Reconstruction of regional mean sea level anomalies from tide gauges using neural networks

Edited: 2011-02-21
TitleReconstruction of regional mean sea level anomalies from tide gauges using neural networks
Publication TypeJournal Article
Year of Publication2010
AuthorsWenzel, M., and J. Schröter
JournalJournal of Geophysical Research (Oceans)
Volume115
Pagination08013
Date Published08/2010
Keywordssea_level, tide_gauge
AbstractThe 20th century regional and global sea level variations are estimated based on long-term tide gauge records. For this the neural network technique is utilized that connects the coastal sea level with the regional and global mean via a nonlinear empirical relationship. Two major difficulties are overcome this way: the vertical movement of tide gauges over time and the problem of what weighting function to choose for each individual tide gauge record. Neural networks are also used to fill data gaps in the tide gauge records, which is a prerequisite for our analysis technique. A suite of different gap-filling strategies is tested which provides information about stability and variance of the results. The global mean sea level for the period January 1900 to December 2006 is estimated to rise at a rate of 1.56 ± 0.25 mm/yr which is reasonably consistent with earlier estimates, but we do not find significant acceleration. The regional mean sea level of the single ocean basins show mixed long-term behavior. While most of the basins show a sea level rise of varying strength there is an indication for a mean sea level fall in the southern Indian Ocean. Also for the the tropical Indian and the South Atlantic no significant trend can be detected. Nevertheless, the South Atlantic as well as the tropical Atlantic are the only basins that show significant acceleration. On shorter timescales, but longer than the annual cycle, the basins sea level are dominated by oscillations with periods of about 50-75 years and of about 25 years. Consequently, we find high (lagged) correlations between the single basins.
URLhttp://adsabs.harvard.edu/abs/2010JGRC..11508013W
DOI10.1029/2009JC005630