A fully coupled meteorology-chemistry model (WRF-Chem, the Weather Research and Forecasting model coupled with chemistry) has been configured to conduct quasi-global simulation for 5 years (2010–2014) and evaluated with multiple observation data sets for the first time. The evaluation focuses on the simulation over the trans-Pacific transport region using various reanalysis and observational data sets for meteorological fields and aerosol properties. The simulation generally captures the overall spatial and seasonal variability of satellite retrieved aerosol optical depth (AOD) and absorbing AOD (AAOD) over the Pacific that is determined by the outflow of pollutants and dust and the emissions of marine aerosols. The assessment of simulated extinction Ångström exponent (EAE) indicates that the model generally reproduces the variability of aerosol size distributions as seen by satellites. In addition, the vertical profile of aerosol extinction and its seasonality over the Pacific are also well simulated. The difference between the simulation and satellite retrievals can be mainly attributed to model biases in estimating marine aerosol emissions as well as the satellite sampling and retrieval uncertainties. Compared with the surface measurements over the western USA, the model reasonably simulates the observed magnitude and seasonality of dust, sulfate, and nitrate surface concentrations, but significantly underestimates the peak surface concentrations of carbonaceous aerosol likely due to model biases in the spatial and temporal variability of biomass burning emissions and secondary organic aerosol (SOA) production. A sensitivity simulation shows that the trans-Pacific transported dust, sulfate, and nitrate can make significant contribution to surface concentrations over the rural areas of the western USA, while the peaks of carbonaceous aerosol surface concentrations are dominated by the North American emissions. Both the retrievals and simulation show small interannual variability of aerosol characteristics for 2010–2014 averaged over three Pacific sub-regions. The evaluation in this study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA, allowing one to further understand the impact of transported pollutants on the regional air quality and climate with high-resolution nested regional modeling.
Aerosols, originating from natural and anthropogenic sources in Europe, North Africa, and East Asia, can be transported thousands of miles downwind across the Pacific Ocean to North America and even beyond. Previous studies using ground-based and satellite measurements and numerical models have estimated about 7–10 days of travel time for aerosols to traverse the Pacific Ocean (Eguchi et al., 2009). Previous studies have shown that aerosols outflowed from the Asian continent could be transported by the mid-latitude prevailing westerlies across the Pacific Ocean and ultimately reach the west coast of North America and beyond, and its efficiency is the largest in spring (e.g., Takemura et al., 2002; Chin et al., 2007; Huang et al., 2008; Yu et al., 2008; Uno et al., 2009, 2011; Alizadeh-Choobari et al., 2014). Takemura et al. (2002) found that the contribution of anthropogenic aerosols to the total aerosol optical thickness is comparable to that of dust during the transport over the North Pacific in spring. Chin et al. (2007) found that the long-range transported dust brought 3 to 4 times more fine particles than anthropogenic pollutants to the total surface fine particles over the USA on annual average with a maximum influence in spring, and over the northwestern USA Yu et al. (2008) estimated that about 25 % of the Asian outflow reaches the west coast of North America, which is about 15 % of the total North American emissions; the transport fluxes are largest in spring and smallest in summer. Uno et al. (2011) also revealed that the dust trans-Pacific path sometimes could be split into two branches: a southern path to the central USA and a northern path that is trapped and stagnant for a longer time and finally subsides over the northwestern USA.
These trans-Pacific aerosols can play an important role in atmospheric
composition (e.g., Yu et al., 2008), air quality (e.g., Jaffe et al., 1999;
VanCuren, 2003; Heald et al., 2006; Chin et al., 2007; Fischer et al., 2009;
Yu et al., 2012; Tao et al., 2016), and regional weather and climate (e.g.,
Lau et al., 2008; Eguchi et al., 2009; Yu et al., 2012; Creamean et al.,
2013; Fan et al., 2014; Huang et al., 2006, 2014) over the US West Coast.
At the surface, Heald et al. (2006) found that Asian anthropogenic aerosol
plume increased aerosol concentrations in elevated regions of the
northwestern USA by 0.16
Previous studies have used global models to quantify the long-range transport of aerosols to the western USA (e.g., Fairlie et al., 2007; Heald et al., 2006; Chin et al., 2007; Hadley et al., 2007). However, simulations were performed at relatively coarse resolutions (typically 1–2 degrees) that cannot fully resolve the large geographical variability of aerosols over the western USA with complex topography (Zhao et al., 2013a). Coarse-resolution simulations also lack the capability to fully resolve aerosol–cloud–precipitation interaction. Some studies have reported regional simulations at relatively high resolutions over the western USA (e.g., Zhao et al., 2013a; Fan et al., 2014; Fast et al., 2014). However, most of them either used sparse in situ observations to provide lateral boundary conditions that are only suitable for idealized or short-term sensitivity studies, or used simulations from global models with inconsistent physics and chemistry schemes to provide lateral boundary conditions, which introduce biases in estimating the contribution and effect of trans-Pacific transported aerosols.
To investigate the impact of trans-Pacific transported aerosols on regional
air quality and climate of the US West Coast, a multi-scale modeling
framework including global simulation at coarse resolutions that captures the
large-scale circulation and provides consistent chemical lateral boundaries
for nested regional simulation at high resolutions is needed. WRF-Chem, the
Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) coupled
with a chemistry component (Grell et al., 2005), is such a modeling
framework. As a state-of-the-art model, WRF-Chem supports nested simulations,
and includes complex aerosol processes and interactions between aerosols and
radiation, clouds, and snow albedo (Zhao et al., 2014). The model has been
used extensively to study aerosols and their impacts on air quality and
climate at regional scales (e.g., Fast et al., 2006, 2009; Gustafson et al.,
2007; Qian et al., 2010; Gao et al., 2011, 2014; Shrivastava et al., 2011;
Chen et al., 2013, 2014; Zhao et al., 2010a, 2011, 2012, 2013a, 2014). Zhao
et al. (2013b) is the first study to use WRF-Chem for quasi-global
(180
Although the quasi-global WRF-Chem simulation described by Zhao et al. (2013b) has been used to provide realistic chemical lateral boundary conditions for multiple regional modeling studies (e.g., Zhao et al., 2014; Fan et al., 2015), its evaluation has not been documented so far. In this study, the WRF-Chem simulation for 2010–2014 is evaluated extensively using observational data. For lack of in situ observations over East Asia and the Pacific Ocean during our simulation period, evaluation is performed mainly using reanalysis and satellite retrieval (e.g., CALIPSO, MODIS, and MISR; see Sect. 3.1 for further definition.) data sets, along with some available ground-based observations from AErosol RObotic NETwork (AERONET) and Interagency Monitoring for Protected Visual Environments (IMPROVE) in the region. We focus on the simulation over the trans-Pacific transport region as a first step to evaluate the simulation for providing consistent lateral chemical boundaries for nested regional simulations used to investigate the impact of transported aerosols on regional air quality and climate. Spatial evolution of aerosols during the trans-Pacific transport as well as their seasonal and annual variability simulated by WRF-Chem will also be characterized.
In the following sections, the detailed setup of WRF-Chem will be described in Sect. 2. In Sect. 3 ground-based measurements and satellite retrievals will be presented. In Sect. 4, we evaluate the WRF-Chem simulated spatial distributions and seasonal and annual variability of aerosols across the Pacific with the observations. The conclusion can be found in Sect. 5.
In this study, WRF-Chem (3.5.1), updated by scientists at Pacific Northwest
National Laboratory (PNNL), is used. The MOSAIC (Model for Simulation
Aerosol Interactions and Chemistry) aerosol module (Zaveri et al., 2008)
coupled with the CBM-Z (carbon bond mechanism) photochemical mechanism
(Zaveri and Peters, 1999) in WRF-Chem is selected in this study. MOSAIC uses
a sectional approach to represent aerosol size distributions with four or
eight discrete size bins in the current version of WRF-Chem (Fast et al.,
2006). All major aerosol components including sulfate
(SO
Aerosol optical properties such as extinction, single scattering albedo
(SSA), and asymmetry factor for scattering are computed as a function of
wavelength for each model grid box. Aerosols are assumed internally mixed in
each bin (i.e., a complex refractive index is calculated by volume averaging
for each bin for each chemical constituent of aerosols). The Optical
Properties of Aerosols and Clouds (OPAC) data set (Hess et al., 1998) is used
for the shortwave (SW) and longwave (LW) refractive indices of aerosols,
except that a constant value of 1.53
Following Zhao et al. (2013b), we use a quasi-global channel configuration
with periodic boundary conditions in the zonal direction and 360
Anthropogenic emissions are obtained from the REanalysis of the TROpospheric
(RETRO) chemical composition inventories (Schultz et al., 2007;
The Moderate Resolution Imaging SpectroRadiometer (MODIS) instrument onboard
the NASA EOS Terra satellite observes Earth in 36 spectral bands from 0.4 to
14.4
The Multi-angle Imaging SpectroRadiometer (MISR) instrument onboard the Terra
spacecraft crosses the Equator at
The Ozone Monitoring Instrument (OMI) onboard the NASA Aura satellite has a
daily global coverage, and crosses the Equator at 13:45 LT. The nadir
horizontal resolution of OMI is 24 km
In this study, we use aerosol extinction profiles retrieved by the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The CALIPSO satellite was launched into a Sun-synchronous orbit on 28 April 2006. CALIOP is a dual-wavelength polarization lidar and is designed to acquire vertical profiles of attenuated backscatter from a near-nadir-viewing geometry during both day and night phase (Winker et al., 2007; Liu et al., 2004, 2008; Hu et al., 2007, 2009). In this study, the aerosol extinction profiles at a nominal horizontal resolution of 5 km from the CALIPSO level 2 profile products are used to evaluate the model. We focus on the CALIOP nighttime observations in cloud-free condition, because nighttime observations have higher accuracy than daytime observations (Winker et al., 2009).
The cloud–aerosol discrimination (CAD) score, which is an indicator that
measures confidence level of the discrimination between clouds (positive
value) and aerosols (negative value), is used to help screen out aerosol
profiles that contain cloud signals. We include the aerosol data with CAD
score between
The AERONET is a globally distributed remote
sensing network for aerosol monitoring from ground stations (Holben et al.,
1998). AERONET uses the Cimel sun–sky photometer that measures AOD in 16 spectral channels (340–1640 nm).
The measurements provide products every 15 min during daytime. In addition, an inversion algorithm is used for the
retrieval of aerosol size distribution, complex refractive index,
single-scattering albedo, and phase function (Dubovik and King, 2000; Dubovik
et al., 2002). The spectral AOD from AERONET has an accuracy of
Observation sites for the AERONET (green stars) and IMPROVE (red-dotted circles) networks used in this study.
Spatial distributions of seasonal-averaged wind fields at 850 hPa from the MERRA reanalysis and the WRF-Chem simulation for the period 2010–2014.
The IMPROVE network was initiated in 1985 by US federal agencies including
EPA, National Park Services, Department of Agriculture-Forest Service, and
other land management agencies as a part of the EPA Regional Haze program
(Malm et al., 1994). The network monitors the visibility conditions and
changes in national parks and wilderness areas on a long-term basis. The
detail sample collection and analytical methodology have been given by Hand
et al. (2011), and the data can be downloaded from
Spatial distributions of seasonal-averaged precipitation from the GPCP observation and the difference between observation and simulation for the period 2010–2014.
Winds and precipitation are two crucial meteorological factors playing
important roles in aerosol emission, transport, and removal. The seasonal
mean wind fields at 850 hPa averaged for the period 2010–2014 from the
WRF-Chem simulation are compared with the Modern-Era Retrospective analysis
for Research and Applications (MERRA) reanalysis data (Rienecker et al.,
2011) (Fig. 2). Strong westerly winds occur over the North Pacific
throughout the seasons with a peak (up to 12 m s
Figure 4 shows the spatial distributions of seasonal mean AOD at 550 nm
across the Pacific from Asia to North America averaged for 2010–2014 from
the retrievals of MODIS and MISR onboard Terra and the corresponding WRF-Chem
simulation. The WRF-Chem simulated AOD at 600 and 400 nm are used to
derive the AOD at 550 nm (using the Ångström exponent). In order to
reduce the sampling discrepancy between the two retrievals, the daily results
from the two satellite retrievals and simulation are sampled and averaged at
the same time and location. This way of averaging leads to the blank areas of
missing values, which are relatively large in JJA. Satellite retrievals show
consistent spatial patterns with the spatial correlation coefficients of
0.65–0.88 for the four seasons. The MODIS retrieval shows higher AOD over the
semi-arid regions (e.g., Northwest China and the southwestern USA) than the
MISR retrieval; however, the MODIS retrieved AOD magnitude over these regions
is significantly overestimated because of its large uncertainties in the
assumed surface reflectance in semi-arid regions (Remer et al., 2005; Levy et
al., 2013). In comparison, the MISR observations in the western USA show
better quality presumably because of the multi-angle capability that allows
for a better characterization of surface reflectance. Both retrievals
indicate that AOD is high over the Asian continent and gradually decreases
across the Pacific. High AOD coincides with the sub-tropical jet
(30–50
Spatial distributions of seasonal mean 550 nm AOD from the
retrievals of MODIS and MISR onboard Terra and the WRF-Chem simulation for
the period 2010–2014. The daily results from MISR, MODIS, and WRF-Chem are
only sampled for averaging when all of them have valid values at the same
location and time. Three sub-regions are denoted by the black boxes: region 1
(20–50
The WRF-Chem simulation generally well captures the observed spatial and
seasonal variability of AOD across the Pacific with the spatial correlation
coefficients of 0.63–0.76 for the four seasons against the MISR retrievals.
The model generally underestimates the retrieved AOD over the North Pacific
(0–60
Since this study focuses on the trans-Pacific transport and evolution of
aerosols, the Pacific is further divided into three sub-regions (region 1:
20–50
Seasonal mean 550 nm AOD from the MISR and MODIS retrievals, and the corresponding WRF-Chem simulation averaged for the period 2010–2014 over the three sub-regions shown in Fig. 4. The values of bars represent the mean. The vertical lines represent 10th and 90th percentile values, and the black dots represent the median values.
The AERONET observations of daily AOD at 550 nm at the three sites (SACOL, Midway Island, and Frenchman Flat) vs. the corresponding WRF-Chem simulation for the period 2010–2014.
Available observations from several AERONET sites (Fig. 1) over East Asia, the Pacific, and the western USA are also compared with the model simulation. Figure 6 shows the comparison of observed and simulated AOD at three representative AERONET sites for 2010–2014 over East Asia, an island of the Pacific, and the western US coast. The observations and simulation agree well at all three sites, and both reflect the AOD gradient from East Asia to the western USA as shown in Fig. 4. Observed AOD is the highest with a mean value of 0.31 at the SACOL site over East Asia and reduces to 0.075 at the Midway_Island site, and 0.045 at the Frenchman_Flat site. The model reproduces exactly these values at the three sites with correlation coefficients of 0.45, 0.65, and 0.64, respectively. About 90 % of simulated AOD is within a factor of 2 of the AERONET measurements.
Monthly mean 550 nm AOD from AERONET (black dots), MODIS (purple triangles), MISR (red five-pointed stars) and the corresponding WRF-Chem simulation (histogram) averaged for the period 2010–2014 at the eastern Asian, the Pacific island, and the western USA sites as shown in Fig. 1.
Figure 7 further shows the monthly variation of AOD averaged at the AERONET
sites over East Asia, the Pacific island, and the western USA (as shown in Fig. 1) from the AERONET observations, MODIS and MISR retrievals, and WRF-Chem
simulation. For the simulated AOD, contributions by dust, BC, OM, sulfate,
and other aerosols are also shown. Over East Asia, the MISR and AERONET
retrievals agree well with the annual mean of 0.37 and 0.33, respectively.
Their monthly variation correlates with a coefficient of 0.8. The MODIS
retrievals with the annual mean of 0.48 generally overestimate AOD against
the AERONET retrievals and correlate with the AEROENT retrieved monthly AOD
with a coefficient of 0.67. The simulation reproduces the AERONET observed
AOD variability with an annual mean of 0.38 and a monthly correlation
coefficient of 0.74. Model results show that anthropogenic aerosols dominate
the AOD from summer to winter, while dust can significantly contribute to the
AOD in spring. Over the island of Pacific (the Midway_Island
site), retrievals from AERONET, MODIS, and MISR are generally consistent with
each other on annual mean with values of 0.14, 0.13, and 0.14, respectively.
The MISR retrievals correlate well with the AERONET retrievals in monthly
variation with a coefficient of 0.70, which is 0.42 for MODIS, showing a
minimum in summer months. The simulated annual mean AOD of 0.14 well
reproduces the AERONET retrieval. The model also captures the AERONET
retrieved monthly variation of AOD with a correlation coefficient of 0.64.
The simulation shows that this monthly variation is largely determined by the
variation of sea-salt aerosol (e.g., Smirnov et al., 2003) and Asian
pollutant outflow. The trans-Pacific transported aerosols (other than
sea-salt) show strong monthly variation with a maximum in April and a minimum
in July. Over the western USA, the MISR and MODIS retrievals well capture the
monthly variation of AERONET retrievals with correlation coefficients of
Seasonal mean EAE from the MODIS retrievals and the corresponding WRF-Chem simulation averaged for the period 2010–2014 over the three sub-regions shown in Fig. 4. The vertical bars represent 10th and 90th percentile values, the filled dots represent the median values, and the triangles and circles represent the mean values.
The wavelength dependence of AOD that can be represented by the EAE is an indicator of aerosol particle size (Ångström, 1929; Schuster et al., 2006). In general, relatively small values of EAE indicate that aerosol size distributions are dominated by coarse aerosols typically associated with dust and sea-salt, while relatively large values of EAE indicate fine aerosols usually contributed by anthropogenic pollution and biomass burning. Figure 8 shows the seasonal mean EAE averaged for 2010–2014 from the MODIS retrievals and the WRF-Chem simulation over the three sub-regions. The retrievals show clearly that the seasonal median EAE values peak at 1.25, 0.74, and 0.89 in JJA and reach a minimum of 0.68, 0.20, and 0.21 in DJF in three sub-regions of the western, central, and eastern Pacific, respectively. This seasonality reflects the fact that photochemistry is most active in JJA to produce fine aerosol particles such as sulfate. In general, the simulation successfully reproduces the observed EAE seasonality with the JJA maximum of 1.09, 0.82, and 0.79 and the DJF minimum of 0.83, 0.42, and 0.35 in the three sub-regions, respectively. The retrievals and simulation also show that the values of EAE are greater in the western Pacific than in the central and eastern Pacific. This pattern may reflect the dominance of the Asian pollutant outflow on the aerosol size distributions over the western Pacific, while the relatively large-size particles of sea-salt dominates in the other two regions. Again, the annual variability of EAE over the three sub-regions is small (not shown).
Seasonal mean AAOD at 500 nm from the OMI retrievals and the corresponding WRF-Chem simulation averaged for the period 2010–2014 over the three sub-regions shown in Fig. 4. The values of bars represent the mean. The vertical lines represent 10th and 90th percentile values, and the black dots represent the median values.
Light absorbing aerosols such as BC and dust play an important role in the atmosphere to absorb radiation and change the heating profiles in the atmosphere. AAOD is an important parameter for evaluating the model performance in simulating light absorbing aerosols. Figure 9 shows the seasonal mean AAOD at 500 nm averaged for 2010–2014 and over the three sub-regions from the OMI retrieval and the WRF-Chem simulation. The model simulated AAOD at 600 and 400 nm are used to derive the AAOD at 500 nm (using the Ångström exponent). Both retrievals and simulation show small interannual variability (not shown). The simulated seasonal mean AAOD of 0.015 over the western Pacific agrees reasonably well with the OMI retrieval of 0.014 in DJF but is higher in the other three seasons, with the largest difference in JJA. The significantly lower AAOD in seasons other than DJF from the OMI retrieval is also shown in the comparison with the AERONET retrieval (to be discussed with Fig. 10). Over the central Pacific, the simulated seasonal mean AAOD of 0.014 and 0.006 in MAM and SON, respectively, generally reproduces the retrieved AAOD of 0.017 and 0.005, but the model overestimates (underestimates) the retrieved values in JJA (DJF) with 0.008 (0.005) from the simulation and 0.004 (0.009) from the retrieval. This difference may reflect the model deficiency in simulating Asian BC outflow over the Pacific in JJA and DJF, but may also result from retrieval uncertainties. The OMI retrievals may have difficulty in distinguishing the ocean color effects from those of low aerosol concentrations in the UV spectral range and ignoring the less-sufficient amounts of absorbing aerosols (Veihelmann et al., 2007; Torres et al., 2013). Jethva et al. (2014) found that the most important source of uncertainty in OMI AAOD is the effect of sub-pixel cloud contamination related to the sensor's coarse spatial resolution, which causes AAOD underestimations for cases of low aerosol load. Over the eastern Pacific, the simulated seasonal mean AAOD of 0.0035, 0.0091, 0.0048, and 0.0042 for DJF, MAM, JJA, and SON, respectively, are generally consistent with the retrieved values of 0.005, 0.007, 0.0012, and 0.003, which show the maximum value in MAM. The most significant difference occurs in JJA. Similar to over the central Pacific, the underestimation of retrieved AAOD over the clean region may contribute to the difference. The retrievals and simulation show large variability of AAOD, and they generally agree within the 10th and 90th percentiles of each other. AAOD is larger over the western Pacific than the central and eastern Pacific, which is consistent with the AOD pattern. The simulation shows that AAOD peaks in MAM followed by JJA over the three sub-regions, which may be due to the stronger outflow of dust and anthropogenic pollutants in the two seasons.
Monthly AAOD from the retrievals of AERONET and OMI and the corresponding WRF-Chem simulation averaged for the period 2010–2014 over the East Asia sites as shown in Fig. 1.
The AERONET retrieval products (version 2) also provide AAOD values but only at the sites and time when the total AOD exceeds a threshold value of 0.4 at 440 nm because the AERONET inversion algorithms require a high signal-to-noise ratio to retrieve some optical products such as AAOD. The total AOD values over the central Pacific and the western USA are less than this threshold value most of the time, and only AAOD values retrieved at the eastern Asian sites are available and reliable. Figure 10 shows the monthly variation of AAOD averaged at the AERONET sites over East Asia (Fig. 1) from the AERONET observation, OMI retrieval, and WRF-Chem simulation. The AERONET retrieval shows the monthly variation of AAOD over East Asia with relatively lower values in JJA probably due to wet removal of aerosols by precipitation and mixing with clean marine air during the eastern Asian summer monsoon (Zhao et al., 2010b). The simulation generally captures the observed monthly variability with the minimum AAOD of 0.035 and 0.032 in July from the simulation and the AERONET retrieval, respectively, and the maximum of 0.055 and 0.054 in October. The model overestimates AAOD in the warm months (May–September) with the mean values of 0.046 and 0.036 from the simulation and retrieval, respectively, and underestimates AAOD in December and January with the mean values of 0.037 and 0.043, respectively. The model positive (negative) biases in AAOD in the warm (cold) months may be partly related to the constant anthropogenic BC emissions applied throughout the seasons, but previous studies have shown that anthropogenic BC emissions over China may have seasonal variation, with roughly 6 vs. 13 % of the annual total BC emission in summer and winter, respectively, estimated in Lu et al. (2011). The simulation shows that AAOD over East Asia is dominated by BC and is partly contributed by dust. Other aerosols contribute to a small amount of AAOD due to the internal mixing of aerosols in the atmosphere (Zhao et al., 2013a). The OMI retrieved AAOD is lower than that from AERONET and WRF-Chem, particularly in JJA and SON. The lower OMI AAOD over East Asia may also indicate its negative biases over the western Pacific (Fig. 9), where the air is significantly affected by the eastern Asian outflow. The biases in the OMI algorithm of retrieving SSA over East Asia may be also applied over the western Pacific.
Column integrated properties of aerosol (e.g., AOD and AAOD) provide useful information with regard to atmospheric aerosol loading but little information on the vertical distribution of aerosols. Previous studies have found that simulated aerosol vertical distributions differ significantly, which can affect the assessments of aerosol impacts on climate and air quality (e.g., Schulz et al., 2006; Textor et al., 2006). CALIPSO with its unique capability provides an opportunity to assess model simulation of aerosol vertical distributions (e.g., Huang et al., 2013). Figure 11 shows the vertical distributions of annual mean aerosol extinction coefficients for 2010–2014 averaged over the three sub-regions from the CALIPSO retrieval and the corresponding WRF-Chem simulation under cloud-free conditions. The model results are sampled for averaging at the locations and times where and when retrievals are available. The CALIPSO retrieval shows clearly that aerosol extinction coefficients peak near several hundred meters above the surface and then decrease with the altitude over the three sub-regions. The extinction coefficients reduce from the western to eastern Pacific. The model generally reproduces the aerosol extinction vertical variation with correlation coefficients of 0.95–0.97. The simulated aerosol extinction coefficients are consistent with the retrievals around 0.5–1 km with difference within 15 %. The difference increases in the free troposphere and below 0.5 km. The simulation is higher than the retrieval in the free troposphere (e.g., about a factor of 2 around 4 km), which may be due to the reduced sensitivity of CALIPSO to tenuous aerosol layers above 4 km (Yu et al., 2010). The lower (up to 30 % lower) simulated extinction coefficients below 0.5 km in all three sub-regions may indicate negative biases in estimating marine aerosol emissions and excessive wet scavenging of the model, as shown in Fig. 4. The in situ measurements over the region are needed for further validating both remote sensing data and the simulation. The simulated mass fraction of each aerosol component (Fig. 12) shows that below 1 km, sea-salt dominates the total aerosol mass over the central and eastern Pacific, while the outflow of anthropogenic aerosols and dust also makes significant contributions over the western Pacific. Above 4 km, dust is the dominant aerosol over all three sub-regions.
Vertical distributions of annual mean extinction from the CALIPSO retrieval and the corresponding WRF-Chem simulation averaged for the period 2010–2014 over the three sub-regions shown in Fig. 4.
The seasonal variation of aerosol extinction profiles averaged for 2010–2014
(Fig. 13) shows the spring maximum, particularly above 2 km, over all three
sub-regions from both the CALIPSO retrievals and the model simulation. This
is likely due to the seasonality of dust outflow over the Pacific (Fig. 14)
that dominates the aerosol masses above 2 km with a peak in spring (e.g.,
Huang et al., 2013). The model reasonably reproduces the retrieved aerosol
extinction vertical variation through the seasons over the three sub-regions
with the correlation coefficients of 0.93–0.98. Over the western Pacific, the
simulation has larger negative biases (up to 35 %) below 1 km in DJF
when sea salt has a relatively larger contribution near the surface (Fig. 14)
than other seasons (up to 15–25 %), and has positive biases above 1 km.
At 1–4 km, the simulated aerosol extinction is higher (up to a factor of 2)
than the retrieval and the difference increases with the altitude. The
comparison between the simulation and retrieval at 1–4 km is the best in
DJF with the difference within 15 %. In JJA, the aerosol mass has the
largest contribution from the anthropogenic pollutant outflow among the
seasons with a peak at
Vertical distributions of annual mean aerosol mass (black solid
line; upper
Vertical distributions of seasonal mean aerosol extinction from the CALIPSO retrieval and the corresponding WRF-Chem simulation averaged for the period 2010–2014 over three sub-regions as shown in Fig. 4.
Vertical distributions of seasonal mean aerosol mass (black solid
line; upper
For lack of in situ observations of aerosol masses over the Pacific,
measurements of surface fine aerosol (PM
At both northwestern and southwestern sites, the model generally captures the
observed monthly variation of dust with the correlation coefficients of 0.61
and 0.55, respectively. Both the observation and simulation show the maximum
dust mass concentration in MAM and the minimum in DJF. The model simulates
higher annual mean surface dust concentrations (0.25 and
0.56
Daily mass concentrations of fine-mode (PM
A sensitivity simulation without dust, fire, and anthropogenic emissions over
North America (10–70
There is a significant difference in BC and OM surface concentrations between
the observations and simulation. At the northwestern sites, the observed BC
and OM show significant seasonal variation with the highest surface
concentration in JJAS. The sensitivity simulation shows that the peak is
dominated by the North American emission that is contributed by biomass
burning with a maximum in JJAS (Chin et al., 2007). The simulation captures
this seasonality to some extent with monthly correlation coefficients of 0.74
and 0.69 for BC and OM, respectively. However, the simulation significantly
underestimates the JJAS peak with 0.05 and 0.49
At the southwestern sites, the impact of biomass burning on the BC and OM
surface concentrations seems relatively small. The observations show the
maximum BC surface concentration of 0.17
A fully coupled meteorology-chemistry model (WRF-Chem) has been configured
to conduct quasi-global simulation for 5 years (2010–2014). The
simulation results are evaluated for the first time with various reanalysis
and observational data sets, including precipitation from GPCP, wind fields
from MERRA, AOD, EAE, and AAOD from MODIS, MISR, OMI, and AERONET, aerosol
extinction profiles from CALIPSO, and aerosol surface mass concentrations
from IMPROVE. In this study, the evaluation and analysis focus on the
trans-Pacific transport region for the purpose of demonstrating the
capability of using the quasi-global WRF-Chem simulation to provide
consistent lateral chemical boundaries for nested regional WRF-Chem
simulations that can be used to investigate the impact of trans-Pacific
transported aerosols on the regional air quality and climate over the
western USA. The main conclusion is summarized below:
The comparison of simulated AOD with the satellite and AERONET retrievals
reveals that the model can well capture the spatial gradient of aerosol mass
loading, decreasing from the western to eastern Pacific, resulting from the
sea-salt loading and the Asian pollutant outflow. The seasonal variation of
aerosols across the Pacific with the maximum AOD in MAM is also reproduced
by the model. The model underestimates AOD over the ocean to the south of
20 The assessment of simulated EAE indicates that the model generally captures
the observed smaller-size aerosols over the western Pacific contributed by the
Asian pollutant outflow compared to the relatively larger particles over the
central and eastern Pacific with more contributions from sea salt. The model
also simulates the consistent seasonality of EAE with observations showing a
minimum in DJF and a maximum in JJA due to the active production of small
particles in warm seasons. The model reasonably simulates the decreasing gradient of OMI derived AAOD
from the eastern to western of Pacific. The simulation shows a peak of AAOD in MAM
due to the strong outflow of dust and anthropogenic pollutants. The
comparison with AERONET retrieved AAOD over East Asia may indicate that the
OMI SSA retrieval has positive biases over East Asia and also the western
Pacific, particularly in JJA. Over East Asia, the model positive (negative)
biases in AAOD in the warm (cold) months may be partly due to the neglect of
the seasonal variability of anthropogenic BC emissions in this study. The model generally captures the CALIPSO retrieved vertical gradient of
aerosol extinction coefficients roughly decreasing with the altitude over the
Pacific. Near the surface, the model biases in estimating marine aerosol
emissions may contribute to the discrepancy between the simulation and
retrievals. The difference between the simulation and retrievals in the free
troposphere may be due to the reduced sensitivity of CALIPSO to the aerosol
layers above 4 km. The model well captures the seasonality of aerosol
extinction profiles with a maximum in MAM, which is largely controlled by the
activity of dust outflow events over the Pacific. Compared with the measurements from the IMPROVE sites over the western USA,
the model simulates reasonable magnitudes and seasonality of the observed
sulfate and nitrate surface concentrations with peaks in JJA and DJF,
respectively. The simulation has relatively larger positive biases of
nitrate surface concentrations in early spring and late fall, which may
reflect the model deficiency in aerosol thermodynamics that the partitioning
of nitrate aerosol to the gas phase in these months is too slow in the
model. The simulation captures the observed seasonality of dust surface
concentrations with the maximum and minimum in MAM and DJF, respectively,
but generally overestimates the observed dust surface concentrations, which
may be due to the excessive trans-Pacific dust. The difference may also be
partly from the observation uncertainties. Over the southwestern USA, the
simulation reproduces the magnitude and seasonality of surface BC
concentrations that show the maximum in DJF, but significant underestimates
the surface OM concentrations in JJA likely due to the negative biases in
SOA production. Over the northwestern USA, the simulation significantly
underestimates surface BC and OM concentrations likely due to the
uncertainties in fire emissions that may not capture the strong local fire
events. Another source of the difference may be due to the discrepancy in
spatial scales between site observations and model outputs for the grid cell
area of 1-degree resolution. In addition, uncertainties in IMPROVE may
also contribute to the discrepancy, in particular for carbonaceous aerosols
that are inferred from optical/thermal measurements. The sensitivity simulation shows that the trans-Pacific transported dust
dominates the dust surface concentrations in the western USA, particularly
in MAM. The trans-Pacific transported sulfate and nitrate can also make
significant contribution to their surface concentrations over the rural
areas of the western USA. The peaks of BC and OM surface concentrations over
the western USA are dominated by the North American emissions. These
sensitivity simulation results may be different to some extent from other
models (e.g., Chin et al., 2007), which could result from the considerable
differences in aerosol composition and vertical distributions due to
differences in model treatments of emissions and removal processes as
revealed by several inter-comparison studies (Barrie et al., 2001; Penner et
al., 2002; Textor et al., 2006). More detailed model inter-comparison of the
trans-Pacific transport of aerosols deserves further study.
Although dust and biomass burning emissions in general have considerable year-to-year variations, the interannual variability of seasonal AOD for 2010–2014 average over the three sub-regions of the Pacific is small as indicated by the retrievals and simulation. It is noteworthy that the trans-Pacific aerosols identified in this study include not only the outflow of Asian pollutants and dust but also European pollutants and African dust that are transported to Asia and then merged with the Asian outflow. This has been recognized by previous studies (e.g., Chin et al., 2007). The evaluation in this study successfully demonstrates that the WRF-Chem quasi-global simulation with some improvements in emission inventories can be used for studying trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA to further understand the impact of transported pollutants on the air quality and regional climate with high-resolution nested regional modeling. It needs to be noted that the aerosol optical properties, such as AOD, AAOD, and EAE, derived from the retrievals and simulation have some different assumptions of the physical and optical parameters; therefore, the link between the model and the satellite data are only qualitative or semi-quantitative. Evaluation of model results with in situ observations would be informative. In situ data even for specific events are valuable especially over Asia and the Pacific where public data are currently sparse or inaccessible, although some observations may be obtained through collaborations. Last but not least, the model biases against observations may also be partly contributed by the uncertainties in emissions. Some recently updated anthropogenic emissions (e.g., Janssens-Maenhout et al., 2015; Li et al., 2015) and other biomass burning emissions with higher temporal and spatial resolutions (e.g., Wiedinmyer et al., 2011) may be used in future studies to investigate the impact of emission uncertainties on trans-Pacific aerosols over the western USA.
The WRF-Chem version 3.5.1 release can be obtained at
The RETRO global anthropogenic emission inventory (Schultz et al., 2007) can
be obtained through the link below:
This research was supported by the Office of Science of the U.S. Department of Energy (DOE) as part of the Regional & Global Climate Modeling (RGCM) program. Jianping Huang acknowledges support from the National Basic Research Program of China (2012CB955301). Hongbin Yu was supported by NASA CALIPSO project (NNX14AB21G) managed by David Considine. This study used computing resources from the PNNL Institutional Computing. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute for the DOE under contract DE-AC05-76RL01830. The CALIPSO data were obtained from the NASA Langley Research Center Atmospheric Sciences Data Center. MODIS and MISR data were obtained from the NASA Atmospheric Science Data Center. OMI data were obtained from the NASA Goddard Earth Sciences Data and Information Services Center. Edited by: J. Williams