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Reduction in Sunshine Duration and Related Factors over Mainland China during 1961–2016

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  • Zihao Feng

    (Key Laboratory of Geomatics and Digital Technology of Shandong Province, Shandong University of Science and Technology, Qingdao 266590, China
    College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Bin Guo

    (Key Laboratory of Geomatics and Digital Technology of Shandong Province, Shandong University of Science and Technology, Qingdao 266590, China
    College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shoujia Ren

    (Key Laboratory of Geomatics and Digital Technology of Shandong Province, Shandong University of Science and Technology, Qingdao 266590, China
    College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Yang Li

    (Nuclear and Radiation Safety Center, Ministry of Ecology and Environment, Beijing 100082, China)

Abstract

As a kind of renewable energy, the development and utilization of solar energy is valued by many countries. Sunshine duration (SD), as an important factor to measure solar energy, has also been widely discussed as relevant in terms of distribution and variation. The spatial patterns and variation trends in SD and related factors (wind speed, precipitation, relative humidity, mean temperature and elevation) over mainland China have been studied based on data from 569 meteorological stations during 1961–2016. The results indicated that annual SD decreased significantly at the rate of −40.7 h/10a over mainland China and the decline trend was the most pronounced in the 1980s. Seasonally, the decline rate in SD was the largest in summer (−16.8 h/10a), followed by winter (−9.9 h/10a), autumn (−9.5 h/10a) and spring (−4.5 h/10a), respectively. Spatially, the decline trend in SD was significantly higher in the eastern region than in the western region during 1961–2016, especially in North China. SD was positively correlated with wind speed (R = 0.76); however, it was negatively correlated with mean temperature (R = −0.60) and precipitation (R = −0.41). Moreover, altitude and population density may affect the values and variations of annual SD over mainland China. This study provides a new perspective for the reduction of SD in mainland of China. The drastic changes in SD, such as abrupt changes and sudden decreases, were closely related to volcanic eruptions. Among them, the mean mutation and sudden decrease of SD in the 1980s were due to the long-time weakening of the aerosol accumulated by multiple volcanic eruptions. After the volcanic eruptions in the early 1990s, volcanic aerosols were gradually dissipating, resulting in a small rebound in SD.

Suggested Citation

  • Zihao Feng & Bin Guo & Shoujia Ren & Yang Li, 2019. "Reduction in Sunshine Duration and Related Factors over Mainland China during 1961–2016," Energies, MDPI, vol. 12(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4718-:d:296472
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    References listed on IDEAS

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    1. Osseweijer, Floor J.W. & van den Hurk, Linda B.P. & Teunissen, Erik J.H.M. & van Sark, Wilfried G.J.H.M., 2018. "A comparative review of building integrated photovoltaics ecosystems in selected European countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 1027-1040.
    2. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    3. Temitayo O. Olowu & Aditya Sundararajan & Masood Moghaddami & Arif I. Sarwat, 2018. "Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey," Energies, MDPI, vol. 11(7), pages 1-32, July.
    4. Aynur Kazaz & Seyda Adiguzel Istil, 2019. "A Comparative Analysis of Sunshine Duration Effects in terms of Renewable Energy Production Rates on The LEED BD + C Projects in Turkey," Energies, MDPI, vol. 12(6), pages 1-9, March.
    5. Antonio Barragán-Escandón & Esteban Zalamea-León & Julio Terrados-Cepeda, 2019. "Incidence of Photovoltaics in Cities Based on Indicators of Occupancy and Urban Sustainability," Energies, MDPI, vol. 12(5), pages 1-26, February.
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