  Partitioning ocean flows into regions dynamically distinct from their
surroundings based on material transport can assist search-and-rescue planning
by reducing the search domain. The spectral clustering method partitions the
domain by identifying fluid particle trajectories that are similar. The
partitioning validity depends on the accuracy of the ocean forecasting, which
is subject to several sources of uncertainty: model initialization, limited
knowledge of the physical processes, boundary conditions, and forcing terms.
Instead of a single model output, multiple realizations are produced spanning a
range of potential outcomes, and trajectory clustering is used to identify
robust features and quantify the uncertainty of the ensemble-averaged results.
First, ensemble statistics are used to investigate the cluster sensitivity to
the spectral clustering method free-parameters and the forecast parameters for
the analytic Bickley jet, a geostrophic flow model. Then, we analyze an
operational coastal ocean ensemble forecast and compare the clustering results
to drifter trajectories south of Martha's Vineyard. This approach accurately
identifies regions of low uncertainty where drifters released within a cluster
remain there throughout the window of analysis. Drifters released in regions of
high uncertainty tend to either enter neighboring clusters or deviate from all
predicted outcomes.
