Abstract. Surface solar radiation (SSR) is an essential factor in the flow of surface
energy, enabling accurate capturing of long-term climate change and
understanding of the energy balance of Earth's atmosphere system. However, the
long-term trend estimation of SSR is subject to significant uncertainties
due to the temporal inhomogeneity and the uneven spatial distribution of in situ observations. This paper develops an observational integrated and
homogenized global terrestrial (except for Antarctica) station SSR dataset (SSRIHstation) by integrating all available SSR observations,
including the existing homogenized SSR results. The series is then
interpolated in order to obtain a 5∘ × 5∘
resolution gridded dataset (SSRIHgrid). On this basis, we further
reconstruct a long-term (1955–2018) global land (except for Antarctica) SSR
anomaly dataset with a 5∘ × 2.5∘ resolution
(SSRIH20CR) by training improved partial convolutional neural network
deep-learning methods based on 20th Century Reanalysis version 3 (20CRv3). Based on this, we
analysed the global land- (except for Antarctica) and regional-scale SSR trends
and spatiotemporal variations. The reconstruction results reflect the
distribution of SSR anomalies and have high reliability in filling and
reconstructing the missing values. At the global land (except for
Antarctica) scale, the decreasing trend of the SSRIH20CR (−1.276 ± 0.205 W m−2 per decade) is smaller than the trend of the SSRIHgrid (−1.776 ± 0.230 W m−2 per decade) from 1955 to
1991. The trend of the SSRIH20CR (0.697 ± 0.359 W m−2 per decade)
from 1991 to 2018 is also marginally lower than that of the SSRIHgrid
(0.851 ± 0.410 W m−2 per decade). At the regional scale, the
difference between the SSRIH20CR and SSRIHgrid is more significant
in years and areas with insufficient coverage. Asia, Africa, Europe and
North America cause the global dimming of the SSRIH20CR, while Europe
and North America drive the global brightening of the SSRIH20CR.
Spatial sampling inadequacies have largely contributed to a bias in the
long-term variation of global and regional SSR. This paper's homogenized
gridded dataset and the Artificial Intelligence reconstruction gridded
dataset (Jiao and Li, 2023) are both available at https://doi.org/10.6084/m9.figshare.21625079.v1.