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The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb are to advance our knowledge of the carbon cycle, in particular land-atmosphere fluxes of the greenhouse gases carbon dioxide (CO2) and methane (CH4), and the effects on these fluxes on the Earth's radiation budget. GeoCarb will retrieve column integrated amounts of CO2 (XCO2), CH4 (XCH4) and CO (XCO; important for understanding tropospheric chemistry), in addition to Solar-Induced Fluorescence (SIF), which is proportional to the photosynthetic activity of vegetation, from hyperspectral resolution measurements in the O2 A-band at 0.76 um, the weak CO2 band at 1.6 um, the strong CO2 band at 2.06 um, and a CH4/CO band at 2.32 um. Unlike it's polar orbiting predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb will be in a Geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high spatial resolution imaging capabilities will provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land-atmosphere CO2 and CH4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the level-2 retrieval algorithms which will be followed by simulation experiments to determine a relatively comprehensive error budget for each target gas. Several sources of uncertainty will be explored including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to, forward model parameters including that from meteorology and spectroscopy. The results indicate that the errors (1σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2, XCH4, and XCO, respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMS errors of 1.06 ppm for XCO2, 8.2 ppb for XCH4, and 2.5 ppb for XCO, respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO, like SIF, are dominated by measurement noise.

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries’ carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2 ) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1◦ × 1 ◦ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites’ (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 PgCO2 yr−1 (0.90–1.25 PgC yr−1 ). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems. This research has been supported by the European Commission, Horizon 2020 Framework Programme (CoCO2 (grant no. 958927 856612/EMME-CARE)) and Copernicus Atmosphere Monitoring Service (grant no. CAMS73), the Australian Research Council (grant nos. DP190100180, DE180100203, DP160100598, LE0668470, DP140101552, DP110103118, DP0879468 and FT180100327), the Environmental Restoration and Conservation Agency (grant no. JPMEERF21S20800), the Korea Meteorological Administration (grant no. KMA2018-00320), the National Aeronautics and Space Administration (grant nos. 20-OCOST20-0004, 80NSSC18K0908, 80NSSC18K0976, 80NSSC20K0006, 80NSSC21K1068, 80NSSC21K1073, 80NSSC21K1077, 80NSSC21K1080, 80HQTR21T0069, NAG512247, NNG05GD07G, NNH17ZDA001N-OCO2 and NNX15AG93G), and the National Oceanic and Atmospheric Administration (grant no. NA18OAR4310266).

International audience; The Thermal And Near infrared Sensor for carbon Observation - Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a "good" XCO2 quality flag, as compared to 3.9 % in v7.3 (6 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

Urban areas, where more than 55% of the global population gathers, contribute more than 70% of anthropogenic fossil fuel carbon dioxide (CO_(2ff)) emissions. Accurate quantification of CO_(2ff) emissions from urban areas is of great importance for formulating global warming mitigation policies to achieve carbon neutrality by 2050. Satellite-based inversion techniques are unique among “top-down” approaches, potentially allowing us to track CO_(2ff) emission changes over cities globally. However, their accuracy is still limited by incomplete background information, cloud blockages, aerosol contamination, and uncertainties in models and priori emission inventories. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO_(2ff) emissions based on the OCO-2 satellite measurements of column-averaged dry-air mole fractions of CO₂ (X_(CO₂)) over a fast-growing Asian metropolitan area: Lahore, Pakistan. We first examined the OCO-2 data availability at global scale. About 17% of OCO-2 soundings over the global 70 most populated cities from 2014 to 2019 are marked as high-quality. Cloud blockage and aerosol contamination are the two main causes of data loss. As an attempt to recover additional soundings, we evaluated the effectiveness of OCO-2 quality flags at the city level by comparing three flux quantification methods (WRF-Chem, X-STILT, and the flux cross-sectional integration method). The satellite/bottom-up emissions (OCO-2/ODIAC) ratios of the high-quality tracks with reduced uncertainties in emissions are better agreed across the three methods compared to the all-data tracks. This demonstrates that OCO-2 quality flags are useful filters of low-quality OCO-2 retrievals at local scales. All three methods consistently suggested that the ratio medians are greater than 1, implying that the ODIAC slightly underestimated CO_(2ff) emissions over Lahore. Additionally, our estimation of the a posteriori CO2ff emission trend was about 734 kt C/year (i.e., an annual 6.7% increase). 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test showed that less than 10% prior uncertainty for 8 tracks (or less than 20% prior uncertainty for 25 tracks) is required to achieve a greater-than-50% trend significant possibility at a 95% confidence level. It implies that the trend is driven by the prior and not due to the assimilation of OCO-2 retrievals. The key to improving the role of satellite data in CO₂ emission trend detection lies in collecting more frequent high-quality tracks near metropolitan areas to achieve significant constraints from X_(CO₂) retrievals.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

NASA's Orbiting Carbon Observatory-3 (OCO-3) was installed on the International Space Station (ISS) on 10 May 2019. OCO-3 combines the flight spare spectrometer from the Orbiting Carbon Observatory-2 (OCO-2) mission, which has been in operation since 2014, with a new Pointing Mirror Assembly (PMA) that facilitates observations of non-nadir targets from the nadir-oriented ISS platform. The PMA is a new feature of OCO-3, which is being used to collect data in all science modes, including nadir (ND), sun-glint (GL), target (TG), and the new snapshot area mapping (SAM) mode. This work provides an initial assessment of the OCO-3 instrument and algorithm performance, highlighting results from the first 8 months of operations spanning August 2019 through March 2020. During the In-Orbit Checkout (IOC) phase, critical systems such as power and cooling were verified, after which the OCO-3 spectrometer and PMA were subjected to a series of rigorous tests. First light of the OCO-3 spectrometer was on 26 June 2019, with full science operations beginning on 6 August 2019. The OCO-3 spectrometer on-orbit performance is consistent with that seen during preflight testing. Signal to noise ratios are in the expected range needed for high quality retrievals of the column-averaged carbon dioxide (CO₂) dry-air mole fraction (XCO₂) and solar-induced chlorophyll fluorescence (SIF), which will be used to help quantify and constrain the global carbon cycle. The first public release of OCO-3 Level 2 (L2) data products, called “vEarly”, is being distributed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). The intent of the vEarly product is to evaluate early mission performance, facilitate comparisons with OCO-2 products, and identify key areas to improve for the next data release. The vEarly XCO2 exhibits a root-mean-squared-error (RMSE) of ≃ 1, 1, 2 ppm versus a truth proxy for nadir-land, TG&SAM, and glint-water observations, respectively. The vEarly SIF shows a correlation with OCO-2 measurements of >0.9 for highly coincident soundings. Overall, the Level 2 SIF and XCO₂ products look very promising, with performance comparable to OCO-2. A follow-on version of the OCO-3 L2 product containing a number of refinements, e.g., instrument calibration, pointing accuracy, and retrieval algorithm tuning, is anticipated by early in 2021.

The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. We report results from a first attempt to determine whether a regional-scale reduction of anthropogenic CO2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO2. For this purpose, we have analysed a small ensemble of satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2. We focus on East China because COVID-19 related CO2 emission reductions are expected to be largest there early in the pandemic. We analysed four XCO2 data products from the satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT). We use a data-driven approach that does not rely on a priori information about CO2 sources and sinks and ignores atmospheric transport. Our approach utilises the computation of XCO2 anomalies, ΔXCO2, from the satellite Level 2 data products using a method called DAM (Daily Anomalies via (latitude band) Medians). DAM removes large-scale, daily XCO2 background variations, yielding XCO2 anomalies that correlate with the location of major CO2 source regions such as East China. We analysed satellite data between January 2015 and May 2020 and compared monthly XCO2 anomalies in 2020 with corresponding monthly XCO2 anomalies of previous years. In order to link the XCO2 anomalies to East China fossil fuel (FF) emissions, we used XCO2 and corresponding FF emissions from NOAA’s (National Oceanic and Atmospheric Administration) CarbonTracker version CT2019 from 2015 to 2018. Using this CT2019 data set, we found that the relationship between target region ΔXCO2 and the FF emissions of the target region is approximately linear and we quantified slope and offset via a linear fit. We use the empirically obtained linear equation to compute ΔXCO2FF, an estimate of the target region FF emissions, from the satellite-derived XCO2 anomalies, ΔXCO2. We focus on October to May periods to minimize contributions from biospheric carbon fluxes and quantified the error of our FF estimation method for this period by applying it to CT2019. We found that the difference of the retrieved FF emissions and the CT2019 FF emissions in terms of the root-mean-square-error (RMSE) is 0.39 GtCO2/year (4 %). We applied our method to NASA’s (National Aeronautics and Space Administration) OCO-2 XCO2 data product (version 10r) and to three GOSAT products. We focus on estimates of the relative change of East China monthly emissions in 2020 relative to previous months. Our results show considerable month-to-month variability (especially for the GOSAT products) and significant differences across the ensemble of satellite data products analysed. The ensemble mean indicates emission reductions by approximately 8 % ± 10 % in March 2020 and 10 % ± 10 % in April 2020 (uncertainties are 1-sigma) and somewhat lower reductions for the other months in 2020. Using only the OCO-2 data product, we obtain smaller reductions of 1–2 % (depending on month) with an uncertainty of ± 2 %. The large uncertainty and the differences of the results obtained for the individual ensemble members indicates that it is challenging to reliably detect and to accurately quantify the emission reduction. There are several reasons for this including the weak signal (the expected regional XCO2 reduction is only on the order of 0.1–0.2 ppm), the sparseness of the satellite data, remaining biases and limitations of our relatively simple data-driven analysis approach. Inferring COVID-19 related information on regional-scale CO2 emissions using current satellite XCO2 retrievals likely requires, if at all possible, a more sophisticated analysis method including detailed transport modelling and considering a priori information on anthropogenic and natural CO2 surface fluxes.

The Orbiting Carbon Observatory 2 (OCO-2) and the Orbiting Carbon Observatory 3 (OCO-3) are NASA Earth Science missions designed to measure carbon dioxide in Earth’s atmosphere. OCO-2 was inserted at the front of the 705 km Afternoon Constellation (A-Train) in August 2014, and flies about 7 minutes ahead of the Aqua spacecraft. OCO-3 was mounted on the Japanese Experiment Module Exposed Facility onboard the International Space Station (ISS) in May 2019. OCO-2 and OCO-3 use three-channel grating spectrometers to measure reflected sunlight within the O2 band at 0.76 micron and two CO2 bands at 1.61 micron and 2.06 micron. The grating of each channel disperses light onto 1016 spectral channels of a focal plane array, yielding spectra with a resolution of ~ 0.04, 0.08, and 0.1 nm, respectively. OCO-2 uses onboard lamps, solar observations, and lunar measurements as well as surface targets for radiometric calibration/validation. OCO-3 is equipped with onboard lamps, and can also observe the Moon a few times each year, but cannot observe the Sun due to its configuration onboard ISS. The OCO missions require a 5% accuracy on absolute radiometric calibration to meet their CO2 accuracy requirements. Here, we describe results from the radiometric comparisons of OCO-2 and Aqua MODIS using OCO-2 nadir observations over eight desert sites and nearly simultaneous MODIS observations with sensor viewing zenith angles of 15±0.5 degree. The MODIS data are collocated into the OCO-2 geolocation grid using a 1 km circular region around each OCO-2 footprint. Without correcting for viewing geometry differences and mismatched spectral response functions, the mean and standard OCO-2/MODIS radiance ratio over the eight sites are determined to be 1.103±0.010, 1.120±0.007, 1.233±0.016 for the OCO-2 three bands, respectively. Here, we report the OCO-2 absolute radiometric calibration obtained from ongoing efforts to develop bi-directional reflectance distribution function (BRDF) models to account for the viewing geometry differences and to reduce the seasonal variations, and by correcting biases due to mismatched spectral response functions. We will also discuss the absolute radiometric comparison of OCO-2 and OCO-3 using simultaneous nadir overpasses of these desert sites.

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