Watson, P.J., 2015. Development of a unique synthetic data set to improve sea-level research and understanding.

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 effects on global coastal populations. Although extensive research has been undertaken into this issue, there remains considerable conjecture and scientific debate about the temporal changes in mean sea level and the climatic and associated physical forcings responsible for them. In particular, over recent years, significant debate has centred around the issue of a measurable acceleration in ocean water-level records, a feature central to projections based on the current knowledge of climate science. The complexity of the dynamic influences and noise embedded within ocean water-level data sets has led sea-level research toward successively more sophisticated time series analytical techniques to estimate the trend. In the absence of an absolute knowledge of the mean sea-level signal (or trend) for a particular record, the accuracy of the trend has increasingly been inferred from the assumed sophistication of the underpinning analytical approach applied to the data record. An innovative and transparent process by which to identify the most efficient analytical technique for isolating the mean sea-level signal is to test such approaches against “synthetic” (or custom built) data sets with a known mean sea-level signal. This paper details the development of a monthly average data set to meet the aforementioned objective.

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