Evaluation of a coupled ocean and sea-ice model (MOM6-NEP10k) over the Bering Sea and its sensitivity to turbulence decay scales

Abstract. Understanding and predicting the ocean environment and marine ecosystem status depends on accurate representations of regional ocean dynamics. Recently, the Modular Ocean Model version 6 (MOM6) has been configured to span the Northeast Pacific Ocean from Baja California to the Chukchi Sea (MOM6-NEP). In this study we present a physical hindcast (1993–2018) simulation of MOM6-NEP where it is coupled to a thermodynamic-dynamic sea-ice module and includes tides. We evaluate performance of this model in the Bering Sea. Various model metrics are benchmarked against in-situ mooring data and satellite observations. The simulation captures the general characteristics of Bering Sea dynamics, particularly with respect to seasonal and interannual variability of the middle shelf water mass properties. Modeling of shear induced mixing was found to be critical to the model's ability to reproduce the observed sharp summer thermocline and its depth. The hindcast simulation reproduces the long-term mean timing of sea-ice arrival and retreat in both the northern and southern Bering Sea, with the remaining mild biases primarily occurring in May over the northern shelf - the modeled sea ice tends to retreat earlier (later) in cold (warm) years than observations. This pattern in biases suggests that the melting rate in the model likely lacks the well-known melt-rate dependency on ice property whereby thicker (thinner) ice melts more slowly (quickly). As a result of high skills in reproducing sea ice areal coverage, the interannual variability of the cold pool (the cold-water mass present on the bottom of the Bering Sea shelf in summer) extent is accurately reproduced by the model. Skillful representation of sea ice and cold pool is essential for understanding ecosystem dynamics and successful fisheries management in the Bering Sea. The findings of this study contribute to the development of reliable oceanographic modeling and forecasting of marine ecosystem conditions to support fisheries management decision making.