  Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine
learning (ML) techniques offers exciting possibilities for advancing the
capacity and speed of established methods and also for making substantial and
serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in
many modern scientific fields, the study of the ocean poses a combination of
unique challenges that ML can help address. The observational data available is
largely spatially sparse, limited to the surface, and with few time series
spanning more than a handful of decades. Important timescales span seconds to
millennia, with strong scale interactions and numerical modelling efforts
complicated by details such as coastlines. This review covers the current
scientific insight offered by applying ML and points to where there is imminent
potential. We cover the main three branches of the field: observations, theory,
and numerical modelling. Highlighting both challenges and opportunities, we
discuss both the historical context and salient ML tools. We focus on the use
of ML in situ sampling and satellite observations, and the extent to which ML
applications can advance theoretical oceanographic exploration, as well as aid
numerical simulations. Applications that are also covered include model error
and bias correction and current and potential use within data assimilation.
While not without risk, there is great interest in the potential benefits of
oceanographic ML applications; this review caters to this interest within the
research community.
