Document Type

Article

Publication Date

2014

DOI

10.1002/gdj3.6

Publication Title

Geoscience Data Journal

Volume

1

Issue

1

Pages

13-19

Abstract

Since 1993, satellite altimetry has provided accurate measurements of sea surface height with near-global coverage. These measurements led to the first definitive estimates of global mean sea-level (GMSL) rise and have improved understanding of how sea levels are changing regionally at decadal time scales. These relatively short records, however, provide no information about the state of the ocean prior to 1993, and with the modern altimetry record spanning only 20 years, the lower frequency signals that are known to be present in the ocean are difficult or impossible to resolve. Tide gauges, on the other hand, have measured sea level over the last 200 years, with some records extending back to 1807. While providing longer records, the spatial resolution of tide gauge sampling is poor, making studies of the large-scale patterns of ocean variability and estimates of GMSL difficult. Combining the satellite altimetry with the tide gauges using a technique known as sea-level reconstruction results in a data set with the record length of the tide gauges and the near-global coverage of satellite altimetry. Cyclostationary empirical orthogonal functions (CSEOFs), derived from satellite altimetry, are combined with historical sea-level measurements from tide gauges to create the Reconstructed Sea Level data set spanning from 1950 to 2009. Previous sea-level reconstructions have utilized empirical orthogonal functions (EOFs) as basis functions, but by using CSEOFs and by addressing other aspects of the reconstruction procedure, an alternative sea-level reconstruction can be computed. The resulting reconstructed sea-level data set has weekly temporal resolution and half-degree spatial resolution.

Original Publication Citation

Hamlington, B.D., Leben, R.R., Strassburg, M.W., & Kim, K.Y. (2014). Cyclostationary empirical orthogonal function sea-level reconstruction. Geoscience Data Journal, 1(1), 13-19. doi: 10.1002/gdj3.6

ORCID

0000-0002-2315-6425 (Hamlington)

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