Uncertainty‐aware machine learning bias correction and filtering for OCO‐2: 1

Using a physics‐based retrieval algorithm, these measurements are inverted to estimate column‐averaged atmospheric carbon dioxide dry‐air mole fractions (XCO 2 ). However, biases are present in the retrieved XCO 2 due to sensor calibration errors and discrepancies between the physics‐based retrieval and nature.

We propose a Random Forest (RF), a non‐linear, interpretable machine learning (ML) technique, to correct these biases. The approach is rigorously validated, comes with quantified uncertainties, and is derived independent of carbon flux models. Compared to the operational approach, our method reduces unphysical variability over land and ocean and shows closer agreement with independent ground‐based observations from the Total Carbon Column Observing Network.