4 research outputs found
Climate reconstruction based on archaeological bivalve shells
Several years of biogeochemical research on bivalve shells yielded in clear proxyrecords carrying potential for reconstruction of paleoseasonal trends in coastal environments. However, the interpretation of the proxy signals is still often problematic. Proxy concentrations can be influenced by several environmental parameters and by physiological processes. With more complex models these problems can be tackled. Two strategies are followed; (1) a statistical black-box model is being developed in parallel with (2) a physiological white-box model.The statistical black-box model can be described as a non-linear multi-proxy model. It is based on chemical measurements in modern bivalve shells and consists of the construction of a curve in a multi-dimensional space. The model describes the variations in the chemical signature of the shell during a full year cycle. The shortest distance from any other data point (e.g. a fossil shell) to the model will give a time point estimation in the annual cycle, which can further be linked to environmental parameters. At present our model approach achieves quite accurate SST reconstructions.A white box model is crucial for understanding the physiological processes and for an unambiguous interpretation of the proxy records. We investigated, in a first phase, in situ the influences of environmental parameters and physiology on the incorporation of proxies in Mytilus edulis at a well documented wave breaker site. In a second phase, in vitro culturing experiments under controlled laboratory conditions were carried out. Experiments were carried out at 8°C and 16°C and at salinities of 18‰ and 28‰. During these experiments mussels were fed under high and low supply regimes. By combining these in situ and in vitro approaches a white box multi-proxy model is generated for the reconstruction of SST and SSS
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Periodic time series modeling of environmental proxy records with guaranteed positive growth rate estimation
Identifying a periodic time-series model from environmental records, without imposing the positivity of the growth rate, does not necessarily respect the time order of the data observations. Consequently, subsequent observations, sampled in the environmental archive, can be inversed on the time axis, resulting in a non-physical signal model. In this paper an optimization technique with linear constraints on the signal model parameters is proposed that prevents time inversions. The activation conditions for this constrained optimization are based upon the physical constraint of the growth rate, namely, that it cannot take values smaller than zero. The actual constraints are defined for polynomials and first-order splines as basis functions for the nonlinear contribution in the distance-time relationship. The method is compared with an existing method that eliminates the time inversions, and its noise sensitivity is tested by means of Monte Carlo simulations. Finally, the usefulness of the method is demonstrated on the measurements of the vessel density, in a mangrove tree, Rhizophora mucronata, and the measurement of Mg/Ca ratios, in a bivalve, Mytilus trossulus