The rise in global temperatures since the 20th century has a significant impact on human activities (Esper et al., 2002; Moberg et al., 2005; IPCC, 2014), resulting in temperature changes at both local-regional and global scales, with a strong influence on upland as well as hydrophytic ecosystems (Cao and Woodward, 1998; Shaver et al., 2000). Warming is expected to have significant effects on ecosystems at high latitudes, where plant growth is mainly limited by temperature (Kittel et al., 2000). The Daxing’an Mountains (DM), which are ranging in the northeast across 200–300 km from east to west and extend over 1220 km from south to north, are one of the most sensitive areas to temperature variation of China (Ding et al., 1994; Wang et al., 1998; Zhang et al., 2016). Previous studies showed that temperature change in this region was linked with solar activities and global land-sea atmospheric circulation. The temperature will rise or fall with the intensity or weakness of solar activity (e.g. the warm period in Middle Ages and the cold period in “Little Ice Age”) (Lean and Rind, 1999; Bond et al., 2001; Herrera et al., 2015).
In recent years, the temperature in northeast China has gradually increased, and the temperature in the growing season has risen significantly (Li and Gong, 2006). To understand the potential impacts of climate variation in this region, it is necessary to thoroughly understand the long-term changes and trends of climate over the last hundred years (Esper et al., 2002; Zhang et al., 2003). However, the historical and instrumental meteorological records are very limited before the 1950s. This poses the most significant impediment to comprehending the processes and mechanisms of past climatic fluctuations in this region (Bao et al., 2012). Thus, understanding the characteristics of long-term paleoclimate records are of great research value for the Daxing’an Mountains, which is fundamental for the prediction of future climate.
Over the past few years, the dendrochronological research has made significant progress in China, such as dendroecological research by Song et al., 2011, Yu et al., 2018; dendrogeomorphological research by Malik et al., 2013, 2017. Besides, because the tree-rings are high-resolution climate proxies containing rich climatic information, from which long paleoclimatic records can develop (Shao et al., 2010). This method has been used to extend limited meteorological data and predicts the impacts of various factors over time in various locations around the world (Pederson et al., 2001; Mann et al., 2009; Cook et al., 2010; Davi et al., 2010; Bao et al., 2012; PAGES 2k Consortium, 2013; Pathi et al., 2017). In recent years, some climate reconstruction projects based on tree rings have also been carried out in the northern Greater Khingan Mountains (Liu et al., 2009; Zhang et al., 2011, 2013; Chen et al., 2012; Yu et al., 2012). However, to date, temperature reconstruction based on tree-ring records were still insufficient in the northern Greater Khingan Mountains (Zhang et al., 2013). Thus, this lack must be addressed. Mangui (the site codes MG) is situated in the northern Daxing’an Mountains. A large area of natural temperate old-growth forests in this location provides an excellent opportunity for the study of dendroclimatology, which will allow us to better understand the historical and current climate changes in the northern Daxing’an Mountains region.
This paper uses tree-ring data for
Our study area is located in Mangui in the northern Daxing’an Mountains, Inner Mongolia Autonomous Region, China (
Location of the sampling site (MG). Northern Greater Higgnan Mountains (HGHM) (Zhang et al., 2013), Inner Mongolia (IM) (Zhang et al., 2011) and Hulunbuir (HLBE) (Shi et al., 2015) were u asterisk sed for comparison. The cities TH(Tahe), NJ (Nenjiang), NH(Nehe), QQHE(Qiqihar), MX(Maoxing) and HEB (Haerbin) were marked with asterisk.
All cores were preprocessed, air-dried, mounted on the wooden holders using conventional dendrochronological techniques, and subsequently were carefully polished with successively finer grit sandpaper in the laboratory (Fritts, 1976; Holmes, 1983; Cook, 1985). The tree rings were all visually cross-dated with a binocular microscope, annual ring widths were subsequently carefully measured to the nearest 0.001 mm by using a Velmex measuring system. The quality control of cross-dating and measurements was checked by using the COFECHA programme (Holmes, 1983). Each individual ring-width series were detrended in order to remove age-related, non-climatic growth trends (Frits, 1976). A negative exponential curve or straight line was applied to preserve as much low-frequency signal as possible (Cook, 1990). In a few cases, a cubic spline with 67% of the series length was employed when anomalous low-frequency growth trends occurred. The detrended data from individual tree cores were combined into the site chronology by using a bi-weight robust mean method to minimize the influence of extreme values, outliers or bias in tree-ring indices (Cook, 1990). Three kinds of chronologies were generated from ARSTAN: residual (RES), autoregressive (ARS) and standard (STD). The STD chronology was used for further analyses because it includes both low-and high-frequency signals. We limited our analyses to the period with an EPS of at least 0.85 to determine the length of credible chronology (Cook and Briffa, 1990; Wigley et al., 1984).
There is no weather station near the sample sites. As such, the interpolated values, based on records from randomly selected 120 of 164 meteorological stations, were used for growth-climate response analysis. (120 weather stations in northeast China were from Chinese Meteorological Data Sharing Service System (
This interpolation method begins by fitting a partial thin-plate smoothing spline model, that is based on relating location and elevation to ground-based observations to estimate a trend surface, and then a simple kriging procedure was employed to the residuals for trend surface correction (Huang et al., 2013; Li et al., 2014). Analysis of regression was conducted between measured and interpolated precipitations and temperatures at the remaining 44 weather stations, to assess the reliability of the interpolated values. There was a high correlation with R2 = 0.99.
Correlation coefficients between tree-ring width indices and climatic variables from 1959 to 2014 were analyzed by using the data of monthly mean temperature and precipitation. Four climate variables were applied for the dendroclimatological analyses, including monthly total precipitation (Prec), monthly maximum temperature (Tmax), monthly mean temperature (Tmean) and monthly minimum temperature (Tmin). The climate data from the previous June to the current September was used for the correlation analysis.
Global-scale climate variables such as El Niño-Southern Oscillation index (ENSO) and the Pacific Decadal Oscillation index (PDO) (
Pearson correlation function and growth-climate response analyses (Blasing et al., 1984; Biondi and Waikul, 2004) were utilized to identify the most accepted model for the climatic reconstruction. Subsequently, a simple linear regression equation between the tree-ring width and the climate variables was computed for the calibrated period of 1959–2014. The parameters for calibration and verification included the Pearson’s correlation coefficient (r), explained variance (r2), reduction of error (RE), coefficient of efficiency (CE), sign test (ST) and product means test (PMT). All statistical analyses were performed by using commercial software, SPSS12.0 (SPSS, Inc., Chicago, IL, USA).
Power spectral analysis was applied to identify reasonable periodicities and performed over the full range of the reconstruction. The spectral properties of the reconstruction series were assessed by using a multi-taper method. In addition, Spatial correlations between the reconstructed Tmean
The chronology statistics are shown in
Statistical features of STD chronology.
Statistic | STD |
---|---|
Mean sensitivity | 0.23 |
Standard deviation | 0.26 |
First order autocorrelation | 0.60 |
Mean correlation within trees | 0.60 |
Variance in first eigenvector (%) | 42.0 |
Signal-to-noise ratio (SNR) | 27.6 |
Mean ring width (mm) | 1.16 |
Expressed population signal (EPS) | 0.91 |
First year where SSS>0.85 (number of trees) | 1880 (3) |
The results of correlation between the MG chronology (STD) and the climatic data revealed that tree-ring width indices were negatively correlated to monthly mean temperatures in almost all months (
Correlation coefficients between the monthly climate variables and tree-ring indices for 1959–2014.
Based on the results of correlation analysis, a linear regression model was used in our study to describe the connection between the tree-ring width and the June-July temperatures. The model was designed as follows:
where Tmean
For the calibration period (1959–2014), the reconstruction accounted for 43.6% of the actual Tmean
Scatter plot of the tree-ring width index and the averaged Tmean6–7 from June to July (1959–2014).
Statistics of calibration and verification test for the common period of 1959–2014.
Calibration | R | R2 | Verification | R | Reduction of error | Coefficient of efficiency | Sign test | Product means test |
---|---|---|---|---|---|---|---|---|
Whole section 1959-2014 | 0.66 | 0.436 | ||||||
Front section 1959-1983 | 0.53 | 0.28 | Back section 1984–2014 | 0.71 | 0.61 | 0.35 | (26+ /5-) | 2.8 |
Back section 1984-2014 | 0.71 | 0.50 | Front section 1959–1984 | 0.53 | 0.61 | 0.43 | (21+ / 5-) | 3.3 |
Rank of years of warm/cold reconstructed mean temperature from June to July (T
Rank | Warm year | Tmax | Cold year | Tmax |
---|---|---|---|---|
2008 | 17.28 | 1888 | 12.66 | |
2003 | 16.85 | 1893 | 13.21 | |
2012 | 16.67 | 1902 | 13.22 | |
1994 | 16.65 | 1932 | 13.33 | |
2014 | 16.59 | 1898 | 13.42 | |
1954 | 16.46 | 1880 | 13.60 | |
2011 | 16.43 | 1991 | 13.62 | |
2004 | 16.38 | 1934 | 13.72 | |
1886 | 16.36 | 1882 | 13.73 | |
1885 | 16.28 | 1892 | 13.81 | |
1975 | 16.26 | 1978 | 13.91 | |
1967 | 16.15 | 1891 | 13.94 | |
1919 | 16.14 | 1895 | 14.04 | |
1883 | 16.13 | 1958 | 14.05 | |
2001 | 16.11 | 1890 | 14.05 | |
2005 | 16.09 | 1927 | 14.10 | |
1939 | 16.07 | 1900 | 14.15 |
Based on model (1), the mean temperature that was reconstructed from 1880 to 2014 for the MG region exhibited a mean of 15.1°C and a standard deviation of σ = 0.88°C. We defined years with values > mean+1σ as a warm year, and values < mean–1σ as a cold year. During the last 134 years, there were 17 warm years, 17 cold years, which accounted for 12.7% of the total reconstruction years, respectively (
(A) Comparison of actual and reconstructed Tmean6–7 from 1959 to 2014 and (B) the reconstructed June-July temperature series since 1880. The smoothed line indicates the 11-year moving average, and red dots represent drought events, blue dots represent flood events.
In this study, summer (June to July) temperatures were the most significant negative correlations with the annual radial growth of
Mean monthly temperature (in °C) and total precipitation (in mm) at Mangui (MG) in the northern Daxing’an Mountains (AD 1959–2014) based on interpolated values from 164 climate stations.
Drought is not only due to lower precipitation but also to higher temperatures. Under normal rainfall conditions, high temperatures can cause severe droughts, and precipitation is accompanied by low temperatures (Yi et al., 2012; Bao et al., 2012). Historical literature evidence shows that many drought and flooding events occurred in Heilongjiang Province after AD 1880 (Wen and Sun, 2007). The high-temperature years that occurred in AD 1883, 1885, 1886, 1919, 1939, and 1954 were linked with drought events (
The dry/wet years of the reconstructed temperature for the Mangui (MG) region in comparison with historical documents (Wen and Sun, 2007).
Dry and wet years | Short description of weather or related events |
---|---|
To further evaluate the reliability of this reconstruction, we compared the reconstructed series with nearby tree-ring-based reconstruction temperature series by Zhang et al., 2013 (the site codes NGHM, 680 m a. s. l., 52°55’ N, 121°06’ E; 193 km from our sampling point), Zhang et al., (2011) (the site codes IM, 51°03’15”–52°08’08” N, 120°00’20” – 121°19’21” E; 120 km from our sampling point) and reconstruction Palmer drought severity index (PDSI) series by Shi et al., 2015 (the site codes HLBE, 515–669 m a. s. l., 49°12’ N, 119°42’ E; 607 km from our sampling point) (
Comparison of June–July mean temperature reconstruction in MG with other tree-ring proxies from surrounding areas: (a) June–July maximum temperature reconstruction in this study; (b) May– September temperature reconstruction in Inner Mongolia (Zhang et al., 2011); (c) May–October temperature reconstruction in northern Greater Higgnan Mountains, China (Zhang et al., 2013); (d) Annual PDSI reconstruction from tree-ring of Mongolian pine in Hulunbuir, Northeast China (Shi et al., 2015). The gray areas mean the common warm/cold periods.
The results showed that the large-scale regional temperature variations had been well captured by our reconstruction, which was significantly positively correlated with regional gridded temperatures (
Spatial correlation of (A) instrumental and (B) reconstructed June–July temperatures with regional gridded June–July temperatures during the period 1959–2014. The asterisk mark in is the sampling position.
The multi-taper method (MTM) of spectral analysis (Wei, 2010) revealed that the reconstructed Tmean
The power spectrum analyses of reconstructed June–July mean temperature.
Spatial correlation for the reconstruction with June–July averaged HadlSST1 SST during the period of 1880–2014.
The significant spectral peaks at 29.7-yr (
As mentioned above, the complex connections with the ENSO, PDO and Solar activity suggested that the temperature in the Mangui area indicated both local-regional climate signals and global-scale climate changes.
The mean temperature from June to July was reconstructed for the period of 1880 to 2014 by using tree-ring data from MG in the northern Daxing’an Mountains, China. The reconstructed temperature series provided essential information concerning temperature variations in this region. During the last 134 years, there were 17 warm years, 17 cold years, which accounted for 12.7% of the total reconstruction years, respectively. Cold episodes occurred in the intervals 1887–1898, while warm episodes occurred in 1994–2014. In and near the study region, the warmer events coincided with dry periods and the colder events consistent with wet conditions. The spatial correlation analyses between the reconstruction series and gridded temperature data revealed that the regional climatic variations were well captured by this study and the reconstruction represented a regional temperature signal for the northern Daxing’an Mountains. In addition, multi-taper method spectral analysis revealed the existence of significant periodicities in our reconstruction. Significant spectral peaks were found at 29.7, 10.9, 2.5, and 2.2 years. The significant spatial correlations between our temperature reconstruction and the El Niño–Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO) and Solar activity suggested that temperature variability in the Mangui area was probably driven by extensive large-scale atmospheric-oceanic variability and solar activity.