ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-7291-2017WRF-Chem simulation of aerosol seasonal variability in the San Joaquin ValleyWuLongtaolongtao.wu@jpl.nasa.govSuHuiKalashnikovaOlga V.JiangJonathan H.https://orcid.org/0000-0002-5929-8951ZhaoChunGarayMichael J.CampbellJames R.https://orcid.org/0000-0003-0251-4550YuNanpengJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USASchool of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, ChinaNaval Research Laboratory, Monterey, CA, USAUniversity of California, Riverside, Riverside, CA, USALongtao Wu (longtao.wu@jpl.nasa.gov)17June20171712729173094November201629November201620April20177May2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/7291/2017/acp-17-7291-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/7291/2017/acp-17-7291-2017.pdf
WRF-Chem simulations of aerosol seasonal variability in the San Joaquin
Valley (SJV), California, are evaluated by satellite and in situ
observations. Results show that the WRF-Chem model successfully captures the
distribution and magnitude of and variation in SJV aerosols during the cold
season. However, aerosols are not well represented in the warm season.
Aerosol simulations in urban areas during the cold season are sensitive to
model horizontal resolution, with better simulations at 4 km resolution than
at 20 km resolution, mainly due to inhomogeneous distribution of
anthropogenic emissions and precipitation that is represented better in the 4 km
simulation. In rural areas, the model sensitivity to grid size is rather
small. Our observational analysis reveals that dust is a primary contributor
to aerosols in the SJV, especially during the warm season. Aerosol
simulations in the warm season are sensitive to the parameterization of dust
emission in WRF-Chem. The GOCART (Goddard Global Ozone Chemistry Aerosol
Radiation and Transport) dust scheme produces very little dust in the SJV,
while the DUSTRAN (DUST TRANsport model) scheme overestimates dust emission.
Vertical mixing of aerosols is not adequately represented in the model based
on CALIPSO (Cloud-Aerosol Lidar and Infrared pathfinder Satellite
Observation) aerosol extinction profiles. Improved representation of dust
emission and vertical mixing in the boundary layer is needed for better
simulations of aerosols during the warm season in the SJV.
Introduction
The San Joaquin Valley (SJV) in the southern portion of the
California Central Valley is surrounded by a coastal mountain range to the west
and the Sierra Nevada range to the east. With cool wet winters and hot dry
summers, the unique natural environment makes SJV one of the most productive
agricultural regions in the world (SJV APCD, 2012 and references therein).
However, SJV is also one of the most polluted regions in the US due to its unique
geographical location. Frequent stagnant weather systems are conducive to air
pollution formation, while the surrounding mountains block air flow and trap
pollution. Large seasonal and spatial variation in aerosol occurrence and
distribution is observed in the SJV. Although significant progress in
improving local air quality in past decades has been achieved through strong
emission controls, PM2.5 (particulate matter with
a diameter ≤ 2.5 µm) concentrations in the SJV remain well
above the National Ambient Air Quality Standards (NAAQS) threshold of
12 µgm-3 on an annual basis and 35 µgm-3 on
a daily basis, occurring mainly during the cold season. Improved understanding
of the aerosol variability and impacts is needed to provide further guidance
for emission control strategies in the SJV.
Air quality models are a useful tool for understanding the formation and
evolution of aerosols and their impacts on air quality, weather and climate.
However, it is quite challenging to accurately simulate aerosol properties
(Fast et al., 2014). Fast et al. (2014) summarized the factors contributing
to the errors in regional-scale modeling of aerosol properties. They include
(1) emission sources, (2) meteorological parameterizations,
(3) representation of aerosol chemistry, (4) limited understanding of the
formation processes of secondary organic aerosol (SOA), (5) spatial
resolution and (6) boundary conditions.
As one of the advanced regional air quality models presently available to the
community, the Weather Research and Forecasting model with Chemistry
(WRF-Chem) has been widely used to study aerosols and their impacts on
regional air quality, weather and climate (e.g., Misenis and Zhang, 2010;
Zhang et al., 2010; Zhao et al., 2010, 2013a, b, 2014; Gao et al., 2011; Wu
et al., 2011a, b, 2013; Fast et al., 2012, 2014; Scarino et al., 2014; Tessum
et al., 2015; Campbell et al., 2016; Hu et al., 2016). For example, Fast et
al. (2014) showed that WRF-Chem simulations at 4 km horizontal resolution
captured the observed meteorology and boundary layer structure over
California in May and June of 2010 and the spatial and temporal variations in
aerosols were reasonably simulated. Aerosol simulations by WRF-Chem are
usually sensitive to both local emission and long-range transport of aerosols
from the boundary conditions provided by the global Model for Ozone and
Related Chemical Tracers, version 4 (MOZART-4). With a similar model set-up,
Zhao et al. (2013b) conducted a 1-year simulation at 12 km horizontal
resolution and found that the WRF-Chem model represented the observed
seasonal and spatial variation in surface particulate matter (PM)
concentration over California. However, underestimation of elemental carbon
(EC) and organic matter (OM) was noticed in the model simulation, with weak
sensitivity to horizontal resolution.
In this study, we focus on simulating aerosol seasonal variability in the
SJV, California, using similar model configurations to those used in Zhao et
al. (2013b) and Fast et al. (2014). This paper serves as the first step for
future investigation of the aerosol impact on regional climate and the water
cycle in California. Previous studies have demonstrated that aerosols are
better simulated at higher model resolution (Misenis and Zhang et al., 2010;
Qian et al., 2010; Stround et al., 2011; Fountoukis et al., 2013). However,
most regional climate studies are still performed with coarse model
resolutions (of the order of 10 km) due to the availability of
computational resources. This study will investigate the sensitivity of
aerosol simulations to horizontal resolution and identify optimal model
physical choices for reasonable representation of aerosol variabilities in
the SJV.
Another application of air quality modeling is to provide initial a priori
fields for remote sensing retrievals. The WRF-Chem model has been proposed as
an input for retrieval algorithms to be developed for the recently selected
NASA MAIA (Multi-Angle Imager
for Aerosols) mission, which aims to map PM component concentrations in major
urban areas (including the SJV, a test bed for the MAIA retrieval algorithm
development). A significant challenge for aerosol remote sensing in
retrieving spatial information on specific aerosol types, especially near the
surface, is caused by the lack of information on the vertical distribution of
aerosols in the atmospheric column and limited instrument sensitivity to
aerosol types over land. The WRF-Chem model will be used to provide
near-real-time estimation of particle properties, aerosol layer heights and
aerosol optical depths (AODs) to constrain the instrument-based PM retrievals.
A reasonable estimate of aerosol properties from WRF-Chem is critical to
ensuring retrieval speed and quality. Considering the sensitivity of WRF-Chem
simulations to various factors such as initial and boundary conditions, model
parameterizations and emission sources (e.g., Wu and Petty, 2010; Zhao et
al., 2010, 2013a, b; Wu et al., 2011a, 2015; Fast et al., 2014; Campbell et
al., 2016; Morabito et al., 2016), careful model evaluations are needed
before the simulations can be used operationally for remote sensing
retrievals. Thus, this study is important for the development of MAIA
retrieval algorithms, which are critical to the success of the MAIA mission.
This paper is organized as follows. Section 2 describes observational
datasets used for model evaluation. Section 3 provides the description of the
WRF-Chem model and experiment setup. Model simulations and their comparison
with observations are discussed in Sect. 4. Section 5 presents the
conclusions.
AOD is a measure of column-integrated light extinction by aerosols and a
proxy for total aerosol loading in the atmospheric column. The Aerosol
Robotic Network (AERONET) provides ground measurements of AOD every
15 min during daytime under clear skies (Holben et al., 1998), with
an accuracy approaching ±0.01 (Eck et al., 1999; Holben et al., 2001;
Chew et al., 2011). The monthly level 2.0 AOD product with cloud screening
and quality control is used in this study. The Ångström exponent (AE) is
an indicator of aerosol particle size. Small (large) AE values are generally
associated with large (small) aerosol particles (Ångström, 1929;
Schuster et al., 2006). The AE between 0.4 and 0.6 µm is derived
from AERONET observed AODs and is used to evaluate the model-simulated AE.
For comparison with simulated AOD, AERONET AOD is interpolated to
0.55 µm from 0.50 to 0.675 µm using the AE. In the
SJV, only one AERONET station at Fresno, California (36.79∘ N,
119.77∘ W), has regular observations throughout the California water
year of 2013 (WY2013) from October 2012 to September 2013.
Daily mean anthropogenic PM2.5 emission rate
(µgm-2h-1) in (a) 20km and (b) 4km
simulations. Domain-averaged emission rate is shown at the top right of each
panel. Red dashed lines in (a) represent the region used for the
domain averages in the discussions. Yellow circle: IMPROVE site; yellow
diamond: EPA CSN site. Three urban sites: Fresno, Bakersfield and Modesto;
two rural sites: Pinnacles and Kaiser.
The Multiangle Imaging Spectroradiometer (MISR) (Diner et al., 1998)
instrument onboard the Terra satellite has provided global coverage of AOD
once a week since December 1999. The standard MISR retrieval algorithm
provides AOD observations at 17.6 km resolution using 16×16 pixels
of 1.1km×1.1km each. About 70 % of MISR AOD
retrievals are within 20 % of the paired AERONET AOD, and about 50 %
of MISR AOD falls within 10 % of the AERONET AOD, except in dusty and
hybrid (smoke and dust) sites (Kahn et al., 2010). We use version 22 of
the Level 3 monthly AOD product at 0.5∘ resolution in this study.
Surface mass concentration
Surface PM2.5 speciation and PM10 (particulate matter with
a diameter ≤ 10 µm) data are routinely collected by two
national chemical speciation monitoring networks: the Interagency Monitoring of
Protected Visual Environments (IMPROVE) and the PM2.5 National Chemical
Speciation Network (CSN) operated by the Environmental Protection Agency (EPA)
(Hand et al., 2011; Solomon et al., 2014). IMPROVE has collected 24 h aerosol
speciation every third day at mostly rural sites since 1988. The same
frequency of aerosol speciation dataset has been collected at EPA CSN sites in
urban and suburban areas since 2000. The observed organic carbon is converted
to OM by multiplying by 1.4 (Zhao et al., 2013b; Hu et al., 2016). Some
precursors of aerosol pollution (such as NO2 and SO2) are
observed hourly by the EPA (data available at
https://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html) and
are used in this study. Selected IMPROVE and EPA CSN sites used in this study
are shown in Fig. 1a.
Aerosol extinction profile
The aerosol extinction coefficient profile reflects the attenuation of the
light passing through the atmosphere due to the scattering and absorption by
aerosol particles as a function of range. Version 3 Level 2 532 nm aerosol
extinction profiles derived from Cloud-Aerosol Lidar with Orthogonal
Polarization (CALIOP) backscatter profiles collected onboard the
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)
satellite are used (Omar et al., 2009; Young and Vaughan, 2009). Seasonal
mean profiles are derived for WY2013 based on the methodology outlined in
Campbell et al. (2012), whereby quality-assurance protocols are applied to
individual profiles before aggregating and averaging the data. We highlight
that no individual profiles are included in the averages if the CALIOP
Level 2 retrieval failed to resolve any extinction within the column, a
potential issue for creating bias that has recently been described by Toth et
al. (2017). Level 2 532 nm aerosol extinction data classify aerosols into
six types: clean marine, dust, polluted continental, clean continental,
polluted dust and smoke. Dust and polluted dust are distinguished in the
averages in this study for their seasonal contribution to total extinction and the
vertical profile in the SJV.
Meteorology
AIRS (Atmospheric Infrared Sounder) onboard the Aqua satellite (Susskind et
al., 2003; Divakarla et al., 2006) has provided global coverage of the
tropospheric temperature and moisture at approximately 01:30 and 13:30 local
time since 2002. AIRS retrievals have a RMSE of
∼ 1 K for temperature and ∼ 15 % for water vapor
(Divakarla et al., 2006). Level 3 monthly temperature and moisture retrievals
(version 6) on a 1∘×1∘ grid are used in this study.
The vertical gradient of equivalent potential temperature (θe)
marks atmospheric stability and is computed from temperature and moisture
profiles observed by AIRS. Vertical profiles from the European Center for
Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim; Dee et
al., 2011) are also used for comparison. Surface observations, including air
temperature, relative humidity (RH) and wind speed, are routinely collected
at the California Irrigation Management Information System (CIMIS;
http://www.cimis.water.ca.gov/). Precipitation used in this study is
the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Daily
Precipitation product at 0.25∘×0.25∘ resolution.
Model description and experiment setup
The WRF-Chem model Version 3.5.1 (Grell et al., 2005) updated by the Pacific
Northwest National Laboratory (PNNL) is used in this study (Zhao et
al., 2014). This study uses the CBM-Z (carbon bond mechanism) photochemical
mechanism (Zaveri and Peters, 1999) coupled with the sectional-bin MOSAIC
(Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme
(Zaveri et al., 2008) as the chemical driver. The major components of
aerosols (nitrate, ammonium, EC, primary OM, sulfate, sea salt, dust, water
and other inorganic matter) as well as their physical and chemical processes
are simulated in the model. For computational efficiency, aerosol particles
in this study are partitioned into four sectional bins with a dry diameter
within 0.039–0.156, 0.156–0.625, 0.625–2.5 and 2.5–10.0 µm.
Zhao et al. (2013a) compared the impact of aerosol size partition on dust
simulations. It showed that the four-bin approach reasonably produces dust mass
loading and AOD compared with the eight-bin approach. The size distribution of
the four-bin approach follows that of the eight-bin approach with coarser
resolution, resulting in ±5 % difference in the ratio of
PM2.5 dust / PM10 dust in dusty regions (more large particles
and less small particles). Dust number loading and absorptivity are biased
high in the four-bin approach compared with the eight-bin approach.
Aerosols are considered to be spherical and internally mixed in each bin
(Barnard et al., 2006; Zhao et al., 2013b). The bulk refractive index for
each particle is calculated by volume averaging in each bin. Mie calculations
as described by Ghan et al. (2001) are used to derive aerosol optical
properties (such as extinction, single-scattering albedo and the asymmetry
parameter for scattering) as a function of wavelength. Aerosol radiation
interaction is included in the shortwave and longwave radiation schemes (Fast
et al., 2006; Zhao et al., 2011). By linking simulated cloud droplet number
with shortwave radiation and microphysics schemes, aerosol cloud interaction
is effectively simulated in WRF-Chem (Chapman et al., 2009). Aerosol snow
interaction is implemented in this version of WRF-Chem (Zhao et al., 2014) by
considering aerosol deposition on snow and the subsequent radiative impacts
through the SNICAR (SNow, ICe, and Aerosol Radiative) model (Flanner and
Zender, 2005, 2006).
The model simulations start on 1 September 2012 and run continuously for
13 months. With the first month used for the model spin-up, our analysis
focuses on WY2013 from October 2012 to September 2013. The model is
configured with 40 vertical levels and a model top at 50 hPa. The
vertical resolution from the surface to 1 km gradually increases from
28 to 250 m. The model center is placed at 38∘ N,
121∘ W, with 250×350 grid points at 4 km horizontal
resolution (referred to as “4km” hereafter; Table 1), covering California
and the surrounding area. To test the sensitivity of the aerosol simulations
to horizontal resolution, one simulation with the same model settings and
domain coverage is conducted at 20 km horizontal resolution (referred to as
“20km” hereafter).
The physics parameterizations used in the simulations include the Morrison
double-moment microphysics scheme (Morrison et al., 2009), Rapid Radiative
Transfer Model for general circulation models (RRTMG) model shortwave and
longwave radiation schemes (Iacono et al., 2008), and the Community Land
Model (CLM) Version 4 land surface scheme (Lawrence et al., 2011). The Yonsei
University (YSU) planetary boundary layer (PBL) scheme (Hong et al., 2006) is
used in all of the simulations, except one sensitivity experiment that uses
the ACM2 (Asymmetric Convective Model with non-local upward mixing and local
downward mixing; Pleim, 2007) PBL scheme (referred to as “20km_P7”
hereafter, Table 1). Previous studies showed that both the YSU and ACM2
schemes have good performance in simulating boundary layer properties (e.g.,
Hu et al., 2010; Xie et al., 2012; Cuchiara et al., 2014; Banks and
Baldasano, 2016; Banks et al., 2016; Chen et al., 2017). Sub-grid convection,
convective transport of chemical constituents and aerosols, and wet
deposition from sub-grid convection are parameterized using the Grell 3-D
ensemble cumulus scheme (Grell and Devenyi, 2002) in the 20 km simulations,
while convective processes are resolved in the 4 km simulations. The
ERA-Interim reanalysis serves as the initial and boundary meteorological
conditions for WRF-Chem. The MOZART-4 global chemical transport model (Emmons
et al., 2010) is used for initial and boundary chemical conditions. Fast et
al. (2014) found that the MOZART-4 model overestimates aerosols in the free
troposphere over California, which is also found in one of our sensitivity
experiments (“20km_BC1” in the Supplement). Following Fast et
al. (2014), the chemical initial and boundary conditions from MOZART-4 are
divided by 2 in all simulations except 20km_BC1.
Experiment description.
Experiment IDExperiment description20kmSimulation with the GOCART dust scheme at 20 km horizontal resolution.20km_D2Same as 20km, but with the DUSTRAN dust scheme.20km_P7Same as 20km_D2, but with the ACM2 PBL scheme.4kmSame as 20km, but at 4 km horizontal resolution.4km_D2Same as 4km, but with the DUSTRAN dust scheme.
Anthropogenic emissions are provided by US EPA 2005 National Emissions
Inventory (NEI05), with area-type emissions on a structured 4 km grid and
point-type emissions at specific latitude and longitude locations (US EPA,
2010). Aerosol emissions include SO4, NO3, EC, organic
aerosols, and total PM2.5 and PM10 masses, and 19 gases (including
SO2, NO, NH3, etc.) are emitted. Anthropogenic
emissions are updated every hour to account for diurnal variability, while
their seasonal variation is not considered in the simulations. A sensitivity
experiment with 2011 NEI emissions (“20km_NEI11” in the Supplement) did
not produce significantly different results from the 2005 NEI emissions.
Biogenic emissions are calculated online using the Model of Emissions of
Gases and Aerosols from Nature (MEGAN) (Guenther et al., 2006). Biomass
burning emissions are obtained from the Global Fire Emissions Database
version 2.1, with 8-day temporal resolution (Randerson et al., 2007) and
monthly updates. Sea salt emissions are derived from the PNNL-updated sea
salt emission scheme that includes the correction of particles with a radius
less than 0.2 µm (Gong, 2003) and dependence on sea surface
temperature (Jaeglé et al., 2011).
Following Zhao et al. (2013b), dust emission is computed from the GOCART
(Goddard Global Ozone Chemistry Aerosol Radiation and Transport) dust scheme
(Ginoux et al., 2001) in the 20 km and 4 km simulations. The GOCART dust
scheme estimates the dust emission flux F as
F=CSspu10m2u10m-ut,
where C is an empirical proportionality constant, S is a source function
for potential wind erosion that is derived from the 1∘×1∘
GOCART database (Freitas et al., 2011), sp is a fraction of each
size class dust in emission, u10m is 10 m wind speed and
ut is a threshold speed for dust emission.
As shown later, a significant amount of dust is observed in the SJV, whereas
the GOCART dust scheme produces little dust. Two sensitivity experiments at
20 km and 4 km horizontal resolution (hereafter referred to as
“20km_D2” and “4km_D2”, respectively) are conducted by switching
the dust emission scheme to the DUST TRANsport model (DUSTRAN) scheme (Shaw
et al., 2008). The DUSTRAN scheme estimates F as
F=αCu*41-fwu*tu*,
where C is an empirical proportionality constant, α is the
vegetation mask, u* is the friction velocity, u*t is a
threshold friction velocity and fw is the soil wetness factor.
The C value in both GOCART and DUSTRAN is highly tunable for different
regions. The original C values, 1.0 µgs2m-5 in GOCART
(Ginoux et al., 2001) and 1.0×10-14gcm-6s-3 in
DUSTRAN (Shaw et al., 2008), are used in this study.
Model simulation results
Shown in Fig. 1a, our model domain includes three urban sites (Fresno,
Bakersfield and Modesto) and two rural sites (Pinnacles and Kaiser) where
surface measurements of aerosols are available. Because aerosol properties
and model performance are similar at all urban sites, our discussion is
focused on the results at Fresno and the simulations for other urban sites
are provided in the Supplement. Model simulations in the rural areas are
presented in the last subsection.
Sensitivity to horizontal resolution
Figure 1 features daily mean anthropogenic PM2.5 emission rates used in
the 20km and 4km simulations, respectively. Although both emission rates
are derived from the 4km NEI05 dataset, localized high emission rates with
sharp gradients are evident in urban areas from the 4km simulation
(Fig. 1b). The 20km simulation exhibits lower emission rates in the urban
areas with weaker gradients due to the reapportionment process (Fig. 1a). As
precipitation is an important process that removes aerosols, we examine the
simulated precipitation for the 20km and 4km runs and find that the
20km simulation produces 51 % more precipitation, although the domain-averaged precipitation is lower in the 20km run than the 4km run
(Fig. 2a).
Consistent with higher emission rates and lower precipitation at Fresno, the
4km run simulates higher AOD than the 20km run in the cold season
(October–November–December and January–February–March; OND and JFM in
Fig. 3). Averaged over a broad area encompassing Fresno and Bakersfield, the
most polluted region in the SJV (red box in Fig. 1a), the AOD is 0.090 in the
4km simulation and 0.073 in the 20km simulation, a 23 % difference. Compared to the MISR
observations, the 4 km simulation reproduces the spatial distribution and
magnitude of AOD in the cold season. However, the AOD difference between the
20 km and 4 km runs is small in the warm season (April–May–June and
July–August–September; AMJ and JAS in Fig. 3), and both runs underestimate
AOD by ∼ 50 % with respect to the MISR observations.
(a) Monthly precipitation (mmday-1) from CPC,
20km and 4km; (b) monthly wind speed (ms-1) from CIMIS,
20km and 4km. 4km_D2 (not shown) is similar to 4km.
Spatial distribution of seasonal mean 550 nm AOD from MISR and the
WRF-Chem (20km and 4km) simulations in WY2013. OND:
October–November–December; JFM: January–February–March; AMJ:
April–May–June; JAS: July–August–September.
Comparing the point values at Fresno in the 4km and 20km simulations
(Fig. 4a), we find similar results: the 4km AOD is closer to the AERONET
measurements and is about 23 % higher than that in the 20km run during
the cold season, while both runs are biased low in AOD during the warm
season. The different model sensitivities to horizontal resolution between
the cold and warm seasons suggest that the dominant aerosol sources may be
different for the two seasons. We will elaborate upon the aerosol composition
in the following section. MISR and AERONET observations display weak seasonal
AOD variation in the SJV and at Fresno, respectively, which is not well
represented in the 20km and 4km simulations (Figs. 3 and 4a).
(a) Monthly mean 550 nm AOD; (b) monthly mean 400–600 nm
Ångström exponent at Fresno, CA, from October 2012 to September
2013.
Aerosol mass (µgm-3) for different species from
observations and the 20km and 4km simulations at Fresno, CA. NH4
observations are from the EPA; other observations are from IMPROVE.
PM2.5_NO3 represents NO3 with a
diameter ≤ 2.5 µm. Similar definition for NH4, EC,
OM and SO4 in the figures.
Aside from AOD, significant seasonal variability in AE (Fig. 4b) is shown at
Fresno. AE exhibits a maximum of about 1.50 in January and a minimum of 0.98 in
April, suggesting relatively small particles in the winter and large
particles in the spring. A relatively large AE value of 1.40 (corresponding
to small particles) is observed in July, possibly related to the wild fires
in late July in the SJV. WRF-Chem captures the seasonal variability in the AE
well, with a correlation of 0.90 in both the 20km and 4km simulations.
The magnitude of AE is also approximately simulated in the cold season, with
a mean of 1.15 (1.20) in the 20km (4km) run compared to 1.33 in the
observation. However, the simulated AE is underestimated by ∼ 30 %
in the warm season, indicating that the simulated particle size is biased
high during this period.
Significant seasonal variability in PM2.5 is observed in the SJV urban
areas (Figs. 5a and S4a and S5a). PM2.5 at Fresno peaks in January
(26.18 µgm-3) and reaches a minimum of
7.03 µgm-3 in June, with an annual non-attainment value of
12.64 µgm-3 (Fig. 5a). Both the 20km and 4km runs
approximately capture the observed seasonal variability in PM2.5, with a
correlation around 0.90 (Table 2). In the cold season, the 4km simulation
overestimates PM2.5 by 27 % while the 20km simulation exhibits a
low bias of 19 % compared with IMPROVE observations at Fresno (Table 3).
The 4km simulation of PM10 is in good agreement with IMPROVE in the
winter (December, January and February), but has significant low biases of
between 30 and 85 % in other months (Fig. 5b). The 20km simulation
underestimates PM10 throughout WY2013.
PM2.5 is a mixture of nitrate (NO3), ammonia (NH4), OM,
EC, sulfate (SO4), dust and other aerosols. High concentrations of
PM2.5 are primarily the result of NO3 at Fresno (Fig. 5c). Both
simulations produce the seasonal variability in NO3 with a
correlation of 0.94, but a high bias of 17 % (75 %) is found in the
20km (4km) simulation during the cold season. As one precursor of
NO3, NO2 is underestimated by 43 % in the 20km run
(Fig. 6a). The overestimation in NO3 and underestimation in
NO2 suggest that the precursor emissions may not be the reason for the
high biases in NO3. NH4 shows a similar performance to
NO3, with an overestimation by 38 % (111 %) in the 20km
(4km) run during the cold seasons (Fig. 5d). As shown later in Sect. 4.3,
both NO3 and NH4 simulations are quite sensitive to the PBL
scheme applied.
Correlation with observations for different species at Fresno, CA.
OM, the second largest species contributing to cold season PM2.5 in the
SJV (Table 3), is significantly underestimated by 82 % in the 20km
simulation (Fig. 5f). The 4km simulation produces higher OM, but it is
still lower than the IMPROVE observations by 63 %. The underestimation of
OM is expected because SOA processes are not included in our model
infrastructure. Fast et al. (2014) used the simplified two-product volatility
basis set parameterization to simulate equilibrium SOA partitioning in
WRF-Chem although SOA was still underestimated in their simulation. Research on how to correctly represent SOA processes in regional
climate models remains ongoing.
Surface aerosol mass (µgm-3) for different species
at Fresno, CA.
SpeciesCold season Warm season OBS20km4km4km_D220km_D220km_P7OBS20km4km4km_D220km_D220km_P7PM2.516.8413.7121.3822.4814.9013.778.444.916.2912.8510.1214.85PM2.5_NO35.436.369.549.226.223.160.840.550.690.790.660.57PM2.5_NH41.421.972.992.881.910.980.400.190.240.200.160.13PM2.5_OM5.390.922.072.070.931.042.470.490.870.870.500.55PM2.5_EC1.080.521.121.130.520.580.320.270.490.490.270.30PM2.5_SO40.870.530.820.810.530.461.040.540.610.600.530.49PM2.5_dust0.900.110.111.651.504.182.080.040.036.495.1610.05PM1031.5514.9322.8128.3220.1024.5234.827.088.6938.1230.1948.02
Both the 20km and 4km simulations reproduce the seasonal variability in EC,
with a correlation of 0.98 between the modeled and observed time series
(Table 2). The 20km simulation underestimates EC by 52 % (16 %) in
the cold (warm) season (Fig. 5e and Table 3). The 4km simulated EC
(1.12 µgm-3) exhibits good agreement with IMPROVE
(1.08 µgm-3) in the cold season, but overestimates EC by
53 % in the warm season.
The seasonal variability in SO4 at Fresno is very different from
other PM2.5 species. It peaks in May at 1.35 µgm-3 and
reaches the minimum of 0.67 µgm-3 in August (Fig. 5g). The
20km simulated SO4 exhibits good correlation of 0.63 with the
observation (Table 2), but is biased low by 28 to 63 % throughout WY2013
(Fig. 5g). Although the observed SO2, the precursor of SO4, has
approximately similar seasonal variation to the observed SO4
(Fig. 6b), the 20km simulated seasonal variability in SO2 resembles
other anthropogenic emissions, with high values in the cold season and low
values in the warm season, out of phase with the simulated SO4 and
the observed SO2. The 4km simulation produces higher SO4
than the 20km run, resulting in better agreement with the observation
(0.82 µgm-3 vs. 0.87 µgm-3) during the cold
season (Fig. 5g and Table 3). However, the 4km run produces an increase in
SO4 by only 13 % comparing to the 20km run in the warm season,
resulting in a correlation of -0.16 between the 4km simulation and the
observation.
(a)NO2 and (b)SO2 from the EPA (OBS)
and the 20km run at Fresno, CA.
To explore the possible cause for the underestimation of SO4 and
SO2 in the warm season in both the 20km and 4km simulations, we
conduct a sensitivity experiment with different chemical boundary conditions
from the baseline runs (20km_BC1 in the Supplement). We find that
SO4 in the SJV is partly contributed to by marine intrusions (the
different chemical boundary conditions between 20km_BC1 and 20km_D2)
throughout the year (Fig. S2g), as pointed out by Fast et al. (2014).
Including the marine intrusions, the 20km_BC1-simulated SO4 tracks
the observation at a correlation of 0.78. Doubled chemical boundary
conditions in the 20km simulation result in a 41 % increase in
SO4 at Fresno, with a stronger increase in the warm season. Compared
to the observed SO4 of 1.04 µgm-3 in the warm
season, the simulated SO4 of 0.79 µgm-3 in the run
is closer to the observation than that simulated in the 20km_D2 run
(0.53 µgm-3). The relative contributions of local emissions
and remote transports (as well as other emission sources, such as wild fires)
to SO4 concentrations in different seasons of the SJV require further
investigation.
(a) PM2.5_dust; (b) PM2.5 and
(c) PM10 from IMPROVE and the 4km and 4km_D2 simulations at
Fresno, CA.
Overall, the 4km simulation produces higher AOD and surface PM than the
20km simulation in urban areas of the SJV, especially during the cold
season, resulting in better agreement with satellite and surface observations
than the 20km simulation. Both the 20km and 4km simulations approximately
capture the seasonal variability in PM2.5 and most of its speciation.
However, significant low biases of AOD and PM10 are found during the
warm season in both simulations. The underestimation also exists in a
sensitivity experiment (not shown) with the same model setups except
initialized in April, indicating that the identified model biases during the
warm season are not caused by potential model drift after a relatively long
simulation period. The relatively good performance in simulating PM2.5
but not PM10 during the warm season suggests that coarse aerosol
particle mass (CM; 10 µm≥ particulate matter with
a diameter > 2.5 µm), mainly dust in the SJV, is not properly
represented in the model. The impact of dust parameterizations is
investigated in the 4km_D2 experiment.
Sensitivity to dust scheme
Limited amounts of PM2.5_dust (dust with
a diameter ≤ 2.5 µm) are observed in the SJV cold season,
with a minimum of 0.37 µgm-3 in December (Fig. 7a). The
amount of PM2.5_dust increases in the warm season, with a peak of
3.86 µgm-3 in September. The 4km simulation produces
comparable PM2.5_dust relative to IMPROVE in the winter, but almost
no dust in other months (Fig. 7 and upper panel in Fig. 8). Conversely, the dust emission rate in the 4km_D2 run is significantly higher
than the 4km run. We have found that the source function, S, for
potential wind erosion in the SJV is set to zero in the 1∘×1∘ GOCART dataset used for the 4km simulation (Fig. 9). An updated
source function, S, at higher resolution is needed for the GOCART dust
scheme to correctly represent dust emissions in the SJV.
The 4km_D2 simulation reproduces the amount of PM2.5_dust in OND
(Fig. 7a). However, it overestimates PM2.5_dust by up to a factor of
3 in the warm season, resulting in an overestimation of PM2.5 by
52 % (Fig. 7b and Table 3). PM2.5_dust is not sensitive to
long-range transport (from chemical boundary conditions in the model
simulation; Fig. S2h). Both the 4km and 4km_D2 simulations capture the
seasonal variability in PM2.5, but not that in PM10 (Fig. 7c). The
magnitude of PM10 in the 4km_D2 run is larger than the 4km
simulation. PM10 in the 4km_D2 run is overestimated in
AMJ but underestimated in JAS,
leading to a comparable season mean of 38.12 µgm-3 with
IMPROVE observing 34.82 µgm-3. The overestimation of AMJ
PM10 and PM2.5_dust in the 4km_D2 run is likely associated
with the high bias in the simulated wind speed (Fig. 2b).
Mean dust emission rate (µgm-2s-1) from the
4km and 4km_D2 runs.
Fraction of erodible surface in the GOCART dataset used in this
study.
Relative contribution (%) of aerosol species from IMPROVE and the
WRF-Chem (4km and 4km_D2) simulations at Fresno, CA, in WY2013. (Panel 1)
Contribution to PM2.5 in the cold season; (panel 2) relative
contribution of PM2.5 and coarse mass (CM) to PM10 in the cold
season; (panel 3) same as panel 1 but in the warm season; (panel 4) same as
Panel 2 but in the warm season. “Other” refers to the difference of
PM2.5 total mass and specified PM2.5 (NO3, NH4, OM,
EC, SO4 and dust).
As for the relative contribution of different aerosol species, IMPROVE
observations at Fresno show that NO3 is the primary contributor
(32.3 %) to PM2.5, while only 5.3 % of PM2.5 is dust in the
cold season (panel 1 of Fig. 10). Both the 4km and 4km_D2 runs roughly
reproduce the relative contributions to PM2.5 in the cold season, with
an overestimation of NO3 and NH4 and an underestimation of
OM, consistent with the time series in Fig. 5. Relative contributions of dust
to PM2.5 are better simulated in the 4km_D2 run (7.3 %) than the
4km one (< 1.0 %). IMPROVE shows that 46.6 % of PM10 is CM in
the cold season (panel 2 of Fig. 10). Both the 4km (6.3 %) and 4km_D2
(20.6 %) runs underestimate the contribution of CM to PM10, mainly
in October and November. In the warm season, dust (24.6 %) becomes the
primary contributor to PM2.5, while the contribution from NO3
decreases to 9.9 % in IMPROVE observations (panel 3 of Fig. 10). Almost
no PM2.5_dust is simulated in the 4km run, while too much
PM2.5_dust is produced in the 4km_D2 (50.5 %) run during the
warm season. The relative contribution of CM to PM10 is too small
(27.6 %) in the 4km run, while the 4km_D2 run reflects a better
relative contribution of 66.3 % as compared to IMPROVE-observed
75.8 % (panel 4 of Fig. 10).
Spatial distribution of seasonal mean 550 nm AOD from the 4km_D2
run in WY2013.
AOD simulations are improved in the 4km_D2 experiment (Fig. 11), with
better agreement found for MISR (Fig. 3) in AMJ. AOD (0.114) in the
4km_D2 run is comparable to observations (0.131) in AMJ but are still
underestimated by 53 % in JAS. Consistent with AOD, the vertical
distribution of aerosol extinction is reasonably simulated during the cold
season in the WRF-Chem simulations, while large discrepancies are found in
the warm season (Fig. 12). As observed by CALIOP at 532 nm, aerosols
are confined below 1 km in the cold season and decrease sharply with
height. During AMJ, aerosols are well mixed between the surface and the
altitude of 1.5 km and then gradually decrease with height. During
JAS, the well-mixed aerosol layer is shallower than that in AMJ and the
vertical profile of aerosol extinction is in-between the cold season and AMJ.
Model simulations roughly capture the bottom-heavy structure of the
extinction profiles observed by CALIOP especially in the cold season, but
significant biases exist. One common problem for all four seasons is the low
bias in the boundary layer and high bias in the free atmosphere. Similar
discrepancy between the model simulations and CALIOP is shown in other
studies (Wu et al., 2011a; Hu et al., 2016). The model does not capture the
well-mixed aerosol layer during AMJ. The difference in the aerosol extinction
profiles between the 4km and 4km_D2 runs is small during the cold
season.
Dust in the boundary layer is a primary factor contributing to aerosol
extinction in the SJV, as illustrated by the differences between the bulk
seasonal CALIOP mean profile and those excluding the contributions of the
dust and polluted dust (CALIOP_nodust) profiles (Fig. 12). Simulated
aerosol extinction falls between the two in all seasons, suggesting that dust
is the primary factor contributing to the model biases in aerosol extinction.
Although a small portion of PM2.5 is dust in the cold season, it
contributes to about 50 % of total aerosol extinction (Fig. 12a and b). A
predominant portion of aerosol extinction in the lower troposphere is
contributed by dust in the warm season (Fig. 12c and d). There, the 4km_D2
simulation produces higher aerosol extinction between 0.3 and 3 km
than the 4km simulation, although it is still lower than CALIOP. The
simulated aerosol extinction in the free troposphere is close to or larger
than CALIOP, suggesting that aerosols transported from remote areas through
chemical boundary conditions (e.g., the differences between the 20km_BC1
and 20km_D2 runs in Fig. S3) may not be the major factors contributing to
the underestimation of dust between 0.3 and 3 km in the SJV.
Overall, the poor simulations of dust play a dominant role in the low bias of
aerosols in the boundary layer during the warm season. Both the GOCART and
DUSTRAN dust emission schemes used in this study have difficulties in
reproducing dust emissions in the SJV, with an underestimation in GOCART and
an overestimation in DUSTRAN (Fig. 7). Improvement on the dust emission
schemes is needed for capturing the seasonal variability in aerosols in the
SJV.
Vertical distribution of seasonal mean 532 nm aerosol extinction
coefficient (km-1) from CALIOP (blue) and the WRF-Chem (4km and
4km_D2) simulations over the red box region in Fig. 1a in WY2013. Blue
dashed lines (CALIOP_nodust) represent the CALIOP profiles without dust
(dust and polluted dust).
Vertical distribution of season mean equivalent potential
temperature (θe; K) from AIRS, ERA-Interim (ERA-I) and the
WRF-Chem (4km_D2, 20km_D2 and 20km_P7) simulations over the red box
region in Fig. 1a in WY2013. The 4km run (not shown) is similar to the
4km_D2 run.
Aerosol mass (µgm-3) for different species from OBS,
the 4km_D2, 20km_D2 and 20km_P7 simulations at Fresno, CA.
NH4 observations are from the EPA; other observations are from IMPROVE.
PM2.5_NO3 represents NO3 with
a diameter ≤ 2.5 µm. Similar definition for NH4, EC,
OM, SO4 and dust in the figures.
Vertical distribution of seasonal mean 532 nm aerosol extinction
coefficient (km-1) from CALIOP, CALIOP_nodust and the WRF-Chem
(4km_D2, 20km_D2 and 20km_P7) simulations over the red box region in
Fig. 1a in WY2013.
Aerosol mass (µgm-3) for different species from
IMPROVE (OBS), the 4km_D2, 20km_D2 and 20km_P7 simulations at
Pinnacles, CA.
Aerosol mass (µgm-3) for different species from
IMPROVE (OBS), the 4km_D2, 20km_D2 and 20km_P7 simulations at
Kaiser, CA.
The role of meteorology
The WRF-Chem simulations approximately reproduce the seasonal variations in
meteorological variables near the surface (correlations > 0.80),
including temperature, RH, wind speed and precipitation (Fig. S6 and
Table S1). All of the model simulations exhibit warm and dry biases near
the surface and in the boundary layer, with cold and wet biases in the free
atmosphere (Figs. S6–S8 and Table S2). The dry bias in the 4km_D2 run is
about 10 % near the surface throughout WY2013. Due to the relative dry
environment (RH < 50 %) in the warm season, the underestimation of
boundary layer aerosol extinction and column-integrated AOD is unlikely
caused by the hygroscopic effects (Feingold and Morley, 2003). In the cold
season, the surface wind speed is underestimated by 0.67 ms-1
(1.00 ms-1) in the 4km_D2 (20km_D2) runs. In the warm
season, the 4km_D2 run overestimates wind speed by 0.78 ms-1,
while the 20km_D2 run has an underestimation of 0.16 ms-1.
These results suggest that wind speed is not a major factor contributing to
the low biases of aerosols in the boundary layer between 0.3 and
3 km. Furthermore, the seasonal variability in precipitation is well
captured in the simulations, while the magnitude of precipitation is weaker
than the observations during the warm season (Table S2). Thus, we conclude
that wet removal processes would not be a primary reason for the aerosol
biases in the warm season.
In the warm season, more aerosols are observed above 1.5 km than in
the cold season (Fig. 12). A well-mixed layer of aerosols is observed below
1.5 km in AMJ (Fig. 12c), consistent with the unstable lower
troposphere below 1.5 km shown in AIRS and ERA-Interim (Fig. 13c).
The WRF-Chem model simulates neutral (or weakly stable) layers below
1.5 km, which may limit uplifting of aerosols from the surface,
failing to create a deep well-mixed layer of aerosols (Fig. 12c). Although
the dust emission at the surface is overestimated in AMJ in the 4km_D2
run, the simulated neutral or weakly stable thermal structure does not favor
convective vertical mixing, resulting in the low biases of aerosols between
0.3 and 3 km.
Similar biases of aerosol and instability in the lower troposphere are also
shown in JAS (Figs. 12d and 13d). The stable boundary layer limits vertical
transport of aerosols from the surface, contributing to the low bias of
column-integrated AOD in JAS (Fig. 11). In JAS (Fig. 12d), aerosol extinction
close to the CALIOP observation is simulated in the free atmosphere,
suggesting that the low bias in AOD is not due to the halved chemical
boundary conditions from MOZART-4. In the cold season, in spite of some
discrepancies in the magnitude of atmospheric stability, all of the
simulations capture the stable lower troposphere (Fig. 13a and b), consistent
with relatively good performance of aerosol simulations in the cold season.
As biases in the model simulations are found mainly within the boundary
layer, a sensitivity experiment is conducted at 20 km resolution using the
ACM2 PBL scheme (20km_P7). Although the changes in the meteorological
variables (Figs. S6–S9) and atmospheric static stability (Fig. 13) are
rather small, the simulated surface NO3 and NH4 in the
20km_P7 run decrease by 50 % compared to the 20km_D2 run
(Fig. 14c, d and Table 3). Considering that more NO3 and NH4
are simulated at 4 km resolution than at 20 km resolution as shown in
Sect. 4.1, the use of the ACM2 PBL scheme at 4 km simulation would largely
resolve the high biases of NO3 and NH4 in the 4km_D2
simulation. The decrease in NO3 and NH4 near the surface is
because more aerosols are transported to the layers above 0.5 km
(Fig. 15a and b), possibly resulting from different convective vertical
mixing in the PBL schemes. However, PM2.5_dust is significantly
overestimated by a factor of 4 in the 20km_P7 simulation (Fig. 14h),
leading to a small decrease in PM2.5 of only 8 % compared with the
20km_D2 run in the cold season. In the warm season, PM2.5_dust in
the 20km_P7 run is overestimated by a factor of 5, causing an
overestimation of PM2.5 and PM10 (Fig. 14a and b). Aerosol
extinctions in the boundary layer above the surface increase in the warm
season (Fig. 15c and d), possibly related to overestimation of dust emissions
and more conducive convective vertical transport in the PBL scheme.
In summary, the WRF-Chem model captures the seasonal variations in
meteorological variables (temperature, RH, wind speed and precipitation),
despite some deviations in magnitude. The low biases in aerosol optical
properties of the warm season likely do not originate from hygroscopic
effects, wet removal processes or dust emissions associated with the wind
speed bias. The model simulates a stable environment in the warm season,
which is opposite to the unstable environment observed. The simulated stable
environment may be most likely responsible for low biases in the aerosol
extinction above the surface (0.3–3 km) and the column-integrated
AOD in the warm season. Switching to the ACM2 PBL scheme leads to improved
vertical displacement of aerosols in the boundary layer, thus an improvement
in the simulations of NO3 and NH4 in the cold season.
However, dust emissions are significantly overestimated with the ACM2 PBL
scheme, which contributes partly to the better simulation of aerosol
extinction in the boundary layer and AOD in the column. These results
highlight that improving the simulation of boundary layer structure and
processes is critical for capturing the vertical profiles of aerosol
extinction.
Results in rural areas
In general, low values of PM concentration are observed in the rural areas,
Pinnacles and Kaiser (Figs. 16 and 17). The rural areas share some similar
model performance to the urban areas, such as the overestimation of
NO3, reasonable simulation of EC, good representation of SO4
in the cold season and underestimation of SO4 in the warm season.
However, the results are not sensitive to model resolution. It suggests that
high resolution is particularly important for heavily polluted areas due to
the inhomogeneity of emission sources, but less important for relatively
lightly polluted areas.
In late July and early August, MODIS (Moderate Resolution Imaging
Spectroradiometer) fire data (not shown) showed active wild fires close to
Kaiser, which resulted in a high local concentration of aerosols (Fig. 17).
Our model simulations with monthly-varying fire emissions fail to reproduce
these fire events. Previous studies (e.g., Grell et al., 2011; Wu et
al., 2011a; Archer-Nicholls et al., 2015) demonstrated that the WRF-Chem
model can capture aerosol distributions from wild fires based on fire
locations from satellite observations. Campbell et al. (2016) further
described the difficulties in constraining total aerosol mass from
operational satellite fire observations and the time needed by the model for
diffusion within the near-surface layers to render both reasonable AOD and
vertical profiles of aerosol extinction. For operational application of the
WRF-Chem model in MAIA retrievals, the observations of daily fire events need
to be more appropriately considered.
Summary
The WRF-Chem (Weather Research and Forecasting model
with Chemistry) model is employed to simulate the seasonal variability in
aerosols in WY2013 (water year 2013) in the San Joaquin
Valley (SJV). Model simulations are evaluated using satellite and in-situ
observations. In general, the model simulations conducted at 4 km resolution
reproduce the spatial and temporal variations in regional aerosols in the
cold season, when aerosols are mainly contributed to by anthropogenic
emissions in the SJV. The magnitude of simulated aerosols in the cold season
however, especially in relatively dense urban areas, is sensitive to model
horizontal resolution. The 4km simulation has comparable magnitude to
available observations, while the 20km simulation underestimates aerosols.
Differences in aerosol simulation fidelity as a function of variable
resolutions are mainly due to the difference in aerosol emissions and
simulated precipitation. Emissions at a higher resolution can better resolve
the inhomogeneity of anthropogenic emissions in the SJV than at lower
resolution. The sensitivity to horizontal resolution is small in rural areas
and during the warm season, where and when the relative contribution of
anthropogenic emissions is small.
Previous studies in the SJV were mainly focused on PM2.5 (particulate
matter with a diameter ≤ 2.5 µm) and during the cold season (e.g.
Chow et al., 2006; Herner et al., 2006; Pun et al., 2009; Ying and Kleeman,
2009; Zhang et al., 2010; Chen et al., 2014; Hasheminassab et al., 2014;
Kelly et al., 2014; Baker et al., 2015; Brown et al., 2016). CALIOP
(Cloud-Aerosol Lidar with Orthogonal Polarization) and IMPROVE (Interagency
Monitoring of Protected Visual Environments) observations show that dust is a
primary contributor to the aerosols in the SJV, especially in the warm
season. Dust contributes 24.6 % to PM2.5 and more than 75.8 %
to PM10 in the warm season. For all seasons, the major component of
aerosol extinction in the boundary layer is dust, as observed by CALIOP,
consistent with Kassianov et al. (2012). For a complete understanding of
aerosol impacts on air quality, weather and climate, the full spectrum of
aerosols should be considered during all seasons.
All the model simulations conducted fail to capture aerosol vertical
distribution and variability in the SJV warm season, largely due to the
misrepresentation of dust emissions, static stability and vertical mixing in
the boundary layer. The GOCART (Goddard Global Ozone Chemistry Aerosol
Radiation and Transport) dust emission scheme significantly underestimates
dust due to the non-active source function, S, for potential wind erosion
used in this study, while the DUSTRAN (DUST TRANsport model) scheme may
overestimate dust emission in the SJV. Along with the bias in dust emissions,
our simulations produce a relatively stable boundary layer in the warm
season, in contrast with observations suggesting a more unstable environment,
leading to a weak vertical mixing of aerosols in the boundary layer. Improved
dust emission and better simulations of the boundary layer properties are
needed for accurate simulation of aerosols in the SJV warm season.
Other biases are also identified in the model simulations. NO3 and
NH4 in the cold season are overestimated in the model, but the
results are sensitive to the choice of the planetary boundary layer scheme.
The secondary organic aerosol processes contribute to the underestimation of
organic matter in this study. The underestimation of sulfate in the warm
season may be caused by the misrepresentation of emissions and the chemical
boundary conditions related to marine intrusions. Aerosols from wild fires
are not captured in the simulations with monthly-updated fire data. Further
investigations are needed to improve model simulations in the SJV for both
scientific and operational applications.
The AERONET observation is available through the following
link: https://aeronet.gsfc.nasa.gov/. The MISR data are available
through the following link:
https://www-misr.jpl.nasa.gov/.
The IMPROVE and EPA data are available through the following link:
http://views.cira.colostate.edu/fed/DataWizard/. The CALIPSO data are
available through the following link:
https://eosweb.larc.nasa.gov/project/calipso/calipso_table. The AIRS
data are available through the following link:
10.5067/AQUA/AIRS/DATA324. The CIMIS data are available through the
following link:
http://wwwcimis.water.ca.gov/.
The CPC data are available through the following link:
https://www.esrl.noaa.gov/psd/data/gridded/data.unified..
The ERA-Interim data are available through the following link:
https://rda.ucar.edu//#!lfd?nb=y&b=proj&v=ECMWF.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-7291-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was carried out at the Jet Propulsion Laboratory, California
Institute of Technology, under a contract with the National Aeronautics and
Space Administration. The authors thank the funding support from the NASA
ACMAP program and JPL PDF program. This work is partially sponsored by
the California Energy Commission under grant #EPC-14-064. The author JRC
acknowledges the support of the NASA ACCDAM program and its manager Hal
Maring. The authors thank the four anonymous reviewers for their helpful
comments.
Edited by: Xiaohong Liu
Reviewed by: four anonymous referees
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