Decadal Internal Sea-Level variability in the Indian Ocean studied using a New Bivariate Regionally Reconstructed Sea-Level Dataset

The low-lying coastal zones of countries that border the Indian Ocean (IO) are home to over 350 million people that are at risk due to sea level (SL) rise. The lack of accurate regional SL rise estimates exacerbates socio-economic impacts associated with SL rise by limiting the ability of regional policymakers to act effectively in aiding planning and adaption strategies. Regional SL trends are heavily influenced by internal climate variability at decadal timescales thus, making the study of long term SL change in the IO extremely important. The interdecadal modulation of the Indian Ocean Basin mode (IOB), Indian Ocean Dipole (IOD) and Subtropical Dipole Pattern (SDP) and their relative contributions to IO regional SL variability is not well understood and can be attributed to the lack in high-quality long SL data records. By combining the long time-series data provided by tide gauges with the information about the spatial covariance of SL from satellite altimetry we reconstruct SL data for the IO from 1950 - 2017 using a bivariate (sea surface temperature) cyclostationary empirical orthogonal function (CSEOF) SL reconstruction technique. We will provide an improved understanding of the contribution of IOB, IOD and SDP to regional IO SL trends using data from carefully selected tide gauges, new reconstructed SL dataset, AVISO and modeled SL data. After removing the fraction of variability associated with internal climate variability, the residual SL trend pattern from the altimeter record will be investigated for trends attributable to anthropogenic forcing. Results from this study will help to improve the understanding of the dynamics of internal and anthropogenic SL change which is essential for understanding the dynamic pathways that link the IO basin to terrestrial climates world-wide. An improved representation of internal SL variability obtained from this study will help predictions of future IO SL and better aid adaptation and mitigation efforts in the densely populated countries facing a threat from IO SL rise.