An unstable tree-growth response to climate in two ...

Dendrochronologia 28 (2010) 225–237

ORIGINAL ARTICLE

An unstable tree-growth response to climate in two 500 year chronologies, North Eastern Qinghai-Tibetan Plateau Chunming Shia,b,c , Valerie Masson-Delmotteb,∗ , Valerie Dauxb,d , Zongshan Lia,c , Qi-Bin Zhanga,∗∗ a

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China Laboratoire des Sciences du Climat et de l’Environnement, UMR 1572, IPSL/CEA/CNRS/UVSQ, Bat 701, L’Orme des Merisiers, CEA Saclay, 91 191 Gif sur Yvette Cedex, France c Graduate School of Chinese Academy of Sciences, Beijing 100049, China d Université Pierre et Marie Curie, Paris, France b

Received 23 October 2009; accepted 23 December 2009

Abstract Two new Juniper tree-ring-width (TRW) chronologies spanning more than 500 years were developed in the Yellow River source area, North Eastern Qinghai-Tibetan Plateau (NE-QTP). For the two studied sites, located approximately 50 km apart, split correlation and coherence analysis reveal unstable tree-growth responses to local moisture availability. While significant correlations are obtained with April–June local precipitation, Palmer Drought Severity Index (PDSI) and river flow from 1948/1954 to 1998 and from 1948/1954 to 1970s, these correlations vanish for the time period 1970s-1998. The local instrumental climate data (precipitation, PDSI and river flow) exhibit opposite correlations with large scale modes of variability (El Niño Southern Oscillation, ENSO, and Pacific Decadal Oscillation, PDO) before and after the 1977 PDO shift. One tree-ring chronology is coherent and anti-phased with instrumental ENSO/PDO indices at 5.2-year frequency. On the longer time span, this TRW chronology is compared with PDO reconstructed from historical Chinese data. This comparison also exhibits unstable multi-decadal relationships, notably in the mid 19th century. Altogether, the comparison between our two chronologies, local instrumental climate records, and ENSO/PDO indices suggest a cautious use of local TRW records for paleoclimate reconstructions. Further studies are needed to explore both the spatial coherency of tree-ring records and the temporal stability of their response to local and large scale climate variability. © 2010 Istituto Italiano di Dendrocronologia. Published by Elsevier GmbH. All rights reserved. Keywords: PDO; ENSO; Tree-ring; Temporal stability; Tibetan plateau; Yellow River

Introduction The Yellow River, supporting 107 million people and booming industry and agriculture in its middle and lower ∗ Corresponding

author. Tel.: +33 169087715; fax: +33 169087716. author. E-mail addresses: [email protected], [email protected] (V. Masson-Delmotte), [email protected] (Q.-B. Zhang). ∗∗ Corresponding

reaches (Wang et al., 2006), has suffered from a trend of steady decreasing discharge to the sea since the 1950s with increasing occurrence of zero-flow days during 1990s (Liu and Xia, 2004; Chang et al., 2007). The Yellow River has been essential for the early development of Chinese civilization, as attested by 4000-year-old early urban history remains found in its central plain. Because the water supply in the headwater region accounts for more than 60% of total runoff to the Yellow River (Zhao et al., 2008), there is a need to understand

1125-7865/$ – see front matter © 2010 Istituto Italiano di Dendrocronologia. Published by Elsevier GmbH. All rights reserved. doi:10.1016/j.dendro.2009.12.002

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C. Shi et al. / Dendrochronologia 28 (2010) 225–237

climate variability that affects water supply in this region, often referred to as the “water tower” of China. The climate and river flow in the source region of the Yellow River are documented through instrumental records starting in the 20th century (Fig. 1; Table 1). They appear closely linked with large scale climate variability, with frequent high annual river flow/precipitation during La Niña years and

opposite situation in El Niño years (Wang et al., 2001; Fu et al., 2007; Zhang et al., 2007). Wang et al. (2006) reports that El Niño events can induce 51% decrease in the discharge of the Yellow River to the sea. The Pacific Decadal Oscillation (PDO), which is a long-living El Niño-like pattern of Pacific climate variability, can modulate the ENSO’s impacts through in-phase or out-phase interaction (Chan and Zhou,

Fig. 1. Characterization of the study area. Top panel: map of tree-ring sampling sites from this study (red, Heibei and Jiangqun), and from earlier studies (black and grey circles for Gou et al., 2007; Li et al., 2008a,b, respectively), weather stations (Xinghai, Tongde, Zeku, Henan, black squares), hydrological station (Tangnaihai, black triangle), and position of the study area within the Qinghai-Tibetan plateau (shaded area). Bottom panel: mean seasonal cycle of monthly precipitation (mm/month) and temperature (◦ C/month) of the four nearby weather stations. Vertical error bars display the range of interannual variability. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)


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Table 1. Location, elevation and time span of the tree chronologies, meteorological and hydrological records. Code

Long., E

Lat., N

Elevation (m)

Time span

Sample sites

Hebei Jiangqun

HB JQ

100.82 100.35

34.75 35.02

3475 3600

1442–2005 1465–2005

Weather stations

Tongde Xinghai Zeku Henan

TD XH ZK HN

100.65 99.98 101.47 101.6

35.27 35.58 35.00 34.73

3290 3324 3664 3500

1954–1998 1960–2002 1958–1990 1960–2002

Hydrological station

Tangnaihai

TNH

100.15

35.50

2546

1919–2007

2005; Imada and Kimoto, 2009). The marked 1976/1977 PDO shift from a ‘cold’ phase to ‘warm’ phase (Minobe, 1997) has strongly affected the Indian Summer Monsoon (ISM) and East Asian Summer Monsoon (EASM) intensities (Chang et al., 2000a,b; Ashok et al., 2000; Wang et al., 2001; Annamalai et al., 2005). This EASM shift has modified precipitation patterns in large area of China since late-1970s (Qian et al., 2007; Ma, 2007). Since the 1970s, the decreasing precipitation downstream of Lanzhou city has thus contributed to the water resource deficit in the mid-low catchments of the Yellow River (Zhang et al., 2009a,b). In summary, the Yellow River flow is influenced by local and large scale climate variability, which exhibit marked changes in the mid 1970s. Past PDO variations have been reconstructed using tree-ring data from North America and Asia (D’Arrigo et al., 1999, 2001; D’Arrigo and Wilson, 2006; Jacoby et al., 2004) or using historical document from China (Shen et al., 2006). These reconstructions have revealed a strong inter-decadal PDO variability prior to the 1976/1977 shift. It is therefore crucial to document the occurrence of such multi-decadal climate shifts and to characterize the full power spectrum of precipitation variability beyond the short instrumental records, using annually resolved climate proxies. Several studies have previously been conducted using high elevation TRW chronologies in the Northeastern QinghaiTibetan Plateau (NE-QTP), near the source region of the Yellow River (Fig. 1, black and grey circles). These studies have shown that moisture availability is a key factor limiting tree growth (Zhang et al., 2003; Shao et al., 2005; Zhang and Qiu, 2007; Li et al., 2008a,b). Through principal component analyses of several chronologies, Gou et al. (2007) have reported the potential of tree rings to reconstruct river flow in the source region of Yellow River. Using two neighbor chronologies, Li et al. (2008a,b) showed a significant correlation between local TRW, precipitation amount and Pacific SST. All these studies have greatly improved our understanding of past climate in QTP. However, most of them were based on calibration models built over the whole instrumental period (around 1950–2000 A.D.) without detailed analyses of the temporal stability of the tree growth–climate relationships. Here, we provide two new tree-ring chronologies spanning more than 500 years, and located 50 km apart, near the head of the Yellow River. Section “Data and methods” describes the construction of the two chronologies. In “Results”, we com-

pare the two chronologies and analyze the stability of their relationships with local and large scale climate variability, using split correlation and coherence analyses. The “Discussion” section is focused on the processes linking local and large scale climate, the possible causes for a changing response of tree growth, and the comparison with previous tree-ring studies.

Data and methods Our study sites are located in the Sanjiangyuan Nature Reserve (31◦ 39 –36◦ 12 N, 89◦ 45–102◦ 23 E) of the NE-QTP (Fig. 1; Table 1). The Yellow River and Yangtze River, which are the largest Chinese rivers, but also the Mekong River, flowing over 6 Asian countries, originate in this area. Treering samples were collected in two natural forest stands in the Animaqin Mountain, far away from human habitation, at Hebei (HB; 34.75◦ N, 100.82◦ E, 3475 m.a.s.l.) and Jiangqun (JQ; 35.02◦ N, 100.35◦ E, 3600 m.a.s.l.) (Fig. 1). Site HB is located in the bottom of a closed narrow valley with thick soil layer and sheltered from strong winds. Site JQ is located on the top of a south facing hill, with steep slope and thin soil. Both sites are within 6 km from the mainstream of the Yellow River. With the aim to select large and old trees, our sampling sites are located below the upper forest limit. The tree species under study is Qilian Juniper (Sabina przewalskii Kom.) which is the dominant species in natural forests of NE-QTP. It is recognized to be a long-living and climate sensitive species (Wang et al., 1983). Several studies have highlighted its response to water stress (Zhang et al., 2003; Shao et al., 2005; Zhang and Qiu, 2007; Li et al., 2008a,b). Recently, a bi-millennial temperature reconstruction has been accomplished by Liu et al. (2009) using this species. During field work, it was decided to sample a maximum of trees and sites, with only one core per tree collected at breast height. Trees with obvious damage or distorted trunk were avoided for sampling. In total, 34 and 30 cores were respectively collected from sites HB and JQ. The distance between sites studied by Gou et al. (2007) (denoted by black circles in Fig. 1) and our HB/JQ sites range from 45 to 105 km and 5 to 70 km respectively, and both sites of Li et al. (2008a,b) (denoted by grey circles) are within 3 km from HB site and around 50 km from JQ.

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Cores were air-dried and fixed to slotted wooden bars, then sanded with progressively finer sand paper up to 600-grit (16 ␮m). Tree-ring widths were measured with a Velmex system (Velmex Inc., Bloomfield, NY, USA) with an accuracy of 1 ␮m. The tree rings were visually cross-dated and the quality of the match was checked with the COFECHA software (Holmes, 1998). Errors in dating and measurements were corrected following microscopic examination of treering characteristics. Cores that could not be well dated were excluded from further analysis to ensure correct assignment of years to tree-ring formation. The tree-ring chronology was developed using the Arstan40c software (Cook and Krusic, 2006). Non-climatic trends were removed from each tree-ring series using a cubic spline with a 50% response frequency cut-off at half length of the series (standard outputs displayed in Fig. 2). The robustness of our results with respect to the detrending method has also been tested by removing long-term trends using an exponential fit (and a linear fit for cores with linear trends) (grey lines, Fig. 2). For each site, the detrended series were averaged into a standard chronology using a bi-weight robust mean calculation to exclude the influence of outliers. Residual chronologies, in which the effect of series autocorrelation was removed, were also developed. The early section of

chronologies with less than five sample replications were truncated. The standard chronologies are displayed in Fig. 2 and their links with meteorological and hydrological data are discussed in the next section. Meteorological data were obtained from four nearby meteorological stations (Xinghai, Tongde, Zeku and Henan) located at similar elevations (3290–3664 m) (Fig. 1; Table 1). The Tongde station is the nearest to both sampling sites (straight-line distance is 40 km to JQ and 75 km to HB) and has been chosen for correlation analyses with our tree-ring chronologies. Hydrological data were taken from Tangnaihai station. The length of the various instrumental records is displayed in Table 1. Palmer Drought Severity Index (PDSI) data spanning the instrumental period (since 1954) were extracted from a globally gridded PDSI database (Dai et al., 2004) for the relevant grid points (from grids 100–102.5◦ E and 32.5–37.5◦ N) to estimate the drought condition at HB and JQ respectively. The pertinence of the PDSI data for our study area is attested by high correlation coefficients with river flow (r > 0.8, p < 0.01). Instrumental PDO (defined as the leading principal component of monthly SST anomaly in the North Pacific poleward of 20◦ N) and Niño 3 indices (SST anomaly averaged over [5◦ S, 5◦ N] and [150◦ W, 90◦ W]) have been downloaded from the NOAA web site

Fig. 2. Number of cores in HB (panel a) and JQ (panel e) chronologies. HB (panel b) and JQ (panel d) standard tree-ring chronologies (black) with a 40-year low pass filter (red and blue). For comparison, chronologies built using an exponential/linear detrending are also displayed (grey). Panel c displays the 40-year sliding correlation of HB and JQ standard chronologies (solid line) and residual chronologies (dashed line): the horizontal green line indicates the 95% confidence level. Panel f shows the reconstructed PDO data (thin black line, Shen et al., 2006), the 40-year low pass filter of PDO (thick black line) and HB chronology (thick red line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)


(http://www.cdc.noaa.gov/data/climateindices/List/) to test the links between the climate of the study area and relevant large scale modes of variability.

Results Characteristics of the tree-ring chronologies TRW chronologies for HB and JQ sites were developed using respectively 27 and 28 successfully cross-dated cores, reaching lengths of 564 years (A.D 1442–2005) for HB and 541 years (A.D. 1465–2005) for JQ. The mean tree-ring annual growth rates are 0.53 mm (HB) and 0.61 mm per year (JQ), with rates of missing rings of 1.3% and 0.6%, respectively. The EPS value was above 0.85 during most period of the two chronologies. The mean annual growth and absent ring rates are similar to those reported by Li et al. (2008a,b) and Gou et al. (2007), with about 0.5 mm per year and around 1–2.6% (note that these studies used two cores per tree, for 13–38 trees). The standard deviation, mean sensitivity, 1year-lag autocorrelation coefficient are 0.23, 0.2, 0.40 and 0.14, 0.12, 0.43 for HB and JQ chronologies respectively. The elevations of our sites are within the elevation range of earlier studies (3400–3700 m). Extreme low growth (2σ below average) at HB site was found in years 1451, 1490, 1591, 1596, 1770 and 1776, and extreme low growth at JQ site occurred in years 1639, 1642, 1770 and 1953. Extreme high growth (2σ above average) at HB site was detected in years 1611, 1612, 1619 and 1955, and extreme high growth at JQ site was found in years 1939, 1943, 1944, 1946. The HB chronology shows more inter-annual and decadal variability than the JQ chronology (Figs. 2 and 3). This appears clearly when comparing standard deviations (0.23 for HB to compare with 0.14 for JQ chronology). HB has smaller mean annual growth rate and higher sensitivity, which is surprising for a “valley” site. There are multi-decadal periods when the two standard chronologies are strongly coherent at the inter-annual scale, and other multi-decadal periods when they seem distinct from each other, as revealed by 40-year running correlation coefficients fluctuating throughout the entire record between 0 and 0.8 (Fig. 2, panel c). At the multi-decadal scale, the highest consistency between HB and JQ chronologies is found during 1480–1530s, 1580–1620s, 1660–1670s, 1740–1760s, 1810–1870s, and 1940–1970s. Wavelet analyses reveal significant (95% confidence level) quasi-centennial and 50-year periodicities for HB and JQ chronologies; for HB, the quasi-centennial signal accounts for 10% of the total variance. Due to uncertainties linked with statistical detrending methods, we focus here on the inter-annual to decadal variability. Strong 2–10 years periodicities are found in both chronologies, coherently and in phase (Fig. 3). HB and JQ chronologies are therefore more coherent at high frequency than at low frequency. This result is robust with respect to the detrending method as tested using alterna-

229

tive standard chronologies produced by negative exponential plus linear detrending.

Correlation with local and large scale climate before and after the PDO shift in 1977 Linear correlations between tree-ring chronologies and local climate (as indicated by monthly precipitation, PDSI, Yellow River stream flow) were investigated using the software DendroClim 2002 (Biondi and Waikul, 2004). The correlation of both chronologies with local climate experienced a dramatic change in the 1970s (Figs. 4–6, panel d), corresponding with the climate transition following the PDO shift in 1976/1977. The chronologies and climate parameters were therefore split into two parts, before and after 1977. Bootstrap correlation coefficients between the monthly climate parameters from previous September to current September and standard tree-ring chronologies were calculated in each part (Figs. 4 and 5). Before 1977, both HB and JQ standard chronologies exhibit non-significant but positive correlation with precipitation. Significant correlation (p < 0.05) is obtained between both sites and both PDSI and April-June river flow. Our interpretation is that meteorological stations are located at elevations lower than the sampled trees and may not capture orographic effects on precipitation. PDSI and river flow integrate the large scale interplay between precipitation and temperature effects (including evapotranspiration and snowmelt) and may be more related to soil moisture availability than precipitation. Based on the time periods showing the strongest correlation (Table 2), pre-monsoon moisture availability (Apr-Jun) can be regarded as the key factor limiting tree growth. This is consistent with former tree-ring studies in this area (Zhang et al., 2003; Shao et al., 2005; Zhang and Qiu, 2007; Li et al., 2008a,b). After 1977, there is no significant linear correlation between any of the standard chronologies, and precipitation, PDSI or river flow. These correlation results are robust with respect to long-term trends as tested using residual chronologies. In order to explore what can cause different climate/growth relationships before and since 1977, we scrutinized the relationships between the climate variables themselves. We have built local climate indices using standardized and averaged temperature and precipitation from the four nearby weather stations (Fig. 1). Local springsummer temperature and precipitation are significantly and negatively correlated (p < 0.05), which is probably the reason why an integrated drought index such as PDSI shows stronger correlation with tree-ring growth and river flow than temperature or precipitation alone. The close coherence between river flow and drought is confirmed by their persistent very high correlation before and after 1977 (r > 0.8, p < 0.01). Because (1) the tree growth–local climate relationships change at the time of the 1976–1977 PDO shift, and (2)

230


Fig. 3. Wavelet analyses of HB (a) and JQ (b) chronologies. The red and blue lines in panel c indicate 95% and 99% confidence levels (software provided by Torrence and Compo, http://paos.colorado.edu/research/wavelets/). Multitaper coherency and phase analysis of HB and JQ chronologies (panel c) calculated using a Matlab program provided by Peter Huybers. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

river flow and PDSI are related to PDO/ENSO (not shown), we now explore directly the links between tree growth and large scale climate indices (PDO and ENSO). It is remarkable that the correlation between PDO/ENSO and spring moisture (e.g., river flow) has reversed sign before and after the 1976/1977 transition (not shown). Before 1977, the HB chronology shows a strong negative correlation with PDO and ENSO (p < 0.05) (Fig. 4), while there is no significant correlation between JQ chronology and monthly PDO and ENSO (Fig. 5). Note that the correlation between HB chronology and PDO/ENSO has the same strength (|R| ∼ 0.5) as the correlation between spring Yellow River flow and PDO/ENSO (Table 2). After 1977, the correlation between HB chronology and PDO/ENSO disappears. A negative relationship appears between JQ chronology and ENSO of the previous year,

which was not observed previously (1925–1977). Fig. 6 makes possible a detailed comparison of year by year coherency between spring ENSO and tree growth. The large/small TRI (tree-ring index) in HB chronology that co-occurred with low/high ENSO indices are highlighted as coherent years (Fig. 6, vertical lines). The “positive” (large growth, low ENSO) coherent years of 1950, 1955, 1962, 1964, 1967 and 1974 are frequently associated with high precipitation/PDSI and low PDO (3 out of 6, Fig. 6). The reversed phenomenon is observed for the “negative” (small growth, high ENSO) coherent years of 1953, 1957, 1963 and 1969 (2 out of 4, Fig. 6). Only 3 coherent years (1989, 1992, and 1996) are found after 1977. This clearly illustrates the non-stable relationships between HB growth, local moisture records and ENSO before/after 1977.


Fig. 4. Linear correlation coefficients between HB chronology and monthly precipitation, PDSI, river flow, PDO, and ENSO (a) during 1948/1954–1977, (b) during 1978–1998. Bars with stars indicate significant at the 95% level tested by a bootstrap method. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Fig. 5. Linear correlation coefficients between JQ chronology and monthly precipitation, PDSI, river flow, PDO, and ENSO (a) during 1948/1954–1977, (b) during 1978–1998. Bars with stars indicate significant at the 95% level as tested with a bootstrap method. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

Table 2. Linear correlation coefficients between standard chronologies, meteorological and hydrological parameters (with identification of the seasons leading to the best correlations). One and two stars indicate correlations respectively significant at 95% and 99% levels as tested with a bootstrap procedure.

HB 1948/1954–1998 JQ 1948/1954–1998 HB 1978–1998 JQ 1978–1998 HB 1948/1954–1977 JQ 1948/1954–1977 HB 1925–1947 JQ 1925–1947 Seasons chosen HB JQ

Precipitation

Temperature

PDSI

River flow

PDO

ENSO

0.24 0.345* 0.21 0.13 0.27 0.53**

0.1 −0.37* 0.01 −0.25 0.17 −0.43*

0.41* 0.45* 0.25 0.2 0.50* 0.61**

0.38* 0.33* 0.17 0.2 0.58** 0.44* 0.52* 0.49*

−0.21 −0.07 0.12 −0.06 −0.52* −0.25 −0.34 −0.27

−0.14 −0.13 0.17 −0.17 −0.48* −0.21 −0.55** −0.25

April–June April–June

April–June April–June

April–July April–June

May–July April–June

May–July May–July

March–May March–May

PDO

ENSO

−0.42*

−0.48*

Precipitation Inter-climate correlation River 1925–1947 Temp. 1954–1977 Temp. 1978–1998 PDSI 1954–1977 PDSI 1978–1998 river 1954–1977 River 1978–1998 PDO 1954–1977 PDO 1978–1998 ENSO 1948/1954–1977 ENSO 1978–1998

Temperature

PDSI

River flow

−0.32 −0.32 0.65** 0.55** 0.6** 0.69** −0.25 0.33 −0.3 0.27

−0.32 −0.69* −0.34 −0.55* −0.02 0.05 −0.2 0.12

0.82** 0.85** −0.2 0 −0.3 0.17

−0.41 0.21 −0.26 0.18

0.59** 0.4

April–June

April–June

April–July

May–July

April–July

Seasons chosen March–May

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Fig. 6. (a) HB chronology (1948–1998), (b) JQ chronology (1948–1998), (c) 21-year running correlation between HB and JQ chronologies, (d) 21-year running correlation between chronologies and their corresponding PDSI (red for HB, green for JQ), (e) precipitation, (f) temperature, (g) PDSI for HB site (PDSI1), and (h) JQ site (PDSI2), (i) Yellow River flow measured at Tanghaihai, (j) PDO index, and (k) NINO3 index; (l) precipitation and (m) temperature from the TD, XH, HN and ZK, the abbreviation of four meteorological stations listed in Table 1. Vertical red and blue dashed lines indicate respectively positive and negative coherent years between HB chronology and Niño 3 index (see text). The green horizontal lines indicate the 95% confidence level of correlation. The seasons for all parameters are the same as in Table 2. For panels e to k, all the time series have been normalized. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

An earlier study had shown a significant correlation between tree growth and Pacific SST (Li et al., 2008a,b). In our case, we obtain no significant result with the JQ chronology (not shown). Fig. 7a and b displays the spatial correlation map between HB chronology and April-June SST derived from Kalnay et al. (1996), using the online software available at: http://www.cdc.noaa.gov/data/correlation/ for two different time intervals. From 1949 to 1977 (Fig. 7a), high HB growth is associated with low SSTs in tropical Indian Ocean and middle/east equatorial Pacific Ocean and with high SSTs in the northwest Pacific Ocean. This is fully consistent with our negative correlation with PDO and Niño 3 indices but highlights the importance of the Indian Ocean SST, known to have close links with Indian monsoon (Li and Yanai, 1996). Nearly no significant correlations remain after the 1976/1977 climate shift (Fig. 7b). In summary, our linear regression analyses (Table 2) reveal that the JQ chronology is best correlated with precipitation and PDSI, and sensitive to spring temperature; HB chronology is best correlated with river flow and significantly influenced by PDO/ENSO (similarly with the river flow itself). The inter-annual variability of HB and JQ chronolo-

gies is more consistent before the 1970s than after. No significant correlation was found between tree-ring chronologies and PDO/ENSO indices before 1925, which may be due to the lower accuracy of observation in early SST and therefore climate indices. Split correlation in recent decades demonstrated unstable linkages of tree growth with local hydro-climate records and with PDO/ENSO, as also observed between local hydro-climate with PDO/ENSO themselves.

Coherence with PDO/ENSO before and after 1860s A Multi Taper Method (Thomson, 1982) analysis of the HB chronology and the instrumental seasonal PDO/ENSO indices (for months defined in Table 2) in the recent interval (1871–2005) clearly reveals a coherent 5.2-year signal (Fig. 8, vertical green line), which is significant at 99% confidence level. At this frequency, the HB chronology is in anti-phase with PDO/ENSO indices (Fig. 8). The same analyses using the reconstructed annual PDO data produces similar results.


233

Fig. 7. Spatial distribution of the linear correlation coefficients between HB chronology and SST. Spatial distribution of the linear correlation coefficients between HB and SST, (a) during 1949–1977 and (b) during 1978–1998, using NCEP/NCAR linear correlation analysis (http://www.cdc.noaa.gov/data/correlation/). The 95% confidence level is displayed with a red dashed line.

PDO indices have been reconstructed using large scale historical dry/wet records from China (Shen et al., 2006). Fig. 2f shows the comparison between the 40year low-pass filters of this PDO reconstruction and HB chronology, which, from the past decades, is expected to be in anti-phase with the PDO index. It is remarkable that these records appear roughly in-phase before the 1860s, and out of phase afterwards. We have therefore specially analyzed the coherency of the records before 1860.

For the period 1470–1860, we have compared our chronologies with the reconstructed annual PDO index (Fig. 9, panels a and b). The HB chronology is in phase and coherent with the reconstructed PDO bi-annual and multidecadal variability with no phase lag. The JQ chronology is less coherent with PDO (Fig. 9, panels c and d). In addition to the unstable association among tree growth, local and large scale climate records across 1970s, the split coherence analysis reveals a changed linkage between reconstructed PDO and HB chronology in mid-19th century.

234


Discussion Differences between HB and JQ

Fig. 8. (a–c) MTM analysis of HB chronology, March to May Niño 3 index , and April–June PDO; (d and e) Coherence and phase analysis of HB chronology and March to May Niño 3 index; (f and g) Coherence and phase analysis of HB chronology and April–June PDO. Red and blue lines indicate 99% and 95% confidence levels, and the vertical green bar highlights the periodicity at 5.2 years consistent in all records. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

With homogeneous precipitation and temperature variability across meteorological stations from our study area (Fig. 6, panels l and m), the marked differences in the growth patterns and links with climate of HB and JQ chronologies were unexpected. We suggest that these differences may arise from contrasting local environments (with different soil moisture/shadow/cloudiness effects). The annual growth at site JQ, located on steep slope with a thin soil, seems mostly driven by changes in local precipitation and temperature, whereas the site HB, located in closed valley bottom, seems more coherent with large scale variations in moisture availability, reflected in PDSI, river flow, and linked with PDO and ENSO. The PDO shift in 1976/1977 has profoundly changed precipitation patterns in large parts of China (Weng et al., 1999), showing a “sandwich pattern” with increased precipitation in northeast China, lower Yangtze River Valley and eastern QTP, and decreased precipitation in southeast coastal China and lower Yellow River Valley (Qian et al., 2007; Qian and Qin, 2008). Prior to the 1860s, HB and JQ chronologies are relatively consistent at intervals roughly coinciding with the low regimes of reconstructed PDO (Fig. 2, panels c and f). The 40-year low-pass of HB chronology and reconstructed PDO are approximately consistent before 1860s (Fig. 2, panel f), suggesting an association of PDO with dry/wet intervals in the studied area before 1860s. Consistent growth patterns at HB and JQ sites only emerge during dry (low growth) intervals.

The links of HB chronology with PDO/ENSO and tropical climate shifts

Fig. 9. Coherency and phase analysis between HB (panels a and b)/JQ (panels c and d) chronologies and the reconstructed PDO from 1470 to 1870. Red and blue lines indicate 99% and 95% confidence levels. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

In this section, we discuss the mechanisms linking local climate in our study area with large scale modes of variability, particularly at the turning points in the 1970s and mid 19th century. ENSO modifies the intensity and location of the Northwestern Pacific Subtropical High (Chang et al., 2000a,b) and has shown a steady influence on East Asian Summer Monsoon (EASM) over the last decades (Wang et al., 2001). The rain-belt of EASM arrives in southeastern coastal China in May, and reaches the latitude of our study area in July (Wang and Linho, 2002), after the critical tree-growth season. Therefore we exclude the possible influences of ENSO through EASM. There is a well-known negative relationship between ENSO and Indian summer monsoon (ISM) prior to mid1970s through persistent warming in Indian Ocean following El Niño events (Annamalai et al., 2005; Yang et al., 2007). A southerly wind prevails in large parts of China from early spring onwards. High pressure over western-north Pacific and


troughs in the southern and eastern flanks of QTP produce upward motion in lower troposphere, bringing precipitation before the large scale monsoon onset (Zhao et al., 2007). The negative correlation of local hydro-climate and HB chronology with Indian Ocean SST and ENSO suggests a linkage between pre-monsoon precipitation of NE-QTP and the early stage of ISM. A warm Indian Ocean SST may reduce the high pressure over sea and its pressure gradient with QTP (Li and Yanai, 1996), decreasing the pre-monsoon intensity and therefore the tree growth in the studied area. Kumar et al. (1999) and Annamalai et al. (2005) reported that, after the mid 1970s, a strong Indian Ocean dipole/zonal mode (IODZM) was induced by a change in Walker circulation. This zonal atmosphere-ocean circulation disrupted the linkage between Indian Monsoon and ENSO (Kumar et al., 1999; Wang et al., 2001; Ashok et al., 2000). This mechanism may be responsible for the decoupled association between local hydro-climate and ENSO after the 1970s, reflected in our tree growth. The effects of PDO/ENSO on regional climate reversed from negative to positive since mid1970s, which erased the teleconnections between tree growth and Pacific SST. Such an instable correlation between tree growth and PDO/ENSO was recently reported by Heinrich et al. (2009) for the eastern coastal Australia, and was also attributed to a shifting association between Pacific SST and local precipitation. HB chronology and PDO show an anti-phase at a periodicity of 5.2 years from the 1870s to present. The relation between the 40-year low-pass variations of HB chronology and PDO exhibit a marked change during the mid-19th century, turning from in-phase to out of phase. It is worth noting that the mid-19th century was also a time of transition in the Pacific Ocean. After 1850, the Pacific intertropical convergence zone has shifted northward from its southernmost position (Julian et al., 2009). Over the past 150 years, the Walker circulation, which is closely linked to ENSO and monsoonal circulation, has weakened in response to global warming (Vecchi et al., 2006).

Possible causes for the reduced response of tree growth to moisture variation after 1970s Compared to the first part of the instrumental period, the tree growth seems insensitive to moisture variability recorded in metrological/hydrological stations since 1970s. This is particularly marked for the relatively moist interval 1982–1988 (Fig. 6). One parameter which can affect moisture availability is the snow cover and its meltwater contribution to spring moisture. Spring snow cover on QTP has been shown to be related to ENSO (Shaman and Tziperman, 2005) but also to westerlies. Since the late 1970s, positive stages of the North Atlantic Oscillation (NAO) during winter have induced an intensification of spring westerlies, producing a sharp increase in QTP snow cover and March-April soil moisture (Zhang et

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al., 2004; Wu et al., 2009). It is expected that an increased snow melt would reduce the correlation between tree growth and spring precipitation. This westerlies shift has also enhanced spring mid-level stratiform cloud cover over the eastern flank of QTP (Li et al., 2005, 2008a,b). Changes in cloudiness can affect photosynthesis and tree growth independently of the precipitation amount. Since the early 1980s, despite increasing temperatures, a sudden decrease in sunshine duration and pan-evaporation has been reported in the source region of the Yellow River (Zhao et al., 2008). Xu et al. (2007) identified the maximum evaporation shift during spring (April and June). Decreased sunshine duration and increased cloud cover could limit tree growth despite wet conditions. We therefore suggest that tree growth has been limited by local water stress prior to 1970s, but the decoupling between local hydro-climate parameters and tree growth since 1970s may be due to increasing snow melt and/or changes in local cloud cover affecting sunshine duration and photosynthesis.

Comparison with previous studies in this area Li et al. (2008a,b) and Gou et al. (2007) suggested that tree growth in this area is highly influenced by spring precipitation and river flow. We confirm the strong impact of springsummer hydrological conditions (precipitation, river flow, PDSI) but show an unstable relationship. Li et al. (2008a,b) also observed a linkage of tree growth and western Pacific SST. Again, our analyses confirm a link with large scale Pacific and Indian Ocean SST but reveal that this correlation is unstable with time for our studied sites.

Conclusions and perspectives In this study we compared the response of tree growth to local and large scale climate at two sites of contrasting local topography and settlings. In these locations, tree growth seems primarily controlled by spring soil moisture availability, especially before 1977. The results show distinct local histories of tree-growth variations, the two tree-ring chronologies being more consistent during dry intervals. Moreover, the relationships with regional and large scale hydro-climate indices reveal temporally unstable results. While frustrating for climate reconstructions, these results highlight the need for systematic sampling strategies to characterize local tree responses and the need to systematically check the temporal response stability for dendroclimatology studies. Other studies have reported unstable tree growth-climate relationships. At high northern latitudes, the “divergence” problem appears as a late 20th century decoupling between tree growth and temperature trends (Briffa et al., 1998a,b; Barber et al., 2000; D’Arrigo et al., 2008). In other areas, stress caused by air pollution has been reported to reduce tree-growth response to precipitation (Wilson and

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Elling, 2004). For our species, a recent study has also reported changing correlation between tree growth and climate since the 1930s (Zhang et al., 2009a,b). Prior to the mid-19th century, the multi-decadal dry/wet intervals of our HB chronology are concurrent with historical low/high PDO regimes. Since the mid-19th century, the HB chronology exhibits a marked inter-annual variability at 5.2 years, which is strongly coherent and anti-phased with PDO/ENSO. Since the late 1970s, tree growth appears decoupled from local moisture records, and also reflects the decoupling between local hydro-climate and large scale climate indices. Several factors can contribute to this decoupling such as changes in large scale atmospheric dynamics, associated with the decoupling of ENSO and Indian Monsoon, and marked by changes in local spring snow cover and cloudiness/sunshine duration. Due to the local characteristics of tree-ring records and the non-stable relationships to climate found in our two chronologies, large scale paleoclimate reconstructions will require the production of huge databases of adequate treering chronologies per area in the QTP, in order to assess the right chronologies to select for climate purposes, their coherencies and their stable or unstable links with climate. It will be also necessary to compare other tree-ring proxies such as cellulose isotopic composition and wood density, in search for an annually resolved proxy with more stable links with local climate rather than ring width.

Acknowledgements This study was supported by the National Natural Science Foundation of China projects No. 40631002, No. 30870461 and No. 40890051, and by the Science development foundation project at the Institute of Yellow River Hydrological Science. The climate data were obtained from the weather information center of China Meteorological Administration. C.S. was supported by the French Embassy in Beijing, China and LSCE acknowledges support from GIS Pluies-Tibet.

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