Ocean currents are an important consideration throughout the life cycle of the many offshore projects. These currents are complex, three dimensional, dynamic and as yet poorly characterized in a statistical sense. Numerical ocean circulation models are increasingly sophisticated and are beginning to capture the structure and variability of complex ocean current systems. The starting point for model-based characterization of currents is a long time series of model outputs obtained at high spatial and temporal resolution. There are an ever-increasing number of model products, but it is not clear how to identify suitable products for a given application. Frequently, a familiar product is chosen that may not be the best choice. Here, we present an alternative approach wherein a collection of model runs, referred to as an ensemble, is used to estimate ocean current statistics at points of interest. Unlike other ensemble methods where the ensemble is used to estimate the statistics directly, we use the ensemble to construct a surrogate ocean model or an emulator using polynomial expansions. This emulator is computationally inexpensive to run and is used to sample the model outputs for large numbers of model inputs to generate full probability distributions of the model state, which can then be used to derive statistics required for design criteria. We have used the above technique to build an emulator for a numerical circulation model of the Gulf of Mexico. We present statistics of the Loop Current derived from this approach and briefly compare it with statistics obtained from measurements and other available long time-series of model outputs. Probability distributions for a sample point in the vicinity of the Loop Current are presented. It is shown that the technique can provide robust statistics and complements existing techniques.