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

摘要

A long standing problem in paleoceanography concerns the reconstruction ofwater temperature from δ18O carbonate. It is problematic in thecase of freshwater influenced environments because the δ18Oisotopic composition of the ambient water (related to salinity) needs to beknown. In this paper we argue for the use of a nonlinear multi-proxy methodcalled Weight Determination by Manifold Regularization (WDMR) to develop atemperature reconstruction model that is less sensitive to salinityvariations. The motivation for using this type of model is twofold: firstly,observed nonlinear relations between specific proxies and water temperaturemotivate the use of nonlinear models. Secondly, the use of multi-proxy modelsenables salinity related variations of a given temperature proxy to beexplained by salinity-related information carried by a separate proxy. Ourfindings confirm that Mg/Ca is a powerful paleothermometer and highlight thatreconstruction performance based on this proxy is improved significantly bycombining its information with the information for other trace elements inmulti-proxy models. Although the models presented here are black-box modelsthat do not use any prior knowledge about the proxies, the comparison ofmodel reconstruction performances based on different proxy combinations doyield useful information about proxy characteristics. Using Mg/Ca, Sr/Ca,Ba/Ca and Pb/Ca the WDMR model enables a temperature reconstruction with aroot mean squared error of ± 2.19 °C for a salinity range between15 and 32.