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 Roten1, John C. Lin1, Lewis Kunik1, Derek Mallia1, Dien Wu2, Tomohiro Oda3,4,5, and Eric A. Kort6 Dustin Roten et al.
  • 1Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT
  • 2Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA
  • 3The Earth from Space Institute, Universities Space Research Association, Columbia, MD, USA
  • 4Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
  • 5Graduate School of Engineering, Osaka University, Suita, Osaka, Japan
  • 6Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA

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 et al.

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 et al.

Model code and software

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

Dustin Roten et al.

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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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