Space-based observations of carbon dioxide (CO2) can contribute to the elimination of important knowledge gaps related to the regional sources and sinks of CO2 . Near-surface sensitive measurements of column-averaged dry-air mole fractions of CO2 (XCO2) in the short-wave infrared spectral region (SWIR) are well suited for this application. These observations can complement measurements from existing surface-based greenhouse gas monitoring networks, especially in data-poor regions, by providing data with dense spatial coverage. However, satellite measurements need to be precise and accurate enough to reduce uncertainties in the characterisation of the sources and sinks. Studies showed that a precision of better than 1 % for regional averages and monthly means and regional biases of less than a few tenth of a part per million (ppm) are required .

The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on-board the European Space Agency's (ESA) Environmental Satellite (ENVISAT) , launched in 2002, was in the time period before mid-2009 the only satellite instrument measuring XCO2 with high surface sensitivity. The long-term time series of surface-sensitive satellite-derived XCO2 starts with SCIAMACHY. SCIAMACHY had observed the Earth's atmosphere until the loss of ENVISAT in April 2012.

The Thermal And Near infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on-board the Greenhouse gases Observing SATellite (GOSAT) , launched in January 2009, and the Orbiting Carbon Observatory-2 (OCO-2) , launched in July 2014, are currently the only satellite instruments yielding XCO2 with high near-surface sensitivity. Both satellite missions are specifically designed to observe XCO2.

Several retrieval algorithms have been developed to evaluate the satellite observations for SCIAMACHY e.g. and for GOSAT e.g.. These algorithms differ e.g. in cloud and aerosol treatment, state vector elements and cloud filtering for more details see, e.g.. One of these algorithms is the Bremen Optimal Estimation DOAS (BESD) retrieval algorithm developed for the evaluation of SCIAMACHY measurements at the University of Bremen . As unaccounted scattering by aerosols and clouds is a major error source for satellite retrievals e.g., BESD aims to reduce this error source by explicitly considering atmospheric scattering . The BESD algorithm has been used to generate a SCIAMACHY XCO2 data product ranging from 2002 to 2012. This data product has been used in several key European projects, e.g. ESA's Climate Change Initiative CCI, www.esa-ghg-cci.org and and the EU's Monitoring of Atmospheric Composition and Climate MACC, project.

Carbon cycle and climate-related research requires consistent and accurate long-term global CO2 data sets. However, global data sets based on observations from different satellite instruments may suffer from inconsistencies originating from the use of different satellite algorithms. We address this potential issue by applying the same retrieval algorithm, the BESD algorithm, to different satellite instruments, SCIAMACHY and TANSO-FTS. Within the European MACC project, after the loss of ENVISAT, the BESD algorithm has been modified to also retrieve XCO2 from TANSO-FTS measurements. The GOSAT/TANSO-FTS BESD XCO2 product was delivered for the assimilation into the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecasting System . Here, we report first results of an assessment of the new GOSAT BESD XCO2 data product. In addition, we discuss results of an investigation concerning the consistency of the SCIAMACHY BESD and GOSAT BESD XCO2 data sets. This analysis includes a comparison of validation results obtained by using data from the Total Carbon Column Observing Network TCCON,, a direct comparison of daily satellite-based XCO2 data and a global comparison with NOAA's CO2 modelling and assimilation system CarbonTracker .

This paper is structured as follows: in Sects. 2 and 3, relevant aspects of the SCIAMACHY and TANSO-FTS instruments are discussed. Section 4 gives a short overview of the SCIAMACHY BESD retrieval algorithm whereas in Sect. 5 the recently developed GOSAT BESD XCO2 retrieval algorithm is introduced. This includes the GOSAT Level 1C generation (fully calibrated total intensity, measurement error and a priori information), the GOSAT Level 2 XCO2 generation as well as the cloud filtering and post-processing. In Sects. 6 and 7 the comparison of the satellite XCO2 data with TCCON and CarbonTracker are described and discussed. Finally, conclusions are given in Sect. 8.

The satellite instrument SCIAMACHY was part of the atmospheric chemistry payload on-board ESA's ENVISAT. The ENVISAT satellite was launched in March 2002. On 8 April 2012, after 10 years of operation, ESA lost contact to ENVISAT and finally had declared the official end of the ENVISAT mission on 9 May 2012. ENVISAT flew on a sun-synchronous daytime (descending) orbit with an equator crossing time of 10:00 local time (LT).

The SCIAMACHY instrument was a passive remote sensing moderate-resolution imaging spectrometer and measured sunlight transmitted, reflected and scattered by the Earth's atmosphere or surface in the ultraviolet, visible and near-infrared wavelength regions in eight spectral channels (214–1750, 1940–2040, 2265–2380 nm) with a spectral resolution between 0.2 and 1.4 nm. The scientific objective of SCIAMACHY was to improve our knowledge of global atmospheric change and related issues of importance to the chemistry and physics of the atmosphere, i.e. the impact of pollution, exchange processes between atmospheric layers, atmospheric chemistry in polar and other regions and the influence of natural phenomena such as volcanic eruptions. Targets of SCIAMACHY were atmospheric gases (e.g. O3, NO2, CH4 and CO2) as well as clouds and aerosols, ocean colour and land parameters. SCIAMACHY measured in three different viewing geometries: nadir, limb and solar/lunar occultation.

For the work presented in this study the nadir mode observations in channel 4 (755–775 nm) and channel 6 (1558–1594 nm) has been used. The integration time of the instrument in the used spectral regions was typically 0.25 s. This provided a typical spatial resolution of ∼60 km across track and ∼30 km along track. By scanning ±32∘ across track, SCIAMACHY achieved a swath width of ∼1000 km.

GOSAT was the first satellite mission dedicated to measuring atmospheric XCO2 and XCH4 . GOSAT is a joint project of the Japanese Aerospace Exploration Agency, the National Institute for Environmental Studies and the Ministry of the Environment. The objectives of GOSAT are to monitor the global distribution of greenhouse gases, to estimate CO2 and CH4 sources and sinks on subcontinental scale and to verify reductions of anthropogenic greenhouse gas emissions . On 23 January 2009, GOSAT was launched in a sun-synchronous daytime orbit with an equator crossing time of 13:00 (LT).

GOSAT carries two satellite instruments, the TANSO-FTS and the Cloud and Aerosol Imager (TANSO-CAI). The TANSO-FTS is a double pendulum interferometer. It measures two orthogonal polarisation directions of reflected or scattered sunlight in three bands (bands 1, 2, 3) in the SWIR between 4800 and 13 200 cm-1 (758–2083 nm). In addition to the SWIR bands, band 4 measures in the thermal infrared between 700 and 1800 cm-1 (5.56–14.3 µm). However, measurements obtained with band 4 are not considered in this paper. TANSO-FTS has a spectral resolution of Δν1≈0.36 cm-1 (Δλ1≈0.02 nm) in band 1 and Δν2,3≈0.26 cm-1 (Δλ2≈0.07 and Δλ3≈0.1 nm) in bands 2 and 3. In order to improve the dynamic range of the instrument, the scientific measurements of TANSO-FTS are performed in two gain modes, medium (M) and high (H), used according to the measured level of intensity. For example, gain M is used over bright surfaces such as deserts. With an instantaneous field of view (IFOV) of 15.8 mrad (∼10.5 km diameter at nadir when projected to the ground), TANSO-FTS can measure ± 35∘ across track and ± 20∘ along track. The typically used scan time of one interferogram is 4 s. Between 4 April 2009 and 31 July 2010, the five-point across track mode was used, which yields footprints separated by ∼158 km across track and ∼152 km along track at the equator e.g.. In order to improve the pointing stability during the scans, on 1 August 2010 the observation mode was changed to a three-point across track mode with footprints separated by ∼263 km across track and ∼283 km along track at the equator.

The TANSO-CAI instrument is a high spatial resolution imager detecting clouds and optically thick aerosol layers within the TANSO-FTS field of view. The TANSO-CAI data products are not used for the BESD algorithm.

The BESD retrieval algorithm has been developed at the University of Bremen to retrieve XCO2 from SCIAMACHY nadir measurements. BESD aims to minimise scattering-related errors of the retrieved XCO2. For this purpose, the algorithm explicitly accounts for scattering. The theoretical basis of BESD and a study of synthetic retrievals is presented in the publication of and validation results are presented in .

The algorithm is a core algorithm within ESA's CCI aiming at delivering high-quality satellite retrievals. Here we use the most recent product version (02.00.08) of SCIAMACHY BESD, which is part of the Climate Research Data Package (CRDP#2) of the CCI project. A detailed description of the current version of BESD can be found in the Algorithm Theoretical Basis Document (ATBD) available at http://www.esa-ghg-cci.org/. Here, only a short overview of the algorithm is given.

The BESD algorithm retrieves several independent parameters from the O2-A band (755–775 nm) in SCIAMACHY's channel 4 and from a CO2 band (1558–1594 nm) in channel 6. An optimal-estimation-based inversion technique is used to derive the most probable atmospheric state from a SCIAMACHY measurement using some a priori knowledge. The state vector consists of 26 elements. These elements include a wavelength shift and the full width half maximum (FWHM) of a Gaussian-shaped instrumental slit function, both fitted separately in the O2 and CO2 fit window. A Lambertian surface albedo with smooth spectral progression expressed as a second-order polynomial (with polynomial coefficients P0, P1 and P2) is fitted separately in both fit windows. A 10-layered CO2 mixing ratio profile, which is separated in equally spaced pressure intervals, is fitted in the CO2 fit window. The correlated a priori errors of the CO2 profile layers provide a degree of freedom of the retrieved XCO2 of ∼1.0. Reanalysis profiles ERA-Interim, of pressure, temperature and humidity provided by the ECMWF are used for the forward model calculation needed to calculate simulated SCIAMACHY spectra. The surface pressure, a shift of the temperature profile and the H2O column-averaged mole fraction are fitted in the O2 and CO2 window simultaneously.

Atmospheric scattering is considered by fitting three scattering-related parameters. A thin ice cloud layer consisting of fractal ice crystals with 50 µm effective radius and a thickness of 0.5 km is defined for the forward model calculations. Within the retrieval, the cloud water path (CWP) and the cloud top height (CTH) are retrieved. Aerosols are considered by using a standard LOWTRAN summer aerosol profile with moderate rural aerosol load. A Henyey–Greenstein phase function is used and the total optical thickness is about 0.136 at 750 nm and 0.038 at 1550 nm. The aerosol retrieval is based on scaling the predefined aerosol profile (aerosol profile scaling (APS) factor). Not only the scattering parameters but also the parameters defining the meteorological situation are fitted simultaneously via a merged fit window approach. Simultaneous fitting in both fit windows transfers information, e.g. in case of scattering parameters, mostly obtained from the O2-A band to the CO2 band.

The forward model is the radiative transfer model SCIATRAN . SCIATRAN calculates the needed radiance spectra and weighting functions, which are the derivatives of the measured radiation. The correlated-k approach of is used to accelerate the radiative transfer calculations. Line parameters from NASA's absorption cross section database ABSCO v4.0 is used for O2. The HITRAN 2008 database are used for the other gases. The calculated spectra are convolved with a Gaussian slit function.

Although BESD has been designed to minimise scattering-related retrieval errors, clouds are still an important potential error source and strict cloud filtering is necessary. BESD filters clouds by using cloud information based on measurements of the Medium Resolution Imaging Spectrometer (MERIS).

The post-processing of the retrieved data includes strict quality filtering and an empirical bias correction. This is needed due to the demanding accuracy requirements on the satellite retrievals. The implemented bias correction for SCIAMACHY BESD is described in the BESD ATBD .

The GOSAT BESD algorithm is based on the SCIAMACHY BESD algorithm which has been modified to also retrieve XCO2 from GOSAT. Here, an overview of the modifications of BESD are given.

The quality of the satellite XCO2 data products and their consistency has been assessed using ground-based TCCON XCO2 observations. In this section a short overview of TCCON is given, the assessment method is described and the comparison results are discussed.

In addition to the comparisons with TCCON, we have also compared the BESD data sets with the model results of CarbonTracker. For this purpose, we have used data of 4 months in 2011: we selected April–May when the atmospheric CO2 concentration in the Northern Hemisphere peaks and August–September where it reaches its minimum.

CarbonTracker is NOAA's modelling and assimilation system and has been developed to estimate global CO2 concentrations and CO2 surface fluxes . We use CarbonTracker version CT2013B downloaded from http://carbontracker.noaa.gov. Global monthly maps of GOSAT BESD, SCIAMACHY BESD and CarbonTracker XCO2 have been generated in a grid of 5∘×5∘. All grid boxes with less than 15 measurements have been excluded to achieve robust results. A global mean offset has been added to GOSAT BESD (1 ppm) and SCIAMACHY BESD (0.4 ppm) to better compare the differences to CarbonTracker. From the intercomparison of the global maps the mean difference, the standard deviation of the difference and the correlation coefficient between the data sets have been computed.

Figure  shows the comparison results for April–May 2011. The GOSAT BESD, SCIAMACHY BESD and CarbonTracker maps show a similar strong latitudinal dependence of XCO2 with high XCO2 in the Northern Hemisphere and low XCO2 in the Southern Hemisphere. The number of grid boxes filled with sufficient observations is larger for SCIAMACHY than for GOSAT BESD. In comparison to CarbonTracker, GOSAT BESD as well as SCIAMACHY BESD has a small mean difference (GOSAT: 0.10 ppm; SCIAMACHY: 0.03 ppm) and a similar standard deviation of the difference (GOSAT: 1.29 ppm; SCIAMACHY: 1.30 ppm). The correlation coefficient between the BESD data sets and CarbonTracker is similarly high (∼0.9). The direct comparison between GOSAT BESD and SCIAMACHY BESD shows a mean difference of 0.09 ppm, a smaller standard deviation of the difference of 1.17 ppm and a similar correlation coefficient (r=0.92) as compared to the difference to CarbonTracker. In addition to the global maps, latitudinal averages of the differences are shown (Fig. , right panel). Generally the latitudinal differences between the data sets are small. We have also computed the standard deviation of the latitudinal differences (σl). The differences between GOSAT BESD or SCIAMACHY BESD to CarbonTracker show a similar σl (GOSAT: 0.42 ppm; SCIAMACHY: 0.44 ppm), but the differences between GOSAT and SCIAMACHY BESD are smaller with σl=0.29 ppm. These results show that the north to south dependence of XCO2 is more consistent between the BESD data sets as compared to CarbonTracker.

The results for August–September 2011 are shown in Fig. . The northern hemispheric carbon uptake in this time period explains the low XCO2 values in the Northern Hemisphere shown in all three data sets. The number of grid boxes is again larger for SCIAMACHY compared to GOSAT BESD. The comparison with CarbonTracker shows for GOSAT and SCIAMACHY a similar small offset (-0.04 ppm). The standard deviation of the difference is somewhat smaller for GOSAT (1.14 ppm) as compared to SCIAMACHY BESD (1.28 ppm) and the correlation coefficient is similar (GOSAT: 0.71; SCIAMACHY: 0.74). The direct comparison of the BESD data sets shows a smaller/similar standard deviation of the difference (1.02 ppm) and has a similarly high correlation coefficient (0.80) as obtained from the comparison with CarbonTracker. The latitudinal averages of GOSAT BESD–CarbonTracker as well as SCIAMACHY BESD–CarbonTracker decrease in a similar way near the equator. As a result the latitudinal averages of the difference between the two BESD data sets are smaller (σl=0.35 ppm) than the difference of either data set to CarbonTracker (GOSAT: 0.68 ppm; SCIAMACHY: 0.62 ppm). These results again show that the north to south dependence of XCO2 is more consistent between the BESD data sets as compared to CarbonTracker.

The remaining differences between GOSAT and SCIAMACHY BESD are likely due to the non-perfect spatial and temporal collocations and a non-perfect BESD algorithm. However, the smaller/similar differences of the BESD data sets as compared to CarbonTracker are another indication for the high degree of consistency between GOSAT and SCIAMACHY BESD.

As consistent long-term data sets of XCO2 are required for carbon cycle and climate-related research, we have investigated whether retrievals of XCO2 from different satellites but evaluated using the same retrieval algorithm are consistent. For this purpose, the BESD algorithm originally developed for SCIAMACHY measurements has been modified and used to also evaluate GOSAT measurements.

The quality of the BESD data products was estimated by a validation study using TCCON observations. This comparison showed that the GOSAT BESD XCO2 data product has a mean offset of -0.30 ppm, a single measurement precision of 2.09 ppm, a mean correlation coefficient of 0.79 and a station-to-station bias of 0.43 ppm. The SCIAMACHY BESD XCO2 data product has a mean offset of -0.05 ppm, a single measurement precision of 2.20 ppm, a mean correlation coefficient of 0.78 and a station-to-station bias of 0.89 ppm (0.67 ppm without Four Corners).

In order to evaluate the consistency of the satellite data products, we compared the data products with the TCCON data for the same time period and performed a direct comparison of the satellite data.

The comparison of the validation results for the years 2010–2011, when the observation periods of SCIAMACHY and GOSAT overlap, showed for both data sets a small mean offset (-0.42 ppm for GOSAT, -0.08 ppm for SCIAMACHY), a similar single measurement precision of 2.04 ppm for GOSAT and 2.12 ppm for SCIAMACHY and a similar mean correlation coefficient for GOSAT (0.71) and SCIAMACHY (0.63). The station-to-station bias for GOSAT is slightly better with 0.48 ppm compared to 0.88 ppm for SCIAMACHY.

The GOSAT BESD and SCIAMACHY BESD XCO2 data show similarities in the comparisons at the TCCON sites. The mean difference from TCCON is at e.g. Bremen (-1.01 ppm for GOSAT and -1.07 ppm for SCIAMACHY) and Darwin (-1.00 ppm for GOSAT and -0.87 ppm for SCIAMACHY) similarly low. Overall, the correlation coefficient between the station biases of both data sets is large (0.83). The single measurement precision has similar small values e.g. at Darwin (1.24 ppm for GOSAT and 1.67 ppm for SCIAMACHY) and a similar high value e.g. at Karlsruhe (2.67 ppm for GOSAT and 2.55 ppm for SCIAMACHY). These similarities, the large correlation coefficient of the station biases and the similarity of the validation results give evidence that the GOSAT BESD XCO2 and the SCIAMACHY BESD XCO2 are generally consistent.

In a direct comparison of the satellite data, we analysed daily averages of GOSAT and SCIAMACHY BESD XCO2. This analysis showed an offset between the data sets of -0.77 ppm, a similar standard difference between the data sets (1.51 ppm) compared to the TCCON comparison (1.28 ppm for GOSAT and 1.60 ppm for SCIAMACHY), a high correlation coefficient (0.80) and smaller/similar station-to-station variations of the mean difference (0.59 ppm) compared to the difference to TCCON (0.54 ppm for GOSAT and 0.85 ppm for SCIAMACHY).

We have also compared global monthly maps and latitudinal averages of the satellite data sets with CarbonTracker XCO2. Results of two time periods, April–May and August–September 2011, were presented. These results showed that the differences between the BESD data sets are smaller/similar as the difference to CarbonTracker.

The remaining differences found between GOSAT and SCIAMACHY are likely not only due to non-perfect collocation (i.e. the observed air masses can be not identical) but likely also to a non-perfect BESD retrieval algorithm. However, the similar scatter of the difference between the data sets compared to the difference to TCCON and CarbonTracker and the smaller/similar station-to-station variation of the differences of the data sets compared to the difference to TCCON indicate a high degree of consistency between the SCIAMACHY and GOSAT XCO2 data sets. These results demonstrates that consistent retrievals can be obtained from different satellite instruments using the same retrieval algorithm.

Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other current or future missions, e.g. OCO-2 , GOSAT-2 and CarbonSat .