March–May Snow Cover Extent Reconstruction for the Past Four Centuries Based on the Tree-Ring Early-Wood on the Southeastern Tibetan Plateau

The snow cover extent (SCE) on the southeastern Tibet Plateau (SETP) has an important impact on the dynamics of the East Asian winter monsoon and the runoff changes of the first and third largest rivers in Asia, namely, the Yangtze River and the Yarlung Zangbo River. Unfortunately, the shortness of instrumental SCE data of a few decades limits our ability to understand its long-term variability before the industrial era. Here, we developed Abies faxoniana tree-ring total ring width (TRW), early-wood width (EWW), and late-wood width (LWW) chronologies for the past four centuries at Little Qamdo Village (XQDV), Markam County, on the SETP. The most significant positive correlation (r = 0.62, p < 0.01) was found between the EWW chronology and SCE from March to May (SCE_3–5). The SCE would affect the onset of the growing season through soil moisture, restricting the early-wood growth of trees. Thus, we presented a reconstruction of SCE_3–5 via EWW chronology since AD 1660 for SETP. We observed two abrupt changes from low to high around the years 1685 and 1998 for our reconstructed SCE_3–5. In addition, we found that the positive anomalies of the reconstructed SCE_3–5 after 1988 cohered with the distinct increase of the East Asian winter monsoon.

Introduction

Variations in SCE have strong impacts on climate change. The expansion of SCE increases surface albedo and reduces absorbed shortwave radiation. When the snow melts, it increases the latent heat sink at the expense of sensible heat, resulting in cooling in the snow-covered regions. Moreover, snow is also a critical component of the hydrological system in middle/high altitude regions, acting as a reservoir of water and a buffer control for river discharge and associated environmental processes (Groisman et al., 1994; You et al., 2002; Barnett et al., 2005; Zhang, 2005; Li et al., 2008; Räisänen, 2008; Zuo et al., 2012; IPCC, 2013; Qin et al., 2014; Huang et al., 2016).

In turn, snow cover extent (SCE) is highly sensitive to the current warming trend. Due to global warming, the beginning of the snow accumulating season (the end of the snow-melting season) will occur later (earlier) in most of the snow-covered regions, and the SCE will decrease except for very few exceptions (Masahiro et al., 2005). For example, the monotonic trend analysis of Northern Hemisphere SCE over the period of 1972–2006 with the Mann–Kendall test reveals significant declines in SCE during spring over North America and Eurasia, with lesser declines during winter and some increases in fall SCE (Déry and Brown, 2007). In particular, a number of studies have attributed the cause of diminishing spring SCE to Northern Hemisphere warming (Brown et al., 2010; McCabe and Wolock, 2010; Brown and Robinson, 2011).

Snow cover extent in China is primarily situated in the northeastern China, the northern part of Xinjiang Province, and the Tibetan Plateau (TP; Wang, 2012; Wang et al., 2012; Li, 2013; Xi and Zhang, 2013). The southeastern Tibetan Plateau (SETP) is one of the regions with a considerable volume of cryospheric extent (e.g., snow, ice, glacier, and permafrost) outside the polar regions (Liu and Chen, 2000; Qin et al., 2006; Kang et al., 2010; Yang et al., 2011; Immerzeel and Bierkens, 2012; Yao et al., 2018). Besides, as the product of snowfall in winter, the climatic effect of snow cover extent on the TP is also reflected in the coupling of its temporal and spatial variability as well as the circulation situation in winter and summer (Wang and Li, 2012; Wang et al., 2015).

As an important part of the global atmospheric circulation, the influence of the East Asian winter monsoon on the snow on the TP cannot be ignored (An, 2000; Mohtadi et al., 2011). On the one hand, since the terrain on the TP is tall and complex, the way the East Asian winter monsoon affects the region is therefore different from that of the eastern region of China. On the other hand, the spatial distribution of snowfall on the TP is quite different. Studies by Xu et al. (2005) and Xiu-zhong et al. (2010) found that the TP showed a basic distribution of lower SCE in the central hinterland and higher SCE in the surrounding areas. Zhu (2007) have revealed that there is higher SCE on the TP in winter and spring when the East Asian winter monsoon is stronger using both numerical simulation and data analysis. Wang et al. (2015) found there was a significant correlation between winter snowfall on the TP and the East Asian winter monsoon during 1961–2010, noting that since the beginning of the 21st century, the East Asian winter monsoon has been weakening and slowing down. The winter snowfall on the TP, meanwhile, has also shown a falling trend.

The spatiotemporal characteristics and extremity of the above effects in the long-term perspective are not known due to the lack of long-term SCE data from the SETP. The ground and satellite-based SCE records for the region are only a few decades long (Li, 1996; Pu et al., 2007), limiting a sufficient time window to assess the natural variability in SCE in the long-term perspective, and thus proxy data are required for the study of past SCE change.

Several studies have used tree-ring chronologies as a predictor to reconstruct SCE worldwide (Woodhouse, 2003; Timilsena and Piechota, 2008; Anderson et al., 2012; Masiokas et al., 2012; Ram and Mahendra, 2013) and the only reconstructed SCE record on the SETP is 300 years in length, providing the variation of SCE on an inter-decadal scale (Fang et al., 2016). However, almost all of these studies were based on the analysis of total ring-width (TRW) sequences, which were usually sensitive to a climate signal covering several months (Fritts, 2001; Speer, 2010). As a matter of fact, the increase in SCE may delay the growing season, restricting the early-wood growth of trees (Vaganov et al., 1999). Besides, SCE also affects soil moisture, which is an important factor limiting the growth of trees in cold and arid areas (Li et al., 2009; Fan et al., 2010). Thus, tree-ring early-wood variations may be an ideal material for reconstruction of SCE. Moreover, the analysis of the early-wood width (EWW) and late-wood width (LWW) chronologies helps us further understand the seasonality of climate changes and their impact on biomass production (Villanueva-Diaz et al., 2007; Griffin et al., 2013; Dannenberg and Wise, 2016; Torbenson et al., 2016).

The objectives of this study are to (1) identify the SCE_3–5 sensitive proxy to reconstruct SCE_3–5 variability over the past four centuries on the SETP based on the TRW, EWW, and LWW chronologies from Abies faxoniana trees and (2) further explore the relationship between our reconstructed SCE_3–5 history and the East Asian winter monsoon.

Materials and Methods

Study Areas

The study area is located at Xiao Qamdo Village (XQDV) (98.7°E, 29.3°N), Markam County, on the SETP (Figure 1), which is in the transition zone between the TP and the Western Sichuan Plateau. The mountainous area has a continental plateau climate with high altitude, low temperature, less precipitation, large evaporation, and various climate types (Ye, 1981). Due to the special physical and geographical conditions, the ecosystem in the region has obvious vulnerability characteristics. The Abies faxoniana is the dominant forest species growing along an elevation gradient from 3,607 to 3,618 m a.s.l. in our study area, and the 50 tree-ring samples in this work were collected in October 2019 from 26 Abies faxoniana trees. Our sampling site is located at the upper limit of the forest, where tree growth is usually sensitive to temperature (Fritts, 1976).

FIGURE 1

Figure 1. The snow cover distribution and the locations of tree-ring sampling site and meteorological station in this study. The map on the right shows the snow cover on the Tibet Plateau derived by Yan et al. (2021). Blue represents the low snow cover, and red represents the high.

Tree-Ring Data

Tree-ring samples were collected and prepared for analysis based on the technique described by Stokes and Smiley (1968) and Fritts (1976). Following the surfacing, samples were scanned using an Epson^® Expression 10000 flatbed scanner at resolutions between 800 and 1,200 dpi, depending on the image clarity. Higher resolution images were required to enable the distinction of narrow rings in periods of suppressed growth if the coarser-resolution was deemed inefficient for this purpose. Dating, measuring, and visual cross-dating of annual rings were carried out using the program WinDENDROTM Density (version 2008b). EWW and LWW measurements were aided by the distinct size differentiation of early-wood and late-wood vessels in Abies faxoniana tree samples. Early-wood and late-wood measurements were based on a defined boundary of 40% of the minimum to maximum relative pixel density in the reflectance values, where the onset of late-wood growth was noted as having denser and compacted vessels that were darker in nature (Supplementary Figure 1). Some early-latewood boundaries were adjusted manually to correct for errors in the automatic detection process.

COFECHA was used to verify cross-dating and provide chronology statistics that describe the strength of intercorrelation between tree-ring samples at the study site (Table 1; Holmes, 1983). The chronologies were computed as the robust mean value of the normalized, detrended, and standardized TRW, EWW, and LWW available each year, using the computer programs ARSTAN (Cook and Holmes, 1986). The TRW, EWW, and LWW measurements were standardized to remove the biological growth trend as well as other low-frequency variations due to stand dynamics. In our study, the chronologies were developed with conservative methods by fitting a negative exponential curve or a straight line to any slope. A cubic spline with a 50% frequency-response cutoff equal to 67% of the series length was also used in a few cases when anomalous growth trends occurred. The reliability of each standard chronology was evaluated by the expressed population signal (EPS) and mean series intercorrelation (Rbar) (Cook and Kairiukstis, 1990). Both Rbar and EPS were calculated for a 50-year moving window with 25-year overlaps along the chronology. The reliable part of the chronologies was defined by a threshold of EPS 0.85 (Wigley et al., 1984). The standard (the detrended index chronology) TRW, EWW, and LWW chronologies were produced and were subsequently used throughout the analyses (Fritts, 1976).

TABLE 1

Table 1. Statistics for each chronology at XQDV.

Climate Data

Monthly maximum (T_max), minimum (T_min), temperature (°C), and monthly total precipitation (Pre) (mm) records for the period of 1954–2018 were obtained from the nearest meteorological station (Qamdo meteorological station; 31.15°E, 97.17°N, 3307.1 m a.s.l.; Figure 2). The gridded SCE (%) database with weekly resolution covering the northern hemisphere (20°–90°N¹; Robinson et al., 2012) was also derived from the Climate Research Unit (CRU)². The SCE on the TP is mainly distributed in the northwest and southeast, with the largest in the northwest. The spatial range of SCE data used in this study is 93° to 98°E and 29° to 31°N, which belongs to the second largest SCE area on the TP. The time span of the database is from 1966 to 2018, and the spatial resolution is 2° × 2°, mainly compiled based on the snowfall and snow depth data monitored by satellites and stations. These data were reduced to annual and seasonal means and totals.

FIGURE 2

Figure 2. The pattern of monthly maximum (T_max), minimum (T_min), temperature (°C), and monthly total precipitation (Pre) (mm) for the period of 1954–2018 and the gridded snow cover extent (SCE) (%) for 1966–2018.

After comparing and analyzing 18 East Asian winter monsoon indexes affecting East Asia, Wang and Chen (2010) classified the East Asian winter monsoon indexes into four categories, namely, East–West pressure difference, low-level wind field characteristics, high-level wind shear, and East Asian Trough, and pointed out that most of the main East Asian winter monsoon indexes showed that the East Asian winter monsoon entered a weakening stage in the 1980s. Other studies have also proved the characteristics of this weakening trend (Zhu, 2008; Liu et al., 2013). For the sake of objectivity, the East Asian trough location index (CW) and the Siberian high-intensity index (SH) during the period of 1961–2010, developed by Wang et al. (2015), are selected to illustrate the linkage of the SCE to the SETP and large-scale circulation in our study.

Methods

To evaluate the relationship between climatic variables and the TRW, EWW, and LWW indices for Abies faxoniana, we used correlation analyses, applied with the software Dendroclim (Biondi and Waikul, 2004). All statistical procedures were evaluated at a level of significance at P < 0.05. SCE modeling was conducted using the transfer function approach (Fritts, 1976; Cook and Kairiukstis, 1990). Multiple stepwise linear regression was used to develop a linear model to estimate the dependent SCE variable from a set of potential tree-ring predictors.

Based on the correlation analysis, a linear regression model was developed to reconstruct SCE_3–5 via the EWW chronology. In view of the short period of instrumental SCE records used in the reconstruction model, we used the leave-one-out verification method (Michaelsen, 1987) to test the robustness of the calibration model. The evaluative statistics included Pearson’s correlation coefficient (r), the t-value derived using the product mean test (PMT), the raw sign test (ST) and the first-difference sign test (ST1), and the reduction of error (RE) (Cook et al., 1999).

We performed the Mann–Kendall (M-K) abrupt test method (M-K method) on the reconstructed SCE_3–5 time series with the trend package in the R program (Yi et al., 2011). The M-K method can determine the time of our reconstructed SCE_3–5 mutation according to the two output sequences (UF and UB): if the UF value is greater than 0, it indicates that the sequence shows an upward trend; if the UF value is less than 0, it indicates a downward trend; when the upward or downward trend exceeds the confidence level (α = 0.05), it indicates that the upward or downward trend is significant; if the UF and UB sequences intersect and the intersection is fallen in the confidence level, the time corresponding to the intersection is the time when the mutation starts.

Results

The Total Ring Width, Early-Wood Width, and Late-Wood Width Chronologies at Xiao Qamdo Village

The TRW, EWW, and LWW chronologies were obtained from Abies faxoniana trees at XQDV and spanned the intervals of 1668–2018, 1660–2018, and 1680–2018, respectively (Table 1). The three standardized chronologies showed a good coherence of variability over the common period of 1680–2018 (r = 0.95, P < 0.001 for EWW vs. TRW; r = 0.84, P < 0.001 for LWW vs. TRW; r = 0.80, P < 0.001 for EWW vs. LWW), exhibiting similar growth patterns. The three chronologies revealed sustained high growth patterns in the early-1700s, late-1700s, mid-1850s, mid-1950s, and 2010s, and suppressed growth patterns in the early-1800s, late-1800s, and early-1900s (Figure 3). Based on inter-series correlation and between-tree correlations (Table 1), it appears that LWW chronology may be less sensitive and responsive to climatic fluctuations. The peak correlations (r = 0.95, P < 0.001) were found between the EWW and TRW chronologies (Supplementary Figure 2), which is reasonable as the EWW accounts for most of the TRW.

FIGURE 3

Figure 3. Standard tree-ring indices and the sample depth at XQDV for (A) LWW (1680–2018), (B) EWW (1660–2018), and (C) TRW (1668–2018). Chronologies are smoothed by a 20-year running average (red curves). Yellow shading indicates years of suppressed growth (below the mean), and green shading indicates periods of rapid growth above the mean.

Climate Response of Total Ring Width, Early-Wood Width, and Late-Wood Width Chronologies

The EWW chronology was significantly positively correlated with monthly SCE for the months of current March (r = 0.52, P < 0.001) and April (r = 0.62, P < 0.001), and the month combination from current March–May (r = 0.59, P < 0.001) during 1979–2013. Interestingly, a significant positive correlation between the EWW chronology and the month combination of the monthly minimum temperature during the pre-growing season was also detected, although the significant correlation was not as high as that with SCE. The strong significant positive correlation between the TRW chronology and monthly minimum temperature occurs in the single month of current December (r = 0.84, P < 0.001), and the month combination from the previous November to current May (r = 0.44, P < 0.01). In contrast, the climate signal reflected by LWW seems to be much weaker because it only shows a significant positive correlation with the monthly minimum temperature in August (r = 0.33, P < 0.01; Figure 4).

FIGURE 4

Figure 4. Correlation between (A) EWW, (B) LWW, and (C) TRW chronologies and climatic variables (SCE, T_min, and T_max) at XQDV. ** Indicates the significance at the 99% level of confidence and * indicates the significance at the 95% level of confidence.

Snow Cover Extent Reconstructions From Early-Wood Width and Validation

A highlight of our study is the SCE signal recorded in the EWW chronology (Figure 4A). The EWW data was used to reconstruct the SCE_3–5 by identifying the particular months/seasons climate variables. The confidence intervals of correlations are at the 95 and 99% confidence levels. Based on the results of the correlation analysis, a linear regression model between the EWW index and the SCE_3–5 for the period of 1979–2013 was developed as follows:

$SC{E}_{3-5}=-0.862+1.567EWW$

(r = 0.62, R² = 38.4%), where SCE_3–5 represents the snow cover extent data from current March to May.

On the whole, the reconstructed and instrumental SCE_3–5 data were in agreement (Figure 5A). According to the test results of the leave-one-out in Table 2, the r value between the reconstructed and the instrumental series is 0.62. ST1 does not reach 95% significance levels, while ST reaches 95% significance levels, indicating that the reconstructed series is more consistent with the instrumental series in low-frequency variation than in high-frequency variation (Liu and Shao, 2000; Figure 5B). The RE value (0.215) is far greater than 0, indicating that the reconstruction results are stable and reliable (Cook et al., 1999). Therefore, based on this model, variations of SCE_3–5 in the study area were reconstructed for the period of 1660–2013 CE (Figure 5B).

FIGURE 5

Figure 5. The reconstructed SCE_3–5 at XQDV, SETP. (A) A comparison between the raw instrumental data and the raw reconstruction data (1979–2013) and (B) the reconstructed SCE_3–5 since 1660 CE and its 20-year moving average. The horizontal light-blue line indicates the mean value of the reconstruction during the period of 1660–2013, and the red broken lines indicate the mean ± 1σ (standard deviation).

TABLE 2

Table 2. Statistical test parameters of the reconstructed SCE_3–5 on the SETP.

Characteristics of SCE_3–5 Over the Past Four Centuries

For the SCE_3–5 reconstruction in the reliable period (1660–2013; Figure 5B), SCE_3–5 varied between 0 and 1.8%, with a mean of 0.7 ± 0.3%. A year in which the SCE_3–5 exceeds the standard of mean ± 1σ (standard deviation) is defined as an abnormal year (Cai et al., 2020). Accordingly, there are 51 years of abnormal high SCE_3–5 in our reconstruction results, of which 15 years (29.4%) fall in the period of 1998–2013; and 49 years of abnormal low SCE_3–5, of which 9 years (18.4%) fall in the period of 1867–1878 (Figure 5B). The top 10 highest SCE_3–5 years are 2010, 1719, 1715, 2011, 1720, 1721, 1854, 2006, 1853, and 1718. The top 10 lowest SCE_3–5 years are 1897, 1887, 1867, 1937, 1885, 1813, 1670, 1703, 1668, and 1709 (Figure 5B). Moreover, the 20-year FFT indicates that the high SCE_3–5 periods in the past four centuries occur during the period of 1700–1750, 1830–1860, and 1983–2013, and the low SCE_3–5 periods occurs during the period of 1660–1700, 1790–1830, and 1860–1940 at the study area (Figure 5B).

The M-K abrupt test shows that the UF(k) curve values are mainly positive from 1680 to 1880, indicating that the reconstructed SCE_3–5 shows an increasing trend during these 200 years. Especially during the period of 1720–1800, the UF(k) statistic exceeds the critical value of the 0.05 significance level (the upper limit is 4.5), and the uptrend is more significant. The UF(k) curve values are mainly negative from 1881 to 2000, indicating that the reconstructed SCE_3–5 shows a decreasing trend during these 120 years. Especially during the period of 1920–1945, the UF(k) statistic exceeds the critical value of the 0.05 significance level (the lower limit is –3), and the downtrend is more significant. The two curves of UF(k) and UB(k) intersect around 1685 and 1998, respectively, and the intersection is between the critical values. It can be therefore judged that the time points of the two mutations are around 1685 and 1998, respectively (Figure 6B).

FIGURE 6

Figure 6. (A) Interannual variation of the anomalies of the East Asian trough location index (CW), Siberian High index (SH), and the reconstructed SCE_3–5 values for 1961–2010 on the SETP, and (B) the M-K abrupt test of reconstructed SCE_3–5 values during the period of 1660–2013. The green horizontal dotted lines indicate the 0.05 significance level.

Cross-field correlations using the EWW chronology and the reconstructed SCE_3–5 developed for the SETP region with gridded CRU scPDSI 4.05 early data (available at http://climexp.knmi.nl; Schrier et al., 2015) were generated for 1955–2013 to understand linkages with regional droughts. The positive correlations between the EWW chronology and reconstructed SCE_3–5 with the corresponding months’ CRU scPDSI were observed on the SETP (P < 0.01; Figure 7). The correlation fields showed distinct dipole arrangements with positive relationships over 35° to 45°N, 75° to 100°E and negative relationships over 20° to 35°N, 75° to 85°E (Figure 7).

FIGURE 7

Figure 7. (A) Spatial correlation between the reconstructed SCE_3–5 and the corresponding months’ gridded PDSI data for 1955–2013. (B) Same as (A) but between EWW and March-April-May gridded PDSI data for 1955–2013. The images were generated using the climate explorer program.

Linkage of East Asian Winter Monsoon With SCE_3–5 on the Southeastern Tibet Plateau

An inverse correlation between CW and SH was identified (r = –0.51, P > 0.01) (Figure 6A). It can be seen from Figure 6A that both the winter season monsoon index and the reconstructed SCE_3–5 values on the SETP had a distinct change around 1988. SH showed a weakening trend for the period of 1961–1987 and then gradually increased in the following 22 years (1989–2010) (Figure 6A). The East Asian trough moved eastward from 1961 to 1988 and slowly moved westward from 1989 to 2010 (Figure 6A).

Furthermore, in order to reveal the relationship between our reconstructed SCE_3–5 on the SETP and the East Asian winter monsoon, Figure 8 shows the correlation coefficients between SCE_3–5 and the 700 hPa zonal wind and 500 hPa geopotential height field. The reconstructed SCE_3–5 and 700 hPa zonal winds have significant characteristics of positive and negative alternating changes in the meridional direction (Figure 8A). This zonal distribution is not only affected by the position change of polar front and subtropical jet stream in winter but also related to the intensity of subtropical jet stream. The distribution area where the 500 hPa geopotential height field and the reconstructed SCE_3–5 are significantly correlated (99%) have obvious monsoon characteristics (Figure 8B). The significant correlation region is mainly located in the East Asian trough, which is greatly affected by sea and land. The regions of Eurasia and the American continent that shifted slightly eastward are non-correlated areas, and the area dominated by the ocean is a positively correlated area. However, it is also positively correlated in Asia and Africa, especially in East Asia south of Mongolia. In our study site, the reconstructed SCE_3–5 is significantly negatively correlated with 700 hPa zonal wind and is significantly positively correlated with 500 hPa geopotential height field.

FIGURE 8

Figure 8. Correlation coefficients between SCE_3–5 at the study site and (A) 700 hPa zonal wind and (B) 500 hPa geopotential height field. The shaded area passed the test at a 99% confidence level.

Discussion

Climate-Growth Relationships and SCE_3–5 Reconstruction

The summer monsoon is not strong enough to bring sufficient precipitation into SETP in the early growing season, during which the growth of ring-width is often the most critical (Gou et al., 2013). Abundant snow melt water plays an important role in promoting the growth of tree-ring at this time (Fang et al., 2015). Climatic conditions before the growing season might affect ring-width growth during the growing period (Fritts, 1976; Camarero et al., 2010). Thicker snow cover can delay spring snowmelt, storing additional water for early-wood growth, which leads to a wider ring (Fang et al., 2015; Li et al., 2019). Thicker snow cover can increase soil moisture content, compensating for water loss caused by drought in spring (Fan et al., 2010). Water deficit in the early stages of the growing season suppresses the rapid expansion of tracheids and cell division in the cambium of trees (Fritts, 1976; Akkemik, 2003). In addition, the thicker snow cover plays an insulating role in maintaining the temperature constant. A higher temperature might benefit the radial growth of spruce trees during the growing season for the growing season is advanced, and there may be less winter damage to the shallow roots of the spruce trees (Liang et al., 2006; Song et al., 2007; Zhu et al., 2009; Zhang et al., 2010, 2015; Zhou et al., 2016; Li X. X. et al., 2017).

Similar to the fact that the change of SCE around the Arctic affects the growth of trees by regulating temperature (Vaganov et al., 1999; Fang et al., 2016), our study also found a significant negative correlation between the reconstructed SCE_3–5 and surface temperature (Supplementary Figure 3), and the significant positive correlations between the reconstructed SCE_3–5 and EWW and CRU scPDSI on the SETP (Figure 7), indicating that the decrease of SCE is mainly modulated by temperature, and then restricted tree growth by reducing soil moisture. During spring, our study area is drier than the circumpolar region (Fan et al., 2010; Fang et al., 2010), and this period is often the most critical for the growth of tree-ring width (Gou et al., 2013). Therefore, the abundant snowmelt water occurred in the study area during spring plays a very important role in promoting the growth of tree-rings.

Comparison With Other Tree-Ring-Based Reconstruction

The change of SCE is not only affected by air temperature but also affects the degree of regional drought. Therefore, we compared our reconstructed March–May SCE series on the SETP with two tree-ring-based reconstructions obtained from the surrounding research region, i.e., the April–June scPDSI reconstruction (Li J. B. et al., 2017) and winter temperature reconstruction (Huang et al., 2019), on the SETP, respectively. All the consequences were smoothed using the 11-year moving average (Figure 9). It has been shown that the long-term changes of March–May SCE are positive correlated with April–June scPDSI (r = 0.52, P < 0.001, n = 354) and winter temperature (r = 0.34, P < 0.001, n = 348), respectively. Similar common variations, such as the warm-wet periods (corresponding to the high SCE periods) during 1710–1730, 1840–1860, and 1990–2010, as well as the cold-dry periods (corresponding to the low SCE periods) during 1760–1770, 1800–1830, 1860–1890, 1900–1930, and 1960–1990 were found in three series (Figure 9). The pluvial conditions observed during 1948–1958 and 1986–1996 for cold arid western Himalaya (Ram and Mahendra, 2013) are consistent with the high SCE_3–5 reconstructed in this study.

FIGURE 9

Figure 9. Comparisons of the (A) reconstructed April–June scPDSI on SETP (1660–2013 CE) (Li J. B. et al., 2017), (B) reconstructed SCE_3–5 from this study (1660–2013 CE), and (C) reconstructed winter temperature on the SETP (1660–2010 CE) (Huang et al., 2019). The heavy black curve corresponds to the 11-year average smoothing series. Shade bars indicate the common fluctuation periods.

Linkage of East Asian Winter Monsoon With SCE_3–5 on the Southeastern Tibet Plateau

In this study, we focus on the analysis of filtered low-frequency SCE_3–5 series, and the reconstructed SCE_3–5 values on the SETP were mainly negative anomalies from 1961 to 1987 (the relatively low snowfall), while positive anomalies from 1988 to 2010 (the relatively high snowfall). This indicates that (1) when the East Asian Trough is eastward, the westerly airflow in the high latitudes over East Asia is relatively zonal, corresponding to the ground where the Siberian High is weaker, and the cold air is generally easterly, which is conducive to winter snowfall on the TP. That is, CW (SH) has a positive (negative) correlation with snowfall on the southeast TP; (2) when the East Asian tough is westward, the meridional circulation is dominated in the mid-high latitudes over East Asia, the Siberian High over the ground is relatively strong, and the TP has less snowfall. That is, CW (SH) has a negative (positive) relationship with snowfall on the southeast TP. The transition point of the positive and negative anomalies of the reconstructed SCE_3–5 is close to the sudden change time of the East Asian winter monsoon Index (in the year 1986; Figure 6A). The M-K abrupt test shows that the reconstructed SCE_3–5 series on the SETP has two abrupt changes from low to high around the years of 1685 and 1998 (Figure 6B), which further convinces us that there is a distinct negative correlation between the reconstructed SCE_3–5 on the SETP and the intensity of the East Asian winter monsoon on the interannual scale. Previous studies have shown that the intensity of the Siberian High is stronger during 1960–1970s and weaker during 1980s. The strongest and weakest periods of the Siberian High in recent centuries fell in the 1960s and late 1980s–1990s, respectively (Gong and Wang, 1999). This is the reason why the SCE_3–5 we reconstructed on the SETP and the Siberian High were relatively poor after 1996.

A study by Shen et al. (2011) revealed that the lower SCE occurred in the periods of 1840–1880 and 1920–1960, and the higher SCE occurred in the intervals of 1800–1840, 1880–1920, and after 1960, with snow accumulation data from several ice cores on the TP during the last two centuries. A partial disparity existed between our SCE_3–5 reconstruction and the finding by Shen et al. (2011), which was mainly because our reconstruction spanned from current March–May, while Shen et al. (2011) concentrated on the annual SCE. Interestingly, the high- and low-value periods in our reconstructed SCE_3–5 series are highly consistent with the annual maximum snow depth reconstructed with tree-rings in the north of Tian Shan Mountain, China (Qin et al., 2016). The possible reason is that the SCE on the SETP is controlled by atmospheric circulations (Bamzai, 2003; Shaman and Tziperman, 2005; You et al., 2011; Cohen et al., 2012). Previous studies have also shown an obvious jump in the winter temperature on the TP since 1987 (Wang et al., 2012). There was a simultaneous jump signal between the East Asian winter monsoon circulation around 1986 and the reconstructed SCE_3–5 values on the SETP, which indicated a significant correlation between the East Asian winter monsoon and winter-spring snowfall.

The reasons for the abrupt change of SCE_3–5 on the SETP from low to high in the late 1980s are the weakening of the East Asian winter monsoon, the enhancement of winter westerly on the southern TP and the increase of snowfall caused by an active westerly disturbance on the interdecadal scale (Liu et al., 2003). The mid-high latitude trough and ridge changes in the northern hemisphere have disturbed the subtropical westerly jet and the positive westerly anomaly on the southern side of the European trough strengthens the subtropical westerly jet. When the westerly airflow encounters the large terrain of the TP to the west, part of it flows eastward and northward and part of it flows to the TP, then the strong southwesterly air brings the warm and humid air in the subtropical region to the TP, forming an atmospheric circulation background that is conducive to the increase of SCE on the TP. Such warm and humid airflow will reduce the formed SCE already, which is also the reason why the 700 hPa zonal wind is negatively correlated with the SCE on the SETP. There is a positive correlation between the SCE_3–5 reconstructed by EWW chronology and the geopotential height at 500 hPa, indicating that when the SCE is large, it corresponds to the high pressure near the ground (Fang et al., 2016). The atmospheric circulation system and the complex local topography on the SETP together lead to produce such variation in our reconstructed SCE_3–5 history on the SETP (Hu and Liang, 2013).

Conclusion

We developed TRW (1668–2018), EWW (1660–2018), and LWW (1680–2018) Abies faxoniana chronologies, respectively, at Xiao Qamdo Village, Markam County, on the SETP and found peak correlation (r = 0.586, P < 0.01) between EWW chronology and SCE from March to May. Hence, we reconstructed the SCE_3–5 on the SETP for the past 354 years, and identified a simultaneous jump signal between the East Asian winter monsoon circulation around 1986 and our EWW-based SCE_3–5 reconstruction on the SETP, which indicates the combined effects of the weakening of East Asian winter monsoon, the enhancement of winter westerly on the SETP and the disturbance of westerly activity.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Author Contributions

FZ, ZZ, and MB did field sampling of the manuscript. XB did data analysis and writing. SJ and CW made preliminary revisions to the manuscript. QY and KF made the final revision of the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was funded by the National Natural Science Foundation of China (41822101, 41971022, and 41772180), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB26020000), Swedish FORMAS Project (Future Research Leaders, 2017-01408), State Administration of Foreign Experts Affairs of China (GS20190157002), and fellowship for Youth Talent Support Program of Fujian Province.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2022.900219/full#supplementary-material

Supplementary Figure 1 | Schematic diagram of early-wood and late-wood for the Abies faxoniana tree-rings.

Supplementary Figure 2 | Correlations between the annual TRW, EWW, and LWW chronologies for the common period of 1680–2018. Correlations are significant at the P < 0.00001 level.

Supplementary Figure 3 | Spatial correlation between the reconstructed SCE_3–5 and the corresponding months’ gridded surface temperature data for 1955–2013.

Footnotes

1. ^ https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00756
2. ^ http://climexp.knmi.nl

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