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Due to the complexity of the multiple scattering problem for shortwave radiative transfer in Earth's atmosphere, operational physical retrieval algorithms commonly use a plane parallel radiative transfer model (RTM). This so-called one-dimensional (1-D) assumption allows practical retrieval algorithms to be implemented. In order to understand the impacts of this assumption for low altitude, unresolved clouds observed by OCO-2, the three-dimensional (3-D) radiative transfer model SHDOM is used to generate synthetic observations which are then processed by the operational retrieval algorithm based on a 1-D RTM. Simulations are performed over three realistic surface spectral albedos, corresponding to snow, vegetation, and bare soil. The results show that the existing cloud screening algorithm has difficulty identifying sub-field of view (FOV), unresolved clouds that fill less than half of the FOV. The unresolved clouds introduce a bias in the retrieved CO2 concentration, as quantified by the dry air mole fraction (XCO2). The biases are relatively small (less than 1 ppm) when the albedo at 2.1 <abbr>μm</abbr> is high, which is common over bare land surfaces. For cases with low 2.1 <abbr>μm</abbr> albedo, such as snow, the bias becomes much larger, up to 5 ppm. These results indicate that the XCO2 retrieval appears robust to 3-D scattering effects from unresolved low level clouds when the short wave infrared surface albedo is large, but for darker surfaces these clouds can introduce significant biases

Estimating bias in the OCO-2 retrieval algorithm caused by 3-D radiation scattering from unresolved boundary layer clouds