Reducing Drift and Bias of a Global Ocean Model by Frequency-Dependent Nudging

Abstract

Frequency-dependent nudging is applied to a coarse resolution (nominal 1°) global ocean model to suppress its drift and bias, and the impact of the nudging on the skill of the model is assessed. The nudging is applied to temperature and salinity in frequency bands centred on 0 and 1 cycles per year. As expected, the nudging significantly reduces the biases in the long-term mean and annual cycle of temperature, salinity, and sea level. By comparing the simulated (i) sea surface temperature with operational analyses based on observations, (ii) vertical profiles of temperature and salinity with observations made by Argo floats, and (iii) sea level with altimeter observations, it is shown that the skill of the model in simulating variability about the annual cycle is also improved. The potential benefit of applying frequency-dependent nudging to the ocean component of a coupled atmosphere–ocean model is discussed.