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Published on: July 3, 2020
[Reconstructing January-June precipitation in Southeastern Shanxi over the past 296 years inferred from tree-ring
Hong-Hua Cao1, Xiao-En Zhao1, Feng Chen1,2
1Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650504, China.
Abstract:
The study of regional historical climate change is limited by the availability of observational data, which is not conducive to understanding long-term climate change. In this study, we used the tree-ring cores of Pinus tabuliformis to establish a tree ring width chronology (RES) from the southeast Shanxi Province, and analyzed the relationship between precipitation and tree-ring width chronology. The results showed that the residual chronology had a good correlation (r=0.636, n=59, P<0.01) with January-June precipitation. A linear regression was used to reconstruct the January-June precipitation for the southeastern Shanxi Province, which accounts for 40.4% of the instrumental precipitation variation during 1724-2019. Dry conditions occurred during 1742-1771, 1830-1848, 1872-1894, 1917-1945, 1961-1981, and 1990-2019, while the periods of 1727-1741, 1772-1829, 1849-1871, 1895-1916 were relatively wet. There were 10 extremely dry years and six extremely wet years during the period from 1724 to 2019. The longest dry periods were 1742-1771 and 1990-2019, while the longest wet period was 1772-1829. Results of spatial climate correlation analyses with gridded land surface data showed that the precipitation reconstruction contained a strong regional precipitation signal for southeast Shanxi Province. Power spectrum analysis of the precipitation reconstruction showed remarkable 2.3, 3.2-3.3, 3.7-3.8, 6.3-6.7, 8.3-8.7 years cycles for the past 296 years, the 2.3 year cycle corresponds to the 'quasi-two-year pulsation', and the 3.2-3.3, 3.7-3.8 and 6.3-6.7 year cycles might have a certain relationship with ENSO. Results of the spatial correlation analysis showed that the reconstructed precipitation series could better represent precipitation changes in the study area.
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