Impact of altimetry data on ENSO ensemble initializations and predictions

Abstract

The El Niño/Southern Oscillation (ENSO) predictions strongly depend on the accuracy and dynamical consistency of the coupled initial conditions. Based on the proposed ensemble Kalman filter (EnKF), a new initialization scheme for the ENSO ensemble prediction system (EPS) was designed and tested in an intermediate coupled model (ICM). The inclusion of this scheme in the ICM leads to substantial improvements in ENSO prediction skill via the successful assimilation of both observed sea surface temperature (SST) and TOPEX/Poseidon/Jason‐1 (T/P/J) altimeter data into the initial ensemble conditions. Comparisons with the original ensemble hindcast experiment show that the ensemble prediction skills were significantly improved out to a 12‐month lead time by improving sea level (SL) initial conditions for better parameterization of subsurface thermal effects. It is clearly demonstrated that improvement in forecast skill can result from the multivariate and multi‐observational ensemble data assimilation.