TANSO-FTS collects high-resolution spectra of reflected sunlight in the near-infrared (NIR) and shortwave-infrared (SWIR) spectral ranges that include the oxygen A-band near 0.76 µm (ABO2 band) at approximately 0.36 cm-1 spectral resolution, and weak and strong CO2 absorption features near 1.6 µm (WCO2 band) and 2.0 µm (SCO2 band), respectively, at 0.27 cm-1 spectral resolution. All three channels simultaneously measure two orthogonal components of polarization approximately every 4.6 s.

Each GOSAT sounding has a circular ground footprint with a diameter of approximately 10.5 km when viewing the local nadir. An agile, two-axis pointing system allows cross-track and along-track motions of ± 35∘ and ± 20∘, respectively. Before August 2010, a five-point cross-track scan was used, yielding footprints that were separated by approximately 150 km in both the down-track and along-track dimensions. Since that time, a three-point cross-track scan has been used, yielding footprint separation of approximately 260 km .

Over water, the TANSO-FTS scan mechanism targets the field of view to collect observations in the direction of the local glint spot, where sunlight is specularly reflected from the surface. Early in the mission, glint observations were collected only within ± 20∘ of the sub-solar latitude. In May 2013, to increase the latitudinal extent of the GOSAT ocean measurements, the scanning strategy was improved to better track the actual specular glint spot, which varies by latitude and season. The latitude range for glint observation was further extended three times in increments of 3∘ in September 2014, June 2015, and January 2016, by not only tracking the exact specular point but also tracking along the principal plane of the specular reflection when the glint spot was out of range of the scan mechanism. In addition, more observations over fossil fuel emission target sites such as mega-cities and power plants have been made in recent years, allowing for detailed emission source studies (e.g., ). Daily observation patterns can be found at https://www.eorc.jaxa.jp/GOSAT/currentStatus_10.html (last access: 10 January 2022).

The TANSO-FTS detectors can be read out using independent medium-gain and high-gain signal chains. Most measurements over land use the instrument's high-gain signal chain (H-gain), while brighter land surfaces are measured using the medium-gain signal chain (M-gain) to avoid saturating the detectors. Over oceans, which appear dark in the SWIR spectral bands, measurements are collected using the high-gain signal chain to maximize the signal.

During the first 7 years of GOSAT operations (2009–2015), data acquisition was temporarily suspended due to one spacecraft and two instrument anomalies, as highlighted in . A rotation failure of a solar paddle in 2014 resulted in a data loss of 6 d. A switch from the primary to secondary pointing mirror in January 2015 resulted in a data loss of approximately 6 weeks, while a temporary shutdown of the cryocooler in August 2015 resulted in a data loss of 13 d.

Since 2015, three additional anomalies interrupted data acquisition. An unexpected shutdown of the instrument occurred in May 2018, resulting in the loss of a week of data. A failure of the second solar panel caused a significant loss of data spanning more than a month in November and December of 2018, and an anomaly of the FTS alignment laser caused a loss of a week of data in June of 2020. In all these cases, the system was able to recover full functionality either through utilization of on-board back-up systems, or through mitigation strategies, and as of the summer of 2021, TANSO-FTS continues to collect science data.

The JAXA L1b algorithm, which has been updated more than 10 times over the 11-year data record, produces an internally consistent set of geometrically, radiometrically, and spectrally calibrated TANSO-FTS radiances. The raw spectral measurements are interferograms, which are calibrated and Fourier transformed to yield spectra. The version 205/210 Level 1b (L1b) geolocated and calibrated radiances provided by JAXA have been used for the ACOS v9 reprocessing. A list of L1b updates for v205/210 can be found in Table 3 of the ACOS v9 Data Users Guide (DUG) . Note that while the current L1b version is now 230, the only differences between this version and 205/210 are in the thermal infrared band (5.6–14.3 µm), which is not used in the ACOS XCO2 retrieval.

After obtaining the calibrated L1b product from JAXA, the ACOS team converts the files to the format needed as input to the ACOS L2 algorithms. The L2FP algorithm uses a simple average of the S and P linear polarizations to produce an approximation of the total measured intensity. Due to cooperation agreements between JAXA and the California Institute of Technology, the distribution of the ACOS GOSAT L1b product is restricted and therefore not publicly available on the NASA DISC. However, the data may be procured by submitting a request to the GOSAT project.

Table  summarizes the evolution of the ACOS L2FP retrieval algorithm from v7 to v10. A similar table, complete through v8, can be found in . The trace gas absorption coefficient tables (ABSCO) were updated from v4.2 in ACOS v7 to ABSCO v5.0 in ACOS v8/9. The ACOS v9 L2FP algorithm is unmodified relative to v8 . However, changes were made in v9 regarding the sampling of the meteorological prior, which does affect ACOS GOSAT estimates of XCO2. The source of the prior meteorology was switched from the European Center for Medium-range Weather Forecast (ECMWF) in ACOS v7, to the NASA Goddard Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) Forward Processing – Instrument Team (FP-IT) product for ACOS v8/9. Both v7 and v8/9 used aerosol priors based on a simple monthly 1∘ latitude by 1∘ longitude climatology constructed from the output aerosol fields of the GMAO Modern-Era Retrospective analysis for Research and Applications (MERRA) product . However, between v7 and v8/9, an additional stratospheric aerosol layer was introduced, as described in Sect. 3.1.1 of . In addition, the prior value of the aerosol optical depth (AOD) for each retrieved aerosol type was lowered from 0.0375 in ACOS v7 to 0.0125 in ACOS v8/9 based on extensive testing. There was no change in the source of the CO2 prior from ACOS v7 to v8/9; both versions adopted the prior developed by the TCCON team for use in the ggg2014 algorithm . An additional change from ACOS v7 to v8/9 was a switch from a purely Lambertian land surface model to a more sophisticated bi-directional reflectance distribution function (BRDF) model.

Several important components of the v9 ACOS L2FP retrieval configured for GOSAT have not changed from v7.3: (i) the surface pressure prior constraint remains set at ±2 hPa, (ii) three empirical orthogonal functions (EOFs) are fit in each spectral band (see Sect. 3.3 in for a full discussion of ACOS EOFs), and (iii) a zero level offset (ZLO) is fit in the state vector to account for non-linearity in the ABO2 signal chain on GOSAT TANSO-FTS .

To support comparisons of the ACOS GOSAT v9 XCO2 product with the OCO-2 v10 product, Table  includes the most recent updates to the ACOS v10 L2FP algorithm. For v10, the ABSCO tables were again updated from v5.0 to v5.1 . The aerosol prior was updated from the MERRA monthly climatology to daily GEOS-FT-IT values, with a tightened prior uncertainty . Finally, the CO2 priors developed by the TCCON team for use in ggg2014 were updated to a revised set of priors developed for use in ggg2020.

GOSAT data from 20 April 2009 through 30 June 2020 were passed through the ACOS L2FP algorithm pipeline, which includes a series of stages where soundings can be rejected or selected for further processing. The throughput of each of these stages for ACOS GOSAT v9 is summarized in Table  and Fig. . The pipeline begins with a series of preprocessing steps, which reject corrupted spectra and screen the remainder to eliminate those with optically thick clouds and/or aerosols . From the full set of measurements (panel a of Fig. ), the remaining soundings are accepted by the L2FP algorithm (18.8 % of the 37.4×106 measured soundings contained in the ACOS GOSAT v9 record) (panel b of Fig. ) and a retrieval of XCO2 is attempted. The majority of the selected soundings successfully converge to a valid solution: 87 % for ACOS GOSAT v9 (16.4 % of the total measured soundings). Soundings can fail to converge for a variety of reasons, including (i) producing non-physical values, such as negative gas mixing ratios or surface pressures (3.9 % of the selected), (ii) converging too slowly and exceeding a predefined number of iterations (3.2 % of the selected), or (iii) having more diverging steps than the predefined maximum (5.9 % of the selected). The 6.1×106 valid soundings were then run through the quality filtering and bias correction procedure discussed in the next section.

All GOSAT soundings that converged to a valid XCO2 value within the L2FP retrieval were input to the quality filtering and bias correction procedure. A modest fraction (4.5 % of the valid soundings) were removed from the final L2Lite product based on screening via the IMAP-DOAS Preprocessor (IDP) CO2 ratio, which indicated the presence of clouds or aerosols. Based on a series of screening criteria derived from comparisons with TCCON and modeled CO2 fields, each sounding that converged within the L2FP is assigned either a “good” (=0) or “bad” (=1) XCO2 quality flag. Generally, for global or regional studies, it is recommended that users retain only the “good” quality soundings, as the soundings flagged as “bad” quality are likely to include biases that compromise their utility for some applications. A global map of the ACOS GOSAT v9 “good” XCO2 sounding density is provided in panels C and D of Fig. . A subset of data variables from the per-orbit L2Std files , along with the quality filter flag and bias-corrected XCO2, are repackaged into the daily aggregated L2Lite NetCDF files .

A fundamental aspect of the quality filtering and bias correction procedures (QF/BC) is the need for XCO2 truth metrics with which to compare the satellite-derived estimates . The development of ACOS GOSAT v9 used XCO2 truth metrics derived from both TCCON measurements and the median CO2 distributions determined from a suite of four atmospheric inversion systems, which do not assimilate satellite CO2 measurements.

TCCON is a well-established validation transfer standard for space-based estimates of XCO2 . For the ACOS GOSAT v9 QF/BC, estimates of XCO2 derived from TCCON measurements using the ggg2014 retrieval algorithm were used . Individual GOSAT soundings were compared to TCCON daily mean XCO2 values. TCCON data were included if (i) they were flagged as good (flag = 0), (ii) they fell within 3 standard deviations of a daily quadratic fit against time (to remove outliers, e.g., due to unscreened cloud), (iii) they covered at least 15 min within a given day, (iv) there were at least three good soundings within the day, and (v) the standard deviation of the good soundings for the day was less than 3 ppm. In the GOSAT-TCCON comparisons described here, an averaging kernel correction was applied to each TCCON XCO2 estimate following , prior to calculating the daily mean value.

Following the criteria defined in , the spatial collocation criteria for GOSAT soundings were those falling within ±2.5∘ latitude and ±5∘ longitude of a TCCON station for most sites. For the Southern Hemisphere (SH) sites poleward of 25∘ S latitude, where the variation of CO2 is low, the spatial box was increased to ±10∘ latitude by 20∘ longitude to increase the number of collocations. For the Edwards TCCON station, which lies in an arid region just north of the polluted Los Angeles metropolitan area, a very specific collocation box of [34.68, 37.46] latitude and [-127.88, -112.88] longitude was used to avoid contamination from the city. Similarly, for the Caltech site, located in Pasadena, California, a latitude box of [33.38, 34.27] and longitude box of [-118.49, -117.55] were used. This avoids collocating GOSAT soundings measured over ocean, the San Gabriel Mountains, and regions too far outside of the Los Angeles basin with the Caltech TCCON data. Finally, only GOSAT soundings acquired within ±2 h of the mean TCCON measurement time were considered. For the quality filtering and bias correction procedure, single sounding level collocations are used to maximize the number of fit points.

Estimates of CO2 from atmospheric inversion systems, or models, provide a useful metric for evaluating satellite-based estimates of XCO2 . In this work, a suite of four models (CarbonTracker, CAMS, CarboScope, and University of Edinburgh) were sampled at the GOSAT sounding times and locations. Brief descriptions of each, along with references, are provided in Table . The models use a variety of land biosphere prior fluxes, inverse solvers and transport models, and assimilate CO2 data only from flasks and continuous analyzers on a wide variety of platforms, e.g., observatories, towers, aircraft, and ships. Specifically, no data from GOSAT, OCO-2, or TCCON are assimilated. The CO2 concentration fields of the models capture the known features of the global atmospheric CO2 distribution, including seasonality, time trends, and inter-annual variability (IAV) due to El Niño–Southern Oscillation. For each GOSAT sounding, the vertical profiles of CO2 from the corresponding grid box of each of the four models are spatiotemporally interpolated (linear in latitude, longitude, and time) to the GOSAT observation point, and the GOSAT averaging kernel is applied to each vertical profile to produce a modeled XCO2 as if viewed from the satellite.

For each GOSAT sounding, a multi-model median (MMM) XCO2 was calculated from the models having a valid XCO2 estimate for that location and time. Unless otherwise noted, the model XCO2 is taken to be that which a perfect OCO-2 would have observed, XCO2,ak; that is, an averaging kernel correction is applied to account for differences between the model profile of CO2 and the ACOS prior in the unmeasured part of the profile: 1XCO2,ak=∑i=120hi{aium,i+(1-ai)ua,i}, where hi is the pressure weighting function on the i=1…20 ACOS model levels, a is the normalized ACOS averaging kernel for CO2, um is the model profile of CO2, and ua is the ACOS prior profile of CO2.

To exclude outliers, models with XCO2 that deviated more than ±1.5 ppm from the initial MMM for that sounding were not included. The sounding was then rejected if more than one of the four models had been excluded, or if the standard deviation amongst the valid models was > 1 ppm. Approximately 85 % of the GOSAT v9 soundings with a good L2FP quality flag had a valid MMM XCO2 value for analysis. The regions with the highest fraction of rejections occur along the Southern Ocean (latitude -60∘), the Amazon and Congo rain forests, and a broad region across northern Asia. Table  lists the model version numbers used for the QF/BC procedure, as well as that used in the evaluation of the final good-quality XCO2 product that will be presented later.

Table  lists the quality filtering variables used for ACOS GOSAT v9 and their corresponding thresholds. Many of the same variables (18 out of 31) were also used in the OCO-2 v9 quality filtering, as seen in Table 5 of . This includes the IDP CO2 and H2O ratios , and the A-band preprocessor dP, i.e., the difference between the retrieved and prior surface pressure from the oxygen A band . Another common variable used for quality filtering is the perturbation in the L2FP CO2 vertical profile relative to the prior, a quantity called “CO2 grad del” (δ ∇CO2), as defined in Eq. (5) of . A number of aerosol-related retrieval parameters are also used, similar to OCO-2 v9. Section 2.5 of the ACOS GOSAT v9 DUG provides additional details on the quality filtering .

Spurious correlations in the estimates of XCO2 with other retrieval variables due to inadequacies in the modeled physics motivate the application of a bias correction . Generally such spurious correlations are found with state vector elements such as retrieved surface pressure, various aerosol parameters, and δ ∇CO2. For each sensor there are also typically offsets by viewing mode, e.g., land glint versus ocean glint, which are accounted for via the bias correction. A general discussion of the ACOS XCO2 bias correction methodology is provided in Sect. 4 of , where the fundamental equation is defined as 2XCO2,bc=XCO2,raw-CP(mode)-CF(j)C0(mode), where CP is the mode-dependent parametric bias, CF is a footprint-dependent bias for footprints 1…8, and C0 represents a mode-dependent global scaling factor. Note that for GOSAT there is no footprint-dependent bias correction term, as is necessary for OCO due to low-level calibration errors across the detector frame. Further, to be consistent with previous ACOS GOSAT data versions, the global divisor is replaced by an additive offset, which is effectively the same because the range of XCO2 variability (∼ 20 ppm) is small relative to the mean XCO2 (∼ 400 ppm).

The explicit formula for application of the ACOS GOSAT v9 correction is provided in Sect. 2.5.6 of the DUG . For both land H-gain and M-gain, a set of five BC variables are used, while ocean H-gain uses only three variables. The difference between the H-gain and M-gain bias correction over land is minor. New for ACOS GOSAT v9 is the use of a correction against time, which is made possible with an 11-year data record; the corrections are 0.05 ppm/yr over land and 0.10 ppm/yr over water. The source of this spurious drift in the bias-corrected XCO2 is currently unclear and is the subject of ongoing study. Although there is some commonality in the quality filtering and bias correction variables used for ACOS GOSAT v9 (compare Tables  and ), they do differ somewhat, as is typically the case with each sensor and data version.

Table  compares the bias correction variables used for ACOS GOSAT v9 with the variables used in the previous ACOS GOSAT v7.3, as well as with OCO-2 v9 and v10. The same few variables have appeared in all recent versions, including L2FP δ ∇CO2, L2FP dP, and L2FP DWS for land soundings. For ocean soundings the bias correction variables have evolved, with the only common one being δ ∇CO2.

Table  summarizes the effect of the quality filtering and bias correction on the ACOS GOSAT XCO2 for v7.3 and v9. For ocean H-gain soundings, the v9 quality flag is substantially more restrictive compared to v7.3, i.e., ≃ 57 % pass rate compared to ≃ 78 %. This is mostly driven by the more extensive latitudinal coverage in the v9 record, which tends to include more soundings with high solar zenith angles (SZA) and low signal-to-noise ratio (SNR), which are more challenging for the L2FP. For H-gain land observations, the two versions have quite similar QF pass rates (≃ 35 %–45 %). The QF pass rate for v9 M-gain land data is ≃ 39 % when compared against models but ≃ 56 % against TCCON. In all cases there is a significant reduction in the scatter of the XCO2 after application of the QF/BC: by a factor of ≃ 2 for ocean H-gain and land M-gain and a factor of 3 for land H-gain. The QF/BC scatter is always slightly lower for v9 compared to v7.3, even though the number of soundings is greater by 1.5 to 10 times for the various scenarios.

Figure  shows the relative magnitudes of the bias correction on the good-quality soundings by season, aggregated to 2.5∘ latitude by 5∘ longitude. The global median bias of -1.8 ppm has been removed for clarity. This highlights gradients and contrasts in the bias correction, which are of importance as gradients in CO2 concentrations are the primary driver of CO2 fluxes in atmospheric inversion systems. In general, the bias correction is necessary to remove spurious contrasts between land and ocean XCO2 values. The strongest relative bias corrections are positive adjustments over the bright land surfaces in M-gain viewing mode, specifically the Sahara in DJF and JJA and Australia in DJF. The land H-gain observations have a mix of relative bias correction values, ranging from mildly negative over high northern latitudes in JJA to moderately positive over northern mid-latitudes in JJA in the western United States and the Middle East. Most of the ocean H-gain observations have a mildly negative relative bias correction, with some mild positive values in the southern tropical oceans in DJF.

It is instructive to compare the ACOS GOSAT v9 product to the earlier v7.3 product to highlight similarities and differences in the quality filter screening. A time series histogram of the monthly throughput of the good-quality-filtered soundings for the v9 product compared to v7.3 is shown in Fig. . The soundings have been binned by month, with the three GOSAT observation modes displayed by color. The v7.3 product did not contain any land M-gain data in the L2Lite files (red in the figure) as the quality filtering and bias correction were not developed for that gain mode in v7.3 due to some unreconciled differences. An important feature of the v9 data record is the extension in time, which runs through June 2020, compared to a termination date of June 2016 for v7.3. Even for the overlapping v7.3 and v9 time period (2009 through mid 2016), there are some differences in the data volume for land H-gain and ocean H-gain observations. This is due to changes in both the details of the QF procedure, including changes in the variable thresholds used to assign QF = good/bad, and to some differences in the convergence characteristics of the L2FP retrieval. Generally, v9 is producing up to 60 % more good-quality data than v7.3 near the end of the overlap period in 2016. There was a substantial increase in the number of good QF soundings from 2010 to 2019, due to the increased latitudinal range of the ocean observations as a result of improvements in the GOSAT pointing strategy, as well as improvements in the sounding selection for ACOS L2FP v9.

Figure  shows sounding density Hovmöller plots comparing ACOS GOSAT v7.3 (a) to v9 (b) with the three GOSAT observation modes combined. Again, the extended time period covered by v9 is evident. The increase in sounding density in the SH beginning in 2016 due to optimization of the GOSAT viewing strategy is prominent in the v9 product. This feature is also seen in the spatial maps showing the fraction of good-quality soundings and the density per grid box, in panels (c) and (d) of Fig. , which was introduced in Sect. . Persistently clear regions, such as the Sahara and the western part of Australia, have as many as 30 % of the observations assigned a good-quality flag. Large regions of the tropical Pacific and Atlantic also contain a relatively high fraction of good-quality soundings. On the other hand, tropical forests and high latitudes in general have low yields of good-quality soundings. This is largely a combination of cloud contamination, dark surfaces at shortwave infrared wavelengths, and low solar illumination conditions, all three of which are problematic for retrieving CO2 from space using reflected sunlight.

There has been a steady increase in the atmospheric burden of CO2 since the onset of the industrial age due mainly to the burning of fossil fuels (e.g., ). In May of 2009, at the beginning of the GOSAT mission, the mean global value of XCO2 reported by the NOAA Global Monitoring Laboratory was 387.95 ppm, while by May of 2020, the mean global value had risen to 413.81 ppm . This yields a secular increase of ≃ 2.35 ppm/yr. For comparison, Fig.  shows the ACOS GOSAT v9 bias-corrected and quality-filtered XCO2 as a function of latitude (15∘ increments) and time (30 d increments) for combined land M and H-gain observations (a) and ocean H-gain observations (b). Using the monthly mean XCO2 values (combined land and ocean) for May 2009 (386.50 ppm) and May 2020 (411.82 ppm), the ACOS GOSAT v9 record has a secular increase of ≃ 2.30 ppm/yr over the 11-year record. This small disagreement in secular trend of approximately 2 % is understandable, given the significant differences in the spatiotemporal sampling of the two data sets. For the interested reader, a thorough comparison of satellite- and surface-derived growth rates in atmospheric CO2 is given in .

The maps in Fig.  show the spatial distribution of XCO2 at 2.5∘ latitude by 5∘ longitude resolution for 2010 (top) and 2019 (bottom), for DJF (left) and JJA (right). The dynamic range of the color scale in each case is 6 ppm. However, due to the secular increase in global CO2 of ≃ 2.3 ppm per year, the scale is centered ≃ 20 ppm higher in 2019 compared to 2010. The strong latitudinal gradients in XCO2 are similar in these two seasons, while the zonal gradient tends to be weakest in MAM (not shown), just before the summer drawdown of CO2 by the land biosphere begins. The increase in the number of ocean H-gain soundings in the later part of the data record is also evident in these maps.

Qualitatively, the patterns in the maps look quite similar from 2010 to 2019, but with increased data coverage. In general, the highest concentrations of XCO2 for the two selected seasons are observed by GOSAT in the Northern Hemisphere (NH) during DJF, especially over northern tropical Africa (between 0 and 15∘ N latitude), large portions of China, and the eastern United States. This stands to reason, as the atmospheric burden of CO2 increases towards a peak during NH winter due to inactivity of the land biosphere, coupled with strong anthropogenic CO2 emissions. During DJF the ACOS GOSAT v9 XCO2 exhibits relatively low concentrations across the entire SH, as would be expected if the Southern Ocean were a strong carbon sink (e.g., ). In JJA, the XCO2 is reduced over the mid-latitude and boreal forests, also expected behavior due to strong photosynthetic uptake of CO2 during this season (e.g., ).

A quantification of differences in the bias-corrected ACOS GOSAT v9 XCO2 data product relative to the v7.3 product is given in Fig.  for the overlapping period. The top row (panels a through c) shows results for the land H-gain observations, while the lower row (panels d through f) shows results for the ocean H-gain observations. Only soundings that were present in both data sets and assigned a good-quality L2FP flag were used in this comparison. This restricts the analysis to April 2009 through June 2016 and also eliminates the v9 land M-gain data, as no M-gain data exist in the v7.3 product. The mean and standard deviation of the ΔXCO2 for the approximately 311 k land soundings at the single sounding level are -0.18 and 0.72 ppm, respectively, as shown in panel (a). When gridded and mapped at 2.5∘ latitude by 5∘ longitude resolution, as shown in panel (b), the majority of the negative signal (v9 XCO2 lower than v7.3) occurs at latitudes greater than approximately 45∘. Most of the land mass at latitudes less than 45∘ has ΔXCO2 values closer to zero, with the largest positive signals appearing over equatorial forests. Furthermore, when the data are gridded and viewed versus time and latitude in 30 d by 15∘ increments, respectively, as in panel (b), we see that the ΔXCO2 signal has an increasing tendency in time; i.e., the v9 XCO2 increases more rapidly in time than v7.3. The cause of this effect is currently unknown, but it is partially due to the implementation in v9 of a time-dependent bias correction term of +0.05 ppm/yr for land observations. This translates into an expected change of about 0.35 ppm in the v9 record over the 2009 to 2016 time span.

For the ocean H-gain observations at the single sounding level (panel d), the mean and standard deviation of the ΔXCO2 are +0.28 and 0.79 ppm, respectively. When gridded and mapped at 2.5∘ latitude by 5∘ longitude resolution (panel e), the spatial distribution is fairly smooth, i.e., low variation in both latitude and longitude. Finally, when the data are gridded versus time and latitude (panel f), the modest variation in latitude is confirmed, but a substantial time dependence is observed, with the ΔXCO2 signal beginning negative in 2009 (v9 XCO2 lower than v7.3), and switching to a positive ΔXCO2 signal by 2016 (v9 XCO2 higher than v7.3). The time-dependent bias correction term for ocean H-gain observations was +0.1 ppm/yr. This translates into an expected change of about 0.7 ppm over the 2009 to 2016 time span in the v9 record, accounting for some but not all of this time-dependent difference between v9 and v7.3.

This direct comparison between the v9 and v7.3 XCO2 product only allows for statements as to their differences. It does not allow one to deduce which is closer to truth. Therefore, an analysis of the v9 XCO2 data product against truth metrics follows. Furthermore, it is difficult to accurately determine the effect that the new v9 XCO2 product will have on atmospheric inversion system results relative to v7.3 without further detailed study.

A list of TCCON stations used in this work, including basic physical information and data citations, is given in Table . For the evaluation against the ACOS v9 XCO2 data, the single sounding collocations described in Sect.  were aggregated into overpass mean values. Essentially the same TCCON data set was used for both the QF/BC procedure as for the evaluation, as no hold-over data were maintained. Also, as described in Sect. , an averaging kernel correction was applied to the TCCON data in order to fairly compare to the satellite data. A one-to-one linear regression of the XCO2 provides a simple quantification of the agreement, as shown in Fig. .

For ocean H-gain observations (Fig. a), the mean (μ) of the differences in XCO2 (ΔXCO2TCCON = GOSAT - TCCON) is essentially zero: -0.01 ppm for the single-sounding (SS) results and +0.01 ppm for the overpass mean (OPM) results. The corresponding standard deviations (σ) are 1.08 and 0.82 ppm for the SS and OPM results, respectively. This indicates that roughly half of the SS error variance is a result of instrument noise or other random high-frequency error sources (1.082=1.2 ppm versus 0.822=0.7 ppm).

For land H-gain observations (Fig. b), μ=+0.09 ppm and +0.14 ppm for the SS and OPM, respectively. The land H-gain σ values are higher than for ocean H-gain: 1.58 and 1.14 ppm for SS and OPM, respectively. Larger variations in ΔXCO2TCCON are expected for land H-gain due to variability in topography and surface brightness, as well as higher likelihood of contamination by cloud and aerosol, all of which are more challenging for the ACOS retrieval. Further, biology and atmospheric transport cause CO2 signals to vary more over land regions, and in addition, instrument noise is higher because the SNRs tend to be lower.

Land M-gain observations have near-zero bias (μ=-0.02 ppm and +0.02 ppm for SS and OPM, respectively) and scatter similar to that for ocean H-gain (σ=1.08 and 0.84 ppm for SS and OPM, respectively), likely driven by lower variability in surface topography and brightness compared to land H-gain observations, as well as higher SNRs over these bright land surfaces.

The correlation in the XCO2 between the data sets in all observation modes is high, with Pearson R2=0.98, 0.98, and 0.99 for ocean H-gain, land H-gain, and land M-gain, respectively. Overall, these results indicate excellent agreement between the bias-corrected and quality-filtered ACOS GOSAT v9 XCO2 product and collocated estimates from TCCON.

Figure  shows the mean absolute error (MAE) between the overpass mean collocated GOSAT and TCCON XCO2, organized by latitude bins, season, and observation mode for v7.3 (panels a and b) and v9 (panels c, d, e). The calculation of the MAE and error bars follow the procedure reported in (Eqs. 3 and 4). The error bars on the MAE represent the scatter around the mean. A smaller error bar, or a lower scatter, implies that the MAE values are more consistent across a group of TCCON stations within a latitude band and season. The MAEs tend to be lower for v9 compared to v7.3, with smaller error bars and increased number of collocations. This is especially true for the SH ocean H-gain data, where the MAE ranges from 0.4 to 0.7 ppm in v9 for all seasons, in contrast to v7.3, which had higher MAE ranging from 0.5 to 0.85 ppm in that region. In the v9 land H-gain data, the MAE is roughly a function of latitude, with the highest values (≃ 1.0 ppm) seen between 60–90∘ N and the lowest values (≃ 0.7 ppm) seen from 30–60∘ S. This stands to reason as lower variability of XCO2 in the SH tends to yield better agreement between satellite- and ground-based observations. The error bars on the v9 land H-gain estimates in the 30–60∘ N latitude range are very small, due in part to the large number of collocations. There are very limited land data between 0 and 30∘ N (approximately 25 % of Earth's surface), due to the sparsity of TCCON stations in this latitude band. Only Burgos (18.5∘ N) and Izaña (28.3∘ N) are located in this range (reference Table ), and many of the collocations from these sites are from GOSAT observations made in ocean H-gain viewing.

Knowledge of the average XCO2 seasonal cycle can be used to disentangle the CO2 growth rate from the seasonal variability, as well as for quantifying potential seasonal biases between satellite- and ground-based XCO2 estimates. fitted a skewed sine wave (see Eq. 1 of ) to the ACOS GOSAT v3.5 XCO2 time series and the TCCON estimates of XCO2 at 16 stations, spanning April 2009 through December 2013. They found that ACOS GOSAT v7.3 captured the seasonal cycle within approximately 1 ppm of the TCCON estimates for all but the European sites and that the satellite- and ground-based CO2 growth rates agreed generally better than 0.2 ppm per year. Here, we provide an update to those results using the 11-year ACOS GOSAT v9 XCO2 data record. For this part of the analysis, a slightly more restrictive set of collocation criteria were implemented, compared to that described in Sect.  for the BC/QF procedure and to that used to generate Fig. . The seasonal cycle analysis required that the TCCON record spanned at least one contiguous year (a full seasonal cycle) and that a minimum of 20 collocations with GOSAT occurred. In addition, the three GOSAT observation modes (ocean H-gain, land M-gain, land H-gain) were combined for each site, and satellite overpass means of XCO2 were aggregated into daily means. This resulted in approximately 7700 daily averages at 26 TCCON stations over the 11-year GOSAT data record.

Figure  shows the results of the seasonal cycle fit for the Lamont, Oklahoma, TCCON station. The one-to-one scatter of the 896 daily averaged XCO2 values (a) indicates a bias of -0.27 ppm for the GOSAT product relative to TCCON, with a standard deviation of 1.25 ppm and a Pearson's R2 of 0.99. The seasonal cycle fits (b) indicate excellent agreement in the secular CO2 increase at this site: 2.34 ppm for both GOSAT and TCCON. The mean seasonal amplitudes indicate a slight disagreement of a few tenths of a ppm, with TCCON showing a slightly higher fitted peak XCO2 value during the spring maximum phase, compared to GOSAT. This is similar to the results for this site reported in Fig. 4 of . The time series of the calculated difference in satellite- and ground-based estimated XCO2 (GOSAT - TCCON), shown in (c), highlights the magnitude of the scatter about the mean bias and suggests that there is no observable time drift in the data at this site.

A summary of the data from each station that met the seasonal cycle collocation criteria is provided in Table . In addition, the full complement of plots is presented in Appendix . Overall, the seasonal cycle analysis at most sites is in agreement, to within the estimated uncertainties. The standard deviation of the mean XCO2 biases for the 26 sites is 0.41 ppm for the ACOS GOSAT v9 record. This compares to a value of 0.51 ppm at 23 stations for ACOS GOSAT v7.3, suggesting an improvement in the quality of the v9 XCO2 product.

The collocation and calculation of the multi-model-mean (MMM) is described in Sect. . Although the model data used for evaluation were very similar to those used in the QF/BC procedure, some minor version updates and extensions in time were included, as indicated in Table . It is important to be aware that there can be a considerable delay between performing the QF/BC procedure and the full generation of the final product, during which time the models are often updated.

Seasonal maps of ΔXCO2MMM (GOSAT v9 minus MMM) are shown in Fig.  for the 11-year data record binned at 2.5∘ latitude by 5∘ longitude. Generally, the agreement between the model-derived values and the satellite estimates is quite good, with an annual mean difference of ≃-0.15 ppm and binned scatter ≃ 0.5 ppm. For ocean H-gain observations, the ΔXCO2MMM tends to be negative (positive) in the Northern (Southern) Hemisphere. Land observations exhibit several distinct sub-continental-scale disagreements, including a strong positive signal over northern tropical Africa in DJF (GOSAT XCO2 higher than MMM).

Figure  shows Hovmöller plots of the ΔXCO2MMM ocean H-gain data at 30 d by 15∘ latitude resolution for v7.3 (a) and v9 (a). The extension in time of v9 is evident, as well as the expansion in the latitude range of the ocean H-gain observations since 2015. A direct comparison between the v7.3 and v9 ΔXCO2MMM values for the overlapping time period, April 2009 through June 2016, reveals a global mean bias and standard deviation of -0.54 and 1.0 ppm for the v7.3 product, and -0.20 and 0.84 ppm for v9, underscoring the improvement.

Of particular note are the strong positive ΔXCO2MMM values in the v9 SH ocean H-gain observations for the latter part of 2014, persisting through most of 2015. This feature is not seen in the v7.3 product, due to a paucity of SH ocean H-gain data. It approximately coincides with the strong 2015–2016 El Ninõ event, where ΔXCO2MMM signals were also seen in the OCO-2 v7 ocean H-gain data, as reported in . It has been hypothesized that the 2015–2016 El Ninõ produced an anomalously strong carbon release from tropical land regions due to higher temperature and below average precipitation . In contrast to the positive SH signal, negative ΔXCO2MMM values (GOSAT lower than MMM) have been observed in the v9 NH oceans since 2016. It is unclear why the satellite and models disagree over such large spatial and temporal scales, but recent work by suggests that the ACOS v7.3 (and to a lesser extent v9) XCO2 values are in fact biased low by approximately 1 to 1.5 ppm, as compared to a new independent evaluation data set generated from combined ship and aircraft measurements over the open oceans. Further investigation into the source of the ACOS GOSAT biases against models is warranted.

Figure  shows spatial maps of ΔXCO2MMM for the truncated time span 2010 through 2015 comparing v7.3 (a and c) and v9 (b and d) for DJF (a and b) and JJA (c and d). There is significant decrease in scatter in the v9 product (≃ 0.5 ppm) relative to v7.3 (≃ 0.75 ppm). The low bias in DJF tropical Pacific vanishes in v9, and the positive bias in JJA extratropical land regions is reduced. The expanded latitudinal extent of the ocean H-gain observations is evident in the v9 maps. One feature that is robust in both v7.3 and v9 is the large positive signal over northern tropical Africa in DJF. This feature was also observed in the OCO-2 v7 and v8 comparisons to a MMM and in v10 XCO2 anomaly maps .

NASA's Orbiting Carbon Observatory-2 (OCO-2) has been collecting science data since September, 2014 from a near-polar low-Earth orbit (705 km altitude), with an afternoon Equator crossing time of ≃ 13:30 local time . Like GOSAT, OCO-2 takes measurements of reflected solar radiation in the oxygen A-band (0.76 µm) and the weak and strong carbon dioxide bands (1.6 and 2.0  µm, respectively), which are used to estimate XCO2 using the ACOS L2FP retrieval algorithm . However, due to differences in the orbit parameters of the two sensors, e.g., a 3 d repeat cycle for GOSAT versus a 16 d repeat cycle for OCO-2 (see Table 2 of ), the number of collocated soundings is somewhat limited. Therefore, some criteria must be defined in order to identify soundings that can be compared in a meaningful way. The underlying assumption of the collocation is that on scales of a few hundred kilometers and several hours, the natural variance in XCO2 is not detectable in satellite-derived estimates from the ACOS L2FP algorithm.

For this study, the coincidence criteria to match OCO-2 soundings to individual GOSAT soundings were as follows: (i) falling within 2∘ latitude and 3∘ longitude, (ii) with a maximum spatial separation of 300 km, and (iii) acquired within ±2 h. Due to the dense nature of the OCO-2 soundings relative to the sparseness of the GOSAT soundings, there are typically between zero and several hundred matched OCO-2 soundings per GOSAT footprint. A lower limit of 10 and an upper limit of 100 (randomly selected) OCO-2 soundings that meet the coincidence criteria were set in order to retain the GOSAT sounding for analysis. The individual L2FP quality flags are applied for both GOSAT and OCO-2 during the collocation procedure, and then the mean value of XCO2 from the 10 to 100 collocated OCO-2 soundings is calculated and subtracted from the corresponding GOSAT XCO2 to produce ΔXCO2OCO-2.

Here we compare ACOS GOSAT v9 against OCO-2 v10 (rather than to the deprecated v9), since we assume that science users will adopt the newest OCO-2 product. Major updates to the version 10 ACOS L2FP algorithm are discussed in Sect. .

One complexity in comparing ACOS GOSAT v9 and OCO-2 v10 is the fact that the two versions of the algorithm used different CO2 priors. Typically, models which assimilate satellite CO2 data take into account the unmeasured part of the prior CO2 profile (specified via the retrieval's averaging kernel) via an averaging kernel correction, as given in Eq. (). Therefore, in order to fairly compare these two data sets as models would assimilate them, we need to remove their difference due to the unmeasured part of the CO2 profile, as follows: 3XCO2′=XCO2+∑i=120hi(1-ai)⋅(ua,i′-ua,i), where h is the XCO2 pressure weighting function, a is the normalized XCO2 averaging kernel, ua is the ACOS v9 CO2 prior profile used for GOSAT, and ua′ is the ACOS v10 CO2 prior profile used for OCO-2. The summation takes place over the 20 vertical levels defined in the ACOS code. In summary, the total adjustment to the ACOS GOSAT XCO2 value is calculated as the contribution of the difference in the vertical CO2 priors at each level weighted by the one minus the averaging kernel at that level. The global mean adjustment due to the CO2 prior correction was approximately 0.2 ppm, with 95 % of corrections between -0.1 and +0.5 ppm.

Spatial maps of ΔXCO2OCO-2 (GOSAT v9 – OCO-2 v10) for the prior-corrected, collocated soundings are shown in Figs.  and for land and ocean, respectively. In each figure the maps are shown by season at 2.5∘ latitude by 5∘ longitude resolution. In all seasons, higher scatter in ΔXCO2OCO-2 is observed over land (≃1 ppm) than over ocean (< 0.7 ppm), likely due to variability of land surface features and/or lower signal-to-noise ratios of the radiance measurements. The annual global mean ΔXCO2OCO-2 for land is near zero (0.06 ppm) and exhibits little variation with season. For ocean H-gain, the global mean ΔXCO2OCO-2 is larger (-0.40 ppm) and varies more significantly by season from -0.2 (DJF) to -0.6 ppm (JJA). The disagreements in the ocean H-gain data tend to be spatially coherent, with a notably large negative difference in the NH in most seasons. Currently, the underlying cause of these disagreements is unknown and could stem from instrument calibration or sampling-related issues, differences in retrieval algorithm versions, or even collocation issues.

The disagreement in XCO2 for ocean H-gain between ACOS GOSAT v9 and OCO-2 v10 is highlighted in panel (a) of Fig. , which shows ΔXCO2OCO-2 for the period September 2014 through December 2020 at 30 d by 15∘ latitude resolution for the ocean H-gain observations. Panel B shows the number of collocated soundings in each bin. A large SH positive difference in ΔXCO2OCO-2 (GOSAT higher than OCO-2 by ≃ 0.5 ppm) is observed for the first 2 years of the time record. Then, in early 2016, there is what appears to be an abrupt jump to a large negative difference (GOSAT lower than OCO-2 by ≃-0.5 ppm) in the NH. From this point forward, ΔXCO2OCO-2 appears to be reasonably stable in time, although there is a persistent low difference in the NH for the remainder of the record.

Figure  shows the ΔXCO2OCO-2 data for the combined land H-gain and M-gain data, similar to Fig. . The main feature here is that the overall variability is larger compared to the ocean H-gain data, which we attribute to biases introduced by variations in both topography and surface albedo. A slightly positive (red) signal is observed during the September to December months in the SH, especially in 2014, 2018, and 2019. Although additional investigation into such signals is warranted, it is beyond the scope of the current work.

A set of summary statistics for the ACOS GOSAT v9 versus OCO-2 v10 XCO2 product is given in Table . The values reported here are on the individual collocations by year and season, rather than the spatially gridded averages as given in Figs.  and . For the land observations, there has been a very slight upward trend in time of the ΔXCO2OCO-2 to slightly more positive values (GOSAT v9 larger than OCO-2 v10 XCO2). On the other hand, for ocean H-gain observations, the general trend has been an increasingly more negative ΔXCO2OCO-2 in time, as is seen in Fig. . Additional investigation will be required to determine the root cause(s) of these differences.