Introduction
Since 1992 a Fourier transform infrared (FTIR) spectrometer (FTS)
in Ny-Ålesund (78.9∘ N, 11.9∘ E) has been used for the
ground-based observation of total column trace gas abundances in the Arctic
via solar absorption spectroscopy . The measurements are
taken within the Infrared Working Group (IRWG) of the Network for the
Detection of Atmospheric Composition Change (NDACC). Since 2002, measurements
in the near-infrared (NIR) spectral region have been performed to retrieve
the dry-air mole fractions (DMFs) of CO2 and CH4 (denoted
here as xCO2 and xCH4) and other gases . These are, since 2005, part of the Total Carbon Column
Observing Network (TCCON). Today, these measurements are widely used as
validation for satellite products, in model comparisons and studies of
sources and sinks.
A large limitation of the availability of these measurements is the absence
of sunlight in the polar winter. At Ny-Ålesund, between October and
March, the sun is permanently below the horizon. However, during this period
the moon is permanently above the horizon around full moon.
Moonlight has already successfully been used as a light source in retrievals
of various trace gas concentrations via the FTS in
Ny-Ålesund in the mid-infrared spectral region and in Antarctica . Here
the employment of liquid-nitrogen-cooled InSb and MCT detectors
ensures low instrumental noise, even under low light conditions. In the NIR,
i.e. > 4000 cm-1, typically extended-range InGaAs diodes
are used. Recently and showed the application
of a thermoelectrically cooled InGaAs detector for the measurement of
reflected sunlight spectra from the Los Angeles basin on a mountaintop site.
The thermoelectrical cooling reduces the detector noise and allows for
higher signal-to-noise ratios in the measured spectrum.
After initial tests at the Bremen TCCON site , a
thermoelectrically cooled InGaAs diode detector was implemented in the Ny-Ålesund FTS
and a time series of xCO2 and xCH4, the total column dry-air
mole fraction, was obtained from spectra measured during polar nights between
2012 and 2016. The resulting product is compared to TCCON solar measurements
as well as model simulations from the MACC reanalysis model for CO2
(v14r2; ) and for CH4 (v10; ), the Jena
CO2 inversion CarboScope s04_v3.7 and the
CarbonTracker 2015 model . Together with the summer TCCON data
from Ny-Ålesund, for the first time the whole seasonal cycle of xCO2
and xCH4 is presented.
In Sects. and , this paper describes the
measurement set-up and the methods used to retrieve the dry-air mole
fractions. Section describes the newly obtained time
series and the comparison to TCCON. Finally we compare our results with model
data in Sect. .
Method
Measurement set-up
The measurements follow the TCCON standard settings wherever possible. A
solar (lunar) tracker is mounted on the roof of the AWIPEV observatory and
the light is reflected into the laboratory underneath and into the FTS. Accurate tracking is ensured by usage of a four-quadrant diode
with feedback to the solar tracker motor controller. The incident light is
focused on an entrance aperture and afterwards parallelized to enter a
Michelson interferometer arrangement of the Bruker IFS 120-5 HR. The movable
retro-reflective mirror is mounted on a sledge on steel rods. Accurate
tracking of the movable mirror's position is provided by a stabilized
internal HeNe laser reference. The light path arrives in the detector
compartment of the instrument, where it is focused through a HeNe
laser filter onto the InGaAs detector. The resulting signal is
amplified and recorded together with the internal laser reference.
In a post processing step the spectra are calculated via a fast Fourier
transform (FFT) routine by the instrument operating software OPUS (by
Bruker). After changing the measurement routine in 2015 to a semi-automated
set-up, less intervention from the operator is required. At the same time,
the interferograms are read directly from the instrument, resulting in raw
data slices that are processed to spectra via the i2s program shipped with
the GGG2014 software suite used within TCCON.
All interferograms have been transformed using Boxcar apodization and the
retrieval code adjusts for the resulting sinc-shaped distortion of the
spectral lines. Using i2s, the DC interferograms have been corrected for
brightness fluctuations. However, the effect of the correction is expected to
be minimal; because of the low resolution, thin cirrus clouds for example
typically lead to brightness fluctuations between consecutive scans and to a
lesser degree to fluctuations within one interferogram record.
Example fit of a measured spectrum (black line) on 25 October 2015,
the corresponding calculated spectrum (blue line), the contribution of the
solar lines (orange) and their residuum (red line) for the retrieved windows
of O2, CO2 and CH4.
The differences between the solar and lunar measurements include the
detector, the spectral resolution, the integration time and the size of the
entrance aperture. Decreasing the resolution leads to a shorter measurement
time and therefore allows for integration of more interferograms in the same
time frame. Increasing the entrance aperture allows for more incident light
on the detector, which increases the signal-to-noise ratio. The impact of
spectral resolution is further discussed in Sect. .
At full moon, the entrance aperture was set to 3.15mm.
Occasionally, a smaller entrance aperture is required, because if the moon is
not full, its image on the aperture wheel requires a smaller aperture to
still ensure that the aperture is uniformly lit. Additionally, the
four-quadrant diode used in the tracking system sometimes has difficulty
centring the non-full lunar image; using a smaller aperture in this case,
again, ensures full illumination of the entrance aperture.
In the TCCON the small entrance aperture samples the centre of the solar disc
and the corresponding solar lines are narrow. Sunlight reflected at the lunar
surface will have a (solar) disc-averaged spectrum; i.e. the solar lines will
be broadened as a result of the different Doppler shifted contributions from
different parts of the solar disc. GFIT includes a setting that switches to a
calculation of a disc-averaged spectrum when the moon is selected as the
source. This approach leads to well-captured solar lines in the spectral fit
residuum (see Fig. ) and therefore indicates the absence of
a bias from using different solar line shapes, as is to be expected. A
potentially introduced bias would be within the limits of the total bias to
the solar measurements (e.g. 0.66±4.56ppm for xCO2 and
-1.94±20.63ppb for xCH4) as discussed in Sect. .
Calculation of dry-air mole fractions
For this analysis the current TCCON standard processing
code GGG2014 was used for both solar and lunar retrievals. The retrieval code
returns vertical columns (VCgas), which have to be converted
to dry-air mole fractions. There are two possibilities to do this. The
standard TCCON processing uses the simultaneously retrieved vertical
O2 column to scale the target gas' vertical column via
xGas=VCgasVCO20.2095.
The dry-air mole fraction of O2 is well known and assumed constant;
therefore systematic errors common to both vertical column retrievals cancel
out using this approach.
However, for the retrieval of O2 the spectral band at 1.27µm (7880cm-1) is used and the detector is much less sensitive
in that region compared to the CO2 and CH4 windows between
5800 and 6400cm-1 (compare Fig. ). This results in a noisier O2 retrieval especially
under low signal-to-noise conditions (see Fig. ).
The second option to calculate the dry-air mole fraction involves the scaling
to atmospheric surface pressure and a correction for the water contained in
the column:
xGas=VCgasp0NAmdryairg‾-VCH2OmH2Omdryair.
Here, xGas denotes the target species' dry-air mole fraction, VCgas
the vertical column and p0 the surface pressure. NA is
Avogadro's number and the molecular masses of water, mH2O=18.01534gmol-1, and dry air, mdryair=28.9644gmol-1, are given. g‾ denotes the column-averaged
gravitational acceleration at the measurement site and is assumed to be
g‾=9.81ms-2.
This approach requires accurate knowledge of the surface pressure p0.
Additionally systematic errors, e.g. pointing errors can affect the
retrieval, as they are not cancelled out via ratio with O2. The
surface pressure measurement is performed at the Ny-Ålesund station of the
Baseline Surface Radiation Network (BSRN), located adjacent to the AWIPEV
observatory and thus the FTS. The raw pressure measurements are
then scaled to compensate for the height difference to the FTS. The
meteorological data are provided by AWIPEV and publicly available at
10.1594/PANGAEA.150000 for years until 2013, with corresponding
updates for more recent years.
In the following, the approach described in Eq. () was
used to retrieve xCO2 and xCH4. The second approach, in
Eq. (), was only used to derive xO2 in
Sect. , which covers the validation with solar
measurements. The main retrieval windows and the fit residuals of an example
spectrum are shown in Fig. . The vertical column of
H2O used for the water correction in Eq. ()
is retrieved simultaneously in several micro-windows in the same spectral
region as the target species.
Differences in the lunar absorption retrieval results (2012–2015)
using the site and time of measurement interpolated atmospheric model
compared to using the model interpolated to site and local noon for both
target species dependent on the lunar zenith angle.
Atmospheric model
Information on the target gas is retrieved from the processed spectra by the
least-square fitting algorithm GFIT (see Sect. ). The
software assumes an a priori profile of the target gas and calculates an
artificial spectrum given additional information on the atmospheric profile.
In TCCON the interpolation of the NCEP/NCAR reanalysis data
to the sites latitude, longitude and local noon is used as an atmospheric
model, resulting in one model profile per day. The NCEP/NCAR reanalysis data
are publicly available and was provided via
http://www.esrl.noaa.gov/psd/ . In case of lunar
measurements, this presents a potential problem around midnight, as
consecutive measurements would use different atmospheric models, i.e. the one
interpolated to local noon.
Same as Fig. but for TCCON solar absorption
measurements for the time between 19 and 24 September 2013. Note the
generally higher differences at high zenith angles. Between 12:30 and 15:00
local time the sun moves behind a mountain at lower zenith angles.
Given that the reanalysis data are available in 6 h time intervals, we
use the model profile interpolated to the site coordinates and the time of
measurement, resulting in specific model profiles for each measurement. These
profiles presumably better reflect the atmospheric conditions, especially at
night. The increased computational effort for this per-spectrum-model
approach is affordable for this comparatively small time series.
A comparison of the differences in retrieved xCO2 and xCH4
between the daily and spectrum-specific model profiles is shown in Fig. for the lunar time series and for selected days in the
TCCON time series in Fig. . The two retrievals show
minimal differences at local noon (as they should), but differences of about
±0.5ppm (CO2) and about ±2ppb (CH4)
can occur later in the day, under quickly varying atmospheric conditions
distant in time from local noon. Note that the measurements showing
potentially large deviations are typically filtered out within TCCON as they
occur at high solar zenith angles.
Analysis of optimal resolution
The resolution used in the TCCON is better than
0.02cm-1, corresponding to a maximum optical path difference
(OPD) of 45cm. Initial tests showed that even with the cooled
detector, the spectral signal-to-noise ratio did not allow for a robust
retrieval unless a lot of spectra were averaged; however, the path of
moonlight through the atmosphere changes rapidly with time. Although this is
more prominent in lower latitudes, it still must be considered here,
especially at large lunar zenith angles. To avoid bias from inaccurate
knowledge of the viewing geometry, the integration time per measurement must
be as small as possible.
One option to decrease the measurement time is to increase the velocity of
the instrument's scanning mirror; however, this has no effect on the spectral
signal-to-noise ratio. The scanner velocity was therefore not changed and
kept at 10kHz to minimize potential differences from the solar
absorption measurements. The second option is to decrease the spectral
resolution, which increases the spectral signal-to-noise ratio. Additionally,
it allows for shorter measurement times and thus for more spectra to be
averaged within the same time, resulting again in an increased
signal-to-noise ratio.
Retrieved xCO2 and xCH4 from cropped interferograms
with different resolutions and different levels of white noise (z axis and
colour bar) added to the spectra.
The influence of resolution on the retrieval can be analysed in further
detail and to circumvent differences arising from a varying atmospheric
state. Previously, investigated this for the TCCON standard
retrieval windows. Here the analysis was repeated with emphasis on lower
resolutions (down to 1.0cm-1) and additionally spectra with
different signal-to-noise ratios were used.
A set of 60 consecutive solar spectra has been selected and the
interferograms cropped at lengths corresponding to a range of maximum optical
path differences between 45 cm (0.02cm-1) and 0.9 cm
(1.0cm-1). The interferograms were reprocessed and the spectra
calculated with the i2s program within the GGG2014 program suite.
In addition to this series of spectra, different magnitudes of white noise
were added to the created spectra to simulate the effect of the lower
signal-to-noise ratio expected in lunar spectra. The signal-to-noise ratios
are calculated from the reprocessed spectra by dividing the maximum mean
signal between absorption lines at about 6000cm-1 by the root
mean square of a blacked-out region of the spectrum. Figure shows the results of the standard retrieval of
xCO2 and xCH4 for the various combinations of resolution and
signal-to-noise ratio of the series.
The decrease in resolution leads to an increase in S / N. Fig.
shows the increase in S / N measured as a function of spectral resolution with
a Bruker 125 HR, normalized to the signal-to-noise ratio at 0.02cm-1, i.e. a
spectrum recorded with 1.0 cm-1 resolution has a 10 times larger
S / N (see blue line). Additionally, the shorter scan length allows to record
more spectra in the same time frame. Averaging leads to an increase in S / N by
a factor of N with N measurements (red line). The combination of
both effects (black line) shows the potential increase in S / N with resolution
for a fixed integration time. A lower resolution would potentially also allow
for a larger entrance aperture. However, at lower resolutions the size of the
entrance aperture is limited by the size of the image of the lunar disc
rather than the resolution.
Spectral signal-to-noise ratio (S / N) as a function of resolution. The
improvement due to lower resolution (blue line) and averaging over larger
number of spectra in the same time frame (red line) and the resulting
relative S / N from both effects (black line), normalized to the S / N at
0.02cm-1.
Mean of the retrieved xCO2 and xCH4 from cropped
interferograms at different resolutions with low and high signal-to-noise
ratio (S / N). Shown is the relative difference to the highest signal-to-noise ratio
and highest resolution.
For better visibility, Fig. shows a subset of the
data from Fig. , showing the mean retrieved
xCO2 and xCH4 DMFs at a given resolution. Two series have
been selected, with high (red) and low (black) signal-to-noise ratios. The
associated errors can be estimated by the standard deviation (1σ) of
the arithmetic mean and do not change much with resolution for a given S / N.
The mean errors and their standard deviation for xCO2 are 4.0±0.6ppm for the low S / N case (black dots in Fig. ) compared to 0.6±0.05ppm for the
high S / N case (red dots). Similarly the errors for CH4 are 18.5±3.2ppb (low S / N, black dots) and 2.9±0.3ppb (high S / N,
red dots).
A distinct cut-off above 0.7cm-1 can be identified in the
xCO2. For higher resolutions, i.e. 0.02–0.7cm-1, no
significant difference is visible in high signal-to-noise conditions. In
general, a lower signal-to-noise ratio of the spectra leads to increased
scatter of the retrieved DMFs but to no significant bias. Table shows the bias in the retrieved DMFs of high and
low signal-to-noise ratio spectra for the two resolutions used in the
measurement set-up later.
showed that lower-resolution solar spectra can be used to
retrieve DMFs with a low-resolution FTS (Bruker EM27/SUN). Recently
investigated errors and biases from a
0.5cm-1 FTS (Bruker EM27) for TCCON relevant species. The three
studies report different biases in
xCO2 when changing the resolution to 0.5cm-1 in the
range from -0.12 to 0.13%. For xCH4,
reported an increase of 0.28% when decreasing
the resolution to 0.49cm-1. In our analysis (see Table ) a consistent decrease in mean ΔxCO2
and ΔxCH4, i.e. the difference between DMFs from low- and high-resolution spectra, is observed when moving to lower resolutions. However,
when considering the assigned errors (1σ standard deviation) this is
not significant, especially under lower signal-to-noise conditions.
Comparison of retrieved xCO2 and xCH4 for different
resolutions from low (OPD=1.8cm=^0.5cm-1, black) and higher (OPD=10.59cm=^0.085cm-1, red) resolution measurements on 7 October
2014.
Comparison of the biases, introduced by lower-resolution
measurements and low signal-to-noise ratio (S / N). Subset of data points from
Fig. .
S / N
Resolution
ΔxCO2
ΔxCH4
(cm-1)
(%)
(%)
>300
0.08
0.03 ± 0.57
0.28 ± 2.61
0.5
0.07 ± 0.65
0.76 ± 3.03
≈30
0.08
-0.13 ± 4.12
0.00 ± 15.03
0.5
-0.20 ± 4.50
0.79 ± 22.89
For the final decision on the best resolution for low S / N conditions the
possible number of recorded spectra per time interval has to be considered.
This number does not increase linearly due to instrumental effects, i.e. the
deceleration of the moving mirror and the time needed for data acquisition
and storage. The first measurements were taken at a reasonably high spectral
resolution of 0.08cm-1 (OPD = 11.25 cm). The measurement set-up
was adjusted after further tests. The benefit of a better signal-to-noise
ratio on the measurement precision lead to finally decreasing the resolution
to 0.5cm-1 (OPD = 1.8 cm) and all measurements from 2015 onwards
were taken with a resolution of 0.5cm-1.
The effect of different resolutions on the retrieved columns can also be
investigated by comparing different measurements taken consecutively with
different resolutions. Figure shows lunar
absorption measurements of the target species on 7 October 2014. The first
and third batch of measurements were taken with a resolution of
0.085cm-1 (OPD = 10.59 cm), while the second batch was measured with
0.5cm-1 (OPD = 1.8 cm) resolution. No significant bias is
observed.
Decreasing the spectral resolution also changes the information content of
the recorded spectral lines. This results in a change in shape of the
measurements averaging kernels and is discussed below.
(a) Averaging kernels of the lunar measurements.
(b) Difference between lunar and solar averaging kernels colour coded
for different spectral resolutions. (c) Differences between low
resolution and TCCON spectra averaging kernels as a function of resolution.
Averaging kernels
The sensitivity of the retrieved dry-air mole fraction of the target gas
depends on the a priori information and the measurement's altitude dependent
sensitivity, i.e. the averaging kernels. The a priori profiles used are the
default TCCON ones. The averaging kernel of a measurement strongly depends on
the retrieval methodology and the information content of the corresponding
spectrum. As such it depends on the viewing geometry as well as the
resolution, the absorption strength and the signal-to-noise ratio. The weight
different altitude levels have in the retrieval can be parameterized as a
function of the zenith angle. As the instrument faces the light source at a
certain zenith angle, the measurement samples different contributions from
the various atmospheric layers. The pressure broadening of the absorption
features shows a specific altitude dependent sensitivity and this information
depends on the chosen resolution and the signal-to-noise ratio of the
measurement.
The set-up of the lunar measurements is similar to that of TCCON
measurements, and
therefore the averaging kernels are quite similar, aside from effects of
resolution and noise for a given zenith angle.
The top panel in Fig. shows the averaging kernels for
the lunar measurements. The middle panel shows the difference from the
standard TCCON ones from Ny-Ålesund, interpolated to the corresponding
zenith angles. The lines are colour gradient coded with their respective
zenith angles and different colour schemes reflect different resolutions.
Pressure broadening leads to spectral lines originating from gases at low
pressure being narrower than those at higher pressure. The narrow part of a
spectral line sampled with fewer points therefore cannot give as much
information as one with higher resolution. This leads to averaging kernels
from low-resolution spectra being less sensitive to the stratosphere and more
sensitive in the lower troposphere than their high-resolution counterparts.
This can be seen in the lower panel of Fig. , where
the difference between standard TCCON averaging kernels and their
lower-resolution counterparts at the same zenith angle is shown. As expected,
decreasing the spectral resolution leads to greater differences between the
averaging kernels.
Comparison of the solar and lunar measurements of xCO2 and
xCH4 in September 2013 (dots) and the corresponding arithmetic means
(lines). Values are given in Table .
Comparison of the retrieved solar, lunar and model DMFs for the two
comparison time periods. Note that xO2 was calculated using the
surface pressure and the offset to the true atmospheric value of 20.95 %
is caused by spectroscopic errors.
xCO2 (ppm)
xCH4 (ppb)
xO2 (%)
March 2013
Solar
397.47 ± 0.67
1773.78 ± 2.99
21.33 ± 0.08
Lunar
396.81 ± 3.89
1775.72 ± 17.64
21.34 ± 0.36
Jena CO2
398.01 ± 0.13
–
–
CT15 CO2
396.89 ± 0.22
–
–
MACC CO2
397.16 ± 0.18
–
–
MACC CH4
–
1784.09 ± 1.06
–
September 2013
Solar
393.16 ± 0.49
1810.26 ± 3.11
21.38 ± 0.06
Lunar
392.15 ± 8.03
1813.62 ± 38.02
21.40 ± 0.60
Jena CO2
391.56 ± 0.26
–
–
CT15 CO2
391.29 ± 0.24
–
–
MACC CO2
392.07 ± 0.39
–
–
MACC CH4
–
1800.79 ± 1.58
–
Validation with solar absorption spectroscopy
The validation of the measurements performed during
the polar night is difficult. In the absence of other options, here we
compare to solar absorption measurements taken within TCCON. In spring and
autumn there are a few consecutive days around the full moon when solar
absorption measurements during the day and lunar absorption measurements
during the night are possible. Such comparison measurements were performed in
March and September 2013. Here the DMFs of xCO2 and xCH4 for
both solar and lunar measurements were retrieved using Eq. (). For the comparison of xO2 Eq. () was used.
Assuming the total column values do not change significantly during that time
period, the means of the two retrievals can be compared directly. Figure shows the comparison results and the calculated means
for a comparison in September 2013. Table shows the
corresponding values of the arithmetic mean and its standard deviation as an
indication of the error for both comparison campaigns in March and September
2013. The same analysis was performed on the available smoothed model output.
The calculated standard deviations of the models of about 0.2ppm
and 0.3ppm for CO2 and 1.0
and 1.6ppb for CH4 for March and September, respectively, indicate that the
assumption of stable DMFs for the observed time frame is reasonable.
The accuracy of the lunar measurements can be determined via the bias of the
lunar compared to the solar measurements and can be deduced from Table as well. In March 2013 the difference between solar and
lunar measurements is 0.66±4.56ppm for xCO2 and -1.94±20.63ppb for xCH4. In the September 2013 campaign a bias
of 1.01±8.52ppm for xCO2 and -3.36±41.13ppb for xCH4 can be observed. The diurnal variability
of the lunar measurements is used to define the precision. As the later
measurements have a higher precision, a typical value achieved in the
2014–2015 winter is used. Here the standard deviations of the daily mean of
2ppm for xCO2 and 10ppb for (xCH4),
corresponding to 0.5% in both cases.
The target accuracy can be estimated via the detrended year-to-year
wintertime variability. Here model output can be used as a proxy. In the
smoothed, detrended MACC CO2 and CH4 model (see Sect. ) the arithmetic mean of the first week of January differs
by 0.55 ppm in xCO2 and 9.84 ppb in xCH4 between 2012 and
2014. At the same time, the standard deviation of all values for the first
week of January between 2012 and 2014 is about 1.8 ppm for xCO2 and
18.8 ppb for xCH4. However, these estimates are potentially subject
to unknown biases in the models, i.e. the model could be biased similarly
every year. Additionally, the seasonal variability surely is an upper limit
for the target precision. Here the seasonal cycle amplitude measured by solar
FTS is about 15 ppm for xCO2 and about 40 ppb for xCH4.
Comparison of the solar and lunar measurements of xO2 in
March and September 2013.
As described in Sect. (see Eq. ),
the dry-air column is calculated using the vertical column of O2,
retrieved from the 7885cm-1 spectral region. Here airglow
emissions in the high atmosphere could potentially disturb the O2
spectra. This can typically be ignored in solar absorption spectra, as the
magnitude of the emissions is negligible, when viewing directly into the sun.
In case of lunar spectra, however, airglow emissions could potentially fill
in the spectral lines and influence the measurements. To test this,
xO2 was retrieved using the surface pressure to calculate the dry-air
column as described in Eq. ().
In both comparison periods, no significant difference between the solar and
lunar retrievals of xO2 can be observed. Note that xO2
retrieved via surface pressure shows an offset of 0.4% in both
cases (lunar and solar). This offset originates in the line parameters used
for the O2 retrieval and is compensated in the xCO2 and
xCH4 retrieval with the TCCON in situ correction.
reported values that are 2.27±0.25%
larger if the surface pressure retrieved dry column was used. Here we find a
mean difference of 1.96±0.14% when calculating the mean and
standard deviation of the solar and lunar mean xO2 values shown in
the sidebars in Fig. . Note that these retrievals were
performed with updated spectroscopy available within GGG2014 compared to that
used by .
Seasonal cycle and model comparison
Method – model comparison
The rigorous comparison of ground-based column
measurements of a trace gas to model simulations requires resampling the
model profile as if it was measured by the instrument.
The smoothed column dry-air mole fraction c^ can be calculated
following , and
by adding the column-integrated a priori profile (ca) to the
difference between the model (x) and the dry TCCON a priori
profile (xa) weighted with the averaging kernel
(a):
c^=ca+hTaTx-xa.
Here, h represents the pressure weighting function (see
).
Given a vertical model profile, the measurement's averaging kernel and the
vertical columns of water vapour and the a priori profile of the target gas,
the smoothed dry-air mole fraction of the model output can be calculated. Due
to the high random error of the lunar FTS measurements, daily means have been
calculated for both the measurements and the model data, after the smoothing
was applied.
Comparison of the daily means of lunar (blue) and solar (red)
xCO2 FTIR measurements to the AK-smoothed MACC CO2 model
v14r2 (top panel, grey). Error bars show the standard error
(σ/N, with N number of measurements). The lower panels show
the model–measurement difference for all models.
Comparison of the daily means of lunar (blue) and solar (red)
xCH4 FTIR measurements to the AK-smoothed MACC CH4 model v10
(grey). Error bars show the standard error (σ/N, with N number
of measurements). The lower panel shows the model–measurement difference.
Results – time series
In this section the FTIR time series is compared
to CO2 model results from three different models: the MACC
CO2 model version 14r2 , the CarbonTracker 2015
model and the Jena CO2 inversion version s04_v3.7
. In case of the CH4 time series, the MACC CH4
v10 is used. As described in Sect. the
model's DMF profile has been smoothed with the corresponding a priori and
averaging kernel of the lunar and solar measurement, respectively. For times
when there are no FTS measurements available, an averaging kernel was
calculated using the solar zenith angle of the corresponding time. In winter
the lunar zenith angle was used instead. For times where no FTS measurements
were possible at all, e.g. sun and moon are below the horizon, a mean zenith
angle of 65∘ was assumed.
Comparison of solar (red) and lunar (blue) xCO2 FTIR
measurements. Error bars show 1σ standard deviation of the daily mean.
The lunar data points have been averaged over one full moon period each. The
shaded grey area shows the 1σ standard deviation of the three model
daily means (MACC, CarbonTracker and Jena) as shown in
Fig. .
The resulting model time series can now be compared directly to the FTS
measurements. Figure shows the comparison of the FTS
and the smoothed model time series for CO2. The CH4
comparison is shown in Fig. .
Results – seasonal cycle
The detrended seasonal cycles of both target species are similar from year to
year. In the following, the detrended seasonal cycles are compared to the
models already discussed in Sect. .
Comparison of solar (red) and lunar (blue) xCH4 FTIR
measurements. Error bars show 1σ standard deviation of the daily mean.
The lunar data points have been averaged over one full moon period each. The
shaded grey area shows the 1σ standard deviation of the MACC
CH4 model daily means as shown in Fig. .
Figure shows the seasonal cycle of xCO2 as
observed with the Ny-Ålesund FTS between 2012 and 2016, detrended with a
linear increase of 2.6ppmyr-1, an offset of 380.0ppm
on 1 January 2012 and condensed to 1 year. The seasonal cycle of xCO2
shows little difference between the three models, and therefore the comparison
can be performed with an model average. The shaded area in Fig. shows the 3σ standard deviation around the
daily mean of the combined model data points of all three models (MACC,
CarbonTracker and Jena). The weighted average of all FTS measurements during
one full moon period is shown (green dots) with error bars corresponding to
the standard error (σ/N) of the daily mean calculated from N
measurements. The weights are chosen to be the inverse squared residual of
the spectral fits.
The difference between the models and the TCCON measurements in summer is
quite small, except for a phase shift in the onset of the downward slope at
the beginning of the growing season decline. In winter the models agree well
with the FTIR lunar absorption measurements, within the given error margin.
In the case of CH4 a similar comparison has been performed and the
results can be seen in Fig. . Here the xCH4
time series have been linearly detrended with an annual increase of
10.6ppbyr-1 and an offset of 1760.0ppb on 1 January 2012.
Figure shows the 3σ standard deviation around
the daily means of the MACC CH4 model (shaded area) compared to the
FTS measurements (red and blue dots) averaged over one full moon measurement
cycle. The error bars correspond to the 1σ standard deviation of the
mean.
In spring/summer the FTS measurements show generally smaller values than the
model and a larger spread. From late summer throughout the winter the
measurements are in better agreement with the model. At specific events in
spring, the FTS measurements show sudden decreases of xCH4 (compare
Figs. and ). This could be
due to the model not being able to capture vertical transport very well,
which has been shown previously by . Here, stratospheric
intrusions during the breakdown of the polar vortex in spring can lead to
large, short-term decreases in xCH4. This is currently being
investigated by using a stratospheric species as a tracer to separate the
xCH4 column in a tropospheric and stratospheric part and exceeds the
scope of this paper.