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

doi:10.21203/rs.3.rs-1448882/v1

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

https://doi.org/10.21203/rs.3.rs-1448882/v1

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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

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.

2.1 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 (R²), 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).

3.1. 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, R² = 50.4%, R²_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. 4a). 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. 4b), 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**
R²	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).

4.1 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 1885 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. 10a-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. 10a-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. 10a-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.

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.

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.

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

Status:

Version 1

Abstract

Figures

1. Introduction

2. Data And Methods

2.1 Study area

2.2 Chronology and climate data

2.3 Statistical methods

3. Results

3.1. Climate-growth relationship

3.2 Transfer function and regional scPDSI reconstruction

4. Discussion

4.1 Tree growth influenced by temperature, precipitation, or scPDSI

4.2 Drought variation of the reconstructed scPDSI_MJ

4.3 The spatial representations of reconstructed scPDSI_MJ

4.3.1 Spatial representations

4.3.2 Causes analysis of drought variations

4.4 The global hydro-climatic signals in reconstructed mean scPDSI_MJ

5. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

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

Status:

Version 1

Abstract

Figures

1. Introduction

2. Data And Methods

2.1 Study area

2.2 Chronology and climate data

2.3 Statistical methods

3. Results

3.1. Climate-growth relationship

3.2 Transfer function and regional scPDSI reconstruction

4. Discussion

4.1 Tree growth influenced by temperature, precipitation, or scPDSI

4.2 Drought variation of the reconstructed scPDSIMJ

4.3 The spatial representations of reconstructed scPDSIMJ

4.3.1 Spatial representations

4.3.2 Causes analysis of drought variations

4.4 The global hydro-climatic signals in reconstructed mean scPDSIMJ

5. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

4.2 Drought variation of the reconstructed scPDSI_MJ

4.3 The spatial representations of reconstructed scPDSI_MJ

4.4 The global hydro-climatic signals in reconstructed mean scPDSI_MJ