Estimation of Global Ocean TOA Instantaneous Clear‐Sky Albedo From CERES for Shortwave Cloud Radiative Effect Analysis Based on a Deep Learning Model

Clouds play a crucial role in Earth's climate system, with clear‐sky albedo being fundamental for estimating cloud albedo and the shortwave (SW) cloud radiative effect (CRE), which are key to understanding Earth's radiative balance. However, direct satellite measurements of theoretical clear‐sky albedo for cloudy pixels are impossible. To address this limitation, we developed a Multi‐Layer Perceptron (MLP) model trained on over 20 million samples from the Clouds and the Earth's Radiant Energy System (CERES) data set, enabling the estimation of instantaneous clear‐sky albedo at the top of the atmosphere (TOA). The MLP model achieves an RMSE of 0.004 and R 2 of 0.96, having a closer agreement with direct observational products compared to other radiation products, and provides the temporally perfect match to the moderate resolution imaging spectroradiometer instantaneous observations. Furthermore, we correct undetected sub‐resolution cloud contamination and sea‐ice contamination within clear‐sky pixels present in CERES observations. Based on clear‐sky albedo across cloudy regions, the estimated instantaneous noon SW CRE is −113.44 W·m −2 . By employing another MLP model to scale the instantaneous clear‐sky albedo to daily values, the estimated daily CRE is −44.51 W·m −2 , which is 1.02 W·m −2 weaker than that from the CERES Synoptic TOA and surface fluxes and clouds (SYN) product, mainly since imperfect temporal match, as well as the differences in aerosol sources and treatment. The deep learning‐derived clear‐sky albedo and the estimated CRE provide a new approach for research on aerosol‐cloud interactions, cloud feedback mechanisms, and model improvements, offering valuable insights into the field.