EGU21-16510, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Searching for weather in varves: use of ultra-high-resolution scanning techniques to reconstruct seasonal meteorological conditions

Paul Zander1, Maurycy Żarczyński2, Wojciech Tylmann2, Shauna-kay Rainford3, and Martin Grosjean1
Paul Zander et al.
  • 1Institute of Geography & Oeschger Centre for Climate Change Research, University of Bern, Switzerland
  • 2Faculty of Oceanography and Geography, University of Gdansk, Poland
  • 3Institute of Plant Sciences & Oeschger Centre for Climate Change Research, University of Bern, Switzerland

Varved lake sediments are recognized as valuable archives of paleoclimatic information due to their precise chronological control. However, paleoclimate reconstructions based on the composition of biochemical varves are relatively rare (Zolitschka et al., 2015). We applied novel high-resolution scanning techniques to the varved sediments of Lake Żabińskie, Poland to obtain spatially resolved geochemical data at a resolution of 60 μm covering the period 1966-2019. Relative abundances of elements were measured in resin-embedded sediment slabs using a Bruker M4 Tornado micro-XRF scanner. Chloropigments-a and bacteriopheopigments-a were measured on a wet sediment core using a Specim Hyperspectral core scanner (Butz et al., 2015). The high resolution of the scanning data, and the relatively thick well-preserved varves (average thickness = 6.4 mm), enables a close examination of seasonal scale sediment composition and varve formation processes. Time series of geochemical variables within each varve year were classified into 4 varve type groups based on the dissimilarity measure ψ for multivariate time series (Benito and Birks, 2020; Gordon and Birks, 1974). Based on a Multivariate Analysis Of Variance test, these groups of years experienced significant (p<0.05) differences in seasonal meteorological conditions, particularly wind speed and temperature.  Additionally, a correlation analysis on mean annual geochemical values from the aforementioned scanning techniques and conventional CNS analysis, and seasonal meteorological data revealed significant (p<0.05) correlations with windiness and temperature. Based on these relationships, we applied generalized additive models to predict spring and summer (MAMJJA) temperature and number of windy days (spring through fall), yielding models with significant predictive power. Based on model selection, the variables with the most predictive power for spring and summer temperature were Ti (negative correlation) and total C. The variables with the most predictive power for windiness were Si, sediment accumulation rate, and varve type. This study highlights the usefulness of high-resolution scanning techniques to improve our understanding of varve formation processes and relationships between varve composition and climate variables in biochemical varves.



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How to cite: Zander, P., Żarczyński, M., Tylmann, W., Rainford, S., and Grosjean, M.: Searching for weather in varves: use of ultra-high-resolution scanning techniques to reconstruct seasonal meteorological conditions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16510,, 2021.

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