Validation of an EnKF System for OGCM Initialization Assimilating Temperature, Salinity, and Surface Height Measurements

Abstract

Abstract Results are presented from a decade-long assimilation run with a 64-member OGCM ensemble in a global configuration. The assimilation system can be used to produce ocean initial conditions for seasonal forecasts. The ensemble is constructed with the Max Planck Institute Ocean Model, where each member is forced by differently perturbed 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis atmospheric fields over sequential 10-day intervals. Along-track altimetric data from the European Remote Sensing and the Ocean Topography Experiment (TOPEX)/Poseidon satellites, as well as quality-controlled subsurface temperature and salinity profiles, are subsequently assimilated using the standard formulation of the ensemble Kalman filter. The applied forcing perturbation method and data selection and processing procedures are described, as well as a framework for the construction of appropriate data constraint error models for all three data types. The results indicate that the system is stable, does not experience a tendency toward ensemble collapse, and provides smooth analyses that are closer to withheld data than an unconstrained control run. Subsurface bias and time-dependent errors are reduced by the assimilation but not entirely removed. Time series of assimilation and ensemble statistics also indicate that the model is not very strongly constrained by the data because of an overspecification of the data errors. A comparison of equatorial zonal velocity profiles with in situ current meter data shows mixed results. A shift in the time-mean profile in the central Pacific is primarily associated with an assimilation-induced bias. The use of an adaptive bias correction scheme is suggested as a solution to this problem.