Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5891-2025
© Author(s) 2025. 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-18-5891-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
11 Sep 2025
Model evaluation paper |
| 11 Sep 2025
| 11 Sep 2025
Offline Fennel: a high-performance and computationally efficient biogeochemical model within the Regional Ocean Modeling System (ROMS)
GRC Geociències Marines, Departament de Dinàmica de la Terra i de l'Oceà, Facultat de Ciències de la Terra, Universitat de Barcelona, 08028 Barcelona, Spain
Jordi Solé
Institute of Marine Sciences (ICM), Physical and Technological Oceanography Department, Spanish National Research Council (CSIC), 08003 Barcelona, Spain
Centre for Ecological Research and Forestry Applications (CREAF), 08193 Cerdanyola del Vallès, Spain
Càtedra d'Economia Blava Sostenible, Universitat de Barcelona, 08028 Barcelona, Spain
Miquel Canals
GRC Geociències Marines, Departament de Dinàmica de la Terra i de l'Oceà, Facultat de Ciències de la Terra, Universitat de Barcelona, 08028 Barcelona, Spain
Càtedra d'Economia Blava Sostenible, Universitat de Barcelona, 08028 Barcelona, Spain
Reial Acadèmia de Ciències i Arts de Barcelona (RACAB), 08001 Barcelona, Spain
Institut d'Estudis Catalans (IEC), Secció de Ciències i Tecnologia, 08001 Barcelona, Spain
Related authors
Aleida Rosquete-Estevez, Júlia Crespin, Luis Yubero, Ana María Mancho, and Marta Umbert
EGUsphere, https://doi.org/10.5194/egusphere-2026-3629, https://doi.org/10.5194/egusphere-2026-3629, 2026
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
Understanding Arctic currents is vital for climate research, but sparse data makes validation difficult. We evaluated six satellite and ocean reanalysis products using real drifting buoys. Products incorporating wind and mixed-layer data reproduced currents best. Crucially, we show that local velocity accuracy does not guarantee realistic transport pathways. To correctly track melting freshwater, tracking frameworks must evaluate entire journeys rather than point-wise speeds.
Marta Umbert, Nina Hoareau, Júlia Crespin, Maria Sánchez-Urrea, Ferran Hernández-Macià, Carolina Gabarró, and Eva De-Andrés
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-429, https://doi.org/10.5194/essd-2026-429, 2026
Preprint under review for ESSD
Short summary
Short summary
Greenland fjords are changing rapidly, but observations in winter and early spring are rare. We collected 73 temperature and salinity profiles from southwest Greenland fjords during two ship campaigns in March and April 2025. The data reveal the start of spring surface freshening, strongest near glacier-fed fjords, while deeper waters changed little. The open dataset will help improve studies of glacier melt, ocean change, and climate models.
Aleida Rosquete-Estevez, Júlia Crespin, Luis Yubero, Ana María Mancho, and Marta Umbert
EGUsphere, https://doi.org/10.5194/egusphere-2026-3629, https://doi.org/10.5194/egusphere-2026-3629, 2026
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
Understanding Arctic currents is vital for climate research, but sparse data makes validation difficult. We evaluated six satellite and ocean reanalysis products using real drifting buoys. Products incorporating wind and mixed-layer data reproduced currents best. Crucially, we show that local velocity accuracy does not guarantee realistic transport pathways. To correctly track melting freshwater, tracking frameworks must evaluate entire journeys rather than point-wise speeds.
Marta Umbert, Nina Hoareau, Júlia Crespin, Maria Sánchez-Urrea, Ferran Hernández-Macià, Carolina Gabarró, and Eva De-Andrés
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-429, https://doi.org/10.5194/essd-2026-429, 2026
Preprint under review for ESSD
Short summary
Short summary
Greenland fjords are changing rapidly, but observations in winter and early spring are rare. We collected 73 temperature and salinity profiles from southwest Greenland fjords during two ship campaigns in March and April 2025. The data reveal the start of spring surface freshening, strongest near glacier-fed fjords, while deeper waters changed little. The open dataset will help improve studies of glacier melt, ocean change, and climate models.
Cited articles
Allen, J. S., Newberger, P. A., and Federiuk, J.: Upwelling Circulation on the Oregon Continental Shelf. Part I: Response to Idealized Forcing, J. Phys. Oceanogr., 25, 1843–1866, https://doi.org/10.1175/1520-0485(1995)025<1843:UCOTOC>2.0.CO;2, 1995.
Aulenbach, B. T., Buxton, H. T., Battaglin, W. A., and Coupe, R. H.: Streamflow and nutrient fluxes of the Mississippi-Atchafalaya River Basin and subbasins for the period of record through 2005, US Geological Survey, https://doi.org/10.3133/ofr20071080, 2007.
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.
Béal, D., Brasseur, P., Brankart, J.-M., Ourmières, Y., and Verron, J.: Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis, Ocean Sci., 6, 247–262, https://doi.org/10.5194/os-6-247-2010, 2010.
Berry, T. and Harlim, J.: Correcting Biased Observation Model Error in Data Assimilation, Mon. Weather Rev., 145, 2833–2853, https://doi.org/10.1175/MWR-D-16-0428.1, 2017.
Bogden, P. S., Malanotte-Rizzoli, P., and Signell, R.: Open-ocean boundary conditions from interior data: Local and remote forcing of Massachusetts Bay, J. Geophys. Res., 101, 6487–6500, https://doi.org/10.1029/95JC03705, 1996.
Crespin, J.: Offline Fennel: Offline biogeochemical model within the Regional Ocean Modeling System (ROMS), Zenodo [code], https://doi.org/10.5281/zenodo.14916223, 2025a.
Crespin, J.: Run files, input data, and output results from online and offline biogeochemical simulations using the Offline Fennel model in ROMS, Zenodo [data set], https://doi.org/10.5281/zenodo.14930138, 2025b.
da Silva, A. M., Young, C. C., and Levitus, S.: Atlas of surface marine data 1994, volume 3: Anomalies of heat and momentum fluxes, US Dept. of Commerce, Washington, D.C., 1994a.
da Silva, A. M., Young, C. C., and Levitus, S.: Atlas of surface marine data 1994, volume 4: Anomalies of fresh water fluxes, US Dept. of Commerce, Washington, D.C., 1994b.
Fennel, K., Laurent, A., Hetland, R., Justić, D., Ko, D. S., Lehrter, J., Murrell, M., Wang, L., Yu, L., and Zhang, W.: Effects of model physics on hypoxia simulations for the northern Gulf of Mexico: A model intercomparison, JGR Oceans, 121, 5731–5750, https://doi.org/10.1002/2015JC011577, 2016.
Fennel, K. and Laurent, A.: N and P as ultimate and proximate limiting nutrients in the northern Gulf of Mexico: implications for hypoxia reduction strategies, Biogeosciences, 15, 3121–3131, https://doi.org/10.5194/bg-15-3121-2018, 2018.
Fennel, K., Wilkin, J., Levin, J., Moisan, J., O'Reilly, J., and Haidvogel, D.: Nitrogen cycling in the Middle Atlantic Bight: Results from a three-dimensional model and implications for the North Atlantic nitrogen budget: nitrogen cycling in the middle Atlantic, Global Biogeochem. Cy., 20, GB3007, https://doi.org/10.1029/2005GB002456, 2006.
Fennel, K., Wilkin, J., Previdi, M., and Najjar, R.: Denitrification effects on air-sea CO2 flux in the coastal ocean: Simulations for the northwest North Atlantic, Geophys. Res. Lett., 35, L24608, https://doi.org/10.1029/2008GL036147, 2008.
Fennel, K., Hetland, R., Feng, Y., and DiMarco, S.: A coupled physical-biological model of the Northern Gulf of Mexico shelf: model description, validation and analysis of phytoplankton variability, Biogeosciences, 8, 1881–1899, https://doi.org/10.5194/bg-8-1881-2011, 2011.
Fennel, K., Hu, J., Laurent, A., Marta-Almeida, M., and Hetland, R.: Sensitivity of hypoxia predictions for the northern Gulf of Mexico to sediment oxygen consumption and model nesting, J. Geophys. Res.-Oceans, 118, 990–1002, https://doi.org/10.1002/jgrc.20077, 2013.
Fennel, K., Mattern, J. P., Doney, S. C., Bopp, L., Moore, A. M., Wang, B., and Yu, L.: Ocean biogeochemical modelling, Nat. Rev. Meth. Primers, 2, 76, 1–21, https://doi.org/10.1038/s43586-022-00154-2, 2022.
Galperin, B., Kantha, L. H., Hassid, S., and Rosati, A.: A Quasi-equilibrium Turbulent Energy Model for Geophysical Flows, J. Atmos. Sci., 45, 55–62, https://doi.org/10.1175/1520-0469(1988)045<0055:AQETEM>2.0.CO;2, 1988.
Große, F., Fennel, K., and Laurent, A.: Quantifying the Relative Importance of Riverine and Open-Ocean Nitrogen Sources for Hypoxia Formation in the Northern Gulf of Mexico, J. Geophys. Res.-Oceans, 124, 5451–5467, https://doi.org/10.1029/2019JC015230, 2019.
Große, F., Fennel, K., Zhang, H., and Laurent, A.: Quantifying the contributions of riverine vs. oceanic nitrogen to hypoxia in the East China Sea, Biogeosciences, 17, 2701–2714, https://doi.org/10.5194/bg-17-2701-2020, 2020.
Hetland, R. D.: Event-driven model skill assessment, Ocean Model., 11, 214–223, https://doi.org/10.1016/j.ocemod.2004.12.001, 2006.
Hetland, R. D. and DiMarco, S. F.: How does the character of oxygen demand control the structure of hypoxia on the Texas–Louisiana continental shelf?, J. Mar. Syst., 70, 49–62, https://doi.org/10.1016/j.jmarsys.2007.03.002, 2008.
Hetland, R. D. and DiMarco, S. F.: Skill assessment of a hydrodynamic model of circulation over the Texas–Louisiana continental shelf, Ocean Model., 43–44, 64–76, https://doi.org/10.1016/j.ocemod.2011.11.009, 2012.
Kim, T. and Khangaonkar, T.: An offline unstructured biogeochemical model (UBM) for complex estuarine and coastal environments, Environ. Model. Softw., 31, 47–63, https://doi.org/10.1016/j.envsoft.2011.11.010, 2012.
Klawonn, I., Bonaglia, S., Whitehouse, M. J., Littmann, S., Tienken, D., Kuypers, M. M. M., Brüchert, V., and Ploug, H.: Untangling hidden nutrient dynamics: rapid ammonium cycling and single-cell ammonium assimilation in marine plankton communities, ISME J., 13, 1960–1974, https://doi.org/10.1038/s41396-019-0386-z, 2019.
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994.
Larsen, J., Brüning, T., She, J., and Mohn, C.: Flexsem: A new method for offline ocean modelling, DCE – Danish Centre for Environment and Energy, Aarhus University, http://dce2.au.dk/pub/TR105.pdf (last access: december 2024), 2017.
Laurent, A. and Fennel, K.: Simulated reduction of hypoxia in the northern Gulf of Mexico due to phosphorus limitation, Elementa, 2, 000022, https://doi.org/10.12952/journal.elementa.000022, 2014.
Laurent, A., Fennel, K., Hu, J., and Hetland, R.: Simulating the effects of phosphorus limitation in the Mississippi and Atchafalaya River plumes, Biogeosciences, 9, 4707–4723, https://doi.org/10.5194/bg-9-4707-2012, 2012.
Laurent, A., Fennel, K., Cai, W., Huang, W., Barbero, L., and Wanninkhof, R.: Eutrophication-induced acidification of coastal waters in the northern Gulf of Mexico: Insights into origin and processes from a coupled physical-biogeochemical model, Geophys. Res. Lett., 44, 946–956, https://doi.org/10.1002/2016GL071881, 2017.
Laurent, A., Fennel, K., Ko, D. S., and Lehrter, J.: Climate Change Projected to Exacerbate Impacts of Coastal Eutrophication in the Northern Gulf of Mexico, J. Geophys. Res.-Oceans, 123, 3408–3426, https://doi.org/10.1002/2017JC013583, 2018.
Marta-Almeida, M., Ruiz-Villarreal, M., Pereira, J., Otero, P., Cirano, M., Zhang, X., and Hetland, R. D.: Efficient tools for marine operational forecast and oil spill tracking, Marine Pollution Bulletin, 71, 139–151, https://doi.org/10.1016/j.marpolbul.2013.03.022, 2013.
Mattern, J. P., Song, H., Edwards, C. A., Moore, A. M., and Fiechter, J.: Data assimilation of physical and chlorophyll a observations in the California Current System using two biogeochemical models, Ocean Model., 109, 55–71, https://doi.org/10.1016/j.ocemod.2016.12.002, 2017.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982.
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.: North American Regional Reanalysis, Bulletin of the American Meteorological Society, 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343, 2006.
Pasquier, B., Holzer, M., Chamberlain, M. A., Matear, R. J., Bindoff, N. L., and Primeau, F. W.: Optimal parameters for the ocean's nutrient, carbon, and oxygen cycles compensate for circulation biases but replumb the biological pump, Biogeosciences, 20, 2985–3009, https://doi.org/10.5194/bg-20-2985-2023, 2023.
Piroddi, C., Coll, M., Liquete, C., Macias, D., Greer, K., Buszowski, J., Steenbeek, J., Danovaro, R., and Christensen, V.: Historical changes of the Mediterranean Sea ecosystem: modelling the role and impact of primary productivity and fisheries changes over time, Sci. Rep., 7, 44491, https://doi.org/10.1038/srep44491, 2017.
Rabalais, N. N., Turner, R. E., Dortch, Q., Justic, D., Bierman, V. J., and Wiseman, W. J.: Nutrient-enhanced productivity in the northern Gulf of Mexico: past, present and future, Hydrobiologia, 475, 39–63, https://doi.org/10.1023/A:1020388503274, 2002.
Ramirez-Romero, E., Jordà, G., Amores, A., Kay, S., Segura-Noguera, M., Macias, D. M., Maynou, F., Sabatés, A., and Catalán, I. A.: Assessment of the Skill of Coupled Physical–Biogeochemical Models in the NW Mediterranean, Front. Mar. Sci., 7, 497, https://doi.org/10.3389/fmars.2020.00497, 2020.
Rocha, C., Edwards, C. A., Roughan, M., Cetina-Heredia, P., and Kerry, C.: A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system, Geosci. Model Dev., 12, 441–456, https://doi.org/10.5194/gmd-12-441-2019, 2019.
Shchepetkin, A. F.: A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate, Journal of Geophysical Research, 108, 3090, https://doi.org/10.1029/2001JC001047, 2003.
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model, Ocean Model., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002, 2005.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Tedesco, L., Piroddi, C., Kämäri, M., and Lynam, C.: Capabilities of Baltic Sea models to assess environmental status for marine biodiversity, Mar. Policy, 70, 1–12, https://doi.org/10.1016/j.marpol.2016.04.021, 2016.
Thyng, K. M., Greene, C. A., Hetland, R. D., Zimmerle, H. M., and DiMarco, S. F.: True Colors of Oceanography: Guidelines for Effective and Accurate Colormap Selection, Oceanography, 29, 9–13, https://doi.org/10.5670/oceanog.2016.66, 2016.
Thyng, K. M., Kobashi, D., Ruiz-Xomchuk, V., Qu, L., Chen, X., and Hetland, R. D.: Performance of offline passive tracer advection in the Regional Ocean Modeling System (ROMS; v3.6, revision 904), Geosci. Model Dev., 14, 391–407, https://doi.org/10.5194/gmd-14-391-2021, 2021.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical turbulence models, J. Mar. Res., 61, 235-265, https://elischolar.library.yale.edu/journal_of_marine_research/9 (last access: December 2024), 2003.
Wanninkhof, R.: Relationship between wind speed and gas exchange over the ocean revisited, Limnol. Ocean Meth., 12, 351–362, https://doi.org/10.4319/lom.2014.12.351, 2014.
Warner, J. C., Sherwood, C. R., Arango, H. G., and Signell, R. P.: Performance of four turbulence closure models implemented using a generic length scale method, Ocean Model., 8, 81–113, https://doi.org/10.1016/j.ocemod.2003.12.003, 2005.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) Modeling System, Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Yu, L., Fennel, K., and Laurent, A.: A modeling study of physical controls on hypoxia generation in the northern Gulf of Mexico: physical controls on hypoxia, J. Geophys. Res.-Oceans, 120, 5019–5039, https://doi.org/10.1002/2014JC010634, 2015a.
Yu, L., Fennel, K., Laurent, A., Murrell, M. C., and Lehrter, J. C.: Numerical analysis of the primary processes controlling oxygen dynamics on the Louisiana shelf, Biogeosciences, 12, 2063–2076, https://doi.org/10.5194/bg-12-2063-2015, 2015b.
Short summary
This study presents the Offline Fennel model, a tool designed to simulate ocean biogeochemical processes efficiently. By using existing hydrodynamic data, the model significantly reduces computation time from 6 h to just 30 min. We tested its accuracy in the northern Gulf of Mexico and found it closely matches physical–biogeochemical coupled simulations. This model allows researchers to conduct more tests and simulations without the need for extensive computational resources.
This study presents the Offline Fennel model, a tool designed to simulate ocean biogeochemical...
