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Keywords = super-long tree-ring chronology

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16 pages, 2416 KiB  
Article
Volcanic Impact Patterns in Tree Rings from Historical Wood in Northern Fennoscandia’s Old Churches
by Oleg I. Shumilov, Elena A. Kasatkina, Mauri Timonen and Evgeniy O. Potorochin
Forests 2025, 16(4), 573; https://doi.org/10.3390/f16040573 - 26 Mar 2025
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Abstract
This study revealed a significant reduction in tree growth across northern Fennoscandia following the 1600 AD eruption of Huaynaputina in Peru, the most powerful volcanic event in South America over the past two millennia. In the analysis, we utilized six tree-ring chronologies, which [...] Read more.
This study revealed a significant reduction in tree growth across northern Fennoscandia following the 1600 AD eruption of Huaynaputina in Peru, the most powerful volcanic event in South America over the past two millennia. In the analysis, we utilized six tree-ring chronologies, which included the Finnish super-long chronology (5634 BC–2004 AD), the Kola Peninsula chronology (1445–2004 AD), and historical chronologies derived from old wooden churches in Finnish Lapland and Karelia, Russia. Using a superposed epoch analysis across these chronologies revealed a significant 24% (p < 0.01) decline in tree-ring growth in 1601 compared to the previous six years. The northernmost records, the Finnish super-long chronology (72%, p < 0.001) and the Sodankylä Old Church chronology (67%, p < 0.001), showed the most pronounced decreases. Statistical analysis confirmed significant (p < 0.05) similarities in tree-ring responses across all chronologies from 1601 to 1608. These findings underscore the reliability of using the 1600 Huaynaputina eruption as a chronological marker for dating historic wooden churches in northern Fennoscandia that were likely built between the late 17th and early 18th centuries. Additionally, analyzing church wood may provide insights into past climate patterns and environmental conditions linked to the eruption. Full article
(This article belongs to the Special Issue Wood as Cultural Heritage Material: 2nd Edition)
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12 pages, 3253 KiB  
Article
Neural Network-Based Climate Prediction for the 21st Century Using the Finnish Multi-Millennial Tree-Ring Chronology
by Elena A. Kasatkina, Oleg I. Shumilov and Mauri Timonen
Geosciences 2024, 14(8), 212; https://doi.org/10.3390/geosciences14080212 - 8 Aug 2024
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Abstract
The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records [...] Read more.
The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records from five weather stations in northern Fennoscandia (65°–70.4° N) revealed an increasing trend, with a range of 0.09 °C/decade to 0.15 °C/decade. However, due to the short duration of instrumental records, it is not possible to accurately assess and predict climate changes on centennial and millennial timescales. In this study, we used the Finnish super-long (~7600 years) tree-ring chronology to create a climate prediction for the 21st century. We applied a method that combines a long short-term memory (LSTM) neural network with the continuous wavelet transform and wavelet filtering in order to make climate change predictions. This approach revealed a significant decrease in tree-ring growth over the near term (2063–2073). The predicted decrease in tree-ring growth (and regional temperature) is thought to be a result of a new grand solar minimum, which may lead to Little Ice Age-like climatic conditions. This result is significant for understanding current climate processes and assessing potential environmental and socio-economic risks on a global and regional level, including in the area of the Arctic shipping routes. Full article
(This article belongs to the Special Issue Advanced Statistical Modelling in Climate Change)
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