Articles | Volume 21, issue 4
https://doi.org/10.5194/os-21-1709-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-21-1709-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
05 Aug 2025
Review article |
| 05 Aug 2025
| 05 Aug 2025
Marine data assimilation in the UK: the past, the present, and the vision for the future
Jozef Skákala
CORRESPONDING AUTHOR
Environmental Intelligence Group, Plymouth Marine Laboratory, Plymouth, United Kingdom
National Centre for Earth Observation, Leicester, United Kingdom
David Ford
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Keith Haines
National Centre for Earth Observation, Leicester, United Kingdom
Department of Meteorology, University of Reading, Reading, United Kingdom
Amos Lawless
National Centre for Earth Observation, Leicester, United Kingdom
School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, United Kingdom
Matthew J. Martin
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Philip Browne
Research Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Marcin Chrust
Research Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Stefano Ciavatta
Research and Development, Mercator Ocean International, Toulouse, France
Alison Fowler
National Centre for Earth Observation, Leicester, United Kingdom
Department of Meteorology, University of Reading, Reading, United Kingdom
Daniel Lea
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Matthew Palmer
Environmental Intelligence Group, Plymouth Marine Laboratory, Plymouth, United Kingdom
Andrea Rochner
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom
Jennifer Waters
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Research Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Deep S. Banerjee
Environmental Intelligence Group, Plymouth Marine Laboratory, Plymouth, United Kingdom
National Centre for Earth Observation, Leicester, United Kingdom
Mike Bell
Ocean, Cryosphere and Climate, Met Office, Exeter, United Kingdom
Davi M. Carneiro
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Yumeng Chen
National Centre for Earth Observation, Leicester, United Kingdom
Department of Meteorology, University of Reading, Reading, United Kingdom
Susan Kay
Environmental Intelligence Group, Plymouth Marine Laboratory, Plymouth, United Kingdom
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Dale Partridge
Environmental Intelligence Group, Plymouth Marine Laboratory, Plymouth, United Kingdom
National Centre for Earth Observation, Leicester, United Kingdom
Martin Price
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Richard Renshaw
Ocean, Cryosphere and Climate, Met Office, Exeter, United Kingdom
Georgy Shapiro
School of Biological and Marine Sciences, University of Plymouth, Plymouth, United Kingdom
James While
Ocean Forecasting R&D, Met Office, Exeter, United Kingdom
Related authors
Deep S. Banerjee and Jozef Skákala
Biogeosciences, 22, 3769–3784, https://doi.org/10.5194/bg-22-3769-2025, https://doi.org/10.5194/bg-22-3769-2025, 2025
Short summary
Short summary
Nitrate is a crucial nutrient in oceans, whose excess can trigger uncontrolled algae growth that damages marine ecosystems. We used machine learning to generate skilled, gap-free, bi-decadal surface nitrate data from sparse observations, revealing areas on the North-West European Shelf that are more vulnerable to excess algae growth if nutrient pollution occurs. We also looked at bi-decadal trends in coastal nitrate and the impact of winter nitrate on spring phytoplankton blooms.
Dale Partridge, Deep Banerjee, David Ford, Ke Wang, Jozef Skakala, Juliane Wihsgott, Prathyush Menon, Susan Kay, Daniel Clewley, Andrea Rochner, Emma Sullivan, and Matthew Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3346, https://doi.org/10.5194/egusphere-2025-3346, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi.
Ieuan Higgs, Ross Bannister, Jozef Skákala, Alberto Carrassi, and Stefano Ciavatta
EGUsphere, https://doi.org/10.48550/arXiv.2504.05218, https://doi.org/10.48550/arXiv.2504.05218, 2025
Short summary
Short summary
We explored how machine learning can improve computer models that simulate ocean ecosystems. These models help us understand how the ocean works, but they often struggle due to limited observations and complex processes. Our approach uses machine learning to better connect the parts of the system we can observe with those we cannot. This leads to more accurate and efficient predictions, offering a promising way to improve future ocean monitoring and forecasting tools.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024, https://doi.org/10.5194/bg-21-731-2024, 2024
Short summary
Short summary
A complex network is a way of representing which parts of a system are connected to other parts. We have constructed a complex network based on an ecosystem–ocean model. From this, we can identify patterns in the structure and areas of similar behaviour. This can help to understand how natural, or human-made, changes will affect the shelf sea ecosystem, and it can be used in multiple future applications such as improving modelling, data assimilation, or machine learning.
Dale Partridge, Ségolène Berthou, Rebecca Millington, James Clark, Lucy Bricheno, Juan Manuel Castillo, Julia Rulent, and Huw Lewis
EGUsphere, https://doi.org/10.5194/egusphere-2025-3654, https://doi.org/10.5194/egusphere-2025-3654, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
Phytoplankton blooms are governed by the availability of light and nutrients, both of which are affected by mixing in the upper layers of the ocean, which is impacted by wave activity on the surface. Most numerical ocean models estimate waves through a parameterisation, here we explicitly resolve waves through a coupled wave model to examine the impact on the strength and timing of phytoplankton blooms, particular during storms when wave activity is elevated.
Deep S. Banerjee and Jozef Skákala
Biogeosciences, 22, 3769–3784, https://doi.org/10.5194/bg-22-3769-2025, https://doi.org/10.5194/bg-22-3769-2025, 2025
Short summary
Short summary
Nitrate is a crucial nutrient in oceans, whose excess can trigger uncontrolled algae growth that damages marine ecosystems. We used machine learning to generate skilled, gap-free, bi-decadal surface nitrate data from sparse observations, revealing areas on the North-West European Shelf that are more vulnerable to excess algae growth if nutrient pollution occurs. We also looked at bi-decadal trends in coastal nitrate and the impact of winter nitrate on spring phytoplankton blooms.
Xue Feng, Matthew J. Widlansky, Tong Lee, Ou Wang, Magdalena A. Balmaseda, Hao Zuo, Gregory Dusek, William Sweet, and Malte F. Stuecker
Ocean Sci., 21, 1663–1676, https://doi.org/10.5194/os-21-1663-2025, https://doi.org/10.5194/os-21-1663-2025, 2025
Short summary
Short summary
Forecasting sea level changes months in advance along the Gulf Coast and East Coast of the United States is challenging. Here, we present a method that uses past ocean states to forecast future sea levels, while assuming no knowledge of how the atmosphere will evolve other than its typical annual cycle near the ocean's surface. Our findings indicate that this method improves sea level outlooks for many locations along the Gulf Coast and East Coast, especially south of Cape Hatteras.
Dale Partridge, Deep Banerjee, David Ford, Ke Wang, Jozef Skakala, Juliane Wihsgott, Prathyush Menon, Susan Kay, Daniel Clewley, Andrea Rochner, Emma Sullivan, and Matthew Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3346, https://doi.org/10.5194/egusphere-2025-3346, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
Gianpiero Cossarini, Andrew Moore, Stefano Ciavatta, and Katja Fennel
State Planet, 5-opsr, 12, https://doi.org/10.5194/sp-5-opsr-12-2025, https://doi.org/10.5194/sp-5-opsr-12-2025, 2025
Short summary
Short summary
Marine biogeochemistry refers to the cycling of chemical elements resulting from physical transport, chemical reaction, uptake, and processing by living organisms. Biogeochemical models can have a wide range of complexity, from a single nutrient to fully explicit representations of multiple nutrients, trophic levels, and functional groups. Uncertainty sources are the lack of knowledge about the parameterizations, the initial and boundary conditions, and the lack of observations.
Ibrahim Hoteit, Eric Chassignet, and Mike Bell
State Planet, 5-opsr, 21, https://doi.org/10.5194/sp-5-opsr-21-2025, https://doi.org/10.5194/sp-5-opsr-21-2025, 2025
Short summary
Short summary
This paper explores how using multiple predictions instead of just one can improve ocean forecasts and help prepare for changes in ocean conditions. By combining different forecasts, scientists can better understand the uncertainty in predictions, leading to more reliable forecasts and better decision-making. This method is useful for responding to hazards like oil spills, improving climate forecasts, and supporting decision-making in fields like marine safety and resource management.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
Short summary
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Liying Wan, Marcos Garcia Sotillo, Mike Bell, Yann Drillet, Roland Aznar, and Stefania Ciliberti
State Planet, 5-opsr, 15, https://doi.org/10.5194/sp-5-opsr-15-2025, https://doi.org/10.5194/sp-5-opsr-15-2025, 2025
Short summary
Short summary
Operating the ocean value chain requires the implementation of steps that must work systematically and automatically to generate ocean predictions and deliver this information. The paper illustrates the main challenges foreseen by operational chains in integrating complex numerical frameworks from the global to coastal scale and discusses existing tools that facilitate orchestration, including examples of existing systems and their capacity to provide high-quality and timely ocean forecasts.
Yann Drillet, Matthew Martin, Yosuke Fujii, Eric Chassignet, and Stefania Ciliberti
State Planet, 5-opsr, 2, https://doi.org/10.5194/sp-5-opsr-2-2025, https://doi.org/10.5194/sp-5-opsr-2-2025, 2025
Short summary
Short summary
This article describes the various stages of research and development that have been carried out over the last few decades to produce an operational reference service for global ocean monitoring and forecasting.
Michael J. Bell, Andreas Schiller, and Stefania Ciliberti
State Planet, 5-opsr, 10, https://doi.org/10.5194/sp-5-opsr-10-2025, https://doi.org/10.5194/sp-5-opsr-10-2025, 2025
Short summary
Short summary
We provide an introduction to physical ocean models, at elementary and intermediate levels, describing the properties they represent, the principles and equations they use to evolve these properties, the physical phenomena they simulate, and the wider context and prospects for their further development. We also outline, at a more technical level, the methods and approximations that they use and the difficulties that limit their accuracy or reliability.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
Short summary
Short summary
The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Ieuan Higgs, Ross Bannister, Jozef Skákala, Alberto Carrassi, and Stefano Ciavatta
EGUsphere, https://doi.org/10.48550/arXiv.2504.05218, https://doi.org/10.48550/arXiv.2504.05218, 2025
Short summary
Short summary
We explored how machine learning can improve computer models that simulate ocean ecosystems. These models help us understand how the ocean works, but they often struggle due to limited observations and complex processes. Our approach uses machine learning to better connect the parts of the system we can observe with those we cannot. This leads to more accurate and efficient predictions, offering a promising way to improve future ocean monitoring and forecasting tools.
Gabriela Martinez-Balbontin, Julien Jouanno, Rachid Benshila, Julien Lamouroux, Coralie Perruche, and Stefano Ciavatta
EGUsphere, https://doi.org/10.5194/egusphere-2025-1246, https://doi.org/10.5194/egusphere-2025-1246, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This study uses machine learning to predict chlorophyll-a levels, which are important for monitoring marine ecosystems and the carbon cycle. By using forecasts of sea surface temperature, salinity, height, and mixed layer depth, we can make global predictions up to six months ahead in just minutes. Our approach is as accurate or better than traditional methods, while being faster and more resource-efficient.
Samantha Petch, Liang Feng, Paul Palmer, Robert P. King, Tristan Quaife, and Keith Haines
EGUsphere, https://doi.org/10.22541/essoar.173343481.12875858/v1, https://doi.org/10.22541/essoar.173343481.12875858/v1, 2025
Short summary
Short summary
The growth rate of atmospheric CO2 varies year to year, mainly due to land ecosystems. Understanding factors controlling the land carbon uptake is crucial. Our study examines the link between terrestrial water storage and the CO2 growth rate from 2002–2023, revealing a strong negative correlation. We highlight the key role of tropical forests, especially in tropical America, and assess how regional contributions shift over time.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Robert R. King, Matthew J. Martin, Lucile Gaultier, Jennifer Waters, Clément Ubelmann, and Craig Donlon
Ocean Sci., 20, 1657–1676, https://doi.org/10.5194/os-20-1657-2024, https://doi.org/10.5194/os-20-1657-2024, 2024
Short summary
Short summary
We use simulations of our ocean forecasting system to compare the impact of additional altimeter observations from two proposed future satellite constellations. We found that, in our system, an altimeter constellation of 12 nadir altimeters produces improved predictions of sea surface height, surface currents, temperature, and salinity compared to a constellation of 2 wide-swath altimeters.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Yumeng Chen, Lars Nerger, and Amos S. Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-1078, https://doi.org/10.5194/egusphere-2024-1078, 2024
Short summary
Short summary
In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Charlotte A. J. Williams, Tom Hull, Jan Kaiser, Claire Mahaffey, Naomi Greenwood, Matthew Toberman, and Matthew R. Palmer
Biogeosciences, 21, 1961–1971, https://doi.org/10.5194/bg-21-1961-2024, https://doi.org/10.5194/bg-21-1961-2024, 2024
Short summary
Short summary
Oxygen (O2) is a key indicator of ocean health. The risk of O2 loss in the productive coastal/continental slope regions is increasing. Autonomous underwater vehicles equipped with O2 optodes provide lots of data but have problems resolving strong vertical O2 changes. Here we show how to overcome this and calculate how much O2 is supplied to the low-O2 bottom waters via mixing. Bursts in mixing supply nearly all of the O2 to bottom waters in autumn, stopping them reaching ecologically low levels.
Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024, https://doi.org/10.5194/bg-21-731-2024, 2024
Short summary
Short summary
A complex network is a way of representing which parts of a system are connected to other parts. We have constructed a complex network based on an ecosystem–ocean model. From this, we can identify patterns in the structure and areas of similar behaviour. This can help to understand how natural, or human-made, changes will affect the shelf sea ecosystem, and it can be used in multiple future applications such as improving modelling, data assimilation, or machine learning.
Eva Álvarez, Gianpiero Cossarini, Anna Teruzzi, Jorn Bruggeman, Karsten Bolding, Stefano Ciavatta, Vincenzo Vellucci, Fabrizio D'Ortenzio, David Antoine, and Paolo Lazzari
Biogeosciences, 20, 4591–4624, https://doi.org/10.5194/bg-20-4591-2023, https://doi.org/10.5194/bg-20-4591-2023, 2023
Short summary
Short summary
Chromophoric dissolved organic matter (CDOM) interacts with the ambient light and gives the waters of the Mediterranean Sea their colour. We propose a novel parameterization of the CDOM cycle, whose parameter values have been optimized by using the data of the monitoring site BOUSSOLE. Nutrient and light limitations for locally produced CDOM caused aCDOM(λ) to covary with chlorophyll, while the above-average CDOM concentrations observed at this site were maintained by allochthonous sources.
Michael Mayer, Takamasa Tsubouchi, Susanna Winkelbauer, Karin Margretha H. Larsen, Barbara Berx, Andreas Macrander, Doroteaciro Iovino, Steingrímur Jónsson, and Richard Renshaw
State Planet, 1-osr7, 14, https://doi.org/10.5194/sp-1-osr7-14-2023, https://doi.org/10.5194/sp-1-osr7-14-2023, 2023
Short summary
Short summary
This paper compares oceanic fluxes across the Greenland–Scotland Ridge (GSR) from ocean reanalyses to largely independent observational data. Reanalyses tend to underestimate the inflow of warm waters of subtropical Atlantic origin and hence oceanic heat transport across the GSR. Investigation of a strong negative heat transport anomaly around 2018 highlights the interplay of variability on different timescales and the need for long-term monitoring of the GSR to detect forced climate signals.
Jonathan Andrew Baker, Richard Renshaw, Laura Claire Jackson, Clotilde Dubois, Doroteaciro Iovino, Hao Zuo, Renellys C. Perez, Shenfu Dong, Marion Kersalé, Michael Mayer, Johannes Mayer, Sabrina Speich, and Tarron Lamont
State Planet, 1-osr7, 4, https://doi.org/10.5194/sp-1-osr7-4-2023, https://doi.org/10.5194/sp-1-osr7-4-2023, 2023
Short summary
Short summary
We use ocean reanalyses, in which ocean models are combined with observations, to infer past changes in ocean circulation and heat transport in the South Atlantic. Comparing these estimates with other observation-based estimates, we find differences in their trends, variability, and mean heat transport but closer agreement in their mean overturning strength. Ocean reanalyses can help us understand the cause of these differences, which could improve estimates of ocean transports in this region.
Richard Renshaw, Eileen Bresnan, Susan Kay, Robert McEwan, Peter I. Miller, and Paul Tett
State Planet, 1-osr7, 13, https://doi.org/10.5194/sp-1-osr7-13-2023, https://doi.org/10.5194/sp-1-osr7-13-2023, 2023
Short summary
Short summary
There were two unusual blooms in Scottish waters in summer 2021. Both turned the sea a turquoise colour visible from space, typical of coccolithophore blooms. We use reanalysis and satellite data to examine the environment that led to these blooms. We suggest unusual weather was a contributory factor in both cases.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
Short summary
Short summary
We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
Hydrol. Earth Syst. Sci., 27, 1723–1744, https://doi.org/10.5194/hess-27-1723-2023, https://doi.org/10.5194/hess-27-1723-2023, 2023
Short summary
Short summary
Gravitational measurements of water storage from GRACE (Gravity Recovery and Climate Experiment) can improve understanding of the water budget. We produce flux estimates over large river catchments based on observations that close the monthly water budget and ensure consistency with GRACE on short and long timescales. We use energy data to provide additional constraints and balance the long-term energy budget. These flux estimates are important for evaluating climate models.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Jeff Polton, James Harle, Jason Holt, Anna Katavouta, Dale Partridge, Jenny Jardine, Sarah Wakelin, Julia Rulent, Anthony Wise, Katherine Hutchinson, David Byrne, Diego Bruciaferri, Enda O'Dea, Michela De Dominicis, Pierre Mathiot, Andrew Coward, Andrew Yool, Julien Palmiéri, Gennadi Lessin, Claudia Gabriela Mayorga-Adame, Valérie Le Guennec, Alex Arnold, and Clément Rousset
Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, https://doi.org/10.5194/gmd-16-1481-2023, 2023
Short summary
Short summary
The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
Short summary
Short summary
We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, https://doi.org/10.5194/tc-16-61-2022, 2022
Short summary
Short summary
Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021, https://doi.org/10.5194/npg-28-633-2021, 2021
Short summary
Short summary
Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
Robert R. King and Matthew J. Martin
Ocean Sci., 17, 1791–1813, https://doi.org/10.5194/os-17-1791-2021, https://doi.org/10.5194/os-17-1791-2021, 2021
Short summary
Short summary
The SWOT satellite will provide a step change in our ability to measure the sea surface height over large areas, and so improve operational ocean forecasts, but will be affected by large correlated errors. We found that while SWOT observations without these errors significantly improved our system, including correlated errors degraded most variables. To realise the full benefits offered by the SWOT mission, we must develop methods to account for correlated errors in ocean forecasting systems.
Marion Mittermaier, Rachel North, Jan Maksymczuk, Christine Pequignet, and David Ford
Ocean Sci., 17, 1527–1543, https://doi.org/10.5194/os-17-1527-2021, https://doi.org/10.5194/os-17-1527-2021, 2021
Short summary
Short summary
Regions of enhanced chlorophyll-a concentrations can be identified by applying a threshold to the concentration value to a forecast and observed field (or analysis). These regions can then be treated and analysed as features using diagnostic techniques to consider of the evolution of the chlorophyll-a blooms in space and time. This allows us to understand whether the biogeochemistry in the model has any skill in predicting these blooms, their location, intensity, onset, duration and demise.
Georgy I. Shapiro and Jose M. Gonzalez-Ondina
Ocean Sci. Discuss., https://doi.org/10.5194/os-2021-77, https://doi.org/10.5194/os-2021-77, 2021
Preprint withdrawn
Short summary
Short summary
An effective method is developed for data assimilation in a high-resolution (child) ocean model in the case when the output from a coarse-resolution data-assimilating model (parent) is available. The basic idea is to assimilate data from the coarser model instead of actual observations. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) does not allow the child model to drift away from reality as it is indirectly controlled by observations via the parent model.
Georgy I. Shapiro, Jose M. Gonzalez-Ondina, and Vladimir N. Belokopytov
Ocean Sci., 17, 891–907, https://doi.org/10.5194/os-17-891-2021, https://doi.org/10.5194/os-17-891-2021, 2021
Short summary
Short summary
This paper presents an efficient method for high-resolution ocean modelling based on a combination of the deterministic and stochastic approaches. The method utilises mathematical tools similar to those developed for data assimilation in ocean modelling. The main difference is that instead of assimilating a relatively small number of observations, the SDD method assimilates all the data produced by a parent model. The method is applied to create an operational Stochastic Model of the Red Sea.
Yumeng Chen, Konrad Simon, and Jörn Behrens
Geosci. Model Dev., 14, 2289–2316, https://doi.org/10.5194/gmd-14-2289-2021, https://doi.org/10.5194/gmd-14-2289-2021, 2021
Short summary
Short summary
Mesh adaptivity can reduce overall model error by only refining meshes in specific areas where it us necessary in the runtime. Here we suggest a way to integrate mesh adaptivity into an existing Earth system model, ECHAM6, without having to redesign the implementation from scratch. We show that while the additional computational effort is manageable, the error can be reduced compared to a low-resolution standard model using an idealized test and relatively realistic dust transport tests.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
Short summary
Short summary
Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
David Ford
Biogeosciences, 18, 509–534, https://doi.org/10.5194/bg-18-509-2021, https://doi.org/10.5194/bg-18-509-2021, 2021
Short summary
Short summary
Biogeochemical-Argo floats are starting to routinely measure ocean chlorophyll, nutrients, oxygen, and pH. This study generated synthetic observations representing two potential Biogeochemical-Argo observing system designs and created a data assimilation scheme to combine them with an ocean model. The proposed system of 1000 floats brought clear benefits to model results, with additional floats giving further benefit. Existing satellite ocean colour observations gave complementary information.
Irene Polo, Keith Haines, Jon Robson, and Christopher Thomas
Ocean Sci., 16, 1067–1088, https://doi.org/10.5194/os-16-1067-2020, https://doi.org/10.5194/os-16-1067-2020, 2020
Short summary
Short summary
AMOC variability controls climate and is driven by wind and buoyancy forcing in the Atlantic. Density changes there are expected to connect to tropical regions. We develop methods to identify boundary density profiles at 26° N which relate to the AMOC. We found that density anomalies propagate equatorward along the western boundary, eastward along the Equator and then poleward up the eastern boundary with 2 years lag between boundaries. Record lengths of more than 26 years are required.
David Andrew Ford
Ocean Sci., 16, 875–893, https://doi.org/10.5194/os-16-875-2020, https://doi.org/10.5194/os-16-875-2020, 2020
Short summary
Short summary
Satellite observations of the ocean were combined with a numerical model to create simulations of the ocean state between 1998 and 2010. Relationships between physical and biogeochemical quantities were assessed to investigate whether observations of different variables are consistent in their representation of the Earth system. Good consistency was found. The results also highlighted ways in which the model could be improved and the respective impacts of using different observations.
Ewan Pinnington, Tristan Quaife, Amos Lawless, Karina Williams, Tim Arkebauer, and Dave Scoby
Geosci. Model Dev., 13, 55–69, https://doi.org/10.5194/gmd-13-55-2020, https://doi.org/10.5194/gmd-13-55-2020, 2020
Short summary
Short summary
We present LAVENDAR, a mathematical method for combining observations with models of the terrestrial environment. Here we use it to improve estimates of crop growth in the UK Met Office land surface model. However, the method is model agnostic, requires no modification to the underlying code and can be applied to any part of the model. In the example application we improve estimates of maize yield by 74 % by assimilating observations of leaf area, crop height and photosynthesis.
Catherine Guiavarc'h, Jonah Roberts-Jones, Chris Harris, Daniel J. Lea, Andrew Ryan, and Isabella Ascione
Ocean Sci., 15, 1307–1326, https://doi.org/10.5194/os-15-1307-2019, https://doi.org/10.5194/os-15-1307-2019, 2019
Short summary
Short summary
Coupled atmosphere–ocean modelling systems allow changes in the ocean to directly and immediately feed back on the atmosphere and enable improved weather prediction and ocean forecasts. This is particularly true if the coupled feedbacks are also considered in the way real-time observations of the atmospheric and oceanic states are used to obtain the initial conditions for the forecasts. Here we demonstrate promising performance from such a coupled system when used for ocean prediction.
Hao Zuo, Magdalena Alonso Balmaseda, Steffen Tietsche, Kristian Mogensen, and Michael Mayer
Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, https://doi.org/10.5194/os-15-779-2019, 2019
Short summary
Short summary
OCEAN5 is the fifth generation of the ocean and sea-ice analysis system at ECMWF. It was used for production of historical ocean and sea-ice states from 1979 onwards and is also used for generating real-time ocean and sea-ice states responsible for initializing the operational ECMWF weather forecasting system. This is a valuable data set with broad applications. A description of the OCEAN5 system and an assessment of the historical data set have been documented in this reference paper.
Dale Partridge, Tobias Friedrich, and Brian S. Powell
Geosci. Model Dev., 12, 195–213, https://doi.org/10.5194/gmd-12-195-2019, https://doi.org/10.5194/gmd-12-195-2019, 2019
Short summary
Short summary
This paper demonstrates the improvements made to an operational ocean forecast model around the Hawaiian Islands by performing a reanalysis of the model over a 10-year period. Using a number of different measurements we show the role a variety of observations play in producing the forecast, in particular the contribution of high-frequency radar.
Prima Anugerahanti, Shovonlal Roy, and Keith Haines
Biogeosciences, 15, 6685–6711, https://doi.org/10.5194/bg-15-6685-2018, https://doi.org/10.5194/bg-15-6685-2018, 2018
Short summary
Short summary
Minor changes in the biogeochemical model equations lead to major dynamical changes. We assessed this structural sensitivity for the MEDUSA biogeochemical model on chlorophyll and nitrogen concentrations at five oceanographic stations over 10 years, using 1-D ensembles generated by combining different process equations. The ensemble performed better than the default model in most of the stations, suggesting that our approach is useful for generating a probabilistic biogeochemical ensemble model.
Steffen Tietsche, Magdalena Alonso-Balmaseda, Patricia Rosnay, Hao Zuo, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, https://doi.org/10.5194/tc-12-2051-2018, 2018
Short summary
Short summary
We compare Arctic sea-ice thickness from L-band microwave satellite observations and an ocean–sea ice reanalysis. There is good agreement for some regions and times but systematic discrepancy in others. Errors in both the reanalysis and observational products contribute to these discrepancies. Thus, we recommend proceeding with caution when using these observations for model validation or data assimilation. At the same time we emphasise their unique value for improving sea-ice forecast models.
Jean-François Legeais, Michaël Ablain, Lionel Zawadzki, Hao Zuo, Johnny A. Johannessen, Martin G. Scharffenberg, Luciana Fenoglio-Marc, M. Joana Fernandes, Ole Baltazar Andersen, Sergei Rudenko, Paolo Cipollini, Graham D. Quartly, Marcello Passaro, Anny Cazenave, and Jérôme Benveniste
Earth Syst. Sci. Data, 10, 281–301, https://doi.org/10.5194/essd-10-281-2018, https://doi.org/10.5194/essd-10-281-2018, 2018
Short summary
Short summary
Sea level is one of the best indicators of climate change and has been listed as one of the essential climate variables. Sea level measurements have been provided by satellite altimetry for 25 years, and the Climate Change Initiative (CCI) program of the European Space Agency has given the opportunity to provide a long-term, homogeneous and accurate sea level record. It will help scientists to better understand climate change and its variability.
Davi Mignac, David Ferreira, and Keith Haines
Ocean Sci., 14, 53–68, https://doi.org/10.5194/os-14-53-2018, https://doi.org/10.5194/os-14-53-2018, 2018
Short summary
Short summary
Four ocean reanalyses and two free-running models are compared to study the meridional transports in the South Atlantic. We analyse the underlying causes of the product differences in an attempt to understand the potential impact (and limitations) of the data assimilation (DA) in improving the simulated ocean states. The DA schemes can consistently constrain the basin interior transports, but not the overturning circulation dominated by the narrow South Atlantic western boundary currents.
Bertrand Bonan, Nancy K. Nichols, Michael J. Baines, and Dale Partridge
Nonlin. Processes Geophys., 24, 515–534, https://doi.org/10.5194/npg-24-515-2017, https://doi.org/10.5194/npg-24-515-2017, 2017
Short summary
Short summary
We develop data assimilation techniques for numerical models using moving mesh methods. Moving meshes are valuable for explicitly tracking interfaces and boundaries in evolving systems. The application of the techniques is demonstrated on a one-dimensional
model of an ice sheet. It is shown, using various types of observations, that
the techniques predict the evolution of the edges of the ice sheet and its height accurately and efficiently.
Enda O'Dea, Rachel Furner, Sarah Wakelin, John Siddorn, James While, Peter Sykes, Robert King, Jason Holt, and Helene Hewitt
Geosci. Model Dev., 10, 2947–2969, https://doi.org/10.5194/gmd-10-2947-2017, https://doi.org/10.5194/gmd-10-2947-2017, 2017
Short summary
Short summary
An update to an ocean modelling configuration for the European North West Shelf is described. It is assessed against observations and climatologies for 1981–2012. Sensitivities in the model configuration updates are assessed to understand changes in the model system. The model improves upon an existing model of the region, although there remain some areas with significant biases. The paper highlights the dependence upon the quality of the river inputs.
David A. Ford, Johan van der Molen, Kieran Hyder, John Bacon, Rosa Barciela, Veronique Creach, Robert McEwan, Piet Ruardij, and Rodney Forster
Biogeosciences, 14, 1419–1444, https://doi.org/10.5194/bg-14-1419-2017, https://doi.org/10.5194/bg-14-1419-2017, 2017
Short summary
Short summary
This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea. These observations were used to validate two physical–biogeochemical ocean model simulations, each of which used different variants of the widely used European Regional Seas Ecosystem Model (ERSEM). The results suggest the ability of the models to reproduce the observed phytoplankton community structure was dependent on the details of the biogeochemical model parameterizations used.
Momme Butenschön, James Clark, John N. Aldridge, Julian Icarus Allen, Yuri Artioli, Jeremy Blackford, Jorn Bruggeman, Pierre Cazenave, Stefano Ciavatta, Susan Kay, Gennadi Lessin, Sonja van Leeuwen, Johan van der Molen, Lee de Mora, Luca Polimene, Sevrine Sailley, Nicholas Stephens, and Ricardo Torres
Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, https://doi.org/10.5194/gmd-9-1293-2016, 2016
Short summary
Short summary
ERSEM 15.06 is a model for marine biogeochemistry and the lower trophic levels of the marine food web. It comprises a pelagic and benthic sub-model including the microbial food web and the major biogeochemical cycles of carbon, nitrogen, phosphorus, silicate, and iron using dynamic stochiometry. Further features include modules for the carbonate system and calcification. We present full mathematical descriptions of all elements along with examples at various scales up to 3-D applications.
J. R. Siddorn, S. A. Good, C. M. Harris, H. W. Lewis, J. Maksymczuk, M. J. Martin, and A. Saulter
Ocean Sci., 12, 217–231, https://doi.org/10.5194/os-12-217-2016, https://doi.org/10.5194/os-12-217-2016, 2016
Short summary
Short summary
The Met Office provides a range of services in the marine environment. To support these services, and to ensure they evolve to meet the demands of users and are based on the best available science, a number of scientific challenges need to be addressed. The paper summarises the key challenges, and highlights some priorities for the ocean monitoring and forecasting research group at the Met Office.
B. Bonan, M. J. Baines, N. K. Nichols, and D. Partridge
The Cryosphere, 10, 1–14, https://doi.org/10.5194/tc-10-1-2016, https://doi.org/10.5194/tc-10-1-2016, 2016
Short summary
Short summary
This paper introduce a moving-point approach to model the flow of ice sheets. This particular moving-grid numerical approach is based on the conservation of local masses. This allows the ice sheet margins to be tracked explicitly. A finite-difference moving-point scheme is derived and applied in a simplified context (1-D). The conservation method is also suitable for 2-D scenarios. This paper is a first step towards applications of the conservation method to realistic 2-D cases.
N. Melia, K. Haines, and E. Hawkins
The Cryosphere, 9, 2237–2251, https://doi.org/10.5194/tc-9-2237-2015, https://doi.org/10.5194/tc-9-2237-2015, 2015
Short summary
Short summary
Projections of Arctic sea ice thickness (SIT) have the potential to inform stakeholders about accessibility to the region, but are currently rather uncertain. We present a new method to constrain global climate model simulations of SIT to narrow projection uncertainty via a statistical bias-correction technique.
E. W. Blockley, M. J. Martin, A. J. McLaren, A. G. Ryan, J. Waters, D. J. Lea, I. Mirouze, K. A. Peterson, A. Sellar, and D. Storkey
Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, https://doi.org/10.5194/gmd-7-2613-2014, 2014
V. N. Stepanov and K. Haines
Ocean Sci., 10, 645–656, https://doi.org/10.5194/os-10-645-2014, https://doi.org/10.5194/os-10-645-2014, 2014
F. Wobus, G. I. Shapiro, J. M. Huthnance, M. A. M. Maqueda, and Y. Aksenov
Ocean Sci., 9, 885–899, https://doi.org/10.5194/os-9-885-2013, https://doi.org/10.5194/os-9-885-2013, 2013
G. Shapiro, M. Luneva, J. Pickering, and D. Storkey
Ocean Sci., 9, 377–390, https://doi.org/10.5194/os-9-377-2013, https://doi.org/10.5194/os-9-377-2013, 2013
Related subject area
Approach: Data Assimilation | Properties and processes: Coastal and near-shore processes
Fusion of Lagrangian drifter data and numerical model outputs for improved assessment of turbulent dispersion
Sloane Bertin, Alexei Sentchev, and Elena Alekseenko
Ocean Sci., 20, 965–980, https://doi.org/10.5194/os-20-965-2024, https://doi.org/10.5194/os-20-965-2024, 2024
Short summary
Short summary
In applications related to search and rescue or pollution mitigation, gaining insights into turbulent processes at a fine scale on the ocean surface is crucial. This research suggests combining numerical model outputs with Lagrangian drifter measurements to enhance the reconstruction of surface current velocity fields. The findings demonstrate a notable enhancement (approximately 50 %) in the model's ability to replicate the movement of passive tracers in the Dover Strait.
Cited articles
Abarbanel, H. D. I., Rozdeba, P. J., and Shirman, S.: Machine learning as statistical data assimilation, Neural Comput., 30, 2025–2055, 2018.
Allen, J. I., Eknes, M., and Evensen, G.: An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea, Ann. Geophys., 21, 399–411, https://doi.org/10.5194/angeo-21-399-2003, 2003.
Alves, O., Balmaseda, M. A., Anderson, D., and Stockdale, T.: Sensitivity of dynamical seasonal forecasts to ocean initial conditions, Q. J. Roy. Meteor. Soc., 130, 647–667, 2004.
Anderson, L. A., Robinson, A. R., and Lozano, C. J.: Physical and biological modeling in the Gulf Stream region: I. Data assimilation methodology, Deep-Sea Res. Pt. I, 47, 1787–1827, https://doi.org/10.1016/S0967-0637(00)00019-4, 2000.
Arcucci, R., Lamya, M., and Guo, Y.-K.: Neural assimilation, Computational Science – ICCS 2020, 20th International Conference, Amsterdam, the Netherlands, 3–5 June 2020, Proceedings, Part VI 20, Springer International Publishing, https://doi.org/10.1007/978-3-030-50433-5_13, 2020.
Balmaseda, M. A.: Ocean analysis at ECMWF: From real-time ocean initial conditions to historical ocean reanalysis, ECMWF Newsletter, 105, 24–42, 2005.
Balmaseda, M. A., Dee, D., Vidard, A., and Anderson, D. L.: A multivariate treatment of bias for sequential data assimilation: Application to the tropical oceans, Q. J. Roy. Meteor. Soc., 133, 167–179, 2007.
Balmaseda, M. A., Vidard, A., and Anderson, D. L.: The ECMWF ocean analysis system: ORA-S3, Mon. Weather Rev., 136, 3018–3034, 2008.
Balmaseda, M. A., Alves, O. J., Arribas, A., Awaji, T., Behringer, D. W., Ferry, N., Fujii, Y., Lee, T., Rienecker, M., Rosati, T., and Stammer, D.: Ocean initialization for seasonal forecasts, Oceanography, 22, 154–159, 2009.
Balmaseda, M. A., Mogensen, K., and Weaver, A. T.: Evaluation of the ECMWF ocean reanalysis system ORAS4, Q. J. Roy. Meteor. Soc., 139, 1132–1161, 2013.
Balmaseda, M. A., Hernandez, F., Storto, A., Palmer, M. D., Alves, O., Shi, L., Smith, G. C., Toyoda, T., Valdivieso, M., Barnier, B., and Behringer, D.: The ocean reanalyses intercomparison project (ORA-IP), J. Oper. Oceanogr., 8, s80–s97, 2015.
Banerjee, D. and Skakala, J.: Improved understanding of eutrophication trends, indicators and problem areas using machine learning (2024), Authorea [preprint], https://doi.org/10.22541/essoar.171405637.76928549/v1, 2025.
Barthélémy, S., Brajard, J., Bertino, L., and Counillon, F.: Super-resolution data assimilation, Ocean Dynam., 72, 661–678, 2022.
Bell, M. J., Forbes, R. M., and Hines, A.: Assessment of the FOAM global data assimilation system for real-time operational ocean forecasting, J. Marine Syst., 25, 1–22, 2000.
Bell, M. J., Martin, M. J., and Nichols, N. K.: Assimilation of data into an ocean model with systematic errors near the equator, Q. J. Roy. Meteor. Soc., 130, 873–893, 2004.
Berthou, S., Renshaw, R., Smyth, T., Tinker, J., Grist, J., Wihsgott, J., Jones, S., Inall, M., Nolan, G., Arnold, A., and Castillo, J. M.: June 2023 marine heatwave over the Northwest European shelf: origins, weather feedback and future recurrence, Research Square, https://doi.org/10.21203/rs.3.rs-3417023/v1, 2023.
Bertino, L., Brasseur, P., Popov, M., Skakala, J., Wakamatsu, T., and Yumruktepe, C.: D4.2 Recommendations for strongly coupled physical-biogeochemical data assimilation. Deliverable report of project H2020 SEAMLESS (grant 101004032) (Version 1), Zenodo, https://doi.org/10.5281/zenodo.7432230, 2022.
Blair, G. S.: Digital twins of the natural environment, Patterns, 2, 1–3, https://doi.org/10.1016/j.patter.2021.100359, 2021.
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, 2019.
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Foundations of Data Science, 2, 55–80, https://doi.org/10.3934/fods.2020004, 2020.
Bonavita, M. and Laloyaux, P.: Machine learning for model error inference and correction, J. Adv. Model. Earth Sy., 12, e2020MS002232, https://doi.org/10.1029/2020MS002232, 2020.
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model, J. Comput. Sci.-Neth., 44, 101171, https://doi.org/10.1016/j.jocs.2020.101171, 2020.
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data assimilation and machine learning to infer unresolved scale parametrization, Philos. T. Roy. Soc. A, 379, 20200086, https://doi.org/10.1098/rsta.2020.0086, 2021.
Brewin, R. J., Ciavatta, S., Sathyendranath, S., Jackson, T., Tilstone, G., Curran, K., Airs, R. L., Cummings, D., Brotas, V., Organelli, E., and Dall'Olmo, G.: Uncertainty in ocean-color estimates of chlorophyll for phytoplankton groups, Frontiers in Marine Science, 4, https://doi.org/10.3389/fmars.2017.00104, 2017.
Brewin, R. J., Ciavatta, S., Sathyendranath, S., Skákala, J., Bruggeman, J., Ford, D., and Platt, T.: The influence of temperature and community structure on light absorption by phytoplankton in the North Atlantic, Sensors-Basel, 19, 4182, https://doi.org/10.3390/s19194182, 2019.
Brewin, R. J., Sathyendranath, S., Platt, T., Bouman, H., Ciavatta, S., Dall'Olmo, G., Dingle, J., Groom, S., Jönsson, B., Kostadinov, T. S., and Kulk, G.: Sensing the ocean biological carbon pump from space: A review of capabilities, concepts, research gaps and future developments, Earth-Sci. Rev., 217, 103604, https://doi.org/10.1016/j.earscirev.2021.103604, 2021.
Bruggeman, J., Bolding, K., Nerger, L., Teruzzi, A., Spada, S., Skákala, J., and Ciavatta, S.: EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters, Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, 2024.
Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y., Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin, G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley, S., Stephens, N., and Torres, R.: ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels, Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, 2016.
Chen, S., Meng, Y., Lin, S., Yu, Y., and Xi, J.: Estimation of sea surface nitrate from space: Current status and future potential, Sci. Total Environ., 899, 165690, https://doi.org/10.1016/j.scitotenv.2023, 2023.
Chen, Y., Partridge, D., and Nerger, L.: Assimilation of carbon data into NEMO-MEDUSA, Authorea [preprint], https://doi.org/10.22541/essoar.174585012.27386970/v1, 2025.
Cheng, S. B., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., Xiao, D. H., Janjic, T., Ding, W. P., Guo, Y. K., Carrassi, A., Bocquet, M., and Arcucci, R.: Machine learning with data assimilation and uncertainty quantification for dynamical systems: A review, IEEE/CAA J. Autom. Sinica, 10, 1361–1387, https://doi.org/10.1109/JAS.2023.123537, 2023.
Chevyrev, I. and Oberhauser, H.: Signature moments to characterize laws of stochastic processes, J. Mach. Learn. Res., 23, 1–42, 2022.
Ciavatta, S., Torres, R., Saux-Picart, S., and Allen, J. I.: Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?, J. Geophys. Res.-Oceans, 116, C12043, https://doi.org/10.1029/2011JC007219, 2011.
Ciavatta, S., Torres, R., Martinez-Vicente, V., Smyth, T., Dall'Olmo, G., Polimene, L., and Allen, J. I.: Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling, Prog. Oceanogr., 127, 74–95, 2014.
Ciavatta, S., Kay, S., Saux-Picart, S., Butenschön, M., and Allen, J. I.: Decadal reanalysis of biogeochemical indicators and fluxes in the North West European shelf-sea ecosystem, J. Geophys. Res.-Oceans, 121, 1824–1845, 2016.
Ciavatta, S., Brewin, R. J. W., Skakala, J., Polimene, L., de Mora, L., Artioli, Y., and Allen, J. I.: Assimilation of ocean-color plankton functional types to improve marine ecosystem simulations, J. Geophys. Res.-Oceans, 123, 834–854, 2018.
Ciavatta, S., Kay, S., Brewin, R. J., Cox, R., Di Cicco, A., Nencioli, F., Polimene, L., Sammartino, M., Santoleri, R., Skakala, J., and Tsapakis, M.: Ecoregions in the Mediterranean Sea through the reanalysis of phytoplankton functional types and carbon fluxes, J. Geophys. Res.-Oceans, 124, 6737–6759, 2019.
Ciavatta, S., Lazzari, P., Álvarez, E., Bertino, L., Bolding, K., Bruggeman, J., Capet, A., Cossarini, G., Daryabor, F., Nerger, L., Popov, M., Skákala, J., Spada, S., Teruzzi, A., Wakamatsu, T., Yumruktepe, V. C., and Brasseur, P.: Control of simulated ocean ecosystem indicators by biogeochemical observations, Prog. Oceanogr., 231, 103384, https://doi.org/10.1016/j.pocean.2024.103384, 2025.
Clark, J. R., Tilstone, G. H., Blackford, J., Ciavatta, S., Ford, D., Kay, S., Land, P. E., McEwan, R., and Renshaw, R.: Using CMEMS satellite and model data to help assess eutrophication status in Northwest European Shelf Seas, in: Copernicus Marine Service Ocean State Report, Issue 4, J. Oper. Oceanogr., 13, s82–s87, https://doi.org/10.1080/1755876X.2020.1785097#S014, 2020.
Cole, H., Henson, S., Martin, A., and Yool, A.: Mind the gap: The impact of missing data on the calculation of phytoplankton phenology metrics, J. Geophys. Res., 117, C08030, https://doi.org/10.1029/2012JC008249, 2012.
Daužickaitė, I., Lawless, A. S., Scott, J. A., and van Leeuwen, P. J.: On time-parallel preconditioning for the state formulation of incremental weak constraint 4D-Var, Q. J. Roy. Meteor. Soc., 147, 3521–3529, https://doi.org/10.1002/qj.4140, 2021.
Davidson, F., Alvera-Azcarate, A., Barth, A., Brassington, G. B., Chassignet, E. P., Clementi, E., De Mey-Frémaux, P., Divakaran, P., Harris, Ch., Hernandez, F., Hogan, P., Hole, L. R., Holt, J., Liu, G., Lu, Y., Lorente, P., Maksymczuk, P., Martin, M., Mehra, A., Melsom, A., Mo, H., Moore, A., Oddo, P., Pascual, A., Pequignet, A. Ch., Kourafalou, V., Ryan, A., Siddorn, J., Smith, G., Spindler, D., Spindler, T., Stanev, E. V., Staneva, J., Storto, A., Tanajura, C., Vinayachandran, P. N., Wan, L., Wang, H., Zhang, Y., Zhu, X., and Zu, Z.: Synergies in operational oceanography: the intrinsic need for sustained ocean observations, Frontiers in Marine Science, 6, 450, https://doi.org/10.3389/fmars.2019.00450, 2019.
Davidson, K., Whyte, C., Aleynik, D., Dale, A., Gontarek, S., Kurekin, A. A., McNeill, S., Miller, P. I., Porter, M., Saxon, R., and Swan, S.: HABreports: online early warning of harmful algal and biotoxin risk for the Scottish shellfish and finfish aquaculture industries, Frontiers in Marine Science, 8, 631732, https://doi.org/10.3389/fmars.2021.631732, 2021.
de Rosnay, P., Browne, P., de Boisséson, E., Fairbairn, D., Hirahara, Y., Ochi, K., Schepers, D., Weston, P., Zuo, H., Balmaseda, M. A., Balsamo, G., Bonavita, M., Borman, N., Brown, A., Chrust, M., Dahoui, M., Chiara, G., English, S., Geer, A., Healy, S., Hersbach, H., Laloyaux, P., Magnusson, L., Massart, S., McNally, A., Pappenberger, F., and Rabier, F.: Coupled data assimilation at ECMWF: current status, challenges and future developments, Q. J. Roy. Meteor. Soc., 148, 2672–2702, https://doi.org/10.1002/qj.4330, 2022.
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. Roy. Meteor. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005.
Dong, B., Haines, K., and Martin, M.: Improved high resolution ocean reanalyses using a simple smoother algorithm, J. Adv. Model. Earth Sy., 13, e2021MS002626, https://doi.org/10.1029/2021MS002626, 2021.
Dong, B., Bannister, R., Chen, Y., Fowler, A., and Haines, K.: Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses, Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, 2023.
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003.
Eyre, J. R.: Observation impact metrics in NWP: A theoretical study. Part I: Optimal systems, Q. J. Roy. Meteor. Soc., 147, 3180–3200, 2021.
Farchi, I., Bocquet, M., Laloyaux, P., Bonavita, M., and Malartic, Q.: A comparison of combined data assimilation and machine learning methods for offline and online model error correction, J. Comput. Sci.-Neth., 55, 101468, https://doi.org/10.1016/j.jocs.2021.101468, 2021.
Fasham, M. J. and Evans, G. T.: The use of optimization techniques to model marine ecosystem dynamics at the JGOFS station at 47 N 20 W, Philos. T. Roy. Soc. Ser. B, 348, 203–209, 1995.
Fennel, K., Gehlen, M., Brasseur, P., Brown, C. W., Ciavatta, S., Cossarini, G., Crise, A., Edwards, C. A., Ford, D., Friedrichs, M. A., Gregoire, M., Jones, E., Hae-Cheol, K., Lamoroux, J., and Murtugudde, R.: Advancing marine biogeochemical and ecosystem reanalyses and forecasts as tools for monitoring and managing ecosystem health, Frontiers in Marine Science, 6, https://doi.org/10.3389/fmars.2019.00089, 2019.
Fiedler, E. K., Martin, M. J., Blockley, E., Mignac, D., Fournier, N., Ridout, A., Shepherd, A., and Tilling, R.: Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM), The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, 2022.
Ford, D.: Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design, Biogeosciences, 18, 509–534, https://doi.org/10.5194/bg-18-509-2021, 2021.
Ford, D. and Barciela, R.: Global marine biogeochemical reanalyses assimilating two different sets of merged ocean colour products, Remote Sens. Environ., 203, 40–54, https://doi.org/10.1016/j.rse.2017.03.040, 2017.
Ford, D. A.: Assessing the role and consistency of satellite observation products in global physical–biogeochemical ocean reanalysis, Ocean Sci., 16, 875–893, https://doi.org/10.5194/os-16-875-2020, 2020.
Ford, D. A., Edwards, K. P., Lea, D., Barciela, R. M., Martin, M. J., and Demaria, J.: Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model, Ocean Sci., 8, 751–771, https://doi.org/10.5194/os-8-751-2012, 2012.
Ford, D. A., Grossberg, S., Rinaldi, G., Menon, P. P., Palmer, M. R., Skákala, J., Smyth, T., Williams, C. A. J., Lorenzo Lopez, A., and Ciavatta, S.: A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts, Frontiers in Marine Science, 9, 1067174, https://doi.org/10.3389/fmars.2022.1067174, 2022.
Fowler, A. M. and Lawless, A. S.: Coupled atmosphere-ocean data assimilation in the presence of model error, Mon. Weather Rev., 144, 4007–4030, 2016.
Fowler, A. M., Simonin, D., and Waller, J.: Measuring theoretical and actual observation influence in the Met Office UKV: application to Doppler radial winds, Geophys. Res. Lett., 47, e2020GL091110, https://doi.org/10.1029/2020GL091110, 2020.
Fowler, A. M., Skákala, J., and Ford, D.: Validating and improving the uncertainty assumptions for the assimilation of ocean-colour-derived chlorophyll a into a marine biogeochemistry model of the Northwest European Shelf Seas, Q. J. Roy. Meteor. Soc., 149, 300–324, https://doi.org/10.1002/qj.4408, 2023.
Francis, D. J., Fowler, A. M., Lawless, A. S., Eyre, J., and Migliorini, S.: The effective use of anchor observations in variational bias correction in the presence of model bias, Q. J. Roy. Meteor. Soc., 149, 1789–1809, https://doi.org/10.1002/qj.4482, 2023.
Friedrichs, M. A., Dusenberry, J. A., Anderson, L. A., Armstrong, R. A., Chai, F., Christian, J. R., Doney, S. C., Dunne, J., Fujii, M., Hood, R., and McGillicuddy Jr., D. J.: Assessment of skill and portability in regional marine biogeochemical models: Role of multiple planktonic groups, J. Geophys. Res.-Oceans, 112, C08001, https://doi.org/10.1029/2006JC003852, 2007.
Fujii, Y., Rémy, E., Zuo, H., Oke, P., Halliwell, G., Gasparin, F., Benkiran, M., Loose, N., Cummings, J., Xie, J., and Xue Y.: Observing system evaluation based on ocean data assimilation and prediction systems: On-going challenges and a future vision for designing and supporting ocean observational networks, Frontiers in Marine Science, 6, 417, https://doi.org/10.3389/fmars.2019.00417, 2019.
Gasparin, F., Guinehut, S., Mao, C., Mirouze, I., Rémy, E., King, R. R., Hamon, M., Reid, R., Storto, A., Le Traon, P.-Y., Martin, M. J., and Masina, S.: Requirements for an Integrated in situ Atlantic Ocean Observing System From Coordinated Observing System Simulation Experiments, Frontiers in Marine Science, 6, 83, https://doi.org/10.3389/fmars.2019.00083, 2019.
Gasparin, F., Cravatte, S., Greiner, E., Perruche, C., Hamon, M., Van Gennip, S., and Lellouche, J. M.: Excessive productivity and heat content in tropical Pacific analyses: disentangling the effects of in situ and altimetry assimilation, Ocean Model., 160, 101768, https://doi.org/10.1016/j.ocemod.2021.101768, 2021.
Geer, A. J.: Learning earth system models from observations: machine learning or data assimilation?, Philos. T. Roy. Soc. A, 379, 20200089, https://doi.org/10.1098/rsta.2020.0089, 2021.
Goodliff, M., Bruening, T., Schwichtenberg, F., Li, X., Lindenthal, A., Lorkowski, I., and Nerger, L.: Temperature assimilation into a coastal ocean-biogeochemical model: assessment of weakly and strongly coupled data assimilation, Ocean Dynam., 69, 1217–1237, 2019.
Gorman, E. T., Kubalak, D. A., Patel, D., Mott, D. B., Meister, G., and Werdell, P. J.: The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission: an emerging era of global, hyperspectral Earth system remote sensing, InSensors, Systems, and Next-Generation Satellites XXIII 2019 Oct 10, vol. 11151, SPIE, 78–84, https://doi.org/10.1117/12.2537146m 2019.
Groom, S., Sathyendranath, S., Ban, Y., Bernard, S., Brewin, R., Brotas, V., Brockmann, C., Chauhan, P., Choi, J. K., Chuprin, A., Ciavatta, S., Cipollini, P., Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T., Kampel, M., Krasemann, H., Lavender, S., Pardo-Martinez, S., Melin, F., Platt, T., Santoleri, R., Skakala, J., Schaeffer, B., Smith, M., Steinmetz, F., Valente, A., and Wang, M.: Satellite ocean colour: Current status and future perspective, Frontiers in Marine Science, 6, 485, https://doi.org/10.3389/fmars.2019.00485, 2019.
Guiavarc'h, C., Roberts-Jones, J., Harris, C., Lea, D. J., Ryan, A., and Ascione, I.: Assessment of ocean analysis and forecast from an atmosphere–ocean coupled data assimilation operational system, Ocean Sci., 15, 1307–1326, https://doi.org/10.5194/os-15-1307-2019, 2019.
Guillet, O., Weaver, A. T., Vasseur, X., Michel, Y., Gratton, S., and Gurol, S.: Modelling spatially correlated observation errors in variational data assimilation using a diffusion operator on an unstructured mesh, Q. J. Roy. Meteor. Soc., 145, 1947–1967, https://doi.org/10.1002/qj.3537, 2019.
Hemmings, J. C., Srokosz, M. A., Challenor, P., and Fasham, M. J.: Assimilating satellite ocean-colour observations into oceanic ecosystem models, Philos. T. Roy. Soc. A, 361, 33–39, 2003.
Hemmings, J. C., Srokosz, M. A., Challenor, P., and Fasham M. J.: Split-domain calibration of an ecosystem model using satellite ocean colour data, J. Marine Syst., 50, 141–179, 2004.
Hemmings, J. C., Barciela, R. M., and Bell, M. J.: Ocean color data assimilation with material conservation for improving model estimates of air-sea CO2 flux, J. Mar. Res., 66, 87–126, 2008.
Hemmings, J. C. P., Challenor, P. G., and Yool, A.: Mechanistic site-based emulation of a global ocean biogeochemical model (MEDUSA 1.0) for parametric analysis and calibration: an application of the Marine Model Optimization Testbed (MarMOT 1.1), Geosci. Model Dev., 8, 697–731, https://doi.org/10.5194/gmd-8-697-2015, 2015.
Higgs, I., Bannister, R., Skákala, J., Carrassi, A., and Ciavatta, S.: Hybrid machine learning data assimilation for marine biogeochemistry, arXiv [preprint], https://doi.org/10.48550/arXiv.2504.05218, 2025.
Holland, M. M., Atkinson, A., Best, M., Bresnan, E., Devlin, M., Goberville, E., Hélaouët, P., Machairopoulou, M., Faith, M., Thompson, M. S., and McQuatters-Gollop, A.: Predictors of long-term variability in NE Atlantic plankton communities, Sci. Total Environ., 952, 175793, https://doi.org/10.1016/j.scitotenv.2024.175793, 2024.
Hu, C.-C. and van Leeuwen, P. J.: A particle flow filter for high-dimensional system applications, Q. J. Roy. Meteor. Soc., 147, 2352–2374, https://doi.org/10.1002/qj.4028, 2021.
Huang, L., Gianinazzi, L., Yu, Y., Dueben, P. D., and Hoefler, T.: DiffDA: a diffusion model for weather-scale data assimilation, arXiv [preprint], https://doi.org/10.48550/arXiv.2401.05932, 2024.
Izett, J. G., Mattern, J. P., Moore, A. M., and Edwards, C. A.: Evaluating Alternate Methods of 4D-Var Data Assimilation in a Coupled Hydrodynamic – Four-Component Biogeochemical Model of the California Current System, Ocean Model., 185, 102253, https://doi.org/10.1016/j.ocemod.2023.102253, 2023.
Jackson, L. C., Dubois, C., Forget, G., Haines, K., Harrison, M., Iovino, D., Köhl, A., Mignac, D., Masina, S., Peterson, K. A., Piecuch, C. G., Roberts, C. D., Robson, J., Storto, A., Toyoda, T., Valdivieso, M., Wilson, C., Wang, Y., and Zuo, H.: The mean state and variability of the North Atlantic circulation: A perspective from ocean reanalyses, J. Geophys. Res.-Oceans, 124, 9141–9170, https://doi.org/10.1029/2019JC015210, 2019.
Johnson, K. and Claustre, H.: Bringing biogeochemistry into the argo age, Eos T. Am. Geophys. Un., https://doi.org/10.1029/2016EO062427, 2016.
Kay, S., McEwan, R., and Ford, D.: North West European Shelf Production Centre, NWSHELF_MULTIYEAR_-BIO_004_011, CMEMS360, Report, 3, 21, https://data.marine.copernicus.eu/product/NWSHELF_MULTIYEAR_BGC_004_011/description (last access: 18 July 2025), 2016.
King, R. R. and Martin, M. J.: Assimilating realistically simulated wide-swath altimeter observations in a high-resolution shelf-seas forecasting system, Ocean Sci., 17, 1791–1813, https://doi.org/10.5194/os-17-1791-2021, 2021.
King, R. R., Lea, D. J., Martin, M. J., Mirouze, I., and Heming, J.: The impact of Argo observations in a global weakly-coupled ocean-atmosphere data assimilation and short-term prediction system, Q. J. Roy. Meteor. Soc., 146, 401–414, https://doi.org/10.1002/qj.3682, 2019.
King, R. R., Martin, M. J., Gaultier, L., Waters, J., Ubelmann, C., and Donlon, C.: Assessing the impact of future altimeter constellations in the Met Office global ocean forecasting system, Ocean Sci., 20, 1657–1676, https://doi.org/10.5194/os-20-1657-2024, 2024.
Kong, C. E., Sathyendranath, S., Jackson, T., Stramski, D., Brewin, R. J., Kulk, G., Jönsson, B. F., Loisel, H., Galí, M., and Le, C.: Comparison of ocean-colour algorithms for particulate organic carbon in global ocean, Frontiers in Marine Science, 11, 1309050, https://doi.org/10.3389/fmars.2024.1309050, 2024.
Kulk, G., Platt, T., Dingle, J., Jackson, T., Jönsson, B. F., Bouman, H. A., Babin, M., Brewin, R. J., Doblin, M., Estrada, M., Figueiras, F. G., Doblin, M., Estrada, M., Figueiras, F. G., Furuya, K., González-Benítez, N., Gudfinnsson, G. H., Gudmundsson, K., Huang, B., Isada, T., Kovač, Z., Lutz, V. A., Marañón, E., Raman, M., Richardson, K., Rozema, P. D., van de Poll, W., Segura, V., Tilstone, G. H., Uitz, G., van Dongen-Vogels, V., Yoshikawa, T., and Sathyendranath, S.: Primary production, an index of climate change in the ocean: satellite-based estimates over two decades, Remote Sens.-Basel, 12, 826, https://doi.org/10.3390/rs12050826, 2020.
Laine, M., Kulk, G., Jönsson, B. F., and Sathyendranath, S.: A machine learning model-based satellite data record of dissolved organic carbon concentration in surface waters of the global open ocean, Frontiers in Marine Science, 11, 1305050, https://doi.org/10.3389/fmars.2024.1305050, 2024.
Land, P. E., Shutler, J. D., Findlay, H. S., Girard-Ardhuin, F., Sabia, R., Reul, N., Piolle, J. F., Chapron, B., Quilfen, Y., Salisbury, J., and Vandemark, D.: Salinity from space unlocks satellite-based assessment of ocean acidification, Environ. Sci. Technol., 49, 1987–1994, https://doi.org/10.1021/es504849s, 2015.
Lary, D. J., Zewdie, G. K., Liu, X., Wu, D., Levetin, E., Allee, R. J., Malakar, N., Walker, A., Mussa, H., Mannino, A., and Aurin, D.: Machine learning applications for earth observation, 165. Observation Open Science and Innovation, ISSI Scientific Report Series, vol. 15, Springer, Cham, https://doi.org/10.1007/978-3-319-65633-5_8, 2018.
Lea, D. J. and Martin, M. J.: Roadmap for the use of JEDI for ocean data assimilation at the Met Office, Internal deliverable report, Met Office, Exeter, UK, 2023.
Lea, D. J., Drecourt, J.-P., Haines, K., and Martin, M. J.: Ocean altimeter assimilation with observational- and model-bias correction, Q. J. Roy. Meteor. Soc., 134, 1761–1774, 2008.
Lea, D. J., Martin, M. J., and Oke, P. R.: Demonstrating the complementarity of observations in an operational ocean forecasting system, Q. J. Roy. Meteor. Soc., 140, 2037–2049, https://doi.org/10.1002/qj.2281, 2014.
Lea, D. J., Mirouze, I., Martin, M. J., King, R. R., Hines, A., Walters, D., and Thurlow, M.: Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model, Mon. Weather Rev., 143, 4678–4694, https://doi.org/10.1175/MWR-D-15-0174.1, 2015.
Lea, D. J., While, J., Martin, M. J., Weaver, A., Storto, A., and Chrust, M.: A new global ocean ensemble system at the Met Office: Assessing the impact of hybrid data assimilation and inflation settings, Q. J. Roy. Meteor. Soc., 148, 1996–2030, https://doi.org/10.1002/qj.4292, 2022.
Lea, D. J., Martin, M. J., Price, M., Roberts-Jones, J., Tennant, W., and Harris, C.: Assessing the impact of including a global ocean ensemble system in the Met Office coupled Numerical Weather Prediction system, Forecasting Research Technical Report 657, Met Office, Exeter, UK, 2023.
Le Traon, P. Y., Dibarboure, G., Jacobs, G., Martin, M., Remy, E., and Schiller, A.: Use of satellite altimetry for operational oceanography, in: Satellite Altimetry Over Oceans and Land Surfaces, edited by: Stammer, D. and Cazenave, A., CRC Press, Boca Raton, https://doi.org/10.1201/9781315151779, 2018.
Leung, T. Y., Lawless, A. S., Nichols, N. K., Lea, D. J., and Martin, M. J.: The impact of hybrid oceanic data assimilation in a coupled model: A case study of a tropical cyclone, Q. J. Roy. Meteor. Soc., 148, 2410–2430, https://doi.org/10.1002/qj.4309, 2022.
Lewis, H. W., Castillo Sanchez, J. M., Arnold, A., Fallmann, J., Saulter, A., Graham, J., Bush, M., Siddorn, J., Palmer, T., Lock, A., Edwards, J., Bricheno, L., Martínez-de la Torre, A., and Clark, J.: The UKC3 regional coupled environmental prediction system, Geosci. Model Dev., 12, 2357–2400, https://doi.org/10.5194/gmd-12-2357-2019, 2019.
Lorenc, A. C., Bell, R. S., and MacPherson, B.: The Meteorological Office analysis correction data assimilation scheme, Q. J. Roy. Meteor. Soc., 117, 59–89, 1991.
Loveday, B. R., Smyth, T., Akpinar, A., Hull, T., Inall, M. E., Kaiser, J., Queste, B. Y., Tobermann, M., Williams, C. A. J., and Palmer, M. R.: Application of a new net primary production methodology: a daily to annual-scale data set for the North Sea, derived from autonomous underwater gliders and satellite Earth observation, Earth Syst. Sci. Data, 14, 3997–4016, https://doi.org/10.5194/essd-14-3997-2022, 2022.
Lyons, T.: Rough paths, signatures and the modelling of functions on streams, arXiv [preprint], https://doi.org/10.48550/arXiv.1405.4537, 2014.
Mao, C., King, R., Reid, R. A., Martin, M. J., and Good, S.: Assessing the Potential Impact of An Expanded Argo Array in An Operational Ocean Analysis System, Frontiers in Marine Science, 7, 905, https://doi.org/10.3389/fmars.2020.588267, 2020.
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., and Xavier, P.: Description of GloSea5: the Met Office high resolution seasonal forecast system, Q. J. Roy. Meteor. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2015.
Mansfield, T., Wihsgott, J., Skakala, J., Menon, P. Gardner, E., Kay, S., and Palmer, M.: Lessons learned in establishing an environmental digital twin, OCEANS25, submitted, 2025.
Martin, M. J., Hines, A., and Bell, M. J.: Data assimilation in the FOAM operational short-range ocean forecasting system: a description of the scheme and its impact, Q. J. Roy. Meteor. Soc., 133, 981–995, 2007.
Martin, M. J., King, R. R., While, J., and Aguiar, A. B.: Assimilating satellite sea-surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system, Q. J. Roy. Meteor. Soc., 145, 705–726, https://doi.org/10.1002/qj.3461, 2019.
Martin, M. J., Remy, E., Tranchant, B., King, R. R., Greiner, E., and Donlon, C.: Observation impact statement on satellite sea surface salinity data from two operational global ocean forecasting systems, J. Oper. Oceanogr., 15, 87–103, https://doi.org/10.1080/1755876X.2020.1771815, 2020.
Martin, M. J., Hoteit, I., Bertino, L., and Moore, A. M.: Data assimilation schemes for ocean forecasting: state of the art, in: Ocean prediction: present status and state of the art (OPSR), edited by: Álvarez Fanjul, E., Ciliberti, S. A., Pearlman, J., Wilmer-Becker, K., and Behera, S., Copernicus Publications, State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025.
McEwan, R., Kay, S., and Ford, D.: Quality Information Document for the CMEMS North West European Shelf Biogeochemical Analysis and Forecast, CMEMS-NWS-QUID-004-002 report, https://doi.org/10.5281/zenodo.4746437, 2021.
Melinc, B. and Zaplotnik, Ž.: 3D-Var data assimilation using a variational autoencoder, Q. J. Roy. Meteor. Soc., 150, 2273–2295, 2023.
Mignac, D., Martin, M., Fiedler, E., Blockley, E., and Fournier, N.: Improving the Met Office's Forecast Ocean Assimilation Model (FOAM) with the assimilation of satellite-derived sea-ice thickness data from CryoSat-2 and SMOS in the Arctic, Q. J. Roy. Meteor. Soc., 148, 1144–1167, https://doi.org/10.1002/qj.4252, 2022.
Mogensen, K. S., Balmaseda, M. A., Weaver, A., Martin, M. J., and Vidard, A.: NEMOVAR: A variational data assimilation system for the NEMO ocean model, ECMWF Newsletter, https://doi.org/10.1002/qj.4708, 2009.
Moore, A. M., Martin, M. J., Akella, S., Arango, H. G., Balmaseda, M., Bertino, L., Ciavatta, S., Cornuelle, B., Cummings, J., Frolov, S., and Lermusiaux, P.: Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: A more complete picture of the state of the ocean, Frontiers in Marine Science, 6, 90, https://doi.org/10.21957/3yj3mh16iq, 2019.
Morrow, R., Fu, L.-L., Ardhuin, F., Benkiran, M., Chapron, B., Cosme, E., d'Ovidio, F., Farrar, J. T., Gille, S. T., Lapeyre, G., Le Traon, P.-Y., Pascual, A., Ponte, A., Qiu, B., Rascle, N., Ubelmann, C., Wang, J., and Zaron, E. D.: Global Observations of FineScale Ocean Surface Topography With the Surface Water and Ocean Topography (SWOT) Mission, Frontiers in Marine Science, 6, p. 232, https://doi.org/10.3389/fmars.2019.00232, 2019.
Nerger, L. and Hiller, W.: Software for ensemble-based data assimilation systems – Implementation strategies and scalability, Comput. Geosci., 55, 110–118, https://doi.org/10.1016/j.cageo.2012.03.026, 2013.
Nerger, L., Brasseur, P., Popov, M., Skakala, J., Teruzzi, A., and Cossarini, G.: Recommendations for weakly coupled physical- biogeochemical data assimilation. Deliverable report 4.1 of project H2020 SEAMLESS (grant 101004032), Zenodo, https://doi.org/10.5281/zenodo.10947688, 2024.
O'Carroll, A. G., Armstrong, E. M., Beggs, H. M., Bouali, M., Casey, K. S., Corlett, G. K., Dash, P., Donlon, C. J., Gentemann, C. L., Høyer, J. L., and Ignatov, A.: Observational needs of sea surface temperature, Frontiers in Marine Science, 20, 420, https://doi.org/10.3389/fmars.2019.00420, 2019.
Palmer, J. R. and Totterdell, I. J.: Production and export in a global ocean ecosystem model, Deep-Sea Res. Pt. I, 48, 1169–1198, 2001.
Park, J. Y., Stock, C. A., Yang, X., Dunne, J. P., Rosati, A., John, J., and Zhang, S.: Modeling global ocean biogeochemistry with physical data assimilation: a pragmatic solution to the equatorial instability, J. Adv. Model. Earth Sy., 10, 891–906, 2018.
Partridge, D., Banerjee, D., Ford, D., Wang, K., Skakala, J., Wihsgott, J., Menon, P., Kay, S., Clewley, D., Rochner, A., Sullivan, E., and Palmer, M.: A Digital Twin Ocean: Can we improve Coastal Ocean Forecasts using targeted Marine Autonomy?, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3346, 2025.
Polton, J., Harle, J., Holt, J., Katavouta, A., Partridge, D., Jardine, J., Wakelin, S., Rulent, J., Wise, A., Hutchinson, K., Byrne, D., Bruciaferri, D., O'Dea, E., De Dominicis, M., Mathiot, P., Coward, A., Yool, A., Palmiéri, J., Lessin, G., Mayorga-Adame, C. G., Le Guennec, V., Arnold, A., and Rousset, C.: Reproducible and relocatable regional ocean modelling: fundamentals and practices, Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, 2023.
Ponte, R. M., Carson, M., Cirano, M., Domingues, C. M., Jevrejeva, S., Marcos, M., Mitchum, G., Van De Wal, R. S. W., Woodworth, P. L., Ablain, M., and Ardhuin, F.: Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level, Frontiers in Marine Science, 6, 437, https://doi.org/10.3389/fmars.2019.00437, 2019.
Raghukumar, K., Edwards, C. A., Goebel, N. L., Broquet, G., Veneziani, M., Moore, A. M., and Zehr, J. P.: Impact of assimilating physical oceanographic data on modeled ecosystem dynamics in the California Current System, Prog. Oceanogr., 138, 546–558, https://doi.org/10.1016/j.pocean.2015.01.004, 2015.
Roemmich, D., Alford, M. H., Claustre, H., Johnson, K., King, B., Moum, J., Oke, P., Owens, W. B., Pouliquen, S., Purkey, S., and Scanderbeg, M.: On the future of Argo: A global, full-depth, multi-disciplinary array, Frontiers in Marine Science, 6, 439, https://doi.org/10.3389/fmars.2019.00439, 2019.
Röhrs, J., Sutherland, G., Jeans, G., Bedington, M., Sperrevik, A. K., Dagestad, K. F., Gusdal, Y., Mauritzen, C., Dale, A., and LaCasce, J. H.: Surface currents in operational oceanography: Key applications, mechanisms, and methods, J. Oper. Oceanogr., 16, 60–88, 2023.
Roy, S., Broomhead, D. S., Platt, T., Sathyendranath, S., and Ciavatta, S.: Sequential variations of phytoplankton growth and mortality in an NPZ model: A remote-sensing-based assessment, J. Marine Syst., 92, 16–29, 2012.
Rozet, F. and Louppe, G.: Score-based data assimilation for a two-layer quasi-geostrophic model, arXiv [preprint], https://doi.org/10.48550/arXiv.2310.01853, 2023.
Saulter, A. N., Bunney, C., King, R. R., and Waters, J.: An Application of NEMOVAR for Regional Wave Model Data Assimilation, Frontiers in Marine Science, 7, 579834, https://doi.org/10.3389/fmars.2020.579834, 2020.
Sauzède, R., Bittig, H. C., Claustre, H., Pasqueron de Fommervault, O., Gattuso, J. P., Legendre, L., and Johnson, K. S.: Estimates of water-column nutrient concentrations and carbonate system parameters in the global ocean: A novel approach based on neural networks, Frontiers in Marine Science, 4, 128, https://doi.org/10.3389/fmars.2017.00128, 2017.
Schepers, D., de Boisséson, E. R., Eresmaa, R. E., Lupu, C. R., and de Rosnay, P. A.: CERA-SAT: A coupled satellite-era reanalysis, ECMWF Newsletter, 155, 32–37, 2018.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., and Woodward, S.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, 2019.
Shapiro, G. I. and Gonzalez-Ondina, J. M.: An efficient method for nested high-resolution ocean modelling incorporating a data assimilation technique, Journal of Marine Science and Engineering, 10, 432, https://doi.org/10.3390/jmse10030432, 2022.
Shapiro, G. I. and Salim, M.: How efficient is model-to-model data assimilation at mitigating atmospheric forcing errors in a regional ocean model?, Journal of Marine Science and Engineering, 11, 935, https://doi.org/10.3390/jmse11050935, 2023.
Shutler, J. D., Gruber, N., Findlay, H. S., Land, P. E., Gregor, L., Holding, T., Sims, R. P., Green, H., Piolle, J. F., Chapron, B., and Sathyendranath, S.: The increasing importance of satellite observations to assess the ocean carbon sink and ocean acidification, Earth-Sci. Rev., 250, 104682, https://doi.org/10.1016/j.earscirev.2024.104682, 2024.
Skákala, J., Ford, D., Brewin, R. J., McEwan, R., Kay, S., Taylor, B., de Mora, L., and Ciavatta, S.: The assimilation of phytoplankton functional types for operational forecasting in the northwest European shelf, J. Geophys. Res.-Oceans, 123, 5230–5247, 2018.
Skákala, J., Bruggeman, J., Brewin, R. J., Ford, D. A., and Ciavatta, S.: Improved representation of underwater light field and its impact on ecosystem dynamics: A study in the North Sea, J. Geophys. Res.-Oceans, 125, e2020JC016122, https://doi.org/10.1029/2020JC016122, 2020.
Skákala, J., Ford, D., Bruggeman, J., Hull, T., Kaiser, J., King, R. R., Loveday, B., Palmer, M. R., Smyth, T., Williams, C. A., and Ciavatta, S.: Towards a multi-platform assimilative system for North Sea biogeochemistry, J. Geophys. Res.-Oceans, 126, e2020JC016649, https://doi.org/10.1029/2020JC016649, 2021.
Skákala, J., Bruggeman, J., Ford, D., Wakelin, S., Akpınar, A., Hull, T., Kaiser, J., Loveday, B. R., O'Dea, E., Williams, C. A., and Ciavatta, S.: The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas, Ocean Model., 172, 101976, https://doi.org/10.1016/j.ocemod.2022.101976, 2022.
Skákala, J., Awty-Carroll, K., Menon, P. P., Wang, K., and Lessin, G.: Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia, Frontiers in Marine Science, 10, 1058837, https://doi.org/10.3389/fmars.2023.1058837, 2023a.
Skákala, J., Wakamatsu, T., Bertino, L., Teruzzi, A., Lazzari, P., Alvarez, E., Cossarini, G., Spada, S., Nerger, L., Vliegen, S., Brankart, J. M., and Brasseur, P.: SEAMLESS target indicator quality in CMEMS MFCs, Deliverable report of H2020 project SEAMLESS (grant 101004032), https://doi.org/10.5281/zenodo.10522305, 2023b.
Skákala, J., Ford, D., Fowler, A., Lea, D., Martin, M. J., and Ciavatta, S.: How uncertain and observable are marine ecosystem indicators in shelf seas?, Prog. Oceanogr., 224, 103249, https://doi.org/10.1016/j.pocean.2024.103249, 2024.
Smith, P. J., Fowler, A. M., and Lawless, A. S.: Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model, Tellus A, 67, 27025, https://doi.org/10.3402/tellusa.v67.27025, 2015.
Smith, P. J., Lawless, A. S., and Nichols, N. K.: Estimating forecast error covariances for strongly coupled atmosphere-ocean 4D-Var data assimilation, Mon. Weather Rev., 145, 4011–4035, https://doi.org/10.1175/MWR-D-16-0284.1, 2017.
Smith, P. J., Lawless, A. S., and Nichols, N. K.: Treating sample covariances for use in strongly coupled atmosphere-ocean data assimilation, Geophys. Res. Lett., 45, 445–454, https://doi.org/10.1002/2017GL075534, 2018.
Smith, P. J., Lawless, A. S., and Nichols, N. K.: The role of cross-domain error correlations in strongly coupled 4D-Var atmosphere-ocean data assimilation, Q. J. Roy. Meteor. Soc., 146, 2450–2465, https://doi.org/10.1002/qj.3802, 2020.
Sonnewald, M., Lguensat, R., Jones, D. C., Dueben, P. D., Brajard, J., and Balaji, V.: Bridging observations, theory and numerical simulation of the ocean using machine learning, Environ. Res. Lett., 16, 073008, https://doi.org/10.1088/1748-9326/ac0eb0, 2021.
Stephens, R. V., O'Neill, C. K., Siddorn, J. R., Cox, A. T., Harris, E., and Orelup, E.: The Mid Atlantic Current Hindcast MACH, Paper presented at the Offshore Technology Conference Asia, Kuala Lumpur, Malaysia, https://doi.org/10.4043/28378-MS, 2018.
Stockdale, T. N., Busalacchi, A. J., Harrison, D. E., and Seager, R.: Ocean modeling for ENSO, J. Geophys. Res.-Oceans, 103, 14325–14355, 1998.
Storkey, D., Blockley, E. W., Furner, R., Giuavarc'h, C., Lea, D., Martin, M. J., Barciela, R. M., Hines, A., Hyder, P., and Siddorn, J. R.: Forecasting the ocean state using NEMO: The new FOAM system, J. Oper. Oceanogr., 3, 3–15, 2010.
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae, J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, 2018.
Sundararaman, H. K. K. and Shanmugam, P.: Estimates of the global ocean surface dissolved oxygen and macronutrients from satellite data, Remote Sens. Environ., 311, 114243, https://doi.org/10.1016/j.rse.2024.114243, 2024.
Tabeart, J. M., Dance, S. L., Lawless, A. S., Nichols, N. K., and Waller, J. A.: Improving the condition number of estimated covariance matrices, Tellus A, 72, 1–19, https://doi.org/10.1080/16000870.2019.1696646, 2020.
Telszewski, M., Palacz, A., and Fischer, A.: Biogeochemical in situ observations – motivation, status, and new frontiers, New Front. Operational Oceanogr., 131–160, https://doi.org/10.17125/gov2018.ch06 2018.
Testor, P., De Young, B., Rudnick, D. L., Glenn, S., Hayes, D., Lee, C. M., Pattiaratchi, C., Hill, K., Heslop, E., Turpin, V., and Alenius, P.: OceanGliders: a component of the integrated GOOS, Frontiers in Marine Science, 6, 422, https://doi.org/10.3389/fmars.2019.00422, 2019.
Tinker, J. and Hermanson, L.: Towards Winter Seasonal Predictability of the North West European Shelf Seas, Frontiers in Marine Science, 8, 698997, https://doi.org/10.3389/fmars.2021.698997, 2021.
Tonani, M., Sykes, P., King, R. R., McConnell, N., Péquignet, A.-C., O'Dea, E., Graham, J. A., Polton, J., and Siddorn, J.: The impact of a new high-resolution ocean model on the Met Office North-West European Shelf forecasting system, Ocean Sci., 15, 1133–1158, https://doi.org/10.5194/os-15-1133-2019, 2019.
Torres, R., Allen, J. I., and Figueiras, F. G.: Sequential data assimilation in an upwelling influenced estuary, J. Marine Syst., 60, 317–329, 2006.
Torres, R., Artioli, Y., Kitidis, V., Ciavatta, S., Ruiz-Villarreal, M., Shutler, J., Polimene, L., Martinez, V., Widdicombe, C., Woodward, E. M., and Smyth, T.: Sensitivity of modeled CO2 air–sea flux in a coastal environment to surface temperature gradients, surfactants, and satellite data assimilation, Remote Sens.-Basel, 12, 2038, https://doi.org/10.3390/rs12122038, 2020.
Vinogradova, N., Lee, T., Boutin, J., Drushka, K., Fournier, S., Sabia, R., Stammer, D., Bayler, E., Reul, N., Gordon, A., and Melnichenko, O.: Satellite salinity observing system: Recent discoveries and the way forward, Frontiers in Marine Science, 6, 243, https://doi.org/10.3389/fmars.2019.00243, 2019.
Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While, J.: Implementing a variational data assimilation system in an operational
Waters, J., Bell, M. J., Martin, M. J., and Lea, D. J.: Reducing ocean model imbalances in the equatorial region caused by data assimilation, Q. J. Roy. Meteor. Soc., 143, 195–208, https://doi.org/10.1002/qj.2912, 2017.
Waters, J., Martin, M. J., Bell, M. J., King, R. R., Gaultier, L., Ubelmann, C., Donlon, C., and Van Gennip, S.: Assessing the potential impact of assimilating total surface current velocities in the Met Office's global ocean forecasting system, Frontiers in Marine Science, 11, 1383522, https://doi.org/10.3389/fmars.2024.1383522, 2024a.
Waters, J., Martin, M. J., Mirouze, I., Remy, E., King, R., Gaultier, L., Ubelmann, C., Van Gennip, S., and Donlon, C.: The impact of simulated Total Surface Current Velocity observations on operational ocean forecasting and requirements for future satellite missions, Frontiers in Marine Science, 11, 1408495, https://doi.org/10.3389/fmars.2024.1408495, 2024b.
Weaver, A. T., Deltel, C., Machu, E., Ricci, S., and Daget, N.: A multivariate balance operator for variational ocean data assimilation, Q. J. Roy. Meteor. Soc., 131, 3605–3625, 2005.
Weaver, A. T., Tshimanga, J., and Piacentini, A.: Correlation operators based on an implicitly formulated diffusion equation solved with the Chebyshev iteration, Q. J. Roy. Meteor. Soc., 142, 455–471, https://doi.org/10.1002/qj.2664, 2016.
Weaver, A. T., Chrust, M., Ménétrier, B., Piacentini, A., Tshimanga, J., Yang, Y., Gürol, S., and Zuo, H.: Using ensemble-estimated background error variances and correlation scales in the NEMOVAR system, in: Report TR/PA/18/15 2018 Jan 10 (p. 39), CERFACS, Toulouse, France, 2018.
While, J. and Martin, M. J.: Variational bias correction of satellite sea-surface temperature data incorporating observations of the bias, Q. J. Roy. Meteor. Soc., 145, 2733–2754, https://doi.org/10.1002/qj.3590, 2019.
While, J., Totterdell, I., and Martin, M. J.: Assimilation of pCO2 data into a global coupled physical-biogeochemical ocean model, J. Geophys. Res., 117, C03037, https://doi.org/10.1029/2010JC006815, 2012.
Williams, N., Byrne, N., Feltham, D., Van Leeuwen, P. J., Bannister, R., Schroeder, D., Ridout, A., and Nerger, L.: The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system, The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, 2023.
Wolff, J. O., Maier-Reimer, E. E., and Legutke, S.: The Hamburg Ocean Primitive Equation Model HOPE, Technical report no. 13, German Climate Computer Center (DKRZ), Hamburg, Germany, 1997.
Wright, A., Lawless, A. S., Nichols, N. K., Lea, D. J., and Martin, M. J.: Assessment of short-range forecast error atmosphere-ocean cross-correlations from the Met Office coupled NWP system, Q. J. Roy. Meteor. Soc., 150, 2783–2797, https://doi.org/10.1002/qj.4735, 2024.
Yool, A., Popova, E. E., and Anderson, T. R.: MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies, Geosci. Model Dev., 6, 1767–1811, https://doi.org/10.5194/gmd-6-1767-2013, 2013.
Yu, L., Fennel, K., Bertino, L., El Gharamti, M., and Thompson, K. R.: Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling, Ocean Model., 126, 13–28, 2018.
Zuo, H., Balmaseda, M. A., and Mogensen, K.: The ECMWF-MyOcean2 eddy-permitting ocean and sea-ice reanalysis ORAP5, Part 1: Implementation, ECMWF Technical Memorandum, 736, 1–44, https://doi.org/10.21957/5awbusgo, 2015.
Zuo, H., Balmaseda, M. A., and Mogensen, K.: The new eddy-permitting ORAP5 ocean reanalysis: description, evaluation and uncertainties in climate signals, Clim. Dynam., 49, 791–811, https://doi.org/10.1007/s00382-015-2675-1, 2017.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Zuo, H., Balmaseda, M. A., de Boisseson, E., Browne, P., Chrust, M., Keeley, S., Mogensen, K., Pelletier, C., and de Rosnay, P.: ECMWF's next ensemble reanalysis system for ocean and sea ice: ORAS6, ECMWF Newsletter, 180, https://doi.org/10.21957/hzd5y821lk, 2024.
Short summary
UK marine data assimilation (MDA) involves a closely collaborating research community. In this paper, we offer both an overview of the state of the art and a vision for the future across all of the main areas of UK MDA, ranging from physics to biogeochemistry to coupled DA. We discuss the current UK MDA stakeholder applications, highlight theoretical developments needed to advance our systems, and reflect upon upcoming opportunities with respect to hardware and observational missions.
UK marine data assimilation (MDA) involves a closely collaborating research community. In this...
Special issue