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Article

Response of Larix sibirica Radial Growth to Climate Change in Kanas, Northern Xinjiang, China

by
Jiannan Hou
1,2,
Feng Chen
3,4,* and
Jianrong Li
1,2
1
College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
2
Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Xinjiang Normal University, Urumqi 830054, China
3
Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
4
Southwest United Graduate School, Yunnan University, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(12), 2137; https://doi.org/10.3390/f15122137
Submission received: 30 October 2024 / Revised: 21 November 2024 / Accepted: 26 November 2024 / Published: 3 December 2024
(This article belongs to the Section Forest Meteorology and Climate Change)
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Abstract

:
Understanding how forest ecosystems respond to climate variability is critical for predicting the impacts of climate change on semi-arid and temperate regions. This study examines the climatic drivers of radial growth in Larix sibirica Ledeb in the Kanas Lake region, northern Xinjiang, China, to explore how climate change may alter forest growth patterns. Using tree-ring chronologies, we examine the relationships between temperature, precipitation, and drought conditions, as well as the influence of large-scale atmospheric circulation patterns on growth. Results indicate that high summer temperatures negatively affect tree growth, while adequate precipitation plays a crucial role in mitigating water stress, especially during key growth periods. Positive correlations with the Palmer Drought Severity Index further underscore the importance of long-term moisture availability. Moreover, the study highlights the role of the El Niño-Southern Oscillation in influencing moisture transport, with significant correlations between sea surface temperatures in the Niño 4 region and tree-ring growth. Future growth simulations under two climate scenarios suggest that moderate warming (SSP 2–4.5) may enhance growth, while more extreme warming (SSP 5–8.5) introduces greater uncertainty and potential growth instability. These findings provide critical guidance for forest management strategies in the face of climate change.

1. Introduction

Climate change is reshaping ecosystems worldwide, significantly altering hydrological cycles, temperature regimes, and the frequency of extreme events such as droughts and heatwaves [1]. Forest ecosystems, which play a crucial role in the global carbon cycle and in regulating climate, are particularly vulnerable to these changes [2]. Increasing temperatures and shifts in precipitation patterns are affecting forest health, productivity, and resilience, leading to changes in growth patterns, species distributions, and ecosystem services [3]. Understanding how forests respond to these changes is critical, as their role in sequestering carbon, regulating water cycles, and supporting biodiversity will be increasingly challenged in the coming decades [4].
Forests in temperate and semi-arid regions, such as those in northern Xinjiang, China, are particularly sensitive to climate variability. Their growth is often constrained by water availability, making these ecosystems particularly vulnerable to changes in precipitation and atmospheric moisture [5]. The dynamics of tree growth in such regions provide valuable insights into how these ecosystems may respond to ongoing and future climatic changes [6]. In this context, dendroclimatology—through the study of tree-ring chronologies—offers a powerful tool for reconstructing past climate variability and understanding tree growth responses to changing environmental conditions [7,8,9]. Tree rings not only capture annual growth patterns but also reflect the complex interplay between temperature, moisture availability, and atmospheric circulation on forest ecosystems [5].
The semi-arid forests around Kanas Lake, dominated by Larix sibirica Ledeb, are both ecologically significant and highly sensitive to climatic changes. Larix sibirica is particularly responsive to summer temperature and moisture variability, which are critical for its growth [7]. Existing studies have established that temperature and precipitation are key drivers of tree growth in this region, with significant correlations between tree-ring width and seasonal climate variables [7,10]. However, these studies often fall short of fully capturing the broader climatic mechanisms, particularly the large-scale atmospheric circulation patterns that govern moisture delivery to this region [5].
A growing body of research highlights the importance of ocean–atmosphere interactions, such as those driven by sea surface temperature (SST) anomalies, in influencing regional climate variability [11,12,13,14,15]. The impact of large-scale circulation patterns, including the transport of moisture from the Pacific and Arctic regions, plays a crucial role in determining precipitation regimes in northern Xinjiang [5,13]. Despite this recognition, few studies have employed advanced methodologies like backward trajectory analysis to trace the specific pathways of atmospheric moisture that influence tree growth in this region [16]. Moreover, while the link between tree growth and climate drivers is well established, there remains a lack of predictive studies that explore how these relationships will evolve under future climate scenarios.
This study seeks to bridge these gaps by integrating tree-ring chronologies with atmospheric circulation analyses, offering a more comprehensive view of the climate drivers shaping Larix sibirica growth. Using correlation analyses, spectral methods, and backward trajectory modeling, we trace moisture sources and evaluate their impact on tree growth during critical periods. Furthermore, this study extends beyond the current understanding of climate–growth relationships by incorporating future growth simulations under projected climate scenarios, providing insights into how ongoing climate change may affect the growth and health of these forests.

2. Materials and Methods

2.1. Research Area Description

The Altai Mountains are located in the center of Asia, with a northwestern-southeastern direction, stretching between Russia, Mongolia, China, and Kazakhstan. The middle part of the southern slope of the mountain range is located in China and belongs to Altay Prefecture, Xinjiang. Kanas Lake (48°03′~48°14′ N, 86°59′~87°07′ E, Figure 1a,b) is located in the northwestern part of Altay Prefecture, which is an alpine freshwater lake close to the border of four countries, and also the second deepest water lake and the deepest moraine weir lake in China. The lake has an elevation of 1370 m, a length of 24 km, a width of 1.2~2.55 km, and an average depth of 120.1 m, and the deepest depth is close to 200 m [17,18]. Observation data from Habahe (HBH), Burzin (BEJ) and Altay (ALT) meteorological stations show that the average annual temperature in Kanas is 4.6 °C, and the average monthly temperature is below 0 °C for as long as 7 months, with the average temperature in July, the hottest month, being 22.2 °C, and the average temperature in January, the coldest month, being −16.0 °C. The annual precipitation ranges from 58.2 to 359.7 mm, with May to August being the concentrated period of rainfall, which accounts for about half of the total annual precipitation (Figure 1c, Table 1). The overall climate is humid and well-watered. The winters are long and cold, and there is no summer at all, with spring and fall coming together. Air currents from the Atlantic and Arctic Oceans enter through the Irtysh River valley, producing abundant precipitation as they rise over the mountains. Forests dominate shady and semi-shady slopes as well as river valleys, while meadows develop on sunny slopes, and swamp vegetation appears in stream depressions and along lakeshores. Vegetation includes mixed forests of Larix sibirica, Picea obovata, and broad-leaved species such as Betula ubescens and populus euphratica Oliv, alongside pure forest patches [18,19].

2.2. Tree-Ring Data

In the fall of 2013, we collected 40 tree-ring cores from 20 Larix sibirica trees in the Jiadengqiao region, located in the southeastern part of Kanas Lake (JDQ, Figure 1a,b, Table 1). The sampling was conducted at breast height (1.3 m) using an increment borer with an inner diameter of 10 mm. To account for potential growth asymmetry, we cored each tree twice in opposite directions (e.g., North-South or East-West), where possible, to ensure a representative sample of radial growth. The sampling sites were carefully selected to be minimally impacted by human activities and showed no visible signs of disease or recent disturbance, which helps to reduce the influence of non-climatic factors such as soil conditions, disease, and competition. Additionally, the sampled trees were spaced sufficiently apart to limit the effects of direct competition on growth.
The cores were prepared for dendrochronological analysis by attaching them to grooved wooden mounts and allowing them to air dry. Afterward, we sanded the surface using progressively finer sandpaper (320–600-grit) to clearly reveal the tree-ring boundaries. Ring widths were measured to an accuracy of 0.001 mm under a binocular microscope using the Lintab measurement system, which was interfaced with the Time Series Analysis Program (Frank Rinntech, Heidelberg, Germany). Cross-dating accuracy was visually confirmed and further validated using the COFECHA (version 6.02P) program [20].
The chronology was developed using the ARSTAN (version 44xp) program [21]. To preserve the climatic signal, non-climatic trends were removed by fitting a negative exponential curve to each core [22]. The Briffa Rbar-weighted method was used to stabilize variance [23]. The reliability of the chronology was assessed by calculating the Expressed Population Signal (EPS) and mean inter-series correlation (Rbar) [24]. As shown in Figure 2a, Sample depth represents the number of tree-ring series included for each year. A depth of 6 or more, achieved after 1840, indicates sufficient replication to ensure reliability. EPS (Figure 2b) quantifies how well the chronology captures the population growth signal, with values above 0.85 considered reliable. In our study, EPS exceeds 0.95 after 1840. Rbar (Figure 2c), which measures coherence among tree-ring series, also stabilizes during this period, confirming the chronology’s robustness for climate signal analysis.

2.3. Data Sources

(1) Observed monthly mean precipitation, temperature, maximum temperature, and minimum temperature data for the period 1961–2013, with no missing values, were obtained from the Habahe, Burzin, and Altay meteorological stations (Figure 3).
(2) Global monthly precipitation and temperature data were obtained from the Climatic Research Unit Time-Series version 4.06 (CRU TS4.06) dataset [25], which has a resolution of 0.5° × 0.5°. Precipitation and temperature data for the period 1954–2013 were extracted within the spatial extent of 87° E–88° E and 47° N–48° N, corresponding to the sampling locations (Figure 3). Additionally, global monthly Palmer Drought Severity Index (PDSI) grid data were sourced from the CRU scPDSI 4.05 early dataset, also at a resolution of 0.5° × 0.5°, covering the same time period and spatial region [26].
(3) The Normalized Difference Vegetation Index (NDVI) was obtained from the 0.5° × 0.5° NOAA/NCEI CDR NDVI analysis month-by-month grid point dataset provided by the National Center for Environmental Information (NCEI) for regions between 47° N–48° N and 87° E–88° E during the years from 1981 to 2013 [27].
(4) Sea surface temperature (SST) data were sourced from the HadISST1 dataset, which employs a 0.5° × 0.5° gridding system [28], providing monthly SST data globally covering the period from 1961 to 2013.
(5) The Niño 4 mean data is obtained from the Extended Reconstructed Sea Surface Temperature version 5 dataset, provided by the Climate Prediction Center. This dataset offers high-resolution SST data, specifically for the Niño 4 region (5° N–5° S, 160° E–150° W), and is used to compute SST anomalies that reflect variations in the central equatorial Pacific [29].
(6) Two Shared Socioeconomic Pathway (SSP) scenarios and 33 models from the Coupled Model Intercomparison Project Phase Six (CMIP6) database were selected to consider the probabilities and uncertainties of future climate change (Table 2). These SSPs integrate historical simulations influenced by both natural and anthropogenic factors, reflecting various environmental and population growth scenarios. The SSPs represent distinct radiative forcing pathways (Table 2): SSP 2–4.5 (+4.5 W m−2; medium forcing) and SSP 5–8.5 (+8.5 W m−2; high-end forcing). The simulations provide diagnostics for precipitation rate (pr; mm day−1) and temperature (tas; °C day−1). To mitigate individual model uncertainties and biases, we employed a multimodel ensemble approach [30,31].

2.4. Methods

Pearson correlation analysis was conducted to assess the response of tree-ring width chronologies to monthly climate variables, including precipitation, average temperature, maximum temperature, minimum temperature, Palmer Drought Severity Index, and Normalized Difference Vegetation Index. The climate variables covered the period from March of the previous year to December of the current year. The most significant climate periods were identified for further analysis, which facilitated the selection of optimal time frames for spatial correlation analysis. This spatial correlation analysis aimed to explore the geographic patterns of how these climate factors influence tree growth across the study area [22].
After the response analysis, we focused on the impact of atmospheric circulation on tree growth. Correlation analyses between tree-ring width and sea surface temperature anomalies revealed significant regions, suggesting potential oceanic moisture sources [32]. Building on this, multi-taper method spectral analysis was applied to detect significant periodicities in the tree-ring width chronologies, utilizing confidence levels of 95% and 99% to identify spectral peaks [33]. This analysis highlighted temporal patterns in tree growth, indicating moisture indices associated with these periodicities and supporting the influence of large-scale ocean-atmosphere interactions. Additionally, moisture flux composites were analyzed for the top 10 widest and narrowest tree-ring growth years using NCEP/NCAR reanalysis data to understand water vapor sources during extreme growth years. This was complemented by backward trajectory analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, tracing the origin of the water vapor and enhancing our understanding of the climate drivers affecting tree growth [34]. The backward trajectory analysis is based on the HYSPLIT model developed by the National Oceanic and Atmospheric Administration. For this study, we generated 240 h backward trajectories at daily intervals in May during the growing season for the years of interest. Trajectories were calculated at multiple vertical levels (e.g., 500 m, 1000 m, and 1500 m above ground level) to capture the influence of both surface and upper-level winds [34].
Finally, we simulate tree growth using the VS-Lite model and monthly precipitation and temperature data from CMIP6. The VS-Lite model uses a “leaky bucket model” to estimate soil moisture based on temperature and precipitation data [35]. Day length is determined by the latitude of the site, with no inter-annual variability for a given date [35]. For each year, the model simulates the radial growth of the tree starting from the minimum monthly growth response to air temperature and soil moisture, which is regulated by insolation. In this case, the temperature and moisture response parameters were determined by a Bayesian parameter estimation method, and the other parameters were set to default values [36]. To validate the model, we first used historical CMIP6 data to simulate tree-ring width values and calculated correlations between these simulated values and the observed tree-ring width indices. After ensuring the reliability of the simulated historical tree-ring width data, we then used future CMIP6 projections to simulate potential future tree growth under various scenarios.

3. Results

3.1. Response of Tree-Ring Width Index to Climate Factors

The correlation analysis between tree-ring width chronologies and climate variables, coupled with the spatial correlation analysis, reveals clear patterns of climate influence on Larix sibirica growth across the study area (Figure 4 and Figure 5).
For temperature, the tree-ring width chronologies exhibit strong negative correlations with mean temperature and maximum temperature during the previous summer (June to August) across all meteorological stations (Figure 4). This pattern is confirmed by the spatial correlation analysis (Figure 5a,b), where significant negative correlations extend to the west of the sampling sites (eastern Kazakhstan), indicating that elevated temperatures, particularly during summer, are associated with reduced tree growth. Negative correlations with minimum temperature (Figure 5c) are also present but are weaker compared to mean and maximum temperatures.
Precipitation plays a contrasting role, showing significant positive correlations with tree-ring width during key months in both the previous and current growing seasons. Specifically, July precipitation in the previous year and June or July precipitation in the current year are strongly correlated with growth at all stations (Figure 4). The spatial analysis (Figure 5d) further supports this, revealing significant positive correlations between tree growth and precipitation from January to July of the current year over a broad area.
The correlation with PDSI (Figure 4 and Figure 5e) is consistently positive from March of the previous year through December of the current year, underscoring the importance of drought conditions. The spatial correlation analysis highlights a widespread positive relationship between higher PDSI values (indicating less drought severity) and increased tree growth, particularly in the western part of the sampling site (northeastern Kazakhstan).
Lastly, NDVI shows a significant positive correlation with tree-ring width during the late growing season (September to November of the current year) (Figure 5f), suggesting that higher vegetation productivity in these months is associated with greater radial growth.

3.2. Atmospheric Circulation Driving Mechanisms

The spatial correlation analysis between the tree-ring width chronology and SST (Figure 6a) shows significant positive correlations in the central and western tropical Pacific Ocean, with the strongest correlations centered in the Niño 4 region. This suggests that SST anomalies in this region exert a notable influence on the radial growth of Larix sibirica. MTM spectral analysis (Figure 6b) identifies significant tree-ring periodicities at 14–15, 3.5–4.3, and 2.3–2.4 years (p < 0.05), consistent with El Niño–Southern Oscillation (ENSO) cycle frequencies. These findings indicate a strong relationship between tree growth and ENSO-related climate variability. Additionally, the correlation analysis between the tree-ring width index and the Niño 4 index from January to July (Figure 6c) further supports this connection, with a statistically significant positive correlation (r = 0.27, p < 0.05). This relationship highlights the influence of ENSO-driven SST anomalies on moisture availability and climate conditions in the study region, which, in turn, affects the growth patterns of Larix sibirica.
The water vapor synthesis analysis (Figure 7a,b) reveals the moisture transport patterns during the highest 10 years (Figure 7a) and lowest 10 years (Figure 7b) in the tree-ring chronology. During the highest growth years, moisture predominantly originates from the Pacific Ocean, with strong water vapor fluxes directed toward the study region from the west and southwest. This suggests that abundant moisture supply from the Pacific is critical for enhanced tree growth. In contrast, during the lowest growth years, moisture fluxes weaken, and the predominant sources shift slightly, indicating less favorable conditions for moisture transport to the region, likely contributing to reduced tree growth.
The backward trajectory analysis for 1958 (highest growth year) (Figure 7c) and 1974 (lowest growth year) (Figure 7d) further supports these findings. In 1958, moisture primarily originated from the Pacific Ocean, with some contribution from the Arctic region, facilitating optimal moisture conditions for tree growth. However, in 1974, moisture sources were more limited, with weaker transport from the Pacific and more dependence on regional sources, resulting in drier conditions and suppressed tree growth. The trajectory differences between the two years highlight the crucial role of Pacific Ocean moisture in supporting Larix sibirica growth, particularly during favorable climatic conditions.

3.3. Future Growth Simulation

The comparison between the actual tree-ring width chronology and the simulated values from the VS-Lite model (Figure 8a) shows a significant correlation (r = 0.426, p < 0.01), indicating that the model captures the general trends in tree growth, though with some deviations, particularly in capturing extreme growth variations. In the future radial growth simulation of trees under SSP 2–4.5 and SSP 5–8.5 scenarios, the simulated results show an overall growth trend; however, the growth pattern is not entirely linear. The growth rates derived from linear regression models (slope of 0.0369 for SSP 2–4.5 with r2 = 0.8665; slope of 0.0311 for SSP 5–8.5 with r2 = 0.6171) reflect only the linear component of the growth trend and do not capture the nonlinear characteristics in the model, such as fluctuations and variability. Under the high-emission SSP 5–8.5 scenario, the variability and uncertainty of tree radial growth are more pronounced, indicating stronger nonlinear growth characteristics. This suggests that extreme climate conditions under SSP 5–8.5 may introduce additional stressors, leading to a more complex and unstable future growth pattern. In contrast, the stronger correlation in SSP 2–4.5 reflects a more linear relationship and reduced variability around the growth trend. While both scenarios show an upward trend in growth, SSP 2–4.5 exhibits a more consistent pattern with less fluctuation, compared to the greater variability seen under SSP 5–8.5.

4. Discussion

4.1. Climate Impacts on Larix sibirica Growth

The climatic response of Larix sibirica in the Kanas region reflects broader patterns observed in temperate and semi-arid ecosystems, where growth is tightly regulated by a combination of temperature, precipitation, and drought conditions. As noted in numerous studies, the significant sensitivity of Larix species to summer temperatures, particularly during the growing season, is indicative of the thermal thresholds that constrain growth in these regions [9,37]. High summer temperatures exacerbate evapotranspiration and soil moisture depletion, leading to heat stress and reduced carbon assimilation, which manifests in narrower tree rings. These findings are well-aligned with established research that has highlighted how conifers, especially in semi-arid environments, exhibit growth suppression under elevated temperature regimes [7,10,38,39,40].
However, while high temperatures present a limiting factor, precipitation plays a crucial mitigating role. The positive correlation between summer rainfall and radial growth underscores the dependency of Larix sibirica on adequate moisture supply, particularly during key growth periods. This reliance on precipitation is a hallmark of coniferous species in semi-arid regions, where water availability often dictates the extent of tree-ring formation [14]. These findings add to a growing body of literature demonstrating that sufficient summer rainfall can alleviate temperature-induced water stress and promote tree growth in water-limited environments [9]. The spatial consistency of this pattern across the study area reaffirms that water availability, especially during the summer months, is a critical driver of forest productivity in the region.
The role of drought, as reflected in the positive relationship with the Palmer Drought Severity Index (PDSI), further emphasizes the importance of long-term moisture conditions in shaping tree growth. The sustained correlation between PDSI and growth indicates that Larix sibirica is highly sensitive to even moderate drought, with multi-year drought events causing cumulative moisture deficits and prolonged growth reductions [7]. These findings align with research from similar ecosystems in Central Asia, where drought has been shown to be a primary factor limiting forest productivity and health [37]. The ability of trees to recover from drought stress and their resilience to prolonged moisture deficits are crucial considerations for forest management, particularly in the context of increasing climate variability.
The observed relationship between tree growth and NDVI provides additional insight into the broader ecosystem interactions that influence Larix sibirica growth. NDVI, as an indicator of vegetation productivity, correlates positively with tree-ring width during periods of favorable environmental conditions, such as adequate moisture and moderate temperatures. This linkage suggests that Larix sibirica growth is not only shaped by direct climatic factors but also by the overall health and productivity of the surrounding ecosystem. Studies linking NDVI to tree growth have similarly highlighted that periods of enhanced vegetation greenness reflect optimal conditions for growth, where the ecological balance between soil moisture, temperature, and photosynthetic activity supports forest vitality [9]. This broader perspective reinforces the idea that tree growth is a complex response to both immediate climatic variables and overarching ecosystem processes.

4.2. Atmospheric Circulation’s Impact on Tree Growth

The ENSO plays a significant role in shaping global climate patterns, particularly through its modulation of ocean-atmosphere interactions, which influences moisture transport in many climate-sensitive regions, including the Altai Mountains. In this study, we found a positive correlation between the radial growth of Larix sibirica and SST in the Niño 4 region, particularly during El Niño phases, when enhanced Pacific moisture transport creates favorable growth conditions. Similar findings have been reported by Pang et al. (2023), who showed that ENSO-driven precipitation variations in the Altai Mountains have a substantial impact on local moisture availability, reinforcing the key role of large-scale ocean-atmosphere interactions in shaping tree-ring growth [39].
ENSO’s influence extends beyond simple precipitation changes by altering moisture transport pathways. HYSPLIT-based backward trajectory analysis reveals a distinct contrast between high-growth and low-growth years. During high-growth years, moisture originates predominantly from the Pacific, consistent with findings from Central Asian regions such as Tajikistan, where Chen et al. (2019) demonstrated that ENSO influences moisture transport, directly affecting tree-ring growth through atmospheric teleconnections [41]. In contrast, low-growth years show a shift toward more regional moisture sources, leading to drier conditions and increased drought stress, as seen in the southern Altai Mountains, where Jiang et al. (2020) found that reduced Pacific moisture transport correlated with decreased tree growth during dry periods [7].
In addition to ENSO, other atmospheric systems such as the Westerlies and the Arctic Oscillation significantly affect regional moisture patterns. Wang et al. (2020) highlighted the importance of the Westerlies in shaping precipitation patterns across Siberia and Central Asia, where moisture from the Atlantic dominates during specific periods [42]. These findings emphasize the complex interplay between multiple atmospheric circulation systems. Moreover, Xiao et al. (2024) demonstrated that interactions between the East Asian summer monsoon and the Westerlies also contribute to precipitation variability in the Altai region, suggesting that the convergence of different climate systems plays a crucial role in moisture availability [43].
Thus, while ENSO remains a predominant driver of moisture transport in this study, the combined effects of the Westerlies and other systems must also be considered. Future research should explore how these interactions influence tree growth dynamics across the Altai Mountains and other Central Asian regions.

4.3. Implications of Future Climate Scenarios for Larix sibirica Growth

The application of the V-S Lite model provides essential insights into how Larix sibirica may respond to future climate conditions in the Kanas Lake region. Simulations under two emission scenarios, SSP 2–4.5 and SSP 5–8.5, indicate an upward trend in tree growth. However, the rate of growth increase is notably more stable under SSP 2–4.5 compared to SSP 5–8.5, where greater variability and uncertainty in growth patterns are observed. This differentiation emphasizes the potential impact of extreme climate scenarios on growth stability, aligning with previous studies on the sensitivity of tree growth to temperature and moisture in semi-arid environments [44,45,46].
Moderate warming under SSP 2–4.5 appears to create conditions favorable for tree growth, aligning with findings in other temperate and semi-arid regions where controlled warming can extend growing seasons and enhance productivity [45,47]. However, the variability observed under the higher emissions scenario (SSP 5–8.5) introduces greater uncertainty. This aligns with the understanding that extreme climate conditions can disrupt the delicate balance between temperature, soil moisture, and physiological processes essential for tree growth. Studies using process-based models like V-S Lite have similarly shown that extreme warming, without sufficient moisture, can severely hinder tree growth in moisture-limited environments [44,47].
The results highlight the importance of water availability as a critical limiting factor in semi-arid environments like the Kanas region, as has been observed in similar studies of Pinus sylvestris var. mongolica and Larix gmelinii [46]. While the V-S Lite model effectively simulates broader climate impacts, its inherent limitations—such as its assumption of static physiological responses—must be acknowledged [48]. The simplification of physiological processes in the V-S Lite model, particularly, the exclusion of intra-annual variations in soil moisture and temperature, suggests that actual tree responses to future climates may be more complex than the model captures. These limitations highlight the need for integrating more detailed, process-based models to better capture tree adaptability and growth variability under prolonged climate stress [44].
In conclusion, while both moderate and extreme warming scenarios indicate increased growth potential, the increased variability and uncertainty under SSP 5–8.5 underscore the critical role of emissions mitigation. Future work should continue to refine growth models by incorporating more dynamic physiological processes and considering the potential for long-term adaptations in tree growth to better predict the impacts of climate change on Larix sibirica and similar species.

5. Conclusions

This study enhances our understanding of the climatic drivers influencing Larix sibirica growth in the Kanas Lake region. The findings indicate that temperature and precipitation, particularly during the summer months, play a critical role in shaping tree-ring width, with warmer temperatures often limiting growth and sufficient precipitation supporting it. The backward trajectory analysis further clarified the sources of moisture influencing the region, revealing that both Pacific and Arctic air masses contribute to precipitation patterns. Moreover, future growth projections under different climate scenarios suggest that rising temperatures and decreasing moisture availability could lead to increased stress on these forests, potentially reducing growth rates and making them more vulnerable to climatic extremes. These insights provide valuable information for developing adaptive forest management strategies in the context of ongoing climate change.

Author Contributions

Conceptualization, F.C.; methodology, F.C.; software, F.C.; validation, F.C. and J.H.; formal analysis, J.H. and J.L.; investigation, J.L.; resources, F.C.; data curation, J.L.; writing—original draft preparation, J.H.; writing—review and editing, J.H. and F.C.; visualization, J.L.; supervision, F.C.; project administration, J.H. and F.C.; funding acquisition, J.H. and F.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Xinjiang Arid Zone Lake Environment and Resources Laboratory Open Project (Project No. XJDX0909-2021-03) and Xinjiang Social Science Fund Project (Grant No. 2023BYJ030).

Data Availability Statement

The tree-ring chronology data generated in this study are available from the corresponding author upon reasonable request. Climate datasets, including CRU, PDSI, NDVI, HadISST1, and General Circulation Model (GCM) outputs from the Coupled Model Intercomparison Project Phase 6, were obtained via the Climatic Explorer platform (https://climexp.knmi.nl) (accessed on 20 November 2024). This platform provides open access to these datasets along with detailed metadata and descriptions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Overview of the study area. Kanas Lake (a) and its surrounding tree-ring sampling sites and locations of meteorological stations (b). Monthly variation characteristics of temperature (T), maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (P) from the Climatic Research Unit Time-Series during 1954–2013 (c).
Figure 1. Overview of the study area. Kanas Lake (a) and its surrounding tree-ring sampling sites and locations of meteorological stations (b). Monthly variation characteristics of temperature (T), maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (P) from the Climatic Research Unit Time-Series during 1954–2013 (c).
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Figure 2. Variation characteristics of the standard chronology of tree-ring width, sample depth (a), expressed population signal (EPS), and mean inter-series correlation (Rbar) since 1738 (b,c).
Figure 2. Variation characteristics of the standard chronology of tree-ring width, sample depth (a), expressed population signal (EPS), and mean inter-series correlation (Rbar) since 1738 (b,c).
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Figure 3. Comparison of interannual variations in mean temperature (a), mean maximum temperature (b), mean minimum temperature (c), and precipitation (d) from the meteorological stations of Habahe, Altai, and Burqin and Climatic Research Unit Time-Series grid data in the study area.
Figure 3. Comparison of interannual variations in mean temperature (a), mean maximum temperature (b), mean minimum temperature (c), and precipitation (d) from the meteorological stations of Habahe, Altai, and Burqin and Climatic Research Unit Time-Series grid data in the study area.
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Figure 4. Correlation analysis between the tree-ring width chronology and the precipitation, mean minimum temperature, mean maximum temperature, and mean temperature of the Habahe meteorological station (a), Altay meteorological station (b), Burqin meteorological station (c), and CRU grid data (d) from March of the previous year to December of the current year, as well as NDVI and PDSI (e). “*” represents the 95% significance level.
Figure 4. Correlation analysis between the tree-ring width chronology and the precipitation, mean minimum temperature, mean maximum temperature, and mean temperature of the Habahe meteorological station (a), Altay meteorological station (b), Burqin meteorological station (c), and CRU grid data (d) from March of the previous year to December of the current year, as well as NDVI and PDSI (e). “*” represents the 95% significance level.
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Figure 5. Spatial correlation analysis between the tree-ring width chronology and the grid data of mean temperature (T) (a), mean maximum temperature (Tmax) (b), mean minimum temperature (Tmin) (c), precipitation (P) (d), Palmer Drought Severity Index (PDSI) (e), and Normalized Difference Vegetation Index (NDVI) (f). The dotted area represents the 95% significance level. The legend shows the color range of different correlation coefficients (r values).
Figure 5. Spatial correlation analysis between the tree-ring width chronology and the grid data of mean temperature (T) (a), mean maximum temperature (Tmax) (b), mean minimum temperature (Tmin) (c), precipitation (P) (d), Palmer Drought Severity Index (PDSI) (e), and Normalized Difference Vegetation Index (NDVI) (f). The dotted area represents the 95% significance level. The legend shows the color range of different correlation coefficients (r values).
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Figure 6. Spatial correlation analysis between the tree-ring width chronology and SST (a), its MTM period analysis (b), and its correlation analysis with the Niño 4 index from January to July (c). The red box in (a) represents the extent of the Niño 4 region.
Figure 6. Spatial correlation analysis between the tree-ring width chronology and SST (a), its MTM period analysis (b), and its correlation analysis with the Niño 4 index from January to July (c). The red box in (a) represents the extent of the Niño 4 region.
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Figure 7. Water vapor synthesis analysis for the highest (a) and lowest (b) 10 years in the tree-ring chronology, and backward trajectory analysis for the highest 1958 (c) and lowest 1974 (d) years. Black stars represent the tree-ring sampling sites, and colored lines indicate the backward trajectory pathways.
Figure 7. Water vapor synthesis analysis for the highest (a) and lowest (b) 10 years in the tree-ring chronology, and backward trajectory analysis for the highest 1958 (c) and lowest 1974 (d) years. Black stars represent the tree-ring sampling sites, and colored lines indicate the backward trajectory pathways.
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Figure 8. Future simulation of tree radial growth. Comparison of actual values of tree-ring width chronology with simulated values from VS-Lite simulations, with shading indicating ±1 root mean square error range (a). Simulated future tree radial growth under different emission scenarios (SSP 2–4.5 and SSP 5–8.5) in the 21st century (b).
Figure 8. Future simulation of tree radial growth. Comparison of actual values of tree-ring width chronology with simulated values from VS-Lite simulations, with shading indicating ±1 root mean square error range (a). Simulated future tree radial growth under different emission scenarios (SSP 2–4.5 and SSP 5–8.5) in the 21st century (b).
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Table 1. Information of sampling points and meteorological data.
Table 1. Information of sampling points and meteorological data.
CodeLat. (N)Lon. (E)Elevation (m)Cores/TreesPeriod
JDQ87.2148.52149040/201734–2013
HBH86.0448.05532.6 1958–2013
ALT88.0347.73735.3 1954–2013
BEJ86.8747.70473.9 1961–2013
CRU86.5–87.547–48 1954–2013
PDSI86.5–87.547–48 1954–2013
NDVI86.5–87.547–48 1981–2013
JDQ: Tree-ring sampling site; HBH, BEJ, ALT: Meteorological stations at Habahe, Burzin, and Altay; CRU: Climatic Research Unit Time-Series; PDSI: Palmer Drought Severity Index; NDVI: Normalized Difference Vegetation Index.
Table 2. Information about the 33 GCM models provided by CMIP6.
Table 2. Information about the 33 GCM models provided by CMIP6.
ModelSourceAccuracy
(Lat × Lon)		ModelSourceAccuracy
(Lat × Lon)
ACCESS-CM2Australia144 × 192GFDL-ESM4America180 × 288
ACCESS-ESM1-5Australia145 × 192GISS-E2-1-GAmerica90 × 144
AWI-CM-1-1-MRGerman96 × 192HadGEM3-GC31-LLEngland144 × 192
BCC-CSM2-MRChina160 × 320INM-CM4-8Russia120 × 180
CanESM5Canada64 × 128INM-CM5-0Russia120 × 180
CanESM5-CanOECanada64 × 128IPSL-CM6A-LREurope143 × 144
CESM2-WACCMAmerica192 × 288KACE-1-0-GKorea80 × 96
CESM2America192 × 288MIROC6Japan128 × 256
CIESMChina384 × 320MIROC-ES2LJapan64 × 128
CMCC-CM2-SR5Italy192 × 288MPI-ESM1-2-HRGerman192 × 384
CNRM-CM6-1France128 × 256MPI-ESM1-2-LRGerman96 × 192
CNRM-CM6-1-HRFrance360 × 720MRI-ESM2-0Japan160 × 320
CNRM-ESM2-1France128 × 256NESM3China96 × 192
EC-Earth3Europe256 × 512NorESM2-LMNorway96 × 144
EC-Earth3-VegEurope256 × 512NorESM2-MMNorway192 × 288
FGOALS-f3-LChina192 × 288UKESM1-0-LLEngland144 × 192
FGOALS-g3China80 × 180
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Hou, J.; Chen, F.; Li, J. Response of Larix sibirica Radial Growth to Climate Change in Kanas, Northern Xinjiang, China. Forests 2024, 15, 2137. https://doi.org/10.3390/f15122137

AMA Style

Hou J, Chen F, Li J. Response of Larix sibirica Radial Growth to Climate Change in Kanas, Northern Xinjiang, China. Forests. 2024; 15(12):2137. https://doi.org/10.3390/f15122137

Chicago/Turabian Style

Hou, Jiannan, Feng Chen, and Jianrong Li. 2024. "Response of Larix sibirica Radial Growth to Climate Change in Kanas, Northern Xinjiang, China" Forests 15, no. 12: 2137. https://doi.org/10.3390/f15122137

APA Style

Hou, J., Chen, F., & Li, J. (2024). Response of Larix sibirica Radial Growth to Climate Change in Kanas, Northern Xinjiang, China. Forests, 15(12), 2137. https://doi.org/10.3390/f15122137

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