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Abstract:
Observations of sea-level variations allow the validation of numerical models applied to reconstruct past and predict future sea-level change. Sea-level indicators (SLIs) are used as the main source for deriving relative sea-level (RSL) variations on time scales for which tide-gauge and satellite measurements are not available. However, the leveling of an SLI relative to present sea level does not provide a direct measure of former RSL, but only an indication according to the conditions under which the sample was deposited. This information depends on the sample type as on its environment and has to be mapped to RSL by an appropriate transfer function. The data itself has to be extracted by an objective procedure from primary information or data compilations usually provided in geological or paleontological literature of different primary focus, quality and detail. In addition to the height information, the precision of dating varies between different indicators and in case of radiocarbon-dated material, the ages have to furthermore calibrated.
Likelihood functions are derived from the indicative meaning of different SLI types and dating uncertainties in order to evaluate model-based sea-level predictions against geological inference. This procedure is accompanied by applying the visual analytics tool SLIVISU developed at GFZ-Potsdam. This tool allows access to a relational database system where all meta information of compiled sea-level indicators are provided as well as the combination with model predictions at location and age of the respective SLIs. In this regard it serves as a tool to validate the model results and impact of the indicators regarding their spatial-temporal context.
This study is part of the 'German Paleo-Climate Modelling Initiative' (PalMod).
