Abstract Many of today's most significant and urgent scientific questions, including those about the impact of human activity on air quality and radiative forcing, require the synthesis of observations of atmospheric constituents and scientific theory. The past three decades have each seen a step‐change in the coverage, accuracy, and precision of constituent observations, and hence the questions they can address, especially from the growing constellation of Earth‐observing satellites. This paper is the first comprehensive description of the design and implementation of NASA's Constituent Data Assimilation System (CoDAS), a software package for assimilating constituent observations into the Goddard Earth Observing System (GEOS), an integrated collection of Earth system models. CoDAS is a generalized, tracer‐agnostic system that extends previous capabilities to assimilate multiple species from multiple sensors configurable at run time. Data assimilation provides a means of monitoring constituent changes, intercomparing heterogeneous types of observations, and reconciling data and models. Assimilation produces an optimal synthesis of the ingested data and model, taking advantage of the fact that the statistics of model‐data differences are simpler than those of the observations themselves. The use of a model enables the propagation of information from all previous observations across space and time, which can improve estimates even where and when data are unavailable.

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.

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO2 concentrations and airborne observations and then isolate the CO2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO2 flux estimates.

In this work, we present the results of an observing system simulation experiment (OSSE) in which we investigate the emergence of a surface-reflectance-dependent bias in retrieved column-averaged dry-air mole fractions of methane (XCH4). Our focus is on single-band retrievals in the shortwave infrared (SWIR) at 2.3 mu m. This particular bias manifests as artificial gradients in XCH4 fields that relate to surface features on the ground and can, for example, cause erroneous estimates of methane source emission rates.We find that even for near-ideal conditions (that being a perfectly calibrated instrument, perfect knowledge of meteorology and trace gas vertical distributions, and an absence of clouds and aerosols) a surface-reflectance-related bias appears in the retrieved XCH4. While the magnitude of the bias is much lower than is observed in, for example, real data from the TROPOspheric Monitoring Instrument (TROPOMI), the overall qualitative shape is strikingly similar. When we study a more realistic scenario by considering synthetic measurements that are affected by aerosols, the surface bias increases in magnitude roughly by a factor of 10. We hold all other properties of the synthetic measurements fixed and thus can make the following statements about these surface biases from the 2.3 mu m absorption band. First, the bias already appears in the near-perfect scenario, meaning that its origin is likely fundamental to XCH4 retrievals from this particular absorption band and using an optimal-estimation-type retrieval approach. Second, the magnitude of the bias increases significantly when aerosols are encountered. As aerosols give rise to a magnification of the bias, we have implemented a retrieval configuration in which the retrieval algorithm knows the true aerosol abundance profiles along with their optical properties. With this configuration, the surface bias returns mostly to the level first seen when synthetic measurements were not affected by aerosols.The results we present in this work should be considered for new missions where XCH4 is a target quantity and the design relies on the 2.3 mu m absorption band. Since the surface bias will likely emerge, it is crucial that a validation approach is planned which sufficiently samples the needed range of surface reflectance in areas of near-uniform methane concentrations in order to capture the bias and thus correct for it.

Large quantities of carbon are stored in Yedoma permafrost. When temperatures rise, its high ice content is a catalyst for rapid degradation, which in turn may cause the release of large quantities of carbon. 40 % to 70 % of the radiative forcing from this release is expected to be in the form of CH4. In this observing system simulation experiment, we examined the capabilities of three atmospheric GHG monitoring platforms i.e. tall towers, and the TROPOMI and MERLIN satellite instruments, to detect changes in CH4 release from increased Yedoma thaw. A set of environments are simulated with the GEOS-5 model: one representing a “natural” emission case as the reference, a second featuring enhanced CH4 release from Yedoma soils. From within these modelled environments, synthetic measurements are generated following best in situ practices and realistic error characterizations. For the satellites we find the lowest detection limits when aggregating measurements over a 112 d period, at Yedoma fluxes of 144 % to 367 % of current conditions. These factors are up to 1.2 times higher when taking transport modelling uncertainties into account. The tall tower network shows a wide range of detection lower limits, the lowest at only 107 % of current fluxes, but has considerably higher lower detection limits when factoring in transport modelling errors. Overall, the individual systems appear to lack the ability to detect and attribute small changes in Yedoma CH4 fluxes, and would either need to be used in combination or require a considerable time to detect changes under higher emission scenarios.

Direct and remote observations of ocean and atmospheric greenhouse gases (GHGs) provide a critical constraint on global atmospheric burden of GHGs as well as the key natural and anthropogenic processes that transfer GHGs between atmosphere and land and ocean reservoirs. The United States (US) has played a large role in providing observations that span global to local scale in both the ocean and the atmosphere relying on both direct measurements of the atmosphere and ocean from ground, tower, ship, balloon and aircraft-based platforms and remote measurements from satellite, upward looking spectrometers, floats and ocean profilers. While these networks have been instrumental in providing a basic understanding of the carbon cycle there are many gaps that need to be filled over the next decade to assess interannual variability in both natural and anthropogenic sources and sinks of GHGs. With no planned US satellite missions for carbon dioxide in this time period there is an urgent need to take advantage of other gap filling opportunities. For methane, the focus on large point sources for satellites also represents a gap that many assumed would be filled in the next decade. These gaps in planned remote sensing satellite missions reinforce the need to focus development of new planforms, networks and tracers for observing atmospheric and ocean GHGs gradients and processes driving these gradients. These processes include climate/carbon feedbacks as well as changes in anthropogenic emissions across multiple scales that allow stakeholders in pursuit of GHG mitigation and carbon capture efforts to be informed and act with the most up-to-date understanding of critical processes in the global and local carbon budgets. We provide an overview of the observing, analysis and information systems that build on "bottom up" systems that currently inform the Global Stocktake. We also will report on efforts to make this information more actionable for emissions mitigation.

Climate change is exerting severe impacts on the Northern high latitudes. Large quantities of carbon stored in Yedoma, a carbon and ice-rich permafrost soil, become catalysts for rapid degradation and subsequent carbon release as temperatures rise. The resulting radiative forcing, expected to be 40 to 70 percent in the form of CH4, necessitates accurate detection and quantification to properly account for these fluxes into the global carbon budget. Greenhouse gas observation systems monitor GHG concentrations to infer surface-atmosphere exchange processes. In this study The pan-Arctic tall tower network, passive TROPOMI, and active MERLIN satellites are considered as monitoring platforms. We employ an observing system simulation experiment (OSSE). Utilising a 4D atmospheric transport model, synthetic observations are generated through the Goddard Earth Observing System model (GEOS). To which established errors and biases are added. We simulate a disturbance scenario of enhanced methane release from anticipated Yedoma thaw. A nature run with 'natural' emissions serves as the basis when compared to an enhanced CH4 release run from Yedoma soils. The strength of the enhancement is scaled to establish lower detection limits Satellite systems demonstrate lowest detection limits when aggregating data over a 112-day period, requiring a 1.9 to 2.8 times increase in fluxes compared to current conditions for reliable change detection, which is increased by a factor 1.2 when considering transport modelling uncertainties. Tall tower networks exhibit varied detection limits, emphasising the influence of tower placement and transport errors. This research not only informs the development of a comprehensive methane observing system but also sheds light on the challenges and strengths of in situ and satellite-based monitoring in high latitude environments.

As the majority of fossil fuel carbon dioxide (CO 2 ) emissions originate from cities, the use of novel techniques to leverage available satellite observations of CO 2 and proxy species to constrain urban CO 2 is of great importance. In this study, we seek to empirically determine relationships between satellite observations of CO 2 and the proxy species nitrogen dioxide (NO 2 ), applying these relationships to NO 2 fields to generate NO 2 ‐derived CO 2 fields (NDCFs) from which CO 2 emissions can be estimated. We first establish this method using simulations of CO 2 and NO 2 for the cities of Buenos Aires, Melbourne, and Mexico City, finding that the method is viable throughout the year. For the same three cities, we next calculate empirical relationships (slopes) between co‐located observations of NO 2 from the Tropospheric Monitoring Instrument and Snapshot Area Mode observations of CO 2 from Orbiting Carbon Observatory‐3. Applying varying combinations of slopes to generate NDCFs, we evaluate methodological uncertainties for each slope application method and use a simple mass balance method to estimate CO 2 emissions from NDCFs. We demonstrate monthly urban CO 2 emissions estimates that are comparable to emissions inventory estimates. We additionally prove the utility of our method by demonstrating how large uncertainties at a grid cell level (equivalent to ∼1–3 ppm) can be reduced substantially when aggregating emissions estimates from NDCFs generated from all NO 2 swaths (about 1%–6%). Rather than rely on prior knowledge of emission ratios, our method circumvents such assumptions and provides a valuable observational constraint on urban CO 2 emissions.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25 degrees to 0.01 degrees). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Abstract. Multi-inverse modeling inter-comparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources such as atmospheric CO2 observations, transport models, and prior fluxes. However, accurately quantifying ensemble CO2 flux errors remains challenging, often relying on the ensemble spread as a surrogate. This study proposes a method to quantify the errors of regional terrestrial biosphere CO2 flux estimates from 10 inverse models within the Orbiting Carbon Observatory-2 (OCO-2) MIP by using independent airborne CO2 measurements for the period 2015–2017. We first calculate the root-mean-square error (RMSE) between the ensemble mean of posterior CO2 concentration estimates and airborne observations and then isolate the CO2 concentration error caused solely by the ensemble mean of posterior terrestrial biosphere CO2 flux estimates by subtracting the errors of observation and transport in seven regions. Our analysis reveals significant regional variations in the average monthly RMSE over three years, ranging from 0.90 to 2.04 ppm. The ensemble flux error projected into CO2 space is a major component that accounts for 58–84 % of the mean RMSE. We further show that in five regions, the observation-based error estimates exceed the atmospheric CO2 errors computed from the ensemble spread of posterior CO2 flux estimates by 1.37–1.89 times, implying an underestimation of the actual ensemble flux error, while their magnitudes are comparable in two regions. By identifying the most sensitive areas to airborne measurements through adjoint sensitivity analysis, we find that the underestimation of flux errors is prominent in eastern parts of Australia and East Asia, western parts of Europe and Southeast Asia, and midlatitude North America, suggesting the presence of systematic biases related to anthropogenic CO2 emissions in inversion estimates. The regions with no underestimation were southeastern Alaska and northeastern South America. Our study emphasizes the value of independent airborne measurements not only for the overall evaluation of inversion performance but also for quantifying regional errors in ensemble terrestrial biosphere flux estimates.

The size, duration, impact, and cost of wildland fire is increasing over the last several decades. A recent Interagency Council for Advancing Meteorological Services (ICAMS)-sponsored workshop focused on the scientific questions and challenges associated with subseasonal-to-seasonal wildfire outlooks. Opinions from this workshop, including recommended cross-agency motivation and activities, are provided.

Monitoring national and global greenhouse gas (GHG) emissions is a critical component of the Paris Agreement, necessary to verify collective activities to reduce GHG emissions. Top-down approaches to infer GHG emission estimates from atmospheric data are widely recognized as a useful tool to independently verify emission inventories reported by individual countries under the United Nation Framework Convention on Climate Change. Conventional top-down atmospheric inversion methods often prescribe fossil fuel CO _2 emissions (FFCO2) and fit the resulting model values to atmospheric CO _2 observations by adjusting natural terrestrial and ocean flux estimates. This approach implicitly assumes that we have perfect knowledge of FFCO2 and that any gap in our understanding of atmospheric CO _2 data can be explained by natural fluxes; consequently, it also limits our ability to quantify non-FFCO2 emissions. Using two independent FFCO2 emission inventories, we show that differences in sub-annual emission distributions are aliased to the corresponding posterior natural flux estimates. Over China, for example, where the two inventories show significantly different seasonal variations in FFCO2, the resulting differences in national-scale flux estimates are small but are significant on the subnational scale. We compare natural CO _2 flux estimates inferred from in-situ and satellite observations. We find that sparsely distributed in-situ observations are best suited for quantifying natural fluxes and large-scale carbon budgets and less suitable for quantifying FFCO2 errors. Satellite data provide us with the best opportunity to quantify FFCO2 emission errors; a similar result is achievable using dense, regional in-situ measurement networks. Enhancing the top-down flux estimation capability for inventory verification requires a coordinated activity to (a) improve GHG inventories; (b) extend methods that take full advantage of measurements of trace gases that are co-emitted during combustion; and (c) improve atmospheric transport models.

Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year‐to‐year increase in atmospheric CO 2 through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding is not well understood. To improve upon this knowledge, the impact of the 2019 anomalously wet conditions over the Midwest and Southern US on CO 2 vegetation fluxes is examined in the context of 2017–2018 when such precipitation anomalies were not observed. CO 2 is simulated with NASA's Global Earth Observing System (GEOS) combined with the Low‐order Flux Inversion, where fluxes of CO 2 are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power as well as with a bias correction based on insitu observations. Net ecosystem exchange CO 2 tracers are separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO 2 observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15%–25% reduction of annual net carbon uptake in 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non‐crop vegetation, annual net carbon uptake is enhanced in 2019 by about 10%–20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

<p>Atmospheric-based approaches have been recognized as promising tools for QA/QC and verification of greenhouse gas (GHG) emission inventories reported by countries. Atmospheric-based approaches also help provide GHG estimates for countries and regions with less robust inventory building capacities, and direct estimates for key subnational levels, which are not often covered by national inventories. Conventional CO<sub>2</sub> flux inversion approaches, unlike urban inversion applications, often prescribe fossil fuel CO<sub>2</sub> emissions (FFCO2) and mostly optimized natural fluxes. Thus, errors in prescribed FFCO2 impact the final flux estimates.</p><p>We implemented two sets of inversions with two different emission inventories in order to examine the impact of the prescribed FFCO2 on the inverse flux estimates. The emission inventory difference was used to approximate potential errors in the prescribed FFCO2. Our inversion result demonstrated how the FFCO2 errors, particularly due to sub-annual seasonal emission pattern differences, can have an impact on posterior emission estimates via flux optimization. Our result also demonstrated that FFCO2 errors from large emitting countries could significantly bias sub-national flux estimates by mis-attributing their flux corrections to natural fluxes to compensate for the FFCO2 errors. The magnitude of the potential errors might be small compared to that of large-scale fluxes. However, the errors could be significant in relation to small sub-national scale fluxes or emissions from lesser emitting countries. We also examined the impact of two observation systems, such as global in-situ network and a satellite, to FFCO2 errors.</p><p>We discuss the existing challenges that need to be addressed to further enhance the use of atmospheric inversions with country level inventories. In addition to improvements in inversions, improving inventories (prescribed FFCO2) should have a direct benefit to improved accuracy of inverse flux estimates. For example, extended data collection at sub-national scales should greatly mitigate potential errors in the prescribed FFCO2. This study highlights the importance of developing GHG emission information in a hybrid fashion to support science and the emission reporting and monitoring.</p>

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding authors: Monika Kopacz, monika.kopacz@noaa.gov; Victoria Breeze, victoria.breeze@noaa.gov

Here we present a first look at the Gross Primary Production (GPP) forecast skill levels achievable with a state‐of‐the‐art subseasonal‐to‐seasonal (S2S) forecast system. Using NASA's retrospective S2S ensemble forecast in conjunction with a terrestrial biosphere model, and using an independent, remote sensing‐based data set for validation, we demonstrate an ability to accurately forecast spring‐summer carbon uptake at multi‐month leads. Averaged across mid‐ and high latitudes of the Northern Hemisphere land, the GPP forecast initialized on January 1 produces statistically significant skill through summer. The skill achieved, however, is spatially variable, with some regions appearing to extract skill from accurate forecasts of snowpack removal and others extracting skill from the initialization of carbon and nitrogen states. Our results reveal some heretofore unexplored facets of climate predictability and provide a look at what might be possible with future S2S forecast systems that are fully integrated with biogeochemical cycles.

Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA's Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4, and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations.

Abstract. NASA’s return to the Moon coincides with explosive growth in exoplanet discovery. Missions are being formulated to search for habitable planets orbiting other stars, making this the ideal time to deploy an instrument suite to the lunar surface to help us recognize a habitable exoplanet when we see it. We present EarthShine, a technically mature, three-instrument suite to observe the whole Earth from the Moon as an exoplanet proxy. EarthShine data will validate and improve models critical for designing missions to image and characterize exoplanets, thus informing observing strategies for flagship missions to directly image exoplanets. EarthShine will answer interconnected questions in Earth and lunar science, exoplanets, and astrobiology, related to the credo “follow the water.” EarthShine can take advantage of current NASA programs to conduct science from the Moon with low-cost, mature space hardware to reduce risk and assure success. Like the 1968 Apollo Earthrise image of our home planet, lonely in the black sky, the appeal of EarthShine to a multidisciplinary array of researchers in Earth Science, Planetary Science, and astrophysics will maximize both its scientific impact and its impact on the general public.

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.