Estimation of long-term gridded cloud radiative kernel and radiative effects based on cloud fraction

作者：Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, Zhaocong Wu · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-2405-2025 · 被引用次数：4 · 研究领域：Atmospheric aerosols and clouds、Solar Radiation and Photovoltaics、Meteorological Phenomena and Simulations

Abstract. The surface shortwave cloud radiative effect (CRE) plays a critical role in modulating the Earth's energy balance and climate change. However, accurately quantifying the CRE remains challenging due to significant uncertainties in downwelling surface shortwave radiation (DSSR) and cloud parameter estimates, especially in the Arctic. This paper introduces a novel approach that enhances the accuracy of CRE estimation by constructing computationally efficient, long-term gridded surface cloud fraction radiative kernels (GCF-CRKs) and integrating refined DSSR estimates and a high-precision cloud fraction (CF). By leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources, we construct a CF-dependent model to refine DSSR estimates. Based on this model, we construct GCF-CRKs using the CF as the sole perturbation parameter to isolate the CF CRE. Our results indicate that this method significantly improves the accuracy of DSSR estimation under partially cloudy conditions (0 % < CF < 100 %), aligning more closely with ground-based observations. In Arctic-wide validation experiments, the root mean square error (RMSE) was decreased by approximately 2.5 W m−2, and the bias was reduced by 1.23 W m−2, which was an improvement of 8.7 % (reduction in RMSE) against the CERES EBAF (Clouds and the Earth's Radiant Energy System Energy Balanced and...