Drought response of tree-ring width in the southern boundary of Pinus tabulaeformis from Mt. Funiu, central China

Drought response of tree-ring width in the southern boundary of Pinus tabulaeformis from Mt. Funiu, central China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Drought response of tree-ring width in the southern boundary of Pinus tabulaeformis from Mt. Funiu, central China Jianfeng Peng, Jinbao Li, Xuan Li, Jiayue Cui, Meng Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1448882/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tree ring data from the southern boundary of Pinus tabulaeformis distribution where is the southern margin of warm temperate, the paper analyzes the response of climate factors along north-south direction to tree growth. The results show that temperature and precipitation in May-June and relative moisture from March to June are main limiting factors on tree growth; however, the temperature in the south of the mountain and the humidity in the north of the mountain have relatively greater influence on tree growth. The regional scPDSI MJ (that is PDSI in May-June) is the most significant and stable limiting factors on tree growth to be used for reconstruction, which explains 50.4% of the variance in the instrumental records from 1963 to 2016 CE. The reconstructed scPDSI MJ revealed that there were 29 extremely dry years (accounting for 12.96%) and 30 extremely wet years (accounting for 13.89%) and it can represent the drought variation in central and eastern monsoon region, and agree with the reconstructed PDSI for Mt. Shennong and the drought/wetness series in Zhengzhou, with correlation coefficients 0.367 (n = 210, p = 0.000) and 0.196 (n = 200, p = 0.005), respectively. Further research found that drought of May-June in central China mainly impacted by local temperature and moisture (including precipitation, soil moisture, potential evaporation and water pressure), and then the Pacific and the Atlantic. These results may provide better understanding of May-June drought variation and service for agricultural production in central China. tree-ring Pinus tabulaeformis Carr. drought variation transition zone central China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Under global warming, the frequency and intensity of extreme weather events are likely to increase dramatically, which will have a more serious impact on ecosystems and human society and require more timely and effective responses (IPCC, 2014 ). The typical continental monsoon climate in the middle and lower reaches of the Yellow River can cause the instability and variability of the climate due to the changes of monsoon strength and the early and late of monsoon rains. These changes may be result in frequent flood and drought in the region and directly related to the harvest of agricultural years. Hence the climate changes in the future may not only affect living environment and ecological security, also put forward severe challenges to the high quality and sustainable development of the middle and lower reaches of the Yellow River region. There is a long history and abundant climatic records in the middle and lower reaches of the Yellow River, and also has some reconstructed drought-flood information based on historical documents but often non-quantitative and discontinuous. Dendroclimatology is a science that reconstructs past climate changes based on tree physiology and tree radial growth characteristics (Fritts,1976;Wu, 1990 ). Tree-ring data have the advantages of high resolution (annual or seasonal) and precise-continuous series for dating, and it has been successfully used to quantitatively characterize the climate change series and regarded as one of the most important proxy indicators in the past climate change research (Fritts,1976; Cook et al, 2010 ). In recent years, dendrochronological studies in eastern China had been developing rapidly (Cai et al, 2013, 2018 ;Cheng et al, 2012, 2015, 2021; Duan et al, 2012 ༛Fang et al, 2017 ༛Peng et al, 2014 , 2018 , 2019 , 2020 ; Shi et al, 2013 , 2017 ; Zhang et al, 2017 ; Zhao et al, 2019 ༛Zheng et al, 2012 , 2016 ). However, dendrochronological study is still limited in Mt. Funiu of western Henan Province as a climatic transition zone (Tian et al, 2009 ༛Shi et al, 2012 ༛Liu et al, 2015 , 2017 ༛Zhao et al, 2019 ༛Peng et al, 2020 ; Yang et al, 2021 ). There are some hydroclimate reconstructions in Mt. Funiu, e.g., temperature (Shi et al, 2009 ༛Tian et al, 2009 ༛Liu et al, 2014 , 2015 ༛Yang et al, 2021 ), relative humidity (Liu et al, 2017 ༛Peng et al, 2020 ), scPDSI (Zhao et al, 2019 ). To some extent, the above studies are helpful to understand climate change in Mt. Funiu and its surrounding areas. To better reveal the impact and formation mechanism on climate change and more understood climate change affecting ecological security and high-quality development in central Plains, further study of tree rings in Mt. Funiu is necessary. This study was mainly used to compare correlation between tree-ring data and north-south climatic factors (including scPDSI grid data) in Mt. Funiu and the transitional characteristics of performance; Secondly the most significant climate limiting factors were selected to carry out climate reconstruction and analysis it’s spatial representations, and finally further to explore the possibility and potential impact of future climate change in this region. 2. Data And Methods 2.1 Study area Mt. Shiren (33º43′39.49″N, 112º15′4.39″E, 1775 m a.s.l.) is located in western Henan province in central eastern Qinling Mountains, central China (Fig. 1 ). It is a transitional zone from subtropical to warm temperate continental monsoon climate with mildly wet summers and cold dry winters. It is also the transition zone from humid to sub-humid. The annual mean temperature is 10.0°C and annual amount of precipitation is 820–860 mm, and mean temperature in January is -3.3°C while July is 20.3°C. The forests canopy cover is about 95%, the dominant vegetation is the mixed forests composed of temperate deciduous broad-leaved and coniferous on the top of the mountain, including P. tabulaeformis carr. and P. armandi Franch LC. The soil is typically brown mountain soil with 30 to 40 cm deep. The species for our study, P. tabulaeformis Carr. , is mainly distributed between 1300 m and 1800 m a.s.l. The species is very sensitive to climate change because the eastern Mt. Funiu is the southern boundary of the Chinese Pine distribution (Xu,1993). 2.2 Chronology and climate data Tree-ring data of P. tabulaeformis Carr is from Mt. Shiren in western Henan province which had been studied tree growth response to east-west climate factors (from Luanchuan and Baofeng meteorological stations) in previous studies (Peng et al., 2020 ; Yang et al., 2021 ). The sampling site is in central eastern Mt. Funiu, central China. The reliable standard chronology from 1801 to 2016 CE, with the starting year determined by adopting a sub-sample signal strength (SSS) threshold of 0.85 (Wigley et al. 1984 ), was developed and used. The chronology contains high signal-to-noise ratio (SNR, 14.543) and the expressed population signal (EPS, 0.936), so it demonstrated a high level of reliability. To better understand transitional characteristics of tree growth in Mt.Funiu, the study selected Songxian meteorological station of northern Mt. Funiu and Neixiang meteorological station of southern Mt. Funiu (Fig. 1 , Table 1 ) along the gradient of temperature and precipitation in the north and south (that is along gradient from warm temperate to subtropical monsoon climate). Climate factors used in this study include monthly mean temperature (T), monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin), monthly total precipitation (P), and monthly mean relative humidity (RH) during 1963–2016 (Fig. 2 ). The self-calibrating Palmer Drought Severity Index (scPDSI, van der Schrier et al., 2013 ) was chosen as a metric to measure the responses of tree growth to moisture conditions. The four grids and a regional mean of the scPDSIs between 33.5 to 34.5N and 111.5 to 112.5E (CRU scPDSI 3.26e, https://climexp.knmi.nl/ ; Fig. 1 , Table 1 ) were also used in this study, respectively. Table 1 Characteristics of climate data used in correlation analyses Climate data Source Latitude(°N) Longitude(°E) Elevation (m a.s.l.) Time Spaning Songxian 34.13 112.06 440.7 1963–2016 Neixiang 33.15 111.88 221.4 1963–2016 scPDSI-1 33.75 111.75 - 1963–2016 scPDSI-2 33.75 112.25 - 1963–2016 scPDSI-3 34.25 111.75 - 1963–2016 scPDSI-4 34.25 112.25 - 1963–2016 Regional scPDSI 33.5–34.5 111.5-112.5 - 1963–2016 2.3 Statistical methods Pearson’s correlation analyses were performed to identify climate-growth relationships between tree-ring standard chronology and climatic factors from two meteorological stations. Climate-growth relationships were investigated from previous March to current November to explore the potential effects of climatic factors on trees growth from the previous year to the current year. Then, a simple linear regression model based on the most dominant climatic factor was selected and reconstructed. The split calibration-verification procedure was used to verify the reconstruction (Cook et al., 1999 ), statistical parameters for this assessment include correlation coefficient (r), R-squared (R 2 ), sign test (ST), reduction of error (RE), and coefficient of efficiency (CE). In general, positive RE and CE indicate a rigorous and reliable reconstruction model (Cook et al. 1999 ). Spectral analyses were performed using Multi-Taper Method (MTM) (Mann and Lee 1996) to detect the periodicities of the reconstruction, and Wavelet analysis (Torrence and Compo, 1998 ) was used to extract strong or weak changes for different cycle signals in the reconstruction. Spatial analyses were performed between the reconstructed scPDSI MJ and scPDSI (1963–2016; 4.05early), temperature, precipitation, potential evaporation, vapor pressure (1963–2016; CRU TS4.04) and soil moisture (1979–2016; CLM/EARi, 0-10cm) (references for European Climate Assessment & Data) to explore regional representation and possible formation mechanisms. The analyses were performed on the KNMI Climate Explorer ( http://climexp.knmi.nl ). 3. Results 3.1. Climate-growth relationship There are significant negative correlations ( p < 0.05) with T, Tmax and Tmin in current May and June (Fig. 3 ), and also significant negative correlations with Tmax in current March and April. There are similar significantly negative correlations with temperatures from two meteorological stations, which indicated the strong effect of temperature on tree growth and the influence time of Tmax to tree growth is longer. In contrast, the chronology shows mostly positive correlations with P in April-May and RH in March-June at both stations. There are also significant positive correlations between chronology and current June P in Songxian station and current July RH from the Neixiang station. Overall, temperature (T, Tmax) of southern meteorological station of Mt. Shiren showed higher correlation with tree growth while the greatest influence of humidity (P, RH) were found from northern meteorological station of Mt. Shiren. The correlations between chronology and scPDSI from previous March to current November are almost all positive, especially significant from current March to August. Almost all correlations between chronology and scPDSI in May and June are over 0.6 ( p < 0.05), the highest correlation in May is scPDSI grid point (0.687, p < 0.05; 33.75N, 112.25E) closest to the sampling site while the highest correlation in June is regional mean scPDSI (0.663, p < 0.05; 33.5-34.5N, 111.5-112.5E). In a general way, seasonal climate is more stable and meaningful than single month climate to tree growth. Based on the principle that a higher correlation indicates a greater impact on tree growth, we combine the monthly data on single scPDSI grid point (33.75N, 112.25E) closest to the sampling site and regional mean scPDSI (33.5-34.5N, 111.5-112.5E). The highest correlations with chronology were both May-June scPDSI, and the highest correlation value of single grid scPDSI was 0.693 ( p < 0.05, 33.75N, 112.25E) while that of regional mean scPDSI was 0.71 ( p < 0.05). Thus, the standard chronology of P. tabulaeformis Carr could be used to reconstruct regional scPDSI MJ . 3.2 Transfer function and regional scPDSI reconstruction According to the above correlation resultss, the regional mean scPDSI in current May-June (scPDSI MJ ) was reconstructed using following linear regression equation based on the least square method: scPDSI MJ = 5.514*Wt – 5.21 (N = 54, r = 0.71, R 2 = 50.4%, R 2 adj = 49.4%, F = 52.795, p < 0.0001) Where, Wt is the index of tree-ring chronology for year t. The reconstructed scPDSI MJ could explain 50.4% of the variance in the instrumental record and 49.4% after an adjustment for the loss of the degree of freedom. A visual comparison also showed that the reconstructed mean scPDSI MJ tracked the observed scPDSI MJ well (Fig. 4 a). The first-order difference data show that there was a significant correlation (r = 0.737, p < 0.001) between the two scPDSI MJ sequences (Fig. 4 b), indicating that the reconstructed scPDSI MJ captured variations of the observed scPDSI MJ at both high and low frequencies. The split sample procedure was used to assess the reliability of the reconstruction. Table 2 shows that all parameters used for calibration and verification periods are significant ( p < 0.01) and RE and CE values are positive, and ST test is also significant ( p < 0.05) and high F values are in all calibration and verification time periods, which means the model is acceptable for scPDSI MJ reconstruction (Cook et al.1999). Table 2 Calibration and verification statistics for reconstructed scPDSI MJ Calibration (1963–1991) Verification (1992–2016) Calibration (1988–2016) Verification (1963–1987) Full calibration (1963–2016) R 0.714** 0.696** 0.814** 0.601** 0.710** R 2 0.510 0.485 0.662 0.361 0.504 CE 0.298 0.324 RE 0.668 0.440 ST 22+/7–** 18+/7–* 22+/7–** 19+/6–* 41 + 13–** F 28.108 21.663 52.948 12.994 52.795 **Significant at the 99% confidence levels, *Significant at the 95% confidence levels. Spectral analysis results (Fig. 5 ) revealed that the scPDSI MJ reconstruction contained 2.3a, 2.86-2.9a, 3.35a, 3.69-3.83a and 6.43a cycles ( p < 0.5), indicating potential ENSO (2-7a, El Niño-Southern Oscillation) impacts (Allan et al. 1996 ; Sun and Wang, 2007). There are also 34.11a, 49.26a cycles ( p < 0.01), which may be related to PDO (Pacific Decadal Oscillation) or AMO (Atlantic Multi-decadal Oscillation) (d'Orgeville et al, 2007 ; Yang et al, 2017 ). 4. Discussion 4.1 Tree growth influenced by temperature, precipitation, or scPDSI Previous studies demonstrated that the maximum temperature (Yang et al, 2021 ) and Relative Humidity (Peng et al, 2020 ) from April to July were main limiting factors at Mt. Shiren. Early summer moisture signals (scPDSI) were also captured and reconstructed in the eastern Mt. Qinling (Zhao et al, 2019 ). However, tree growth is the result of the interaction of temperature and precipitation, and temperature often affects tree growth through its effects on water availability. So temperature-raised water deficiency induces water stress to suppress cell division and expansion (Fritts, 1976 ) and form narrow ring. The sampling site located in the eastern Mt. Funiu, the eastern extension of the Mt. Qinling, here is a climatic transitional belt from subtropical to warm-temperate continental monsoon. There are higher temperature and more precipitation in the subtropical zone of the southern Mt. Funiu, belong to humid climate region, while belong to sub-humid climate region in warm temperate zone of northern Mt. Funiu, so tree growths exist different response to climate in north and south Mt. Funiu. The correlation results found that tree growth in higher altitude responds similarly to climate factors on the north and south meteorological stations as described above, these are temperature and precipitation in May and June are mainly limiting factors. However, also some different response in horizontal scale which temperature (T, Tmax) of southern meteorological station (Neixiang) of Mt. Shiren had greater influence on tree growth while the greatest influence humidity (P, RH) were from northern meteorological station (Songxian). To better understand the interaction between temperature and humidity, scPDSI was used for further research. We chose 4 single grid-points and 1╳1 grid-point (33.5-34.5N, 111.5-112.5E) regional mean scPDSI near sampling site as examples to conduct correlation analyses, and found that the correlation results were consistent, with significant positively correlations from current March to August. The regional mean grid-point (33.5-34.5N, 111.5-112.5E) scPDSI contains both the sampling point and the Songxian meteorological station, and there is a good correspondence between the response of tree growth to scPDSI and the response to the precipitation of the Songxian meteorological station. This also proved that the tree growths in sub-humid areas were mainly restricted by moisture. But the highest correlation with chronology was regional mean scPDSI (r = 0.71, p < 0.05) in current May-June, while be 0.675 ( p = 0.000) with relation humidity and 0.583 ( p = 0.000) with precipitation in current May-June. In other words, with temperatures rising in May-June, deficiency of soil moisture limits tree growth and produce narrow rings due to a lack of precipitation before the rainy season and a high evapotranspiration rate. However, the scPDSI as a comprehensive drought index well represents the joint effects of regional temperature, precipitation and soil moisture, so scPDSI may better reflect moisture variation. The moving correlation result shows that regional mean scPDSI MJ (Fig. 6 ) is the most significant and stable, hence it could be used for reconstruction. 4.2 Drought variation of the reconstructed scPDSI MJ Table 3 Moderately to extreme drought/wet events derived from tree-ring reconstructions and the corresponding descriptions of historical records. scPDSI MJ (1801–2016, this study) RH AJ (1801–2016, Peng, 2020) scPDSI MJJ (1868–2005, Zhao, 2019) Historical records in Henan Province (CCMDC,2006) Extremely dry period 1801–1802 1807,1810 1813–1814 1835 1847 1867 1879 –1881 1891–1892 1900 1907 1929 1932 1935 1941 1945 1955 1968 1988 1992, 2000 –2001 2011 1801–1802 1807–1810 1813–1814 1821 1835–1836 1847 1867 1879 –1881 1891–1892 1900 1907 1929 1932 1935 1941 1945 1955 1968, 1988 1992, 2000 –2001 2007 2011 1879 1900 1923,1926, 1929 1994,1995 2000 Not available Drought from spring to summer Drought from spring to summer in 1813 and spring drought in 1814 Severe drought from spring to summer Drought from spring to summer Drought from spring to summer in Gongyi, Henan A mega-drought caused a great famine over Henan and Shaanxi so on provinces in northern China in during 1876–1879 Not available Severe drought from spring to autumn over Henan and Shaanxi Drought from spring to summer Severe drought from spring to autumn over Henan, the river and pond dry up Drought from summer to autumn over Henan Severe spring drought over Henan Severe drought from spring to autumn in 1941and 1942 Severe drought and locust disaster over Henan Severe drought from spring to early summer Drought from spring to summer in the most of Henan Severe drought following the drought of 1985–1987 Severe spring drought Severe drought from February to May (the worst one since 1950) Severe drought from January to February since October 2010 ( the lowest for the same period since 1951) Extremely wet period 1831 1840 1862–1864 1866 1868–1872 1875 1885 1894 1898 1901 1905 1911–1913 1915 1946 1950 1983 1984 1990 1991 1998, 2010 1869 1883 1 885 1894 1895 1898 1905 1906 1910–1912 1933,1934, 1936,1944 1948,1949 1973,1980, 1983 1984 1990 Spring and summer rains from north to south of the Yellow River Not available Not available Flood in March in Yexian Flood in summer and autumn over Henan in 1869 Not available Flood in summer in Lingbao and Shanxian (northwestern Henan) Not available Severe flood in summer at Yi and Luo river (Henan), Shangnan (Shaanxi) Flood in Fangcheng (Henan) Severe flood in spring and summer over Henan Persistent flood in summer and autumn over Henan during 1910–1913 Flood in summer and autumn over Henan Persistent rainfall in spring and summer in the most of Henan Not available Rainstorm in April and May in the north Henan in 1983 From June to September, there were 5 large-scale rainstorms over Henan in 1984 Not available Rainstorm in September in Nanzhao (Henan) Not available Low temperature and rain in spring, heavy rain in summer The five driest years 1880 (-2.999) 1835 (-2.795) 1955 (-2.723) 1929 (-2.629) 1907 (-2.552) 1880 (57.82%) 1835 (60.05%) 1955 (60.24%) 1929 (60.50%) 1907 (60.71%) 1879(-3.61) 2000(-2.94) 1929(-2.53) 1926(-2.33) 1923(-2.28) Ding-Wu disaster, Shanxi and Henan famine, extreme drought 4.3 The spatial representations of reconstructed scPDSI MJ Spatial correlation analyses of actual or reconstructed regional mean scPDSI MJ with scPDSI (1963–2016; scPDSI, 4.05early), temperature, precipitation, potential evaporation, vapor pressure (1963–2016; CRU TS4.04) and soil moisture (1979–2016; CLM/EARi, 0-10cm) were performed to determine the spatial distribution and explore possible causes of drought/wet variations. 4.3.1 Spatial representations Figure 8 demonstrates that the actual and reconstructed regional scPDSI MJ are significant positive correlations with scPDSI (1963–2016; scPDSI, 4.05early) around the study area. Obviously, the reconstructed scPDSI MJ may represent the drought/wetness variation in central and eastern monsoon region, especially in central China region and south adjacent area. These regions are the main grain producing areas in China, so it is very important to research drought/wet variation for grain production security in China. In order to verify the reliability of regional representation, we compared reconstructed scPDSI MJ with the reconstructed PDSI of Mt. Shennong (SNPDSI, Fig. 1 ) and drought/wetness index from Zhengzhou (DWZZ, CMA 1981; Fig. 1 Zhengzhou). The results found that the severe drought events showed better consistency, and correlation coefficients are 0.367 (n = 210, p = 0.000) between scPDSI MJ and SNPDSI, 0.196 (n = 200, p = 0.005) between scPDSI MJ and DWZZ, and 0.190 (n = 200, p = 0.007) between SNPDSI and DWZZ (Fig. 9 ). These statistically validated the reliability and spatial representation of the reconstructed scPDSI MJ . The gray bars represent the same drought periods in three series. 4.3.2 Causes analysis of drought variations The drought variation of a region is closely related to regional climatic elements. The spatial correlation results show significant negative correlations with temperature and potential evaporation and significant positive correlations with precipitation and vapor pressure and soil moisture in central China region and south adjacent region (e.g., Huanghuai region and the central-western Jianghuai region) (Fig. 10 a-j), however the scale is slightly different. In terms of the perspective of affected area, temperature and soil moisture on north and south sides of the sampling site (Mt. Shiren) have similar influence on tree growth (Fig. 10 , a-b and i-j), while precipitation, potential evaporation and water pressure from the north of mountains have more influence than the south of mountains (Fig. 10 , c-d, e-f and g-h). These also show that drought of central China in May-June is probably mainly impacted by local temperature and moisture (including precipitation, soil moisture, potential evaporation and water pressure) on both sides of the transition zone. 4.4 The global hydro-climatic signals in reconstructed mean scPDSI MJ Based on the significant characteristics of monsoon climate in eastern China, this study continues to discuss the relationship between tree growth at sampling sites and global sea surface temperature (SST, NASA MERRA-2 Tsfc, 1980–2016; The relationship between tree growth and land surface temperature has been analyzed in Fig. 10 a-b). Figure 11 shows there are some significant negative correlations with SST from the northern Pacific Ocean and the northern Atlantic Ocean and significant positive correlation with SST from the southeastern Pacific Ocean and the northeastern Indian Ocean with reconstructed scPDSI MJ in this study, but the overall correlation is weak. These results also confirm that the periodic changes (2.3-6.43a ENSO cycles ( p < 0.5); 34.11 and 49.26 PDO or AMO cycles ( p < 0.01)) of tree growth in the transition zone could be related to the SST changes in the Pacific and Atlantic Oceans. The conclusion, which the drought from May to June had a certain relationship with SST from the northwestern Pacific Ocean (that is PDO cycle), was similar to previous studies (Ma, 2007 ; Zheng et al, 2014 ), and same as spectral analysis previously. Example, the monsoon from the Pacific Ocean usually arrives at the middle and lower reaches of the Yangtze River in June, and the monsoon rainy season begins in the study area in late July, so the study area in May-June is a period of low rainfall. The high temperature of the northwestern Pacific Ocean surface in May-June reduces the thrust of the monsoon, making it difficult for water vapor to reach the northern China, while heat and evaporation on land lead to water shortages for limiting tree growth (Fig. 10 a-b) and forming narrow ring. The spatial correlations from Fig. 11 also show that local continental temperature influence (above 0.6) is higher than that of the Pacific Ocean surface temperature (from 0.3 to 0.5), this also indicates that sea surface temperature changes in the Pacific Ocean had a greater impact on tree growth in the study area, but the impact is weaker than that on land in May and June. 5. Conclusion Based on sampling site located in an ecological sensitive area from north subtropical to warm temperate climatic transition zone, the correlation analysis between tree-ring chronology and north-south longitudinal meteorological data was conducted. The results found that temperature and precipitation in May-June and relative humidity from March to June are main limiting factors; however, the temperature in the south of the mountain and the moisture (including precipitation and relative humidity) in the north of the mountain had greater influence to tree growth. The regional scPDSI MJ is the most significant (0.71, p < 0.05) and stable to be used for reconstruction, which explains 50.4% (49.4% after adjustment of the degree of freedom) of the variance in the instrumental records from 1963 to 2016 CE. The reconstructed scPDSI MJ revealed that there are 29 extremely dry years (accounting for 12.96%) and 30 extremely wet years (accounting for 13.89%). The five driest years were 1880 (-2.999), 1835 (-2.795), 1955 (-2.723), 1929 (-2.629) and 1907 (-2.552) over the reconstruction period. The reconstructed scPDSI MJ can represent the drought variation in central and eastern monsoon region and agree with the reconstructed PDSI for Mt. Shennong and the drought/wet series in Zhengzhou which correlation coefficients are 0.367 (n = 210, p = 0.000) and 0.196 (n = 200, p = 0.005), respectively. Further research found that drought of central China in May-June was mainly impacted by local temperature and moisture (including precipitation, soil moisture, and potential evaporation and water pressure) on both sides of the transition zone, and the second one is by the Pacific and the Atlantic. These results may provide better understanding of May-June drought variation and service for agricultural production in central China. Declarations Acknowledgements : The authors thank Kexian Li and Lei Zhao from Mt. Shiren National Nature Reserve Bureau and Liang Chen, Kunyu Peng, and Jinrui Dan for their help during field work. The authors also thank the National Field Scientific Observation and Research Station of Forest Ecosystem in Dabie Mountains, Henan Province. Funding: This research was funded by National Natural Science Foundation of China (No. 41671042; 42077417). Author Contributions: JFP and JBL conceived and designed the original study. JFP, XL, MP performed statistical analyses. XL, JYC, MP, JKL and XXW interpreted the data. JFP wrote original manuscript. JBL critically revised manuscript. Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Allan RJ, Lindesay JA, Parker DE (1996) El Nino Southern Oscillation and Climatic Variability. CSIRO, Collingwood, Victoria Cai QF, Liu Y (2013) The June–September maximum mean temperature reconstruction from Masson pine ( Pinus massoniana Lamb.) tree rings in Macheng, southeast China since 1879 AD. 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Climatic Change 33: 409–445 Peng JF, Li JB, Wang T, Huo JX, Yang L (2019) Effect of altitude on climate-growth relationships of chinese white pine ( Pinus armandii ) in the northern Funiu mountain, central China. Climatic Change 154: 273–288 Peng JF, Li JB, Yang L, Li JR, Huo JX (2020) A 216-year tree-ring reconstruction of April-July relative humidity from Mt. Shiren, central China. International Journal of Climatology 40: 6055–6066 Peng JF, Liu YZ, Wang T (2014) A tree-ring record of 1920’s-1940’s droughts and mechanism analyses in Henan Province. Acta Ecologica Sinica 34(13): 3509–3518 (in Chinese) Peng JF, Peng KY, Li JB (2018) Climate-growth response of Chinese white pine ( Pinus armandii ) at different age groups in the Baiyunshan National Nature Reserve, central China. Dendrochronologia 49: 102–109 Shi JF, Cook ER, Li JB, Lu, HY (2013) Unprecedented January-July warming recorded in a 178-year tree-ring width chronology in the Dabie mountains, southeastern China. 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Science China- Earth Science 50:145–152 (in Chinese) The compilation of China meteorological disaster Canon (2006) China meteorological disaster Canon. China Meteorological Press, Beijing (in Chinese) Tian QH, Liu Y, Cai QF, Bao G, Wang WP, Xue WL, Zhu WJ, Song HM, Lei Y (2009) The Maximum Temperature of May-July Inferred from Tree-ring in Funiu Mountain since 1874 AD. Acta Geographica Sinica 64(7): 879–887 (in Chinese) Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79: 61–78 van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901–2009. Journal of Geophysical Research-Atmospheres 118, 4025–4048 Wigley TML, Briffa KR, Jones PD (1984) On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. Journal of Applied Meteorology and Climatology 23: 201–213 Wu XD (1990) Tree ring and climate change. China Meteorological Press, Beijing (in Chinese) Xu HC (1993) Tree growth Chinese Pine. China Forest Industry Press, Beijing: 125–126 (in Chinese). Yang L, Li JR, Peng JF, Huo JX, Chen L (2021) Temperature variation and influence mechanism of Pinus tabulaeformis ring width recorded since 1801 at Yao Mountain, Henan Province. Acta Ecologica Sinica 41(1):79–91 Yang Q, Ma ZG, Fan XG, Yang ZL, Xu ZF, Wu PL (2017) Decadal modulation of precipitation patterns over eastern China by sea surface temperature anomalies. Journal of Climate 30(17): 7017–7033 Zhang Y, Tian QH, Guillet S, Stoffel M (2017) 500-yr. precipitation variability in Southern Taihang Mountains, China, and its linkages to ENSO and PDO. Climatic Change 144(3): 419–432 Zhao YS, Shi JF, Shi SY, Ma XQ, Zhang WJ, & Wang BW, Sun XG, Lu HY, Bräuning A (2019) Early summer hydroclimatic signals are captured well by tree-ring earlywood width in the eastern Qinling mountains, central china. Climate of the Past 15(3): 1113–1131 Zheng QY, Shen BZ, Gong ZQ, Zhou J, Hu JG (2014) Relationship between North Pacific Oscillation in May and Drought /Flood in Summer in North China. Plateau Meteorology 3:775–785 Zheng YH, Shao XM, Lu F, Li Y (2016) February-may temperature reconstruction based on tree-ring widths of Abies fargesii from the Shennongjia area in central China. International Journal of Biometeorology 60: 1175–1181 Zheng YH, Zhang Y, Shao XM, Yin ZY, & Jin Z (2012) Temperature variability inferred from tree-ring widths in the Dabie mountains of subtropical central China. Trees 26(6): 1887–1894 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1448882","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":92563904,"identity":"d8e50db6-c22b-4d39-94df-0e62e9e0e164","order_by":0,"name":"Jianfeng Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACxuYDIMqGgY0BhMAggYCWNrCCNBK0MLCBFRwGM4nTwtzGe/Bxwa/ziX3SDWwPPu44zMDPnmPA8HMHPofxJRvP7Lud2CZzgN1w5pnDDJI9bwwYe8/g0TK/x0yat+e2MZtEAps0b9thBoMbOQbMjG34bOEx/83bcw6i5S9Qiz0RWsyYeX4ckANrYQTZIkFQC1+yNG9DshybzME2yd62dB6JM88KDvbi0WIIDLHPPH/seORnNx+T+NlmLcffnrzxwU98Whp4QFYBWRKMDSABHhBxALcGBgZ5sJo/IC34lI2CUTAKRsGIBgC/KEoE30MIYQAAAABJRU5ErkJggg==","orcid":"","institution":"Henan University","correspondingAuthor":true,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Peng","suffix":""},{"id":92563905,"identity":"dc36645e-f681-4e29-9ab7-ce927eff9676","order_by":1,"name":"Jinbao Li","email":"","orcid":"","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Jinbao","middleName":"","lastName":"Li","suffix":""},{"id":92563906,"identity":"e564aa24-e8f4-4705-a624-c02dde5790a5","order_by":2,"name":"Xuan Li","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Li","suffix":""},{"id":92563907,"identity":"a2f87ddf-ce2d-4006-b1cb-50397124a41a","order_by":3,"name":"Jiayue Cui","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Jiayue","middleName":"","lastName":"Cui","suffix":""},{"id":92563908,"identity":"3637936c-3bc2-4906-b204-310e016089f6","order_by":4,"name":"Meng Peng","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2022-03-14 07:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1448882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1448882/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19586901,"identity":"218302e8-44f4-45cf-acde-26e12837caeb","added_by":"auto","created_at":"2022-03-24 19:29:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14001573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap showing the study area (Mt. Shiren), Songxian and Neixiang meteorological stations, the scPDSI grid points, and regional mean value.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/755744848cb4d52fef2a1970.jpeg"},{"id":19586808,"identity":"a7f45271-a967-4caa-8acf-97a83034bc32","added_by":"auto","created_at":"2022-03-24 19:28:55","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":557451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTmax, T, Tmin, P and RH from Songxian (A) and Neixiang (B) meteorological stations during 1963-2016\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/d5abac696199166b62601617.jpeg"},{"id":19586892,"identity":"f2ce78fa-4022-4a38-aef6-55954b3a959a","added_by":"auto","created_at":"2022-03-24 19:29:46","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":639776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation coefficients between the chronology and climatic factors (T, Tmax, Tmin, P, RH and scPDSI) in the Songxian (brown bar) and the Neixiang stations (green bar), respectively. \u003c/strong\u003eThe horizontal dashed line represents p\u0026lt; 0.05.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/15acbc6a7fa4d8ef72ab6197.jpeg"},{"id":19586930,"identity":"44070d5c-a2ce-42fd-adf4-2cec31a0721e","added_by":"auto","created_at":"2022-03-24 19:29:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) Comparison of the observed (solid line) and the reconstructed (dot line) scPDSI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMJ\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e during the period 1963-2016 CE at Mt. Shiren. b) Comparison of the first-order difference sequence of the observed (solid line) and reconstructed (dot line) scPDSI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMJ\u003c/strong\u003e\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"Fig04.png","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/1030a8cfac80ff938d1d3732.png"},{"id":19586983,"identity":"9ed40a14-98c5-4ec9-8261-37e0875f4fdc","added_by":"auto","created_at":"2022-03-24 19:29:59","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1641337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the MTM power spectrum from reconstructed scPDSI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMJ\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e from 1801 to 2016 CE\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eThe red and cyan lines indicate 99% and 95%, respectively.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/c6723b8bd810d17a8aa90e2f.jpeg"},{"id":19586891,"identity":"fd9b0bb4-6542-4294-a466-1d9a6c8953d0","added_by":"auto","created_at":"2022-03-24 19:29:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe moving correlation result of regional mean scPDSI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMJ\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig06.png","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/0258fc8673204e339dbc1473.png"},{"id":19586899,"identity":"904ab70a-2c57-4469-b363-0e4d40dc04f2","added_by":"auto","created_at":"2022-03-24 19:29:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReconstructed scPDSI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMJ\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e and 11 year smoothing curve\u003c/strong\u003e (blue line) \u003cstrong\u003eat Mt. Shiren during 1801-2016.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig07.png","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/db2aeac8c97071e398945108.png"},{"id":19587009,"identity":"0318df66-dd1f-4159-8b18-f1512b99fccc","added_by":"auto","created_at":"2022-03-24 19:30:14","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":466155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial correlation between actual (left) and reconstructed (right) scPDSI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMJ\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e and scPDSI (1963-2016; 4.05early).\u003c/strong\u003e The white rectangle represents the reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e area (33.5-34.5N, 111.5-112.5E).\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/d3ac18eb36154d2b890596eb.jpeg"},{"id":19586898,"identity":"26843421-5f0f-46ae-a237-07c5e1f72e89","added_by":"auto","created_at":"2022-03-24 19:29:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":28791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of a) reconstructed \u003c/strong\u003eregional scPDSI\u003csub\u003eMJ\u003c/sub\u003e\u003cstrong\u003e, b) reconstructed PDSI from Mt. Shennong (Peng et al. 2012), and c) drought/wet index in Zhengzhou (CMA, 1981). \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe gray bars represent the same drought periods in three series.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig09.png","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/0f394b9d967baa5f8680efc8.png"},{"id":19586928,"identity":"90af5580-e9d1-45b9-ab77-b970474cf183","added_by":"auto","created_at":"2022-03-24 19:29:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":438773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial correlation analyses between actual (left) and reconstructed scPDSI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMJ\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (right). a-b) temperature, c-d) precipitation, e-f) potential evaporation, g-h) vapor pressure (CRU TS4.04, 1963-2016), i-j) soil moisture (1979-2016, CLM/EARi, 0-10cm).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/6d8dae035dd60d7c04619d66.png"},{"id":19586974,"identity":"4c039533-26b6-481f-8dad-2dee566acdb7","added_by":"auto","created_at":"2022-03-24 19:29:57","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":281154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between tree growth and global sea surface temperature \u003c/strong\u003e(SST, NASA MERRA-2 Tsfc, 1980-2016)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/12f44deccd0fa259514718a3.png"},{"id":19702783,"identity":"3c2c615c-088a-4976-96c6-01492a0163f0","added_by":"auto","created_at":"2022-03-28 20:05:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2179734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1448882/v1/8bfc0a40-cb53-4bc0-89b1-eeb1355b72cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Drought response of tree-ring width in the southern boundary of Pinus tabulaeformis from Mt. Funiu, central China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUnder global warming, the frequency and intensity of extreme weather events are likely to increase dramatically, which will have a more serious impact on ecosystems and human society and require more timely and effective responses (IPCC, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The typical continental monsoon climate in the middle and lower reaches of the Yellow River can cause the instability and variability of the climate due to the changes of monsoon strength and the early and late of monsoon rains. These changes may be result in frequent flood and drought in the region and directly related to the harvest of agricultural years. Hence the climate changes in the future may not only affect living environment and ecological security, also put forward severe challenges to the high quality and sustainable development of the middle and lower reaches of the Yellow River region.\u003c/p\u003e \u003cp\u003eThere is a long history and abundant climatic records in the middle and lower reaches of the Yellow River, and also has some reconstructed drought-flood information based on historical documents but often non-quantitative and discontinuous. Dendroclimatology is a science that reconstructs past climate changes based on tree physiology and tree radial growth characteristics (Fritts,1976;Wu, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Tree-ring data have the advantages of high resolution (annual or seasonal) and precise-continuous series for dating, and it has been successfully used to quantitatively characterize the climate change series and regarded as one of the most important proxy indicators in the past climate change research (Fritts,1976; Cook et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, dendrochronological studies in eastern China had been developing rapidly (Cai et al, 2013, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;Cheng et al, 2012, 2015, 2021; Duan et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e༛Fang et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e༛Peng et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shi et al, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e༛Zheng et al, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, dendrochronological study is still limited in Mt. Funiu of western Henan Province as a climatic transition zone (Tian et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e༛Shi et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e༛Liu et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e༛Zhao et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e༛Peng et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There are some hydroclimate reconstructions in Mt. Funiu, e.g., temperature (Shi et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e༛Tian et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e༛Liu et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e༛Yang et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), relative humidity (Liu et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e༛Peng et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), scPDSI (Zhao et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To some extent, the above studies are helpful to understand climate change in Mt. Funiu and its surrounding areas. To better reveal the impact and formation mechanism on climate change and more understood climate change affecting ecological security and high-quality development in central Plains, further study of tree rings in Mt. Funiu is necessary.\u003c/p\u003e \u003cp\u003eThis study was mainly used to compare correlation between tree-ring data and north-south climatic factors (including scPDSI grid data) in Mt. Funiu and the transitional characteristics of performance; Secondly the most significant climate limiting factors were selected to carry out climate reconstruction and analysis it\u0026rsquo;s spatial representations, and finally further to explore the possibility and potential impact of future climate change in this region.\u003c/p\u003e "},{"header":"2. Data And Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eMt. Shiren (33\u0026ordm;43\u0026prime;39.49\u0026Prime;N, 112\u0026ordm;15\u0026prime;4.39\u0026Prime;E, 1775 m a.s.l.) is located in western Henan province in central eastern Qinling Mountains, central China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is a transitional zone from subtropical to warm temperate continental monsoon climate with mildly wet summers and cold dry winters. It is also the transition zone from humid to sub-humid. The annual mean temperature is 10.0\u0026deg;C and annual amount of precipitation is 820\u0026ndash;860 mm, and mean temperature in January is -3.3\u0026deg;C while July is 20.3\u0026deg;C. The forests canopy cover is about 95%, the dominant vegetation is the mixed forests composed of temperate deciduous broad-leaved and coniferous on the top of the mountain, including \u003cem\u003eP. tabulaeformis\u003c/em\u003e carr. and \u003cem\u003eP. armandi\u003c/em\u003e Franch LC. The soil is typically brown mountain soil with 30 to 40 cm deep. The species for our study, \u003cem\u003eP. tabulaeformis Carr.\u003c/em\u003e, is mainly distributed between 1300 m and 1800 m a.s.l. The species is very sensitive to climate change because the eastern Mt. Funiu is the southern boundary of the Chinese Pine distribution (Xu,1993).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Chronology and climate data\u003c/h2\u003e \u003cp\u003eTree-ring data of \u003cem\u003eP. tabulaeformis\u003c/em\u003e Carr is from Mt. Shiren in western Henan province which had been studied tree growth response to east-west climate factors (from Luanchuan and Baofeng meteorological stations) in previous studies (Peng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The sampling site is in central eastern Mt. Funiu, central China. The reliable standard chronology from 1801 to 2016 CE, with the starting year determined by adopting a sub-sample signal strength (SSS) threshold of 0.85 (Wigley et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), was developed and used. The chronology contains high signal-to-noise ratio (SNR, 14.543) and the expressed population signal (EPS, 0.936), so it demonstrated a high level of reliability.\u003c/p\u003e \u003cp\u003eTo better understand transitional characteristics of tree growth in Mt.Funiu, the study selected Songxian meteorological station of northern Mt. Funiu and Neixiang meteorological station of southern Mt. Funiu (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) along the gradient of temperature and precipitation in the north and south (that is along gradient from warm temperate to subtropical monsoon climate). Climate factors used in this study include monthly mean temperature (T), monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin), monthly total precipitation (P), and monthly mean relative humidity (RH) during 1963\u0026ndash;2016 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe self-calibrating Palmer Drought Severity Index (scPDSI, van der Schrier et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) was chosen as a metric to measure the responses of tree growth to moisture conditions. The four grids and a regional mean of the scPDSIs between 33.5 to 34.5N and 111.5 to 112.5E (CRU scPDSI 3.26e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climexp.knmi.nl/\u003c/span\u003e\u003cspan address=\"https://climexp.knmi.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were also used in this study, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of climate data used in correlation analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate data Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude(\u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude(\u0026deg;E)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003cp\u003e(m a.s.l.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime Spaning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSongxian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e440.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1963\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeixiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1963\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003escPDSI-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1963\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003escPDSI-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1963\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003escPDSI-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1963\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003escPDSI-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1963\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional scPDSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.5\u0026ndash;34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.5-112.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1963\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical methods\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analyses were performed to identify climate-growth relationships between tree-ring standard chronology and climatic factors from two meteorological stations. Climate-growth relationships were investigated from previous March to current November to explore the potential effects of climatic factors on trees growth from the previous year to the current year.\u003c/p\u003e \u003cp\u003eThen, a simple linear regression model based on the most dominant climatic factor was selected and reconstructed. The split calibration-verification procedure was used to verify the reconstruction (Cook et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), statistical parameters for this assessment include correlation coefficient (r), R-squared (R\u003csup\u003e2\u003c/sup\u003e), sign test (ST), reduction of error (RE), and coefficient of efficiency (CE). In general, positive RE and CE indicate a rigorous and reliable reconstruction model (Cook et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpectral analyses were performed using Multi-Taper Method (MTM) (Mann and Lee 1996) to detect the periodicities of the reconstruction, and Wavelet analysis (Torrence and Compo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) was used to extract strong or weak changes for different cycle signals in the reconstruction.\u003c/p\u003e \u003cp\u003eSpatial analyses were performed between the reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e and scPDSI (1963\u0026ndash;2016; 4.05early), temperature, precipitation, potential evaporation, vapor pressure (1963\u0026ndash;2016; CRU TS4.04) and soil moisture (1979\u0026ndash;2016; CLM/EARi, 0-10cm) (references for European Climate Assessment \u0026amp; Data) to explore regional representation and possible formation mechanisms. The analyses were performed on the KNMI Climate Explorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://climexp.knmi.nl\u003c/span\u003e\u003cspan address=\"http://climexp.knmi.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e "},{"header":"3. Results","content":"\u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1. Climate-growth relationship\u003c/h2\u003e \u003cp\u003eThere are significant negative correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with T, Tmax and Tmin in current May and June (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and also significant negative correlations with Tmax in current March and April. There are similar significantly negative correlations with temperatures from two meteorological stations, which indicated the strong effect of temperature on tree growth and the influence time of Tmax to tree growth is longer. In contrast, the chronology shows mostly positive correlations with P in April-May and RH in March-June at both stations. There are also significant positive correlations between chronology and current June P in Songxian station and current July RH from the Neixiang station. Overall, temperature (T, Tmax) of southern meteorological station of Mt. Shiren showed higher correlation with tree growth while the greatest influence of humidity (P, RH) were found from northern meteorological station of Mt. Shiren.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlations between chronology and scPDSI from previous March to current November are almost all positive, especially significant from current March to August. Almost all correlations between chronology and scPDSI in May and June are over 0.6 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the highest correlation in May is scPDSI grid point (0.687, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 33.75N, 112.25E) closest to the sampling site while the highest correlation in June is regional mean scPDSI (0.663, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 33.5-34.5N, 111.5-112.5E).\u003c/p\u003e \u003cp\u003eIn a general way, seasonal climate is more stable and meaningful than single month climate to tree growth. Based on the principle that a higher correlation indicates a greater impact on tree growth, we combine the monthly data on single scPDSI grid point (33.75N, 112.25E) closest to the sampling site and regional mean scPDSI (33.5-34.5N, 111.5-112.5E). The highest correlations with chronology were both May-June scPDSI, and the highest correlation value of single grid scPDSI was 0.693 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 33.75N, 112.25E) while that of regional mean scPDSI was 0.71 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Thus, the standard chronology of \u003cem\u003eP. tabulaeformis\u003c/em\u003e Carr could be used to reconstruct regional scPDSI\u003csub\u003eMJ\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Transfer function and regional scPDSI reconstruction\u003c/h2\u003e \u003cp\u003eAccording to the above correlation resultss, the regional mean scPDSI in current May-June (scPDSI\u003csub\u003eMJ\u003c/sub\u003e) was reconstructed using following linear regression equation based on the least square method:\u003c/p\u003e \u003cp\u003escPDSI\u003csub\u003eMJ\u003c/sub\u003e = 5.514*Wt \u0026ndash; 5.21\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;54, r\u0026thinsp;=\u0026thinsp;0.71, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;50.4%, R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;49.4%, F\u0026thinsp;=\u0026thinsp;52.795, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003cp\u003eWhere, Wt is the index of tree-ring chronology for year t.\u003c/p\u003e \u003cp\u003eThe reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e could explain 50.4% of the variance in the instrumental record and 49.4% after an adjustment for the loss of the degree of freedom. A visual comparison also showed that the reconstructed mean scPDSI\u003csub\u003eMJ\u003c/sub\u003e tracked the observed scPDSI\u003csub\u003eMJ\u003c/sub\u003e well (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The first-order difference data show that there was a significant correlation (r\u0026thinsp;=\u0026thinsp;0.737, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the two scPDSI\u003csub\u003eMJ\u003c/sub\u003e sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), indicating that the reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e captured variations of the observed scPDSI\u003csub\u003eMJ\u003c/sub\u003e at both high and low frequencies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe split sample procedure was used to assess the reliability of the reconstruction. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that all parameters used for calibration and verification periods are significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and RE and CE values are positive, and ST test is also significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and high F values are in all calibration and verification time periods, which means the model is acceptable for scPDSI\u003csub\u003eMJ\u003c/sub\u003e reconstruction (Cook et al.1999).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCalibration and verification statistics for reconstructed\u003c/b\u003e scPDSI\u003csub\u003eMJ\u003c/sub\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalibration (1963\u0026ndash;1991)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerification (1992\u0026ndash;2016)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalibration (1988\u0026ndash;2016)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVerification (1963\u0026ndash;1987)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFull calibration (1963\u0026ndash;2016)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.714**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.696**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.601**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.710**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22+/7\u0026ndash;**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18+/7\u0026ndash;*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22+/7\u0026ndash;**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19+/6\u0026ndash;*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41\u0026thinsp;+\u0026thinsp;13\u0026ndash;**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**Significant at the 99% confidence levels, *Significant at the 95% confidence levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpectral analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed that the scPDSI\u003csub\u003eMJ\u003c/sub\u003e reconstruction contained 2.3a, 2.86-2.9a, 3.35a, 3.69-3.83a and 6.43a cycles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5), indicating potential ENSO (2-7a, El Ni\u0026ntilde;o-Southern Oscillation) impacts (Allan et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Sun and Wang, 2007). There are also 34.11a, 49.26a cycles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which may be related to PDO (Pacific Decadal Oscillation) or AMO (Atlantic Multi-decadal Oscillation) (d'Orgeville et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yang et al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e "},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.1 Tree growth influenced by temperature, precipitation, or scPDSI\u003c/h2\u003e \u003cp\u003ePrevious studies demonstrated that the maximum temperature (Yang et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Relative Humidity (Peng et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) from April to July were main limiting factors at Mt. Shiren. Early summer moisture signals (scPDSI) were also captured and reconstructed in the eastern Mt. Qinling (Zhao et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, tree growth is the result of the interaction of temperature and precipitation, and temperature often affects tree growth through its effects on water availability. So temperature-raised water deficiency induces water stress to suppress cell division and expansion (Fritts, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) and form narrow ring.\u003c/p\u003e \u003cp\u003eThe sampling site located in the eastern Mt. Funiu, the eastern extension of the Mt. Qinling, here is a climatic transitional belt from subtropical to warm-temperate continental monsoon. There are higher temperature and more precipitation in the subtropical zone of the southern Mt. Funiu, belong to humid climate region, while belong to sub-humid climate region in warm temperate zone of northern Mt. Funiu, so tree growths exist different response to climate in north and south Mt. Funiu. The correlation results found that tree growth in higher altitude responds similarly to climate factors on the north and south meteorological stations as described above, these are temperature and precipitation in May and June are mainly limiting factors. However, also some different response in horizontal scale which temperature (T, Tmax) of southern meteorological station (Neixiang) of Mt. Shiren had greater influence on tree growth while the greatest influence humidity (P, RH) were from northern meteorological station (Songxian).\u003c/p\u003e \u003cp\u003eTo better understand the interaction between temperature and humidity, scPDSI was used for further research. We chose 4 single grid-points and 1╳1 grid-point (33.5-34.5N, 111.5-112.5E) regional mean scPDSI near sampling site as examples to conduct correlation analyses, and found that the correlation results were consistent, with significant positively correlations from current March to August. The regional mean grid-point (33.5-34.5N, 111.5-112.5E) scPDSI contains both the sampling point and the Songxian meteorological station, and there is a good correspondence between the response of tree growth to scPDSI and the response to the precipitation of the Songxian meteorological station. This also proved that the tree growths in sub-humid areas were mainly restricted by moisture. But the highest correlation with chronology was regional mean scPDSI (r\u0026thinsp;=\u0026thinsp;0.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in current May-June, while be 0.675 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) with relation humidity and 0.583 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) with precipitation in current May-June. In other words, with temperatures rising in May-June, deficiency of soil moisture limits tree growth and produce narrow rings due to a lack of precipitation before the rainy season and a high evapotranspiration rate. However, the scPDSI as a comprehensive drought index well represents the joint effects of regional temperature, precipitation and soil moisture, so scPDSI may better reflect moisture variation.\u003c/p\u003e \u003cp\u003eThe moving correlation result shows that regional mean scPDSI\u003csub\u003eMJ\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) is the most significant and stable, hence it could be used for reconstruction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Drought variation of the reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerately to extreme drought/wet events derived from tree-ring reconstructions and the corresponding descriptions of historical records.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003escPDSI\u003csub\u003eMJ\u003c/sub\u003e (1801\u0026ndash;2016, this study)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRH\u003csub\u003eAJ\u003c/sub\u003e (1801\u0026ndash;2016, Peng, 2020)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003escPDSI\u003csub\u003eMJJ\u003c/sub\u003e (1868\u0026ndash;2005, Zhao, 2019)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHistorical records in Henan Province (CCMDC,2006)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremely dry period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1801\u0026ndash;1802\u003c/p\u003e\u003cp\u003e1807,1810\u003c/p\u003e\u003cp\u003e1813\u0026ndash;1814\u003c/p\u003e\u003cp\u003e1835\u003c/p\u003e\u003cp\u003e1847\u003c/p\u003e\u003cp\u003e1867\u003c/p\u003e\u003cp\u003e\u003cb\u003e1879\u003c/b\u003e\u0026ndash;1881\u003c/p\u003e\u003cp\u003e1891\u0026ndash;1892\u003c/p\u003e\u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1907\u003c/p\u003e\u003cp\u003e\u003cb\u003e1929\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1932\u003c/p\u003e\u003cp\u003e1935\u003c/p\u003e\u003cp\u003e1941\u003c/p\u003e\u003cp\u003e1945\u003c/p\u003e\u003cp\u003e1955\u003c/p\u003e\u003cp\u003e1968\u003c/p\u003e\u003cp\u003e1988\u003c/p\u003e\u003cp\u003e1992,\u003c/p\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u0026ndash;2001\u003c/p\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1801\u0026ndash;1802\u003c/p\u003e\u003cp\u003e1807\u0026ndash;1810\u003c/p\u003e\u003cp\u003e1813\u0026ndash;1814\u003c/p\u003e\u003cp\u003e1821\u003c/p\u003e\u003cp\u003e1835\u0026ndash;1836\u003c/p\u003e\u003cp\u003e1847\u003c/p\u003e\u003cp\u003e1867\u003c/p\u003e\u003cp\u003e\u003cb\u003e1879\u003c/b\u003e\u0026ndash;1881\u003c/p\u003e\u003cp\u003e1891\u0026ndash;1892\u003c/p\u003e\u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1907\u003c/p\u003e\u003cp\u003e\u003cb\u003e1929\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1932\u003c/p\u003e\u003cp\u003e1935\u003c/p\u003e\u003cp\u003e1941\u003c/p\u003e\u003cp\u003e1945\u003c/p\u003e\u003cp\u003e1955\u003c/p\u003e\u003cp\u003e1968,\u003c/p\u003e\u003cp\u003e1988\u003c/p\u003e\u003cp\u003e1992,\u003c/p\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u0026ndash;2001\u003c/p\u003e\u003cp\u003e2007\u003c/p\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1879\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1923,1926,\u003c/p\u003e\u003cp\u003e\u003cb\u003e1929\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1994,1995\u003c/p\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eDrought from spring to summer\u003c/p\u003e\u003cp\u003eDrought from spring to summer in 1813 and spring drought in 1814\u003c/p\u003e\u003cp\u003eSevere drought from spring to summer\u003c/p\u003e\u003cp\u003eDrought from spring to summer\u003c/p\u003e\u003cp\u003eDrought from spring to summer in Gongyi, Henan\u003c/p\u003e\u003cp\u003eA mega-drought caused a great famine over Henan and Shaanxi so on provinces in northern China in during 1876\u0026ndash;1879\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eSevere drought from spring to autumn over Henan and Shaanxi\u003c/p\u003e\u003cp\u003eDrought from spring to summer\u003c/p\u003e\u003cp\u003eSevere drought from spring to autumn over Henan, the river and pond dry up\u003c/p\u003e\u003cp\u003eDrought from summer to autumn over Henan\u003c/p\u003e\u003cp\u003eSevere spring drought over Henan\u003c/p\u003e\u003cp\u003eSevere drought from spring to autumn in 1941and 1942\u003c/p\u003e\u003cp\u003eSevere drought and locust disaster over Henan\u003c/p\u003e\u003cp\u003eSevere drought from spring to early summer\u003c/p\u003e\u003cp\u003eDrought from spring to summer in the most of Henan\u003c/p\u003e\u003cp\u003eSevere drought following the drought of 1985\u0026ndash;1987\u003c/p\u003e\u003cp\u003eSevere spring drought\u003c/p\u003e\u003cp\u003eSevere drought from February to May (the worst one since 1950)\u003c/p\u003e\u003cp\u003eSevere drought from January to February since October 2010 ( the lowest for the same period since 1951)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremely\u003c/p\u003e\u003cp\u003ewet period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1831\u003c/p\u003e\u003cp\u003e1840\u003c/p\u003e\u003cp\u003e1862\u0026ndash;1864\u003c/p\u003e\u003cp\u003e1866\u003c/p\u003e\u003cp\u003e1868\u0026ndash;1872\u003c/p\u003e\u003cp\u003e1875\u003c/p\u003e\u003cp\u003e\u003cb\u003e1885\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1894\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1898\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1901\u003c/p\u003e\u003cp\u003e\u003cb\u003e1905\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1911\u0026ndash;1913\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1915\u003c/p\u003e\u003cp\u003e1946\u003c/p\u003e\u003cp\u003e1950\u003c/p\u003e\u003cp\u003e\u003cb\u003e1983\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1984\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1991\u003c/p\u003e\u003cp\u003e1998,\u003c/p\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1869\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1883\u003c/p\u003e\u003cp\u003e1\u003cb\u003e885\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1894\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1895\u003c/p\u003e\u003cp\u003e\u003cb\u003e1898\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1905\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1906\u003c/p\u003e\u003cp\u003e\u003cb\u003e1910\u0026ndash;1912\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1933,1934,\u003c/p\u003e\u003cp\u003e1936,1944\u003c/p\u003e\u003cp\u003e1948,1949\u003c/p\u003e\u003cp\u003e1973,1980,\u003c/p\u003e\u003cp\u003e\u003cb\u003e1983\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1984\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpring and summer rains from north to south of the Yellow River\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eFlood in March in Yexian\u003c/p\u003e\u003cp\u003eFlood in summer and autumn over Henan in 1869\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eFlood in summer in Lingbao and Shanxian (northwestern Henan)\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eSevere flood in summer at Yi and Luo river (Henan), Shangnan (Shaanxi)\u003c/p\u003e\u003cp\u003eFlood in Fangcheng (Henan)\u003c/p\u003e\u003cp\u003eSevere flood in spring and summer over Henan\u003c/p\u003e\u003cp\u003ePersistent flood in summer and autumn over Henan during 1910\u0026ndash;1913\u003c/p\u003e\u003cp\u003eFlood in summer and autumn over Henan\u003c/p\u003e\u003cp\u003ePersistent rainfall in spring and summer in the most of Henan\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eRainstorm in April and May in the north Henan in 1983\u003c/p\u003e\u003cp\u003eFrom June to September, there were 5 large-scale rainstorms over Henan in 1984\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eRainstorm in September in Nanzhao (Henan)\u003c/p\u003e\u003cp\u003eNot available\u003c/p\u003e\u003cp\u003eLow temperature and rain in spring, heavy rain in summer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe five driest years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1880 (-2.999)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1835 (-2.795)\u003c/p\u003e\u003cp\u003e1955 (-2.723)\u003c/p\u003e\u003cp\u003e\u003cb\u003e1929 (-2.629)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1907 (-2.552)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1880 (57.82%)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1835 (60.05%)\u003c/p\u003e\u003cp\u003e1955 (60.24%)\u003c/p\u003e\u003cp\u003e\u003cb\u003e1929 (60.50%)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1907 (60.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1879(-3.61)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e2000(-2.94)\u003c/p\u003e\u003cp\u003e\u003cb\u003e1929(-2.53)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1926(-2.33)\u003c/p\u003e\u003cp\u003e1923(-2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDing-Wu disaster, Shanxi and Henan famine, extreme drought\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 The spatial representations of reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eSpatial correlation analyses of actual or reconstructed regional mean scPDSI\u003csub\u003eMJ\u003c/sub\u003e with scPDSI (1963\u0026ndash;2016; scPDSI, 4.05early), temperature, precipitation, potential evaporation, vapor pressure (1963\u0026ndash;2016; CRU TS4.04) and soil moisture (1979\u0026ndash;2016; CLM/EARi, 0-10cm) were performed to determine the spatial distribution and explore possible causes of drought/wet variations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3.1 Spatial representations\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e demonstrates that the actual and reconstructed regional scPDSI\u003csub\u003eMJ\u003c/sub\u003e are significant positive correlations with scPDSI (1963\u0026ndash;2016; scPDSI, 4.05early) around the study area. Obviously, the reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e may represent the drought/wetness variation in central and eastern monsoon region, especially in central China region and south adjacent area. These regions are the main grain producing areas in China, so it is very important to research drought/wet variation for grain production security in China.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn order to verify the reliability of regional representation, we compared reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e with the reconstructed PDSI of Mt. Shennong (SNPDSI, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and drought/wetness index from Zhengzhou (DWZZ, CMA 1981; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Zhengzhou). The results found that the severe drought events showed better consistency, and correlation coefficients are 0.367 (n\u0026thinsp;=\u0026thinsp;210, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) between scPDSI\u003csub\u003eMJ\u003c/sub\u003e and SNPDSI, 0.196 (n\u0026thinsp;=\u0026thinsp;200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) between scPDSI\u003csub\u003eMJ\u003c/sub\u003e and DWZZ, and 0.190 (n\u0026thinsp;=\u0026thinsp;200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) between SNPDSI and DWZZ (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These statistically validated the reliability and spatial representation of the reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe gray bars represent the same drought periods in three series.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3.2 Causes analysis of drought variations\u003c/h2\u003e \u003cp\u003eThe drought variation of a region is closely related to regional climatic elements. The spatial correlation results show significant negative correlations with temperature and potential evaporation and significant positive correlations with precipitation and vapor pressure and soil moisture in central China region and south adjacent region (e.g., Huanghuai region and the central-western Jianghuai region) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea-j), however the scale is slightly different. In terms of the perspective of affected area, temperature and soil moisture on north and south sides of the sampling site (Mt. Shiren) have similar influence on tree growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, a-b and i-j), while precipitation, potential evaporation and water pressure from the north of mountains have more influence than the south of mountains (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, c-d, e-f and g-h). These also show that drought of central China in May-June is probably mainly impacted by local temperature and moisture (including precipitation, soil moisture, potential evaporation and water pressure) on both sides of the transition zone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 The global hydro-climatic signals in reconstructed mean scPDSI\u003csub\u003eMJ\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eBased on the significant characteristics of monsoon climate in eastern China, this study continues to discuss the relationship between tree growth at sampling sites and global sea surface temperature (SST, NASA MERRA-2 Tsfc, 1980\u0026ndash;2016; The relationship between tree growth and land surface temperature has been analyzed in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea-b). Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows there are some significant negative correlations with SST from the northern Pacific Ocean and the northern Atlantic Ocean and significant positive correlation with SST from the southeastern Pacific Ocean and the northeastern Indian Ocean with reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e in this study, but the overall correlation is weak. These results also confirm that the periodic changes (2.3-6.43a ENSO cycles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5); 34.11 and 49.26 PDO or AMO cycles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01)) of tree growth in the transition zone could be related to the SST changes in the Pacific and Atlantic Oceans. The conclusion, which the drought from May to June had a certain relationship with SST from the northwestern Pacific Ocean (that is PDO cycle), was similar to previous studies (Ma, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zheng et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and same as spectral analysis previously. Example, the monsoon from the Pacific Ocean usually arrives at the middle and lower reaches of the Yangtze River in June, and the monsoon rainy season begins in the study area in late July, so the study area in May-June is a period of low rainfall. The high temperature of the northwestern Pacific Ocean surface in May-June reduces the thrust of the monsoon, making it difficult for water vapor to reach the northern China, while heat and evaporation on land lead to water shortages for limiting tree growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea-b) and forming narrow ring. The spatial correlations from Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e also show that local continental temperature influence (above 0.6) is higher than that of the Pacific Ocean surface temperature (from 0.3 to 0.5), this also indicates that sea surface temperature changes in the Pacific Ocean had a greater impact on tree growth in the study area, but the impact is weaker than that on land in May and June.\u003c/p\u003e "},{"header":"5. Conclusion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cp\u003eBased on sampling site located in an ecological sensitive area from north subtropical to warm temperate climatic transition zone, the correlation analysis between tree-ring chronology and north-south longitudinal meteorological data was conducted. The results found that temperature and precipitation in May-June and relative humidity from March to June are main limiting factors; however, the temperature in the south of the mountain and the moisture (including precipitation and relative humidity) in the north of the mountain had greater influence to tree growth.\u003c/p\u003e \u003cp\u003eThe regional scPDSI\u003csub\u003eMJ\u003c/sub\u003e is the most significant (0.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and stable to be used for reconstruction, which explains 50.4% (49.4% after adjustment of the degree of freedom) of the variance in the instrumental records from 1963 to 2016 CE. The reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e revealed that there are 29 extremely dry years (accounting for 12.96%) and 30 extremely wet years (accounting for 13.89%). The five driest years were 1880 (-2.999), 1835 (-2.795), 1955 (-2.723), 1929 (-2.629) and 1907 (-2.552) over the reconstruction period. The reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e can represent the drought variation in central and eastern monsoon region and agree with the reconstructed PDSI for Mt. Shennong and the drought/wet series in Zhengzhou which correlation coefficients are 0.367 (n\u0026thinsp;=\u0026thinsp;210, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) and 0.196 (n\u0026thinsp;=\u0026thinsp;200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), respectively.\u003c/p\u003e \u003cp\u003eFurther research found that drought of central China in May-June was mainly impacted by local temperature and moisture (including precipitation, soil moisture, and potential evaporation and water pressure) on both sides of the transition zone, and the second one is by the Pacific and the Atlantic. These results may provide better understanding of May-June drought variation and service for agricultural production in central China.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors thank Kexian Li and Lei Zhao from Mt. Shiren National Nature Reserve Bureau and Liang Chen, Kunyu Peng, and Jinrui Dan for their help during field work. The authors also thank the National Field Scientific Observation and Research Station of Forest Ecosystem in Dabie Mountains, Henan Province.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by National Natural Science Foundation of China (No. 41671042; 42077417).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eJFP and JBL conceived and designed the original study. JFP, XL, MP performed statistical analyses. XL, JYC, MP, JKL and XXW interpreted the data. JFP wrote original manuscript. JBL critically revised manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllan RJ, Lindesay JA, Parker DE (1996) El Nino Southern Oscillation and Climatic Variability. CSIRO, Collingwood, Victoria\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai QF, Liu Y (2013) The June\u0026ndash;September maximum mean temperature reconstruction from Masson pine (\u003cem\u003ePinus massoniana\u003c/em\u003e Lamb.) tree rings in Macheng, southeast China since 1879 AD. 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Trees 26(6): 1887\u0026ndash;1894\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"tree-ring, Pinus tabulaeformis Carr., drought variation, transition zone, central China","lastPublishedDoi":"10.21203/rs.3.rs-1448882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1448882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTree ring data from the southern boundary of \u003cem\u003ePinus tabulaeformis\u003c/em\u003e distribution where is the southern margin of warm temperate, the paper analyzes the response of climate factors along north-south direction to tree growth. The results show that temperature and precipitation in May-June and relative moisture from March to June are main limiting factors on tree growth; however, the temperature in the south of the mountain and the humidity in the north of the mountain have relatively greater influence on tree growth. The regional scPDSI\u003csub\u003eMJ\u003c/sub\u003e (that is PDSI in May-June) is the most significant and stable limiting factors on tree growth to be used for reconstruction, which explains 50.4% of the variance in the instrumental records from 1963 to 2016 CE. The reconstructed scPDSI\u003csub\u003eMJ\u003c/sub\u003e revealed that there were 29 extremely dry years (accounting for 12.96%) and 30 extremely wet years (accounting for 13.89%) and it can represent the drought variation in central and eastern monsoon region, and agree with the reconstructed PDSI for Mt. Shennong and the drought/wetness series in Zhengzhou, with correlation coefficients 0.367 (n\u0026thinsp;=\u0026thinsp;210, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) and 0.196 (n\u0026thinsp;=\u0026thinsp;200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), respectively. Further research found that drought of May-June in central China mainly impacted by local temperature and moisture (including precipitation, soil moisture, potential evaporation and water pressure), and then the Pacific and the Atlantic. These results may provide better understanding of May-June drought variation and service for agricultural production in central China.\u003c/p\u003e","manuscriptTitle":"Drought response of tree-ring width in the southern boundary of Pinus tabulaeformis from Mt. Funiu, central China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-24 18:15:31","doi":"10.21203/rs.3.rs-1448882/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ef175f4-5084-4c62-a69c-4910566154eb","owner":[],"postedDate":"March 24th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-03-28T08:59:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-24 18:15:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1448882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1448882","identity":"rs-1448882","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}