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The observing strategy of the Geostationary Carbon Observatory (GeoCarb), which is a “step and stare” approach, can lead to distortions in the instrument spectral response function (ISRF) when there are gradients in brightness across instrument field of view. These distortions induce errors in the retrieved trace gases. In order to minimize these errors, the GeoCarb instrument design was modified to include a “slit homogenizer” whose purpose is to scramble the pattern of the incoming light and effectively remove the ISRF distortions caused by the variations in illumination across the slit. As a risk reduction, GeoCarb procured six different homogenizers and had them tested for performance in a benchtop optical system. The major finding is that the homogenizer performance depends strongly on the polarization of the incoming light, with the sensitivity growing as a function of wavelength. The width of the ISRF is substantially smaller when the light is vertically polarized (orthogonal to the slit length) compared to horizontally polarized (parallel to the slit length), and the throughput is accordingly reduced. These effects are due to the effects of the gold coating and high incidence angles present in the GeoCarb homogenizer design, which was verified using a polarization-dependent model generalized from previous homogenizer modeling work. The results strongly recommend controlling the polarization of the light entering a similar implementation using a polarizer, depolarizer, or polarization scrambler for other instruments attempting to mitigate scene illumination non-uniformity effects, as well as a robust characterization of the polarization sensitivity of all key subsystems.

Measurements of column averaged, dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Obersvatory-2 (OCO-2) contain systematic errors and regional scale biases; often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlate with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction of error variance over the operational method using a robust data driven, non-linear method. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a non-linear bias correction, which allows for an increase of sounding throughput by 16 % while maintaining the residual error of the operational correction. The method can readily be applied to future ACOS algorithm updates, OCO-2’s companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.

Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often co-emitted with CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using Measurements of Pollution in The Troposphere (MOPITT) v8 data is performed to estimate fire emissions which are then converted into CO2 fire emissions (called FIREMo) through the use of the emission ratio. These optimized CO2 fire emissions are used to rebalance the CO2 net ecosystem exchange (NEEMo) and respiration (RhMo) with the global CO2 growth rate. Subsequently, in a second step, these rebalanced fluxes are used as priors for a CO2 inversion to derive the NEE and ocean fluxes constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ (IS) CO2 data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Emissions Database (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal, and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 (FIRE3) and GFED4.1s (FIRE4) fires, as well as with an inversion using both Carnegie–Ames–Stanford Approach (CASA)-GFED3 NEE and GFED3 fire priors (priorCMS). After comparison at the different scales, the inversions are evaluated against Total Carbon Column Observing Network (TCCON) data. Comparison of the flux estimates shows that at the global scale posterior net flux estimates are more robust than the different prior flux estimates. However, at the regional scale, we can observe differences in fire emissions among the priors, resulting in differences among the NEE prior emissions. The derived NEE prior emissions are rebalanced in concert with the fires. Consequently, the differences observed in the NEE posterior emissions are a result of the balancing with fires and the constraints provided by CO2 observations. Tropical net flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger net CO2 sources are derived with posterior fire emissions using either FIRE4 or FIREMo in the OCO-2 inversion, in particular for most tropical regions during the 2015 El Niño year. Similarly, larger net CO2 sources are also derived with posterior fire emissions in the in situ data inversion for Tropical Asia. Evaluation with CO2 TCCON data shows lower biases with the three rebalanced priors than with the prior using CASA-GFED3. However, posteriors have average bias and scatter very close each other, making it difficult to conclude which simulation performs better than the other. We observe that the assimilated CO2 data have a strong influence on the global net fluxes among the different inversions. Inversions using OCO-2 (or IS) data have similar emissions, mostly as a result of the observational constraints and to a lesser extent because of the fire prior used. But results in the tropical regions suggest net flux sensitivity to the fire prior for both the IS and OCO-2 inversions. Further work is needed to improve prior fluxes in tropical regions where fires are a significant component. Finally, even if the inversions using the FIREMo prior did enhance the biases over some TCCON sites, it is not the case for the majority of TCCON sites. This study consequently pushes forward the development of a CO–CO2 joint inversion with multi-observations for a possible stronger constraint on posterior CO2 fire and biospheric emissions.

International audience; Australia experienced multi-year drought and record high temperatures, and massive forest fires occurred across the southeast in 2019 and early 2020. In the fire-affected forest areas, understory and often tree canopies were burned, and in-situ observations in late 2020 reported rapid vegetation recovery, including grasses, shrubs, and tree canopies from burned-but-not-dead eucalyptus trees. Considering the strong fire resilience and resistance of eucalyptus trees and above-average rainfall in 2020, we assessed how much and how quickly vegetation structure and biomass changed from loss to post-fire and drought recovery in 2020 for all forest areas in Australia. Here, we analyzed space-borne optical, thermal, and microwave images to assess changes in the structure and function of vegetation using four vegetation indices (VIs), leaf area index (LAI), solar-induced chlorophyll fluorescence (SIF), gross primary production (GPP), and aboveground biomass (AGB). We found that all eight variables show large losses in 2019, driven by fires and climate (drought and high temperature), but large gains in 2020, resulting from the high resilience of most trees to fire and rapid growth of understory vegetation under wet condition in 2020. In 2019, the forest area has an AGB loss of 0.20 Pg C, which is ~15% of the pre-fire AGB. Attribution analyses showed that both fire and climate (prior and co-occurring severe drought and record high temperatures) are responsible for the AGB loss in 2019, approximately 0.09 Pg C (fire) and 0.11 Pg C (climate), respectively. In 2020, the forest area has a total AGB gain of 0.26 Pg C, composed of 0.22 Pg C from fire-affected forest area and 0.04 Pg C from fire-unaffected forest area. Fire-adapted Eucalyptus forests and above-average annual precipitation in 2020 brought by a moderate La Niña drove the recovery of vegetation cover, productivity, and AGB. The results from this study shows the potential of multiple sensors for monitoring and assessing the impacts of fire and climate on the forest areas in Australia and their post-fire recovery.

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 Pg CO2 yr−1 (0.90–1.25 Pg C 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.

Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often used as a tracer of CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using MOPITT v8 data is performed to estimate fire emissions which are then converted in CO2 fire emissions through the use of emission ratio. These CO2 fire emissions allow us, then, to estimate adjusted CO2 Net Ecosystem Exchange (NEE) and respiration which are then used as priors for CO2 inversions constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Database Emissions (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 and GFED4.1s, as well as with an inversion using CASA-GFED3 fire and NEE priors. After comparison at the different scales, the inversions are evaluated against TCCON data. Results show that variations in posterior flux are much smaller across different prior mean fluxes when compared with the data assimilated. However, at global scale and for most of the regions, while the net fluxes remain robust, we can observe differences in fire emissions among the priors, resulting in large adjustments in the Net Ecosystem Exchange (NEE) to match the fires and observations. Tropical flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger CO2 net sources are observed when using GFED4.1s and MOPITT CO prior in CO2 OCO-2 inversions than compared with the other priors, particularly during the 2015 El Niño event for most Tropical regions. Larger CO2 net sources with MOPITT CO and GFED4.1s priors are also observed in Tropical Asia in CO2 in situ inversions than compared with the other priors during the 2015–2016 El Niño period and shows large net emissions than compared to OCO-2 inversions. Evaluation with TCCON suggests that the re-balanced posterior simulated give biases and accuracy very close each other where biases have decreased and variability matches better the validation data than with the CASA-GFED3. Further work is needed to improve prior fluxes in Tropical regions where fires are a significant component.

The Geostationary Carbon Cycle Observatory (GeoCarb), to be launched in 2023, will be capable of measuring solar-induced chlorophyll fluorescence (SIF) and add to the current space-based record which started in 1995. GeoCarb will be unique as it will be the first geostationary satellite capable of sensing SIF over the American continents. Consequently, SIF measurements from GeoCarb can be performed much more flexibly compared to polar-orbiting platforms. With its scan mirror assembly, the instrument can point to any location on the Earth disc. This will allow measurements to be collected at various times of day, and measurement locations can be re-visited several times within a day. In expectation of the launch, we conduct an extensive, SIF-focused simulation study and explore the capability and limitations of the instrument and its particular sampling approach. Using cloud information from real measurements, as well as other observation- and model-based data, we produce over four million atmospheric simulations of GeoCarb measurements that the instrument would see throughout a full day. We then apply dedicated SIF retrieval algorithms on the simulated spectra and investigate the results along with cloud-screening performance and emergent retrieval biases and subsequent bias correction. Finally, we make comparisons with currently operating instruments where appropriate and show future science users of GeoCarb SIF what a typical day of measurements will yield.

The Geostationary Carbon Cycle Observatory (GeoCarb) will make measurements of greenhouse gases over the contiguous North and South American landmasses at daily temporal resolution. The extreme flexibility of observing from geostationary orbit induces an optimization problem, as operators must choose what to observe and when. The proposed scanning strategy for the GeoCarb mission tracks the sun's path from east to west and covers the entire area of interest in five coherent regions in the order of tropical South America east, tropical South America west, temperate South America, tropical North America, and temperate North America. We express this problem in terms of a geometric set cover problem, and use an incremental optimization (IO) algorithm to create a scanning strategy that minimizes expected error as a function of the signal-to-noise ratio (SNR). The IO algorithm used in this studied is a modified greedy algorithm that selects, incrementally at 5 min intervals, the scanning areas with the highest predicted SNR with respect to air mass factor (AF) and solar zenith angle (SZA) while also considering operational constraints to minimize overlapping scans and observations over water. As a proof of concept, two experiments are performed applying the IO algorithm offline to create an SNR-optimized strategy and compare it to the proposed strategy. The first experiment considers all landmasses with equal importance and the second experiment illustrates a temporary campaign mode that gives major urban areas greater importance weighting. Using a simple instrument model, we found that there is a significant performance increase with respect to overall predicted error when comparing the algorithm-selected scanning strategies to the proposed scanning strategy.

We evaluate terrestrial net ecosystem-atmosphere exchange (NEE) of CO2 from nine global inversion systems that inferred fluxes from four CO2 observational sources. We use 98 flights in the central ...

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.

The ability of current global models to simulate the transport of CO2 by mid-latitude, synoptic-scale weather systems (i.e. CO2 weather) is important for inverse estimates of ...

Enhanced CO2 mole fraction bands were often observed immediately ahead of cold front during the Atmospheric Carbon and Transport (ACT)-America mission and their formation mechanism is undetermined....

Spatial–temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate and biodiversity in the Brazilian Amazon. Here we investigate interannual changes in AGB and forest area by analysing satellite-based annual AGB and forest area datasets. We found that the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, the net AGB loss was three times smaller in 2019 than in 2015. During 2010–2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in a net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds that of deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority. Carbon loss from forests occurs through deforestation or the degradation of existing forest. The loss of forest area in the Brazilian Amazon was higher in 2019 than following drought and an El Nino event in 2015, yet degradation drove three times more biomass loss than deforestation from 2010 to 2019.

Since its selection as a NASA Earth Venture mission in late 2016, the Geostationary Carbon Cycle Observatory (GeoCarb) has been in steady development. Launch is planned for 2024, and the instrument will be hosted on a commercial platform in geostationary orbit. Featuring a geostationary view over the western hemisphere, GeoCarb will be able to provide atmospheric total-column trace gas amounts to help answer scientific questions related to the carbon cycle of North and South America such as the quantification of regional- and urban-scale carbon dioxide emissions.GeoCarb’s instrument design features a two-arm grating-type spectrometer with four separate bands at wavelengths 0.765 µm, 1.606 µm, 2.065 µm and 2.323 µm in order to measure atmospheric absorption features of oxygen (O2), carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO). With the spacecraft position being fixed relative to Earth and the instrument’s scan mirror assembly, GeoCarb will be able to selectively point at locations visible from its position over the American continents. As a result, very different sampling strategies can be employed, compared to polar orbiting instruments which are generally limited to revisit periods of days and weeks. For routine operations, the North and South American land masses will be scanned at least once per day – depending on the final choice of scanning strategy, large portions of the American continents could be measured twice per day. Thanks to the flexible scanning capability, there is also the possibility for special campaigns which can feature many repeated measurements over targets of special interest throughout a single day.In this presentation, we summarize the most recent development status of the GeoCarb instrument and the various retrieval algorithms that will be used for data product generation. We will share updates on the impact of sub-slit scene inhomogeneity on retrieval results, and how a slit homogenizer can mitigate those effects. Further, we report on our analyses regarding the correction of the so-called keystone optical aberration. Finally, we provide a detailed overview of GeoCarb’s capabilities for the retrieval of solar-induced chlorophyll fluorescence.

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations (FAO) in 2010, ranking sixth in the world, which is based on a forest definition with tree height > 2 meters . Here, we report a new forest cover data product that used the FAO forest definition ( tree cover > 10 % and tree height > 5 meters at observation time or mature) and was derived from microwave (Phased Array type L-band Synthetic Aperture Radar, PALSAR) and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) images and validated with very high spatial resolution images, Light Detection and Ranging (LiDAR) data from the Ice, Cloud, and land Elevation Satellite (ICESat), and in situ field survey sites. The new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over Australia. PALSAR/MODIS forest map has an overall accuracy of ~95% based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover types. Compared with the canopy height and canopy coverage data derived from ICESat LiDAR strips, PALSAR/MODIS forest map has 73% of forest pixels meeting the FAO forest definition, much higher than the other four widely used forest maps (ranging from 36% to 52%). PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information System. This new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

The TROPOspheric Monitoring Instrument (TROPOMI) is the satellite instrument on board the Copernicus Sentinel-5 Precursor satellite launched on 13 October 2017. Data products include column estimates of ozone, nitrogen dioxide, methane, carbon monoxide, among others, and feature unprecedent high spatial resolution. These new data products provide opportunities to gain detailed insights into emissions and their sources on scales previously not feasible. We present results from five student teams investigating TROPOMI observations in a semester-long statistical and machine learning analysis practicum under the joint guidance of an atmospheric scientist and statistician. The analysis follows agile practices, where initial results inform the next analysis step. The focus is on the United States, specifically the investigation of methane emissions from natural gas production in key geological basins.

The community has been measuring greenhouse gases from space for two decades, starting with SCIAMACHY and proceeding through GOSAT, OCO-2, TROPOMI, GOSAT-2, OCO-3, with many more to come in the future. The GeoCarb mission was selected in 2016 under the Earth Venture Mission program by NASA. GeoCarb will measure CO2, CH4, and CO from geostationary orbit aboard a commercial communications satellite as a hosted payload starting in 2023. In this presentation, we will discuss mission technical progress and program updates, including the recent passage into Phase C on Jan 1, 2020 and plans moving forward with integration and test and eventual launch. Additionally, we will discuss plans for how best to proceed in this brave new world of a true constellation of greenhouse gas sensors, including cross-calibration and use of the data for flux determination.

Previous studies done for the TROPOMI mission (e.g. Meister et al (2017), Hu et al (2016), Landgraf et al (2016)) has shown that inhomogeneous illumination within the field of view causes distortio...

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

The Orbiting Carbon Observatory-2 has been on orbit since 2014, and its global coverage holds the potential to reveal new information about the carbon cycle through the use of top-down atmospheric inversion methods combined with column average CO2 retrievals. We employ a large ensemble of atmospheric inversions utilizing different transport models, data assimilation techniques and prior flux distributions in order to quantify the satellite-informed fluxes from OCO-2 Version 7r land observations and their uncertainties at continental scales. Additionally, we use in situ measurements to provide a baseline against which to compare the satellite-constrained results. We find that within ensemble spread, in situ observations and satellite retrievals constrain a similar global total carbon sink of 3.7 ± 0.5 PgC, and 1.5 ± 0.6 PgC per year for global land, for the 2015–2016 annual mean. This agreement breaks down on smaller regions, and we discuss the differences between the experiments. Of particular interest is the difference between the different assimilation constraints in the tropics, with the largest differences occurring in tropical Africa, which could be an indication of the global perturbation from the 2015–2016 El Niño. Evaluation of posterior concentrations using TCCON and aircraft observations gives some limited insight into the quality of the different assimilation constraints, but the lack of such data in the tropics inhibits our ability to make strong conclusions there.

The Geostationary Carbon Observatory (GeoCarb) will make measurements of greenhouse gases over the land mass in the western hemisphere. The extreme flexibility of observing from geostationary orbit induces an optimization problem, as operators must choose what to observe and when. We express this problem in terms of an optimal subcovering problem, and use an Incremental Optimization (IO) algorithm to create a scanning strategy that minimizes expected error as a function of the signal-to-noise ratio (SNR), and show that this method outperforms the human selected strategy in terms of global error distributions.

The second NASA Earth Venture Mission, Geostationary Carbon Cycle Observatory (GeoCarb), will provide measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and solar-induced fluorescence (SIF) from Geostationary Orbit (GEO). The GeoCarb mission will deliver daily maps of column concentrations of CO2, CH4, and CO over the observed landmasses in the Americas at a spatial resolution of roughly 10 x 10 km. Persistent measurements of CO2, CH4, CO, and SIF will contribute significantly to resolving carbon emissions and illuminating biotic processes at urban to continental scales, which will allow the improvement of modeled biogeochemical processes in Earth System Models as well as monitor the response of the biosphere to disturbance. This is essential to improve understanding of the Carbon-Climate connection. In this paper, we introduce the instrument and the GeoCarb Mission, and we demonstrate the potential scientific contribution of the mission through a series of CO2 and CH4 simulation experiments. We find that GeoCarb will be able to constrain emissions at urban to continental spatial scales on weekly to annual time scales. The GeoCarb mission particularly builds upon the Orbiting Carbon Obserevatory-2 (OCO-2), which is flying in Low Earth Orbit.

We show that transport differences between two commonly used global chemical transport models, GEOS‐Chem and TM5, lead to systematic space‐time differences in modeled distributions of carbon dioxide and sulfur hexafluoride. The distribution of differences suggests inconsistencies between the transport simulated by the models, most likely due to the representation of vertical motion. We further demonstrate that these transport differences result in systematic differences in surface CO2 flux estimated by a collection of global atmospheric inverse models using TM5 and GEOS‐Chem and constrained by in situ and satellite observations. While the impact on inferred surface fluxes is most easily illustrated in the magnitude of the seasonal cycle of surface CO2 exchange, it is the annual carbon budgets that are particularly relevant for carbon cycle science and policy. We show that inverse model flux estimates for large zonal bands can have systematic biases of up to 1.7 PgC/year due to large‐scale transport uncertainty. These uncertainties will propagate directly into analysis of the annual meridional CO2 flux gradient between the tropics and northern midlatitudes, a key metric for understanding the location, and more importantly the processes, responsible for the annual global carbon sink. The research suggests that variability among transport models remains the largest source of uncertainty across global flux inversion systems and highlights the importance both of using model ensembles and of using independent constraints to evaluate simulated transport., Key Points There are systematic differences in transport between two commonly used chemical transport models, TM5 and GEOS‐ChemThese systematic differences lead to significant and meaningful posterior flux uncertainties in atmospheric CO2 flux inversions

Remotely sensed observations of atmospheric composition require an estimate of surface pressure. This estimate can either come from an instrument with sensitivity in an O2 absorption feature in the spectrum, or it can be provided by a numerical weather prediction (NWP) model. In this work, the authors outline an information-based methodology for setting measurement requirements for an active lidar measurement of O2 in the context of the Active Sensing of Carbon Emissions over Nights, Days and Seasons (ASCENDS) mission. The results indicate that the impacts of correlations in the environmentally induced vertical weighting function errors between CO2 and O2 measurements are nontrivial and that the choice of CO2 and O2 wavelengths can lead to a stricter or looser requirement than that of surface pressure considerations alone, which would indicate about a 0.1 % precision for 1mb accuracy. Furthermore, the less sensitive the CO2 measurement is to surface pressure errors, the more difficult it will be for an O2 observation to provide a useful measurement.

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty., A substantial portion of model uncertainty arises from model parameters and structures. Here, the authors show that alternative model structures with data-driven parameters project greater uncertainty in soil carbon responses to climate change than the conventional soil carbon model.

We investigate the ability of column-integrated trace gas measurements from a geostationary satellite to constrain surface fluxes at regional scale. The proposed geoCARB instrument measures CO2, CO and CH4 at a maximum resolution of 3 km east–west × 2.7 km north–south. Precisions are 3 ppm for CO2, 10 ppb for CO and 18 ppb for CH4. Sampling frequency is flexible. Here we sample a region at the location of Shanghai every 2 daylight hours for 6 days in June. We test the observing system by calculating the posterior uncertainty covariance of fluxes. We are able to constrain urban emissions at 3 km resolution including an isolated power-plant. The CO measurement plays the strongest role; without it our effective resolution falls to 5 km. Methane fluxes are similarly well-estimated at 5 km resolution. Estimating the errors for a full year suggests such an instrument would be a useful tool for both science and policy applications.

Remotely sensed observations of atmospheric composition require an estimate of surface pressure. This estimate can either come from an instrument with sensitivity in an O2 absorption feature in the spectrum, or it can be provided by a numerical weather prediction (NWP) model. In this work, the authors outline a information-based methodology for setting measurement requirements for an active measurement of O2 in the context of the Active Sensing of Carbon Emissions over Nights, Days and Seasons (ASCENDS) mission. The results indicate that the impacts of correlations in the spectroscopic errors between CO2 and O2 measurements are nontrivial, and actually tighten the requirement for the O2 measurement by 5–10%.