Introduction
Atmospheric carbon dioxide (CO2) and methane (CH4) are major
greenhouse gases, the global annual mean concentrations of which have increased
rapidly from 278 to 396 ppm for CO2 and from 722 to 1824 ppb for
CH4 over the last 200 years (WMO, 2014). At present, radiative forcing
of atmospheric CO2 and CH4 accounts for approximately 65 and
17 % of the total radiative forcing by long-lived greenhouse gases,
respectively (WMO, 2014). To accurately predict future atmospheric CO2
and CH4 concentrations and their impact on climate, it is necessary to
understand how sources and sinks of CO2 and CH4 are distributed
around the globe and how they change over time. Although the sources and
sinks can be estimated from an inversion of surface air sample/in situ
measurements, column abundances are also useful in constraining emissions
(Yang et al., 2007; Keppel-Aleks et al., 2012) as well as sources and sinks
of CO2 (Chevallier et al., 2011). The Total Carbon Column Observing
Network (TCCON) was established to derive column-averaged dry-air mole
fractions of CO2 and CH4 (XCO2 and XCH4), in addition to
several other trace gases (Wunch et al., 2011a).
Global XCO2 and XCH4 distributions are also derived from
space-based instruments: the Scanning Imaging Absorption Spectrometer for
Atmospheric Chartography onboard Envisat (Bovensmann et al., 1999), the
Thermal And Near-infrared Sensor for carbon Observation–Fourier Transform
Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite
(GOSAT) (Kuze et al., 2009), and the Orbiting Carbon Observatory-2
(XCO2 only, Crisp et al., 2004; Boesch et al., 2011; Frankenberg et
al., 2015). While satellite-based instruments can provide a global view, it
is necessary to continuously validate these satellite products using data
from instruments that have superior measurement precision and accuracy.
Satellite data are validated using ground-based (g-b) high-resolution
FTS data obtained from TCCON (Butz et al.,
2011; Cogan et al., 2012; Guerlet et al., 2013; Morino et al., 2011; Nguyen
et al., 2014; Oshchepkov et al., 2012; Reuter et al., 2011; Wunch et al.,
2011b; Yoshida et al., 2013) and aircraft profile CO2 and CH4 data
collected with instruments installed on commercial airliners and chartered
aircraft (Inoue et al., 2013, 2014). Guerlet et al. (2013), however, pointed
out that additional validation sites with various atmospheric and land
surface conditions would be useful for improving retrieval algorithms.
We installed a high-resolution FTS instrument at Saga University
(33.24∘ N, 130.29∘ E; 8 m above sea level), Japan, in
June 2011, and solar spectrum measurements have been performed since the end
of July 2011. This FTS is in operation following the TCCON requirements and
to make a contribution to validating the satellite products. Air masses in
the troposphere over Saga are dominated by transport from the Asian
continent; however, depending on meteorological conditions, they may also be
advected from the Pacific Ocean, especially in the summer season (Uchino et
al., 2014). Because Saga and its surroundings are affected by continental
aerosols and volcanic dust (Hidemori et al., 2014; Sakai et al., 2014), we
also monitor aerosols with a sky radiometer and a Mie lidar, which are
useful for evaluating the influence of aerosols on satellite retrievals. In
the present study, we describe FTS instruments and analysis methods in
Sects. 2 and 3, respectively, and present in Sect. 4 the calibration of the
g-b FTS XCO2 and XCH4 values using aircraft measurements, time
series of the g-b FTS data for the period of July 2011 to December 2014,
short-term summer variations, and application of these data for validating
TANSO-FTS products.
Instruments
Solar spectrum measurements
An FTS observation system was installed at Saga University, Japan, in June
2011. Saga is located on the Tsukushi Plane, which covers an area of 1200 km2,
and is sandwiched between the Tsukushi Mountains in the north and
the Ariake Sea in the south. Between 1981 and 2010, monthly mean
precipitation amounts for June and July exceeded 300 mm.
(http://www.jma.go.jp/jma/indexe.html). The city consists of cultivated land
and urban areas (http://www.biodic.go.jp/vg_map/vg_html/en/html/vg_map_frm_e.html).
Spectral measurements were taken with a Bruker IFS 125HR FTS instrument that
has a maximum spectral resolution of 0.0035 cm-1 (defined as
0.9/maximum optical path difference). The FTS is housed in a recycled
12-foot shipping container located on the grounds of Saga University. The
container is insulated and equipped with an air conditioning system to keep
internal temperature and humidity stable. Sunlight is directed into the
container by a solar tracker (Bruker A547N), mounted on top of the
container, and then introduced into the FTS by a folding mirror. The solar
tracker is positioned inside a sliding dome to allow the tracker to move
into every position, even in the closed state. The solar tracker features a
quadrant photoelectric detector and a feedback system that enables the
tracker to adjust the azimuth and elevation angles to keep solar radiation
at a maximum. In order to protect the solar tracker from dust, we built a
case made of polyvinyl chloride side surfaces and a top made of
high-transmission glass (Asahi Glass, JFL5). To indicate the effect of the
glass cover on the measured spectra and retrieved values, we show the
measured spectra (Fig. S1 in the Supplement) and the retrieved XCO2 values (Figs. S2 and
S3) before and after the glass cover was installed. These data were acquired
at the JAXA Tsukuba Space Center (36.01∘ N, 140.13∘ E),
Japan, in June 2010, before the instruments were located at Saga. Figure S1
indicates that the glass cover did not cause a significant fringe pattern on
the measured spectra. In the spectral range above ∼5000 cm-1,
transmittance of the glass is approximately 90 %, and the
wavenumber dependence is small. Although the transmittance decreases from
90 % as the wavenumber becomes lower, spectra with a signal-to-noise ratio
(SNR) of ∼400 at 5000 cm-1 can be obtained through the
glass. Figures S2 and S3 indicate that a bias and degradation in XCO2
were not observed and that the effect of the glass cover on the XCO2
retrieval is negligibly small. The container has a precipitation sensor,
allowing the sliding dome to close automatically when the sensor detects
changes in conductivity due to rain and so on. An uninterruptible power
supply is integrated to bridge power failures of up to 2 h.
The FTS is equipped with two room temperature detectors, an indium gallium
arsenide diode (InGaAs; 4000–12 000 cm-1) and a silicon diode (Si;
9500–25 000 cm-1). A spectral range from 3900 to 14 500 cm-1
can be measured simultaneously using a dual channel acquisition
mode, with a ∼10 000 cm-1 cutoff dichroic filter (Optics
Balzers). In addition to the room temperature detectors, liquid nitrogen
cooled indium antimonide (InSb; 1850–10 000 cm-1) and mercury cadmium
telluride (MCT; 600–12 000 cm-1) detectors are installed, although
their data were not used in this study. The solar absorption spectra are
acquired with a spectral resolution of 0.02 cm-1, a scanner velocity of
7.5 kHz, and an aperture diameter of 1 mm. A calcium fluoride (CaF2)
beam splitter is used. Two scans, one forward and one backward, are
performed and individual interferograms are recorded. One measurement for a
single scan takes about 110 s. The pressure inside the FTS is kept at
∼0.03 hPa with an oil-free scroll pump (Adixen, ACP15) to
maintain stability of the system and to ensure clean and dry conditions.
Auxiliary data
A meteorological station, which consists of sensors for measuring surface
pressure (Vaisala, PTB330), atmospheric temperature and relative humidity
(Vaisala, HMP-155D), wind direction and speed (R. M. Young, 05103), rain
amounts (Climatec, CTK-15PC), and solar and long-wave radiation (Hukseflux,
RA01), is installed next to the FTS container. Solar and long-wave radiation
are measured using a pyranometer and a pyrgeometer, respectively, which are
part of a two-component radiation sensor. Data are recorded on a laptop
computer with a frequency of 0.1 Hz through a data logger (Campbell,
CR1000). Additionally, a sky radiometer, a Mie lidar, and an ozone
differential absorption lidar are installed at Saga University for the
purpose of validating the satellite data (Uchino et al., 2012a; Morino et
al., 2013) and monitoring atmospheric aerosol and ozone.
Instrumental line shape (ILS) evaluation
For accurate retrievals of column abundance and/or column-averaged dry-air
mole fractions, a good optical alignment of the FTS is crucial, and
monitoring of the ILS is important. The monitoring
of the ILS is performed by spectral measurement of an HCl gas cell (length
10 cm, diameter 4 cm, filling pressure 5 mbar) located inside the FTS
instrument and by spectral analysis using the LINEFIT 14.5 software (Hase et
al., 1999, 2013). Figure 1a shows time series of the modulation efficiency
amplitudes at the maximum optical pass difference (OPD), which indicate
deviations of the actual ILS width to that of an ideal ILS. Figure 1b shows
time series of the modulation efficiency phases averaged over the entire
OPD, which indicate a measure for symmetry of the ILS. The average loss in
modulation efficiency amplitude at maximum OPD is 3.0±1.2 %, and
we found that the ILS remained nearly constant during the ∼ 3.5 yr of
operation.
(a) Modulation efficiency amplitude at the maximum optical pass
difference (OPD) and (b) modulation efficiency phase averaged over the whole
OPD, which are evaluated from HCl cell spectra using the LINEFIT 14.5
software.
Results
Comparison with aircraft measurements
The g-b FTS data were corrected with TCCON common scale factors empirically
determined using aircraft profiles over multiple TCCON sites to place the
TCCON data on the World Meteorological Organization (WMO) standard reference
scales (Wunch et al., 2010; Messerschmidt et al., 2011; Geibel et al.,
2012). We compared Saga FTS data with independent aircraft profiles to
ensure that the TCCON common scale factors can be applied to the Saga FTS
data. Several aircraft observation campaigns in Japan were performed for
calibration of the g-b FTS measurements and validation of the GOSAT products
(Tanaka et al., 2012). The aircraft measurements over Saga were performed
using a Beechcraft King Air 200T on 9 and 13 January 2012 and 15 January 2013.
The diameter of spiral flights was less than 10 km, and maximum
altitudes were approximately 7 and 10 km for the 2012 and 2013 campaigns,
respectively. Instrument settings used during the aircraft measurements are
described in detail in Tanaka et al. (2012). During the aircraft campaigns
in 2012, CO2 profiles were measured in situ with a non-dispersive
infrared gas analyzer (NDIR; LI-COR, LI-840) onboard the aircraft. In
addition, flask sampling was performed at eight altitude levels to check
accuracy of the in situ CO2 profile and to obtain other trace gas
concentrations such as CH4, CO, N2O, H2, and SF6. During
the aircraft campaigns in 2013, in addition to the NDIR measurements and
flask sampling, alternative CO2 and CH4 profiles were measured in
situ with cavity ring-down spectroscopy (CRDS; Picarro, G2301-m). For
comparisons to the g-b FTS data, NDIR and flask data were used as aircraft
profiles of CO2 and CH4 for 2012, respectively, while the CRDS
data were used as the aircraft profiles for 2013. Note that the CO2
profiles measured with NDIR and CRDS in 2013 are in agreement within ±0.2 ppm (Tanaka et al., 2015). Precision of the aircraft data is
estimated to be 0.39 ppm and 4.5 ppb for the CO2 and CH4 mole
fractions, respectively (Tanaka et al., 2015). Figure 3a and b
show the measured CO2 and CH4 profiles for 15 January 2013,
respectively. Additional vertical profiles of pressure, temperature,
relative humidity, wind direction, and wind speed were obtained by the Japan
Weather Association under contract with the NIES using GPS radiosondes
(Meisei Electric Co., RS-01G). Three temperature profiles measured on 15 January 2013 are shown in Fig. 3c.
Comparison between XCO2 values derived from g-b FTS and
aircraft measurement. The values in the second, fourth, and fifth columns
represent the results obtained using tropopause heights determined from NCEP
reanalysis data and radiosonde temperature profiles (in brackets) over Saga.
Aircraft data are weighted by the column averaging kernel of the g-b FTS.
Date
Tropopause
Maximum flight
FTS
Aircraft
FTS-AircraftAircraft [%]
height [km]
height [km]
[ppm]
[ppm]
9 Jan 2012
14.425 (14.333)
7.1
395.60
395.04 (395.04)
0.14 (0.14)
13 Jan 2012
15.521 (14.967)
7.0
395.17
394.51 (394.49)
0.17 (0.17)
15 Jan 2013
15.945 (8.667)
10.2
397.24
396.39 (396.72)
0.21 (0.13)
Same as Table 1 but for XCH4 values.
Date
Tropopause
Maximum flight
FTS
Aircraft
FTS-AircraftAircraft [%]
height [km]
height [km]
[ppm]
[ppm]
9 Jan 2012
14.425 (14.333)
7.1
1.827
1.823 (1.823)
0.22 (0.22)
13 Jan 2012
15.521 (14.967)
7.0
1.825
1.828 (1.827)
-0.16 (-0.11)
15 Jan 2013
15.945 (8.667)
10.2
1.836
1.838 (1.840)
-0.11 (-0.22)
Since the altitude ranges of the aircraft measurements were limited to
approximately 0.5–7 or 0.5–10 km, the aircraft in situ profiles were
extended below and above the flight altitude to cover the entire CO2
and CH4 profiles based on the assumption that the mole fractions in the
boundary layer and the upper troposphere are constant against altitude.
Aircraft in situ data were extrapolated to the surface using the lowest
aircraft data. When aircraft measurements were not conducted up to the height
of the tropopause, which was determined from the NCEP reanalysis data, the
highest aircraft data were extended up to tropopause height. Above the
highest aircraft height or the tropopause height, GFIT a priori profiles
were attached to the aircraft data. Although the tropopause height can also
be obtained from radiosonde temperature data measured during the aircraft
observation campaign, the tropopause height determined from the NCEP
reanalysis data was used in expanding the aircraft data to the stratosphere
because it was used in creating the a priori profiles. The effect of the
difference in tropopause height determination is evaluated below. Completed
CO2 and CH4 profiles are shown in Fig. 3a and b, respectively.
In situ (a) CO2 and (b) CH4 profiles measured by
instruments onboard aircraft over the Saga FTS site on 15 January 2013.
The black line represents the measured data and the red line is the integrated profile
(see text); dashed and dotted lines indicate tropopause height determined
from NCEP reanalysis data and radiosonde temperature profiles, respectively.
(c) Temperature profiles measured by radiosonde launched from Saga on
15 January 2013.
The XCO2 values for the integrated aircraft profile were calculated
according to the method of Wunch et al. (2010) for comparison with the
retrieved FTS XCO2:
c^s=γca=VCCO2,akaircraft-γVCCO2,aka prioriVCair,
where ca is the a priori XCO2, γ is the retrieved scale
factor, VCair is the vertical column of dry-air, and
VCCO2,akaircraft
and VCCO2,aka priori
are vertical columns of CO2 from aircraft and a priori profiles,
respectively, with a column averaging kernel applied. The effect of the
column averaging kernel of FTS was taken into account to equalize the
sensitivities of the CO2 mole fraction at each altitude for the total
column. Since FTS data averaged over a time window of ±3 h
relative to the time of the aircraft measurements are compared to the
aircraft data, column averaging kernels averaged over the same time window
were used for the calculation. In addition, the column averaging kernels are
the average of the used retrieval windows. Table 1 lists the integrated
aircraft and average FTS XCO2 values. The differences in XCO2
between FTS and aircraft measurements are within ±0.21 %. The
differences in XCH4 between the two measurements are within ±0.22 % (Table 2). Tables 1 and 2 include results obtained using tropopause
heights determined from radiosonde temperature profiles. The differences in
tropopause height introduce errors of up to 0.11 % in estimating aircraft
XCO2 and XCH4 values. The error components below the maximum
flight altitude were estimated by adding twice the precision of the aircraft
data to the profile and re-integrating the profile (Wunch et al., 2010):
0.54 ppm for XCO2 and 6.1 ppb for XCH4. The stratospheric errors
in the aircraft XCO2 and XCH4 were estimated by shifting the a
priori profile by 1 km: 0.19 ppm for XCO2 and 7.1 ppb for XCH4.
The total errors were calculated as the root sum squares of the three
errors, and we estimated the total errors in the aircraft XCO2 and
XCH4 to be 0.66 ppm and 9.6 ppb, respectively. Nevertheless, since
uncertainties (2σ) in the TCCON common scale factor are
approximately 0.2 and 0.4 % for XCO2 and XCH4, respectively
(Wunch et al., 2010), we find that the Saga FTS fall within this range of
uncertainties and can be calibrated to the WMO standard reference scales.
Time series
Figure 4a, b, and c show time series of XCO2, XCH4, and XCO
values observed at Saga during the period from July 2011 to December 2014.
Both seasonal and interannual variations can be seen. We determined the
seasonal and trend components in the time series using a fitting procedure
described by Thoning et al. (1989), which is based on a low-pass filtering
technique using FFT. Series of harmonic functions with 12- and 6-month
periods were employed to represent seasonal variations, low-pass filter with
a 2-year cutoff frequency was used for the long-term trend, and low-pass
filter with a 150-day cutoff frequency was used for the short-term trend.
The summation of the harmonic functions and the long- and short-term trends
is treated as the fitting curve of XCO2. For XCH4 and XCO, the
summation of the harmonic functions and the long-term trend is regarded as
the fitting curve. The fitting curves and the long-term trends of the
retrieved values are shown in Fig. 4. Standard deviations of the differences
between the retrieved values and the fitting curves are 0.81, 12.1,
and 13.3 ppb for XCO2, XCH4, and XCO, respectively.
Time series of (a) XCO2, (b) XCH4, and (c) XCO values at
Saga for the period of July 2011 to December 2014. Colors correspond to
those of the trajectories for DOY 170–260 shown in Fig. 5b. Fitting
curves (grey solid lines) and long-term trends (grey dashed lines) are also
shown.
The peak-to-peak seasonal amplitude of XCO2 was 6.9 ppm over Saga
during July 2011 and December 2014, with a seasonal maximum and minimum in
the average seasonal cycle during May and September, respectively. The
long-term trend of the retrieved XCO2 shows a monotonic increase. The
growth rate of XCO2, which is the derivative of the long-term trend
over time, is almost constant, and we obtained an average growth rate of
2.3 ppm yr-1 for XCO2, similar to the global mean growth rate based on
sampling measurements (WMO, 2014). The XCH4 time series is
characterized by large variability of XCH4 during the summer season,
which is discussed in the next section, as well as an increasing trend with
an average growth rate of 9.5 ppb yr-1. The XCO time series has
features of a seasonal cycle along with multiple peaks and a moderately
decreasing trend with a growth rate of -1.0 ppb yr-1.
Source of short-term variations
During the summer season, relatively low XCO2 values, approximately
3–4 ppm lower than the fitting curve, were observed. As for XCH4, the
observed XCO2 values in the summer season indicate a larger variability
compared to the other seasons. In order to investigate the causes of this
variation, backward trajectory calculations were performed with the NCEP
Global Data Assimilation System data using the Hybrid Single-Particle
Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2013;
Rolph, 2013). Ten-day isentropic backward trajectories were started at 2.6 km
altitude (approximately 700 hPa) above the Saga FTS site. This height was
selected because the change in potential temperature at 700 hPa correlates
with the XCO2 variation (Keppel-Aleks et al., 2012). Figure 5a and b
show the results of the backward trajectories for days of year (DOY) 1–90
(January to March; winter season) and 170–260 (mid-June to mid-September;
summer season), respectively. The trajectories for the summer season were
classified into three types, depending on the origin of the air masses. When
the start point of the trajectory was located north of 35∘ N and
west of 145∘ E, the trajectories were classified as type I. When the
start point of the trajectory was located south of 35∘ N and west
of 120∘ E, the trajectories were classified as type II. When the
start point was located anywhere else, the trajectories were classified as
type III. However, provided that an air mass was located for a longer period
of time west of 130∘ E, the trajectories were classified as type
II. The trajectories for types I, II, and III relate to transport of air
masses from the Asian continent (China), Southeast Asia, and the Pacific
Ocean, respectively, and are colored in green (China), red (Southeast Asia),
and blue (Pacific) in Fig. 5b. For the remaining days (April to mid-June and
mid-September to December) not shown in Fig. 5, the transport from the Asian
continent is dominant. Ishizawa et al. (2015) demonstrated that the
XCH4 variation at Saga was consistent with those obtained from the g-b
FTS data at Tsukuba (36.05∘ N, 140.12∘ E) and the GOSAT
TANSO-FTS data in East Chinese and Japanese areas. Additionally, on the basis of
simulation output from the global atmospheric transport model of NIES
(Belikov et al., 2013), they concluded that pressure pattern (i.e., wind
pattern) is attributed to the XCH4 variation on synoptic scale
including the East Chinese and Japanese areas during the summer seasons, and this
statement is consistent with the fact that, in summer 2013, the types I and
II of the trajectory calculations were dominant and the larger variability
of XCH4 was observed at Saga.
Ten-day isentropic backward trajectories from Saga at 03:00 UT on
(a) DOY 1–90 and (b) DOY 170–260. The trajectories are started from an
altitude of 2.6 km (approximately 700 hPa). The trajectories of DOY 170–260
are classified into three types, depending on of air mass transport, and the
classification approach is described in the text. The numbers of trajectory
corresponding to type I (green), type II (red), and type III (blue) are 28,
30, and 45, respectively.
Then, in order to derive short-term variations of XCO2, XCH4, and
XCO, the respective long-term trends and seasonal cycles were subtracted
from the observed values, and the residual values are referred to as ΔXCO2, ΔXCH4, and ΔXCO. Figure 6a and b show
correlation plots of ΔXCO /ΔXCO2 and ΔXCH4/ΔXCO, respectively, whose values are represented by the
daily mean values. Figures 5b and 6a indicate that most of the low-XCO2
events in the summer season are driven by long-range transport of air masses
associated with strong biospheric uptake over the Asian continent (type I).
Wada et al. (2007) reported that low-CO2 events at ground level in the
summer season were observed at Minamitorishima Island (24.3∘ N,
154.0∘ E; Fig. S6) in the Northwest Pacific. We note that the air
masses passing over Saga are expected to travel toward Minamitorishima Island. With
regard to XCO, high-XCO events correspond to transport of air masses from
the Asian continent (type I) or Southeast Asia (type II), while low-XCO
events correspond to air mass transport from the Pacific Ocean (type III).
Table 3 summarizes correlation coefficients and slopes of ΔXCO /ΔXCO2
and ΔXCH4/ΔXCO for the different DOY and trajectory types.
The negative slope of the ΔXCO /ΔXCO2 ratio for the type
I is gentler than for the type II, which is due to the transport of the
air masses that experienced the strong biospheric uptake of CO2 over
the Asian continent (i.e., stronger XCO2 decreases for the type I).
However, statistical t test indicates that the correlation for the type I as
well as the type III is not significant at 95 % confidence. For the winter
season, it is probable that the burning of fossil fuel causes the positive
steep slope of the ΔXCO /ΔXCO2 ratio.
Correlation plots of (a) ΔXCO and ΔXCO2 and
(b) ΔXCH4 and ΔXCO, which are represented by the
differences between g-b FTS data and fitting curve values. Black circles are
data for DOY 1–90; colored crosses are data for DOY 170–260. Colors
correspond to those of the trajectories shown in Fig. 5b. Solid and
dashed lines denote linear fits to DOY 1–90 and DOY 170–260 data,
respectively. The linear fits to DOY 170-260 were performed separately for
each trajectory type.
As shown in Fig. 6b, the slopes of the ΔXCH4/ΔXCO ratio
for the summer season are steeper than that for the winter season, and the
differences between the types I–III for the summer season are smaller than
the differences between the summer and the winter seasons. The differences
in the slopes for the types I–III are statistically insignificant. The slope
of the ΔXCH4/ΔXCO ratio for the type III is formed from
the air masses with the lowest values for both ΔXCH4 and
ΔXCO, which were transported from the Pacific Ocean, where CH4
and CO emissions are low. The slope of the ΔXCH4/ΔXCO
ratio for the type II is attributable to the air masses with the highest
ΔXCH4 and ΔXCO values, which were transported from
Southeast Asia, where CH4 emissions from rice fields significantly
increase during the summer (Bergamaschi et al., 2009) and CO concentrations
are high (Worden et al., 2010). Consequently, the slopes of the ΔXCH4/ΔXCO ratio for the types I–III become almost equivalent.
For the winter season, although the ΔXCO values remain high, the
decrease in CH4 emissions over Asia during the winter causes the gentle
slope of the ΔXCH4/ΔXCO ratio.
Correlation coefficients and slopes of ΔXCO /ΔXCO2 and ΔXCH4/ΔXCO. The correlation coefficients
and slopes for DOY 170–260 are indicated separately for types I–III.
ΔXCO /ΔXCO2
ΔXCH4/ΔXCO
Period/trajectory type
Correlation coefficient
Slope [ppb ppm-1]
Correlation coefficient
Slope [ppb ppb-1]
DOY 1–90
0.62
16.6
0.91
0.84
DOY 170–260 Type I
-0.34
-3.15
0.80
1.04
DOY 170–260 Type II
-0.52
-14.3
0.92
1.08
DOY 170–260 Type III
-0.04
-0.52
0.86
1.14
The ΔXCO /ΔXCO2, ΔXCO /ΔXCH4, and
ΔXCH4/ΔXCO2 ratios at Saga over the period from
2011 to 2014 were compared with ΔCO /ΔCO2, ΔCO /ΔCH4, and ΔCH4/ΔCO2 ratios at
Hateruma Island, Japan (24.05∘ N, 123.80∘ E; Fig. S6),
that were derived from in situ observation over the period from 1999 to 2010
(Tohjima et al., 2014). Since the temporal and spatial distributions of
CO2, CH4, and CO are attributed mainly to their emissions and
following transports, the ratios among CO2, CH4, and CO derived
from column observation are comparable to those derived from in situ
observation (Wong et al., 2015). The ΔXCO /ΔXCO2,
ΔXCO /ΔXCH4, and ΔXCH4/ΔXCO2
ratios for the summer season (-5.94, 0.70, and -9.40 ppb ppm-1)
are comparable with the ΔCO /ΔCO2, ΔCO /ΔCH4, and ΔCH4/ΔCO2 ratios for
the summer season in Tohjima et al. (2014), while the ΔXCO /ΔXCO2, ΔXCO /ΔXCH4, and ΔXCH4/ΔXCO2 ratios for the winter season (16.6, 1.19, and
7.33 ppb ppm-1) are significantly smaller than those found by Tohjima et
al. (2014). Assuming that their emissions for the winter season have similar
spatial distribution over East Asia, the comparison results imply that the
emissions over the period from 2012 to 2014 relatively decrease in the order
corresponding to CO, CH4, and CO2 or increase in the order
corresponding to CO2, CH4, and CO, compared to before 2010.
However, since ΔXCO2/ΔXCO ratios in China, which have
been derived from satellite XCO2 and XCO observations, differ depending
on megacity (Silva et al., 2013), the differences in observed ratios at Saga
and Hateruma Island might reflect the differences in regional emissions from
East Asia. When the fitting procedure described in Sect. 4.2 was performed for
only the type I data, the growth rates were 2.4, 8.0, and -1.9 ppb yr-1 for XCO2, XCH4, and XCO,
respectively. Compared to the case of using the entire data, the growth rate
of XCO2 increased and those of XCH4 and XCO decreased. This would
support the guess concerning the regional differences. In order to separate
the temporal and spatial contributions to the differences in observed
ratios, continuous observations and a top-down approach with high spatial
resolution (e.g., Turner et al., 2015) are required.
Comparisons between g-b FTS and TANSO-FTS NIES L2 data
As described in the previous section, except for transport from the Pacific
Ocean during the summer season, air masses over Saga are derived from the
Asian continent, where large aerosol optical depth is observed (van
Donkelaar et al., 2010). From observations of aerosols at Fukue Island
(∼150 km west-southwest of Saga, Fig. S6), the transport of
continental aerosols is indicated (Hidemori et al., 2014). Therefore, the
Saga g-b FTS data are believed to be appropriate for validation of the
XCO2 and XCH4 retrievals from the satellite-borne short-wavelength
infrared (SWIR) spectra in moderately aerosol-loaded scenes. We compared the
g-b FTS XCO2 and XCH4 data with those derived from the SWIR
spectra measured with TANSO-FTS onboard GOSAT. The TANSO-FTS XCO2 and
XCH4 products used here are general public user subsets of version
02.21 (before 24 May 2014) and version 02.31 (after 16 June 2014). Figure 7a
and b show a time series of XCO2 and XCH4 from the g-b FTS and
TANSO-FTS measurements. The TANSO-FTS data are selected within a
±2.0∘ latitude/longitude rectangular area centered on the FTS site, while
the complete g-b FTS data are presented in those figures. The TANSO-FTS can
observe seasonal variations of XCO2 and XCH4 similar to the g-b
FTS, but the TANSO-FTS data show a higher degree of scattering than the g-b
FTS data.
Time series of (a) XCO2 and (b) XCH4 for g-b FTS and
TANSO-FTS data. Black circles are the g-b FTS data; green circles are the
TANSO-FTS data within a ±2.0∘ latitude/longitude rectangular
area centered on the FTS site.
In order to accurately compare physical quantities obtained from two kinds
of remote sensing instruments, it is necessary to consider the effects of
differences in a priori profile and vertical resolution (i.e., column
averaging kernel). First, to conform to a common a priori profile, the
TANSO-FTS data were adjusted to the TCCON a priori profile (Wunch et al.,
2011b). The average difference between the adjusted and the raw TANSO-FTS
XCO2 data (adjusted minus raw data) is -0.02 ppm with a standard
deviation of 0.17 ppm. The average difference for XCH4 data is -4.34±0.84 ppb. Secondly, the TCCON data were smoothed by the TANSO-FTS
column averaging kernel to simulate what the TANSO-FTS would observe,
provided that the TCCON data were true. The average difference between the
smoothed and the raw TCCON XCO2 data (smoothed minus raw data) is
-0.08 ppm with a standard deviation of 0.12 ppm. The average difference
for the XCH4 data is 0.02±0.18 ppb. We then compared the
TANSO-FTS data adjusted to the TCCON a priori profile with the TCCON data
smoothed by the TANSO-FTS column averaging kernel.
A correlation plot for TANSO-FTS and g-b FTS XCO2 values is shown in
Fig. 8a, and a correlation plot for XCH4 is shown in Fig. 8b. When the
g-b FTS data were collected within ±30 min of the GOSAT overpass
time (around 1325 local time), the average data and corresponding TANSO-FTS
data are plotted. Therefore, the number of TANSO-FTS data in the correlation
plots (Fig. 8) is less than that in the time series (Fig. 7). The average
difference between TANSO-FTS XCO2 values and g-b FTS data is 0.40±2.51 ppm (average difference ± standard deviation). As for
XCH4, the TANSO-FTS data are biased low by 7.6 ppb with a standard
deviation of 13.7 ppb, compared to the g-b FTS data. The correlation
coefficients amount to 0.74 and 0.68 for XCO2 and XCH4,
respectively. The average differences between TANSO-FTS and g-b FTS data are
within the range of validation results using other TCCON site data (Yoshida
et al., 2013) but with slightly larger standard deviations.
Correlation plots of (a) XCO2 and (b) XCH4 values
derived from the g-b FTS and the TANSO-FTS spectra. The g-b FTS data within
±30 min of the GOSAT overpass time are averaged. Solid and dashed
lines denote linear fit with an intercept of 0 and 1-to-1 line,
respectively.
Particulate effects on the TANSO-FTS NIES L2 retrievals
Figure 9a and b illustrate the differences between TANSO-FTS and g-b FTS
values for XCO2 and XCH4 as a function of the aerosol optical
thickness (AOT) values at 870 nm, which were observed with the sky
radiometer (Kobayashi et al., 2006) located at Saga. We note that the AOT is
here defined as the integral of the aerosol optical depth (AOD) along the
entire vertical extent of the atmosphere (Bohren and Clothiaux, 2006). The
match-up was limited to a ±1.0∘ latitude/longitude rectangular
area to highlight the local effect of aerosols on the differences between
TANSO-FTS and g-b FTS data. The sky radiometer data were selected in the
same manner as the g-b FTS data (i.e., average of data within ± 30 min
of the GOSAT overpass time). The correlation coefficients of the
difference between TANSO-FTS and g-b FTS data and the AOT values are -0.25
and -0.07 for XCO2 and XCH4, respectively, and t values of
statistical t test are 2.4 and 0.66 (the number of data point n=90),
suggesting the correlation is significant for only XCO2 at 95 %
confidence. The differences between TANSO-FTS and g-b FTS data are
independent of the Ångström exponent, which is a measure of the size
of the aerosol particles, for both XCO2 and XCH4.
Next, the effects of aerosol and cirrus profiles on the TANSO-FTS XCO2
and XCH4 retrievals were investigated using data measured with a lidar
at Saga (Uchino et al., 2012b). The total number of coincidence measurements
with the g-b FTS, TANSO-FTS, and lidar at Saga was 31. On the basis of
vertical profiles of the backscattering ratio (R) and total depolarization
ratio at 532 nm (Dep.), and of the backscatter-related wavelength exponent
between 532 and 1064 nm (Alp), which were measured with the lidar, we
categorized the state of the atmospheric particulates (aerosol/cloud) into
three types (tropospheric aerosol, cirrus cloud, and low cloud). The
tropospheric aerosol was further categorized into large-AOD tropospheric
aerosol and normal tropospheric aerosol (clear sky), depending on whether an
AOD measured with the lidar was larger or smaller than 0.5. The numbers of
the respective types resulted in 4 for the large-AOD tropospheric aerosol,
19 for the normal tropospheric aerosol, 4 for the cirrus cloud, and 4 for
the low cloud. Since the low cloud scenes were likely cloudy just within the
lidar receiver field of view (FOV) and was clear within the TANSO-FTS
instantaneous FOV, the low cloud scenes were not treated. If only the normal
tropospheric aerosol scenes were considered, the correlations of the
difference between TANSO-FTS and g-b FTS data and the AOT values are not
significant for XCO2 as well as for XCH4. Therefore, large-AOD
tropospheric aerosol and cirrus clouds would cause the negative correlation.
We present distinctive case studies relevant for the large-AOD tropospheric
aerosol and the cirrus cloud, and their overall impacts on the XCO2 and
XCH4 retrievals, since there are not enough data for the large-AOD
tropospheric aerosol and cirrus cloud scenes to statistically show the
relationship between the particulate types and the differences in
XCO2/ XCH4 between TANSO-FTS and g-b FTS.
(a) Differences between XCO2 values derived from TANSO-FTS
and g-b FTS spectra with respect to aerosol optical thickness at 870 nm
measured with the sky radiometer at Saga. Color scale represents
Ångström exponent derived from the sky radiometer measurements.
(b) Same as Fig. 9a but for XCH4 values.
Vertical profiles of the backscattering ratio (cobalt), total
depolarization ratio (green), and backscatter-related wavelength exponent
(pink), measured with the Mie lidar at Saga on (a) 29 May 2012 and
(b) 8 November 2013.
Figure 10a and b show vertical profiles of the backscattering ratio, the
total depolarization ratio, and the backscatter-related wavelength exponent.
Aerosols on 29 May 2012 in Fig. 10a were uniformly distributed below a
height of 3 km and the AOD for 0–10 km was large (i.e., 1.28, assuming an
aerosol extinction-to-backscatter ratio of 50 sr). For this large-AOD
tropospheric aerosol scene, the differences between TANSO-FTS data closest
to the Saga site and g-b FTS data are -4.82 ppm for XCO2 and -7.2 ppb
for XCH4. The mean biases for the large-AOD tropospheric aerosol
scenes are -1.36±1.96 ppm for XCO2 and -9.9±7.7 ppb
for XCH4. On 8 November 2013 shown in Fig. 10b, thin cirrus clouds was
observed around 8.5 km and the AOD was 0.018 assuming an aerosol
extinction-to-backscatter ratio of 20 sr. Total AOD, including aerosols
below 2 km was 0.49 for the altitude range of 0–15 km. The differences
between TANSO-FTS data closest to the Saga site and g-b FTS data are
4.01 ppm for XCO2 and 19.5 ppb for XCH4. The mean biases of XCO2
and XCH4 for the cirrus cloud scenes are 2.04±2.17 ppm and
-1.0±12.0 ppb, respectively. The differences between the mean
biases for the large-AOD tropospheric aerosol and cirrus cloud scenes are
significant for XCO2 and not significant for XCH4. It is unclear
what made the XCO2 retrieval sensitive (or the XCH4 retrieval
insensitive) to the particulate type. While the difference in the spectral
range practically used for the retrieval (XCO2: bands 1–3; XCH4:
bands 1–2) might affect the retrieval results, further investigations are
necessary to figure out the cause. As a whole, effects of aerosols/cirrus
clouds on the TANSO-FTS XCO2 retrievals result in a weak negative
correlation of the differences between TANSO-FTS and g-b FTS data against
AOT as well as in a large scatter of TANSO-FTS data. We note that the
effects of the difference in aerosol type on the XCO2 and XCH4
retrievals would be dependent on treatment of aerosol profile in each
retrieval algorithm. A treatment of cirrus clouds in the TANSO-FTS NIES
XCO2 and XCH4 retrievals will be incorporated in the next version
of the Level 2 algorithm (Y. Yoshida, personal communication, 2015).