Marine Heatwaves in the Bay of Bengal: A Synthesis of Observed Trends, Climate Drivers, Modeling Gaps, and Future Challenges

Author: Tasnim Binth Abin, Priyanka Mazumder, Dr. Mohammad Muslem Uddin, Saif Mehjabin, Longwei Zhang
DOI: doi.org/10.70279/bmj-v1-1098

Marine heatwaves (MHWs) are increasingly recognized as a major expression of ocean warming, with rising ecological and socio-economic consequences. The Bay of Bengal (BoB) is particularly vulnerable because strong upper-ocean stratification, large freshwa ter inputs, and monsoon-driven variability favor surface heat retention and amplify temperature extremes. This review synthesizes four decades of BoB-relevant MHW research to summarize observed patterns, dominant drivers, detection and modeling prac tices, and key gaps that limit forecasting and preparedness. A structured, multi-source literature synthesis was conducted using peer-reviewed articles, conference papers, and institutional reports spanning 1982–2024. Studies were included if they examined the BoB or adjacent eastern Indian Ocean, applied percentile-based MHW definitions consistent with established approaches, and reported observational, reanalysis, or model-based anal yses. Evidence from satellite SST products and reanalysis-based studies indicates that Bay of Bengal MHWs have become more frequent, longer-lasting, and more intense since the early 1980s. Findings were organized across spatial–seasonal variability, physical drivers, climate teleconnections, detection constraints, and modeling approaches. Across the reviewed literature, BoB MHW behavior is linked to coupled local–remote processes, including air–sea heat-flux anomalies, stratification and mixed-layer dynamics, mesoscale eddies, and modulation by climate models such as ENSO and the IOD. Reported metrics vary partly due to differences in baseline selection, detection settings, and data limitations, highlighting the need for more consistent reporting. Modeling practice remains weighted toward retrospective detection tools, while process-resolving high-resolution regional models and hybrid (dynamical + data-driven) forecasting approaches remain underutilized for BoB applications. Strengthening BoB preparedness requires standardizing detection parameters, improving obser vation and validation of stratification and mixing processes, and expanding the use of high-reso lution and hybrid forecasting frameworks to support early warning and adaptation planning.