ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-7509-2017Methane emissions from dairies in the Los Angeles BasinViatteCamillecamille@gps.caltech.eduLauvauxThomashttps://orcid.org/0000-0002-7697-742XHedeliusJacob K.https://orcid.org/0000-0003-2025-7519ParkerHarrisonChenJiahttps://orcid.org/0000-0002-6350-6610JonesTaylorFranklinJonathan E.DengAijun J.GaudetBrianVerhulstKristalhttps://orcid.org/0000-0001-5678-9678DurenRileyWunchDebrahttps://orcid.org/0000-0002-4924-0377RoehlColeenDubeyManvendra K.https://orcid.org/0000-0002-3492-790XWofsySteveWennbergPaul O.https://orcid.org/0000-0002-6126-3854Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USADepartment of Meteorology, Pennsylvania State University, University Park, PA, USAEarth System Observations, Los Alamos National Laboratory, Los Alamos, NM, USASchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USAnow at: Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germanynow at: Department of Physics, University of Toronto, Toronto, ON, CanadaCamille Viatte (camille@gps.caltech.edu)21June20171712750975281April201627April201611April201725April2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/7509/2017/acp-17-7509-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/7509/2017/acp-17-7509-2017.pdf
We estimate the amount of methane (CH4) emitted by the largest
dairies in the southern California region by combining measurements from four
mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic
13/12CH4 measurements from a CRDS analyzer (Picarro), and a
high-resolution atmospheric transport simulation with a Weather Research and
Forecasting model in large-eddy simulation mode (WRF-LES).
The remote sensing spectrometers measure the total column-averaged dry-air
mole fractions of CH4 and CO2 (XCH4 and XCO2) in the
near infrared region, providing information on total emissions of the
dairies at Chino. Differences measured between the four EM27/SUN ranged from
0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million)
for XCH4 and XCO2, respectively. To assess the fluxes of the
dairies, these differential measurements are used in conjunction with the
local atmospheric dynamics from wind measurements at two local airports and
from the WRF-LES simulations at 111 m resolution.
Our top-down CH4 emissions derived using the Fourier transform
spectrometers (FTS) observations of 1.4 to 4.8 ppt s-1 are in the low end of
previous top-down estimates, consistent with reductions of the dairy farms
and urbanization in the domain. However, the wide range of inferred fluxes
points to the challenges posed by the heterogeneity of the sources and
meteorology. Inverse modeling from WRF-LES is utilized to resolve the
spatial distribution of CH4 emissions in the domain. Both the model and
the measurements indicate heterogeneous emissions, with contributions from
anthropogenic and biogenic sources at Chino. A Bayesian inversion and a
Monte Carlo approach are used to provide the CH4 emissions of 2.2 to
3.5 ppt s-1 at Chino.
Introduction
Atmospheric methane (CH4) concentration has increased by 150 % since
the pre-industrial era, contributing to a global average change in radiative
forcing of 0.5 W m-2 (Forster et al., 2007; Myhre et al., 2013; IPCC, 2013).
Methane is naturally emitted by wetlands, but anthropogenic emissions now
contribute to more than half of its total budget (Ciais et al., 2013), ranking
it the second most important anthropogenic greenhouses gas after carbon
dioxide (CO2).
The United Nations Framework Convention on Climate Change (UNFCCC,
http://newsroom.unfccc.int/) aims to reduce CH4 emissions by reaching
global agreements and collective action plans. In the United States (USA),
the federal government aims to reduce CH4 emissions by at least 17 %
below 2005 levels by 2020 by targeting numerous key sources such as (in
order of importance) agriculture, energy sectors (including oil, natural
gas, and coal mines), and landfills (Climate Action Plan, March 2014).
Methane emissions are quantified using bottom-up and top-down
estimates. The bottom-up estimates are based on scaling individual
emissions and process level information statistically (such as the number of
cows, population density or emission factor) with inherent approximations.
Top-down estimates, based on atmospheric CH4 measurements, often
differ from these reported inventories both in the total emissions and the
partitioning between the different sectors and sources (e.g., Hiller et al.,
2014). In the USA, the disagreement in CH4 emissions estimated can reach
a factor of 2 or more (Miller et al., 2013; Kort et al., 2014), and
remains controversial regarding the magnitude of emissions from the
agricultural sector (Histov et al., 2014). Thus, there is an acknowledged
need for more accurate atmospheric measurements to verify the bottom-up
estimates (Nisbet and Weiss, 2010). This is especially true in urban
regions, such as the Los Angeles Basin, where many different CH4
sources (from farmlands, landfills, and energy sectors) are confined to a
relatively small area of ∼ 87 000 km2 (Wunch et al., 2009;
Hsu et al., 2010; Wennberg et al., 2012; Peischl et al., 2013; Guha et al.,
2015; Wong et al., 2015). Therefore, improved flux estimations at local
scales are needed to resolve discrepancies between bottom-up and top-down
approaches and improve apportionment in CH4 sources.
Inventories of CH4 fluxes suggest that emissions from US agriculture
increased by more than 10 % between 1990 and 2013 (EPA, 2015), and by more
than 20 % since between 2000 and 2015 in California (CARB, 2015). In
addition, these emissions are projected to increase globally in the future
due to increased food production (Tilman and Clark, 2014). Livestock in
California have been estimated to account for 63 % of the total
agricultural emissions of greenhouse gases (mainly CH4 and N2O);
dairy cows represented more than 70 % of the total CH4 emissions from
the agricultural sectors in 2013 (CARB, 2015). State-wide actions are now
underway to reduce CH4 emissions from dairies (ARB,
2015). Measurements at the local scale with high spatial and
temporal resolution are needed to assess CH4 fluxes associated with
dairy cows and to evaluate the effectiveness of changing practices to
mitigate CH4 emissions from agriculture.
Space-based measurements provide the dense and continuous data sets needed to
constrain CH4 emissions through inverse modeling (Streets et al.,
2013). Recent studies have used the Greenhouse gases Observing SATellite
(GOSAT – footprint of ∼ 10 km diameter) observations to
quantify mesoscale natural and anthropogenic CH4 fluxes in Eurasia
(Berchet et al., 2015) and in the USA (Turner et al., 2015). However, it is
challenging to estimate CH4 fluxes at smaller spatial scales using
satellite measurements due to their large observational footprint (Bréon
and Ciais, 2010). Nevertheless, recent studies used the SCanning Imaging
Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY – footprint
of 60 km × 30 km) to assess emissions of a large CH4 source in
the USA
(Leifer et al., 2013; Kort et al., 2014).
Small-scale CH4 fluxes are often derived from in situ measurements
taken at the surface and from towers (Zhao et al., 2009), and/or in situ
and remote-sensing measurements aboard aircraft (Karion et al., 2013;
Peischl et al., 2013; Lavoie et al., 2015; Gordon et al., 2015). A recent
study emphasized the relatively large uncertainties of flux estimates from
aircraft measurements using the mass balance approach in an urban area
(Cambaliza et al., 2014).
Ground-based solar absorption spectrometers are powerful tools that can be
used to assess local emissions (McKain et al., 2012). This technique has
been used to quantify emissions from regional to urban scales (Wunch et al.,
2009; Stremme et al., 2013; Kort et al., 2014; Lindenmaier et al., 2014;
Hase et al., 2015; Franco et al., 2015; Wong et al., 2015; Chen et al.,
2016; Kille et al., 2017).
In this study, we use four mobile ground-based total column spectrometers
(called EM27/SUN, Gisi et al., 2012) to estimate CH4 fluxes from the
largest dairy-farming area in the South Coast Air Basin (SoCAB), located in
the city of Chino, in San Bernardino County, California. The Chino area was
once home to one of the largest concentrations of dairy farms in the United
States (USA), however rapid land-use change in this area may have caused
CH4 fluxes from the dairy farms change rapidly in both space and time.
Chen et al. (2016) used differential column measurements (downwind minus
upwind column gradient ΔXCH4 across Chino) recorded on
favorable meteorological conditions (e.g., constant wind direction) to
verify emissions reported in the literature. In this study, the same column
measurement network is employed in conjunction with meteorological data and
a high-resolution model to estimate CH4 emissions at Chino for
several different days, including more varying wind conditions. The approach
proposed here allows us to describe the spatial distributions of CH4
emissions within and around the feedlot at very high resolution by using an
advanced atmospheric modeling system applicable to any convective
meteorological conditions (Gaudet et al., 2017).
In Sect. 2 of this paper, the January 2015 field campaign at Chino is
described with details on the mobile column and in situ measurements. In
Sect. 3, we describe the new high-resolution Weather Research and Forecasting (WRF)
model with large-eddy simulations (LES) setup. In Sect. 4, results of CH4
fluxes estimates are examined. Limitations of this
approach, as well as suggested future analyses are outlined in Sect. 5.
Three different days of measurements during the field campaign at
Chino (∼ 9 × 6 km) on 15, 16, and 24 January 2015.
Panels (a–c) show the chosen locations of the
four EM27/SUN (black, red, green, and blue pins correspond to the Caltech,
LANL, Harvard1, and Harvard2 instruments, respectively). The red marks on
the map correspond to the dairy farms. Lower panels show wind roses of
10 min averages of wind directions and wind speeds measured at the two
local airports (at Chino on d–f, and at Ontario on g–i).
Map provided by Google Earth V 7.1.2.2041, US Dept. of State
Geographer, Google, 2013, Image Landsat, Data SIO, NOAA, US, Navy, NGA, and GEBCO.
Measurements in the Los Angeles Basin dairy farmsLocation of the farms: Chino, California
Chino (34.02∘ N, -117.69∘ W) is located in the eastern
part SoCAB, called the Inland Empire, and has historically been a major
center for dairy production. With a growing population and expanding housing
demand, the agricultural industry has shrunk in this region and grown in the
San Joaquin Valley (California Central Valley). The number of dairies
decreased from ∼ 400 in the 1980s to 95 in 2013 (red area of
Fig. 1a–c). Nevertheless, in 2013 ∼ 90 % of the southern California dairy cow population (California
Agricultural Statistics, 2013) remained within the Chino area of
∼ 6 × 9 km (Fig. 1). These feedlots are a major point source
of CH4 in the Los Angeles Basin (Peischl et al., 2013).
Mobile column measurements: EM27/SUN
Atmospheric column-averaged dry-air mole fractions of CH4 and
CO2 (denoted XCH4 and XCO2; Wunch at al., 2011) have been
measured using four ground-based mobile Fourier transform spectrometers (FTS).
The mobile instruments were developed by Bruker Optics and are all
EM27/SUN models. The four FTS (two owned by Harvard University, denoted
Harvard 1 and 2, one owned by Los Alamos National Laboratory, denoted LANL,
and one owned by the California Institute of Technology, denoted Caltech)
were initially gathered at the California Institute of Technology in
Pasadena, California in order to compare them against the existing Total
Carbon Column Observing Network (TCCON, Wunch et al., 2011) station and to
each other, over several full days of observation. The instruments were then
deployed to Chino to develop a methodology to estimate greenhouses gas
emissions and improve the uncertainties on flux estimates from this major
local source. Descriptions of the capacities and limitations of the mobile
EM27/SUN instruments have been published in Chen et al. (2016) and Hedelius
et al. (2016). Using Allan analysis, it has been found out that the
precision of the differential column measurements ranges between 0.1 and 0.2 ppb
with a 10 min averaging time (Chen et al., 2016). For this analysis, we need
to ensure that all the data from the EM27/SUN instruments are on the same
scale. Here, we reference all instruments to the Harvard2 instrument.
Standardized approaches (retrieval consistency, calibrations between the
instruments) are needed to monitor small atmospheric gradients using total
column measurements from the EM27/SUN. Indeed we ensured all retrievals used
the same algorithm, calibrated pressure sensors, and scaled retrievals
according to observed, small systematic differences to reduce instrumental
biases (Hedelius et al., 2016).
These modest-resolution (0.5 cm-1) spectrometers are equipped with
solar trackers (Gisi et al., 2011) and measure throughout the day. To
retrieve atmospheric total column abundances of CH4, CO2, and
oxygen (O2) from these near-infrared (NIR) solar absorption spectra, we
used the GGG software suite, version GGG2014 (Wunch et al., 2015). Column
measurements at Chino were obtained on 5 days: 15,
16, 22 and 24 January, and the 13 August 2015. Of these days, 15, 16, and 24 January are
sufficiently cloud-free for analysis. These days have different
meteorological conditions (i.e., various air temperatures, pressures, wind
speeds and directions), improving the representativeness of the flux estimates at Chino.
Figure 1 shows measurements made on 15, 16, and
24 January. Wind speeds and directions, shown in the bottom panels of
Fig. 1, are measured at the two local airports inside the domain (the Chino
airport indicated on Fig. 1d–f and the Ontario airport Fig. 1g–i).
Wind measurements from these two airports, located at less than
10 km apart, are made at an altitude of 10 m above the surface. The
exact locations of the four EM27/SUN spectrometers (colored symbols in
Fig. 1a–c) were chosen each morning of the
field campaign to optimize the chance of measuring upwind and downwind of
the plume. On 15 and 16 January, the wind speed was low
with a maximum of 3 ms-1 and in a highly variable direction all day
(Fig. 1d, e, and g, h); therefore the four EM27/SUN spectrometers were
placed at each corner of the source area to ensure that the plume was
detected by at least one of the instruments throughout the day. On the
contrary, the wind on 24 January was in a constant direction from the
northeast and was a relatively strong 8–10 ms-1 (Fig. 1f and i),
so the instruments were located such that one spectrometer (Harvard2)
was always upwind (blue symbols in Fig. 1) and the others are downwind of
the plume and at different distances from the sources (black, green, and red
symbols in Fig. 1).
In situ measurements: Picarro
The EM27/SUN column measurements are supplemented by ground-based in situ
measurement using a commercial Picarro instruments during the January campaign.
The Picarro instruments use a cavity ring-down spectroscopy (CRDS) technique
that employs a wavelength monitor and attenuation to characterize species abundance.
In situ 12CH4, CO2, and 13CH4 measurements were
performed on 15, 16, and 22 January, and 13 August 2015 at roughly 2 m
away from the LANL EM27/SUN (Fig. 1a–c) with a Picarro G2132-I instrument (Arata et al., 2016,
http://www.picarro.com/products_solutions/isotope_analyzers/). This Picarro, owned by LANL,
utilize a 1/4′′ synflex inlet tube placed approximately 3 m a.g.l. (above ground
level) to sample air using a small vacuum pump. Precisions on
12CH4, CO2, and 13CH4 measurements are 6 ppb, 2 ppm,
and 0.6 ‰, respectively.
To locate the major CH4 sources in the dairy farms area, a second
Picarro G2401 instrument (http://www.picarro.com/products_solutions/trace_gas_analyzers/) from the Jet
Propulsion Laboratory (JPL, Hopkins et al., 2016) was deployed on
15 January 2015. Precision on CH4 measurements is ∼ 1 ppb.
WRF-Chem simulation domains for the four grid resolutions (3 km;
1 km; 333 m; 111 m), with the corresponding topography based on the Shuttle
Radar Topographic Mission digital elevation model at 90 m resolution). The
16 rectangular areas (2 × 2 km2) are shown on the LES domain map and
numerate by pixel numbers (Fig. 10).
Model simulationsDescription of WRF-LES model
The Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) is
an atmospheric dynamics model used for both operational weather forecasting
and scientific research throughout the global community. Two key modules
that supplement the baseline WRF system are used here. First, the chemistry
module WRF-Chem (Grell et al., 2005) adds the capability of simulating
atmospheric chemistry among various suites of gaseous and aerosol species.
In this study, CH4 is modeled as a passive tracer because of its long
lifetime relative to the advection time at local scales. The longest travel
time from the emission source region to the instrument locations is less
than 1 h, which is extremely short compared to the lifetime of CH4
in the troposphere (∼ 9 years). Therefore, no specific
chemistry module is required. The version of WRF-Chem used here (Lauvaux et
al., 2012) allowed for the offline coupling between the surface emissions,
prescribed prior to the simulation, and their associated atmospheric
tracers. Second, we make use of the large-eddy simulation (LES) version of
WRF (Moeng et al., 2007) on a high-resolution model grid with 111 m
horizontal grid spacing. A key feature of the simulation is the explicit
representation of the largest turbulent eddies of the planetary boundary
layer (PBL) in a realistic manner. The more typical configuration of WRF
(and other atmospheric models) is to be run at a somewhat coarser resolution
that is incapable of resolving PBL eddies. An advantage in this study is
that the effect of the most important PBL eddies to vertical turbulent
transport (i.e., the largest eddies) are not parameterized. By having a
configuration with the combination of CH4 tracers and PBL eddies, we
can realistically predict the evolution of released material at scales of
the order of the PBL depth or smaller. The WRF-LES mode has been evaluated
over Indianapolis, IN and compared to the commonly used mesoscale mode of
WRF (Gaudet et al., 2017). The representation of plume structures in the
horizontal and in the vertical is significantly improved at short distances
(< 8 km) compared to mesoscale simulations at 1 km resolution, while
the meteorological performance of WRF-LES remains similar to coarser domains
due to the importance of boundary nudging in the nested-domain
configuration. Thus, the representation of the CH4 plumes in this study
should be significantly improved with the LES mode configuration by Gaudet et al. (2017).
In this real case experiment, the model configuration consists of a series
of four one-way nested grids, shown in Fig. 2 and described further in Supplement S1.
Each domain contains 201 × 201 mass
points in the horizontal, with 59 levels from the surface to 50 hPa, and the
horizontal grid spacings are 3 km, 1 km, 333 m, and 111 m. All four domains
use the WRF-Chem configuration. The model 3 km, 1 km, and 333 m grids are
run in the conventional mesoscale configuration with a PBL parameterization,
whereas the 111 m grid physics is LES. The initial conditions for the cases
are derived from the National Centers for Environmental Prediction (NCEP)
0.25∘ Global Forecast System (GFS) analysis fields (i.e., 0 h
forecast) at 6 h intervals. The simulations are performed from 12:00 to
00:00 UTC (04:00 to 16:00 LT) only, which corresponds to daylight hours
when solar heating of the surface is present and measurements are made.
Data assimilation to optimize meteorological fields is performed using four-dimensional data assimilation (FDDA; Deng et al., 2009) for the 3 km and
1 km domains. The assimilation improves the model performance significantly
(Rogers et al., 2013; Deng et al., 2017) without interfering with mass
conservation and the continuity of the airflow. Surface wind and
temperature measurements, including from the Ontario (KONT) and Chino (KCNO)
airport stations, and upper-air measurements were assimilated within the
coarser grids using the WRF-FDDA system. However, no observations of any
kind were assimilated within the 333 and 111 m domains; therefore, the
influence of observations can only come into these two domains through the
boundary between the 333 m and 1 km grids. Wind measurements at fine scale
begin to resolve the turbulent perturbations, which would require
additional prefiltering. These measurements are used to evaluate the WRF
model performances at high resolutions.
Based on the terrain elevation in the LES domain (Fig. 2), target
emissions are located in a triangular-shaped valley with the elevation
decreasing gradually towards the south. However, hills nearly surround the
valley along the southern perimeter. Meanwhile, the foothills of the San
Gabriel Mountains begin just off the 111 m domain boundary to the north. As
a result, the wind fields in the valley are strongly modified by local
topography and can be quite different near the surface than at higher levels.
The definition of the prior error covariance matrix B is most problematic
because little is known about the dairy farm emissions except the presence
of cows distributed in lots of small areas. However, we assume no error
correlation as it is known that groups of cows are distributed randomly
across our inversion domain. For the definition of the variances in B
(i.e., diagonal terms), no reliable error estimate is available because
nonagricultural emissions are suspected. The lack of error estimate
directly impacts the inverse emissions, and therefore results in the
generation of unreliable posterior error estimates. Instead, we develop a
Monte Carlo approach using a simulated annealing (SA) technique which will
define the range of flux estimates for each grid point according to the
observed XCH4 mole fractions. We test the initial errors in the emissions by
creating random draws (i.e., random walk perturbing the emissions
iteratively) with an error of about 200 % compared to the expected
emissions (based on the dairy cows' emissions from CARB, 2015). We then
generated populations of random solutions and iterated 2000 times with the
SA algorithm. Overall, the SA approach allows us to explore the entire space
of solutions without any prior constraint. However, we assume here that each
pixel is independent, possibly causing biased estimates of CH4
emissions. To avoid this problem, we only used the range of emission values
for each pixel to construct our prior emission errors but discarded the
total emissions from the SA. Instead, we performed a Bayesian inversion to
produce total emissions for the area using the diagnosed emissions from the
SA as our prior emission errors.
Bayesian optimization using WRF-LES
Due to the absence of an adjoint model in LES mode, the
inverse problem is approached with Green's functions, which correspond to
the convolution of the Chino dairies emissions and the WRF-LES model
response. For the two independent simulations (15 and 16 January),
16 rectangular areas of 2 × 2 km2 (Fig. 2) are defined
across the feedlots to represent the state vector (x) and therefore the
spatial resolution of the inverse emissions, which correspond to the entire
dairy farm area of about 8 × 8 km2 once combined together. The
16 emitting areas continuously release a known number of CH4 molecules
(prior estimate) during the entirety of the simulations, along with 16 individual
tracers representing the 16 areas of the dairies. The final
relationship between each emitting grid cell and each individual measurement
location is the solution to the differential equation representing the
sensitivity of each column measurement to the different 2 × 2 km2
areas. The WRF-LES results are sampled every 10 min at each sampling
location to match the exact measurement times and locations of the EM27/SUN instruments.
The inversion of the emissions over Chino is performed using a Bayesian
analytical framework, described by the following equation:
x=x0+BHTHBHT+R-1y-Hx0,
with x the inverse emissions, x0 the prior emissions, B the prior
emission error covariance, R the observation error covariance, H the
Green's functions, and y the observed column dry-air mole fractions. The
dimension of the state vector is 16, and we assume constant CH4
emissions for each individual day. The column observations (here the vector y)
correspond to the local enhancements (i.e., the contributions of local
sources), the background conditions having been subtracted beforehand. Here, we
defined the background as the daily minimum for both days, measured by
multiple sensors depending on the wind direction and the relative position
of the sensor. Figure 3 shows that CH4 background values vary between
1.830 and 1.832 ppm, with a minimal value of 1.825 ppm on 16 January. We
used two distinct daily minimums as our final CH4 background mixing
ratios . The lack of CH4 inventory for the Los Angeles Basin and the impact of
transport errors on simulated CH4 mixing ratios are likely to produce
larger uncertainties on the background conditions. For these reasons, upwind
observations were used to define the background, assuming that spatial
gradients across our simulation domain are small compared to atmospheric
signals from Chino. The CH4 observations used here, after subtracting
the background value, correspond to local signals of about 10 ppb (with a
peak at 25 ppb) compared to an uncertainty of about 2 ppb on the background
values. Two maps of 16 emission estimates are produced corresponding to the
2 × 2 km2 areas for the 2 days (15 and 16 January). A
combined inversion provides a third estimate of the emissions using
10 min average column data from both days. The metric used to select the
best solutions is the mean absolute error (or absolute differences) between
the simulated and observed column fractions. We store the solutions
exhibiting a final mismatch of less than 0.01 ppm to minimize the mismatch
between observed and simulated column fractions. The optimal solution and
the range of accepted emission scenarios are shown in Fig. S2. The space
of solutions provide a range of accepted emissions for each 2 × 2 km2
area that can be used as a confidence interval in the inversion results. The
posterior emissions from the Bayesian inversion are then compared to the
confidence interval from the SA to evaluate our final
inverse emissions estimates and the posterior uncertainties. The results are
presented in Sect. 4.3.
One minute average time series of XCH4(a–c)
and XCO2(d–f) measured by the four
EM27/SUN (black, red, green, and blue marks correspond to the Caltech, LANL,
Harvard1, and Harvard2 spectrometers, respectively).
Transport errors in the WRF-LES simulation can impact the accuracy of the
inversion and need to be addressed in the optimization. Deng et al. (2017)
studied the sensitivity of inverse emissions due to different transport
scenarios. To quantify the impact of transport errors on the inverse fluxes,
an ensemble approach would be necessary to propagate transport errors in the
inverse solution (e.g., Evensen, 1994). Ensemble-based techniques remain
computationally expensive, especially for LES simulations. Instead, we aimed
to reduce the transport errors using the WRF-FDDA system to limit the
errors in wind direction, wind speed, and PBL height. The improvement in
model performance is significant, as demonstrated in Deng et al. (2017),
reducing the wind speed and wind direction random errors by half, while
removing biases in the three variables. Remaining uncertainties are
described in the observation error covariance matrix R by balancing the
normalized Chi-squared distance (Lauvaux and Davis, 2014) varying between
0.5 and 3 ppb for all the 10 min column measurements.
ResultsObservations of XCH4 and XCO2 in the dairy farms
Figure 3 shows the 1 min average time series of XCH4
(Fig. 3a–c) and XCO2 (Fig. 3d–f) derived from the four EM27/SUN. For
days with slow wind (∼ 3 m s-1), i.e., on 15 and 16 January
(Fig. 1d, e, and g, h), the maximum
gradients observed between the instruments are 17 and 22 ppb (parts per
billion), and 2 and 3 ppm (parts per million), for XCH4 and
XCO2, respectively. Assuming that the observed Xgas changes are
confined to the PBL, gradients in this layer are about 10 times larger.
Gradients observed on 15 and 16 January are higher than those
of XCH4 and XCO2 of 2 ppb and 0.7 ppm observed on a windy day,
the 24 January. The XCH4 and XCO2 variabilities captured by the
instruments are due to changes in wind speed and direction, i.e., with high
XCH4 signals when the wind blows from the dairies to the instruments.
Thus, the EM27/SUN are clearly able to detect variability of greenhouses
gases at local scales (temporal is less than 5
min, and spatial is less than 10 km) indicating that these mobile column measurements have the
potential to provide estimates of local source emissions.
Estimation of fluxes with EM27/SUN column measurements
Total column measurements are directly linked to total emissions (McKain et
al., 2012) and are sensitive to surface fluxes (Keppel-Aleks et al., 2012).
To derive the total emissions of trace gases released in the atmosphere from
a source region, the ”mass balance” approach is often used. In its simplest
form, the XCH4 fluxes can be written as in Eq. (2), but this
requires making assumptions on the homogeneity of the sources and wind
shear in the PBL.
FXCH4=ΔXCH4V(z)m(θ)SCair(z),
where FXCH4 is the flux (molecules s-1 m-2),
ΔXCH4 is the XCH4 enhancement between the upwind and the downwind region
(ppb), V is the average wind speed (ms-1) from both airports, m is
the distance in meters that air crosses over the dairies calculated as a
function of the wind direction θ, and SCair(z)
is the vertical column density of air (molecules m-2). The distances
that air masses cross over the dairies (m) before reaching a receptor (EM27/SUN)
are computed for each day, each wind direction, and each
instrument (see complementary information Sect. S3).
Equation (2) can be reformulated as
ΔXCH4=ΔtFXCH4SCair(z),
where Δt=m(θ)V(z) is the
residence time of air over the dairies (in seconds).
A modified version of this mass balance approach has been used by Chen et
al. (2016) to verify that the XCH4 gradients measured by the EM27/SUN
are comparable to the expected values measured at Chino during the CalNex
aircraft campaign (Peischl et al., 2013). In Chen et al. (2016), XCH4
enhancements measured between upwind and two of the downwind sites on
24 January (day of constant wind direction; Fig. 1f and i)
are compared to the expected value derived from Peischl's emission numbers,
which were determined using the bottom-up method and aircraft measurements.
They found that the measured XCH4 gradient of ∼ 2 ppb,
agrees within the low range of the 2010 value. However, this differential
approach, using upwind and downwind measurements, reduces the flux estimates
to only 1 day (24 January), since the wind speed and direction were
not constant during the other days of field measurements.
In this study, we extend the analysis of the Chino data set using the mass
balance approach on steady-wind day (on 24 January) for all the FTS
instruments (i.e., three downwind sites), as well as employing the other two
days of measurements (15 and 16 January) in conjunction the
WRF-LES model to derive a flux of XCH4 from the dairy farms. We
exclude measurements from 22 January and 13 August because of
the presence of cirrus clouds during those days, which greatly reduce the
precision of the column measurements. Our XCH4 signal measured by the
FTS can be decomposed as the sum of the background concentration and the
enhancements due to the local sources:
XCH4,measured=XCH4,background+ΔXCH4.
Gradients of XCH4(ΔXCH4) are calculated relative to
one instrument for the 3 days. The XCH4 means (and standard
deviations) over the 3 days of measurements at Chino are 1.824 (±0.003),
1.833 (±0.007), 1.823 (±0.003),
and 1.835 (±0.010) ppm for the Caltech, Harvard1, Harvard2, and LANL
instruments, respectively. The Harvard2 XCH4 mean and standard
deviation are the lowest of all the observations; therefore these
measurements are used as background measurements. This background site is consistent
with wind directions for almost all observations, except for small periods
of time on 16 January, which highlights the limitation of our method.
Gradients of XCH4(ΔXCH4) for an instrument i
(i.e., Caltech, Harvard1, or LANL) are the differences between each 10 min
average XCH4 measured by i and the simultaneous 10 min average
XCH4 measured by the Harvard2 instrument. Details on the residence
time calculation can be found in Sect. S3. Time series of anomalies for
individual measurement days are presented in Fig. 4.
Time series of the 10 min-average XCH4 anomaly
(ΔXCH4, in ppb) computed relative to the Harvard2 instrument for
15 January (a), 16 January (b), and on 24 January 2015 (c).
Emissions of CH4 at Chino.
StudyTimeSourcesCH4CH4ofemissionemissionstudy(Gg yr-1)(ppt s-1)Peischl et al. (2013)2010inventory (dry manure + cows)282.5Peischl et al. (2013)2010aircraft measurements24–742.1–6.5Wennberg et al. (2012)2010inventory (wet manure + cows)1665.8CARB (2015)2015inventory (dry manure + cows)191.7Chen et al. (2016)2015FTS measurements only19–322.4–3.32This study2015FTS measurements only16–551.4–4.8This study2015WRF inversions25–392.2–3.5
1 Value reported for the SoCAB, apportioned for Chino in this study.
2 Chen et al. (2016) values are used.
Assuming the background levels XCH4 are similar at all the instrument
sites within 10 km distance and steady state wind fields, Eq. (3) can be written as
XCH4,i-XCH4,Harvard2∝ti-tHarvard2⋅FXCH4.
A graphical representation of Eq. (5) is shown in Fig. 5 in which
ΔXCH4, the measured gradients by the four FTS during
24 January, are plotted as a function of Δt, so that the slope
corresponds to a flux in ppb s-1 or ppt s-1 (parts per trillion). In this figure
the slope of the blue lines (dark and light ones) represents the flux
measured at Chino in previous studies (Peischl et al., 2013). These studies
estimating CH4 fluxes at Chino in 2010 reported a bottom-up value of
28 Gg yr-1 with a range of top-down measurements from 24 to 74 Gg yr-1 (Table 1).
To compare these values (in Gg yr-1) to the fluxes derived from column average
(in ppt s-1), we used Eq. (6):
Fcol=F×109a⋅Y⋅SCair(z)⋅mgNa1012,
where Fcol is the column average flux in ppt s-1, F the flux in Gg yr-1,
a the area of Chino (m2) , Y the number of seconds in a year,
SCair(z) the vertical column density of air
(molecules m-2), mg the molar mass of CH4 (g mol-1), and
Na the Avogadro constant (mol-1).
On 24 January, when the wind speed is higher than on the other days
(Fig. 1f and i), the residence time over the dairies (Δt) is
reduced by a factor of 30. The mean Δt from the closest
to the furthest instruments to the upwind site are 4 min for Caltech
(black square, Fig. 5), 13 min for Harvard2 (green square, Fig. 5),
and 16 min for LANL (red square, Fig. 5). The XCH4 fluxes
estimated using the mean states (mass balance approach) are 4.8, 1.6, and
1.4 ppt s-1 for the Caltech, LANL, and Harvard2 downwind instruments. For that
day, the high wind speed causes a reduction of the methane plume width
across the feedlot, which may increase uncertainties on the mass-balance
approach since the FTS measurements may only detect a small portion of the
total plume. Overall, the FTS network infers XCH4 emissions at
Chino,
which are at the low end of previous top-down estimates reported by Peischl
et al. (2013), consistent with the decrease in cows and farms in
the Chino area over the past several years.
Estimated fluxes using FTS observations on 24 January. The
10 min anomalies (relative to the Harvard2 instrument) are plotted
against the time that air mass took to travel over the dairies, so that the slopes
are equivalent to XCH4 fluxes (in ppb s-1, Eq. 5). The blue (and
cyan) line represents the fluxes (and half of the value) estimated at Chino
in 2010 (Peischl et al., 2013). The squares are the medians of the data
which correspond to the estimated fluxes using the FTS observations (in
black, red and green for the Caltech, LANL, and Harvard2 instruments).
Vertical profiles of mean horizontal wind velocity errors (a, b)
and direction (c, d) averaged from the WMO radiosonde sites
available across the 3 km domain, with the mean absolute error (in red), the
root mean square error (in black), and the mean error (in blue). Only
measurements from 00:00 UTC radiosondes were used in the evaluation.
However, the flux estimated using the closest instrument/shortest residence
time (i.e., Caltech) exceeds the value from previous studies by almost a
factor of 2. The other values from LANL and Harvard2, on the other hand,
are lower than previous published values. This analysis demonstrates that,
even with the day of steady-state winds and the simple geometry, the mass
balance still has weaknesses, since it does not properly explain the
differences seen at the three downwind sites. The close-in site exhibits
the highest apparent emission rate possibly due to the proximity of a large
CH4 source. This exhibits delusive approximations implied by this
method (i.e., spatial inhomogeneity of XCH4 sources completely averaged
out and conservative transport in the domain) even on the “golden day” of
strong steady-state wind pattern. Therefore, when investigating emissions at
local scales these assumptions can be dubious and lead to errors in the flux estimates.
Spatial study of the CH4 fluxes using WRF-LES data
Analysis of the spatial sources at Chino is developed in this section using
the WRF-LES model and in Sect. 4.4 with in situ Picarro measurements.
To map the sources of CH4 at Chino with the model, we focus on the
2 days of measurements during which the wind changed direction regularly
(i.e., 15 and 16 January; Fig. 1d, e, and g, h). This provides the model
with information on the spatial distribution of CH4 emissions.
WRF-LES model evaluation
The two WRF-Chem simulations were evaluated for both days (15 and 16 January)
using meteorological observations (Figs. 6 and 7). EM27
XCH4 measurements from 24 January correspond to a constant wind direction
and therefore are less suitable for mapping CH4 emissions. The
triangulation of sources requires changes in wind direction when using a
static network of sensors. Starting with the larger region on the 3 km grid
where WMO sondes are available (Fig. 6), model verification for both days
indicates that wind speed errors averaged over the domain are about
1 ms-1 in the free atmosphere and slightly larger in the PBL (less than
2 ms-1). For wind direction, the mean absolute error (MAE) is less than
20∘ in the free atmosphere and increases towards the surface,
reaching a maximum of about 50∘ there. In the PBL, where local
enhancements are located, the mean error (ME) remains small, oscillating
between 0 and 10∘. At higher resolutions, the comparison between
observed and WRF-predicted surface wind speed (Fig. 7) indicates that WRF
is able to reproduce the overall calm wind conditions for both days at both
WMO stations, Chino (KCNO) and Ontario (KONT). However, measurements below
1.5 ms-1 are not reported following the WMO standards, which limit the
ability to evaluate the model over time. On 15 January at KCNO,
consistent with the observations, all domains except the 3 km grid predict
no surface wind speeds above 2 ms-1 from 16:00 to 19:00 UTC, except
for one time from the 111 m LES domain. After this period, the 111 m LES
domain successfully reproduces the afternoon peak in wind speed of about
3 ms-1, only slightly smaller than the observed values (3.6 ms-1
at Chino and 3.9 ms-1 at Ontario airports). However, we should not
expect perfect correspondence between the observations and the instantaneous
LES output unless a low-pass filter is applied to the LES to average out
the turbulence. On 16 January 2015, the model wind speed at KONT
remained low throughout the day, in good agreement with the (unreported)
measurements and also with available observations.
Mean horizontal 10 m wind velocity in ms-1 measured at
Chino (KCNO) and Ontario (KONT) airports for 15 and 16 January
(black circles) compared to the simulated wind speed for different
resolutions using WRF hourly averaged results. When black circles indicate
zero, the wind velocity measurements are below the WMO minimum threshold
(i.e., 1.5 m s-1).
Dispersion of tracers in LES mode: 15 and 16 January 2015
We use the 15 January 2015 case as an example showing the detail in
the local winds that can be provided by the high-resolution LES domain.
Prior to approximately 19:00 UTC (11:00 LT) a brisk easterly flow is
present in the valley up to a height of 2 km; however, near the surface, a
cold pool up to several hundred meters thick developed with only a very weak
easterly motion. A simulated tracer released from a location near the east
edge of the Chino area stays confined to the cold pool for this period
(Fig. 8, upper row panels). Solar heating causes the cold pool to break down
quite rapidly after 19:00 UTC, causing the low-level wind speed to become
more uniform with height (around 3 ms-1 from the east) and allowing
the tracer to mix up to a height of about 1 km (Fig. 8, middle row panels).
Beginning around 22:00 UTC (14:00 LT), however, a pulse of easterly flow
scours out the valley from the east, while a surge of cooler westerly flow
approaches at low levels from the west, undercutting the easterly flow. By
00:00 UTC (16:00 LT) the tracer seems to be concentrated in the cooler
air just beneath the boundary of the two opposing airstreams (Fig. 8, lower row panels).
The tracer released (right column panels in Fig. 8) from an emitting 2 × 2 km2
pixel shows complex vertical structures and two different regimes
over the day. At 18:00 UTC, the tracer is concentrated near the surface,
except toward the west with a maximum at 600 m high. At 21:00 UTC, the
tracer is well-mixed in the vertical across the entire PBL, from 0 to about
∼ 1 km, corresponding to convective conditions of daytime. At
00:00 UTC, the stability increased again, generating a low vertical plume
extent with complex structures and large vertical gradients along the
transect. Several updrafts and downdrafts are visible at 18:00 and 00:00 UTC,
indicated by the shift in wind vectors and the distribution of the
tracer in the vertical (Fig. 8). These spatial structures are unique to
the LES simulation, as the PBL scheme of the mesoscale model does not
reproduce turbulent eddies within the PBL.
In the horizontal, convective rolls and large tracer gradients are present,
with visible fine-scale spatial structures driven by the topography
(i.e., hills in the south of the domain) and turbulent eddies. Figure 9 (left
panel) illustrates the spatial distribution of the mean horizontal wind at
the surface over the 111 m simulation domain at 18:00 UTC, just prior to the
scouring out of the cold pool near a large Chino feedlot. It can be seen
that the near-surface air that fills the triangular valley in the greater
Chino area is nearly stagnant, while much stronger winds appear on the
ridges to the south. There are some banded structures showing increased wind
speed near KONT to the north of the main pool of stagnant air. Figure 9
(right panel) illustrates the wind pattern for the 18:00 UTC 16 January
case. The same general patterns can be seen, with the main
apparent differences being reduced wind speed along the southern high
ridges, and more stagnant air in the vicinity of KONT along with elevated
wind speed bands near KCNO. These results emphasize how variable the wind
field structures can be from point to point in the valley.
Vertical transects across the 111 m west–east WRF-LES simulation
domain (pixels 5, 6, 7, and 8) at 18:00 UTC of 15 January (a–c),
21:00 UTC (d–f), and 00:00 UTC (g–i). From left to right,
simulated data are shown for potential temperature (in K, a, d, g), mean
horizontal wind speed and direction (in ms-1 and degree, (b, e, h),
and passive tracer concentration released from an eastern pixel of
the emitting area (pixel 5, c, f, i), to illustrate the relationship
between the three variables.
Mean horizontal wind field (in ms-1) in the first level of
the domain at 111 m resolution simulated by WRF-LES for 15 January (a),
and 16 January 2015 (b) at 18:00 UTC. High
wind speeds were simulated over the hills (southern part of the domain)
whereas convective rolls, corresponding to organized turbulent eddies, are
visible in the middle of the domain (i.e., over the feedlots of Chino),
highlighting the importance of turbulent structures in representing the
observed horizontal gradients of CH4 concentrations. The locations of
the Chino (KCNO) and Ontario (KONT) airports and the counties border (white
line) are indicated.
Emissions of CH4 (in mol km-2 h-1) for the 16 pixels
(2 × 2 km2 shown in Fig. 2) describing the dairies for both days,
i.e., 15 January (a) and 16 January 2015 (b). The
probability density function from the simulated annealing is shown in the
background. The Bayesian mean emissions (see Sect. 3.2) for the 2 days
combined are shown in black (dash line) and for the individual day (brown triangles).
Bayesian inversion and error assessment
We present the inverse emissions from the Bayesian analytical framework with
probability distribution functions from the SA in Fig. 10.
The Bayesian analytical solution was computed for both days, assuming a
flat prior emission rate of 2150 mol km-2 h-1 corresponding to a
uniform distribution of 115 000 dairy cows over 64 km2 emitting methane
at a constant rate of 150 kg of CH4 per year (CARB, 2015), plus 18 kg
annually per cow from dry manure management assumed to be on site (Peischl
et al., 2013). The colored contours in Fig. 10 represent the probability
density (or confidence level) defined by the SA
analysis for the 2 days of the campaign. The Bayesian averages are
moderately correlated with high confidence solutions from the SA. However,
the highest value (pixel 2) coincides with high confidence for large
emission values (> 50 % probability of emissions at 8000 mol km-2 h-1
or higher in pixels 2 or 3) which confirms that large flux
signals are fairly well constrained in the inverse solution. Other pixels
(i.e., 6 to 11) show a wide range of high confidence values meaning that the
inverse solution is more uncertain at these locations, with few pixels being
completely unconstrained (i.e., with low probabilities from the SA analysis
such as pixels 15 and 16). This would possibly suggest that only the largest
emissions could be attributed with sufficient confidence using these tools.
The spatial distribution of the emissions is shown in Fig. 12, which
directly corresponds to the pixel emissions presented in Fig. 10. The
largest sources are located in the southern part of the dairy farms area,
and in the northeastern corner of the domain. Additional interpretation of
these results is presented in the following section. The combination of the
results from two dates (15 and 16 January) is necessary in
order to identify the whole southern edge of the feedlots as a large source.
Sensitivity results are presented in the discussion and in S4 and S5.
The triangulation of sources performed by
the inversion produced consistent results using different configurations of
EM27 sensors for each day. Inversion results cover the entire domain with
all wind directions being observed over the 2 days (see Fig. 1d, e and g, h).
Additional sensitivity tests were performed to evaluate the
impact of instrument errors, introducing a systematic error of 5 ppb in
XCH4 measured by one of the EM27/SUN. The posterior emissions increased
by 3–4 Gg yr-1 for a +5 ppb bias, almost independent of the location of the
biased instrument. This represents ∼ 10 % of the total emission at Chino.
Spatial study of the CH4 emissions at Chino using Picarro measurements
During the field campaign in January 2015, in situ measurements of CH4,
CO2, as well as δ13C are collected simultaneously with a
Picarro instrument at the same site as the LANL EM27/SUN. Fossil-related
CH4 sources, such as power plants, traffic, and natural gas, emit
CH4 with an isotopic depletion δ13C ranging from -30 to
-45 ‰, whereas biogenic methane sources, such as those
from enteric fermentation and wet and dry manure management in dairies and
feedlots emit in the range of -65 to -45 ‰ (Townsend-Small et
al., 2012). During the January 2015 campaign, the
δ13C at Chino ranged from -35 to -50 ‰, indicating
a mixture of fossil and biogenic sources, respectively. Most of the air
sampled included a mixture of both sources. However, the measurements with
the highest CH4 concentrations had the lowest δ13C signatures,
suggesting that the major CH4 enhancements measured by the Picarro
instrument can be attributed to the dairy farms and not the surrounding urban sources.
On 16 and 22 January, the Picarro and the LANL EM27/SUN were
installed at the southwestern side of the largest dairies in Chino (red pin,
Fig. 1b), near a wet lagoon that is used for manure management
(, 150 m away). For these days, the Picarro measured enhancements of
CH4 up to 20 ppm above background concentrations, demonstrating that the
lagoon is a large source of CH4 emissions in the Chino area. The
location of the lagoon was identified and verified by satellite imagery,
visual inspection, and also with measurements from the second Picarro
instrument deployed in the field on 15 January 2015. With this
instrument, CH4 spikes up to 23 ppm were observed near the wet manure
lagoon. The measurements from both Picarros and the LANL EM27/SUN instrument
near the lagoon suggested that this is a significant local source of
CH4 emissions in the Chino area.
As opposed to column measurements, Picarro measurements are very sensitive
to the dilution effect of gases in the PBL. With a low boundary layer,
atmospheric constituents are concentrated near the surface, and the
atmospheric signal detected by the in situ surface measurements is enhanced
relative to the daytime, when the PBL is fully developed. For this reason,
additional Picarro measurements were made at night on 13 August 2015,
when the PBL height is minimal. Between 04:00 and 07:00 LT, we performed
Picarro measurements at different locations in Chino to map the different
sources of CH4 and verify that the large sources observed in January,
such as the lagoon, are still emitting in summer. Figure 11 shows the
scatter plot of 1 min-average anomalies of CH4(ΔCH4)
vs. CO2(ΔCO2), colored by the δ13C values,
measured by the Picarro on the night of 13 August 2015. During that night,
the isotopic range of δ13C in sampled
methane ranged from -45 to -65 ‰.
These low δ13C values are consistent with the expectation that
the sources of CH4 in the Chino area are dominated by biogenic
emissions from dairy cows. In the feedlots (side triangles, Fig. 11),
ΔCH4 and ΔCO2 are well correlated (r2= 0.90),
because cows emit both gases (Kinsman et al., 1995). The observed
ΔCH4/ΔCO2 emission ratio, 48 ± 1.5 ppb ppm-1, is in
good agreement with a previous study measuring this ratio from cows' breath
(Lassen et al., 2012). Measurements obtained at less than 1 m away
from cows (circles, Fig. 11) had the lowest the δ13C
observed, ∼-65 ‰, and these points scale
well with the linear correlation observed during the survey. This confirms
that the emission ratio derived by surveying the feedlots is representative of
biogenic emissions related to enteric fermentation. For, measurements obtained
next to the lagoon (diamond marks, Fig. 11), the 12CH4
concentrations were enhanced by up to 40 ppm above background levels observed
that night, while the relative enhancement of CO2 was much smaller.
This extremely large CH4 enhancement relative to CO2 indicates a
signature of CH4 emissions from wet manure management (lagoon),
confirming that there is significant heterogeneity in the CH4 sources
within the Chino dairy area.
Scatter plot of 1 min-average anomalies (from the 5 min
smoothed) of CH4 vs. CO2, color coded using the delta CH4 values,
measured by the Picarro on 13 August from 04:00 to 07:00 LT.
Discussion
The fluxes derived by the FTS observations and the WRF-LES inversions, as
well as previous reported values, are summarized in Table 1.
The top-down CH4 estimate using FTS observations in Chino provides a
range of fluxes from 1.4 to 4.8 ppt s-1 during January 2015 (Table 1), which
are on the lower end of previously published estimates. These values of
CH4 flux estimates for January 2015 based on the FTS measurements are
consistent with the decrease in cows in Chino over the past several years as
urbanization has spread across the region. The mass balance approach uses a
simple characterization of the background XCH4 that can be applied to
any deployment of EM27 sensors. As described in Sect. S3, emissions are estimated
using the average residence time between the sensor locations based on
meteorological measurements. The wind direction has not been considered here
to perform a site selection and define background XCH4 mole fractions.
Therefore, the range of emissions from our analysis may be larger possibly
due to variations in the observed enhancements when the mean wind direction
changes frequently over the day. The approach presented here could be
improved by collecting wind direction measurements co-located to EM27
sensors to help define the boundary conditions (as described in Lauvaux et al., 2016).
Considering the decrease in the number of dairy cows by ∼ 20 % from 2010
to 2015, and using the emission factor of 168 kg yr-1 per head (CARB, 2015
inventory: enteric fermentation + dry manure management), the CH4
flux associated with dairy cows at Chino decreased from 2.0 to 1.7 ppt s-1,
which agrees well with our low flux estimates derived from FTS observations.
However, fluxes derived using the simple mass balance approach differ from
each other, exhibiting the limitations of this method, even on a golden
day (steady-state wind day on 24 January). The WRF-LES inversions
(Figs. 10 and 12) and mobile in situ measurements using the Picarro
instrument (Fig. 11) indicate that the CH4 sources are not
homogeneous within this local area. In addition, wind measurements from the
two local airports typically disagree regarding the direction and speed
(Fig. 1d–i), and the WRF-LES tracer results
indicate nonhomogeneous advection of tracers (Fig. 8, right panels).
Figure 12 shows the map of the a posteriori XCH4 fluxes (mean of 15 and 16 January
runs) from the WRF-LES simulations, superimposed on a Google
Earth map, with the location of dairy farms represented by the red areas.
The domain is decomposed into 16 boxes (Sect. 3.2), in which the colors
correspond to the a posteriori emissions derived from the WRF-LES inversions. Red (blue)
colors of a box mean more (less) CH4 emissions compared to the a priori
emissions, which correspond to the dairy cow emissions contained in the
CARB 2015 inventory (emission factor multiplied by the number of cows).
Results of the inversion exhibit more CH4 emissions at the southern and
the northeastern parts of the domain, as well as emissions corresponding to dairy cows
in the center of the area.
Map of the a posteriori XCH4 fluxes (mean of 15 and 16 January
runs) from the WRF-LES simulations normalized by the a priori
emissions and superimposed on a Google Earth map, where the dairy farms are
represented by the red areas as shown in Fig. 1. The domain is decomposed
in 16 boxes (2 km × 2 km), in which the colors correspond to the a posteriori emissions
from the WRF-LES inversions. Red (blue) colors mean more (less) CH4
emissions than dairy cows in that box. A multiplicative ratio of 1 is
equivalent to a flux of 2150 mol km-2 h-1. The locations of the
lagoon (yellow pin) and the power plant (blue pin) are also added to the
map. Map provided by Google Earth V 7.1.2.2041, US Dept. of State
Geographer, Google, 2013, Image Landsat, Data SIO, NOAA, US, Navy, NGA, and GEBCO.
The higher CH4 emissions from the southwestern part of the domain can
be attributed to the wet manure lagoon (yellow pin, Fig. 12) in January 2015.
During the night of 13 August 2015, Picarro measurements
confirmed that the lagoon was still wet and emitted a considerable amount of
CH4 relative to CO2 (Fig. 12). The second mobile Picarro
instrument from JPL was deployed on 15 January 2015 and measured
CH4 spikes up to 23 ppm near the wet manure lagoon. The WRF-LES model
also suggests higher methane fluxes in these regions (red boxes, Fig. 12).
The CARB 2015 inventory estimates that manure management practices under wet
(e.g., lagoon) conditions emit more CH4 than the dairy cows themselves:
187 kg CH4 cow-1 yr-1 from wet manure management,
18 kg CH4 cow-1 yr-1 from dry management practices, and
150 kg CH4 cow-1 yr-1 from enteric fermentation in the stomachs
of dairy cows. Therefore, we expect that measurements in which the lagoon
emissions were detected by our instruments will lead to higher methane
fluxes in the local region compared to measurements that detect emissions
from enteric fermentation in cows alone. Bottom-up emission inventory of
CH4 is 2 times higher when considering wet lagoons (Wennberg et al.,
2012) instead of dry management practices (Peischl et al., 2013) at Chino
(Table 1). The location and extent of wet lagoons in the Chino region is not
expected to be constant with time and could be altered due to changing land
use and future development in the area. Bottom-up estimates of CH4
emissions from dairies in the Chino region could be further improved if the
extent and location of wet manure lagoons were well known.
The WRF-LES model also suggests higher methane fluxes in the southeast (red
boxes, Fig. 12). No dairy farms are located in these areas, but an
interstate pipeline is located nearby; thus these CH4 enhancements
could be attributed to natural gas. The 13CH4 Picarro measurements
indicate that the Chino area is influenced by both fossil- and biogenic-related
methane sources. A recent study has suggested the presence of considerable
fugitive emissions of methane at Chino
(http://www.edf.org/climate/methanemaps/city-snapshots/los-angeles-area),
probably due to the advanced age of the pipelines. Natural gas leaks in the
Chino area were not specifically targeted during the time of this field
campaign and cannot be confirmed using available data. This possibility
should thus be confirmed by future studies.
In addition to possible fugitive emissions at Chino, the inversion also
predicts higher CH4 flux in the northeastern region of the study
domain, which is in the vicinity of a power plant that reportedly emits a
CH4 flux roughly equivalent to one cow per year (only including enteric
fermentation) (http://www.arb.ca.gov/cc/reporting/ghg-rep/reported_data/ghg-reports.htm).
Further analysis and measurements of fossil methane
sources in the Chino area would help to verify potential contributions from
fossil methane sources, including power plants and/or fugitive natural gas pipeline emissions.
Overall, FTS and in situ Picarro measurements, as well as WRF-LES
inversions, all demonstrate that the CH4 sources at Chino are
heterogeneous, with a mixture of emissions from enteric fermentation, wet
and dry manure management practices, and possible additional fossil methane
emissions (from natural gas pipeline and power plants). The detection of
CH4 emissions in the Chino region and discrepancies between top-down
estimates could be further improved with more FTS observations and
concurrent in situ methane isotopes measurements combined with
high-resolution WRF-LES inversions. This would improve the spatial detection
of the CH4 emissions at Chino in order to ameliorate the inventories
among the individual sources in this local area.
Summary and conclusions
In January 2015, four mobile low-resolution FTS (EM27/SUN) were deployed in
a ∼ 6 × 9 km area in Chino (California) to assess CH4
emissions related to dairy cows in the SoCAB farms. The network of column
measurements captured large spatial and temporal gradients of greenhouses
gases emitted from this small-scale area. Temporal variabilities of
XCH4 and XCO2 can reach up to 20 ppb and 2 ppm, respectively,
within less than a 10 min interval with respect to wind direction
changes. This study demonstrates that these mobile FTS are therefore capable
of detecting local greenhouses gas signals and these measurements can be
used to improve the verification of XCO2 and XCH4
emissions at local scales.
Top-down estimates of CH4 fluxes using the 2015 FTS observations in
conjunction with wind measurements are 1.4–4.8 ppt s-1, which are in the
low end of the 2010 estimates (Peischl et al., 2013), consistent with the
decrease in cows in the Chino area. During this campaign, FTS measurements
were collected in close proximity to the sources (less than a few kilometers) in
order to capture large signals from the local area. The main advantage of
this type of deployment strategy is to better constrain the emissions while
avoiding vertical mixing issues in the model with the use of column
measurements in the inversion (Wunch et al., 2011). Therefore, the model
transport errors, which often limit the capacity of the model flux
estimates, are considerably reduced. However, the close proximity of the
measurements to the sources makes the assumptions on the homogeneity of the
sources and wind patterns questionable.
The FTS and the Picarro measurements detected various CH4 signatures
over Chino, with extreme CH4 enhancements measured near a wet
lagoon (Picarro and FTS measurements enhanced by 40 ppm CH4 and 60 ppb
XCH4, respectively) and possible fugitive fossil-related CH4
emissions in the area (indicated by higher δ13C values than
expected from biogenic emissions alone).
Wind speed and direction measurements derived from the two local airports
(less than 10 km apart), as well as the WRF meteorological simulations at
different FTS sites, differ greatly, suggesting that an
assumption of steady horizontal wind can be improved upon in the use of the
mass balance approach in our study.
This study demonstrates the value of using mobile column measurements for
the detection of local CH4 enhancements and the estimation of CH4
emissions when these measurements are combined with modeling.
High-resolution (111 m) WRF-LES simulations were performed on two dates,
constrained by four column measurements each day, to map the heterogeneous
CH4 sources at Chino. The optimized emissions (i.e., average a
posteriori flux) over the domain are 1.3 ppt s-1 when only considering the
boxes in the center of the domain and 2.6 ppt s-1 when all the boxes are
averaged. A major emitter (a wet manure lagoon) was identified by the
inversion results, and is supported by in situ 13CH4 measurements
collected during the campaign. The CH4 flux estimates are within the
range of the top-down mass balance emissions derived with the four FTS and
estimates reported by Peischl et al. (2013) (i.e., 2.1 to 6.5 ppt s-1), showing
that column measurements combined with high-resolution modeling can detect
and be used to estimate CH4 emissions.
The instrumental synergy (mobile in situ and column observations) coupled
with a comprehensive high-resolution model simulations allow the estimation of
local CH4 fluxes, and can be useful for improving emission
inventories, especially in a complex megacity area, where the different
sources are often located within small areas.
This study highlights the complexity of estimating emissions at local scale
when sources and wind can exhibit heterogeneous patterns. Long-term column
observations and/or aircraft eddy covariance measurements could improve estimations.
The atmospheric data are available upon request (camille.viatte@latmos.ipsl.fr) or as an electronic
Supplement to the paper.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-7509-2017-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank NASA and the W. M. Keck Institute for Space Studies for
financial support. MKD acknowledges NASA CMS support of the EM27/SUN
deployment and LANL-LDRD 20110081DR for acquisition of the instrument.
Jia Chen, Taylor Jones, Jonathan E. Franklin, and Steve Wofsy gratefully acknowledge
funding provided by the National Science Foundation through MRI Award 1337512.
A portion of this research was carried out at the Jet Propulsion
Laboratory, California Institute of Technology, under a contract with the
National Aeronautics and Space Administration. The January campaign participants
are Camille Viatte, Jacob Hedelius, Harrison Parker, Jia Chen, Johnathan Franklin,
Taylor Jones, Riley Duren, and Kristal Verhulst.
Edited by: P. Monks
Reviewed by: two anonymous referees
ReferencesArata, C., Rahn, T., and Dubey, M. K.: Methane Isotope Instrument Validation
and Source Identification at Four Corners, New Mexico, United States, J.
Phys. Chem. A, 120, 1488–1494, 10.1021/acs.jpca.5b12737, 2016.ARB – Air Resources Board: concept paper, full report,
available at: http://www.arb.ca.gov/cc/shortlived/concept_paper.pdf,
last access: 7 May 2015.Berchet, A., Pison, I., Chevallier, F., Paris, J.-D., Bousquet, P., Bonne, J.-L.,
Arshinov, M. Y., Belan, B. D., Cressot, C., Davydov, D. K., Dlugokencky, E. J.,
Fofonov, A. V., Galanin, A., Lavrič, J., Machida, T., Parker, R., Sasakawa,
M., Spahni, R., Stocker, B. D., and Winderlich, J.: Natural and anthropogenic
methane fluxes in Eurasia: a mesoscale quantification by generalized atmospheric
inversion, Biogeosciences, 12, 5393–5414, 10.5194/bg-12-5393-2015, 2015.Breon, F. M. and Ciais, P.: Spaceborne remote sensing of greenhouse gas
concentrations, Comptes Rendus Geoscience, 342, 412–424, 10.1016/j.crte.2009.09.012, 2010.California Agricultural Statistics, United States Department of Agriculture,
National Agricultural Statistics Service, Pacific Regional, Field Office
California, full report, available at: http://www.nass.usda.gov/Statistics by_State/California/Publications/California_Ag_Statistics/ CALivestockandDairy.pdf
(last access: 16 June 2017), 2013.Cambaliza, M. O. L., Shepson, P. B., Caulton, D. R., Stirm, B., Samarov, D.,
Gurney, K. R., Turnbull, J., Davis, K. J., Possolo, A., Karion, A., Sweeney,
C., Moser, B., Hendricks, A., Lauvaux, T., Mays, K., Whetstone, J., Huang, J.,
Razlivanov, I., Miles, N. L., and Richardson, S. J.: Assessment of uncertainties
of an aircraft-based mass balance approach for quantifying urban greenhouse gas
emissions, Atmos. Chem. Phys., 14, 9029–9050, 10.5194/acp-14-9029-2014, 2014.CARB – California Air Resources Board: California Greenhouse Gas Emission
Inventory, 2015 Edn., available at: http://www.arb.ca.gov/cc/inventory/data/data.htm
(last access: 6 June 2017), 2015.Chen, J., Viatte, C., Hedelius, J. K., Jones, T., Franklin, J. E., Parker, H.,
Gottlieb, E. W., Wennberg, P. O., Dubey, M. K., and Wofsy, S. C.: Differential
Column Measurements Using Compact Solar-Tracking Spectrometers, Atmos. Chem.
Phys., 16, 8479–8498, 10.5194/acp-16-8479-2016, 2016.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré,
C., Myneni, R. B., Piao, S., and Thornton, P.: Carbon and Other
Biogeochemical Cycles, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner,
G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V.,
and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013.
Deng, A., Stauffer, D., Gaudet, B., Dudhia, J., Hacker, J., Bruyere, C., Wu,
W., Vandenberghe, F., Liu, Y., and Bourgeois, A.: Update on WRF-ARW
end-to-end multi-scale FDDA system, in: 10th Annual WRF Users' Workshop,
23 June 2009, Boulder, CO, 2009.Deng, A., Lauvaux, T., Davis, K.J., Gaudet, B. J., Miles, N. L., Richardson,
S. J., Wu, K., Sarmiento, D. P., Hardesty, R. M., Bonin, T. A., Brewer, W.
A., and Gurney, K. R.: Toward reduced transport errors in a high resolution
urban CO2 inversion system, Elementa, 2017, 5–20, doi.org/10.1525/elementa.133, 2017.EPA – Environmental Protection Agency: Sources of Greenhouses Gases
Emissions: addresses anthropogenic emissions from agricultural activities
(not including fuel combustion and sewage emissions, which are addressed in
the Energy and Waste chapters), full report, available at:
http://www.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2015-Chapter-5-Agriculture.pdf
(last access: 19 January 2017), 2015.Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys.
Res., 99, 10143–10162, 10.1029/94JC00572, 1994.Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey,
D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R.,
Raga, G. M. S., and Van Dorland, R.: Changes in Atmospheric Constituents and in
Radiative Forcing, in: Climate Change 2007: The Physical Science Basis,
Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Solomon, S., Quin, D.,
Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H.,
Cambridge University Press, Cambridge, UK, 500–657, 10.1017/CBO9781107415324, 2007.Franco, B., Hendrick, F., Van Roozendael, M., Müller, J.-F., Stavrakou, T.,
Marais, E. A., Bovy, B., Bader, W., Fayt, C., Hermans, C., Lejeune, B., Pinardi,
G., Servais, C., and Mahieu, E.: Retrievals of formaldehyde from ground-based
FTIR and MAX-DOAS observations at the Jungfraujoch station and comparisons with
GEOS-Chem and IMAGES model simulations, Atmos. Meas. Tech., 8, 1733–1756,
10.5194/amt-8-1733-2015, 2015.
Gaudet, B. J., Lauvaux, T., Deng, A., and Davis, K. J.: Exploration of the
impact of nearby sources on urban atmospheric inversions using large eddy
simulation, Elementa, in review, 2017.Gisi, M., Hase, F., Dohe, S., and Blumenstock, T.: Camtracker: a new camera
controlled high precision solar tracker system for FTIR-spectrometers, Atmos.
Meas. Tech., 4, 47–54, 10.5194/amt-4-47-2011, 2011.Gisi, M., Hase, F., Dohe, S., Blumenstock, T., Simon, A., and Keens, A.:
XCO2-measurements with a tabletop FTS using solar absorption spectroscopy,
Atmos. Meas. Tech., 5, 2969–2980, 10.5194/amt-5-2969-2012, 2012.Gordon, M., Li, S.-M., Staebler, R., Darlington, A., Hayden, K., O'Brien, J.,
and Wolde, M.: Determining air pollutant emission rates based on mass balance
using airborne measurement data over the Alberta oil sands operations, Atmos.
Meas. Tech., 8, 3745–3765, 10.5194/amt-8-3745-2015, 2015.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled online chemistry within the WRF model, Atmos.
Environ., 39, 6957–6975, 2005.Guha, A., Gentner, D. R., Weber, R. J., Provencal, R., and Goldstein, A. H.:
Source apportionment of methane and nitrous oxide in California's San Joaquin
Valley at CalNex 2010 via positive matrix factorization, Atmos. Chem. Phys.,
15, 12043–12063, 10.5194/acp-15-12043-2015, 2015.Hase, F., Frey, M., Blumenstock, T., Groß, J., Kiel, M., Kohlhepp, R.,
Mengistu Tsidu, G., Schäfer, K., Sha, M. K., and Orphal, J.: Use of
portable FTIR spectrometers for detecting greenhouse gas emissions of the
megacity Berlin – Part 2: Observed time series of XCO2 and XCH4,
Atmos. Meas. Tech. Discuss., 8, 2767–2791, 10.5194/amtd-8-2767-2015, 2015.Hedelius, J. K., Viatte, C., Wunch, D., Roehl, C. M., Toon, G. C., Chen, J.,
Jones, T., Wofsy, S. C., Franklin, J. E., Parker, H., Dubey, M. K., and Wennberg,
P. O.: Assessment of errors and biases in retrievals of XCO2,
XCH4, XCO, and XN2O from
a 0.5 cm-1 resolution solar-viewing spectrometer, Atmos. Meas. Tech., 9,
3527–3546, 10.5194/amt-9-3527-2016, 2016.Hiller, R. V., Neininger, B., Brunner, D., Gerbig, C., Bretscher, D.,
Künzle, T., Buchmann, N., and Eugster, W.: Aircraft-based CH4 flux
estimates for validation of emissions from an agriculturally dominated area
in Switzerland, J. Geophys. Res.-Atmos., 119, 4874–4887, 10.1002/2013JD020918, 2014.Histov, A. N., Johnson, K. A., and Kebreab, E.: Livestock methane emissions
in the United States, P. Natl. Acad. Sci. USA, 111, E1320, 10.1073/pnas.1401046111, 2014.Hopkins, F. M., Kort, E. A., Bush, S. E.,Ehleringer, J. R., Lai, C.-T.,
Blake, D. R., and Randerson, J. T.: Spatial patterns and source attribution
of urban methane in the Los Angeles Basin, J. Geophys. Res.-Atmos., 121,
2490–2507, 10.1002/2015JD024429, 2016.Hsu, Y.-K., VanCuren, T., Park, S., Jakober, C., Herner, J., FitzGibbon, M.,
Blake, D. R., and Parrish, D. D.: Methane emissions inventory verification
in southern California, Atmos. Environ., 44, 1–7, 10.1016/j.atmosenv.2009.10.002, 2010.
IPCC – Intergovernmental Panel on Climate Change: Climate Change 2013: the
physical science basis, in: Contribution of working group I to the fifth
Assessment report of the Intergovernmental Panel On Climate Change, edited by: Stocker,
T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J.,
Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University
Press, Cambridge, UK and New York, NY, USA, 1535 pp., 2013.Karion, A., Sweeney, C., Pétron, G., Frost, G., Michael Hardesty, R.,
Kofler, J., Miller, B. R., Newberger, T., Wolter, S., Banta, R., Brewer, A.,
Dlugokencky, E., Lang, P., Montzka, S. A., Schnell, R., Tans, P., Trainer,
M., Zamora, R., and Conley, S.: Methane emissions estimate from airborne
measurements over a western United States natural gas field, Geophys. Res.
Lett., 40, 4393–4397, 10.1002/grl.50811, 2013.Keppel-Aleks, G., Wennberg, P. O., Washenfelder, R. A., Wunch, D., Schneider,
T., Toon, G. C., Andres, R. J., Blavier, J.-F., Connor, B., Davis, K. J.,
Desai, A. R., Messerschmidt, J., Notholt, J., Roehl, C. M., Sherlock, V.,
Stephens, B. B., Vay, S. A., and Wofsy, S. C.: The imprint of surface fluxes
and transport on variations in total column carbon dioxide, Biogeosciences,
9, 875–891, 10.5194/bg-9-875-2012, 2012.Kille, N., Baidar, S., Handley, P., Ortega, I., Sinreich, R., Cooper, O. R.,
Hase, F., Hannigan, J. W., Pfister, G., and Volkamer, R.: The CU mobile
Solar Occultation Flux instrument: structure functions and emission rates of
NH3, NO2 and C2H6, Atmos. Meas. Tech., 10, 373–392,
10.5194/amt-10-373-2017, 2017.Kinsman, R., Sauer, F. D., Jackson, H. A., and Wolynetz, M. S.: Methane and Carbon
Dioxide Emissions from Dairy Cows in Full Lactation Monitored over a Six-Month
Period, J. Dairy Sci., 78, 2760–2766, 10.3168/jds.S0022-0302(95)76907-7, 1995.Kort, E. A., Frankenberg, C., Costigan, K. R., Lindenmaier, R., Dubey, M. K.,
and Wunch, D.: Four corners: The largest US methane anomaly viewed from
space, Geophys. Res. Lett., 41, 6898–6903, 10.1002/2014GL061503, 2014.Lassen, J., Lovendahl, P., and Madsen, J.: Accuracy of noninvasive breath
methane measurements using Fourier transform infrared methods on individual
cows, J. Dairy Sci., 95, 890–898, 10.3168/jds.2011-4544, 2012.Lauvaux, T. and Davis, K. J. : Planetary boundary layer errors in mesoscale
inversions of column-integrated CO2 measurements, J. Geophys. Res.-Atmos.,
119, 490–508, 10.1002/2013JD020175, 2014.Lauvaux, T., Schuh, A., Bocquet, M., Wu, L., Richardson, S., Miles, N., and
Davis, K.: Network design for mesoscale inversions of CO2 sources and sinks,
Tellus B, 64, 17980, 10.3402/tellusb.v64i0.17980, 2012.Lauvaux, T., Miles, N. L., Deng, A., Richardson, S. J., Cambaliza, M. O.,
Davis, K. J., Gaudet, B., Gurney, K. R., Huang, J., Karion, A., Oda, T.,
Patasaruk, R., Razlivanov, I., Sarmiento, D., Shepson, P., Sweeney, C.,
Turnbull, J., and Wu, K.: High resolution atmospheric inversion of urban
CO2 emissions during the dormant season of the Indianapolis Flux
Experiment (INFLUX), J. Geophys. Res.-Atmos., 121, 5213–5236,
10.1002/2015JD024473, 2016.Lavoie, T. N., Shepson, P. B., Cambaliza, M. O. L., Stirm, B. H., Karion, A.,
Sweeney, C., Yacovitch, T. I., Herndon, S. C., Lan, X., and Lyon, D.:
Aircraft-Based Measurements of Point Source Methane Emissions in the Barnett
Shale Basin, Environ. Sci. Technol., 49, 7904–7913, 10.1021/acs.est.5b00410, 2015.Leifer, I., Culling, D., Schneising, O., Farrell, P., Buchwitz, M., and
Burrows, J. P.: Transcontinental methane measurements: Part 2. Mobile
surface investigation of fossil fuel industrial fugitive emissions, Atmos.
Environ., 74, 432–441, 10.1016/j.atmosenv.2013.03.018, 2013.Lindenmaier, R., Dubey, M. K., Henderson, B. G., Butterfield, Z. T., Herman,
J. R., Rahn, T., and Lee, S.-H.: Multiscale observations of CO2, 13CO2, and
pollutants at Four Corners for emission verification and attribution, P.
Natl. Acad. Sci. USA, 111, 8386–8391, 2014.McKain, K., Wofsy, S. C., Nehrkorn, T., Eluszkiewicz, J., Ehleringer, J. R.,
and Stephens, B. B.: Assessment of ground-based atmospheric observations for
verification of greenhouse gas emissions from an urban region, P. Natl.
Acad. Sci. USA, 109, 8423–8428, 10.1073/pnas.1116645109, 2012.Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E.,
Biraud, S. C., Dlugokencky, E. J., Eluszkiewicz, J., Fischer, M. L.,
Janssens-Maenhout, G., Miller, B. R., Miller, J. B., Montzka, S. A.,
Nehrkorn, T., and Sweeney, C.: Anthropogenic emissions of methane in the
United States, P. Natl. Acad. Sci. USA, 110, 20018–20022, 10.1073/pnas.1314392110, 2013.Moeng, C.-H., Dudhia, J., Klemp, J., and Sullivan, P.: Examining two-way
grid nesting for large eddy simulation of the PBL using the WRF model, Mon.
Weather Rev., 135, 2295–2311, 10.1175/MWR3406.1, 2007.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J.,
Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T.,
Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and
Natural Radiative Forcing, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner,
G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V.,
and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013.Nisbet, E. and Weiss, R.: Top-down versus bottom-up, Science, 328, 1241–1243,
10.1126/science.1189936, 2010.Peischl, J., Ryerson, T. B., Brioude, J., Aikin, K. C., Andrews, A. E.,
Atlas, E., Blake, D., Daube, B. C., de Gouw, J. A., Dlugokencky, E., Frost,
G. J., Gentner, D. R., Gilman, J. B., Goldstein, A. H., Harley, R. A.,
Holloway, J. S., Kofler, J., Kuster, W. C., Lang, P. M., Novelli, P. C.,
Santoni, G. W., Trainer, M., Wofsy, S. C., and Parrish, D. D.: Quantifying
sources of methane using light alkanes in the Los Angeles basin, California,
J. Geophys. Res.-Atmos., 118, 4974–4990, 10.1002/jgrd.50413, 2013.
Rogers, R. E., Deng, A., Stauffer, D. R., Gaudet, B. J., Jia, Y., Soong, S.,
and Tanrikulu, S.: Application of the Weather Research and Forecasting Model for
Air Quality Modeling in the San Francisco Bay Area, J. Appl. Meteorol., 52, 1953–1973, 2013.Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the Advanced
Research WRF version 3, NCAR Technical Note 475, http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf
(last access: 16 June 2017), 2008.Streets, D. G., Canty, T., Carmichael, G. R., de Foy, B., Dickerson, R. R.,
Duncan, B. N., Edwards, D. P., Haynes, J. A., Henze, D. K., Houyoux, M. R.,
Jacob, D. J., Krotkov, N. A., Lamsal, L. N., Liu, Y., Lu, Z., Martin, R. V.,
Pfister, G. G., Pinder, R. W., Salawitch, R. J., and Wecht, K. J.: Emissions
estimation from satellite retrievals: a review of current capability, Atmos.
Environ., 77, 1011–1042, 10.1016/j.atmosenv.2013.05.051, 2013.Stremme, W., Grutter, M., Rivera, C., Bezanilla, A., Garcia, A. R., Ortega,
I., George, M., Clerbaux, C., Coheur, P.-F., Hurtmans, D., Hannigan, J. W.,
and Coffey, M. T.: Top-down estimation of carbon monoxide emissions from the
Mexico Megacity based on FTIR measurements from ground and space, Atmos.
Chem. Phys., 13, 1357–1376, 10.5194/acp-13-1357-2013, 2013.Tilman, D. and Clark, M.: Global diets link environmental sustainability and
human health, Nature, 515, 518–522, 10.1038/nature13959, 2014.Townsend-Small, A., Tyler, S. C., Pataki, D. E., Xu, X., and Christensen, L. E.:
Isotopic measurements of atmospheric methane in Los Angeles, California,
USA reveal the influence of “fugitive” fossil fuel emissions, J.
Geophys. Res., 117, D07308, 10.1029/2011JD016826, 2012.Turner, A. J., Jacob, D. J., Wecht, K. J., Maasakkers, J. D., Lundgren, E.,
Andrews, A. E., Biraud, S. C., Boesch, H., Bowman, K. W., Deutscher, N. M.,
Dubey, M. K., Griffith, D. W. T., Hase, F., Kuze, A., Notholt, J., Ohyama, H.,
Parker, R., Payne, V. H., Sussmann, R., Sweeney, C., Velazco, V. A., Warneke,
T., Wennberg, P. O., and Wunch, D.: Estimating global and North American methane
emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem.
Phys., 15, 7049–7069, 10.5194/acp-15-7049-2015, 2015.US Climate Action Plan: Strategy to reduce methane, full report,
https://obamawhitehouse.archives.gov/sites/default/files/strategy_to_reduce_methane_emissions_2014-03-28_final.pdf
(last access: 16 June 2017), March 2014.Wennberg, P. O., Mui, W., Wunch, D., Kort, E. A., Blake, D. R., Atlas, E. L.,
Santoni, G. W., Wofsy, S. C., Diskin, G. S., Joeng, S., and Fischer, M. L.:
On the sources of methane to the Los Angeles atmosphere, Environ. Sci.
Technol., 46, 9282–9289, 10.1021/es301138y, 2012.Wong, K. W., Fu, D., Pongetti, T. J., Newman, S., Kort, E. A., Duren, R., Hsu,
Y.-K., Miller, C. E., Yung, Y. L., and Sander, S. P.: Mapping CH4 : CO2
ratios in Los Angeles with CLARS-FTS from Mount Wilson, California, Atmos. Chem.
Phys., 15, 241–252, 10.5194/acp-15-241-2015, 2015.Wunch, D., Wennberg, P. O., Toon, G. C., Keppel-Aleks, G., and Yavin, Y. G.:
Emissions of greenhouse gases from a North American megacity, Geophys. Res.
Lett., 36, L15810, 10.1029/2009GL039825, 2009.Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J.,
Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The
total carbon column observing network, Philos. T. Roy. Soc. A,
369, 2087–2112, 10.1098/rsta.2010.0240, 2011.
Wunch, D., Toon, G. C., Sherlock, V., Deutscher, N. M., Liu, C., Feist, D.
G., and Wennberg, P. O.: The Total Carbon Column Observing Network's GGG2014
Data Version, 43, 10.14291/tccon.ggg2014.documentation.R0/1221662, 2015.
Zhao, C., Andrews, A. E., Bianco, L., Eluszkiewicz, J., Hirsch, A.,
MacDonald, C., Nehrkorn, T., and Fischer, M. L.: Atmospheric inverse
estimates of methane emissions from Central California, J. Geophys. Res.,
114, D16302, 10.1029/2008JD011671, 2009.