Influence of interannual variability in estimating the rate and acceleration of present-day global mean sea level

Abstract Recent studies have shown that the global mean sea level (GMSL) is accelerating. For improved process understanding and sea level projections, it is crucial to precisely estimate the GMSL acceleration due to externally-forced global climate change. For that purpose, the internal climate variability-related signal of the GMSL needs to be removed from the GMSL record. In the present study, we estimate how the observed GMSL rate has evolved with time over the altimetry era (1993-present), with the objective of determining how it is influenced by the interannual variability. We find that the GMSL rate computed over 5-year moving windows, displays significant interannual variability around 6–7 years and 12–13 years, preventing from robust acceleration estimation. To remove from the observed GMSL time series, the interannual variability, possibly related to internal climate modes, like ENSO, PDO, IOD, NAO or AMO, we use two methods previously widely applied in the literature: (1) multiple linear regression of the GMSL against some climate indices, and (2) Empirical Orthogonal Function (EOF) decomposition of the gridded sea level data to isolate the interannual signal. Although the interannual signal of the corrected GMSL time series is reduced, a cycle around 6–7 years still remains in the GMSL rate. We discuss possible sources of the remaining 6-7-year cycle, including the limitation of the methods used to remove the interannual variability.