A 300-year Vietnam hydroclimate and ENSO variability record reconstructed from tree ringδ18O



[1] A tree ring δ18O chronology is developed for the past 300 years (1705–2004) using 6 cypress trees from northern Vietnam to reconstruct long-term hydroclimatic variations in the summer monsoon season. To the best of our knowledge, this is the first well-replicated tree ringδ18O chronology from Southeast Asia, as well as the longest yet produced. Response analyses reveal that tree ring δ18O is significantly correlated with temperature, precipitation, and the Palmer Drought Severity Index (PDSI) during the period May–October, with highest correlation to the PDSI. Our δ18O chronology accounts for 44% of the PDSI variance, and is in good agreement with a 52-year tree ringδ18O chronology from northern Laos (r = 0.77), indicating that regional hydroclimatic signals are well recorded in the δ18O data. Spatial correlation analyses with global sea surface temperatures suggest that the tropical Pacific plays an important role in modulating hydroclimate over the study region. Further, the δ18O chronology correlates significantly with El Niño–Southern Oscillation (ENSO)-related indices, and is therefore used to reconstruct the annual Multivariate ENSO Index. Because previously published ENSO reconstructions are based mainly on proxy records originating from North America and/or the tropical Pacific, the future development of a tree ringδ18O network from mainland Southeast Asia could lead to an independent and more robust reconstruction of ENSO variability.

1. Introduction

[2] Monsoon droughts and floods are seriously affecting the agrarian economy of densely populated regions in Southeast Asia, and predictions of monsoon activity are a matter of vital economic importance. Such predictions require reliable information on historic patterns of variability in monsoon systems. However, instrumental meteorological data in this area are spatially and temporally limited, as compared with those in India and China, and rarely extend to the early 20th century [Lawrimore et al., 2011; New et al., 2001]. High-resolution long-term proxy records are therefore necessary to improve our understanding of Asian monsoon variability.

[3] Tree rings have been used as one of the best natural archives of past climate because of their high temporal resolution and accuracy of dating, and the broad geographical distribution of trees [Hughes, 2002]. The development of statistically calibrated and verified hydroclimatic (PDSI) reconstructions from rare and long-lived cypress trees (Fokienia hodginsii) growing in Vietnam [Buckley et al., 2010; Sano et al., 2009] represents a major recent contribution to dendroclimatic studies in mainland Southeast Asia. Of particular note is the evidence for multidecadal droughts in the mid-14th, early 15th, and mid-18th centuries. Interestingly, these prolonged droughts, some of which are recorded by other proxy records from India [Borgaonkar et al., 2010; Sinha et al., 2007], China [Zhang et al., 2008], Thailand [Buckley et al., 2007] and Myanmar [D'Arrigo et al., 2011], coincided with periods of serious social unrest, suggesting that droughts may have influenced the stability of societal structures [Buckley et al., 2010; Sinha et al., 2011]. However, despite the recent progress in dendroclimatic reconstruction, the dynamics and causes of climatic variability in this region are yet to be fully understood. For example, ring widths used in the previous dendroclimatic reconstructions mainly reflect the influence of early monsoon (March–May) climates, and hence do not provide a record of actual summer monsoon variability. In addition, the ring width chronology from northern Vietnam accounts for only 19% of the variance in the PDSI, owing largely to the fact that non-climatic disturbances (especially those related to closed-canopy environmental conditions) strongly influence the growth patterns of individual trees [Sano et al., 2009].

[4] Oxygen isotope data offer a promising approach to the interpretation of tree ring data because the isotopic data are less influenced by environmental heterogeneity than are ring width variations [e.g., Anderson et al., 1998; Raffalli-Delerce et al., 2004; Sano et al., 2012]. The reliability of oxygen isotope data stems from the fact that tree ring δ18O is primarily controlled by two climatic factors: δ18O of source water and relative humidity [e.g., Robertson et al., 2001; Saurer et al., 1997], both of which are closely related to monsoon activities. In fact, an increasing number of studies conducted at lower latitudes reveal that δ18O of tree cellulose can be used to investigate past precipitation and/or relative humidity, with or without the presence of visible annual rings [Anchukaitis and Evans, 2010; Anchukaitis et al., 2008; Evans and Schrag, 2004; Managave et al., 2010; Poussart and Schrag, 2005; Poussart et al., 2004]. Furthermore, our preliminary analyses using cypress trees from northern Laos show that tree ring δ18O is much more sensitive to precipitation, temperature, and PDSI than is ring width [Xu et al., 2011].

[5] Here, we present a precisely dated tree ring δ18O chronology for the last 300 years, based on 6-tree data from the cypressF. hodginsiifrom northern Vietnam. To the best of our knowledge, this is the first well-replicated tree ringδ18O chronology from Southeast Asia, as well as the longest yet produced. We use this record to first reconstruct the PDSI during the summer monsoon season (May–October) in northern Vietnam. In addition to their use in local climate reconstruction, tree ring records from the Indochina Peninsula are known to be proxies for ENSO variability. For example, Buckley et al. [2010]identified a significant correlation between December–February NINO 3.4 sea surface temperatures (SSTs) and tree ring width records from southern Vietnam. Similarly, we found a close relationship between ENSO-related indices and tree ringδ18O values over the 135-year period of available instrumental data. Based on these correlations, we used theδ18O chronology to reconstruct the annual Multivariate ENSO Index (MEI) [Wolter and Timlin, 2011]; this is one of the first ENSO reconstructions based on data from the Indochina Peninsula. Moreover, we note that the time-stable association between tree ringδ18O and the ENSO during the 135-year period has been obtained using a very limited number of samples from a single site.

2. Oxygen Isotope Theory

[6] Trees take up soil water through roots without any isotopic fractionation [White et al., 1985], and then transport it to leaves through xylem. The oxygen isotopic composition in leaf water is controlled by transpiration through stomata, which involves preferential loss of lighter isotopes and consequent enrichment in δ18O. This effect can be described by the modified Craig–Gordon equation:

display math

where δ18Ol, δ18Ox, and δ18Oa are the δ18O of leaf water, xylem water, and atmospheric water vapor, ε* and εk are equilibrium and kinetic isotopic fractionation factors, and ea and ei are ambient and intercellular water vapor pressures. The equilibrium fractionation, associated with the phase change from liquid to vapor, varies slightly with temperature; at 20°C, ε* = 9.8‰ [Majoube, 1971]. Kinetic fractionation results for the diffusion of vapor into unsaturated air give εk ≈ 26.5‰ [Farquhar et al., 1989]. Relative humidity (h) can be substituted for the vapor pressure ratio (ea/ei) [Dongmann et al., 1974], as leaf interstitial space is saturated with water vapor. δ18Ox is considered equal to δ18O of precipitation (δ18Op). By assuming that atmospheric water vapor is isotopically equilibrated with precipitation, δ18Oa can be replaced by δ18Op − ε*. Thus, equation (1) can be simplified to:

display math

[7] The δ18O of cellulose is affected by biochemical fractionation associated with synthesis of sucrose in the leaf, and the extent to which carbon-bound oxygen undergoes exchange with xylem water (precipitation) during cellulose synthesis. These processes can be described as follows [Roden et al., 2000; Sternberg, 2009]:

display math

where δ18Oc represents the δ18O of cellulose, f is the fraction of oxygen that exchanges with xylem water, εo is the net biological fractionation factor between the xylem water and the exchanged oxygen in the carbohydrate during cellulose synthesis, and 27‰ is the equilibrated isotopic exchange ratio between leaf water and sucrose during photosynthesis. The value of f is estimated to be 0.42 for εo = 27‰ [Roden et al., 2000]. Overall, this model indicates that the δ18O of cellulose is mainly controlled by the δ18O of precipitation and relative humidity.

3. Materials and Methods

3.1. Tree Ring Data

[8] Tree ring samples were collected from old growth F. hodginsii growing at an elevation of 1700–2000 m above sea level (masl) in the Mu Cang Chai (MCC) area, northern Vietnam (Figure 1). Paired increment cores were obtained from 60 trees (total of 120 cores). Cross-dating was performed by visually matching ring width variations and then statistically validating the matches using the program COFECHA [Holmes, 1983]. The data set included tree ring widths of 42 precisely dated cores from 22 trees, used in a previous reconstruction of the March–May PDSI for the last 535 years [Sano et al., 2009].

Figure 1.

The study region, showing the locations of the Mu Cang Chai (MCC) site (21°40′N, 104°06′E), the Sapa meteorological station, and the PL site [Xu et al., 2011]. The area of the grid box averages of the CRU TS3.1 and PDSI data sets used in this study is enclosed by the thick solid line.

[9] We selected 6 cores (trees) from the dated samples for isotopic analysis, based on the following criteria: 1) ring widths were wide enough to produce samples for isotopic measurement; and 2) cores consisted of more than 300–400 rings, to reduce potential biases related to aging. Isotopic analyses were conducted on all 6 cores for the period 1905–2004. Four of the cores were selected for analyses of the period 1805–1904, and 3 of the cores were selected for analyses of the period 1705–1804.

[10] We employed the ‘plate’ method to extract cellulose directly from a wood plate (transverse section) with a thickness of 1.0 mm, rather than from individual rings; this allowed us to process thousands of rings simultaneously. The chemical protocol for extracting cellulose was based on a modified Jayme–Wise method [Green, 1963; Loader et al., 1997]. Every annual ring on the cellulose plates was separated from adjacent rings with a scalpel under a microscope. Further details of the plate method are given by Xu et al. [2011].

[11] We loaded duplicate cellulose samples (sample size, 120–180 μg) onto silver foil, and determined 18O/16O ratios using an isotope ratio mass spectrometer (ThermoQuest Delta Plus XP) interfaced with a pyrolysis-type elemental analyzer (ThermoQuest TCEA). The18O/16O ratios were expressed as δ18O (‰) deviation relative to the VSMOW standard. The standard deviation was derived from repeated measurements of standard materials (Merck cellulose and IAEA C3 cellulose values were 0.20‰; n = 376). The measured values of the standard materials were used to correct the (slight) instrumental drift observed during the series of measurements.

[12] All 6 time series were individually normalized over the entire time period, such that the mean was 0 and the variance was 1 for the reference period 1905–2004; the series were then averaged to produce the final δ18O chronology for the period 1705–2004. We used the expressed population signal (EPS) [Wigley et al., 1984], calculated using the sample size and mean inter-series correlation (Rbar), to evaluate the strength of common variations.

3.2. Climate Analyses

[13] We employed linear correlation analyses between the tree ring δ18O and monthly climate data within 12-month windows (prior November through current October) to identify theδ18O–climate relationship. Because the duration of the 20-year observation data of the Sapa station closest to our site is too short for a meaningful correlation analysis, the CRU TS3.1 gridded precipitation and temperature data sets (http://badc.nerc.ac.uk/data/cru) for the period 1948–2004 were used instead. To test the consistency of the data sets, we compared monthly precipitation data of the CRU TS3.1 with those of the GPCC V5 [Rudolf et al., 2010]; the mean correlation of 0.80 for the 1948–2004 period validated the use of the CRU TS3.1 data set. In addition, the gridded PDSI data set produced by Dai et al. [2004] was utilized for the response analysis. Positive and negative values of the PDSI correspond to wet and dry conditions, respectively. We generated an average from grid points located between 15.0°N and 22.5°N and 100°E and 105°E (Figure 1) to obtain representative data for the region.

[14] The 20-year records of monthly mean temperature and precipitation at Sapa are presented inFigure 2 as the general climate of the study regions. The mean annual precipitation is 2,685 mm, 61% of which falls in the monsoon season of May through August. Rainfall in the early monsoon (March–April) and late monsoon (September–October) seasons jointly contributes 28% of annual precipitation, while the remaining 4 months (November–February) comprise the dry season. The hottest season is June through August during the monsoon.

Figure 2.

Monthly mean temperature and precipitation (1985–2004) at the Sapa instrumental station.

[15] To identify the impacts of large-scale climate variations on local climate (and hence tree ringδ18O), our chronology was correlated with global SSTs from the HadISST1 data set [Rayner et al., 2003]. For this purpose, we utilized the Climate Explorer (http://www.knmi.nl/) of Royal Netherlands Meteorological Institute (KNMI) [van Oldenborgh and Burgers, 2005]. We also compared the tree ring δ18O chronology with ENSO-related indices, such as the NINO 3.4 SST derived from the HadISST1 data set, the Southern Oscillation Index (SOI) [Trenberth, 1984], and the MEI [Wolter and Timlin, 2011]. The MEI was originally defined as the first principal component of 6 observed atmosphere–ocean variables located between 30°N and 30°S latitude and 100°E and 70°W longitude (excluding the Atlantic Ocean) for the 1950–1993 period [Wolter and Timlin, 1993, 1998, 2011]. We used a modified version of the MEI (extended to 1871) defined as the first principal component of the two most important variables (SST and sea level pressure) observed in the above mentioned domain [Wolter and Timlin, 2011]. The modified MEI is considered as a reasonable substitute for the original MEI, and provides a more balanced and complete variable for monitoring the ENSO state as compared with NINO 3.4 SST or SOI data [Wolter and Timlin, 2011].

4. Results and Discussion

4.1. Signal Strength of Tree Ring δ18O Data

[16] The means (and standard deviations) of δ18O values for the 6 trees, calculated for the common period 1905–2004, range from 22.4‰ (0.86‰) to 23.6‰ (1.08‰). The mean δ18O values are 1.0‰–1.3‰ less than those obtained for 4 trees of Fokienia hodginsii from northern Laos [Xu et al., 2011]. Because of the relatively higher latitude of our sampling site (see Figure 1), δ18O values of precipitation (source water) are expected to be more depleted at our site than at the Laos site, reflecting the ‘latitude effect’ of precipitation δ18O [Dansgaard, 1964]. The standard deviations of the δ18O values for the 6 trees for each year, calculated after adjusting the mean δ18O values to 0, range from 0.15‰ to 1.32‰ (mean, 0.56‰), which is comparable to values of 0.08‰ to 1.42‰ (mean, 0.65‰) obtained from the 4 trees from northern Laos [Xu et al., 2011].

[17] Our tree ring δ18O data from 6 trees show significant inter-series correlations over the past 300 years, as determined by running Rbar statistics (0.59–0.80) calculated using 50-year windows and a lag-time of 25 years (Figure 3). We found relatively low Rbar values during the last 100 years, and the tree ring δ18O data show overall increasing trends during this time. Nevertheless, even the lowest Rbar value of 0.59 is highly significant, suggesting that climatic signals are well recorded in the δ18O data. In contrast, ring width-index series derived from the same 6 trees show relatively weak inter-series correlations (0.10–0.36) [Sano et al., 2009], which is attributed to the effects of endogenous disturbances on radial growth, such as competition with neighboring trees in a closed-canopy forest site [Fritts, 1976]; δ18O values, on the other hand, are less influenced by such disturbances. The EPS for the δ18O data attains the generally accepted threshold value of 0.85 or greater over the entire period of the last 300 years (Figure 3), although the sample size is smaller in the earlier portions of the chronology. Therefore, the chronology can be considered to provide robust estimates of mean tree ring δ18O in the region.

Figure 3.

Tree ring δ18O data with sample size, and the running expressed population signal (EPS) and mean inter-series correlation (Rbar) calculated using 50-year windows and a lag-time of 25 years.

[18] It should be noted that age-related long-term trends in tree ringδ18O have been reported [Esper et al., 2010; Nakatsuka, 2007; Treydte et al., 2006], although the physiological mechanisms underlying these trends have yet to be fully analyzed. For example, Treydte et al. [2006]identified an age-dependent decrease in theδ18O chronology of juniper by analyzing biologically younger and older tree rings for the same time period; however, the age dependency had little effect on their millennium-long climate reconstruction. Other studies have also noted a decrease inδ18O series with increasing tree age, a trend which is observed in different species and at different locations. In contrast, our δ18O chronology shows no significant changes in δ18O during the period 1705–1804 (a possible slight increase of 0.021‰ per decade). In this study, the 3 tree cores selected for analyses of the period 1705–1804 have rings dating to 1560, 1596 and 1624, and none of these rings contain pith; therefore, these trees were at least 100 years old in 1705. Although we did not measure younger and older tree rings for the same time period, and therefore cannot directly evaluate the effect of tree age on δ18O, our findings suggest that the age effect does not significantly influence our δ18O chronology. Young et al. [2011] have recently reported that age effects are not observed in tree rings whose cambial ages exceed ca. 50 years. The results of Young et al. [2011] indicate that the aging effect does depend on species and location.

4.2. Climate Signals in Tree Ring δ18O and the PDSI Reconstruction

[19] The δ18O chronology shows significant positive correlations with temperatures from May to August, and negative correlations with precipitation in May and August (Figure 4). Overall, the May–October monsoon season optimizes the temperature (r = 0.64, p < 0.001, effective degrees of freedom (edf) = 54) and precipitation signals (r = −0.59, p < 0.001, edf = 55) in the chronology. As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation and relative humidity levels, both of which lead to enriched δ18O in tree rings. More specifically, an inverse relationship between the δ18O of precipitation and the amount of precipitation is observed at lower latitudes, which is referred to as the ‘amount effect’ [Araguás-Araguás et al., 1998; Dansgaard, 1964]. Lower levels of precipitation correlate with higher δ18O values of the precipitation, resulting in δ18O enrichment of source water taken up by trees. On the other hand, lower humidity causes higher vapor pressure gradients between leaf interstitial spaces and the atmosphere, resulting in preferential loss of lighter isotopes and consequent enrichment of δ18O in leaf water [Roden et al., 2000]. In addition, higher temperatures stimulate evaporation of soil water, leading to soil-waterδ18O enrichment, thus affecting the composition of source water taken up by trees. Overall, our results are consistent with the model of tree ring cellulose δ18O, as explained in section 2.

Figure 4.

Correlations between tree ring δ18O and monthly (prior-year November to current-year October) and seasonal (May–October) temperature, precipitation, and PDSI for the period 1948–2004. To account for autocorrelation, the correlation coefficients were tested for significance using the number of effective degrees of freedom.

[20] The strength of the tree ring δ18O response to climate is consistent across a variety of climate parameters; moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor. Generally, lower precipitation levels correlate with higher δ18O values of the precipitation (amount effect); at the same time, higher temperatures (usually associated with lower precipitation levels) also lead to δ18O enrichment of soil water; acting together, these factors cause δ18O enrichment of source water. In addition, lower precipitation and higher temperature usually result in lower relative humidity levels, leading to increased leaf water δ18O. Importantly, these associated climatic parameters contribute to tree ring δ18O enrichment or depletion in a similar way (direction), indicating that tree ring δ18O values can be used as a pure proxy of past hydroclimate (wet–dry conditions); this interpretation is supported by relatively high correlations between δ18O and the monthly PDSI during the period May–October, with the highest correlation occurring in the May–October season (r = −0.66, p < 0.001, edf = 50). The PDSI is a dry–wet metric based upon a water balance model that considers precipitation, temperature, and soil characteristics [Palmer, 1965]. Although the mechanisms underlying variations in tree ring δ18O can generally be attributed to δ18O of source water and evaporative enrichment of leaf water δ18O, in pluvial monsoon regions these effects seem to be well represented by the PDSI, a simplified climatic variable. Therefore, PDSI values during the monsoon season are considered to be a strong predictor of the variance in tree ring δ18O values.

[21] The correspondence of our δ18O chronology with climatic factors is consistent with the findings of a 52-year tree ringδ18O chronology of F. hodginsii from Phu Leuy (PL) in northern Laos [Xu et al., 2011]. Furthermore, a direct comparison between the Vietnam and Laos δ18O chronologies is highly significant (r = 0.77, p < 0.001, edf = 48) (Figure 5), even though these sampling sites are 150 km distant from each other, indicating that regional hydroclimatic signals are well recorded in our δ18O chronology.

Figure 5.

Comparison of normalized tree ring δ18O chronologies from northern Vietnam (this study) and northern Laos [Xu et al., 2011] for the common period 1951–2002.

[22] We scrutinized the fidelity of a linear regression model used to reconstruct May–October PDSI, by conducting split-period calibration-verification tests commonly used in dendroclimatology [Cook and Kairiukstis, 1990]. More specifically, the PDSI reconstructed by regression upon one half of the climate data (calibration period) was tested against the actual PDSI of the remaining half of the data (verification period) withheld from regression. This test was repeated by switching the calibration and verification periods. As shown in Table 1, the reduction of error (RE) and the coefficient of efficiency (CE) tests are both positive, indicating the validity of the regression model. Thus, we were able to reconstruct summer monsoon PDSI back to 1705, based on a regression model that accounts for 44% of the PDSI variance (Figures 6a and 6c).

Table 1. Calibration and Verification Statistics for the Period of 1948–2004
Calibration PeriodR2Verification PeriodrRECE
  • a

    Here p < 0.01.

  • b

    RE, CE > 0.

Full (1948–2004)0.439a
Early half (1948–1975)0.252aLate half (1976–2004)0.687a0.557b0.327b
Late half (1976–2004)0.471aEarly half (1948–1975)0.502a0.502b0.151b
Figure 6.

(a) Actual and reconstructed PDSI values and linear regression trends. (b) Same as Figure 6a but for the Multivariate ENSO Index (MEI), including running 21-year correlations between the data sets. (c) Reconstructions of the May–October PDSI (left axis) and the November–October MEI (right axis) for the period 1705–2004. The red and blue lines represent 30-year splined values and the mean values of the reconstructions, respectively. Note that the right axis for the MEI is inverted because of the inverse PDSI-MEI correlation.

[23] The reconstructed PDSI values reveal that the 18th century was generally wet, followed by a dry period in 1820–1870 and a wet period in 1870–1900 (Figure 6c). The dry epoch in the early to mid 19th century is also suggested by pre-monsoon (March–May) PDSI reconstruction based on ring width records originating from the same site as that of this study [Sano et al., 2009]. For other periods, on the other hand, the δ18O reconstruction agrees less well with the ring width record. Perhaps the most notable difference between the δ18O and ring width records is the absence of evidence for a multidecadal drought in the mid-eighteenth century in theδ18O-based reconstruction. This is not surprising because of differences in the reconstructed seasons. Recently,Kajikawa et al. [2012]showed a seasonal dependence of various features of the Asian monsoon over the last 30 years, such as trends toward earlier onset of the monsoon and increase in May precipitation (but no long-term changes in July and August precipitation). These findings point out that PDSI reconstructions for different seasons usingδ18O and ring width data are both important for analyses of seasonal changes in Asian monsoon patterns over long time periods.

[24] Another noteworthy feature of our δ18O-based reconstruction is a trend in decreasing PDSI during the latter half of the 20th century, which parallels similar trends in meteorologically based PDSI (Figure 6a). Singhrattna et al. [2005] also observed a weakening of monsoon precipitation in central Thailand over the last 50 years. In addition, tree ring δ18O from the Himalaya [Sano et al., 2012] and Tibet [Grießinger et al., 2011], and varve-thickness data from lake sediments in Tibet [Chu et al., 2011], also show a decrease in monsoon rainfall over the past two centuries, indicating that the summer monsoon has weakened across wide areas of South and Southeast Asia. A modeling study reveals that a weakening trend of monsoon precipitation found in northern India and the eastern Tibetan Plateau over the latter half of the 20th century is deducible from the atmosphere's response to an increase in observed SSTs over the tropical Pacific and Indian Ocean [Zhou et al., 2008]. These findings suggest that elevated SSTs may also be responsible for the recent decrease observed in our reconstructed PDSI values.

[25] We further examined the reconstructed PDSI for the period of 1705–2004 in terms of its spectral power, using the multitaper method [Mann and Lees, 1996]. Significant peaks that fall within the range of ENSO variability occur at 3.8 and 4.2–4.4 years (see Figure 7). On the other hand, multidecadal cycles, which are prominent in ring width records from the Indochina Peninsula [Buckley et al., 2007, 2010; Sano et al., 2009], lack significant power in our data set. Overall, a persistent multicentury trend is the most notable feature of our δ18O chronology; this trend may be related to an increase in tropical SSTs, as a warming trend over the last 200 years has been observed in a reconstruction of large-scale tropical SST patterns [Wilson et al., 2006].

Figure 7.

Multitaper method power spectra for the reconstructed PDSI (AD 1705–2004). Peaks above the red line are significant at the p < 0.01 level.

4.3. Links With the El Niño-Southern Oscillation

[26] Our δ18O chronology shows a significant positive correlation with annual SSTs over most of the tropical Pacific and Indian Ocean, in particular the central Pacific (Figure 8), indicating that El Niño and La Niña phases result in dry and wet conditions, respectively, in the study region. The δ18O chronology is also significantly correlated with annual (previous November to current October) ENSO-related indices during the 1871–2004 period, especially annual NINO3.4 SST (r = 0.67, p < 0.001, edf = 114), the SOI (r = −0.60, p < 0.001, edf = 117), and the MEI (r = 0.70, p < 0.001, edf = 110). The long-term linear trends of increasing tree ringδ18O and decreasing reconstructed PDSI since the late 19th century is also consistent with that of increasing NINO3.4 SST and MEI, and of decreasing SOI. Furthermore, the influence of tropical Pacific climates on tree ring δ18O is temporally stable, as revealed by running 21-year correlations with MEI data (significance level,p < 0.01) (Figure 6b). Surprisingly, the explained variance for the MEI (49%) is higher than that for the local PDSI (44%). We attribute this, at least in part, to the necessity of using a PDSI data set based on records mostly from Thailand. Overall, our results indicate the potential of tree ring δ18O for reconstructing ENSO. For further investigation, we established a linear regression model to estimate the annual MEI over the last 300 years (Figures 6b and 6c), which shows variations that are virtually identical to those of the PDSI reconstruction.

Figure 8.

Spatial correlations between tree ring δ18O and annual (prior-year November to current-year October) sea surface temperatures (HadISST1 data set) for the period 1871–2004. All time series were individually detrended using linear regressions to remove long-term (common) trends in the series before calculating correlation coefficients. Correlations of >|0.23| are significant at thep < 0.01 level, assuming 120 effective degrees of freedom.

[27] Over the last decade, considerable effort has been devoted to reconstructions of ENSO variability using tree rings and other proxy records, based mainly on data from North America and/or the tropical Pacific [e.g., Braganza et al., 2009; Cook et al., 2008; D'Arrigo et al., 2005; Li et al., 2011; Mann et al., 2000; Wilson et al., 2010]. While existing ENSO reconstructions are in good agreement for the period since 1871 (the time at which instrumental records commenced), there is relatively limited agreement about ENSO variability prior to this time [Wilson et al., 2010]. As presented in Table 2, our reconstructed MEIs also show markedly weakened associations with published ENSO reconstructions before the instrumental period. Among the ENSO reconstructions, the ‘center of action’ (COA) reconstruction [Wilson et al., 2010] is perhaps the most reliable, as its calibration model accounts for over 80% of the instrumental variance. The importance of the COA reconstruction is further emphasized by the fact that all of the original proxy (coral) records are located in the central and eastern Pacific (the core region where ENSO develops), while other reconstructions rely largely on proxy records located in regions that are teleconnected with the central Pacific. Unfortunately, the COA reconstruction is not considered robust prior to 1850, because of lack of replication [Wilson et al., 2010]; this lack of sample replication may be responsible for reduced inter-series correlations before the mid-19th century.

Table 2. Correlation Matrix Calculated for 7 ENSO Reconstructions From the 1871–1980 and 1705–1870 Periodsa
Source Proxy LocationReferenceThis StudyWilson COAWilson TELCookMannLi
Central/Eastern PacificWilsonb (COA)0.57     
Indo-Pacific BasinaWilsonb (TEL)0.600.80    
Southwest N AmericaCookc0.460.600.48   
Near GlobalMannd0.490.640.530.82  
North AmericaLie0.440.340.280.690.67 
Pacific BasinBraganzaf (R5)0.520.550.540.710.720.60
Central/Eastern PacificWilsonb (COA)0.09 ns     
Indo-Pacific BasinaWilsonb (TEL)0.13 ns0.03 ns    
Southwest N AmericaCookc0.220.00 ns0.16 ns   
Near GlobalMannd0.250.15 ns0.14 ns0.48  
North AmericaLie0.12 ns−0.06 ns0.06 ns0.610.39 
Pacific BasinBraganzaf (R5)0.26−0.02 ns0.220.660.590.48

[28] It should be noted that the ENSO reconstructions of Mann et al. [2000], Cook et al. [2008], Braganza et al. [2009], and Li et al. [2011]are in moderate agreement prior to 1871. This is due partly to the shared tree ring data from southwest North America for these reconstructions. The cross correlations, however, do indicate some degree of coherence among these reconstructions. On the other hand, the ‘teleconnection’ (TEL) reconstruction, based on 1 ice core and 11 coral records from the Indo-Pacific basin (but out of the region used for the COA reconstruction) [Wilson et al., 2010], is less well correlated with those reconstructions [Braganza et al., 2009; Cook et al., 2008; Li et al., 2011; Mann et al., 2000] for the period 1705–1870, although the TEL record is considered as robust since 1727. These results, taken together with the fact that the TEL series is entirely independent of the reconstructions produced by Cook et al. [2008] and Li et al. [2011], indicate that the teleconnected relationship between the tropical central/eastern Pacific and the regions where proxy records are located may not be temporally stable [see Wilson et al., 2010].

[29] The significant time-stable correlation between tree ringδ18O and ENSO records over the 135-year period of available instrumental data is the most striking feature of our analysis. Our results imply that northern Indochina may be a ‘hot spot’ firmly teleconnected with the tropical Pacific, although whether the teleconnected relationship was stable before the instrumental period remains unclear. In this context, the reason that the hydroclimate of northern Indochina is closely linked to ENSO has yet to be resolved by numerical modeling experiments. Recently,Liu et al. [2012] revealed a significant correlation between a tree ring δ18O chronology from southwestern China and the SOI for the periods 1902–1930 and 1980–2004. However, no significant correlation was observed during the period 1930–1980, indicating a complex association between the ENSO and regional climate. Evidence of the non-stationarity between ENSO and the local climate (and thus tree ringδ18O values) indicates the importance of investigating long-term changes in atmospheric circulation patterns based on widely spaced proxy records in monsoon Asia. Nevertheless, our overall results indicate the significant potential for generating independent and robust reconstructions of ENSO variability using tree ringδ18O from mainland Southeast Asia, and for investigating the geographical influence of ENSO before the instrumental period.

5. Conclusions

[30] We developed a robust 300-year tree ringδ18O chronology from living F. hodginsii in northern Vietnam. Our tree ring δ18O data show significant correlations with precipitation, temperature, and PDSI in the summer monsoon season, and is in good agreement with a tree ring δ18O chronology from northern Laos. The reconstructed PDSI shows a decreasing trend over the latter half of the 20th century, which was likely induced by an overall increase in sea surface temperature over the tropical Pacific and Indian oceans. The present study demonstrates that a limited number of samples from a single site can produce a significant time-stable correlation between tree ringδ18O and ENSO records during the instrumental period. Our δ18O chronology is significant because it is the first published reconstruction of ENSO variability based on records from the Indochina Peninsula. Continued efforts toward the development of a tree ring δ18O network for mainland Southeast Asia will contribute to increasingly robust reconstructions of ENSO variability.


[31] This study was funded by a Grant-in-Aid for Scientific Research from the Japanese Society for the Promotion of Science (23-10262 and 23242047) and an FS research grant from the Research Institute of Humanity and Nature (RIHN), Japan. We thank the RIHN for generously allowing the use their isotope ratio mass spectrometer. We also thank three anonymous reviewers for valuable comments and suggestions that improved the manuscript.