Abstract.The budget of eddy kinetic energy (EKE) in the Red Sea, including the sources, redistributions and sink, is examined using a high-resolution eddy-resolving ocean circulation model. A pronounced seasonally varying EKE is identified, with its maximum intensity occurring in winter, and the strongest EKE is captured mainly in the central and northern basins within the upper 200 m. Eddies acquire kinetic energy from conversion of eddy available potential energy (EPE), from transfer of mean kinetic energy (MKE), and from direct generation due to time-varying (turbulent) wind stress, the first of which contributes predominantly to the majority of the EKE. The EPEto-EKE conversion occurs almost in the entire basin, while the MKE-to-EKE transfer appears mainly along the shelf boundary of the basin (200 m isobath) where high horizontal shear interacts with topography. The EKE generated by the turbulent wind stress is relatively small and limited to the southern basin. All these processes are intensified during winter, when the rate of energy conversion is about four to five times larger than that in summer.The EKE is redistributed by the vertical and horizontal divergence of energy flux and the advection of the mean flow. As a main sink of EKE, dissipation processes is ubiquitously found in the basin. The seasonal variability of these energy conversion terms can explain the significant seasonality of eddy activities in the Red Sea.

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e. an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step, and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every three days. Real time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

[1] A 1/36°high-resolution nested structure ocean model for Indonesian seas that combines atmospheric and tidal forcings (hereafter referred to as IP3-tide) has been developed based on the modified Princeton Ocean Model (POM). Prior to model application, in 2004, we analyzed the IP3-tide by using observational data, derived data, and a global atlas model. A comparison between IP3-tide with 13 tide gauge points and 11 T/P points resulted in 76-92% certainty. Correlation of temperature from scattered depths and points between the model and XBT data reached 97.5% agreement. The modeled velocity successfully captured the low and high frequency variability shown in INSTANT mooring and TAO/TRITON data. Explicit simulation of tidal processes by regional ocean circulation model improved the representation of circulation in the Indonesian seas. At the tidal frequencies, vertical mixing is increased due to the impact of baroclinic tides and horizontal mixing is enhanced by presence of barotropic tidal motion. Enhanced mixing is responsible for eroding the salinity maximum found in the water masses advected from the Pacific Ocean. On the other hand, seasonal variability changes the vertical density structure of water column, which influences the distribution of internal tidal waves. These results demonstrated the importance of explicit tide simulation by regional ocean circulation model for correct presentation of ocean circulation structure and its variability in the Indonesian seas.