Abstract

One of the most fundamental, critical environmental issues confronting mankind into the foreseeable future remains the ominous spectre of climate change, in particular the pace at which impacts will occur and our capacity to adapt. Sea level rise is one of the key artefacts of climate change that will have profound impacts on global coastal populations. It is estimated that some 600 million people live within the Low Elevation Coastal Zone (contiguous areas along the coastlines of the world less than 10m above Mean Sea Level) and considered vulnerable to storm surges and projected sea level rise. Although extensive research has been undertaken into sea level rise, there remains considerable conjecture and scientific debate about the temporal changes in MSL and the climatic and associated physical forcings responsible for them. The improvement of analytical techniques to isolate MSL (or trend) from the contamination of the decadal and inter-decadal (and longer) cyclical influences and noise remains the overwhelming aim of researchers in the sea level rise field. In effect, comparison from one estimate of MSL to another, in part has become an indirect qualitative view of the merit of the analytical approach applied. An innovative and transparent process by which to identify the most appropriate analytical technique for isolating the MSL signal is to test such approaches against "synthetic† (or custom built) data sets with a known MSL signal. Testing of contemporary analytics against a "synthetic†, physics based data set will substantially improve the rigour and confidence around current estimates of MSL and its temporal characteristics to better inform projection modelling endeavours and improve public education around the issue of sea level rise. This paper provides a summary of the process associated with the development of the core synthetic data set used for testing purposes proposed as part of a post graduate research program. A more detailed discussion will be published in an upcoming issue of the Journal of Coastal Research.

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