Tree-rings, a key ecological indicator of environment and climate change

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Tree-rings, a key ecological indicator of environment and climate change Zhihua Zhang ∗ College of Global Change and Earth System Science, Beijing Normal University, Xinjiekouwai Street, No. 19, Beijing 100875, China

a r t i c l e

i n f o

Article history: Received 18 April 2014 Received in revised form 21 July 2014 Accepted 25 July 2014 Keywords: Tree-rings Ecological indicators Climate Change

a b s t r a c t Due to wide spatial distribution, high annual resolution, calendar-exact dating, and high climate sensitivity, tree-rings play an important role in reconstructing past environment and climate change over the past millennium at regional, hemispheric or even global scales, so tree-rings can help us to better understand climate behaviour and its mechanisms in the past and then predict variation trends for the future. In this paper, we will review latest advances in tree-ring-based climate reconstructions in China and their applications in modelling past local/regional climate change, capturing historical climatic extreme events, as well as analyzing their link to large-scale climate patterns. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction As a fundamental indicator for climate, ecosystem and environment, tree-rings have been widely applied in studies of environment and climate change. In China, since most reliable meteorological records are only about 60 years long and limited historical documents do not adequately examine past climate changes, tree-rings become one of the most important palaeoenvironmental and palaeoclimatic proxies in China. Not only living trees, but also dead or sub-fossil trees can be used to develop tree-ring chronologies. Since tree radial growth is always subject to complex climatic and environmental influences, tree-ring chronologies can contain key information about climate and environment. The research on tree-rings has been conducted in China since the 1930s, but until 1980s, due to increasing tree-ring sampling at each site and applying advanced statistical methods to analyze tree-rings, tree-rings become an important tool in the study of past climate and environment of China (Wu et al., 1987). The long tree-ring chronologies can be used to extend limited meteorological records, analyze interannual to multidecadal climate fluctuations, and evaluate the impacts of various climatic factors over time. Moreover, tree-ring-based reconstructions of climate and environment have significant advantages over other proxies, e.g. wide spatial distribution, high annual resolution, calendarexact dating, and high climate sensitivity (Frank et al., 2010). All over the world, tree-rings can give long-term climate changes that

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occurred as far as the past approximately 2000 years, but there are only a few tree chronologies that extend back more than 1000 years. The world’s longest continuous tree-ring chronology extends over more than 7000 years (Frank et al., 2010). In China, tree-ring chronologies have been already used to reconstruct historical temperature/precipitation for at least the last 1000 years on the Tibetan Plateau and for the last several hundred years in central China. For examples, Shao et al. (2010) developed a 3585-year tree-ring width chronology from the northeastern Qinghai-Tibetan Plateau. Yang et al. (2014) developed a tree-ring width chronology spanning 4500-year by using subfossil, archaeological, and living-tree juniper samples from the northeastern Tibetan Plateau. Increased spatial distribution of tree-ring data in China provides nice opportunities to study past pan-Asian climate variability and ˜ its link to large-scale climate patterns such as the El Nino-Southern Oscillation (ENSO), the Asian monsoon variability and the North Atlantic Oscillation (NAO) (e.g. Hua et al., 2014; Chen et al., 2006, 2013; Qin et al., 2011; Lu et al., 2013; Peng and Liu, 2013; Wu et al., 2013; Cai and Liu, 2013; Sun and Liu, 2013 ). In this paper, we will review latest advances in Chinese tree-ring research. Since tree-ring data have been shown to have strong, linear correlations with climate variables, with the help of tree-ring width, density, or isotope data, Chinese researchers use tree-ring chronologies to reconstruct temperature, precipitation, runoff, drought, cloud cover and so on that extend back several centuries to millennia. In Section 2, we will introduce how to obtain tree-ring samples during field works. In Section 3, we will introduce main statistical methods used for analyzing tree-ring data, reconstructing various climatic factors from tree-ring data and discovering their links to large-scale climate patterns. In Sections 4–8, we will review lat-

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est tree-ring-based climate reconstructions in different regions of China and their applications in modelling past local/regional climate change, capturing historical climatic extreme events, as well as analyzing their link to large-scale climate patterns. In Section 9, we give some discussions and conclusion.

cloud cover. For tree ı13 C, due to ı13 C depletion in atmospheric CO2 by fossil fuel emissions, ı13 C tree-ring records generally show a prominent downward trend of 1–2% starting around 1850 AD. It must be removed prior to any climatic reconstruction. In addition, wood densities are also often used in climate reconstruction.

2. Field works and materials

3. Statistical analysis for tree-ring chronologies

In order to obtain tree-ring data, a lot of field work is needed to be done (Fritts, 1976; Breitenmoser et al., 2012; McCarroll and Loader, 2004; Holmes et al., 2009; Sheppard et al., 2004; Braker, 2002; Hughes, 2002; Liang et al., 2003; Palmer, 1965; Esper et al., 2002, 2003). First, we need to decide the location of sample sites carefully. In order to maximize the temperature signal, sample sites should be chosen in upper-elevation tree-line locations and cold mountain valley environments. But for precipitation, sample sites should be chosen in a steep, rocky, south facing slope. Next, we need to choose suitable trees for sampling in each sample site. Since most reliable meteorological records in China are about 60 years long, trees selected for sampling must have the age of above 60 years. After removing a cylinder of wood roughly 5 mm in diameter along the radius of a tree, core samples are collected at breast height (about 1.3 m above the ground) from trees by using an increment borer. Finally, tree-ring core samples are brought back to the laboratory and treated according the following standard process: Step 1 (Tree-ring width measurement). Before further analysis, core samples are needed to be air dried and polished with successively finer grades of sandpaper until annual rings could be distinguished easily. In general, some samples may be discarded due to irregular rings or missing rings. After that, tee-ring widths are measured with a precision of 0.01 mm or 0.001 mm by using a LINTAB system or similar system. Step 2 (Cross-dating). The process of tree-ring cross-dating is to match patterns of wide and narrow rings over time to ensure the exact dating of each annual ring to its calendar year of growth, If a similar pattern of wide and narrow rings in living trees could be found on the samples taken from old dead trees, then the samples would be considered cross-dated, so cross-dating can extend treering chronologies based only on living trees much further back in time. It is well known that some growth rings are often missing in trees because of critical hydrological conditions. Cross-dating permits the reliable identification of “false rings” and missing or partially missing rings. Cores with any ambiguities of cross-dating are often excluded from further analysis. Finally, the cross-dated tree-ring series can be quality checked using the COFECHA software Step 3 (Tree-ring width chronologies). In order to combine samples with differences in growth rates and eliminate nonclimatic, age-related growth trends, with the help of the negative exponential function/linear regression function and the spline function, the untreated tree-ring width data are detrended and/or standardized by using Programme ARSTAN. Finally, we obtain three kinds of tree-ring width chronologies: the standard chronology, the residual chronology and the autoregression standard chronology Except for tree-ring widths, tree-ring isotopic data are also a powerful tool for reconstructing climatic conditions with perfect annual resolution. An important advantage of tree-ring isotopic data is that they can be used for climate reconstructions without detrending. In order to obtain isotopic information, each ring of tree core samples are cut using a scalpel blade under a binocular microscope and cellulose are extracted, then tree ı18 O and ı13 C values of cellulose are measured by using a stable isotope ratio mass spectrometer (e.g. Thermal Chemical Elemental Analyzer). Since the principal source of water for tree growth is precipitation and its oxygen isotopic ratio (ı18 O) is related to temperature and precipitation amount, so the tree-ring cellulose ı18 O may record past changes in temperature, precipitation, relative humidity, or

After tree-ring chronologies are obtained in tree-ring labs, various advanced statistical methods (von Storch and Zwiers, 1999; Zhang and Moore (2011, 2012); Zhang et al., 2014a,b; Marengo et al., 2013; Pascual et al., 2013) can be used to analyze these chronologies, develop tree-ring-based climate reconstruction and examine the link between local/regional climate and global climate. Correlation analysis can be used to examine the relationships between tree-ring parameters and climate factors. Main tree-ring parameters include tree-ring width, density, and isotopic data. Main climate factors include temperature, precipitation, runoff, drought, cloud cover and so on. In tree-ring research, researchers always first compute the correlation coefficient between each treering parameter and each climatic factor, then researchers find a tree-ring parameter and a climatic factor whose correlation coefficient is largest. Finally, researchers will choose this tree-ring parameter to reconstruct this climatic factor that extends back several centuries to millennia. In addition, correlation analysis is also used in examining the link between this reconstructed climatic series and global climate. Linear regression and curvilinear regression are main tools to develop a tree-ring-based climate reconstruction. Since tree-ring data have been shown to have strong, linear correlations with climate factors, with the help of tree-ring width, density, or isotope data, researchers can use tree-ring chronologies to reconstruct temperature, precipitation, runoff, drought, cloud cover and so on that extend back several centuries to millennia. In detail, if researchers find a significant relationship between a tree-ring parameter and a climatic factor (e.g. temperature) in correlation analysis, with the help of linear regression, a climate reconstruction is estimated using this climatic factor as the dependent variable and the treering parameter as the independent variable. Recently, in order to obtain better climate reconstruction, many researchers begin to use curvilinear regression instead of linear regression. Principal component analysis can detect spatial pattern of treering-based climate reconstructions from different regions of China and discover the dominant modes of variability. Power Spectral Analysis, Multi-taper Method, and Wavelet Analysis can be used to analyze interannual to multi-decadal variability contained in the reconstructed climatic series. Researchers often choose red noise as climatic background noise and combine power spectral analysis/multi-taper method/wavelet analysis with statistical significance tests. By this way, researchers can extract significant cycles in the tree-ring-based climate reconstructions and ˜ large-scale climate patterns such as the El Nino-Southern Oscillation (ENSO), the Asian monsoon variability and the North Atlantic Oscillation (NAO). If there are some common significant cycles between the reconstructed climatic series and some large-scale climate pattern, it may suggest a link between local climate and this large-scale climate pattern. 4. Case studies in Northeast China Northeast China consists of Liaoning province, Jilin province, Heilongjiang province and eastern Inner Mongolia autonomous region. Many parts of Northeast China are covered with undisturbed old-growth forest, so many tree-ring researches are carried out in this region. In order to obtain better climate reconstruction,

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researchers always first use correlation analysis to discover which tree-ring parameter is most sensible to local climate factors, and then use this tree-ring parameter to reconstruct local climate factors. 4.1. Hulunbuir region The Hulunbuer region (Fig. 1.) is located in the Eastern Inner Mongolia, northeastern China, between 47.08–53.231◦ N and 115.22–126.061◦ E. Since the Hulunbuir region is a transitional zone between semiarid and arid conditions, forest and steppe, and agricultural land and pastureland, it is extremely sensitive and vulnerable to climate and environment changes. Therefore, Hulunbuir region become an ideal region for Chinese scientists to carry out tree-ring research. Liu et al. (2009a) collected tree core samples and found that tree-ring width (TRW) chronologies are highly correlated with total precipitation from prior July to current June with r = 0.711 (P < 0.0001), so Liu et al. reconstructed total precipitation from the previous July to current June (P76 ) since 1865 AD by using the following transfer function: P76 = 222.408 × TRW + 133.115.

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During the calibration period 1952–2003, Liu et al.’s reconstructed total precipitation from prior July to current June tracked the observation very well. Later on, using new tree-ring data, Chen et al. (2012a, c) also gave an August–June precipitation reconstruction over 1806–2007 AD in this region. Gao et al. (2013) gave the third precipitation reconstruction, i.e. they reconstructed precipitation from prior August to current July (P87 ) by: P87 = 210.47 × TRW + 144.61

with precipitation in February and May of the current year, and in September of the preceding year, and correlated negatively with mean monthly temperature in current May and solar radiation over most of the year. Liang et al. (2009) further researched the relation between Normalized Difference Vegetation Index (NDVI) and tree-ring width data. NDVI is often taken as an indicator for the photosynthetic activity and for the Net Primary Productivity (NPP) of the terrestrial vegetation. Liang et al. used the first principal component (PC1) of tree-ring chronologies from 1850 to 2004 and found that PC1 are highly correlated with the growing-season steppe NDVI and the total April–July precipitation. Since MTM spectral analysis of PC1 time series discovered 17.4 and 20.5 years significant periods, Liang concluded that the recent greening trend in the Xinlin Hot Region should be a part of the moisture-driven natural variability and appeared every 17–20 years. 4.3. Mohe region The Mohe region (Fig. 1), the northernmost area of China, is situated in the northern Greater Higgnan Mountains. Zhang et al. (2014a) analyzed tree core samples from Mohe region. They reconstructed the total precipitation from the previous August to the current July (P87 ) since AD 1724 using the tree-ring width (TRW) chronologies. The transfer function is as follows: P87 = 40.98 + 389.56 × TRW

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Since 7 of 10 driest years indicated by the above reconstruction agree with historical documents, Zhang et al.’s precipitation reconstruction can capture the extremely dry signals well. 4.4. Qianshan Mountain

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Gao et al. indicated that their precipitation reconstruction are significantly correlated with the intensity of the East Asia Summer ˜ Monsoon, El Nino/Southern Oscillation, Pacific Decadal Oscillation, Arctic Oscillation and North Atlantic Oscillation. Bao et al. (2012) reconstructed April–September mean maximum temperature (MMT) from 1868 to 2008 for the Hulunbuir region by tree-ring width chronologies through the following transfer function: MMT = −2.807 × TRW + +21.288

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Based on Bao et al.’s reconstruction, five severe warm events of two years or more occurred in 1905–1909, 1996–1997, 2000–2001, 2003–2005 and 2007–2008., while five cold events occurred during the periods 1880–1881, 1897–1900, 1948–1949, 1955–1960 and 1962–1964. Bao et al. further found significant correlations between the reconstructed MMT and Pacific ˜ which may suggest the influences of Decadal Oscillation/Nino large-scale atmospheric–oceanic variability on regional temperature and droughts in Hulunbuir grassland. In addition, Chen et al. (2012a, c) reconstructed monthly vegetation cover dynamics for the growing season from 1891 to 2006. 4.2. Xinlin Hot Region Xinlin Hot Region (Fig. 1) is located in the south of the Hulunbuer region and another representative typical steppe zone in China. The Xilin River Basin is located in the Xinlin Hot Region with an area of 3900 km2 . Within this basin the average elevation varies between 1100 and 1400 m, with a dry, cold continental climate and vegetation dominated by dry perennial grasses Liang et al. (2001) took 42 cores from 21 dominant Meyer spruce trees in the Xilin River Basin and measured tree-ring width on each core. Liang et al. showed that radial growth is positively correlated

The Qianshan Mountain (Fig. 1) is located in Southern Northeast China. Since the Qianshan Mountain is strongly influenced by the East Asian Summer Monsoon, it has a warm temperate continental monsoon climate with four distinct seasons, with coinciding warm and rainy periods. Liu et al. (2013a) conducted their tree-ring studies in Qianshan Mountain. The correlation analysis showed that tree-ring width (TRW) index is significantly positively correlated with the precipitation and negatively correlated with the monthly mean temperature and mean maximum temperature (MMT) during the growing season months of current year. More importantly, Liu et al. discovered the linear relationships between the observed May–July MMT from meteorological stations and tree-ring width chronologies as follows MMT = −1.78 × TRW + 28.81

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Based on it, the average of May–July MMT for the Qianshan Mountain region from 1745 to 2012 was reconstructed. The spectral analyses revealed that the Qianshan MMT reconstruction has 128.2-, 64.1-, 18.6-, 3.46-, 3.19-, 2.43-, 2.15- and 2.10-year quasicycles at a 95% confidence level during the past 268 years. 4.5. Shenyang City Chen et al. (2011a) collected tree-ring samples of Chinese Pine from three sites in or near Shenyang (Fig. 1), the capital and largest city of Liaoning Province, China. They discovered that there is a significant positive correlation between tree-ring width data and local January–May precipitation and a weak correlation between tree-ring width data and June–October precipitation. It means that previous winter–spring precipitation is crucial for tree growth and less sensitive to moisture deficiency in the growing season. Based on it, Chen et al. used stepwise multiple regressions to reconstruct January–May precipitation and dryness/wetness index for 1771–2002. Their reconstruction captures certain local rainfall

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Fig. 1. Location of main tree-ring sample sites in China: (1) Hulunbuir Region, (2) Xinlin Hot Region, (3) Mohe region. (4) Qianshan Mountain, (5) Shenyang City, (6) Changbai Mountain, (7) Altai Mountains, (8) Tianshan Mountains, (9) Helan Mountains, (10) Luoshan Mountains, (11) Kongtong Mountains, (12) Qilian Mountains, (13) Heihe River, (14) Changling–Shoulu region, (15) Anyemaqen Mountains, (16) Yushu region, (17) Qinling Mountains, (18) Bomi region, (19) Zedang region, (20) Namling region, (21) Gaoligong Mountains, (22) Daqin region, (23) Yulong Snow Mountains, (24) Miyaluo Town, (25) Ningwu Region, (26) Heng Mountain, (27) Lüliang Mountains, (28) Fenhe River, (29) Xiaowutai Mountain, (30) Dabie Mountains, (31) West Tianmu Mountains, (32) Pearl River Delta, (33) Wuyi Mountains.

variability and dryness/wetness signals, such as three unusually dry decades (1920s, 1850s and 1960s), an unusually dry halfcentury (1850–1899). In addition, based on tree-ring samples of Chinese Pine collected near Shenyang, Chen et al. found that Chinese pines show significant synchronous response to solar activity, in the periodic band 5–8 years, 10–16 years and 20–30 years and validate the strongest PDO influence signal at 26 years. In addition, Cui et al. (2013) used tree-ring data to detect the response of trees to environmental pollution in Shenyang during the last century.

4.6. Changbai Mountains The Changbai Mountains (Fig. 1) in Northeast China are covered with a large area of undisturbed temperate old-growth forest

which makes Changbai Mountain an ideal setting to examine climate–growth relationships. Zhu et al. (2009) gave a 250-year February–April temperature (T24 ) reconstruction based on treering width chronologies as follows: T24 = −10.9 + 2.35 × TRW(t) + 4.12 × TRW(t + 1)

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Four cold periods of 1784–1815, 1827–1851, 1878–1889 and 1911–1945, and two warm periods of 1750–1783 and 1855–1877 were identified by Zhu et al. Moreover, Zhu et al. suggested this reconstruction may be used as an indicator of the East Asian Winter Monsoon intensity. Later on, Yu et al. (2013b) found that at lower elevations tree radial growth is controlled mainly by precipitation, while at the upper limit of its higher elevation zone it is much more affected by minimum temperature. After 1970, tree radial growth decreased at lower elevations under climate change

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characterized by warming and drought, but increased at higher elevations. 5. Case studies in Northwest China Northwestern China includes the autonomous regions of Xinjiang and Ningxia and the provinces of Sha’anxi, Gansu and Qinghai, as well as Western Inner Mongolia. The total area of the region is approximately one-third of China’s land area. Deserts occupy a vast area in northwestern China, so most of tree-ring related researches are carried out in mountain regions. 5.1. Altai Mountains The Altai Mountains (Fig. 1) are located in the north part of Xinjiang Autonomous Region and their climate can be characterized by a typically continental climate. Zhang et al. (2008) reconstructed precipitation series in the recent 524 years and found nine wet periods and nine dry periods. By partial correlation analysis, Wu et al. (2014) showed that tree radial growth in lower altitudinal plots is significantly and negatively correlated to the temperature in June, August and September of the previous year and the temperature in March, May, June and July of the current year, while tree radial growth in higher altitudinal plots does not show close correlations to temperature in any month except for prior July at the highest altitudinal plot. Since high temperature during spring and summer will increase drought stress, Wu et al. further concluded that drought stress during pre- and early growing season is a limiting factor for tree growth in the low altitudinal forest belt while the limiting effects of drought stress for tree growth in higher altitudinal Altai Mountains are weakened and disappear. 5.2. Tianshan Mountains The Tianshan Mountains (Fig. 1) are located in the central part of Xinjiang autonomous region. The Tianshan Mountains play an important role in determining the climatic processes in northern central Asia In the western Tianshan Mountains, based on the mean latewood density (LWD) chronologies, Yu et al. (2013a) reconstructed May–August minimum temperatures (MinT58 ) with good accuracy for the period AD 1657–2008 by MinT58 = −10.5 + 23.2 × LWD

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Yu et al.’s temperature reconstruction showed a typically warm May–August from 1657 to 1738, 1855 to 1899 and 1977 to 2008, and the relatively cold periods in 1738–1854 and 1900–1976. Moreover, they revealed that the end of the 20th century is the warmest period in the past 352 years. Yuan et al. (2013) developed three tree-ring maximum latewood density chronologies which can extend back to the early 16th and late 17th centuries. For the central Tienshan Mountains, Wang et al. (2005) developed three ring-width chronologies in the Tianchi Nature Reserve at different altitudes: low forest border (1600–1700 m a.s.l.), interior forest (2100–2200 m a.s.l.), and upper treeline (2600–2700 m a.s.l.), and found that precipitation in August of the prior growth year plays an important role on tree’s radial growth. Later on, Zhang et al. (2013) used tree-ring width chronologies from Baluntai region to reconstruct total precipitation from the previous July to current June (P76 ) during the period of 1464–2005: P76 = 62.93 + 148.93 × TRW

(8)

Power spectral and wavelet analysis of Zhang et al.’s reconstruction demonstrated the existence of significant ∼100-y, ∼60-y, ∼50-y, ∼16-y, ∼10-y and ∼2-y cycles of variability.

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In the eastern Tianshan Mountains, Wang et al. (2007) used tree-ring width data to reconstruct moisture index sequence and discovered 6 remarkable moist periods and 7 remarkable dry periods. 5.3. Helan Mountains The Helan Mountains (Fig. 1) are located in the north part of Ningxia autonomous region and run north-south parallel to the north-flowing Yellow River. Liu et al. (2004a) analyzed ı18 O values in tree-ring samples from the northern part of the Helan Mountains. Between 1961 and 1999, the ı18 O ranges from 2.55% to 2.93%. The ı18 O values in tree-rings from this study is found not to be correlated with the mean annual temperature or the growing season (May to September) temperature, while a significant positive correlation is found between the ı18 O and the total annual precipitation. Therefore ı18 O can be used as a proxy of summer monsoon strength. Liu et al. (2008) further found that high values of ı18 O correspond to high precipitation periods and ı18 O record in treerings captures dry years appeared in 1878, 1908, 1918, 1940, 1966, 1981, 1989, 1994 and wet years in 1897, 1901, 1909, 1949, 1956, 1964, 1979, 1995. In addition, based on the detrended discrimination (DSS) series from tree-ring ı13 C values from Helan Mountain, Liu et al. (2004b) reconstructed February to July precipitation (P27 ) since 1804 using linear regression as follows: LogP27 = 7.264 × DSS + 1.878

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The historically documented drought of 1926–1930, and the wet period in the 1940s are captured in Liu’s reconstruction. 5.4. Luoshan Mountains and Tengger Desert The Luoshan Mountains (Fig. 1) are in the south part of the Tengger Desert of Ningxia autonomous region and are surrounded by land subject to desertification. Wang et al. (2013) used tree-ring width chronologies to reconstruct annual Palmer Drought Severity Index (PDSI) in the Tengger Desert for the period 1897–2007 AD as follows PDSI = 4.90 + 4.15 × TRW

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Palmer Drought Severity Index (PDSI) is a standardized measure of surface moisture conditions. Positive PDSI values indicate wet conditions, while negative values indicate dry conditions. Wang et al.’s reconstructed PDSI showed three obvious droughts in the 1910s, 1920s–1930s and the early 21st century, as documented in the literature. 5.5. Kongtong Mountains The Kongtong Mountains (Fig. 1) are in the east part of Gansu province and are a transitional area between monsoon and nonmonsoon. Their climate can be characterized by a continental climate. Fang et al. (2012) found that tree growth in this area is highly correlated (0.844) with the Palmer Drought Severity Index (PDSI) from May to July, so Fang et al. developed a May–July PDSI reconstruction spanning 1615–2009 and discovered extremely dry in 1723–1727 and 1928–1932 and significant wet in 1696–1700, 1753–1757 and 1963–1969. At the same region, Song et al. (2013) reconstructed mean temperatures from February to September (T29 ) during the past 283 years: T29 = 1.41 × TRW + 14.21

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Based on their reconstruction, Song et al. further indicated that PDSI is mainly affected by temperature in the Kongtong Mountains, while precipitation only plays a small role in the PDSI.

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5.6. Qilian Mountains The Qilian Mountains (Fig. 1) are located in the border between Gansu Province and Qinghai Province and are on the northern edge of the Tibet Plateau. Gou et al. (2005) developed four tree-ring width chronologies in Qilian Mountains and found the sensitivity of the tree-ring chronologies to climate decreases with altitude. Liu et al. (2009b) indicated that due to the temperature effect on precipitation ı18 O, tree-ring ı18 O values in the Qilian Mountains are most strongly correlated with the mean temperature from the previous November to the current February. Moreover, they found that tree-ring ı18 O values are significantly related to variations in the summer NAO index from June to August. Chen et al. (2011b) reconstructed total annual (July–June) precipitation (P76 ) over the AD 1768–2009 by tree-ring width chronologies as follows: P76 = 12.025 + 74.967 × TRW

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Chen et al.’s precipitation reconstruction revealed severe drought events in 1876–1879, 1923–1932, 1957–1962. Kang et al. (2013) research extreme drought events in the years 1877–1878, and 1928, and found that the former drought distribution pattern ˜ events, while the latter is associis probably attributable to El Nino ated with warm and cold air masses. In addition, Liu et al. (2013a–d) reconstructed PDSI and found that droughts lasting over four years are in 1877–1885, 1889–1893, 1925–1932, and 2004–2007. At the same time, Deng et al. (2013) also reconstruct the average PDSI from previous September to current August of the eastern Qilian Mountains (PDSI98 ) for the period AD 1856–2009 by tree-ring width chronologies: PDSI98 = 5.18 × TRW + 6.08

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Deng et al.’s reconstruction captured 49.9% of the actual PDSI variance during the observation period 1951–2005. Sun et al. (2013) also reconstructed a 450-year Palmer Drought Severity Index (PDSI) series based on tree-ring width chronology and found that the drought variations in the middle Qilian Mountains have significant periodicities of 2.05–2.31, 54.44, 98, and 122.5 years at a 99% confidence level.

Asian summer monsoon. Liu et al. (2013c) reconstructed the total precipitation from the previous to the current June (P76 ) during AD 1853–2007 by tree-ring width (TRW) chronologies: P76 = 131.533 × TRW + 185.528

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and found that precipitation in the 1920s is significantly low and droughts appeared continually for several years thereafter. 5.9. Anyemaqen Mountains The Anyemaqen Mountains (Fig. 1) in the northeastern Tibetan Plateau belong to a transition-strip of monsoon and nonmonsoon climate, featuring semi-arid conditions. Peng et al. (2013) developed a Palmer Drought Severity Index (PDSI) reconstruction since AD 1411 and regional extreme dry years are found in 1451, 1455, 1480, 1488, 1490, 1591, 1649, 1770, 1776, 1824, 1831, 1879, 1922, 1927 and 1992. 5.10. Yushu region The Yushu region (Fig. 1) is the source region of the Yangtze River in Qinghai province. Its climate is affected by both the South Asian and the East Asian monsoon. Liang et al. (2008) collected tree-ring width data from four sites and reconstructed the mean summer (June–August) minimum temperature for the past 379 years. A regional chronology was developed by averaging all four standard chronologies from these four sites. This reconstruction successfully captures recent abrupt climatic changes (e.g. Tambora eruption). 5.11. Qinling Mountains The Qinling Mountains (Fig. 1) are a major east–west mountain range in southern Sha’anxi province, China and constitute an important geographic demarcation line between the North and South of China. Liu et al. (2012) collected tree core samples and indicated that numerical mix method (equivalent to unweighted mean) with tree-ring stable isotope data can capture more climatic signal and so provides a series quite satisfactory for climate reconstruction.

5.7. Heihe River 6. Case studies in Southeast China The Heihe River is a major river system in Qilian Mountains. Precipitation in Qilian Mountains affects runoff of the Heihe River significantly. Liu et al. (2010) discovered a strong relationship between tree-ring width chronologies and total precipitation from the previous July to the current June (P76 ) from 1634 to 2000 AD, so in order to reconstruct total precipitation P76 , they use the following linear regression model: P76 = 164.24 × TRW + 236.196

(14)

Similarly, they also reconstructed the runoff data of Heihe river from previous September to current June, R96 from 1430 to 2007 AD as follows: R96 = 18.505 × TRW + 16.718

(15)

In the lower sections of Heihe river (i.e. Badain Jaran Desert), different from other researchers, Xiao et al. (2012) collected treerings from shrubs and found three wet periods: the 1840s to early 1850s, the early 1890s to the 1900s and the late 1970s to the mid1980s in this region. In addition, Peng et al. (2013) also did some further research on this region. 5.8. Changling–Shoulu region The Changling and Shoulu Mountains are located in the north of Lanzhou City, Gansu province and belong to the margin of the East

Southwestern China includes the autonomous regions of Xizang and Guangxi and the provinces of Sichuan, Yunnan and Guizhou. Main tree-ring related researches are carried in Hengduan Mountains and southern Tibetan Plateau. 6.1. Bomi region Climate variability on the Tibetan Plateau is critical for understanding of hemispheric-scale climate features and is especially sensitive to climate change. Based on tree-ring width chronology of Balfour spruce on Bomi region (Fig. 1) of Tibetan Plateau, Zhu et al. (2011) reconstructed summer temperature over the period 1385–2002. Their reconstruction can capture most temperature variability in the history of Tibetan Plateau, and showed atypically warm Augusts in 1446–1494, 1509–1522, 1553–1567, 1797–1812, 1845–1905, and 1918–2002, and cool Augusts in 1385–1416, 1426–1445, 1495–1508, 1523–1552, 1568–1686, 1695–1718, 1725–1796, 1813–1844, and 1906–1917. Liu et al. (2013b) also collected tree-ring core samples in Bomi region. Both Zhu et al.’s reconstruction and Liu et al.’s reconstruction reveal the same extreme growth events. Moreover, Liu et al. developed a treering cellulose ı18 O chronology for the southeastern Tibetan Plateau from 1600 to 2008 which reveals a weakening Indian Summer Monsoon intensity since around 1900. After that, Liu et al. (2014a,b)

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examined the relationship between ı13 C and ı18 O in tree-ring samples and found that the climatic signal recorded in tree-ring ı18 O is more stable than that of ı13 C. In addition, Wang and Zhang (2011) investigated solar activity signals in this region by using tree-ring data. 6.2. Zedang region Zedang region is located at the northern bank of the middle reaches of the Yarlung Tsangpo River Valley in central-south Tibet. The average elevation of this area is 4000 m above sea level. Liu et al. (2011) reconstructed annual precipitation from the prior July to the current June (P76 ) during the past 528 years by tree-ring width chronologies as follows: P76 = 174.17 × TRW + 179.53

(17)

Their reconstruction showed that dry conditions occurred in AD 1500–1550s, 1710–1800s, 1850–1870s and 1900–1920s, while wet conditions occurred during AD 1550–1630s, 1680–1720s, 1800–1850s and 1960–1990s. 6.3. Namling region Namling region (Fig. 1) is between the Gangdese and the Nyainqentanglha Mountains of Tibet. Its climate can be characterized by a semi-arid plateau temperate climate. Liu et al. (2013d) investigated changes in the tree growth/climate relationships along an elevation gradient and found that the correlations between tree-ring width and early winter temperature vary from significantly positive at the low-elevation site to weakly positive at the high-elevation site

part of Hengduan Mountains. After careful research, Liu et al. found that the strongest correlations emerge for tree-ring ı18 O with total cloud cover from June to October (r = −0.45; P = 0.001), with relative humidity from August to October (r = −0.40, P < 0.003), and with precipitation from August to October (r = −0.39, P < 0.004), respectively. Since the obtained tree-ring ı18 O values are detected to be negatively correlates with the Indian summer monsoon indices (1948 to 2004), the western North Pacific monsoon (1948 to 2004), and with the East Asian summer monsoon (1902 to 2000), Liu concluded that these monsoons strongly influence the climate of Yulong Snow Mountains. 6.7. Miyaluo Town The Miyaluo Town is situated in the transition zone from the Chengdu Plain to the Tibetan Plateau. The climate of the region belongs to a monsoon mountain climate. Li et al. (2013a) collected tree-ring samples from Rhododendron przewalskii (a dwarf shrub species) in this region. Since radial growth of R. przewalskii is significantly and negatively correlated to late winter temperature (January–February), mainly driven by maximum temperatures, they recommended using R. przewalskii for climate–growth studies in alpine regions where no forests are present 7. Case studies in North China North China includes the provinces of Shanxi, Hebei and Shandong, Beijing City and Tianjin City. Most of tree-ring researches in this region are carried out in Shanxi province and surroundings. 7.1. Ningwu region

6.4. Gaoligong Mountains The climate in Sothern Hengduan Mountains is strongly influenced by the south-west Asian monsoon from the Indian Ocean. The Gaoligong Mountains (Fig. 1) are just located in the west margin of southern Hengduan Mountains. Based on a linear regression model, Fan et al. (2010) reconstructed May–August mean temperature for the Gaoligong Mountains region back to A.D. 1585, and revealed that several cold episodes occurred during 1600s, 1620s, 1640–1650s, 1700s, 1730–1740, 1760s, 1810–1820, 1850s, 1900–1910s and 1955–1970s, while warm episodes occurred during 1610s, 1660–1680s, 1710–1720s, 1750s, 1780–1790s, 1825–1840, 1920–1950 and 1980–present. 6.5. Daqin region The Daqin region (Fig. 1) is in the centre of southern Hengduan Mountains. Fan et al. (2008) collected tree-ring samples from five sites in this region. With the help of principal component analysis and linear regression, they reconstructed annual mean temperature from the previous October to the current September (T109 ) for the past 250 years as follows: T109 = 5.182 + 0.351 × TRW

7

(18)

Their reconstruction shows that the centre of southern Hengduan Mountain experienced some cool episodes during the 1810s, 1860s, and during 1960–1980. In addition, based on maximum latewood density, Fan et al. (2009) developed a warm season (April–September) temperature reconstruction for the period A.D. 1750–2006.

The Ningwu region (Fig. 1) in the Shanxi Province is located at the margin of the East Asian Summer Monsoon region. Li et al. (2011) found that there are significant negative correlations between the tree-ring cellulose ı18 O and precipitation/relative humidity during the summer months and this correlation is not influenced by recent rapid warming. It means that this tree-ring cellulose ı18 O can be used as a proxy for summer precipitation and relative humidity. Li et al. also showed that the tree-ring cellulose ı18 O is a significantly negative correlated with Summer PDSI. In addition, based on the tree-ring cellulose ı18 O, Li et al. reconstructed inter-annual variations in the precipitation ı18 O during the summer period from 1954 to 2003. Li et al. (2013b) further reconstructed the local May–July temperature from 1686 to 2003 7.2. Heng Mountain The Heng Mountain is in the north part of Shanxi province and belongs to the transition zone between temperate grassland and warm-temperate deciduous broad-leaved forest. Cai et al. (2013) reconstructed the mean temperature from May to June (T56 ) back to 1767 AD: T56 = −2.016 × TRW + 20.106

(19)

Their reconstruction captures a cold period in 1849–1910, two other relatively cold periods in 1800–1812 and 1938–1965, and three comparatively warm periods in 1815–1846, 1917–1934 and 1980–2008. 7.3. Lüliang Mountains

6.6. Yulong Snow Mountains Liu et al. (2012) showed that the tree-ring ı18 O values range from 12.8 to 18.6‰ in the Yulong Snow Mountains (Fig. 1), southern

The Lüliang Mountains are located in the western part of the Shanxi Province and serve as a watershed for the Yellow River. Cai and Liu (2013) developed five tree-ring width chronologies and

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indicated that the trees in this region can provide common regional climate information, and combinations of multiple species are more successful in reconstructing the climate data than single species. 7.4. Fenhe river The Fenhe river is the largest river system in Shanxi province. Sun et al. (2013) reconstructed historical runoff variations from March to July (R37 ) in the upper Fenhe River since AD 1799 by using tree-ring width chronologies: R37 = 8 : 045 × TRW − 1.428

(20)

Based on it, Sun et al. indicated that AD 2001 is not only the driest year for the instrumental period, but also the driest year since AD 1799 in the upper Fenhe River. 7.5. Xiaowutai Mountain The Xiaowutai Mountain is located in Hebei Province and is the main peak of the Taihang Mountains. It lies on the boundary between temperate sub-humid and semi-arid climate. Yan et al. (2013) showed that the standard and residual chronologies of Chinese pine have significant correlations between spring precipitation, 0 cm ground temperature in May, average relative humidity of April and May, hours of sunshine and percentage of sunshine in May. 8. Case studies in center and south China Due to lack of undisturbed old-growth forest, only few tree-ring researches are carried out in centre and south China. 8.1. Dabie Mountains The Dabie Mountains (Fig. 1) are a major mountain range located in central China. Shi et al. (2013) collected tree-ring width data from Taiwan pine in the Dabie Mountains and reconstructed the January–July average minimum temperature in the period of 1834 to 2010. Tree-ring-based reconstruction captures observed temperature quite well at inter-annual to multi-decadal time scales and explains 57.6% variance of actual January–July average minimum temperature. 8.2. West Tianmu Mountains Zhao et al. (2006) collected tree-ring samples in the West Tianmu Mountain (Fig. 1), Zhejiang province and used tree-ring ı13 C values to reconstruct atmospheric CO2 concentration (Ca ) as follows: 13

13

Ca = 8598 + 810.922 × ı C + 19.748 × (ı C)

2

(21)

Based on it, Zhao indicated that between 1685 and 1840 the evaluated atmospheric CO2 concentration was stable, but after 1840 it exhibited a rapid increase.

8.4. Wuyi Mountains Chen et al. (2012b) used tree-ring width (TRW) records near Wuyi Mountains (Fig. 1), Fujian province to reconstruct July–October minimum temperature (MT710 ) for the period AD 1803–2008 as follows: MT710 = 22.662 − 0.150 × TRW(t) − 1.567 × TRW(t − 1)

(22)

According to this reconstruction, cool periods with belowaverage temperature occurred in AD 1803–1807, 1816–1832, 1854–1861, 1872–1894, 1907–1913 and 1928–1972, while relatively warm periods occurred in AD 1808–1815, 1833–1853, 1862–1871, 1895–1906, 1914–1927 and 1973–2008. Chen et al. further showed that there is a strong relationship between the reconstruction and summer Asian-Pacific Oscillation (APO) which suggest linkages of regional temperature variability with the AsianPacific climate system. 9. Conclusion Tree-rings are a key ecological indicator for studies of climate and environment variability. China is not only sensitive to regional environmental changes, but may also be closely linked to global change. However, due to the lack of long-term meteorological records, it is hard to get a comprehensive understanding of past climate and environment change in China. Because of precise dating, high continuity, high time resolution and widespread, tree-ring records have been widely applied in past climate and environment change studies over China. In the recent ten years, tree-ring parameters (ring width, density or stable isotopes) taken from different regions of China are analyzed at various tree-ring laboratories in China. Due to lack of undisturbed old-growth forest, most of treering researches are only carried out in west China or northeast China. These researches provide high-resolution climate reconstructions for the late Holocene in highly sensitive regions in China. Together with paleoclimate models, all these climate reconstructions are necessary for modelling past local/regional climate change, capturing historical climatic extreme events, exploring the links to the large-scale circulation on different time scales, and analyzing the impacts of hemispheric or global climate change on China climate. Current tree-ring researches in China mainly focus on treering width data. Tree-ring stable isotopes have many advantages over tree-ring width in climate reconstruction, e.g. tree-ring stable isotopes have no age-related growth trends. It will be expected to have a more great progress on the research of tree-ring stable isotopes in the future. Acknowledgements This research is partially supported by the National Key Science Programme for Global Change Research nos. 2013CB956604 and 2010CB950504; the Fundamental Research Funds for the Central Universities (Key Programme) no. 105565GK; the Beijing Higher Education Young Elite Teacher Project; and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China.

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Please cite this article in press as: Zhang, Z., Tree-rings, a key ecological indicator of environment and climate change. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.07.042