Chemical Geology 291 (2012) 104–115

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Chemical Geology

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Research paper

Tree-ring δ18O in southwestern China linked to variations in regional cloud cover andtropical sea surface temperature

Xiaohong Liu a,⁎, Wenling An a, Kerstin Treydte b, Xuemei Shao c, Steven Leavitt d, Shugui Hou a, Tuo Chen a,Weizhen Sun a, Dahe Qin a

a State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy Sciences, Lanzhou 730000, Chinab Swiss Federal Research Institute WSL, Dendro Sciences Unit, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerlandc Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, Chinad Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ 85721, USA

⁎ Corresponding author at: State Key Laboratory of CArid Regions Environmental and Engineering ResearchSciences, Donggang West Road No. 320, Lanzhou 73007342; fax: +86 931 827 1124.

E-mail address: liuxh@lzb.ac.cn (X. Liu).

0009-2541/$ – see front matter © 2011 Elsevier B.V. Alldoi:10.1016/j.chemgeo.2011.10.001

a b s t r a c t

a r t i c l e i n f o

Article history:Received 20 September 2010Received in revised form 12 September 2011Accepted 4 October 2011Available online 19 October 2011

Editor: B. Sherwood Lollar

Keywords:Oxygen isotopesAbies forrestiiLatewoodAsian monsoonENSOSouthwestern China

In this paper, we present the first annual oxygen isotope (δ18O) record (1902 to 2004) from the latewoodcellulose of trees growing in a temperate-moist forest in southwestern China. Tree-ring δ18O ranges from12.8 to 18.6‰ and averages 15.2‰. δ18O in the latewood negatively correlates with total cloud cover, relativehumidity, and precipitation during the moist months (August to October) from 1951 to 2004 and, to a lesserextent, positively correlates with the temperature in June. Spatial correlation analysis revealed that tree-ringlatewood δ18O is more strongly related to variability in regional cloud cover and precipitation in autumn.Tree-ring δ18O negatively correlates with the indices of the Indian summer monsoon (1948 to 2004), thewestern North Pacific monsoon (1948 to 2004), and with the East Asian summer monsoon (1902 to 2000),suggesting that these monsoons strongly influence the regional climate. Under global warming of last centu-ry, we found a strong inverse correlation between tree-ring δ18O and the Southern Oscillation index in differ-ent periods, indicating a complex association between the El Niño-Southern Oscillation and the regionalclimate, which in turn affects tree-ring δ18O. Correlation with the global sea surface temperature (SST) indi-cates that climatic conditions in the Indian Ocean might play a dominant role in modulating cloud cover andprecipitation over the study region during the monsoon seasons. We also found a significant correlation be-tween tree-ring δ18O and SST over the western-north Pacific Ocean. Our results suggest strong links betweentropical oceans and an ENSO-like climate with the oxygen-isotope sources and discrimination for trees in thestudy region.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

The Asian monsoons play an important role in the global climatesystem, and are a dominant feature of Asia's climate. These mon-soons are associated with a thermal contrast between the Eurasianland mass and the Indian and western north Pacific Oceans (Ding,1994; Wang and Linho, 2002; Wang et al., 2008). SouthwesternChina is located on the southeastern side of the Tibetan Plateau, fac-ing the Bay of Bengal and the South China Sea. The Bay of Bengaland the South China Sea are important tropical bodies of waterthat provide abundant water vapor to adjacent landmasses. The dis-tinct geography of southwestern China moderates the impacts of themonsoons and these bodies of water on the regional climate,

ryospheric Sciences, Cold andInstitute, Chinese Academy of00, China. Tel.: +86 931 496

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especially in terms of moisture conditions, but the climate is none-theless controlled by interactions among the Indian summer mon-soon (ISM), the East Asian summer monsoon (EASM), and thewestern north Pacific summer monsoon (WNPSM) (Wang andLinho, 2002), leading to maximum precipitation during the summer(Wang and Li, 2007).

The El Niño-Southern Oscillation (ENSO) phenomenon is an impor-tant natural interannual climate fluctuation that affects the Asian mon-soons (Charles et al., 1997; Kumar et al., 1999) and therefore affects theclimate of many Asian countries, leading to alternations between highprecipitation accompanied by flooding and periods of severe drought(Ropelewski and Halpert, 1987; Trenberth and Shea, 1987; Allan et al.,1996). The ENSO has been proven to exert a strong influence on the cli-mate of China (Huang and Wu, 1989; Gong andWang, 1999; Qin et al.,2005; Wang and Li, 2007). Long-term changes in atmospheric circula-tion patterns such as the ISM, EASM, and ENSO have had and will con-tinue to have a significant influence on precipitation amounts in China(Wang and Linho, 2002), and will therefore affect the precipitationδ18O signatures recorded in this region (Araguás-Araguás et al., 1998).

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However, a scarcity of long-term instrumental and reconstructed cli-mate data for remote regions of monsoonal Asia has impeded progresstoward explaining the complex mechanisms that govern the relation-ships between the Asian monsoons and variability in the regionalclimate.

The distribution patterns of precipitation amount and δ18O aremainly controlled by geographic parameters, such as altitude and dis-tance from the coast, and by meteorological parameters, such as airtemperature, relative humidity, and the amount of precipitation(Dansgaard, 1964; Araguás-Araguás et al., 2000; Lee and Fung,2008). In addition, they are controlled by the sources of the moistureand by transport processes (Araguás-Araguás et al., 1998; Tian et al.,2007; Liu et al., 2008). Moreover, changes in the moisture sourceand strong convective activity can result in pronounced differencesin the mean annual δ18O of the regional precipitation (Lawrenceet al., 2002; Liu et al., 2008).

In practice, spatial variation in precipitation δ18O results from theinterplay among these factors. A number of proxies for precipitationδ18O from mid- to high-latitude continental environmental archives,such as ice-cores (Yao and Thompson, 2002) and tree-ring cellulose(Robertson et al., 2001; Treydte et al., 2006), have been developedand used successfully to reconstruct a region's palaeohydrology andpalaeoclimate. Cellulose oxygen isotope ratios (δ18O) from treerings offer the possibility of high-resolution climate reconstruction,particularly in temperate regions (McCarroll and Loader, 2004;Treydte et al., 2007) and tropical regions (Poussart et al., 2003;Evans and Schrag, 2004). The δ18O of plant tissues reflects the varia-tion in three factors: δ18O values in the source water (Saurer et al.,1997; Danis et al., 2006), evaporative enrichment of 18O in leafwater due to transpiration (Barnard et al., 2007), and biochemicalfractionation during the synthesis of organic matter (Gessler et al.,2009). Thus, the cellulose records a signal that integrates both theconditions at the leaf interface (~60% of the oxygen atoms) and theisotopic ratio in the source water (~40% of the oxygen atoms)(Roden et al., 2000). This signal therefore varies as a function of tem-perature, storm trajectories and source regions, and the amount andtype of precipitation (Miller et al., 2006; Bale et al., 2010). Most stud-ies have shown that the δ18O value in tree rings can be used to recon-struct δ18O variations in precipitation during the growing season andseasonal temperatures (Saurer et al., 1997; Rebetez et al., 2003; Daniset al., 2006; Roden and Ehleringer, 2007), or δ18O variations in thewater supply at the beginning of tree growth (Robertson et al.,2001; Treydte et al., 2006). Several recent studies have also focusedon the reconstruction of variations in the ENSO and other atmospher-ic circulation patterns based on tree-ring widths (Fowler, 2008; Xu etal., 2009) and isotopic parameters such as tree-ring δ18O and δD (Liuet al., 2004; Welker et al., 2005; Roig et al., 2006; Feng et al., 2007; Liuet al., 2009). These results suggest that changes in tree-ring δ18O canbe used as an effective proxy for local or regional climate change andatmospheric circulation patterns in certain regions where instrumen-tal meteorological data are sparse or are only available for short timeperiods.

Southwestern China has been recognized as an area that is sensi-tive to variations in climate associated with variations in the ENSO(Allan et al., 1996; Xie and Zhang, 2000) and the Asian monsoons(Zhang et al., 1996; Qin et al., 2005). These phenomena can combineto produce extreme inter-annual climatic variability that is associatedwith different moisture transfer trajectories (Wang and Linho, 2002).Thus, the study area is an excellent location for establishing potentiallinks between the annual and seasonal variations in δ18O values inwood cellulose and macro-scale climate variations over long timescales. To date, meteorological records for southwestern China areonly available for relatively short periods and are sparsely distributed.Therefore, it is necessary to rely on high-resolution proxy data, suchas tree-ring isotopic parameters, to shed more light on climate vari-ability in southwestern China.

In this study, we present a tree-ring δ18O chronology for latewoodcellulose from trees in the Yunnan–Guizhou Plateau of southwesternChina, and investigate its response to changes in the local climate andseveral macro-scale atmospheric circulation patterns. We hypothe-size that our chronology records climatic information related to theδ18O values of the source water taken up by the trees and related toclimate variability during the previous and current growing seasons(Saurer et al., 1997; Robertson et al., 2001; Danis et al., 2006). Giventhe existence of linkages between the climate of southwesternChina, the ENSO, and the Asian monsoons (Allan et al., 1996; Wangand Linho, 2002; Fowler, 2008), the present study attempts to estab-lish relationships between tree-ring δ18O and the local climate, and toinvestigate the linkage of δ18O with the Asian monsoons, ENSO varia-tions, and related variations in the sea surface temperature (SST).

2. Materials and methods

The Yulong Snow Mountains, located in the Hengduan range at27° N latitude on the southeastern edge of the Tibetan Plateau andnorth of Lijiang in China's Yunnan province, are the southernmostglaciated area of Eurasia (Fig. 1). The climate at Lijiang is dominatedby the ISM and EASM during the summer and by the southern branchof the westerly airflow during the winter. The annual mean tempera-ture and mean annual precipitation are 12.6 °C and 970.0 mm, re-spectively, at the Lijiang meteorological station, 20 km from ourstudy site. The lowest mean monthly temperatures of Decemberand January are above 6 °C. The maximum precipitation occurs duringthe summer monsoon months (from June to September; Fig. 2), andthe vapor source for monsoon rainfall mainly originates in theSouth China Sea and the Indian Ocean (Dang, 2007). The highest at-mospheric mean relative humidity occurs in September, 2 monthsafter the maximum precipitation. Causal linkages between climaticcontrols originating over the western Pacific Ocean and the SouthChina Sea are referred to as the El Niño-Southern Oscillation(ENSO). This phenomenon is an important meteorological compo-nent that contributes to the inter-annual climate variability in ourstudy area (Xie and Zhang, 2000; Qin et al., 2005; Wang and Li, 2007).

The main tree species in the Yulong SnowMountains are fir (Abiesforrestii), spruce (Picea likiangensis), pine (Pinus densata), and Chinesehemlock (Tsuga dumosa). There is a high forest cover in the study re-gion (30 to 40% of the total area) and many shrub and herbaceousspecies grow in the understory of the forest. In the present study,we collected cores from the dominant Abies forrestii trees in a temper-ate forest of the Yulong Snow Mountains near Lijiang (27°6.1′N,100°13.4′E), at an elevation of about 3260 m (Fig. 1), growing in awell-drained brown soil with a high humus content and weak acidity(Zheng, 1983). The fir trees have a shallow root distribution, whichmeans that the water they access is mainly from precipitation andshallow soil water sources (Zheng, 1983). The data from a forest ob-servation station, which is located about 10 km south of the samplingsite at an altitude of 2392 m (from 2007 to 2009), reveal monthlymean temperatures in April and October of 7.8 and 8.8 °C, respective-ly. Based on the assumption that these trees start their physiologicalactivity when the air temperature is greater than 4 to 6 °C (He,2000), the trees typically grow from April to October in the studyregion.

We obtained two cores from each of 20 trees at breast height,using 5-mm increment borers (Haglöf, Mora, Sweden). Tree-ringcores were prepared following the standard dendrochronologicaltechniques outlined in Stokes and Smiley (1968). After crossdating,annual rings were measured under a binocular stereoscope with aLINTAB 6 measuring machine (Rinntech, Heidelberg, Germany) con-nected to a computer, with 0.01-mm accuracy.

We selected nine trees with homogeneous growth patterns andpooled their annual latewood samples prior to cellulose extraction(Leavitt, 2008; Treydte et al., 2006; Liu et al., 2009). The period

Fig. 1. Location of the study site in the Yunnan–Guizhou Plateau of southwestern China. The Asian monsoon has been divided into three subregions (ISM, Indian Summer Monsoon;EASM, East Asian summer monsoon; WNPSM, western north Pacific summer monsoon) according to Wang and Linho (2002). The sampling site is located in a broad transitionalzone between the three sub-monsoon regions.

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from 1902 to 2004 was used for oxygen isotope analysis to coincidewith the instrumental and gridded climatic records that are availablefor the region.

The extraction of latewood cellulose should reduce the potentialsources of uncertainties related to storage effects from the previousseason's photosynthetic products, which are mostly incorporated inthe earlywood (Helle and Schleser, 2004; Kagawa et al., 2005). Wefirst milled the pooled annual latewood samples (b80 μm), and thenextracted α-cellulose using a method based on those of Green(1963) and Loader et al. (1997). To obtain better homogenization ofthe cellulose, we used a JY92-2D ultrasound machine (Scientz Indus-try, Nibgbo, China) to break the cellulose fibers, according to themethod of Laumer et al. (2009).

The δ18O values were determined using a High Temperature Con-version Elemental Analyzer (TC/EA) coupled to a Finnigan MAT-253mass spectrometer (Thermo Electron Corporation, Bremen, Germa-ny) at the State Key Laboratory of Cryospheric Sciences, ChineseAcademy of Sciences. The δ18O analysis was repeated four times foreach annual cellulose sample, from which we calculated the meanvalues. We measured the ratio for a benzoic acid working standard

Fig. 2. Total monthly precipitation, mean temperature, and relative humidity at theLijiang meteorological station from 1951 to 2004. Values represent mean±the inter-annual standard deviation.

with a known δ18O value (IAEA-601, δ18O=23.3‰) every sevenmea-surements to monitor the analytical precision and to calibrate thesamples for analytical accuracy (Liu et al., 2009). The cellulose stan-dard IAEA-C3 (δ18O=32.2‰) was also used to calibrate the tree-ring oxygen measurements. The resulting analytical uncertainty wasless than 0.3‰.

To determine the concurrent and time-lagged relationships be-tween tree-ring δ18O and selected climate variables, we calculatedPearson's correlation coefficients (Blasing et al., 1984) over the periodfrom 1951 to 2004 between tree-ring δ18O and each of the monthly-resolution climate variables: mean temperature, total precipitation,mean relative humidity, and total cloud cover. All data were obtainedfrom the Lijiang meteorological station (26°52′N, 100°13′E, at 2392 melevation; a standard meteorological station operated by the ChinaMeteorological Administration), which is about 20 km south of ourstudy site; records at this station were available from 1951 to 2004.Significance levels were tested using a bootstrap procedure (Guiot,1991) and statistical significance was defined at pb0.05. To test thespatial coherency between the tree-ring δ18O and local to regional cli-matic variations, we computed the spatial correlations between tree-ring δ18O and a regional gridded meteorological data set, CRU TS 2.1(Mitchell and Jones, 2005). The variables we considered includedmean temperatures (June to July), total precipitation (August to Octo-ber), and total cloud cover (June to October) from 1951 to 2002, withsignificance at pb0.10.

Based on the NCEP/NCAR reanalysis data (Kalnay et al., 1996;http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml),Wang et al. (2001) used the ISM and the WNPSM indices to revealinterannual variability in their spatial and temporal structures andtheir relationships to the ENSO. In the present study, we investigatedthe responses of tree-ring δ18O to major atmospheric circulation pat-terns using the ISM and WNPSM indices defined by Wang et al.(2001) and the EASM index defined by Guo (1983), which is basedon sea-level pressure differences between two longitudes (110°Eand 160°E) at latitudes ranging from 20°N to 50°N.

We also examined the correlations between the tree-ring δ18Oand monthly values of the Southern Oscillation index (SOI, http://www.bom.gov.au/climate/current/soihtm1.shtml). The anomalousSST values associated with El Niño events correspond to negative

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SOI values and constitute the warm (low) phase of ENSO. Therefore,we also examined the correlations between tree-ring δ18O and theSST anomalies in different regions, which include the SST of El Niño3 (SSTANiño3; http://www.cpc.ncep.noaa.gov/data), spring SST varia-tions in the Nansha islands (Yu et al., 2001), and annual SST variationsinferred from coral δ18O values in the Xisha Islands (Sun et al., 2004)of the South China Sea and SST variations in the southwestern equa-torial Indian Ocean (Charles et al., 1997). To test the associations be-tween tree-ring δ18O and SST variations, we computed correlationcoefficients between the tree-ring δ18O and a regional gridded SSTdata set, HadISST (Rayner et al., 2003; http://climexp.knmi.nl).

3. Results

3.1. Tree-ring δ18O and its response to climate

The tree-ring δ18O ranged from 12.8 to 18.6‰ (Fig. 3a), and theoverall mean (±SD) was approximately 15.2±1.3‰, which is lowerthan those in most previous studies (Liu et al., 2004; Treydte et al.,2006; Reynolds-Henne et al., 2007; Hilasvuori et al., 2009; Liu et al.,2009). Tree-ring δ18O was significantly (pb0.05) higher from 1968to 2004 than from 1902 to 1967, averaging around 16.2 and 14.8‰,

Fig. 3. Tree-ring δ18O chronology and number of tree-ring cores used for the oxygen isotopicfrom 1902 to 2004. (b) Correlations between tree-ring δ18O and environmental parametercover from 1951 to 2004 at the Lijiang meteorological station. Months followed by “/p” indithrough October during the current year; JJASO = June through October. Asterisks indicate

respectively (Fig. 3a). The range among tree-ring δ18O values since1902 was 5.8‰. There were two upward shifts in tree-ring δ18O,which occurred around the 1960s and from 1976 to 1977, and thesemay be related to climate changes in response to shifts in the regionalatmospheric circulation patterns at these times (Guo et al., 2003;Vecchi et al., 2008). The first-order autocorrelation for the tree-ringδ18O series is 0.37, indicating some influence of previous years onthe latewood cellulose.

Correlation analysis was performed to detect significant relation-ships between the tree-ring cellulose δ18O series and the four climaticfactors (Fig. 3b). In this analysis, we used the monthly mean temper-ature, monthly total precipitation, monthly mean air relative humid-ity, and monthly mean total cloud cover obtained from themeteorological station at Lijiang from 1951 to 2004. Despite a positivecorrelation between mean temperature and latewood cellulose δ18Oin the current June (r=0.34; pb0.05) (Fig. 3b), negative correlationsto all the moisture related variables are found during the late growingseason from June/August to October (Fig. 3b). The strongest correla-tions emerged for tree-ring δ18O with total cloud cover from June toOctober (r=−0.45; P=0.001), with relative humidity from Augustto October (r=−0.40, Pb0.003), and with precipitation from Augustto October (r=−0.39, Pb0.004), respectively.

analysis; error bars represent the standard error of the mean. (a) Tree-ring δ18O valuess (monthly mean temperature, precipitation, relative humidity (RH)), and total cloudcate values in the previous year; all other values are for the current year. AO = Augustsignificant correlations (pb0.05).

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Spatial correlations between tree-ring δ18O and gridded cloudcover over southern Asia from 1951 to 2002 show significant negativerelationships (June to October) in the region surrounding the sam-pling site, and particularly south of the sample site, indicating thatthe moisture source in the study region originates from the south(Fig. 4a). The strongest negative correlations with cloud cover ex-tended into eastern India, to approximately 90°E, and into northernMyanmar and Vietnam, to approximately 20°N. Spatial correlationsbetween tree-ring δ18O and gridded precipitation (August to Octoberfrom 1951 to 2002) were significantly negative in a similar region, butthe areas with the strongest negative correlation were smallerthan those for regional cloud cover and lay farther north (Fig. 4b).However, no significant spatial correlation fields were seen betweenthe tree-ring δ18O and gridded temperatures from May to June(data not shown). These results confirm that our tree-ring δ18Ovalues represent regional to large-scale moisture conditions.

3.2. Linkages between tree-ring δ18O and atmosphericcirculation patterns

Inter-decadal changes in atmospheric circulation patterns mayalter the δ18O value in precipitation (Jouzel et al., 1997; Conroy andOverpeck, 2008; Birk and Edwards, 2009) and may, therefore, alterthe isotopic composition of the soil water used by trees. In southwest-ern China, precipitation and its δ18O values are linked to the intensi-ties of the Asian monsoons, including the ISM, EASM, and NWPSMsystems (Araguás-Araguás et al., 1998; Wang et al., 2001; Wang andLinho, 2002). Based on the compartmentalization of Wang and

Fig. 4. Spatial correlations between tree-ring δ18O and (a) regional gridded total cloudcover (June to October) and (b) precipitation (August to October) from 1951 to 2002.Only values significant at pb0.10 are shown. The red dots in both panes indicate thesampling site.

Linho (2002), our sampling site is located in the transitional zoneamong the ISM, EASM, and WNPSM. These systems all influence theclimate of our study region. We therefore examined the correlationsbetween tree-ring cellulose δ18O and the ISM and WNPSM indices(Wang and Linho, 2002) and between tree-ring cellulose δ18O andthe EASM index (Guo et al., 2003). Based on data for the common pe-riod (from 1948 to 2004) for the three indices, the tree-ring δ18O issignificantly and negatively correlated with ISM (r=−0.29,P=0.027) and WNPSM (r=−0.35, P=0.007) in the currentSeptember (Fig. 5a, b). The correlation between the current EASMand tree-ring δ18O is significant (r=−0.36; Pb0.001) from 1902 to2000. However, the strongest relationship is between the EASMindex of the previous year (defined as EASM-1) and tree-ring δ18O(r=−0.50, Pb0.001) from 1902 to 2000 (Fig. 5c). The structure ofthe long-term trends in the tree-ring δ18O and EASM changes syn-chronously but in opposite directions (Fig. 5c). Starting around the1960s, these two series show the most obvious change in oppositedirections.

It is known that southwestern China is influenced by ENSO events,resulting in extreme interannual climatic variability (Chen, 2001; Jieand Zhang, 2000). Therefore, it is reasonable to expect that oxygen-isotope records from trees in southwestern China would be influ-enced by variability in the ENSO. Using the Southern Oscillationindex (SOI) as an indicator of the ENSO status, the correlation coeffi-cients with tree-ring δ18O (1902 to 2004) (Fig. 6a); are significant(Pb0.05) from the previous December to the current June. The stron-gest relationships occur in March (r=−0.42) and April (r=−0.37).The mean December to July SOI is negatively correlated (r=−0.44;Pb0.0001) with the tree-ring δ18O record from 1902 to 2004 as awhole (Fig. 6b). However, the correlations are strong and significantfrom 1902 to 1930 (r=−0.613, Pb0.001) and from 1980 to 2004(r=−0.526, Pb0.007), but there is no significant correlation from1930 to 1980 (Fig. 6b). Surprisingly, the most significant correlationsbetween the tree-ring δ18O and SOI correspond to warming periods(1900 to 1930 and 1980 to 2004) in China (Fig. 6c).

Based on the close linkage between the ENSO, SSTs, and rainfall inAsian monsoonal regions (Charles et al., 1997; Kumar et al., 1999;Yang and Lau, 2004), we tested the responses of tree-ring δ18O tomeasured and reconstructed SST variations. As shown in Fig. 7a, theSST of the Niño3 region from February to May is significantly and pos-itively correlated (r=0.43, Pb0.001) with the tree-ring δ18O from1950 to 2004. During this period, increased SST is generally accompa-nied by a corresponding increase in the tree-ring δ18O to positivevalues. Moreover, tree-ring δ18O strongly correlated with thespring-SST record inferred from coral δ18O in the Nansha Islands(9°33′N, 112°54′E) in the South China Sea (Yu et al., 2001), withr=0.58 from 1951 to 1998. Tree-ring δ18O is also significantly andpositively correlated to SST variations (Fig. 7b) in the Xisha Islands(Sun et al., 2004) (r=0.373, Pb0.001) from 1906 to 1992. Moreover,the tree-ring δ18O is negatively correlated with the δ18O variations ina coral colony (r=−0.224, P=0.031, from 1902 to 1994) in thesouthwestern Indian Ocean (4°37′S, 55°49′E; Charles et al., 1997), in-dicating a negative correlation between the tree-ring δ18O and IndianOcean SST (Fig. 7c).

The correlations between the gridded Indian Ocean SST anomaliesand the tree-ring δ18O in southwestern China are positive and statis-tically significant (r>0.40, Pb0.05; Fig. 8a) from 1902 to 2004, withthe region containing the strongest correlations confined to between50°E and 100°E, within a band ±10° around the equator. Moreover,the positive influence of SST variations over the western north PacificOcean on the tree-ring δ18O is obvious (r>0.2, Pb0.05; Fig. 8a), and isfocused in the region between the WNPSM and the EASM (10°N to20°N, 110°E to 130°E) (Wang and Linho, 2002). The relationship be-tween SST over the South China Sea and the tree-ring δ18O was alsosignificant, but relatively weak (rb0.40; Fig. 8a). Using the period1951 to 2004 to repeat this analysis because of the better data quality,

Fig. 5. Comparisons between tree-ring δ18O and (a) the India summer monsoon index (ISM) (Wang et al., 2001) and (b) the western North Pacific monsoon index (WNPSM) (Wanget al., 2001) from 1948 to 2004 and (c) the East Asian summer monsoon index (EASM) from 1902 to 2004 (Guo et al., 2003). EASM-1 represents the EASM index from the previousyear. Red and blue lines indicate 5-year moving averages.

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we found that the spatial pattern of correlation does not changegreatly in the Indian Ocean, but the region with a strong correlationin the northwestern Pacific Ocean decreases to a relatively smallarea, with weaker correlations (rb0.4; Fig. 8b).

4. Discussion

4.1. Absolute δ18O values in tree-ring cellulose

Compared with the results from other studies (Roden et al., 2000;Saurer et al., 2002; Liu et al., 2004; Treydte et al., 2006; Cullen andGrierson, 2007; Hilasvuori et al., 2009), our cellulose δ18O, with amean of 15.2‰, is lower than those in most previous studies. Wefound only one case in which tree-ring δ18O was comparable, with avalue of approximately 16.0‰ reported by Saurer et al. (2002).Tree-ring δ18O depends strongly on the δ18O value of soil water,which is related mainly to the isotope composition in rain water(the water source), residence time in the soil, evaporation effects,and leaf water enrichment due to transpiration through stomata(Yakir and Sternberg, 2000; Roden et al., 2000; Waterhouse et al.,2002).

Based on a precipitation δ18O model developed for China (Liu etal., 2008), the mean regional precipitation δ18O value is −11.2‰.Because of the “amount effect” (Lee and Fung, 2008) that resultsfrom the high levels of precipitation in southwestern China, the rain-fall δ18O values in summer are more negative near Lijiang, with sum-mer precipitation δ18O around −12.0‰ in 2003 (Pang et al., 2006).The temperature effect on the rainfall δ18O is insignificant in ourstudy region (Pang et al., 2006; Zhao et al., 2011). In our study, thelatewood of the tree rings mainly incorporates the effects of the sum-mermonsoonal rainfall, which therefore leads to correspondingly low

cellulose δ18O values. The mean relative humidity during the growingseason is greater than 80%, which may result in lower transpirationand leaf water enrichment and thus, also a relatively low oxygen iso-tope ratio in cellulose. As Sternberg (2009) noted in a recentreview, cellulose δ18O can be calculated using a simple equation:δ18Ocellulose=δ18Owater-source+26.7‰. On this basis, the tree-ringcellulose δ18O should equal 14.7‰ when the δ18O of the sourcewater is −12.0‰, consistent with the results presented here (Panget al., 2006).

4.2. Climate–isotope relationships

As expected, the current growing season's climate dominates theδ18O signals (Fig. 3b), because latewood was analyzed. At the Gan-haizi observation station in the forest belt (27°6.2′N, 100°15.3′E,3100 m a.s.l.) of our study region, latewood forms from late August(13.5 °C) to October (8.8 °C). According to phenological observationsof fir (He, 2000), tree sap flow and tree cell division cease when theair temperature falls below 5 °C (Liang et al., 2009a,b). Therefore,tree sap flow and cell wall development are likely to continueuntil at least October, and the influence of the climate variablesfrom August to October can leave their imprint on tree-ring δ18O(Fig. 3b).

Tree-ring δ18O was significantly and negatively correlated to allmoisture-related variables such as total cloud cover, mean relativehumidity, and precipitation during the current summer (Fig. 3b),which includes the months with the most rain and highest relativehumidity (Fig. 2). These correlations might have been even strongerif the elevations of the weather station and study site were closer.Correlations partly reflect the inter-relationships among all these var-iables (Pb0.006, from 1950 to 2004) and support our hypothesis that

Fig. 6. (a) Correlation coefficients between tree-ring δ18O and the monthly Southern Oscillation index (SOI) from 1902 to 2004. Months followed by “/p” indicate values in the pre-vious year. The dashed line indicates the pb0.05 significance level; bars that extend below this line are significant. (b) Comparison of the tree-ring δ18O anomaly with the SOI fromthe previous December (Dec/p) to the current July. The y-axis for the SOI index has been reversed to facilitate the comparison. (c) Temperature variations over China from 1900 to2005 (Wang and Li, 2007). Dashed red lines indicate linear warming trends of 1900–1930 and 1980–2005.

110 X. Liu et al. / Chemical Geology 291 (2012) 104–115

the isotopic abundance in latewood cellulose is related to moistureconditions during the growing season, which affect physiological pro-cesses and tree growth.

Increased cloud cover will increase the probability of rainfall withlow δ18O and decrease the atmospheric vapor-pressure deficit(Reinhardt and Smith, 2008). Both the source water signature andless leaf 18O enrichment by evapotranspiration result in low tree-

ring δ18O values, which explains the negative correlation to totalcloud cover in our study region.

The relationship between mean relative humidity and tree-ringδ18O reflects the effect of relative humidity on stomatal conductanceand transpiration rates (Roden et al., 2000). Increased moisture dur-ing the rainy season minimizes the evaporative enrichment of leafδ18O by reducing the intercellular-to-extracellular vapor pressure

Fig. 7. (a) Comparisons of tree-ring δ18O and spring SST anomalies in the South China Sea (Yu et al., 2001) and SST in Niño-3. Variations in (b) SST in the Xisha Islands of the SouthChina Sea (Sun et al., 2004) and (c) SST over the southwestern Indian ocean (Charles et al., 1997) inferred from coral δ18O. (d) Mean tropical (30°N to 30°S) SST variations (Wilsonet al., 2006). The y-axis for each parameter has been scaled to anomalies.

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gradient and the intercellular-to-ambient specific humidity gradient(Roden et al., 2000; Shu et al., 2005). During the dry season, weexpect lower specific humidity to increase evaporative enrichmentof 18O in leaf water, resulting in increased tree-ring δ18O. Severalstudies of trees from temperate climates have reported similar rela-tionships between tree-ring δ18O and relative humidity (Rameshet al., 1986; Robertson et al., 2001; Shu et al., 2005). In southwesternChina where monsoon conditions occur, rainfall δ18O has been corre-lated with the amount of rainfall rather than with temperature(Dansgaard, 1964; Pang et al., 2006). Elsewhere, this “amount” effecthas been used to estimate the activities of past tropical storms andhurricanes whose history is recorded in the isotopic composition ofprecipitation (Miller et al., 2006). An inverse relationship betweenthe tree-ring δ18O and summer rainfall is consistent with a negativerelationship between rainfall δ18O and the total amount of rainfallduring the growing season (Fig. 3b). Overall, under monsoon-typeconditions, cloud cover may be an indicator of atmospheric circula-tion patterns, which reflects the integrative influences of climatic fac-tors on tree-ring oxygen discrimination.

Compared to the moisture-related variables, temperature has arelatively small effect on both tree-ring δ18O (only significant inJune) and rainfall δ18O (Pang et al., 2006). From May to June, temper-ature increases to levels above 15 °C, whereas precipitation increaseslater in the summer (Fig. 2). This causes leaf water evaporation todominate the source water signal, leading to higher xylem celluloseδ18O values. This indirectly influences evaporative enrichment, andthis hypothesis is supported by the significant negative relationship(r=−0.65, Pb0.0001, from 1951 to 2004) between temperatureand relative humidity during the growing season. Thus, the tree-

ring δ18O reflects the direct effects of humidity on stomatal conduc-tance and evaporative enrichment of leaf water influenced by air tem-perature (Roden et al., 2000; Robertson et al., 2001; McCarroll andLoader, 2004).

These factors indicate that in the wet temperate forest of the studyarea, higher temperatures will increase soil evaporation and a tree'stranspiration while stimulating photosynthesis and these processeswould increase tree-ring δ18O values. However, during the latewoodproduction period, the dominant factors that affect tree-ring δ18Ovalues switch from temperature to water conditions during the sum-mer monsoon, which significantly affects stomatal conductance, andexplains the negative correlations between tree-ring δ18O valuesand precipitation, relative humidity and cloud cover (Fig. 3b).

The earlywood and latewood of tree rings are composed of organicmatter originating from photosynthate stored in the previous yearand from photosynthate produced during the current growing season,respectively (Kagawa et al., 2005, 2006). These two periods have differ-ent precipitation water sources, from the previous year's monsoon andthe late current monsoon seasons, respectively. Our latewood δ18Omainly reflects the wet conditions late in the growing season in ourstudy region. Our results therefore support suggestions from other au-thors (Treydte et al., 2007; Boda et al., 2011) that tree-ring δ18O valuesin a wet temperate region provide a promising tool to investigate thevariability in moisture conditions and their driving factors.

4.3. Tree-ring δ18O and large-scale atmospheric circulation

A recent study demonstrated that tree-ring δ18O could be a reli-able proxy for the strength of the variation in the East Asian summer

Fig. 8. Spatial correlations between tree-ring δ18O and the gridded SST (February toSeptember) covering different periods: (a) from 1902 to 2004, (b) from 1951 to2004. Values significant at pb0.10 are shown. The black squares indicate the samplingsite.

112 X. Liu et al. / Chemical Geology 291 (2012) 104–115

monsoon in arid northwestern China (Liu et al., 2004) and found apositive correlation between precipitation and yearly tree-ring δ18O.However, the relationship between precipitation and tree-ring late-wood δ18O in the temperate monsoonal region of southwesternChina shows a different seasonal pattern (Fig. 3b), with negative cor-relations between latewood δ18O and the periods with the highestrainfall and atmospheric relative humidity during the summer andautumn (Figs. 2, 3b), suggesting that regional water-vapor conditionsgovern the interannual variability in the tree-ring δ18O.

This negative correlation can also be explained by the amount ef-fect on precipitation δ18O in the study region (Pang et al., 2006). Asthe monsoon intensity increases, more rainfall occurs during thegrowing season, and precipitation δ18O becomes more negative(Zhang et al., 2007), leading to more negative tree-ring δ18O values.This is why negative relationships exist among tree-ring δ18O andthe three monsoon indices (ISM, WNPSM and EASM). Although theclimate of southwestern China is mainly affected by the ISM, the cor-relation between the tree-ring δ18O and the ISM was not very strong(Fig. 5a). Pang et al. (2007) found that from 1991 to 2003, the δ18O ina snow profile in the Yulong Snow Mountains was negatively corre-lated with the South Asian summer monsoon index defined byWang and Zhen (1999), with r=−0.55 (Pb0.05).

In our study, the correlation between the tree-ring δ18O and theISM from 1950 to 2004 was weak but significant (R=−0.29,P=0.027), and this correlation coefficient was stronger than that inthe study by Pang et al. (2007). Moreover, the δ18O value in thesource water (from precipitation) may be further modified by a tree'sphysiological processes. There is some evidence that oxygen isotope

ratios in precipitation during the latemonsoonalmonths are influencedby recycling of monsoon rainfall (Pang et al., 2006), i.e., some of thewater taken up by the trees re-enters the atmosphere as a result of tran-spiration and soil evaporation, and contributes to cloud formation andsubsequent rainfall. These factors will all contribute to the noise in thefinal tree-ring δ18O chronology and will weaken the correlations be-tween tree-ring δ18O and the ISM. Around the 1960s, an abrupt changein the relationship between the tree-ring δ18O and the EASM intensitywas obvious, with the trendsmoving in opposite directions (Fig. 5c), in-dicating a substantial influence of EASMactivity on the climate of south-western China (Shi and Zhu, 1996).

From 1902 to the 1930s and then after the 1980s, most ENSOevents (with negative SOI values) corresponded to periods withhigh tree-ring δ18O values (Fig. 6b). This shows a clear connection be-tween years with low SOI values and years with high tree-ring δ18Ovalues. The ENSO warm phase decreases precipitation in southernChina (Xiao and Gong, 2000). This would result in anomalously highδ18O values in cellulose that formed during the growing season. Thefact that the significant relationships between tree-ring δ18O andSOI occurred against a background of increasing temperature overChina (Fig. 6c) can be explained partly by linkages between ENSOphases and climate, especially for temperature in China (Xu et al.,2009). The low tree-ring δ18O values in our study appear to be relatedto ENSO events (negative values of the SOI).

Overall, the relationship of ENSO to tree-ring δ18O was significant(Fig. 6b). However, the patterns of the correlation between δ18O andENSO events were inconsistent among periods. This instability ofENSO was also observed by Trenberth and Hoar (1997). The instabil-ity of ENSO could be reflected in the connections between tree-ringδ18O and regional climate during different periods. During the devel-oping phase of ENSO, a significant abnormality develops in the EASMcirculation and precipitation (Zhang et al., 1996), corresponding toless transport of water vapor. The resulting decrease in precipitationand in its δ18O variability caused by local climate change may changethe relationships between SOI and tree-ring δ18O. In most cases, ENSOis strongly associated with precipitation over land. However, a recentstudy by Kumar et al. (1999) showed a weakening correlation be-tween ENSO and the ISM, and only about 60% of the El Niño and LaNiña variations affected the regional climate in India (Kane, 2006).This indicates that not every ENSO event affects local climate, andtherefore that ENSO events may not always affect tree-ring δ18O(Xiao and Gong, 2000). Moreover, the narrow seasonal response ofthe climate-sensitive tree-ring δ18O values may not coincide withthe seasonality of the local ENSO teleconnection during certain pe-riods (Stahle et al., 1998). Instability in the correlations between cli-mate and tree-ring isotope ratios was also observed on a long-termscale in recent studies from the Alps (Reynolds-Henne et al., 2007)and Finland (Hilasvuori et al., 2009).

The anomalous SSTs associated with ENSO events correspond tonegative SOI values. This anomaly links globally with local climatesin the tropical and extra-tropical latitudes of both hemispheres(Tourre and White, 1995). When SSTs are high, the EASM is weak,resulting in decreasing precipitation and more positive δ18O valuesin precipitation (Pang et al., 2006; Zhang et al., 2007). On the otherhand, when SSTs are low, the EASM is strong and monsoon precipita-tion increases, leading to lower δ18O values in rainfall. Thus, the pos-itive correlation (r=0.58, Pb0.001) between SST in the NanshaIslands (Yu et al., 2001) and the tree-ring δ18O (Fig. 7a) appears tobe expected on both annual and decadal scales, especially from1955 to 1980. When SSTs in the Niño3 region are high, temperatureson the Tibetan Plateau would also increase (Xu et al., 2009). Thiswould lead to higher evaporation from the soil and higher vegetationtranspiration, resulting in a positive correlation between tree-ringδ18O and temperatures in May and June (Fig. 3b). Our analysis com-paring tree-ring δ18O with SSTs revealed the classical SST signatureof ENSO (Trenberth et al., 1997).

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Kumar et al. (1999) pointed out that the inverse relationship be-tween ENSO and the ISM has broken down in recent decades, andthat the relationship between Niño3 SST and ISM rainfall has weak-ened since 1980. However, in our study, the connection betweentree-ring δ18O and SSTs in the South China Sea remained stable dur-ing this period. Based on the coral Sr content in the Xisha Islands(16°51′N, 112°20′E) and the coral δ18O in the western Indian Ocean(4°37′S, 55°49′E), SST in both regions increased steadily despite theoccurrence of several cooling events (Fig. 7b,c). There are a numberof distinctive similarities and dissimilarities in the temporal patternsof change reflected in each of these records. Tree-ring δ18O anomaliesand SSTs in the Xisha Islands reveal a similarity on a decadal scale,suggesting that climate in the study region is coherent with the cli-matic regime of the South China Sea (Fig. 7b). However, the oppositetrends were observed in two SST series from different oceans in thelow-frequency domain during some periods (e.g., 1955 to 1975;Fig. 7b,c). A comparison between the tree-ring δ18O and the meantropical SST anomaly (Wilson et al., 2006) also indicates a close link-age, with r=0.44 (pb0.0001) from 1902 to 2003 (Fig. 7d). Our corre-lation analyses using gridded SST data (Fig. 8) revealed substantiallinkages between tree-ring δ18O and SSTs in the Indian Ocean andthe western north Pacific Ocean, and particularly in the IndianOcean, suggesting a dominant influence of the climate in the IndianOcean on climatic variability in southwestern China, as well as a smal-ler contribution from the western north Pacific Ocean. Based on tree-ring width analyses, Sano et al. (2009) also demonstrated a connec-tion between soil moisture availability over northern Vietnamand SST anomalies in the Indian Ocean and north-western PacificOcean.

5. Conclusions

We measured tree-ring δ18O of latewood for the period 1902 to2004 from a subalpine wet temperate forest in southwestern China,and compared the data with various climate variables, SSTs and circu-lation indices. Although the individual correlations with the regionalinstrumental measurements as well as the large-scale circulation var-iables are significant, the percentage of explained variance in theserelationships is not sufficiently high to develop robust reconstruc-tions. Nonetheless, these correlations help identify the climate pa-rameters and ocean/atmospheric circulation patterns that moststrongly influence tree-ring δ18O values. Negative correlations be-tween tree-ring δ18O values and precipitation, relative humidity,and total cloud cover during the wettest part of the growing seasonsuggest that both stomatal conductance and the source water signa-ture drive tree-ring oxygen isotope values under the study area'stemperature and humidity conditions. The negative correlations be-tween the SOI index, the EASM index, and tree-ring δ18O, and the pos-itive correlation between SSTs and tree-ring δ18O, support thepresence of a source water signal in tree-ring δ18O at our site and in-dicate the potential of using tree-ring δ18O to investigate the drivingforces in atmospheric circulation patterns. Instability in the relation-ships between tree-ring δ18O and the ENSO index and correlationswith SSTs in oceans surrounding the study area suggests that addi-tional factors may be contributing to the climatic variability inthis region. Continued efforts to develop a multi-species isotopic net-work for long-term scales using earlywood and latewood tree-ringδ18O will shed more light on the regional climatic variability andmonsoon activities, and on the signatures of ENSO events in monsoonChina.

Acknowledgments

This research was supported by the Knowledge Innovation Projectof the Chinese Academy Sciences (KZCX2-YW-QN308), by the Self-determination Project of the State Key Laboratory of Cryospheric

Sciences (SKLCS09-03), and by the National Natural Science Founda-tion of China (408900501, 40871002). We thank the journal's anony-mous reviewers and the journal's editor, whose comments andsuggestions were helpful in improving the quality of this paper.

Appendix A. Supplementary data

Supplementary data to this article can be found online at doi:10.1016/j.chemgeo.2011.10.001.

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