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Atmospheric inversions provide estimates of carbon dioxide (CO2) fluxes between the surface and atmosphere based on atmospheric CO2 concentration observations. The number of CO2 observations is projected to increase severalfold in the next decades from expanding in situ networks and next‐generation CO2‐observing satellites, providing both an opportunity and a challenge for inversions. This study introduces the TRACE Regional Atmosphere–Carbon Ensemble (TRACE) system, which employ an ensemble‐based simultaneous state and parameter estimation (ESSPE) approach to enable the assimilation of large volumes of observations for constraining CO2 flux parameters. TRACE uses an online full‐physics mesoscale atmospheric model and assimilates observations serially in a coupled atmosphere–carbon ensemble Kalman filter. The data assimilation system was tested in a series of observing system simulation experiments using in situ observations for a regional domain over North America in summer. Under ideal conditions with known prior flux parameter error covariances, TRACE reduced the error in domain‐integrated monthly CO2 fluxes by about 97% relative to the prior flux errors. In a more realistic scenario with unknown prior flux error statistics, the corresponding relative error reductions ranged from 80.6% to 88.5% depending on the specification of prior flux parameter error correlations. For regionally integrated fluxes on a spatial scale of 106 km2, the sum of absolute errors was reduced by 34.5%–50.9% relative to the prior flux errors. Moreover, TRACE produced posterior uncertainty estimates that were consistent with the true errors. These initial experiments show that the ESSPE approach in TRACE provides a promising method for advancing CO2 inversion techniques. Plain Language Summary: To gain a better understanding of the main drivers behind atmospheric carbon dioxide (CO2) variations and trends, it is essential to accurately quantify CO2 exchanges—also known as fluxes—between the atmosphere and other components of the Earth system. It is generally not possible to directly measure surface CO2 fluxes at regional scales; however, fluxes can be inferred from a network of atmospheric CO2 concentration observations combined with atmospheric modeling and inversion methods, which seek to find the most likely fluxes given prior knowledge and observational evidence. This paper presents a new regional inversion framework called TRACE Regional Atmosphere–Carbon Ensemble (TRACE) for deriving CO2 fluxes at high resolution in space and time. One of the major innovations in TRACE is the ensemble‐based dual state and parameter estimation approach, which makes it computationally feasible to ingest large volumes of observations into the system. A series of experiments were carried out in a domain over North America to test the new framework in a controlled setting. In the experiments, we used synthetic tower observations of atmospheric CO2 concentrations to constrain parameters controlling terrestrial biogenic and oceanic CO2 fluxes. The results show that TRACE is capable of accurately estimating both the magnitude and spatial pattern of regional CO2 fluxes. Key Points: An ensemble‐based simultaneous state and parameter estimation approach is introduced for regional CO2 flux inversionsThe new dual‐state TRACE Regional Atmosphere–Carbon Ensemble (TRACE) framework provides a flexible platform for conducting coupled atmosphere–carbon data assimilationControlled experiments show that TRACE is effective at constraining regional CO2 fluxes using in situ CO2 concentration observations [ABSTRACT FROM AUTHOR]

Computational requirements often impose limitations on the spatial and temporal resolutions of atmospheric CO2 inversions, increasing aggregation and representation errors. This study enables higher spatial and temporal resolution inversions with spatial and temporal error structures similar to those used in other published inversions by representing the prior flux error covariances as a Kronecker product of spatial and temporal covariances and by using spectral methods for the spatial correlations. Compared to existing inversion systems that are forced to degrade the resolution of the problem in order to bring the dimensionality down to computationally tractable levels, this inversion framework is able to take advantage of mesoscale transport simulations and more of the complexity of spatial and temporal covariances in the surface CO2 fluxes. This approach was successfully implemented over one month with an identical‐twin observing system simulation experiment (OSSE) using a set of assumptions about the prior flux uncertainties compatible both with continental‐scale uncertainty estimates and with comparisons of vegetation models to flux towers. The demonstration illustrates the potential of the newly developed inversion system to use high‐temporal‐resolution information from the North American tower network, to extract high‐resolution information about CO2 fluxes that is inaccessible to coarser resolution inversion systems, and to simultaneously optimize an ensemble of prior estimates. This demonstration sets the stage for regional flux inversions that can take full advantage of the high‐resolution data available in tower CO2 records and mesoscale atmospheric transport reanalyses, include more realistic prior error structures, and explore specifying prior fluxes with ensembles. Plain Language Summary: This paper describes a new inverse framework for deriving surface carbon dioxide (CO2) fluxes at high spatial and temporal resolutions from atmospheric mole fraction measurements. We demonstrate the potential of the system over North America by inverting for subdaily biogenic CO2 fluxes while accounting for the complexity of spatio‐temporal structures in flux error covariances in a data‐based manner. Key Points: A new inversion system uses spectral methods for spatial correlations to do a 27 km subdaily inversion over North AmericaThe software package allows simultaneous optimization of multiple prior mean flux estimatesThe system is demonstrated over continental North America for a network of seven towers [ABSTRACT FROM AUTHOR]

The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature‐based climate‐change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower‐cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower‐cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low‐frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower‐cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint. Plain Language Summary: Vegetation around the world is critical to how our planet functions. Plants regulate how sunlight, rain, and even carbon dioxide are used and recycled. In order to learn about plant responses to rain, sunlight, temperature, and climate change, scientists measure the exchanges of water, energy, and carbon between the atmosphere and the land surface. However, the instruments used for these measurements are expensive, so they are only installed in a small number of locations around the entire planet. This is a problem for two reasons: First, these instruments are more common in more wealthy countries giving a distorted view of the world. Second, often only one instrument is installed, meaning we must assume that the area measured is exactly the same as the entire landscape, when this is often not true. To solve this problem, we built a much cheaper version of the instruments, which costs just one‐quarter of the usual price. To test our new instruments, we installed them next to the conventional instruments in two locations. We found strong agreement between our cheaper instruments and the high‐cost ones. This is good because cheaper instruments could be used more widely to improve the measurement of important ecosystems. Key Points: Lower‐cost, low‐frequency eddy covariance (EC) systems yield evapotranspiration and CO2 fluxes that correspond strongly with high‐frequency EC systemsLower‐cost EC systems enable increased spatial sampling and replication, improving confidence in retrieved evapotranspiration and CO2 fluxesLower‐cost EC systems may therefore address the undersampling and spatial biases in existing EC studies and networks [ABSTRACT FROM AUTHOR]

This study systematically examines the regional uncertainties and biases in carbon dioxide (CO2) mole fractions from two of the state‐of‐the‐art global CO2 analysis products, namely, the Copernicus Atmosphere Monitoring Service (CAMS) real‐time atmospheric analysis from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the CarbonTracker Near‐Real‐Time (CT‐NRT) reanalysis from the National Oceanic and Atmospheric Administration (NOAA), by evaluation against hundreds of hours of airborne in situ measurements from the summer 2016 and winter 2017 Atmospheric Carbon and Transport (ACT)‐America field campaigns. Both the CAMS and CT‐NRT analyses agree reasonably well with the independent ACT‐America airborne CO2 measurements in the free troposphere, with root‐mean‐square deviations (RMSDs) between analyses and observations generally between 1 and 2 ppm but show considerably larger uncertainties in the atmospheric boundary layer where the RMSDs exceed 8 ppm in the lowermost 1 km of the troposphere in summer. There are strong variations in accuracy and bias between seasons, and across three different subregions in the United States (Mid‐Atlantic, Midwest, and South), with the largest uncertainties in the Mid‐Atlantic region in summer. Overall, the RMSDs of the CAMS and CT‐NRT analyses against airborne data are comparable to each other and largely consistent with the differences between the two analyses. The current study provides uncertainty estimates for both analysis products over North America and suggests that these two independent estimates can be used to approximate regional CO2 analysis uncertainties. Both statistics are important in future studies in quantifying the uncertainties in regional CO2 mole fraction and flux estimates. Key Points: Two global CO2 analysis products are compared with airborne in situ data collected during the first two ACT‐America field campaignsBoth analyses agree reasonably well with observations but show considerable biases in CO2 in the Mid‐Atlantic region during summer 2016The two independent analysis products can be used to quantify the overall analysis uncertainties in estimated CO2 mole fractions [ABSTRACT FROM AUTHOR]

Abstract: Carbon cycling in the coastal zone affects global carbon budgets and is critical for understanding the urgent issues of hypoxia, acidification, and tidal wetland loss. However, there are no regional carbon budgets spanning the three main ecosystems in coastal waters: tidal wetlands, estuaries, and shelf waters. Here we construct such a budget for eastern North America using historical data, empirical models, remote sensing algorithms, and process‐based models. Considering the net fluxes of total carbon at the domain boundaries, 59 ± 12% (± 2 standard errors) of the carbon entering is from rivers and 41 ± 12% is from the atmosphere, while 80 ± 9% of the carbon leaving is exported to the open ocean and 20 ± 9% is buried. Net lateral carbon transfers between the three main ecosystem types are comparable to fluxes at the domain boundaries. Each ecosystem type contributes substantially to exchange with the atmosphere, with CO2 uptake split evenly between tidal wetlands and shelf waters, and estuarine CO2 outgassing offsetting half of the uptake. Similarly, burial is about equal in tidal wetlands and shelf waters, while estuaries play a smaller but still substantial role. The importance of tidal wetlands and estuaries in the overall budget is remarkable given that they, respectively, make up only 2.4 and 8.9% of the study domain area. This study shows that coastal carbon budgets should explicitly include tidal wetlands, estuaries, shelf waters, and the linkages between them; ignoring any of them may produce a biased picture of coastal carbon cycling. [ABSTRACT FROM AUTHOR]