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Article

Response of Tree-Ring Oxygen Isotopes to Climate Variations in the Banarud Area in the West Part of the Alborz Mountains

by
Yajun Wang
1,
Shengqian Chen
1,*,
Haichao Xie
1,*,
Yanan Su
1,
Shuai Ma
2 and
Tingting Xie
3
1
Alpine Paleoecology and Human Adaptation Group (ALPHA), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, Lanzhou University, Lanzhou 730000, China
3
College of Water Resources and Architectural Engineering, Tarim University, Alar 843300, China
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(8), 1238; https://doi.org/10.3390/f16081238
Submission received: 1 July 2025 / Revised: 21 July 2025 / Accepted: 26 July 2025 / Published: 28 July 2025
(This article belongs to the Section Forest Meteorology and Climate Change)
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Abstract

Stable oxygen isotopes in tree rings (δ18O) serve as important proxies for climate change and offer unique advantages for climate reconstruction in arid and semi-arid regions. We established an annual δ18O chronology spanning 1964–2023 using Juniperus excelsa tree-ring samples collected from the Alborz Mountains in Iran. We analyzed relationships between δ18O and key climate variables: precipitation, temperature, Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET). Correlation analysis reveals that tree-ring δ18O is highly sensitive to hydroclimatic variations. Tree-ring cellulose δ18O shows significant negative correlations with annual total precipitation and spring PDSI, and significant positive correlations with spring temperature (particularly maximum temperature), April VP, and spring PET. The strongest correlation occurs with spring PET. These results indicate that δ18O responds strongly to the balance between springtime moisture supply (precipitation and soil moisture) and atmospheric evaporative demand (temperature, VP, and PET), reflecting an integrated signal of both regional moisture availability and energy input. The pronounced response of δ18O to spring evaporative conditions highlights its potential for capturing high-resolution changes in spring climatic conditions. Our δ18O series remained stable from the 1960s to the 1990s, but showed greater interannual variability after 2000, likely linked to regional warming and climate instability. A comparison with the δ18O variations from the eastern Alborz Mountains indicates that, despite some differences in magnitude, δ18O records from the western and eastern Alborz Mountains show broadly similar variability patterns. On a larger climatic scale, δ18O correlates significantly and positively with the Niño 3.4 index but shows no significant correlation with the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). This suggests that ENSO-driven interannual variability in the tropical Pacific plays a key role in regulating regional hydroclimatic processes. This study confirms the strong potential of tree-ring oxygen isotopes from the Alborz Mountains for reconstructing hydroclimatic conditions and high-frequency climate variability.

1. Introduction

Tree-ring oxygen isotopes (δ18O) are highly sensitive to climate change and have emerged as important proxies for studying high-resolution variations in past temperature, precipitation, and evapotranspiration dynamics. Unlike traditional dendrochronological parameters such as ring width or density, tree-ring δ18O directly reflects environmental moisture sources, temperature fluctuations, and water-use efficiency during the growing season. This direct relationship provides greater stability and clearer underlying physical mechanisms [1,2,3]. Tree-ring δ18O records have proven particularly valuable in arid regions [4,5], monsoon margins [6,7], and high-latitude areas [8,9,10], providing crucial evidence for identifying past extreme climate events and long-term trends. However, despite the global abundance of related studies, the response mechanisms of tree-ring δ18O to climate change in specific regions—such as the Alborz Mountains, an alpine arid transition zone—require further exploration.
The Alborz Mountains in northern Iran form a crucial mountain range linking the Caspian Sea coast with the Iranian Plateau. Their complex topography and climatic gradients make this region particularly important for climate change research. The area experiences influences from Mediterranean, monsoonal, and continental climates, resulting in pronounced fluctuations between wet and dry conditions. Recent years have seen increased dendroclimatic research in the Alborz Mountains, focusing primarily on tree-ring width [11,12,13,14,15,16,17,18,19], stable isotopes, particularly δ18O and δ13C [20,21,22], and quantitative wood anatomy [23,24,25,26,27]. These studies have investigated the response of tree radial growth to hydroclimatic variability. Reconstructions of precipitation and temperature based on tree-ring data have revealed decadal-scale variability in regional moisture and temperature patterns, which are closely associated with large-scale internal climate variability, including the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) [28,29,30]. Foroozan et al. [20,22] developed a δ18O chronology for the eastern Alborz region that demonstrates high sensitivity to changes in moisture sources and evapotranspiration processes, and reconstructed the total precipitation for April–June of both the previous and the current year. Despite this initial and valuable progress, high-resolution studies of tree-ring oxygen isotopes remain limited, particularly in mountainous areas characterized by high climatic variability and frequent wet–dry alternations. This gap highlights the urgent need for additional research to deepen our understanding of regional climate–hydrology dynamics.
This study used a tree-ring oxygen isotope analysis of Juniperus excelsa from the western part of the Alborz Mountains to investigate how δ18O responds to hydroclimatic factors, reveal the physiological responses of tree growth to moisture variability, and assess the potential for regional climate change reconstruction. We aim to help fill the spatial gap in tree-ring isotope studies in the high mountains of Central and Western Asia, providing scientific insight for ecosystem adaptation studies and regional water resource management.

2. Materials and Methods

2.1. Tree-Ring Sampling

The tree-ring samples were collected from the western part of the Alborz Mountains in Iran, near Banaroud Village in Tarom County, Zanjan Province (Figure 1). The study area experiences a temperate continental climate characterized by hot, dry summers and cold winters with relatively higher precipitation. The sampling area features open terrain with gentle slopes and is characterized by typical semi-arid montane shrub–grassland vegetation. Trees are sparsely distributed, primarily composed of Juniperus excelsa. Individual trees generally range from 2.5 to 5 m in height, with diameters of approximately 20 to 35 cm and crown diameters typically ranging from 1.5 to 4 m. The sampling site is located at approximately 37°07′ N and 48°42′ E, at an elevation ranging between 1000 and 1200 m. Healthy, naturally grown, and unmanaged Juniperus excelsa trees were sampled in October 2023. A total of 30 trees were sampled at breast height using an increment borer (Haglöf Sweden, Långsele, Sweden), yielding 61 cores.

2.2. Cross-Dating and Oxygen Isotope Analysis

We followed standard tree-ring processing procedures for sample preparation. The collected cores were dried, mounted, sanded, and preliminarily dated by visual inspection. Tree-ring widths were measured using a LINTAB measuring system (Rinntech, Heidelberg, Germany) with 0.01 mm resolution. The COFECHA program (v6.06P) [31] was used to verify cross-dating accuracy and measurement quality. The ARSTAN program (v41d) [32] was used to generate tree-ring width index chronologies, which span from 1924 to 2023. Based on the mass of samples and the clarity of the tree rings, four cores with complete and continuous ring structures were selected for oxygen isotope analysis.
Each annual growth ring was separated and sectioned, then subjected to chemical treatments to extract α-cellulose. This process removed oils, ash, soluble substances, lignin, and hemicellulose, retaining the most stable isotopic signal. The extracted cellulose samples were freeze-dried and analyzed for δ18O values using high-temperature pyrolysis–isotope ratio mass spectrometry (TC/EA-IRMS) (Thermo Fisher Scientific, Bremen, Germany). During measurement, international reference materials were used for calibration, and results were expressed relative to VSMOW (Vienna Standard Mean Ocean Water). One internal standard was inserted for every seven samples to monitor instrument stability. Final analytical precision was determined from repeated measurements of laboratory standards and was controlled within ±0.28‰.

2.3. Climate Data and Analysis Methods

Climate data were publicly accessible from the Climatic Research Unit (CRU) TS 4.08 dataset (see Table 1) with a 0.5° × 0.5° spatial resolution [33]. Monthly data for total precipitation (P), mean temperature (Tmean), mean maximum temperature (Tmax), mean minimum temperature (Tmin), Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET) were extracted from the 0.5°grid cell (37.0°–37.5° N, 48.5°–49.0° E) encompassing the sampling site. Climate data were selected for 1964–2023 to align with the oxygen isotope chronology.
The annual total precipitation in the study area is 578.0 mm. Precipitation increases gradually during February and March, reaching a peak of 65.5 mm in April. From May onward, precipitation declines to its lowest point in July (18.9 mm). Although precipitation increases slightly in August and September, it remains relatively low compared to the rest of the year. The highest monthly precipitation occurs in October (73.5 mm), followed by gradual decreases, though November and December precipitation remains higher than summer levels. The annual mean temperature is 10.3 °C, with the highest temperature occurring in July (22.5 °C). The region exhibits pronounced seasonal patterns with opposing moisture and heat availability, as the warmest months (June to August) are also the driest (Figure 2).
Large-scale climate indices were also used to explore their potential influence on local climate variability, including the Arctic Oscillation (AO) index, the North Atlantic Oscillation (NAO) index (see Table 1), and the Niño 3.4 sea surface temperature (SST) index, widely accepted as a standard indicator of ENSO [34] (see Table 1). The sources of the datasets used in this study are summarized in Table 1.
Pearson correlation analysis in SPSS software (v22) was used to examine relationships between the tree-ring δ18O series and climatic variables (precipitation, temperature, PDSI, PET), as well as large-scale climate indices (AO, NAO, Niño 3.4 SST). Since climatic factors and indices from the previous year may influence current-year δ18O values, the analysis included variables from the 12-month period spanning the previous November through the current October. For clarity, “annual” in the following correlation analysis refers to the period from the previous November to the current October.

3. Results

3.1. Characteristics of the δ18O Series

The tree-ring cellulose δ18O series from four selected trees are shown in Figure 3. The combined dataset has a mean value of 31.2‰ and a standard deviation of 1.4‰. Due to a slight deviation in the mean δ18O value of one core (30.59‰) compared to the others (31.73‰, 31.67‰, 31.18‰), each series was divided by its own mean for normalization. The four individual series begin in 1948, 1960, 1964, and 1972, respectively. Due to the high inter-series correlation observed among the trees, we constructed a regional δ18O chronology by averaging the individual series to better represent regional climate conditions. The chronology’s effective start year is 1964, the first year in which at least three cores were available for analysis. The resulting δ18O chronology spans 60 years from 1964 to 2023. Statistical analysis reveals an average inter-series correlation (Rbar) of 0.6 and an average expressed population signal (EPS) of 0.85. These values indicate a strong common climatic signal within the dataset, making it well-suited for both climate response analysis and historical reconstruction.
The δ18O series (Figure 3) shows clear interdecadal variability. Values increased notably in the 1950s (based on 1–2 tree cores), remained relatively stable from the 1960s to the 1990s, and exhibited greater interannual fluctuations after the onset of the 21st century.

3.2. Correlation Between δ18O and Climate Factors

Figure 4 presents the correlation analysis between the δ18O chronology and climatic factors from the previous November to the current October. The results demonstrate that δ18O variations respond sensitively to changes in climatic conditions.

3.2.1. Precipitation Relationships

The δ18O chronology shows significant negative correlations with precipitation in March (r = −0.335, p < 0.01) and April (r = −0.419, p < 0.01), indicating the influence of soil water storage during the early growing season [35]. Seasonal analysis reveals significant negative correlations with winter (r = −0.258, p < 0.05), spring (r = −0.482, p < 0.01), and annual total precipitation (r = −0.516, p < 0.01), with the strongest correlation observed for annual total precipitation. These results suggest that the tree-ring δ18O series captures winter, spring, and annual total precipitation variability, with the annual signal being most prominent.

3.2.2. Temperature Relationships

The δ18O chronology demonstrates strong sensitivity to temperature changes, with particularly pronounced correlations with maximum temperatures. Significant positive correlations with maximum temperature occur in five months: previous December (p < 0.05), February (p < 0.05), March (p < 0.01), April (p < 0.01), and August (p < 0.05). Mean temperatures show significant positive correlations in four months: previous December (p < 0.05), March (p < 0.01), April (p < 0.01), and August (p < 0.05). Minimum temperatures correlate significantly in three months: March (p < 0.05), April (p < 0.05), and August (p < 0.05). Seasonal analysis reveals that δ18O correlates positively with winter (p < 0.05), spring (p < 0.01), and annual (p < 0.01) mean maximum and mean temperatures. For minimum temperature, significant positive correlations occur with spring (p < 0.01), autumn (p < 0.05), and annual (p < 0.05) values. The strongest correlation is with spring maximum temperature (r = 0.599, p < 0.01), indicating that spring temperature variations are most prominently recorded in the δ18O series.

3.2.3. Drought Index Relationships

The δ18O chronology shows significant negative correlations with PDSI throughout the analysis period, with the strongest correlation in July (r = −0.608, p < 0.01). Seasonal analysis reveals significant negative correlations for all seasons and annually (p < 0.01), with the highest correlation in summer (r = −0.609, p < 0.01). This indicates that the δ18O series contains strong signals of coupled climatic variability.

3.2.4. Vapor Pressure and Evapotranspiration Relationships

The δ18O chronology correlates significantly and positively with vapor pressure (VP) in April (r = 0.318, p < 0.05), with additional significant correlations for spring and annual (p < 0.05).
For potential evapotranspiration (PET), δ18O shows significant positive correlations in previous December (p < 0.05), February (p < 0.05), March (p < 0.01), April (p < 0.01), and May (p < 0.05), with the strongest positive correlation in March (r = 0.551, p < 0.01). Seasonal analysis reveals significant positive correlations with winter (p < 0.05), spring (p < 0.01), and annual (p < 0.01) mean PET, with the strongest correlation occurring in spring (r = 0.625, p < 0.01).
The correlation analysis demonstrates that δ18O variations are significantly influenced by climatic changes, with the most prominent relationships observed with spring conditions, particularly spring PET, which shows the highest correlation coefficient (r = 0.625, p < 0.01). This indicates that tree-ring δ18O is primarily controlled by variations in spring evapotranspiration intensity and moisture conditions.

3.3. Correlation Between δ18O and Large-Scale Climate Indices

To explore relationships between tree-ring δ18O and large-scale climate patterns, we calculated correlations with the Arctic Oscillation (AO) index, the North Atlantic Oscillation (NAO) index, and the Niño 3.4 sea surface temperature (SST) index. Results show that δ18O does not correlate significantly with AO or NAO indices. However, δ18O exhibits significant responses to Niño 3.4 SST variations, showing significant negative correlations with monthly SSTs from previous November to current April, with the strongest negative correlation in February (r = −0.449, p < 0.01) (Figure 5).

4. Discussion

Tree-ring δ18O variations are jointly influenced by moisture sources, evapotranspiration processes, and temperature-regulating mechanisms.

4.1. Sensitivity Mechanisms of Tree-Ring δ18O to Climate Factors

4.1.1. Precipitation Effects

The significant negative correlation between tree-ring δ18O and March–April precipitation likely results from the well-known “amount effect” [36]. The negative correlation with winter precipitation may relate to snowmelt dynamics [37]. During snowmelt, isotopic enrichment of surface water occurs through evaporation and sublimation processes [38]. The δ18O signal of precipitation is incorporated into tree-ring δ18O through water uptake by roots and cellulose synthesis [39]. When winter precipitation is abundant, it typically accompanies lower temperatures and weaker evaporation, limiting δ18O enrichment in precipitation and resulting in lower tree-ring δ18O values.
The negative correlation with spring precipitation reflects the onset of tree growth. Abundant spring precipitation provides sufficient soil moisture, allowing increased water uptake and stomatal conductance. Although evapotranspiration is enhanced, faster leaf water turnover limits isotopic enrichment, and the input water tends to have lower δ18O values. These factors combine to decrease tree-ring δ18O. Conversely, when precipitation is low, stomatal closure increases water residence time in leaves, enhancing isotopic enrichment and raising δ18O values [40,41].

4.1.2. Temperature Effects

The significant positive correlation between tree-ring δ18O and spring temperature, particularly maximum temperature, reflects the strong influence of temperature changes on evapotranspiration processes and leaf water isotopic composition. Rising spring temperatures enhance soil moisture evaporation and strengthen isotopic enrichment, increasing δ18O in source water [42]. Additionally, high temperatures boost tree transpiration, and under high temperature and evapotranspiration conditions, leaf water turnover rates decrease, leading to greater isotopic enrichment and higher δ18O values in synthesized cellulose. The high sensitivity of δ18O to spring maximum temperature highlights the critical regulatory role of temperature changes on tree water-use strategies and isotopic processes under climate warming.

4.1.3. Drought Index Effects

The negative correlation between tree-ring δ18O and PDSI reflects the regulatory mechanism of climatic moisture conditions on tree water sources and evapotranspiration processes. High PDSI values indicate humid conditions with sufficient soil moisture, while low PDSI suggests drought conditions and limited water availability. The correlation mechanism between δ18O and PDSI essentially parallels that between δ18O and annual total precipitation. Similar negative correlations between δ18O and PDSI have been reported in southern China [43], the western Himalayas [42], and Vietnam [44].

4.1.4. Vapor Pressure and Evapotranspiration Effects

The significant positive correlation between tree-ring δ18O and April vapor pressure reflects the response to temperature-driven transpiration changes. In April, when precipitation is relatively high in the study area and temperatures rise, vapor pressure increases as warmer air holds more moisture. Rising temperatures enhance leaf transpiration and intensify isotopic fractionation, leading to higher δ18O values in tree rings.
The positive correlation with PET reflects the direct impact of increased evaporative demand on leaf water isotopic composition. Higher PET typically indicates elevated temperatures, stronger radiation, and drier air, conditions that intensify leaf water evaporation and enhance isotopic fractionation, leading to δ18O enrichment. High PET often causes partial stomatal closure, slowing leaf water turnover and further amplifying enrichment effects, ultimately recorded as higher δ18O values in tree-ring cellulose. Similar positive correlations between tree-ring δ18O and PET have been found in southeastern Tibet [45].

4.1.5. Integrated Response Pattern

Tree-ring δ18O shows significant negative correlations with precipitation and PDSI, and significant positive correlations with temperature, VP, and PET, with the strongest correlation with PET. This indicates that tree-ring δ18O in the study area is highly sensitive to combined drought signals of increased evaporative demand and insufficient water supply. Favorable moisture conditions (high precipitation, low temperature) result in higher stomatal conductance and lower transpiration rates, leading to lower δ18O values. Conversely, dry conditions (reduced precipitation, higher temperatures) lead to decreased stomatal conductance and increased transpiration, resulting in higher δ18O values [1,46]. Overall, δ18O variations primarily reflect regional moisture condition fluctuations, particularly those driven by evapotranspiration-dominated water balance processes.

4.2. Comparison with the δ18O Record from the Eastern Alborz Mountains

In our δ18O series, a notable increase in δ18O values occurred in the 1950s (based on 1–2 tree cores), possibly reflecting a drier or warmer period. From the 1960s to the 1990s, the values remained relatively stable, indicating limited moisture variability. Since the early 2000s, however, interannual fluctuations have intensified, likely associated with factors such as enhanced evapotranspiration and reduced effective moisture due to regional warming [47]. Continued warming may further increase evaporation and reduce effective moisture, leading to greater δ18O variability or higher values. These results underscore the value of tree-ring δ18O as a proxy for tracking regional hydroclimatic changes.
To assess the similarities and differences between our results and nearby δ18O variations, we further compared our δ18O record with that from the eastern Alborz Mountains [20], as well as with the precipitation reconstruction based on δ18O for the combined previous spring and current spring (py spring + spring) period [22] (Figure 6). The comparison reveals that, overall, two δ18O series both exhibit broadly consistent temporal trends, suggesting similar responses to hydroclimatic variability. Several periods—particularly during the middle and late portions of the record—demonstrate synchronous increases or decreases, indicating the influence of shared climatic drivers. Meanwhile, the two curves also show certain differences in specific years, particularly when one exhibits pronounced fluctuations while the other remains relatively stable.
The comparison between our δ18O series and the precipitation series from the eastern Alborz reveals a generally clear inverse relationship, further confirming that δ18O is primarily controlled by the “amount effect” [36] and serves as a reliable proxy for regional moisture variability. During the period 1948–1963, δ18O and precipitation exhibited opposite trends, and despite only 1–2 tree cores being available prior to 1963 in our study, the δ18O data still hold important reference value. Similar inverse relationships are also evident during other periods, such as 1965–1980 and the late 1990s to early 2000s, with increased precipitation corresponding to decreased δ18O values. In the late 2000s to early 2010s, although precipitation continued to rise, our δ18O record remained relatively stable or slightly declined, still maintaining this negative correlation pattern.
Overall, the δ18O variations in the western and eastern Alborz Mountains exhibit broadly consistent trends, although differences in the magnitude of changes may occur during certain periods. These discrepancies may be attributed to local environmental conditions at the sampling sites, differences in moisture sources, or varying physiological responses of trees to drought stress. Such differences highlight the importance of multi-site sampling and the integration of multiple proxies, which can enhance the representativeness and robustness of tree-ring δ18O records in reflecting regional climate variability.

4.3. Response Mechanisms Analysis to Large-Scale Climate Indices

Large-scale climate modes such as the AO, NAO, and ENSO are known to exert widespread influences on hydroclimatic variability across different regions of the world. For example, a positive AO phase is associated with warming over Eurasia and North America [48], positive NAO phases are typically associated with warmer and wetter winter conditions over northern Europe and parts of central Asia, but can induce drier or colder anomalies in the eastern Mediterranean [49]. Similarly, ENSO events have been shown to impact precipitation and temperature patterns globally, including the Middle East, East Asia, and even extratropical regions, through atmospheric teleconnections [50,51].
Although our δ18O record represents a single site in the Alborz Mountains, the climatic signals preserved in the isotopic variations may reflect the influence of these large-scale circulation modes, which often act coherently across wide spatial domains. Therefore, comparisons with these indices provide meaningful insights into potential teleconnections.

4.3.1. ENSO Influence

The significant negative correlation between tree-ring δ18O and the Niño 3.4 index indicates strong responses to equatorial Pacific sea surface temperature anomalies. During El Niño events (positive Niño 3.4 index), δ18O values tend to decrease. This relationship likely stems from tropical Pacific sea surface temperatures rising during El Niño events, which disrupt global atmospheric circulation patterns and enhance moisture transport to the Middle East, resulting in increased regional precipitation.
Ma et al. [52] demonstrated that during the warm phase of the Pacific Decadal Oscillation (PDO), elevated central and eastern equatorial Pacific sea surface temperatures trigger eastward-propagating Rossby waves, leading to negative geopotential height anomalies over western Asia and inducing abnormal upward motion. The PDO also alters meridional temperature gradients in the troposphere, causing westerly jet streams to shift southward and intensify, enhancing the moisture budget over western Asia. Furthermore, the PDO induces high-pressure anomalies in low-latitude regions, guiding moisture transport from low latitudes to western Asia and further increasing regional moisture supply. This results in increased precipitation and lower δ18O values, revealing the significant influence of tropical oceanic processes on regional hydroclimatic conditions.

4.3.2. Limited Atlantic Influence

Tree-ring δ18O shows no significant correlation with the Arctic Oscillation (AO) or North Atlantic Oscillation (NAO) indices, reflecting the relatively weak influence of North Atlantic circulation anomalies on the regional hydroclimate. Other studies have found that spring precipitation reconstructed from tree rings in the eastern Alborz Mountains does not correlate significantly with large-scale climate indices representing westerly influences (such as NAO) [22], indicating minimal NAO impact on spring precipitation in northern Iran.
These results suggest that equatorial Pacific sea surface temperature anomalies may be important remote driving factors influencing the hydroclimatic balance and tree transpiration dynamics in the Alborz Mountain region.
Although the tree-ring δ18O series in this study is relatively short and not yet sufficient for long-term climate reconstruction, this study provides valuable insights into δ18O response to climate factors and demonstrates the potential for using δ18O to reconstruct past climate variability in the Alborz Mountains.

5. Conclusions

We established a tree-ring δ18O chronology based on Juniperus excelsa samples collected from the western Alborz Mountains and analyzed its response to key climatic factors, including precipitation, temperature, PDSI, VP, and PET. The results demonstrate that tree-ring δ18O variations correlate significantly and negatively with spring precipitation and PDSI, and significantly and positively with spring temperature, with the strongest positive correlation observed with spring PET.
Tree-ring δ18O in the study area is sensitive to changes in spring climatic conditions and contains signals of climatic variability, particularly those related to spring PET. The δ18O series shows clear interdecadal variability, with a marked rise in the 1950s (based on 1–2 tree cores), stable patterns from the 1960s to 1990s, and increased interannual fluctuations after 2000. The δ18O trends in the western Alborz since the 1940s largely align with those in the eastern Alborz, suggesting a similar response to climate variability. The significant negative correlation with the Niño 3.4 index suggests that δ18O variations may be indirectly influenced by El Niño events.
Continued efforts to identify longer tree-ring records in this region and using tree-ring δ18O to reconstruct high-resolution climate variability over extended periods will help fill geographical gaps in δ18O studies in the Alborz Mountains and expand the spatial coverage of tree-ring δ18O data.

Author Contributions

Conceptualization, Methodology, Data curation, Formal analysis, Writing—original draft, Y.W.; Methodology, Formal analysis, Writing—review and editing, S.C.; Formal analysis, Resources, Writing—review and editing, H.X.; Formal analysis, Writing—review and editing, Y.S.; Data curation, Writing—review and editing, S.M.; Resources, Writing—review and editing, T.X. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was supported by the National Natural Science Foundation of China No. 42171162; Young Elite Scientists Sponsorship Program by CAST No. 2022QNRC001; NSFC-INSF Joint Research Project No. 42261144670; and the National Natural Science Foundation of China No. 42101158.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

Acknowledgments

We are grateful to Hamid Lahijani, Vilma Bayramzadeh, and Pedram Attarod for their assistance in collecting tree cores.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Locations of the sampling sites and CRU climate data grid. The digital elevation model is provided by the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn, accessed on 15 February 2021).
Figure 1. Locations of the sampling sites and CRU climate data grid. The digital elevation model is provided by the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn, accessed on 15 February 2021).
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Figure 2. Monthly total precipitation and monthly mean temperature throughout the year.
Figure 2. Monthly total precipitation and monthly mean temperature throughout the year.
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Figure 3. Time series plots of tree-ring cellulose δ18O. (a) Individual trees (b) Normalized mean δ18O.
Figure 3. Time series plots of tree-ring cellulose δ18O. (a) Individual trees (b) Normalized mean δ18O.
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Figure 4. Correlation between δ18O and climate factors. (a) δ18O–P, Tmean, Tmax. Tmin (b) δ18O–PDSI, VP, PET. The solid line indicates the 0.01 significance level, the dashed line indicates the 0.05 significance level, −11 represents the previous November, DJF refers to winter, MAM to spring, JJA to summer, and SON to autumn, ANN to annual.
Figure 4. Correlation between δ18O and climate factors. (a) δ18O–P, Tmean, Tmax. Tmin (b) δ18O–PDSI, VP, PET. The solid line indicates the 0.01 significance level, the dashed line indicates the 0.05 significance level, −11 represents the previous November, DJF refers to winter, MAM to spring, JJA to summer, and SON to autumn, ANN to annual.
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Figure 5. The correlation between δ18O and the February Niño 3.4 index. (a) this study (b) Niño 3.4 SST.
Figure 5. The correlation between δ18O and the February Niño 3.4 index. (a) this study (b) Niño 3.4 SST.
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Figure 6. Comparison of our δ18O series with the δ18O and precipitation series from the eastern Alborz Mountains. (a) this study (b) eastern Alborz δ18O record (c) eastern Alborz δ18O-based precipitation reconstruction.
Figure 6. Comparison of our δ18O series with the δ18O and precipitation series from the eastern Alborz Mountains. (a) this study (b) eastern Alborz δ18O record (c) eastern Alborz δ18O-based precipitation reconstruction.
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Table 1. Summary of datasets used in this study.
Table 1. Summary of datasets used in this study.
Dataset NameVariable(s)Spatial ResolutionTemporal CoverageSource
CRU TS v4.08P, T, PET, et al.0.5° × 0.5° global grid1901–2023https://climexp.knmi.nl, accessed on 24 April 2025
AO IndexSea-level pressure anomalyNorthern Hemisphere1950–presenthttps://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao.shtml, accessed on around 14 May 2025
NAO IndexSea-level pressure anomalyNorth Atlantic Region1950–presenthttps://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml, accessed on around 14 May 2025
Niño 3.4 SST IndexSSTEquatorial Pacific1870–presenthttps://www.psl.noaa.gov/data/index.html, accessed on around 14 May 2025
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Wang, Y.; Chen, S.; Xie, H.; Su, Y.; Ma, S.; Xie, T. Response of Tree-Ring Oxygen Isotopes to Climate Variations in the Banarud Area in the West Part of the Alborz Mountains. Forests 2025, 16, 1238. https://doi.org/10.3390/f16081238

AMA Style

Wang Y, Chen S, Xie H, Su Y, Ma S, Xie T. Response of Tree-Ring Oxygen Isotopes to Climate Variations in the Banarud Area in the West Part of the Alborz Mountains. Forests. 2025; 16(8):1238. https://doi.org/10.3390/f16081238

Chicago/Turabian Style

Wang, Yajun, Shengqian Chen, Haichao Xie, Yanan Su, Shuai Ma, and Tingting Xie. 2025. "Response of Tree-Ring Oxygen Isotopes to Climate Variations in the Banarud Area in the West Part of the Alborz Mountains" Forests 16, no. 8: 1238. https://doi.org/10.3390/f16081238

APA Style

Wang, Y., Chen, S., Xie, H., Su, Y., Ma, S., & Xie, T. (2025). Response of Tree-Ring Oxygen Isotopes to Climate Variations in the Banarud Area in the West Part of the Alborz Mountains. Forests, 16(8), 1238. https://doi.org/10.3390/f16081238

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