Robust Uneven Shift of Extreme Storm Surges Observed in Data Sparse Northeast Indian Ocean Cities

Md. Rezuanul Islam , Htut Naing Thwin, Hiroshi Takagi, Yohei Sawada

Reanalysis-driven storm surge datasets enable extreme analysis in data-sparse regions, but most studies translate these time series into extremes using a single statistical model, leaving model-selection uncertainty unquantified. In this study, we analyze ERA5-forced surge residual dataset (1950–2024) from Copernicus Climate Change Service for 11 Northeast Indian Ocean (NIO) cities using an ensemble of nonstationary extreme value and Bayesian formulations to estimate return levels, implied return period changes in 2000 relative to 1950 baselines, and trends. Our multi-model ensemble analyses reveal that for inner NIO cities (e.g., South 24 Parganas, Patuakhali, Chittagong, Cox’s Bazar)–near the head of the Bay of Bengal–the 1950’s 50-year surge residual level (RL50) becomes more frequent by 2000 (typically a 33- to 39-year event corresponds to increase in median annual exceedance probability up to 51%), even though surge residual annual-maxima trends are negative (~ - 1 mm/year). These changes are not uniform across the NIO as several western NIO cities (e.g., Colombo, Chennai) show the opposite tendency, with longer implied return periods (typically a 66- to 104-year event) by 2000. Statistical model choice substantially affects design levels and their interpretation in areas with high storm surges. For example, Gumbel or Bayesian median estimates of RL50 can correspond to roughly a median of RL25 under Frechet tail assumptions for inner NIO cities, highlighting nontrivial structural uncertainty. Finally, we show that bias of High Resolution Model Intercomparison Project (HighResMIP)– ERA5 depends on the statistical model used to estimate return levels, and that an ensemble-of-models evaluation provides a conservative and transparent basis for benchmarking climate-model surge extremes against reanalysis.