The San Joaquin Valley of California is known for
excessive ozone air pollution owing to local production combined with
terrain-induced flow patterns that channel air in from the highly populated
San Francisco Bay area and stagnate it against the surrounding mountains.
During the summer, ozone violations of the National Ambient Air Quality
Standards (NAAQS) are notoriously common, with the San Joaquin Valley having
an average of 115 violations of the current 70 ppb standard each year
between 2012 and 2016. Because regional photochemical production peaks with
actinic radiation, most studies focus on the daytime, and consequently the
nocturnal chemistry and dynamics that contribute to these summertime high-ozone events are not as well elucidated. Here we investigate the hypothesis
that on nights with a strong low-level jet (LLJ), ozone in the residual
layer (RL) is more effectively mixed down into the nocturnal boundary layer
(NBL) where it is subject to dry deposition to the surface, the rate of
which is itself enhanced by the strength of the LLJ, resulting in lower
ozone levels the following day. Conversely, nights with a weaker LLJ will
sustain RLs that are more decoupled from the surface, retaining more ozone
overnight, and thus lead to more fumigation of ozone the following mornings,
giving rise to higher ozone concentrations the following afternoon. The
relative importance of this effect, however, is strongly dependent on the
net chemical overnight loss of Ox (here [Ox] ≡ [O3]
+ [NO2]), which we show is highly uncertain, without knowing the
ultimate chemical fate of the nitrate radical (NO3). We analyze
aircraft data from a study sponsored by the California Air Resources Board
(CARB) aimed at quantifying the role of RL ozone in the high-ozone
events in this area. By formulating nocturnal scalar budgets based on pairs
of consecutive flights (the first around midnight and the second just after
sunrise the following day), we estimate the rate of vertical mixing between
the RL and the NBL and thereby infer eddy diffusion coefficients in the top
half of the NBL. The average depth of the NBL observed on the 12 pairs of
flights for this study was 210(±50) m. Of the average -1.3 ppb h-1
loss of Ox in the NBL during the overnight hours from midnight
to 06:00 PST, -0.2 ppb h-1 was found to be due to horizontal advection,
-1.2 ppb h-1 due to dry deposition, -2.7 ppb h-1 to chemical loss
via nitrate production, and +2.8 ppb h-1 from mixing into the NBL
from the RL. Based on the observed gradients of Ox in the top half of
the NBL, these mixing rates yield eddy diffusivity estimates ranging from
1.1 to 3.5 m2 s-1, which are found to inversely correlate with the
following afternoon's ozone levels, providing support for our hypothesis.
The diffusivity values are approximately an order of magnitude larger than
the few others reported in the extant literature for the NBL, which further
suggests that the vigorous nature of nocturnal mixing in this region, due to
the LLJ, may have an important control on daytime ozone levels.
Additionally, we propose that the LLJ is a branch of what is colloquially
referred to as the Fresno eddy, which has been previously proposed to
recirculate pollutants. However, vertical mixing from the LLJ may counteract
this effect, which highlights the importance of studying the LLJ and Fresno
eddy as a single interactive system. The synoptic conditions that are
associated with strong LLJs are found to contain deeper troughs along the
California coastline. The LLJs observed during this study had an average
centerline height of 340 m, average speed of 9.9 m s-1 (σ=3.1 m s-1),
and a typical peak timing around 23:00 PST. A total of 7 years of
915 MHz radioacoustic sounding system and surface air quality network data
show an inverse correlation between the jet strength and ozone the following
day, further suggesting that air quality models need to forecast the
strength of the LLJ in order to more accurately predict ozone violations.
Introduction
The main source of air for California's southern San Joaquin Valley (SSJV)
is incoming maritime flow from the San Francisco Bay area, which gets
accelerated toward the southern end of the valley as a consequence of the
valley–mountain circulation (Rampanelli et al., 2004; Schmidli and Rottuno,
2010). The local sources of ozone precursors are scattered along this
primary inflow path to the SSJV. The ozone buildup in the SSJV results from
both the large amount of local upwind sources and the Tehachapi Mountains to
the south, which block the flow and prevent advection out of the region
(Dabdub et al., 1999; Pun et al., 2000). Because of this tendency for the
air to stagnate, both daytime and nocturnal mesoscale dynamics are likely
important in the phenomenology of ozone pollution in this area.
Under typical fair-weather conditions over the continents, thermals are
generated near the surface beginning shortly after sunrise, buoyantly
forcing a convectively mixed layer, which is known more generally as the
daytime atmospheric boundary layer (ABL). As solar heating increases the
Earth's surface temperature throughout the day, this layer reaches its
maximum height by late afternoon, typically between 700 and 900 m in the SJV
during summer months (Bianco et al., 2011). Around sunset, when the solar
heating abates, the convective thermals shut off and no longer power
turbulent mixing in the boundary layer. The result of the subsequent
radiative cooling of the ground throughout the night forms a stable,
nocturnal boundary layer (NBL), typically extending between 100 and 500 m
(Stull, 1988) above the surface. The erstwhile convective layer from the
daytime, after spinning down and no longer actively mixing, functions as a
residual reservoir for pollutants and other trace gases from daytime
emissions and photochemical production. This layer overlying the NBL is
known as the residual layer (RL).
During both daytime and nighttime, mixing can occur between the boundary
layer and the layer of air above. In the daytime over land in clear-sky
conditions, this process of entrainment is driven by convective thermals
that penetrate into the laminar free troposphere above and then sink back
into the convective layer; it may be augmented by wind shear near the top
of the boundary layer (Conzemius and Fedorovich, 2006). Entrainment has been
shown to be a significant factor for near-surface air quality and more
generally for scalar budgets, as the two interacting layers often have
different trace gas concentrations (Lehning et al., 1998; Trousdell et al.,
2016; Vilà-Guerau de Arellano et al., 2011). At night, another type of
gas exchange can occur between the aforementioned NBL and the RL by
shear-induced mixing. Extensive observations of the structure of the NBL
indicate that a localized wind maximum near the top of the NBL, known as a
low-level jet (LLJ), is often present (Banta et al., 2002; Garratt, 1985;
Kraus et al., 1985). This LLJ is able to drive sheer production of
turbulence, thereby promoting the mixing between these layers despite the
stable stratification. In this study, we suggest that the LLJ in the SSJV is
part of the northerly flow component of what is colloquially referred to as
the Fresno eddy. As we attempt to show, the interaction between the LLJ and
larger Fresno eddy is complex and raises an important question about whether
the eddy simply recirculates ozone to exacerbate air pollution in the
region, or whether the LLJ associated with the eddy induces enough vertical
mixing to significantly deplete RL ozone and mitigate daytime ozone maxima.
Our study uses aircraft observations in a large area of the SSJV (see Fig. 3),
and thus these nocturnal mesoscale features are an important aspect of the
scope of our work.
The Fresno eddy can drive both vertical mixing and regional horizontal
advection. Monthly averaged wind speeds from June through August of the LLJ
in the SSJV up to 12 m s-1 have been reported (Bianco et al., 2011),
suggesting that shear-induced downward mixing of RL ozone in this region may
be particularly strong. It has been previously shown that RL ozone can have
a substantial correlation with ground-level ozone the following day (Aneja
et al., 2000; Zhang and Rao, 1999). Using SODAR data from the Swiss plateau,
Neu (1995) estimated that about 75 % of the following day's early
afternoon ozone was due to vertical mixing from the RL into the NBL. They
also found a good correlation (r=0.74) between weaker turbulence in the
RL, inferred from the amount of time wind maxima at night were observed
below 150 m, and the aforementioned early afternoon ozone levels. Coupling
of the RL and NBL via intermittent turbulence has also been shown to
correlate with overnight ozone spikes at ground-level monitoring stations
(Salmond and McKendry, 2005). Because of the complexity of intermittent
nocturnal turbulence, the spatial and temporal distributions of these spikes
are unknown, and thus the extent to which these ozone spikes
help to deplete the RL ozone or contribute to the following day's ozone is unknown. A
study from southern Taiwan also found that RL ozone plays an important role
in the following day's ozone concentrations, with fumigation of this ozone
into the developing daytime boundary layer accounting for 48 % of the
daily surface maximum (Lin, 2008).
Owing to the complex topography and stable stratification overnight, the
dynamics of the NBL and RL in California are difficult to model. Bao et al. (2008)
report that while the Weather Research and Forecasting (WRF) model
is able to qualitatively capture the LLJ, systematic errors up to 2 m s-1
are observed, with root mean square errors of 4–5 m s-1.
Above 2000 m, a similar magnitude of errors in the model's ability to
forecast wind is observed, and since the LLJ is influenced by this upper-level synoptic forcing, there is a need for more systematic study of the
background synoptic conditions associated with strong and weak LLJs.
At the core of our observational method, we recognize that most scalar
budgets are driven by horizontal advection, vertical mixing, local
emissions and uptake, and net chemical production (including chemical gains
and/or losses). While many previous studies of daytime ozone budgets
(Kleinman et al., 1994; Conley et al., 2011; Lehning et al., 1998; Lenschow
et al., 1981; Trousdell et al., 2016) have shown that photochemical
production is important, and a few nocturnal studies have highlighted
significant losses of ozone in the dark (Brown et al., 2006; Stutz et al.,
2010), we present here the first complete budget to include the mixing and
chemistry overnight. The nocturnal ozone chemistry is primarily driven by
its well-known reaction with NO2 to form the nitrate (NO3)
radical. The nitrate radical has many different loss pathways including
combining with NO2 to equilibrate with N2O5 (which can
undergo hydrolysis on surfaces), reacting with hydrocarbons, or reacting
with NO to regenerate O3 and NO2 (Brown et al., 2006, 2007; Wood
et al., 2005). As we will attempt to show, the chemical fate of the nitrate
radical is highly uncertain and plays a critical role in the net overnight
loss of ozone and consequently in our ability to predict the following
day's ozone level. Additionally, dry deposition of the chemical species of
interest cannot be ignored for scalar budgets (Conley et al., 2011; Faloona
et al., 2009). While the aforementioned studies focused on daytime scalar
budgets, to our knowledge, no attempts have been made at nocturnal scalar
budgets using aircraft data. Our goal is to test whether more nocturnal
mixing between the RL and NBL, induced by wind-shear turbulence beneath a
strong LLJ, will deplete ozone in the RL, making less available to fumigate
the following morning and seed further photochemical production. One
advantage of the present study is that we use airborne data to sample a
large area, which overcomes the limitations of studies using ground
monitoring stations that may be influenced by the intermittent bursts of
turbulence and confounds of uncertain horizontal advection. We will proceed
with this in three ways: first, we introduce a method for analyzing
the nocturnal scalar budgets of flight data, which is similar to that of the
daytime scalar budgets, and attempt to estimate the eddy diffusivity of
Ox in the NBL on each night of the field campaign (Sect. 3.1 and 3.2).
Second, to determine whether our findings can be generalized to
climatological timescales, we analyze synoptic conditions around the LLJ and
look at a broader dataset of LLJ strength and the following afternoon's
ozone concentrations using radioacoustic sounding system (RASS) and
California Air Resources Board (CARB) ground network data (Sect. 3.3 and 3.4).
Lastly, we look at other metrics of NBL turbulence in our campaign
data such as turbulent kinetic energy (TKE), bulk Richardson number (BRN),
and elevated mixed layers in order to further support our findings (Sect. 3.5 and 3.6).
Nocturnal Ox budgeting methodologyAirborne data collection
Aircraft data were collected by a Mooney Bravo and Mooney Ovation, which are
fixed-wing single-engine airplanes operated by Scientific Aviation, Inc. The
wings are modified to sample air through inlets, which flow to the onboard
analyzers. Temperature and relative humidity data were collected by a
Visalia HMP60 humidity and temperature probe, ozone was measured with a dual-beam ozone absorption monitor (2B Technologies, model 205), and NO was
measured by chemiluminescence (ECO PHYSICS, model CLD 88). NOx was
measured by utilizing a photolytic converter (model 42i BLC2-395
manufactured by Air Quality Design, Inc.). For flights performed in 2016, a
pre-reaction chamber was also installed to monitor and subtract the changing
background signal, reducing the detection threshold to <50 ppt.
Frequent calibrations were performed in the field, generally once per
deployment, with zero and span checks daily. Calibrations for NO
measurements were performed with a NIST-traceable standard by Scott-Marrin,
Inc. Calibrations for NOx measurements were performed by titrating the
NO standard with an ozone generator (2B Technologies, model 206 Ozone
Calibration Source.) During routine operation on the aircraft, the lamp of
the photolytic converter was toggled on and off at 20 s intervals
during the flights (corresponding to approximately 1.5 km horizontal and 50 m
vertical displacements by the aircraft), requiring linear interpolation
for continuous NO and NO2 data. The pre-reaction chamber was toggled on
for a 40 s period every 10 min in order to measure the background
signals of NO and NOx, and the background signals were subtracted from
the measurement. The interpolated NO2 signal was noted to decay
approximately exponentially after powering up, which sometimes affected the
first 15–30 min of flight. The presumed artifact was successfully
replicated in the laboratory with a constant NO2 concentration and was
removed by exponential detrending.
Winds are measured using a dual-hemisphere global positioning system
combined with direct airspeed measurements, as described in Conley et al. (2014).
The winds are measured at 1 Hz, and the power spectra are observed to
fit the Kolmogorov scaling law within the inertial subrange (approximately
0.12–0.5 Hz in the daytime convective boundary layer corresponding to
roughly 150–600 m spatial scales). At night, the -5/3 slope is observed
from 0.02 to 0.5 Hz (Fig. 1), corresponding to length scales of 150–3700 m,
the largest of which are likely contributions from buoyancy waves. This is
evident by the calculated Brunt–Väisälä frequencies (Fig. 2),
which have an average value of 0.023 Hz in the NBL. For simplicity's sake, we
consider anything smaller than this buoyancy frequency to be “turbulence”
and use 1/NBV∼50 s as the sampling time to
observe wind variances, though we recognize that this cutoff is somewhat
arbitrary. The TKE is estimated by correcting the
observed wind variance of a given detrended 50 s signal with the
integrated nocturnal power spectra beyond the Nyquist frequency (0.5 Hz)
using a -5/3 extrapolation, which indicates that approximately 11 % of
the total variance is not directly captured by the system. Only horizontal
winds are measured, and thus similarity assumptions are required to estimate
vertical wind variance (σw2). While some similarity
relationships have been reported for the NBL (Nieuwstadt, 1984), we were not
able to measure the governing parameters. However, Banta et al. (2006)
reported a meta-analysis of NBL studies with an average σw2/σu2 of 0.39, where σu2 is
the streamwise variance. We applied this correction to our TKE measurements
to account for the missing vertical wind variance.
Power spectra for nighttime winds averaged over 309
5 min samples. The average airspeed was 76.6 m s-1.
Mean and standard deviation profile of
Brunt–Väisälä frequencies for all late-night flights. The mean
value within the stable boundary layers is 0.023 s-1.
Data were collected on five separate deployments (10–12 September 2015, 2–4 June 2016,
28–29 June 2016, 24–26 July 2016, 12–18 August 2016). During a given
deployment, four flights per day were conducted (07:00, 11:00, 15:00, and 22:00 PST). Each
deployment consisted of stationing the airplane at Fresno Yosemite
International Airport (FAT), with each flight comprising a transect to
Bakersfield Meadows Field Airport (BFL) and back, spanning approximately 2 h
and 15 min (Fig. 3). Profiles of the full boundary layer and above
were taken at Fresno and Bakersfield. Along the Fresno–Bakersfield transect,
altitude legs of 500, 1000, and 1500 m a.g.l. were conducted in a randomized
order. Low passes were also flown over the Tulare (TLR), Delano (DLO), and
Bakersfield airports, but in 2016 we replaced the low approaches at Tulare
with Visalia (VIS) to coincide with the NOAA lidar deployment. All of these
airports are within a few hundred meters of California Highway 99, or in the
case of Fresno and Bakersfield within an urban center. If time permitted on
any given flight, we typically completed an extra profile at Visalia or
flew west toward Hanson to better sample the nocturnal LLJ.
Flight paths of all aircraft deployments in this field
campaign (green). Airports where low approaches were conducted (red
triangles) and with ground ozone monitors (blue crosses) are shown. From north to
south, the airports are Fresno Yosemite International Airport (FAT), Visalia
Municipal Airport (VIS), Delano Municipal Airport (DLO), and Bakersfield
Meadows Field Airport (BFL). From north to south, the CARB ground ozone
network stations are Fresno Sierra Skypark no. 2, Clovis N Villa Avenue,
Fresno Garland, Fresno Drummond Street, Parlier, Visalia N Church Street,
Hanford S Irwin Street, Shafter Walker Street, Bakersfield 5558 California
Avenue, Edison, and Bakersfield Municipal Airport.
The nocturnal scalar budget analyses presented here utilize all late-night
(∼21:45–00:00 PST) flights for which a subsequent flight was
conducted the following morning (∼06:15–08:30 PST). The
dates (before midnight PST) of the late-night flights for the 12 overnight
periods are shown in Table 1. Additionally, late-night flights without a
subsequent morning flight were flown on 12 September 2015 and 26 July 2016,
and morning flights without a preceding late-night flight were flown on 10 September 2015,
24 July 2016, 12 August 2016, and 14 August 2016. These
additional flights are included in the analyses here that refer exclusively
to either the late-night or morning flights, but they were not used for the
scalar budgets.
Scalar budget analysis
Here we aim to test the importance of nocturnal mixing on the ozone
budget in this region by applying a scalar budgeting technique to the
aircraft data in order to estimate an eddy diffusivity between the NBL and
the RL. To address this objective, we use a similar method that has been
presented with daytime scalar budgets (Conley et al., 2011; Faloona et al.,
2009; Trousdell et al., 2016) to further demonstrate the overall
practicality of this methodology.
The nocturnal budget equation is formulated by the Reynolds-averaged
conservation equation for a scalar – in this case Ox – in a turbulent
medium. Ox is defined here as NO2+O3 in order to avoid the
effects of the titration of O3 by NO. If not depleted by chemical oxidation
to NO3 and further reaction products, NO2 will photolyze the
following day to reproduce ozone in a photostationary state so it can act as
an overnight reservoir of ozone. The chemical loss of Ox is then
computed by the reaction between O3 and NO2 to form nitrate, and
the ultimate fate of nitrate will affect the overall Ox loss. In the
stable nighttime environment we will treat the mixing between the RL and NBL
by using an eddy diffusivity. The NBL Ox budget can thus be represented
as
∂[Ox]∂t=-αkO3+NO2O3NO2-u‾Δ[Ox]Δx‾-v‾Δ[Ox]Δy‾1+-[O3]SFC⋅vdh+KzΔ[Ox]Δzh,
where the term on the left represents the change in concentration with
respect to time. The leftmost term on the right side of Eq. (1) represents the
net loss of Ox due to chemical reaction of the resultant NO3 and
contains an unknown constant of proportionality, α, which depends on
the subsequent reaction pathway of NO3 and can range from 0 to 3. For
reasons discussed later, α is assumed to be ∼1.5 for
this analysis. The next two terms represent changes due to advection by the
horizontal wind, followed by terms representing the dry deposition of ozone
to the surface, and finally the vertical turbulent mixing term that uses the
vertical gradient and the eddy diffusivity, Kz – a number that
encapsulates the strength of the overnight mixing. The storage (left-hand
side) term, chemical loss, advection, surface ozone, and NBL height can be
calculated using the aircraft data. Combining those measurements with an
estimated 0.2 cm s-1 nighttime dry deposition velocity of ozone in the
SSJV (an average from a study over cotton, grass, mixed deciduous forest,
and vineyard field sites by Padro, 1996), we can indirectly estimate
Kz. In the following sections, we detail the methods for estimating the
terms in Eq. (1).
Mean and ±1 standard deviation (swathes) of
potential temperature, ozone, NO, NO2, wind speed, and turbulent
kinetic energy (mean only) from all late-night flights.
NBL height
Profiles of wind speed, potential temperature, NO2, and O3 from
each night and morning flight were analyzed to make a best guess of the NBL
height, h. Figure 4 shows the average scalar profiles from all 15 late-night
flights to illustrate the typical gradients in the lower portion of the
atmosphere. One method of determining h is to observe the lowest elevation
at which ∂θ/∂z becomes close to adiabatic, as the
layer below that physically represents air that is in thermodynamic
communication with the radiatively cooled surface (Stull, 1988). Another
method is to use the level of wind maximum, or LLJ height, when one is
present. We found that both of these estimates typically yielded similar
values of h. On nights when there was significant disagreement between the
two different estimates, the vertical jump (or sharpest gradient) of Ox
in the height region of the NBL–RL interface was considered, as this likely
points to a region of maximum mixing. In such cases, we averaged the height
at which the steepest gradient was observed with the estimates obtained from
the other two methods. It should be noted that some subjectivity was
involved for determining a final value of h for each night because wind
maxima and thermal gradients were not always clearly defined in the
profiles. All of the aforementioned factors lead to an estimated uncertainty
of ±100 m for all of the NBL heights obtained. The average conditions
from the late-night and morning flights are presented in Table 1.
NBL heights, ozone, NO2, Brunt–Väisälä
(BV) frequencies, bulk Richardson number (BRN), turbulent kinetic energy
(TKE), and LLJ maximum wind speeds observed during the late-night and
morning flight pairs. Maximum daily 8 h average ozone (MDA8) values are
from the following day and are an average of the 11 ground networks in our
flight region.
Flight dateNBL heightNBL O3NBL NO2MDA8BV frequencyBRNTKELLJ maxσu/Uxh (m)(ppbv)(ppbv)(ppbv)N (s-1)(m2 s-2)Ux (m s-1)9 Sep 201525045.416.582.70.0250.680.358.10.0912 Sep 201513031.218.567.20.0180.890.704.00.223 Jun 201626052.76.087.80.0210.230.3512.00.054 Jun 201622059.06.192.30.0260.800.505.90.1229 Jun 201615043.09.991.90.0220.280.4110.00.0825 Jul 201619044.212.085.50.0220.710.436.40.1026 Jul 201632051.68.794.80.0230.990.568.00.0813 Aug 201615049.813.992.10.0170.410.619.10.0815 Aug 201625042.511.674.30.0230.371.0210.30.0816 Aug 201621044.814.186.80.0250.520.719.40.1017 Aug 201617048.315.991.50.0241.350.746.20.1218 Aug 201619048.812.692.20.0251.000.715.60.17Average20846.812.186.60.0230.690.597.90.11SD536.53.87.90.0030.320.192.20.04
For the domain of interest, all measured NO2 and O3 data were
averaged for each 20 m altitude bin in order to generate mean vertical
profiles of Ox. Separate profiles were created for the late-night
flight and the subsequent morning flight. The height of the NBL for each
night (h) was used as the upper altitude limit when averaging observations to
obtain advection, chemical loss, and time rate of change (storage) terms for
the budget equation, since the budget equation is meant to be applied to the
NBL. The overnight average Ox profile was subtracted from the sunrise
profile and divided by the time difference between the midpoints of each
flight to compute the storage term.
Nocturnal chemical loss of Ox
The chemical loss term in Eq. (1) is expected to be an important
component of the NBL Ox budget. Both NO2 and NO3 are able to
regenerate ozone in the presence of sunlight and participate in the same
sequence of reactions; therefore, the species are normally grouped together
into a family referred to as odd oxygen (Ox=O3+NO2+2NO3+3N2O5) (Brown et al., 2006; Wood
et al., 2005). However, since we did not measure NO3 and
N2O5, in this study we estimate Ox as merely the sum of
O3+NO2 because these are expected to exceed to concentrations of
the other Ox species by 1–2 orders of magnitude (Brown et al., 2003;
Smith et al., 1995). Considering Ox is useful for our study because the
family is conserved in the rapid oxidation of NO by O3 (Reaction R1 below)
yielding NO2, which may be quickly photolyzed to regenerate O3
once the sun rises as part of the standard daytime photostationary state.
Aside from dry deposition to the Earth's surface, NOx chemistry is the
main loss of ozone at night, counteracting its role in production during the
daytime (Brown et al., 2006, 2007). The chemical loss of ozone at night
begins with the production of the nitrate radical (Reaction R2).
R1NO+O3→NO2+O2R2NO2+O3→NO3+O2NO3 photolyzes rapidly once the sun rises, so the ultimate net loss of
ozone depends on the loss of nitrate in the dark. The loss occurs mainly via
three general channels. In one channel, dinitrogen pentoxide is formed (Reaction R3),
which has a backwards reaction and can be a source of NO2 if not
deposited onto moist surfaces or aerosols to form nitric acid via hydrolysis
(Reaction R4).
R3NO3+NO2+M↔N2O5+MR4N2O5+H2O→2HNO3Net (R1-R4)NO+2O3+NO2→2NOzNOz=NOy-NOx represents the family of products
of NOx oxidation. In another channel, nitrate is lost by reaction with
a wide array of organic compounds. This process can typically be represented
by Reaction (R5), but in some cases, organic compounds can become rearranged to produce an
NO2 molecule (Reactoin R5a) (Brown et al., 2006).
R5NO3+(VOC,etc.)→organicnitratesR5aNO3+(VOC,etc.)→organicnitrates+NO2Net (R1, R2, R5)NO+2O3→NOzNet (R1, R2, R5a)NO+2O3→NOz+NO2
However, in urban environments with nocturnal sources of NO, nitrate is
reduced back to NO2 by very rapid reaction.
R6NO+NO3→2NO2Net (R1, R2, R6)2NO+2O3→2NO2
If the hydrolysis of N2O5 (Reaction R4) is the dominant NO3 sink, then
the net reaction leads to a loss of three Ox molecules per nitrate produced
(Reaction R2). However, if the dominant loss is reaction with VOCs then the net
reaction leads to between one (Reaction R5a) and two (Reaction R5) Ox molecules lost per R2.
And if there is sufficient NO, Reaction (R6) will dominate the nitrate loss, leading to
no net Ox loss per Reaction (R2). Thus, determining the dominant loss of nitrate
is crucial for any analysis of the diurnal budget of ozone.
Reaction (R6) has often been ignored at night under the presumption that
local sources of NO are sparse and Reaction (R1) will outcompete Reaction (R6)
(Brown et al., 2007; Stutz et al., 2010). However, at observed values
of 30 ppb of O3 and an estimated 20 ppt of NO3 (Smith et al.,
1995), the lifetime of NO (∼80s) with respect to Reaction (R1) would
be nearly equivalent to that of Reaction (R6). Our measurements indicate ground-level
NO of about 0.6 ppb at midnight (σ=1 ppb), corroborated by the
CARB surface air quality network, increasing in the early morning hours to
2–4 ppb. However, both the ground network and aircraft observations may be
biased high to the regional average because of their proximity to California
Highway 99 and other urban centers (Fig. 3). Nevertheless, the rate of
Reaction (R6) is 2.6×10-11 cm3 s-1 molec-1 (Sander et
al., 2006), extremely rapid relative to the others, such that even 60 ppt of
NO (an order of magnitude lower than what our measurements indicate) would
result in an NO3 lifetime of only 25 s. Hence, we conclude that
Reaction (R6) should not be ignored in general as it may ultimately reduce the
chemical loss rate of Ox overnight.
There is then a further question as to whether any VOCs would be able to
compete with this channel of NO3 consumption. An investigation into the
most rapid VOC reactions with NO3 per Atkinson et al. (2006) and
Gentner et al. (2014a) is presented in Table 2. In this analysis,
concentrations of VOCs are estimated from available reports in the SJV,
which given its roughly 5 million acres of irrigated land (Li et al., 2016)
may vary widely from one location to another due to the presence of diverse
crop canopies. The estimated lifetime of NO3 due to the VOC reactions
in Table 2 is 12.2 s, about 5 times the lifetime of NO3 with
respect to the presence of 0.6 ppb of NO (2.5 s). We note that
although there are few direct observations of NO3 in the SSJV, the
CalNex campaign conducted one flight that measured concentrations of about
10–40 ppt shortly after sunset on 24 May 2010
(https://esrl.noaa.gov/csd/groups/csd7/measurements/2010calnex/P3/DataDownload/index.php,
last access: 30 April 2018).
Smith et al. (1995) present DOAS measurements from 15 nights in July and
August 1990 (their Fig. 6a) from a site 32 km southeast of Bakersfield
suggesting that NO3 concentrations in the SSJV peak around 30 pptv
within an hour or two after sunset and plateau in the middle of the night
around 10 ppt, then decline to zero by sunrise. The variability of NO3
reported in that study is high, with nocturnal values ranging from near zero
to over 50 ppt. Under a simplified, steady-state model, the expected
lifetime of NO3 can be estimated using the second-order reaction rate
for Reaction (R2) for the formation of the nitrate radical and combining all of the
loss channels into a single lifetime (τNO3):
τNO3=[NO3]k2NO2[O3].
Using the average NBL ozone and NO2 from Table 1, an NO3
concentration of 10 ppt would imply its lifetime to be about 25 s,
which is about twice as large as our estimate from Table 2. Based on these
direct measurements of NO3, our lifetime calculations likely represent
a lower bound and further illustrate the uncertainty given the sensitivity
to the unconstrained VOCs and our NO measurements, which have an envelope of
error that spans a large range of possible nitrate loss lifetimes.
Estimations of VOC reactions with nitrate in the
summertime nocturnal boundary layer for the SSJV. Reaction rates from
Atkinson and Arey (1998), Table 2, and Atkinson et al. (2006).
VOCkBest guessτNO3Sourcecm3 mlc-1 s-1pptso-cresol3.33 (10-11)10120Estimate1linalool2.22 (10-11)5036Arey et al. (1991)33-methylfuran1.90 (10-11)9235Steiner et al. (2008)b-caryophyllene1.67 (10-11)13190Gentner et al. (2014b)6-methyl-5-hepten-2-one1.67 (10-11)10241Estimate2limonene1.33 (10-11)26117CalNexmyrcene1.11 (10-11)29124Gentner et al. (2014b)sabinene9.52 (10-12)31284CalNexb-phellandrene8.33 (10-12)10482Estimate2Phenol7.41 (10-12)10542Estimate2a-pinene6.06 (10-12)47142CalNexb-pinene2.67 (10-12)34654CalNextrans-2-butene7.94 (10-13)130389Steiner et al. (2008)isoprene6.94 (10-13)68853CalNexcamphene6.54 (10-13)78502CalNexNET12.2
1 Drew Gentner of Yale University, personal communication. 2 No measurements
reported in the SSJV; an order of magnitude estimate is made based on typical aerosol concentrations.
3 Arey et al. (1991) reported 70 ppt in an orange grove. We estimate 50 ppt as an SJV average.
With longer lifetimes of nitrate loss with respect to the VOC and NO
reactions, we are faced with the possibility that the hydrolysis of
N2O5 is also an important loss channel, increasing the amount of
Ox molecules lost per nitrate molecule formation in Reaction (R2).
Smith et al. (1995) report that the lifetime of NO3 was found to be highly dependent
on relative humidity, with lifetimes ranging from seconds to 10 min when
the relative humidity is above 45 % (presumably due to N2O5
hydrolysis) but between 10 and 60 min when below the 45 % threshold.
Figure 5 shows the diurnal cycle of temperature and relative humidity
observed at the airports in our flight region during the days of our
campaign compared with the 2015–2016 1 June–30 September averages. The
>45 % relative humilities observed at FAT and VIS imply that
the hydrolysis of N2O5 is an important sink for NO3.
Given the importance of nitrate loss to VOCs and NO, but some importance
of the N2O5 hydrolysis, we use a best estimate that each effective
collision of NO2 and O3 will lead to the net loss of approximately
1.5(±0.5) molecules of Ox from the net effects of the entire
series of reactions outlined above. This is a “center of the envelope”
estimate for the possible range of 0–3. Although our measurements are
unable to constrain this coefficient, the ultimate fate of the nitrate
radical can be seen to have a critical role in quantifying the net loss of
Ox overnight, and without a greater understanding of the nitrate
budget, predicting this loss rate is uncertain.
Consequently, we calculate the net Reaction (R1–R6) for the nocturnal
chemical loss rate of Ox as a constant multiple of Reaction (R2).
The second-order rate equation for the net chemical loss of Ox is calculated by
dOxdtchemicalloss=-αkO3+NO2O3NO2,
where α can range from 0 to 3 and, per the discussion above, is
estimated to be 1.5±0.5 (uncertainty discussed in Sect. 3.2). To
estimate a value for the second-order rate constant (kO3+NO2), we start
with the temperature-dependent function for this reaction (Sander et al.,
2006):
kO3+NO2=1.2(10-13)⋅e-2450T,
where T is the temperature in Kelvin. For the domain being analyzed, an
instantaneous value of kO3+NO2 is determined at each data point. These
values of kO3+NO2 are then averaged to obtain a constant value for the
given night. It should be noted that small errors in the value of k that are
within the order of our temperature fluctuations were found not to have a
measurable impact on the chemical loss term. To estimate the chemical loss
of Ox, the initial 20 m altitude bins for NO2 and O3 are
taken from the late-night and morning profiles. In each bin, the
concentrations are linearly interpolated between the late-night and morning
values so that there is an estimation of the current average concentration
within that bin at every time during the night.
Diurnal plots of temperature and relative humidity during
flight days of the Residual Layer Ozone campaign (individual days: grey
lines, campaign average: blue lines) compared to 1 June–30 September 2015
and 2016 averages (red lines) at the Fresno (FAT), Visalia (VIS), and
Bakersfield (BFL) airports as part of the Automated Weather Observing System (AWOS)
network. Hours are in Pacific Standard Time (PST).
Horizontal advection by mean wind
The advection term in Eq. (1) is calculated by first collecting all
1 s Ox data points for the late-night and morning flights
separately. For each flight, a multiple linear regression is fit through the
1 s Ox data for latitude (y), longitude (x), and altitude (z),
allowing for estimations of the horizontal gradients of Ox
(∂[Ox]/∂x and ∂[Ox]/∂y) in the
horizontal advection term. The r2 values of the regressions ranged from
0.25 to 0.69, and the number of data points that they contained ranged from
2813 to 5323. Typical values of the horizontal Ox gradients were of
order 0.1±0.02 ppb km-1. To compute the total advection term
within the NBL on a given flight, these gradients are combined with the mean
wind speeds.
AdvectionOx=-∂Ox∂x⋅u‾+∂Ox∂y⋅v‾
Per convention, u is the mean x component (zonal) wind and v is the mean
y component (meridional) wind. The same procedure is repeated for the morning
flights, and the advection terms from the late-night and morning flights are
averaged together.
Dry deposition of Ox
Dry deposition of ozone is presumed to be an important sink of Ox at
the surface, the flux of which can be parameterized as the product of the
surface ozone values (measured directly from the aircraft) and the
deposition velocity for ozone. There are reports of ozone deposition in the
area of our field campaign from a 1994 study using the eddy covariance
technique (Padro, 1996). The findings of that study suggest nocturnal ozone
deposition velocities are several times smaller than their daytime
counterparts, but we infer that the overall process is still important for
the budget in the NBL because of the smaller mixed-layer depth (Eq. 1).
Based on an abundance of observations of nocturnal ozone dry deposition
velocities reported in the literature over a broad variety of grassland and
agricultural surfaces similar to those found in the SSJV (Pederson et al.,
1995; Pio et al., 2000; Mészáros et al., 2009; Neirynck et al., 2012; Lin et
al., 2010), all ranging about 0.1–0.3 cm s-1, we estimate a
dry deposition velocity of 0.2 cm s-1 (±0.1 cm s-1) for our
purposes. We ignore NO2 deposition on the basis that crop canopies can
either be a small source or sink of NO2 at the surface (Walton et al.,
1997). The amount of Ox lost overnight due to deposition would be
within our stated uncertainty (±0.86 ppb h-1) as long as
|vdNO2|<∼2.5 cm s-1,
an assumption supported by the literature (Pilegaard et al., 1998; Walton et al., 1997).
Vertical turbulent mixing between the NBL and the RL
Finally, a vertical flux divergence for Ox must be estimated for
Eq. (1), which is represented by the last two terms. For the top part of
the NBL, the flux of Ox can be interpreted as an eddy diffusivity
(Kz) multiplied by the vertical gradient of Ox between the NBL and
RL. For each flight, a linear regression through the 1 s Ox data
within the NBL–RL interface is used to determine ∂[Ox]/∂z (for the last term in Eq. 1) in the upper
portion of the NBL that appeared to contain the strongest Ox gradient.
The average r2 value of the 24 regressions was 0.11, and the number of
data points that they contained ranged from 116 to 2166. Typical values of
the vertical Ox gradients were ∼0.07±0.04 ppb m-1.
The layers used for the regression fit were 100–200 m thick and
did not extend below 70 m a.g.l. to avoid capturing the region where the
Ox sink due to surface deposition and/or reaction with freshly emitted
NO likely accounts for the vertical gradient in Ox (Fig. 6). The eddy
diffusivity can now be solved for with all of the other terms estimated.
The contribution of vertical mixing to the budget can be visualized as an
inferred difference between Ox profiles that are observed and Ox
profiles that are predicted from other terms in Eq. (1). Figure 6 shows
an example of the observed profiles of Ox on the late-night and morning
flights for the series performed on 4 June 2016. The height of the NBL is
shown (green), and the lower bound of the layer used in the vertical
gradient fit is shown (yellow). The dashed profiles show the expected
profile that would have been observed on the morning flight if only
advection (blue), chemical loss (green), or both advection and chemical loss
(red) processes were occurring. The observed morning Ox (magenta) is
inferred to exceed the predicted morning Ox (red) due to the vertical
mixing term in the scalar budget equation.
Ox profiles from 4 June 2016 overnight analysis, NBL
height (green line), and lower bound to vertical mixing gradient (yellow
line). The solid lines are observations and the dashed lines are calculated
based on expected changes due to horizontal advection (blue), chemical loss
(green), and the sum of the two (red).
Results and discussionOx scalar budget results
Results of the scalar budget analysis for all 12 paired late-night and
morning flights are presented in Table 3. An error propagation analysis
(discussed in Sect. 3.2) is presented for each term in the budget, and
for the Kz values.
Results from the nocturnal scalar budget for all terms.
Estimated error (see Sect. 3.2) in parenthesis.
Of note is the fact that, on average, the chemical loss is expected to be a
little more than twice as large as the physical loss from dry deposition.
For dry deposition, the average lifetime of ozone is 28 h (200 m / 0.002 m s-1),
and for chemical loss it is 12 h. Both losses of Ox added
together are about triple the observed time rate of change, and thus the
physical and chemical losses are largely (∼2/3) compensated for
by vertical mixing. Because the RL consistently contains more ozone than the
stable NBL, turbulent mixing will result in a transfer of ozone into the
NBL. While NO2 is observed to be higher in the NBL than in the RL (by
about 3–5 ppbv), it is a much smaller contribution to Ox (O3
is less than NO2 by anywhere from 10 to 20 ppbv.) Thus, vertical mixing at
the top of the NBL, influenced by the strength of the LLJ, is inherently a
source term of Ox to the lower NBL.
Error analysis
Here we estimate the uncertainties for each term in the budget equation and those for the resultant eddy diffusivities. The storage term error
is computed by first taking the standard deviation of 1 s ozone
measurements divided by the square root of the number of samples, then the
standard error of the means for both the late-night and morning profiles are
combined. This analysis is carried out in 20 m altitude bins separately and
then averaged together because there is more uncertainty at lower altitudes
due to fewer measurements. The advection term error is computed from the
standard error of the slopes of the regression fit, with errors propagating
for each of the four advection components for both the u and v components of
wind. To compute the chemical loss error, the large uncertainty of the
α coefficient must be taken into consideration. Based on our
analysis concluding that all channels of nitrate loss are probably
non-negligible, we infer that α is between 0.5 and 2.5 with a 95 %
confidence interval. Thus, 1 standard error for the α
coefficient is about 0.5. An error propagation is then carried out for each
20 m altitude bin using the standard deviations of the O3 and NO2
measurements divided by the square root of the sample size. As previously
stated, the estimated standard errors of the NBL height and surface
deposition of ozone are taken to be 100 m and 0.1 cm s-1, respectively.
The surface ozone standard error is computed as the standard deviation of
the aircraft measurements divided by the square root of the sample size, and
the vertical Ox gradient uncertainty is computed by the standard error
of the regression slope. The uncertainties in the vertical mixing,
deposition flux, and diffusivity values can then be computed by standard
error propagation. The resultant relative error estimates of the nighttime
diffusivities are about 50 %, and errors of this order seem reasonable
based on a technique that assumes the closure of four independently measured
terms. Past studies using similar airborne budgeting methods have estimated
relative uncertainties ranging from 15 % to 75 % (Conley et al., 2011; Faloona
et al., 2009; Kawa and Pearson, 1989; Trousdell et al., 2016).
The Fresno eddy and LLJ
The formation of the Fresno eddy begins when the daytime northwesterly
valley wind continues into the late evening, decoupling from the surface and
forming an LLJ (Davis, 2000). The Tehachapi Mountains will typically
topographically block the flow of the LLJ (Lin and Jao, 1995). The eddy is
formed during the hours before dawn when this northwesterly flow interacts
with southeasterly nocturnal downslope flow coming from the high southern
Sierra Nevada Mountains, although there is some question as to the extent to
which the southeasterly flow observed in the morning hours is merely the
result of a topographic deflection and recirculation of the nocturnal jet.
The Coriolis force helps to circulate this flow; however, a mesoscale low is
not thought to develop (Bao et al., 2008; Lin and Jao, 1995). We note that
the valley flow peaks around midnight, while the katabatic drainage flow
peaks near dawn, so these two components of the Fresno eddy are not
time coherent. The initial northwesterly wind and a topographic blockage are
both critical for determining whether or not the eddy will form on a given
night (Lin and Jao, 1995).
One complicating factor for our scalar budget analysis is the influence that
this eddy will have on our measurements of advection. If an eddy is
recirculating a scalar quantity, using a simple linear fit model as we did
in Sect. 2.2.3 to estimate advection would be questionable, especially if
the flight area only covered a small portion of the larger mesoscale
circulation. Zhong et al. (2004) use a series of 915 MHz RASSs to analyze
low-level winds in the SSJV. Their Fig. 4 shows that at night, the
northwesterly LLJ is formed in the SJV, and a weak katabatic southerly flow
is observed in the foothills to the east at the Trimmer site. As the night
progresses, the eddy becomes more coherent as the northwesterly jet relaxes,
while the southerly flow strengthens and expands westward. After daybreak,
the eddy appears to deform and disintegrate, with much of the SSJV
experiencing a strong southerly wind.
This pattern is roughly consistent with our aircraft observations,
suggesting the presence of a Fresno eddy during our flights. An analysis of
the average wind vectors and their consistency for all nocturnal and morning
flights in the approximate NBL (0–300 m a.g.l.) and RL (300–700 m a.g.l.)
is shown in Fig. 7. The wind consistency is defined as the ratio of the
vector-averaged wind speed to the magnitude-averaged wind speed, with values
close to 1 indicating a consistent wind direction (Stewart et al., 2002;
Zhong et al., 2004). The nocturnal LLJ can clearly be seen to fill most of
the SSJV in both the NBL and RL. In the morning RL, there is localized
consistent southerly flow closest to the foothills, some of which may be
regarded as surprisingly strong. The lower-level winds in the morning are
consistent with the deformed eddy. We note that caution should be exercised
in directly comparing our flight data to the analysis from
Zhong et al. (2004) as our flights specifically targeted high-ozone events, which
we based primarily on high temperature stagnation conditions (see Fig. 5).
Thus, the synoptic and mesoscale conditions during our flights may be
systematically different from the climatological norms presented in Zhong et al. (2004).
From this analysis, we conclude that it is likely that our dataset captures
the bulk of the dominant flow (and thus advection) on both the late-night
and morning flights, which are averaged and interpolated. The average
advection term for the 12 nights presented is -0.24 ppb h-1, which is
nearly an order of magnitude smaller than the chemical loss and storage
terms. The small average contribution from advection is consistent with
previous findings from daytime scalar budgets performed over the oceans
(Conley et al., 2011; Faloona et al., 2009) and in the SJV (Trousdell et
al., 2016) and what might be expected in the presence of a recirculating
eddy. Lastly, it is noted that individually adjusting each flight to have an
advection term of zero (to assume full eddy recirculation) results in only a
3 % change to the average of the diffusivity values, which further
supports the idea that the influence of advection on our scalar budget
analysis is minimal.
Wind consistency for late-night flights and morning
flights in the NBL (0–300 m) and the RL (300–700 m).
Since the LLJ is hypothesized to contribute to the variability of maximum
daytime ozone concentration, we explored the synoptic patterns that are
associated with differing strengths of the LLJ. A total of 7 years of data
(2010–2016) from the 915 MHz sounder located in Visalia, CA, are compiled to
obtain the LLJ speed and the height at which it was observed. For this
analysis, we define the nocturnal LLJ speed as the maximum hourly averaged
wind speed observed below 1000 m averaged in 100 m vertical bins from 23:00
to 07:00 PST, specifically during the summer months (defined here as 1 June–30 September).
The 1000 m cutoff is used to ensure that the wind maximum
captured is related to the LLJ at the top of the NBL rather than
free-tropospheric wind. Using this definition, the LLJ had an average height
of 340 m, an average speed of 9.9 m s-1 (σ=3.1 m s-1),
and a typical peak timing around 23:00 PST. The 700 mb level corresponds to
approximately 3000 m, well above the Pacific Coast Range but approximately
in line with the top of the southern Sierras.
To analyze possible synoptic influences on the jet strength, daily average
synoptic charts from the North American Regional Reanalysis (NARR) are
created in Figs. 8 and 9 for days when the LLJ strength was less than
7 m s-1 (N=147 nights) and greater than 12 m s-1 (N=165 nights).
Both the strong and weak low-level jets show a climatological trough
pattern, but the mean trough axis is situated about 100 km to the east for
the strong cases (Fig. 8b). We also note that the pressure gradient is at
least twice as strong for the stronger low-level jets and that the synoptic
pattern of the weak jets favors a southerly geostrophic wind aloft, which
directly opposes the up-valley northwesterly thermally driven flow. We also
find a positive correlation between the LLJ strength and the upwelling
index (r2=0.3018, p<10-5), calculated by NOAA's
Pacific Fisheries Environmental Lab at 33∘ N, 119∘ W
(https://www.pfeg.noaa.gov/products/PFEL/modeled/indices/upwelling/NA/upwell_menu_NA.html, last access:
8 August 2018).
The indices are primarily driven by the
strength and position of the North Pacific High, which, when strong, acts to
push the 700 mb trough farther eastward, as seen in Fig. 8b, and is
associated with lower sea surface temperatures and thus enhanced thermal
forcing of the coupled sea breeze and valley wind. These findings are
consistent with the Lin and Jao (1995) modeling study that showed that the
Fresno eddy (and associated LLJ) did not form when the synoptic flow over
the coastal range was westerly. Beaver and Palazoglu (2009) found that
maximum daily 8 h average ozone (MDA8) exceedances were more frequent in
the central and southern San Joaquin Valley when an offshore ridge or
onshore high was present, consistent with Fig. 8a. The results of our
study suggest that this may be at least partially explained by the presence
of a weaker LLJ under those synoptic conditions.
Although the LLJ and Fresno eddy are not synonymous, we propose that the
northwesterly LLJ could be the dominant feature of the eddy's northerly flow
component. This leads to an important question about the role of the Fresno
eddy in modulating the daily ozone peak. Beaver and Palazoglu (2009) purport
that ozone levels in the central SJV are particularly high on days when the
morning southerly wind at Parlier, a site about midway between Fresno and
Visalia, is strong, concluding that recirculation from the downslope branch
of the Fresno eddy significantly controls the day's buildup of ozone.
However, mixing induced by LLJs in other parts of the world has been shown
to decrease ozone levels the following day (Hu et al., 2013; Neu,
1995). Thus, it may be the case that a Fresno eddy associated with a
particularly strong LLJ may decrease ozone the following day if the
recirculation of ozone and its precursors does not overcompensate for
overnight losses due to vertical mixing down to the surface. We suggest that
the Fresno eddy, when present, will act to recirculate pollutants regardless
of the strength of the LLJ. That is, a stronger eddy will not recirculate
pollutants any more than a weaker one will. Thus, the nighttime dynamical
conditions that will lead to the greatest ozone levels the following day may
consist of a Fresno eddy just coherent enough to effectively recirculate
pollutants, but without an associated LLJ so strong as to deplete the RL
ozone by vertical mixing. There is currently no established link in the
literature between the Fresno eddy and LLJ strength. Thus, future research
should investigate which of these two nocturnal mechanisms (recirculation
from the eddy or RL depletion by vertical mixing) will dominate the ozone
budget on any given night, taking into consideration the different possible
structures and timing of the Fresno eddy as well as the synoptic conditions
that engender them.
In addition to the synoptic patterns discussed above, slightly lower surface
temperatures across the entire region are observed during stronger LLJs
(Fig. 9). This could either be a consequence of the synoptic flow (southerly
geostrophic flow will generally bring warm air advection) or itself be an
underlying precursor to the LLJ. In the latter case, a ∼2 K
greater temperature difference between the delta region and the SSJV for
strong LLJs (seen in Fig. 9) will lead to more up-valley thermal forcing,
resulting in stronger winds that decouple from the surface at night. The
higher temperatures associated with the weak nocturnal jets may make for a
twofold mechanism for high ozone: the high temperatures either cause
increased photochemical production or result from increased
meteorological stagnation, and a lack of mixing overnight induced by the LLJ
causes less depletion of the RL ozone. Warmer nights may also result in
less dry deposition of Ox through stomatal pores.
North American Regional Reanalysis 700 mb geopotential
height (m) for low-level jet speeds less than 7 m s-1(a) and greater
than 12 m s-1(b).
North American Regional Reanalysis 2 m air temperature
(∘C) difference between cases in which the low-level jet speed
exceeds 12 m s-1 and cases in which it is below 7 m s-1 at 01:00 PST.
Positive values indicate warmer surface temperatures for strong jets.
Vertical mixing and next-day ozone
As seen in Fig. 4, the average LLJ height is 200–400 m, which
approximately corresponds to the average NBL depth. Likely due to the
shear induced by the LLJ, turbulence is seen to be vigorous at night with
TKE values about 50 % of daytime values during convective conditions.
Further, TKE increases toward the surface, a condition that Banta et al. (2006)
refer to as a “traditional” stable boundary layer. As previously
mentioned, the physical significance of turbulent mixing overnight in
relation to SJV air pollution remains somewhat of an open question. On the
one hand, Beaver and Palazoglu (2009) suggest that a stronger Fresno eddy
circulation is associated with higher ozone pollution. On the other hand,
greater coupling between the NBL and RL, induced by turbulence generated
from the LLJ, could reduce the amount of ozone stored in the RL reservoir,
rendering cleaner air the following day. To test this hypothesis, the
relationship between the eddy diffusivity values found in our study and
regional mean surface ozone from the CARB network is analyzed.
The thermals generated by solar heating after sunrise initiate a fumigation
process whereby as the daytime boundary layer develops, the ozone that was
in the RL is mixed downward. The change in surface ozone concentration
(d[O3]/dt) due to fumigation peaks at around 08:00 PST and continues
until about 10:00. The relationship between our estimated eddy diffusivities
and ozone during the fumigation period is strongest at 10:00 PST, after
the bulk of the fumigation has occurred (r2=0.29, p=0.07). A
negative correlation between eddy diffusivities and the maximum 1 h
ozone, 24 h average ozone, and MDA8 was also found, with the strongest
relationship for the MDA8 (r2=0.46, p=0.015), as shown in Fig. 10.
This supports our hypothesis that stronger NBL turbulence is associated
with lower ozone the following day.
Correlation between overnight eddy diffusivity and
maximum daily 8 h average ozone (MDA8) the following day. All values are
averages of 11 CARB surface network stations that are within the flight
region.
Because this analysis consisted of only 12 flights, we explored a larger
dataset that might support the hypothesis that a stronger LLJ reduces ozone
the following day. A total of 7 years of LLJ speeds obtained from the Visalia sounder
from 2010–2016 are combined with the CARB surface network ozone monitoring
site at Visalia N Church St (36.3325∘ N, 119.2908∘ W; 30 m
elevation) for analysis. Only calendar days 152 through 273 (June–September)
are included. The LLJ, hypothesized to be the main contribution
to the variability in overnight mixing between the RL and NBL, is compared
with MDA8 observed the following day, as shown in Fig. 11. It can be seen
that a stronger nocturnal LLJ is correlated, albeit weakly, with lower ozone
the following day (r2=0.181, p<10-5). A single outlier
was removed for which the LLJ exceeded 25 m s-1. This overall relationship
supports our hypothesis that the LLJ leads to stronger mixing, which in turn
leads to more RL ozone depletion.
Correlation between nocturnal low-level jet speed and
the following day's MDA8 in Visalia, CA, for calendar days 152–273 from
2010 to 2016.
The physical processes of RL Ox depletion once it mixes down into the
NBL represent a further question. The main destruction processes of Ox in the
NBL are chemical loss and dry deposition. One possibility is that surface
sources of NO2 contribute to the excess nocturnal chemical depletion of
Ox in the NBL. However, the chemical loss of Ox is not thought to
vary significantly between the RL and NBL because the increase in NO2
in the NBL is compensated for by the decrease in O3 (see Fig. 4), although
this assumes that there are no other chemical differences that alter the
reaction fate of nitrate (i.e., α in Eq. 1). Another possibility is
that the deposition velocity of ozone may be enhanced by a reduction of
aerodynamic resistance under a stronger LLJ. The dry deposition of any gas
may be characterized by a series of resistances (Wesely, 1989):
vd=1ra+rb+rc,
where ra is the aerodynamic resistance, rb is the viscous sub-layer
resistance, and rc is the surface (or canopy) resistance. Figure 4 in
Padro (1996) suggests that for ozone at night, ra∼rc∼250 s m-1.
rb is likely nonzero (Massman et
al., 1994) but is typically several times smaller than the other resistances
(Georgiadis et al., 1995; Pilegaard et al., 1998), so we assume that
ra=rb+rc=250 s m-1 to yield our estimated
deposition velocity of 0.2 cm s-1. Combining an estimate of aerodynamic
resistance due to mass transfer (ra=Uu∗-2, where
u∗2 is the momentum flux) and parameterizing the momentum flux
as a function of 10 m wind speed, U10, and a drag coefficient
CD (u∗2=CDU102), we roughly approximate
ra as
ra∼1CDU10.
In the 7 years of LLJ data at Visalia, the 10 m wind speed is correlated
with the jet strength (r2=0.309, p<10-5). On
average, U10 was 1 for 5 m s-1 jets and 2.5
for 15 m s-1 jets. Assuming an average U10 of 1.75 m s-1 and
ra of 250 s m-1, this would imply that CD∼2.3×10-3. A sensitivity analysis indicates that the difference in U10
between strong and weak jets would result in an approximate 40 % change in
vd. We thus conclude that the LLJ likely plays a significant role in
modulating the dry deposition rate, whereby a strong jet decreases ra and
thus increases vd, further contributing to a loss of ozone overnight. It
is important to note that what we have presented is only a rough estimate of
the variability of ra, and thus future studies should measure these
parameters with more precision in order to better estimate the degree to
which the LLJ can modulate dry deposition in the SJV. The average error of
Kz due to the uncertainty in vd is calculated to be
∼0.50 m2 s-1 and is included in our error propagation analysis.
Eddy diffusivity and other estimates of turbulence
Here we attempt to build confidence in the eddy diffusivity estimates by
analyzing additional metrics of turbulence. We find that nocturnally and
spatially averaged TKE in the NBL ranges from 0.35 to 1.02 m2 s-2,
which is very comparable to values obtained in other NBL studies
(Banta et al., 2006; Lenschow et al., 1988). Table 1 shows the TKE, LLJ
speed, and the ratio of the streamwise variance to LLJ speed (σu/Ux) for each night. The average value of σu/Ux in this study is 0.11, approximately double what was
reported in Banta et al. (2006). There is no detectable relationship between
our calculated NBL TKE and eddy diffusivities, LLJ speed, or MDA8 the
following day, which implies that the eddy diffusivities calculated from the
scalar budget analysis may be a better measure of nocturnal mixing strength
than TKE.
Our budget method of estimating turbulent dispersion differs from some other
attempts that have been made for stably stratified environments. Clayson and
Kantha (2008) applied a technique that had been previously used in oceans to
the free troposphere, where turbulence is sparse and intermittent, much like
in the NBL. Their method involves using high-resolution soundings to
estimate a length scale of overturning eddies, known as the Thorpe scale
(Thorpe, 2005), which is then used to obtain estimates of turbulent
dissipation rate and subsequently eddy diffusivity. This is done by
relating the Thorpe scale to the Ozmidov scale, whereby if the Brunt–Väisälä
frequency (NBV) is known, the TKE dissipation rate (ε) can be
estimated. Eddy diffusivity can then be estimated as a product of the TKE
dissipation and N-2:
Kz=γεNBV-2,
where γ is the mixing efficiency, which can vary between 0.2 and 1 (Fukao et
al., 1994). From the nocturnal power spectra (Fig. 1) we use a Kolmogorov
fit to estimate ε, which is determined to be approximately 4.8×10-6 m2 s-3 for the overall altitude range of our nighttime
flights (surface to ∼3000 m), but a median of 3.0×10-4 m2 s-3
is observed in the NBL. Using the average NBL
Brunt–Väisälä frequency of 0.023 Hz, a mixing efficiency of 0.6,
and the median NBL ε results in an eddy diffusivity of 0.34 m2 s-1,
which is about 3 times smaller than the lower end of
our range (1.1–3.5 m2 s-1). A recent study of vertical mixing
based on scalar budgeting of radon-222 in the NBL by Kondo et al. (2014)
estimated 7-day average overnight diffusivities of 0.05–0.13 m2 s-1,
which are an order of magnitude below our estimates inferred from
the Ox budget. However, Wilson (2004) conducted a meta-analysis of
radar-based estimates of eddy diffusivity in the free troposphere, which is
also a generally stable environment, and found a range of 0.3–3 m2 s-1.
Pisso and Legras (2008) estimated diffusivities of about 0.5 in
the lower stratosphere during Rossby-wave-induced intrusions of midlatitude
air into the subtropical region. A modeling study by Hegglin et al. (2005)
reports diffusivities of 0.45–1.1 m2 s-1 in the lower
stratosphere with an average Brunt–Väisälä frequency of 0.021 Hz,
indicating a similar turbulent environment to ours. Finally, Lenschow et al. (1988)
analyzed flight data in the NBL over rolling terrain in Oklahoma
and found eddy diffusivities for heat (Kh) of ∼0.25 m2 s-1
for the upper half of the NBL and ∼1 m2 s-1 for the lower half. To our knowledge, the latter is the
most comparable observational finding within the NBL to our range of
diffusivities. Nevertheless, the variability in the reported values leads to
the conclusion that vertical diffusivity in very stable environments is
poorly understood, and further research is necessary to illuminate its
phenomenology. More specifically, while it is possible that the diffusivity
measurements in this study are biased high (e.g., due to overestimates of
the chemical loss parameter α), it is also possible that the LLJ and
other mesoscale wind features of the complex terrain account for stronger
nocturnal mixing in the SSJV compared to those in other stable environments.
Lastly, we estimate the BRN on each late-night
flight within the NBL using 100 m bins to estimate wind shear. A range
of Richardson numbers between 0.23 and 1.34 is obtained, and the estimates
are seen to have a slight negative relationship with eddy diffusivities as
illustrated in Fig. 12. The weak correlation is probably the result of the
limited dataset coupled with the challenging nature of both the eddy
diffusivity and BRN measurements.
Eddy diffusivities and bulk Richardson numbers (BRNs)
derived from aircraft observations.
Nocturnal elevated mixed layers
During the late-night flights in stable environments, the flight crew
reported many patches of turbulence. While most of these subjective reports
were during low approaches and thus likely attributable to wind shear
between the LLJ and the surface, they noted that some patches corresponded
to what appeared to be elevated mixed layers, i.e., layers of air in which
virtual potential temperature was observed to decrease with height. These
layers may be of special interest to our analysis of overnight mixing, since
absolutely unstable layers of air generate turbulence and vertical mixing.
The time series of all late-night flights was scanned for any period during
which (1) the aircraft maintained an ascent (or descent) rate of at least
1.4 m s-1, and (2) during a given elevation span of at least 100 m, a virtual
potential temperature decrease with height was observed. The process was
repeated for a thickness of at least 50 m.
The locations of the layers greater than 50 m thickness, along with their
elevation and lapse rate, are shown in Fig. 13. One feature of note is
that the layers appear to be more prominent over urban areas, such as
Fresno, Visalia, and Bakersfield. This may lead one to suspect that some of
these layers are driven by an urban heating effect; however, this seems
unlikely as the unstable layers appear mostly above the NBL wherein the
communication with the surface is relatively rapid. Rather, the appearance
of these layers clustering around urban areas may be the result of a flight
sampling bias and thus may not be significant. Another feature worth noting
is that more unstable layers are observed closer to the Tehachapi Pass. One
possible explanation for this is that the katabatic flow down the mountain
slopes detrains along the way and is carried over the valley by local
advection before mixing with surrounding air. Given that these layers are
found from near the bottom of the RL all the way up to 2.5 km, it is
possible that they contribute to the overnight mixing of Ox from the RL
to the NBL by maintaining a fairly well-mixed lower atmosphere over the
valley. Further research, both observational and modeling based, is needed
to explore this possibility.
Detected nocturnal elevated mixed layers with at least
50 m thickness, with elevations shown.
The unstable layers are not found to have more TKE than the rest of the
atmosphere. While this may reflect the limitations of the method used to
estimate turbulence from this low-cost wind measurement system, it is
consistent with the study by Cho et al. (2003) that found no relationship
between turbulence and static stability in the free troposphere.
Interestingly, their analysis of aircraft data collected over the Pacific
Ocean up to 8 km of altitude found unstable layers in 6 % to 25 % (depending on
the layer thickness definition of 100 to 10 m) of their profiles above the
boundary layer (Cho et al., 2003). Because the aircraft moves more than 10
times faster horizontally than vertically during profiling, the observations
of the elevated mixed layers may be an artifact of localized temperature
gradients that are more prominent in the horizontal dimension. To confirm
that this is not the case, we examined the wind quivers in the unstable
layers along with the direction of the colder air. The cooler air was not
systematically detected in any one direction, which supports the hypothesis
that they are true vertical temperature gradients.
To analyze the stability, wind shear, and turbulence from a climatological
standpoint, a July–August 2016 composite of the 915 MHz Visalia sounder data
is presented in Fig. 14. Even in the 2-month averages, some nocturnal
unstable layers are detectable between 500 and 1500 m, which further
supports the existence of persistent elevated mixed layers that may
contribute to the overnight mixing of pollutants in the lower troposphere over
the valley.
Stability and wind quivers for the Visalia 915 MHz
sounder, 1 July–31 August 2016.
Conclusions
We have demonstrated a method for performing a nocturnal Ox budget
analysis using aircraft data and applied it to estimate the effects of
turbulent mixing in the NBL, which can be used to help understand many air
quality issues in the SJV. Inherently, eddy diffusivity estimates for any
given night will have a large uncertainty due to the indirect nature of the
measurement and the limited flight durations. However, the overall
between-flight consistency and the correlations of the eddy diffusivities
with both the Richardson number and surface ozone suggest that this method
is informative. We obtain eddy diffusivity values between 1.1 and 3.5 m2 s-1,
which are larger but approximately within the same order
of magnitude of values that have been obtained from other studies in the
free troposphere, lower stratosphere, and NBL. One limitation of our study
is the lack of sample size, with only 12 pairs of overnight and morning
flights. Nevertheless, we believe this study demonstrates the importance of
focused flight strategies that measure the individual terms of the scalar
budget equation and highlights the significant influence that synoptic and
mesoscale meteorological conditions can have on the overnight destruction of
ozone, thereby impacting the following day's peak concentrations.
The larger set of RASS and ARB surface network data from Visalia, CA, shows a
correlation between LLJ speed and the MDA8 the following afternoon for
summertime months, further suggesting a link between nocturnal mixing and
the ensuing day's ozone levels. In particular, we note that 5 out of 6 days
when the Visalia, CA, ozone MDA8 exceeded 90 ppb were preceded by a weak LLJ
(<7 m s-1). Similarly, the correlations between the
aircraft-estimated eddy diffusivities and MDA8 the following day also
suggest that vertical mixing in the NBL plays an important role in
controlling ozone concentrations. While we cannot unequivocally infer a
causal relationship in the data between a strong LLJ, stronger mixing, and
reduced ozone levels, we propose a feasible process link with a stronger
LLJ leading to greater mixing, which helps deplete the ozone reservoir in
the RL by bringing it into the NBL overnight. There it is subject to dry
deposition at the surface, wherein the deposition velocity itself may be
modulated by the strength of the LLJ. Because the near-surface winds are
accelerated by an overlying jet, a stronger LLJ reduces aerodynamic
resistance, resulting in more efficient transport to surfaces and stomata
where ozone can be taken up. Subsequently, when thermals begin to form after
sunrise the following morning, there is less ozone to fumigate downward. We
propose that the LLJ is a branch of the Fresno eddy, and the vertical mixing
it induces may offset some of the next-day ozone enhancement that results
from the eddy recirculating pollutants. Our findings highlight the crucial
need for models to capture the LLJ and Fresno eddy with sufficient
resolution, and policy makers may consider putting more stringent emission
limitations on days when synoptic and mesoscale patterns appear to favor
weak nocturnal mixing. Of course, in addition to nocturnal mixing,
the photochemical production of ozone, as well as advection, will play a major
role in the ultimate daytime peak ozone levels observed (Trousdell et al.,
2016), which is likely why the correlation between nighttime turbulence and
afternoon ozone is not always high.
The relative importance of these dynamical effects depends on the exact
magnitude of the chemical loss of Ox overnight. We suggest that the
ultimate fate of the NO3 radical plays a very important role in the
Ox budget's chemical loss term, and thus it likely impacts the following
day's maximum ozone concentration. The loss of the nitrate radical at night
can occur from N2O5 hydrolysis, reaction with VOCs, or a very
rapid reaction with small NO concentrations, and there is considerable
uncertainty regarding which reactions dominate without concurrent
measurements of NO3, N2O5, and VOCs. Thus, the lifetime of
NO3 can range from seconds to several minutes, which affects the
chemical loss term in the scalar budget equation. It is therefore crucial to
measure the lifetime of NO3 in future studies that analyze the NBL
ozone or Ox budget. We also suggest more direct estimates of
aerodynamic resistance and nocturnal ozone deposition at the surface by
ground-based eddy covariance flux measurements in conjunction with future
airborne studies.
Data availability
All of the aircraft data used in this analysis can be found
at https://www.esrl.noaa.gov/csd/groups/csd3/measurements/cabots/
(Caputi and Faloona, 2016; last access: 27 March 2019). NARR and
Visalia 915 MHz sounder data can be accessed from the Earth Science Research
Laboratory website (https://www.esrl.noaa.gov/, ESRL, 2018;
last access: 10 September 2018), and CARB ground network data
can be accessed from the CARB website (https://ww2.arb.ca.gov/,
ARB, 2018; last access: 27 March 2018). The data can
also be obtained by contacting the PI.
Author contributions
IF designed the research study and DC, JS, and SC carried it out. DC and SC
designed the scalar budget code and DC carried out the analysis. Other
analyses were performed by DC, IF, JS, NF, and JT. DC prepared and submitted
the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This project was supported by a grant sponsored by the California Air
Resources Board, Research Division contract no. 14-308. We thank Steven
S. Brown, William P. Dube, and Nick Wagner of the NOAA Chemical Sciences
Division, Allen Goldstein of the University of California at Berkeley, and
Drew Gentner of Yale University for freely sharing their data from the
CalNex mission. We also thank Laura Bianco of the NOAA Physical Sciences
Division for sharing the Chowchilla boundary layer height data. Ian Faloona
was supported in part by the California Agricultural Experiment Station,
Hatch project CA-D-LAW-2229-H.
Review statement
This paper was edited by Robert Harley and reviewed by two anonymous referees.
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