Ultra-High-Resolution Climatological Monthly Mean Regional Model Output for China’s Coastal Seas

Description

This dataset was generated by a research team at the Institute of Oceanology, Chinese Academy of Sciences, using the independently developed ROMS-based MPIOS and its subregional models. Daily mean output from the parent model was used to prescribe the boundary conditions for the nested model, and output for December 30 of the final parent-model year was used to initialize the nested simulation. Atmospheric forcing was derived from COADS, and tidal forcing was obtained from TPXO8, incorporating 10 tidal constituents: M2, S2, N2, K2, K1, O1, P1, Q1, Mf, and Mm. The model was first integrated under climatological forcing until it reached climatological equilibrium, followed by an additional five-year integration. Monthly mean fields from the final simulation year were archived. Spatial correlations between the simulated annual mean sea surface height, sea surface temperature, and sea surface salinity and their satellite-observed counterparts all exceeded 0.99, with root-mean-square errors of 0.0538 m, 0.5435 °C, and 0.3591 PSU, respectively. The model successfully reproduced the transport, seasonal variability, and vertical structure of major currents, including the Kuroshio in the East China Sea, and flows through key passages. The simulated amplitudes and phases of the M2 and K1 tidal constituents also agreed closely with observations, supporting the use of this dataset for studies of regional circulation and its underlying dynamical mechanisms. The dataset consists of 12 files named ECS_1x30_monthXX_crop, where XX denotes the month. Each file contains longitude and latitude coordinates, two-dimensional sea surface height, and three-dimensional temperature, salinity, and velocity fields.Temporal coverage: Model climatological monthly means. Spatial coverage: 116°–136°E, 21°S–41°N.

Steps to reproduce

The data can be read through any software which can open and proceed NetCDF files, as Matlab, Python-xarray, Panoply etc.

Institutions

• Institute of Oceanology

  Shandong, Qingdao

Categories

Funders

• Qingdao National Laboratory for Marine Science and Technology

  Shandong, Qingdao

  Grant ID: LSKJ202202504

Licence