Extreme sea level rise along the Indian Ocean coastline: observations and 21st century projections

Abstract

Anthropogenic sea-level rise poses challenges to coastal areas globally. The combined influence of rising mean sea level (MSL) and storm surges exacerbate the extreme sea level (ESL). Increasing ESL poses a major challenge for climate change adaptation of nearly 2.6 billion inhabitants in the Indian Ocean region. Yet, knowledge about past occurrences of ESL and its progression is limited. Combining multiple tide-gauge and satellite-derived sea-level data, we show that ESL has become more frequent, longer-lasting and intense along the Indian Ocean coastlines. We detect a 2-3-fold increase in ESL occurrence, with higher risk along the Arabian Sea coastline and the Indian Ocean Islands. Our results reveal that rising MSL is the primary contributor to ESL increase (more than 75%), with additional contribution from intensifying tropical cyclones. A two-fold increase in ESL along the Indian Ocean coastline is detected with an additional 0.50C warming of the Indian Ocean relative to pre-industrial levels. Utilizing the likely range (17th-83rd percentile as the spread) of IPCC MSL projections with considerable inter-model spread, we show that the Indian Ocean region will be exposed annually to the present-day 100-year ESL event by 2100, irrespective of the greenhouse-gas emission pathways, and by 2050 under the moderate-emission-mitigation-policy scenario. The study provides a robust regional estimate of ESL and its progression with rising MSL, which is important for climate change adaptation policies.

Rapid Emergence of the Indian Ocean Extreme Sea Level

“…MSL simulations from IPCC AR6 models provide relatively reliable simulation for the Indian Ocean coastline, accurately capturing historic climate variability and observed MSL changes 11,31 . Using the same projections, we analysed the median change of MSL under SSP585 at 29 TG locations in the Indian Ocean by the end of the 21 st century relative to the baseline of 1995-2014 (blue bars, Fig.…”

“…The present-day ESL distribution has been taken from 11 . They have fitted a generalised extreme value (GEV) distribution 42 to the observed ESLs from the TG observations.…”

“…This is especially true for the Indian Ocean region, which stands out as a hotspot facing an increasing threat due to both a higher ESL rise and densely populated coastal belts. ESL along the tide gauge (TG) stations of Arabian Sea, Bay of Bengal, and Indian Island derived from satellite-derived daily sea level anomalies 11,12 for the period of 1993-2020 Ocean showing increasing trend (Fig. 1c).…”

Exploring the nonstationarity of coastal sea level probability distributions

“…Such studies aim to characterize tails of sea level distributions in recent periods or in future scenarios via extreme value theory (EVT) (Coles, 2001) and often quantify changes in extremes solely as a function of changes in distributional mean (Tebaldi et al, 2021). A shift in the mean sea level is in fact recognized to be the primary driver of changes in tails of the distributions (Vousdoukas et al, 2018; Sreeraj et al, 2022. Few studies also explored extreme value analysis by considering changes in the median and width of the fitted generalized extreme value distributions.…”

Exploring the non-stationarity of coastal sea level probability distributions

“…Such studies aim to characterize tails of sea level distributions in recent periods or in future scenarios via Extreme Value Theory (EVT) [Coles, 2001], and often quantify changes in extremes solely as a function of changes in distributional mean [Tebaldi et al, 2021]. A shift in the mean sea level is in fact recognized to be the primary driver of changes in tails of the distributions [Vousdoukas et al, 2018, Sreeraj et al, 2022 While recent studies have focused on trends in the mean sea level or on large extremes through EVT, there has been less work quantifying how shapes of sea level probability distributions have been changing in the observational record and how they may change in a warming climate. This is no easy task, as quantifying trends in distributions and their significance under internal climate variability is not a well defined problem and many measures could be adopted.…”