The potential of clear-sky carbon dioxide satellite retrievals

Abstract

Abstract. Since the launch of the Greenhouse Gases Observing Satellite (GOSAT) in 2009, retrieval algorithms designed to infer the column-averaged dry-air mole fraction of carbon dioxide (X CO 2 ) from hyperspectral near-infrared observations of reflected sunlight have been greatly improved. They now generally include the scattering effects of clouds and aerosols, as early work found that absorption-only retrievals, which neglected these effects, often incurred unacceptably large errors, even for scenes with optically thin cloud or aerosol layers. However, these "full-physics" retrievals tend to be computationally expensive and may incur biases from trying to deduce the properties of clouds and aerosols when there are none present. Additionally, algorithms are now available that can quickly and effectively identify and remove most scenes in which cloud or aerosol scattering plays a significant role.In this work, we test the hypothesis that non-scattering, or "clear-sky", retrievals may perform as well as full-physics retrievals for sufficiently clear scenes. Clear-sky retrievals could potentially avoid errors and biases brought about by trying to infer properties of clouds and aerosols when none are present. Clear-sky retrievals are also desirable because they are orders of magnitude faster than full-physics retrievals. Here we use a simplified version of the Atmospheric Carbon Observations from Space (ACOS) X CO 2 retrieval algorithm that does not include the scattering and absorption effects of clouds or aerosols. It was found that for simulated Orbiting Carbon Observatory-2 (OCO-2) measurements, the clear-sky retrieval had errors comparable to those of the fullphysics retrieval. For real GOSAT data, the clear-sky retrieval had errors 0-20 % larger than the full-physics retrieval over land and errors roughly 20-35 % larger over ocean, depending on filtration level. In general, the clear-sky retrieval had X CO 2 root-mean-square errors (RMSEs) of less than 2.0 ppm, relative to Total Carbon Column Observing Network (TCCON) measurements and a suite of CO 2 models, when adequately filtered through the use of a custom genetic algorithm filtering system. These results imply that non-scattering X CO 2 retrievals are potentially more useful than previous literature suggests, as the filtering methods we employ are able to remove measurements in which scattering can cause significant errors. Additionally, the computational benefits of non-scattering retrievals means they may be useful for certain applications that require large amounts of data but have less stringent error requirements.

The impact of improved aerosol priors on near-infrared measurements of carbon dioxide

“…This is because these contaminants can modify the light path seen by the satellite's sensor in ways that are difficult to quantify. Completely neglecting clouds and aerosols in measurements of X CO 2 can result in errors that exceed 1 % (around 4 ppm of CO 2 ) and can be much larger for scenes containing significant contamination (O'Brien and Rayner, 2002;Aben et al, 2007;Butz et al, 2009). Even when scenes are heavily screened to remove clouds and aerosols, a nonscattering retrieval performs 20 %-40 % worse than one that includes some way to account for scattering effects (Nelson et al, 2016).…”

Validation of OCO-2 error analysis using simulated retrievals

“…In addition, certain tests, where the L2 true state is directly related to the retrieval vector, were simplified by using the older version of the retrieval algorithm which contains a less complicated aerosol scheme. In the older L2 algorithm versions (pre B3.5), the state vector for all soundings always included the same four aerosol types; cloud water, cloud ice, Kahn 1 (a mixture of course and fine mode dust aerosols) and Kahn 2 (carbonaceous 25 mode aerosols) (described more in Nelson et al (2016)). Both Kahn 1 and 2 types contain some sulfate and sea salt aerosols as well.…”

“…Preprocessor code described in Taylor et al (2016) was run on the cloudy-sky L1b simulations to provide realistic cloud screening prior to running the L2 retrieval.…”

Application of a PCA‐Based Fast Radiative Transfer Model to XCO2 Retrievals in the Shortwave Infrared

“…Ignoring scattering altogether, on the other hand, can lead to significant errors in the top-of-the-atmosphere (TOA) radiances, which then leads to errors in the retrieved quantities (Aben et al, 2007;. Nelson et al (2016) have shown that even after filtering for clear-sky conditions, neglecting scattering and absorption by clouds and aerosols can lead to increased errors unless restrictive filtering of the scenes is performed.…”