Bias: the unmentionable problem in ocean data assimilation

James Carton and Gennady Chepurin

Department of Meteorology at the University of Maryland in College Park


Numerical models of ocean circulation are subject to systemic errors resulting from errors in model physics, numerics, inaccurately specified initial conditions, and errors in surface forcing. Here we describe an effort to characterize systematic errors in today's ocean data assimilation systems. We find that the bias in different assimilation systems is similar, strongly suggesting that the causes are similar as well. Next, focusing on the tropical Pacific, we develop a two-stage bias correction procedure and show its impact on the quality of the analysis. Finally, if time is available we will discuss what ocean data assimilation products may be expected in the near future.