Surface Solar Radiation Data and its Long-term Variation

With the rapid development of satellite observation technology, scientists have been able to make relatively accurate estimates of the energy budget at the top of the Earth's atmosphere. However, the estimation error in the Earth's surface energy imbalance is still more than an order of magnitude greater than the changes in radiative forcing caused by the increase in greenhouse gases. Among the various components contributing to the energy imbalance estimation at the Earth's surface, downward shortwave solar radiation represents one of the most important sources of error. At the same time, surface solar radiation (SSR) is not only an important driving data for simulating land surface processes, but also an important indicator for the utilization of renewable solar photovoltaic energy. Therefore, accurate observation of SSR has become a key variable in reducing the associated error. The long-term variations of SSR have always been a major scientific concern for researchers across various fields. This paper starts with a comparison of existing SSR data products, discusses the bias and uncertainty issues in the long-term variations of SSR, and clarify the importance of developing high-quality SSR observation baseline data products. On this basis, the paper introduces a series of researches in recent years, based on the most complete SSR station observation data to date, systematically considering its inhomogeneity and sampling problems, performing systematic homogenization processing and AI reconstruction on station series, and estimating the long-term variations and uncertainty levels of SSRs at global and regional scales. These studies offer new evidence for global and regional climate change observation, detection attribution, and future projection. Finally, the paper presents an outlook on the existing and future challenges in the research on SSR data and its long-term variations.