In this paper, we study the spatio-temporal variability of the sound speed in the Bay of Bengal (BoB) estimated from the Argo observation data during 2011-2020. We perform domain-wide and region specific analysis of the sound speed structure and identify the regions and times of higher variabilities. The domain-wide spatio-temporal variability in the sound speed is maximum in the thermocline layers near 110 m depth. This variability is smaller at around 35-40m depth but increases in the surface layers. The regions of higher temporal and spatial variability vary with depth and time. In the surface layers, the variability is large in the northern part of the Bay but in the subsurface and the layers underneath, it is large along the entire western boundary from the north to south. Due to the combined impact of temperature inversion and the positive salinity gradient, the northern BoB experiences a significant positive vertical gradient in sound speed above the sonic layer depth (SLD) during the postmonsoon and winter periods. This gradient supports strong surface ducting and formation of the shadow zone below the SLD.

Congratulations to Aaron Charous, who won first prize for the Early Career Presenter in Computational Acoustics at the 181st Meeting of the Acoustical Society of America in Seattle. The Computational Acoustics Technical Specialty Group (CA TSG) held its second Young/Early Career Presenter competition at this meeting, with 10 entrants. Three awards are made of up to USD $250 each. The award winners were selected based on the quality of the presented paper, comprising both the content and its delivery. Congrats Aaron!

MSEAS members Tony, Yoland, Abhinav, and Pierre celebrated their birthdays in October and November, and Chris celebrated his 10-year work anniversary with MSEAS in October, and the group gathered outside the Student Center to celebrate!

Abhinav Gupta’s machine learning framework was highlighted by the Mechanical Engineering department in the article “Computing for Ocean Environments“. MSEAS work on using dynamical system models to make predictions for the ocean environment was illustrated in the article, explaining the new machine learning framework that helps make up for the lack of resolution or accuracy in ocean models.