Accounting for aerosol scattering in the CLARS retrieval of column averaged CO_2 mixing ratios

Abstract

The California Laboratory for Atmospheric Remote Sensing Fourier transform spectrometer (CLARS‐FTS) deployed at Mount Wilson, California, has been measuring column abundances of greenhouse gases in the Los Angeles (LA) basin in the near‐infrared spectral region since August 2011. CLARS‐FTS measures reflected sunlight and has high sensitivity to absorption and scattering in the boundary layer. In this study, we estimate the retrieval biases caused by aerosol scattering and present a fast and accurate approach to correct for the bias in the CLARS column averaged CO2 mixing ratio product, X_(CO2). The high spectral resolution of 0.06 cm^(−1) is exploited to reveal the physical mechanism for the bias. We employ a numerical radiative transfer model to simulate the impact of neglecting aerosol scattering on the CO_2 and O_2 slant column densities operationally retrieved from CLARS‐FTS measurements. These simulations show that the CLARS‐FTS operational retrieval algorithm likely underestimates CO_2 and O_2 abundances over the LA basin in scenes with moderate aerosol loading. The bias in the CO_2 and O_2 abundances due to neglecting aerosol scattering cannot be canceled by ratioing each other in the derivation of the operational product of X_(CO2). We propose a new method for approximately correcting the aerosol‐induced bias. Results for CLARS X_(CO2) are compared to direct‐Sun X_(CO2) retrievals from a nearby Total Carbon Column Observing Network (TCCON) station. The bias‐correction approach significantly improves the correlation between the X_(CO2) retrieved from CLARS and TCCON, demonstrating that this approach can increase the yield of useful data from CLARS‐FTS in the presence of moderate aerosol loading.

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