Global trends in downward surface solar radiation from spatial interpolated ground observations during 1961-2019

Abstract

AbstractDownward surface solar radiation (SSR) is a crucial component of the Global Energy Balance, affecting temperature and the hydrological cycle profoundly, and it provides crucial information about climate change. Many studies have examined SSR trends, however they are often concentrated on specific regions due to limited spatial coverage of ground based observation stations. To overcome this spatial limitation, this study performs a spatial interpolation based on a machine learning method, Random Forest, to interpolate monthly SSR anomalies using a number of climatic variables (various temperature indices, cloud coverage, etc.), time point indicators (years and months of SSR observations), and geographical characteristics of locations (latitudes, longitudes, etc). The predictors that provide the largest explanatory power for interannual variability are diurnal temperature range and cloud coverage. The output of the spatial interpolation is a 0:5° ×0:5° monthly gridded dataset of SSR anomalies with complete land coverage over the period 1961-2019, which is used afterwards in a comprehensive trend analysis for i) each continent separately, and ii) the entire globe.The continental level analysis reveals the major contributors to the global dimming and brightening. In particular, the global dimming before the 1980s is primarily dominated by negative trends in Asia and North America, while Europe and Oceania have been the two largest contributors to the brightening after 1982 and up until 2019.