Sea-Level Trend Uncertainty With Pacific Climatic Variability and Temporally-Correlated Noise

Sam Royston*, Christopher S. Watson, Benoît Legrésy, Matt A. King, John A. Church, Machiel S. Bos

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

11 Citations (Scopus)
250 Downloads (Pure)

Abstract

Recent studies have identified climatic drivers of the east-west see-saw of Pacific Ocean satellite altimetry era sea level trends and a number of sea-level trend and acceleration assessments attempt to account for this. We investigate the effect of Pacific climate variability, together with temporally-correlated noise, on linear trend error estimates and determine new time-of-emergence (ToE) estimates across the Indian and Pacific Oceans. Sea-level trend studies often advocate the use of auto-regressive (AR) noise models to adequately assess formal uncertainties, yet sea level often exhibits colored but non-AR(1) noise. Standard error estimates are over- or under-estimated by an AR(1) model for much of the Indo-Pacific sea level. Allowing for PDO and ENSO variability in the trend estimate only reduces standard errors across the tropics and we find noise characteristics are largely unaffected. Of importance for trend and acceleration detection studies, formal error estimates remain on average up to 1.6 times those from an AR(1) model for long-duration tide gauge data. There is an even chance that the observed trend from the satellite altimetry era exceeds the noise in patches of the tropical Pacific and Indian Oceans and the south-west and north-east Pacific gyres. By including climate indices in the trend analysis, the time it takes for the observed linear sea-level trend to emerge from the noise reduces by up to 2 decades.

Original languageEnglish
Pages (from-to)1978-1993
Number of pages16
JournalJournal of Geophysical Research: Oceans
Volume123
Issue number3
Early online date14 Mar 2018
DOIs
Publication statusPublished - Mar 2018

Keywords

  • Climate variability
  • Noise
  • Sea level
  • Temporal correlation
  • Trend error
  • Uncertainty

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