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Article

Shifts in Climatic Influences on Radial Growth of Scots Pine in the Central Scandinavian Mountains with an Evident Transition in the 1970s

by
Ulrika Gomm
*,
Emilia Bromfält
,
Selma Kling
and
Qiong Zhang
Department of Physical Geography, Stockholm University, SE-10691 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Climate 2024, 12(7), 97; https://doi.org/10.3390/cli12070097
Submission received: 18 May 2024 / Revised: 29 June 2024 / Accepted: 2 July 2024 / Published: 4 July 2024
(This article belongs to the Section Climate and Environment)
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Abstract

:
Radial growth of trees, as reflected by tree ring width, serves as a vital proxy for past climate conditions, offering insights into climate dynamics over centennial and millennial time scales. Traditionally, in the high altitudes and latitudes of the central Scandinavian Mountains, summer temperatures, particularly in July, have significantly influenced the radial growth of Scots pine. This research aims to reassess the climatic determinants of Scots pine radial growth in Jämtland, central Scandinavian Mountains, by incorporating a refined analysis that considers temperature, precipitation, and snow depth, and their correlations with tree growth over time. Using a dynamic moving window heatmap correlation analysis, this study revisits a Scots pine chronology to explore the evolving climatic influences on radial growth. This approach allows for the identification of temporal shifts in growth-limiting factors. We observe a notable transition in the 1970s, marking a shift where water availability, rather than temperature, emerges as a critical limiting factor for radial growth at both the beginning and the end of the growing season. This shift is reflective of the broader global trend of decreasing tree growth response to increasing temperatures in the latter half of the 20th century, underscoring the significant impact of ongoing climate change on forest ecosystems. The results highlight the necessity for adaptive forest management strategies that consider the changing dynamics of climatic influences on tree growth. Furthermore, our study contributes to the broader understanding of forest growth patterns in the face of climate change, with substantial implications for ecological research and forest management.

1. Introduction

In the cold, high latitudes of the northern hemisphere, the growth rate of trees has historically been constrained by low temperatures and short growing seasons. Tree ring width (TRW) chronologies are therefore considered to be good temperature proxies. These chronologies are predicated on the assumption that tree growth, particularly in these climatically challenges regions, is primarily temperature-limited [1,2,3]. However, since the global warming trend started in the 1970s, northern trees seem to have become less sensitive to temperature. Studies by Wilmking et al. [4] and Harvey et al. [5] report a diminishing sensitivity in tree growth to increasing temperatures, suggesting a possible geographical redistribution of the climatic drivers essential for radial tree growth. The transition to growth limitation by water availability, as opposed to temperature, marks a significant shift in the tree growth dynamics of boreal and temperate zones on a global scale [6].
Dendroclimatology aims to unravel the relationship between tree growth patterns and climatic variables, such as the correlation between ring widths and temperature. The discipline acknowledges various limiting factors—principally climate—that influence tree ring growth. These factors exhibit temporal variability, affecting tree growth under specific conditions. For example, the cambial activity of trees near the treeline show distinct growth patterns, with annual tree ring increments closely following the climatic conditions of the region [7,8,9,10]. Scots pine (Pinus sylvestris L.), in particular, has been instrumental in constructing temperature-sensitive chronologies within Scandinavia, highlighting the nuanced relationship between tree growth and climate.
Decoupling of tree growth and temperature has been reported in recent decades across the Northern Hemisphere, a phenomenon referred to as the divergence problem. This intriguing pattern, where trees exhibit loss of climate sensitivity and diminished growth responses despite rising temperature, remains a subject of debate and is not fully understood [11,12,13]. The onset of global warming in the late 20th century has complicated the interplay between tree growth and climate factors. Varied tree growth responses to climate factors are observed across different Northern Hemisphere regions before and after the 1970s, implying a complex, region-specific climate–growth relationship [14,15]. Notably, in alpine areas, local variations in elevation, topography, and stand composition might trigger growth decline through drought stress under a warming climate, with older trees displaying a pronounced vulnerability. On the other hand, temperature might enhance tree growth in higher elevations [1,16,17,18,19].
Recent research challenges the notion of a static relationship between climate variables and TRW. Instead, it proposes a dynamic perspective that considers temporal variability or non-stationarity, moving away from traditional linear relationships between tree growth and the environment [4,20]. This evolving understanding acknowledges the complex, often lagged responses of trees to environmental factors, integrating climate conditions over several years [20,21,22].
The primary aim of our study is to examine the impacts of ongoing climate warming on radial tree growth and the redistribution of climate drivers in the central Scandinavian Mountains. To achieve this, we employ multiple dynamic heatmap correlation analysis to investigate the relationships between the TRW proxy and key climate variables (temperature, precipitation, and snow depth) in the county of Jämtland, Sweden. Using the Treeclim R package, we provide a detailed re-examination of the Handölan chronology, offering a novel investigation into this geographic region located on the border between the maritime and continental climate zones.

2. Materials and Methods

2.1. Study Site

The study site is situated along the Handölan river in the central part of the Scandinavian Mountains (Figure 1), a region shaped by Pleistocene glaciations. This history has given the area a diverse mix of glacial sediments, river deposits, and peatlands, creating a special foundation for studying the environment [23,24]. Located in a narrow corridor of the mountain range, the site benefits from a maritime climate. This is in contrast to the drier continental influences prevailing further east [25]. The Handölan chronology is based on cores from dendrological samples taken from Scots pines at the treeline west of the Handölan river.
The topography in the region is characterised by gently sloping mountains, approximately 800–1000 m a.s.l. with peaks up to 1700 m (Figure 1). Valleys, mainly running from west to east such as those in the Snasahögarna mountains, allow moist winds from the North Atlantic Ocean to move through the area. This orographic effect significantly increases precipitation in the mountains compared to the eastern lowlands, enhancing the region’s climatic complexity [23].
Storlien, located in Jämtland county, shows the wider weather patterns that are impacting the area. Over the past century, there has been a notable increase in the frequency of days with above-average temperatures, with an overall warming of approximately 2 °C between 1900 and 2020. This warming trend has accelerated in the latter half of the 20th century, evidenced by an increase of 1 °C in the annual mean temperature from 1970 to 2000 (Figure 2). Concurrently, the growing season in Jämtland has extended by approximately one month, now starting in mid-May and ending in October. This change is attributed to an earlier onset of spring temperatures, defined by a period of four consecutive days with daily mean temperatures exceeding 5 °C [26]. However, the altitude of the study site introduces a moderating effect, resulting in a shorter growing season than observed at lower elevations in Storlien [23].
Precipitation in Storlien shows large monthly to inter-annual variations since 1925. There is a slightly decreasing trend in precipitation over the entire study period. Concurrent with the extension of the growing season, notable shifts in the timing of snow events have been documented. Specifically, the onset of snowfall has moved earlier into the fall, whereas the conclusion of snowfall now extends further into the spring. This shift is evident in the latter half of 20th century (Figure 3). More details on the meteorological stations can be found in Section 3.2 Meteorological variables.

2.2. The Handölan TRW chronology

The Handölan TRW chronology spans from 1566 to 2002 AD and was sampled during several years as part of the master Jämtland chronology, which extends over 7000 years [23,27,28]. The sampling site is located along a 4 km S-N long transect next to the Handölan river, situated on the lower eastern slope of the Snasahögarna mountain massif. Sampling involved collecting two cores from each living Scots pine at 1.3 m above the ground level. Each year consists of data from 40 and 50 trees, except for the 1999–2002 AD period, which includes approximately 20 trees. The variation in the number of trees sampled annually is primarily due to the logistical and environmental constraints encountered during different sampling years. For the majority of the study period, the aim has been to sample between 40 and 50 trees annually to ensure robust statistical power and representativeness of the chronology. The selection criteria aimed for homogeneity, focusing on trees near the treeline at altitudes of approximately 650–700 m a.s.l., with the treeline defined by the presence of 3–10 mature and productive trees [29]. The Handölan chronology is standardized using a negative exponential function to adjust for biological growth trends, thereby isolating the climate signal reflected in the TRW index [23]. This study focuses on a truncated interval of the chronology, specifically from 1925 to 2000 AD (Figure 4), to analyse shifts in climate factors affecting tree growth. The Handölan TRW dataset is provided as Supplementary Material (Table S1).

2.3. Meteorological Variables

The following meteorological variables are included in the correlation analysis, monthly mean temperature, total monthly precipitation, and maximum monthly snow depth [30]. The selection of Storlien meteorological stations was due to their proximity, acknowledging potential discrepancies caused by station relocation over time, especially before and after the year 1964 (Table 1). It is important to note that the meteorological data series have not been statistically homogenized.

2.4. Correlation Analysis Using Treeclim R Package

Employing the Treeclim R package, specifically the dcc function, we conducted a heatmap moving window correlation analysis to elucidate the temporal dynamics of climate influence on TRW. This method visualizes temporal variability in climate and tree growth relationships through color gradients, representing correlation strength over time [31]. A 30-year moving window for Pearson correlation, with significance level set at p < 0.05, facilitated this analysis over the selected intervals (1925–2000 AD for temperature and precipitation, 1926–2000 AD for snow depth). The analysis spanned sixteen months for temperature and precipitation—June to December of the preceding year and January to September of the current year—to capture the potential lag effects and include the whole growing season. For snow depth, an eight-month period with consistent snow coverage (October to December of the preceding year and January to May of the current year) was analyzed. The correlation coefficients were statistically reinforced through bootstrapping, performed 1000 times to ensure robustness.

3. Results

The results are presented through heatmaps with a 30-year moving window correlation analysis, adding a temporal dimension to our climatic examination. This approach allows us to unravel temporal relationships between tree growth (as indicated by TRW) and key climate variables: monthly mean temperature, total monthly precipitation, and maximum monthly snow depth. Each relationship is indicated with a nuanced color gradient, illustrating the strength and frequency of correlation over time.

3.1. Monthly Mean Temperature

Analysis reveals significant temporal variability in the correlation between TRW and mean temperature. Throughout the study period, the highest correlations are observed during April, July, and September of the current year, and August and December of the preceding year (Figure 5). July temperatures show a robust and consistent positive correlation with TRW across a larger part of the study period, indicating a pronounced sensitivity of tree growth to summer temperature.
Notably, the early part of our study period exhibits predominately positive correlation across most months. However, this pattern shifts around 1934–1979 AD, where a general weakening is observed, with slightly higher correlation in December (previous year), and April and July (current year). Post-1970 analysis indicates a pivot, with current April and September temperatures correlating negatively with TRW. Intriguingly, during this latter period, July (current year) and August and December (previous year) maintain a significant positive correlation with TRW. This period also aligns with increasing mean temperatures for September and April post-1975, suggesting a complex interaction between rising temperatures and tree growth dynamics.

3.2. Total Monthly Precipitation

The moving window heatmap for correlation between monthly total precipitation and TRW shows a varying correlation pattern over the study period (Figure 6). Initial analyses highlight a positive correlation between TRW and precipitation in March and April of the current year, in the beginning of the study period. Post-1970, September shows a notable shift from negative to a significant positive correlation with TRW. The preceding months of September and October show similar patterns but weaker correlations.

3.3. Maximum Monthly Snow Depth

The correlation between TRW and maximum snow depth displays significant temporal shifts, especially in the January–May window of the current year (Figure 7). The early period marks a significant positive correlation, particularly pronounced before the 1950 shift towards negative correlations in March–May. April, and briefly in March, experiences a significantly negative correlation during this phase. Notably, the measured snow depth records an increasing trend from February to May between 1925–1960 AD, from an average of 800 mm to approximately 1500 mm. The decades surrounding 1970 show a resurgence of positive correlation in April and May, contrasting a persistently weak negative correlation in March. The winter months (December to February) exhibit a transition from a weak positive correlation to weak negative correlation post-1970, suggesting an evolving climatic influence on snow depth dynamics and its subsequent impact on tree growth.

4. Discussion

The observed warming trend since the 1970s has notably extended the growing season in Jämtland by an entire month, a change that is roughly evenly split between the beginning and the end of the season [26]. Our findings align with a broader climatic shift observed since the latter part of the 20th century, characterised by a redistribution of climate factors influencing tree growth. The traditional dominance of temperature as a limiting factor for tree growth is gradually giving way to an increased influence of water availability, as previously discussed by Babst et al. [6], Wilmking et al. [4] and Harvey et al. [5]. Particularly noteworthy is the diminished climate sensitivity of TRW to rising temperatures during September post-1970s. This can be seen by the negative correlation between TRW and temperature for this month, opposite to earlier studies that highlighted the significant impacts of September temperatures on tree growth [32].
Moreover, our analysis suggests a shift in the TRW’s climate sensitivity, with an increasing response to precipitation in September, similar to those reported by Düthorn et al. [33]. This change could be indicative of a more pronounced reliance on water availability during this time of year. Further, TRW proxy indicates an acute sensitivity to snow depth in May, likely providing critical soil moisture in the beginning of the growing season. This observation diverges from previous studies that reported a lesser climate sensitivity to precipitation during the same month in both central and northern regions of the Scandinavian Mountains [33,34]. However, the role of summer temperatures, especially in July, remains a consistent limiting factor for tree growth throughout the whole study period, reaffirming its importance both before and after the pivotal 1970s transition [14,32,34].
Our investigation acknowledges several limitations that could potentially impact the robustness of our results, particularly concerning the utilization of climate data compiled from multiple weather stations. A significant concern is the lack of homogenization across these datasets, coupled with our decision to retain data points identified by the SMHI as potentially unreliable. This approach introduces a level of uncertainty into our correlation analysis, which needs careful consideration when interpreting the findings.
Despite these limitations, our study reveals a pivotal transition around 1970 in the climate sensitivity to radial tree growth, aligning with a period of marked temperature increases. Historically, summer mean temperatures have exerted a considerable influence on tree growth in the northern latitudes. However, our findings highlight a notable divergence from this trend in the latter part of the 20th century, as shown by the negative correlation observed for mean monthly temperature in September. This period marks a discernible shift towards moisture, particularly snow depth in late spring, as a critical driver of tree growth in the beginning of the growing season.
The ongoing global warming, with additional arctic amplification in the northern latitudes, is likely to accelerate and enhance these shifts in climatic factors limiting tree growth. This evolving dynamic challenges the traditional application of uniformitarianism within paleoclimatology, especially concerning the use of dendrochronology for reconstructing past climate. Our findings prompt a reevaluation of established principles in light of observed changes, suggesting the need for further research within central Scandinavian Mountains to fully understand the implications of these shifts.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cli12070097/s1, Table S1: The Handölan TRW dataset.

Author Contributions

Conceptualization, U.G., E.B. and S.K.; methodology, U.G., E.B. and S.K.; software, U.G., E.B. and S.K.; validation, U.G., E.B. and S.K.; formal analysis, U.G., E.B. and S.K.; investigation, U.G., E.B. and S.K.; resources, U.G., E.B. and S.K.; data curation, U.G., E.B. and S.K.; writing—original draft preparation, U.G., E.B. and S.K.; writing—review and editing, U.G., E.B., S.K. and Q.Z.; visualization, U.G., E.B. and S.K.; supervision, U.G., E.B. and S.K.; project administration, U.G., E.B. and S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The climate data can be found at the website of the Swedish Meteorological and Hydrological Institute (SMHI): https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska-observationer#param=airtemperatureInstant,stations=core, accessed on 11 December 2023. The Handölan TRW chronology is part of the master Jämtland TRW chronology which can be found at https://bolin.su.se/data/?k=RCS, accessed on 14 December 2023.

Acknowledgments

We would like to thank Josefine Axelsson for valuable comments during the writing process, and Björn Gunnarson for providing TRW datasets and specific knowledge about the sampling site.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Fritts, H.C. Chapter 1: Dendrochronology and Dendroclimatology. In Tree Rings and Climate; Academic Press: London, UK, 1976; pp. 1–54. [Google Scholar]
  2. Speer, J.H. Fundamentals of Tree-Ring Research; University of Arizona Press: Tucson, AZ, USA, 2010. [Google Scholar]
  3. Villalba, R.; Luckman, B.H.; Boninsegna, J.; D’Arrigo, R.D.; Lara, A.; Villanueva-Diaz, J.; Masiokas, M.; Argollo, J.; Soliz, C.; LeQuesne, C.; et al. Chapter 7. Dendroclimatology from Regional to Continental Scales: Understanding Regional Processes to Reconstruct Large-Scale Climatic Variations Across the Western Americas. In Dendroclimatology: Progress and Prospects; Hughes, M., Swetnam, T., Diaz, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; Volume 11, pp. 175–227. [Google Scholar]
  4. Wilmking, M.; van der Maaten-Theunissen, M.; van der Maaten, E.; Scharnweber, T.; Buras, A.; Biermann, C.; Gurskaya, M.; Hallinger, M.; Lange, J.; Shetti, R.; et al. Global assessment of relationships between climate and tree growth. Glob. Chang. Biol. 2020, 26, 3212–3220. [Google Scholar] [CrossRef] [PubMed]
  5. Harvey, J.E.; Smiljanić, M.; Scharnweber, T.; Buras, A.; Cedro, A.; Cruz-García, R.; Drobyshev, I.; Janecka, K.; Jansons, Ā.; Kaczka, R.; et al. Tree growth influenced by warming winter climate and summer moisture availability in northern temperate forests. Glob. Chang. Biol. 2020, 26, 2505–2518. [Google Scholar] [CrossRef] [PubMed]
  6. Babst, F.; Bouriaud, O.; Poulter, B.; Trouet, V.; Girardin, M.P.; Frank, D.C. Twentieth century redistribution in climatic drivers of global tree growth. Sci. Adv. 2019, 5, 1–9. [Google Scholar] [CrossRef]
  7. Fritts, H.C.; Swetnam, T.W. Dendroecology: A Tool for Evaluating Variations in Past and Present Forest Environments. In Advances in Ecological Research; Begon, M., Fitter, A.H., Ford, E.D., MacFadyen, A., Eds.; Academic Press: Cambridge, MA, USA, 1989; Volume 19, pp. 111–188. [Google Scholar] [CrossRef]
  8. Briffa, K.R.; Bartholin, T.S.; Eckstein, D.; Jones, P.D.; Karlén, W.; Schweingruber, F.H.; Zetterberg, P. A 1400-year tree-ring record of summer temperatures in Fennoscandia. Nature 1990, 346, 434–439. [Google Scholar] [CrossRef]
  9. Grudd, H. Torneträsk tree-ring width and density ad 500–2004: A test of climatic sensitivity and a new 1500-year reconstruction of north Fennoscandian summers. Clim. Dyn. 2008, 31, 843–857. [Google Scholar] [CrossRef]
  10. Helama, S.; Mielikäinen, K.; Timonen, M.; Eronen, M. Finnish supra-long tree-ring chronology extended to 5634 BC. Nor. Geogr. Tidsskr. Nor. J. Geogr. 2008, 62, 271–277. [Google Scholar] [CrossRef]
  11. Wilson, R.; D’Arrigo, R.; Buckley, B.; Büntgen, U.; Esper, J.; Frank, D.; Luckman, B.; Payette, S.; Vose, R.; Youngblut, D. A matter of divergence: Tracking recent warming at hemispheric scales using tree ring data. J. Geophys. Res. Atmos. 2007, 112, 1–17. [Google Scholar] [CrossRef]
  12. D’Arrigo, R.; Wilson, R.; Liepert, B.; Cherubini, P. On the ‘Divergence Problem’ in Northern Forests: A review of the tree-ring evidence and possible causes. Glob. Planet. Chang. 2008, 60, 289–305. [Google Scholar] [CrossRef]
  13. Büntgen, U.; Frank, D.; Neuenschwander, T.; Esper, J. Fading temperature sensitivity of Alpine tree growth at its Mediterranean margin and associated effects on large-scale climate reconstructions. Clim. Chang. 2012, 114, 651–666. [Google Scholar] [CrossRef]
  14. Babst, F.; Poulter, B.; Trouet, V.; Tan, K.; Neuwirth, B.; Wilson, R.; Carrer, M.; Grabner, M.; Tegel, W.; Levanic, T.; et al. Site- and species-specific responses of forest growth to climate across the European continent. Glob. Ecol. Biogeogr. 2013, 22, 706–717. [Google Scholar] [CrossRef]
  15. Liu, J.; Li, Z.S.; Keyimu, M.; Wang, X.; Liang, H.; Feng, X.; Gao, G.; Fu, B. Accelerated warming in the late 20th century promoted tree radial growth in the Northern Hemisphere. J. Plant Ecol. 2023, 16, rtac077. [Google Scholar] [CrossRef]
  16. Kienast, F.; Schweingruber, F.H. Dendroecological Studies in the Front Range, Colorado, U.S.A. Arct. Alp. Res. 1986, 18, 277–288. [Google Scholar] [CrossRef]
  17. Primicia, I.; Camarero, J.J.; Janda, P.; Čada, V.; Morrissey, R.C.; Trotsiuk, V.; Bače, R.; Teodosiu, M.; Svoboda, M. Age, competition, disturbance and elevation effects on tree and stand growth response of primary Picea abies forest to climate. For. Ecol. Manag. 2015, 354, 77–86. [Google Scholar] [CrossRef]
  18. Gleason, K.E.; Bradford, J.B.; Bottero, A.; D’Amato, A.W.; Fraver, S.; Palik, B.J.; Battaglia, M.A.; Iverson, L.; Kenefic, L.; Kern, C.C. Competition amplifies drought stress in forests across broad climatic and compositional gradients. Ecosphere 2017, 8, e01849. [Google Scholar] [CrossRef]
  19. Wang, X.; Yang, B. Divergent tree radial growth at alpine coniferous forest ecotone and corresponding responses to climate change in northwestern China. Ecol. Indic. 2021, 121, 107052. [Google Scholar] [CrossRef]
  20. Peltier, D.M.P.; Ogle, K. Tree growth sensitivity to climate is temporally variable. Ecol. Lett. 2020, 23, 1561–1572. [Google Scholar] [CrossRef] [PubMed]
  21. Ljungqvist, F.C.; Thejll, P.; Björklund, J.; Gunnarson, B.E.; Piermattei, A.; Rydval, M.; Seftigen, K.; Støve, B.; Büntgen, U. Assessing non-linearity in European temperature-sensitive tree-ring data. Dendrochronologia 2020, 59, 125652. [Google Scholar] [CrossRef]
  22. Peltier, D.M.P.; Anderegg, W.R.L.; Guo, J.S.; Ogle, K. Contemporary tree growth shows altered climate memory. Ecol. Lett. 2022, 25, 2663–2674. [Google Scholar] [CrossRef]
  23. Linderholm, H.W.; Gunnarson, B.E.; Liu, Y. Comparing Scots pine tree-ring proxies and detrending methods among sites in Jämtland, west-central Scandinavia. Dendrochronologia 2010, 28, 239–249. [Google Scholar] [CrossRef]
  24. SGU. SGU (Sveriges Geologiska Undersökning) Kartvisaren: Jordarter 1:750,000 Mittnorden. 2023. Available online: https://apps.sgu.se/kartvisare/kartvisare-jordarter-mittnorden-750-tusen.html (accessed on 18 December 2023).
  25. SMHI. Sveriges Meteorologiska och Hydrologiska Institut. Normal Kontinentala och Marina Områden. 2017. Available online: https://www.smhi.se/data/meteorologi/temperatur/normal-kontinentala-och-marina-omraden-1.4182 (accessed on 12 December 2023).
  26. Bergström, T. Klimatanalys för fjällområdet i Jämtlands Län. SMHI Rapport 44, Sveriges Meteorologiska och Hydrologiska Institut (SMHI), Norrköping. 2021. Available online: https://www.lansstyrelsen.se/publikation?entry=12&context=35 (accessed on 10 December 2023).
  27. Gunnarson, B.E.; Linderholm, H.W. Low-frequency summer temperature variation in central Sweden since the tenth century inferred from tree rings. Holocene 2002, 12, 667–671. [Google Scholar] [CrossRef]
  28. Linderholm, H.W. Twentieth-Century Scots Pine Growth Variations in the Central Scandinavian Mountains Related to Climate Change. Arct. Antarct. Alp. Res. 2002, 34, 440–449. [Google Scholar] [CrossRef]
  29. Linderholm, H.W.; Gunnarson, B.E. Summer Temperature Variability in Central Scandinavia during the Last 3600 Years. Geogr. Ann. Ser. A Phys. Geogr. 2005, 87, 231–241. [Google Scholar] [CrossRef]
  30. SMHI. Sveriges Meteorologiska och Hydrologiska Institut. Ladda ner Meteorologiska Observationer. 2023. Available online: https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska-observationer#param=airtemperatureInstant,stations=core (accessed on 1 December 2023).
  31. Zang, C.; Biondi, F. Treeclim: An R package for the numerical calibration of proxy-climate relationships. Ecography 2015, 38, 431–436. [Google Scholar] [CrossRef]
  32. Linderholm, H.W. Climatic influence on Scots pine growth on dry and wet soils in the central Scandinavian mountains, interpreted from tree-ring width. Silva Fenn. 2001, 35, 415–424. [Google Scholar] [CrossRef]
  33. Düthorn, E.; Holzkämper, S.; Timonen, M.; Esper, J. Influence of micro-site conditions on tree-ring climate signals and trends in central and northern Sweden. Trees 2013, 27, 1395–1404. [Google Scholar] [CrossRef]
  34. Macias, M.; Timonen, M.; Kirchhefer, A.J.; Lindholm, M.; Eronen, M.; Gutiérrez, E. Growth Variability of Scots Pine (Pinus sylvestris) along a West-East Gradient across Northern Fennoscandia: A Dendroclimatic Approach. Arct. Antarct. Alp. Res. 2004, 36, 565–574. [Google Scholar] [CrossRef]
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Figure 1. Location map of the Handölan tree ring sampling site, with regional (A) and local (B) topography of the central Scandinavian Mountains. Meteorological stations near Storlien, to the northwest of the sampling site, are marked along with sampling sites being part of the larger Jämtland master chronology.
Figure 1. Location map of the Handölan tree ring sampling site, with regional (A) and local (B) topography of the central Scandinavian Mountains. Meteorological stations near Storlien, to the northwest of the sampling site, are marked along with sampling sites being part of the larger Jämtland master chronology.
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Figure 2. The annual mean temperature anomaly from 1900–2000 AD in Storlien, with a reference value from 1951–1980 average. The data is from the weather stations described in Table 1 and Figure 1.
Figure 2. The annual mean temperature anomaly from 1900–2000 AD in Storlien, with a reference value from 1951–1980 average. The data is from the weather stations described in Table 1 and Figure 1.
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Figure 3. First and last days of snow in Storlien (1925–2000 AD). The bars show the number of days passed between the first and last day of snow during one season. The dots indicate first and last day of snow of the season. The data is based on the weather stations described in Table 1 and Figure 1.
Figure 3. First and last days of snow in Storlien (1925–2000 AD). The bars show the number of days passed between the first and last day of snow during one season. The dots indicate first and last day of snow of the season. The data is based on the weather stations described in Table 1 and Figure 1.
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Figure 4. Handölan TRW index and sample depth (1925–2000 AD). Standardized with a negative exponential function.
Figure 4. Handölan TRW index and sample depth (1925–2000 AD). Standardized with a negative exponential function.
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Figure 5. Moving window correlation between the Handölan TRW data and mean monthly temperature, with significant correlations marked by a white asterisk. The analysis covers a 16-month period from June of the previous year to September of the current year, within a moving 30-year window, bootstrapped 1000 times.
Figure 5. Moving window correlation between the Handölan TRW data and mean monthly temperature, with significant correlations marked by a white asterisk. The analysis covers a 16-month period from June of the previous year to September of the current year, within a moving 30-year window, bootstrapped 1000 times.
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Figure 6. Moving window correlation analysis between the Handölan TRW data and total monthly precipitation, with signficant correlations marked by a white asterisk. The analysis covers a 16-month period from June of the previous year to September of the current year, within a 30-year moving window, bootstrapped 1000 times.
Figure 6. Moving window correlation analysis between the Handölan TRW data and total monthly precipitation, with signficant correlations marked by a white asterisk. The analysis covers a 16-month period from June of the previous year to September of the current year, within a 30-year moving window, bootstrapped 1000 times.
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Figure 7. Moving window correlation analysis between the Handölan TRW data and maximum monthly snow depth, with significant correlations marked by a white asterisk. The analysis covers an 8-month period from October of the previous year to May of the current year, within a 30-year moving window, bootstrapped 1000 times.
Figure 7. Moving window correlation analysis between the Handölan TRW data and maximum monthly snow depth, with significant correlations marked by a white asterisk. The analysis covers an 8-month period from October of the previous year to May of the current year, within a 30-year moving window, bootstrapped 1000 times.
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Table 1. Geographical and dataset information for the meteorological stations used in this study. It includes the type of data collected at each station and the respective time period covered by each dataset.
Table 1. Geographical and dataset information for the meteorological stations used in this study. It includes the type of data collected at each station and the respective time period covered by each dataset.
SMHI StationN, E (°)Elevation (m a.s.l.)Data 1Time Interval
Storlien63.3158, 12.1009595T, P, S1924–1963
Storlien-Storvallen63.2826, 12.1218583P, S1964–2001
Storlien-Visjövalen63.3028, 12.1253642T1964–2001
1 T = monthly mean temperature, P = total monthly precipitation, S = daily snow depth.
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Gomm, U.; Bromfält, E.; Kling, S.; Zhang, Q. Shifts in Climatic Influences on Radial Growth of Scots Pine in the Central Scandinavian Mountains with an Evident Transition in the 1970s. Climate 2024, 12, 97. https://doi.org/10.3390/cli12070097

AMA Style

Gomm U, Bromfält E, Kling S, Zhang Q. Shifts in Climatic Influences on Radial Growth of Scots Pine in the Central Scandinavian Mountains with an Evident Transition in the 1970s. Climate. 2024; 12(7):97. https://doi.org/10.3390/cli12070097

Chicago/Turabian Style

Gomm, Ulrika, Emilia Bromfält, Selma Kling, and Qiong Zhang. 2024. "Shifts in Climatic Influences on Radial Growth of Scots Pine in the Central Scandinavian Mountains with an Evident Transition in the 1970s" Climate 12, no. 7: 97. https://doi.org/10.3390/cli12070097

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