A 382-year reconstruction of August mean minimum temperature from tree-ring maximum latewood density on the southeastern Tibetan Plateau, China

2016 ◽  
Vol 37 ◽  
pp. 1-8 ◽  
Author(s):  
Hanxue Liang ◽  
Lixin Lyu ◽  
Muhammad Wahab
2021 ◽  
Vol 251 ◽  
pp. 106712
Author(s):  
Maierdang Keyimu ◽  
Zongshan Li ◽  
Guohua Liu ◽  
Bojie Fu ◽  
Zexin Fan ◽  
...  

2013 ◽  
Vol 104 ◽  
pp. 23-33 ◽  
Author(s):  
Xiaohong Liu ◽  
Xiaomin Zeng ◽  
Steven W. Leavitt ◽  
Wenzhi Wang ◽  
Wenling An ◽  
...  

2021 ◽  
Vol 17 (6) ◽  
pp. 2381-2392
Author(s):  
Maierdang Keyimu ◽  
Zongshan Li ◽  
Bojie Fu ◽  
Guohua Liu ◽  
Fanjiang Zeng ◽  
...  

Abstract. Trees record climatic conditions during their growth, and tree rings serve as proxy to reveal the features of the historical climate of a region. In this study, we collected tree-ring cores of hemlock forest (Tsuga forrestii) from the northwestern Yunnan area of the southeastern Tibetan Plateau (SETP) and created a residual tree-ring width (TRW) chronology. An analysis of the relationship between tree growth and climate revealed that precipitation during the non-growing season (NGS) (from November of the previous year to February of the current year) was the most important constraining factor on the radial tree growth of hemlock forests in this region. In addition, the influence of NGS precipitation on radial tree growth was relatively uniform over time (1956–2005). Accordingly, we reconstructed the NGS precipitation over the period spanning from 1600–2005. The reconstruction accounted for 28.5 % of the actual variance during the common period of 1956–2005. Based on the reconstruction, NGS was extremely dry during the years 1656, 1694, 1703, 1736, 1897, 1907, 1943, 1982 and 1999. In contrast, the NGS was extremely wet during the years 1627, 1638, 1654, 1832, 1834–1835 and 1992. Similar variations of the NGS precipitation reconstruction series and Palmer Drought Severity Index (PDSI) reconstructions of early growing season from surrounding regions indicated the reliability of the present reconstruction. A comparison of the reconstruction with Climate Research Unit (CRU) gridded data revealed that our reconstruction was representative of the NGS precipitation variability of a large region in the SETP. Our study provides the first historical NGS precipitation reconstruction in the SETP which enriches the understanding of the long-term climate variability of this region. The NGS precipitation showed slightly increasing trend during the last decade which might accelerate regional hemlock forest growth.


1994 ◽  
Vol 42 (1) ◽  
pp. 88-99 ◽  
Author(s):  
Malcolm K. Hughes ◽  
Wu Xiangding ◽  
Shao Xuemei ◽  
Gregg M. Garfin

AbstractMay-June (MJ) and April-July (AJ) precipitation at Huashan in north-central China has been reconstructed for the period A.D. 1600 to 1988 using tree-ring density and width fromPinus armandii. MJ precipitation (based on ring width and maximum latewood density) calibrated and cross-validated against local instrumental data more strongly than AJ precipitation (based only on ring width). A major drought was reconstructed for the mid- and late 1920s, confirmed by local documentary sources. This drought (culminating in 1929) was the most severe of the 389-yr period for MJ and second most severe for AJ, after an event ending in 1683. Neither reconstruction shows much spectral power at frequencies lower than 1 in 10 yr, but both show concentrations of power between 2.1 and 2.7 yr and 3.5 to 9 yr. There are significant correlations between the two reconstructions and a regional dryness/wetness index (DW) based on documentary sources, particularly at high frequencies. These correlations are focused in the 7.6- to 7.3-, 3.8- to 3.6-, and 2.5-yr periods. Using singular spectrum analysis, quasiperiodic behavior with a period close to 7.2 yr was identified in the MJ precipitation reconstruction and in the DW index based on documents.


2011 ◽  
Vol 107 (3-4) ◽  
pp. 633-643 ◽  
Author(s):  
Feng Chen ◽  
Yu-jiang Yuan ◽  
Wen-shou Wei ◽  
Shu-long Yu ◽  
Zi-ang Fan ◽  
...  

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