A 564-year annual minimum temperature reconstruction for the east central Tibetan Plateau from tree rings

Accepted Manuscript A 564-year annual minimum temperature reconstruction for the east central Tibetan Plateau from tree rings

Teng Li, Jinbao Li PII: DOI: Reference:

S0921-8181(17)30027-9 doi: 10.1016/j.gloplacha.2017.08.018 GLOBAL 2639

To appear in:

Global and Planetary Change

Received date: Revised date: Accepted date:

18 January 2017 24 August 2017 24 August 2017

Please cite this article as: Teng Li, Jinbao Li , A 564-year annual minimum temperature reconstruction for the east central Tibetan Plateau from tree rings, Global and Planetary Change (2017), doi: 10.1016/j.gloplacha.2017.08.018

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ACCEPTED MANUSCRIPT A 564-year annual minimum temperature reconstruction for the east central Tibetan Plateau from tree rings Teng Lia,*, Jinbao Lia a

Department of Geography, University of Hong Kong, Pokfulam, Hong Kong.

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* Corresponding author. E-mail address: [email protected] (T. Li).

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ACCEPTED MANUSCRIPT Highlights

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A 709-year tree-ring width chronology of Sabina tibetica Kom. was developed from the east central Tibetan Plateau. Annual (pApril-cMarch) minimum temperature was reconstructed for the past 564 years. The level of warming during 1989-2014 is unprecedented over the past 564 years. The Atlantic Multidecadal Oscillation (AMO) has a crucial influence on multidecadal temperature variations in the study area over the past five centuries.

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ACCEPTED MANUSCRIPT Abstract Minimum temperatures have increased rapidly on the

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we present a 709-year tree-ring width chronology from

Keywords Tree-ring; Annual minimum temperature; Sabina tibetica; Tibetan Plateau; Atlantic Multidecadal Oscillation

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ACCEPTED MANUSCRIPT 1 Introduction The Tibetan Plateau (TP) is one of the world's most unique geographical features, with an average altitude over 4000 m above sea level (a.s.l.) and an area of about 2.5×106 km2 (Messerli and Ives, 1997; Kang et al., 2010). It is regarded as Asia’s “water tower”, suppling freshwater to more than 1.4 billion people (Viviroli et al.,

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2007; Immerzeel et al., 2010). The TP is one of the most sensitive zones to climate change, and its climate and environment have changed dramatically since the early twentieth century (Liu and Chen, 2000; Du et al., 2004; Yan and Liu, 2014). The TP has experienced a persistent and rapid warming since the 1960s, with a positive rate of 0.36 °C/decade that is much higher than the global average of 0.20 °C/decade

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(Duan and Wu, 2006; You et al., 2008; Hansen et al., 2010). This exceptional warming trend continued unabated during the recent “global warming hiatus” period

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when there is an apparent pause in global warming (Kosaka and Xie, 2013; Yan and Liu, 2014). Recent studies show that the increasing trend in minimum temperature

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(Tmin) is greater than that of maximum temperature (Tmax), with the asymmetric warming trend most remarkable over the central and eastern TP (You et al., 2008;

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Yan and Liu, 2014). Therefore, it is vital to investigate long-term Tmin variations on the TP to better understand the nature of the current warming and the underlying

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forcing mechanisms.

Instrumental climate records over the TP are short, scarce and unevenly

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distributed, making it difficult to place current climate variability in a long-term perspective. Tree-rings provide reliable information on past climate change, owing to their annual resolution, accurate dating, and high sensitivity to climate (Fritts, 1976). Although considerable efforts have been devoted to mean temperature (Tmean) reconstructions (Liang et al., 2008; Liu et al., 2009; Fan et al., 2010; Zhu et al., 2011; Duan and Zhang, 2014; Wang et al., 2014), few studies have paid attention to the Tmin variations on the TP (Shao and Fan, 1999; Gou et al., 2007; He et al., 2014; Liang et al., 2016). Among them, Shao et al. (1999) reconstructed regional winter

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ACCEPTED MANUSCRIPT Tmin variations on the western Sichuan Plateau for the period 1654-1994. Based on a ring-width chronology, Gou et al. (2007) built a reconstruction of 425-year winter half-year (October–April) Tmin on the northeastern TP. He et al. (2014) developed a six-hundred-year annual (January-December) Tmin record for the central TP, and Liang et al. (2016) reconstructed the August mean Tmin variations over 1630-2011 on

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the southeastern TP. However, no study has been conducted in the east central TP, which limits our understanding of long-term Tmin variations in this area, as well as Tmin's spatiotemporal variability over the TP. The objectives of this study are to fill this research gap by presenting a new annual Tmin reconstruction based on tree-rings from the east central TP, and to reveal the major driving force of Tmin changes at

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multidecadal time scales.

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2 Materials and methods 2.1. Tree-ring data

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In total, 107 tree-ring samples from 52 trees were collected from old-growth forests on the east central TP. The forests are pristine with no evidence of human or

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distinct natural disturbance. Trees are highly isolated from each other growing in open canopy stands. Increment cores were taken from the Tibetan juniper (Sabina tibetica

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Kom.) trees in two nearby sites (LIT and TAN) in Litang county, at altitudes ranging from 4050 to 4220 m a.s.l. (Fig. 1 and Table 1). In the following analysis, all the

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samples from the two sites were merged in light of their similar growing environments and growth response to climate. All the tree cores were measured on a Velmex ring-width measuring system with an accuracy of 0.001 mm after being air dried, mounted, and sanded, following standard dendrochronological techniques (Stokes and Smiley, 1968). The program COFECHA was used to check the quality and accuracy of the cross-dating and measurement (Holmes, 1983). Eighty-eight cores from 45 trees were finally used to build the mean ring-width chronology (Table. 1).

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ACCEPTED MANUSCRIPT In dendroclimatic studies the non-climatic growth trend contained in raw ring-width measurements is removed by standardizing each measurement to its local mean (Fritts, 1976; Holmes, 1983). We calculated tree-ring indices as ratios between the raw ring-width series and a fitted age-dependent growth curve, with extreme values reduced by a biweight robust mean method (Cook and Kairiukstis, 1990;

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Melvin et al., 2007). In comparison to computing indices by the residual method (Cook and Peters, 1997), the ratio method provided a better fit to climate data in terms of magnitude and overall correlation (Figs. S1 and S2). After detrending, a single variance-stabilized chronology was built using the Rbar weighted method (Osborn et al., 1997; Frank et al., 2007), and the “signal-free” method was applied to minimize

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the “trend distortion” problem (Melvin, 2004; Melvin and Briffa, 2008). The final variance stabilized, “signal-free” chronology was used to reconstruct the Tmin

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variation in the following analysis. The expressed population signal (EPS) with a threshold value of 0.85 was employed to assess the reliability of the chronology over

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time (Wigley et al., 1984). 2.2. Climate data

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The instrumental data were collected from two local meteorological stations (Litang, 30 ºN, 100.27 ºE, 3949 m a.s.l., and Daocheng, 29.05 ºN, 100.3 ºE, 3728 m

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a.s.l.) (Fig. 1), with coverage from 1957 to 2014. The climate parameters include monthly Tmean, Tmax, Tmin, and total precipitation. Based on the observational data,

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the annual Tmean of Litang (Daocheng) is approximately 3.5 °C (4.5 °C), with a January Tmean of -5.2 °C (-5.1 °C) and a July Tmean of 11.0 °C (12.1 °C), respectively (Fig. 2). Annual total precipitation of Litang (Daocheng) is approximately 730.3 mm (638.8 mm), with June-September precipitation accounting for 81.0% (87.3%) of the annual total at each station. Regional monthly temperature and precipitation series were built by averaging the records from the Litang and Daocheng meteorological stations to minimize spatial heterogeneity. 2.3. Statistical Methods

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ACCEPTED MANUSCRIPT Pearson’s correlation analysis was conducted to reveal the statistical relationship of tree growth with temperature (Tmean, Tmax, and Tmin) and precipitation from previous April to current September. In addition to monthly correlations, seasonal and yearly correlations were calculated, because averaged climate indices may be more responsible for ring-width variations (Cook et al., 1999). A linear regression model

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between the chronology and the targeted climate parameter was used to build the reconstruction (Fritts, 1976; Cook and Kairiukstis, 1990). The quality and stability of the regression model was tested with the leave-one-out cross-validation (LOOCV) method (Michaelsen, 1987).

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3 Results

The ring-width chronology covers the period from 1306 to 2014, with a mean

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segment length of 315 years (Fig. 3a). The reliable portion of the chronology, based on the EPS cutoff value (0.85), with at least eight cores, is from 1451 to 2014 (Fig.

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3b). The Rbar ranges from 0.26 to 0.53 with an average value of 0.36 (Fig. 3b). Based on these statistics, the chronology is suitable for dendroclimatic study as it contains a

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fairly strong and stable common signal over time. Significant positive correlations (p < 0.05) are found between tree-ring indices and

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Tmean from previous April to current September, except current May and July (Fig. 4a). The correlations between tree-rings and precipitation are significantly positive in

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previous April and November, and current March to May. These results suggest that temperature plays a more crucial role on tree growth. Therefore, we further analyzed the correlations between tree-rings and Tmin and Tmax on monthly, seasonal, and yearly scales. As shown in Fig. 4b, significant positive correlations between tree-rings and Tmin are found in all the months investigated. As for Tmax, significant positive correlations with tree-rings are found in previous July to September, current February, August and September. Further analysis indicates that the annual Tmin from previous April to current March (pApril-cMarch) has the highest (r = 0.80, p < 0.001)

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ACCEPTED MANUSCRIPT correlation with tree growth. These results suggest that the pApril-cMarch Tmin is the most critical limiting factor on tree growth. Based on the above climate-tree growth relationship, we reconstructed the annual pApril-cMarch Tmin over the study area through a linear regression model. The reconstruction model explained 64.5% of the instrumental pApril-cMarch Tmin

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variance (63.9% after adjustment for loss of degree of freedom) during 1958-2014 (Fig. 5a). The LOOCV test generated a positive reduction of error (RE = 0.45), suggesting that the reconstruction model is reliable. To assess the influence of trend on the reconstruction model, we compared the first-differenced reconstructed and instrumental pApril-cMarch Tmin data during 1959-2014 (Fig. 5b). The statistically

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significant correlation coefficient (r = 0.50, p < 0.01) and F value (F = 17.70, p < 0.001) indicate the robustness of the reconstruction. The positive RE value (0.25) of

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LOOCV test indicates that the reconstruction model estimates the year-to-year pApril-cMarch Tmin variability with good accuracy. These statistics highly validate

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the reconstruction.

Based on this regression model, we reconstructed the annual pApril-cMarch Tmin

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changes on the east central TP for the past 564 years (1451-2014; Fig. 6). The average and standard deviation (SD) of the reconstructed Tmin are -2.86 °C and 0.47 °C,

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respectively. To evaluate the Tmin anomalies, warm/cold years are defined when the temperature is above/below one standard deviation, respectively. There are 60 warm

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years and 67 cold years in the annual Tmin reconstruction. Warm years are more frequent in the sixteenth century (32 years), while cold years are mostly found in the twentieth century (19 years) and fifteenth century (15 years). The warmest and coldest mean annual minimum temperatures in the reconstruction are found in the years 2010 and 1456, respectively. Based on a 21-year low-pass filter, six major warm periods are found during 1490-1623, 1713-1729, 1784-1812, 1868-1877, 1918-1954, 1989-2014, and six major cold periods occurred during 1451-1489, 1624-1712,

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ACCEPTED MANUSCRIPT 1730-1783, 1813-1853 1878-1917, and 1955-1988. The recent warming during 1989-2014 is unprecedented over the past 564 years.

4 Discussion 4.1. Climate-tree growth relationships

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Based on the correlations between tree-rings and climatic factors, we found that the annual pApril-cMarch Tmin plays a crucial role on tree growth in the study area. Previous studies showed that prior year temperature may influence current year tree growth on the TP (He et al., 2014; Wang et al., 2014). It was also found that tree growth is most sensitive to the Tmin variations at many sites over the central and

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eastern TP (Liang et al., 2008; Zhu et al., 2011; He et al., 2014; Shi et al., 2015). Minimum temperatures may have strong influence on radial tree growth at high

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elevations by affecting root growth and cambium activity. Low nighttime air temperatures can decrease soil temperature and thus limit root growth and water

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uptake activity (Körner, 1999). Recent studies suggest that air temperature influences the onset of xylem growth, and a roughly 5–6 °C daily air Tmin threshold exists for

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the onset of xylem activity (Gruber et al., 2010; Swidrak et al., 2011). Minimum temperatures influence the cambium activity through their effects on the division and

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enlargement of conifer tracheids during the growing season (Deslauriers et al., 2003), and their influence on the lignification progress of xylem cells mainly occurs at night

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(Hosoo et al., 2002).

Significant positive correlations between tree-rings and March-May precipitation indicate that precipitation benefits tree growth during the early growing season by modulating water availability. This indicates that moisture availability during the early growing season plays a role on tree growth, a common phenomenon over the TP (Qin et al., 2003; Fan et al., 2008; Li et al., 2008a; Liu et al., 2012; He et al., 2013; Jiang et al., 2017; Li et al., 2017). Nonetheless, it is comparatively less important than the Tmin at our sampling sites.

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ACCEPTED MANUSCRIPT 4.2. Co-variability of temperature changes on the TP To validate our Tmin reconstruction, we compared it with five tree-ring reconstructed Tmin series from nearby regions (Fig. 7), including an August Tmin reconstruction from the southeastern TP (Liang et al., 2016), a winter Tmin reconstruction on the western Sichuan Plateau (Shao and Fan, 1999), a prior year

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January-December Tmin reconstruction on the central TP (He et al., 2014), an October-April Tmin reconstruction on the northeastern TP (Gou et al., 2007), and a January-August Tmin reconstruction in the Qilian Mountains (Zhang et al., 2014). All these Tmin reconstructions have been normalized and smoothed with a 21-year low-pass filter for direct comparison. The cold period during the 1450s-1480s, found

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in this study, coincides with that observed in the Tmin reconstructions from the Qilian Mountains and the central TP (He et al., 2014; Zhang et al., 2014). The cold period

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from the 1620s-1710s is found in the Tmin reconstructions from the central and northeastern TP (Gou et al., 2007; He et al., 2014). The cold period during the

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1730s-1780s is consistent with the August Tmin variations in the southeastern TP (Liang et al., 2016), the winter Tmin variations on the west Sichuan Plateau (Shao and

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Fan, 1999), and the October-April Tmin variations on the northeastern TP (Gou et al., 2007). There are similar temperature anomalies during the 1810s-1850s between this

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study and the Tmin reconstructions from the southeastern TP (Liang et al., 2016), the central TP (He et al., 2014), and the Qilian Mountains (Zhang et al., 2014). However,

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the cold period in the 1650s-1690s is not observed in the Tmin series on the southeastern TP (Liang et al., 2016) and the Qilian Mountains (Zhang et al., 2014). There are also differences between our reconstruction and the Tmin reconstructions from the central TP and the Qilian Mountains during the cold period in the 1880s-1910s (He et al., 2014; Zhang et al., 2014). The discrepancies between these records may reflect seasonal characteristics of Tmin variations at these sites, or site-specific temperature anomalies related to varied tree growth sensitivity under different growing environments (Walther et al., 2002; Classen et al., 2015).

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ACCEPTED MANUSCRIPT Regardless, the above results suggest that the Tmin variations on the eastern TP exhibit high degree of coherence during the past five centuries. We further compared our annual Tmin reconstruction with two tree-ring based Tmean series from nearby regions, including an April-September Tmean (Duan and Zhang, 2014), and an annual Tmean reconstruction on the southeastern TP (Duan and

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Zhang, 2014; Wang et al., 2014). As shown in Fig. 8, common variations are found between these reconstructions, such as cold periods in the 1450s-1480s, 1620s-1710s, 1730s-1780s, and 1950s-1980s, as well as the recent warming since the 1980s. The cold period during the 1620s-1640s is also reported in the summer temperature sensitive tree-ring records on the southeastern TP (Bräuning and Mantwill, 2004;

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Liang et al., 2016). The cold period during the 1730s-1780s corresponds well with the ice-core δ18О records from Dasuopu in northern Himalaya (Thompson et al., 2000)

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and Puruogangri on the central TP (Thompson et al., 2006). Furthermore, the tree-ring records also witnessed glacier advance in the southern TP during the 1760s-1780s

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(Bräuning, 2006). The cold period of 1880s-1910s, captured in our reconstruction, is also noted in other tree-ring based temperature reconstructions over the TP (Liang et

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al., 2008; Fan et al., 2009; Yang et al., 2010). Overall, our annual pApril-cMarch Tmin reconstruction is significantly (p < 0.01) correlated at 0.40 and 0.51 with the

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April-September Tmean reconstruction produced by Duan and Zhang (2014) and the annual Tmean reconstructions of Wang et al. (2014) during 1563-2011 and 1451-2014,

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respectively.

Our reconstruction contains a pronounced warming trend since the 1980s, suggesting that the tree-ring records have captured a strong warming signal in the study area. The pronounced warming is also recorded in many types of tree-ring records over the TP (Figs. 7 and 8), such as the Qilian juniper (S. przewalskii) in Wulan and the Qilian Mountains on the northeastern TP (Zhu et al., 2008; Zhang et al., 2014), the Tibetan juniper (S. tibetica) in Qamdo of the eastern TP (Wang et al., 2014), and the Balfour spruce (P. likiangensis) on the southeastern TP (Duan and

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ACCEPTED MANUSCRIPT Zhang, 2014). Researchers contend that recent increase in tree growth may be related to an increase in precipitation or a growth release resulting from canopy disturbances (Zhang et al., 1999; Fraver and White, 2005). However, precipitation increase is unlikely a major cause of rapid tree growth at our sampling sites, as there is no pronounced increase in precipitation in the study area (Fig. S3). Human activities,

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such as tree cutting or road construction, are equally unlikely responsible for recent tree growth enhancement, as our sampling sites are located in pristine, open-canopy forests without human disturbance. Considering the evidence presented here, we suggest the recent notable tree growth increase is associated with rapid temperature increase at high elevations. Many studies found that tree growth in high-elevation

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regions is more responsive to temperature change than tree growth in low-elevation regions (Buckley et al., 1997; Villalba et al., 1997; Splechtna et al., 2000; Savva et al.,

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2006; Salzer et al., 2009). At high-elevations, a significant increase in temperature extends the length of the growing season, which will have positive influence on tree

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growth (Savva et al., 2006). Snowmelt at high-elevations due to higher temperature increases soil moisture and promotes fast root growth, which eventually benefits tree

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growth (Peterson et al., 2002).

Our reconstruction showed an unprecedented tree growth decline during the first

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decade of the twenty-first century (Fig. 6), a phenomenon that is observed in many tree-ring records in and outside of the TP, such as in cypress (J. przewalskii) in

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Xiqing Mountains of the northeastern TP (Gou et al., 2007), in Balfour spruce (P. likiangensis) in Basu County of the southeastern TP (Liang et al., 2016), in Masson pine (P. massoniana) in northwestern Yichang of South Central China (Cai et al., 2016), in white spruce (P. glauca) in interior Alaska (Barber et al., 2000), and in larch (L. gmelinii) in Tura of central Siberia (Sidorova et al., 2009). Warming induced drought stress may be responsible for this tree growth decline. Anomalously high temperature intensifies evapotranspiration and the shortage of soil moisture during the growing season, which will become very stressful for tree growth when if not

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ACCEPTED MANUSCRIPT compensated by sufficient precipitation (Barber et al., 2000; D'Arrigo et al., 2004; Tiwari et al., 2017).

4.3. Temperature relationship with the AMO Many studies have suggested that climate variability over the TP may be

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influenced by macroscale atmospheric circulations, such as the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), or El Niño/Southern Oscillation (ENSO) (Grigholm et al., 2009; Fang et al., 2010; Xu et al., 2010; Wang et al., 2014; Wang et al., 2015; Shi et al., 2017). We calculated the correlation coefficients of our Tmin reconstruction with the AMO (Trenberth and Shea, 2006),

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PDO (Mantua et al., 1997), and ENSO (Huang et al., 2015) indices during the instrumental era. Results show that the Tmin reconstruction is significantly correlated

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with the AMO (r = 0.25, p < 0.01) and insignificantly with the PDO (r = 0.06, p > 0.1) and ENSO (r = 0.05, p > 0.1) indices, suggesting that the AMO may have a crucial

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influence on temperature variability over the east central TP. To further investigate this relationship, we compared our Tmin reconstruction with two AMO

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reconstructions and two AMO-sensitive sea surface temperature (SST) records (Fig. 9), including AMO indices estimated from the interpolated Kaplan SST data set

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(Kaplan et al., 1998), AMO indices derived from tree-ring chronologies (Gray et al., 2004), the coral-based SST record from the Yucatan Peninsula (Vásquez-Bedoya et

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al., 2012), and the foraminifer Mg/Ca-based SST reconstruction from the Caribbean (Wurtzel et al., 2013). A significant positive correlation (r = 0.51, p < 0.01) is found between our Tmin reconstruction and the Kaplan SST-based AMO index during 1856-2014 (Kaplan et al., 1998) (Fig. 9a, b). Our pApril-cMarch Tmin reconstruction also agree well with the two AMO-sensitive SST records (Fig. 9a, c, e), with significant correlations of 0.24 (p < 0.01) with the coral-based SST record during 1773-2009 (Vásquez-Bedoya et al., 2012), and 0.38 (p < 0.01) with the foraminifer Mg/Ca-based SST record during

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ACCEPTED MANUSCRIPT 1451-2008 (Vásquez-Bedoya et al., 2012; Wurtzel et al., 2013). The positive correlations are consistent with the notion that coral growth and calcification rates rise under warm SSTs and decline under cold SSTs (Slowey and Crowley, 1995; Lough and Barnes, 2000). Our pApril-cMarch Tmin reconstruction shows coherent variations with the Mg/Ca-based SST record during the 1450s-1530s, 1730s-1780s,

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1890s-1910s and the 1990s to present (Fig. 9a, e). In particular, our pApril-cMarch Tmin reconstruction shows a high degree of coherency with the two AMO indices and two SST records over the recent 130 years. Although the overall correlation between our Tmin reconstruction and the tree-ring based AMO index (Gray et al., 2004) is insignificant, they share several common warm and cold periods, such as warm

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phases in the 1570s-1580s, 1780s, and 1870s, and cold phases in the 1820s-1850s, 1880s-1910s, and 1970s (Fig. 9a, d). Regardless, the relationship between our Tmin

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reconstruction and the AMO indices indicates that positive/negative temperature anomalies on the east central TP are largely concurrent with warm/cold phases of the

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AMO, respectively. The close relationship between our temperature reconstruction and the AMO is consistent with other studies over the TP (Feng and Hu, 2008; Wang

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et al., 2013; Wang et al., 2014; Liang et al., 2016; Jiang et al., 2017). Instrumental records and climate models suggest that AMO variations may cause

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significant temperature changes across Europe and East Asia (Knight et al., 2006; Li and Bates, 2007; Wang et al., 2009; Sutton and Dong, 2012). Observational records

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indicate that the warm phases of the AMO are coincident with positive temperature anomalies over Europe, consistent with climate model simulations (Knight et al., 2006; Sutton and Dong, 2012). Such a close relationship between AMO variations and temperature changes are also found in East Asia, with warm phases of the AMO coincident with an air temperature increase over East Asia (Li and Bates, 2007; Wang et al., 2009). The relationship between warm/cold phases of the AMO and multidecadal positive/negative temperature anomalies over East Asia has persisted over the past

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ACCEPTED MANUSCRIPT millennium (Wang et al., 2013). Nonetheless, mechanisms of the AMO influence on Asian temperature are not fully understood. One possible explanation is that the warm-phase AMO influences temperature variability through the enhancement of the middle and upper troposphere heating over Eurasia, which strengthens the East Asian summer monsoon and weakens the East Asian winter monsoon (Lu et al., 2006; Wang

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et al., 2009; Ding et al., 2014). In summer, the warm-phase AMO causes positive subtropical western Pacific geopotential height anomalies and strong subtropical anticyclones, intensifying the East Asian summer monsoon (Lu et al., 2006; Wang et al., 2009). In winter, the warm-phase AMO induces strong mid-latitude westerly winds (Dong et al., 2006; Grossmann and Klotzbach, 2009), causing surface low air

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pressure from the North Atlantic to extend over the Eurasian continent (Li and Bates, 2007; Wang et al., 2009). These changes weaken the Siberian-Mongolian Cold High

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system and the East Asian cold air activity, reducing the intensity of the East Asian winter monsoon (Li and Bates, 2007; Wang et al., 2009; Ding et al., 2014). In this

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regard, the warm/cold phases of the AMO influence the positive/negative temperature anomalies through the effects on the East Asian monsoon system.

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Another possibility is related to the Rossby wave propagation in the mid- and high-latitudes that propagates from the central North Atlantic across Eurasia, heating

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(cooling) the middle and upper troposphere of the Asian continent during the warm (cold) phases of the AMO, respectively (Hoskins and Ambrizzi, 1993; Li et al., 2008b;

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Shimizu and de Albuquerque Cavalcanti, 2011). By perturbing the Rossby wave propagation, the warm/cold phases of the AMO may result in positive/negative temperature anomalies over East Asia, including the eastern TP (He et al. 2014; Wang et al. 2013, 2014).

5 Conclusions With tree-ring width data of S. tibetica Kom. from the east central TP, a 564-year annual (pApril-cMarch) minimum temperature reconstruction was developed after the

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ACCEPTED MANUSCRIPT climate-tree

growth

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analysis.

Comparison

with

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temperature-sensitive tree-ring records from nearby regions indicates that our reconstruction is representative of large-scale Tmin variations over the eastern TP. Our reconstruction shows an unprecedented warming trend in recent decades, suggesting that tree growth tracks well the strong warming signal in this region.

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Significant positive correlations between our reconstruction and the AMO indices suggest that the AMO may have a crucial influence on multidecadal temperature variations on the east central TP. Future studies should aim at developing a larger tree-ring network with longer chronologies over the TP in order to fully understand the nature of recent Tmin anomalies and their long-term connections with major

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climate factors such as the AMO.

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Acknowledgments

This research was funded by the HKU Seed Funding Program for Basic Research (No.

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201309159002) and Hong Kong RGC Project (No. 27300514). Tree-ring data in this

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study are available on the NOAA paleoclimate database (www.ncdc.noaa.gov).

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Barber, V.A., Juday, G.P. and Finney, B.P., 2000. Reduced growth of Alaskan white spruce in the twentieth century from temperature-induced drought stress. Nature 405, 668-673. Bräuning, A., 2006. Tree-ring evidence of ‘Little Ice Age’glacier advances in southern Tibet. The Holocene 16, 369-380. Bräuning, A. and Mantwill, B., 2004. Summer temperature and summer monsoon history on the Tibetan plateau during the last 400 years recorded by tree rings. Geophys. Res. Lett. 31, L24205, DOI: 10.1029/2004GL020793. Buckley, B., Cook, E., Peterson, M. and Barbetti, M., 1997. A changing temperature response with elevation for Lagarostrobos franklinii in Tasmania, Australia. Clim. Change 36, 477-498. Cai, Q., Liu, Y., Wang, Y., Ma, Y. and Liu, H., 2016. Recent warming evidence inferred from a tree-ring-based winter-half year minimum temperature reconstruction in northwestern Yichang, South Central China, and its relation to the large-scale circulation anomalies. Int. J. Biometeorol. 60, 1885-1896. 16

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Fig. 1 Map of the Tibetan Plateau showing the location of the tree-ring sampling sites and nearby meteorological stations. Red triangles indicate the tree sites in this study (LIT and TAN). Green squares indicate the Litang (LT) and Daocheng (DC) meteorological station. Black circles denote the tree sites of five Tmin reconstructions

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(HY (Zhang et al., 2014), XQ (Gou et al., 2007), QZ (He et al., 2014), RS (Liang et al., 2016), and CX (Shao and Fan, 1999)), and white circles denote the tree sites of two Tmean reconstructions (ZJ (Wang et al., 2014) and BZ (Duan and Zhang, 2014)). Fig. 2 Monthly Tmean, Tmin, Tmax (line with symbols) and monthly total precipitation (bar) from a Litang and b Daocheng meteorological stations during

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Fig. 3 a Tree-ring width chronology developed from two sites of S. tibetica Kom. in

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the east central TP (blue line) and the corresponding sample size (grey shading). b Running EPS and Rbar statistics. Dashed horizontal line denotes the 0.85 cutoff value.

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Fig. 4 Correlations of tree-rings with a monthly precipitation and Tmean, and b monthly Tmin and Tmax records from previous April to current September during

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Fig. 5 Comparison of a instrumental (solid line) and reconstructed (dash line) pApril-cMarch Tmin during the common period 1958-2014, and b their first-differenced data during 1959-2014. Fig. 6 Tree-ring based pApril-cMarch Tmin reconstruction for the east central TP from 1451 to 2014. The bold line denotes a 21-year low-pass filter. The dashed line indicates the mean value of the reconstruction.

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ACCEPTED MANUSCRIPT Fig. 7 Comparison of the pApril-cMarch Tmin reconstruction with other Tmin records on the eastern TP. a The pApril-cMarch Tmin reconstruction in this study, b August Tmin reconstruction on the southeastern TP (Liang et al., 2016), c winter Tmin reconstruction on the western Sichuan Plateau (Shao and Fan, 1999), d prior year January-December Tmin reconstruction on the central TP (He et al., 2014), e

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October-April Tmin reconstruction on the northeastern TP (Gou et al., 2007), and f January-August Tmin reconstruction in the Qilian Mountain (Zhang et al., 2014). All series have been normalized for direct comparison. The bold line in each panel denotes a 21-year low-pass filter. Vertical shading indicates cold periods in our reconstruction.

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Fig. 8 Comparison of the pApril-cMarch Tmin reconstruction with two Tmean records on the TP. a The pApril-cMarch Tmin reconstruction in this study, b April–

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Fig. 9 Comparison of the pApril-cMarch Tmin reconstruction with the AMO indices. a The pApril-cMarch Tmin reconstruction in this study, b Kaplan SST-based AMO

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2004), e foraminifer Mg/Ca-based SST reconstruction from Caribbean (Wurtzel et al., 2013). All series have been normalized for direct comparison. The bold line in each panel denotes a 21-year low-pass filter. Vertical shading indicates cold periods in our reconstruction.

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ACCEPTED MANUSCRIPT Table. 1 Site information and tree-ring chronology statistics. Elevation (m) 4220 4050

Cores/ Time span SD MS AC1 Trees (AD) 34/17 1317-2014 0.28 0.21 0.81 54/28 1306-2014 0.30 0.20 0.86 88/45 1306-2014 0.29 0.20 0.84 RC, regional chronology; SD, standard deviation; MS, mean sensitivity; AC1, first-order autocorrelation; Rbar, within-trees rbar; EPS, expressed population signal.

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Location (latitude, longitude) 30.24 ºN, 100.26 ºE 30.23 ºN, 100.26 ºE

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Site Code LIT TAN RC

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0.55 0.54 0.56

0.94 0.97 0.92