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Abstract

The Estimating the Circulation and Climate of the Ocean (ECCO) consortium provides a framework in which the adjoint method of data assimilation is applied to a general circulation model to provide a dynamically self-consistent estimate of the time-varying ocean state, which is constrained by observations. In this study, the sensitivity of the solution to the constraints provided by various datasets is investigated in a regional setting in the North Pacific. Four assimilation experiments are performed, which vary by the data used as constraints and the relative weights associated with these data. The resulting estimates are compared to two of the assimilated datasets as well as to data from two time series stations not used as constraints. These comparisons demonstrate that increasing the weights of the subsurface data provides overall improvement in the model-data consistency of the estimate of the state of the North Pacific Ocean. However, some elements of the solution are degraded. This could result from incompatibility between datasets, possibly because of hidden biases, or from errors in the model physics made more evident by the increased weight on subsurface data. The adjustments to the control parameters of surface forcing and initial conditions necessary to obtain the more accurate fit to the data are found to be within prior error bars.