm, Ohara et al.,
2007). Seasonal variations of anthropogenic emissions are considered in
China and India using monthly scaling factors taken from Kurokawa et al. (2013). Global biomass burning emissions of BC are taken from the Global
Fire Emissions Database version 3 (GFEDv3; van der Werf et al., 2010) with a
monthly temporal resolution. More details about the anthropogenic and
biomass burning emissions of BC are discussed by Mao et al. (2016).
We conduct two simulations driven by GEOS-4 for the years 1986–2006 (VMETG4)
and by MERRA for 1980–2010 (VMET). Our analysis centers on the period of
1986–2006, the years for which both GEOS-4 and MERRA data are available.
Both simulations are preceded by 1-year spin-up. In each simulation,
meteorological parameters are allowed to vary year to year, but
anthropogenic and biomass burning emissions of BC are fixed at the year 2010
levels. The simulations thus represent the impact of variations in
meteorological parameters on the interannual variations of BC. We also
conduct simulation (VNOC) to quantify the contributions of non-Chinese
emissions to BC. The configurations of the model simulation are the same as
those in VMET, except that anthropogenic and biomass burning emissions in
China are set to zero. The evaluations of GEOS-Chem aerosol simulations in
China using the MERRA and GEOS-4 data are discussed in studies by
Mao et al. (2016) and Yang et al. (2015), respectively. In addition, we have
systematically evaluated the BC simulations for 1980–2010 in China from the
GEOS-Chem model (Li et al., 2016; Mao et al., 2016). We would like to point
out that simulated BC concentrations are likely underestimated because of
the biased low emissions (e.g., Bond et al., 2013; Xu et al., 2013; Mao et
al., 2016) and coarse resolution of the model used. We have discussed the
adjustment of the biased low BC emissions using the scaling factor in our
previous study by Mao et al. (2016). The adjustment of the BC emissions is
not included in the present study as we aim to discuss the impact of
variations in meteorological parameters on BC.
(a) Normalized East Asian summer monsoon index (EASMI,
bars, left y axis) and the simulated June–July–August (JJA) mean surface
BC concentrations (lines, right y axis, µg m-3) averaged
over eastern China (20–45∘ N, 110–125∘ E) from model
simulation VMET (red line) for 1980–2010 and from VMETG4 (blue line) for
1986–2006. EASMI are calculated based on MERRA (red bars) and GEOS-4 (blue
bars) assimilated meteorological data following Li and Zeng (2002).
(b) Same as panel (a), but for normalized East Asian winter
monsoon index (EAWMI) and the simulated December–January–February (DJF)
mean surface BC concentrations. EAWMIs are calculated following Wu and
Wang (2002).
The definition of EAM index
The interannual variations in the strength of the EAM are commonly
represented by the indexes. Following Zhu et al. (2012) and Yang et al. (2014), we use the EASM index
(EASMI, Fig. 1a) introduced by Li and Zeng (2002) based on the GEOS-4 meteorological parameters
for 1986–2006 or the MERRA data for 1980–2010 (referred to as
EASMI_GEOS and EASMI_MERRA, respectively). The
EASMI calculated using the reanalyzed NCEP/NCAR datasets (Kalnay et al.,
1996; Zhu et al., 2012, referred to as EASMI_NCEP, not shown)
agrees well (r > 0.97) with EASMI_GEOS for
1986–2006 and with EASMI_MERRA for 1980–2010, indicating
that both the GEOS-4 and MERRA data have a good representation of the
strength of the EASM. Positive values of EASMI indicate strong summer
monsoon years, while negative values indicate weak monsoon years.
Correlation coefficients among different definitions of the strength
of the East Asian winter monsoon (EAWM), and between the EAWM index (EAWMI)
and simulated December–January–February (DJF) mean surface BC
concentrations averaged over eastern China (110–125∘ E,
20–45∘ N). Simulated BC concentrations are from model simulations
VMETG4 and VMET, and corresponding monsoon indexes are calculated based on
GEOS-4- and MERRA-assimilated meteorological data.
Correlation
GEOS–4 (1986–2006)
MERRA (1986–2006)
MERRA (1980–2010)
EAWMIa
BC
EAWMI
BC
EAWMI
BC
EAWMI_Tb
0.63
-0.57
0.58
-0.16
0.56
-0.29
EAWMI_Vc
0.51
-0.31
0.56
-0.50
0.54
-0.40
EAWMI_Ud
0.77
-0.42
0.82
-0.72
0.73
-0.69
EAWMI_P1e
0.65
-0.33
0.72
-0.38
0.77
-0.41
EAWMI_P2f
0.71
-0.61
0.72
-0.68
0.70
-0.66
a EAWMIi=norm(∑20∘N70∘N(P1i-P2i)). P1i is the DJF mean sea level pressure over
110∘ E. P2i is the DJF mean
sea level pressure over 160∘ E (Wu and Wang, 2002).b EAWMI_Ti=T‾‾-T‾i.
T‾i is the DJF mean surface temperature over the region of
20–40∘ N and 110–135∘ E for year i.
T‾‾ is the mean of T‾i (Yan et al.,
2009).c EAWMI_Vi=V‾‾-V‾i.
V‾i is the DJF mean 850 hpa meridional wind over the region
of 20–40∘ N and 110–135∘ E for year i.
V‾‾ is the mean of
V‾i (Yan et al., 2009).d EAWMI_Ui=U‾1i-U‾2i.
U‾1i is the DJF mean 300 hpa zonal wind over the region of
27.5–37.5∘ N and 110–170∘ E for year i.
U‾2i is the DJF mean 300 hpa zonal wind over the region of
50–60∘ N and 80–140∘ E for year i (Jhun and Lee,
2004).e EAWMI_P1i=P‾1i-P‾2i.
P‾1i is the DJF mean sea level pressure over the region of
30–55∘ N and 110–130∘ E for year i. P‾2i
is the DJF mean sea level pressure over the region of 20–40∘ N and
150–180∘ E for year i (Yan et al.,
2009).f EAWMI_P2i=P‾1i. P‾1i is the
DJF mean sea level pressure over the region of 40–60∘ N and
80–120∘ E for year i (Yan et al., 2009).
Numerous studies have shown that the intensity of the EAWM is closely tied
with wind, air temperature, and precipitation (e.g., Guo, 1994; Ji et al.,
1997; Chen et al., 2000; Jhun and Lee, 2004; Yan et al., 2009). The
definitions of the EAWM index (EAWMI) are thus quite different in the
previous studies (Table 1). Here we calculate the EAWMI (Fig. 1b) as the sum
of zonal sea level pressure differences (110∘ E vs. 160∘ E)
over 20–70∘ N, following Wu and Wang (2002). The EAWMIs in GEOS-4
and MERRA (referred to as EAWMI_GEOS and EAWMI_MERRA) in the present study
show strong correlations with those based on surface temperature, wind, and
pressure (r=0.51–0.82, Table 1) and are generally consistent with those
in NECP (referred to as EAWMI_NCEP), with the correlation coefficients
larger than 0.94. The EAWMIs in GEOS and MERRA are thus reliable to represent
the strength of the EAWM. Similarly, negative (positive) values of EAWMI
indicate weak (strong) winter monsoon years.
Impact of EASM on interannual variation of BC
Simulated JJA BC in GEOS-4 and MERRA
Fig. 1a also shows simulated JJA surface concentrations of BC averaged over
eastern China (110–125∘ E, 20–45∘ N). Simulated JJA
surface concentrations of BC have strong interannual variations, which range
from 0.95 to 1.04 µg m-3 with a deviation from the mean (DM) of
-5.3 to 4.2 % in VMET and 0.65–0.78 µg m-3 with a DM
of -6.8 to 12.5 % in VMETG4. During the period of 1986–2006, JJA
surface BC concentrations on average are 0.30 µg m-3 (44 %)
higher in MERRA than in GEOS-4. Our analyses indicate that different
precipitation patterns between GEOS-4 and MERRA likely account for the
abovementioned differences in BC concentrations using the two meteorological
fields.
We find that the JJA mean precipitation is stronger in GEOS-4 than in MERRA
in most of China, except in southern China (Fig. S1 in the Supplement). In Fig. 2a1, we
further compare the differences in precipitation between GEOS-4 and MERRA
averaged over eastern China. The JJA mean precipitation in GEOS-4 is 2.5 mm d-1 (29 %) stronger than that in MERRA for 1986–2006. The resulting
wet deposition (Fig. 2b1) is also higher by 0.018 kg s-1 (11 %) in
GEOS-4 than in MERRA. The stronger precipitation in GEOS-4 thus results in
significantly lower surface BC concentrations. Note that MERRA is likely
more reliable than the previous versions of GMAO metrological data products
(e.g., GEOS-4 and GEOS-5) as MERRA has significantly improved the convection
and then precipitation and water vapor by comparing to the reanalyses
(Rienecker et al., 2011).
(a1) JJA and (a2) DJF mean precipitation
(mm d-1) averaged over eastern China for 1986–2006 from GEOS-4 (blue
lines) and MERRA (red lines) meteorological data. DJF mean precipitation is
multiplied by 5 in panel (a2). (b1, b2) Same as
panels (a1) and (a2), respectively, but for wet deposition
(kg s-1).
Correlation between JJA BC and EASMI
In simulations VMET and VMETG4, we find that monsoon strength has large
impacts on summertime BC concentrations over eastern China. JJA surface
concentrations of BC negatively correlate with both EASMI_GEOS4 and EASMI_MERRA (Fig. 1a). The correlation coefficient
between simulated surface BC concentrations and the EASMI_GEOS4 is -0.7 for 1986–2006, and those for the EASMI_MERRA
are -0.5 for 1980–2010 and -0.4 for 1986–2006. Simulated surface BC
concentrations are thus high (low) in the weak (strong) EASM years.
Figure 3a shows the spatial distributions of the correlation coefficients
between BC surface concentrations and EASMI_GEOS4 or
EASMI_MERRA. Negative correlations are found in central and
northeastern China, with the strongest negative correlations in eastern China
and the Tibetan Plateau (< -0.8). Positive correlations are
found over southern and northwestern China, with the largest values in southern
China (> 0.7). The correlation coefficients in GEOS-4 and MERRA
show similar spatial distribution and magnitude, except that positive
correlations are found in larger regions in MERRA than in GEOS-4. Our
results are generally consistent with those from Zhu et al. (2012), which
reported that surface concentrations of PM2.5 in GEOS-4 were higher in
northern China (110–125∘ E, 28–45∘ N) but lower in
southern China (110–125∘ E, 20–27∘ N) in the weak EASM
years compared to the strong monsoon years.
(a) Correlation coefficients between EASMI and JJA mean
surface BC concentrations during 1986–2006. (b) Correlation
coefficients between EAWMI and DJF mean surface BC concentrations during
1986–2006. Simulated BC concentrations are from model simulations VMETG4
(left) and VMET (right), and monsoon indexes are calculated based on GEOS-4
(left) and MERRA (right) assimilated meteorological data. The dotted areas
indicate statistical significance with 95 % confidence from a two-tailed
Student's t test.
(a1) Absolute (µg m-3) and
(a2) percentage (%) differences in simulated JJA mean surface BC
concentrations between weakest (1988, 1993, 1995, 1996, and 1998) and
strongest (1990, 1994, 1997, 2004, and 2006) EASM years during 1986–2006
from model simulations VMETG4 and VMET. (b1, b2) Same as
panels (a1) and (a2), respectively, but for absolute
(µg m-3) and percentage (%) differences in simulated DJF
mean surface BC concentrations between weakest (1990, 1993, 1997, 1998, and
2002) and strongest (1986, 1996, 2001, 2005, and 2006) EAWM years. The
enclosed areas are defined as northern China (NC; 110–125∘ E,
28–45∘ N) and southern China (SC; 110–125∘ E,
20–27∘ N).
Differences in BC between weak and strong EASM years
In order to quantify to what degree the strength of the EASM influences
surface BC concentrations in China, we examine the differences in the JJA
mean surface BC concentrations between the 5 weakest (1988, 1993, 1995, 1996,
and 1998) and the 5 strongest (1990, 1994, 1997, 2004, and 2006) EASM years
during 1986–2006 (Fig. 4a). We select these weakest (or strongest) monsoon
years based on the five largest negative (or positive) values of the
normalized EASMI in both GEOS-4 and MERRA within 1986–2006. The selected
monsoon years are thus slightly different to those from previous studies
(Zhu et al., 2012; Yang et al., 2014) only based on GEOS-4 (weakest monsoon
years 1988, 1989, 1996, 1998, and 2003 and strongest monsoon years 1990,
1994, 1997, 2002, and 2006). The spatial distribution of the differences in
BC concentrations between the weakest and strongest summer monsoon years is
in good agreement with the distribution of the correlation coefficients
between concentrations and EASMI (Fig. 3a). The differences in JJA mean
surface BC concentrations are highest in northern China, with a maximum
exceeding 0.3 µg m-3 (40 %). Relative to the strongest
summer monsoon years, JJA surface BC concentrations in GEOS-4 in the weakest
summer monsoon years are 0.09 µg m-3 (11 %) higher over
northern China and 0.03 µg m-3 (11 %) lower over southern
China (Table 2). The corresponding values in MERRA are 0.04 µg m-3 (3 %) higher over northern China and 0.04 µg m-3
(10 %) lower over southern China. In eastern China, JJA surface BC
concentrations in the weakest monsoon years are higher on average by 0.05 µg m-3 (9 %) in GEOS-4 and by 0.02 µg m-3 (2 %)
in MERRA. The difference in surface BC concentrations between the weakest
and strongest summer monsoon years in each region is comparable or even
larger than the corresponding standard deviation of JJA mean surface BC for
1986–2006 (Table 2). The different patterns of BC concentrations between
northern and southern China can also be seen in Fig. 5a, which shows the
height–latitude plot of the differences in BC concentrations averaged over
110–125∘ E between the 5 weakest and 5 strongest monsoon
years. BC concentrations in the whole troposphere are lower south of
27∘ N but higher north of 27∘ N in the weakest monsoon
years compared to the strongest years. The different patterns of BC
concentrations between GEOS-4 and MERRA in Fig. 5a are likely because of the
different convection schemes used in the two meteorological data sets (Rienecker
et al., 2011).
Simulated JJA (DJF) mean surface BC concentrations
(µg m-3) in the 5 weakest and 5 strongest EASM (EAWM)
years during 1986–2006. Results are from simulations VMETG4 and VMET
averaged over northern China (NC; 110–125∘ E, 28–45∘ N),
southern China (SC; 110–125∘ E, 20–27∘ N), and eastern
China (EC; 110–125∘ E, 20–45∘ N).
Month
Region
Surface concentrations of BC (µg m-3)
GEOS-4
MERRA
Weak
Strong
Diff.a
Meanb
SDc
Weak
Strong
Diff.
Mean
SD
JJA
SC
0.24
0.27
-0.03 (-11 %)
0.26
0.02
0.37
0.41
-0.04 (-10 %)
0.39
0.02
NC
0.94
0.85
0.09 (11 %)
0.89
0.05
1.30
1.26
0.04 (3 %)
1.27
0.03
EC
0.72
0.67
0.05 (9 %)
0.70
0.03
1.02
1.00
0.02 (2 %)
1.00
0.02
DJF
SC
0.90
0.80
0.10 (12 %)
0.85
0.06
1.14
1.10
0.04 (3 %)
1.12
0.04
NC
1.76
1.63
0.13 (8 %)
1.68
0.08
2.76
2.62
0.14 (5 %)
2.68
0.10
EC
1.37
1.50
0.12 (9 %)
1.43
0.07
2.26
2.15
0.11 (5 %)
2.20
0.07
a The difference is (weakest–strongest) and the
relative difference in
percentage is in parentheses.b,c The mean and the standard deviation of simulated JJA (DJF)
mean surface BC concentrations for 1986–2006.
(a) Height–latitude cross section of differences in
simulated JJA mean BC concentrations (µg m-3) between the 5
weakest and 5 strongest EASM years during 1986–2006. Plots are averaged
over a longitude range of 110–125∘ E from model simulations
VMETG4 (a1) and VMET (a2). (b) Same as
(a), but for differences in DJF between 5 weakest and 5
strongest EAWM years.
The composite analyses of JJA (DJF) horizontal fluxes of BC
(kg s-1) for two selected boxes (northern China (110–125∘ E,
28–45∘ N) and southern China (110–125∘ E,
20–27∘ N), from the surface to 10 km) based on simulations VMETG4
and VMET. The values are averages over the 5 weakest and 5 strongest
EASM (EAWM) years during 1986–2006. For horizontal fluxes, positive values
indicate eastward or northward transport and negative values indicate
westward or southward transport. Also shown are the corresponding wet
deposition of BC (kg s-1) for the two selected boxes.
Boundary
GEOS-4
MERRA
Weakest
Strongest
Difference∗
Weakest
Strongest
Difference∗
JJA, northern China (110–125∘ E, 28–45∘ N)
South
+2.24
+0.97
+1.27
+1.93
+0.92
+1.01
North
+3.44
+4.06
-0.62
+3.90
+4.57
-0.67
West
+6.60
+4.20
+2.40
+8.72
+7.51
+1.21
East
+12.48
+9.20
+3.28
+3.60
+2.31
+1.29
Net
inflow 1.01
inflow 1.60
Deposition
14.06
13.35
0.70
13.26
11.76
1.50
JJA, southern China (110–125∘ E, 20–27∘ N)
South
+0.62
+0.70
-0.08
+0.61
+0.60
+0.01
North
+1.79
+0.88
+0.91
+1.67
+0.95
+0.72
West
+0.94
+0.13
+0.81
+0.47
+0.12
+0.35
East
+0.33
+0.42
-0.09
+0.18
+0.27
-0.09
Net
outflow 0.09
outflow 0.27
Deposition
2.46
3.02
-0.56
2.26
2.84
-0.58
DJF, northern China (110–125∘ E, 28–45∘ N)
South
-6.35
-8.24
+1.89
-4.51
-5.96
+1.45
North
-0.37
-0.71
+0.34
+0.64
-0.28
+0.92
West
+11.60
+11.41
+0.19
+12.01
+12.90
-0.89
East
+22.77
+21.67
+1.10
+23.55
+24.53
-0.98
Net
inflow 0.64
inflow 0.62
Deposition
9.48
9.24
0.24
9.17
8.75
0.42
DJF, southern China (110–125∘ E, 20–27∘ N)
South
-3.09
-3.61
+0.52
-2.77
-3.47
+0.70
North
-5.23
-6.68
+1.45
-4.40
-5.59
+1.19
West
+1.03
-0.64
+1.67
+1.24
+0.25
+0.99
East
+2.68
+2.13
+0.55
+0.98
+0.88
+0.10
Net
inflow 0.19
inflow 0.40
Deposition
4.78
4.52
0.26
4.79
4.51
0.28
∗ The difference is (weakest–strongest).
(a) Differences in JJA 850 hPa wind (vector, m s-1)
between the 5 weakest and 5 strongest EASM years during 1986–2006 from
GEOS-4 (left) and MERRA (right) data. (b) Same as
panel (a), but for differences in DJF wind between 5 weakest and 5
strongest EAWM years.
Zhu et al. (2012) have shown that the impacts of the EASM on aerosol
concentrations in eastern China are mainly from the changes in atmospheric
circulation. Figure 6a shows composite differences in JJA 850 hPa wind (m s-1) between the 5 weakest and 5 strongest EASM years from the
GEOS-4 and MERRA data. Relative to the strongest EASM years, anomalous
northerlies over northern China and anomalous northeasterlies over the
western North Pacific in the weakest monsoon years prevent the outflow of
pollutants from northern China. In addition, the southerly branch of the
anomalous anticyclone in the south of the middle and the lower reaches of the
Yangtze River and nearby oceans strengthen the northward transport of
aerosols from southern China to northern China. As a result, an anomalous
convergence in northern China leads to an increase in BC concentrations in
the region, while an anomalous anticyclone in the south of the middle and
lower reaches of the Yangtze River results in the decreased BC
concentrations in southern China (Fig. 4a). The convergence and divergence
can also be seen in Fig. 7a, which shows anomalous vertical transport of BC
concentrations averaged over 110–125∘ E. Compared to the strong
monsoon years, the increased surface BC concentrations in northern China
lead to higher upward mass fluxes of BC concentrations north of
25∘ N in both MERRA and GEOS-4. In southern China, the lower
surface BC concentrations in the weakest EASM years result in the decreased
upward fluxes south of 25∘ N. The pattern of the anomalous
vertical transport of BC concentrations thus confirms the anomalous
convergence in northern China and anomalous divergence in southern China in
the weakest monsoon years.
(a) Differences in simulated upward mass flux of JJA BC
(kg s-1) between the 5 weakest and 5 strongest EASM years during
1986–2006. Plots are averaged over a longitude range of 110–125∘ E
from model simulations VMETG4 (left) and VMET (right). (b) Same as
panel (a), but for differences in DJF between the 5 weakest and 5
strongest EAWM years.
The differences in winds between the weak and strong monsoon years lead to
differences in horizontal transport of BC. In Table 3 we summarize the
differences in simulated horizontal mass fluxes of JJA BC at the four lateral
boundaries of the box in northern and southern China (Fig. 4a, from the
surface to 10 km) based on simulations VMETG4 and VMET. The boxes are
selected as BC concentrations in the regions are higher or lower in the
weakest monsoon years than in the strongest monsoon years (Fig. 4a). In
northern China, the weakest (strongest) monsoon years in GEOS-4 show inflow
BC fluxes of 2.24 (0.97) kg s-1 at the south boundary and of 6.60
(4.20) kg s-1 at the west boundary, and they show outflow BC fluxes of
3.44 (4.06) kg s-1 at the northern boundary and of 12.48
(9.20) kg s-1 at the eastern boundary. The total effects are thus
outflow fluxes of 7.08 kg s-1 in the weakest monsoon years and of
8.09 kg s-1 in the strongest monsoon years, resulting in a net effect
of a larger BC inflow of 1.01 kg s-1 in the weakest monsoon years
compared to the strongest monsoon years. Similarly, simulation results in
MERRA show a net effect of a larger BC inflow of 1.60 kg s-1 in the
weakest monsoon years compared to the strongest monsoon years. The larger
inflow of BC in the weakest monsoon years thus leads to the higher surface BC
concentrations in northern China. In southern China, we find inflow BC fluxes
of 0.62 (0.70) kg s-1 at the southern boundary and of 0.94
(0.13) kg s-1 at the western boundary, and we find outflow BC fluxes
of 1.79 (0.88) kg s-1 at the northern boundary and of 0.33
(0.42) kg s-1 at the eastern boundary in the weakest (strongest)
monsoon years in GEOS-4. The resulting effect is larger outflow fluxes of BC
by 0.09 kg s-1 in the weakest monsoon years
(0.56 kg s-1) compared to the strongest monsoon years
(0.47 kg s-1). In MERRA, the weakest monsoon years also show larger
outflow fluxes of BC by 0.27 kg s-1, compared to the strongest monsoon
years. These results indicate that the differences in transport of BC due to
the changes in atmospheric circulation are a dominant mechanism through which
the EASM influences the variations of JJA BC concentrations in eastern China.
We also examine the impact of the changes in precipitation associated with
the strength of the summer monsoon on BC concentrations, which is not as
dominant as that of the winds. Compared to the strongest EASM years,
increases in wet deposition of BC is found in the weakest monsoon years
north of 28∘ N in eastern China (Table 3), as a result of the high
aerosol concentrations in the region and also the increased rainfall in the
lower and middle reaches of the Yangtze River (around 30∘ N). In
the region south of 28∘ N in eastern China, we find decreased wet
deposition of BC in the weakest monsoon years, mainly because of the low BC
concentrations in that region.
We would like to point out that warming trend is not a significant factor in
the variations of BC concentrations in the present study, as emissions are
fixed at the 2010 levels and warming trend in the emissions is thus excluded.
In addition, Yang et al. (2016) have systematically examined the trends of
metrological parameters and PM2.5 in eastern China for 1985–2005. They
found a positive trend in temperature and a negative trend in precipitation,
while they found no significant trend in BC concentrations.
Simulated JJA (DJF) mean all-sky direct radiative forcing (DRF) of
BC (W m-2) at the top of the atmosphere (TOA) in the 5 weakest and
5 strongest EASM (EAWM) years during 1986–2006. Results are from
simulation VMET averaged over eastern China (110–125∘ E,
20–45∘ N), northern China (110–125∘ E,
28–45∘ N), the North China Plain (110–125∘ E,
37–45∘ N), the Central China Plain (110–125∘ E,
28–36∘ N), and southern China (110–125∘ E,
20–27∘ N).
Month
Region
TOA DRF of BC, MERRA (W m-2)
Weak
Strong
Difference∗
JJA
Southern China
0.34
0.40
-0.06 (14 %)
Northern China
1.41
1.38
0.04 (3 %)
Eastern China
1.08
1.07
0.01 (1 %)
DJF
Southern China
1.04
1.07
-0.03 (3 %)
Northern China
1.65
1.62
0.03 (2 %)
Central China Plain
2.11
2.14
-0.03 (1 %)
North China Plain
1.08
0.97
0.11 (11 %)
Eastern China
1.46
1.45
0.01 (1 %)
∗ The difference is (weakest–strongest) and the relative
difference in percentage is in parentheses.
Impact of EASM on vertical profile and DRF of BC
Previous studies have shown that vertical distribution of BC is critical for
the calculation of the BC DRF (e.g., Bond et al., 2013; Li et al., 2016).
The calculation of the BC DRF is dependent on several factors, e.g., BC
lifetime and radiative forcing efficiency (radiative forcing exerted per
gram of BC), which are significantly influenced by vertical distribution of
BC. Vertical profile of BC affects its wet scavenging and hence its lifetime
(Bond et al., 2013). The direct radiative forcing efficiency of BC was enhanced
considerably when BC was located at high altitude, largely because of the
radiative interactions with clouds (Samset et al., 2013). For example, BC
above 5 km accounts for ∼ 40 % of the global DRF of BC
(Samset et al., 2013). We would like to point out that few aircraft
observations of BC vertical profiles are available in China. Previous studies
have evaluated the GEOS-Chem-simulated vertical profiles of BC by using
datasets from aircraft campaigns for the regions of the northwest Pacific,
North America, and the Arctic (Park et al., 2005; Drury et al., 2010; Wang
et al., 2011).
Figure 8a compares the simulated JJA mean all-sky DRF of BC at the TOA in the
5 weakest and 5 strongest EASM years during 1986–2006. Model results
are from simulation VMET. The BC DRF is calculated using the rapid radiative
transfer model for GCMs (RRTMG; Heald et al., 2014), which is discussed in
detail by Mao et al. (2016). We find that the BC DRF is highest
(> 3.0 W m-2) over northern China in JJA. The spatial
distributions of the differences in the BC DRF between the weakest and
strongest monsoon years are similar to those in BC concentrations (Fig. 4a).
Relative to the strongest monsoon years, the TOA DRF of BC shows an increase
north of 28∘ N, while it shows a reduction south of 27∘ N in the
weakest monsoon years. The BC DRF in northern China is 0.04 W m-2
(3 %, Table 4) higher in the weakest compared to the strongest monsoon years, with a
maximum of 0.3 W m-2 in Jiangsu Province. In southern China, the
weakest monsoon years have a lower DRF by 0.06 W m-2 (14 %). As a
result, the TOA DRF of BC in eastern China is 0.01 W m-2 (1 %) higher
in the weakest monsoon years than in the strongest monsoon years. Note that
the estimated DRF is associated with large uncertainties due to the BC
mixing state used in the model, which assumes external mixing of aerosols and
gives a lower-bound estimate of BC DRF. Internal mixing of BC with
scattering aerosols in the real atmosphere likely increases the estimates of
DRF (e.g., Jacobson, 2001).
(a) Simulated JJA mean
all-sky direct radiative forcing (DRF) of BC (W m-2) at the top of the
atmosphere (TOA) in the (a1) 5 weakest and (a2) 5 strongest
EASM years during 1986–2006 from model simulation VMET. Also shown are the
(a3) absolute (W m-2) and (a4) percentage (%)
differences between the 5 weakest and 5 strongest EASM years.
(b) Same as (a), but for simulated DJF mean all-sky TOA DRF
of BC in the 5 weakest and 5 strongest EAWM years. The enclosed areas are
defined as northern China (NC; 110–125∘ E, 28–45∘ N), the
North China Plain (NCP; 110–125∘ E, 36–45∘ N), the
Central China Plain (CCP; 110–125∘ E, 28–36∘ N), and
southern China (SC; 110–125∘ E, 20–27∘ N).
In Fig. 9a we further compare the vertical distribution of simulated JJA
mean all-sky DRF of BC in the 5 weakest and 5 strongest EASM years,
averaged over 110–125∘ E. A maximum BC DRF (> 2 W m-2) is shown at an altitude of approximately 3–10 km because of the
larger direct radiative forcing efficiency of BC at high altitude. We find
the largest BC-induced forcing at the latitude of 30–40∘ N in the
weakest monsoon years and 35–40∘ N in the strongest monsoon
years. The shift of the center of the highest BC DRF is likely due to the
different vertical distributions of BC concentrations between the weakest
and strongest monsoon years (Fig. 5a). BC DRF is higher by > 0.13 W m-2 (10–20 %) over 30–35∘ N in the 5 weakest EASM
years compared to the 5 strongest EASM years, which are consistent with
those in Fig. 8a.
(a) Height–latitude cross sections of simulated JJA mean
all-sky DRF of BC (W m-2) in the (a1) 5 weakest and
(a2) 5 strongest EASM years during 1986–2006. Also shown are the
(a3) absolute (W m-2) and (a4) percentage (%)
differences between the 5 weakest and 5 strongest EASM years. Plots are
averaged over a longitude range of 110–125∘ E from model simulation
VMET. (b) Same as (a), but for simulated DJF mean all-sky
DRF of BC in the 5 weakest and 5 strongest EAWM years.
Figure 10a1 shows the simulated vertical profiles of JJA BC mass
concentrations (µg m-3) averaged over eastern China for
1986–2006. The simulated BC concentrations are higher in MERRA than in
GEOS-4 below 3 km. We find that the vertical profiles of JJA BC in GEOS-4
generally show larger interannual variations than those in MERRA. The
variations of JJA BC in MERRA (GEOS-4) range from –5 to 4 % (–7
to 12 %) at the surface, -25 to 16 % (-23 to 23 %) at 1 km,
-35 to 42 % (-32 to 46 %) at 2 km, -23 to 32 %
(-25 to 67 %) at 3 km, -13 to 10 % (-18 to 71 %) at 4 km, and -10 to 7 % (-14 to > 76 %) at 5–8 km. The
differences in vertical profiles of BC in MERRA between the weakest and
strongest EASM years (1998–1997) is -46 to 7 %, with the largest
difference of -0.09 µg m-3 at ∼ 2 km (Fig. 10a2). We further compare the difference in simulated vertical profiles
of JJA BC between the 5 weakest and 5 strongest EASM years averaged
over northern and southern China in MERRA. The decreased BC concentrations
throughout the troposphere in the weakest monsoon years lead to a reduction
in the BC DRF in southern China, while the increased BC concentrations below
2 km result in a significant increase in the BC DRF in northern China (Table 4).
(a1) Simulated vertical profiles of JJA BC mass
concentrations (µg m-3) averaged over 1986–2006. The error
bars represent the minimum and maximum values of BC. Results are averages
over eastern China from model simulations VMETG4 (blue) and VMET (red).
(a2) Differences in simulated vertical profiles of JJA BC mass
concentrations (µg m-3) between the 5 weakest and 5
strongest EAM years (solid lines) during 1986–2006, and between the weakest
and strongest EASM years (1998–1997, dotted lines). Results are averages
over eastern China, northern China, and southern China from model simulation
VMET. (b1) Same as panel (a1), but for simulated DJF BC
mass concentrations. (b2) Same as panel (a2), but for
differences in DJF between the 5 weakest and 5 strongest EAWM years and
between the weakest and strongest EAWM years (1990–1996). Results are
averages over eastern China, the North China Plain, the Central China Plain,
and southern China.
Studies have shown that the impact of non-Chinese emissions is significant on
vertical profiles and hence DRF of BC in China; the contribution of
non-Chinese emissions to concentrations and DRF of BC in China is larger than
20 % at 5 km altitude and about 17–43 %, respectively (e.g., Li et al.,
2016). Figure 11a shows vertical distribution of simulated JJA mean all-sky
DRF of BC due to non-Chinese emissions in the 5 weakest and 5 strongest
EASM years, averaged over 110–125∘ E. Model results are from
simulation VNOC, in which the anthropogenic and biomass burning emissions
are turned off in China. The non-Chinese emissions induce a high (> 0.16 W m-2) BC DRF above ∼ 5 km due to the significant
contributions of non-Chinese emissions to BC concentrations at high altitudes.
Compared to the 5 strongest EASM years, the simulated DRF of BC due to
non-Chinese emissions in the weakest EASM years is larger (by ∼ 10 %) at 25–40∘ N because of the
higher (by > 10 %) BC concentrations transported to the region (Fig. 12a).
Same as Fig. 9, but for the contributions from non-Chinese emissions
to simulated all-sky DRF of BC.
(a) Height–latitude cross sections of contributions of
non-Chinese emissions to simulated JJA mean BC concentrations
(µg m-3) in the (a1) 5 weakest and
(a2) 5 strongest EASM years during 1986–2006. Also shown are the
(a3) absolute (µg m-3) and (a4) percentage
(%) differences between the 5 weakest and 5 strongest EASM years.
Plots are averaged over a longitude range of 110–125∘ E from model
simulation VMET. (b) Same as (a), but for simulated DJF
mean BC concentrations in the 5 weakest and 5 strongest EAWM years.
Impact of EAWM on interannual variation of BC
Simulated DJF BC in GEOS-4 and MERRA
Simulated DJF surface BC concentrations averaged over eastern China also have
strong interannual variations, ranging from 1.30 to
1.58 µg m-3 (-8.9 to 10.8 %) in GEOS-4 for 1986–2006
and from 2.05 to 2.31 µg m-3 (-7.0 to 5.2 %) in MERRA
for 1980–2010 (Fig. 1b). DJF mean surface concentrations of BC for
1986–2006 are 0.77 µg m-3 (54 %) higher in MERRA than in
GEOS-4. Again, the consistently stronger precipitation in GEOS-4 (by
0.3 mm d-1, 21 % on average) largely accounts for the lower
surface BC concentrations (Figs. S1 and 2a2). The DJF mean precipitation
averaged for 1986–2006 is higher in GEOS-4 than in MERRA in most of China
(Fig. S1), except in the delta of the Yangtze River in eastern China. The
resulting differences in BC wet deposition between GEOS-4 and MERRA show
similar patterns as those in precipitation (not shown). The DJF mean wet
deposition of BC in GEOS-4 is generally higher (by 0.007 kg s-1,
5 % on average) than that in MERRA for 1986–2006, except in 1998
(Fig. 2b2). In addition, we find that the planetary boundary layer height
(PBLH) partially accounts for the abovementioned differences in surface BC
concentrations between GEOS-4 and MERRA. The DJF mean PBLH is generally
higher in GEOS-4 than in MERRA by 11.6 m (2 %, Fig. S2). The lower PBLH
in MERRA suppresses the convection and thus leads to higher BC concentrations
at the surface.
Correlation between DJF BC and EAWMI
Figure 1b shows the normalized EAWMI and simulated DJF mean surface BC
concentrations averaged over eastern China from simulation VMET for
1980–2010 and from VMETG4 for 1986–2006. The correlation coefficient
between the surface BC concentrations and the EAWMI_GEOS4 is
-0.7 for 1986–2006, and those between surface BC and the
EAWMI_MERRA are -0.6 and -0.7, respectively, for 1980–2010
and for 1986–2006. Different definitions of the EAWMI also show negative
correlations with simulated DJF surface BC concentrations (Table 1, r=-0.16 to -0.72). This negative correlation between simulated DJF mean
surface BC concentrations and the EAWMIs over eastern China indicates that
surface BC concentrations are generally high in the weak winter monsoon
years. The correlation coefficients in GEOS-4 and MERRA show similar spatial
distribution and magnitude; negative correlations are found in most of
China, while positive correlations are found over southwestern China (Fig. 3b).
Differences in BC between weak and strong EAWM years
Figure 4b shows the differences in simulated DJF mean surface BC
concentrations (µg m-3) between the weakest (1990, 1993, 1997,
1998, and 2002) and strongest (1986, 1996, 2001, 2005, and 2006) EAWM years
during 1986–2006 from model simulations using the GEOS-4 and MERRA data. The
spatial distribution of the differences in concentrations is in good
agreement with the distribution of the correlation coefficients between the
EAWMI and surface BC (Fig. 3b). In eastern China, DJF surface BC
concentrations in GEOS-4 are 0.12 µg m-3 (9 %) higher in
the weakest winter monsoon years than in the strongest years (Table 2). The
corresponding values are 0.11 µg m-3 (5 %) higher in
MERRA. In northern China, simulated surface BC concentrations are higher in
the weakest monsoon years than in the strongest monsoon years by
0.13 µg m-3 (8 %) in GEOS-4 and by
0.14 µg m-3 (5 %) in MERRA. In southern China, the
corresponding concentrations are higher by 0.10 µg m-3
(12 %) and 0.04 µg m-3 (3 %), respectively, in GEOS-4
and in MERRA. The difference in surface BC concentrations between the weakest
and strongest winter monsoon years over each region is deemed significant by
comparing with the corresponding standard deviation of DJF mean surface BC
for 1986–2006 (Table 2).We find that the region over 30–40∘ N has
lower BC concentrations in the weakest monsoon years. These lower
concentrations are also shown in Fig. 5b, which represents the
height–latitude of differences in simulated DJF mean BC concentrations
between the 5 weakest and 5 strongest EAWM years during 1986–2006 and
averaged over 110–125∘ E from model simulations VMETG4 and VMET.
Increased BC concentrations in the weakest monsoon years are found north
of 20∘ N in both GEOS-4 and MERRA, except for the region over
30–40∘ N and above 1 km.
The changes in atmospheric circulation again likely account for the
increased BC concentrations in the weak winter monsoon years in eastern
China. Figure 6b shows the composite differences in DJF 850 hPa wind (m s-1) between the 5 weakest and 5 strongest EAWM years from the
GEOS-4 and MERRA data. The differences in wind in GEOS-4 show a similar
pattern as those in MERRA. In DJF, northerly winds are weaker in the weaker
monsoon years than in the stronger monsoon years. As a result, anomalous
southwesterlies are found in the weakest monsoon years along the coast of
eastern China and anomalous southeasterlies control northern China and
northeast China, which does not favor the outflow of pollutants from eastern
China (Table 3). Figure 7b shows the differences in simulated upward mass flux
of DJF BC (kg s-1) between the 5 weakest and 5 strongest EAWM
years. The differences are averaged over the longitude range of
110–125∘ E. Compared to the strongest monsoon years, increases in
upward mass flux of BC concentrations are found over 20–30∘ N and
north of 40∘ N in the troposphere in the weakest monsoon years,
confirming the increased surface BC concentrations in northern and southern
China (Figs. 4b and 5b). We find decreased upward transport of BC over
30–40∘ N in the weakest monsoon years, which is consistent with
decreased concentrations in the region of static winds (Fig. 6b). Our
results are consistent with studies from Li et al. (2015) and Zhou et
al. (2015), which showed that the change in wind speed and wind direction
was the major factor of the negative correlation between the increased
winter fog–haze days and the weakening of the EAWM in China.
In Table 3 we further summarize the differences in horizontal fluxes of DJF
BC at the four lateral boundaries of the northern and southern boxes (Fig. 4b, from the surface to 10 km) between the 5 weakest and 5 strongest
EAWM years based on simulations VMETG4 and VMET. Both northern and southern
China show increased BC concentrations in the weakest monsoon years compared
to
the strongest monsoon years (Fig. 4b). In the southern box, we find a larger
inflow of BC by 1.67 (0.99) kg s-1 at the western boundary, a smaller inflow by
1.45 (1.19) kg s-1 at northern boundary, a smaller outflow by 0.52 (0.70) kg s-1 at the southern boundary, and a larger outflow by
0.55 (0.10) kg s-1 at eastern boundary from simulation VMETG4 (VMET). The net effect in
southern China is a larger inflow of BC by 0.19 (0.40) kg s-1 in the
weakest EAWM years compared to the strongest EAWM years, which leads to
higher BC concentrations in the weakest EAWM years. As we discussed in Sect. 3.3, the larger outflow fluxes of BC by 0.09 (0.27) kg s-1 result in
the lower BC concentrations in southern China in the weakest EASM years than
in the strongest EASM years. Different patterns of atmospheric circulation
between summer and winter monsoons thus lead to the different distributions
of BC in southern China. In northern China, there is a net effect of larger
inflow of BC by 0.64 (0.62) kg s-1 because of the anomalous southerlies
in the weakest monsoon years. The anomalous southerlies in northern China
thus prevent the outflow of pollutants and lead to an increase in BC
concentrations in the region in the weakest monsoon years. Compared to the
strongest EAWM years, enhanced wet deposition of BC is found in the weakest
monsoon years in both northern and southern China (Table 2), likely because
of the increased BC concentrations and precipitation in the corresponding
regions. Weaker upward transport in the weakest monsoon years compared to the
strongest years in northern China (Fig. 7b) is also not a dominant factor
that contributes to the higher surface BC concentrations in the region
(Tables 2 and 3).
Impact of EAWM on vertical profile and DRF of BC
Figure 8b shows the simulated DJF mean all-sky TOA DRF of BC in the 5
weakest and strongest EAWM years during 1986–2006 based on simulation
VMET. The simulated BC DRF is high in eastern China, with the largest values
(> 5.0 W m-2) in the Sichuan Basin. In northern China, the
TOA DRF of BC is 0.03 W m-2 (2 %, Table 4) higher in the weakest
monsoon years than in the strongest monsoon years, consistent with the higher
BC concentrations in the region (Fig. 4b). We further separate northern
China into two regions, the Central China Plain (110–125∘ E,
28–36∘ N) and the North China Plain (110–125∘ E,
37–45∘ N). Relative to the 5 strongest monsoon years, the BC
DRF in the weakest monsoon years is higher in the North China Plain by
0.11 W m-2 (11 %) but lower in the Central China Plain by 0.03 W m-2 (1 %). In the Central China Plain, although the surface
concentrations are higher by 0.08 µg m-3 (2 %) in the weakest
monsoon years, the corresponding DRF is lower partially because of the lower
column burdens of tropospheric BC (by 0.04 mg m-2, 1 %, from surface
to 10 km; Figs. 5b2 and 10b2). In southern China, the DRF is 0.03 W m-2 (3 %) lower in the weakest monsoon years than in the strongest
monsoon years. In contrast, both surface concentrations (higher by 0.04 µg m-3, 3 %) and column burdens (higher by 0.02 mg m-2,
2 %) of BC are higher in the weakest monsoon years. We attribute these
discrepancies largely to the vertical distributions of BC concentrations, as
discussed in the following paragraph. In Fig. 9b we further compare the
vertical distribution of simulated DJF DRF of BC in the 5 weakest and
5 strongest EAWM years, averaged over 110–125∘ E. The
BC-induced forcing is large (> 2.8 W m-2) at the latitude of
20–40∘ N and at an altitude of 5–10 km. BC DRF is higher by
> 0.1 W m-2 (> 10 %) north of 35∘ N
in the 5 weakest EAWM years than in the 5 strongest EAWM years,
consistent with the values shown in Fig. 8b.
The abovementioned differences in spatial patterns of DRF and BC
concentrations are likely because of the vertical distributions of BC
concentrations. In general, the simulated vertical profiles of DJF BC
concentrations over eastern China are higher in MERRA than in GEOS-4, but
the interannual variations are larger in GEOS-4 than in MERRA (Fig. 10b1).
The variations of DJF BC in MERRA (GEOS-4) range from -7 to 5 %
(-9 to 11 %) at the surface, -12 to 10 % (-13 to 27 %)
at 1 km, -19 to 14 % (-13 to 62 %) at 2 km, -14 to 15 %
(-17 to 57 %) at 3 km, -17 to 16 % (-22 to 61 %) at 4 km, and -17 to > 14 % (-22 to > 67 %) at
5–8 km. We find that the difference in vertical profiles of BC over
eastern China in MERRA between the weakest and strongest EAWM years
(1990–1996) is -0.08 to 0.2 µg m-3 (-11 to 12 %)
below 10 km, with the largest difference at the surface and ∼ 1.5 km (Fig. 10b2). We further compare the differences in simulated
vertical profiles of DJF BC mass concentrations between the 5 weakest and
5 strongest EAWM years from model simulation VMET, averaged over southern
China, the Central China plain, and the North China Plain. Relative to
the strongest monsoon years, decreased BC concentrations are found in the
weakest monsoon years from 2 to 5 km in southern China and from 1 to 6 km in
the Central China Plain. The decreased BC concentrations above 1–2 km lead
to the reduction in the DRF in the two regions. In contrast, the higher DRF
of BC in the North China Plain in the weakest monsoon years is because of
the increased BC concentrations throughout the troposphere.
The lower concentrations above 1–2 km in the weakest monsoon years in
southern China and the Central China Plain are likely because of the weaker
vertical convection at the corresponding altitudes in the weakest monsoon
years than in the strongest monsoon years. We calculate the horizontal and
vertical fluxes of BC in two boxes of southern China and the Central China
Plain from 1 to 6 km (Table 5). In vertical direction, the two boxes have
upward fluxes in both lower and upper boundaries. Relative to the strongest
monsoon years, the southern box has a net outflow of 0.07 kg s-1 in
the weakest monsoon years; the central China Plain shows a net downward flux
of 0.11 kg s-1. The corresponding net horizontal fluxes are relatively
smaller at about 0.03 kg s-1 in southern China and 0.01 kg s-1
in the Central China Plain. The weaker vertical fluxes above 1–2 km in the
weakest monsoon years thus result in lower BC concentrations at the
elevated altitudes and therefore the reduction in the DRF in the two regions.
Figure 11b shows the vertical distribution of simulated DJF mean all-sky DRF
of BC due to non-Chinese emissions in the 5 weakest and 5 strongest EAWM
years, averaged over 110–125∘ E. The non-Chinese emissions induce a
high (> 0.35 W m-2) BC DRF at 15–35∘ N. We also
find a higher (by > 5 %) DRF of BC north of 25∘ N in
the weakest EAWM years than in the strongest years due to the larger BC
concentrations at the low troposphere in the weakest EAWM years (Fig. 12b).
The composite analyses of DJF horizontal and vertical fluxes of BC
(kg s-1) for two selected boxes (the Central China Plain
(110–125∘ E, 27–36∘ N) and southern China
(110–125∘ E, 20–27∘ N), from 1 to 6 km) based on
simulation VMET. The values are averages over the 5 weakest and 5
strongest EAWM years during 1986–2006. For fluxes, positive values indicate
eastward, northward, or upward transport and negative values indicate
westward, southward, or downward transport.
Boundary
Weakest
Strongest
Difference∗
Net
DJF, Central China Plain (110–125∘ E, 28–36∘ N)
South
+1.29
+0.98
+0.31
Inflow 0.01
North
+0.53
+0.07
+0.46
West
+7.84
+8.89
-1.05
East
+7.39
+8.61
-1.21
Upper
+0.99
+1.24
-0.25
outflow 0.11
Bottom
+5.22
+5.56
-0.34
DJF, southern China (110–125∘ E, 20–27∘ N)
South
-0.08
-0.20
+0.12
inflow 0.03
North
+0.91
-0.67
+0.24
West
+4.40
+4.37
+0.03
East
+1.70
+1.82
-0.12
Upper
+0.09
+0.06
+0.03
outflow 0.07
Bottom
+1.12
+1.16
-0.04
∗ The difference is (weakest–strongest).
Summary and conclusions
We quantified the impacts of the EASM and EAWM on the interannual variations
of mass concentrations and DRF of BC in eastern China for 1986–2006 and
examined the relevant mechanisms. We conducted simulations with fixed
anthropogenic and biomass burning emissions at the year 2010 levels and
driven by GEOS-4 for 1986–2006 and by MERRA for 1980–2010.
We found that simulated JJA and DJF surface BC concentrations averaged over
eastern China were higher in MERRA than in GEOS-4 by 0.30 µg m-3
(44 %) and 0.77 µg m-3 (54 %), respectively. Our analyses
indicated that generally higher precipitation in GEOS-4 than in MERRA
largely accounted for the differences in BC concentrations using the two
meteorological fields.
In JJA, simulated BC concentrations showed interannual variations of -5
to 4 % in MERRA (-7 to 12 % in GEOS-4) at the surface and -35
to 42 % in MERRA (-32 to > 76 % in GEOS-4) above 1 km.
The differences in vertical profiles of BC between the weakest and strongest
EASM years (1998–1997) reached up to -0.09 µg m-3 (-46 %)
at 1–2 km. Simulated JJA surface BC concentrations negatively correlated
with the strength of the EASM (r=-0.7 in GEOS-4 and -0.4 in MERRA),
mainly because of the changes in atmospheric circulation. Relative to the 5
strongest EASM years, simulated JJA surface BC concentrations in the 5
weakest EASM years were higher over northern China by 0.09 µg m-3 (11 %) in GEOS-4 and by 0.04 µg m-3 (3 %) in MERRA.
The corresponding concentrations were lower over southern China by 0.03 µg m-3 (11 %) and 0.04 µg m-3 (10 %). The
resulting JJA mean TOA DRF of BC was 0.04 W m-2 (3 %) higher in
northern China but 0.06 W m-2 (14 %) lower in southern China.
In DJF, the changes in meteorological parameters alone led to interannual
variations in BC concentrations ranging from -7 to 5 % in MERRA
(-9 to 11 % in GEOS-4) at the surface and -19 to > 14 % in MERRA (-22 to > 67 % in GEOS-4) above 1 km.
Simulated DJF surface BC concentrations negatively correlated with the EAWMI
(r=-0.7 in GEOS-4 and -0.7 in MERRA), indicating higher DJF surface BC
concentrations in the weaker EAWM years. We also found that the changes in
atmospheric circulation likely accounted for the increased BC concentrations
in the weak EAWM years. In winter, anomalous southerlies in the weak monsoon
years did not favor the outflow of pollutants, leading to an increase in BC
concentrations. Compared to the 5 strongest EAWM years, simulated DJF
surface BC concentrations in the 5 weakest EAWM years were higher in
northern China by 0.13 µg m-3 (8 %) in GEOS-4 and by 0.14 µg m-3 (5 %) in MERRA. The corresponding concentrations were
also higher in southern China by 0.10 µg m-3 (12 %) and 0.04 µg m-3 (3 %). The resulting TOA DRF of DJF BC
was 0.03 W m-2 (2 %) higher in northern China but 0.03 W m-2 (2 %) lower
in southern China. In southern China, the decreased BC concentrations above
1–2 km in the weakest EAWM years led to the reduction in BC DRF, likely due
to the weaker vertical convection in the corresponding altitudes. Simulated
BC concentrations above 1–2 km were lower in the weakest EAWM year (1990) than
in the strongest year (1996), with the largest value of -0.08 µg m-3 (-11 %) in eastern China.
Different patterns of atmospheric circulation between summer and winter
monsoons lead to the different distributions of BC in southern and northern
China. Note that these different changes in BC concentrations and DRF between
northern and southern China due to the EAM would be useful for proposing
efficient air quality regulation in different regions of China. It is also
worth pointing out that the BC DRF is also dependent on factors such as cloud
and background aerosol distributions (Samset et al., 2013), which can be
influenced by the strength of the EAM (Liu et al., 2010; Zhu et al., 2012).
In addition, the strength of the EAWM would influence the following summer
monsoon via changes in factors such as circulation and precipitation (e.g.,
Chen et al., 2000), and they would further affect the aerosol concentrations
and radiative forcing. These aspects should be further investigated in future
studies.