Correcting 3D cloud effects in X CO 2 retrievals from the Orbiting Carbon Observatory-2 (OCO-2)

Steffen Mauceri, Steven T. Massie, K. Sebastian Schmidt

Atmospheric measurement techniques · 21 March 2023 · NASA, California Institute of Technology (Caltech)

In one sentence. Interpretable non-linear ML bias correction removes 3D cloud artifacts from OCO-2 XCO2, cutting unphysical variability by 31% over land and 55% over ocean.

Abstract

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.

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