Identifying the Best Performing Time Series Analytics for Sea Level Research

Integrating Long Tide Gauge Records with Projection Modelling Outputs. A Case Study: New York

“…Although every effort has been made to take advantage of state-of-the-art analytical techniques to improve the resolution of the mean sea level signal from the tide gauge record, in reality time series analysis techniques are inherently limited by the ubiquity of end effects. Extensive time series analysis testing and optimization for sea level research [24,31] has limited these influences, but, notwithstanding, the broadened error margins at the ends of the respective records take some account of the uncertainties of the respective mean estimates (mean sea level and velocity) near the ends of such records. The utility of such analyses will therefore continue to improve as the length of the overlapping records increase into the future.…”

Development of a Unique Synthetic Data Set to Improve Sea-Level Research and Understanding

“…The necessity to remove these influences from the data to enhance acceleration estimates is well noted in the literature (e.g., Calafat and Chambers, 2013;Calafat, Chambers, and Tsimplis, 2012;Chambers, Merrifield, and Nerem, 2012;Dangendorf et al, 2014;Douglas, 1992;Haigh et al, 2014). The NAO was one of the key dynamic features embedded within the synthetic data set (Watson, 2015) used to test time series analysis techniques for their utility in isolating relative mean sea level from conventional ocean water level data sets with improved accuracy (Watson, 2016a). From this testing, SSA (which underpins the msltrend package used to decompose records in this study) proved an optimal technique to separate out these complex oscillatory signals with timevarying amplitudes and noise from the low-amplitude and low-frequency signal of mean sea-level rising over time.…”

“…Under various forecast scenarios, the current rate of global averaged sea-level rise of 3.4 6 0.4 mm/y (CU Sea Level Research Group, 2016; Nerem et al, 2010) is expected to increase to rates of the order of 10-20 mm/y by 2100 (Watson, 2016a). Figure 8 provides a visual comparative analysis of how the velocity and acceleration time series might change at Cuxhaven, Germany, and Kronstadt, Russian Federation, based on a relative mean sea-level rise of 800 mm from present to 2100.…”

“…Whilst the techniques espoused in this paper represent a concerted effort to test and specifically identify enhanced techniques to isolate mean sea level with improved temporal accuracy (Watson, 2015(Watson, , 2016a this work should be considered a staging point for ongoing improvements in such critical areas, in particular the identification of statistically robust change points in the record.…”

“…Improved understanding of how and when climate change effects will occur and evolve over time will be critical to developing robust strategies to adapt and minimise risks (Watson, 2016a). The prominence of the climate change issue has placed more emphasis on examination of the extensive global repository of relative mean sea-level records (Holgate et al, 2012), which, along with temperature and carbon dioxide, remain the key proxy data sets used to monitor and quantify changes in the global climate system (Watson, 2016c).…”