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
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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
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Robert R. King, Matthew J. Martin, Lucile Gaultier, Jennifer Waters, Clément Ubelmann, and Craig Donlon
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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
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Preprint archived
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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
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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, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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