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).
Nine SSR datasets are collected to derive the global SSR variable. In particular, six datasets contain data from observational stations: two global ground-based measurement datasets (GEBA, WRDC) and four homogenized products at the regional and country levels (Europe, China, Japan and Italy). Three of the adopted datasets are reanalysis data: fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), 20th Century Reanalysis version 3 (20CRv3) data and the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulation output (125). Specifically, the ERA5 data are used to fill the data over oceans and Antarctica, and 20CRv3 data and CMIP6 simulations are used for AI model training and reconstruction.
This paper first homogenizes and grids the most extensive collection of available global SSR station observations. Then, the missing grid boxes and years are spatially interpolated using a convolutional neural network (CNN) approach to obtain a globally covered land surface SSR anomaly dataset. Finally, the reconstructed datasets are initially analysed and evaluated.
The data quality is good.
| # | number | name | type |
| 1 | 41975105 | National Natural Science Foundation of China |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.FIGSHARE.DB6659.2024
https://doi.org/10.6084/m9.figshare.21625079.v1
