The purpose of this special issue is to illustrate the role of a sustainable coastal research infrastructure in supporting monitoring, sciences, and management of the coastal marine areas. As such the JERICO-RI research infrastructure is most suitable, gathering 34 partners in Europe with the same overarching objective: to strengthen and enlarge a solid and transparent European network of coastal observatories and to provide an operational service for the timely, continuous, and sustainable delivery of high-quality environmental (physical, biogeochemical, and biological) data and services related to the marine environment in European coastal seas. Six scientific areas are targeted, from the sensor development to the data analysis and the scientific results. These are the following:

1. the pelagic biodiversity with phytoplankton and harmful algal blooms;
2. the benthic biodiversity and habitats;
3. the contaminant transports;
4. the coastal transport and hydrology;
5. the carbonate systems and C cycle; and
6. the coastal operational oceanography and modelling.

Since its inception, data assimilation has proved to be enormously useful in the most varied fields throughout the Earth sciences. It is certainly essential in meteorology, where the short-range forecasts would otherwise be almost impossible. In oceanography, its development has been slower, partly due to the smaller number of continuous and stable observations and partly due to fewer studies that show the importance of ocean forecasts for societal benefit. However, recently, these techniques have been used more and more widely, both in operational oceanography and to produce climate reconstructions. Although the techniques are similar to those used in the atmospheric field, they have to deal with particularities due to the different environment, where the boundary conditions, open and closed, have greater importance and the sparsity of observations pose unique challenges. This special issue is open to contributions describing data assimilation techniques, both methodological and case studies, in the oceanographic field. We welcome original work describing new techniques or new types of observations that cover every aspect of data assimilation, including varied applications of data assimilation, both in coastal seas and in the open ocean. This issue was inspired by the session OS4.7 at EGU2022.

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.

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.

Clouds over the ocean and their radiative properties are partially influenced by the biology, biogeochemistry and physics of the surface ocean. For example, the production of sea spray via bubble bursting associated with breaking waves can be influenced by biogenic material present in surface seawater, and biogenic material carried in sea spray aerosol can also influence ice-nucleating properties in air overlying the ocean. Furthermore, phytoplankton produce a range of compounds, including gases and particles, which when emitted to the atmosphere participate in the atmospheric chemistry. Our recent ship campaign Sea2Cloud had a primary focus of investigating the link between marine particle emissions and the biogeochemical properties of the seawater in subantarctic and subtropical waters, examining new particle formation processes and the relationship between surface ocean biogeochemistry and sea spray aerosol in dedicated ship-borne experiments. In parallel, ambient air aerosol, gas and cloud properties were measured along the ship track, which will allow for testing of these relationships in modelling exercises. Along with the ship campaign, longer-term measurements of aerosol properties were monitored at the Baring Head GAW research station (NZ) as well as continuously ongoing onboard the R/V Marion Dufresne over the years 2021–2022. These measurements will be used for the inspection of the representativeness of the ship campaign's process-orientated results. The Sea2Cloud project is running from July 2018 to July 2023, with potential additional laboratory and ship-borne measurements in the Southern Ocean by January 2023.

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.

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.

Since its inception, data assimilation has proved to be enormously useful in the most varied fields throughout the Earth sciences. It is certainly essential in meteorology, where the short-range forecasts would otherwise be almost impossible. In oceanography, its development has been slower, partly due to the smaller number of continuous and stable observations and partly due to fewer studies that show the importance of ocean forecasts for societal benefit. However, recently, these techniques have been used more and more widely, both in operational oceanography and to produce climate reconstructions. Although the techniques are similar to those used in the atmospheric field, they have to deal with particularities due to the different environment, where the boundary conditions, open and closed, have greater importance and the sparsity of observations pose unique challenges. This special issue is open to contributions describing data assimilation techniques, both methodological and case studies, in the oceanographic field. We welcome original work describing new techniques or new types of observations that cover every aspect of data assimilation, including varied applications of data assimilation, both in coastal seas and in the open ocean. This issue was inspired by the session OS4.7 at EGU2022.

Clouds over the ocean and their radiative properties are partially influenced by the biology, biogeochemistry and physics of the surface ocean. For example, the production of sea spray via bubble bursting associated with breaking waves can be influenced by biogenic material present in surface seawater, and biogenic material carried in sea spray aerosol can also influence ice-nucleating properties in air overlying the ocean. Furthermore, phytoplankton produce a range of compounds, including gases and particles, which when emitted to the atmosphere participate in the atmospheric chemistry. Our recent ship campaign Sea2Cloud had a primary focus of investigating the link between marine particle emissions and the biogeochemical properties of the seawater in subantarctic and subtropical waters, examining new particle formation processes and the relationship between surface ocean biogeochemistry and sea spray aerosol in dedicated ship-borne experiments. In parallel, ambient air aerosol, gas and cloud properties were measured along the ship track, which will allow for testing of these relationships in modelling exercises. Along with the ship campaign, longer-term measurements of aerosol properties were monitored at the Baring Head GAW research station (NZ) as well as continuously ongoing onboard the R/V Marion Dufresne over the years 2021–2022. These measurements will be used for the inspection of the representativeness of the ship campaign's process-orientated results. The Sea2Cloud project is running from July 2018 to July 2023, with potential additional laboratory and ship-borne measurements in the Southern Ocean by January 2023.

The purpose of this special issue is to illustrate the role of a sustainable coastal research infrastructure in supporting monitoring, sciences, and management of the coastal marine areas. As such the JERICO-RI research infrastructure is most suitable, gathering 34 partners in Europe with the same overarching objective: to strengthen and enlarge a solid and transparent European network of coastal observatories and to provide an operational service for the timely, continuous, and sustainable delivery of high-quality environmental (physical, biogeochemical, and biological) data and services related to the marine environment in European coastal seas. Six scientific areas are targeted, from the sensor development to the data analysis and the scientific results. These are the following:

1. the pelagic biodiversity with phytoplankton and harmful algal blooms;
2. the benthic biodiversity and habitats;
3. the contaminant transports;
4. the coastal transport and hydrology;
5. the carbonate systems and C cycle; and
6. the coastal operational oceanography and modelling.