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Bias correction and variability attribution analysis of surface solar radiation from MERRA-2 reanalysis

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Abstract

Clarifying the long-term variability of surface solar radiation (SSR) and its driving factors is critical to understanding the energy distribution and climate change. The accuracy of reanalysis SSR products has always been questionable, and serious deviations have significant implications for use. In this study, an effective method to reduce the average bias has corrected the systematic error of the monthly MERRA-2 reanalysis SSR from 1980 to 2015 based on ground observation. This method reduces the error of MERRA-2 SSR and has a good correction effect in Northern North America (NNA), Southern North America (SNA), Europe (EUR), North Africa and the Middle East (NAM), Russia (RUS), East Asia (EA) and Oceania (OCE). The correction reduced the monthly and annual mean errors by 59.61% and 59.15%, respectively. The corrected SSR reproduced seasonal and annual variation similar to the observed data in most areas. In addition, we have developed a method to analyze the influencing factors of SSR, quantifying the contribution of cloud fraction, aerosol optical depth (AOD), and water vapor to the SSR annual variability. In some areas of high cloudiness, such as the Mississippi Plain, the southern Arabian Plateau and the Yunnan-Guizhou Plateau, the negative contribution of clouds to the annual variability of SSR is over − 40%. Dust has an important influence on the downward trend of SSR in NAM. For some highly polluted areas, such as the North China Plain, the contribution of AOD to SSR variability is more than − 35%.

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Code availability

Not applicable.

Data availability

The MERRA-2 reanalysis data is available online (from https://disc.sci.gsfc.nasa.gov/ datasets?keywords=%22MERRA-2%22&page = 1&source = Models%2FAnalyses%20 MERRA-2). The GEBA station data was obtained online (from https://geba.ethz.ch/data-retrieval.html) and the CMA station data was obtained online (from https://data.cma.cn/data/cdcdetail/dataCode/RADI_MUL_CHN_DAY.html).

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 42171386, 41905032, and 41975044). We are grateful to NASA GMAO for providing the MERRA-2 dataset.

Funding

This work was financially supported by the National Natural Science Foundation of China (No. 41905032, 42171386, and 41975044).

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    Authors and Affiliations

    1. Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

      Ziyan Wang, Ming Zhang, Huaping Li & Lunche Wang

    2. Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

      Ming Zhang & Lunche Wang

    3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote sensing, Wuhan University, Wuhan, China

      Wei Gong & Yingying Ma

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    1. Ziyan Wang
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    Contributions

    All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ziyan Wang, Ming Zhang and Huaping Li. The first draft of the manuscript was written by Ziyan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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    Correspondence to Ming Zhang.

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    Wang, Z., Zhang, M., Li, H. et al. Bias correction and variability attribution analysis of surface solar radiation from MERRA-2 reanalysis. Clim Dyn 61, 5613–5628 (2023). https://doi.org/10.1007/s00382-023-06873-w

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