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TanSat is the 1st Chinese carbon dioxide (CO$_{2}$) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO$_{2}$ retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O$_{2}$ A band retrieval. Accordingly, we extend the previous TanSat single CO$_{2}$ weak band retrieval to a combined O$_{2}$ A and CO$_{2}$ weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO$_{2}$ retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO$_{2}$ retrieval. We show that our new approach produces a significant improvement on the XCO$_{2}$ retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of ���0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO$_{2}$ retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO$_{2}$ processing.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra spacecraft were expected to have an accuracy of 10 % prior to launch in 1999. Here we evaluate MOPITT version 7 joint TIR-NIR (V7J) accuracy and precision, and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings, and ground based measurements. 1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. 2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. 3) Using a small region approximation (SRA) a new filtering scheme is developed and applied based on additional quality indicators such as signal-to-noise. After applying these new filters, the root mean squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 ppb to 2.55 ppb. 4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. 5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground and satellite based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO, and tend to pull concentrations away from the prior, and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Using five long-running ground-based atmospheric observatories in Europe, we demonstrate the utility of long-term, stationary, ground-based measurements of atmospheric total columns for verifying annual methane emission inventories. Our results indicate that the methane emissions for the region in Europe between Orléans, Bremen, Białystok, and Garmisch-Partenkirchen are overestimated by the state-of-the-art inventories of the Emissions Database for Global Atmospheric Research (EDGAR) v4.2 FT2010 and the high-resolution emissions database developed by the Netherlands Organisation for Applied Scientific Research (TNO) as part of the Monitoring Atmospheric Composition and Climate project (TNO-MACC_III), possibly due to the disaggregation of emissions onto a spatial grid. Uncertainties in the carbon monoxide inventories used to compute the methaneemissionscontributetothediscrepancybetweenour inferred emissions and those from the inventories.

We present 5 years of GOSAT XCH4 retrieved using the "proxy" approach. The Proxy XCH4 data are validated against ground-based TCCON observations and are found to be of high quality with a small bias of 4.8 ppb (∼ 0.27 %) and a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (ameasure of the relative accuracy) is found to be 4.2 ppb. For the first time the XCH4 / XCO2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppb ppm−1 (∼ 0.30 %), single-sounding precision of 0.033 ppb ppm−1 (∼ 0.72 %)). The uncertainty relating to the model XCO2 component of the Proxy XCH4 is assessed through the use of an ensemble of XCO2 models. While each individual XCO2 model is found to agree well with the TCCON validation data (r = 0.94–0.97), it is not possible to select one model as the best from our comparisons. The median XCO2 value of the ensemble has a smaller scatter against TCCON (a standard deviation of 0.92 ppm) than any of the individual models whilst maintaining a small bias (0.15 ppm). This model median XCO2 is used to calculate the Proxy XCH4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty. We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and find typically that the model XCO2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased summertime reflected sunlight. We assess the significance of these model and retrieval uncertainties on flux inversion by comparing the GOSAT XCH4 against modelled XCH4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that already provided by the assimilated surface measurements.

We present lower/middle tropospheric column-averaged CH4 mole fraction time series measured by nine globally distributed ground-based FTIR (Fourier transform infrared) remote sensing experiments of the Network for the Detection of Atmospheric Composition Change (NDACC). We show that these data are well representative of the tropospheric regional-scale CH4 signal, largely independent of the local surface small-scale signals, and only weakly dependent on upper tropospheric/lower stratospheric (UTLS) CH4 variations. In order to achieve the weak dependency on the UTLS, we use an a posteriori correction method. We estimate a typical precision for daily mean values of about 0.5% and a systematic error of about 2.5%. The theoretical assessments are complemented by an extensive empirical study. For this purpose, we use surface in situ CH4 measurements made within the Global Atmosphere Watch (GAW) network and compare them to the remote sensing data. We briefly discuss different filter methods for removing the local small-scale signals from the surface in situ data sets in order to obtain the in situ regional-scale signals. We find good agreement between the filtered in situ and the remote sensing data. The agreement is consistent for a variety of timescales that are interesting for CH4 source/sink research: day-to-day, monthly, and inter-annual. The comparison study confirms our theoretical estimations and proves that the NDACC FTIR measurements can provide valuable data for investigating the cycle of CH4.

Recent studies have suggested the potential importance of abiotic degradation in arid ecosystems. In this study, the role of photo- and thermal degradation in ecosystem CO2 and CO exchange is assessed. A field experiment was performed in Italy using an FTIR-spectrometer (Fourier Transform Infrared) coupled to a flux gradient system and to flux chambers. In a laboratory experiment, field samples were exposed to different temperatures and radiation intensities. No photodegradation-induced CO2 and CO fluxes of in literature suggested magnitudes were found in the field nor in the laboratory study. In the laboratory, we measured CO2 and CO fluxes that were derived from thermal degradation. In the field experiment, CO uptake and emission have been measured and are proposed to be a result of biological uptake and abiotic thermal degradation-production. We suggest that previous studies, addressing direct photodegradation, have overestimated the role of photodegradation and observed fluxes might be due to thermal degradation, which is an indirect effect of radiation. The potential importance of abiotic decomposition in the form of thermal degradation, especially for arid regions, should be considered in future studies.

NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, X$_{CO_{2}}$, in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and X$_{CO_{2}}$ from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 X$_{CO_{2}}$ retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 X$_{CO_{2}}$ data quality throughout its mission.

This paper investigates the scientific value of retrieving H218O and HDO columns in addition to H216O columns from high-resolution ground-based near-infrared spectra. We present a set of refined H216O, H218O, and HDO spectral windows. The retrieved H216O, H218O, and HDO columns are used for an a posteriori calculation of columnar δD and δ18O. We estimate the uncertainties for the so-calculated columnar δD and δ18O values. These estimations include uncertainties due to the measurement noise, errors in the a priori data, and uncertainties in spectroscopic parameters. Time series of δ18O obtained from ground-based FTIR (Fourier transform infrared) spectra are presented for the first time. For our study we use a full physics isotopic general circulation model (ECHAM5-wiso). We show that the full physics simulation of HDO and H218O can already be reasonably predicted from the H216O columns by a simple linear regression model (scatter values between full physics and linear regression simulations are 35 and 4‰ for HDO and H218O, respectively). We document that the columnar δD and δ18O values as calculated a posteriori from the retrievals of H216O, H218O, and HDO show a better agreement with the ECHAM5-wiso simulation than the δD and δ18O values as calculated from the H216O retrievals and the simple linear regression model. This suggests that the H218O and HDO column retrievals add complementary information to the H216O retrievals. However, these data have to be used carefully, because of the different vertical sensitivity of the H216O, H218O, and HDO columnar retrievals. Furthermore, we have to note that the retrievals use reanalysis humidity profiles as a priori input and the results are thus not independent of the reanalysis data.

We present lower/middle tropospheric columnaveraged CH4 mole fraction time series measured by nine globally distributed ground based FTIR (Fourier transform infrared) remote sensing experiments of the Network for the Detection of Atmospheric Composition Change (NDACC). We show that these data are well representative of the tropospheric regional scale CH4 signal largely independent of the local surface small scale signals and only weakly dependent on upper tropospheric/lower stratospheric (UTLS) CH4 variations. In order to achieve the weak dependency on the UTLS we use an a posteriori correction method. We estimate a typical precision for daily mean values of about 0.5 and a systematic error of about 2.5 . The theoretical assessments are complemented by an extensive empirical study. For this purpose we use surface in situ CH4 measurements made within the Global Atmosphere Watch (GAW) briefly discuss different filter methods for removing the local small scale signals from the surface in situ data sets in order to obtain the in situ regional scale signals. We find good agreement between the filtered in situ and the remote sensing data. The agreement is consistent for a variety of timescales that are interesting for CH4 source/sink research: day to day monthly and inter annual. The comparison study confirms our theoretical estimations and proves that the NDACC FTIR measurements can provide valuable data for investigating the cycle of CH4.

The Amazon Basin contains large wetland ecosystems which are important sources of methane (CH4). Spaceborne observations of atmospheric CH4 can provide constraints on emissions from these remote ecosystems, but lack of validation precludes robust estimates. We present the first validation of CH4 columns in the Amazon from the Greenhouse gases Observing SATellite (GOSAT) using aircraft measurements of CH4 over five sites across the Amazon Basin. These aircraft profiles, combined with stratospheric results from the TOMCAT chemical transport model, are vertically integrated allowing direct comparison to the GOSAT XCH4 measurements (the column-averaged dry air mole fraction of CH4). The measurements agree within uncertainties or show no significant difference at three of the aircraft sites, with differences ranging from −1.9 ppb to 6.6 ppb, while at two sites GOSAT XCH4 is shown to be slightly higher than aircraft measurements, by 8.1 ppb and 9.7 ppb. The seasonality in XCH4 seen by the aircraft profiles is also well captured (correlation coefficients from 0.61 to 0.90). GOSAT observes elevated concentrations in the northwest corner of South America in the dry season and enhanced concentrations elsewhere in the Amazon Basin in the wet season, with the strongest seasonal differences coinciding with regions in Bolivia known to contain large wetlands. Our results are encouraging evidence that these GOSAT CH4 columns are generally in good agreement with in situ measurements, and understanding the magnitude of any remaining biases between the two will allow more confidence in the application of XCH4 to constrain Amazonian CH4 fluxes.

The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations and released as a SWIR L2 product from the National Institute for Environmental Studies (NIES). XCO2 and XCH4 retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (-8.85 and 4.75 ppm for XCO2 and -20.4 and 18.9 ppb for XCH 4, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g., solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (-1.48 and 2.09 ppm for XCO2 and -5.9 and 12.6 ppb for XCH4, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased, especially at northern mid- and high-latitudinal areas. © Author(s) 2013.

Global distributions of atmospheric ammonia (NH3) measured with satellite instruments such as the Infrared Atmospheric Sounding Interferometer (IASI) contain valuable information on NH3 concentrations and variability in regions not yet covered by ground-based instruments. Due to their large spatial coverage and (bi-)daily overpasses, the satellite observations have the potential to increase our knowledge of the distribution of NH3 emissions and associated seasonal cycles. However the observations remain poorly validated, with only a handful of available studies often using only surface measurements without any vertical information. In this study, we present the first validation of the IASI-NH3 product using ground-based Fourier transform infrared spectroscopy (FTIR) observations. Using a recently developed consistent retrieval strategy, NH3 concentration profiles have been retrieved using observations from nine Network for the Detection of Atmospheric Composition Change (NDACC) stations around the world between 2008 and 2015. We demonstrate the importance of strict spatio-temporal collocation criteria for the comparison. Large differences in the regression results are observed for changing intervals of spatial criteria, mostly due to terrain characteristics and the short lifetime of NH3 in the atmosphere. The seasonal variations of both datasets are consistent for most sites. Correlations are found to be high at sites in areas with considerable NH3 levels, whereas correlations are lower at sites with low atmospheric NH3 levels close to the detection limit of the IASI instrument. A combination of the observations from all sites (Nobs Combining double low line 547) give a mean relative difference of ĝ'32.4ĝ€±ĝ€(56.3)ĝ€%, a correlation r of 0.8 with a slope of 0.73. These results give an improved estimate of the IASI-NH3 product performance compared to the previous upper-bound estimates (-50 to +100%).

Understanding the atmospheric distribution of water (H$_{2}$O) is crucial for global warming studies and climate change mitigation. In this context, reliable satellite data are extremely valuable for their global and continuous coverage, once their quality has been assessed. Short-wavelength infrared spectra are acquired by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) aboard the Greenhouse gases Observing Satellite (GOSAT). From these, column-averaged dry-air mole fractions of carbon dioxide, methane and water vapor (XH$_{2}$O) have been retrieved at the National Institute for Environmental Studies (NIES, Japan) and are available as a Level 2 research product. We compare the NIES XH$_{2}$O data, Version 02.21, with retrievals from the ground-based Total Carbon Column Observing Network (TCCON, Version GGG2014). The datasets are in good overall agreement, with GOSAT data showing a slight global low bias of -3.1%± 17.7%, reasonable consistency over different locations (station bias of -3.1%±9.5%) and very good correlation with TCCON (R = 0.95). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH$_{2}$O amounts can reach 6000–6500ppm when the atmospheric water content is high, the correlated NIES values do not exceed 5500 ppm. This could be due to a dry bias of TANSO-FTS in situations of high humidity and aerosol content. We also determined that the GOSAT-TCCON differences directly depend on the altitude difference between the TANSO-FTS footprint and the TCCON site. Further analysis will account for these biases, but the NIES V02.21 XH$_{2}$O product, after public release, can already be useful for water cycle studies.

Global observations of column-averaged dry air mole fractions of carbon dioxide (CO2), denoted by XCO2 , retrieved from SCIAMACHY on-board ENVISAT can provide important and missing global information on the distribution and magnitude of regional CO2 surface fluxes. This application has challenging precision and accuracy requirements. In a previous publication (Heymann et al., 2012), it has been shown by analysing seven years of SCIAMACHY WFM-DOAS XCO2 (WFMDv2.1) that unaccounted thin cirrus clouds can result in significant errors. In order to enhance the quality of the SCIAMACHY XCO2 data product, we have developed a new version of the retrieval algorithm (WFMDv2.2), which is described in this manuscript. It is based on an improved cloud filtering and correction method using the 1.4 μm strong water vapour absorption and 0.76 μm O2-A bands. The new algorithm has been used to generate a SCIAMACHY XCO2 data set covering the years 2003–2009. The new XCO2 data set has been validated using ground-based observations from the Total Carbon Column Observing Network (TCCON). The validation shows a significant improvement of the new product (v2.2) in comparison to the previous product (v2.1). For example, the standard deviation of the difference to TCCON at Darwin, Australia, has been reduced from 4 ppm to 2 ppm. The monthly regional-scale scatter of the data (defined as the mean intra-monthly standard deviation of all quality filtered XCO2 retrievals within a radius of 350 km around various locations) has also been reduced, typically by a factor of about 1.5. Overall, the validation of the new WFMDv2.2 XCO2 data product can be summarised by a single measurement precision of 3.8 ppm, an estimated regional-scale (radius of 500 km) precision of monthly averages of 1.6 ppm and an estimated regional-scale relative accuracy of 0.8 ppm. In addition to the comparison with the limited number of TCCON sites, we also present a comparison with NOAA's global CO2 modelling and assimilation system CarbonTracker. This comparison also shows significant improvements especially over the Southern Hemisphere.

Carbon monoxide (CO) and ozone (O3) have been measured in the Western Pacific (43° N to 35° S) during a ship campaign with Research Vessel Sonne in fall 2009. Observations have been performed using ship-based solar absorption Fourier Transform infrared spectrometry, flask sampling, balloon sounding, and in-situ Fourier Transform infrared analysis. The results obtained are compared to the GEOS-Chem global 3-D chemistry transport model for atmospheric composition. In general, a very good agreement is found between the GEOS-Chem model and all instruments. The CO and O3 distributions show a comparable variability suggesting an impact from the same source regions. Tagged-CO simulations implemented in the GEOS-Chem model make it possible to differentiate between different source processes and source regions. The source regions are verified with HYSPLIT backward trajectory calculations. In the Northern Hemisphere fossil fuel combustion in Asia is the dominant source. European and North American fossil fuel combustion also contribute to Northern Hemispheric CO pollution. In the Southern Hemisphere contributions from biomass burning and fossil fuel combustion are dominant; African biomass burning has a significant impact on Western Pacific CO pollution. Furthermore, in the tropical Western Pacific enhanced upper tropospheric CO within the tropical tropopause layer mainly originates from Indonesian fossil fuel combustion and can be transported into the stratosphere. The source regions of the measured O3 pollution are simulated with a tagged-O3 simulation implemented in the GEOS-Chem model. Similar source regions compared to the tagged-CO simulations are identified by the model. In the Northern Hemisphere contributions from Asia, Europe, and North America are significant. In the Southern Hemisphere emissions from South America, south-east Africa, and Oceania significantly contribute to the measured O3 pollution.

資料番号: PA1110076000

Shale gas: factual scientific argument for and against ; the scientific perspective of the expert network of the Shale Gas Information Platform SHIP

Shale gas: factual scientific argument for and against ; the scientific perspective of the expert network of the Shale Gas Information Platform SHIP

Model simulations of column-averaged methane mixing ratios (XCH4) are extensively used for inverse estimates of methane (CH4) emissions from atmospheric measurements. Our study shows that virtually all chemical transport models (CTM) used for this purpose are affected by stratospheric model-transport errors. We quantify the impact of such model transport errors on the simulation of stratospheric CH4 concentrations via an a posteriori correction method. This approach compares measurements of the mean age of air with modeled age and expresses the difference in terms of a correction to modeled stratospheric CH4 mixing ratios. We find age differences up to ~ 3 years yield to a bias in simulated CH4 of up to 250 parts per billion (ppb). Comparisons between model simulations and ground-based XCH4 observations from the Total Carbon Column Network (TCCON) reveal that stratospheric model-transport errors cause biases in XCH4 of ~ 20 ppb in the midlatitudes and ~ 27 ppb in the arctic region. Improved overall as well as seasonal model-observation agreement in XCH4 suggests that the proposed, age-of-air-based stratospheric correction is reasonable. The latitudinal model bias in XCH4 is supposed to reduce the accuracy of inverse estimates using satellite-derived XCH4 data. Therefore, we provide an estimate of the impact of stratospheric model-transport errors in terms of CH4 flux errors. Using a one-box approximation, we show that average model errors in stratospheric transport correspond to an overestimation of CH4 emissions by ~ 40 % (~ 7 Tg yr−1) for the arctic, ~ 5 % (~ 7 Tg yr−1) for the northern, and ~ 60 % (~ 7 Tg yr−1) for the southern hemispheric mid-latitude region. We conclude that an improved modeling of stratospheric transport is highly desirable for the joint use with atmospheric XCH4 observations in atmospheric inversions.

Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO₂, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH₄, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH₄ precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.

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Within the NDACC (Network for the Detection of Atmospheric Composition Change), more than 20 FTIR (Fourier-transform infrared) spectrometers, spread worldwide, provide long-term data records of many atmospheric trace gases. We present a method that uses measured and modelled XCO2 for assessing the consistency of these NDACC data records. Our XCO2 retrieval setup is kept simple so that it can easily be adopted for any NDACC/FTIR-like measurement made since the late 1950s. By a comparison to coincident TCCON (Total Carbon Column Observing Network) measurements, we empirically demonstrate the useful quality of this suggested NDACC XCO2 product (empirically obtained scatter between TCCON and NDACC is about 4‰ for daily mean as well as monthly mean comparisons, and the bias is 25‰). Our XCO2 model is a simple regression model fitted to CarbonTracker results and the Mauna Loa CO2 in situ records. A comparison to TCCON data suggests an uncertainty of the model for monthly mean data of below 3‰. We apply the method to the NDACC/FTIR spectra that are used within the project MUSICA (multi-platform remote sensing of isotopologues for investigating the cycle of atmospheric water) and demonstrate that there is a good consistency for these globally representative set of spectra measured since 1996: the scatter between the modelled and measured XCO2 on a yearly time scale is only 3‰.