Intercomparison of atmospheric CO2 and CH4 abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations
Authors: Tu Qiansi , Hase Frank , Blumenstock Thomas , Kivi Rigel , Heikkinen Pauli , Sha Mahesh Kumar , Raffalski Uwe , Landgraf Jochen , Lorente Alba , Borsdorff Tobias , Chen Huilin , Dietrich Florian , Chen Jia 
Published: September 2020

Abstract. We compare the atmospheric column-averaged dry-air mole fractions of carbon
dioxide (XCO2) and methane (XCH4) measured with a pair of
COllaborative Carbon Column Observing Network (COCCON) spectrometers at
Kiruna and Sodankylä (boreal areas). We compare model data provided by
the Copernicus Atmosphere Monitoring Service (CAMS) between 2017 and 2019 with XCH4 data from the recently launched Sentinel-5 Precursor (S5P)
satellite between 2018 and 2019. In addition, measured and modeled gradients
of XCO2 and XCH4 (ΔXCO2 and ΔXCH4) on
regional scales are investigated. Both sites show a similar and very good
correlation between COCCON retrievals and the modeled CAMS XCO2 data,
while CAMS data are biased high with respect to COCCON by 3.72 ppm (±1.80 ppm) in Kiruna and 3.46 ppm (±1.73 ppm) in Sodankylä on
average. For XCH4, CAMS values are higher than the COCCON observations
by 0.33 ppb (±11.93 ppb) in Kiruna and 7.39 ppb (±10.92 ppb)
in Sodankylä. In contrast, the S5P satellite generally measures lower
atmospheric XCH4 than the COCCON spectrometers, with a mean difference
of 9.69 ppb (±20.51 ppb) in Kiruna and 3.36 ppb (±17.05 ppb)
in Sodankylä. We compare the gradients of XCO2 and XCH4 (ΔXCO2 and ΔXCH4) between Kiruna and
Sodankylä derived from CAMS analysis and COCCON and S5P measurements to
study the capability of detecting sources and sinks on regional scales. The
correlations in ΔXCO2 and ΔXCH4 between the
different datasets are generally smaller than the correlations in XCO2
and XCH4 between the datasets at either site. The ΔXCO2 values
predicted by CAMS are generally higher than those observed with COCCON with
a slope of 0.51. The ΔXCH4 values predicted by CAMS are mostly higher
than those observed with COCCON with a slope of 0.65, covering a larger
dataset than the comparison between S5P and COCCON. When comparing CAMS
ΔXCH4 with COCCON ΔXCH4 only in S5P overpass days
(slope = 0.53), the correlation is close to that between S5P and COCCON
(slope = 0.51). CAMS, COCCON, and S5P predict gradients in reasonable
agreement. However, the small number of observations coinciding with S5P
limits our ability to verify the performance of this spaceborne sensor. We
detect no significant impact of ground albedo and viewing zenith angle on
the S5P results. Both sites show similar situations with the average ratios of
XCH4 (S5P/COCCON) of 0.9949±0.0118 in Kiruna and 0.9953±0.0089 in Sodankylä. Overall, the results indicate that the COCCON
instruments have the capability of measuring greenhouse gas (GHG) gradients
on regional scales, and observations performed with the portable
spectrometers can contribute to inferring sources and sinks and to
validating spaceborne greenhouse gas sensors. To our knowledge, this is the
first published study using COCCON spectrometers for the validation of
XCH4 measurements collected by S5P.