We developed a new tree‐ring width chronology for a cypress species ( Sabina saltuaria ) in the Wolong Natural Reserve, in the western Sichuan Plateau of China. The goals of this study are to (1) investigate the climatic response of the new chronology and reconstruct the temperature history of the past 172 years, (2) use various climate proxy records to validate this reconstructed temperature series within a confined region, and finally (3) detect the significant cycles and related climatic fingerprints of the reconstruction.

The western Sichuan Plateau consists of the southeastern part of the Tibetan Plateau (Liu et al. , 2001 ; Fu et al. , 2004 ). Several climate systems, i.e. the eastern Asian monsoon, the Indian monsoon, and the continental westerlies, play an active role in driving the regional climate patterns of this region (Song et al. , 2006 ). This region is largely covered by alpine coniferous forests and tree‐ring data from those forests at the upper elevation limit should ideally serve as high‐resolution evidence of past climate variation (Figure 1 ). Based on the living spruce ( Picea balfouriana ) trees of Shanuli Mountain, Shao and Fan ( 1999 ) developed a 345‐year winter temperature reconstruction, which is the first climate reconstruction based on tree rings in this region. Significant progress has been made recently in deriving annually resolved estimates of climatic fluctuations derived from tree‐ring data in west Sichuan, and several tree‐ring‐based temperature reconstructions from various geographical sites of this region are available (Song et al. , 2007 ; Qin et al. , 2008 ; Wu et al. , 2008 ; Li et al. , 2011a ; Yu et al. , 2012 ). In comparison to the nearby Tibetan Plateau (Bräuning, 2001 ; Yang et al. , 2003 , 2009 , 2010 ; Zhang et al. , 2003 ; Liang et al. , 2008 ; Fan et al. , 2010 ; Shao et al. , 2010 ; He et al. , 2013 ; Wang et al. , 2014 ), however, the western Sichuan Plateau still receives little attention and only a limited set of tree‐ring chronologies have been built (Qin et al. , 2008 ; Wu et al. , 2008 ; Yu et al. , 2012 ). Increased efforts to obtain new tree‐ring archives from the western Sichuan Plateau are recommended to improve our understanding of the spatial and temporal details of climatic variations for this region.

Knowledge of climate conditions over past centuries is indispensable in determining whether modern climate change is unprecedented in a pre‐industrial context, and adds the benefit of allowing the anticipation of the probability of future episodes (Mann et al. , 2008 ; Cook et al. , 2010 ; Marcott et al. , 2013 ; Stern and Kaufmann, 2014 ). Development of this understanding is hindered by instrumental measurements that usually cover only the last 50–100 years. This necessitates the use of natural climate archives such as tree rings to capture climate information in the context of longer time scales (Fritts, 1976 ; Schweingruber, 1996 ). Conditions on the Tibetan Plateau, one of the Earth's most imposing geomorphic features, have catalysed a considerable body of research in dendroclimatology, with a series of climate reconstructions having been built (Zhang et al. , 2003 ; Liang et al. , 2008 ; Yang et al. , 2009 ; Shao et al. , 2010 ; He et al. , 2013 ; Wang et al. , 2014 ). Those studies have enabled the successful reconstruction of the 3500‐year tree‐ring record of annual precipitation on the northeastern Tibetan Plateau (Yang et al. , 2014 ). Key gaps in our understanding of climate change for the Tibetan Plateau still remain, primarily due to sparse coverage of tree‐ring sample sites, despite intensified recent efforts (Cook et al. , 2010 ; Yang et al. , 2010 , 2014 ). Improved knowledge of these efforts is still needed to create additional climate reconstructions on the Tibetan plateau for regions without sufficient tree‐ring data, such as the western Sichuan Plateau.

The climate parameters were used for climate‐growth analysis over the common period with the chronology: 1956–2010 for the climate station, 1950–2002 for CRU grid‐boxes, and 1950–2005 for the PDSI grid. Climate variables were lagged to determine if the climate during the previous growing season (beginning of the previous October) affected growth during the current growing season (through current October). The use of lagged climate variables is considered necessary because tree growth is partially dependent upon climatic conditions several months prior to ring formation (Fritts, 1976 ).

We used correlation and response function analyses to determine which climate variables were most significantly related to radial growth at the study site. Both types of analysis were performed using the DENDROCLIM2002 program (Biondi and Waikul, 2004 ). Response function analysis yields a rigorous evaluation of climatic effects on tree growth and to reduce the effects resulting from multicollinearity among independent variables (Grissino‐Mayer et al. , 1989 ).

Although the Dengsheng ecological station is the nearest climate station to the sample site (approximately 3 km from the study site), the extremely limited and incomplete meteorological records (available only from 1999 to 2008) make it difficult to perform reliable calibration with the targeted tree‐ring data. Consequently, the chronology is compared to climate data of high quality (1956–2010) from the national meteorological station (Dujiangyan station, 698 m) closet to our sample site. The data set consist of monthly mean temperature (TEM), monthly total precipitation (PRE), monthly minimum (TMN), and maximum (TMX) temperatures. We further used gridded climate data for analyses because they provide a more regional signal than the station records do. The gridded climate data included monthly mean (TEM), minimum (TMN) and maximum (TMX) temperatures and monthly total precipitation (PRE), which were obtained from the Climatic Research Unit (CRU), East Anglia, UK ( http://www.cru.uea.ac.cn : 0.5° × 0.5°; CRUts2.1; Mitchell and Jones, 2005 ). Mean values were calculated from four grid‐boxes covering the region between 20.75°–31.25°N and 102.75°–103.25°E over the 1950–2002 period. In addition, the climate data used in this study included the monthly Palmer Drought Severity Index (PDSI) data developed by Dai et al. ( 2004 ). The PDSI data set covering 1950–2005 is available on a 2.5° × 2.5° grid. The grid point (28.75°N, 101.25°E) nearest to our sampling site was used in this study.

We also calculated several descriptive statistics pertaining to the chronology generated by the COFECHA and ARSTAN programs: expressed population signal (EPS), average inter‐series correlation (Rbar), mean sensitivity (MS), signal‐to‐noise ratio (SNR), first‐order serial autocorrelation (AC1), and the variance in the first eigenvector (VFE). The EPS statistics was calculated to represent the variance agreement between a finite sample size and a theoretical infinite sample size. Though a specific range of EPS values of acceptable statistical quality cannot be given, Wigley et al. ( 1984 ) suggested a threshold of 0.85 as reasonable. Rbar is a measure of how well the samples within a chronology correlate. A high Rbar suggests accurate crossdating and a common signal, likely a regional climate response (Grissino‐Mayer, 2001 ). Both EPS and Rbar were calculated in a 30‐year running window with 15‐year overlaps along the chronology. MS reflects year‐to‐year change in tree growth. Samples from a site with a high MS have considerable variation in ring width, while those from a site with low MS are complacent and unlikely to exhibit a strong climate response (Fritts, 1976 ). AC1 determines if biological persistence is an issue in the chronology. SNR is used to detect the high‐frequency variation of tree‐ring width series, and VFE to estimate the strength of observed common signals among the trees.

The crossdated tree‐ring data were standardized using the ARSTAN program (Cook and Kairiukstis, 1990 ). Standardization converts the ring widths into dimensionless indices, removes age‐growth trends, and allows the raw data to be averaged into a single index chronology. The raw data were conservatively detrended using linear regression lines or negative exponential curves. A cubic spline with a 50% frequency‐response cut‐off equal to 67% of the series length was used in a few cases (two series) when the significant growth reversal at the ends of the series resulted in poor curve fits based on the conservative methods. The measured values were divided by the ‘growth’ curve values to produce a detrended and standardized series. Ring‐width chronology was calculated using a mean‐value function (combining the detrended tree‐ring series), and the mean of each year was computed as a biweight robust estimate (Cook and Kairiukstis, 1990 ). To reduce the potential influence of the changing sample depth, the variance of the chronology was also stabilized using the method described by Osborn et al. ( 1997 ). Three different outputs were created: standard (STD), residual (RES), and ARSTAN (ARS). Because the standard chronology can maximize low‐ to high‐frequency common variance, presumably in response to climate, we chose it to analyse the consistency of the climate response.

Increment cores, usually two from each living S. saltuaria tree, were taken approximately at 1 m height using Haglof increment borers (Grissino‐Mayer, 2003 ). After extraction, all samples were stored in plastic straws and appropriately labelled. In total, 70 cores from 35 S. saltuaria trees were collected. Sampling at the study site was completed in the autumn of 2012. In the laboratory, samples were air dried and mounted on slotted wooden boards, and then polished with progressively finer sandpaper (from 240‐grid to 600‐grid) to reveal individual cell boundaries (Orvis and Grissino‐Mayer, 2002 ). The widths of all rings of each core were measured to 0.001 mm on a Lintab tree‐ring station (Rinntech Inc., Heidelberg, Germany). We further used COFECHA, a computer program that uses segmented time‐series correlation techniques, to check the quality of visual crossdating (Holmes, 1983 ). Finally, 62 cores of 31 trees were crossdated successfully.

The S. saltuaria forest study site in the Wolong Natural Reserve, approximately 3 km southeast of the Dengsheng ecological station (Figure 2 (b)), is located on the south‐facing slope. In this stand, S. saltuaria trees have a relatively narrow elevation range (from 3600 to 3700 m) and make up the treeline ecotone. S. saltuaria is associated with Quercus semicarpifolia and Rhododendron spp. and is found growing as sparse forest landscape. Our S. saltuaria sampling site (3700 m, 102.99°E, 30.85°N) is close to its upper elevation limit, and trees can reach approximately 15 m with an average diameter of about 50 cm at breast height (Figure 2 (c)).

S. saltuaria is endemic to China and is distributed at the southeastern portion of the Tibetan plateau. S. saltuaria usually occurs as a dominant constituent of treeline vegetation (Zhang et al. , 2007 ). It is commonly found in the elevation zone between 3600 and 4000 m, where it forms dense stands of krummholz or sparse tree forests. On the Sejila Mountain of eastern Tibet, S. saltuaria is distributed in elevations between 4320 and 4520 m on the south‐facing slopes. This should be the highest elevation zone in which this species grows (Guo et al. , 2010). The presence and dominance of S. saltuaria at the treeline sites depend on its extreme tolerance to the climate conditions, such as freezing damage, winter desiccation, and ice blast (Körner, 1998 ; Zhang et al. , 2007 ).

Lying in the eastern climatic zone of the Tibetan Plateau, the reserve is characterized by a temperate sub‐humid climate (Cui et al. , 2005 ). In the winter months (from November to April), the main climate determinants are the continental air masses, resulting in sunny and dry weather. By contrast, the climate during the summer months (from May to October) is primarily influenced by the southeastern Asian monsoon, causing warm–wet conditions. Data from the Desheng Ecological Station within the reserve (2730 m, 102.97°E, 30.85°N; Figure 2 (b)) (Liu et al. , 2006 ), located near our sample site, exemplify the local climate. Mean annual temperature is 8.4 °C. Mean monthly minimum (January) and maximum (July) temperatures are approximately –1.7 and 17.0 °C, respectively. The mean annual precipitation is approximately 900 mm, and the rainy season (May–October) accounts for approximately 80% of the total annul precipitation. The mean frost‐free period is of approximately 190 days. The time from mid‐May to late September approximately corresponds to the growing season (Song et al. , 2006 ).

The Wolong Natural Reserve is located in the eastern portion of the western Sichuan Plateau, which acts as a transition zone from the Chengdu Plain to the Tibetan Plateau. The reserve has a territory of 2000 km 2 (102.87°–103.4°E, 30.75°–31.42°N, Figure 2 (a)) (An et al. , 2001 ) and was established as a biosphere reserve within the reserve network of UNESCO's Man and Biosphere program in 1980 (Fu et al. , 2004 ). The reserve was established to protect alpine and sub‐alpine ecosystems and endangered species such as giant pandas ( Ailuropoda melanoleuca ), golden monkeys ( Rhinopithecus roxellanae ), and dove trees ( Davidia involucrata ) (Liu et al. , 2001 ; Fu et al. , 2004 ). The reserve, which includes contrasting topographic conditions, ranges in elevation from 1200 to 6250 m (Taylor et al. , 1991 ). Within the reserve, mountain peaks are generally above 5000 m a.s.l., e.g. Balang Mountain (102.9°E, 30.93°N, 5040 m a.s.l.) and Four Girls Mountain (102.9°E, 31.11°N, 6250 m a.s.l.). The Pitiao River runs through the reserve and flows into the Min River (the first tributary of the Yangtze River) (Figure 2 (b)). The vegetation within the reserve includes evergreen broadleaf, deciduous, and sub‐alpine coniferous forests, as well as alpine meadows along lower to upper elevation gradients (He et al. , 2009 ). Soils are predominantly sandstone, shale, and limestone derived from sedimentary and metamorphic parent materials. Soil depth varies with slope and is deepest on gently sloping plateaus and shallowest on steep slopes (Taylor and Qin, 1988 ).

The reconstruction covered 172 years, spanning from 1840 to 2011, beginning on the date at which the EPS value was constantly above the threshold of 0.85 (Figure 7 (b)). The reconstruction showed decadal variations and revealed several prolonged warm and cold periods after smoothing with an 11‐year moving average filter. The continuous multi‐decadal warm periods occurred during the 1927–1960 interval, while other warm periods were considerably shorter in duration and occurred during the 1853–1862 and 1997–2011 intervals. By comparison, the most pronounced cold period lasted for 36 years, occurring during the 1961–1996 interval. Another unusually low temperature was present during the 1886–1926 interval, lasting about 41 years. The reconstruction also had one cold episode in 1863–1882.

The relationships between the tree‐ring chronology and seasonal groupings of climate variables identified annual (prior October to current September) mean instrumental temperature as the most appropriate predictand for reconstruction. The regression model for the calibration period was Y = 1.503 X + 13.88, where Y = annual mean temperature (°C) and X = tree‐ring indices. The predictor variable accounted for 54.1% of the variance of the predictand (Table 2 ). Our reconstruction paralleled the general tendency of the observed temperature (Figure 7 (a)) and the leave‐one‐out cross‐validation test showed that reduction of error (RE) was strongly positive, indicating no bias in the calibration/verification exercises. The sign test (ST) and the product mean test (PMT), which describes how well the predicted value tracks the direction of actual data, exceeded the 99% confidence level. These analyses indicated that the model performed adequately for developing the final reconstruction.

Correlation and response function analyses between CRU gridded climate data and tree‐ring chronology. TMP, PRE, TMN, and TMX represent mean temperature, precipitation, minimum temperature, and maximum temperature, respectively. The dashed and dotted lines represent significant effects at levels of P < 0.05 and P < 0.01 for the simple correlation analysis. * over the black boxes represent significant effects at levels of P < 0.05 for the response function analysis.

The correlation and response analyses with CRU temperature data exhibited rough similarities to the calculations with instrumental temperature data, but the magnitudes of correlations were apparently lower (Figure 5 ). The magnitudes of correlations were weaker for precipitation when compared with temperature variables, and the strong relationships seen were the negative correlations with spring (March) and autumn (September) precipitation. Tree growth had significant negative response with PDSI from prior October to current October in the correlation analysis, but only autumn (September to October) PDSI was important in explaining tree growth in the response function analysis (Figure 6 ).

Correlation and response function analyses between meteorological climate data and tree‐ring chronology. TMP, PRE, TMN, and TMX represent mean temperature, precipitation, minimum temperature, and maximum temperature, respectively. The dashed and dotted lines represent significant effects at levels of P < 0.05 and P < 0.01 for the simple correlation analysis. * over the black boxes represent significant effects at levels of P < 0.05 for the response function analysis.

The correlation and response analyses with instrumental climate data showed that temperature was most influential in determining annual tree growth (Figure 4 ). Tree growth indicated highly significant similarities in growth responses to the temperature variables (mean, minimum, and maximum temperatures). The magnitudes of positive correlations were strongest for summer (July) and autumn (September) temperatures, and strong positive correlations were also seen for the temperatures from late winter to early spring (February to April). Precipitation was only weakly correlated with tree growth, and the ring‐width record showed negative responses only to summer (June to July) precipitation.

A tree‐ring chronology for S. saltuaria of 241 years in length, from 1771 to 2011, was constructed (Figure 3 (a)). The chronology that began with fewer than five individual tree‐ring series was truncated to eliminate weakly replicated portions. The mean segment length, or the average number of rings per core, was 174 years (from 99 to 272 years), a value that set the ceiling for decadal variability retention. The EPS statistic is greater than 0.85 over the period from 1840 to 2011 (Figure 3 (c)), indicating that this period had satisfactory quality for the chronology. The EPS threshold corresponded to a sample depth of 30 cores (Figure 3 (d)). The mean Rbar (0.36) calculated over a 30‐year running window was higher than the 95% confidence level (0.349), indicating similar year‐to‐year fluctuations in radial growth indices among trees. The chronology had a slightly high mean sensitivity (0.218) and standard deviation (0.332 mm), indicating high year‐to‐year fluctuations in radial growth indices (Table 1 ). The value of AC1 for the chronology was relatively high (0.69), suggesting that annual growth is affected by the growth of the previous year (biological persistence) (Fritts, 1976 ). The relatively high level of SNR (9.96) and VFE (20.43%) indicated that the trees constituting the mean chronology had strong common signals.

4 Discussion

4.1 The association of tree growth with climate High‐latitude alpine treelines are generally thought to be limited by temperature (Körner, 1998). At the global scale, the treelines of the world's mountains seem to follow a common isotherm: a seasonal mean ground temperature of 6.7 °C (Körner and Paulsen, 2004). Most studies of tree‐growth‐climate interaction at the treeline have reported the fidelity of local summer temperature sensitivity embedded in tree‐ring width variability (Esper et al., 2002; Mann et al., 2008). The resulting chronologies have been used to reconstruct either regional or detailed spatial patterns of past temperature variability at various locations (Villalba et al., 2003; Wilson et al., 2007; Fan et al., 2009; Esper et al., 2012; Büntgen et al., 2013). In this study, the tree‐ring records show a strong and consistent relationship with summer (July) temperature measurements (Figures 4 and 5). This finding parallels the generally observed tendency of strong temperature controls on tree growth at alpine treelines (Körner and Paulsen, 2004). Calculated from the instrumental data of the Dujiangyan meteorological station (the lapse rate for saturated air of 0.6 °C per 100 m) (Buckley et al., 1997), the minimum (TMN), mean (TMP), and maximum (TMX) temperatures in July of the sample site were 3.52, 6.71, and 10.61 °C, respectively. This result indicates that the thermal conditions in July for S. saltuaria stands are below even the threshold temperatures (TMN = 4–5 °C, TMP = 8–9 °C, TMX = 13–14 °C) at which cell division and differentiation are active for the alpine coniferous forests (Rossi et al., 2008). This provides independent confirmation that summer temperature is a critical factor limiting production and differentiation of xylem cells at the sample site. The overall positive effect of an increase in autumn (September) temperatures on tree growth could also be related to a direct physiological influence. During the late growing season, radial increments are almost halted, and the storage of carbohydrates may be oriented towards secondary cell wall building, while late cell growing may continue for another several weeks (Schmitt et al., 2000; Bouriaud et al., 2004). Relatively warm climate in autumn can leave enough time to complete secondary wall synthesis and lignin deposition of latewood before winter (Gričar et al., 2005; Rossi et al., 2006), and the completed thickening of wood elements in autumn can decrease the risk of direct frost damage to plant tissues when exposed to cold climate spells in winter (Lazarus et al., 2004; Misson et al., 2004). The high autumn temperatures will benefit the process of cell wall thickening of coniferous trees in several ways, such as by activating enzymes, by increasing the ability of membranes to transport substances, and by decreasing the duration of tracheid development in the secondary thickening zone (Antonova and Stasova, 1997; Lebourgeois et al., 2005). The chronology yielded several of the significant correlations with temperatures from later winter to early spring (prior December to current March). A warm winter may influence the breaking of dormancy and the resumption of physiological activity in the tree, and thus may increase the duration of the current growing season (Maherali et al., 2004). Furthermore, warm climates in winter can decrease the probability of freezing‐induced embolism of the previous year's early wood vessels, which are necessary for the spring recovery of hydraulic conductivity (Cochard et al., 1992; Lebourgeois et al., 2004). Later in the season, a warm early spring can hasten budburst and cambial reactivation after winter dormancy and thus also promote growth (Lebourgeois et al., 2012). Moreover, tree‐growth benefits from higher temperatures in early spring, which can make the winter snow cover, melt earlier, making enough moisture available to satisfy the water demand of trees in the early growing season (Vaganov et al., 1999; Fan et al., 2008). In addition, we found high precipitation and the PDSI generally to have a negative influence on tree growth. This finding indicates that rainy and cloudy weather may cause an increased risk of delayed snow melt and freezing injury in winter that can inhibit the cambial activity at the beginning of the growing season (Vaganov et al., 1999; Auclair et al., 2010). Furthermore, a pluvial climate in the growing season may result in a low mean temperature, and low air and soil temperatures may limit tree growth by affecting water availability and thus the photosynthetic rate (DeLucia, 1986; Gruber et al., 2009).

4.2 Changes in reconstructed temperature over time We compared our reconstruction with other temperature‐sensitive tree‐ring records in surrounding regions to identify whether our results can accurately represent large‐scale climate features. The positive relationship is most statistically significant for our reconstruction and the winter minimum temperature reconstruction for the Shanuli Mountain of western Sichuan over the period from 1840 to 1989 (r = 0.544, P < 0.01) (Shao and Fan, 1999). This positive relationship may be due to the relatively short distance between the two sampling sites. Our reconstruction also preserves a marked resemblance of decadal‐scale temperature variation with summer temperature reconstruction for northwestern Yunnan during the period from 1840–2001 (r = 0.457, P < 0.01) (Li et al., 2011b) and summer temperature reconstruction for the source region of the Yangtze River on the Tibetan Plateau during the period from 1840 to 2002 (r = 0.467, P < 0.01) (Liang et al., 2008). The comparisons described above are indicative of the capacity of the reconstruction in this study to capture temperature signal over a well‐defined spatial domain. The climate was relatively stable prior to 1900, aside from a descending temperature trend that occurred from 1864 to 1882. This pattern agrees with existing tree‐ring temperature proxies for the Gaoligong Mountains of northwestern Yunnan (Fan et al., 2010) and the source region of the Yangtze River on the Tibetan Plateau (Liang et al., 2008). The early 20th century (1900s–1920s) of the reconstruction was characterized by a relatively cold epoch. This temperature depression has also been captured by tree‐ring data for the Miyaluo Natural Reserve in western Sichuan analysed by Li et al. (2011a) and for the central Hengdual Mountains analysed by Fan et al. (2009). The reconstruction indicated that temperatures were persistently above average from the 1930s–1950s, a fact also clearly recorded in tree‐ring‐based temperature reconstructions for the Himalayan region (Yadav et al., 1999; Cook et al., 2003) and the central Hengduan Mountains (Fan et al., 2009; Li et al., 2011b). Based on the multi‐proxy materials, Wang et al. (2001) reconstructed the annual temperature series of China over the past millennium and indicated that temperature anomalies in 1920s–1940s were the warmest 30 consecutive years of the 20th century, and that the 1940s were among the warmest decades in southwest China. These results roughly agree with the findings of this study. By combining instrumental temperature data with long, temperature‐sensitive proxy records, Mann et al. (1999, 2008) demonstrated the 20th‐century warming to have been abrupt and truly exceptional in the context of at least the past millennium. However, we found that climate warmth in the 20th century is not anomalous in our reconstruction, and the mean temperature (15.3 °C) of the 20th century was even somewhat lower than that of the 19th century (15.37 °C). Interestingly, the most characteristic feature of the 20th century was an unusual cold spell that occurred during the latter half of this century (1961–1996), and nine of the top ten coldest years took place during this cold period. Three major glaciers of the Hengduan Mountains – the Hailuogou Glacier (29.59°N, 101.87°E, 2940 m) of the Gongga Mountain, the Mingyong Glacier (28.43°N, 98.68°E, 5000 m) of the Meili Snow Mountain, and the Baishui No.1 Glacier (27.12°N, 100.2°E, 4800 m) of the Jade Dragon Snow Mountain – all maintained a state of stagnation or speed‐down recession during the 1970s–1980s (He et al., 2003), indicating a relative cold climate prevailed on a regional scale. The temperature signal of δ18O variations preserved in ice cores recovered from the Puruogangri ice field (33.73–37.73°N, 88.33–89.83°E, 5500–6500 m) in the central Tibetan Plateau also experienced a cold episode from 1975 to 1985 that was exceptionally cold from the perspective of the past 600 years (Yang et al., 2009). Based on tree‐ring data for Smith fir (Juniperus tibetica) trees of the central Tibetan Plateau, He et al. (2013) identified a pronounced cold spell over the last five decades (1948–2003) that was comparable to those of any cold period over the past 600 years, including the most pronounced cold spells of the Little Ice Age. An anomalous temperature depression in the late 20th century has been proven to be a determined phenomenon of the temperature‐sensitive tree‐ring data for the Tibetan Plateau and its adjoining regions (Shao and Fan, 1999; Cook et al., 2003; Bräuning and Mantwill, 2004; Yadav et al., 2004; Liang et al., 2008; Fan et al., 2009; Li et al., 2011b; Wang et al., 2014). China experienced significant decreases in solar radiation (−4.5 W m−2 per decade) over the latter half of the 20th century (Che et al., 2005; Qian et al., 2006), and surface cooling induced by aerosol radiative forcing is at a maximum over the Sichuan Basin of southwest China (Giorgi et al., 2002). A significant decrease in the summertime maximum temperature for a large portion of southeastern China near the Sichuan Basin over this time period has been detected (Kaiser and Qian, 2002). Analysis of meteorological data from the Dujiangyan station reveals that the study area has experienced a significant decrease (–75.76 h decade−1) in sunshine duration from 1960 to 2000. The annual mean temperature also decreased (−0.18 °C decade−1) for the period from 1960 to 1990. Decreasing incoming radiation in recent decades of the study area reduces the amount of solar radiation available for photosynthesis, which has negative impact on tree growth (D'Arrigo et al., 2008). These unprecedented adverse climate conditions may have negative impact on forest growth in western Sichuan and its nearby regions during the late 20th century. The climate has undergone a significant warming trend since the year 1997. The warming rate for the 1997–2011 period was 0.4 °C decade−1, and the mean temperature for this period was 0.44 °C higher than the mean temperature of the reconstruction. 2005 was the warmest year in the reconstruction with a temperature value of 0.83 °C higher than the mean value. The surface temperature also caused the year 2005 to be regarded as one of the two warmest years of global instrumental records (Solomon et al., 2007).