scholarly journals A 414-year tree-ring-based April–July minimum temperature reconstruction and its implications for the extreme climate events, northeast China

2016 ◽  
Vol 12 (9) ◽  
pp. 1879-1888 ◽  
Author(s):  
Shanna Lyu ◽  
Zongshan Li ◽  
Yuandong Zhang ◽  
Xiaochun Wang

Abstract. A ring-width series was used as a proxy to reconstruct the past 414-year record of April–July minimum temperature at Laobai Mountain, northeast China. The chronology was built using standard tree-ring procedures for providing comparable information in this area while preserving low-frequency signals. By analyzing the relationship between the tree-ring chronology of Korean pine (Pinus koraiensis) and meteorological data, we found that the standard chronology was significantly correlated with the April–July minimum temperature (r =  0.757, p < 0.01). Therefore, the April–July minimum temperature since 1600 (more than six trees, but the expressed population signal (EPS) is greater than 0.85 since 1660) was reconstructed by this tree-ring series. The reconstruction equation accounted for 57.3 % of temperature variation, and it was proved reliable by testing with several methods (e.g., sign test, product mean test, reduction of the error, and coefficient of efficiency). Reconstructed April–July minimum temperature on Laobai Mountain showed six major cold periods (1605–1616, 1645–1677, 1684–1691, 1911–1924, 1930–1942, and 1951–1969) and seven major warm periods (1767–1785, 1787–1793, 1795–1807, 1819–1826, 1838–1848, 1856–1873, and 1991–2008) during the past 414 years. The reconstructed low-temperature periods in the 17th and early 18th century were consistent with the Little Ice Age (LIA) in the Northern Hemisphere, and the rate of warming in the 19th century was significantly slower than that in the late 20th century. In addition, the reconstructed series was fairly consistent with the historical and natural disaster records of extreme climate events (e.g., cold damage and frost disaster) in this area. This temperature record provides new evidence of past climate variability, and can be used to predict the climate trend in the future in northeast China.

2016 ◽  
Author(s):  
S. Lyu ◽  
Z. Li ◽  
Y. Zhang ◽  
X. Wang

Abstract. A ring-width series was used as a proxy to reconstruct the past 413-year record of April-July minimum temperature at Laobai Mountain, northeastern China. Chronology was built using standard tree-ring procedures for providing comparable information in this area while preserving low-frequency signals. By analyzing the relationship between the tree-ring chronology of the Korean pine (Pinus koraiensis) and meteorological data, we found that the standard chronology was significantly correlated with the April-July minimum temperature (r = 0.76). Therefore, the April-July minimum temperature since 1600 was reconstructed by this tree-ring series. The reconstruction equation accounted for 57.3 % of temperature variation, and it proved reliable by testing with several methods (e.g., sign test, product mean test, reduction of error, and coefficient error). Reconstructed April-July minimum temperature in Laobai Mountains showed five cool periods (1605–1681, 1684–1690, 1747–1756, 1914–1922, and 1953–1966) and eight warm periods (1697–1704, 1767–1785, 1787–1793, 1795–1801, 1803–1808, 1816–1826, 1835–1878, and 1987–2008) during the past 413 years. The reconstructed low temperature periods in the 17th and early 18th century were consistent with the Little Ice Age in the Northern Hemisphere, and the rate of warming in the 19th century was significantly slower than that in late 20th century. In addition, the reconstructed series was fairly consistent with the historical and natural disaster records of extreme climate events (e.g., cold damage and frost disaster) in this area, and it exhibited 128-60-, 23-22-, 12-10-, 4.0-2.7-, and 2.4-year periods of warm-cool changes, which may be related to variations in sunspot activity or other large-scale interactions between the ocean and the atmosphere.


2019 ◽  
Vol 96 ◽  
pp. 669-683 ◽  
Author(s):  
Enliang Guo ◽  
Jiquan Zhang ◽  
Yongfang Wang ◽  
Lai Quan ◽  
Rongju Zhang ◽  
...  

2020 ◽  
Author(s):  
Mingqi Li ◽  
Guofu Deng ◽  
Xuemei Shao ◽  
Zhi-Yong Yin

Abstract. Inter-annual variations in precipitation play important roles in management of forest ecosystems and agricultural production in Northeast China. This study presents a 270-year precipitation reconstruction of winter to early growing season for the central Lesser Khingan Mountains, Northeast China based on tree-ring width data from 99 tree-ring cores of Pinus koraiensis Sieb. et Zucc. from two sampling sites near Yichun. The reconstruction explained 43.9 % of the variance in precipitation from the previous October to current June during the calibration period 1956–2017. At the decadal scale, we identified four dry periods that occurred during AD 1748–1759, 1774–1786, 1881–1886 and 1918–1924, and four wet periods occurring during AD 1790–1795, 1818–1824, 1852–1859 and 2008–2017, and the period AD 2008–2017 was the wettest in the past 270 years. Power spectral analysis and wavelet analysis revealed cyclic patterns on the inter-annual (2–3 years) and inter-decadal (~11 and ~32–60 years) timescales in the reconstructed series, which may be associated with the large-scale circulation patterns such as the Arctic Oscillation and North Atlantic Oscillation through their impacts on the Asian polar vortex intensity, as well as the solar activity.


2021 ◽  
Vol 12 ◽  
Author(s):  
Qianjuan Shan ◽  
Hongbo Ling ◽  
Hangzheng Zhao ◽  
Mengyi Li ◽  
Zikang Wang ◽  
...  

Frequent extreme climate events have attracted considerable attention around the world. Malus sieversii in Xinjiang is the ancestor of cultivated apple, and it is mainly distributed in the Ili river valley at end of the Tianshan Mountains. Wild fruit forests have been degraded, but the cause remains unclear. In order to identify whether extreme climate events caused this degradation reanalysis data and atmospheric circulation indices were used to determine the trends and the reasons for extreme climate changes. Subsequently, we further investigated the effect of extreme climate events on wild fruit forest using characteristics of extreme climate indices and tree-ring chronology. We found increasing trends in both extreme precipitation and warm indices, and decreasing trends in cool indices. Extreme climate events were mainly associated with the Atlantic Multidecadal Oscillation (AMO). Analysis of data of wind and geopotential height field at 500 hPa showed that strengthening wind, increasing geopotential height, cyclone and anti-cyclone circulation drivers contributed to extreme climate events. In the non-degraded region, there were significant positive correlations between tree-ring chronology and both extreme precipitation and extreme warm indices (except for warm spell duration indicator). The other extreme indices (except for heavy rain days) had a large correlation range with tree-rings in a 4–8-year period. These results indicated that extreme precipitation and extreme warm indices intensified M. sieversii growth of the non-degraded region on multi-time scales. In contrast, the degraded region showed insignificant negative relationship between tree-ring chronology and both extreme precipitation and extreme warm indices [except for warm spell duration index (WSDI)], and significant negative correlations in a 4–8-year period were detected between tree-ring chronology and most of the extreme precipitation indices, including heavy rain days, very wet days, cold spell duration indicator, simple precipitation intensity index (SDII), and annual total precipitation. Under the long disturbance of inappropriate anthropic activities, extreme climate has caused the outbreak of pests and diseases resulting in the degeneration of wild fruit forest. Our study provides scientific guidance for the ecosystem conservation in wild fruit forest in China, and also across the region.


2013 ◽  
Vol 4 (2) ◽  
pp. 92-102 ◽  
Author(s):  
Zhao Chun-Yu ◽  
Wang Ying ◽  
Zhou Xiao-Yu ◽  
Cui Yan ◽  
Liu Yu-Lian ◽  
...  

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