Articles | Volume 21, issue 2
https://doi.org/10.5194/cp-21-357-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-21-357-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret
Timon Netzel
Institute for Geoscience, Sect. Meteorology, University of Bonn, Auf dem Hügel 20, 53121 Bonn, Germany
Andrea Miebach
Bonn Institute of Organismic Biology, Sect. Paleontology, University of Bonn, Nussallee 8, 53115 Bonn, Germany
Thomas Litt
CORRESPONDING AUTHOR
Bonn Institute of Organismic Biology, Sect. Paleontology, University of Bonn, Nussallee 8, 53115 Bonn, Germany
Andreas Hense
Institute for Geoscience, Sect. Meteorology, University of Bonn, Auf dem Hügel 20, 53121 Bonn, Germany
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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
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The extreme events heatwaves, droughts, heavy precipitation, floods and wind storms affect socio-economic systems and generate strong public attention. They are embedded into atmospheric dynamics and are statistically rare events. Here we compile the contributions of twenty one articles of the inter-journal NHESS/ASCMO/WCD special issue by project ClimXtreme parallel to results from thirty three more publication. The conclusions underline the complexity of the results.
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Clim. Past, 19, 1043–1060, https://doi.org/10.5194/cp-19-1043-2023, https://doi.org/10.5194/cp-19-1043-2023, 2023
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Data–data and data–model vegetation comparisons are commonly based on comparing single vegetation estimates. While this approach generates good results on average, reducing pollen assemblages to single single plant functional type (PFT) or biome estimates can oversimplify the vegetation signal. We propose using a multivariate metric, the Earth mover's distance (EMD), to include more details about the vegetation structure when performing such comparisons.
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Short summary
New probabilistic methods for local quantitative paleoclimate reconstructions are introduced within a Bayesian framework and applied to plant proxy data from Lake Kinneret (Israel). Recent climate data and arboreal pollen from the lake's sediment are added as predefined boundary conditions. The results provide a reconstruction of the mean December–February temperature and annual precipitation, along with their associated uncertainty ranges, in this region during the Holocene.
New probabilistic methods for local quantitative paleoclimate reconstructions are introduced...