Estimation of model errors generated by atmospheric forcings for ocean data assimilation: experiments in a regional model of the Bay of Biscay

摘要

The characterization of model errors is an essential step for effective data assimilation into open-ocean and shelf-seas models. In this paper, we propose an experimental protocol to properly estimate the error statistics generated by imperfect atmospheric forcings in a regional model of the Bay of Biscay, nested in a basin-scale North Atlantic configuration. The model used is the Hybrid Coordinate Ocean Model (HYCOM), and the experimental protocol involves Monte Carlo (or ensemble) simulations. The spatial structure of the model error is analyzed using the rep-resenter technique, which allows us to anticipate the subsequent impact in data assimilation systems. The results show that the error is essentially anisotropic and inhomogeneous, affecting mainly the model layers close to the surface. Even when the forcings errors are centered around zero, a divergence is observed between the central forecast and the mean forecast of the Monte Carlo simulations as a result of nonlinearities. The 3D structure of the representers characterizes the capacity of different types of measurement (sea level, sea surface temperature, surface velocities, subsurface temperature, and salinity) to control the circulation. Finally, data assimilation experiments demonstrate the superiority of the proposed methodology for the implementation of reduced-order Kalman filters.