Articles | Volume 17, issue 14
https://doi.org/10.5194/gmd-17-5619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-5619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
25 Jul 2024
Model description paper |
| 25 Jul 2024
| 25 Jul 2024
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Jorn Bruggeman
CORRESPONDING AUTHOR
Bolding & Bruggeman ApS, 5466 Asperup, Denmark
Karsten Bolding
Bolding & Bruggeman ApS, 5466 Asperup, Denmark
Lars Nerger
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Anna Teruzzi
National Institute of Oceanography and Applied Geophysics – OGS, 34010 Trieste, Italy
Simone Spada
National Institute of Oceanography and Applied Geophysics – OGS, 34010 Trieste, Italy
Jozef Skákala
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
National Centre for Earth Observation, Plymouth, PL1 3DH, UK
Stefano Ciavatta
Mercator Ocean International, 31400 Toulouse, France
Related authors
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.
Qing Li, Jorn Bruggeman, Hans Burchard, Knut Klingbeil, Lars Umlauf, and Karsten Bolding
Geosci. Model Dev., 14, 4261–4282, https://doi.org/10.5194/gmd-14-4261-2021, https://doi.org/10.5194/gmd-14-4261-2021, 2021
Short summary
Short summary
Different ocean vertical mixing schemes are usually developed in different modeling framework, making the comparison across such schemes difficult. Here, we develop a consistent framework for testing, comparing, and applying different ocean mixing schemes by integrating CVMix into GOTM, which also extends the capability of GOTM towards including the effects of ocean surface waves. A suite of test cases and toolsets for developing and evaluating ocean mixing schemes is also described.
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.
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025, https://doi.org/10.5194/os-21-1709-2025, 2025
Short summary
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.
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.
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.
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.
Frauke Bunsen, Judith Hauck, Sinhué Torres-Valdés, and Lars Nerger
Ocean Sci., 21, 437–471, https://doi.org/10.5194/os-21-437-2025, https://doi.org/10.5194/os-21-437-2025, 2025
Short summary
Short summary
Computer models are often used to estimate the ocean's CO2 uptake due to a lack of direct observations. Because such idealized models do not match precisely with the real world, we combine real-world observations of ocean temperature and salinity with a model and study the effect on the modeled air–sea CO2 flux (2010–2020). The corrections of temperature and salinity have their largest effect on regional CO2 fluxes in the Southern Ocean in winter and a small effect on the global CO2 uptake.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
Anna Teruzzi, Ali Aydogdu, Carolina Amadio, Emanuela Clementi, Simone Colella, Valeria Di Biagio, Massimiliano Drudi, Claudia Fanelli, Laura Feudale, Alessandro Grandi, Pietro Miraglio, Andrea Pisano, Jenny Pistoia, Marco Reale, Stefano Salon, Gianluca Volpe, and Gianpiero Cossarini
State Planet, 4-osr8, 15, https://doi.org/10.5194/sp-4-osr8-15-2024, https://doi.org/10.5194/sp-4-osr8-15-2024, 2024
Short summary
Short summary
A noticeable cold spell occurred in Eastern Europe at the beginning of 2022 and was the main driver of intense deep-water formation and the associated transport of nutrients to the surface. Southeast of Crete, the availability of both light and nutrients in the surface layer stimulated an anomalous phytoplankton bloom. In the area, chlorophyll concentration (a proxy for bloom intensity) and primary production were considerably higher than usual, suggesting possible impacts on fishery catches.
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.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Carolina Amadio, Anna Teruzzi, Gloria Pietropolli, Luca Manzoni, Gianluca Coidessa, and Gianpiero Cossarini
Ocean Sci., 20, 689–710, https://doi.org/10.5194/os-20-689-2024, https://doi.org/10.5194/os-20-689-2024, 2024
Short summary
Short summary
Forecasting of marine biogeochemistry can be improved via the assimilation of observations. Floating buoys provide multivariate information about the status of the ocean interior. Information on the ocean interior can be expanded/augmented by machine learning. In this work, we show the enhanced impact of assimilating new in situ variables (oxygen) and reconstructed variables (nitrate) in the operational forecast system (MedBFM) model of the Mediterranean Sea.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
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.
Simone Spada, Anna Teruzzi, Stefano Maset, Stefano Salon, Cosimo Solidoro, and Gianpiero Cossarini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-170, https://doi.org/10.5194/gmd-2023-170, 2023
Revised manuscript under review for GMD
Short summary
Short summary
In geosciences, data assimilation (DA) combines modeled dynamics and observations to reduce simulation uncertainties. Uncertainties can be dynamically and effectively estimated in ensemble DA methods. With respect to current techniques, the novel GHOSH ensemble DA scheme is designed to improve accuracy by reaching a higher approximation order, without increasing computational costs, as demonstrated in idealized Lorenz96 tests and in realistic simulations of the Mediterranean Sea biogeochemistry
Giovanni Coppini, Emanuela Clementi, Gianpiero Cossarini, Stefano Salon, Gerasimos Korres, Michalis Ravdas, Rita Lecci, Jenny Pistoia, Anna Chiara Goglio, Massimiliano Drudi, Alessandro Grandi, Ali Aydogdu, Romain Escudier, Andrea Cipollone, Vladyslav Lyubartsev, Antonio Mariani, Sergio Cretì, Francesco Palermo, Matteo Scuro, Simona Masina, Nadia Pinardi, Antonio Navarra, Damiano Delrosso, Anna Teruzzi, Valeria Di Biagio, Giorgio Bolzon, Laura Feudale, Gianluca Coidessa, Carolina Amadio, Alberto Brosich, Arnau Miró, Eva Alvarez, Paolo Lazzari, Cosimo Solidoro, Charikleia Oikonomou, and Anna Zacharioudaki
Ocean Sci., 19, 1483–1516, https://doi.org/10.5194/os-19-1483-2023, https://doi.org/10.5194/os-19-1483-2023, 2023
Short summary
Short summary
The paper presents the Mediterranean Forecasting System evolution and performance developed in the framework of the Copernicus Marine Service.
Valeria Di Biagio, Riccardo Martellucci, Milena Menna, Anna Teruzzi, Carolina Amadio, Elena Mauri, and Gianpiero Cossarini
State Planet, 1-osr7, 10, https://doi.org/10.5194/sp-1-osr7-10-2023, https://doi.org/10.5194/sp-1-osr7-10-2023, 2023
Short summary
Short summary
Oxygen is essential to all aerobic organisms, and its content in the marine environment is continuously under assessment. By integrating observations with a model, we describe the dissolved oxygen variability in a sensitive Mediterranean area in the period 1999–2021 and ascribe it to multiple acting physical and biological drivers. Moreover, the reduction recognized in 2021, apparently also due to other mechanisms, requires further monitoring in light of its possible impacts.
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
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary
Short summary
Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Alexandre Mignot, Hervé Claustre, Gianpiero Cossarini, Fabrizio D'Ortenzio, Elodie Gutknecht, Julien Lamouroux, Paolo Lazzari, Coralie Perruche, Stefano Salon, Raphaëlle Sauzède, Vincent Taillandier, and Anna Teruzzi
Biogeosciences, 20, 1405–1422, https://doi.org/10.5194/bg-20-1405-2023, https://doi.org/10.5194/bg-20-1405-2023, 2023
Short summary
Short summary
Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and monitor ocean health. Here, we demonstrate the use of the global array of BGC-Argo floats for the assessment of biogeochemical models. We first detail the handling of the BGC-Argo data set for model assessment purposes. We then present 23 assessment metrics to quantify the consistency of BGC model simulations with respect to BGC-Argo data.
Juan Pablo Almeida, Lorenzo Menichetti, Alf Ekblad, Nicholas P. Rosenstock, and Håkan Wallander
Biogeosciences, 20, 1443–1458, https://doi.org/10.5194/bg-20-1443-2023, https://doi.org/10.5194/bg-20-1443-2023, 2023
Short summary
Short summary
In forests, trees allocate a significant amount of carbon belowground to support mycorrhizal symbiosis. In northern forests nitrogen normally regulates this allocation and consequently mycorrhizal fungi growth. In this study we demonstrate that in a conifer forest from Sweden, fungal growth is regulated by phosphorus instead of nitrogen. This is probably due to an increase in nitrogen deposition to soils caused by decades of human pollution that has altered the ecosystem nutrient regime.
Hao-Cheng Yu, Yinglong Joseph Zhang, Lars Nerger, Carsten Lemmen, Jason C. S. Yu, Tzu-Yin Chou, Chi-Hao Chu, and Chuen-Teyr Terng
EGUsphere, https://doi.org/10.5194/egusphere-2022-114, https://doi.org/10.5194/egusphere-2022-114, 2022
Preprint archived
Short summary
Short summary
We develop a new data assimilative approach by combining two parallel frameworks: PDAF and ESMF. This allows maximum flexibility and easy implementation of data assimilation for fully coupled earth system model applications. It is also validated by using a simple benchmark and applied to a realistic case simulation around Taiwan. The real case test shows significant improvement for temperature, velocity and surface elevation before, during and after typhoon events.
Anna Teruzzi, Giorgio Bolzon, Laura Feudale, and Gianpiero Cossarini
Biogeosciences, 18, 6147–6166, https://doi.org/10.5194/bg-18-6147-2021, https://doi.org/10.5194/bg-18-6147-2021, 2021
Short summary
Short summary
During summer, maxima of phytoplankton chlorophyll concentration (DCM) occur in the subsurface of the Mediterranean Sea and can play a relevant role in carbon sequestration into the ocean interior. A numerical model based on in situ and satellite observations provides insights into the range of DCM conditions across the relatively small Mediterranean Sea and shows a western DCM that is 25 % shallower and with a higher phytoplankton chlorophyll concentration than in the eastern Mediterranean.
Qing Li, Jorn Bruggeman, Hans Burchard, Knut Klingbeil, Lars Umlauf, and Karsten Bolding
Geosci. Model Dev., 14, 4261–4282, https://doi.org/10.5194/gmd-14-4261-2021, https://doi.org/10.5194/gmd-14-4261-2021, 2021
Short summary
Short summary
Different ocean vertical mixing schemes are usually developed in different modeling framework, making the comparison across such schemes difficult. Here, we develop a consistent framework for testing, comparing, and applying different ocean mixing schemes by integrating CVMix into GOTM, which also extends the capability of GOTM towards including the effects of ocean surface waves. A suite of test cases and toolsets for developing and evaluating ocean mixing schemes is also described.
Lars Nerger, Qi Tang, and Longjiang Mu
Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, https://doi.org/10.5194/gmd-13-4305-2020, 2020
Short summary
Short summary
Data assimilation combines observations with numerical models to get an improved estimate of the model state. This work discusses the technical aspects of how a coupled model that simulates the ocean and the atmosphere can be augmented by data assimilation functionality provided in generic form by the open-source software PDAF (Parallel Data Assimilation Framework). A very efficient program is obtained that can be executed on high-performance computers.
Cited articles
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.
Andersen, T. K., Bolding, K., Nielsen, A., Bruggeman, J., Jeppesen, E., and Trolle, D.: How morphology shapes the parameter sensitivity of lake ecosystem models, Environ. Model. Softw., 136, 104945, https://doi.org/10.1016/j.envsoft.2020.104945, 2021.
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015.
Bagniewski, W., Fennel, K., Perry, M. J., and D'Asaro, E. A.: Optimizing models of the North Atlantic spring bloom using physical, chemical and bio-optical observations from a Lagrangian float, Biogeosciences, 8, 1291–1307, https://doi.org/10.5194/bg-8-1291-2011, 2011.
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017.
Bertino, L., Evensen, G., and Wackernagel, H.: Sequential Data Assimilation Techniques in Oceanography, Int. Stat. Rev., 71, 223–241, https://doi.org/10.1111/j.1751-5823.2003.tb00194.x, 2003.
Brankart, J.-M., Testut, C.-E., Béal, D., Doron, M., Fontana, C., Meinvielle, M., Brasseur, P., and Verron, J.: Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations, Ocean Sci., 8, 121–142, https://doi.org/10.5194/os-8-121-2012, 2012.
Brasseur, P., Gruber, N., Barciela, R., Brander, K., Doron, M., El Moussaoui, A., Hobday, A., Huret, M., Kremeur, A.-S., Lehodey, P., Matear, R., Moulin, C., Murtugudde, R., Senina, I., and Svendsen, E.: Integrating Biogeochemistry and Ecology Into Ocean Data Assimilation Systems, Oceanography, 22, 206–215, https://doi.org/10.5670/oceanog.2009.80, 2009.
Brewin, R. J. W., Ciavatta, S., Sathyendranath, S., Jackson, T., Tilstone, G., Curran, K., Airs, R. L., Cummings, D., Brotas, V., Organelli, E., Dall'Olmo, G., and Raitsos, D. E.: Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups, Front. Mar. Sci., 4, 104, https://doi.org/10.3389/fmars.2017.00104, 2017.
Brewin, R. J. W., Sathyendranath, S., Platt, T., Bouman, H., Ciavatta, S., Dall'Olmo, G., Dingle, J., Groom, S., Jönsson, B., Kostadinov, T. S., Kulk, G., Laine, M., Martínez-Vicente, V., Psarra, S., Raitsos, D. E., Richardson, K., Rio, M.-H., Rousseaux, C. S., Salisbury, J., Shutler, J. D., and Walker, P.: 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. and Bolding, K.: A general framework for aquatic biogeochemical models, Environ. Model. Softw., 61, 249–265, https://doi.org/10.1016/j.envsoft.2014.04.002, 2014.
Bruggeman, J., Bolding, K., and Nerger, L.: EAT: Ensemble and Assimilation Tool (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.11111437, 2024a.
Bruggeman, J., Teruzzi, A., Spada, S., and Skákala, J.: EAT example applications (v2.3), Zenodo [data set], https://doi.org/10.5281/zenodo.11111361, 2024b.
Brüning, T., Li, X., Schwichtenberg, F., and Lorkowski, I.: An operational, assimilative model system for hydrodynamic and biogeochemical applications for German coastal waters, Hydrographische Nachrichten, 118, 6–15, https://doi.org/10.23784/HN118-01, 2021.
Burchard, H., Bolding, K., and Ruiz Villarreal, M.: GOTM – a General Ocean Turbulence Model. Theory, applications and test cases., Tech. Rep. EUR 18745 EN, European Commission, 1999.
Burchard, H., Bolding, K., Kühn, W., Meister, A., Neumann, T., and Umlauf, L.: Description of a flexible and extendable physical-biogeochemical model system for the water column, J. Marine Syst., 61, 180–211, https://doi.org/10.1016/j.jmarsys.2005.04.011, 2006.
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.
Campbell, J. W.: The lognormal distribution as a model for bio-optical variability in the sea, J. Geophys. Res.-Oceans, 100, 13237–13254, https://doi.org/10.1029/95JC00458, 1995.
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Climate Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018.
Ciavatta, S., Lazzari, P., Alvarez, E., Bruggeman, J., Capet, A., Cossarini, G., Daryabor, F., Nerger, L., Skakala, J., Teruzzi, A., Wakamatsu, T., and Yumruktepe, C.: D3.2 Observability of the target indicators and parameter sensitivity in the 1D CMEMS sites. Deliverable report of project H2020 SEAMLESS (grant 101004032), Zenodo [data set], https://doi.org/10.5281/zenodo.6580236, 2022.
Daewel, U. and Schrum, C.: Simulating long-term dynamics of the coupled North Sea and Baltic Sea ecosystem with ECOSMO II: Model description and validation, J. Marine Syst., 119–120, 30–49, https://doi.org/10.1016/j.jmarsys.2013.03.008, 2013.
Doron, M., Brasseur, P., Brankart, J.-M., Losa, S. N., and Melet, A.: Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical–biogeochemical model, J. Marine Syst., 117–118, 81–95, https://doi.org/10.1016/j.jmarsys.2013.02.007, 2013.
Egbert, G. D. and Erofeeva, S. Y.: Efficient Inverse Modeling of Barotropic Ocean Tides, J. Atmos. Ocean. Tech., 19, 183–204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002.
Eknes, M. and Evensen, G.: An Ensemble Kalman filter with a 1-D marine ecosystem model, J. Marine Syst., 36, 75–100, https://doi.org/10.1016/S0924-7963(02)00134-3, 2002.
E.U. Copernicus Marine Service Information (CMEMS): Atlantic-European North West Shelf-Ocean Biogeochemistry Reanalysis, Marine Data Store (MDS), https://doi.org/10.48670/moi-00058, last access: 19 July 2024.
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.
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.
Fournier, A., Nerger, L., and Aubert, J.: An ensemble Kalman filter for the time-dependent analysis of the geomagnetic field, Geochem. Geophys. Geosyst., 14, 4035–4043, https://doi.org/10.1002/ggge.20252, 2013.
Gharamti, M. E., Samuelsen, A., Bertino, L., Simon, E., Korosov, A., and Daewel, U.: Online tuning of ocean biogeochemical model parameters using ensemble estimation techniques: Application to a one-dimensional model in the North Atlantic, J. Marine Syst., 168, 1–16, https://doi.org/10.1016/j.jmarsys.2016.12.003, 2017a.
Gharamti, M. E., Tjiputra, J., Bethke, I., Samuelsen, A., Skjelvan, I., Bentsen, M., and Bertino, L.: Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites, Ocean Model. (Oxf), 112, 65–89, https://doi.org/10.1016/j.ocemod.2017.02.006, 2017b.
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, https://doi.org/10.1007/s10236-019-01299-7, 2019.
Gordon, H. R. and McCluney, W. R.: Estimation of the Depth of Sunlight Penetration in the Sea for Remote Sensing, Appl. Optics, 14, 413–416, https://doi.org/10.1364/ao.14.000413, 1975.
Gropp, W., Lusk, E., and Skjellum, A.: Using MPI: Portable Parallel Programming with the Message-Passing Interface, MIT Press, Cambridge, MA, 1994.
Harris, R.: The L4 time-series: the first 20 years, J. Plankton Res., 32, 577–583, https://doi.org/10.1093/plankt/fbq021, 2010.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Hoteit, I., Triantafyllou, G., Petihakis, G., and Allen, J. I.: A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model, Ann. Geophys., 21, 389–397, https://doi.org/10.5194/angeo-21-389-2003, 2003.
Kurtz, W., He, G., Kollet, S. J., Maxwell, R. M., Vereecken, H., and Hendricks Franssen, H.-J.: TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model, Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, 2016.
Leibniz Institute for Baltic Sea Research: ERGOM: Ecological ReGional Ocean Model, https://ergom.net/ (last access: 19 July 2024), 2015.
Lenartz, F., Raick, C., Soetaert, K., and Grégoire, M.: Application of an Ensemble Kalman filter to a 1-D coupled hydrodynamic-ecosystem model of the Ligurian Sea, J. Marine Syst., 68, 327–348, https://doi.org/10.1016/j.jmarsys.2006.12.001, 2007.
Li, Q., Bruggeman, J., Burchard, H., Klingbeil, K., Umlauf, L., and Bolding, K.: Integrating CVMix into GOTM (v6.0): a consistent framework for testing, comparing, and applying ocean mixing schemes, Geosci. Model Dev., 14, 4261–4282, https://doi.org/10.5194/gmd-14-4261-2021, 2021.
Mamnun, N., Völker, C., Vrekoussis, M., and Nerger, L.: Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model, Front. Mar Sci., 9, 984236, https://doi.org/10.3389/fmars.2022.984236, 2022.
Mattern, J. P., Dowd, M., and Fennel, K.: Sequential data assimilation applied to a physical–biological model for the Bermuda Atlantic time series station, J. Marine Syst., 79, 144–156, https://doi.org/10.1016/j.jmarsys.2009.08.004, 2010.
Moore, A. M., Martin, M. J., Akella, S., Arango, H. G., Balmaseda, M., Bertino, L., Ciavatta, S., Cornuelle, B., Cummings, J., Frolov, S., Lermusiaux, P., Oddo, P., Oke, P. R., Storto, A., Teruzzi, A., Vidard, A., and Weaver, A. T.: Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean, Front. Mar Sci., 6, 90, https://doi.org/10.3389/fmars.2019.00090, 2019.
Mu, L., Liang, X., Yang, Q., Liu, J., and Zheng, F.: Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017, J. Glaciol., 65, 813–821, https://doi.org/10.1017/jog.2019.55, 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., Hiller, W., and Schröter, J.: The Parallel Data Assimilation Framework: Experiences with Kalman filtering, in: Use of High Performance Computing in Meteorology, in: Use of High Performance Computing in Meteorology, World Scientific, 63–83, https://doi.org/10.1142/9789812701831_0006, 2005.
Nerger, L., Janjić, T., Schröter, J., and Hiller, W.: A Unification of Ensemble Square Root Kalman Filters, Mon. Weather Rev., 140, 2335–2345, https://doi.org/10.1175/MWR-D-11-00102.1, 2012.
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020.
Pardini, F., Corradini, S., Costa, A., Esposti Ongaro, T., Merucci, L., Neri, A., Stelitano, D., and de' Michieli Vitturi, M.: Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption, Atmosphere (Basel), 11, 359, https://doi.org/10.3390/atmos11040359, 2020.
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, https://doi.org/10.1002/2017MS001223, 2018.
Pelc, J. S., Simon, E., Bertino, L., El Serafy, G., and Heemink, A. W.: Application of model reduced 4D-Var to a 1D ecosystem model, Ocean Model. (Oxf), 57–58, 43–58, https://doi.org/10.1016/j.ocemod.2012.09.003, 2012.
Penny, S. G., Akella, S., Alves, O., Bishop, C., Buehner, M., Chevallier, M., Counillon, F., Draper, C., Frolov, S., Fujii, Y., Karspeck, A., Kumar, A., Laloyaux, P., Mahfouf, J.-F., Martin, M., Peña, M., de Rosnay, P., Subramanian, A., Tardif, R., Wang, Y., and Wu, X.: Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges and Recommendations, WWRP 2017-3, World Meteorological Organization, https://library.wmo.int/idurl/4/57666 (last access: 19 July 2024), 2017.
Pingree, R. D. and Griffiths, D. K.: Tidal fronts on the shelf seas around the British Isles, J. Geophys. Res., 83, 4615, https://doi.org/10.1029/JC083iC09p04615, 1978.
Pradhan, H. K., Völker, C., Losa, S. N., Bracher, A., and Nerger, L.: Assimilation of Global Total Chlorophyll OC-CCI Data and Its Impact on Individual Phytoplankton Fields, J. Geophys. Res.-Oceans, 124, 470–490, https://doi.org/10.1029/2018JC014329, 2019.
Pradhan, H. K., Völker, C., Losa, S. N., Bracher, A., and Nerger, L.: Global Assimilation of Ocean-Color Data of Phytoplankton Functional Types: Impact of Different Data Sets, J. Geophys. Res.-Oceans, 125, e2019JC015586, https://doi.org/10.1029/2019JC015586, 2020.
Roemmich, D., Alford, M. H., Claustre, H., Johnson, K., King, B., Moum, J., Oke, P., Owens, W. B., Pouliquen, S., Purkey, S., Scanderbeg, M., Suga, T., Wijffels, S., Zilberman, N., Bakker, D., Baringer, M., Belbeoch, M., Bittig, H. C., Boss, E., Calil, P., Carse, F., Carval, T., Chai, F., Conchubhair, D. Ó., d'Ortenzio, F., Dall'Olmo, G., Desbruyeres, D., Fennel, K., Fer, I., Ferrari, R., Forget, G., Freeland, H., Fujiki, T., Gehlen, M., Greenan, B., Hallberg, R., Hibiya, T., Hosoda, S., Jayne, S., Jochum, M., Johnson, G. C., Kang, K., Kolodziejczyk, N., Körtzinger, A., Traon, P.-Y. Le, Lenn, Y.-D., Maze, G., Mork, K. A., Morris, T., Nagai, T., Nash, J., Garabato, A. N., Olsen, A., Pattabhi, R. R., Prakash, S., Riser, S., Schmechtig, C., Schmid, C., Shroyer, E., Sterl, A., Sutton, P., Talley, L., Tanhua, T., Thierry, V., Thomalla, S., Toole, J., Troisi, A., Trull, T. W., Turton, J., Velez-Belchi, P. J., Walczowski, W., Wang, H., Wanninkhof, R., Waterhouse, A. F., Waterman, S., Watson, A., Wilson, C., Wong, A. P. S., Xu, J., and Yasuda, I.: On the Future of Argo: A Global, Full-Depth, Multi-Disciplinary Array, Front. Mar. Sci., 6, 439, https://doi.org/10.3389/fmars.2019.00439, 2019.
Santana-Falcón, Y., Brasseur, P., Brankart, J. M., and Garnier, F.: Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic Ocean, Ocean Sci., 16, 1297–1315, https://doi.org/10.5194/os-16-1297-2020, 2020.
Schachtschneider, R., Saynisch-Wagner, J., Klemann, V., Bagge, M., and Thomas, M.: An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model, Nonlin. Processes Geophys., 29, 53–75, https://doi.org/10.5194/npg-29-53-2022, 2022.
Simon, E. and Bertino, L.: Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model, J. Marine Syst., 89, 1–18, https://doi.org/10.1016/j.jmarsys.2011.07.007, 2012.
Simon, E., Samuelsen, A., Bertino, L., and Mouysset, S.: Experiences in multiyear combined state–parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter, J. Marine Syst., 152, 1–17, https://doi.org/10.1016/j.jmarsys.2015.07.004, 2015.
Skákala, J., Bruggeman, J., Brewin, R. J. W., 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. J., 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. J., and Ciavatta, S.: The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas, Ocean Model. (Oxf), 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, Front. Mar. Sci., 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 (D6.1), https://doi.org/10.5281/zenodo.10522305, 2023b.
Sournia, A.: Is there a shade flora in the marine plankton?, J. Plankton Res., 4, 391–399, https://doi.org/10.1093/plankt/4.2.391, 1982.
Teruzzi, A., Bolzon, G., Feudale, L., and Cossarini, G.: Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment, Biogeosciences, 18, 6147–6166, https://doi.org/10.5194/bg-18-6147-2021, 2021a.
Teruzzi, A., Di Biagio, V., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Coidessa, G., and Cossarini, G.: Mediterranean Sea Biogeochemical Reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system) (Version 1), Copernicus Marine Service [data set], https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC _006_008_MEDBFM3, 2021b.
Thomas, R. Q., Figueiredo, R. J., Daneshmand, V., Bookout, B. J., Puckett, L. K., and Carey, C. C.: A Near-Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time, Water. Resour. Res., 56, e2019WR026138, https://doi.org/10.1029/2019WR026138, 2020.
Torres, R., Allen, J. I., and Figueiras, F. G.: Sequential data assimilation in an upwelling influenced estuary, J. Marine Syst., 60, 317–329, https://doi.org/10.1016/j.jmarsys.2006.02.001, 2006.
Torres, R., Artioli, Y., Kitidis, V., Ciavatta, S., Ruiz-Villarreal, M., Shutler, J., Polimene, L., Martinez, V., Widdicombe, C., Woodward, E. M. S., Smyth, T., Fishwick, J., and Tilstone, G. H.: 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.
Van Leeuwen, P. J., Cheng, Y., and Reich, S.: Nonlinear Data Assimilation, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-18347-3, 2015.
Vichi, M., Lovato, T., Butenschön, M., Tedesco, L., Lazzari, P., Cossarini, G., Masina, S., Pinardi, N., Solidoro, C., and Zavatarelli, M.: The Biogeochemical Flux Model (BFM): Equation Description and User Manual. BFM version 5.2., Bologna, Italy, 104 pp., 2020.
Yumruktepe, V. Ç., Samuelsen, A., and Daewel, U.: ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic, Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, 2022.
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.
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply...
