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Tropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric carbon dioxide (CO2) have greatly increased spatial coverage over tropical regions, providing the potential for improved estimates of terrestrial fluxes. Despite this advancement, the spread among satellite‐based and in‐situ atmospheric CO2 flux inversions over northern tropical Africa (NTA), spanning 0–24°N, remains large. Satellite‐based estimates of an annual source of 0.8–1.45 PgC yr−1 challenge our understanding of tropical and global carbon cycling. Here, we compare posterior mole fractions from the suite of inversions participating in the Orbiting Carbon Observatory 2 (OCO‐2) Version 10 Model Intercomparison Project (v10 MIP) with independent in‐situ airborne observations made over the tropical Atlantic Ocean by the National Aeronautics and Space Administration (NASA) Atmospheric Tomography (ATom) mission during four seasons. We develop emergent constraints on tropical African CO2 fluxes using flux‐concentration relationships defined by the model suite. We find an annual flux of 0.14 ± 0.39 PgC yr−1 (mean and standard deviation) for NTA, 2016–2018. The satellite‐based flux bias suggests a potential positive concentration bias in OCO‐2 B10 and earlier version retrievals over land in NTA during the dry season. Nevertheless, the OCO‐2 observations provide improved flux estimates relative to the in situ observing network at other times of year, indicating stronger uptake in NTA during the wet season than the in‐situ inversion estimates. Plain Language Summary: Satellite carbon dioxide (CO2) observations over land imply a major revision to our understanding of the global carbon cycle linked to large emissions from northern tropical Africa (NTA) during the dry season, from October to May. We use aircraft observations made over the Atlantic Ocean in four seasons to evaluate flux models driven by a range of ground and satellite observations. Our results show that models using satellite observations over land overestimate annual emissions from NTA by approximately 1 PgC yr−1, concentrated in the dry season. At other times of year, satellite CO2 observations provide improved estimates of NTA exchange, with a stronger CO2 uptake during the wet season. Key Points: Emergent constraints derived from aircraft carbon dioxide (CO2) measurements and inversions estimate a near neutral northern tropical African CO2 budgetInversions using satellite observations overestimate annual emissions from northern tropical Africa (NTA) by approximately 1 PgC yr−1Satellite CO2 observations imply a strong sink during the wet season over NTA [ABSTRACT FROM AUTHOR]

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime. Plain Language Summary: The magnitude and distribution of China's terrestrial carbon sink remain underconstrained; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals from NASA's Carbon Observatory 2 (OCO‐2) could help reduce this uncertainty. This study revisited China's terrestrial carbon sink estimates based on state‐of‐the‐art OCO‐2 XCO2 inversions, including the Global Carbon Assimilation System OCO‐2 inversion, the Copernicus Atmosphere Monitoring Service OCO‐2 inversion, and those in the OCO‐2 Model Inter‐comparison Project. We found that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. This result suggests that the OCO‐2 XCO2 inversions allow an improved understanding of China's land carbon sink over in situ CO2 inversions and bottom‐up biosphere model simulations, including better representations in spatial distributions and seasonal cycles and more plausible interannual variations. These improvements suggest that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. Key Points: Orbiting Carbon Observatory 2 (OCO‐2) inversions reveal the largest carbon sink in China is in the south on an annual basis, while in the northeast during peak growing seasonThe seasonal cycle appears to be well constrained in the monsoon climate zonesOCO‐2 inversions are able to capture the impacts of climate extremes on China's carbon sink interannual variability [ABSTRACT FROM AUTHOR]

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space‐based CO2 measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO2, and the recent progresses on the inversion of China's land carbon sink using satellite CO2 measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite‐based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space‐based CO2 measurements. [ABSTRACT FROM AUTHOR]

The spatiotemporal variability of terrestrial biospheric carbon dioxide (CO2) fluxes over South Asia has large uncertainty. The Orbiting Carbon Observatory 2 (OCO‐2) satellite provides much‐needed retrievals of column‐average CO2 on a global‐scale, with the highest sensitivity to surface CO2 fluxes and spatiotemporal resolution available to‐date. This study conducted global inverse model simulations, assimilating in situ (IS) data and OCO‐2 retrievals, to assess optimized CO2 net ecosystem exchange (NEE) fluxes for South Asia. Annual Net Biome Exchange (NBE = NEE + biomass burning) fluxes over South Asia were estimated to be near neutral (0.04 ± 0.14 PgC yr−1) using both IS and OCO‐2 observations. The most robust result found by assimilating OCO‐2 observations was the constraint imposed on the seasonal cycle of NBE fluxes. The amplitude of the seasonal cycle of NEE was found to be larger than previously assumed. The OCO‐2 inversion led to an NBE seasonal amplitude of 0.34 PgC month−1, which was larger compared to IS constrained NBE (0.19 PgC month−1) and MsTMIP ensemble mean NEE (0.16 PgC month−1). Moreover, OCO‐2 data imposed a phase shift in the NBE seasonal cycle predicted by the prior model. The larger magnitude of NEE seasonality, and phase shift, simulated when assimilating OCO‐2 observations are in general agreement with previous studies assimilating regional aircraft observations in addition to global IS observations. This result suggests that OCO‐2 provides valuable data that allows for the estimate of NBE on a regional scale in a similar manner as regional in situ aircraft networks. Plain Language Summary: The terrestrial biosphere plays a significant role in the global carbon budget. As biosphere‐atmosphere exchange is one of the largest sources of uncertainty in the global carbon cycle, it is important that we better understand the sources and sinks of biospheric carbon dioxide (CO2). A major limitation for estimating CO2 fluxes from the terrestrial biosphere has historically been the scarcity of measurement data. However, to alleviate this issue, NASA's Orbiting Carbon Observatory 2 (OCO‐2) satellite was launched in 2014 with the goal to improve our understanding about the regional exchange of CO2 between the terrestrial biosphere and atmosphere. This study applied OCO‐2 data focusing on South Asia, a region with highly uncertain spatiotemporal variability of terrestrial biospheric fluxes. When using OCO‐2 data in this study, a larger seasonal amplitude of biospheric CO2 fluxes was estimated compared to what has previously been assumed for this region. A noticeable difference in the temporal variability of the CO2 flux seasonality was also determined when using satellite data. The results of this study suggest that OCO‐2 provides data sufficient for estimating biospheric CO2 fluxes at a regional scale in a similar manner as regional aircraft networks. Key Points: Retrievals of column‐averaged carbon dioxide from NASA's OCO‐2 satellite are applied to constrain biospheric fluxes in South AsiaThe seasonal amplitude of South Asian biospheric fluxes were estimated to be larger than previously assumed for this regionBiospheric CO2 flux seasonality estimated by assimilating OCO‐2 data compares favorably to independent measurements and satellite‐based vegetation indices [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory 2 (OCO-2) satellite has been provided 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 has 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 ten atmospheric inversions all characterized by different transport models, data assimilation algorithm 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 been also used to provide a baseline against which to compare the satellite-driven results. The times series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiments using either OCO-2 or IS data. We then evaluate the inversion ensemble based on dataset from 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. [ABSTRACT FROM AUTHOR]

Abstract The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications. However, the sparse observation network in and around the African continent means that Africa is one of the weakest links in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major source of interannual variability in global atmospheric CO2. Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability are likely to undergo substantial increases through the 21st century.

To check the accuracy of column-average dry air CO2 mole fractions ("XCO2 ") retrieved from Orbiting Carbon Overvatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT)-America flight campaigns. Here we do a lagged correlation analysis of these MFLL-OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances of under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km. The OCO-2 satellite takes many CO2 measurements with small (~3 km²) fields of view (FOVs) in a thin (<10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ~6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10-second-long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10-second averages and their uncertainties, including the constant-correlation-with-distance error model currently being used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL-OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant-correlation error model. To implement this new model, the data are averaged first across 2-second spans, to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. A small percentage of the data that cause non-physical negative averaging weights in the model are thrown out. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land. The new correlation model gives 10-second XCO2 averages that are only a few tenths of a ppm different from the constant-correlation model. Over land, the uncertainties in the mean are also similar, suggesting that the +0.3 constant correlation coefficient currently used in the model there is accurate. Over the oceans, the twice-the-land correlation lengths that we assume here result in a significantly lower uncertainty on the mean than the +0.6 constant correlation currently gives - measurements similar to the MFLL ones are needed over the oceans to do better. Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in those inversion methods that choose to assimilate each X??푂2 retrieval individually, and to account for correlations between separate 10-second averages when these are assimilated instead. [ABSTRACT FROM AUTHOR]

Interannual variations in temperature and precipitation impact the carbon balance of terrestrial ecosystems, leaving an imprint in atmospheric CO2. Quantifying the impact of climate anomalies on the net ecosystem exchange (NEE) of terrestrial ecosystems can provide a constraint to evaluate terrestrial biosphere models against and may provide an emergent constraint on the response of terrestrial ecosystems to climate change. We investigate the spatial scales over which interannual variability in NEE can be constrained using atmospheric CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT). NEE anomalies are calculated by performing a series of inversion analyses using the GEOS-Chem adjoint model to assimilate GOSAT observations. Monthly NEE anomalies are compared to "proxies", variables that are associated with anomalies in the terrestrial carbon cycle, and to upscaled NEE estimates from FLUXCOM. Statistically significant correlations (P<0.05) are obtained between posterior NEE anomalies and anomalies in soil temperature and FLUXCOM NEE on continental and larger scales in the tropics, as well as in the northern extratropics on subcontinental scales during the summer (R2≥0.49), suggesting that GOSAT measurements provide a constraint on NEE interannual variability (IAV) on these spatial scales. Furthermore, we show that GOSAT flux inversions are generally better correlated with the environmental proxies and FLUXCOM NEE than NEE anomalies produced by a set of terrestrial biosphere models (TBMs), suggesting that GOSAT flux inversions could be used to evaluate TBM NEE fluxes. [ABSTRACT FROM AUTHOR]

This study assesses the impact of different state-of-the-science global biospheric CO2 flux models, when applied as prior information, on inverse modeling top-down estimates of terrestrial CO2 fluxes obtained when assimilating Orbiting Carbon Observatory 2 (OCO-2) observations. This is done with a series of Observing System Simulation Experiments (OSSEs) using synthetic CO2 column-average dry air mole fraction (XCO2) retrievals sampled at the OCO-2 satellite spatio-temporal frequency. The OSSEs used the four-dimensional variational (4D-Var) assimilation system with the GEOS-Chem global chemical transport model (CTM) to estimate CO2 net ecosystem exchange (NEE) fluxes using synthetic OCO-2 observations. The impact of biosphere models in inverse model estimates of NEE is quantified by conducting OSSEs using the NASA-CASA, CASA-GFED, SiB-4 and LPJ models as prior estimates and using NEE from the multi-model ensemble mean of the Multiscale Synthesis and Terrestrial Model Intercomparison Project as the truth. Results show that the assimilation of simulated XCO2 retrievals at OCO-2 observing modes over land results in posterior NEE estimates which generally reproduce true NEE globally and over terrestrial TransCom-3 regions that are well-sampled. However, we find larger spread among posterior NEE estimates, when using different prior NEE fluxes, in regions and seasons that have limited OCO-2 observational coverage and a large range in bottom-up NEE fluxes. Posterior NEE estimates had seasonally-averaged posterior NEE standard deviation (SD) of ~ 10 % to ~ 50 % of the multi-model-mean NEE for different TransCom-3 land regions with significant NEE fluxes (regions/seasons with a NEE flux ≥ 0.5 PgC yr−1). On a global average, the seasonally-averaged residual impact of the prior model NEE assumption on posterior NEE spread is ~ 10–20 % of the posterior NEE mean. Additional OCO-2 OSSE simulations demonstrate that posterior NEE estimates are also sensitive to the assumed prior NEE flux uncertainty statistics, with spread in posterior NEE estimates similar to those when using variable prior model NEE fluxes. In fact, the sensitivity of posterior NEE estimates to prior error statistics was larger compared to prior flux values in some regions/times of the Tropics and Southern Hemisphere where sufficient OCO-2 data was available and large differences between the prior and truth were evident. Overall, even with the availability of dense OCO-2 data, noticeable residual differences (up to ~ 20–30 % globally and 50 % regionally) in posterior NEE flux estimates remain that were caused by the choice of prior model flux values and the specification of prior flux uncertainties. [ABSTRACT FROM AUTHOR]

We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions. [ABSTRACT FROM AUTHOR]

We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of Observing System Simulation Experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions as well as inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that in the absence of relative biases between in situ CO2 and OCO-2 XCO2, XCO2-based estimates of terrestrial flux for TRANSCOM-scale land regions are more robust to transport model differences compared to corresponding in situ CO2 inversions. This, however, does not hold for oceanic fluxes or flux estimates for zonal bands. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions. [ABSTRACT FROM AUTHOR]

The sources and sinks of important climatic trace gases such as carbon dioxide (CO2) are often deduced from spatial and temporal variations in atmospheric concentrations. Reducing uncertainties in our understanding of the contemporary carbon budget and its underlying dynamics, however, requires significantly denser observations globally than is practical with in situ measurements. Space-based measurements appear technically feasible but require innovations in data analysis approaches. We develop a variational data assimilation scheme to estimate surface CO2 fluxes at fine time/space scales from such dense atmospheric data. Global flux estimates at a daily time step and model-grid spatial resolution ( here) are rapidly achieved after only a few dozen minimization steps. We quantify the flux errors from existing, planned and hypothetical surface and space-borne observing systems. Simulations show that the planned NASA Orbital Carbon Observatory (OCO) satellite should provide significant additional information beyond that from existing and proposed in situ observations. Improvements in data assimilation techniques and in mechanistic process models are both needed to fully exploit the emerging global carbon observing system. [ABSTRACT FROM AUTHOR]

The sources and sinks of important climatic trace gases such as carbon dioxide (CO2) are often deduced from spatial and temporal variations in atmospheric concentrations. Reducing uncertainties in our understanding of the contemporary carbon budget and its underlying dynamics, however, requires significantly denser observations globally than is practical with in situ measurements. Space-based measurements appear technically feasible but require innovations in data analysis approaches. We develop a variational data assimilation scheme to estimate surface CO2 fluxes at fine time/space scales from such dense atmospheric data. Global flux estimates at a daily time step and model-grid spatial resolution ( here) are rapidly achieved after only a few dozen minimization steps. We quantify the flux errors from existing, planned and hypothetical surface and space-borne observing systems. Simulations show that the planned NASA Orbital Carbon Observatory (OCO) satellite should provide significant additional information beyond that from existing and proposed in situ observations. Improvements in data assimilation techniques and in mechanistic process models are both needed to fully exploit the emerging global carbon observing system. [ABSTRACT FROM AUTHOR]

In this article the authors discuss the Mauna Loa carbon dioxide (CO2) time series begun by C. D. Keeling in 1958 and maintained by the Scripps Institution of Oceanography and the U.S. National Oceanic and Atmospheric Administration's (NOAA's) Earth System Research Laboratory (ESRL). They discuss the time series' role in providing evidence of the impact of human-induced atmospheric increases of CO2 and the impact of NOAA budget cuts on climate change research.

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space-based CO 2 measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO 2 , and the recent progresses on the inversion of China's land carbon sink using satellite CO 2 measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite-based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space-based CO 2 measurements., (© 2022 John Wiley & Sons Ltd.)