Development of a High-Resolution Multiscale Modeling and Prediction System for Bay of Bengal, Part I: Climatology-Based Simulations

Arun Chakraborty, Avijit Gangopadhyay
2016 Open Journal of Marine Science  
A high-resolution (10 km × 10 km) multiscale ocean modeling system was developed for shortterm (1 -2 weeks) ocean state hindcasting/forecasting in the Bay of Bengal (BOB) region. This paper is Part I of a two-part series of studies. The Regional Ocean Modeling System (ROMS) was implemented and initialized with Levitus 1/4˚ climatological fields for short-term forecasting. The results from these climatology-based model simulations for three representative months (February, June and October) in
more » ... e and October) in three different seasons (winter, summer and autumn) are discussed herein. This high-resolution model implementation simulates most of the observed dominant circulation features. The multiscale features during February include an anticyclonic basin-scale gyre with a strong western boundary current (WBC) in the western basin, the formation of several shallow mesoscale eddies in the head of the Bay and a cyclonic sub-basin-scale Myanmar Gyre in the northeast. During June, no well-defined boundary current is simulated along the Indian coast; instead, alternating cyclonic and anticyclonic eddies appear along the east coast with cross-basin eastward flow to support a deep cyclonic Andaman Gyre. In October, a basin-scale cyclonic gyre with a continuous well-defined East India Coastal Current (EICC), weak inflow from the Malacca Strait to the Andaman Sea and advection of BOB water into the Arabian Sea via the Palk Strait are simulated well by the model. A number of mesoscale eddies appear on the eastern half of the basin during October. Physical pattern of simulated eddies and transports across selected sections are validated against available drifter climatology, ARGO data and previous observations. Application of this system to synoptic short-term predictions for October 2008 will be presented in Part II. Keywords Simulation, Bay of Bengal, WBC, EICC Introduction The importance of predicting the state of the ocean (currents, temperature, salinity and sea level) in real time has long been recognized. Recently, the Integrated Ocean Observing System (IOOS, http://oceanservice.noaa.gov/facts/ioos.html), Global Ocean Observing System (GOOS, http://www.ioc-goos.org) and GOOS in the Indian Ocean (IOGOOS, http://www.incois.gov.in/portal/iogoos/home.jsp) have given impetus to different countries to focus on regional ocean prediction research. The GOOS program is a permanent system for observation, modeling and analysis of ocean variables to support operational ocean services for the globe. The goal of IOGOOS is to promote activities for the development of operational oceanography in the Indian Ocean region [1]. Based on this motivation, a real-time ocean hindcast/forecast system is being developed for the Bay of Bengal (BOB) region, which is a tropical ocean basin with three sides bounded by land and an open southern boundary (Figure 1(a) ). Typically, there are two ways to set up the initialization field for an ocean prediction modeling system. In one method, the model is first spun up to equilibrium with climatological water-mass fields forced with atmospheric fields over a period of time (typically 3 -10 years) [2] . Available data are then assimilated with realistic (and real-time) atmospheric model forecast fields in the prognostic mode. Various assimilation schemes such as nudging [3] [4], Objective Interpolation (OI) [5], 3DVAR [6] [7] and 4DVAR [8] [9] are employed to maximize data utilization and minimize model-data differences at the observation locations. Examples of such prediction systems are the Global Ocean Data Assimilation Experiment (GODAE, http://www.godae.org), Hybrid Coordinate Ocean Model (HYCOM, http://www.hycom.org), Navy Coastal Ocean Model (NCOM, http://www7320.nrlssc.navy.mil/global_ncom), UK Met Office (http://www.metoffice.gov.uk) model, and the models run by the Bureau of Meteorology, Australia (http://www.bom.gov.au). In the second method, the initialization field is obtained by a careful reconstruction of the three-dimensional ocean with prevalent mesoscale and submesoscale features embedded in an appropriate climatology [10]- [12] . Typical data assimilation and forcing are applied during the forecast period. The first method works well when there are adequate observations available in space and time and when these observations sample the dominant variability of the system. The second method works well when prior knowledge of the regional synoptic variability has been captured well by regional feature models that are first validated and then employed for initialization. The former method is applicable to targeted sampling of a particular phenomenon, while the latter depends on the persistence and robustness of the dominant circulation features. Both of these methods require climatological fields as background, and it is the fidelity of the climatology that determines the robustness of the prediction system. The circulation system in the Bay of Bengal consists of a complex interplay of seasonally robust fronts/currents with transient eddies superimposed on a background of seasonally changing large-scale gyres. The scarcity of data (both historical and in real time) makes developing a real-time prediction system a challenging task. Our approach was to develop such a system in two phases. First, as described herein, we implemented a high-resolution regional modeling system initialized with the available high-resolution climatological data to help us understand the fidelity of the climatological fields in developing robust, large-scale currents/fronts and gyres and probably a number of mesoscale features. This step was carried out individually for three particular months that represent the three seasonal variation phases of the Bay's monsoon-dominated circulation pattern: the pre-monsoon (February), monsoon (June) and post-monsoon (October) periods. In the second phase, we investigate the impact of introducing SST and ARGO data into the climatological background for October 2008. Together, these two studies establish a modeling framework for developing a fully operational forecast system following either (or both) of the two well-known approaches discussed above. Several modeling [13]-[18], observational [19]-[26] and ship-drift [27] studies have shown a distinct seasonal cycle of surface circulation, with seasonally reversing coastal currents along the eastern coast of India in the Bay of Bengal. During the month of February, the prominent surface circulation feature in the Bay is a basin-scale
doi:10.4236/ojms.2016.61013 fatcat:g22pt5selnckvc2s2ktnybcfa4