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JING Yuan-Yuan, LI Shuanglin, WAN Jiang-Hua, LUO Fei-Fei. Relationships between the Oxygen Isotopes in East Asian Stalagmites and Large-Scale Atmospheric and Oceanic Modes. Atmospheric and Oceanic Science Letters, 2014, 7(6): 540-545  
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Relationships between the Oxygen Isotopes in East Asian Stalagmites and Large-Scale Atmospheric and Oceanic Modes
JING Yuan-Yuan1,2, LI Shuanglin1,*, WAN Jiang-Hua1, LUO Fei-Fei1
1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2. College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
*Corresponding author: LI Shuanglin,shuanglin.li@mail.iap.ac.cn
Abstract

The stalagmite δ18O record is known to be associated with the climate, but the specifics of the relationship remain unclear. It may not represent variation in air temperature or precipitation, but instead reflect integral changes of monsoon circulation, especially water vapor sources (the so-called "circulation effect"). Since large-scale atmospheric-oceanic modes such as the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) exert significant effects on Asian monsoon, in this paper the authors investigate the relationships of the East Asian stalagmite δ18O record with these modes. The last three centuries form the focus of our study, for which the authors use reconstructed as well as instrumental data. Considering the impacts of human activity, our analysis is conducted with respect to two periods-the pre- and post-industrial periods. The results show significant lead-lag connections: a positive correlation peaks when the PDO leads East Asian stalagmite δ18O by 3 years, which is persistent over the past 300 years; while the relationships of the AMO and NAO with the East Asian stalagmite δ18O record show significant differences in the post-industrial relative to the pre-industrial period. This implies that the East Asian stalagmite δ18O record may primarily reflect the PDO signal.

Keyword: stalagmite; oxygen isotope; large-scale circulation; lead-lag correlation; East Asian climate
1 Introduction

Under equilibrium conditions of carbonate deposition, stalagmite δ18O is an important hydrogeochemical proxy in paleoclimate studies ( Wang et al., 2001). Previous research has suggested that δ18O may be an indicator of temperature ( Johnson and Ingram, 2004) or precipitation ( Fleitmann et al., 2004), while other studies have indicated neither ( Zhang et al., 2008). Instead, it could be a reflection of the strength of monsoon circulation, but this is by no means a certainty. Recent research has connected δ18O with the variability of water vapor sources ( Dayem et al., 2010). Based on an overall consistency of the δ18O record within China, as well as its reflection of the 1976-77 climate shift in the Pacific Ocean ( Miller et al., 1994), Tan (2009) put forward the "circulation effect" of stalagmite δ18O to explain the consistency. This effect refers to the fact that stalagmite δ18O reflects the ratio of water vapor from the remote Indian Ocean to that from the adjacent Pacific Ocean, and therefore the change in large-scale atmospheric general circulation rather than the variability of temperature or precipitation. Water vapor from the Indian Ocean travels a longer distance than that from the Pacific Ocean before reaching East Asia, implying the presence of a lighter oxygen isotope in monsoon rainfall in China when the water vapor from the Indian Ocean plays a larger role than that from the Pacific Ocean due to the Rayleigh distillation process ( Rayleigh, 1896). More recently, Tan (2013) analyzed the connection of δ18O with precipitation, the western Pacific subtropical high (a key circulation system of the East Asian Summer Monsoon), and the southwesterly East Asian Summer Monsoon, and found that the δ18O in precipitation does indeed reflect the effect of El Niño-Southern Oscillation (ENSO) on the transportation of water vapor. In the summer following an El Niño event, the water vapor contributing substantially to the precipitation in China primarily originates from the subtropical western Pacific. In addition to the circulation effect, some other studies have proposed connections of stalagmite δ18O with water vapor origin, transport path, and subtropical cyclones ( LeGrande and Schmidt, 2009; Maher and Thompson, 2012). Based on these connections, single stalagmite δ18O records have been calibrated to precipitation and circulation ( Zhang et al., 2008; He et al., 2009).

On the other hand, previous research has revealed significant influences of large-scale atmospheric-oceanic modes on monsoon circulation. The Atlantic Multidecadal Oscillation (AMO) is characterized by a basin-scale variability of sea surface temperature (SST) in the North Atlantic with a period of 65-80 years. It influences the East Asian Monsoon circulation by changing the air-sea interaction in the western Pacific Ocean and tropospheric air temperature over Eurasia. In warm AMO phases, the Indian Summer Monsoon is intensified with warmer East Asian temperature and stronger East Asian Summer Mon-soon ( Lu et al., 2006; Li and Bates, 2007; Li et al., 2008; Wang et al., 2009; Wang et al., 2013a, b). Undoubtedly, this will affect water vapor sources and the δ18O in precipitation. The Pacific Decadal Oscillation (PDO) is the decadal variability mode of North Pacific SST, featuring a periodicity of 20-30 years. It too is associated with the East Asian Summer Monsoon circulation via its influence on the Walker circulation and sea level pressure over Mongolia. In the positive phases of the PDO, the cross- equatorial flow is weaker around the Indochina Peninsula, resulting in a weaker summer monsoon with more rainfall in southeastern China but less rainfall in northern China ( Ma, 2007; Zhu et al., 2010; Li et al., 2011; Qian and Zhou, 2014; Yu et al., 2014). The North Atlantic Oscillation (NAO) is characterized by opposite changes of sea level pressure between the Azores and Iceland. The NAO in winter ( Wang and Shi, 2001) and the summer NAO affects the East Asian monsoon circulation and transportation of water vapor via its influence on the pressure over Eurasia and the intraseasonal oscillation in the tropical Indian Ocean ( Sun et al., 2008, 2009; Yuan and Sun, 2009; Sun and Wang, 2012; Linderholm et al., 2011). The NAO has a wide spectrum, and its decadal component has a significant influence on the East Asian and South Asian Summer Monsoon ( Cui et al., 2013). Nonetheless, the large-scale atmospheric-oceanic modes could affect precipitation δ18O by changing water vapor via the monsoon circulation. Thus, it is meaningful to explore the relationship between stalagmite δ18O and these decadal atmospheric-oceanic modes. Such a study might not only verify the link between East Asian stalagmite δ18O and large- scale monsoon circulation, but also reveal the potential climate signal in stalagmite δ18O. These ideas motivated the present reported work. Considering that the interdecadal evolution of East Asian climate and the atmospheric-oceanic modes are asynchronous (e.g., Li and Luo, 2013), we analyzed their lead-lag connections. Because the anthropogenic influence on climate is substantial, the analysis was conducted for two periods, before and after the industrial revolution.

2 Data and method
2.1 Instrumental data

The instrumental cave stalagmite δ18O data were acquired from the National Climatic Data Center/National Oceanic and Atmospheric Administration (NCDC/NOAA). We used four caves within the mainland China region: Huangye in Gansu (33.58°N, 105.11°E); Wanxiang in Gansu (33.32°N, 105.0°E); Dayu in Shaanxi (33.13°N, 106.3°E); and Heshang in Hubei (30.45°N, 110.42°E) ( Hu et al., 2008; Tan et al., 2009, 2011; Zhang et al., 2008). We chose these four caves because: 1) their temporal resolutions are high, and they all cover a common period (around 1720 to the present day) despite their different timespans; 2) the time series of these four caves are well correlated with one another; and 3) the caves are located in central China, where the AMO, PDO, and NAO exert strong influences ( Zhu and Yang, 2003; Wang et al., 2009; Linderholm et al., 2011). An East Asian δ18O index (referred to as 4CAVE) was obtained by averaging the standardized δ18O in the four caves. Using monthly data from the Climate Research Unit, University of East Anglia, UK (http://www.cru.uea.ac.uk/~timm/grid), we constructed an instrumental temperature indexand precipitation index by averaging data in the sector of (30.25-33.75°N, 104.75-110.75°E). The instrumental AMO, PDO, and NAO indices (hereafter referred to as AMO_inst, PDO_inst, and NAO_inst, respectively) were calculated with reanalysis data, and downloaded from http://www. esrl.noaa.gov/psd/gcos_wgsp/Timeseries/.

2.2 Reconstructed data

Reconstructed atmospheric-oceanic mode indices were obtained from the NCDC/NOAA. We used two reconstructed AMO indices-one by Mann et al. (2009) with tree rings, stalagmite and other proxies covering 500-2006 AD (Anno Domini), and another by Gray et al. (2004) with tree rings covering 1572-1985 AD. The former is hereafter referred to as AMO_Mann, and the latter as AMO_Gray. The reconstructed PDO index used was that of Shen et al. (2006), which is based on the reconstructed East China drought-flood index covering the period 1470-1998 AD, and is hereafter referred to as PDO_Shen. Two reconstructed NAO indices were used-one by Cook et al. (2002) based on ice cores, tree rings, and other proxies spanning the period 1400-1998 AD, and another by Trouet et al. (2009) based on drought and flood indices spanning the period 1049-1995 AD. The former is hereafter referred to as NAO_Cook, and the latter as NAO_ Trouet. The period after 1720 forms the focus of our analysis since the reconstructed series during this period show good accordance with one another.

2.3 Methodology

The main statistical method used is correlation analysis. The significance of the correlation between two autocorrelated time series was assessed using the effective number of degrees of freedom, Neff, which can be given by the approximation ( Pyper and Peterman, 1998; Li et al., 2013)

(1)

where N is the sample size and and are the autocorrelations of two sampled time series, X and Y, at time lag j, respectively.

3 Results
3.1 Correlation in the post-industrial period

Table 1 (first row) shows the simultaneous correlation coefficients (CC) between 4CAVE and East Asian temperature, precipitation, and three atmospheric-oceanic mode indices, separately. In agreement with previous studies ( Zhang et al., 2008; Tan, 2011), 4CAVE shows negative correlation with temperature and precipitation. It is positively correlated with AMO and PDO, but negatively correlated with NAO. Considering the previously reported significant lead-lag connection between East Asian temperature and atmospheric-oceanic modes ( Li and Luo, 2013; Li et al., 2013), we analyzed the lead-lag connections between δ18O and atmospheric-oceanic modes.

Table 1 Simultaneous correlation coefficients (CC_sim) and maximum lead-lag correlation (CC_(lag)) of stalagmite oxygen isotopes (4CAVE) with temperature index (TI), precipitation index (PI), and three instrumental atmospheric-oceanic mode indices for the period 1860-2006 AD. The values in parentheses separated by a comma are the effective degrees of freedom ( Neff) and the threshold for the CC with the 90% confidence level, while the values in square brackets following the CC in the second row are the corresponding number of lag years

Figure 1 shows the evolution of CC with different lead-lag times of up to 30 years. When AMO_inst leads 4CAVE by one year (black curve in Fig. 1), the positive correlation reaches a maximum (second row in Table 1). When PDO_ inst leads 4CAVE by two years, their positive correlation reaches a maximum (0.27). NAO_ inst is most negatively correlated (correlation coefficient of -0.50) with 4CAVE, which occurs when it leads 4CAVE by 20 years. Compared with the simultaneous correlation, the lead-lag correlations are more significant, especially between NAO_inst and 4CAVE. Considering the strong influence of human activity, we next investigated their relationship in the pre-industrial period with reconstructed data.

Figure 1 Lead-lag correlations between 4CAVE and instrumental atmospheric-oceanic mode indices in the period 1860-2006 AD. The positive part of the horizontal axis represents 4CAVE leading the atmospheric-oceanic modes, and the dashed lines denote the 90% confidence levels for the time series using the effective number of degrees of freedom.

3.2 Pre-industrial correlation

We validated the quality of the reconstructed atmospheric-oceanic indices from two aspects. First, we compared the simultaneous CC between the reconstructed data and the instrumental data in the post-industrial period, 1860-2006. From Table 2, AMO_Mann and AMO_ Gray coincide with AMO_inst, with CC greater than 0.85. PDO_Shen also agrees well with PDO_inst in the post- industrial period. NAO_Cook and NAO_Trouet are generally in accordance with NAO_inst. The results indicate that the reconstructed data and the instrumental data are in good agreement.

Table 2 CC_sim between reconstructed atmospheric-oceanic mode indices and instrumental indices in the post-industrial period. Values in parentheses are the Neff and the threshold for the CC with the 90% confidence level.
Table 3 CC_(lag) of stalagmite oxygen isotopes (4CAVE) with the reconstructed atmospheric-oceanic mode indices in the period 1860-2006 AD (first row) and 1720-1860 AD (second row). In the second row, the degrees of freedom are defined as N = n/11 - 1, where n is the sample size. Values in parentheses are the Neff or N and the threshold for the CC with the 90% confidence level, while the values in square brackets following the CC are the corresponding number of lag years.

Second, we investigated the lead-lag correlation between the δ18O and the instrumental atmospheric-oceanic mode with the reconstructed data in the post-industrial period. From Fig. 2, similar to AMO_inst, when AMO_ Mann leads 4CAVE by three years, the positive correlation reaches a maximum (first row in Table 3); and when AMO_Gray leads 4CAVE by 12 years, the most positive CC is 0.40. When PDO_Shen leads 4CAVE by three years, the positive CC reaches a maximum (0.27). Both NAO_Cook and NAO_Trouet are most negatively correlated with 4CAVE when leading 4CAVE. The above analyses verified the consistency between the reconstructed data and the instrumental data after 1860 AD,suggesting that the reconstructed data are reliable. Because no instrumental data in the pre-industrial period are available, the reconstructed data are meaningful for studying the connection of δ18O with large-scale atmospheric-oceanic modes.

Figure 2 Lead-lag correlations between 4CAVE and reconstructed atmospheric-oceanic mode indices in the period 1860-2006 AD. The positive part of the horizontal axis represents 4CAVE leading the atmospheric-oceanic modes, and the dashed lines denote the 90% confidence levels for the time series using the effective number of degrees of freedom: (a) AMO; (b) PDO; (c) NAO.

Albeit a minor difference, AMO_Mann and AMO_ Gray both show negative correlation with δ18O when the AMO leads 4CAVE in the pre-industrial period (Fig. 3). This is opposite to the post-industrial period. As for PDO_Shen, similar to the post-industrial period, it has the highest CC (approximately 0.1) when it leads 4CAVE by three years in the pre-industrial period. This indicates that the relationship between the PDO and δ18O is stable during the past 300 years. When 4CAVE leads NAO_Cook by one year, the positive CC reaches a maximum. Meanwhile, NAO_Trouet has the strongest positive CC when it leads 4CAVE by three years. Compared with the post-industrial period, the strongest CC between δ18O and the NAO is reversed.

Figure 3 Lead-lag correlations between 4CAVE and reconstructed atmospheric-oceanic mode indices in the period 1720-1860 AD. The positive part of the horizontal axis represents 4CAVE leading the atmospheric-oceanic modes, and the dashed lines denote the 90% confidence levels for the time series using N: (a) AMO; (b) PDO; (c) NAO.

4 Concluding remarks

Based on the data quality, feasibility, and sensitivity to large-scale atmospheric-oceanic modes, we selected four caves in central China and established 4CAVE. The simultaneous and lead-lag correlations of 4CAVE with three large-scale atmospheric-oceanic modes (AMO, PDO, and NAO) in the past 300 years were studied. The conclusions are as follows:1) In the post-industrial period, East Asian stalagmite δ18O and large-scale atmospheric-oceanic modes show a significant lead-lag correlation on the decadal timescale, but no significant simultaneous correlation.

2) The PDO and East Asian δ18O show a steady correlation during the past 300 years, with a positive value peaking when the PDO leads δ18O by 3 years.

3) The relationships between East Asian stalagmite δ18O and the AMO and NAO are much weaker during the pre-industrial period.

The results indicate that East Asian stalagmite δ18O may primarily reflect the impact of the PDO signal. This is in accordance with some previous work ( Yu et al., 2014), in which it was shown that positive PDO is linked to a stronger western Pacific subtropical high and less precipitation in southern China. This causes a weaker Rayleigh distillation process for the northward water vapor transport, resulting in heavier δ18O in stalagmites ( He et al., 2009). As an ENSO-like decadal SST mode, the warm-phase PDO could exert a similar impact to El Niño. In the summer following an El Niño event, the Northwestern Pacific Anticyclone that emerges in the lower troposphere plays an important role in East Asian summer precipitation ( Wang et al., 2000; Zhang and Sumi, 2002). This anticyclone could intensify the western Pacific subtropical high, and result in stronger southwesterlies around the southwestern rim of the subtropical high. This would bring more moisture from the Pacific Ocean ( Zhang and Sumi, 2002), which may be the mechanism for the connection between δ18O and the PDO.

The AMO influences both East Asian summer rainfall ( Lu et al., 2006; Wang et al., 2009) and South Asian summer rainfall ( Li et al., 2008). The NAO affects the transport of water vapor and East Asian summer rainfall via its influence on extratropical systems ( Sun et al., 2008, 2009; Yuan and Sun, 2009; Sun and Wang, 2012) or the intraseasonal oscillation in the tropical Indian Ocean ( Cassou, 2008). These previous studies have explained the connection between δ18O and the AMO and NAO; however, this relationship is not consistent in the post- industrial and pre-industrial periods. Their weaker influences of the AMO and NAO on East Asian water vapor transport relative to the PDO may account for the inconsistency. This is similar to temperature, upon which the AMO exerts the most significant influence ( Luo and Li, 2014). Model experiments and quantitative analysis are required for resolving this issue.

Acknowledgments. The study was jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA11010401) and the National Natural Science Foundation of China (Grant No. NSFC41375085). The authors are grateful to Professor TAN Ming of the Institute of Geology and Geophysics, Chinese Academy of Sciences (IGG/CAS) for his guidance regarding this work.

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