Assimilation of along-track altimeter data in the tropical Pacific region of a global OGCM ensemble

Identical-twin experiments are performed with an ocean general-circulation model ensemble to investigate the potential for correction of subsurface ocean model states through assimilation of altimetric sea level observations with the Ensemble Kalman Filter (EnKF). The EnKF provides a convenient extension to existing ensemble prediction systems. Observations are simulated for the tropical Pacific by sampling a truth run at 10-day intervals at the TOPEX/POSEIDON along-track measurement points and adding realistic instrument and orbit errors. Ensemble spread is generated by perturbing the best-guess forcing fields. The perturbations are based on a multivariate EOF decomposition of differences between two reanalysis products. The effectiveness of the assimilation is investigated by comparison of the forecasts and analyses with a control run and with the truth. Time series of subsurface state variables along the equator show that the analyses are closer to the truth than the control in all cases, indicating a significant potential for improved ENSO forecast initialization. A second assimilation run with an Ensemble Square-Root Filter (ESRF) shows that the analyses are very similar to those from the EnKF. However, ensemble spread in the subsurface state variables is found to be a poor proxy for the true analysis error in this experiment, in particular in the case of the ESRF. While the sea level analyses remain close to the truth, persistent offsets are introduced in the subsurface state, suggesting a role for bias correction schemes in ensemble methods.