Total Carbon Column Observing Network Sites Research Articles

The Total Carbon Column Observing Network's GGG2020 data version

Abstract. The Total Carbon Column Observing Network (TCCON) measures column-average mole fractions of several greenhouse gases (GHGs), beginning in 2004, from over 30 current or past measurement sites around the world using solar absorption spectroscopy in the near-infrared (near-IR) region. TCCON GHG data have been used extensively for multiple purposes, including in studies of the carbon cycle and anthropogenic emissions, as well as to validate and improve observations from space-based sensors. Here, we describe an update to the retrieval algorithm used to process the TCCON near-IR solar spectra and to generate the associated data products. This version, called GGG2020, was initially released in April 2022. It includes updates and improvements to all steps of the retrieval, including but not limited to the conversion of the original interferograms into spectra, the spectroscopic information used in the column retrieval, post hoc air mass dependence correction, and scaling to align with the calibration scales of in situ GHG measurements. All TCCON data are available through https://tccondata.org/ (last access: 22 April 2024) and are hosted on CaltechDATA (https://data.caltech.edu/, last access: 22 April 2024). Each TCCON site has a unique DOI for its data record. An archive of all the sites' data is also available with the DOI https://doi.org/10.14291/TCCON.GGG2020 (Total Carbon Column Observing Network (TCCON) Team, 2022). The hosted files are updated approximately monthly, and TCCON sites are required to deliver data to the archive no later than 1 year after acquisition. Full details of data locations are provided in the “Code and data availability” section.

Approximation of multi-year time series of XCO2 concentrations using satellite observations and statistical interpolation methods

Long-term time series of CO2 concentrations are required for studying e.g., the effects of rising CO2 concentrations on plants and ecosystems. Different methods for approximating column-averaged dry-air mole fractions of CO2 (XCO2) have been developed to substitute missing local CO2 measurements.This study presents a novel method (XCO2SAT+) for determining monthly XCO2 concentrations for selected sites, based entirely on observations from multiple satellites as well as statistical methods in the case of missing satellite data. The product currently covers a period of 16 years from 2003 to 2018. XCO2SAT+ results were validated against data of five European sites of the Total Carbon Column Observing Network (TCCON) and compared to two established XCO2 approximation methods. All three approximation methods were compared to CO2 measurements of German towers from the Integrated Carbon Observation System (ICOS).The mean absolute error (MAE) between the XCO2SAT+ method and TCCON site Karlsruhe, Germany ranges between 0.5 and 1.1 ppm (0.1–0.3%) for years 2014–2018. Mean differences between XCO2SAT+ and XCO2 approximation method CAMS-GCM shows a slightly better MAE of 0.1–0.3 ppm for CAMS-GCM at two German TCCON sites. Multi-year comparisons with ground-level ICOS measurements revealed a comparable seasonality of CO2 concentrations during the vegetation period, but underestimated high winter concentrations, a pattern also observed when comparing with CAMS-GCM. XCO2SAT+ is a suitable alternative and simple method for providing long-term time series of monthly averaged XCO2 concentrations for selected sites, expanding the methodological toolbox for in-situ CO2 research. The new method, which so far provides XCO2 concentrations, will thus serve as a first step towards providing further CO2 data sources, which could be used to further improve numerical models such as atmospheric models or crop growth models and to study CO2 impacts in agriculture and natural ecosystems.

Using portable low-resolution spectrometers to evaluate Total Carbon Column Observing Network (TCCON) biases in North America

Abstract. EM27/SUN devices are portable solar-viewing Fourier transform spectrometers (FTSs) that are being widely used to constrain measurements of greenhouse gas emissions and validate satellite trace gas measurements. On a 6-week-long campaign in the summer of 2018, four EM27/SUN devices were taken to five Total Carbon Column Observing Network (TCCON) stations in North America, to measure side by side, to better understand their durability, the accuracy and precision of retrievals from their trace gas measurements, and to constrain site-to-site bias among TCCON sites. We developed new EM27/SUN data products using both previous and current versions of the retrieval algorithm (GGG2014 and GGG2020) and used coincident AirCore measurements to tie the gas retrievals to the World Meteorological Organization (WMO) trace gas standard scales. We also derived air-mass-dependent correction factors for the EM27/SUN devices. Pairs of column-averaged dry-air mole fractions (denoted with an X) measured by the EM27/SUN devices remained consistent compared to each other during the entire campaign, with a 10 min averaged precision of 0.3 ppm (parts per million) for XCO2, 1.7 ppb (parts per billion) for XCH4, and 2.5 ppb for XCO. The maximum biases between TCCON stations were reduced in GGG2020 relative to GGG2014 from 1.3 to 0.5 ppm for XCO2 and from 5.4 to 4.3 ppb for XCH4 but increased for XCO from 2.2 to 6.1 ppb. The increased XCO biases in GGG2020 are driven by measurements at sites influenced by urban emissions (Caltech and the Armstrong Flight Research Center) where the priors overestimate surface CO. In addition, in 2020, one EM27/SUN instrument was sent to the Canadian Arctic TCCON station at Eureka, and side-by-side measurements were performed in March–July. In contrast to the other TCCON stations that showed an improvement in the biases with the newer version of GGG, the biases between Eureka's TCCON measurements and those from the EM27/SUN degraded with GGG2020, but this degradation was found to be caused by a temperature dependence in the EM27/SUN oxygen retrievals that is not apparent in the GGG2014 retrievals.

Optimizing 4 years of CO2 biospheric fluxes from OCO-2 and in situ data in TM5: fire emissions from GFED and inferred from MOPITT CO data

Abstract. Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often co-emitted with CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using Measurements of Pollution in The Troposphere (MOPITT) v8 data is performed to estimate fire emissions which are then converted into CO2 fire emissions (called FIREMo) through the use of the emission ratio. These optimized CO2 fire emissions are used to rebalance the CO2 net ecosystem exchange (NEEMo) and respiration (RhMo) with the global CO2 growth rate. Subsequently, in a second step, these rebalanced fluxes are used as priors for a CO2 inversion to derive the NEE and ocean fluxes constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ (IS) CO2 data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Emissions Database (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal, and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 (FIRE3) and GFED4.1s (FIRE4) fires, as well as with an inversion using both Carnegie–Ames–Stanford Approach (CASA)-GFED3 NEE and GFED3 fire priors (priorCMS). After comparison at the different scales, the inversions are evaluated against Total Carbon Column Observing Network (TCCON) data. Comparison of the flux estimates shows that at the global scale posterior net flux estimates are more robust than the different prior flux estimates. However, at the regional scale, we can observe differences in fire emissions among the priors, resulting in differences among the NEE prior emissions. The derived NEE prior emissions are rebalanced in concert with the fires. Consequently, the differences observed in the NEE posterior emissions are a result of the balancing with fires and the constraints provided by CO2 observations. Tropical net flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger net CO2 sources are derived with posterior fire emissions using either FIRE4 or FIREMo in the OCO-2 inversion, in particular for most tropical regions during the 2015 El Niño year. Similarly, larger net CO2 sources are also derived with posterior fire emissions in the in situ data inversion for Tropical Asia. Evaluation with CO2 TCCON data shows lower biases with the three rebalanced priors than with the prior using CASA-GFED3. However, posteriors have average bias and scatter very close each other, making it difficult to conclude which simulation performs better than the other. We observe that the assimilated CO2 data have a strong influence on the global net fluxes among the different inversions. Inversions using OCO-2 (or IS) data have similar emissions, mostly as a result of the observational constraints and to a lesser extent because of the fire prior used. But results in the tropical regions suggest net flux sensitivity to the fire prior for both the IS and OCO-2 inversions. Further work is needed to improve prior fluxes in tropical regions where fires are a significant component. Finally, even if the inversions using the FIREMo prior did enhance the biases over some TCCON sites, it is not the case for the majority of TCCON sites. This study consequently pushes forward the development of a CO–CO2 joint inversion with multi-observations for a possible stronger constraint on posterior CO2 fire and biospheric emissions.

Investigating the Performance of Carbon Monoxide and Methane Observations from Sentinel-5 Precursor in China

Since its launch on 13 October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) mission has been measuring the solar radiation backscattered by Earth’s atmosphere and surface. In this study, we evaluate the TROPOMI operational methane (CH4) and carbon monoxide (CO) products’ performance results covering about 3 years using the only two global Total Carbon Column Observing Network (TCCON) sites in China, i.e., the Hefei site and the Xianghe site. These two sites have recently joined the TCCON, and this study uses the both sites simultaneously to validate the TROPOMI products over China for the first time. We found that the systematic bias with rescaling between the TROPOMI CO products and the Hefei site is on average 1.78 ± 6.35 ppb or 1.18 ± 5.35%. The systematic bias with rescaling between the TROPOMI CO products and the Xianghe site is on average 5.33 ± 14.24 ppb or 3.85 ± 10.30%. Both the stations show a correlation above 0.9. The TROPOMI CO data are systematically higher than the two TCCON sites measurements in China. We found that the systematic bias with rescaling between the TROPOMI CH4 products and the Hefei site is on average −4.13 ± 11.65 ppb or −0.22 ± 0.62%. The systematic bias between the TROPOMI CH4 products and Xianghe site is on average −7.25 ± 10.72 ppb or −0.39 ± 0.57%. Both the stations show a correlation above 0.9. The TROPOMI CH4 data are systematically lower than the two TCCON sites measurements in China. We found that the bias between the TROPOMI and the two sites’ data as a function of the coincident radius around the two sites is mostly affected by localized emissions for both CO and CH4. We also observe a CO decreasing trend and a CH4 increasing trend in the year-to-year relative changes from 2019 to 2021. Validating against reference from Hefei and Xianghe TCCON site demonstrates the high quality of TROPOMI CO and CH4 data over China.

Retrieved XCO2 Accuracy Improvement by Reducing Aerosol-Induced Bias for China’s Future High-Precision Greenhouse Gases Monitoring Satellite Mission

China is developing the High-precision Greenhouse gases Monitoring Satellite (HGMS), carrying a high-spectral-resolution lidar (HSRL) for aerosol vertical profiles and imaging grating spectrometers for CO2 measurements at the same time. By providing simultaneous evaluation of the aerosol scattering effect, HGMS would reduce the bias of the XCO2 retrievals from the passive sensor. In this work, we propose a method to reduce aerosol-induced bias in XCO2 retrievals for the future HGMS mission based on the correlation analysis among simulated radiance, XCO2 bias, and aerosol optical depth (AOD) ratio. We exercise the method with the Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals and AOD ratio inferred from the OCO-2 O2 A-band aerosol parameters at 755 nm and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD at 532 nm at several Total Carbon Column Observing Network (TCCON) sites in Europe. The results showed that 80% of measurements from OCO-2 were improved, and data from six TCCON sites show an average of 2.6 ppm reduction in mean bias and a 68% improvement in accuracy. We demonstrate the advantage of fused active–passive observation of the HGMS for more accurate global XCO2 measurements in the future.

On the consistency of methane retrievals using the Total Carbon Column Observing Network (TCCON) and multiple spectroscopic databases

Abstract. The next and current generations of methane-retrieving satellite instruments are reliant on the Total Carbon Column Observing Network (TCCON) for validation. Understanding the biases inherent in TCCON and satellite methane retrievals is as important now as when TCCON started in 2004. In this study we highlight possible biases between different methane products by assessing the retrievals of the main methane isotopologue 12CH4. Using the TCCON GGG2014 retrieval environment, retrievals are performed using five separate spectroscopic databases from four separate TCCON sites (namely, Ascension Island, Ny-Ålesund, Darwin and Tsukuba) over the course of a year. The spectroscopic databases include those native to TCCON, GGG2014 and GGG2020; the high-resolution transmission molecular absorption database 2016 (HITRAN2016); the Gestion et Etude des Informations Spectroscopiques Atmosphériques 2020 (GEISA2020) database; and the ESA Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS). We assess the biases in retrieving methane using the standard TCCON windows and the methane window used by the Sentinel-5 Precursor (S5P) TROPOspheric Ozone Monitoring Instrument (TROPOMI) for each of the different spectroscopic databases. By assessing the retrieved 12CH4 values from individual windows against the standard TCCON retrievals, we find bias values of between 0.05 and 2.5 times the retrieval noise limit. These values vary depending on the window and TCCON site, with Ascension Island showing the lowest biases (typically <0.5) and Ny-Ålesund or Tsukuba showing the largest. For the spectroscopic databases, GEISA2020 shows the largest biases, often greater than 1.5 across the TCCON sites and considered windows. The TROPOMI spectral window (4190–4340 cm−1) shows the largest biases of all the spectral windows, typically >1, for all spectroscopic databases, suggesting that further improvements in spectroscopic parameters are necessary. We further assess the sensitivity of these biases to locally changing atmospheric conditions such as the solar zenith angle (SZA), water vapour and temperature. We find evidence of significant non-linear relationships between the variation in local conditions and the retrieval biases based on regression analysis. In general, each site, database and window combination indicates different degrees of sensitivity, with GEISA2020 often showing the most sensitivity for all TCCON sites. Ny-Ålesund and Tsukuba show the most sensitivity to variations in local condition, while Ascension Island indicates limited sensitivity. Finally, we investigate the biases associated with retrieving 13CH4 from each TCCON site and spectroscopic database, through the calculation of δ13C values. We find high levels of inconsistency, in some cases >100 ‰ between databases, suggesting more work is required to refine the spectroscopic parameters of 13CH4.

Influences of aerosols and thin cirrus clouds on GOSAT XCO2 and XCH4 using Total Carbon Column Observing Network, sky radiometer, and lidar data

ABSTRACT The column-averaged, dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) were retrieved from short-wavelength infrared (SWIR) spectra observed by the Greenhouse gases Observing SATellite (GOSAT). Continuous measurements of SWIR spectra have been made via GOSAT since 2009, but there has been insufficient investigation of the effects of cirrus clouds and aerosols on the observations. In this work, we investigated the influences of aerosols and cirrus clouds on the differences between GOSAT observations and Total Carbon Column Observing Network (TCCON) data for XCO2 and XCH4 (ΔXCO2 and ΔXCH4) at three sites: Tsukuba and Saga in Japan, and Lauder in New Zealand. We used aerosol optical thickness (AOT), Angstrom exponents (AEs), and single scattering albedo (SSA), all obtained from sky radiometer observations, as well as vertical profiles of aerosols and thin cirrus clouds from lidar observations. Matchups were performed within ±0.1° latitude/longitude rectangular areas of each TCCON site, and within 30 min of the GOSAT overpass time. The results show a negative slope between ΔXCO2 and AOT at 500 nm determined from sky radiometer data at Tsukuba and Saga. The GOSAT XCO2 values tended to be lowered in the presence of cirrus clouds and dense boundary-layer aerosols. Moreover, a significant negative ΔXCO2 was observed at times of large AOTs that resulted from dust-like events. At Lauder, ΔXCO2 was negatively correlated with the AOT at 500 nm, although the AOT at this site was generally small. The mean ± standard deviation for ΔXCO2 and ΔXCH4 at Lauder are −0.80 ± 1.83 (ppm) and −5.27 ± 10.79 (ppb) with correlation coefficients r between GOSAT and TCCON of 0.94 and 0.83, respectively. Both ΔXCO2 and ΔXCH4 were significantly and negatively correlated with the AOT during Sep-Oct-Nov. In addition, stratospheric aerosols caused large negative biases of ΔXCO2 and ΔXCH4 at Lauder despite the small stratospheric aerosol optical depth at that site.

Long-term column-averaged greenhouse gas observations using a COCCON spectrometer at the high-surface-albedo site in Gobabeb, Namibia

Abstract. In this study, we present column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) from a recently established measurement site in Gobabeb, Namibia. Gobabeb is a hyperarid desert site at the sharp transition zone between the sand desert and the gravel plains, offering unique characteristics with respect to surface albedo properties. Measurements started in January 2015 and are performed utilizing a ground-based Fourier transform infrared (FTIR) EM27/SUN spectrometer of the COllaborative Carbon Column Observing Network (COCCON). Gobabeb is the first measurement site observing XCO2 and XCH4 on the African mainland and improves the global coverage of ground-based remote-sensing sites. In order to achieve the high level of precision and accuracy necessary for meaningful greenhouse gas observations, we performed calibration measurements for 8 d between November 2015 and March 2016 with the COCCON reference EM27/SUN spectrometer operated at the Karlsruhe Institute of Technology. We derived scaling factors for XCO2, XCH4 and XCO with respect to the reference instrument that are close to 1.0. We compare the results obtained in Gobabeb to measurements from the Total Carbon Column Observing Network (TCCON) sites at Réunion Island and Lauder. We choose these TCCON sites because, while 4000 km apart, the instruments at Gobabeb and Réunion Island operate at roughly the same latitude. The Lauder station is the southernmost TCCON station and functions as a background site without a pronounced XCO2 seasonal cycle. We find a good agreement for the absolute Xgas values, apart from an expected XCH4 offset between Gobabeb and Lauder due to significantly different tropopause height, as well as representative intraday variability between TCCON and COCCON. Together with the absence of long-term drifts, this highlights the quality of the COCCON measurements. In the southern hemispheric summer, we observe lower XCO2 values at Gobabeb compared to the TCCON stations, likely due to the influence of the African biosphere. We performed coincident measurements with the Greenhouse Gases Observing Satellite (GOSAT), where GOSAT observed three nearby specific observation points, over the sand desert south of the station, directly over Gobabeb and over the gravel plains to the north. GOSAT H-gain XCO2 and XCH4 agree with the EM27/SUN measurements within the 1σ uncertainty limit. The number of coincident soundings is limited, but we confirm a bias of 1.2–2.6 ppm between GOSAT M-gain and H-gain XCO2 retrievals depending on the target point. This is in agreement with results reported by a previous study and the GOSAT validation team. We also report a bias of 5.9–9.8 ppb between GOSAT M-gain and H-gain XCH4 measurements which is within the range given by the GOSAT validation team. Finally, we use the COCCON measurements to evaluate inversion-optimized CAMS model data. For XCO2, we find high biases of 0.9 ± 0.5 ppm for the Orbiting Carbon Observatory-2 (OCO-2) assimilated product and 1.1 ± 0.6 ppm for the in situ-driven product with R2 > 0.9 in both cases. These biases are comparable to reported offsets between the model and TCCON data. The OCO-2 assimilated model product is able to reproduce the drawdown of XCO2 observed by the COCCON instrument at the beginning of 2017, as opposed to the in situ-optimized product. Also, for XCH4, the observed biases are in line with prior model comparisons with TCCON.

Spatio-Temporal Validation of AIRS CO2 Observations Using GAW, HIPPO and TCCON

Carbon dioxide (CO2) is a significant atmospheric greenhouse gas and its concentrations can be observed by in situ surface stations, aircraft flights and satellite sensors. This paper investigated the ability of the CO2 satellite observations to monitor, analyze and predict the horizontal and vertical distribution of atmospheric CO2 concentration at global scales. CO2 observations retrieved by an Atmospheric Infrared Sounder (AIRS) were inter-compared with the Global Atmosphere Watch Program (GAW) and HIAPER Pole-to-Pole Observations (HIPPOs), with reference to the measurements obtained using high-resolution ground-based Fourier Transform Spectrometers (FTS) in the Total Carbon Column Observing Network (TCCON) from near-surface level to the mid-to-high troposphere. After vertically integrating the AIRS-retrieved values with the column averaging kernels of TCCON measurements, the AIRS observations are spatio-temporally compared with HIPPO-integrated profiles in the mid-to-high troposphere. Five selected GAW stations are used for comparisons with TCCON sites near the surface of the Earth. The results of AIRS, TCCON (5–6 km), GAW and TCCON (1 km) CO2 measurements from 2007 to 2013 are compared, analyzed and discussed at their respective altitudes. The outcomes indicate that the difference of about 3.0 ppmv between AIRS and GAW or other highly accurate in situ surface measurements is mainly due to the different vertical altitudes, rather than the errors in the AIRS. The study reported here also explores the potential of AIRS satellite observations for analyzing the spatial distribution and seasonal variation of CO2 concentration at global scales.

Validation of XCO<sub>2</sub> and XCH<sub>4</sub> retrieved from a portable Fourier transform spectrometer with those from in situ profiles from aircraft-borne instruments

Abstract. Column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured by a solar viewing portable Fourier transform spectrometer (FTS, EM27/SUN) have been characterized and validated by comparison using in situ profile measurements made during the transfer flights of two aircraft campaigns: Korea-United States Air Quality Study (KORUS-AQ) and Effect of Megacities on the Transport and Transformation of Pollutants at Regional and Global Scales (EMeRGe). The aircraft flew over two Total Carbon Column Observing Network (TCCON) sites: Rikubetsu, Japan (43.46∘ N, 143.77∘ E), for the KORUS-AQ campaign and Burgos, Philippines (18.53∘ N, 120.65∘ E), for the EMeRGe campaign. The EM27/SUN was deployed at the corresponding TCCON sites during the overflights. The mole fraction profiles obtained by the aircraft over Rikubetsu differed between the ascending and the descending flights above approximately 8 km for both CO2 and CH4. Because the spatial pattern of tropopause heights based on potential vorticity values from the ERA5 reanalysis shows that the tropopause height over the Rikubetsu site was consistent with the descending profile, we used only the descending profile to compare with the EM27/SUN data. Both the XCO2 and XCH4 derived from the descending profiles over Burgos were lower than those from the ascending profiles. Output from the Weather Research and Forecasting Model indicates that higher CO2 for the ascending profile originated in central Luzon, an industrialized and densely populated region about 400 km south of the Burgos TCCON site. Air masses observed with the EM27/SUN overlap better with those from the descending aircraft profiles than those from the ascending aircraft profiles with respect to their properties such as origin and atmospheric residence times. Consequently, the descending aircraft profiles were used for the comparison with the EM27/SUN data. The EM27/SUN XCO2 and XCH4 data were derived by using the GGG2014 software without applying air-mass-independent correction factors (AICFs). The comparison of the EM27/SUN observations with the aircraft data revealed that, on average, the EM27/SUN XCO2 data were biased low by 1.22 % and the EM27/SUN XCH4 data were biased low by 1.71 %. The resulting AICFs of 0.9878 for XCO2 and 0.9829 for XCH4 were obtained for the EM27/SUN. Applying AICFs being utilized for the TCCON data (0.9898 for XCO2 and 0.9765 for XCH4) to the EM27/SUN data induces an underestimate for XCO2 and an overestimate for XCH4.

Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the boreal forest with Orbiting Carbon Observatory-2, Total Carbon Column Observing Network, and EM27/SUN measurements

Abstract. Seasonal CO2 exchange in the boreal forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2. Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize boreal forest seasonal cycles across longitudes with a spatially and temporally rich data set, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry air mole fractions of CO2 (XCO2) in boreal forest regions. In addition to the standard quality control (QC) filters recommended for the Atmospheric Carbon Observations from Space (ACOS) B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to boreal forest observations (boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of XCO2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankylä, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN Fourier transform infrared spectrometers (FTSs) at Fairbanks, Alaska, USA. The EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50∘ N than the B8 QC, and the boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While boreal QC results in a substantial increase in passable retrievals, this is accompanied by increases in the standard deviations in biases at boreal forest sites from ∼1.4 parts per million (ppm) with B9 QC to ∼1.6 ppm with boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead, responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at Sodankylä and East Trout Lake than at Fairbanks. Long-term coincident observations from TCCON, EM27/SUN, and satellites from multiple locations would be necessary to determine whether the reduced seasonal variability in bias at Fairbanks is due to geography or instrumentation. Monthly average biases generally varied between −1 and +1 ppm at the three sites considered, with more negative biases in spring (March, April, and May – MAM) and autumn (September and October – SO) but more positive biases in the summer months (June, July, and August – JJA). Monthly standard deviations in biases ranged from approximately 1.0 to 2.0 ppm and did not exhibit strong seasonal dependence, apart from exceptionally high standard deviation observed with all three QC methods at Sodankylä in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of air mass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of XCO2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these boreal forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Intercomparison of low- and high-resolution infrared spectrometers for ground-based solar remote sensing measurements of total column concentrations of CO<sub>2</sub>, CH<sub>4</sub>, and CO

Abstract. The Total Carbon Column Observing Network (TCCON) is the baseline ground-based network of instruments that record solar absorption spectra from which accurate and precise column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4), CO (XCO), and other gases are retrieved. The TCCON data have been widely used for carbon cycle science and validation of satellites measuring greenhouse gas concentrations globally. The number of stations in the network (currently about 25) is limited and has a very uneven geographical coverage: the stations in the Northern Hemisphere are distributed mostly in North America, Europe, and Japan, and only 20 % of the stations are located in the Southern Hemisphere, leaving gaps in the global coverage. A denser distribution of ground-based solar absorption measurements is needed to improve the representativeness of the measurement data for various atmospheric conditions (humid, dry, polluted, presence of aerosol), various surface conditions such as high albedo (>0.4) and very low albedo, and a larger latitudinal distribution. More stations in the Southern Hemisphere are also needed, but a further expansion of the network is limited by its costs and logistical requirements. For this reason, several groups are investigating supplemental portable low-cost instruments. The European Space Agency (ESA) funded campaign Fiducial Reference Measurements for Ground-Based Infrared Greenhouse Gas Observations (FRM4GHG) at the Sodankylä TCCON site in northern Finland aims to characterise the assessment of several low-cost portable instruments for precise solar absorption measurements of XCO2, XCH4, and XCO. The test instruments under investigation are three Fourier transform spectrometers (FTSs): a Bruker EM27/SUN, a Bruker IRcube, and a Bruker Vertex70, as well as a laser heterodyne spectroradiometer (LHR) developed by the UK Rutherford Appleton Laboratory. All four remote sensing instruments performed measurements simultaneously next to the reference TCCON instrument, a Bruker IFS 125HR, for a full year in 2017. The TCCON FTS was operated in its normal high-resolution mode (TCCON data set) and in a special low-resolution mode (HR125LR data set), similar to the portable spectrometers. The remote sensing measurements are complemented by regular AirCore launches performed from the same site. They provide in situ vertical profiles of the target gas concentrations as auxiliary reference data for the column retrievals, which are traceable to the WMO SI standards. The reference measurements performed with the Bruker IFS 125HR were found to be affected by non-linearity of the indium gallium arsenide (InGaAs) detector. Therefore, a non-linearity correction of the 125HR data was performed for the whole campaign period and compared with the test instruments and AirCore. The non-linearity-corrected data (TCCONmod data set) show a better match with the test instruments and AirCore data compared to the non-corrected reference data. The time series, the bias relative to the reference instrument and its scatter, and the seasonal and the day-to-day variations of the target gases are shown and discussed. The comparisons with the HR125LR data set gave a useful analysis of the resolution-dependent effects on the target gas retrieval. The solar zenith angle dependence of the retrievals is shown and discussed. The intercomparison results show that the LHR data have a large scatter and biases with a strong diurnal variation relative to the TCCON and other FTS instruments. The LHR is a new instrument under development, and these biases are currently being investigated and addressed. The campaign helped to characterise and identify instrumental biases and possibly retrieval biases, which are currently under investigation. Further improvements of the instrument are ongoing. The EM27/SUN, the IRcube, the modified Vertex70, and the HR125LR provided stable and precise measurements of the target gases during the campaign with quantified small biases. The bias dependence on the humidity along the measurement line of sight has been investigated and no dependence was found. These three portable low-resolution FTS instruments are suitable to be used for campaign deployment or long-term measurements from any site and offer the ability to complement the TCCON and expand the global coverage of ground-based reference measurements of the target gases.

New ground-based Fourier-transform near-infrared solar absorption measurements of XCO<sub>2</sub>, XCH<sub>4</sub> and XCO at Xianghe, China

Abstract. The column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) have been measured with a Bruker IFS 125HR Fourier-transform infrared (FTIR) spectrometer at Xianghe (39.75∘ N, 116.96∘ E, north China) since June 2018. This paper presents the site, the characteristics of the FTIR system and the measurements. The instrumental setup follows the guidelines of the Total Carbon Column Observing Network (TCCON): the near-infrared spectra are recorded by an InGaAs detector together with a CaF2 beam splitter, and the HCl cell measurements are recorded regularly to derive the instrument line shape (ILS) showing that the instrument is correctly aligned. The TCCON standard retrieval code (GGG2014) is applied to retrieve XCO2, XCH4 and XCO. The resulting time series between June 2018 and July 2019 are presented, and the observed seasonal cycles and day-to-day variations in XCO2, XCH4 and XCO at Xianghe are discussed. In addition, the paper shows comparisons between the data products retrieved from the FTIR measurements at Xianghe and co-located Orbiting Carbon Observatory-2 (OCO-2) and Tropospheric Monitoring Instrument (TROPOMI) satellite observations. The comparison results appear consistent with validation results obtained at TCCON sites for XCO2 and XCH4, while for XCO they highlight the occurrence of frequent high-pollution events. As Xianghe lies in a polluted area in north China where there are currently no TCCON sites, this site can fill the TCCON gap in this region and expand the global coverage of the TCCON measurements. The Xianghe FTIR XCO2, XCH4 and XCO data can be obtained at https://doi.org/10.18758/71021049 (Yang et al., 2019).

Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the TanSat observations using the ACOS (Atmospheric CO2 Observations from Space) algorithm. The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for 2 years of TanSat data from 2017 to 2018. Based on the correlation of the XCO2 error over land with goodness of fit in three spectral bands at 0.76, 1.61 and 2.06 μm, we applied an a posteriori bias correction to TanSat retrievals. For overpass averaged results, XCO2 retrievals show a standard deviation (SD) of ~2.45 ppm and a positive bias of ~0.27 ppm compared to collocated TCCON sites. The validation also shows a relatively higher positive bias and variance against TCCON over high-latitude regions. Three cases to evaluate TanSat target mode retrievals are investigated, including one field campaign at Dunhuang with measurements by a greenhouse gas analyzer deployed on an unmanned aerial vehicle and two cases with measurements by a ground-based Fourier-transform spectrometer in Beijing. The results show the retrievals of all footprints, except footprint-6, have relatively low bias (within ~2 ppm). In addition, the orbital XCO2 distributions over Australia and Northeast China between TanSat and the second Orbiting Carbon Observatory (OCO-2) on 20 April 2017 are compared. It shows that the mean XCO2 from TanSat is slightly lower than that of OCO-2 with an average difference of ~0.85 ppm. A reasonable agreement in XCO2 distribution is found over Australia and Northeast China between TanSat and OCO-2.

Net CO<sub>2</sub> fossil fuel emissions of Tokyo estimated directly from measurements of the Tsukuba TCCON site and radiosondes

Abstract. We present a simple statistical approach for estimating the greenhouse gas emissions of large cities using accurate long-term data of column-averaged greenhouse gas abundances collected by a nearby FTIR (Fourier transform infrared) spectrometer. This approach is then used to estimate carbon dioxide emissions from Tokyo. FTIR measurements by the Total Carbon Column Observing Network (TCCON) derive gas abundances by quantitative spectral analysis of molecular absorption bands observed in near-infrared solar absorption spectra. Consequently these measurements only include daytime data. The emissions of Tokyo are derived by binning measurements according to wind direction and subtracting measurements of wind fields from outside the Tokyo area from measurements of wind fields from inside the Tokyo area. We estimate the average yearly carbon dioxide emissions from the area of Tokyo to be 70±21±6MtCyr-1 between 2011 and 2016, calculated using only measurements from the TCCON site in Tsukuba (north-east of Tokyo) and wind-speed data from nearby radiosondes at Tateno. The uncertainties are estimated from the distribution of values and uncertainties of parameters (±21) and from the differences between fitting residuals with polynomials or with sines and cosines (±6). Our estimates are a factor of 1.7 higher than estimates using the Open-Data Inventory for Anthropogenic Carbon dioxide emission inventory (ODIAC), but when results are scaled by the expected daily cycle of emissions, measurements simulated from ODIAC data are within the uncertainty of our results. The goal of this study is not to calculate the best possible estimate of CO2 emissions but to describe a simple method which can be replicated easily and uses only observation data.

TCCON and NDACC X<sub>CO</sub> measurements: difference, discussion and application

Abstract. Column-averaged dry-air mole fraction of CO (XCO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometer networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC XCO measurements are investigated and discussed based on six NDACC–TCCON sites using data over the period 2007–2017. A direct comparison shows that the NDACC XCO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and the absolute bias between the NDACC and TCCON data is within 2 % at Saint-Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON XCO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error varies in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error is significant because it is higher than the reported uncertainty, particularly at Southern Hemisphere sites. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC XCO measurements become more consistent (5.6 %–8.5 %) with a mean value of 6.8 % at these sites. To determine the sources of the remaining bias, regular AirCore measurements at Orléans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON XCO measurements are 6.1 ± 1.6 % and 8.0 ± 3.2 % smaller than the AirCore measurements at Orléans and Sodankylä, respectively, indicating that the scaling factor of TCCON XCO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON XCO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR XCO data, and especially TCCON XCO data, with model or satellite data.

Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals

Methane ( CH 4 ) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane ( XCH 4 ). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43 ∘ S and 53.13 ∘ N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4 . These differences are linked to the regional CH 4 sources and sinks, and call for further research.

Evaluation of Bias Correction Methods for GOSAT SWIR XH2O Using TCCON data

This study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH2O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing Network (TCCON). They included an empirically multilinear regression method, altitude bias correction method, and combination of altitude and empirical correction for three cases defined by the temporal and spatial collocation around TCCON site. The results showed that large altitude differences between GOSAT observation points and TCCON instruments are the main cause of bias, and the altitude bias correction method is the most effective bias correction method. The lowest biases result from GOSAT SWIR XH2O data within a 0.5° 0.5° latitude longitude box centered at each TCCON site matched with TCCON XH2O data averaged over ±15 min of the GOSAT overpass time. Considering land data, the global bias changed from −1.3 ± 9.3% to −2.2 ± 8.5%, and station bias from −2.3 ± 9.0% to −1.7 ± 8.4%. In mixed land and ocean data, global bias and station bias changed from −0.3 ± 7.6% and −1.9 ± 7.1% to −0.8 ± 7.2% and −2.3 ± 6.8%, respectively, after bias correction. The results also confirmed that the fine spatial and temporal collocation criteria are necessary in bias correction methods.

Validation of Column-Averaged Dry-Air Mole Fraction of CO2 Retrieved from OCO-2 Using Ground-Based FTS Measurements

In order to correctly use the column-averaged atmospheric CO2 dry-air mole fraction (XCO2) data in the CO2 flux studies, XCO2 measurements retrieved from the Orbiting Carbon Observatory-2 (OCO-2) in 2015 were compared with those obtained from the global ground-based high-resolution Fourier Transform Spectrometer (FTS) participating in the Total Carbon Column Observing Network (TCCON). The XCO2 retrieved from three observing modes adopted by OCO-2, i.e., nadir, target, and glint, were separately validated by the FTS measurements at up to eight TCCON stations located in different areas. These comparisons show that OCO-2 glint mode yields the best qualitative estimations of CO2 concentration among the three operational approaches. The overall results regarding the glint mode show no obvious systematic biases. These facts may indicate that the glint concept is appropriate for not only oceans but also land regions. Negative systematic biases in nadir and target modes have been found at most TCCON sites. The standard deviations of XCO2 retrieved from target and nadir modes within the observation period are simi-lar, and larger than those from glint mode. We also used the FTS site in Beijing, China, to assess the OCO-2 XCO2 in 2016. This site is located in a typical urban area, which has been absent in previous studies. Overall, OCO-2 XCO2 agrees well with that from FTS at this site. Such a study will benefit the validation of the newly launched TanSat products in China.