A global ocean state estimation using tidal mixing parameterizations and observed turbulent mixing data
Recent data synthesis experiments showed that adjusting mixing coefficients through data assimilation approach is a promising way to reduce a global misfit between a model simulation and ocean observations, and to improve an ocean state estimation. However, those experiments do not impose any constraints on mixing coefficients, although they are closely related to the energy budget. Aiming for a data synthesis experiment that is energetically consistent with the known constraint on the ocean energy budget, and can assimilate observed turbulent mixing data, we are developing a new quasi-global four-dimensional variational data-assimilation system, based on our system for the Estimated STate of the global Ocean for Climate research (ESTOC). We will introduce the new system, and present some preliminary results of long-term data synthesis experiments using this system.
Dr. Satoshi Osafune JAMSTEC