BACKGROUND AND OBJECTIVES: The innovativeness of this study lies in achieving a comprehensive understanding of the seasonal variations and oceanic characteristics of the Bay of Bengal by addressing the complex interplay of large-scale ocean-atmosphere dynamics. The study aimed to understand the upper ocean characteristics of the Bay of Bengal by analyzing the surface variables such as salinity and temperature using a high-resolution model simulation. To accomplish this, advanced high-resolution numerical simulations were employed, utilizing the coastal and regional ocean community model. This model was crucial for investigating and analyzing the circulation features throughout the entire Bay of Bengal, contributing knowledge and insights about the coastal and regional oceanographic community.
METHODS: To investigate the temporal variability of the upper ocean in the Bay of Bengal, climatological simulations were performed over eight years using the coastal and regional ocean community model. Including a three-year spin-up phase facilitated the adjustment of the model to initial conditions and the attainment of equilibrium, ensuring its fidelity to real-world conditions. The follow-up analyses and comparisons were performed five years after the spin-up phase. The primary objective of this study was to examine the temporal evolution of the kinetic energy throughout the eight-year simulation. The volume-averaged kinetic energy was computed, revealing a gradual increase throughout the simulation, with particularly pronounced enhancements observed during the monsoon period. A Taylor diagram was used for predicting the model with the other data sets.
FINDINGS: The analysis is performed above the surface and sub-surface oceanic layers with prominent dynamics. The temperature and salinity for the surface and sub-surface layers were validated and analyzed for their seasonal variations. The simulations were validated against the existing satellite, reanalysis, and in situ data.
CONCLUSIONS: The innovativeness of this study lies in its successful demonstration of the seasonal variability of temperature and salinity in the Bay of Bengal. Through extensive validations, it establishes the model to accurately simulate the climatological surface features of the Bay of Bengal. This study highlights the effectiveness of numerical models when combined with observations, and the data were reanalyzed, showcasing their utility as valuable tools for studying oceanic conditions. The utilization of a Taylor diagram further supports the validation and excellent performance of the model compared to other available datasets. During the simulation, there is a high correlation (0.96) between the evolution of the salinity and temperature values obtained from the model and the corresponding data from the World Ocean Atlas. This indicates a strong agreement between the model-based simulations and the assimilated data, as supported by the notable correlation values of 0.96 for salinity and temperature. These findings reinforce the existing knowledge regarding the influential role of monsoon winds in shaping the circulation patterns within the Bay of Bengal. Overall, this study contributes to advancing our understanding of the ocean dynamics of the region and underscores the importance of considering seasonal variations for comprehensive oceanographic research, coastal management, climate modeling, and future studies in the Bay of Bengal.
- During the Winter and post-monsoon, the open bay of the southern area has high salinity distribution ranging from 34 to 35 psu;
- Based on the correlation coefficient values, it is evident that the CROCO model demonstrates a robust positive correlation (0.95) with the WOA dataset, which is considered the benchmark for comparison;
- The CROCO model analysis shows that the temperature in the western boundary has a higher range of 27℃ to 28℃ during winter;
- Summer heat subsides, the water temperature starts to decrease, particularly with a more pronounced cooling effect observed along the northern side of the bay during post-monsoon.
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