Abstract The Orbiting Carbon Observatory‐2 and ‐3 (collectively termed “OCO‐2/3,” hereafter) missions, together, provide precise and accurate global data records that contribute to a better understanding of the variability in atmospheric carbon dioxide (CO 2 ). The retrieval algorithm used to process the satellite data, Atmospheric Carbon Observations from Space (ACOS), continues to be updated. The latest v11.2 OCO‐2 and v11 OCO‐3 data releases include significant improvements compared to past data versions. The Total Carbon Column Observing Network (TCCON), a network of ground‐based Fourier Transform Spectrometers (FTS), has historically, been the validation data source for OCO‐2/3. The COllaborative Carbon Column Observing Network (COCCON), an emerging network of ground‐based portable Fourier Transform Infrared Spectrometers (EM27/SUN), aids satellite validation efforts by providing observations in geographic locations where there are no TCCON measurements being made. This study provides the first global comparison between the OCO‐2/3 data sets and the COCCON observations. Currently, the COCCON data set, most useful for global analysis, is version 1 (v1), which our analysis shows is typically lower than the satellites by ∼0.50–1.00 ppm, with scatter against OCO‐2/3 roughly similar to TCCON versus OCO‐2/3. This analysis includes comparisons of results from the latest v2.x COCCON data version for the sites with the data available. Comparisons with the v2.x COCCON data version generally show improved comparisons against OCO‐2/3. Our analysis illustrates the ways in which the COCCON data set, used globally, regionally, or in specific locations, can provide insight into the quality of satellite observations of atmospheric CO 2 .

The Orbiting Carbon Observatory-2 and -3 (collectively termed "OCO-2/3," hereafter) missions, together, provide precise and accurate global data records that contribute to a better understanding of the variability in atmospheric carbon dioxide (CO2). The retrieval algorithm used to process the satellite data, Atmospheric Carbon Observations from Space (ACOS), continues to be updated. The latest v11.2 OCO-2 and v11 OCO-3 data releases include significant improvements compared to past data versions. The Total Carbon Column Observing Network (TCCON), a network of ground-based Fourier Transform Spectrometers (FTS), has historically, been the validation data source for OCO-2/3. The COllaborative Carbon Column Observing Network (COCCON), an emerging network of ground-based portable Fourier Transform Infrared Spectrometers (EM27/SUN), aids satellite validation efforts by providing observations in geographic locations where there are no TCCON measurements being made. This study provides the first global comparison between the OCO-2/3 data sets and the COCCON observations. Currently, the COCCON data set, most useful for global analysis, is version 1 (v1), which our analysis shows is typically lower than the satellites by similar to 0.50-1.00 ppm, with scatter against OCO-2/3 roughly similar to TCCON versus OCO-2/3. This analysis includes comparisons of results from the latest v2.x COCCON data version for the sites with the data available. Comparisons with the v2.x COCCON data version generally show improved comparisons against OCO-2/3. Our analysis illustrates the ways in which the COCCON data set, used globally, regionally, or in specific locations, can provide insight into the quality of satellite observations of atmospheric CO2.

The newly established United States Greenhouse Gas Center (U.S. GHG Center) is a multi-agency partnership between the National Aeronautics and Space Administration (NASA), the Environmental Protection Agency (EPA), the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) that aims to accelerate the production and delivery of actionable, trusted greenhouse gas (GHG) information from the federal government and non-public sector to a variety of users through a coordinated data system, reflecting transparency and open source science principles in both data and methods. The US GHG Center acts as an enabler of collaboration with networks of interagency, international, intergovernmental and private sector partners to increase confidence in setting, assessing, and meeting climate change mitigation goals, with a preliminary focus on carbon dioxide and methane. The US GHG Center is also a critical element in the implementation of the “National Strategy to Advance an Integrated US Greenhouse Gas Measurement, Monitoring, and Information System”. Initial focus areas include 1) Gridded anthropogenic greenhouse gas emissions, 2) Natural sources and sinks, and 3) New Observations for tracking large emission events. The US GHG Center web portal includes a prototype data catalogue, exploratory data analysis capabilities, a collaborative science environment for data analysis and exploration, as well as an interactive visual interface for storytelling. Examples of products currently available on the GHG Center portal include methane and carbon dioxide concentration anomalies and emissions from airborne and space-based instruments, including from NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer in orbit on the International Space Station, EPA’s gridded U.S. anthropogenic methane greenhouse gas inventory data, gridding methodologies and visualizations, NOAA’s Observation Package (ObsPack) data products that bring together atmospheric greenhouse gas observations from a variety of sampling platforms, as well as multi-model land flux and ecosystem exchange estimates.

This report is about an intercomparison experiment that was performed to compare the methane plume detection and quantification based on EMIT remote sensing measurements. The experiment consisted of eight teams analyzing four measurement scenes from EMIT and reporting back results such as plume origins, plume outlines, estimated emission rates, and uncertainties. Comparisons of results across the team are reported, as well as the aggregate statistics, such as box and whisker plots of the emission rates, scatter of the plume origins, and coefficient of variance (COV) for the results. The small size of the datasets limits the generalizability of the results. One key outcome of this work was the development of methods for comparison of results, including application of rotation to plume origins to evaluate in the context of the wind field. We also tested the impact of holding variables constants, such as the wind speed. In this report, we show that the emission rate estimates have COVs of 30% to 50% in the majority of cases, and using the same wind speed across all teams did not drastically change this. The plume origin analysis showed that the cross wind spread of plume origin results was 50 m to 110 m in all but one case, and across wind was 60 m to 120 m in 13 of the 15 cases. The EMIT footprint is 60m, so the variations were roughly 1 to 2 pixels. In future work, we will build on the methods developed here and include measurements over controlled releases where the bias of the emission estimates can be assessed.

In this paper, we characterize major anthropogenic point sources in the South African Highveld region using Orbiting Carbon Observatory-3 (OCO-3) Snapshot Area Map (SAM) carbon dioxide (CO 2 ) and Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO 2 ) observations. Altogether we analyze six OCO-3 SAMs. We estimate the emissions of six power stations (Kendal, Kriel, Matla, Majuba, Tutuka and Grootvlei) and the largest single emitter of greenhouse gas (GHG) in the world, Secunda CTL synthetic fuel plant. We apply the cross-sectional flux method for the emission estimation and we extend the method to fit several plumes at the same time. Overall, the satellite-based emission estimates are in good agreement (within the uncertainties) as compared to emission inventories, even for the cases where several plumes are mixed. We also discuss the advantages and challenges of the current measurement systems for GHG emission monitoring and reporting, and the applicability of different emission estimation approaches to future satellite missions such as the Copernicus CO 2 Monitoring Mission (CO2M) and the Global Observing SATellite for GHGs and Water cycle (GOSAT-GW), including the joint analysis of CO 2 and NO 2 observations.

The Orbiting Carbon Observatory (OCO) -2 and -3 instruments, launched in 2014 and 2019 respectively, require accurate and precise calibration to achieve science goals. While onboard lamps and solar diffusers have proven very useful to track changes over short time scales, these calibration sources have themselves exhibited substantial degradation over the extended mission life. OCO-2 has relied on lunar measurements to estimate "slow" irreversible changes in radiometric response, and OCO-3 is attempting to do the same. This work describes how each instrument observes the Moon and highlights key differences in how these datasets are acquired. Several near-coincident observations also present the opportunity to join the two records, which will benefit both missions.

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 National Academy of Sciences convened a committee to generate a report titled “Development of a Framework for Evaluating Global Greenhouse Gas Emissions Information for Decision Making”. The report: Described approaches used to develop global anthropogenic greenhouse gas emissions inventories, including the use of surveys Discussed the potential uses and limitations of these approaches Provided a framework to evaluate emissions information and inventories Presented several case studies to demonstrate how the framework could be applied to evaluate emissions information and inventory approaches and identify strengths and opportunities for improvement for each case study. Identified ways to improve methodological transparency, sustainability and continuity of relevant observations, and product confidence in global anthropogenic greenhouse gas emissions inventories To accomplish this the committee identified a series of “pillars” that were deemed ideal characteristics of GHG inventories for decisionmaking. Three dominant GHG inventory development approaches were identified from the existing work and ranked within the series of pillars. The committee made a series of recommendations for continued development of inventories to enhance their quality and utility to decisionmakers.In this presentation I will describe the major content of the report, describing the evaluation matrix and a summary of the committee’s recommendations.

The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (XCO2​​​​​​​) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO2. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1 %–0.5 % in XCO2, residual biases and some sources of error remain unaccounted for (Wunch et al., 2017; Worden et al., 2017; Torres et al., 2019). Forward model and other errors can lead to systematic biases in the retrieved XCO2, which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations (Nassar et al., 2017; Kiel et al., 2021). In this study we explore algorithm-induced biases through the use of simulated OCO-3 snapshot area mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vEarly product which contains artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated Level 1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the new OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g., MicroCarb, GeoCarb, GOSAT-GW, CO2M).

The Orbiting Carbon Observatory-3 (OCO-3) measures carbon dioxide and solar-induced fluorescence from the International Space Station (ISS). It uses the flight spare spectrometers from its predecessor, OCO-2, and produces spectral images in three near-infrared channels. Its preflight radiometric calibration was performed at the National Aeronautics and Space Administration's (NASA) Jet Propulsion Laboratory and is constantly updated inflight to account for both gradual and abrupt changes in instrument response that display a wavelength dependence within the bands. Some of these are caused by the accumulation of contaminants and their subsequent removal after scheduled decontamination events, but sudden changes of the overall gain state of the two longest wavelength bands, unrelated to contamination, are also observed. They were found to be triggered by instrument resets as well as by decontamination events. OCO-3 cannot perform solar calibration due to its position on the ISS and the inflight updates to its radiometric calibration depend solely on its on-board calibrator, which consists of three lamps and a reflective diffuser. The lamps are observed with different cadences and thus degrade at different rates. Information from all three lamps is combined to provide high temporal resolution and minimize the impact of lamp aging on the gain degradation coefficients that describe the changes in the radiometric response of the instrument inflight. We use thousands of soundings from ocean scenes to assess the relative calibration within the two shortest wavelength bands. Here we present the algorithm developed for the inflight relative radiometric calibration of OCO-3 Version 10 products and discuss the state of the calibration.

We quantify CO2 emissions from Europe’s largest fossil fuel power plant, the Bełchatόw Power Station in Poland, using CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions on 10 occasions from March 2017 to June 2022. The space-based CO2 emission estimates reveal emission changes with a trend that is consistent with the independent reported hourly power generation trend that results from both permanent and temporary unit shutdowns. OCO-2 and OCO-3 emission estimates agree with the bottom-up emission estimates within their respective 1σ uncertainties for 9 of the 10 occasions. Different methods for defining background values and corresponding uncertainties are explored in order to better understand this important potential error contribution. These results demonstrate the ability of existing space-based CO2 observations to quantify emission reductions for a large facility when adequate coverage and revisits are available. The results are informative for understanding the expected capability and potential limitations of the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) and other future satellites to support monitoring and verification of CO2 emission reductions resulting from climate change mitigation efforts such as the Paris Agreement.

The tropics is the nexus for many of the remaining gaps in our knowledge of environmental science, including the carbon cycle and atmospheric chemistry, with dire consequences for our ability to describe the Earth system response to a warming world. Difficulties associated with accessibility, coordinated funding models and economic instabilities preclude the establishment of a dense pan-tropical ground-based atmospheric measurement network that would otherwise help to describe the evolving state of tropical ecosystems and the associated biosphere-atmosphere fluxes on decadal timescales. The growing number of relevant sensors aboard sun-synchronous polar orbiters provide invaluable information over the remote tropics, but a large fraction of the data collected along their orbits is from higher latitudes. The International Space Station (ISS), which is in a low-inclination, precessing orbit, has already demonstrated value as a proving ground for Earth observing atmospheric sensors and as a testbed for new technology. Because low-inclination orbits spend more time collecting data over the tropics, we argue that the ISS and its successors, offer key opportunities to host new Earth-observing atmospheric sensors that can lead to a step change in our understanding of tropical carbon fluxes.

The Orbiting Carbon Observatory 2 (OCO-2) satellite has been providing information to estimate carbon dioxide (CO2) fluxes at global and regional scales since 2014 through the combination of CO2 retrievals with top–down atmospheric inversion methods. Column average CO2 dry-air mole fraction retrievals have been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of 10 atmospheric inversions all characterized by different transport models, data assimilation algorithms, and prior fluxes using first OCO-2 v7 in 2015–2016 and then OCO-2 version 9 land observations for the longer period 2015–2018. Inversions assimilating in situ (IS) measurements have also been used to provide a baseline against which the satellite-driven results are compared. The time series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiment using either OCO-2 or IS data. We then evaluate the inversion ensemble based on the dataset from the Total Carbon Column Observing Network (TCCON), aircraft, and in situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region, which seems to show the impact of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions, and the estimation of carbon emissions over tropical Africa require further analysis.

Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at the regional to city scale. Very few studies have focused on combustion efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for interpreting and deriving spatially resolved ERs from asynchronous satellite measurements, including (1) variations in meteorological conditions given the mismatch in satellite overpass times, (2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), (3) interferences from the biosphere and biomass burning, and (4) the mismatch in the daytime variations of CO and CO2 emissions. In this study, we extended an established emission estimate approach to arrive at spatially resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influences of the confounding factors listed above and further attribute intra-urban variations in ERs to certain sectors, we leveraged a Lagrangian atmospheric transport model with an urban land cover classification dataset and reported ERCO values from the sounding level to the overpass and city level. We found that the differences in overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially resolved ERCO. Specifically, a time difference of >3 h typically led to dramatic changes in wind directions and urban plume shapes, thereby making the calculation of accurate sounding-specific ERCO difficult. After removing such cases from consideration and applying a simple plume shift method when necessary to account for changes in wind direction and speed, we discovered significant contrasts in combustion efficiencies between (1) two megacities versus two industry-oriented cities and (2) different regions within a city, based on six nearly coincident overpasses per city. Results suggest that the ERCO impacted by heavy industry in Los Angeles is slightly lower than the overall city-wide value (<10 ppb-CO/ppm-CO2). In contrast, the ERCO related to heavy industry in Shanghai is much higher than Shanghai's city mean and more aligned with the city means of two selected industry-oriented cities in China (approaching 20 ppb-CO/ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our unique approach (i.e., without using sector-specific information from emission inventories) provides new insights into assessing combustion efficiency within a city from future satellite missions, such as those that will map column CO2 and CO concentrations simultaneously with high spatiotemporal resolutions.

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 (<1 ×106 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.

Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO2 emissions (larger than 1.0 ktCO2 h-1 per 10-2 square degree grid cell) over the globe with a simple plume cross-sectional inversion approach. We have compared our results with a global gridded and hourly inventory. The corresponding OCO emission retrievals explain more than one third of the inventory variance at the corresponding cells and hours. We have binned the data at diverse time scales from the year (with OCO-2) to the average morning and afternoon (with OCO-3). We see consistent variations of the median emissions, indicating that the retrieval-inventory differences (with standard deviations of a few tens of percent) are mostly random and that trends can be calculated robustly in areas of favorable observing conditions, when the future satellite CO2 imagers provide an order of magnitude more data.

NASA?s Orbiting Carbon Observatory-3 (OCO-3) was designed to support the quantification and monitoring of anthropogenic CO2 emissions. Its Snapshot Area Map (SAM) and target mode measurements provide an innovative dataset for carbon studies on sub-city scales. Unlike any other current space-based instrument, OCO-3 has the ability to scan large contiguous areas of emission hot spots like cities, power plants, and volcanoes. These measurements result in dense, fine-scale spatial maps of column averaged dry-air mole fractions of carbon dioxide (XCO2). For the first time, we present and analyze XCO2 distributions over the Los Angeles megacity (LA) derived from OCO-3 SAM and target mode observations. Urban XCO2 enhancements range from 0 - 6 ppm (median enhancements <^> 2 ppm) relative to a clean background and show excellent agreement with nearby ground-based TCCON measurements of XCO2. OCO-3?s dense observations reveal intra-urban variations of XCO2 over the city that have never been observed from space before. The spatial variations are mainly driven by the complex fossil fuel emission patterns and meteorological conditions in the LA Basin and are in good agreement with those from co-located TROPOMI measurements of co-emitted NO2. Differences between measured and simulated XCO2 enhancements from two models (WRF-Chem and X-STILT) are typically below 1 ppm with larger differences for some sub regions. Both models capture the observed intra-urban XCO2 gradients. Further, OCO-3?s multi-swath measurements capture about three times as much of the city emissions compared to single-swath overpasses. OCO-3?s frequent target and SAM mode observations will pave the way to constrain urban emissions at finer, sub-city scales.

Abstract. 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 (< 1M out of 24M). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (one sigma) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Similarly, global mean biases are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of order 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). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the data. This dataset 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 dataset, which begins in September 2014.

The Orbiting Carbon Observatory 3 (OCO-3) was installed on the International Space Station (ISS) in May 2019 and began routine operations in August 2019 to continue global CO2 and solar-induced chlorophyll fluorescence (SIF) observations using the flight spare instrument from OCO-2. The first version of the data, called vEarly, was released in early 2020, and an update, v10, is being prepared. The growing OCO-3 dataset includes the standard ocean and land measurements, as well as a large set of validation measurements over TCCON stations and a new locally focused measurement. The new Snapshot Area Map (SAM) mode, where 80km by 80km areas are sampled with 2km by 2km footprints in 2 minutes is measurement approach unique to OCO-3. This is a new observation mode made possible by the agile pointing mirror assembly of OCO-3. Data has been collected over hundreds of cities, volcanos, over areas of interest to the terrestrial carbon community, and in coordination with field campaigns. The cross comparison of OCO-3 and OCO-2 data, for radiances, XCO2, and SIF is underway to gain insights into data quality and to create and OCO-3 dataset that can be used seamlessly with OCO-2 measurements. We will discuss these intercomparisons, highlighting a few examples, such as the OCO-2 and OCO-3 target and SAM measurements in Los Angeles that were collected on the same day. Highlights from validation activities and global XCO2 data characteristics will be presented, as well as details of the SAM collection statistics and most sampled regions. The value of the OCO-3 dataset for characterization of diurnal patterns will also be shared. Highlights of the key scientific findings from the mission to date will be included. Finally, looking forward, I will also discuss the mission status, including the expectations for the remaining mission life and progress on developing an improved data version to be released in late spring/early summer 2021.