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

Abstract

Downward 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.

An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach

“…The annual average anomaly variations in regions and globally show that Asia, Africa, Europe and North America are the four contributors to the global dimming, while Europe and North America are two major contributors to the brightening. This is in general agreement with the results obtained by previous machine learning (Yuan et al, 2021). In addition, the discrepancy between the SSRIH 20CR and SSRIH grid is more significant in low-coverage areas (right) than in high-coverage regions (left).…”

“…Machine learning is increasingly being used in spatial interpolation, such as the spatial reconstruction of surface temperature datasets (Huang et al, 2022;Kadow et al, 2020) or the spatial and temporal reconstruction of turbulence resolution (Fukami et al, 2021). Furthermore, it shows high accuracy and low uncertainty in reproducing and predicting SSR (Leirvik and Yuan, 2021;Tang et al, 2016;Yuan et al, 2021). However, long-term homogenized SSR datasets with global terrestrial coverage have yet to be developed, resulting in significant uncertainties in assessing global SSR variation (Jiao et al, 2022).…”

Forcing for Multidecadal Surface Solar Radiation Trends Over Northern Hemisphere Continents

“…Cutforth and Judiesch (2007) document dimming in Canada from 1958 to the 1990s. Using a machine learning method to map surface solar radiation observations over the globe, Yuan et al (2021) show brightening from 1982 through 2019 in central and eastern Canada, Mexico, and the Great Plains of the U.S. He et al (2018) show that dimming from 1952 to 1980 was widespread over all of China, however, from 1994 to 2010 brightening was confined to the southern half of the country, while dimming occurred in the North China Plain.…”

“…Using a machine learning method to map surface solar radiation observations over the globe, Yuan et al. (2021) show brightening from 1982 through 2019 in central and eastern Canada, Mexico, and the Great Plains of the U.S. He et al.…”

Climate Transition Risk and the Impact on Green Bonds

“…Long-and short-term climatic trends include changes in the distribution of temperature, precipitation, and cloudiness. Observational studies have found that temperature increases over time, with an increase in all regions on Earth and with an increase in the level, variability, and drivers of the level of temperature; see, for example, Storelvmo et al (2016), Yuan et al (2021), and Kotz et al (2021).…”