We present a 260-year annual Palmer drought
severity index (PDSI) reconstruction based on a tree-ring width
chronology of Scots pine (
Drought as one of the major natural disasters is being more frequently influenced by climate change in the world (Cook et al., 2010; Dai, 2011, 2013; Davi et al., 2006; Li et al., 2016). Severe droughts can threaten agriculture and human social activities, and also have a devastating impact on human lives and the survival of native and domestic plants and animals (Cook et al., 2010; Dong et al., 2013; Shen, 2008; Sun, 2007). Drought is one of the most severe and frequent natural disasters in China, especially in semi-arid and arid regions (Bao et al., 2015; Chen et al., 2015; Cook et al., 2010; Dong et al., 2013; Liang et al., 2006; Shen, 2008; Sun and Liu, 2013; Xu, 1998). For example, the drought in the 1920s affected almost all of northern China, accompanied by severe economic and social losses (Dong et al., 2013; Liang et al., 2006; Shen, 2008; Sun, 2007). Recent studies indicate a trend of increasing drought frequency, persistence and severity due to global warming in many regions of the world (Bao et al., 2015; Cook et al., 2010; Dai, 2011, 2013; Schrier et al., 2013). A rapid and pronounced warming accompanied by a decrease in precipitation has occurred in China, especially in high-latitude and high-altitude regions (Bao et al., 2015; Chen et al., 2015; Cook et al., 2010; Dai, 2013; Sun and Liu, 2013; Zhu et al., 2017), leading to severe and prolonged drought in recent decades, such as from 1999 to 2002 (Bao et al., 2015; Liu et al., 2009; Shen, 2008).
Sampling sites and weather station distribution map. The red circles, star and black square are the sampled sites, Palmer drought severity index (PDSI) grid point and the weather station, respectively. The red box represents the northern Daxing'an Mountains (this study). The blue and green boxes represent the east and west-central Mongolian Plateau, respectively.
The Daxing'an Mountains in northeast China are a transition area from
semi-humid climate in the east to more arid conditions in the west (Bao et
al., 2015; Zhao et al., 2002). The Asian monsoon system directly affects the
occurrence, intensity and severity of droughts and floods (Bao et al., 2015;
Cook et al., 2010; Liang et al., 2006; Wang et al., 2013, 2005; Zhao et al.,
2002) that have devastating effects on human society and economy as well as
natural ecosystems (Sun, 2007; Xu, 1998). For example, the drought in 2009
affected 81 million people in northeast China and more than
720 000 ha of farmland suffered from water
shortages
(
To better characterize current drought conditions and project those of the future, an improved understanding of past drought variability and potential forcing mechanisms is necessary. However, the shorter meteorological records in the Daxing'an Mountains that only started in the 1950s limited our understanding of the long-term regime of past droughts. Tree rings can serve as an important high-resolution proxy for long-term drought reconstructions (Cook et al., 2010; Dai, 2011; Pederson et al., 2013), and several hydroclimate reconstructions (Bao et al., 2015; Lv and Wang, 2014; Wang and Lv, 2012) have been conducted in northern China. Cook et al. (2010) also reconstructed the June–July–August Dai Palmer drought severity index (PDSI) in 534 grid points (Monsoon Asia Drought Atlas, MADA) in monsoon-affected Asia using 327 tree-ring width chronologies. However, some disagreement occurs between the MADA and the tree-ring-based local drought reconstructions and instrumental drought data, especially in eastern Asia, which might be an insufficient tree-ring datum in eastern Asia used in MADA (Li et al., 2015; Liu et al., 2017). Additional drought reconstructions in eastern Asia are needed to gain a more thorough understanding of the variability in the Asian monsoon. Many researchers use the PDSI, calculated from a water balance equation, incorporating air temperature and precipitation, to estimate drought periodicity and intensity (Bao et al., 2015; Cook et al., 2010; Dai, 2011; Sun and Liu, 2013). Here, we present a 260-year reconstruction of annual PDSI using tree-ring chronologies from the Daxing'an Mountains to identify the timing of droughts and their correlation with eastern Mongolian Plateau climate as well as their potential forcing mechanisms.
Site description and statistical characteristic of the
The Daxing'an Mountains, in northeast Inner Mongolia and the northwest Heilongjiang Province, form an important natural geographic divide between the Pacific Ocean and the northwestern semi-arid inland area (Fig. 1). It is known to be a transition zone from the semi-humid to semi-arid region or from monsoon to non-monsoon climates (Zhao et al., 2002). The summer monsoons from the southeast are blocked by the mountains and cannot penetrate further to the northwest. The western region is more arid, and the eastern region is wetter. Summer weather is clarified by periodic incursions of warm, humid air masses from low-latitude oceans, while dry and cold air in winter air masses invade from high latitudes.
Monthly total precipitation (
This study was conducted in high-latitude forests in the Daxing'an Mountains,
northeast China. The forests are dominated by Dahurian larch (
Tree-ring cores were sampled from four Scots pine-dominated sites that are rarely disturbed in the central Daxing'an Mountains in May 2011 and 2012. Each sampled tree was selected to avoid the influence of identifiable stand disturbances (including animal and human disturbance, windstorm, snow and fire damage) and any obvious abnormal growth. The distance between sample sites is more than 100 km (Fig. 1). A total of 120 cores were obtained from living old trees at breast height (approximately 1.3 m) (Table 1) using a 5.15 mm diameter increment borer (500 mm length, two screws, Haglöf Sweden, Längsele, Sweden). All cores were dried, mounted, surfaced and cross-dated following standard techniques of dendrochronology (Cook and Kairiukstis, 1990; Fritts, 1976). Ring widths were measured with a precision of 0.001 mm using a Velmex measuring system (Velmex, Inc., Bloomfield, NY, USA).
The quality of cross-dating and measurement was evaluated using the COFECHA program (Holmes, 1983). Two cores with weak correlation to the master chronology were excluded from further analysis. Successively, the age-related trends were removed by fitting a cubic smoothing spline with a 50 % frequency response cut-off at two-thirds of the series length using the ARSTAN program (Cook and Kairiukstis, 1990). The tree-ring index was calculated as the ratio of the observed value to the estimated growth curves. Autocorrelation was removed by autoregressive modeling, and the site chronology was calculated using a bi-weighted robust mean (Cook and Kairiukstis, 1990).
Four chronologies have high values of standard deviation, mean sensitivity,
mean series correlation and agreement within population. They reflect high
interannual variation and a strong common signal and are excellent proxies
for regional climate. Since the four chronologies fit well (Table 2), we
merged all samples to develop a single robust regional chronology (Fig. S1 in
the Supplement). Running RBAR (mean correlation between series) and EPS
(expressed population signal) statistics were calculated using a 51-year
interval of the chronology with a 25-year overlap to assess confidence in the
chronology. RBAR averages variance among ring width series in a chronology,
which estimates chronology signal strength (Cook and Kairiukstis, 1990). EPS
estimates the degree to which the chronology represents a hypothetical
chronology based on a finite number of trees that match a hypothetically
perfect chronology; EPS values greater than 0.85 are generally considered
an acceptable threshold for a reliable chronology (Wigley et al., 1984). The
regional chronology spanned the period from 1725 to 2010, and the reliable
interval (
Five-chronology correlation matrix over the common period (1793–2010).
Climate data were obtained from the National Meteorological Information
Center (
Pearson correlation coefficients between tree-ring index of
A linear regression model was used to reconstruct the drought variation, and
a traditional split-period calibration and verification method was applied to
examine the model fitness (Fritts, 1976). Statistical parameters included the
We also carried out the superposed epoch analysis (SEA) between the nearby
forest fire events and the drought series to further validate the accuracy of
our reconstruction because seasonal or annual droughts are usually a key
factor of forest fire severity in the Daxing'an Mountains (Shen, 2008; Sun,
2007). Two regional forest fire chronologies (Mengkeshan and Pangu)
reconstructed by tree-ring scars in nearby forests were used (Yao et al.,
2017). The SEA was carried out using the software package FHAES V2.0.0
(
To identify spatiotemporal patterns of drought variation in northeast Asia
and their relationship with our reconstructed drought series, we analyzed the
correlations between our series and other four hydroclimatic reconstruction
series in the Daxing'an Mountains and the Mongolian Plateau (Fig. 1). To
better visualize the comparison, all series described above were standardized
using
To evaluate the extreme dry and wet years in the historical period, we
defined extreme dry and wet years with the annual PDSI value being lower or
higher than average
Calibration and verification statistics of the PDSI reconstruction.
The reconstruction PDSI series in the Daxing'an Mountains, northeast
China.
A spectral analysis was applied to identify the periodicity of dry–wet
variation and possible effects of large-scale climate using the multi-taper
method (MTM) (Mann and Lees, 1996). To further confirm the linkage between
large-scale climate and regional drought, we analyzed their relationship with
Pearson correlation analysis. Teleconnections between the reconstructed
drought series and the global sea surface temperature (
The radial growth of Scots pine was significantly (
Reconstructed extreme dry–wet years and annual PDSI of the Daxing'an Mountains.
The linear model for PDSI reconstruction is
The long-term droughts and pluvial in the central Daxing'an Mountains during the last 260 years.
Spatial correlation fields between
The instrumental and reconstructed PDSIs of the central Daxing'an Mountains have similar trends and are parallel to each other during the calibration period (Fig. 4). However, the reconstructed PDSI did not capture the magnitude of extreme dry or wet conditions. Spatial correlation analysis showed that the instrumental and reconstructed PDSIs had a strong and similar spatial correlation pattern with the northeast Asia gridded Dai-PDSI (Fig. 5).
The reconstructed annual PDSI with an 11-year moving average exhibited a mean
of 0.48 and a standard deviation (SD) of
Compared with the severe single-year droughts, multi-year droughts had a greater effect on tree growth, and we defined the dry and wet periods as those when the 11-year moving average PDSI was more than 0.5 SD from the mean for at least 2 consecutive years. Four dry periods, 1751–1752, 1812–1817, 1847–1866 and 1908–1927, and four wet periods 1757–1771, 1881–1902, 1952–1955 and 1989–2004 were identified (Table 5). The dry periods of 1847–1866 and 1906–1927 were the longest, spanning 20 years, while the longest wet period, from 1881–1902, lasted for 22 years (Table 5). The multi-year drought in 1847–1866 was the most serious due to the long duration and intensity, and the period 1906–1927 was the second most significant drought (Table 5). Wet periods in 1757–1771 and 1989–2004 were the most remarkable in terms of their intensity and duration (Table 5).
Spectral analysis revealed that the historical PDSI variation in the Daxing'an Mountains showed several significant (95 % or 99 % confidence level) periodicities at 46.5–78.7 (99 %), 12, 5–6 (99 %) and 2–3 (99 %) years, which corresponded to significant cycle peaks presented in Fig. 6.
Multi-taper method power spectrum of the reconstructed PDSI during the period 1751–2010. The 95 % and 99 % confidence levels relative to red noise are shown and the numbers refer to the significant period in years.
Scots pine is an drought-tolerant species and drought stress is thought to be the main climate factor limiting its radial growth in semi-arid or semi-humid regions, such as in the Mongolian Plateau and western Daxing'an Mountains (Bao et al., 2015; Davi et al., 2006; Liu et al., 2009; Pederson et al., 2013). Previous dendroclimatic studies from these regions suggest that radial growth of Scots pine is sensitive to humidity, precipitation or drought (e.g., PDSI, SPEI), and most analyses have reconstructed hydroclimatic history (Bao et al., 2015; Liu et al., 2009). In these areas, the radial growth of Scots pine usually has a typical climate (drought) response pattern with positive tree growth response to increasing precipitation and a negative response to increasing temperature (Bao et al., 2015; Davi et al., 2006; Liu et al., 2009). This typical drought response pattern is usually found in other drought or wetness tree-ring reconstructions (Li et al., 2016; Liu et al., 2017). In this study, the correlation between tree-ring index and monthly precipitation and temperature revealed that the radial growth of Scots pine was mainly limited by water, which is consistent with the physiological characteristics of tree species living in semi-arid regions. A significant positive relationship between the tree-ring index and PDSI in all months supported moisture as the main limiting factor of Scots pine radial growth (Fig. 3b).
Comparisons of
The drought response was also found in Dahurian larch (Wang and Lv, 2012), another important conifer tree species in the study area. However, the typical drought response to temperature was not obvious, and the radial growth of Scots pine was not significantly negatively correlated with the growing season (July–September) temperature (Fig. 3a). On the contrary, a significant positive response of radial growth to the non-growing season temperature was found. It is possible that higher winter temperatures could protect dormant buds from frost damage (Chen et al., 2012). The positive correlation with spring temperature could be due to earlier and larger snow melting, which supplies the spring soil water, and eventually stimulates tree growth (Hollesen et al., 2015; Zhu et al., 2017). This unusual drought response pattern might be due to the relatively humid climate and the northern latitude of our study sites, where the positive effect of temperature was greater than the negative effect resulting from drought stress (Wang and Song, 2011). Similar drought response patterns were also found in tree-ring-based drought reconstructions in the middle Qilian Mountains (Sun and Liu, 2013) and the Tian Shan Mountains of western China (Chen et al., 2015).
We used the local historical document records to verify our PDSI
reconstruction for the timing of extreme dry years or periods. During the
last 260 years, 60.1 % (
Spatial correlation analysis indicated a strong pattern between our
reconstruction and gridded scPDSI in northeast Asia (Fig. 7), and our
reconstruction also represented drought/wet variations in surrounding
geographic regions. During the common periods, our reconstruction shared a
similar dry–wet fluctuation with precipitation of the A'li River (Wang and
Lv, 2012) and SPEI of the Hulun Buir steppe (Bao et al., 2015) both in the low
and high frequency (Fig. 7b–d). Significant (
Comparisons of the drought reconstruction and other large-scale
climate oscillations.
It is important to note that our drought reconstruction and the MADA by Cook
et al. (2010) from the same PDSI grid showed a complete opposite trend
(
Correlation coefficients between the large-scale climate indices (AMO,
PDO, NAO, SNAO, TSI and Niño3) and the local annual mean temperature,
total precipitation and instrumental Dai-PDSI, as well as the
Note: AMO, PDO, NAO, SNAO, TSI and Niño3 indicate the Atlantic
Multidecadal Oscillation reconstruction from Mann et al. (2009), the Pacific
Decadal Oscillation reconstruction from Mann et al. (2009), the multi-decadal
winter North Atlantic Oscillation reconstruction from Trouet et al. (2009),
the summer NAO based on the 20C reanalysis sea-level pressure reconstruction
(SNAO), the total solar irradiance reconstruction from IPCC AR5 and the
Niño3 reconstruction from Mann et al. (2009), respectively. All the above
data were downloaded from
On a larger spatial scale, the streamflow reconstruction of Selenga River in
the west-central Mongolian Plateau from Davi et al. (2006) presented a
significant positive correlation with our drought reconstruction in low
frequency (
Spectral analysis revealed that several significant cycles existed in our drought series (Fig. 6). The significant high-frequency 2.0- to 5.8-year periodicities were within the 2- to 7-year cycles of ENSO (Li et al., 2013), so the drought variations in the Daxing'an Mountains might be related to ENSO. Similarly, the local dry–wet changes related to ENSO have been confirmed by other tree-ring-based hydroclimatic reconstructions in northeast China (Bao et al., 2015; Lv and Wang, 2014; Wang and Lv, 2012), northwest China (Chen et al., 2015; Sun and Liu, 2013) and the Mongolian Plateau (Davi et al., 2006). A strong connection appears between our reconstruction and annual SSTs over the Pacific Ocean, especially near the Equator, the north Pacific, as well as the east and west coasts of the Pacific Ocean (Fig. S3). The significant positive correlation between the Niño3 index and the dry–wet index in both low and high frequencies (Table 6, Fig. 8b) also confirmed the potential links between ENSO and the dry–wet variations in the Daxing'an Mountains. Although the mechanisms need to be further studied, the close relationship between the oscillatory changes of North Atlantic SST and the Asian monsoon have been demonstrated (Zuo et al., 2013). ENSO might indirectly influence dry–wet changes in the Daxing'an Mountains by affecting the local climate (Shuai et al., 2016). Wang et al. (2013) found that the ENSO could potentially drive or affect the Asian monsoon, which in turn affects temperature and precipitation to drive local drought variations, as a possible driving mechanism (Fig. 9). Significant positive correlations between the Niño3 index and local climate (temperature and precipitation) further confirm our inference (Table 6).
Spatial correlations between the annual east Asian monsoon index and
the local
The 12-year cycle indicated that dry–wet changes in the Daxing'an Mountains might be influenced by solar activity (Shindell et al., 1999). Several previous studies have demonstrated that solar activity can influence the local dry–wet variations (Chen et al., 2015; Hodell et al., 2001; Sun and Liu, 2013). In northeastern China, Hong et al. (2001) also found the signals of solar activity in a 6000-year record of drought and precipitation. Significant positive correlations between the total solar irradiance (TSI; reconstruction from IPCC AR5) and the dry–wet index in the Daxing'an Mountains in low and high frequencies, and between the TSI and the local climate (temperature and precipitation), further confirmed a possible relationship between solar activity and local drought (Table 6, Fig. 8b). Wang et al. (2005) found a potential link between the Asian monsoon and solar changes. Dry–wet changes in the Daxing'an Mountains might be driven by the Asian monsoon which is influenced by solar activities (Fig. 9).
Cycles of 46.5–48.8 years might be related to the PDO since it coincided with the 50- to 70-year cycle of PDO (Macdonald and Case, 2005). This was verified by the strong connection between our drought reconstruction and annual SSTs over the Pacific Ocean (Fig. S3). The cycles/signals of PDO widely exist in most tree-ring-based drought reconstructions (Bao et al., 2015; Chen et al., 2015; Sun and Liu, 2013; Wang and Lv, 2012), and many studies have confirmed that PDO can influence drought conditions in China (Bao et al., 2015; Cook et al., 2010; Ma, 2007). The potential linkages between the PDO and local drought in the Daxing'an Mountains is further confirmed by the significant positive correlations between the PDO index (Mann and Lees, 1996) and the dry–wet index in low and high frequencies (Table 6, Fig. 8b). The positive/warm phase of PDO usually corresponds to the dry period, while the negative/cold phase corresponds to the wet period (Ma, 2007). For example, the severe drought in the 1920s–1930s corresponds to the PDO negative phase. Significant positive correlations between the PDO index and local climate (Fig. 9) suggest that the PDO might affect the dry–wet changes in the Daxing'an Mountains by regulating the intensity or location of the Asian monsoon (Bao et al., 2015; Cook et al., 2010; Ma, 2007). Similar results were found in a nearby tree-ring-based drought reconstruction (Bao et al., 2015).
Composite anomaly maps of the 200 hPa vector wind and geopotential
height, and the SSTs (from January to December) for the 10
wettest
The 73-year drought cycle might be derived from oscillatory changes in the North Atlantic SST (Knudsen et al., 2011). Spatial correlations between our drought series and annual SSTs also show a strong teleconnection across the Atlantic Ocean (Fig. S3), which further confirmed potential linkages between the North Atlantic SSTs and dry–wet changes in the Daxing'an Mountains. Although our research area is far from the Atlantic, some studies have confirmed that large-scale climate oscillations in the Atlantic Ocean (such as the Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO), as well as summer NAO (SNAO)) could affect local climate and tree growth in China (Bates, 2007; Linderholm et al., 2011, 2013; Sun et al., 2008; Wang et al., 2011). Most tree-ring drought reconstructions also found the signals of oscillatory changes correlated with the North Atlantic SSTs (e.g., AMO, NAO and SNAO), such as in the Daxing'an Mountains (Lv and Wang, 2014; Wang and Lv, 2012), eastern Mongolian Plateau (Bao et al., 2015; Liu et al., 2009), west-central Mongolia (Davi et al., 2006) and northwest China (Chen et al., 2015; Sun and Liu, 2013). Furthermore, we also identified a significant negative–positive correlation between the dry–wet change in the Daxing'an Mountains and the AMO, NAO and SNAO index both in low and high frequency (Table 6, Fig. 8c). The strong AMO signal (Wang et al., 2011) and teleconnections with SNAO (Linderholm et al., 2013) also have been found in tree-ring widths of Scots pine in northeast China and east-central Siberia during the last 400 years. These studies all confirmed that oscillatory changes in the North Atlantic SST (e.g., AMO, NAO and SNAO) could drive dry–wet changes in the Daxing'an Mountains. Although its mechanism needs to be further studied, the close relationship between the oscillatory changes in the North Atlantic SST and the Asian monsoon has been demonstrated. Recent studies have shown that the AMO (Wang et al., 2013), NAO (Feng and Hu, 2008) and SNAO (Linderholm et al., 2011) all could drive or affect the Asian monsoon. In this study, although only the AMO index was significantly correlated with local climate (Table 6, Fig. 8c), it also confirmed that the oscillatory changes in the North Atlantic SST, especially the AMO, could drive wet–dry changes in the Daxing'an Mountains by influencing the Asian monsoon (Bao et al, 2015; Chen et al., 2015; Cook et al., 2010; Li et al., 2015; Linderholm et al., 2011; Sun et al., 2008).
Previous studies have found that drought variation in northeast Asia may be associated with Asian monsoon activity (Bao et al., 2015; Chen et al., 2015; Cook et al., 2010; Li et al., 2015; Linderholm et al., 2011; Sun et al., 2008). In wet years, the strengthened southerlies and easterlies entered inland China associated with a positive pattern over northeast Asia and some negative height-anomaly centers in west Russia and south Asia as well as the Indian and north Pacific oceans, which strengthened the westerly circulation (Fig. 10a, c). In dry years, however, strengthened southerlies and southwesterlies entered northeast China associated with a positive pattern over east Asia and western Russia, and some negative height-anomaly centers in southern Russia and south Asia as well as the Indian and south Pacific oceans (Fig. 10a, c).
The composite of 200 hPa geopotential height of the most humid 10 years (positive anomaly) in the central-north Daxing'an Mountains is opposite to that of the most arid 10 years (negative anomaly) (Fig. 10c, d). Positive and negative SST anomalies were also found in the western and northern Pacific Ocean during the wettest and driest years (Fig. 10e, f). In the wet years, abundant moisture is transported from the Pacific Ocean through Mongolian Plateau to the Daxing'an Mountains via the strong east Asian monsoon's southeasterly moisture flux joined with a strong westerly circulation (Fig. 10a). This negative anomaly combined with positive SST in the western and northern Pacific Ocean led to an enhanced dry jet (southwesterlies) across/toward the Daxing'an Mountains (Fig. 10b, c, e). Several studies have reported that the dry and wet variations in northeast Asia are strongly linked with the Asian monsoon and SSTs in the Pacific and Atlantic oceans (Bao et al., 2015; Chen et al., 2015; Cook et al., 2010; Li et al., 2015; Linderholm et al., 2011). In addition, the potential evaporation pattern in the Daxing'an Mountains is extremely low in the wettest years, and it also supports the above remote-connection assumptions (Fig. S4).
We developed a 260-year (1751 to 2010) tree-ring chronology of Scots pine
(
The PDSI reconstruction in the central Daxing'an Mountains will be uploaded to NOAA, and all the data published in this study will be available for non-commercial scientific purposes.
The supplement related to this article is available online at:
For this article, XW and SH initiated the study, LZ and QY performed field sampling and data preprocessing, LZ performed statistical analyses and wrote the manuscript, DJC and SH wrote partial discussion and revised the whole manuscript, and XW performed partial analyses and produced figures.
The authors declare that they have no conflict of interest.
This research was supported by the Key Project of the China National Key Research and Development Program (2016YFA0600800), the National Natural Science Foundation of China (nos. 41471168 and 31770490), the Program for Changjiang Scholars and Innovative Research Team in University (IRT-15R09) and the Fundamental Research Funds for the Central Universities (2572016AA32). We also thank Yongxian Lu and Lei Zhang of Northeast Forestry University for their assistance in the field. Edited by: Nathalie Combourieu Nebout Reviewed by: two anonymous referees