ALBERT

Description: An empirical model for the temperature of subsurface water entrained into the ocean mixed layer (Te) is presented and evaluated to improve sea surface temperature anomaly (SSTA) simulations in an intermediate ocean model (IOM) of the tropical Pacific. An inverse modeling approach is adopted to estimate Te from an SSTA equation using observed SST and simulated upper-ocean currents. A relationship between Te and sea surface height (SSH) anomalies is then obtained by utilizing a singular value decomposition (SVD) of their covariance. This empirical scheme is able to better parameterize Te anomalies than other local schemes and quite realistically depicts interannual variability of Te, including a nonlocal phase lag relation of Te variations relative to SSH anomalies over the central equatorial Pacific. An improved Te parameterization naturally leads to better depiction of the subsurface effect on SST variability by the mean upwelling of subsurface temperature anomalies. As a result, SSTA simulations are significantly improved in the equatorial Pacific; a comparison with other schemes indicates that systematic errors of the simulated SSTAs are significantly small—apparently due to the optimized empirical Teparameterization. Cross validation and comparisons with other model simulations are made to illustrate the robustness and effectiveness of the scheme. In particular it is demonstrated that the empirical Te model constructed from one historical period can be successfully used to improve SSTA simulations in another.

Description: A new intermediate coupled model (ICM) is presented and employed to make retrospective predictions of tropical Pacific sea surface temperature (SST) anomalies. The ocean dynamics is an extension of the McCreary baroclinic modal model to include varying stratification and certain nonlinear effects. A standard configuration is chosen with 10 baroclinic modes plus two surface layers, which are governed by Ekman dynamics and simulate the combined effects of the higher baroclinic modes from 11 to 30. A nonlinear correction associated with vertical advection of zonal momentum is incorporated and applied (diagnostically) only within the two surface layers, forced by the linear part through nonlinear advection terms. As a result of these improvements, the model realistically simulates the mean equatorial circulation and its variability. The ocean thermodynamics include an SST anomaly model with an empirical parameterization for the temperature of subsurface water entrained into the mixed layer (Te), which is optimally calculated in terms of sea surface height (SSH) anomalies using an empirical orthogonal function (EOF) analysis technique from historical data. The ocean model is then coupled to a statistical atmospheric model that estimates wind stress (τ) anomalies based on a singular value decomposition (SVD) analysis between SST anomalies observed and τ anomalies simulated from ECHAM4.5 (24-member ensemble mean). The coupled system exhibits realistic interannual variability associated with El Niño, including a predominant standing pattern of SST anomalies along the equator and coherent phase relationships among different atmosphere–ocean anomaly fields with a dominant 3-yr oscillation period. Twelve-month hindcasts/forecasts are made during the period 1963–2002, starting each month. Only observed SST anomalies are used to initialize the coupled predictions. As compared to other prediction systems, this coupled model has relatively small systematic errors in the predicted SST anomalies, and its SST prediction skill is apparently competitive with that of most advanced coupled systems incorporating sophisticated ocean data assimilation. One striking feature is that the model skill surpasses that of persistence at all lead times over the central equatorial Pacific. Prediction skill is strongly dependent on the season, with the correlations attaining a minimum in spring and a maximum in fall. Cross-validation experiments are performed to examine the sensitivity of the prediction skill to the data periods selected for training the empirical Te model. It is demonstrated that the artificial skill introduced by using a dependently constructed Te model is not significant. Independent forecasts are made for the period 1997–2002 when no dependent data are included in constructing the two empirical models (Te and τ). The coupled model has reasonable success in predicting transition to warm phase and to cold phase in the spring of 1997 and 1998, respectively. Potential problems and further improvements are discussed with the new intermediate prediction system.