We consider the problem of detecting multiple changepoints in large oceanographic data sets. In this setting the amount of data being collected is continually increasing and consequently the number of changepoints will also increase with time. An efficient and accurate analysis of such data is of considerable interest to those working in the energy sector as understanding the characteristics of the ocean environment is central to reliable design and operation of marine and coastal structures. Detecting the presence of changepoints in oceanographic time-series is of particular importance, since statistical and engineering modelling of the ocean environment, structural loading and response typically assumes stationarity of the environment (in time). Drawing on recent work on efficient search methods by Killick et al. (2011), we compare and contrast the eff_ect of diff_erent approaches to this data, focusing in particular on computational and statistical aspects. The talk will conclude by highlighting the importance of such computationally efficient methods in an oceanographic setting.