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Uncertainties in the global and continental surface solar radiation variations: inter-comparison of in-situ observations, reanalyses, and model simulations

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Abstract

Surface solar radiation (SSR) and energy balance are closely related to the Earth’s climate, water cycle, atmospheric conditions, and thus the human habitat, and accurate tracking of the energy cycle is essential. However, a certain degree of uncertainty still exists in current studies of long-term SSR changes due to the lack of a state-of-the-art benchmark observational dataset. To understand this issue, we present a systematic comparison of SSR from in-situ observations, the Fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), and the The Coupled Model Intercomparison Project—Phase 6 (CMIP6) in this paper. Our results show that: (1) The reverse interpolated station SSR series based on the ERA5 and the CMIP6 multi-models ensemble (MME) reflects the observed SSR values at the local/station scale to a certain degree. Although the underestimation/overestimation varies slightly in different regions/stations, the overall Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are about 15% and 21%, respectively of the observations. (2) Both the ERA5 SSR and the CMIP6 MME roughly reflect the seasonal variability and the spatial distributions of the SSR’s climatological means (1971–2000) from gridded in-situ observations. (3) The SSR from the ERA5 is more consistent with observations than the CMIP6 MME in the temporal variations, long-term trend, and the uncertainties at global and continental scales. The latter has insignificant fluctuations in interannual variability, consistent with the trend in-situ observations only from 1950 to 1990, but fails the significant test (at 5% level) after that except for some regions. Therefore, developing a higher quality global long-term SSR benchmark climatic dataset with more complete coverage is still of great significance for optimizing surface energy balance estimates and studies of the impact of human activities on climate change.

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Data availability

The GEBA data can be downloaded from https://geba.ethz.ch/. The WRDC data were available from http://wrdc.mgo.rssi.ru/. The CMA data were accessible from http://data.cma.cn/data/cdcdetail/dataCode/RADI_MUL_CHN_MON.html. C-LAST 2.0 were collected from https://doi.pangaea.de/10.1594/PANGAEA.919574. The ERA5 data were obtained at https://apps.ecmwf.int/data-catalogues/era5/. The CMIP6 data can be retrieved from https://esgf-node.llnl.gov/search/cmip6/.

Notes

  1. There is a problem with the historic SSR in-situ data in Australia prior to 1988: the original data have been artificially detrended, as the Weather service there was afraid that the instruments were drifting. Therefore, the trend in the historic Australian SSR data prior to 1988 is artificially mostly flat.

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Acknowledgements

This study is supported by the Natural Science Foundation of China (Grant: 41975105) and the National Key R&D Program of China (Grant: 2018YFC1507705; 2017YFC1502301). The Global Energy Balance Archive (GEBA) is co-funded by the Federal Office of Meteorology and Climatology MeteoSwiss within the framework of GCOS Switzerland. Global dimming and brightening research at ETH Zurich is supported by the Swiss National Science Foundation (Grant No. 200020 188601).

Funding

This work was funded by Natural Science Foundation of China (Grant no: 41975105); National Key R&D Program of China (Grant nos: 2018YFC1507705, 2017YFC1502301); Swiss National Science Foundation (Grant no: 200020 188601).

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Boyang Jiao: Software, Data curation, Writing- Original draft preparation, Visualization, Investigation. Qingxiang Li: Methodology, Supervision, Conceptualization, Validation, Writing—Review and Editing. Wenbin Sun: Software, Data curation. Wild Martin: Writing—Review and Editing.

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Correspondence to Qingxiang Li.

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Jiao, B., Li, Q., Sun, W. et al. Uncertainties in the global and continental surface solar radiation variations: inter-comparison of in-situ observations, reanalyses, and model simulations. Clim Dyn 59, 2499–2516 (2022). https://doi.org/10.1007/s00382-022-06222-3

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  1. Qingxiang Li