Preprints
https://doi.org/10.5194/acp-2022-315
https://doi.org/10.5194/acp-2022-315
06 May 2022
 | 06 May 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

The Information Content of Dense Carbon Dioxide Measurements from Space: A High-Resolution Inversion Approach with Synthetic Data from the OCO-3 Instrument

Dustin Roten, John C. Lin, Lewis Kunik, Derek Mallia, Dien Wu, Tomohiro Oda, and Eric A. Kort

Abstract. 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.

Dustin Roten, John C. Lin, Lewis Kunik, Derek Mallia, Dien Wu, Tomohiro Oda, and Eric A. Kort

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-315', Anonymous Referee #1, 17 Jun 2022
  • RC2: 'Comment on acp-2022-315', Anonymous Referee #2, 18 Jul 2022
  • AC1: 'Comment on acp-2022-315', Dustin D. Roten, 13 Sep 2022
Dustin Roten, John C. Lin, Lewis Kunik, Derek Mallia, Dien Wu, Tomohiro Oda, and Eric A. Kort

Model code and software

Bayesian Inversion Code Kunik et al. https://doi.org/10.5281/zenodo.2655990

Dustin Roten, John C. Lin, Lewis Kunik, Derek Mallia, Dien Wu, Tomohiro Oda, and Eric A. Kort

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Latest update: 02 May 2024
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Short summary
The systems used to monitor carbon dioxide (CO2) emissions from urban areas provides a means to observe and quantify emissions reductions from policy-related reduction efforts. Space-based instruments, such as NASA's Orbiting Carbon Observatory-3 (OCO-3), provides detailed "snapshots" of CO2 emissions from many megacities around the world. This work quantifies the amount of emission "information" contained in these snapshots and uses this information to update previous estimates of urban CO2.
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