Improving model biases in an ESM with an isopycnic ocean component by accounting for wind work on oceanic near-inertial motions.

Pierre Daniel de Wet1,2, Mats Bentsen2,3 and Ingo Bethke2,3, (1)University of Bergen, Geophysical Institute, Bergen, Norway, (2)Bjerknes Centre for Climate Research, Bergen, Norway, (3)Uni Climate, Uni Research AS, Bergen, Norway
Abstract:
It is well-known that, when comparing climatological parameters such as ocean temperature and salinity to the output of an Earth System Model (ESM), the model exhibits biases. In ESMs with an isopycnic ocean component, such as NorESM, insufficient vertical mixing is thought to be one of the causes of such differences between observational and model data. However, enhancing the vertical mixing of the model’s ocean component not only requires increasing the energy input, but also sound physical reasoning for doing so. Various authors have shown that the action of atmospheric winds on the ocean’s surface is a major source of energy input into the upper ocean. However, due to model and computational constraints, oceanic processes linked to surface winds are incompletely accounted for. Consequently, despite significantly contributing to the energy required to maintain ocean stratification, most ESMs do not directly make provision for this energy. In this study we investigate the implementation of a routine in which the energy from work done on oceanic near-inertial motions is calculated in an offline slab model. The slab model, which has been well-documented in the literature, runs parallel to but independently from the ESM’s ocean component. It receives wind fields with a frequency higher than that of the coupling frequency, allowing it to capture the fluctuations in the winds on shorter time scales. The additional energy calculated thus is then passed to the ocean component, avoiding the need for increased coupling between the components of the ESM. Results show localised reduction in, amongst others, the salinity and temperature biases of NorESM, confirming model sensitivity to wind-forcing and points to the need for better representation of surface processes in ESMs.