Sample Bias Estimation for Cloud-Free Aerosol Effects Over Global Oceans

Abstract

Satellite-based methods for estimating the top-of-atmosphere shortwave direct radiative effect (SWRE) either use the spatial distribution of aerosol optical thickness (AOT) coupled with radiative transfer calculations or combine the AOT with broadband radiative energy data sets such as the Clouds and the Earth's Radiant Energy System (CERES). The first approach typically utilizes the AOT at a spatial resolution of from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the second method relies on the same AOT, but it is convolved within the CERES footprint and has spatial resolutions that are greater than . Therefore, the SWRE may vary as a result of this difference in spatial resolution that we call sample bias. We correct for this sample bias using the AOT reported at the MODIS and the CERES product levels coupled with the radiative efficiency (SWRE per-unit optical depth) for 13 regions over the ocean as a function of season between December 2003 and November 2004 and demonstrate that the sample biases are seasonally and spatially dependent. Overall, nearly 75% of the pixels over the global oceans require a sample bias adjustment of some form. However, the adjustment is large , which is less than 7% of the time, primarily during the spring and summer months, in association with large dust aerosol concentrations with large optical depth gradients. If sample biases are not accounted for, they will globally reduce the SWRE by an average of 30% (4.1 versus ), although regionally, the adjustment could be larger (). We argue that these bias corrections are robust and simpler to use when compared with methods that employ narrow- to broadband relationships.