Error Subspace Statistical Estimation (ESSE), the uncertainty prediction and data assimilation methodology employed for real-time ocean forecasts, is based on a characterization and prediction of the largest uncertainties. This is carried out by evolving an error subspace of variable size. We use an ensemble of stochastic model simulations, initialized based on an estimate of the dominant initial uncertainties, to predict the error subspace of the model fields. The dominant error covariance (generated via an SVD of the ensemble generated error covariance matrix) is used for data assimilation. The resulting ocean fields are provided as the input to acoustic modeling, allowing for the prediction and study of the spatiotemporal variations in acoustic propagation. The ESSE procedure is a classic case of Many Task Computing: These codes are managed based on dynamic workflows for (1) the perturbation of the initial mean state, (2) the subsequent ensemble of stochastic PE model runs, (3) the continuous generation of the covariance matrix, (4) the successive computations of the SVD of the ensemble spread until a convergence criterion is satisfied, and (5) the data assimilation. Its ensemble nature makes it a many task data intensive application and its dynamic workflow gives it heterogeneity. Subsequent acoustics propagation modeling involves a very large ensemble of very short in duration acoustics runs. We study the execution characteristics and challenges of a distributed ESSE workflow on a large dedicated cluster and the usability of enhancing this with runs on Amazon EC2 and the Teragrid and the I/O challenges faced.