Dustin Roten

NASA's Orbiting Carbon Observatory‐2 (OCO‐2) and OCO‐3 have been observing Earth's atmosphere from space since September 2014 and August 2019, respectively. The version 11 (v11) Atmospheric Carbon Observations from Space (ACOS) Level 2 full physics (L2FP) retrieval algorithm has been used to provide estimates of the column‐averaged dry‐air mole fraction of carbon dioxide () from OCO measurements of surface reflected near‐infrared solar radiation. In this work, updates to the ACOS v11 retrieval algorithm are discussed, and the quality filtering and bias correction of the products are described. estimates from OCO‐2 and OCO‐3 are compared to Total Carbon Column Observing Network (TCCON) observations and to atmospheric inversion models, which serve as truth‐proxies for . In addition, a direct comparison of collocated soundings between OCO‐2 and OCO‐3 is performed. Variations in v11 over small areas show improvement in agreement between actual and theoretical uncertainties, suggesting a reduction in systematic errors compared to v10, especially for OCO‐3. There is reduced variance in the new v11 product relative to truth‐proxies, including less divergence in the values with time against models. The collocations of observations between OCO‐2 and OCO‐3 indicate an overall mean difference of <0.2 ppm across the overlapping time range. Furthermore, OCO‐2 and OCO‐3 agree better with each other than they do with the validation sources, suggesting a well conditioned and unified set of concentrations that can be used concurrently for carbon cycle studies to provide a benchmark record spanning September 2014 through August 2026.

A growing number of space‐based platforms, like the Orbiting Carbon Observatory (OCO‐2 and OCO‐3) missions, observe Earth's atmospheric carbon dioxide concentrations with high accuracy and precision. With the original goal of constraining natural fluxes at regional to global scales, these instruments have now become popular tools for studying anthropogenic emissions from cities around the world. As signatories of the Paris Climate Agreement are expected to produce nationally determined contributions (NDC) to global carbon emissions, continued monitoring, reporting, and verification (MRV) of these estimates will be essential. While the use of OCO‐2 and OCO‐3 missions for MRV purposes is increasing, several physical and environmental factors limit data collection. Using the continental United States as a test case, the influences of orbital mechanics and environmental factors on local‐ and national‐level emissions estimates are explored through a series of linear and multi‐linear regressions to predict each instrument's effective revisit time. Results suggest that, due to environmental factors, western regions of the U.S. are more likely to be constrained at a sub‐annual scale than eastern regions, with effective instrument revisit times days. East coast cities have effective revisit times days; however, this varies seasonally. The characteristics of the instruments' orbits also vary the frequency of urban observations in both space and time. Implications for observation‐derived emission estimates at local and national scales and remedies for such shortcomings in future missions are discussed.

The concentration of carbon dioxide (CO₂) in Earth's atmosphere is increasing due to human activities and the resulting effects on the global climate system have initiated several policy‐driven approaches to reduce emissions of this greenhouse gas. Quantifying the effectiveness of such policies requires both bottom‐up and top‐down approaches to estimate CO₂ emissions. This work investigates, for the first time, the potential of using Snapshot Area Map observations from NASA's OCO‐3 instrument to disaggregate sector‐specific emissions from instrument observations. Optimized sector‐specific timeseries were produced using Bayesian inversion techniques and compared to proxy activity data from transportation, commercial maritime, and industrial sectors in the Los Angeles Basin. Results demonstrate that dense space‐based observations of atmospheric CO₂ are capable of disentangling sector‐specific CO₂ fluxes, paving the way for accurate monitoring of the effects of carbon‐reduction policies and operational carbon monitoring systems.

Bottom-up accounting methods of carbon dioxide (CO₂) emissions can provide high-resolution emissions estimates at a global scale; however, the necessary in situ observations to verify these emissions are limited in coverage. Space-based observations of CO₂ in the Earth’s atmosphere expand this coverage to a near-global scale to inform carbon cycle science and record emission trends. This work applied an observing system simulation experiment (OSSE) to characterize the flux information contained in “Snapshot Area Map” (SAM) CO₂ measurements from the Orbiting Carbon Observatory-3 (OCO- 3). Unlike previous space-based carbon-observing systems, OCO-3 SAMs provide spatially dense observations of CO₂ over targeted urban areas at unprecedented coverage. A Bayesian inversion using synthetic data was applied to these SAMs to explore their effectiveness in optimizing estimates of fossil fuel CO₂ (FFCO₂) emissions from the Los Angeles Basin. Results demonstrated that errors in the locations of large point sources diminished the inversion’s ability to reduce errors at the sub-city-level. Furthermore, reductions in atmospheric transport error exacerbated these issues. Only after geolocation errors in large point source locations were removed and atmospheric transport error was reduced did individual SAM observations provide modest corrections to prior flux estimates. The aggregation of multiple SAMs proved to be effective in reducing systematic errors in manufacturing- and transportation-related estimates, demonstrating the need for similar measurements in future space-based missions.

A growing constellation of satellites is providing near‐global coverage of column‐averaged CO₂ observations. Launched in 2019, NASA’s OCO‐3 instrument is set to provide XCO₂ observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO‐3 observations and future space‐based column‐observing missions. However, OCO‐3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X‐STILT model to generate upwind influence footprints with less computational expense. The method uses X‐STILT generated influence footprints from a key subset of OCO‐3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO‐3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO‐3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO₂ missions are also discussed.