Intercomparison of atmospheric CO<sub>2</sub> and CH<sub>4</sub> 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

Abstract

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.

MUCCnet: Munich Urban Carbon Column network

“…Besides these urban studies, column measurements are also used to investigate local sources: Chen et al (2016) and Viatte et al (2017) determined the source strength of dairy farms in Chino, California. By combining column measurements with a computational fluid dynamics (CFD) model, Toja-Silva et al (2017) verified the emission inventory of the largest gas-fired power plant in Munich.…”

Intercomparison of arctic XH<sub>2</sub>O observations from three ground-based Fourier transform infrared networks and application for satellite validation

“…The TROPOspheric Monitoring Instrument (TROPOMI) is the single payload onboard the Sentinel-5 Precursor (S5P) satellite and was launched in October 2017 (Veefkind et al, 2012). It is a nadir-viewing grating spectrometer, covering the ultraviolet to shortwave infrared (SWIR) band.…”

Spatial distributions of <i>X</i><sub><i>CO</i><sub>2</sub></sub> seasonal cycle amplitude and phase over northern high latitude regions

“…The role of fluxes within each 5 • latitude by 20 • longitude zone in determining the X CO2 SCA are assessed here using flux estimates from the same model frameworks that are used for previous comparisons of X CO2 SCA in this analysis. The GEOS-Chem CO 2 simulation uses a combination of flux inventories (Hoesly et al, 2018;Randerson et al, 2018;van der Werf et al, 2017) and CarbonTracker2019 (Jacobson et al, 2020) biospheric fluxes from land and ocean, while the CAMS v19r1 model framework includes a flux inversion that is internally consistent with model estimates of X CO2 (Chevallier, 2020b). Boreal zones, designating the Asian Boreal region as a major sink for CO 2 , and suggesting that anomalously large X CO2 SCA in the Asian Boreal region may be partially influenced by enhancements in CO 2 uptake within that region.…”