Ocean Science 

Despite their socio-economic and environmental impacts, extreme events in the marine environment are generally poorly understood, simulated, or predicted. In particular, the dynamics of these events involve multiple temporal and spatial scales and can be driven by different mechanics and complex feedback involving the ocean and its interaction with the other spheres of the climate system. In response to climate change, the frequency and magnitude of these events could dramatically change, resulting in significant impacts on human life and property as well as on marine ecosystems. Hence, advancing our understanding of the dynamics leading to extremes in marine environments is crucial for improving their predictability, which, in turn, will help to achieve environmental and social sustainability. With this rationale, the special issue will focus on marine extremes at multiple temporal and spatial scales. Studies discussing ocean dynamics and air–sea interactions that can influence the evolution of marine extremes are particularly welcome; the inclusion of work using techniques ranging from in situ to remote-sensing observations and from numerical models to innovative artificial intelligence techniques (e.g. machine learning) is also encouraged. This diversity of topics will make for a highly multidisciplinary special issue, open to multiple applications in the field of marine extremes. Contributions related to the investigation of marine heat waves, marine storminess, storm surge and sea level rise, dense-water formation, deep-ocean extreme events, etc. are other examples of potential topics. The special issue has been stimulated by the EGU General Assembly 2023, session OS4.6, but all submissions on this topic are welcome, regardless of whether authors participated in the conference.

In 2024–2025 Ocean Science will be 20 years old, during which time it has been internationally at the forefront of high-quality, open-access, and open-peer-review publishing in our field of science. To celebrate this jubilee, we are publishing a special collection of “reviews and perspectives” papers, looking back at how ocean sciences have advanced over the last 20 years and looking forward to how they might advance over the next 20 years. The Ocean Science Jubilee special issue will be a compilation of influential papers across the broad range of ocean science topics covered by the journal. Accepted reviews and perspectives papers may also be included in the https://encyclopedia-of-geosciences.net/.

Submission of reviews and perspectives papers is open to everyone. We welcome the perspectives of early-career scientists. You do not have to be invited to submit a paper to the special issue. Reviews and perspectives papers are welcome on any topic and discipline within ocean sciences, as with any paper in Ocean Science. Manuscripts will undergo the usual rigorous and open peer review.

The 54th International Liège Colloquium has focused on the topic “machine learning and data analysis in oceanography”. Data-driven approaches to understand and predict the ocean dynamics have gained significant traction in recent years thanks to the increased number, coverage and quality of ocean observations; improved numerical ocean modeling; the availability of massively parallel computing devices (like graphical processing units); and the advancement of optimization schemes and machine learning (ML) models able to constrain high-dimensional and non-linear systems. Many potential applications of such statistical or ML models (sometimes in combination with dynamical modeling derived from first principles) have been developed. These applications include identifying patterns and features as well as regions with common dynamics and deriving related quantities from easily observed ones (such as estimation of subsurface dynamics from partial observations or chlorophyll concentration from radiances), infilling missing data in satellite observations and adaptive sampling methods guided by machine learning. The spatial and temporal scales from different observation systems have become more and more diverse, which requires new techniques and approaches to combine them in an optimal way for estimating the state of the ocean. Statistical or ML models can also be used to identify bad or questionable observations and can help in quality control and quality assessment of observational data products. Data-driven techniques also have a high potential to complement classical numerical modeling as they allow new ways to represent subgrid parameterization, surrogate models, optimal ensemble predictions, and new and efficient approaches for stochastic modeling and data assimilation to be proposed. While many recent advancements are within machine learning applications, progress in more traditional data-driven analysis techniques such as optimal interpolation, variational analysis and Kalman Filtering, among others, is also within the scope of this special issue. We welcome all submissions that fit this description.

Coastal oceanographic processes present important differences to deep-water oceanography, resulting in higher prediction errors, where bottom topography exerts a strong control on wave/current/turbulence fields. These fields are modified by many additional factors that include interactions with coastal infrastructure, stratification, river discharges, and many non-linear effects such as breaking waves or nearshore circulation typical of shallow-water domains.

The special issue invites papers tackling simulations, observations, and analyses of coastal interactions, supporting the combinations of coastal observations (in situ and remote) and new modelling approaches (structured or unstructured grids) to reduce predictive uncertainties or to include new processes (interactions with groundwater flows, damping by biotopes, etc.). The special issue welcomes aggregation of data sets (e.g. different Sentinels or combinations with other satellites); new land boundary formulations; advanced assimilation or error bounding techniques; and, in general, original papers that contribute to the advancement of coastal oceanography. Papers proposing criteria to enhance applications or addressing the multiple scales in coastal processes, from storm events to decadal or climatic scales, are also welcome.

The special issue invites papers from recent and ongoing research projects dealing with green engineering for coastal zones and from EGU sessions in coastal oceanography and which can provide advancements in facing the many challenges experienced by present and future coasts. The wide range of possible topics is open to the interaction of nature-based solutions with coastal processes, uncertainties in coastal decision-making, and the role of operational coastal systems in reducing the growing risks in these areas. Contributions exploring the potential and currently open issues of non-linear response functions, support from artificial intelligence, or downscaled oceanographic projections are also covered.

The special issue will build upon a number of recent EU and international projects dealing with green engineering for coastal zones. Among them, there are many H2020 projects (e.g. the REST-COAST project; https://rest-coast.eu/) and an increasing number of special sessions dedicated to these topics (e.g. EGU but also Coastal Sediments 2023). The special issue is open and welcomes contributions within its scope.

This special issue describes efforts establishing high-resolution regional ocean and biogeochemical modelling capabilities for Modular Ocean Model 6 (MOM6) and selected initial applications. MOM is amongst the most widely used ocean models for global climate and earth system applications, with a lineage of scientific contributions exceeding 50 years. MOM6 represents an ambitious evolution of previous MOM formulations, embracing numerous recent modelling advances and an open development approach. MOM6 and its associated biogeochemical components have been successfully deployed for the Sixth Coupled Model Intercomparison Project (CMIP6) and other international global climate and ocean prediction efforts. The resolution and regional optimality of global simulations, however, are inevitably limited by the computational, forcing, and parameterization demands of global climate-scale simulations. Ocean modelling frameworks capable of simulating and connecting physical, biogeochemical, and ecosystem processes from ocean basins to coasts and estuaries are needed to understand the role of oceans in global change and to anticipate global change impacts on marine ecosystems and coastal communities. Development of robust regional modelling capabilities for MOM6 (i.e. regional MOM6) creates such a framework, but the extension of MOM6 to high-resolution regional applications presents many challenges.

The papers in this collection present the overall design and implementation of regional MOM6, describe new parameterizations intended for regional applications, present a first generation of regional MOM6 configurations from across the global ocean, and offer select initial applications in ocean science. Advances in horizontal grid generation and boundary condition formulation are highlighted, including those enabling a more seamless transmission of physical and biogeochemical information from global to regional scales and those required to handle flexible Lagrangian vertical coordinates. The robustness of physical and biogeochemical configurations and parameterizations – many of which were developed for global applications – is explored in higher-resolution implementations spanning environments from the Arctic to equatorial waters. Analysis of tradeoffs between model skill and computational cost highlights algorithmic improvements critical for producing decision-relevant ensembles that span a range of ocean futures. The collected works provide a foundation for the expanded application of regional MOM6 to understand and predict ocean conditions across scales.

This is a traditional style special issue open to all papers within the topic. We anticipate that contributions will be primarily to GMD initially but that there will be a growing number of applications suitable for OS once the core development papers have been published. The indefinite ending date will allow for a greater number of initial applications to be published in OS and enable eventual documentation of generational updates planned for some configurations.