Bridging observations, theory and numerical simulation of the ocean using machine learning

Abstract

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 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.

Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning

“…To be truly appropriate for application to the physical sciences, the source of skill from ML should be transparent. At the root of this need is a necessity that the ML is based on something physical and not random chance (Balaji, 2020;Irrgang et al, 2021;Sonnewald et al, 2021). The interpretability and explainability of THOR comes from a combination of the equation transform at its core (Sonnewald et al, 2019), the engineering of its input features, and the LRP explanation of its predictive skill.…”

“…Using supervised ML, being able to explain the source of predictive skill and move beyond a "black box" approach, to create transparency, is often nontrivial. This difficulty should not detract from the importance of transparent ML applications, as leveraging the combination of domain knowledge and emerging ML techniques such as AFA could be of pivotal importance for applications within the physical sciences (Balaji, 2020; Irrgang et al, 2021;McGovern et al, 2019;Sonnewald et al, 2021;Toms et al, 2020). When used as a "black box", a NN will be trained to make desired prediction, and while it can be skillful in making these predictions, it could have skill rooted in chance more than physics.…”

“…In this sense, it could be possible to develop a feedback to the theoretical components of THOR, and use the relevance maps to gain a better understanding of the unifying features. Such approaches are discussed further in Sonnewald et al (2021).…”

“…These mechanisms can be seen as governing the circulation or being a direct product of its strength (Kuhlbrodt et al, 2007;Wunsch & Ferrari, 2004). Overall, the field of oceanography is increasingly starting to use advanced ML methods, as reviewed in Sonnewald et al (2021). To infer subsurface dynamics, ML has been employed to predict currents at 1000m from satellites (Chapman & Charantonis, 2017), and subsurface structure from idealized simulations (Manucharyan et al, 2021).…”

“…Unsupervised ML is particularly useful in SONNEWALD AND LGUENSAT 10.1029/2021MS002496 this context, as the labels can be assigned without bias. Second, adoption of ML within the physical sciences suffers from a lack of trust that stems from a lack of a transparent understanding of the source of predictive skill (Irrgang et al, 2021;Rudin, 2019;Sonnewald et al, 2021). Ensuring that what is learned by the machine is physically meaningful, and not due to trivial coincidences, is important for example for reliability and generalization (Balaji, 2020), and to avoid underspecification (D'Amour et al, 2020).…”