Preprints
https://doi.org/10.5194/amt-2022-202
https://doi.org/10.5194/amt-2022-202
 
18 Jul 2022
18 Jul 2022
Status: this preprint is currently under review for the journal AMT.

Correcting 3D cloud effects in XCO2 retrievals from OCO-2

Steffen Mauceri1, Steven Massie2, and Sebastian Schmidt2 Steffen Mauceri et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 2Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Colorado 80303, USA

Abstract. The Orbiting Carbon Observatory-2 makes space-based radiance measurements in the Oxygen A-band and the Weak and Strong carbon dioxide (CO2) bands. Using a physics-based retrieval algorithm these measurements are inverted to column-averaged atmospheric CO2 dry-air mole fraction (XCO2). However, the retrieved XCO2 are biased due to calibration issues and mismatches between the physics-based retrieval and nature. Using multiple linear regression, the biases are empirically mitigated. However, a recent analysis revealed remaining biases in the proximity of clouds caused by 3D cloud radiative effects (Massie et al., 2021) in the current processing version B10. Using an interpretable non-linear machine learning approach, we developed a bias correction model to address these 3D cloud biases. The model is able to reduce unphysical variability over land and ocean by 31 % and 55 %, respectively. Additionally, the 3D cloud bias corrected XCO2 show better agreement with independent ground-based observations from the Total Carbon Column Observation Network (TCCON). Overall, we find that OCO-2 underestimates XCO2 over land by -0.4 ppm in the tropics and northward of 45° N. The approach can be expanded to a more general bias correction and is generalizable to other greenhouse gas missions, such as GeoCarb, GOSAT-3 and CO2M.

Steffen Mauceri et al.

Status: open (until 23 Aug 2022)

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Steffen Mauceri et al.

Steffen Mauceri et al.

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Short summary
The Orbiting Carbon Observatory-2 makes space-based measurements of reflected sun light. Using a retrieval algorithm these measurements are converted to CO2 concentrations in the atmosphere. However, the converted CO2 concentrations contain errors for observations close to clouds. Using a simple machine learning approach, we developed a model to correct these remaining errors. The model is able to reduce errors over land and ocean by 31 % and 55 %, respectively.