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Article

July Mean Temperature Reconstruction for the Southern Tibetan Plateau Based on Tree-Ring Width Data during 1763–2020

by
Wenzheng Nie
1,2 and
Mingqi Li
1,2,*
1
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(11), 1911; https://0-doi-org.brum.beds.ac.uk/10.3390/f13111911
Submission received: 27 September 2022 / Revised: 8 November 2022 / Accepted: 11 November 2022 / Published: 14 November 2022
(This article belongs to the Section Forest Meteorology and Climate Change)
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Abstract

:
Long-term climate records are essential for understanding past climate change and its driving forces, which could provide insights for adapting to future climate change. This paper presents a reconstruction of the July mean temperature based on the Smith fir tree-ring width data over 1763–2020 for the southern Tibetan Plateau (TP). The reconstruction explained 50.1% of the variance in the instrumental temperature records during the calibration period 1979–2020. The reconstruction matched well with other summer temperature reconstructions from neighboring regions and Northern Hemisphere temperatures. A significant warming trend was found from the 1960s, and the warming accelerated since the 1990s. In the reconstructed series, multiple-taper method analysis and wavelet analysis revealed significant periodicities of 2–4-year, 20–30-year, and 70–80-year. Moreover, the El Niño Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO) significantly influenced the July mean temperature in our study area. Our reconstruction can provide valuable data for climate change studies.

1. Introduction

The Tibetan Plateau (TP) is a key region for climate change study [1,2]. The TP has experienced significant warming in recent years, and the warming rate is faster than that of the same latitude [3,4,5,6]. Rapid climate warming has led to aggregated environmental degradation in the TP [7,8]. The instrumental data show that the warming rate significantly varies across seasons and regions [5,9,10]. However, the instrumental data on the TP cover a short period of time, which limits our understanding of climate change history and the driving mechanisms of the TP.
Tree-ring data are an important proxy for revealing historical climate variability [11,12,13]. In the past few decades, a number of climate reconstruction studies based on tree-ring data have been conducted in the TP [14,15,16,17,18,19,20]. However, the current tree-ring studies are unevenly spatially distributed, mainly in the eastern TP, where the forest cover is high. Moreover, there are some gaps in the seasons of the climate reconstruction studies. As tree-ring width (TRW) typically reflects the seasonal or annual climate [21,22,23], only in a few areas did the TRW reflect the climate of a single month, and even fewer can be used for climate reconstruction [14,24,25]. July is the month with the highest temperature in the southern TP, which has an extremely important impact on plant growth. However, to date, there has been no reconstruction of the July mean temperature in the TP.
Smith fir distributed in high elevation, especially near upper tree lines, is considered to have great potential for climate reconstruction [16,26]. However, there were few studies using this tree species for climate reconstruction. Liang et al. found that the TRW chronology of Smith fir in the Sygera Mountains was limited by summer (June–August) temperature and reconstructed the summer temperature for the period 1765–2006 [18]. However, their reconstruction did not extend to the present day and, therefore, did not capture the rapid warming trend in the recent decade, which prevented its comparison with past changes. In this study, we reconstructed the July mean temperature based on the TRW chronology for a new study area in the southern TP. We compared our reconstruction with several other reconstructions in this region and the Northern Hemisphere. Through this approach, we aimed to further understand climate change history and its possible driving forces over the past two and a half centuries.

2. Materials and Methods

2.1. Study Area

The study region is situated in the Nyingchi area, southern TP (Figure 1a), with a characteristic alpine canyon and mountain river valley landscape. Smith fir (Abies forrestii var. smithii Viguié and Gaussen) is one of the dominant species, mainly found at an altitude of 2900–4300 m. The climate of the study area is significantly influenced by the South Asian monsoon systems, characterized by warm and humid summers and cool and dry winters. Meteorological records from the Milin Station (Figure 1b) show that the annual mean temperature was 8.8 °C during 1979–2020, with 15.9 °C in July (the warmest month) and 0.3 °C in January (the coldest month). The mean annual precipitation was 696.9 mm, with approximately 67% occurring from June to September.

2.2. Tree Ring Samples and TRW Chronology Development

Our tree-ring samples were collected in July 2021 from the pure Smith fir forest in the upper tree line on the north slope of Milin (ML1) (28°42′ N/93°33′ E, 4212 m a.s.l.) and on the west slope of Langxian (LX1) (28°46′ N/93°05′ E, 4310 m a.s.l.). One or two cores (in most cases) were extracted at breast height using 10 mm diameter increment borers. A total of 56 cores from 33 living trees at ML1 and 59 from 29 living trees at LX1 were collected, and the cores were not decayed or broken. Both sampling sites were far from the village and showed no human interference.
In the laboratory, the cores were sanded using sandpaper and rigorously visually cross-dated, and then the tree-ring widths were measured using the LINTAB measurement system with 0.01 mm precision. The quality of measurements and cross-dating were checked using the COFECHA program [27]. Those samples that could not be cross-dated were removed from further analyses. In total, 107 cores from 59 trees were successfully cross-dated (51 from 30 trees in ML1 and 56 from 29 trees in LX1).
The TRW series from the two sites were highly correlated. Thus, we combined all TRW series to develop the regional chronology using the RCSigFree software (http://www.ldeo.columbia.edu/treering-laboratory/resources/software) (accessed on 28 November 2021) to remove the non-climatic and tree-age-related growth trends [28]. The negative slope linear regression was used to fit the non-climatic growth trends, and the tree-ring indices were derived by the ratios between the TRWs and the fitted values of a given year. The bi-weight robust mean was utilized to reduce the effects of outliers [29]. The expressed population signal (EPS) denotes the representativeness of a sample to the population chronology as a measure of signal quality, with a value greater than 0.85 generally regarded as satisfactory for dendroclimatic studies [30]. In addition, the first-differenced chronology was produced by calculating the deviation of the current year’s value from that of the previous year.

2.3. Climatic Data

We chose the climatic data from the nearest meteorological station to our sampling sites, Milin (94°08′ E, 29°08′ N, 2950 m a.s.l.), which started monitoring in 1979. The selected climatic variables included the monthly mean temperature (Tmean), the monthly mean minimum temperature (Tmin), the monthly mean maximum temperature (Tmax), and monthly precipitation (Pre). The climatic data were obtained from the National Meteorological Information Center (https://data.cma.cn/) (accessed on 20 March 2021).

2.4. Tree Growth–Climate Relationships, Calibration, and Verification Method

To investigate the major limiting factor and a potential target for reconstruction, Pearson’s correlation coefficients between the TRW chronology and climatic variables (Tmean, Tmin, Tmax, and Pre) were calculated during the instrumental period 1979–2020. As the tree growth could be affected by the current and previous year’s climate [31], the analysis was performed over a 14-month climate window, extending from the previous September to the current October. We also calculated the correlation coefficients between the first-differenced series of the TRW chronology and climate variables to identify tree growth–climate relationships in the high-frequency domain.
Based on the correlation analysis results, a transfer function was derived in which the TRW chronology was the independent variable, and the selected climatic factor was the dependent variable. The leave-one-out cross-validation method [32] was used to validate the transfer function. The validation statistics included Pearson’s correlation coefficient (R), the sign test (ST), the first-differenced sign test (ST1), the product-mean test (PMT), and the reduction in error (RE). The sign test and the product-mean test measure how well the estimated series follows the direction of variation in the actual series [31]. RE provides a rigorous test of shared variance between actual and estimated series, with a positive value considered as the transfer function is valid [33].
To test the spatial representativeness of the reconstruction, we also calculated the spatial correlation between the reconstructed series and the gridded dataset (CRUts 4.05) for the period 1979–2020 using KNMI Climate Explorer (https://climexp.knmi.nl/) (accessed on 1 September 2022).

2.5. Climatic Periodicity and Possible Driving Forces of July Temperature Variability

The multiple-taper method (MTM) spectral analysis and wavelet analysis were used to identify the climatic cycle in the reconstructed series. We also analyzed the possible impact of AMO and ENSO on the July temperature of the study area. The monthly sea surface temperature data of the Nino3 region (SST-Nino3) used in this study were downloaded from the Koninklijk Nederlands Meteorologisch Instituut (https://climexp.knmi.nl/) (accessed on 16 July 2022). Two AMO series reconstructed by Mann [34] and Wang et al. [35] were used in this study.

3. Results

3.1. Regional TRW Chronology

The TRW chronology (Figure 2) covered the period 1723–2020, while the chronology was considered with sufficient samples beginning in 1763, as the EPS reached 0.85. Figure 2 shows the sample depth, running EPS, and running Rbar for the TRW chronology.

3.2. Tree Growth–Climate Relationships

Figure 3 shows the correlation coefficients of the TRW chronology with Tmean, Tmax, Tmin, and Pre from the Milin Station. Significant positive correlations were found between TRW and temperature from the previous September to the current October at the 0.01 level, except for the previous October, current March, May, and October temperature (Figure 3a). The highest correlation was found between the TRW and the July Tmean, with a correlation coefficient of 0.71. The TRW was significantly and positively correlated with the current January and March precipitation and significantly negatively correlated with the current July precipitation, but these correlation coefficients were low. For the first-differenced data (Figure 3b), the highest significant positive correlations were found between the TRW chronology and the current July Tmean (r = 0.62, p < 0.001). TRW was significantly and negatively correlated with the temperature in current April and May, but the correlation coefficient was low. July precipitation was significantly and negatively correlated with TRW but still lower than that of the July Tmean. In addition, the correlation coefficients between the TRW chronology and the seasonal temperatures, such as June–August and July–August temperatures, were also calculated. Additionally, the correlation coefficients in both the raw and the first-differenced data were significantly lower than the July Tmean. Therefore, the July Tmean is the major limiting factor of tree growth at our sampling sites in both low- and high-frequency domains. Moreover, a comparison of raw data results with first-differenced data results showed that the significant positive correlation between the TRW chronology and temperature in months other than July was due to a similar warming trend.

3.3. July Mean Temperature Reconstruction

Based on the tree growth–climate relationships (Figure 3), we decided to reconstruct the July mean temperature (Tmean7) using the TRW chronology. The linear transfer function was chosen based on the linear relationship between the TRW chronology and Tmean7 (Figure 4a). The following transfer function was derived for the calibration period 1979–2020:
Tmean7 = 2.84 + 12.82TRW
The model explained 50.1.% (Radj2 = 49.8%) of the variance in the Tmean7. The leave-one-out cross-validation was used to evaluate the reliability of the transfer model (Table 1). The ST was statistically significant at the 0.01 level and ST1 at 0.05. The values of RE and PMT were high, suggesting good estimation skills. The variation in the reconstructed temperature showed good agreement with the observed temperature (Figure 4b).

3.4. Temporal Variation in the Reconstructed Temperature and Climatic Cycle

Figure 4c shows the reconstructed series for the period from 1763 to 2020. The reconstructed temperature revealed strong inter-annual and decadal variations, providing a valuable long series to evaluate local climate variability. The mean temperature was calculated using the values for the period 1763–2020, and extreme temperatures were defined using a range of ±2σ beyond the mean. The extremely cold years in the reconstructed series included 1813, 1817, and 1819, with the coldest year in 1819. The extremely warm years included 1765, 1768, 1771, 2001, 2008, 2009, 2012, 2013, 2016, 2017, 2018, and 2020. Furthermore, the warm/cold period was defined using a range of ±1σ beyond the mean in the 11-year moving running average smoothed series. According to this classification, the reconstruction series showed cold periods during 1812–1822, 1837–1842, and 1880–1894, with the coldest decade in the past 228 years being the 1810s. The warm epochs occurred during 1764–1772, 1796–1800, and 2000–2020. A rapid warming trend can be observed over the past 60 years, and the past decade has been the warmest in the past 258 years.
The spatial correlation was calculated between the reconstructed (Figure 5) and the gridded Tmean7 for the period 1979–2020 using the KNMI Climate Explorer. For the original and first-differenced data, the reconstructed Tmean7 significantly correlated with the gridded Tmean7 over a region covering approximately 25°–35° N and 85°–105° E (r > 0.5, p < 0.1), both presenting a similar spatial pattern of the correlation between the observed and the gridded data.
The MTM and wavelet analyses (Figure 6) show the 2–4-year, 20–30-year, and 70–80-year cycles in the reconstructed series. The 20–30-year climatic cycle was significant before 1860 CE and disappeared after 1860 CE. The 70–80-year climatic cycle was significant in the MTM analysis result but fell outside the confidence region in the wavelet analysis result.

4. Discussion

4.1. The Relationship between TRW and Climates

The Smith fir TRW showed the highest correlation with mean temperature in July, which was found to be the warmest month in our study area. A previous study revealed that the Smith fir of the Sygera Mountains, which were close to our study area, also showed a high correlation with the July mean temperature [16,18]. This information appears physiologically meaningful, as the Smith fir at the upper timberline with active cambial cell division occurs in July [16]. The Smith fir TRW in our study area and nearby areas was also highly correlated with the July minimum temperature, maybe because low air and soil temperatures could limit cambial activity by affecting water availability and the photosynthetic rate [18,36].
Our results highlight that there is consistency in the response of Smith fir to July temperatures, despite being located in different regions. The TRW chronology of Smith fir distributed at high elevations usually reflects temperature variations [16,18]. In contrast to other studies of Smith fir, the correlation coefficient between our TRW and July temperature was much higher. This may be due to the much colder and wetter environment in our sampling sites. In addition, the correlation coefficients between the Smith fir TRW and temperature in months other than July relatively differed from region to region.
Our Smith fir TRW showed a significant negative correlation with the spring temperature in the first-differenced chronology, which has been found in previous Yunnan fir (Abies georgei Orr var. georgei) TRW study in southeastern TP [25]. This phenomenon may indicate that the moisture condition early in spring is a limiting factor for the radial growth of Smith fir at our sampling sites. High spring temperatures can trigger drought stress by increasing evapotranspiration, delaying lignification and xylem cell production, which in turn affects radial growth [37,38]. The negative correlation between TRW and precipitation in July is because the temperature is lower in July with more precipitation.

4.2. Temperature Variation and Comparison with other Temperature Reconstructions

We compared our reconstructed temperature with two TRW- and one MXD-based temperature reconstruction for the land surface July temperature of southeastern TP [14,18,39] and Northern Hemisphere from CRU (NH1) (https://crudata.uea.ac.uk/cru/data/temperature) (accessed on 23 May 2021) (Figure 7). Our reconstructed series showed a high correlation with the reconstructed series in the nearby regions and the Northern Hemisphere’s July temperature series. The correlation coefficients between our reconstruction and Liang 2009, Zhu 2011, Duan 2019, and NH1 were 0.55, 0.30, 0.29, and 0.73, respectively. The lower correlation coefficient with Duan 2019 may be related to using different tree-ring indicators and longer distances.
On the decadal scale, several warm and cold periods of our reconstruction corresponded well with four other temperature series (Figure 7). In addition, the common modern warming in the past few decades (1990–2020) existed in all the series. However, there were differences between our reconstruction with other temperature series on the inter-annual and decadal scales. The warming rate since 1990 in our series was more rapid than other series in the TP and is slightly higher than the Northern Hemisphere’s land surface average warming rate in July. In addition, our reconstruction is more consistent with temperature variability in the Northern Hemisphere on a decadal time scale. These discrepancies were probably induced by local climatic variation and the different detrending techniques applied to the tree-ring data. We developed TRW chronologies using different detrending methods and found that the warming has occurred since the 1960s. However, the more rapid warming rate since 1990 and the warmer period 1763–1780 appeared when we applied the negative linear regression detrending method and the “signal-free” method.
Our reconstruction showed that the cold periods occurred in 1813–1822, 1837–1841, and 1882–1894. The 1810s was the coldest decade in the past 258 years in our reconstruction. This cold period occurred widely throughout the Northern Hemisphere, possibly related to multiple eruptions in combination with a solar minimum phase [9,40,41,42,43,44]. In addition, the cold period in 1813–1822 and 1882–1894 also happened in the neighboring region, which was recorded by the tree ring and historical documents. The cold period in 1837–1842 occurred in parts of the TP [18], the Northern Hemisphere [41,45], and eastern China [46], and the cold period 1882–1894 occurred in most parts of the TP [9,43,47,48] and eastern China [49].
The warm periods in our reconstruction occurred in 1764–1772, 1796–1800, and 2000–2020. The warm period in 1764–1772 occurred in parts of the TP [24,50,51] and the continental-scale temperature in Asia [44]. However, some studies on the TP presented this period as a cold period [14,18,52], which indicates a large spatial/seasonal variation in temperature during this period. The warm period in 1796–1800 occurred over a wider area of the TP [14,18,20,39,51,53]. Moreover, the instrumental and tree-ring data showed that modern climate warming significantly varied across seasons and regions. Our reconstruction stress that the rise in July mean temperature on the southern TP occurred in the 1960s, and the warming rate accelerated in the 1990s. The 2010s was the warmest phase in the past 258 years.

4.3. Possible Driving Forces for Temperature Variability in Our Study Area

The 2–4-year cycles in the reconstructed series coincided with the cyclicity (2–8 years) of the El Niño Southern Oscillation (ENSO). Our reconstructed series and April–July SSTNiño3 were significantly and positively correlated (R = 0.26, p < 0.01, and R = 0.19, p < 0.05 after the first difference) during 1870–2020. In addition, our reconstructed series and April–July Southern Oscillation Index (SOI) were significantly and negatively correlated (R = −0.14, p < 0. 1, and R = −0.20, p < 0.05 after the first difference) during 1870–2020. The approximate 2–8-year climatic cycle has been found in many studies on the TP, showing that the ENSO may have significant effects on the TP temperature [54,55].
The 20–30-year and approximate 70–80-year cycles in our reconstructed series coincided with the cyclicity (20–30-year and 50–70-year cycles) of the Atlantic Multi-Decadal Oscillation (AMO), which, as reported previously, may have an important impact on the TP [6,24,35,50]. A high positive correlation (r = 0.44, p < 0.001, and r = 0.65, df = 9, p < 0.05 for 30-year smoothing series) existed between our reconstructed series and the AMO index reconstructed by Mann (2009) [34] for the overlapping period 1763–2006. In addition, our reconstructed series was significantly and positively correlated with the AMO index reconstructed by Wang et al. (2017) [35] over the period 1763–2010 (r = 0.38, p < 0.001, and r = 0.65, df = 9, p < 0.05 for 30-year smoothing series).

5. Conclusions

We developed a well-replicated Smith fir TRW chronology on two new sampling sites in the southern TP. Based on the tree growth–climate relationship, we reconstructed the July mean temperature in southern TP for the period 1763–2020. The transfer function represented 50.1% of the variance in the observed temperature for the calibration period of 1979–2020. The reconstruction presented three cold periods, namely 1812–1822, 1837–1842, and 1880-1894, with the coldest decade in the past 258 years being the 1810s. The warm periods occurred during 1764–1772, 1796–1800, and 2000–2020, and a significant warming trend has happened since the 1960s. In addition, the ENSO and AMO have significantly affected the July mean temperatures in the southern TP. This study could provide an important reference for climate change study in the TP and is also helpful for further understanding the climate response of the Smith fir.

Author Contributions

Methodology, W.N.; validation, W.N.; formal analysis, W.N.; resources, M.L.; data curation, W.N.; writing—original draft preparation, W.N.; writing—review and editing, M.L.; visualization, W.N.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Second Tibetan Plateau Scientific Expedition and Research Program of China (grant No. 2019QZKK0301) and the National Natural Science Foundation of China (grant No. 41977391 and 41571194).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study may be available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations of the tree ring sampling sites and the Milin Meteorological Station (a) and temperature and precipitation data from the Milin Meteorological Station during 1979–2020 (b).
Figure 1. Locations of the tree ring sampling sites and the Milin Meteorological Station (a) and temperature and precipitation data from the Milin Meteorological Station during 1979–2020 (b).
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Figure 2. (a) Composite tree-ring width (TRW) chronology (thin black line) with an 11-year moving running average smoothing filter (heavy red line) and the sample depth (grey line); (b) the running expressed population signal (EPS) calculated over 51 years with a 1-year lag. The dotted line indicates the 0.85 criterion; (c) Rbar statistics calculated over 51 years with a 1-year lag in the chronology.
Figure 2. (a) Composite tree-ring width (TRW) chronology (thin black line) with an 11-year moving running average smoothing filter (heavy red line) and the sample depth (grey line); (b) the running expressed population signal (EPS) calculated over 51 years with a 1-year lag. The dotted line indicates the 0.85 criterion; (c) Rbar statistics calculated over 51 years with a 1-year lag in the chronology.
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Figure 3. Correlation coefficients between tree ring width (TRW) chronology and climatic factors (monthly mean temperature, monthly mean maximum/minimum temperature, and monthly precipitation) for the original (a) and first–differenced (b) data.
Figure 3. Correlation coefficients between tree ring width (TRW) chronology and climatic factors (monthly mean temperature, monthly mean maximum/minimum temperature, and monthly precipitation) for the original (a) and first–differenced (b) data.
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Figure 4. Scatter plot of the observed and estimated July mean temperature, regression line (red line) and equation (a); graph of the observed and estimated July mean temperature for the calibration period 1979–2020 (b); reconstructed July mean temperatures (thin black line) and 11-year moving running average smoothing filter (thick red line) both based on tree-ring width (TRW) data from 1763 to 2020, the gray area denotes the confidence interval at 95%, the red dashed lines denote the mean ±1σ, and the black dashed lines denote the mean ±2σ (c).
Figure 4. Scatter plot of the observed and estimated July mean temperature, regression line (red line) and equation (a); graph of the observed and estimated July mean temperature for the calibration period 1979–2020 (b); reconstructed July mean temperatures (thin black line) and 11-year moving running average smoothing filter (thick red line) both based on tree-ring width (TRW) data from 1763 to 2020, the gray area denotes the confidence interval at 95%, the red dashed lines denote the mean ±1σ, and the black dashed lines denote the mean ±2σ (c).
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Figure 5. Spatial correlation fields of the observed (a,b) and reconstructed (c,d) Tmean7 with the gridded Tmean7. Spatial correlation for the original (a,c) and first-differenced (b,d) data for the period 1979–2020. The green dots show the locations of sampling sites, and the black flag shows the location of the meteorological station.
Figure 5. Spatial correlation fields of the observed (a,b) and reconstructed (c,d) Tmean7 with the gridded Tmean7. Spatial correlation for the original (a,c) and first-differenced (b,d) data for the period 1979–2020. The green dots show the locations of sampling sites, and the black flag shows the location of the meteorological station.
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Figure 6. Multiple-taper method (MTM) spectral analysis (a) and wavelet analysis (b) of the reconstructed July temperature. The blue (red) solid line shows the 95% (90%) confidence level in (a); the black line in (b) denotes the 95% confidence level.
Figure 6. Multiple-taper method (MTM) spectral analysis (a) and wavelet analysis (b) of the reconstructed July temperature. The blue (red) solid line shows the 95% (90%) confidence level in (a); the black line in (b) denotes the 95% confidence level.
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Figure 7. Comparisons of the July mean temperature reconstruction (a) with temperature series in the Tibetan Plateau (TP) (c,d) and Northern Hemisphere (NH) (b) temperature series; (b) Northern Hemisphere’s land surface July temperature from CRU; (c) June–August mean temperature reconstruction in the Sygera Mts. in southeast TP [18]; (d) MXD–based August–September mean temperature reconstruction in southeast TP [39]; (e) August mean temperature reconstruction in southeast TP [14].
Figure 7. Comparisons of the July mean temperature reconstruction (a) with temperature series in the Tibetan Plateau (TP) (c,d) and Northern Hemisphere (NH) (b) temperature series; (b) Northern Hemisphere’s land surface July temperature from CRU; (c) June–August mean temperature reconstruction in the Sygera Mts. in southeast TP [18]; (d) MXD–based August–September mean temperature reconstruction in southeast TP [39]; (e) August mean temperature reconstruction in southeast TP [14].
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Table
Table 1. Statistics of calibration and validation results.
Table 1. Statistics of calibration and validation results.
Calibration Leave-One-Out Verification
PeriodR2Radj2FSE rSTST1PMTRE
1979–202050.1%49.8%41.20.5 0.8132+/10− **29+/12− *4.50.47
SE: the standard error; ST: sign test; ST1: first-difference sign test; PMT: the product-mean test; RE: the reduction in error; * p < 0.05; ** p < 0.01.
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Nie, W.; Li, M. July Mean Temperature Reconstruction for the Southern Tibetan Plateau Based on Tree-Ring Width Data during 1763–2020. Forests 2022, 13, 1911. https://0-doi-org.brum.beds.ac.uk/10.3390/f13111911

AMA Style

Nie W, Li M. July Mean Temperature Reconstruction for the Southern Tibetan Plateau Based on Tree-Ring Width Data during 1763–2020. Forests. 2022; 13(11):1911. https://0-doi-org.brum.beds.ac.uk/10.3390/f13111911

Chicago/Turabian Style

Nie, Wenzheng, and Mingqi Li. 2022. "July Mean Temperature Reconstruction for the Southern Tibetan Plateau Based on Tree-Ring Width Data during 1763–2020" Forests 13, no. 11: 1911. https://0-doi-org.brum.beds.ac.uk/10.3390/f13111911

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