Regional dynamics of Bay of Bengal is studied using Open Boundary Condition (OBC) in Modular Ocean Model (MOM) to understand the effect of primary and secondary mesoscale features on various bulk ocean products and turbulent fluxes. A horizontal resolution of 0.1o is adopted to resolve a range of mesoscale features. In order to parametrize the vertical mixing, K-Profile Parametrization (KPP) scheme is implemented. The results show successful implementation of OBC in a regional domain with exchange of mass and energy and conservation of mass at the boundaries. The Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) are validated with SODA reanalysis, which are found to be in good agreement. The Mixed Layer Depth (MLD) pattern is very well represented, albeit the magnitude is slightly under-predicted in comparison with SODA reanalysis. Additionally, analysis of flow energetics reveal regional differences in turbulent kinetic energy (K), production flux (P), buoyancy flux (B), and dissipation (ε). Our results clearly reveal the presence of inverse energy-cascade in the southern Bay of Bengal, wherein energy flows back into mean flow structures from the turbulent eddies. The re-energization of mean flow structures is likely to allow the large-scale circulation to persist for longer periods, thereby modifying the local dynamics. Further analysis shows that the B term is the major source of turbulence production in the north and central Bay of Bengal regions. These results convey the various mechanisms by which energy is produced and transferred in different regions of Bay of Bengal.
Eddy-freshwater interaction is studied in the north Bay of Bengal (BoB) with a high-resolution simulation using the Regional Ocean Modeling System. Following observations, the model simulates the trapping and homogenization of river water by a cyclonic mesoscale eddy on a sub-monthly time scale from October-November of 2015. As fresh river water is trapped in the eddy, it is characterized by strong vertical and lateral gradients in salinity. Within a few weeks, these gradients relax along with the progressive homogenization of freshwater within the eddy. A mixed layer salinity budget shows the…
The Bay of Bengal (BoB) is a high recipient of freshwater flux from rivers and precipitation, making the region strongly stratified. The strong stratification results in a thick barrier layer formation, which inhibits vertical mixing making this region a low-productive zone. In the present study, we attempt to model the pH of the BoB region and understand the role of different governing factors such as sea-surface temperature (SST), sea-surface salinity (SSS), dissolved inorganic carbon (DIC), and total alkalinity (TALK) on the seasonality of sea-surface pH. We run a set of sensitivity experim…
Freshwater from rivers influences Indian summer monsoon rainfall and regional tropical cyclones by shallowing the upper layer and warming the subsurface ocean in the Bay of Bengal. Here, we use in situ and satellite data with reanalysis products to showcase how river water can experience a significant increase in salinity on subseasonal timescales. This involves the trapping and homogenization of freshwater by a cyclonic eddy in the Bay. Specifically, in October 2015, river water is shown to enter a particularly long-lived eddy along with its attracting manifolds within a period of two weeks. …
Horizontal currents in the Bay of Bengal were measured on eight cruises covering a total of 8600 $km$ using a 300 $kHz$ Acoustic Doppler Current Profiler (ADCP). The cruises are distributed over multiple seasons and regions of the Bay. Horizontal wavenumber spectra of these currents over depths of 12--54 m and wavelengths from 2--400 km were decomposed into vortical and divergent components assuming isotropy. An average of across and along track spectra over all cruises shows that the spectral slope of horizontal kinetic energy for wavelengths of 10--80 $km$ scales with an exponent of $-1.7 \p…
Influx of Bay of Bengal waters and stirring trends in the Arabian Sea based on satellite altimetry
Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Indian Institute of Technology Madras（印度马德拉斯理工学院）；Indian Institute of Science（印度科学学院）；International Centre for Theoretical Sciences（国际理论科学中心）
Mixing in the Bay of Bengal, driven by altimetry derived daily geostrophic surface currents, is studied on subseasonal timescales. Hovm{ö}ller and wavenumber-frequency diagrams with power spectra confirm the multiscale nature of the flow. Advection of bands immediately brings out the chaotic nature of mixing in the Bay via repeated straining and filamentation of the tracer field. A principal finding is that mixing is local, i.e., of the scale of the eddies, and does not span the entire basin. Indeed, Finite Time Lyapunov Exponent (FTLE), Relative Dispersion (RD) and Finite Size Lyapunov Expone…
The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, seve…
The prediction skill of a numerical model can be enhanced by calibrating the sensitive parameters that significantly influence the model forecast. The objective of the present study is to improve the prediction of surface wind speed and precipitation by calibrating the Weather Research and Forecasting (WRF) model parameters for the simulations of tropical cyclones over the Bay of Bengal region. Ten tropical cyclones across different intensity categories between 2011 and 2017 are selected for the calibration experiments. Eight sensitive model parameters are calibrated by minimizing the predicti…
The present study explores the capabilities of advanced machine learning algorithms in predicting the sea-surface $p$CO$_2$ in the open oceans of the Bay of Bengal (BoB). We collect the available observations (outside EEZ) from the cruise tracks and the mooring stations. Due to the paucity of data in the BoB, we attempt to predict $p$CO$_2$ based on the Sea Surface Temperature (SST) and the Sea Surface Salinity (SSS). Comparing the MLR, the ANN, and the XGBoost algorithm against a common dataset reveals that the XGBoost performs the best for predicting the sea-surface $p$CO$_2$ in the BoB. Usi…
Cyclone Amphan, a super cyclone in the Bay of Bengal after 21 years, intensified from a cyclonic storm (CAT 1) to a super cyclone (CAT 5) in less than 36 hours. It went on to make landfall over West Bengal as a Very Severe Cyclonic Storm (VSCS) with winds close to 155 kmph. Here, we analyze the dynamics that led to its rapid intensification, given that the system struggled to develop initially despite the presence of ripe conditions like high Sea Surface Temperature (SST) in the Bay. Our analysis clearly reveals that a Convectively Coupled Kelvin Wave (CCKW) from upper troposphere might have i…
Imperial College London（伦敦帝国理工学院）；Princeton University（普林斯顿大学）；NOAA/Geophysical Fluid Dynamics Laboratory（美国国家海洋和大气管理局/地球物理流体动力学实验室）；University of Washington（华盛顿大学）；Institut Pierre-Simon Laplace（皮埃尔-西蒙·拉普拉斯研究所）；IRD（法国发展研究院）；Sorbonne Université（索邦大学）；LOCEAN-IPSL（皮埃尔-西蒙·拉普拉斯环境研究所海洋学与气候实验室）；CNRS（法国国家科学研究中心）；Laboratoire des Sciences du Climat et de l’Environnement（气候与环境科学实验室）；CEA Saclay（法国原子能和替代能源委员会萨克雷中心）
The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision-making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to p…
Evaluation of Deep Neural Operator Models toward Ocean Forecasting
Department of Mechanical Engineering, Center for Computational Science and Engineering（机械工程系，计算科学与工程中心）；Department of Electrical Engineering and Computer Science（电子工程与计算机科学系）；Massachusetts Institute of Technology, Cambridge, MA（麻省理工学院）
$ $From June--September, the summer monsoon current (SMC) flows eastward south of Sri Lanka and bends northeastward to form a swift jet that enters the Bay of Bengal (BoB). As such, it is a crucial part of the water exchange between the Arabian Sea (AS) and BoB. The processes that determine the evolution, intensification and meandering of the SMC are only partly understood. They involve both local and remote forcing by the wind, as well as interactions with westward-propagating Rossby waves and eddies. In this study, we investigate these processes using an Indian-Ocean general circulation mode…
