Analysis of oceanic heat content in the Bay of Bengal

Abstract

The primary source of energy for the intensification and movement of cyclones is Oceanic Heat Content (OHC). The Bay of Bengal (BOB) is one of the largest cyclone-prone regions in the world. The variation of OHC and its distribution have been investigated using 52 years (1950 to 2001) worth of Simple Ocean Data Assimilation (SODA) analysis and available Array of Real-time Geostrophic Oceanography (ARGO) datasets. The vertical and horizontal advection terms of the Heat Flux (Hflux) components of the heat budget are dominant in the southern BOB due to the intrusion of high-salinity water from the Arabian Sea and Indian Ocean, but each term is equally important in the northern BOB due to the freshwater river discharge influence and mixing for the heat distribution in the BOB. The inter-annual variations of OHC show that during the positive Indian Ocean Dipole (IOD) years (1961, 1973, 1994 and 1997), it goes below normal within 3–4 months while during warm El-Nino/Southern Oscillation (ENSO) years (1963/64, 1982/83, 1986/87 and 1997/98), it increases and goes above normal value within a year. However, for concurrent positive IOD and ENSO events (1997/98), the rate of increase of OHC is low. The Cyclonic Heat Potential (CHP) and Sea Surface Height (SSH) are highly correlated in this basin except in the northwestern part of the BOB. Analyzing the recent six years (2002–2007) of SODA and Indian Meteorological Department (IMD) cyclogenesis points from Cyclone Atlas datasets, it is found that anticyclonic (cyclonic) eddies region with CHP greater than 70×10⁷ J/m² (50×10⁷ J/m²) and correlations greater than 0.9 for both SSH and 26 °C isothermal depth (D₂₆) can be a potential region of cyclogenesis (deep depression). It has also been found that the cyclone tracks can be explained from CHP distribution for pre-monsoon and post-monsoon cyclone events. The realistic high resolution short term hindcast experiments using Regional Ocean Modeling System (ROMS) setup with QuikSCAT winds for cyclonic events also support the above findings.