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[Precipitation variation reconstructed based on tree-ring width data for the past 399 years in the eastern Yinshan
Ming-Qi Li1,2, Xue-Mei Shao1,2, Wen-Zheng Nie1,2
1Key Laboratory of Land Pattern and Simulation, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Scien-ces, Beijing 100101, China.
Abstract:
Based on the tree-ring increment cores of Pinus tabuliformis collected from the eastern Yinshan Mountains, the tree-ring width chronology was developed. The correlation coefficients were calculated between the chronology and monthly mean temperature and monthly precipitation during the instrumental period of AD 1952-2007. The results showed that the highest correlation was found between the total precipitation from September of previous year to June of current year and the chronology (r=0.73, n=56, P<0.01). Based on the correlation, the September of previous year to June of current year total precipitation variation was reconstructed in the eastern Yin-shan Mountains during the past 399 years (AD 1619-2017). The reconstruction explained 54.9% of the variation in the total precipitation from September of previous year to June of current year for the calibration period (AD 1952-2007). Both the 'leave-one-out' cross validation and split-period validation showed that the model was relatively robust, with sufficient skills of estimation and high reliability. At the decadal scale, there were four wet periods (AD 1619-1663, AD 1705-1711, AD 1945-1963, and AD 1979-2017)) and four dry periods (AD 1734-1767, AD 1786-1814, AD 1839-1867, and AD 1888-1942) in the past 399 years. Among those periods, the AD 1979-2017 was the wettest period, and AD 1888-1942 was the longest dry duration with the driest period at the late 1920s. Results of power spectral analysis revealed cyclic fluctuations of precipitation series on 2-7 years and 125 years. In addition, the comparison with other reconstructions and spatial correlation analysis indicated that the reconstructed precipitation series well represented regional scale precipitation variation.
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