Introduction
Methane trends and uncertainty
On decadal timescales, methane (CH4) affects the atmospheric radiative
balance more strongly than carbon dioxide (CO2) (IPCC,
2007, Fig. 2.21). Since pre-industrial times, CH4 emissions have risen
by a factor of 2.5 (Dlugokencky et al., 2011; Khalil and Rasmussen,
1995), while estimates of its lifetime has decreased; it is now estimated at
∼ 8.5 years (Sonnemann and Grygalashvyly, 2014).
Atmospheric CH4 growth almost ceased between 1999 and 2006 but has
resumed since 2007 (Nisbet et al., 2014; Schwietzke et al., 2016).
Several processes are proposed to underlie this trend (Ghosh et al.,
2015; John et al., 2012), with recent isotopic shifts suggesting wetlands are
the dominant driver (Nisbet
et al., 2016); however, high uncertainty in emission inventories (IPCC,
2013) complicates interpretation of the underlying mechanism(s).
The dominant CH4 loss arises from reaction with hydroxyl (OH), whose
concentration has been increasing in recent decades (John
et al., 2012), causing a decrease in the estimated CH4 lifetime of
0.5 % yr-1 (Karlsdóttir and Isaksen, 2000).
Overall, the estimate of the CH4 lifetime has decreased by
∼ 40 % from an estimated 12 years in 2007 (IPCC,
2007). Rigby et al. (2017) suggest a decline in OH is
likely to have contributed to increasing CH4 since 2007. The recent
discovery of a new significant CH4 loss mechanism, terrestrial uptake
(Fernandez-Cortes et al., 2015), illustrates the need to
understand loss mechanisms better (Allen, 2016).
Large CH4 budget uncertainties remain for many sources (IPCC, 2013)
with greater uncertainty in future trends from global warming feedback
(Rigby et al., 2008) and
increasing anthropogenic activities (Kirschke et al., 2013; Saunois et
al., 2016; Wunch et al., 2009). Emphasizing these uncertainties are recent
studies that suggest underestimation by a factor of 1.5 in the important
anthropogenic CH4 source, fossil fuel industrial (FFI) emissions
(Brandt et al., 2014). Tellingly, this
discrepancy was only noted recently (Miller et al.,
2013), in part because the US CH4 monitoring network is too sparse to
constrain emissions at “regional to national scales” (Dlugokencky et
al., 2013). Furthermore, isotopic data indicate even larger underestimation
by a factor of 1.6–2.1 (Schwietzke et al., 2016). FFI
emissions are the largest (Brandt et al., 2014; EPA, 2017) or second-largest,
after agriculture (Saunois et al., 2016), anthropogenic
contributor to the global CH4 budget. These uncertainties strongly
argue for the need for new, robust methodologies for flux derivation.
Methane flux estimation
Various approaches have been developed to derive surface emissions from
CH4 concentration measurements including direct flux assessment – i.e.,
measurement of winds and concentrations through a plane – and/or by the
comparison of upwind and downwind mass budgets (Peischl et al., 2015, 2016;
White et al., 1976), data-driven mass balance (e.g., Karion et al., 2013),
tracer / tracer ratio (LaFranchi et al., 2013), and assimilation inverse
models (e.g., Jeong et al., 2013, 2012; Saunois et al., 2017). Challenges
for the last of those approaches include the needs for accurate meteorological
transport models and good a priori emission distributions (Miller et al.,
2013; Peischl et al., 2016; Smith et al., 2015). Miller et al. (2013)
concluded that bottom-up inventories (EPA, 2013; European Commission, 2010)
significantly underestimate husbandry and FFI emissions. To apportion
CH4 to FFI versus biological sources, the tracer–tracer approach has
been applied using ethane, whose emission ratio to CH4 requires tight
constraint (Peischl et al., 2013; Simpson et al., 2012; Wennberg et al.,
2012). In practice, this emission ratio is an a priori assumption in the
assessment.
Direct assessment approaches have advantages over inversion approaches.
Direct approaches allow explicit uncertainty evaluation and do not require
an a priori emission spatial distribution, which may be unknown. Direct approaches
also do not require the ability to model atmospheric transport accurately
across the study region. In areas of complex topography or highly variable
winds, this transport can challenge assimilation approaches, which also are
challenged in areas with poorly characterized (or unknown) or highly
variable sources, particularly if the measurement network is sparse. For
direct assessment approaches, data collection should be rapid if winds
and/or emissions are variable and at adequate data density to characterize
fine-scale structure.
Study motivation
Herein we report on a novel application of fused airborne and surface in situ data
to directly estimate CH4 emissions using an anomaly approach rather
than a more typical mass balance approach due to a lateral gradient in the
upwind data. A direct approach does not require accurate winds over the
study domain, only in the measurement plane. The approach was applied to
1164 km of airborne data collected on 19 August 2015 by NASA's Alpha Jet
Atmospheric eXperiment (AJAX), while AMOG (AutoMObile greenhouse Gas)
Surveyor collected 1074 km of contemporaneous mobile surface data. Both
platforms measure carbon dioxide (CO2), CH4, water vapor
(H2O), and ozone (O3), as well as winds, pressure, relative
humidity (RH), and temperature (T). The surface and airborne datasets were
collected in a downwind curtain or plane oriented approximately orthogonal
to the winds, to characterize the full planetary boundary layer (PBL) from
surface to above the PBL.
Additionally, the surface survey route was designed to include an ascent to
∼ 2.2 km above sea level to allow PBL characterization. Data
fusion between measurement platforms was validated by a vertical profile
intercomparison for 0.5 to 1.5 km altitude by AMOG Surveyor leveraging
topographic relief.
The southern San Joaquin Valley, California
Most California oil production lies in the San Joaquin Valley (SJV), as do
most of California agriculture, including many intensive dairies
(Gentner et al., 2014), and
major north–south transportation arteries. For this study, data were
collected for the Kern River oil fields (Kern Front oil field, Kern River
oil field, and the Poso Creek oil field, referred to herein as the Kern
Fields), located adjacent to northwest Bakersfield (Fig. 1a). These
adjacent oil fields create a strong CH4 source that largely is isolated
from confounding plumes from other SJV sources. This area includes complex
wind flow patterns across and around the “toe” of Sierra Nevada
foothills, which extend into the Kern Front and Kern River oil fields. Here,
topographic steering ensures predictable prevailing northwesterly winds blow
across the Kern Fields.
(a) Full surface and airborne data for 19 August 2015
mapped over California topography. White arrow shows Bakersfield. Data key in
panel. (b) Study area map showing direction of daytime prevailing
winds and nearby mountain topography (Google Earth, 2016). See Supplement
Fig. S1 for a high-altitude (20 km) photo of the entire study area and
surrounding terrain.
Strong orographic forcing also arises from tall bluffs (∼ 100 m)
on the Kern River Valley's south bank, which separates the Kern River oil
field from the urban city of Bakersfield (pop. 364 000 in 2013). The
fine-scale wind structure that results from orographic forcing on transport
dictated an anomaly approach for flux derivation, as did the presence of
strong CH4 structures (plumes) in the valley's lowest air. In the
anomaly approach, transects must extend beyond a reasonably well defined
plume.
Topography (i.e., mountain ranges) plays a locally dominant role in overall
southern California airflows, where upper-level winds locally force the
lower-level flows that transport pollutants (Bao et al.,
2008). The SJV is delimited on the east by the Sierra Nevada and
on the west by the coastal Transverse Ranges (Fig. 1a).
Transport between the SJV and adjacent air basins is poor due to
California's mountain ranges. The SJV features weak surface winds
(Bao et al., 2008), with the worst air quality in the United
States occurring in the cities of Bakersfield and Delano (American Lung
Association, 2016) in the SJV.
Pacific Ocean air primarily enters the SJV through the San Francisco Bay
area and the Carquinez Strait, where it splits north into the Sacramento
Valley and south into the SJV (Zhong et al., 2004). This flow
extends up to ∼ 1 km altitude (Zhong et al., 2004).
These winds are near orthogonal to the 600 km long Central Valley of
California – i.e., cross-slope. South of Bakersfield, winds shift to from
the west due to mountains that guide SJV air out into the Mojave Desert,
where it affects air quality for up to hundreds of kilometers distant
(VanCuren, 2015). Although the Tehachapi Pass is the main exit
pathway of SJV air, other passes also transport air into the Mojave Desert.
These flows are augmented by high inland temperatures relative to the
Pacific Ocean, which creates a horizontal pressure gradient that drives
local upslope flows during the day and returning downslope nocturnal flows
(Zhong et al., 2004). The pressure gradient is maximal around
sunset, although winds peak ∼ 4 h later, shortly before
midnight. This pressure gradient is controlled by the semi-permanent Pacific
high, situated offshore central California, which diverts storms far to the
north during summer. This pressure feature drives prevailing
west-southwesterly winds at the regional scale in the California south coast
air basins (Boucouvala and Bornstein, 2003).
Methodology
Experimental design
Data were collected as part of the GOSAT-COMEX Experiment
(Greenhouse gases Observing SATellite–CO2 and Methane Experiment (GCE)) campaign. GCE was developed to characterize emissions on spatial scales
from decameter (in situ surface, imaging spectroscopy) to kilometer (in
situ airborne) to deca-kilometer (satellite) in an area of complex
topography. GCE design combined in situ mobile surface and airborne data
with GOSAT satellite data. In situ data serve to assess the satellite
pixel–plume overlap. Key GCE requirements are relatively steady, strong,
isolated emissions and predictable, steady winds. Prevailing study area
winds are from the west-northwest, veering to westerly winds to the southeast
of Bakersfield (Fig. 1). Prevailing wind directions are highly reliable due
to topographic control.
GCE developed from the COMEX campaign (Krautwurst et al.,
2016), which combined in situ airborne and surface observations with both imaging
and non-imaging spectroscopy to explore synergies for GHG emission
estimation (Thompson et al., 2015). COMEX focused on southern
California CH4 sources, including husbandry, landfills, natural geology,
and petroleum hydrocarbon refining and production.
GCE combines airborne and surface data collected at dramatically different
speeds. AJAX collects data at ∼ 500 km h-1, capturing a
snapshot of atmospheric winds and plume structure. Surface GCE data are
collected quasi-Lagrangian, starting northwest (upwind) and proceeding
southeast and then east (downwind). This enables useful data collection even
when a CH4 plume drifts into the study area after the upwind survey –
data collection proceeds downwind faster than advection. The surface route
was designed carefully to traverse all targeted GOSAT pixels using rarely
used (low-traffic) surface roads and requires ∼ 100 min.
Airborne and surface surveys are timed so that the downwind data plane
(Krings et al., 2011) is surveyed concurrent with the
satellite overpass. Data planes extend from the surface (AMOG) to above the
PBL (AJAX), reducing uncertainty by providing a more complete atmospheric
characterization including below where airplanes are permitted to fly
(∼ 500 m in an urban area). AJAX and AMOG profile data are
fused by an interpolation approach that imposes the observed vertical
structure, and the flux through the data curtain is calculated (Sect. 2.5).
GCE first incorporates an AMOG Surveyor upwind transit from Delano (100 m)
on the SJV floor to Sierra Alta (1800 m) and higher to confirm that upwind
CH4 plumes do not threaten to impact the study area during the
experiment; otherwise the survey is aborted. A key mission abort criterion
is wind compliance. Specifically, winds must not be too light (typically
less than ∼ 2 m s-1) or variable (> 30∘)
and must flush nocturnal accumulations before the GOSAT
overpass (i.e., no CH4 cloud at or nearby upwind of the site. This
means that winds cannot be light as recently as several hours prior and must
be prevailing. The upwind transit provides vertical profile information
including PBL height and vertical structure. AJAX repeats this upwind
transect to compare wind profiles with AMOG; however, discrepancies in the
transects arise from the road following terrain and the airplane needing to
avoid peaks along the ridge.
AMOG Surveyor
Mobile atmospheric surface measurements have been conducted for many years
using a customized van (Lamb et al., 1995) or a recreational
vehicle (Farrell et al., 2013; Leifer et al., 2013). Recently, the
development of cavity-enhanced absorption spectroscopy (CEAS) analyzers has
opened the way for rapid and highly accurate trace gas measurements
(Leen et al., 2013) without the need for
onboard compressed gases as in gas chromatography (Farrell et
al., 2013), although periodic calibration with gas standards is important,
albeit typically not on board the platform. This allows for smaller vehicle
survey platforms at lower logistical overhead (Leifer et al., 2014;
McKain et al., 2015; Pétron et al., 2012; Yacovitch et al., 2015). A
competing sensor technology that has been used in mobile survey data
collection is open-path spectroscopy (Sun et al., 2014). Mobile
survey platforms can incorporate older technology such as fluorescence to,
for example, measure ozone, O3.
Mobile surface data were collected by AMOG Surveyor (Leifer et al.,
2014), a modified commuter car (see Supplement Sect. S2.1 for additional
details). AMOG Surveyor provides mobile high-speed, high-spatial-resolution
observations of meteorology (winds, temperature, pressure), trace gases
(greenhouse and others), and remote sensing parameters. AMOG Surveyor uses a
range of trace gas analyzers and careful design with respect to wind flow
around the vehicle to characterize strong spatial heterogeneity at up to
highway speeds.
Two-dimensional winds are measured by a sonic anemometer (VMT700, Vaisala)
mounted 1.4 m above the roof (at 3.0 m above the surface) and above vehicle
flow streamlines for slow to highway speeds. Estimated accuracy is
approximately 10∘ and 0.3 m s-1 for wind speeds above 1.5 m s-1
(see Supplement for further details).
A high-flow vacuum pump (GVB30, Edwards Vacuum) draws air down sample
lines from 5 and 3 m above ground for GHG and ozone (O3) analyzers. The
5 m sample line height references low speed/stopped (< a few m s-1)
AMOG sample collection. At high speed (> 10 m s-1)
the sample tube flexes backwards to 3 m height to avoid destructively
hitting obstacles at high speed. This protects the sample line from hitting
bridges, tree branches, etc. Greenhouse gases, CO2, CH4, and
H2O are measured at up to 10 Hz by an off-axis integrated cavity
output spectrometer–cavity-enhanced absorption spectroscopy analyzer, with a 1 s
accuracy of 1 ppb for CH4 (ICOS-CEAS, 911-0010, Los Gatos Research,
Inc.). Calibration is with a Scott-Marrin CH4 and CO2 atmospheric
standard. A fluorescence analyzer measured O3 at 0.25 Hz (49C,
Thermo Fisher Scientific, MA, USA). This difference does not arise from
calibration differences; the AMOG Surveyor O3 analyzer was
cross-calibrated with the AJAX calibration source to 1 ppb accuracy. AMOG
Surveyor's full trace gas suite (carbonyl sulfide, carbon monoxide, nitric
oxide, nitrogen dioxide, hydrogen sulfide, sulfur dioxide, total sulfur,
ammonia) was not deployed on 19 August 2015.
The greenhouse gas analyzer is calibrated using a Scotty's whole-air
standard before and/or after each data collection, with the calibration
factor closest to the day of flight being applied to each raw CO2 and
CH4 measurement. Calibration factors have been shown to agree within
less than 1 ppb. The calibration factor includes a linear correction for
cell pressure, which can drop at higher altitudes. This pressure calibration
has been shown to be linear from 140 mtorr down to 28 mtorr.
Relevant recent AMOG Surveyor improvements since Leifer et al. (2014)
include a high-speed thermocouple (50416-T, Cooper-Atkins) and a
high-accuracy (0.2 hPa) pressure sensor (61320V RM Young Co.). Both are mounted
in a passive roof radiation shield (7710, Davis Instruments) to largely
eliminate dynamic pressure effects from the airflow. Position information is
critical to accurate wind measurements and is provided by redundant (two)
global navigation satellite systems (19X HVS, Garmin) that use the GLONASS,
GPS, Galileo, and QZSS satellites at 10 Hz (WGS84). AMOG Surveyors'
analyzers' and sensor data are logged asynchronously on a single computer.
Custom software integrates the data streams and provides real-time
visualization of multiple parameters in the Google Earth environment.
AJAX
AJAX (Fig. 2b) collected airborne in situ measurements of CO2,
CH4, and H2O by cavity ring-down spectroscopy (G2301-m, Picarro Inc.); O3
(Model 205, 2B Technologies Inc.); and meteorological parameters including
3-D winds by the Meteorological Measurement System
(https://earthscience.arc.nasa.gov/mms), a NASA-developed system with
accuracy of ±1 m s-1. The greenhouse gas analyzer is calibrated
using NOAA whole-air standards; calibrations are performed before and/or
after each flight, with the calibration factor closest to the day of flight
being applied to each raw CO2 and CH4 measurement. Further
corrections include applying water vapor corrections provided by
Chen et al. (2010) to calculate
CO2 and CH4 dry mixing ratios. Data are quality-control-filtered
for deviations in instrument cavity pressure, to improve in-flight precision.
Study platforms. (a) AutoMObile trace Gas (AMOG) Surveyor,
Kern River oil field in background. Photo courtesy of Ira Leifer.
(b) The Alpha Jet Atmospheric eXperiment (AJAX) aircraft, photo
courtesy of Akihiko Kuze, JAXA. See Supplement Sect. S1 for further details.
Overall CH4 measurement uncertainty is typically < 2.2 ppb,
including contributions from accuracy of the standard, precision (1σ
over 6 min), calibration repeatability, in-flight variance due to cavity
pressure fluctuations, and uncertainty due to water corrections and pressure
dependence (based on environmental chamber studies). See Hamill et
al. (2016), Tanaka et al. (2016), and Yates et al. (2013)
for further aircraft and instrumentation details, and Supplement Sect. S2.2.
Background estimation and data fusion
The flux (Q(x, z)) in moles per second per square meter with respect to lateral transect
distance (x) and altitude (z) through the x–z plane is the product of the
normal winds (UN(x, z)) in meters per second and the plume concentration anomaly
(C′(x, z)) or mole fraction in ppm (Leifer et al., 2016).
Q(x,z)=k(z)UN(x,z)C′(x,z)=k(z)UN(x,z)(C(x,z)-CB(x,z))
k(z) converts from ppm to moles. Interpolation of C′ and UN
is linear within the PBL and is assumed uniform above the PBL. To calculate
Q(x, z) requires C′ relative to background (CB(x, z)).
Initially surface data that were collected for an upwind surface transect
were used to derive CB, using the assumption of vertical uniformity
for “background”.
Unfortunately, the upwind data showed a lateral gradient, which coupled with
uncertainty in precisely where the downwind air originated (given the
topography, which features a gentle incline towards the northeast, this
gradient is unsurprising, in retrospect). Thus a very small shift in the
winds between the upwind and downwind curtains results in a significant
shift in CB, with a very large effect on Q. As a result, the more
traditional upwind–downwind mass balance approach was abandoned for an
anomaly approach.
In the anomaly approach, CB(x, z) was derived from evaluating
CB(x<xmax/2, z) and CB(x>xmax/2, z),
denoted CBL(z) and CBR(z), respectively, where xmax is the lateral
extent of the data curtain. Then, CB(x,z) was derived from a first-order
linear polynomial fit of CBL(z) and CBR(z).
Both CBL(z) and CBR(z) are derived from the amplitude of a Gaussian
fit to the left and right probability density functions (ΦL(C(x<xmax/2,z)) and (ΦR(C(x>xmax/2,z)), respectively, for each flight transect level. Specifically,
for ΦL and ΦR,Gaussian functions are fit to model the
plume distribution (ΦP) and the background distribution (ΦB). In these data, ΦB is well fit by a single Gaussian,
while ΦP is best described by multiple Gaussian functions. Then,
CBL(z) and CBR(z) are defined such that
∫ΦBL(CBL(z))=0and∫ΦBR(CBR(z))=0,
where ΦBL and ΦBR are the background ΦB for
the left and right halves of the data plane, respectively. Concentration is
not a conserved value; thus C′ is converted into mass (N′) by the ideal gas law
(k in Eq. 1) for spatial integration to derive the total emissions (E), which
is the integration of the flux through the plane (Q):
E=∫x1x2∫0z=PBLQ(x,z)dzdx.
Interpolation, prior to integration, is linear.
Uncertainty evaluation for emission calculation
The flux calculation has two source of uncertainty: accuracy and
representativeness. Specifically, background concentration profiles may be
incorrect, while winds, which are measured accurately, could be
unrepresentative, as could concentrations due to temporal variability over
the period needed to make the measurements. Monte Carlo simulations based on
observed data variability were run to assess uncertainty. Instrumental
accuracy uncertainty is far less than spatial and temporal variability.
Thus, spatial and temporal variability are the dominant source of
uncertainty (Leifer et al., 2016).
(a) Pre-survey, upwind AMOG surface and AJAX airborne
methane (CH4) and winds for vertical profile on the Delano–Alta
Sierra transect (α-α′). Inset shows area map. (b) Post-survey,
downwind AMOG surface profile ascent Edison–Breckenridge
(ε-ε′) and descent Breckenridge–Bodfish–Caliente
(τ-τ′). Upwind profile visible top left. Planetary boundary layer
(PBL) identified.
Monte Carlo simulations were based on 1 standard deviation in the observed
UN(z) around the mean for each flight transect altitude level on
the right and left sides, i.e., UN_L(z) and UN_R(z).
Gaussian distributions with half-widths of 1σ based on the values of
UN_L(x,z) and UN_R(x,z) were formed for each
transect altitude. The distribution was randomly sampled to populate
UN(x,z) and then interpolated as described above. Other
variables were Monte Carlo-simulated in the same manner; i.e., a Gaussian
distribution was calculated for the left and right portions of the data based
on 1 standard deviation in the observations of the variable around its mean.
Variables then were randomly sampled and interpolated. Specifically, Monte
Carlo simulations also addressed CB and C. Because
instrumentation error is so much less than spatial and temporal variability,
Monte Carlo simulation of CB represents uncertainty in the source
of the background (upwind) air, which could have some veering from the east
or west coupled with convergence in the horizontal plane. One million Monte
Carlo simulations were run for a flux uncertainty calculation.
Surface altitude (z) above mean sea level profiles for west–east
Delano–Alta Sierra transect (Fig. 3a, α-α′) for AMOG and AJAX
(a) methane (CH4), (b) carbon dioxide (CO2),
(c) ozone (O3), (d) temperature (T), and
(e) relative humidity (RH). Also shown in (d) are the
dry, average, and wet adiabatic lapse rates. Data key in panel; planetary
boundary layer (PBL) labeled. Green arrow shows extrapolation of AJAX trend
to Shirley Meadows altitude (2058 m).
Results
Profile data
Four vertical profiles (surface and airborne) were collected to understand
PBL evolution during the survey (2 h) and across the survey domain spanning
the experiment. Primary changes were development of near-surface winds and a
slight increase in the PBL. AMOG Surveyor and AJAX collected pre-survey
intercomparison vertical profiles ∼ 30 km north of the Kern Fields
between the small town of Delano on the SJV floor (100 m) up to a meadow
(2058 m) above Shirley Meadows on a ridge of the Greenhorn Mountains in the
Sierra Nevada range (Fig. 3). This profile spans a wide range of
topography, from grasslands on rolling hills to tall pine trees near Alta
Sierra; see Supplement Fig. S5 for surface images along the profile. AMOG
Surveyor also conducted a post-survey, downwind vertical atmospheric profile
to 1800 m a.s.l. Approximately 15 min of data were collected in an open
(200–300 m) field above Shirley Meadows that was fairly exposed with only
thin stands of pine trees on terrain falling steeply off to both sides. The
wind direction and speeds for the field were consistent with winds at Alta
Sierra, several hundred meters below, where AMOG was surrounded by tall
trees. The field was above the top of the AJAX profile.
AMOG Surveyor's vertical ascent was collected before the AJAX profile to
enable concurrent AMOG and AJAX data collection for the Kern Fields. The AMOG
Surveyor ascent and descent were from 18:48 to 21:09 (20:08 UTC at crest), while
AJAX flew a descent pattern from 20:58 to 21:04 UTC. AMOG Surveyor's descent
was shortened to ∼ 1000 m altitude (Glenville, CA) to allow
AMOG to reach the Kern Fields nearly concurrent with AJAX and GOSAT.
AMOG and AJAX profile data overlapped between 500 and 2000 m. There was very
good agreement between the two platforms for CO2 and CH4 for
altitudes between 1.55 and 2 km (Fig. 4a and b), 99.9 and 99.7 %.
AMOG and AJAX CH4 concentrations decreased notably from the well-mixed
PBL to the near-surface layer, from ∼ 2.07 ppm (500–750 m) to
∼ 1.93 ppm (250–300 m). AJAX also showed a decrease in
CO2 from 403 ppm to below 400 ppm. The CO2 decrease was consistent
with a shift to agricultural air, where CO2 vegetative uptake reduces
CO2 concentrations. The PBL grew from 600 to 900 m between AMOG's
ascent and descent and then to 1500 m by the time of AJAX's descent based on
the CH4, CO2, and O3 data.
The PBL was identified at ∼ 1580–1600 m based on both surface
and airborne RH and T vertical profiles.
Winds were not useful for deriving the location of the PBL. Diurnal heating
is apparent between the two AMOG Surveyor T profiles but does not change the
lapse rate. Because AJAX flies above the surface where AMOG collects data,
AJAX temperatures are lower. In the lower atmosphere, the lapse rate was
6.9 ∘C km-1 for AJAX between 500 and 900 m, while the AMOG
lapse rate from 200 to 900 m was a similar 5.6 ∘C km-1. Between
950 and the top of the PBL, AMOG lapse rates were much shallower, 2.5 ∘C km-1,
with a jump in temperature at 900 m. Above the PBL,
the AMOG-measured lapse rate was 3.5 ∘C km-1, close to the
wet adiabatic lapse rate (Fig. 4d).
Above the PBL, O3 concentrations between AMOG and AJAX were
∼ 20 ppb different, although the AMOG and AJAX profile slopes
(dO3 / dz) were the same. If the trend in AJAX O3(z) from
1600 to 1850 m is extended to z=2058 m (Fig. 3c, green arrow), there is
agreement with AMOG Shirley Meadows (open field) O3 concentrations. This
similar slope but different absolute value could indicate O3 loss as it
diffused down through the pine canopy to the surface (and AMOG). Tall pine
trees (30+ m) dominate above ∼ 1700, except for Shirley Meadows,
where, as noted, there was good agreement. For 900<z<1400 m, AJAX–AMOG agreement was better for the descent, which was closer
in time to AJAX than the ascent. This shift likely was associated with
formation of the daytime PBL.
Surface altitude (z) above sea level profiles for
Edison–Breckenridge ascent (red) and descent (blue) to Bodfish and then
Caliente profile (magenta) (Fig. 3b) for AMOG Surveyor (a) methane
(CH4), (b) carbon dioxide (CO2), (c) relative
humidity (RH), (d) temperature (T), north wind
(Unorth) for (e) ascent and (f) descent (dots
show 50 m altitude-binned averaged), and (g) ozone (O3).
Planetary boundary layer (PBL) labeled.
Altitude (z) profiles for (a) west (upslope) and
(b) north (cross slope) wind components from AMOG and AJAX for
overlapping altitudes of the Delano–Alta Sierra transit (Fig. 3, α-α′), 100 m altitude rolling-averaged data for AJAX, AMOG, and AMOG
upper 5 % of winds. Data key in figure.
In this upwind profile, AJAX observed elevated O3 that was well mixed
down to 500 m, while earlier AMOG showed well-mixed O3 down to only
1100 m. There also was a small (∼ 10 ppb) O3 enhancement at the
top of the PBL in both the airborne and surface profiles. The highest O3
concentrations were observed by AMOG in Shirley Meadows, where visibility was
low due to smoke aerosols from the Rough Fire (NASA, 2015). Air above the PBL
was more humid than elsewhere in the profile, except for the lowest 50 m
above the valley floor, which was enriched in CH4, CO2, and RH,
possibly from nocturnal accumulation and agriculture including irrigation
RH inputs. There were thin layers in the atmosphere that suggest remnant
structures from the prior day. For example, at ∼ 550 m the air changed
character, with a jump in CO2 by ∼ 10 ppm and of O3 by
∼ 10 ppb, and a decrease in the CH4 altitude gradient
(dCH4 / dz).
Air was more polluted at greater altitude above the PBL in the upwind
(Delano–Alta Sierra) profile for O3 for both platforms with air
10–20 ppb greater than in the PBL. Additionally, AJAX CH4 and CO2
were significantly higher above the PBL. The AMOG CH4 and CO2 data
are less clear, presumably because AMOG data were prior to the disappearance
of the nocturnal, stably stratified PBL. This was consistent with visual
observations of haze by AMOG from Shirley Meadows as well as by the AJAX
pilot. Additionally, air above the PBL was more humid.
A downwind ascent profile in the SJV was collected from Edison, CA, to the
high flanks of Breckenridge Mountain, followed by a descent behind
Breckenridge Mountain to Caliente, CA, through the tiny town of Bodfish (Fig. 3b).
This descent was separated from the SJV by a ridge and includes dryer,
clean air that is representative of air from around Lake Isabella, a fairly
isolated mountain valley. The downwind profile was collected
quasi-Lagrangian in that the time separating the two profiles (about 4 h)
is comparable to the transport time (75 km at a mean wind speed of 4 m s-1,
implying 5 h for transport). Thus, the downwind profile was
for close to the same air. Over these hours, there was some additional PBL
growth, ∼ 100 m growth to ∼ 1675 m, with highly
uniform CH4 between 1000 m and the top of the PBL (Fig. 5a).
Thus, the PBL remained fairly stable over the course of the study. Air in
both the upper PBL and above was cleaner with lower humidity and CH4
concentrations. Unfortunately, the O3 analyzer overheated during the
ascent and resumed collecting data on the descent at ∼ 1500 m.
Direct comparison between AMOG and AJAX winds is inappropriate because AMOG
winds are affected strongly by obstacles including hills, trees, and
buildings. However, in many instances, terrain is open or gently rolling
hills, and there tend to be regions of stronger winds that we propose are
representative of free-atmosphere winds. AMOG data were altitude-binned, and
the strongest winds in each bin were compared with AJAX (Fig. 6). Agreement
is generally good (within 15–20 %) between the upper 5 % of AMOG
cross-slope (west) winds in each altitude-averaged band (Fig. 6a). For the
upslope wind (north) agreement is better (within 5–10 %) for a larger
range of altitudes (Fig. 6b). This allows fusions of the upper 5 % of AMOG
winds with AJAX winds. Over the full altitude range, the median differences
were 38 and 27 % for the north and east wind components, respectively; see Supplement
Fig. S7 for the altitude variation in the agreement.
Kern Fields and Bakersfield greenhouse gas emissions
Methane
On 19 August 2015, winds over the Kern Fields were prevailing (northwesterly)
and fairly strong (∼ 3 m s-1) on the ground and somewhat
stronger aloft (Fig. 7). Potential plumes from the only nearby upwind dairy
(Fig. 7a, white arrow) were directed by winds to pass to the west of the oil
fields; agricultural fields in this part of the SJV are dry. As a result,
surface topography like the Kern River Bluffs imposed only small wind
modification at the surface and at altitude. Southeast of Bakersfield, winds
veered to westerlies towards passes in the Sierra Nevada that
connect to the Mojave Desert. The downwind survey included two plume
transits on agricultural roads with negligible to no traffic. These transits
clearly show the plume's eastward drift, passing to the north of the small
town of Arvin, CA.
Combined AJAX and AMOG winds and in situ (a) methane
(CH4) and (b) carbon dioxide (CO2) for the Kern Fields on
19 August 2015 for prevailing wind conditions. White arrow to the west of
Kern Front oil field shows location of nearby dairy. Greek letters identify
two downwind curtains. Red star in (b) locates origin for transect
γ–γ′. Data keys in figure.
The background CH4 plane CB(x,z) was extracted from the CH4
data outside the plume – CBL(z) and CBR(z) (see Eq. 2) –
immediately downwind of the Kern Fields (transect γ–γn). CB showed a slight
increase towards the east of ∼ 20 ppb (Supplement Fig. S6a). The
normal wind (UN) was fairly uniform across the data plane,
including downwind of the canyon (Fig. 8e). Thus, the CH4 flux
(QCH4(x,z) shows similar spatial patterns to CH4'(x,z). Emissions from the Kern Fields were dominated by a large, focused
CH4 plume (or group of plumes) in the core of a much broader, dispersed,
and poorly defined plume. This structure is evident in both surface AMOG data
and in the lowest AJAX altitude for plane γ–γn, with both
showing the strongest peak at x=4.5 km (Fig. 8b, dashed lines). Total
estimated emissions (E) were 63.5 ± 50 % Mol s-1
(equivalent to 32 Gg yr-1). Uncertainty is from the Monte Carlo
simulations, described in Sect. 2.5.
(a) Methane (CH4) altitude (z) profiles for 19
August 2015 for AJAX (black) and AMOG (gray) data. (b) Interpolated,
fused AJAX and AMOG CH4 data with respect to lateral east distance
(x) relative to 119.0023∘ W, 35.3842∘ N for data plane
γ–γ′ (Fig. 7). Dashed lines show data locations.
(c) CH4 anomaly (CH4′) relative to the background data
plane (Supplement Fig. S6a). (d) Vertical normal wind profile
(UN) from AJAX (black) and AMOG (gray) data during
ascent/descent; (e) interpolated, fused UN; and
(f) CH4 flux (QCH4) for the Kern Fields. Data
key in panels.
(a) Vertical carbon dioxide (CO2) altitude (z)
profile data for 19 August 2015 for AJAX (black) and AMOG (gray) data.
(b) Interpolated, fused AJAX and AMOG CO2 data curtain with
respect to lateral east distance, x, relative to 119.0023∘ W,
35.3842∘ N for curtain γ–γ′ (Fig. 7b). Dashed lines
show data locations. (c) CO2 anomaly (CO2′).
(e) Vertical normal wind profile (UN).
(e) Interpolated, fused UN and (f) CO2
flux (QCO2) for the Kern River and Kern Front oil fields for 19
August 2015. Data key in panels.
Within the plume, concentrations are elevated at 1200 m altitude relative to
500 m and the surface, indicating buoyant rise. Additional evidence for
buoyant rise is provided by two small plumes at x∼1.7 and 5.7 km
that were centered at the top of the PBL but were not also observed in surface
and mid-altitude data. The upper AJAX flight line was several hundred meters
below the top of the PBL (at ∼ 1580 m, Fig. 4), which
constrains the main plume and was centered vertically in the PBL.
Concentrations above the PBL were determined from AJAX descent and ascent
data (Fig. 4), in agreement with AMOG data above the PBL. These observations
show that the plume was not well mixed across the PBL. Another important
feature is that the upper-altitude clean-air intrusion at x∼6.5 km
lies downwind of the north–south canyon to the southeast of Round Mountain (Figs. 8b, 7a for location). This intrusion does not penetrate
down to 500 m and represents a downslope airflow of cleaner upper-level air.
For comparison, a recent bottom-up estimate of CH4 emissions based on
production data for the Kern Fields estimated 10–40 Gg CH4 yr-1
(68 % confidence level), by combining oil and gas production data with
US-EPA emissions factors for associated wells (Jeong et al., 2014).
Other CH4 sources are unlikely to confuse this interpretation as
petroleum system emissions are ∼ 20 times larger than
estimated nearby livestock and landfill CH4 emissions of
∼ 2.3 and 1.4 Gg yr-1, respectively (Calgem,
2014).
Carbon dioxide
Background CO2 for data curtain γ–γ' (Supplement Fig. S6b)
was highly uniform. Given the strong
crosswinds and care taken to avoid trailing other vehicles on the
low-trafficked China Loop Road, these data passed quality review – CO2
exhaust contamination manifests as a dramatic increase in the standard
deviation whenever AMOG intersects a vehicle exhaust's turbulent plume.
There was a shallow CO2 layer constrained to the lower 100 to 200 m
with ∼ 10 ppm enhancement (Fig. 9a), also observed in the
CO2 vertical profile (Fig. 4b), a layer that was characterized by
elevated relative humidity. Further evidence that these broad spatial
CO2 emissions are real is from the spatial similarity to CO2
enhancements in the lowest AJAX flight data (Fig. 9c). For example, the
surface CO2 plume was strongest at x∼4.5 km in AMOG and
AJAX data. The broad spatial extent of these emissions, similar to the broad
CH4 emissions, suggests a relationship to field-scale (engineering or
geological) processes. Overall CO2 emissions were 1730 ± 50 % Mol s-1
(equivalent to 2.4 ± 1.2 Tg yr-1).
There was a strong CO2 anomaly in a focused plume at x=5 km and z=1 km. This plume likely relates to the two cogeneration power plants
located in the Kern River oil field. Further support for this interpretation
is its co-location with a similarly focused CH4 plume at the same
location. This power-plant-related feature is a persistent feature that has
been observed in other surveys (Leifer, unpublished data). The upper
clean-air intrusion in the CH4 data curtain also is apparent in the CO2
data (Fig. 9b), in front of the north–south canyon to the southeast of Round Mountain (Fig. 7).
Based on a reservoir CO2 : CH4 gas ratio of
92.2 % : 1.7 % (Lillis et al., 2008) and
32 Gg yr-1 CH4 emissions, the Kern Fields CO2 emissions
were predicted to be 1.8 Tg yr-1, which is fairly consistent with the
directly derived emissions of 2.4 Tg yr-1. Both these values are
somewhat lower than the inventory for the cogeneration plants in Kern River
oil field, 3.1 Tg yr-1 (CARB, 2016). The disagreement with inventory
likely arises from the cogeneration plant only being active some of the
time, confirmed by data from the GOSAT-COMEX campaign.
Discussion
Experimental design and real-time visualization
Ideally, GCE airborne and surface data are collected first upwind and then
downwind. However, AJAX airborne data are not collected in a Lagrangian
sense as would be necessary for a slower, less maneuverable airborne platform
thanks to its extreme speed and maneuverability. This allows collection of
near-snapshot (∼ 30 min) data. Slower, AMOG surface data
were collected in a quasi-Lagrangian sense, reducing the likelihood of
confounding interference in the study area from non-FFI SJV inputs due to
wind shifts after the pre-survey (for non-nominal winds the collection is
aborted). Given the AJAX–AMOG speed difference, concurrent surface and
airborne data could not be collected both upwind and downwind, and thus,
concurrency was prioritized for downwind. For flight efficiency and to
provide downwind concurrency with AMOG, AJAX flew a triangle that allowed
AJAX to complete transects at three altitudes in close to AMOG's
upwind–downwind survey time.
After the Kern Fields survey, AJAX returned to base, while AMOG collected
additional surface data, exploring the fate of emissions from the Kern
Fields. The word “exploring” is significant, as real-time visualization
of winds, CH4, and O3 guided the downwind surveying. Data were
collected to test the hypothesis that there was a relationship between wind
strength and the specific outflow path from the SJV to Mojave Desert –
specifically, that more northerly passes, which require greater wind veering
from the prevailing direction, are preferred at lower winds speeds. The AMOG survey first
confirmed that outflow was not up the Kern River Valley and then collected
a downwind vertical profile into the Sierra Nevada to search for
outflow through a pass near Breckenridge Mountain. After confirming its
absence, AMOG then investigated in the Tehachapi Pass, where the outflow was
identified. Thus, on 19 August 2015, when winds were strong, the outflow was
by the most direct pathway – the Tehachapi Pass.
Experimental design and uncertainty reduction
The experimental design reduced uncertainty by characterizing the PBL
through surface and airborne data fusion so that a well-mixed PBL is not
required. Note that, for a well-mixed PBL, surface–airborne data fusion does not
reduce uncertainty. The benefit arises for a not-well-mixed PBL where a
significant fraction of the plume mass lies below the lowest altitude at which the
airplane can fly. In such cases, surface data inclusion adds information to
the PBL characterization. For example, flights can face airspace
restrictions in cities, during airport approaches, in military airspace, and/or for
safety.
Aerial survey altitudes were designed to span from near the top of the PBL
to as low as permissible and include an intermediate level (0.5, 1, 1.2 km).
Thus, surface data added information on the lowest third of the 1.6 km thick
PBL. This lower portion of the PBL is more important on days when the PBL is
shallower.
Observations showed that the well-mixed PBL assumption was poor as far as
10–20 km downwind. One solution is to collect data even further downwind,
where the PBL should be better mixed (White et
al., 1976); however, secondary (potentially uncharacterized) sources
downwind of the study area and upwind of the downwind data plane add
confounding anomalies. Also, wind flow complexity can lead to transport
orthogonal to the overall downwind direction, leading to flux leakage out of
the plume. The likelihood of plume loss increases over greater distances.
And finally, as the PBL evolves with time, it imposes an evolving structure
on the wind and concentration vertical profiles, which also challenge the
well-mixed PBL assumption – particularly if transport to the downwind plane
requires hours.
The in situ analyzers record concentration and winds with very high accuracy,
albeit only at a single location and time. Thus, in situ uncertainty arises mostly
from inadequate characterization of temporal variability and spatial
heterogeneity in winds and emissions over the survey time period. The best
strategy is to minimize study time; however, there is a necessary tradeoff
between spatial resolution and study time. AJAX collects data quickly,
allowing survey completion within far less than typical atmospheric change
timescales. Similarly, the surface survey route was designed to minimize
collection time, primarily on rural/agricultural roads carefully selected to
avoid traffic congestion and traffic lights. The surface survey requires
∼ 90 min to complete and is conducted quasi-Lagrangian.
GCE treats uncertainty explicitly, allowing improvements in the data
collection strategy to reduce uncertainty. For example, the east–west
downwind transect was lengthened from earlier data collections to characterize
background concentrations better. GCE also does not require an a priori emission
distribution and thus explicitly incorporates emissions from super-emitters,
normal emitters, and distributed sources, improving robustness of the
findings. In contrast, inversion models require a reasonable spatial a priori
emission distribution and the ability to model transport across the study
domain. However, complex wind flows from fine-scale topographic structures,
as observed for the Kern Fields, challenge transport modeling.
Profile intercomparison
This study leveraged terrain to provide profile information with a surface
mobile platform, which was compared with airborne data. In this study, the
two were combined to provide more complete coverage of the atmosphere than a
single platform could, at a fraction of the cost (not to mention logistical
complexity) of having two airborne platforms. Whereas the approach worked
well in the San Joaquin Valley, further research is needed to confirm its
utility in other settings.
Above the PBL, there was excellent agreement between surface and airborne
concentration profile data, while concentration profiles within the PBL show
significant differences between the two profiles, likely related to air mass
shifts and diurnal heating during the time between the profiles (Fig. 4).
Winds above the PBL were in poor agreement, with the north component in the
opposite direction (Fig. 6). Underlying this discrepancy was a mountain
peak, which clearly caused large-scale alterations in the wind flow field.
Within the PBL, agreement between unfiltered surface AMOG winds and AJAX
winds was poor, unsurprising because surface winds are strongly affected by
obstacles. However, by filtering AMOG winds (collected 3 m above the
surface) for the strongest 5 %, agreement was within 15–20 % for the
along-slope – i.e., north – winds and better for upslope winds (west).
Specific exceptions were when AMOG was in a dense grove of pines, and when
AJAX flew behind into the lee of a mountain peak. Surface winds are
modulated by a wide range of surface factors, including trees, steep hills
and hillocks, steep slopes, rolling hills, and structures
(Supplement Fig. S5). However, a combination of gusts (among thin wooded terrain
on steep slopes) and the limited spatial extent of most obstacles underlies
the agreement between the filtered AMOG and AJAX wind profiles. Agreement is
better for the upper portions of the PBL (within 10–20 %), where Sierra
Nevada slopes are steeper. In contrast, the slope lower in the PBL
is gentle, and surface boundary layer effects are more pronounced, biasing
wind speeds slower.
The wind orientation to the slope affects the comparison because topography
imposes wind-field structure at large and small scales. Where winds advect
air upslope, transport incorporates a non-negligible vertical component that
is missed by the 2-D sonic anemometer used in the study reported here.
Currently, AMOG measures 3-D winds, as does AJAX.
Some of the discrepancy between AMOG and AJAX wind profiles could have
arisen from temporal changes between the two profiles; however, this is
unlikely for two reasons. First, the top of the PBL was identified four
times over the course of the study and remained stable within 100 m across
the domain. Second, surface wind observations remained relatively
constant after the mid-morning shift to daytime conditions (breakup of
nocturnal stratification). However, the poor agreement between AJAX and AMOG
vertical concentration profiles within the PBL suggests significant air mass
shifts – highlighting the need for better concurrence.
GHG FFI emissions
Emissions for the Kern Fields were estimated at
32 ± 16 Gg CH4 yr-1, with CH4 emissions ∼ 20 % above EPA
inventories, and 2.4 ± 1.2 Tg CO2 yr-1. The broad CO2
plume suggests emissions from the geologic reservoir – likely along the
same pathways associated with CH4 leakage – in addition to the focused
and not continuous emissions from the cogeneration power plants. On China
Loop Road (where the CO2 surface plume was transected), strong
crosswinds and light traffic would have prevented significant vehicular
CO2 contamination. Additionally there are no upwind (non-oil-field)
roads, only the foothills of the Sierra Nevada.
For comparison, a recent bottom-up estimate of CH4 emissions from the
Kern Fields estimated 25 ± 15 Gg CH4 yr-1 by combining oil
and gas production data with emissions factors for associated wells used by
US-EPA (Jeong et al., 2014); i.e., 19 August 2015 CH4 emissions
were a third above inventories. The derived flux lies within the inventory
uncertainty but is higher, consistent with a recent metastudy of field
studies of FFI production emissions, which showed significant
underestimation in the EPA budget (Brandt et al., 2014; Miller et al.,
2013). A number of factors likely play a role, including the age of the Kern
River oil field (over a century); production factors (steam injection);
shallowness of the reservoir (< 300 m); location in a tectonically
active area, which creates alternate migration pathways from the reservoir
(Leifer et al., 2013); and the recent expansion of the number of
wells in the Kern Front oil field (from Google Earth timeline imagery). Many
of these factors are common to other production fields in California, the
US, and globally. Given the importance of FFI to the overall budget, even
small underestimation could be highly significant. Thus, this uncertainty
highlights the need for improved measurement tools to reduce the significant
uncertainty in the CH4 budget and for satellite measurement validation,
particularly for complex terrain and in the source's near field.