Measurements of atmospheric column-averaged dry-air mole fractions of carbon dioxide
(XCO2), methane (XCH4), and carbon monoxide (XCO) have been collected
across the Pacific Ocean during the Measuring Ocean REferences 2 (MORE-2) campaign in June
2019. We deployed a shipborne variant of the EM27/SUN Fourier transform spectrometer (FTS) on
board the German R/V Sonne which, during MORE-2, crossed the Pacific Ocean
from Vancouver, Canada, to Singapore. Equipped with a specially manufactured fast solar tracker,
the FTS operated in direct-sun viewing geometry during the ship cruise reliably delivering solar
absorption spectra in the shortwave infrared spectral range (4000 to 11000 cm-1). After
filtering and bias correcting the dataset, we report on XCO2, XCH4, and
XCO measurements for 22 d along a trajectory that largely aligns with 30∘ N of
latitude between 140∘ W and 120∘ E of longitude. The dataset has been scaled to the
Total Carbon Column Observing Network (TCCON) station in Karlsruhe, Germany, before and after the
MORE-2 campaign through side-by-side measurements. The 1σ repeatability of hourly means of
XCO2, XCH4, and XCO is found to be 0.24 ppm, 1.1 ppb, and
0.75 ppb, respectively. The Copernicus Atmosphere Monitoring Service (CAMS) models
gridded concentration fields of the atmospheric composition using assimilated satellite
observations, which show excellent agreement of 0.52±0.31ppm for XCO2,
0.9±4.1ppb for XCH4, and 3.2±3.4ppb for XCO (mean
difference ± SD, standard deviation, of differences for entire record) with our
observations. Likewise, we find excellent agreement to within 2.2±6.6ppb with the
XCO observations of the TROPOspheric MOnitoring Instrument (TROPOMI) on the Sentinel-5
Precursor satellite (S5P). The shipborne measurements are accessible at
10.1594/PANGAEA.917240.
Introduction
The greenhouse gases carbon dioxide (CO2) and methane (CH4) and the air pollutant
carbon monoxide (CO) are the target constituents of a range of currently orbiting and planned
Earth observing satellite missions e.g.,. The latest addition to the
fleet of spaceborne sensors is the Sentinel-5 Precursor (S5P) satellite with its TROPOspheric
Monitoring Instrument (TROPOMI) in orbit since October 2017 . TROPOMI retrieves,
among other constituents, the column-averaged dry-air mole fractions of CH4 (XCH4)
and CO (XCO) from spectra of backscattered sunlight
in the shortwave-infrared (SWIR)
spectral range . In parallel to the expansion
of the fleet of greenhouse gas sensors in orbit, the European Centre for Medium Range Weather
Forecasts (ECMWF) operates the Copernicus Atmosphere Monitoring Service (CAMS) on behalf of the
European Commission. CAMS assimilates satellite measurements of atmospheric composition to forecast
the global CO2, CH4, and CO concentrations at high spatial and temporal
resolution using the Integrated Forecasting System
(IFS). During our campaign in June
2019, CAMS assimilated XCO2 and XCH4 measurements from the Greenhouse gases
Observing SATellite (GOSAT) , CH4 and CO measurements from the
Infrared Atmospheric Sounding Interferometer (IASI) , and CO measurements from the Measurement of Pollution in the Troposphere (MOPITT)
instrument. The ultimate goal is to monitor the mitigation of anthropogenic greenhouse gas emissions and
air pollution from global to regional scales . Validation of the XCO2,
XCH4, and XCO satellite data mostly relies on ground-based direct-sun spectroscopic
observations conducted by the Total Carbon Column Observing Network (TCCON)
supplemented by the emerging Collaborative Carbon Column Observing Network (COCCON)
that measure the column-averaged dry-air mole fractions with similar column sensitivity to the
satellites. Likewise, the CAMS model uses TCCON as an evaluation tool
. Most of the observatories of the TCCON and COCCON are located at
continental sites. Thus, validation of the satellites and models over the oceans is limited to a few
island and coastal observatories (in particular Ascension, Reunion, Tenerife, Japan,
California). For the satellite retrievals, ocean–land biases e.g., can occur
since the ocean surface is dark, and thus, satellites typically have to resort to glint geometry
e.g., or retrievals above clouds e.g.,, which
impose difficulties different from the typical clear-sky nadir observations above land.
To enable the evaluation of satellites and models over the oceans, developed a
shipborne prototype of the EM27/SUN Fourier transform spectrometer (FTS)
which is the instrument used within the COCCON . The EM27/SUN has proven to be a reliable
instrument for XCO2 and XCH4 measurements in various studies ranging from ad hoc
networks covering a larger region of interest to mobile deployments
for the quantification of localized CO2 and CH4 sources. The latest variant of the
EM27/SUN incorporates a second spectral detector channel that enables XCO measurements to be conducted
simultaneously with observations of XCO2 and XCH4. The shipborne
observations by were conducted on board the R/V Polarstern during a cruise from Cape Town, South Africa, to Bremerhaven, Germany, in March
and April 2014. These measurements were used for evaluating XCO2 and XCH4
observations of the Greenhouse Gases Observing Satellite (GOSAT) and for improving the
interhemispheric gradient modeled by the IFS for the CAMS CO2 and CH4 analysis and
forecasting system .
Here, we report on the further developments of the shipborne EM27/SUN prototype toward routine use
as a validation tool over the open oceans. To demonstrate the performance and robustness of the
instrumentation and its suitability for satellite and model validation, we deployed the instrument
on the German R/V Sonne during the MORE-2 (Measuring Oceanic REferences 2)
campaign which led from Vancouver, Canada, to Singapore between 30 May and 5 July
2019. Figure shows the track of the research vessel over the Pacific Ocean. We
report on technical developments (Sect. ), the data processing chain and
data quality assessment (Sect. ), and comparisons to TROPOMI's XCO
measurements and CAMS' analysis fields of XCO2, XCH4, and XCO
(Sect. ) over the Pacific Ocean. The data collected are publicly available at
https://doi.org/10.1594/PANGAEA.917240 for evaluating other datasets, and the shipborne
instrument is recommended for routine deployment on ships.
Track of the R/V Sonne (gray line) during the MORE-2 campaign starting from Vancouver, Canada, on 30 May 2019 and entering port in Singapore on 5 July
2019. The blue dots are the locations of all quality-assured EM27/SUN measurements. The map is
provided by .
Instrumentation
The EM27/SUN is a commercially available FTS which was developed in cooperation by Bruker Optics
and the Karlsruhe Institute of Technology (KIT) . The spectrometer has the
dimensions 42×27×35cm3 and weighs about 25 kg. The EM27/SUN uses a
CaF2 beam splitter and a RockSolid™ pendulum interferometer with two cube corner
mirrors. The maximum optical path difference of 1.8 cm supports a spectral resolution of
0.50 cm-1. After the sunlight passed the interferometer, a parabolic off-axis mirror
focuses it on an InGaAs photodetector with a spectral range of 5500–11 000 cm-1, further
called the SWIR-1 channel. Another mirror decouples about 40 % of the beam on a second
spectrally extended InGaAs photodetector covering the spectral range of 4000–5500 cm-1, called the SWIR-3 channel. Typical exposure times are on the order of 6 s for
a single interferogram. Spectra have been generated from raw DC-coupled interferograms using the
preprocessor used by the COCCON network, which has been developed in the framework of the ESA
project COCCON-PROCEEDS . As suggested by , we use water
vapor absorption lines to measure the instrumental line shape (ILS).
The EM27/SUN is mechanically robust, but it would not withstand precipitation or sea spray. For the
shipborne variant, we assembled a small container that houses the EM27/SUN FTS with its solar
tracker, a laptop, and several ancillary sensors (GPS, pressure, temperature) similar to
. During the entire MORE-2 campaign, we placed the container outside on the port
side of the observation deck of the R/V Sonne, which was the uppermost
continuously accessible deck available. We chose this spot to avoid obstruction of the direct light
path from the sun to the instrument by ship structures. The container is a white lacquered K470
Zarges aluminum box (IP65 waterproof) with dimensions of 95×69×48cm3 and mass of
13.4 kg when empty. Figure shows a photograph of the container
deployed on the ship. The solar tracker is a modified version of the custom-built setup used by
consisting of a mirror assembly on two perpendicular rotation stages that
allow for pointing to any azimuth and elevation position of the sun in the overhead sky. For the
ship deployment, we covered the solar tracker with a protective housing that has a fused silica
wedged window transmitting the incoming sunlight. The solar tracker housing is positioned on top of
the box and attached to the rotation stages moving with the azimuth and elevation rotations. The
precision required for the pointing of the solar tracker is 0.05∘ relative to the center of
the sun to keep mole fraction uncertainties due to pointing errors below 0.1 %. Our
tracking system satisfied this requirement for 79 % of the measurements for which the sun was within
the field of view (FOV) of the solar tracker. We observed the largest pointing deviations when high
cirrus clouds were present and when the sun was close to the zenith where the azimuth rotation has a
singularity. Our filter criteria reliably remove such observations (see
Sect. ).
In addition to the main solar tracker, we mounted a f-theta fisheye lens (Fujinon FE185C057HA-1)
with a field of view of 185∘×185∘ under a protective acrylic glass dome
onto the lid of the box. A camera (IDS UI-3280CP-M-GL Rev.2) observes the sky through the fisheye
lens and provides the position of the sun with an accuracy better than 2∘ when the sun is
not within the FOV of the solar tracker. The ambient pressure and temperature sensors, as well as the
GPS antenna, are mounted on the lid as well. The box is equipped with a Pfannenberg PF 66000 fan for
ventilation on one side and an air outlet on the other side to prevent the box from overheating,
both of which are covered by protective lids against sea spray and precipitation. During the whole MORE-2
campaign, the box interior temperature never exceeded 40 ∘C. Inside the box, a
Raspberry Pi 3 Model B is the central control unit. It allows for remote access via a network to
connect to the laptop controlling the EM27/SUN, an Advantech Ark-2150 embedded PC running the solar
tracking software , and a central storage unit (Synology DS2018). The
Raspberry Pi continuously reads the ancillary sensors for the box interior temperature, the ambient
pressure and temperature, and the GPS position of the instrument. The electronics runs on
24 V DC provided by an AC C-TEC 2410-10 uninterrupted power supply (UPS). In case of a power
cut, the Raspberry Pi securely shuts down all devices within the approximately 60 s backup
time of the UPS. The whole container weighs about 80 kg and consumes 190 W via a
regular 230 V AC line if the measurement electronics is running at full power. The
ventilation consumes an additional 160 W if switched on, which was necessary throughout the
campaign.
Photograph of the instrument container on board R/V Sonne(a)
and the solar tracker housing (b). The solar tracker housing (A in a), the ambient
sensors (B in a), and the fisheye camera (C in a) are mounted on top of the
box. The solar tracker housing (b) consists of a cylinder which rotates around a
horizontal axis in the elevation direction. The cylinder is mounted on a cube which is able to
rotate around a vertical axis in the azimuth direction. Sunlight enters the tracker through a
fused silica wedged window.
Data processing
The quality assessment and the retrievals of XCO2, XCH4, and XCO largely
follow . Therefore, we summarize the methods here, mostly highlighting the
differences and new aspects compared to our precursor study.
Retrieval of XCO2, XCH4, and XCO
The spectral retrieval of the targeted gas concentrations from direct-sun absorption spectra is
based on forward modeling of the spectra given a priori concentrations of the molecular absorbers
and then iteratively adjusting the concentrations to optimally (in a least squares sense) fit the
measured spectra. The spectral retrieval calculates total column number densities of the target
gases [GAS] which are a posteriori ratioed by the total column number density of oxygen
[O2] to yield the column-averaged dry-air mole fraction XGAS of the target gas according to
XGAS=[GAS][O2]⋅0.2094,
where 0.2094 is the constant column-averaged dry-air mole fraction of molecular oxygen. Referencing
the target gas column [GAS] to the oxygen column cancels out instrument- and retrieval-related errors common to both retrievals.
For the spectral retrieval, we use a variant of the RemoTeC algorithm which is in
use for satellite observation from GOSAT , the Orbiting Carbon
Observatory (OCO-2) , and TROPOMI . We have adapted RemoTeC for
transmittance calculations applicable to ground-based direct-sun measurements such as those conducted
here. Since the spectral resolution of the EM27/SUN is insufficient to extract profile information
from the absorption line shapes, RemoTeC retrieves a scaling parameter on the a priori absorber
profiles. The spectral retrieval windows for CO2, CH4, and O2 are located in
the SWIR-1 channel and almost identical to the ones used by . The retrieval
window for CO is located in the new SWIR-3 channel. Table collects the
information on window selection and interfering absorbers. The absorption cross sections of all
species are generated from the HITRAN2016 database . Meteorological parameters
such as pressure and temperature profiles are taken from the National Centres for Environmental
Prediction (NCEP) available at . These NCEP FNL (Final) Operational Model Global
Tropospheric Analyses fields are from the Global Data Assimilation System (GDAS) and have a spatial
resolution of 1∘×1∘ and a temporal resolution of 6 h. The a priori
profiles for CO2 and CH4 are taken from CAMS greenhouse gas analysis
and for CO from the near-real-time operational analysis
. The profiles are interpolated to the time and location of each
individual EM27/SUN measurement. CAMS provides CO profiles with 60 model levels on a
0.4∘×0.4∘ grid and 6 h temporal resolution for the campaign period in
June 2019. The CAMS CO2 and CH4 profiles have 136 model levels on a
0.25∘×0.25∘ horizontal grid with 6 h temporal resolution.
Spectral windows with target and interfering absorbers. CIA refers to collision-induced absorption.
The measurements collected during the MORE-2 campaign require quality filtering since cloudy or
partially cloudy scenes need to be screened and we want to avoid measurements that are contaminated
by the exhaust plume of the ship. To this end, suggested a cascade of three
criteria: a filter based on the DC part of the recorded interferograms, a filter based on the
deviation between spectroscopically derived surface pressure and surface pressure measured in situ, and a filter based on the visual identification of steep slopes in the XCO2 time series.
During the MORE-2 campaign, our FTS recorded the interferograms with the slowly varying DC part
included. The DC part is indicative of the overall incoming radiance, and thus, it can be used to
track clouds that obstruct the direct-sun view. The DC filter criterion screens measurements either
if the DC part IDC is too small to be direct sunlight or if the fluctuation DCfluc,
defined as
DCfluc≡max(IDC)min(IDC)-1,
exceeds 5 %. The DC filter removes 8.39 % of the dataset.
The surface pressure filter compares the surface pressure measured in situ by the ship's meteorological station and the surface pressure calculated from the spectroscopic measurements, as suggested by . We calculate the spectroscopic pressure from the measured total column number densities of [O2] and [H2O] with
3pdry=[O2]⋅MO2NA⋅ξO2⋅g,4pH2O=[H2O]⋅MH2ONA⋅g,
where MGAS is the molar mass of the gas molecule, NA Avogadro's constant, ξO2=0.2314 (the dry-air mass mixing ratio of oxygen), and g the gravitational constant.
We scale the spectroscopic pressure to the in situ pressure with a factor of 0.9693 such that the ratio
Rpsf≡0.9693⋅pdry+pH2Opin situ
scatters around unity within the measurement noise under unperturbed conditions. Any measurement for
which Rpsf deviates by more than 0.3 % from unity is excluded from further
processing, which led to a rejection for 6.2 % of the data.
The third quality filter screens the (rare) situation when our EM27/SUN measurements detected the
ship's exhaust plume. During the MORE-2 campaign, this happened in the morning of 8 June 2019 when
the ship's exhaust plume crossed the light path. The observations show a steep increase of
2 ppm (parts per million) in XCO2, while XCH4 and XCO show no increase. We removed 179
measurements (corresponding to 55 min). After applying all filters, a total of 32 859
(84.38 %) direct-sun measurements pass the filter process and are considered for further
processing.
Bias corrections
After retrieving and quality filtering the XCO2, XCH4, and XCO
concentrations, the records require bias correction for a spurious dependency on the solar zenith
angle (SZA) causing an artificial diurnal cycle e.g., and for species-dependent
scaling factors that adjust our spectroscopic measurements to observations of the TCCON whose
stations have been compared and scaled to standards of the World Meteorology Organization
WMO;. We determine the SZA dependency for each species from observations above
the Pacific in background air where the columns are expected to be constant and the scaling factors
to TCCON via the ratio of side-by-side measurements.
The spurious dependency on SZA causes an underestimation of the column-averaged dry-air mole
fractions at high SZAs for each of the target species. suggested an empirical
correction as a function of SZA Θ according to
Xcorr, GAS(Θ)=XGAS(Θ)χGAS(Θ),
where Xcorr, GAS(Θ) is the corrected column-averaged dry-air mole fraction of the gas under consideration and χGAS(Θ) is a third-order polynomial of the form
χGAS(Θ)=aΘ3+bΘ+c,
with a, b, and c being free fitting parameters. We perform the fit by splitting our time series into
morning and afternoon parts at the lowest SZA of the day and consider only those half days which
contain a measurement at SZA=(45.0±0.5)∘, which was the case for 26
half days. We reference each measurement to the observation closest to SZA=45∘,
i.e., we choose χGAS(45∘)=1. Furthermore, we identified half days which showed
actual atmospheric variability by fitting the SZA dependency in a first attempt and calculating the
standard deviation (SD) of the fit residuum. We removed a half day if less than 97 % of its
observations were within 2σ of this fit since this indicates actual atmospheric variability
which we do not want to misinterpret as spurious SZA dependency. This results in removing 2, 9, and
9 half days from the XCO2, XCH4, and XCO records for the fit of the
correction polynomial, respectively. Figure shows the corresponding data and the
fitted correction polynomials for each target species, and Table lists the
parameters defined in Eq. () and the coefficients of determination for each fit. The
lowest coefficient of determination is found for CO most likely due to the stronger
atmospheric variability compared to CO2 and CH4.
Parameters a, b, and c (and coefficients of determination R2) used for correcting the spurious SZA dependency of the measurements.
SZA dependency of the retrieved XCO2 (upper panel), XCH4 (middle
panel), and XCO (lower panel) and the inferred correction polynomial (solid
line). Negative SZAs denote morning and positive SZAs afternoon measurements.
After correcting the SZA dependency, we adjust our measurements to those of the TCCON station at
KIT, Karlsruhe, to ensure traceability to WMO standards . To this end, we performed
side-by-side measurements at Karlsruhe for a day before (30 April 2019) and after (23 July 2019) the
ship campaign. Figure shows the TCCON measurements alongside the EM27/SUN
measurements before and after scaling. Following , we calculate the scaling
factor as the mean ratio γGAS between the TCCON and the EM27/SUN 1 h means for
both days according to
γGAS=〈XGASTCCON〉h〈XGASEM27〉hday
for each of the target species. Table lists the scaling factors
γGAS and their error bars, which we calculate as the standard error σγ of the mean using the hourly mean variances σγ,h2;
9σγ=∑hNσγ,h2N2,10σγ,hγh2=σ〈XGASTCCON〉h〈XGASTCCON〉h2+σ〈XGASEM27〉h〈XGASEM27〉h2,
where σ() is the SD of the hourly mean and N the total number of hours of
side-by-side observations. For CO2, the scaling factors are consistent within the error
bars, and for CH4, the scaling factors before and after the campaign are consistent at
roughly 3 ‰, though the differences are larger than the combined error bars. For CO, the
scaling factors before and after the campaign differ by roughly 2 %, which is substantially more
than the error bars. We identify a change in the ILS as the most likely candidate for this
difference in the scaling factors. The ILS was measured under laboratory conditions before and after
the campaign. We also performed ILS measurements at the beginning of the campaign on 2 June 2019,
yet it was impossible to assure laboratory conditions there since the measurements were conducted
on deck. The pre-campaign ILS differs from the one taken on board the R/V Sonne most likely
due to rough handling during the transport from Germany to Vancouver and, in consequence, a slight
change in the optical alignment. We could not detect any change in the ILS after shipment back to
Germany from Singapore. Thus, we adjust our measurements with the factors derived from the TCCON
side-by-side measurements on 23 July 2019. Even after applying the scaling factor,
Fig. shows that there is some residual differences between the EM27/SUN and TCCON
data growing towards the afternoon. At present, the origin of these differences is
unclear. suggest further investigations of the TCCON XCO scaling factor
based on comparisons of the TCCON to the Network for the Detection of Atmospheric Composition Change
(NDACC) and AirCore measurements. Should the TCCON scaling factor be updated in the future, our
XCO data will be scaled accordingly.
Side-by-side measurements of XCO2(a, b), XCH4(c, d), and XCO(e, f) by the shipborne EM27/SUN and the TCCON station at Karlsruhe on 30 April 2019 (a, c, e) and 7 July 2019 (b, d, f). EM27/SUN measurements before and after scaling are shown in orange and blue, and TCCON records are shown in red.
Scaling factors γ for EM27/SUN XCO2, XCH4, and XCO observations as derived from the side-by-side measurements at the TCCON station Karlsruhe (Eq. ). The uncertainty is the standard error of the mean of the hourly data (Eq. ).
Species30 April 201923 July 2019CO21.0271±0.00021.0272±0.0002CH41.0251±0.00031.0222±0.0002CO0.9436±0.00180.9653±0.0017Comparison to TROPOMI and CAMS
Figure shows the quality-filtered and bias-corrected XCO2,
XCH4, and XCO records for our cruise over the Pacific Ocean. The trajectory
largely follows 30∘ N of latitude between 140∘ W and 120∘ E of longitude,
crossing the date line on 14 June 2019. For statistical analysis, we calculate hourly means of our
records and use the campaign averaged SD of the hourly means as a measure for our
precision, which amounts to 0.24 ppm (0.06 %), 1.1 ppb (0.06 %; parts per billion), and
0.75 ppb (1.03 %) for XCO2, XCH4, and XCO, respectively. The data
records clearly show that we sampled background air masses for most of the time. We calculate a
campaign mean and SD throughout the longitudinal section for each species, finding
means and SDs as little as 411.6±0.6ppm for XCO2,
1835±7ppb for XCH4, and 71±5ppb for XCO.
XCO2(a), XCH4(b), and XCO(c) measured by the shipborne EM27/SUN (blue) above the Pacific alongside coincident CAMS
atmospheric composition analysis data (green) and coincident XCO satellite observations
by TROPOMI (orange). EM27/SUN measurements and CAMS data are hourly averages, while TROPOMI
observations are averaged per overflight. Single TROPOMI measurements are marked small in the
background.
Figure compares our observations to column-averaged dry-air mole fractions
we calculated from vertical profiles of the CAMS atmospheric composition analyses. These profiles
are the same as those we use as a priori for our retrieval. Therefore, the differences between our
retrievals and CAMS have no contribution from the a priori profiles being different from CAMS. Furthermore,
Fig. shows TROPOMI XCO observations for which we apply coincidence
criteria of 0.5∘ radius and 4 h time span. The TROPOMI XCO data are available at
and have been retrieved by the SICOR algorithm which allows for
retrievals above clouds. The latter capability is important over the ocean since the ocean is dark
implying large noise unless the ocean-glint spot is observed or clouds offer a bright reflection
target. After filtering with TROPOMI's quality flag (i.e., the internal quality descriptor bounded
by 0 and 1 must be larger than 0.5), we find 1783 coincident TROPOMI XCO measurements
distributed among 19 d. Although TROPOMI has XCH4 measurement capabilities, we do not
discuss these here since there is currently no ocean data available. Likewise, we do not show any
OCO-2 or GOSAT data since the number of coincidences was 43 and 9, respectively, and limited to
individual days, which we consider too few for a robust statistical analysis. The SICOR algorithm
uses the global chemistry transport model TM5 as an a priori source, which
introduces a difference in the comparison to our EM27/SUN CO measurements with a priori profiles from CAMS . We calculate the difference due to the a priori profiles for each EM27/SUN
observation and find it to be 0.11±0.40ppb (campaign mean ± SD)
with a maximum of 0.92 ppb. This contribution is small but not entirely negligible compared
to the differences we find between our data and TROPOMI CO.
Differences between CAMS and our shipborne EM27/SUN (green) for XCO2(a), XCH4(b), and XCO(c) and between TROPOMI
XCO and our EM27/SUN (orange). The EM27/SUN measurements were subtracted from the CAMS
data and coincident TROPOMI observations.
Figure depicts the differences between CAMS and our data and between TROPOMI
and our data. We average the differences to CAMS over the entire campaign and calculate the standard
deviations of the differences, which amount to 0.52±0.31ppm for XCO2,
0.9±4.1ppb for XCH4, and 3.2±3.4ppb for XCO (see also
Table ). Thus, CAMS shows excellent agreement with the shipborne measurements
within 1σ for CH4 and CO and 2σ for CO2. Maximum differences
between the hourly means of CAMS and EM27/SUN observations amount to 1.0±0.2ppmXCO2, 13.8±1.3ppbXCH4, and 10.3±0.5ppbXCO,
in which the range is the propagated error using the SDs of the hourly mean values. For
XCH4, CAMS tentatively shows an underestimation by a few parts per billion around the date line and an
overestimation around 160∘ E. Further, the intra-day variability of XCH4 shows a
systematic difference on the order of a few parts per billion. However, there is no consistent intra-day pattern
that fits all the campaign days. Likewise for XCO, there is an intra-day residual pattern on
the order of a few parts per billion but no consistency that informs us of potential model errors or shortcomings of
the shipborne measurements.
Comparison of the EM27/SUN observations to CAMS model data and coincident TROPOMI XCO measurements. The data indicate the mean differences ± the SD of the differences for the entire record.
Absolute differences between TROPOMI XCO observations and our EM27/SUN
measurements plotted as a function of the SICOR/TROPOMI retrieval parameters scattering layer
height (a), scattering optical thickness (b), interfering CH4
(c, from weak two-band total column), and interfering H2O(d). The
color code indicates (logarithmic) occurrence, and the red line is a linear fit with its
coefficient of determination R2 given in the upper right of each plot.
TROPOMI XCO also shows very good agreement with our data. The mean difference and standard
deviation among the entire campaign record is 2.2±6.6ppb without any systematic pattern
correlating with the position in the Pacific Ocean. improved the SICOR/TROPOMI
CO data product in comparison to TCCON observations by adjusting the spectroscopic database,
decreasing the global mean bias below 1 ppb compared to TCCON station records with an
SD of 2.6 ppb and a TCCON station-to-station bias variation of
1.8 ppb. We investigated whether the small residual differences correlate with the cloud
parameters or interfering absorber abundances that SICOR/TROPOMI retrieves simultaneously with
XCO. Figure shows the correlations of the differences with the layer
height and the scattering optical thickness of the cloud layer, as well as the atmospheric methane
and water column retrieved by SICOR/TROPOMI. find a retrieval bias in the case
of CO enhancements in combination with clouds, which we can not assess from our background
concentration observations. Furthermore, we find no correlation of the XCO differences with
the atmospheric methane and water vapor columns retrieved by SICOR/TROPOMI. These two species show
spectroscopic interferences with the CO absorption lines in the SWIR-3 band, and thus, they
could be candidates for inducing retrieval errors. Overall, our evaluation suggests that
SICOR/TROPOMI provides robust results over clouds and for ocean-glint observations.
Data availability
The XCO2, XCH4, and XCO records are available for download on PANGAEA at 10.1594/PANGAEA.917240. The CAMS CO2 and CH4 data used in the paper is the official CAMS GHG analysis (10.24380/654b-gm83, ). The data for CO2 and CH4 is available via request to Copernicus Service Desk by emailing to copernicus-support@ecmwf.int or via the CAMS enquiry portal in https://atmosphere.copernicus.eu/help-and-support (last access: 29 January 2020). The CO data is from the CAMS NRT analysis available for download at 10.24380/hhra-8c27.
Conclusions
We deployed an EM27/SUN FTS on board the German R/V Sonne on the MORE-2
campaign cruise from Vancouver to Singapore leaving port on 30 May 2019 and arriving on 5 July
2019. Compared to our precursor study , our instrument setup was able to
withstand environmental conditions, and it ran largely without requiring on-site operating
personnel. Plus, the sun-viewing FTS was augmented by another detector to collect solar absorption
spectra of CO in addition to CO2 and CH4. We provide records
of the column-averaged dry-air mole fractions XCO2, XCH4, and XCO for 22 d
of measurements on the Pacific Ocean largely following 30∘ N of latitude. Our observations
are representative of global background conditions; thus, they are useful for assessing the
performance of atmospheric models and satellite measurements without perturbations due to local
atmospheric variability, and they add to the largely land-based validation data provided by the TCCON
and the COCCON. Our measurements show an overall precision (hourly SDs averaged for
the whole campaign) of 0.24 ppm for CO2, 1.1 ppb for CH4, and
0.75 ppb for CO. Systematic errors due to residual pointing uncertainties, sampling
of the ship's exhaust plume, and a spurious dependency on the SZA are treated by filtering flawed
data and by empirical corrections. We made our observations compatible with the TCCON through
side-by-side measurements before and after the campaign at the TCCON station Karlsruhe. Through
comparisons to our data, we evaluate the performance of the CAMS model for XCO2,
XCH4, and XCO and the performance of XCO measurements by the TROPOMI
instrument on board the Sentinel-5 Precursor satellite. Averaged over the entire campaign, the
differences to CAMS amount to 0.52±0.31ppm for XCO2,
0.9±4.1ppb for XCH4, and 3.2±3.4ppb for
XCO. Furthermore, we find the TROPOMI XCO of
2.2±6.6ppb to be in excellent agreement with the ground-based observations. The instrument is a valuable asset for the
validation of satellite observations over sea. In the future, we plan to fully automate our
instrument design for routine deployment on ships to enrich validation opportunities over the open
oceans where other opportunities are sparse.
Author contributions
MK and RK developed the shipborne instrument and operated it during MORE-2. FH contributed heavily to the adjustment process of the EM27/SUN to the TCCON. SK was the chief scientist during MORE-2. AAP, AI, and JB provided the CAMS analyses. JL and TB contributed to the TROPOMI comparison. AB developed the research question. All authors read and provided comments on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge funding for the MORE-2 campaign by BMBF (German Federal Ministry of Education and Research). The development of the COCCON preprocessing tool has been supported by ESA in the framework of the COCCON-PROCEEDS project. The Copernicus Atmosphere Monitoring Service is operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission as part of the Copernicus program (http://copernicus.eu, last access: 8 January 2021).
Financial support
This research has been supported by the BMBF (German Federal Ministry of Education and Research) (grant no. FKZ 03G0268TD).
Review statement
This paper was edited by David Carlson and reviewed by David Griffith and one anonymous referee.
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