Abstract
To improve our understanding of the climate process and to assess the human impact on current global warming, past climate reconstruction is essential. The chemical composition of a bivalve shell is strongly coupled to environmental variations and therefore ancient shells are potential climate archives. The nonlinear nature of the relation between environmental condition (e.g. the seawater temperature) and proxy composition makes it hard to predict the former from the latter, however. In this paper we compare the ability of three nonlinear system identification methods to reconstruct the ambient temperature from the chemical composition of a shell. The comparison shows that nonlinear multi-proxy approaches are potentially useful tools for climate reconstructions and that manifold based methods result in smoother and more precise temperature reconstruction.
Original language | English |
---|---|
Pages (from-to) | 104-111 |
Number of pages | 8 |
Journal | Computer Methods and Programs in Biomedicine |
Volume | 104 |
Issue number | 2 |
Publication status | Published - 2011 |
Keywords
- Climate reconstruction
- Nonlinear multi-proxy models
- manifold learning
- support vector regression
- bivalve
- sclerochronology