Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-2961-2025
© 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-18-2961-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
Hoa Nguyen
CORRESPONDING AUTHOR
Helmholtz-Zentrum Hereon, Institute of Coastal Research, Max-Planck-Str. 1, 21502 Geesthacht, Germany
Institute for Environment and Resources, Vietnam National University, HoChiMinh City, Vietnam
Ute Daewel
Helmholtz-Zentrum Hereon, Institute of Coastal Research, Max-Planck-Str. 1, 21502 Geesthacht, Germany
Neil Banas
Department of Mathematics & Statistics, University of Strathclyde, 26 Richmond St., Glasgow, G1 1XH, UK
Corinna Schrum
Helmholtz-Zentrum Hereon, Institute of Coastal Research, Max-Planck-Str. 1, 21502 Geesthacht, Germany
Related authors
No articles found.
Feifei Liu, Ute Daewel, Jan Kossack, Kubilay Timur Demir, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 3699–3719, https://doi.org/10.5194/bg-22-3699-2025, https://doi.org/10.5194/bg-22-3699-2025, 2025
Short summary
Short summary
Ocean alkalinity enhancement (OAE) boosts oceanic CO₂ absorption, offering a climate solution. Using a regional model, we examined OAE in the North Sea, revealing that shallow coastal areas achieve higher CO₂ uptake than offshore where alkalinity is more susceptible to deep-ocean loss. Long-term carbon storage is limited, and pH shifts vary by location. Our findings guide OAE deployment to optimize carbon removal while minimizing ecological effects, supporting global climate mitigation efforts.
Kubilay Timur Demir, Moritz Mathis, Jan Kossack, Feifei Liu, Ute Daewel, Christoph Stegert, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 2569–2599, https://doi.org/10.5194/bg-22-2569-2025, https://doi.org/10.5194/bg-22-2569-2025, 2025
Short summary
Short summary
This study examines how variations in the ratios of carbon, nitrogen, and phosphorus in organic matter affect carbon cycling in the northwest European shelf seas. Traditional models with fixed ratios tend to underestimate biological carbon uptake. By integrating variable ratios into a regional model, we find that carbon dioxide uptake increases by 9 %–31 %. These results highlight the need to include variable ratios for accurate assessments of regional and global carbon cycles.
Alberto Elizalde, Naveed Akhtar, Beate Geyer, and Corinna Schrum
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-64, https://doi.org/10.5194/wes-2025-64, 2025
Preprint under review for WES
Short summary
Short summary
As green energy demand rises, offshore wind farms in the North Sea are expanding. This study examines the uncertainties in power output predictions, considering turbine arrangements and weather conditions. Using an advanced climate model, we found that power output can vary by up to 13 %. These findings are vital for accurate economic and environmental planning. This research will contribute to a better understanding of the potential of offshore wind energy.
David Johannes Amptmeijer, Andrea Padilla, Sofia Modesti, Corinna Schrum, and Johannes Bieser
EGUsphere, https://doi.org/10.5194/egusphere-2025-1494, https://doi.org/10.5194/egusphere-2025-1494, 2025
Short summary
Short summary
This paper combines a literature review with a 1D coupled Hg speciation and bioaccumulation model to assess how feeding strategy influences inorganic and methylmercury levels at the food web's base. We find that filter feeders have higher MeHg concentrations, while suspension feeders show very low MeHg. These results highlight feeding strategy as a key driver in MeHg bioaccumulation variability.
David Johannes Amptmeijer, Elena Mikhavee, Ute Daewel, Johannes Bieser, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2025-1486, https://doi.org/10.5194/egusphere-2025-1486, 2025
Short summary
Short summary
In this study, we analyze mercury bioaccumulation, including both methylated and inorganic Hg. While methylmercury is the primary toxin of concern, modeling inorganic Hg bioaccumulation reveals its role in marine mercury cycling. We find that bioaccumulation strongly influences mercury dynamics, increasing methylmercury levels. This effect is more pronounced in well-mixed coastal waters than in permanently stratified deep waters.
Lucas Porz, Wenyan Zhang, Nils Christiansen, Jan Kossack, Ute Daewel, and Corinna Schrum
Biogeosciences, 21, 2547–2570, https://doi.org/10.5194/bg-21-2547-2024, https://doi.org/10.5194/bg-21-2547-2024, 2024
Short summary
Short summary
Seafloor sediments store a large amount of carbon, helping to naturally regulate Earth's climate. If disturbed, some sediment particles can turn into CO2, but this effect is not well understood. Using computer simulations, we found that bottom-contacting fishing gears release about 1 million tons of CO2 per year in the North Sea, one of the most heavily fished regions globally. We show how protecting certain areas could reduce these emissions while also benefitting seafloor-living animals.
Peter Arlinghaus, Corinna Schrum, Ingrid Kröncke, and Wenyan Zhang
Earth Surf. Dynam., 12, 537–558, https://doi.org/10.5194/esurf-12-537-2024, https://doi.org/10.5194/esurf-12-537-2024, 2024
Short summary
Short summary
Benthos is recognized to strongly influence sediment stability, deposition, and erosion. This is well studied on small scales, but large-scale impact on morphological change is largely unknown. We quantify the large-scale impact of benthos by modeling the evolution of a tidal basin. Results indicate a profound impact of benthos by redistributing sediments on large scales. As confirmed by measurements, including benthos significantly improves model results compared to an abiotic scenario.
Philipp Heinrich, Stefan Hagemann, Ralf Weisse, Corinna Schrum, Ute Daewel, and Lidia Gaslikova
Nat. Hazards Earth Syst. Sci., 23, 1967–1985, https://doi.org/10.5194/nhess-23-1967-2023, https://doi.org/10.5194/nhess-23-1967-2023, 2023
Short summary
Short summary
High seawater levels co-occurring with high river discharges have the potential to cause destructive flooding. For the past decades, the number of such compound events was larger than expected by pure chance for most of the west-facing coasts in Europe. Additionally rivers with smaller catchments showed higher numbers. In most cases, such events were associated with a large-scale weather pattern characterized by westerly winds and strong rainfall.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
Short summary
Short summary
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
Short summary
Short summary
We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Ricardo González-Gil, Neil S. Banas, Eileen Bresnan, and Michael R. Heath
Biogeosciences, 19, 2417–2426, https://doi.org/10.5194/bg-19-2417-2022, https://doi.org/10.5194/bg-19-2417-2022, 2022
Short summary
Short summary
In oceanic waters, the accumulation of phytoplankton biomass in winter, when light still limits growth, is attributed to a decrease in grazing as the mixed layer deepens. However, in coastal areas, it is not clear whether winter biomass can accumulate without this deepening. Using 21 years of weekly data, we found that in the Scottish coastal North Sea, the seasonal increase in light availability triggers the accumulation of phytoplankton biomass in winter, when light limitation is strongest.
Cited articles
Asmus, R. M. and Asmus, H.: Mussel beds: limiting or promoting phytoplankton?, J. Exp. Mar. Biol. Ecol., 148, 215–232, https://doi.org/10.1016/0022-0981(91)90083-9, 1991. a, b, c
Baird, D., Asmus, H., and Asmus, R.: Trophic dynamics of eight intertidal communities of the Sylt-Rømø Bight ecosystem, northern Wadden Sea, Mar. Ecol. Prog. Ser., 351, 25–41, 2007. a
Beven, K. and Binley, A.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992. a
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
Chen, H., Xu, J., Zhang, B., and Fuhlbrigge, T.: Improved parameter optimization method for complex assembly process in robotic manufacturing, Industrial Robot: An International Journal, 44, 21–27, https://doi.org/10.1108/IR-03-2016-0098, 2017. a
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. a, b, c
Daewel, U. and Schrum, C.: Low-frequency variability in North Sea and Baltic Sea identified through simulations with the 3-D coupled physical–biogeochemical model ECOSMO, Earth Syst. Dynam., 8, 801–815, https://doi.org/10.5194/esd-8-801-2017, 2017. a
Daewel, U., Schrum, C., and Macdonald, J. I.: Towards end-to-end (E2E) modelling in a consistent NPZD-F modelling framework (ECOSMO E2E_v1.0): application to the North Sea and Baltic Sea, Geosci. Model Dev., 12, 1765–1789, https://doi.org/10.5194/gmd-12-1765-2019, 2019. a, b, c, d
Dowd, M.: Estimating parameters for a stochastic dynamic marine ecological system, Environmetrics, 22, 501–515, 2011. a
Falls, M., Bernardello, R., Castrillo, M., Acosta, M., Llort, J., and Galí, M.: Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon, Geosci. Model Dev., 15, 5713–5737, https://doi.org/10.5194/gmd-15-5713-2022, 2022. a, b, c
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., McGillicuddy Jr., D. J., Moore, J. K., Schartau, M., Spitz, Y. H., and Wiggert, J. D.: 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. a
Fulton, E. A.: Approaches to end-to-end ecosystem models, J. Marine Syst., 81, 171–183, 2010. a
Fulton, E. A., Smith, A. D., and Johnson, C. R.: Effect of complexity on marine ecosystem models, Mar. Ecol. Prog. Ser., 253, 1–16, 2003. a
Garcia-Gonzalo, E. and Fernandez-Martinez, J. L.: A brief historical review of particle swarm optimization (PSO), Journal of Bioinformatics and Intelligent Control, 1, 3–16, 2012. a
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. a
Horn, S., Coll, M., Asmus, H., and Dolch, T.: Food web models reveal potential ecosystem effects of seagrass recovery in the northern Wadden Sea, Restor. Ecol., 29, e13328, https://doi.org/10.1111/rec.13328, 2021a. a
Horn, S., Meunier, C. L., Fofonova, V., Wiltshire, K. H., Sarker, S., Pogoda, B., and Asmus, H.: Toward Improved Model Capacities for Assessment of Climate Impacts on Coastal Bentho-Pelagic Food Webs and Ecosystem Services, Frontiers in Marine Science, 8, 567266, https://doi.org/10.3389/fmars.2021.567266, 2021b. a
Houska, T.: Uncertainty analysis of complex hydro-biogeochemical models, PhD thesis, Justus-Liebig University Gießen, https://doi.org/10.22029/jlupub-10422, 2017. a
Huse, G. and Fiksen, Ø.: Modelling encounter rates and distribution of mobile predators and prey, Prog. Oceanogr., 84, 93–104, 2010. a
Jones, E., Parslow, J., and Murray, L.: A Bayesian approach to state and parameter estimation in a Phytoplankton–Zooplankton model, Aust. Meteorol. Ocean., 59, 7–16, 2010. a
Kern, S., McGuinn, M. E., Smith, K. M., Pinardi, N., Niemeyer, K. E., Lovenduski, N. S., and Hamlington, P. E.: Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models, Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, 2024. a, b
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P.: Optimization by simulated annealing, Science, 220, 671–680, 1983. a
Kohlmeier, C. and Ebenhöh, W.: Modelling the biogeochemistry of a tidal flat ecosystem with EcoTiM, Ocean Dynam., 59, 393–415, 2009. a
Kuhn, A. M. and Fennel, K.: Evaluating ecosystem model complexity for the northwest North Atlantic through surrogate-based optimization, Ocean Model., 142, 101437, https://doi.org/10.1016/j.ocemod.2019.101437, 2019. a
Mai, J., Craig, J. R., and Tolson, B. A.: Simultaneously determining global sensitivities of model parameters and model structure, Hydrol. Earth Syst. Sci., 24, 5835–5858, https://doi.org/10.5194/hess-24-5835-2020, 2020. a
Marini, D. and Corney, J. R.: Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes, J. Intell. Manuf., 32, 611–631, 2021. a
Martens, P.: Über die Qualität und Quantität der Sekundär- und Tertiärproduzenten in einem marinen Flachwasserökosystem der westlichen Ostsee, PhD thesis, Christian-Albrechts-Universität Kiel, https://oceanrep.geomar.de/id/eprint/40068/ (last access: 16 May 2025), 1975. a
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E.: Equation of state calculations by fast computing machines, J. Chem. Phys., 21, 1087–1092, 1953. a
Miller, C. B.: Biological oceanography, John Wiley & Sons, ISBN 9781444311129, 2009. a
Monsalve-Bravo, G. M., Lawson, B. A. J., Drovandi, C., Burrage, K., Brown, K. S., Baker, C. M., Vollert, S. A., Mengersen, K., McDonald-Madden, E., and Adams, M. P.: Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data, Sci. Adv., 8, eabm5952, https://doi.org/10.1126/sciadv.abm5952, 2022. a
Nguyen, H.: 1D GOTM-ECOSMO E2E Parameterisation by Particle Swarm Optimizer (PSO), Zenodo [code and data set], https://doi.org/10.5281/zenodo.13904053, 2024. a, b
Pant, S.: Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems, J. R. Soc. Interface, 15, 20170871, https://doi.org/10.1098/rsif.2017.0871, 2018. a
Poli, R., Kennedy, J., and Blackwell, T.: Particle swarm optimization, Lect. Notes Comput. Sc., 1, 33–57, https://doi.org/10.1007/s11721-007-0002-0, 2007. a, b, c
Prieß, M., Koziel, S., and Slawig, T.: A Fast and Robust Optimization Methodology for a Marine Ecosystem Model Using Surrogates, https://macau.uni-kiel.de/receive/macau_mods_00001811 (last access: 16 May 2025), 2011. a
Prieß, M., Piwonski, J., Koziel, S., Oschlies, A., and Slawig, T.: Accelerated parameter identification in a 3D marine biogeochemical model using surrogate-based optimization, Ocean Model., 68, 22–36, 2013. a
Razavi, S. and Gupta, H. V.: A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory, Water Resour. Res., 52, 423–439, https://doi.org/10.1002/2015WR017558, 2016. a
Reimer, J.: Optimization of Model Parameters, Uncertainty Quantification and Experimental Designs for a Global Marine Biogeochemical Model, arXiv [preprint], https://doi.org/10.48550/arXiv.1912.07412, 2019. a
Rick, J. J., Scharfe, M., Romanova, T., van Beusekom, J. E. E., Asmus, R., Asmus, H., Mielck, F., Kamp, A., Sieger, R., and Wiltshire, K. H.: An evaluation of long-term physical and hydrochemical measurements at the Sylt Roads Marine Observatory (1973–2019), Wadden Sea, North Sea, Earth Syst. Sci. Data, 15, 1037–1057, https://doi.org/10.5194/essd-15-1037-2023, 2023. a
Rückelt, J., Sauerland, V., Slawig, T., Srivastav, A., Ward, B., and Patvardhan, C.: Parameter optimization and validation of a marine biogeochemical model using a hybrid algorithm, https://macau.uni-kiel.de/receive/macau_mods_00001841 (last access: 16 May 2025), 2009. a
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global sensitivity analysis. The primer, John Wiley & Sons, https://doi.org/10.1002/9780470725184, 2008. a, b
Schartau, M., Wallhead, P., Hemmings, J., Löptien, U., Kriest, I., Krishna, S., Ward, B. A., Slawig, T., and Oschlies, A.: Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling, Biogeosciences, 14, 1647–1701, https://doi.org/10.5194/bg-14-1647-2017, 2017. a, b
Schrum, C.: Thermohaline stratification and instabilities at tidal mixing fronts: results of an eddy resolving model for the German Bight, Cont. Shelf Res., 17, 689–716, https://doi.org/10.1016/S0278-4343(96)00051-9, 1997. a
Schrum, C. and Backhaus, J. O.: Sensitivity of atmosphere–ocean heat exchange and heat content in the North Sea and the Baltic Sea, Tellus A, 51, 526–549, https://doi.org/10.1034/j.1600-0870.1992.00006.x, 1999. a
Schrum, C., Alekseeva, I., and John, M.: Development of a coupled physical–biological ecosystem model ECOSMO: Part I: Model description and validation for the North Sea, J. Marine Syst., 61, 79–99, https://doi.org/10.1016/j.jmarsys.2006.01.005, 2006. a, b
Sengupta, S., Basak, S., and Peters, R. A.: Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives, Machine Learning and Knowledge Extraction, 1, 157–191, 2019. a
Stanev, E. V., Wolff, J.-O., Burchard, H., Bolding, K., and Flöser, G.: On the circulation in the East Frisian Wadden Sea: numerical modeling and data analysis, Ocean Dynam., 53, 27–51, 2003. a
Umlauf, L., Burchard, H., and Bolding, K.: GOTM Sourcecode and Test Case Documentation Version 4.0, GOTM Team, https://gotm.net/manual/stable/pdf/a4.pdf (last access: 16 May 2025), 2016. a
Willmott, C. J.: On the validation of models, Phys. Geogr., 2, 184–194, 1981. a
Willmott, C. J., Robeson, M., and Matsuura, K.: Short Communication A refined index of model performance, Int. J. Climatol., 2094, 2088–2094, https://doi.org/10.1002/joc.2419, 2012. a, b
Yingshan, W., Weijun, S., Lei, W., Yanzhao, L., Wentao, D., Jizu, C., and Xiang, Q.: How Do Different Reanalysis Radiation Datasets Perform in West Qilian Mountains?, Front. Earth Sci., 10, https://doi.org/10.3389/feart.2022.852054, 2022. a
Yumruktepe, V. Ç., Mousing, E. A., Tjiputra, J., and Samuelsen, A.: An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas, Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, 2023. a
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Parameterization is key in modeling to reproduce observations well but is often done manually....