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

• Main Authors: A. Merrelli, R. Bennartz, C. W. O'Dell, T. E. Taylor
• Format: Article
• Language: English
• Published: Copernicus Publications 2015-04-01
• Series: Atmospheric Measurement Techniques
• Online Access: http://www.atmos-meas-tech.net/8/1641/2015/amt-8-1641-2015.pdf
• ISSN: 1867-1381; 1867-8548

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 (<i>X</i>_CO₂). 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 <i>X</i>_CO₂ 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.