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CO2 is one of the most important greenhouse gases. Its concentration and distribution in the atmosphere have always been important in studying the carbon cycle and the greenhouse effect. This study is the first to validate the XCO2 of satellite observations with total carbon column observing network (TCCON) data and to compare the global XCO2 distribution for the passive satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT), which are on-orbit greenhouse gas satellites. Results show that since GOSAT was launched in 2009, its mean measurement accuracy was −0.4107 ppm with an error standard deviation of 2.216 ppm since 2009, and has since decreased to −0.62 ppm with an error standard deviation of 2.3 ppm during the past two more years (2014–2016), while the mean measurement accuracy of the OCO-2 was 0.2671 ppm with an error standard deviation of 1.56 ppm from September 2014 to December 2016. GOSAT observations have recently decreased and lagged behind OCO-2 on the ability to monitor the global distribution and monthly detection of XCO2. Furthermore, the XCO2 values gathered by OCO-2 are higher by an average of 1.765 ppm than those by GOSAT. Comparison of the latitude gradient characteristics, seasonal fluctuation amplitude, and annual growth trend of the monthly mean XCO2 distribution also showed differences in values but similar line shapes between OCO-2 and GOSAT. When compared with the NOAA statistics, both satellites’ measurements reflect the growth trend of the global XCO2 at a low and smooth level, and reflect the seasonal fluctuation with an absolutely different line shape.

Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100 000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.

Satellite XCO2 retrievals have been widely used in estimating fossil fuel carbon (FFC) emissions at point and urban scales. However, at the regional scale, it remains a significant challenge. Furthermore, current global and regional atmospheric inversions often overlook the uncertainties associated with FFC emissions. To meet the needs of the global carbon stocktake, we developed an inversion method based on Bayesian statistical theory and OCO-2 satellite XCO2 observations to optimize FFC emissions alongside terrestrial ecosystem carbon fluxes (NEE). The methodology’s core is to distinguish the contributions of NEE and FFC to the observed concentrations using their different spatial distributions. We designed an observing system simulation experiment to invert the 2016 FFC emissions. The results showed that posterior FFC emissions were significantly optimized during the non-growing seasons in the regions with high emissions, with the optimization effect diminishing as emissions shrank. Average FFC emissions uncertainty reductions are in the range of 13–82% in the non-growing season for the eight largest emitting regions globally. By assuming the same uncertainty reduction for FFC emissions in both the growing and non-growing seasons, we can optimize annual emissions for high-emission areas. We believe this study provides a new idea for the inversion of FFC emissions at the regional scale, which is important for achieving the goal of carbon neutrality.

The 2019-20 Australian mega-bushfires, which raged particularly over New South Wales and Victoria, released large amounts of toxic haze and CO2. Here, we investigate whether the resulting CO2 enhancement can be directly detected by satellite observations, based on National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 (OCO-2) column-averaged CO2 (XCO2) product. We find that smoke from wildfires can greatly obscure satellite observations, making the available XCO2 mainly locate over outer fringes of plumes downwind of the major mega-bushfires in eastern Australia in three orbit observations during November-December 2019, with their enhancements of approximately 1.5 ppm. This fire-induced CO2 enhancement is further confirmed using an atmospheric transport model, Goddard Earth Observing System-Chem, forced by satellite observation-derived fire product Global Fire Emissions Database, version 4.1 and wind observations, with comparable simulated XCO2 enhancements. Model simulation also suggests that the sensitivity of the downwind maximum XCO2 enhancement is 0.41 ± 0.04 ppm for 1 TgC d−1 fire emissions. In sum, though detectable to some extent, it remains a challenge to get the accurate maximum XCO2 enhancements due to the gaps in XCO2 detections obscured by smoke. Understanding the capability of OCO-2 XCO2 detection is prerequisite for monitoring and constraining wildfire CO2 emissions by inversions.

In view of the urgent need for high coverage and high-resolution atmospheric CO2 data in the study of carbon neutralization and global CO2 change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO2 datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a 1° × 1° half-monthly average XCO2 dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO2 interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and R2 is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO2 dataset.

Flash droughts– the rapid drying of land and intensification of drought conditions—have devasting impacts to natural resources, food supplies, and the economy. Less is currently known about the drivers of flash droughts and their impact on landscape carbon losses. We leverage carbon and water cycle data from NASA OCO‐2 and Soil Moisture Active and Passive missions to quantify flash drought impacts on U.S. carbon exchange. On average, pre‐onset carbon uptake fully offsets post‐onset losses, creating a carbon neutral biosphere over a ±3 month period surrounding flash drought onset. This contrasts with ecosystem models, which underestimate pre‐onset uptake and overestimate post‐onset loss. Furthermore, spaceborne observations of solar induced fluorescence (SIF) provide a reliable indicator of flash droughts at lead times of 2–3 months, due to feedbacks between vegetation growth and soil water loss. This study is expected to improve understanding of flash drought impacts on carbon exchange, and facilitate flash drought early warning. Plain Language Summary Flash droughts have devasting impacts to the environment, natural resources, and society, and are difficult to predict. Here, we use NASA models and satellite observations to determine (a) the impact of flash drought on carbon exchange in land ecosystems, and (b) the extent to which satellite remote sensing can improve flash drought early warning. We find that beneficial environmental conditions occurring prior to onset of flash drought leads to increases in carbon uptake in ecosystems compared to normal conditions. This anomalous uptake of carbon in ecosystems is, on average, sufficient to fully offset inevitable decreases in carbon uptake associated with hot dry conditions following onset of flash drought, leading to a net zero impact of flash drought on carbon exchange over the 6‐month period surrounding drought onset. Moreover, we find the satellite observations of solar induced chlorophyll fluorescence (SIF), representing a re‐emission of radiation by plants following absorption of sunlight for growth, are extremely well correlated to soil moisture losses associated with flash drought at lead times of 6–12 weeks across diverse landscapes and ecoregions in North America. Satellite SIF thus shows promise as a reliable early warning indicator of flash drought, at sufficient lead time conducive to decision making. Key Points Solar induced fluorescence offers early warning (∼2–3 months) for stealth drought events Pre‐drought carbon gains fully offset post‐drought carbon loss Terrestrial biosphere models overestimate total carbon loss

Fire CO2 emissions are a critical component of the global carbon cycle, yet their estimates remain highly uncertain. This study introduces a satellite‐constrained inversion framework that jointly optimizes fire emissions and net ecosystem exchange using OCO‐2 XCO2 retrievals. An observing system simulation experiment demonstrates the approach's capability to improve emission estimates, especially in regions where fires occur during the non‐growing season. Applied to Africa, the inversion yields fire emissions of 1.18 ± 0.22 PgC yr−1 for 2015–2016––about 20% higher than GFED4s and GFAS averages. Regionally, emissions were underestimated in northern Africa (∼0.25 PgC yr−1) due to missing burned area and overestimated in southern Africa (∼0.05 PgC yr−1) due to inflated fuel assumptions. The inversion reduces inter‐inventory discrepancies by 88% and reveals pronounced landscape‐dependent biases. These findings highlight the potential of XCO2‐based joint inversions to enhance regional emission estimates and improve representations of fire–carbon–climate feedbacks in Earth system models.

Although satellite sensors provide global observations, factors such as cloud interference and narrow swath widths frequently result in partial data gaps which constrain the continuous spatiotemporal analysis of the column-averaged dry air mole fraction of CO2 (XCO2). To address this challenge, this study develops a novel multi-stage fusion framework that integrates GOSAT and OCO-2 data using inverse error variance weighting and a dynamic bias correction technique, generating a seamless monthly XCO2 dataset for East Asia (2016–2024). Validation against TCCON measurements (RMSE = 1.22 ppm; R2 = 0.96) and WDCGG data (RMSE = 2.85 ppm; R2 = 0.76) demonstrates the high accuracy of the product. The results show that the growth rate consistently exceeds 2.2 ppm/year, with clear seasonal patterns characterized by spring maxima and summer minima. Spatially, the locus of rapid growth has shifted toward central and western China, reflecting patterns of regional economic development, while substantial concentrations still persist in the industrialized regions of eastern China, Japan, and South Korea. This study provides new insights into regional atmospheric CO2 dynamics and emphasizes the efficacy of dynamic bias correction in data fusion.

The magnitude and spatial pattern of anomalous net biome exchange (NBE) induced by the 2015/16 El Niño over Amazonian rainforests remain uncertain. We here investigated them using multi‐model posterior NBE products in the Orbiting Carbon Observatory‐2 (OCO‐2) version 10 modeling intercomparison project. Results suggest that relative to the annual NBE average in 2017/18, larger anomalous carbon release occurred over the eastern and northern Amazonian rainforests in 2015/16, with a total flux of approximately 0.4 PgC yr−1 after assimilating satellite‐observed column CO2 concentrations (XCO2) over land. We further find that this anomalous spatial pattern was predominantly determined by soil dryness, while the total positive NBE anomaly was dominated by higher temperature with its contribution of approximately 68～70%. We believe that atmospheric inversions assimilating more satellite‐observed XCO2 in future can provide us more comprehensive understanding how Amazonian rainforests cope with the abiotic stresses induced by El Niño events. Plain Language Summary Interannual variability of carbon flux associated with its drivers over Amazonian rainforests are not fully understood. We here used several groups' newly available posterior CO2 flux estimates to comprehensively investigate the net carbon flux anomaly induced by the 2015/16 extreme El Niño. A total net carbon flux anomaly of approximately 0.4 PgC yr−1 was estimated, which showed larger carbon release over the eastern and northern Amazonian rainforests. We further suggest that although dry conditions greatly shaped the spatial pattern of the anomalous carbon flux, the total carbon flux anomaly was controlled by the higher temperature, with its contribution of approximately 68∼70%. Key Points Net biome exchange (NBE) anomalies over Amazonian rainforests induced by the 2015/16 El Niño were investigated based on multiple atmospheric inversions The spatial pattern of NBE anomaly was regulated by soil water with larger anomalies over the eastern and northern Amazonian rainforests The total NBE anomaly was estimated at about 0.4 PgC yr−1 in 2015/16 relative to the average in 2017/18, dominated by higher temperature

Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO2) emissions. For a better understanding of CO2 variation trends over Asia, in this study, the column-averaged CO2 dry air mole fraction (XCO2) derived from the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker (CT) was compared with that of Greenhouse Gases Observing Satellite (GOSAT) from September 2009 to August 2019 and with Orbiting Carbon Observatory 2 (OCO-2) from September 2014 until August 2019. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the five regions of Asia; i.e., Central Asia, East Asia, South Asia, Southeast Asia, and Western Asia. The results show that XCO2 from GOSAT is higher than the XCO2 simulated by CT by an amount of 0.61 ppm, whereas, OCO-2 XCO2 is lower than CT by 0.31 ppm on average, over Asia. The mean spatial correlations of 0.93 and 0.89 and average Root Mean Square Deviations (RMSDs) of 2.61 and 2.16 ppm were found between the CT and GOSAT, and CT and OCO-2, respectively, implying the existence of a good agreement between the CT and the other two satellites datasets. The spatial distribution of the datasets shows that the larger uncertainties exist over the southwest part of China. Over Asia, NOAA CT shows a good agreement with GOSAT and OCO-2 in terms of spatial distribution, monthly averaged time series, and seasonal climatology with small biases. These results suggest that CO2 can be used from either of the datasets to understand its role in the carbon budget, climate change, and air quality at regional to global scales.