New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret
<p>Quantitative local paleoclimate reconstructions are an important tool for gaining insights into the climate history of the Earth. The complex age–sediment–depth and proxy–climate relationships must be described in an appropriate way. Bayesian hierarchical models are a promising method for d...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-02-01
|
| Series: | Climate of the Past |
| Online Access: | https://cp.copernicus.org/articles/21/357/2025/cp-21-357-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540970227335168 |
|---|---|
| author | T. Netzel A. Miebach T. Litt A. Hense |
| author_facet | T. Netzel A. Miebach T. Litt A. Hense |
| author_sort | T. Netzel |
| collection | DOAJ |
| description | <p>Quantitative local paleoclimate reconstructions are an important tool for gaining insights into the climate history of the Earth. The complex age–sediment–depth and proxy–climate relationships must be described in an appropriate way. Bayesian hierarchical models are a promising method for describing such structures.</p>
<p>In this study, we present a new age–depth transformation in a Bayesian formulation by determining the uncertainty information of depths in lake sediments at a given age. This enables data-driven smoothing of past periods, which allows better interpretation.</p>
<p>We introduce a systematic, machine-learning-based way to establish probabilistic transfer functions which connect spatial distributions of temperature and precipitation to the spatial presence of specific biomes. This includes consideration of various machine learning (ML) algorithms for solving the classification problem of biome presence and absence, taking into account uncertainties in the proxy–climate relationship. For the models and biome distributions used, a simple feedforward neural network provides the optimal choice of the classification problem.</p>
<p>Based on this, we formulate a new Bayesian hierarchical model that generates local paleoclimate reconstructions. This is applied to plant-based proxy data from the lake sediment of Lake Kinneret (LK). Here, a priori information on the recent climate in this region and data on arboreal pollen from this lake are used as boundary conditions. To solve this model, we use Markov chain Monte Carlo (MCMC) sampling methods. During the inference process, our new method generates taxa weights and biome climate ranges. The former shows that less weight needs to be given to <i>Olea europaea</i> to ensure the influence of the other taxa. In contrast, the highest weights are found in <i>Quercus calliprinos</i> and Amaranthaceae, resulting in appropriate flexibility under the given boundary conditions. In terms of climate ranges, the posterior probability of the Mediterranean biome reveals the greatest change, with an average boreal winter (December–February) temperature of <span class="inline-formula">10<sup>∘</sup>C</span> and an annual precipitation of 700 mm for Lake Kinneret during the Holocene. The paleoclimate reconstruction for this period shows comparatively low precipitation of about 400 mm during 9–7 and 4–2 cal ka BP. The respective temperatures fluctuate much less and stay around 10 °C.</p> |
| format | Article |
| id | doaj-art-c3d2fdf97a674f6db09a39de7bb4bafe |
| institution | Kabale University |
| issn | 1814-9324 1814-9332 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Climate of the Past |
| spelling | doaj-art-c3d2fdf97a674f6db09a39de7bb4bafe2025-02-04T11:22:09ZengCopernicus PublicationsClimate of the Past1814-93241814-93322025-02-012135738010.5194/cp-21-357-2025New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake KinneretT. Netzel0A. Miebach1T. Litt2A. Hense3Institute for Geoscience, Sect. Meteorology, University of Bonn, Auf dem Hügel 20, 53121 Bonn, GermanyBonn Institute of Organismic Biology, Sect. Paleontology, University of Bonn, Nussallee 8, 53115 Bonn, GermanyBonn Institute of Organismic Biology, Sect. Paleontology, University of Bonn, Nussallee 8, 53115 Bonn, GermanyInstitute for Geoscience, Sect. Meteorology, University of Bonn, Auf dem Hügel 20, 53121 Bonn, Germany<p>Quantitative local paleoclimate reconstructions are an important tool for gaining insights into the climate history of the Earth. The complex age–sediment–depth and proxy–climate relationships must be described in an appropriate way. Bayesian hierarchical models are a promising method for describing such structures.</p> <p>In this study, we present a new age–depth transformation in a Bayesian formulation by determining the uncertainty information of depths in lake sediments at a given age. This enables data-driven smoothing of past periods, which allows better interpretation.</p> <p>We introduce a systematic, machine-learning-based way to establish probabilistic transfer functions which connect spatial distributions of temperature and precipitation to the spatial presence of specific biomes. This includes consideration of various machine learning (ML) algorithms for solving the classification problem of biome presence and absence, taking into account uncertainties in the proxy–climate relationship. For the models and biome distributions used, a simple feedforward neural network provides the optimal choice of the classification problem.</p> <p>Based on this, we formulate a new Bayesian hierarchical model that generates local paleoclimate reconstructions. This is applied to plant-based proxy data from the lake sediment of Lake Kinneret (LK). Here, a priori information on the recent climate in this region and data on arboreal pollen from this lake are used as boundary conditions. To solve this model, we use Markov chain Monte Carlo (MCMC) sampling methods. During the inference process, our new method generates taxa weights and biome climate ranges. The former shows that less weight needs to be given to <i>Olea europaea</i> to ensure the influence of the other taxa. In contrast, the highest weights are found in <i>Quercus calliprinos</i> and Amaranthaceae, resulting in appropriate flexibility under the given boundary conditions. In terms of climate ranges, the posterior probability of the Mediterranean biome reveals the greatest change, with an average boreal winter (December–February) temperature of <span class="inline-formula">10<sup>∘</sup>C</span> and an annual precipitation of 700 mm for Lake Kinneret during the Holocene. The paleoclimate reconstruction for this period shows comparatively low precipitation of about 400 mm during 9–7 and 4–2 cal ka BP. The respective temperatures fluctuate much less and stay around 10 °C.</p>https://cp.copernicus.org/articles/21/357/2025/cp-21-357-2025.pdf |
| spellingShingle | T. Netzel A. Miebach T. Litt A. Hense New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret Climate of the Past |
| title | New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret |
| title_full | New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret |
| title_fullStr | New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret |
| title_full_unstemmed | New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret |
| title_short | New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret |
| title_sort | new probabilistic methods for quantitative climate reconstructions applied to palynological data from lake kinneret |
| url | https://cp.copernicus.org/articles/21/357/2025/cp-21-357-2025.pdf |
| work_keys_str_mv | AT tnetzel newprobabilisticmethodsforquantitativeclimatereconstructionsappliedtopalynologicaldatafromlakekinneret AT amiebach newprobabilisticmethodsforquantitativeclimatereconstructionsappliedtopalynologicaldatafromlakekinneret AT tlitt newprobabilisticmethodsforquantitativeclimatereconstructionsappliedtopalynologicaldatafromlakekinneret AT ahense newprobabilisticmethodsforquantitativeclimatereconstructionsappliedtopalynologicaldatafromlakekinneret |