Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm has been applied to multi-year records of observations from NASA's Orbiting Carbon Observatory -2 and -3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO<sub>2</sub>) column-averaged dry-air mole fraction (XCO<sub>2</sub>). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO<sub>2</sub> estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO<sub>2</sub> estimates from the two sensors for the time period August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling, but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 ppm and &minus;0.3 ppm for land and ocean observations, respectively. Comparison of XCO<sub>2</sub> estimates to collocated Total Carbon Column Observing Network (TCCON) measurements show root mean squared errors (RMSE) of approximately 0.8 ppm and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO<sub>2</sub> fields derived from atmospheric inversion systems that assimilated only near-surface CO<sub>2</sub> observations, i.e., did not assimilate satellite CO<sub>2</sub> measurements, yielded RMSEs of 1.0 ppm and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of errors in small areas, as well as biases across land-ocean crossings, also show encouraging results, for each sensor and in their agreement. Taken together, our results demonstrate a broad consistency of OCO-2 and OCO-3 XCO<sub>2</sub> measurements, suggesting they may be used together for scientific analyses.

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