Many evidences indicate that the drought in Northern China has progressively intensified, threatening people’s lives and properties. Drought has become one of the most severe environmental problems in Northern China, and thus has serious impacts on the regional social and economic development. The present study investigated the seasonal Palmer drought severity index (PDSI) over the central-western Da Hinggan Mountains (CW-DHM), northeastern China. 283 cores of Mongolian pine trees from 6 locations were used to generate a regional ring-width chronology. The chronology in CW-DHM was significantly correlated with the May–July PDSI, with an explained variance of 49% (r = 0.700, 1951–2013, p < 0.0001). The regional May–July PDSI (PDSI5−7) from the CW-DHM was reconstructed from 1825 to 2013 AD. The ensemble empirical mode decomposition method (EEMD) and multi-taper method (MTM) spectral analyses revealed that the cycles in the reconstructed PDSI5−7 were close to those of the ENSO and solar activity. This suggests that both the ENSO and solar activity have strong influence on the PDSI5−7 variation in the CW-DHM region. In addition, EEMD also revealed that the Pacific decadal oscillation and the Atlantic multi-decadal oscillation influenced the drought variation in this region. The PDSI5−7 reconstruction showed a long-term declining (dry) trend during the period of the 1950s–2010s. This drying trend was also detected in the PDSI data of other parts of China after the 1950s. We believe that these phenomena may be related to a large extent with the weakening of the East Asian summer monsoon.
Drought is one of the most destructive natural disasters, and it has an extremely serious impact on regional agriculture, water resources, and environmental quality. Regional drought, as one of the characteristics of global climate change, has become a significant content of global climate change research (Fu 2008). The Palmer drought severity index (PDSI) is an indicator of the extent of drought (Palmer 1965; Dai 2011). The acquisition of the PDSI is of great importance in understanding the degree of climate change in the past. However, it is difficult to study PDSI variation in periods before the instrumental era because the period of contemporary meteorological observations is far too short. For example, more than 90% of the meteorological observations in China were recorded only since the 1950s (Zhang 1996).
China is a monsoonal country, and the climate in China displays complex variation. The PDSI variation in eastern China is always correlated with the strength of the East Asian summer monsoon (EASM) (Ding et al. 2008; Li et al. 2011). Drought and flood (or the dry and wet events) caused by a strong or weak EASM often bring huge damage to people’s lives and properties. Many studies have indicated that the drought in Northern China has progressively intensified (Fu 2008; Fu et al. 2008). Drought has become one of the most severe environmental problems in northern China and thus has serious influences on the region’s social and economic development (Ma and Fu 2005).
Using tree rings to investigate past PDSI variation is an important aspect of tree ring research. Analysis of tree ring data, with the advantages of high resolution, wide distribution, and easy sampling, has become an important approach to study past PDSI changes (Cook et al. 2010). Variation in the PDSI has been reconstructed based on the integrated impact of temperature and precipitation on tree growth within China (Song and Liu 2011; Cai and Liu 2013; Sun et al. 2012). However, these studies have mainly focused on one particular site, and thus regionally integrated research is scarce.
Although Chinese dendrochronologists have obtained a series of results from northeastern China (Bao et al. 2015; Liu et al. 2009, 2011, 2016; Sun et al. 2016; Peng et al. 2013; Zhu et al. 2009; Shi et al. 2015; Chen et al. 2011), and the levels of precipitation, temperature, relative humidity, and SPEI (Standardized precipitation evapotranspiration index) have been reconstructed for the past 200 years through these studies, there has been little PDSI research. In particular, our knowledge of PDSI variation in this region remains inadequate. In this paper, we present a regional PDSI reconstruction for the past 189 years using tree-ring width data from six locations in the central-western Da Hinggan Mountains (CW-DHM).
Materials and methods
Tree ring data and the CW-DHM regional tree-ring width chronology development
A total of 283 cores of Mongolian pine (Pinus sylvestris var. mongolica) trees from 6 sites (Fig. 1) were used to generate a regional ring-width chronology. Among these 6 sites, 5 were previously studied; and the new site was Hongweiqiao (HWQ, 48°16.53′N, 120°02.90′E, altitude 300–390 m), located on the western slope of the National Honghuaerji Forest Park, Inner Mongolia in the CW-DHM. In the HWQ site, 51 cores were collected from 28 trees in August 2009.
The samples from HWQ were dried and mounted following standard dendrochronological methods (Stokes and Smiley 1996; Phipps 1985). Samples were polished until the annual rings are clearly visible. The other five sites were Hulunbeier (HLBE, Liu et al. 2009), Nuogannuoer (NGNE, Bao et al. 2015), Shenshuiquan (SSQ, Liu et al. 2016), Baritu 01 (BRT01, Sun et al. 2016), and Baritu 02 (BRT02, Sun et al. 2016). The descriptions of all these six sites are given in Table 1.
Since the 6 sites are closed and same tree species, we put all 283 original ring-width series were together to generate a regional chronology for CW-DHM using the ARSTAN program (Cook 1985). During the process, the relatively conservative negative exponential or linear functions were selected to eliminate climate-independent growth trends in each series. All of the series were then combined into a master chronology by computing a robust bi-weight mean. Then, the standard (STD), residual (RES) and autoregressive (ARS) chronologies were obtained. The STD chronology was selected in the following analysis (Fig. 2) since it contains both low- and high-frequency signals. The tree-ring chronology of CW-DHM spans the years 1748–2013 for a total of 266 years. The rate of missing rings was 0.08%; the mean correlation among all of the series (r1) was 0.54, and the average mean sensitivity was 0.25. These values suggest that the growth of trees and their controlling environmental factors in the CW-DHM area have been very consistent. An EPS (expressed population signal) value greater than 0.85 is generally considered to be an acceptable threshold for a reliable chronology (Cook and Kairiukstis 1990; Wigley et al. 1984). As a result, the valid period of our chronology was 1825–2013. The signal strength of the STD chronology was evaluated over time using moving correlations (Rbar) and EPS (D’Arrigo et al. 2005). The statistical characteristics of the STD chronology are shown in Table 2.
To evaluate the climatic response of the regional tree-ring width chronology, two climatic parameters, precipitation (P) and mean temperature (T), were obtained from the Climatic Research Unit time-series (CRU TS) 3.24 (University of East Anglia Climatic Research Unit 2008) in 0.5° × 0.5° gridded data sets (47.5°N–50°N, 117.5°E–122.5°E, 1951–2013 AD). These two parameters were used to conduct further research. The distributions of the monthly temperature and precipitation are shown in Fig. 3.
The index is a comprehensive reflection of the changes in precipitation and temperature. In this paper, the PDSI data (Dai 2011, https://climexp.knmi.nl/) covering 47.5°N–50°N, 117.5°E– 122.5°E (1951–2013 AD) was used to test the ring width-drought response.
Data used in comparisons
To investigate the regional-scale climate signal variability, spatial correlation analyses were performed using the KNMI Climate Explorer (http://www.knmi.nl) (Mitchell and Jones 2005). To verify the reliability and spatial representativeness of our reconstructed PDSI, we used the following data for comparison tests.
Tree-ring width-based Selenge River, Mongolia (MSR) streamflow reconstruction (Davi et al. 2006);
Tree-ring width and historical documents based the North-Central China (NC) summer precipitation reconstruction (Yi et al. 2012);
Tree-ring width-based PDSI reconstruction for Mt. Kongtong (KT) on the Loess Plateau, China (Song and Liu 2011).
To investigate the climate responses of the CW-DHM tree-ring width STD chronology, Pearson’s correlation (r) and partial correlations were calculated. The split calibration–verification method was used to test the stability and reliability of the transfer functions (Cook et al. 1999; Fritts 1991). These procedures were performed by calibrating climate data from one subperiod (1951–1980 and 1981–2013) and verifying the reconstruction using the remaining data (1984–2013 and 1951–1983). The verification of this result was evaluated by using the correlation coefficients (r), sign tests (ST), reduction of error tests (RE), the coefficients of efficiency (CE), and the product means (t). Values of RE and CE greater than zero indicate rigorous model efficiency (Cook et al. 1999).
The ensemble empirical mode decomposition method (EEMD, Wu et al. 2009) was employed to decompose our reconstructed data set. The first step in this method provides a relatively consistent reference size distribution by adding white noise to the target data. In the second step, data with white noise are decomposed into IMFs (intrinsic mode functions). After several iterations of steps one and two, the cumulative effect of the added white noise is reduced to a negligible level, and ensemble means of corresponding IMFs of decompositions with the same time scales are obtained (Sang et al. 2012; Shi et al. 2011).
CW-DHM regional tree-ring width climate response
As the goal of this study was to achieve a multi-site based regional scale PDSI reconstruction, before pooling all sites together for reconstructing, it was necessary to calculate the correlations of all six chronologies and local PDSI data, since the other five studies dealt with precipitation, SPEI, and humidity. The chronologies from six sites were significantly correlated with local PDSI (Table 3).
The correlation between the STD chronology from CW-DHM and the climate records (temperature and precipitation) of the CRU TS3.24 did not meet the minimum requirement of the explained variance in dendroclimatic reconstructions. For example, the highest positive and negative correlations were 0.608 for total precipitation from the prior August to the current July and − 0.439 for the mean temperature from the prior December to the current July, respectively. Therefore, we calculated the correlation between the STD chronology and the regional PDSI. All the chronology has significant correlation with monthly PDSI. However, after the month’s combination, the greatest correlation was with May–July mean PDSI, which was determined to be the main factor limiting tree growth in the CW-DHM region. It also has clear physiological significance of tree growth. The correlation coefficient between the STD chronology and May–July PDSI (PDSI5−7) reached 0.70 at the 99.9% significance confidence level (n = 63) (Table 4; Fig. 4).
It is worth noting that in order to make a comparison between the results from diverse detrending methods, the signal-free regional curve standardization (SF-RCS, Esper et al. 2003) was also used. However, as seen in the results, although the chronology acquired by the SF-RCS method showed significant correlation (0.627, p < 0.0001, n = 63) for the May–July PDSI, this was lower than the correlation coefficient value of 0.70 obtained by the negative exponential method. This demonstrates that the SF-RCS method was not suitable for our study. The analysis will thus focus on the CW-DHM STD chronology gained from the negative exponential method.
The CW-DHM STD chronology reflects the regional PDSI5−7 variations, but we wish to know the specific roles of temperature and precipitation in the changes in regional PDSI.
The results of the partial correlation analysis (Table 4) showed that the correlation between PDSI5−7 and the May to July mean temperature was − 0.15 (n = 63, p < 0.251) when the precipitation was fixed, and the correlation between PDSI5−7 and the May to July mean precipitation was 0.65 (n = 63, p < 0.0001) (Table 4) when the mean temperature was fixed. This was consistent with the results illustrated in Fig. 4, i.e., that tree rings in this area are controlled by the growth season water availability.
CW-DHM regional PDSI5−7 reconstruction during 1825–2013 AD
A transfer function was designed to reconstruct the May–July PDSI variation during 1825 to 2013 AD (189 years) in the CW-DHM region:
(n = 63, 1951–2013 AD, r = 0.700, p < 0.0001, R2 = 49%, R2adj = 48.1%, sd = 1.30, D/W = 1.46); where STDt is the CW-DHM regional tree-ring standard chronology at year t. The correlation coefficient between the reconstructed PDSI5−7 and the observed PDSI was 0.70; the percentage of explained variance was 49%. After an adjustment for the loss of degrees of freedom, the explained variance was 48.1%. The standard deviation (SD) was 1.30. The Durbin–Watson statistic (D/W, Durbin and Watson 1950) was 1.46. With a sample size n of 63, a D/W value between 1.45 and 2.55 indicates no autocorrelation in the series.
The original observation series and the reconstructed series matched quite well, as did their first-order difference (Fig. 5a, b). The correlation between the two series after the first-order differencing was 0.68 (n = 62, p < 0.0001).
During the calibration–verification period, the results of the split-sample method (Table 5) indicated that the correlation coefficients (r) and the product means (t) as well as the sign tests (ST) in all of the periods were significant at the 99% confidence level. The reduction of the error (RE) and the coefficient of efficiency (CE) values in all verification periods were positive, indicating that the transfer functions (1) had reasonable predictive power (Cook et al. 1999). All of these statistical parameters indicated that the regression model was stable and reliable and that it could be used to reconstruct the CW-DHM PDSI5−7 variations.
Wet and drought events recorded in the CW-DHM regional PDSI5−7 reconstruction
We reconstructed the PDSI5−7 history for the CW-DHM region during 1825–2013 AD (Fig. 5c) using Eq. (1). The mean value of PDSI5−7 during 1825–2013 AD was − 0.21, and the standard deviation (σ) was 1.20. We defined an extreme wet year as one where the reconstructed PDSI5−7 value was greater than the mean + 1σ (0.99), while an extreme drought year was similarly defined as a value lower than the mean − 1σ (–1.41). Extremely wet and dry years accounted for 14.3% (27 years) and 15.3% (29 years), respectively. The top 10 drought years and wet years are listed in Table 6.
Spatial representativeness of the reconstructed PDSI5−7 over the CW-DHM region and tele-connections with other regions
To examine the spatial representativeness of our PDSI5−7 reconstruction, a spatial correlation analysis was conducted using KNMI climate explorer (https://climexp.knmi.nl/). Spatial correlation results showed that the observed and reconstructed series of PDSI5−7 in the CW-DHM had similar spatial distribution patterns (Fig. 6), indicating that our reconstruction was highly reliable.
Aside from the above spatial analysis, further comparisons were made between our PDSI5−7 reconstruction and Mongolian Selenge River streamflow reconstruction (Davi et al. 2006, Fig. 7a, a1), the North-Central China summer (June to August) precipitation reconstructions (Yi et al. 2012, Fig. 7b, b1), and PDSI reconstruction in the Loess Plateau, China (Song and Liu 2011, Fig. 7c, c1).
Periodicity analysis of the reconstructed PDSI5−7 over the CW-DHM region
The MTM showed that the PDSI5−7 reconstruction in the CW-DHM region had 18.62- and 3.44-year quasi-cycles over the past 189 years at the 99% confidence level (Fig. 8). Note that there also existed 20.88-, 6.87-, 4.59-, 3.58-, 3.51-, 3.30-, and 2.30-year cycles at the 95% confidence level (Fig. 8).
Ensemble empirical mode decomposition maximum entropy spectral analysis of the reconstructed PDSI5−7 over the CW-DHM region
The ensemble empirical mode decomposition maximum entropy spectral analysis (EEMD-MESA, Wu et al. 2009) method was used to derive five main intrinsic functions (Fig. 9, IMF1–IMF5) and two trends (IMF6 and IMF7). The variance contribution rates were 21.7%, 21.8%, 22.1%, 16%, 12.3%, 4.7%, and 1.3%, respectively. From IMF1 to IMF5, the main cycles were 3.42-, 8.98-, 18.97-, 35.33-, and 64.1-years at the 95% confidence level, respectively. Since the length of the series is only 189 years, there was no period for IMF6 and IMF7.
The 189-year PDSI5−7 variation characteristics over the CW-DHM region, and their spatial and temporal links to central-eastern China
The area east of the Da Hinggan Mountains is a temperate monsoon region that is impacted by the EASM (Fu et al. 1998; Zhao et al. 2002). The EASM brings abundant water vapor, with a mean annual precipitation of more than 400 mm. Since the Da Hinggan Mountains form a rain shadow, blocking the EASM, the area west of the mountains is characterized by a temperate continental climate with annual mean precipitation below 400 mm, and precipitation mainly occurs from June to August (Wang 1997).
The intensity of the EASM has a strong influence on the dry-wet/drought-flood cycles in the Da Hinggan Mountains region.
Our reconstruction represented the CW-DHM region, part of northeastern China (Fig. 6), and it captured many drought and wet years. The partial extreme drought years, such as 1851, 1892–1893, 1906, 1951, and 1986–1987 AD, and some wet events (Table 6) are consistent with previous studies. Overall, the frequency of wet years (14.3%) was slightly less than that of drought years (15.3%) in the CW-DHM region during the past 189 years. However, the Ding-Wu Disaster (1876–1878), the most serious drought event of the nineteenth century in central-northern China (Zhang and Liang 2010; Kang et al. 2013; Hao et al. 2010), did not appear in our reconstruction. This illustrates that during this remarkable drought event, there was no synchronization between the humid CW-DHM region and dry region of central-northern China.
The climate difference between the Northeast and central-northern China is also reflected in the factors that affected the variation in the PDSI. A very interesting observation is that the partial correlation analysis (Table 4) revealed that the PDSI5−7 in the CW-DHM region was mainly controlled by large-scale precipitation variation, not temperature. This was quite different to the results for the Loess Plateau in central-northern China, where the PDSI is mainly controlled by temperature instead of precipitation (Song and Liu 2011). This is because there are different precipitation and temperature in the CW-DHM region and the Loess Plateau.
After 21-year smoothing filtering, the persistent intervals of wet periods were the 1840s–1850 s, the mid-1860s–early 1870s, the 1910s–early 1920s, the late 1930s–late 1960s, and the mid-1970s–early 1980s. The dry periods were the 1830s–1840 s, the late 1870s–1880 s, the 1890s–1900 s, the late 1920s–early 1930s, and the late 1980s–2010 s.
Another notable feature displayed in our PDSI5−7 reconstruction is that the PDSI5−7 had a long-term declining (dry) trend during the period of the 1950s–2010s (Fig. 5c). This drying trend was also detected after the 1950s in the relative humidity series for Yaoshan in central China (Liu et al. 2017a), the relative humidity series for Tianmu Mountain in eastern China (Liu et al. 2018), the Mongolian Selenge River streamflow data (Davi et al. 2006; Fig. 7a, a1), the North-Central China summer (June–August) precipitation (Yi et al. 2012; Fig. 7b, b1), and the PDSI reconstruction for the Loess Plateau, China (Song and Liu 2011; Fig. 7c, c1). As we noted above, the eastern part of China is strongly influenced by the EASM; a stronger monsoon brings larger amounts of water vapor and vice versa. The precipitation-related factors, such as PDSI and relative humidity, have a tight relationship with the EASM. As such, the decreasing trends in all of these curves are related to the amount of monsoon precipitation in China having gradually reduced since the 1950s. At the same time, the temperatures in central-northern China were rising (Yi et al. 2012). That is to say, the slowly decreasing trends of PDSI5−7 and relative humidity since the 1950s may have been triggered by the weakening of the Asian summer monsoon in North China. The simulations from models showed that the forcing of sea surface temperatures, primarily in the central and eastern Pacific, was able to induce most of the observed weakening of the EASM circulation (Li et al. 2010). Increased anthropogenic aerosol emission strongly masks warm-ocean-warmer-land, negating to the rainfall increases due to the greenhouse gases warming, and leading to a further weakening of the EASM circulation, through increasing atmospheric stability (Lau and Kim 2017). There was an apparent rise in the last few years (Fig. 7), which was mainly due to the more precipitation anomaly in 2012 and 2013 (Li et al. 2015; Ma et al. 2013). In these 2 years, high over the Okhotsk sea were strong. Because of the blocking of high over Okhotsk, the cold air accumulated in the northwest region in Inner Mongolia. In addition, the anticyclone from the northwest Pacific continuously transported water vapor to this place. Therefore, the cold and warm air flow met here and led to more rainfall.
Possible mechanisms affecting PDSI5−7 variation in the CW-DHM region
The quasi-cycle of approximately 2 years (Fig. 8) obviously corresponds to that of the Tropospheric Biennial Oscillation (TBO, Meehl and Arblaster 2002) that is common in climate change and has been noticed in many previous tree-ring studies.
The main cycles of 18.62- and 20.88-year in our reconstruction (Fig. 8) apparently are related to solar activity double cycles (~ 22-year, Han and Han 2002). This cycle is also confirmed by an 18.97-year cycle in the IMF3 (Fig. 9c), which had the greatest variance of 22.1% in the set of IMFs in the EEMD decomposition (Fig. 9). Along with the IMF5 (Fig. 9e; see the following discussion), this demonstrated that solar activity had influence on PDSI5−7 variation in the CW-DHM region.
Other relatively notable cycles are from 3.3- to 6.87-year (Fig. 8). These appear to be clear signals of the ENSO (Kaplan et al. 1998; Liu et al. 2017b). The IMF1 (Fig. 9a) and IMF2 (Fig. 9b) also displayed those cycles. The contributions of these two modes account for 43.5% (21.7% + 21.8%) of the total variance. This suggests that ENSO, as suggested in a previous study (Davi et al. 2006), may play an obvious role in the PDSI5−7 change in the CW-DHM. When El Niño occurs, the sea-surface temperature in the equatorial east Pacific is higher, resulting in weakening of the Walker circulation and Hadley circulation in the west Pacific. Because of the weakening of the Hadley circulation, the intensity and location of the western pacific subtropical high are stronger and more southerly, respectively, and the convergence flow on its northern side becomes notably strong, therefore, there is less rainfall in northern China (Su and Wang 2007). So, the CW-DHW turns to be drought.
The 35.33-year cycle in the IMF4 (Fig. 9d) accounts for 16% of the total variance; the cycle may be related to the 35-year quasi-cycle of the PDO (Mohtadi et al. 2016). Previous studies have shown that the impacts of the PDO on the study areas were significant (Bao et al. 2015; Davi et al. 2006). Dry periods in the CW-DHM corresponded to warm phases of the PDO, and vice versa. The PDO influences on precipitation change in northern China by modulating the strength of the Asian summer monsoon and the location of the subtropical high (Shen et al. 2006). A study using observed data also revealed that there was a significant negative correlation between the PDSI in northern China and the PDO index during the period 1900–2010 (Qian and Zhou 2014).
In addition, the 35.33-year cycle is very consistent with the famous Brukner quasi-cycle in climatic processes (Zhang 1976), which can be interpreted as a result of the nonlinear effect of solar activity and variability on atmospheric processes (Raspopov et al. 2000). This cycle has been detected in many studies, including the precipitation from May to August over China’s lower reaches of the Yangtze River during 1885–1972 AD (Zhang 1976), the precipitation from May to August in Inner Mongolia of Northern China (Liu et al. 2001), and the precipitation from July to August in the Beijing and Tianjin regions during 1891–1972 AD (Zhang 1976).
The 64.1-year cycle of IMF5 (Fig. 9e) accounts for 12.3% of the total variance and may be coherent with solar activity and the Atlantic multi-decadal oscillation (AMO, Mohtadi et al. 2016). The solar radiation and AMO may influence climate variation with a cycle of around 60 years (Kerr 2000; Sutton and Hodson 2005; Mazzarella 2007). This cycle was also found in tree-ring temperature series from the Tibetan Plateau (Shao and Fan 1999; Liang et al. 2008).
Recently, the relationship between the AMO and the Asian monsoon climate has been receiving greater attention. The AMO can be regarded as a pacemaker of the Northern Hemisphere surface temperature (Zhang and Delworth 2007) and global air temperature (Kravtsov and Spannagle 2008). It has been observed that the warm-phase AMO tends to enhance air temperatures in East Asia and northern India, resulting in more rainfall, particularly in the summer (Wang et al. 2009). Usually, AMO variation can cause the change of the North Atlantic circulation system which likely affects the west wind belt trough system. Therefore, the climate in northeastern China is affected by AMO variations due to its downstream location (Qian et al. 2014). By combining the observational data and numerical simulation models, it has been shown that more precipitation is induced in northern China during the warm phase of the AMO (Li and Bates 2007).
The above discussion implies that IMF4 and IMF5 together indicated that the both PDO and AMO have influences on drought variation in this region.
IMF6 and IMF7 (Fig. 9f, g) indicate the megatrends variation of the PDSI5−7, and there is no apparent periodicity. This is reflected in the long-term tendency of the PDSI5−7 over the CW-DHM. However, these two trends have variance contributions to the total variation of the PDSI5−7, although less than the other functions.
In this study, the seasonal Palmer drought severity index (PDSI) was reconstructed using Pinus sylvestris tree-ring width data from the past 189 years at six locations in the central-western Da Hinggan Mountains (CW-DHM), northeastern China. The reconstruction captured many severe drought and wet events. Both the ensemble empirical mode decomposition method (EEMD) and MTM cycle analysis indicated that ENSO and solar activity have influences on PDSI5−7 variations in the CW-DHM region. In addition, the results of EEMD also revealed that the PDO and AMO influence drought variations in the study area. Overall, the factors that influence the PDSI5−7 variations on the CW-DHM region appear to be very complex.
The PDSI5−7 had a long-term declining (dry) trend during the period of the 1950s–2010s. To a large extent, this drying trend was caused by the weakening of the Asian summer monsoon, which may be potentially linked to the global warming caused by human activity during this period of time.
The phenomenon of the recent weakening of the East Asia Summer Monsoon, which causes decreased precipitation and enhanced drought tendency in northern China, is an important issue. Limitations of space prevent a more complete discussion of this topic. However, this is an issue of some significance because the drought in Northern China is threatening people’s lives and properties. We believe that this issue deserves further study and discussion.
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The authors thank Drs. Guang Bao, Bo Sun, Na Liu, Ying Lei, Yanchao Wang and Mr. Baoyin Shen for their great help. This study was jointly supported by grants from the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDJ-SSW-DQC021, NSFC 41630531, XDPB05, GJHZ1777) and the Key Project of Institute of Earth Environment, CAS (01) and the State Key Laboratory of Loess and Quaternary Geology (020).
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Liu, R., Liu, Y., Li, Q. et al. Seasonal Palmer drought severity index reconstruction using tree-ring widths from multiple sites over the central-western Da Hinggan Mountains, China since 1825 AD. Clim Dyn 53, 3661–3674 (2019). https://doi.org/10.1007/s00382-019-04733-0
- Central-western Da Hinggan Mountains (CW-DHM)
- Northeastern China
- Tree-ring width
- PDSI reconstruction
- East Asian summer monsoon (EASM)