A Robust Regional Downscaling Ocean Modeling for the Kuroshio Region off Japan

Yusuke Uchiyama, Kobe University, Kobe, Japan
For dynamically consistent, high-resolution, yet cost-effective regional oceanic downscaling modeling, an empirical three-dimensional (3D) density estimate based on publicly available datasets is utilized for the Regional Oceanic Modeling System (ROMS) at submesoscale eddy-permitting lateral resolution of 3 km with simple data assimilation, i.e., temperature-salinity (TS) nudging. We rely on a method built upon the two-layer model to reconstruct a mesoscale 3D temperature and salinity field, referred to as TUMSAT-TS, using near real-time altimeter-derived dynamic height along with Argo float profiling data. The TUMSAT-TS is first validated using in situ hydrographic data, then is implemented in the Japan Coastal Ocean Predictability Experiment (JCOPE2)-ROMS downscaling system for the Kuroshio region off Japan. We explore the usability of TUMSAT-TS by carrying out four comparative simulations with TS nudging towards the (1) TUMSAT-TS and (2) JCOPE2-TS fields, (3) without the nudging, and (4) without the nudging but with a homogeneous constant horizontal eddy viscosity of 100 m2/s. Whereas the unassimilated case (3) fails to properly account for the Kuroshio, both datasets individually are found to help reproduce the mesoscale variability of the Kuroshio, as well as its transient paths, volume transport, associated kinetic energy (KE) and eddy kinetic energy (EKE), and seasonally varying stratification. The additional eddy viscosity in case (4) helps improve the reproducibility to some extent.

We further conducted one more nested regional modeling at submesoscale eddy-resolving lateral resolution of 1 km. Without nudging or additional background lateral eddy viscosity, the 1-km model successfully reproduces surface KE and EKE around the Kuroshio. It is therefore suggested that inclusion of submesoscale turbulent mixing adequately dissipates surface KE and EKE to represent synoptic and mesoscale variability more realistically.