Introduction
The emissions of ammonia (NH3) to the atmosphere are highly uncertain
(e.g., Pinder et al., 2006; Beusen et al., 2008; Galloway et al., 2008;
Henze et al., 2009; Schlesinger, 2009). Nitrogen dioxide (NOx= NO + NO2) and sulfur dioxide (SO2) photo-oxidize in the atmosphere
to form nitric acid (HNO3) and sulfuric acid (H2SO4),
respectively, which react with atmospheric gas-phase ammonia (NH3(g))
to form ammonium sulfate ((NH4)2SO4) and ammonium nitrate
(NH4NO3) aerosol. Uncertainty in NH3 emissions therefore
leads to significant uncertainties in the concentrations of secondary
inorganic aerosols. Ammonium sulfate and nitrate aerosols contribute to fine
particulate matter concentrations (PM2.5), and thus to decreased
visibility, altered climate, and acidification and eutrophication in
sensitive ecosystems (e.g., Paulot and Jacob, 2014; RoTAP, 2012; Bricker et
al., 2007; Martin et al., 2004).
PM2.5 also causes adverse health effects (WHO, 2016; Pope et al., 2004).
In particular, some regions in the San Joaquin Valley (SJV) in California
have been designated as non-attainment areas for PM2.5, with NH3
emissions contributing more than half of the inorganic PM2.5 in the
state (Schiferl et al., 2014), depending on ambient conditions and
concentrations (Lonsdale et al., 2012). During the NOAA California Research
at the Nexus of Air Quality and Climate Change (CalNex) campaign in May and
June of 2010, however, concentrations of PM2.5 rarely exceeded the
National Ambient Air Quality Standard (NAAQS) in the SJV, as PM2.5
exceedances here generally occur in the winter. While emissions of NOx
and SO2 are relatively well constrained, are regulated by the United
States Environmental Protection Agency (US EPA), and are predicted to
continually decrease due to air quality regulations and emission reducing
technologies (Pinder et al., 2006; Henze et al., 2009), NH3
emissions are not currently regulated and are predicted to stay constant or
increase in the US over the next several decades in the US due to an
increasing population and associated increases in farming and agricultural
activities (Moss et al., 2010). Climate change is also predicted to increase
NH3 emissions (+0–40 % in northern–central Europe), with larger
countries having the largest uncertainty in emissions variations (Skjøth
and Geels, 2013).
Anthropogenic NH3 sources in the SJV are dominated by agricultural
activities, with livestock waste estimated to contribute about 74 % of
total anthropogenic NH3 to the atmosphere and chemical fertilizer use
another 16 % (Simon et al., 2008). Agricultural emissions of NH3 can
be highly variable due to factors such as the differences in fertilizer
application, the diet provided to livestock, and waste management and storage
practices of farmers (Hristov et al., 2011; Sawycky et al., 2014). In
addition, while NH3(g) can be quickly deposited to the surface,
causing soil acidification, water eutrophication, and an imbalance of
ecosystems when in excess (e.g., Carfrae et al., 2004), the air–surface
exchange of NH3 is bi-directional, with the direction of the NH3
flux between the land and the atmosphere varying with temperature, relative
humidity, vegetation and soil type, maintenance (e.g., cutting and tilling
practices), and fertilizer applications (Nemitz et al., 2001; Zhang et al.,
2010; Ellis et al., 2011; Bash et al., 2013; Sawycky et al., 2014). This
complexity in the emission and deposition of NH3, along with the rapid
reactions of NH3 with HNO3 and H2SO4 and the consequently
short (∼ 1-day) atmospheric lifetime of NH3, leads to large
temporal and spatial variability as seen in in situ measurements (e.g.,
Langford et al., 1992; Carmichael et al., 2003; Nowak et al., 2010; Walker et
al., 2013) and in satellite retrievals (e.g., Clarisse et al., 2013; Pinder
et al., 2011; Heald et al., 2012; Sun et al., 2015; Shephard and
Cady-Pereira, 2015; Shephard et al., 2011, 2015).
Recent studies have recognized a diurnal pattern in NH3 emissions from
livestock attributed to potential differences in farm management practices,
livestock housing outflow patterns, and variations in soil moisture,
temperature, and wind speed (Hensen et al., 2009; Zhu et al., 2015a, b). To
account for this, a diurnal variability scheme was implemented in global
simulations using the global three-dimensional chemical transport model,
GEOS-Chem, and was shown to decrease NH3 concentrations globally (Zhu et
al., 2015a). That study also calculated the bi-directional exchange of
NH3, which decreased NH3 concentrations in the US in the months of
October through April and increased it in the month of July (Zhu et al.,
2015a). Bash et al. (2013) also explored the sensitivity of modeled NH3
concentrations to a bi-directional NH3 scheme that used meteorological
factors, including temperature, wind speed, agricultural crop flux values,
and a nitrogen soil geochemistry parameterization in the CMAQ model. They
found that over the continental US their model run with the bi-directional
NH3 scheme decreased the total dry deposition of NH3 by 45 %,
thus increasing atmospheric NH3 concentrations and NHx wet
deposition by 10 and 14 %, respectively. Wichink Kruit et al. (2012) use
the DEPosition of Acidifying Compounds (DEPAC) surface–atmospheric exchange
module in a CTM and saw an increase in atmospheric NH3 almost everywhere
in their model domain, including decreased NH3 deposition, with a
remaining underestimation in agricultural areas.
Previous studies have also shown that errors in NH3 emissions are a
common contributing factor to modeled PM2.5 and NH3 bias (e.g.,
Schiferl et al., 2014). Skjøth et al. (2011) discuss their method for
calculating dynamic NH3 emissions that includes distributions of
agricultural NH3 in Europe. Their method is designed for use in chemical
transport models and their results show considerable improvements made in the
agricultural NH3 sector, particularly in areas with detailed records of
agricultural practices. Inverse modeling studies have been used to reduce the
uncertainty in NH3 emissions as well, generally by assimilating surface
observations of the wet deposition of ammonium (NH4+) in
precipitation. Gilliland et al. (2003) used the CMAQ model to determine that
the 1990 version of the US EPA National Emissions Inventory (NEI)
overestimated total emissions of NH3 by 20 %. Gilliland et
al. (2006) performed a similar study for the 2001 NEI and found that total
emissions of NH3 were represented well, but needed to be increased in
summer and reduced in winter. Henze et al. (2009) used the adjoint of the
GEOS-Chem global chemical transport model to assimilate the Inter Agency Monitoring of Protected Visual
Environments (IMPROVE) observations and found that total US NH3
emissions for 1998 were overestimated.
More recently, satellite observations of NH3 have been incorporated into
inverse studies. By assimilating satellite retrievals of NH3
concentrations from the Tropospheric Emission Spectrometer (TES; Beer et al.,
2008; Shephard et al., 2011) aboard the NASA Aura satellite, it has been
found that NH3 emission sources in GEOS-Chem are broadly underestimated
(Zhu et al., 2013). Heald et al. (2012) and Walker et al. (2012) used IMPROVE
data and satellite retrievals of NH3 from the Infrared Atmospheric
Sounding Instrument (IASI, Van Damme et al., 2014) to show that NH3
emissions are likely underestimated in GEOS-Chem for California, leading to a
local underestimate of NH4(p). Other infrared nadir sounders have
been used to provide satellite observations of NH3. For example,
Shephard and Cady-Pereira (2015) demonstrated the ability of the Crosstrack
Infrared Sounder (CrIS) aboard the joint NOAA-NASA Suomi National
polar-orbiting satellite to measure daily, spatially distributed tropospheric
NH3 in California, and in preliminary results found it correlated well
with Deriving Information on Surface Conditions from Column and Vertically
Resolved Observations Relevant to Air Quality (DISCOVER-AQ) aircraft
measurements in the SJV in January 2013.
Investigating the formation, transport, and fate of NH3(g) and
NH4(p) in California was one of the major goals of the CalNex field
campaign, which provided measurements from flights and surface sites (Ryerson
et al., 2012) in the Los Angeles Basin and in the Central Valley. Nowak et
al. (2012) used these data to demonstrate the importance of ammonium nitrate
formation downwind of the Los Angeles urban core and dairy facilities further
east. They found that NH3 emissions from these dairy farms were
underestimated by a factor of 3 or more, thus indicating the need for better
representation in this emission sector. Kelly et al. (2014) in general saw
well-correlated comparisons of CMAQ model estimates to measurements from the
EPA's Chemical Speciation Network. Their model tended to underpredict
NHx (NHx= NH3(g)+ NH4(p)) during the day at
the Bakersfield, CA, site and significantly overpredict NH3(g) at
night. They suggest that this model bias may be due to emissions from
livestock and dairy farms being too low and lacking in variability in this
region or to errors in crustal cation predictions and the missing effects of
organic acids and amines on inorganic aerosol thermodynamics (Kelly et al.,
2014).
Distribution of NH3 emissions across California (background)
on 12 May 2010 at 19:00 UTC as well as P3 flight tracks (small circles),
TES transect (green squares), and the Bakersfield site (red star) with the
county lines shown in white.
Model estimates of the planetary boundary layer (PBL) height are essential in
correctly quantifying changes in atmospheric pollutant concentrations,
especially for short-lived pollutants like NH3. Such estimates are
difficult at fine spatial and temporal scales, especially in the complex
terrain of the SJV. Scarino et al. (2014) studied the PBL and mixed layer
heights during CalNex using WRF and high spectral resolution lidar (HSRL)
data taken during the campaign. They found that, in general, there is good
agreement between the WRF modeled output and measured values; however, in the
California Central Valley there is a WRF mixed-layer height overprediction
and an inability to represent the diurnal growth of the mixed layer in the
early part of the day. Additionally they suggest that future improvements
will require a focus on mixing layer characteristics, soil moisture, and
temperature. Baker et al. (2013) explored how well the WRF model
configuration used to drive the CMAQ simulations of Kelly et al. (2014)
simulates PBL height during CalNex, using two versions of WRF. The study
shows that both WRF versions simulate the PBL and mixing layers well within
the SJV, as well as other large-scale flow patterns, but underpredict local
wind speed and temperature. A strong aerosol gradient is used to identify the
top of the PBL in HSRL measurements; this strong gradient may also be present
in a nighttime residual layer. Baker et al. (2013) take this into account by
identifying the surface-attached mixed layer, which they assume as the lowest
significant gradient in such a circumstance.
In this study, we use the CalNex observations of NH3(g) and NH4(p)
and the CMAQ model to evaluate the estimates of NH3 emissions in the
SJV contained in the California Air Resources Board (CARB) inventory (Fig. 1). While previous NH3 model evaluation efforts using CalNex data have
focused on the NEI inventory (Kelly et al., 2014; Heald et al., 2012; Walker
et al., 2012), the CARB inventory is used in the development of California's
State Implementation Plans (SIPs) under the Clean Air Act, and so ensuring
the accuracy of this emission inventory is important to the design of air
quality policy for the SJV and California in general. In addition, previous
studies have not taken advantage of the high-resolution observations of
NH3(g) made by the TES satellite instrument over Bakersfield during the
CalNex campaign. Here we evaluate the consistency of the satellite,
aircraft, and surface observations of NH3(g) and NH4(p) during the
CalNex campaign and then use these observations, along with lidar retrievals
of PBL height, to investigate the biases in the magnitude and diurnal cycle
of emissions of NH3(g) from the CARB inventory in the SJV. We also
explore the sensitivity of modeled NH3 concentrations to bi-directional
NH3 exchange using the bi-directional NH3 flux scheme in
CMAQv5.0.2.
Section 2 briefly describes the data sources used in this study, while
Sect. 3 describes the CARB emission inventory and the configurations used
for the WRF, the Hybrid Single-Particle Lagrangian Integrated Trajectory
(HYSPLIT), and CMAQ model runs. The performance of the CARB inventory used
in our CMAQ simulations, along with model sensitivity studies, is presented
in Sect. 4. Section 5 discusses the remaining errors in our final model
configuration in detail and makes suggestions for further model
improvements, while our conclusions are discussed in Sect. 6.
Data
NOAA WP-3 aircraft
The NOAA WP-3 aircraft completed 18 research flights during the CalNex
campaign, which included measurements of NH3(g) and NH4(p).
NH3(g) was measured at 1 s (∼ 100 m) intervals using
chemical ionization mass spectrometry (CIMS) with an uncertainty of ±30 % as described in detail in Nowak et al. (2007). The CIMS instrument
sampled air through a 0.55 m long heated teflon inlet with a fast flow.
Measurement artifacts were accounted for by quantifying and subtracting the
background signal originating from NH3 desorption from instrument
surfaces. The background signal was determined in flight by actuating a
teflon valve at the inlet tip once every half hour to divert the sample air
through a scrubber that removes NH3 from the ambient air stream (Nowak
et al., 2007). Additionally, standard addition calibrations from a NH3
permeation tube were performed several times each flight to determine
instrument sensitivity. Submicron NH4(p) was measured at 10 s
(∼ 1 km) intervals with an uncertainty of ∼ 30 % using a compact time-of-flight aerosol mass spectrometer from Aerodyne
(c-TOF AMS, Bahreini et al., 2009). In this study we focused on the flights
of 24 May and 16 and 18 June when the WP-3 was sampling air in the SJV
(Fig. 1). The quality-controlled flight data were reported at a merged
time resolution of 1 s, which we averaged to 1 min values (the
approximate time it takes the WP-3 to cross a 4 km CMAQ grid box) and then
matched the sample times and locations to the corresponding time and
location of the CMAQ hourly concentration output.
Bakersfield surface observations
Bakersfield, California, is located in the southern part of the SJV
(35.35∘ N, 118.97∘ W; 20 m a.s.l.) and there is a general
north-to-south orographic air flow in this region, with a tendency for
emissions to get trapped in the valley due to the nearby mountains (Baker et
al., 2013). At the Bakersfield ground site the Ambient Ion Monitor Ion
Chromatograph (AIM-IC, Ellis et al., 2010; Markovic et al., 2012) was used to
measure NH3(g) on an hourly basis, with an uncertainty of
±20 % and a detection limit of 41 ppt. The sampling inlet for the
AIM-IC consists of an enclosure mounted at 4.5 m above ground, including a
virtual impactor, parallel plate denuder, and particle supersaturation
chamber, connected to the ion chromatography systems via several 20 m
perfluoroalkyl sampling lines carrying the dissolved analytes (Markovic et
al., 2014). This design reduces artifacts by minimizing the inlet surface
area prior to scrubbing the NH3 from the gas phase in the denuder, and
by separating the gas- and particle-phase constituents while the sample flow
is still at ambient temperature and relative humidity (Markovic et al.,
2012). In addition, size-resolved, sub-micron non-refractory NH4(p)
measurements were taken at 5 min intervals using an Aerodyne Aerosol Mass
Spectrometer (AMS, Liu et al., 2012). We averaged these data to 1 h time
resolution in order to compare to the hourly CMAQ model output, which allowed
for the evaluation of the ability of CMAQ to simulate the diurnal cycle of
NH3 concentrations. When NH4(p) measurements are available, we
compare model results to NHx to reduce our sensitivity to
gas-to-particle partitioning errors in the model; otherwise we compare to
NH3(g).
TES NH3 retrievals
During CalNex, TES made special observations (transects) near the
Bakersfield, CA, surface site with a horizontal separation of 12 km on six
different afternoons. TES is a nadir-viewing Fourier-transform infrared
(FTIR) spectrometer with a high spectral resolution of 0.06 cm-1 and a
nadir footprint of 5.3 km × 8.3 km. TES flies aboard the NASA Aura
spacecraft, which is in a sun-synchronous orbit with an Equator crossing time
around 01:30 and 13:30 local solar time. Beer et al. (2008) reported the
first satellite observations of boundary layer NH3(g) using the TES
instrument. Shephard et al. (2011) developed and tested a full
NH3(g) retrieval algorithm. The retrieval is based on an optimal
estimation approach that minimizes the differences between the TES Level 1B
spectra and a radiative transfer calculation that uses absorption
coefficients calculated with the AER line-by-line radiative transfer model –
LBLRTM (Clough et al., 2006). The a priori profiles and covariance matrices
for TES NH3 retrievals are derived from GEOS-Chem model simulations of
the 2005 global distribution of NH3.
The TES NH3(g) retrievals generally have a region of maximum
sensitivity between 700 hPa and the surface. While the retrieval is
performed on 14 pressure levels, the number of degrees of freedom for signal
(DOFS) is generally not greater than 1. Therefore at any given single profile
level the retrieved volume-mixing ratio (VMR) of NH3 is highly
influenced by the a priori profile. Rather than attempting to
analyze data from individual retrieval
levels, it is often desirable to express the retrieved information in a
representation where the influence of the a priori is reduced and the
information available is collapsed to a single point. To address this issue,
Shephard et al. (2011) developed a Representative Volume Mixing Ratio (RVMR)
metric for NH3(g) based on similar techniques used previously for
CH4 (e.g., Payne et al., 2009; Wecht et al., 2012; Alvarado et al.,
2015) and CH3OH (e.g., Beer et al., 2008). This RVMR represents a TES
sensitivity weighted average value where the influence of the a priori
profile is reduced as much as possible; it generally ranges from 20 to
60 % of the retrieved surface value for NH3(g). The minimum
detection level for TES NH3(g) retrievals is an RVMR of
approximately 0.4 ppbv, corresponding to a profile with a surface-mixing
ratio of about 1–2 ppbv (Shephard et al., 2011).
Pinder et al. (2011) showed that the TES NH3 retrievals were able to
capture the spatial and seasonal variability of NH3 over eastern North
Carolina and that the retrievals compared well with in situ surface
observations of NH3, while Alvarado et al. (2011) showed that TES
NH3 retrievals can also capture the higher concentrations of NH3
in forest fires in Canada. Sun et al. (2015) demonstrated that under optimal
conditions (i.e., good thermal contrast and NH3 amounts significantly
above the TES level of detectability), TES NH3 agreed very well with in
situ aircraft and surface measurements taken in the California Central
Valley during the DISCOVER-AQ 2013 campaign.
There are at least three issues that have to be considered when using
NH3 satellite profiles to evaluate model predictions: (a) the vertical
resolution of the satellite profile is substantially coarser than that of
the model profile; (b) the DOFS for NH3 are generally less than 1.0;
and (c) the retrieved satellite profile reflects the influence of the choice
of a priori profile (Rodgers and Connor, 2003). Thus, in order to use these
TES observations to evaluate CMAQ model predictions of the concentrations of
NH3(g), we first interpolate the hourly CMAQ NH3 profile
predicted for 13:00 local solar time (expressed as the natural logarithm of
the mixing ratio) to the TES pressure grid. We then apply the TES
observation operator to the interpolated CMAQ NH3 profile to derive a
model TES profile (xTES). Finally, we apply the
sensitivity weighting to calculate the model RVMR (CMAQRVMR). This
value represents the RVMR that would have been retrieved if (a) TES had
sampled a profile identical to the CMAQ-simulated profile and (b) the
retrieval errors due to jointly retrieved parameters, other model
parameters, and instrument noise were negligible. The observation operator
equation is
xTES=xa+A(xCMAQ-xa)
and the RVMR is calculated as
CMAQRVMR=W×xTES
where xa is a vector of the TES a priori NH3 concentrations,
A is the averaging kernel matrix, xCMAQ is a
vector of the interpolated CMAQ NH3 values, and W is a weighting
vector (Rodgers and Connor, 2003; Shephard et al., 2011). W basically
weights each level according to the sensitivity of the TES instrument at that
level. It is calculated by summing the most significant rows of the averaging
kernel at each level (see the appendix in Shephard et al., 2011, for
details).
PBL heights
Several studies have used lidar observations of aerosol profiles to
determine the height of the planetary boundary layer (PBL) by identifying
regions of large gradients in aerosol concentrations with height (e.g.,
Tucker et al., 2009; Lewis et al., 2013; Scarino et al., 2014; Hegarty et
al., 2015). Scarino et al. (2014) and Tucker et al. (2009) define the mixed
layer measured by the HSRL as “the volume of atmosphere in which aerosol
chemical species emitted within the boundary layer are mixed and dispersed”.
The NASA Langley Research Center (LaRC) airborne HSRL measured mixed layer
heights during the CalNex campaign and the Carbonaceous Aerosol and
Radiative Effects Study (Scarino et al., 2014), both of which we used in
this study.
Models
WRF-ARW
CMAQ v5.0.2 was driven with meteorology provided by WRF ARW Version 3.5
(Skamarock and Klemp, 2008) that was configured with three nested domains of
36, 12, and 4 km horizontal grid spacing and 41 vertical layers. Shortwave
and longwave radiation was calculated using the Rapid Radiative Transfer
Model code for General Circulation Model applications (RRTMG, Mlawer et al.,
1997; Iacono et al., 2008). The YonSie University (YSU, Hong et al., 2006)
non-local turbulent PBL scheme and the Noah land surface scheme (Chen and
Dudhia, 2001) were used. Initial and boundary conditions for WRF were
provided by the North American Regional Reanalysis (NARR, Mesinger et al.,
2006), which is recognized as state-of-the-science for North America
(Bukovsky and Karoly, 2007). The WRF runs were 32 h simulations initialized
every 24 h at 00:00 UTC with analysis nudging of winds, temperature and
humidity above the PBL on the outer 36 km domain only, as in Nehrkorn et
al. (2013). The WRF outputs for UTC hours 09:00 to 32:00 from each
consecutive simulation were combined to form a continuous time series and the
initial 8 h of each simulation were discarded as spin-up time. The 8 h
spin-up time and 32 h simulation length are longer than the 6 h spin-up
time and 30 h simulation length used by Nehrkorn et al. (2013), but were
necessary to perform 24 h daily CMAQ runs using the 24 h daily CARB
emissions files that started at 08:00 UTC. The WRF output was then converted
to CMAQ-model-ready files using the Meteorology-Chemistry Interface Processor
version 4.2 (MCIP).
CMAQ
We ran CMAQ on the inner 4 km WRF domain using the SAPRC07 chemical
mechanism (Hutzell et al., 2012; Carter et al., 2010a, b), which corresponds
to the model-ready emission files for CalNex provided by CARB, and to the
CMAQ AERO6 aerosol module with aqueous chemistry. Biogenic emissions,
photolysis rates, and deposition velocities were all calculated inline. There
were few clouds in California during this study period and thus lightning
NOx emissions were negligible; however, lightning NOx emissions
were also calculated inline in CMAQ. Initial and horizontal boundary
conditions for CMAQ were provided by GEOS-Chem simulations on a
2∘ × 2.5∘ latitude–longitude grid for May and June
2010 following the approach of Lapina et al. (2014).
CMAQ emissions inputs for the state of California were provided as
model-ready files by CARB, which prepared them using the Modeling Emissions
Data System on a 4 km × 4 km grid scale (available at
http://orthus.arb.ca.gov/calnex/data/calnex2010.html). The emission
change log is provided at
ftp://orthus.arb.ca.gov/pub/outgoing/CalNex/2010/modelready/Change Log
for Posted Inventories.pdf. In this inventory, the NH3 emissions in SJV
are assumed to be constant throughout the day (i.e., no diurnal cycle), and
are constant day-to-day in a given month. While emissions do vary
month-to-month, we do not explore seasonal variation in this study, since the
measurement campaign only occurred during the months of May and June. As the
CARB model-ready files had no out-of-state emission sources, our initial
simulations were run using the CARB emissions for California, the GEOS-Chem
boundary conditions, and no out-of-state emissions. We quantified the
potential error in gas-phase NH3(g), Aitken and accumulation mode
aerosol NH4(p), and NHx in the SJV from neglecting
out-of-state agricultural NH3 emissions by using the agricultural
NH3 emissions from the NEI2011 platform, which we re-gridded from 12 km
to our model's 4 km scale while keeping California state emissions constant.
We performed this sensitivity test for a 7-day case study between 25 and 31
May with a 4-day spin-up. Adding these out-of-state emissions had a
negligible impact on the modeled NH3 concentrations in the SJV (less
than 0.001 % change), as the prevailing winds are mostly out of the north
and northwest. Additionally, we tested the effect that errors in the boundary
conditions from GEOS-Chem might have on the model runs. Doubling NH3
boundary conditions for the same 7-day case study also had little impact on
NH3 concentrations in the SJV (less than 0.001 % change), which was
expected based on the short lifetime of NH3.
Finally, we also ran CMAQv5.0.2 using the bi-directional NH3 flux
scheme as developed by Bash et al. (2013) that uses fertilizer
application data, crop type, soil type, and meteorology from MCIP output to
calculate soil emissions potential and NH4 to simultaneously
calculate NH3 deposition and emission fluxes for the CMAQ US domain.
This scheme uses the US Department of Agriculture's Environmental Policy
and Integrated Climate (EPIC) model (Cooter et al., 2012) as contained in
the Fertilizer Emissions Scenario Tool (FEST-C).
In order to evaluate CMAQ v5.0.2 modeled NH3 in the SJV we ran three
different scenarios for a month-long case study that covers the record of the
Bakersfield surface observations (22 May–22 June 2010). The model scenarios
include (1) a baseline model run (CMAQbase), in which the model was
set up as described above, utilizing the CARB emissions inventory,
(2) CMAQB, which
ran with the baseline setup but also included the bi-directional NH3
scheme described in Sect. 3.2, and finally (3) CMAQAB, which
included both the bi-directional NH3 scheme and diurnally varying
emissions in the SJV, as described in Sect. 4.1.
HYSPLIT
In order to explore the sources influencing the Bakersfield concentrations,
we ran the HYSPLIT model. Using meteorological inputs from the WRF 4 km
domain discussed in Sect. 3.1, we generated 36 h back trajectories with
Version 4 of the HYSPLIT model (Draxler and Hess, 1998) initiated from 100 m
above ground level (a.g.l.) at Bakersfield at 17:00 PDT on 18 June back to
20:00 PDT on 17 June. Results from these runs are briefly discussed in
Sect. 4.1 and shown in Fig. S1 in the Supplement.
Summary statistics of the modeled NHx, NH3(g) and
NH4(p) concentration comparisons to the ground measurements for all
three model runs. Mean bias (MB) = mean (modeled–measured); mean
normalized bias (MNB) = mean ([modeled–measured]/measured). Note that
low r2 values, less than 0.10, are highlighted in
italics.
NHx
NH3(g)
NH4(p)
Model run
Slope
r2
MB
MNB
MB
MNB
MB
MNB
(ppbv)
( %)
(ppbv)
( %)
(ppbv)
( %)
CMAQbase
-2.49 ± 0.15
0.001
8.24
72.54
8.63
78.79
-0.40
-52.96
CMAQB
1.22 ± 0.07
0.01
4.57
45.74
4.99
50.60
-0.41
-55.92
CMAQAB
0.85 ± 0.05
0.05
-1.23
-10.70
-0.79
-14.01
-0.44
-60.24
Model evaluation
The following subsections describe the evaluations of all three-model
scenarios using the three different measurement datasets from the CalNex
campaign. Section 4.1 describes the modeled evaluation using surface
measurements, Sect. 4.2 using the aircraft measurements, and finally
Sect. 4.3, utilizing the TES satellite measurements.
(a) The average hourly ratio of modeled to measured
NH3 (dashed line) and NHx (solid line) mixing ratios at the
Bakersfield ground site for the CMAQbase (blue), CMAQB
(green) and CMAQAB (purple) cases, and the average modeled RVMR to
TES RVMR ratio (green dot) in local time. (b) Box plot of average
hourly NHx mixing ratios at the Bakersfield ground site for the measured
(black), CMAQbase (blue) and CMAQAB (purple) cases,
averaged over all measurement days during CalNex where the box plots show the
inter-quartile range and median line (red) within the box and outliers
(whiskers), with the solid lines showing the mean for that day.
Evaluation of modeled transport and diurnal variability of NH3(g)
using surface observations
Table 1 shows that the CMAQbase scenario has a NHx positive
mean bias (MB) of 8.24 ppbv and a mean normalized bias (MNB) of 72.5 %
over the month-long surface data record; we focus on NHx so as to
minimize the effects of possible model errors in gas-to-particle partitioning
on our analysis, as discussed later in this section. NH3 has a slightly
higher bias, with NH4(p) having a lower MB of -0.40 ppbv, which
has a small influence on total NHx. However, this bias is not constant
throughout the day, as can be seen in the CMAQbase results (blue
line) shown in Fig. 2. Figure 2a shows the average hourly ratio of
CMAQbase modeled NHx versus measured concentrations for the
Bakersfield ground site, averaged over all days of the CalNex campaign; these
ratios are derived from the box plots shown in Fig. 2b. The model bias shows
a clear diurnal cycle, with CMAQbase significantly overestimating
surface NHx concentrations at night by up to a factor of 4.5 and
generally underestimating NHx during the daytime by a factor of 0.6
between 13:00 and 14:00 local time, consistent with the average
TESRVMR observations near Bakersfield at about 13:30 local solar
time, which is plotted as the green dot in Fig. 2a and further discussed in
Sect. 4.3. These results suggest that constant daily agricultural NH3
emissions in the CARB inventory (blue line Fig. S2) may be misrepresenting
the observed diurnal emission patterns. This is consistent with previous work
done in North Carolina; Wu et al. (2008) found that NH3 emissions from
livestock feed lots show a strong diurnal cycle, peaking at midday.
Wind rose of measured wind direction and NH3 on the left, and
CMAQAB modeled wind direction and NH3 on the right where
contours represent the number of data points (hourly) per wind direction.
Note the difference in scale, where values are in ppb.
Besides errors in emissions, another contributing factor to the modeled bias
of NH3(g) could be errors in gas-to-particle partitioning of
NH3(g) to NH4(p). Figure 2a also shows that there is very
little difference between the NHx (solid blue) and NH3(g)
(dashed blue) lines, indicating that only a small fraction of total
NH3(g) is converted into NH4(p) in this region,
consistent with Baker et al. (2013). Thus, errors in gas-particle
partitioning of NH3 in CMAQ, while important for accurately estimating
PM2.5 concentrations, cannot account for the diurnal errors in NHx
we have observed.
Another potential source of diurnal errors in modeled NHx are diurnal
variations in meteorology, which could alter the source regions to which the
Bakersfield site was sensitive throughout the day. Differences between
modeled and true NH3 emission errors at upwind sites would thus appear
as diurnal errors in NHx. We ran a HYSPLIT case study for 18 June, where
back trajectories were run for eight different times during the day
(Fig. S1). During the CalNex campaign, the daytime flow is generally from the
north/northwest and is funnelled through the California Central Valley
towards Bakersfield. During the nighttime there is a shift in wind direction
to sources coming from the southeast. Cooling air from up in the eastern
mountain ranges causes a mountain drainage effect into the southern valley
area. This interaction of the mountain drainage combined with the typical
low-level jet from the northern Central Valley creates a Fresno Eddy, as described in
Michelson and Bao (2008). Figure 3 shows a wind rose for all points included
in Fig. 2, where measured wind direction and NHx concentrations are
shown on the left, and modeled wind direction and NHx concentrations are
shown on the right. It can be seen in Fig. S5a that the nighttime wind
measurements from the southeast generally have lower wind speeds
(< 4 m s-1) and that the model does not capture the variation
of these wind speeds very well. This may be due to some timing errors in that
the model may not capture true winds within a 4 km grid box, which
corresponds to about 1–2 h in real time. In general, many of the higher
modeled NHx concentrations appear to be occurring during nighttime when
the model should have winds out of the southeast, thus there is large model
bias for these points. As indicated by the performed HYSPLIT
back-trajectories, and the description of air flow in the southern valley, we
assume that although the measurements indicate the immediate wind direction
was out of the southeast, the air mass's long-range transport still travelled
over the Central Valley to accumulate emissions from that region before being
recirculated by the Fresno Eddy to eventually come from the southeast. Thus,
an overestimate of emissions in the Central Valley at night could still
contribute to a model overestimate of measurements coming out of the
southeast, rather than this air mass having come from a cleaner source,
east of the mountains. Additionally, for the remaining time periods and
majority of measurements not out of the southeast at nighttime, the model
does a better job at simulating wind speeds (Fig. S3), with a large model
bias in NHx concentrations remaining. Thus diurnal changes in transport
are likely not the only contributing factor to the diurnal mismatch shown in
modeling results.
The CalNex ground measurements at the Bakersfield site (solid
black) compared to the CMAQbase (solid blue), CMAQAB (purple) and
CMAQB (green) simulations for a month of model runs. The top panel
(a) shows NHx, (b) shows NH3(g), (c) NH4(p), and temperature (K)
and (d) wind speed on the left and wind direction on the right axis.
Time series of WRF predicted planetary boundary layer heights and
HSRL calculated mixed layer heights for three flight sections in the San
Joaquin Valley (two during CalNex and one during a CARES campaign).
Diurnal errors in the PBL height estimates could also be responsible for the
diurnal error pattern in the CMAQ NHx concentrations at Bakersfield
(Fig. 2). We used daytime HSRL measurements taken in the SJV during CalNex to
evaluate our WRF-simulated PBL heights. Figure 5 shows 2 min averages of the
HSRL calculated mixed layer height compared to the WRF PBL for three daytime
flights that passed over the SJV. The modeled and measured heights show good
agreement, with a slope of 0.76, r2 of 0.70, and mean bias of 87 m.
Thus errors in daytime PBL height do not seem to account for much of the
underestimate in modeled daytime NHx. Scarino et al. (2014), when
comparing all CalNex HSRL flight measurements to their configuration of the
WRF-Chem model, found similar results. In summary, gas-to-particle
partitioning and PBL height errors are likely not responsible for the
diurnally varying measurement to model biases.
CARB NH3 emissions in the SJV are constant both diurnally and
day-to-day, with an hourly flux of around 0.23 moles s-1 for the
Bakersfield area (Fig. S2). The Bakersfield ground measurements, however,
indicate there should be a diurnal pattern of lower emissions at night and
higher emissions during the day, as has been previously reported of NH3
emissions from livestock (e.g., Bash et al., 2013; Zhu et al., 2015a) and
other agricultural NH3 sectors (Skjøth et al., 2011). The intense
agricultural activities in the SJV generate large NH3 emissions, with
concentrations often exceeding 5 ppb as indicated in the ground
measurements, making this an NH3-rich region relative to the ambient
sulfate concentrations. In this regime, since there is not enough sulfate to
react with all the NH3, a simple box model over the Bakersfield site,
with wind speed, deposition, and PBL height variation held constant, would
show a linear relationship between additional NH3 emissions and the
NHx concentration (Seinfeld and Pandis, 2006). Thus we expect errors in
other parameters (PBL height, deposition, etc.) to affect modeled
NH3(g) and NHx concentrations to a greater degree, and we
investigate these parameters below.
To test our hypothesis that the diurnal errors in NHx concentrations are
due to diurnal errors in NH3 emissions, we explored two additional model
scenarios to attempt to improve the diurnal cycle of NH3 emissions in
the CMAQ model. We found that including the bi-directional flux of NHx
in the CMAQB case (green lines) significantly reduces the nighttime
concentration peaks of ground-site-measured NH3. However, there is still
a clear model NHx overestimate overall (MB of 4.57 ppb and large MNB of
45.74 %; see Table 1), and the low correlation is not improved (r2= 0.01). The CMAQB scenario also shows overestimates following
the day's maximum in temperature (Fig. 4). At night this bias is reduced
relative to the total concentrations.
We then applied a scaling factor to all NH3 area sources per grid box in
the SJV, based on the CMAQbase bias relative to the ground
measurements. To do this, we first calculated the total NH3 area source
emissions for each grid box, based on additional information on the emissions
breakdown from the CARB inventory. For Kern County, where Bakersfield, CA, is
located, pesticide/fertilizer applications dominate the NH3 emissions
inventory at 72 %, followed by farming operations (that include handling
of all livestock and excrement) at 25 %, and other sources for the
remaining fraction. Table S2 in the Supplement describes the fraction of
NH3 emissions for counties in the SJV. We then calculated the emissions
for each hour based on the hourly average ground measurements and considering
the NH3-rich conditions. Note that the adjusted maximum emissions vary
by about a factor of 4.5 from the minimum at night to the midday peak, as can
be seen in Fig. S1 (solid red line), which is more modest than the factor of
10 variation seen in livestock feedlots (Bash et al., 2013; J. Bash, personal
communication, 2015). We then reran CMAQ with both these adjusted emissions
and the bidirectional NH3 scheme (the CMAQAB run) to assess
the impact. Despite applying the scaling factor to all emissions instead of
solely to the feedlots as in Bash et al. (2013), the CMAQAB model
predictions, shown as the purple lines in Fig. 4, match the measurements
(black line) better than the CMAQbase or CMAQB scenarios
over the day and night, with large outliers seemingly reduced, consistent
with Bash et al. (2013). The mean nighttime bias for CMAQAB was
reduced by about a factor of 2 and the overall bias of NHx reduced to
-1.23 ppbv (Table 1); this model version does particularly well between
the hours of 01:00 and 06:00 (see Fig. 2a). The fact that adding the
diurnally varying emission profile reduces the model bias, even though the
emissions are dominated by fertilizer applications that should be accounted
for by the bi-directional NH3 scheme, suggests that the bi-directional
NH3 scheme in CMAQ v5.0.2 is not correctly accounting for the diurnal
variations in NH3 flux in the SJV. Furthermore, when we compare the
modeled NH3 to measured values coming from just the southeast at night
(Fig. S5), the model bias is reduced by about a factor of 3.5. This suggests
that although the model may not capture the immediate wind direction and wind
speed at night, as explained above, because of the long-range transport down
the Central Valley that evolves into the Fresno Eddy, reducing emissions in
this upwind region also reduces model bias for these points in time. However,
we note that the correlation of all three-model scenarios remains very low
(r2 < 0.06), suggesting further model errors, such as the
neglect of any day-to-day variation in NH3 emissions in our simulations.
Summary statistics of the modeled to measured NHx concentration
comparisons following the SJV flights. Mean bias (MB) = mean
(modeled–measured); mean normalized bias (MNB) = mean
([modeled–measured]/measured). Note that low r2 values, less than 0.10,
are highlighted in italics.
NHx
NH3(g)
NH4(p)
Date
Time
Slope
r2
MB
MNB
MB
MNB
MB
MNB
(PDT)
(ppbv)
( %)
(ppbv)
( %)
(ppbv)
( %)
CMAQbase
20100524
16:00–22:00
0.20 ± 0.01
0.31
-1.95
-2.010
-1.74
-18.24
-0.14
-58.70
16:00–18:00
0.68 ± 0.05
0.77
-0.20
-10.79
-0.04
-32.46
-0.08
-53.19
18:00–22:00
0.18 ± 0.01
0.29
-2.40
-0.213
-2.24
-14.65
-0.14
-60.10
20100616
13:00–18:00
0.30 ± 0.02
0.43
-5.92
-8.980
-4.90
-3.59
-0.24
-45.32
20100618
13:00–18:00
0.18 ± 0.02
0.10
-8.12
-18.97
-7.85
-28.9
-0.26
-75.20
CMAQB
20100524
16:00–22:00
0.36 ± 0.03
0.09
5.56
351.82
5.71
453.86
-0.10
-39.32
16:00–18:00
-1.57 ± 0.24
0.19
6.59
506.18
6.71
639.07
-0.07
-31.92
18:00–22:00
0.31 ± 0.03
0.11
5.30
31.28
5.46
407.1
-0.11
-41.18
20100616
13:00–18:00
0.76 ± 0.06
0.04
6.27
248.03
6.63
279.85
-0.22
-33.82
20100618
13:00–18:00
0.37 ± 0.04
0.02
4.26
394.88
4.41
458.88
-0.21
-52.37
CMAQAB
20100524
16:00–22:00
0.38 ± 0.03
0.17
6.15
369.73
6.30
474.89
-0.10
-38.48
16:00–18:00
-1.61 ± 0.25
0.16
6.94
526.88
7.07
664.26
-0.07
-31.17
18:00–22:00
0.32 ± 0.02
0.22
5.95
330.05
6.10
427.07
-0.11
-40.33
20100616
13:00–18:00
0.80 ± 0.06
0.10
7.83
264.1
8.19
297.58
-0.22
-33.83
20100618
13:00–18:00
0.42 ± 0.05
0.03
5.59
425.7
5.76
494.16
-0.21
-50.36
(a) The hourly output of CMAQbase NHx is shown in the
background with the measured (1 min average) NHx concentrations
within the modeled hour shown as the dots for the daytime flight on 16 June
2010 and (b) a nighttime flight on 24 May 2010, and (c) the same nighttime
flight but for the CMAQAB scenario.
As noted above, the results for NH3(g) generally track the results
for NHx already discussed. In contrast, the model usually underpredicts
the small amount of NH4(p) observed (on average < 1 ppbv,
Fig. 4c) by a factor of 2, with little variation between the model scenarios
(Table 1, MB of NHx for CMAQbase, CMAQB and
CMAQAB of -0.40, -0.41 and -0.44, respectively). These model
errors in NH4(p) reflect not only model errors in total NHx,
but also errors in the formation of HNO3(g) and SO4(p)
(Fig. S3). HNO3(g) is overestimated in all model simulations up to
a factor of 4, with concentrations not changing between model cases.
SO4(p) measured concentrations are minimal, do not appear to have
any trend and also do not change with model cases. However, as our interest
in this study is in constraining NH3 emissions, not inorganic aerosol
formation, we do not investigate these errors further here.
Evaluation of modeled vertical distribution of NH3(g) using
aircraft observations
The aircraft observations in the SJV indicate a large underestimate (range
of factors about 1 to 5) in CMAQbase modeled NHx concentrations
above the surface, as shown in Table 2 (all flights in SJV) and Fig. 6
(two flights). The variation in model concentrations in the background of
Fig. 6 are due to the aircraft flying in and out of different horizontal
grid boxes in the model. The 24 May flight shows a strong
CMAQbase NHx underestimate of about a factor of 5 when considering
the entire flight with a low correlation (r2) of 0.31 and a mean bias
of -1.95 ppbv. This significant underestimate could potentially be due to an
underestimate of vertical mixing at night (discussed below); when only data
before 18:00 PDT is considered (assuming this is before the collapse of the
convective boundary layer) the underestimate is only a factor of
∼ 1.5 and the r2 is 0.77, a considerably better
and statistically significant result. However, model comparisons to flight
data on 16 and 18 June before 18:00 PDT, likely before the
boundary layer collapse on these days, show a significant model
underestimate and low r2 values, thus there may be other
contributing factors to this bias and lack of correlation, such as errors in
vertical transport and the neglect of day-to-day variability in the
emissions.
A daytime versus evening flight measurement evaluation of CMAQbase
shows a clear difference in the vertical distribution of NHx. At night
(24 May flight, Fig. 6b), the model contains most of the NHx
in the lowest model level, whereas during the day (16 June flight) it
vertically mixes the NHx (Fig. 6a). Based on the higher NHx
concentrations that the aircraft is measuring these results could suggest (1) vertical mixing is stronger than simulated in the model during both day and
night flights or (2) that there is a residual layer of NHx at night that
is not captured by the model or (3) there is a non-local source that is also
not well captured by the model.
Gas-phase NH3 can either be deposited to or emitted from the surface
depending on the land-type, land-use, and ambient concentrations (Bash et
al., 2015; Fowler et al., 2009). The CMAQbase run does not take
this into consideration, but when bi-directional NH3 is calculated with
a diurnal emission factor included in CMAQAB, NH3 dry
deposition should generally decrease, increasing the net land–atmosphere
flux (Bash et al., 2013). The CMAQAB model run shown in Fig. 6c is
consistent with these results (and inconsistent with the hypothesis that
vertical mixing is underestimated in the model) as the vertically distributed
concentration of NHx significantly increases from the CMAQbase
case to the CMAQAB case. The transport of NH3 also seems to
increase, this being a potential explanation for the plume entering the plot
domain around 21:00 PDT in the bottom curtain plot. The total column
concentration of NHx also increases, leading to a significant positive
model bias for the CMAQAB scenario (e.g., in the earlier part of
the flight in Fig. 6c and Table 2), suggesting a possible overestimation of
total NHx emissions by the bi-directional NH3 scheme and further
enhanced by adding a diurnal emission factor during the afternoon and evening
hours when the flights took place. This indicates that the diurnal factor
application in NH3 emissions at the surface grids does not significantly
change the concentrations aloft, where the flight measurements are taking
place compared to the CMAQAB case, resulting in remaining model
bias and requiring further investigation.
Summary statistics of the CMAQRVMR to TESRVMR
NH3 comparisons for four CalNex overpasses (05/28, 05/30, 06/13,
06/15). Mean bias (MB) = mean (modeled–measured); mean normalized bias
(MNB) = mean ([modeled–measured]/measured).
Model run
Slope
r2
MB
MNB
(ppbv)
(%)
CMAQbase
0.47
0.64
-2.57
-30.21
CMAQB
0.93
0.60
0.84
14.40
CMAQAB
1.02
0.60
1.31
19.57
NH3 representative volume mixing ratios (RVMRs) on 12 May 2010
during the CALNEX campaign for (a) TES special observations,
(b) modeled RVMR for CMAQ and (c) the difference between
each RVMR near the Bakersfield, CA, surface site, with the white diamond
locating the Bakersfield measurement site.
Scatter plot of CMAQbase (blue), CMAQB (green)
and CMAQAB (purple) versus TES NH3 representative volume
mixing ratios for TES special observation passes (TESRVMR) during
the CalNex campaign, with statistics discussed in Table 3.
Evaluation of modeled NH3(g) with TES NH3 retrievals
Applying the TES operator to the CMAQ profiles and calculating the
CMAQRVMR allows us to compare the satellite and model datasets
quantitatively, as described in Sect. 2.3. Surface NH3 from the
CMAQbase run (Fig. 7a) and the TES NH3 RVMR (Fig. 7b) along a
sample TES transect both identify the regions of large NH3 sources and
the spatial changes along the transect and demonstrate that the
CMAQRVMR is underestimated for the base run, particularly at higher
NH3 RVMRs. Similar results were found for other transects and summarized
in Tables 3 and S2. The time of the satellite overpass occurs just prior to
the peak of emissions in the emission factor applied to the CMAQAB
case which in turn increases the RVMR bias to 1.31 ppbv and increases the
regression slope to 1.02 (purple line Fig. 8) as compared to a bias of
-2.57 and slope of 0.40 in the CMAQbase case. The slope of the
linear regression of CMAQAB RVMR suggests that CMAQ run with
bi-directional ammonia along with the applied emissions factor slightly
overestimates NH3 concentrations, indicating that the magnitude of the
emissions factor may be too high at the time of satellite overpass. The
inclusion of the emission factor in this CMAQAB case has a higher
bias than the bi-directional model run, CMAQB. This demonstrates
the importance of using highly time-resolved observations of NH3 to
determine the diurnal cycle of NH3 along with polar-orbiting satellite
retrievals of NH3 to improve the spatial and seasonal distribution of
the emissions, as noted in Zhu et al. (2013). In other words, if we had
relied solely on the TES observations at 13:30 local solar time to evaluate
the CMAQbase runs, we would have incorrectly assumed that the CARB
inventory was a factor of 2.4 too low for total NH3 emissions, whereas
the surface data demonstrate that the problem is primarily in the diurnal
cycle of the emissions.
Modeled RVMR can be very sensitive to errors in the modeled vertical
distribution of NH3. We investigated this by comparing each level of
the TES retrieved NH3 profile with the corresponding CMAQ profile level
after the observation operator is applied. Figure 9 shows box-and-whisker
plots of this comparison for the CMAQbase and CMAQAB model
scenarios (CMAQB not shown). This plot differs from that in Shephard et al. (2015) in that it includes the average of layers below 908 mb, which
introduce an RVMR bias due to levels that are below 1000 mb. The CMAQAB
case shows the smallest bias of the three modeled scenarios in the lowest
pressure level (∼ 1 ppb) with the higher levels showing little
bias as well (∼ 0.08 ppb). Thus comparing the TES and CMAQ
profiles level-by-level indicates that the CMAQAB scenario demonstrates
the least bias in simulating the TES retrievals, consistent with the
conclusions based on the surface observations in Sect. 4.1.
Box plots of (a) TES NH3 retrieval by pressure level,
(b) TES NH3 retrieval averaging kernel (AK) diagonal,
(c) difference between the TES NH3 retrieval and
CMAQbase modeled NH3 interpolated to TES levels with an AK
applied for the baseline model run and (d) same as (c) but
for the CMAQAB run. Box plots show the mean (green), median (red),
interquartile range (IQR, blue box), whiskers at 1.5 IQR and outliers beyond
that.
Discussion
The results in Sect. 4 show that the CMAQAB model scenario that
included both the bi-directional NH3 scheme and the diurnally adjusted
emissions provided results that were much closer to the surface measurements
(Sect. 4.1) and satellite (Sect. 4.3) observations than the
CMAQbase runs, with measurement uncertainties explained in Sect. 2.
The CMAQAB simulations did result in a large overestimate of NHx
concentrations higher in the atmosphere as measured by the aircraft (Sect. 4.2). Here we discuss the remaining errors in the CMAQAB scenario,
suggest possible explanations for these errors, and make suggestions for the
direction of future research.
Model bias in both the night and daytime simulation of surface NHx is
reduced in the CMAQAB scenario. The total bias is significantly
reduced from the factors 4.5 at night and 0.6 during the day compared to the
CMAQbase scenario (Fig. 4a). In CMAQAB, the model does
well between the hours of 01:00 and 06:00 local time (Fig. 2a), perhaps
related to the lower emissions at this time of day when adjusted emissions
are used assuming the linear relationship of emissions with concentrations.
The remaining diurnal bias shows a relative model underestimate with a factor
of ∼ 0.6 at 10:00 local time and a relative model overestimate peaking
at ∼ 1.7 at 19:00 local time (Fig. 4c), with average CMAQAB
modeled concentrations slightly higher in the afternoon and peaking around
19:00 (Fig. 4d). It is interesting to note that the CMAQAB bias
relative to surface concentrations is small near the TES overpass time (e.g.,
crossing 0 % between 13:00 and 14:00 local time, Fig. 4c), which is
consistent with the small bias seen in the comparison with the TES
observations in Sect. 4.3. Furthermore, the aircraft results for the
CMAQAB scenario discussed in Sect. 4.2 also show a large relative
overestimate in the afternoon and evening when the flights took place
(Table 2), consistent with the afternoon and evening overestimates seen in
the surface data.
Thus all three datasets suggest that the remaining errors in modeled NHx
concentrations may be due to the diurnal profile of the net land–atmosphere
NH3 flux in the CMAQAB run peaking too late in the day. One
possibility to this is that the diurnal cycle we applied to the
non-fertilizer NH3 emissions, which was based on the ambient
measurements of NH3, is peaking too late in the day. However, as the
peak of our assumed diurnal profile for these emissions (Fig. S1) is
consistent with the peak in surface temperature (13:00, Fig. 4d), we consider
this explanation less likely than remaining errors in the bi-directional
NH3 scheme for fertilizer emissions.
These errors in the bi-directional NH3 scheme could be due to errors in
the dynamic emissions response of the bi-directional NH3 scheme to local
temperature, wind direction and speed (Bash et al., 2013). However, Fig. 4d
shows that the modeled surface temperature and wind speed are not that far
off from the values observed at the Bakersfield site for the majority of
measurements out of the northwest, and for those out of the southeast that
are not captured in the model, we believe that the long-range transport of
these winds through the Central Valley prior to entering the Fresno Eddy are
dominating the emissions profile of that air mass, thus influencing the final
concentration of that air mass. Thus the remaining errors are less likely
related to errors in atmospheric meteorological conditions, and are more
likely due to errors in the land–air interactions and the dependence of soil
conditions (e.g., soil temperature, pH, and water content) on meteorology and
crop management practices as calculated within the bi-directional NH3
scheme (Cooter et al., 2012). The scheme calculation assumes two soil layers
(0.01 and 0.05 m) that independently exchange NHx with the canopy,
which then exchanges NHx with the surface layer of the atmosphere (Bash
et al., 2013). If the calculation of the response of soil properties in these
layers to surface meteorology and crop management practices is incorrect
(e.g., the soil layers do not heat up or cool down quickly enough with the
change in surface temperature), that would affect the amount of NHx
available from the soil as well as the rate at which the soil NH4+ is
converted to NO3- through nitrification (Bash et al., 2013). This
would result in errors in the flux of NHx from the soil to the canopy,
thus altering the canopy compensation point and the net atmospheric flux.
The aircraft results may also suggest errors in the vertical mixing of
NHx during the afternoon and evening (e.g., the peak of the PBL height
and the collapse). While we consider this effect to be likely less important
to the remaining errors in CMAQAB than the potential errors in the
bi-directional NH3 scheme already discussed, an overestimate of vertical
mixing during the afternoon would overestimate the flux of NHx from the
surface layer of the atmosphere to the upper levels, reducing the
concentrations, which is consistent with the aircraft overestimate. In
addition, the soil–canopy–surface atmosphere system would respond to this
overestimate of vertical mixing by increasing the net flux of NHx from
the soil to the atmosphere in order to maintain equilibrium, resulting in a
total overestimate of the emissions of NHx during the afternoon and
evening.
We thus recommend that future work to improve the simulation of atmospheric
NHx concentrations in the SJV focus on bottom-up and top-down approaches
that will better estimate the diurnal changes in the canopy compensation
point that determines the net flux from the land to the atmosphere in the
bi-directional NH3 scheme (Bash et al., 2013). This scheme was
originally developed using field-scale observations taken in North Carolina,
USA (Walker et al., 2013), so it is not surprising that this approach may
require modifications to work in the SJV. We recommend, first, that the CARB
NH3 inventory be updated to better separate NH3 emissions from
fertilizer and livestock sectors. The Bash et al. (2013) scheme assumes that
these two sectors will dominate NH3 emissions, while the CARB inventory
divides fertilizer/pesticide use from “farming operations”; thus, it is
unclear whether these other farming practices are dominated by livestock or
not. Second, crop management data (e.g., fertilizer amount, timing, form, and
distribution) used in EPIC (and thus in the CMAQ bi-directional NH3
scheme) are based on data for the entire West Coast of the US (i.e.,
California, Oregon, and Washington), and thus may not be representative of
farming practices in the SJV. Better crop management data specific to the
SJV, as well as more SJV-specific data on soil moisture and heating rates,
may thus help in removing some of the remaining errors in the
CMAQAB scenario. Third, in order to better connect these bottom-up
emission estimates to the measured atmospheric concentrations, we recommend
that top-down studies focus not just on correcting the net NHx flux to
the atmosphere, but also determine the diurnally varying biases in the canopy
compensation point that determines these net fluxes. This may require the
development of adjoint methods and models (e.g., Zhu et al., 2015a) that can
retrieve time-varying correction factors for the canopy compensation point,
rather than just for the net flux itself.
Conclusions
We used NH3 retrievals from the NASA Tropospheric Emission Spectrometer,
as well as surface and aircraft observations of NH3(g) and
submicron NH4(p) gathered during the CalNex campaign, to evaluate
the ability of the CMAQ model run with the CARB emission inventory to
simulate ambient NH3(g) and NH4(p) concentrations in
California's San Joaquin Valley. We find that CMAQ simulations of NH3
driven with the CARB inventory are qualitatively and spatially consistent
with TES satellite observations, with a correlation coefficient (r2) of
0.64. However, the surface observations at Bakersfield indicate a diurnally
varying model bias and low correlation, with CMAQbase
overestimating NH3 at night by at times more than 50 ppbv and
underestimating it during the day by up to 10 ppbv. The surface, satellite,
and aircraft observations all suggest that the afternoon NH3 emissions
in the CARB inventory used in CMAQbase are underestimated by at
least a factor of 2, while the nighttime overestimate of NH3 is likely
due to a combination of overestimated nighttime NH3 emissions and
underestimated nighttime deposition. Thus the diurnally constant NH3
emissions used by CARB in the SJV appear to misrepresent the diurnal emission
cycle.
Using the bi-directional NH3 scheme in CMAQ (CMAQB) resulted in
reduced NHx concentrations at night and a slight increase during the
day, overall reducing the model bias relative to the surface and satellite
observations. However, this scenario substantially increased the simulated
mixing ratio of NHx at higher altitudes, leading to an increased bias
relative to the aircraft observations. In addition, errors in the simulation
of the nighttime surface concentrations remained in this scenario.
In order to evaluate the diurnal impact of NH3 emissions, we used the
surface observations at Bakersfield to derive an empirical diurnal cycle of
NH3 emissions in the NH3-rich region of the SJV in which
nighttime and midday emissions differed by about a factor of 4.5. Despite the
model not capturing winds out of the southeast at night, adding a diurnal
profile to the CMAQ bi-directional NH3 simulations
(CMAQAB) while keeping the daily total NH3 emissions constant
at the CARB values significantly reduced the model bias at night relative to
the surface observations, on top of the already reduced bias from the
CMAQB simulations. Comparisons with the TES RVMR showed a slight
increase in the bias for the CMAQAB scenario relative to
CMAQB, but further examination of the modeled and retrieved
vertical profiles suggests that this is primarily due to ∼ 1 ppb
differences in the lowest retrieved level, with the CMAQAB scenario
showing little bias (0.08 ppbv) relative to the TES NH3 profile above
this surface level. However, despite nighttime reduction in model bias in the
CMAQAB, scenario sizable errors (up to 20 ppbv) in the afternoon
and evening NH3 and low model correlations remained, possibly due to the
net land–atmosphere NH3 flux calculated by the bi-directional NH3
scheme peaking too late in the day due to errors in the calculated response
of the soil conditions (e.g., soil temperature, pH, and water content) to
meteorology and crop management practices.
We recommend that future work on modeling NHx emissions in the SJV
include (a) updating the CARB NH3 inventory to account for NH3 from
fertilizer, livestock, and other farming practices separately, (b) adding
information on crop management practices specific to the SJV region to the
EPIC-FESTC system, and (c) top-down studies that focus not just on correcting
the net NHx flux to the atmosphere, but also on determining the
diurnally varying biases in the canopy compensation point that determines
these net fluxes.