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A Nonlinear Multi-proxy Model Based on Manifold Learning to Reconstruct Water Temperature from High Resolution Trace Element Profiles in Biogenic Carbonates : Volume 3, Issue 3 (22/07/2010)

By Bauwens, M.

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Book Id: WPLBN0003990658
Format Type: PDF Article :
File Size: Pages 34
Reproduction Date: 2015

Title: A Nonlinear Multi-proxy Model Based on Manifold Learning to Reconstruct Water Temperature from High Resolution Trace Element Profiles in Biogenic Carbonates : Volume 3, Issue 3 (22/07/2010)  
Author: Bauwens, M.
Volume: Vol. 3, Issue 3
Language: English
Subject: Science, Geoscientific, Model
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Beelaerts, V., Barbé, K., Schoukens, J., Ohlsson, H., Dehairs, F., & Bauwens, M. (2010). A Nonlinear Multi-proxy Model Based on Manifold Learning to Reconstruct Water Temperature from High Resolution Trace Element Profiles in Biogenic Carbonates : Volume 3, Issue 3 (22/07/2010). Retrieved from

Description: Earth System Sciences & Department of Analytical and Environmental Chemistry, Vrije Universiteit Brussel, Elsene, Belgium. A long standing problem in paleoceanography concerns the reconstruction of water temperature from Δ18O carbonate, which for freshwater influenced environments is hindered because the isotopic composition of the ambient water (related to salinity) affects the reconstructed temperature. In this paper we argue for the use of a nonlinear multi-proxy method called Weight Determination by Manifold Regularization to develop a temperature reconstruction model that is less sensitive to salinity variations. The motivation for using this type of model is twofold: Firstly, observed nonlinear relations between specific proxies and water temperature motivate the use of nonlinear models. Secondly, the use of multi-proxy models enables salinity related variations of a given temperature proxy to be explained by salinity-related information carried by a separate proxy. Our findings confirm that Mg/Ca is a powerful paleothermometer and highlight that reconstruction performance based on this proxy is improved significantly by combining its information with the information of other trace elements in multi-proxy models. Using Mg/Ca, Sr/Ca, Ba/Ca and Pb/Ca the WDMR model enabled a temperature reconstruction with a root mean squared error of ±2.19 °C for a salinity range between 15 and 32.

A nonlinear multi-proxy model based on manifold learning to reconstruct water temperature from high resolution trace element profiles in biogenic carbonates

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