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TEMPORAL STABILITY OF CLIMATIC SIGNAL RECORDED BY STABLE CARBON ISOTOPE COMPOSITION OF TREE RINGS α-CELLULOSE—A CASE STUDY FOR SUWAŁKI REGION

Published online by Cambridge University Press:  20 December 2024

Sławomira Pawełczyk Open the ORCID record for Sławomira Pawełczyk [Opens in a new window] ,
Anna Pazdur  and
Barbara Benisiewicz Open the ORCID record for Barbara Benisiewicz [Opens in a new window]
Sławomira Pawełczyk*
Affiliation:
Division of Geochronology and Environmental Isotopes, Institute of Physics – CSE, Silesian University of Technology, Konarskiego 22B, 44-100 Gliwice, Poland
Anna Pazdur
Affiliation:
Division of Geochronology and Environmental Isotopes, Institute of Physics – CSE, Silesian University of Technology, Konarskiego 22B, 44-100 Gliwice, Poland
Barbara Benisiewicz
Affiliation:
Division of Geochronology and Environmental Isotopes, Institute of Physics – CSE, Silesian University of Technology, Konarskiego 22B, 44-100 Gliwice, Poland
*
*Corresponding author; email: [email protected]
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Abstract

Investigations of stable carbon isotope composition in α-cellulose extracted from tree rings of pines (Pinus sylvestris L.) growing in the unpolluted Suwałki region, northeastern part of Poland, are undertaken. The presented carbon isotope record covers the period of 1931–2003. Values of δ13C measured in the tree ring α-cellulose are compared to meteorological data. These δ13C values in tree ring cellulose respond to summer temperature, insolation, relative humidity, and precipitation. The best correlation is observed between relative humidity and carbon isotope data. The August relative humidity is found more influential on δ13C values than relative humidity for any other month or combination of months (r = –0.65). Relations between isotopic and meteorological data demonstrate that precipitation influences the stable carbon isotopic ratios to a lower extent than humidity. The intensity and duration of summer rainfall events can determine this effect. The temporal stability of climate-proxy connections is an important issue in paleoclimatic reconstruction. Therefore, the temporal stability of climatic signals recorded by stable carbon isotopes is analyzed in this research using the moving correlation function for moving intervals with a 25-year window. Based on those investigations the highest time stability of correlation was found for the carbon isotope and the August relative humidity. More variability is observed for the correlation of δ13C values with precipitation.

Keywords

δ13Cclimatic conditionsprecipitationtemporal stabilitytree rings
Type
Conference Paper
Information
Radiocarbon , First View , pp. 1 - 13
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
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© The Author(s), 2024. Published by Cambridge University Press on behalf of University of Arizona

INTRODUCTION

Tree rings represent an integration of the conditions corresponding to each growing season. Tree ring widths and maximum latewood density may be used to reconstruct some aspects of the climatic changes, e.g., summer temperature or precipitation. Such reconstructions are known mainly for high altitudes or latitudes where climatic impact is the dominating factor for tree growth (Esper et al. Reference Esper, Cook and Schweingruber2002). However, tree ring reconstruction often fails if a more complex set of growth-limiting conditions exists. Variation of stable carbon isotope composition (δ13C) in the subsequent rings may be a valuable parameter for reconstructing growth conditions in temperate climate regions (Loader and Switsur Reference Loader and Switsur1995). McCarrol and Loader (Reference McCarroll and Loader2004) included a summary of papers that have explored tree ring isotope time series for palaeoenvironmental research. Treydte et al. (Reference Treydte, Frank, Esper, Andreu, Bednarz, Berninger, Boettger, D’Alessandro, Etien and Filot2007) presented results of carbon and oxygen isotopes investigations from 23 sites from Finland to Morocco including Suwałki. Cellulose as the main and stable wood component is traditionally used for isotope analyses in paleoclimatological studies (McCarrol and Loader Reference McCarroll and Loader2004; Boettger et al. Reference Boettger, Haupt, Knoller, Weise, Waterhouse, Rinne, Loader, Sonninen, Jungner, Masson-Delmotte, Stievenard, Guillemin, Pierre, Pazdur, Leuenberger, Filot, Saurer, Reynolds, Helle and Schleser2007).

A tree grows under specific environmental conditions, like temperature, precipitation, humidity, light intensity, and change in soil moisture, and these conditions influence the photosynthesis process and respectively the degree of carbon isotope fractionation in corresponding tree rings (Stuiver et al. Reference Stuiver, Burk and Quay1984; Robertson et al. Reference Robertson, Switsur, Carter, Barker, Waterhouse, Briffa and Jones1997; Schleser et al. Reference Schleser, Helle, Lucke and Vos1999). Tree-ring stable carbon isotope ratios (δ13C) in environments of low moisture stress are likely to be controlled primarily by the photosynthetic rate. Therefore, sunshine, rather than temperature, represents the more direct controlling factor. Temperature reconstructions based on tree-ring δ13C results thus rest on the assumption that temperature and sunshine are strongly linked (Young et al. Reference Young, McCarroll, Loader and Kirchhefer2010, Reference Young, Gagen, Loader, McCarroll, Grudd, Jalkanen, Kirchhefer and Robertson2019). At sites where summer moisture stress restricts stomatal conductance to CO2, or where moisture stress is involved in the covariation of multiple climate variables (precipitation, temperature, cloudiness etc.) carbon isotope variability can be used to reconstruct summer precipitation and drought (Gagen et al. Reference Gagen, Battipaglia, Daux, Duffy, Dorado-Liñán, Hayles, Martínez-Sancho, McCarroll, Shestakova, Treydte, Siegwolf, Brooks, Roden and Saurer2022). Processes controlling carbon isotopic fractionation in trees are not fully explained, yet. According to the model developed by Francey and Farquhar (Reference Francey and Ferquhar1982), carbon fractionation occurs in the leaf during CO2 diffusion through stomata and during carboxylation. Many investigations demonstrated the influence of climatic conditions on δ13C values in tree rings (Freyer and Belacy Reference Freyer and Belacy1983; Stuiver and Braziunas Reference Stuiver and Bruziunas1987; Lipp et al. Reference Lipp, Trimborn, Fritz, Moser, Becker and Frenzel1991; Szczepanek et al. Reference Szczepanek, Pazdur, Pawełczyk, Bőttger, Haupt, Hałas, Bednarz, Krąpiec and Szychowska-Krąpiec2006; Andreu-Hayles et al. Reference Andreu-Hayles, Ummenhofer, Mariano Barriendos, Schleser, Helle, Leuenberger, Gutiérrez, Edward and Cook2017; Young et al. Reference Young, Gagen, Loader, McCarroll, Grudd, Jalkanen, Kirchhefer and Robertson2019; Chakraborty et al. Reference Chakraborty, Reif, Matzarakis, Helle, Faßnacht and Saha2022; Gagen et al. Reference Gagen, Battipaglia, Daux, Duffy, Dorado-Liñán, Hayles, Martínez-Sancho, McCarroll, Shestakova, Treydte, Siegwolf, Brooks, Roden and Saurer2022). In trees, the dominant environmental factors that control the δ13C of leaf sugars are those which determine stomatal conductance and the rate of photosynthesis. Dry conditions are related to low stomatal conductance and lead to reduced carboxylation discrimination against δ13C (Tuzet et al. Reference Tuzet, Perrier and Leuning2003).

Nonstationary relationships between climatic parameters and isotopic data are rarely discussed in the literature (Aykroyd et al. Reference Aykroyd, Lucy, Pollard, Carter and Robertson2001; Daux et al. Reference Daux, Edouard, Masson-Delmotte, Stievenard, Hoffmann, Pierre, Mestre, Danis and Guibal2011; Naulier et al. Reference Naulier, Savard, Bégin, Marion, Nicault and Bégin2015; Savard and Daux Reference Savard and Daux2020). Temporal variability in the strength of proxy-climate correlations was found in several cases (Reynolds-Henne et al. Reference Reynolds-Henne, Siegwolf, Treydte, Esper, Henne and Saurer2007; Hilasvuori et al. Reference Hilasvuori, Berninger, Sonninen, Tuomenvirta and Jungner2009; Rinne et al. Reference Rinne, Loader, Switsur, Treydte and Waterhouse2010; Young et al. Reference Young, McCarroll, Loader and Kirchhefer2010; Daux et al. Reference Daux, Edouard, Masson-Delmotte, Stievenard, Hoffmann, Pierre, Mestre, Danis and Guibal2011; Boettger et al. Reference Boettger, Haupt, Friedrich and Waterhouse2014). Identifying periods of instability in climate-isotope data correlations is very important in producing reliable reconstructions.

In addition to climatic factors, many other factors can affect measured values of δ13C in annual tree rings such as the choice of wood component, or the effect of respired CO2 (McCaroll and Loader Reference McCarroll and Loader2004). Furthermore, CO2 assimilated by the plant comes from different sources: natural, as well as anthropogenic. The increase in the burning of fossil fuels in industrial areas since the 19th century caused the emission of CO2 to the atmosphere and changes in carbon isotopic composition in the atmosphere and following in plants. Depression of δ13C values by about 1.5‰ results from the assimilation of 13C-depleted CO2 due to emission from fossil fuels connected with anthropogenic activity (Donngara and Verrica Reference Dongarra and Varrica2002; McCaroll and Loader Reference McCarroll and Loader2004; McCaroll et al. Reference McCaroll, Gagen, Loader, Robertson, Anchukaitis, Los, Young, Jalkanen, Kirchhefer and Waterhouse2009). For most tree ring δ13C series this trend is apparent (Freyer and Balacy Reference Freyer and Belacy1983; Treydte et al. Reference Treydte, Schleser, Schweingruber and Winiger2001), but not for all series (Tans and Mook Reference Tans and Mook1980; Robertson et al. Reference Robertson, Switsur, Carter, Barker, Waterhouse, Briffa and Jones1997).

The main purpose of isotopic studies for the Suwałki region is to reconstruct climate changes in the past based on δ13C in tree rings and to check human impact on the environment. Climate reconstruction requires prior calibration to establish a statistical relationship between isotopic and climatic data. It is also important to check whether other factors, especially anthropogenic ones, affect δ13C values. The purpose of the research presented in here is to determine the correlation of δ13C with climatic factors and check whether they change over time. The next stage of the research will be to check the influence of human activities on δ13C values. In this case, studies of radiocarbon concentrations, which were carried out for the annual increments of a tree from neighbor Augustów (Pawełczyk and Pazdur Reference Pawełczyk and Pazdur2004), can help in the conclusion. Such a comprehensive study will make it possible to choose the best calibration to reconstruct the climate for the Suwałki region.

DESCRIPTIVE BACKGROUND

Sampling

The research area is situated not far from Suwałki city (54°06 ’N, 22°57’E) in the northeastern part of Poland close to the Polish–Lithuanian border. There are valuable forest complexes in the vicinity of the study area relatively unchanged by human activity. Material for isotopic investigations covering the years 1931–2003 comes mainly from the Wigry National Park.

The coring of living pine trees (Pinus sylvestris L.) was taken with a Pressler increment borer (0.5 cm in diameter). For the mentioned period, 14 trees were subjected to dendrochronological studies. Cores contained 124–231 rings. The pine dendrochronological standard for the Suwałki region is fully described in Szychowska-Krąpiec and Krąpiec (Reference Szychowska-Krąpiec and Krąpiec2005). The quality of the tree ring chronology was evaluated by calculating the expressed population signal (EPS) statistics. The EPS was higher than 0.85 and therefore was reliable along the whole length of the series.

Each sample used for isotope analyses consists of annual tree rings pooled from the four best cross-dated trees. It is a number proven to be satisfactory for a representative isotope site record (Borella et al. Reference Borella, Leuenberger, Saurer and Siegwolf1998; Treydte et al. Reference Treydte, Schleser, Schweingruber and Winiger2001, Reference Treydte, Frank, Esper, Andreu, Bednarz, Berninger, Boettger, D’Alessandro, Etien and Filot2007; Leavitt Reference Leavitt2008).

Cellulose Extraction

Isotope analyses were carried out on the α-cellulose separated from the wood samples. The technique for the extraction of α-cellulose is based on the method presented by Green (Reference Green and Whistler1963) and Loader et al. (Reference Loader, Robertson, Barker, Switsur and Waterhouse1997). The extraction procedure of α-cellulose is fully described in Pawełczyk et al. (Reference Pawełczyk, Pazdur and Hałas2004) and Pazdur et al. (Reference Pazdur, Korput, Fogtman, Szczepanek, Hałas, Krąpiec and Szychowska-Krąpiec2005)

Isotopic Measurements

The δ13C measurements presented in this paper were part of an isotopic study carried out for the Suwałki site. The entire project covered measurements for 400 years in the case of carbon and oxygen isotopes and for 100 years in the case of hydrogen isotopes. The use of simultaneous measurements of δ13C and δ18O has made it possible to significantly reduce the number of measurements.

At the UFZ-Umweltforschungszentrum in Leipzig-Halle, δ13C measurements in α-cellulose were carried out simultaneously with δ18O measurements using an online technique with an IRMS-DELTA plus XL spectrometer (Thermo Finnigan) connected to an HTP reactor (HEKAtech, Germany), in which the pyrolysis process took place (Knöller et al. Reference Knöller, Böttger, Weise and Gehre2005).

The δ13C ratios in cellulose are classically obtained by combusting the samples to CO2 in an Elemental Analyzer (EA) coupled to an isotope ratio mass spectrometer. In previous studies, the high-temperature pyrolysis (HTP) technique has been reported as useful for determining δ18O and δ13C isotopic ratios at the same time from a single cellulose sample operating at 1400°C or above. Despite the presence of reactive carbon, HTP produces reasonably good δ13C values as confirmed by higher agreement with measurements obtained by the elemental analyzer system and the HTP (Knoller et al. Reference Knöller, Böttger, Weise and Gehre2005; Andreu-Hayles et al. Reference Andreu-Hayles, Levesque, Martin-Benito, Huang, Harris, Oelkers, Leland, Martin-Fernandez, Anchukaitis and Helle2019).

The results are given as in the usual δ notation in ‰ versus VPDB scale calibrated using IAEA-CH-3 (δ13C = –24.72‰) and IAEA-CH-6 (δ13C = –10.45‰) standards. The error margins of these methods are ±0.2‰ for carbon.

Site Description and Climatic Data

The area of Wigry National Park is covered by glacial sediments from the Würm Glaciation (tills and fluvioglacial sands with cobbles and boulders) reaching 150 m deep. The spectrum of soils developed on this allochthonous material includes rusty (aerosols) and podzolic soils. Tree stands encompass mainly Norway spruce and Scots pine (Migaszewski et al. Reference Migaszewski, Gałuszka and Pasławski2005).

Climatically, the Suwałki Region substantially differs from other regions of Poland. It is the coldest part of Poland, apart from the mountains, with annual mean temperatures around 6.3ºC (for years 1931–2003). The climate of the Suwałki Region is quite harsh, with some continental features. The spring reaches this region usually three weeks later than in southwestern Poland. The annual sum of precipitation is relatively low (591 mm/year—mean value for years 1931–2003), and it achieves the highest value in the summer season (80 mm/month). Predominates, the summer rainfall, which is characteristic of areas with continental climate features. On average, the summer rainfall consists of 63% of the total annual rainfall, and for different years it ranges from 46 to 81%. On average, there are 163 days per year with more than 0.1 mm of precipitation. The highest number of these is in winter and there are fewer in the warm season, meaning that rainfall is more abundant then and is often stormy. Heavy rain of more than 10.0 mm happens 14 days a year, mostly in the summer, from June to September. The winters are long and frosty, with long-lasting snow and ice cover (between 90 and 110 days in a year), and the number of days with average temperatures below the freezing point (0ºC) amounts to 108–119 a year. The vegetation period lasts for about 180–190 days. The highest relative humidity is here, as in Poland, in the cool season, especially in winter, when the average monthly relative humidity exceeds 90%. The driest period is in May and June—when the monthly average relative humidity drops below 70% (Kondracki Reference Kondracki1998; Krzysztofiak and Olszewki Reference Krzysztofiak and Olszewski1999; Woś Reference Woś1999).

The instrumental meteorological data used for the investigation started in 1931, in the case of temperature and precipitation, and from 1954, in the case of humidity and insolation. The data were obtained from the meteorological observatory in Suwałki (54°07’N, 22°58’E), which is located within 40 km of the sampling sites. An ombrothermic diagram of the region of Suwałki is presented in Figure 1.

Figure 1 An ombrothermic diagram of the region of Suwałki.

Ensuring the quality of climate data is of key importance when using long data series to carry out analyses related to the impact of variability of meteorological conditions over a selected area on other measured values. The Institute of Meteorology and Water Management – National Research Institute undertook activities aimed at developing complete information related to the daily variability of thermal and precipitation conditions in Poland. The official way of calculating characteristics, especially mean daily air temperature, changed over different periods and depended on the measuring program of the station. For example, to calculate the official value of mean daily air temperature since 1966, the following formula was used:

(1) $$\rm {Tsr = (T00 + T03 +T06 + T09+ T12 +T15 +T18 +T21)/8,} $$

where:

Txx = air temperature at measurement date xx (UTC),

For earlier data, a slightly different formula was used:

(2) $$\rm{Tsr = (T06 + T12+ 2 * T18)/4}. $$

Other climate data were also prepared with similar care.

Calculation Methods

The autocorrelation analyses were performed using MATLAB. An unbiased statistic provides a more accurate estimate of the population parameter. A biased statistic will either underestimate or overestimate the population parameter. When the bias is removed, the autocorrelation appears noisier at higher lags.

In our study, the correlations between isotopic and climatic data were calculated using the DendroClim2002 program. Each bootstrap estimate is obtained by generating 1000 samples (selected as random with replacement), and then running numerical computations for each sample (Biondi and Waikul Reference Biondi and Waikul2004).

A simple, but effective way to explore the stability of calibration models is to compute them for multiple periods or intervals, which, ideally, should be systematically selected to minimize bias. Using a moving interval technique (constant length progressively slid by one year) the temporal stability of the correlation between isotope chronology and climate was investigated by Biondi and Waikul (Reference Biondi and Waikul2004), and Cullen and Grierson (Reference Cullen and Grierson2006). For the Suwałki region investigations of the temporal stability of climatic signal were performed for 25-year moving windows with the first period being 1931–1955, the second 1932–1956, and so on. To perform moving correlation functions DendroClim2002 requires an interval greater or equal to twice the number of climatic predictors (for this case—temperature, precipitation, humidity, or insolation of selected months or seasons). To meet this requirement the number of monthly predictors was reduced to 12 by choosing periods (months, seasons), based on results shown in Table 1.

Table 1 Correlation coefficients (R) estimated using DENDROCLIM2002 (Biondi and Waikul Reference Biondi and Waikul2004) between isotopic data for pine (Pinus sylvestris L.) and meteorological data (T—temperature, P—precipitation, H—humidity, S—insolation). Bolded values denote the significant values (at the 95% confidence level). Capital letters in the month descriptions denote the previous year.

* Years 1932–2003.

** Years 1955–2003.

Values of δ13C in tree rings from Suwałki are presented in Figure 2. For the carbon, there are values of raw δ13C and corrected according to McCarroll and Loader (Reference McCarroll and Loader2004), due to changes in stable carbon isotope ratios of atmospheric CO2 since industrialization.

Figure 2 Isotopic (δ13C uncorrected and corrected) and meteorological records (T—temperature, P—precipitation, H—humidity, S—insolation, data covers the period May–August) for Suwałki region.

RESULTS AND DISCUSSION

Autocorrelation Analyses

Biased and unbiased autocorrelation function estimates for isotope chronology of raw and CO2atm corrected δ13C, were determined. Although the unbiased estimator is more appreciated from the statistics point of view, it increases in an unjustified manner for large lags. The autocorrelation analysis is therefore performed for lags, where the two estimators are similar, i.e., for about 25% of total lags, as presented in Figure 3. It can be concluded that isotope chronologies are not fully random, and they exhibit significant deterministic components because the envelopes of the estimates do not rapidly decay. For all cases, significant first-order autocorrelation could be seen. This points to a permanent influence on isotope values for the current year by those of the previous year. This is probably due to the tree’s use of reserves accumulated during the previous growing season. Reserves stored late in the growing season are used for early wood formation the following spring.

Figure 3 Autocorrelation functions for (a) δ13C, (b) corrected δ13C.

Trends for Stable Carbon Isotope Ratios

For the entire period from 1931 to 2003, the raw δ13C values for the pine tree ring cellulose from the Suwałki Region show no significant trend (slope = –0.0003, R2 = 5 × 10−5). However, for the corrected δ13C values, an increasing trend is observed (slope = 0.0187, R² = 0.2528). A decreasing trend in the raw δ13C values, reflecting the global trend of δ13C in atmospheric CO2 due to fossil fuel burning, was expected. When examining two shorter periods within the study, 1963–1983 and 1984–2003, changes in trends for both δ13C and δ13Ccorrected are evident. For the period 1963–1983, a decreasing trend is observed (δ13C: slope = –0.508, R²= 0.4799; δ13Ccorrected: slope = –0.227, R² = 0.1516), aligning with expectations. The Suwałki Region is relatively clean and free from significant industrial impact, so the local effect of δ13C decrease should be minimal, although the global effect should be visible. In contrast, for the period 1984-2003, an increasing trend is observed (δ13C: slope = 0.0389, R² = 0.0901; δ13Ccorrected: slope = 0.067, R² = 0.2264). This change is likely related to the increase in SO2 emissions, also evident in the Suwałki region. There was an EMEP (European Monitoring and Evaluation Programme) station in Suwałki from 1979 to 1992, and later a station in Diabla Góra. These stations, located in relatively clean areas, were meant to record background conditions for northeastern Poland. Nevertheless, an increase in SO2 concentrations in the air was recorded in this area in the 1980s and early 1990s. Gaseous SO2 can cause the closure of stomata, resulting in less negative carbon isotope composition (Rinne et al. Reference Rinne, Loader, Switsur, Treydte and Waterhouse2010).

Additionally, in the last 20 years of the 20th century and the early 21st century, the Suwałki region experienced significant fluctuations in temperature and precipitation. During this period, an increase in temperature (slope = –0.0817, R² = 0.3691) and sunshine (slope = 2.5631, R² = 0.2883) was observed, along with a decrease in humidity (slope = –0.2975, R² = 0.3639) and annual precipitation (slope = 2.487, R² = 0.1256). Less negative δ13C values correlate with warm and low humidity conditions in summer, while more negative values are associated with cooler and humid summers (Cullen and Grierson Reference Cullen and Grierson2006).

Relationship between Climate and Stable Carbon Isotope Ratios

Relationships between isotope values for tree rings and monthly climate data were modeled using a bootstrapped correlation function in DendroClim2002 (Biondi and Waikul Reference Biondi and Waikul2004). Each bootstrap estimate is obtained by generating 1000 samples (selected as random with replacement), and then running numerical computations for each sample (Biondi and Waikul Reference Biondi and Waikul2004). The correlation coefficients between annual isotope data (δ13C, δ13Ccorrected) in tree ring cellulose and temperature, insolation, sum of precipitation as well as humidity are presented in Table 1.

It was found that in the same case, a stronger correlation between climatic and isotopic data is observed in the case of uncorrected carbon isotopic data. Therefore, correlation is presented for corrected as well as for uncorrected data. In the case of carbon isotopes, the highest correlation coefficient due to temperature (r=0.38, n=72 for correlation with August temperature) for the period 1932–2003 exists for uncorrected δ13C data, but when considering the shorter time interval 1955–2003 a higher correlation coefficient occurs for the corrected δ13C (r=0.51, n=49, for correlation with August temperature. If periods combined from several months are considered, higher correlation coefficients (r=0.54, n=72) are obtained when analyzing the relationship between uncorrected δ13C data and the mean temperature of the May–August period. The positive correlation between summer temperature and δ13C is consistent with results from the literature (McCaroll and Loader Reference McCarroll and Loader2004). The correlation between carbon isotope and insolation is a little stronger than in the case of correlation with temperature (r=0.47, n=49 for insolation in June in case of uncorrected δ13C, and r=0.60, n=49 for the insolation in May–August in the case of the corrected value of δ13C). Insolation, rather than temperature, represents the more direct controlling factor. The correlation between the sum of precipitation and carbon isotopic data is stronger for the corrected data in the case of the longer period 1932–2003 (r=–0.35, n=72, p<0.005 for precipitation in August), but for the shorter period 1955–2003 is stronger for corrected data (r=–0.45, n=49, p<0.005 for precipitation in August). If periods combined from several months are considered, higher correlation coefficients are obtained also for May–August precipitation (n=–0.46, n=72) for the corrected δ13C data. The relative humidity values yield the best correlation between climate and carbon isotope data. The August relative humidity value is more influential than the relative humidity of any other month or combination of months, in determining the δ13C values of cellulose (r=–0.65, n=49, p<0.001 in the case of uncorrected data). Many investigators reported that the δ13C of organic matter is strongly related to water status and humidity (Roden and Ehleringer Reference Roden, Lin and Ehleringer2007). Water stress or high vapor pressure deficits cause stomatal closure and reduce intercellular CO2 concentrations and discrimination against 13C by Rubisco. Thus, δ13C in organic matter has been used to estimate plant water-use efficiency (Farquhar et al. Reference Farquhar, Ehleringer and Hubick1989). For carbon, it was found that the photosynthetic rate is controlled primarily by photon flux (sunshine) and temperature, whereas stomatal conductance is by air humidity and soil moisture (McCarroll and Pawellek Reference McCarroll and Pawellek2001; Gagen et al. Reference Gagen, McCarroll, Loader, Robertson, Jalkanen and Anchukaitis2007). Values of δ13C in environments of low moisture stress are likely to be controlled primarily by photosynthetic rate. The results do not make it clear which factor—stomatal conductance or photosynthetic rate—is the dominant factor controlling δ13C in the study area. Based on the presented results it can be proven that precipitation influences the stable isotopic carbon ratios less than the relative humidity especially in the case of uncorrected δ13C.

Temporal Stability of Climatic Signal Recorded by Stable Carbon Isotope Composition of Tree Rings α-Cellulose

The temporal stability of climate-proxy connections is an important issue in paleoclimatic reconstruction. The temporal stability of estimated correlation coefficients is presented in Figure 4 for the months and seasons of the highest correlation between climatic and isotopic data. In Figure 4 changes in the correlation coefficient for the calculated 25-year interval from 1966–1990 to 1979–2003 can be observed.

Figure 4 Temporal stability of correlations between δ13C and climate factor for (a) uncorrected values of δ13C (b) corrected values of δ13C in 25-year time windows. Asterisks denote statistically significant correlations (p<0.05). The description on the vertical axis indicates which meteorological data and from which months were used to determine the correlation with the isotopic data (numbers denote months, and letters: T—temperature, P—precipitation, H—humidity, S—insolation).

Various potential reasons are provided for the instability of the strength of the isotope data-climate correlation. The article by Savard and Daux (Reference Savard and Daux2020) enumerates the following:

  1. 1. sampling and data treatment artifacts

  2. 2. stand dynamics

  3. 3. effects of rising CO2

  4. 4. climate change

  5. 5. pollution

The method of sample preparation and handling was designed to mitigate the first two factors. To address the third factor, a correction for CO2 emissions was applied. Consequently, the last two factors could have influenced the lack of stability in the signals.

Non-stationary relationships between climatic parameters and isotopic data encompass climatic data from the final two decades of the twentieth century and the early years of the twenty-first century. Many elements demonstrate significant year-to-year variability and temporal fluctuations. Changes of particular climate elements are interrelated. The primary causative factors are both anthropogenic changes (greenhouse gas emissions resulting in increased greenhouse effect and global warming, local sources of air pollution) and natural changes: (1) circulation factors: changes in the intensity and location of atmospheric activity centers, changes in the frequency of advection from a specific sector, and the frequency of cyclonic and anticyclonic systems over Poland and (2) radiation factors (changes in values of global solar radiation, sunshine duration and cloudiness). These changes, especially visible after the 1980s, affect the trends of most climatic elements, meteorological phenomena and indices (Falarz et al. Reference Falarz and Falarz2021). It is also noteworthy that δ13C values may contain an anthropogenic signal associated with elevated SO2 emissions, which could have overlapped with the climate signal in the 1980s and early 1990s. Similar observations on the impact of SO2 are presented in the article Boettger et al. (Reference Boettger, Haupt, Friedrich and Waterhouse2014).

CONCLUSION

  • The relationships between δ13C in tree-ring cellulose and meteorological data discussed above showed that, in the case of the Suwałki region, temperature and precipitation affect stable carbon isotopes less than insolation and humidity.

  • The results do not make it clear which factor—stomatal conductance or photosynthetic rate—is the dominant factor controlling δ13C in the Suwałki region because a similar coefficient factor can be observed for humidity and insolation.

  • In most cases, for the combined periods (several months) larger correlation coefficients than for one month were observed.

  • The reported results confirm that stable isotope compositions of carbon can be regarded as indicators of climatic changes.

  • Temporal variability in the strength of δ13C-climate correlations was found.

ACKNOWLEDGMENTS

The authors would like to express thanks to Tatjana Boettger and Marika Haupt for the isotope measurements. This work is a part of an EU ISONET project No. EVK2-CT-2002-0014 (400 years of Annual Reconstructions of European Climate Variability using a High Resolution Isotopic Network). It was supported by the State Committee for Scientific Research under a special grant for the ISONET project SPB-12/RMF-1/2003. The publication was supported by EU within: “The modern methods of the monitoring of the level and isotopic composition of atmospheric CO2” (project no. FESL.10.25-IZ.01-06C9/23-00).

Footnotes

Selected Papers from the 24th Radiocarbon and 10th Radiocarbon & Archaeology International Conferences, Zurich, Switzerland, 11–16 Sept. 2022

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Figure 0

Figure 1 An ombrothermic diagram of the region of Suwałki.

Figure 1

Table 1 Correlation coefficients (R) estimated using DENDROCLIM2002 (Biondi and Waikul 2004) between isotopic data for pine (Pinus sylvestris L.) and meteorological data (T—temperature, P—precipitation, H—humidity, S—insolation). Bolded values denote the significant values (at the 95% confidence level). Capital letters in the month descriptions denote the previous year.

Figure 2

Figure 2 Isotopic (δ13C uncorrected and corrected) and meteorological records (T—temperature, P—precipitation, H—humidity, S—insolation, data covers the period May–August) for Suwałki region.

Figure 3

Figure 3 Autocorrelation functions for (a) δ13C, (b) corrected δ13C.

Figure 4

Figure 4 Temporal stability of correlations between δ13C and climate factor for (a) uncorrected values of δ13C (b) corrected values of δ13C in 25-year time windows. Asterisks denote statistically significant correlations (p<0.05). The description on the vertical axis indicates which meteorological data and from which months were used to determine the correlation with the isotopic data (numbers denote months, and letters: T—temperature, P—precipitation, H—humidity, S—insolation).