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[1] In this paper, we show that a linear, continuously stratified ocean model reproduces observed wind-driven intraseasonal sea level variability in the coastal waveguide of the Northern Indian Ocean (NIO). Sensitivity experiments with intraseasonal wind forcing selectively applied in the equatorial region, Bay of Bengal, and Arabian Sea show that a large part of the basin-scale sea level variations are driven by zonal wind fluctuations along the equator. Within the NIO coastal waveguide, the contribution of remote equatorial forcing decreases from~80-90% in the Andaman Sea to~50% northeast of Sri Lanka and then increases tõ 60-70% along the west coast of India. During the southwest monsoon, intraseasonal wind variations become stronger over the NIO, resulting in a larger contribution of local wind forcing to sea level variability along the west (up to 60%) and east (up to 40%) coasts of India.

The strong seasonal cycle of sea level along the west coast of India (WCI) has important consequences for ecosystem and fisheries, and the Lakshadweep high/low in the southeast Arabian Sea is important for fisheries and the Indian summer monsoon. Previous studies suggested that WCI sea level variability is primarily driven by remote wind forcing from the Bay of Bengal and equatorial Indian Ocean through coastal Kelvin wave propagation. Using a linear ocean model, we demonstrate that wind forcing in a relatively small region around the southern tip of India and east of Sri Lanka contribute to ~60% of this variability. Wind variations from the rest of the Bay and the equator only account respectively for ~20% and ~10%. Sea level signals forced by the “southern tip” winds extend westward into the eastern Arabian Sea through Rossby wave propagation, with more than 50% contribution in the Lakshadweep high/low region.

Surface layer temperature inversion (SLTI), a warm layer sandwiched between surface and subsurface colder waters, has been reported to frequently occur in conjunction with barrier layers in the Bay of Bengal (BoB), with potentially commensurable impacts on climate and postmonsoon tropical cyclones. Lack of systematic measurements from the BoB in the past prevented a thorough investigation of the SLTI spatiotemporal variability, their formation mechanisms, and their contribution to the surface temperature variations. The present study benefits from the recent Research Moored Array for African‐Asian‐Australian Monsoon Analysis and Prediction (RAMA) buoys located in BoB along 90°E at 4°N, 8°N, 12°N, and 15°N over the 2006–2014 period. Analysis of data from these RAMA buoys indicates that SLTI forms after the summer monsoon and becomes fully developed during winter (December–February). SLTI exhibits a strong geographical dependency, with more frequent (80% times during winter) and intense inversions (amplitude, ΔT ∼ 0.7°C) occurring only in the northern BoB compared to central and southern Bay. SLTI also exhibits large interannual and intraseasonal variations, with intraseasonal amplitude significantly larger (ΔT ∼ 0.44°C) than the interannual amplitude (∼0.26°C). Heat budget analysis of the mixed layer reveals that the net surface heat loss is the most dominant process controlling the formation and maintenance of SLTI. However, there are instances of episodic advection of cold, low‐saline waters over warm‐saline waters leading to the formation of SLTI as in 2012–2013. Vertical processes contribute significantly to the mixed layer heat budget during winter, by warming the surface layer through entrainment and vertical diffusion.

Ocean modellers use bathymetric datasets like ETOPO5 and ETOPO2 to represent the ocean bottom topography. The former dataset is based on digitization of depth contours greater than 200 m, and the latter is based on satellite altimetry. Hence, they are not always reliable in shallow regions. An improved shelf bathymetry for the Indian Ocean region (20 • E to 112 • E and 38 • S to 32 • N) is derived by digitizing the depth contours and sounding depths less than 200 m from the hydrographic charts published by the National Hydrographic Office, India. The digitized data are then gridded and used to modify the existing ETOPO5 and ETOPO2 datasets for depths less than 200 m. In combining the digitized data with the original ETOPO dataset, we apply an appropriate blending technique near the 200 m contour to ensure smooth merging of the datasets. Using the modified ETOPO5, we demonstrate that the original ETOPO5 is indeed inaccurate in depths of less than 200 m and has features that are not actually present on the ocean bottom. Though the present version of ETOPO2 (ETOPO2v2) is a better bathymetry compared to its earlier versions, there are still differences between the ETOPO2v2 and the modified ETOPO2. We assess the improvements of these bathymetric grids with the performance of existing models of tidal circulation and tsunami propagation.