Bias correction and variability attribution analysis of surface solar radiation from MERRA-2 reanalysis

Clarifying the long-term variability of surface solar radiation (SSR) and its driving factors is critical to understanding the energy distribution and climate change. The accuracy of reanalysis SSR products has always been questionable, and serious deviations have significant implications for use. In this study, an effective method to reduce the average bias has corrected the systematic error of the monthly MERRA-2 reanalysis SSR from 1980 to 2015 based on ground observation. This method reduces the error of MERRA-2 SSR and has a good correction effect in Northern North America (NNA), Southern North America (SNA), Europe (EUR), North Africa and the Middle East (NAM), Russia (RUS), East Asia (EA) and Oceania (OCE). The correction reduced the monthly and annual mean errors by 59.61% and 59.15%, respectively. The corrected SSR reproduced seasonal and annual variation similar to the observed data in most areas. In addition, we have developed a method to analyze the influencing factors of SSR, quantifying the contribution of cloud fraction, aerosol optical depth (AOD), and water vapor to the SSR annual variability. In some areas of high cloudiness, such as the Mississippi Plain, the southern Arabian Plateau and the Yunnan-Guizhou Plateau, the negative contribution of clouds to the annual variability of SSR is over − 40%. Dust has an important influence on the downward trend of SSR in NAM. For some highly polluted areas, such as the North China Plain, the contribution of AOD to SSR variability is more than − 35%.