Articles | Volume 10, issue 1
https://doi.org/10.5194/gmd-10-453-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-453-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
01 Feb 2017
Model description paper |
| 01 Feb 2017
Bottom RedOx Model (BROM v.1.1): a coupled benthic–pelagic model for simulation of water and sediment biogeochemistry
Evgeniy V. Yakushev
CORRESPONDING AUTHOR
Norwegian Institute for Water Research (NIVA), Gaustadalléen 21,
0349 Oslo, Norway
P.P. Shirshov Institute of Oceanology RAS, Nakhimovskiy prosp. 36,
117991, Moscow, Russia
Elizaveta A. Protsenko
Norwegian Institute for Water Research (NIVA), Gaustadalléen 21,
0349 Oslo, Norway
P.P. Shirshov Institute of Oceanology RAS, Nakhimovskiy prosp. 36,
117991, Moscow, Russia
Jorn Bruggeman
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, UK
Philip Wallhead
Norwegian Institute for Water Research (NIVA Vest), Thormøhlensgate
53 D, 5006 Bergen, Norway
Svetlana V. Pakhomova
Norwegian Institute for Air Research (NILU), P.O. Box 100, 2027
Kjeller, Norway
P.P. Shirshov Institute of Oceanology RAS, Nakhimovskiy prosp. 36,
117991, Moscow, Russia
Norwegian University of Science and Technology (NTNU), 7491
Trondheim, Norway
Shamil Kh. Yakubov
P.P. Shirshov Institute of Oceanology RAS, Nakhimovskiy prosp. 36,
117991, Moscow, Russia
Richard G. J. Bellerby
State Key Laboratory for Estuarine and Coastal Research, East China
Normal University, Shanghai, China
Norwegian Institute for Water Research (NIVA Vest), Thormøhlensgate
53 D, 5006 Bergen, Norway
Raoul-Marie Couture
Norwegian Institute for Water Research (NIVA), Gaustadalléen 21,
0349 Oslo, Norway
University of Waterloo, Earth and Environmental Sciences, Ecohydrology
Group, 200 University Avenue West, N2L3G2, Waterloo, Canada
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Shamil Yakubov, Philip Wallhead, Elizaveta Protsenko, and Evgeniy Yakushev
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-299, https://doi.org/10.5194/gmd-2017-299, 2017
Preprint withdrawn
Short summary
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Aquatic biogeochemical processes can strongly interact, especially in polar regions, with processes occurring in adjacent ice and sediment layers, yet there are few modelling tools to simulate these systems in a fully coupled manner. We have developed a 1D transport model that allows simultaneous simulation of the biogeochemistry of 3 different media: ice, water, and sediments. Description of transportation processes in ice, water, and sediments for both solutes and solids was provided.
Philip John Wallhead, Jörg Schwinger, Jerry Tjiputra, Trond Kristiansen, and Richard Garth James Bellerby
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-76, https://doi.org/10.5194/essd-2025-76, 2025
Preprint under review for ESSD
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We developed a novel method to combine ocean data from observations and models, and applied it to produce gridded estimates of nutrients, oxygen, dissolved inorganic carbon and total alkalinity concentrations at latitudes >40° N and years 1980–2020. The new estimates showed improved accuracy and coverage relative to previous estimates, but highlighted remaining uncertainty in some poorly sampled regions. The work was largely motivated by a need for accurate input data for regional ocean models.
Eva Friis Møller, Asbjørn Christensen, Janus Larsen, Kenneth D. Mankoff, Mads Hvid Ribergaard, Mikael Sejr, Philip Wallhead, and Marie Maar
Ocean Sci., 19, 403–420, https://doi.org/10.5194/os-19-403-2023, https://doi.org/10.5194/os-19-403-2023, 2023
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Melt from the Greenland ice sheet and sea ice both influence light and nutrient availability in the Arctic coastal ocean. We use a 3D coupled hydrodynamic–biogeochemical model to evaluate the relative importance of these processes for timing, distribution, and magnitude of phytoplankton production in Disko Bay, west Greenland. Our study indicates that decreasing sea ice and more freshwater discharge can work synergistically and increase primary productivity of the coastal ocean around Greenland.
Xiaoshuang Li, Richard Garth James Bellerby, Jianzhong Ge, Philip Wallhead, Jing Liu, and Anqiang Yang
Geosci. Model Dev., 13, 5103–5117, https://doi.org/10.5194/gmd-13-5103-2020, https://doi.org/10.5194/gmd-13-5103-2020, 2020
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We have developed an ANN model to predict pH using 11 cruise datasets from 2013 to 2017,
demonstrated its reliability using three cruise datasets during 2018 and applied it to
retrieve monthly pH for the period 2000 to 2016 on the East China Sea shelf using the
ANN model in combination with input variables from the Changjiang biology Finite-Volume
Coastal Ocean Model. This approach may be a valuable tool for understanding the seasonal
variation of pH in poorly observed regions.
Shamil Yakubov, Philip Wallhead, Elizaveta Protsenko, and Evgeniy Yakushev
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-299, https://doi.org/10.5194/gmd-2017-299, 2017
Preprint withdrawn
Short summary
Short summary
Aquatic biogeochemical processes can strongly interact, especially in polar regions, with processes occurring in adjacent ice and sediment layers, yet there are few modelling tools to simulate these systems in a fully coupled manner. We have developed a 1D transport model that allows simultaneous simulation of the biogeochemistry of 3 different media: ice, water, and sediments. Description of transportation processes in ice, water, and sediments for both solutes and solids was provided.
Markus Schartau, Philip Wallhead, John Hemmings, Ulrike Löptien, Iris Kriest, Shubham Krishna, Ben A. Ward, Thomas Slawig, and Andreas Oschlies
Biogeosciences, 14, 1647–1701, https://doi.org/10.5194/bg-14-1647-2017, https://doi.org/10.5194/bg-14-1647-2017, 2017
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Plankton models have become an integral part in marine ecosystem and biogeochemical research. These models differ in complexity and in their number of parameters. How values are assigned to parameters is essential. An overview of major methodologies of parameter estimation is provided. Aspects of parameter identification in the literature are diverse. Individual findings could be better synthesized if notation and expertise of the different scientific communities would be reasonably merged.
Fenjuan Hu, Karsten Bolding, Jorn Bruggeman, Erik Jeppesen, Morgens R. Flindt, Luuk van Gerven, Jan H. Janse, Annette B. G. Janssen, Jan J. Kuiper, Wolf M. Mooij, and Dennis Trolle
Geosci. Model Dev., 9, 2271–2278, https://doi.org/10.5194/gmd-9-2271-2016, https://doi.org/10.5194/gmd-9-2271-2016, 2016
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We present a redesign and further development of a complex and well-known aquatic ecosystem model (PCLake) into the Framework for Aquatic Biogeochemical Models (FABM). So PCLake can run in different hydrodynamic environments, ranging from 0-D to 3-D. We introduce the methods and technical details about how the model was re-designed into a modular structure and the new features of PCLake enabled by FABM. We further present a benchmark test case to verify the new model implementation.
Momme Butenschön, James Clark, John N. Aldridge, Julian Icarus Allen, Yuri Artioli, Jeremy Blackford, Jorn Bruggeman, Pierre Cazenave, Stefano Ciavatta, Susan Kay, Gennadi Lessin, Sonja van Leeuwen, Johan van der Molen, Lee de Mora, Luca Polimene, Sevrine Sailley, Nicholas Stephens, and Ricardo Torres
Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, https://doi.org/10.5194/gmd-9-1293-2016, 2016
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ERSEM 15.06 is a model for marine biogeochemistry and the lower trophic levels of the marine food web. It comprises a pelagic and benthic sub-model including the microbial food web and the major biogeochemical cycles of carbon, nitrogen, phosphorus, silicate, and iron using dynamic stochiometry. Further features include modules for the carbonate system and calcification. We present full mathematical descriptions of all elements along with examples at various scales up to 3-D applications.
Jonathan Beecham, Jorn Bruggeman, John Aldridge, and Steven Mackinson
Geosci. Model Dev., 9, 947–964, https://doi.org/10.5194/gmd-9-947-2016, https://doi.org/10.5194/gmd-9-947-2016, 2016
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This paper is a description of how very different higher and lower trophic level models (Ecopath with Ecosim) and ERSEM, respectively, can be coupled together using a metadata coupling system together with a number of examples of short- and long-range projections for end to end modelling.
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Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
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Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
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Jan D. Zika and Taimoor Sohail
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Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini
Geosci. Model Dev., 17, 7347–7364, https://doi.org/10.5194/gmd-17-7347-2024, https://doi.org/10.5194/gmd-17-7347-2024, 2024
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This study presents a new global wave model that improves predictions of sea-states in tropical areas. By employing advanced techniques to correct wind field errors and simulate distant swells, our new global wave model allows for the first time direct comparisons in shallow waters close to shore. The model's predictions were validated both globally using satellite data and nearshore with data collected around several tropical islands.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
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Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Qian Wang, Yang Zhang, Fei Chai, Y. Joseph Zhang, and Lorenzo Zampieri
Geosci. Model Dev., 17, 7067–7081, https://doi.org/10.5194/gmd-17-7067-2024, https://doi.org/10.5194/gmd-17-7067-2024, 2024
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We coupled an unstructured hydro-model with an advanced column sea ice model to meet the growing demand for increased resolution and complexity in unstructured sea ice models. Additionally, we present a novel tracer transport scheme for the sea ice coupled model and demonstrate that this scheme fulfills the requirements for conservation, accuracy, efficiency, and monotonicity in an idealized test. Our new coupled model also has good performance in realistic tests.
Adrien Garinet, Marine Herrmann, Patrick Marsaleix, and Juliette Pénicaud
Geosci. Model Dev., 17, 6967–6986, https://doi.org/10.5194/gmd-17-6967-2024, https://doi.org/10.5194/gmd-17-6967-2024, 2024
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Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations is made challenging by errors that result in unwanted mixing, compromising simulation realism. Here we illustrate the spurious effect that tides can have on simulations of south-east Asia. Although they play an important role in determining the state of the ocean, they can increase numerical errors and make simulation outputs less realistic. We also provide insights into how to reduce these errors.
Mohamed Ayache, Jean-Claude Dutay, Anne Mouchet, Kazuyo Tachikawa, Camille Risi, and Gilles Ramstein
Geosci. Model Dev., 17, 6627–6655, https://doi.org/10.5194/gmd-17-6627-2024, https://doi.org/10.5194/gmd-17-6627-2024, 2024
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Water isotopes (δ18O, δD) are one of the most widely used proxies in ocean climate research. Previous studies using water isotope observations and modelling have highlighted the importance of understanding spatial and temporal isotopic variability for a quantitative interpretation of these tracers. Here we present the first results of a high-resolution regional dynamical model (at 1/12° horizontal resolution) developed for the Mediterranean Sea, one of the hotspots of ongoing climate change.
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, https://doi.org/10.5194/gmd-17-6415-2024, 2024
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Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
Geosci. Model Dev., 17, 6123–6136, https://doi.org/10.5194/gmd-17-6123-2024, https://doi.org/10.5194/gmd-17-6123-2024, 2024
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Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, GPU-accelerated computing is introduced in WAve Modeling Cycle 6 (WAM6). With this effort, global high-resolution wave simulations can now run on GPUs up to tens of times faster than the currently available models can on a CPU node with results that are just as accurate.
Krysten Rutherford, Laura Bianucci, and William Floyd
Geosci. Model Dev., 17, 6083–6104, https://doi.org/10.5194/gmd-17-6083-2024, https://doi.org/10.5194/gmd-17-6083-2024, 2024
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Nearshore ocean models often lack complete information about freshwater fluxes due to numerous ungauged rivers and streams. We tested a simple rain-based hydrological model as inputs into an ocean model of Quatsino Sound, Canada, with the aim of improving the representation of the land–ocean connection in the nearshore model. Through multiple tests, we found that the performance of the ocean model improved when providing 60 % or more of the freshwater inputs from the simple runoff model.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
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The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 17, 5851–5882, https://doi.org/10.5194/gmd-17-5851-2024, https://doi.org/10.5194/gmd-17-5851-2024, 2024
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The carbonate system is typically studied using measurements, but modeling can contribute valuable insights. Using a biogeochemical model, we propose a new representation of total alkalinity, dissolved inorganic carbon, pCO2, and pH in a highly dynamic Mediterranean coastal area, the Bay of Marseille, a useful addition to measurements. Through a detailed analysis of pCO2 and air–sea CO2 fluxes, we show that variations are strongly impacted by the hydrodynamic processes that affect the bay.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2281, https://doi.org/10.5194/egusphere-2024-2281, 2024
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Vertical mixing is an important process e.g. for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes, TKE and KPP, with different parameter settings, in two different ocean models, and show that most effects from mixing scheme parameter changes are model dependent.
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, https://doi.org/10.5194/gmd-17-5497-2024, 2024
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Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Zhaoru Zhang, Chuan Xie, Chuning Wang, Yuanjie Chen, Heng Hu, and Xiaoqiao Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-128, https://doi.org/10.5194/gmd-2024-128, 2024
Revised manuscript accepted for GMD
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A coupled fine-resolution ocean-ice model is developed for the Ross Sea and adjacent regions in Antarctica, a key area for the formation of global ocean bottom water — the Antarctic Bottom Water (AABW) that affects the world ocean circulation. The model has high skills in simulating sea ice production driving the AABW source water formation and water mass properties when assessed against observations. A model experiment shows significant impact of ice shelf melting on the AABW characteristics.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
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This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
EGUsphere, https://doi.org/10.5194/egusphere-2024-1414, https://doi.org/10.5194/egusphere-2024-1414, 2024
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The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the HadGEM3 coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the sea floor exerts on ocean currents, and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
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We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
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Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Antoine Mathieu, Yeulwoo Kim, Tian-Jian Hsu, Cyrille Bonamy, and Julien Chauchat
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-16, https://doi.org/10.5194/gmd-2024-16, 2024
Revised manuscript accepted for GMD
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Most of the tools available to model sediment transport do not account for complex physical mechanisms such as surface wave driven processes. In this study, a new model sedInterFoam allows to reproduce numerically complex configurations to investigate coastal sediment transport applications dominated by surface waves and gain insight into the complex physical processes associated with breaking waves and morphodynamics.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, https://doi.org/10.5194/gmd-17-4095-2024, 2024
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Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
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The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
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In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
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Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
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Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
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The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
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A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
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We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
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Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024, https://doi.org/10.5194/gmd-17-1023-2024, 2024
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This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
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We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Luca Arpaia, Christian Ferrarin, Marco Bajo, and Georg Umgiesser
Geosci. Model Dev., 16, 6899–6919, https://doi.org/10.5194/gmd-16-6899-2023, https://doi.org/10.5194/gmd-16-6899-2023, 2023
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We propose a discrete multilayer shallow water model based on z-layers which, thanks to the insertion and removal of surface layers, can deal with an arbitrarily large tidal oscillation independently of the vertical resolution. The algorithm is based on a two-step procedure used in numerical simulations with moving boundaries (grid movement followed by a grid topology change, that is, the insertion/removal of surface layers), which avoids the appearance of very thin surface layers.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, France Van Wambeke, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 16, 6701–6739, https://doi.org/10.5194/gmd-16-6701-2023, https://doi.org/10.5194/gmd-16-6701-2023, 2023
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While several studies have shown that mixotrophs play a crucial role in the carbon cycle, the impact of environmental forcings on their dynamics remains poorly investigated. Using a biogeochemical model that considers mixotrophs, we study the impact of light and nutrient concentration on the ecosystem composition in a highly dynamic Mediterranean coastal area: the Bay of Marseille. We show that mixotrophs cope better with oligotrophic conditions compared to strict auto- and heterotrophs.
Trygve Halsne, Kai Håkon Christensen, Gaute Hope, and Øyvind Breivik
Geosci. Model Dev., 16, 6515–6530, https://doi.org/10.5194/gmd-16-6515-2023, https://doi.org/10.5194/gmd-16-6515-2023, 2023
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Surface waves that propagate in oceanic or coastal environments get influenced by their surroundings. Changes in the ambient current or the depth profile affect the wave propagation path, and the change in wave direction is called refraction. Some analytical solutions to the governing equations exist under ideal conditions, but for realistic situations, the equations must be solved numerically. Here we present such a numerical solver under an open-source license.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
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Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Doroteaciro Iovino, Pier Giuseppe Fogli, and Simona Masina
Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, https://doi.org/10.5194/gmd-16-6127-2023, 2023
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This paper describes the model performance of three global ocean–sea ice configurations, from non-eddying (1°) to eddy-rich (1/16°) resolutions. Model simulations are obtained following the Ocean Model Intercomparison Project phase 2 (OMIP2) protocol. We compare key global climate variables across the three models and against observations, emphasizing the relative advantages and disadvantages of running forced ocean–sea ice models at higher resolution.
Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, and Kai H. Christensen
Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, https://doi.org/10.5194/gmd-16-5401-2023, 2023
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A model to predict ocean currents, temperature, and sea ice is presented, covering the Barents Sea and northern Norway. To quantify forecast uncertainties, the model calculates ensemble forecasts with 24 realizations of ocean and ice conditions. Observations from satellites, buoys, and ships are ingested by the model. The model forecasts are compared with observations, and we show that the ocean model has skill in predicting sea surface temperatures.
Jin-Song von Storch, Eileen Hertwig, Veit Lüschow, Nils Brüggemann, Helmuth Haak, Peter Korn, and Vikram Singh
Geosci. Model Dev., 16, 5179–5196, https://doi.org/10.5194/gmd-16-5179-2023, https://doi.org/10.5194/gmd-16-5179-2023, 2023
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The new ocean general circulation model ICON-O is developed for running experiments at kilometer scales and beyond. One targeted application is to simulate internal tides crucial for ocean mixing. To ensure their realism, which is difficult to assess, we evaluate the barotropic tides that generate internal tides. We show that ICON-O is able to realistically simulate the major aspects of the observed barotropic tides and discuss the aspects that impact the quality of the simulated tides.
Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael L. Forget, Christian Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, and Shubha Sathyendranath
Geosci. Model Dev., 16, 4639–4657, https://doi.org/10.5194/gmd-16-4639-2023, https://doi.org/10.5194/gmd-16-4639-2023, 2023
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While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Rafael Santana, Helen Macdonald, Joanne O'Callaghan, Brian Powell, Sarah Wakes, and Sutara H. Suanda
Geosci. Model Dev., 16, 3675–3698, https://doi.org/10.5194/gmd-16-3675-2023, https://doi.org/10.5194/gmd-16-3675-2023, 2023
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We show the importance of assimilating subsurface temperature and velocity data in a model of the East Auckland Current. Assimilation of velocity increased the representation of large oceanic vortexes. Assimilation of temperature is needed to correctly simulate temperatures around 100 m depth, which is the most difficult region to simulate in ocean models. Our simulations showed improved results in comparison to the US Navy global model and highlight the importance of regional models.
David Byrne, Jeff Polton, Enda O'Dea, and Joanne Williams
Geosci. Model Dev., 16, 3749–3764, https://doi.org/10.5194/gmd-16-3749-2023, https://doi.org/10.5194/gmd-16-3749-2023, 2023
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Validation is a crucial step during the development of models for ocean simulation. The purpose of validation is to assess how accurate a model is. It is most commonly done by comparing output from a model to actual observations. In this paper, we introduce and demonstrate usage of the COAsT Python package to standardise the validation process for physical ocean models. We also discuss our five guiding principles for standardised validation.
Katherine Hutchinson, Julie Deshayes, Christian Éthé, Clément Rousset, Casimir de Lavergne, Martin Vancoppenolle, Nicolas C. Jourdain, and Pierre Mathiot
Geosci. Model Dev., 16, 3629–3650, https://doi.org/10.5194/gmd-16-3629-2023, https://doi.org/10.5194/gmd-16-3629-2023, 2023
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Bottom Water constitutes the lower half of the ocean’s overturning system and is primarily formed in the Weddell and Ross Sea in the Antarctic due to interactions between the atmosphere, ocean, sea ice and ice shelves. Here we use a global ocean 1° resolution model with explicit representation of the three large ice shelves important for the formation of the parent waters of Bottom Water. We find doing so reduces salt biases, improves water mass realism and gives realistic ice shelf melt rates.
Daniele Bianchi, Daniel McCoy, and Simon Yang
Geosci. Model Dev., 16, 3581–3609, https://doi.org/10.5194/gmd-16-3581-2023, https://doi.org/10.5194/gmd-16-3581-2023, 2023
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We present NitrOMZ, a new model of the oceanic nitrogen cycle that simulates chemical transformations within oxygen minimum zones (OMZs). We describe the model formulation and its implementation in a one-dimensional representation of the water column before evaluating its ability to reproduce observations in the eastern tropical South Pacific. We conclude by describing the model sensitivity to parameter choices and environmental factors and its application to nitrogen cycling in the ocean.
Cited articles
Alldredge, A. L. and Gotschalk, C.: In situ settling behavior of marine snow, Limnol. Oceanogr., 33, 339–35, https://doi.org/10.4319/lo.1988.33.3.0339, 1988.
Aller, R. C.: Transport and reactions in the bioirrigated zone, in: The Benthic Boundary Layer: Transport Processes and Biogeochemistry, edited by: Boudreau, B. and Jørgensen, B. B., Oxford Press, 2001.
Almroth, E., Tengberg, A., Andersson, J. H., Pakhomova, S., and Hall, P. O. J.: Effects of resuspension on benthic fluxes of oxygen, nutrients, dissolved inorganic carbon, iron and manganese in the Gulf of Finland, Baltic Sea, Cont. Shelf Res., 29, 807–818, https://doi.org/10.1016/j.csr.2008.12.011, 2009.
Anderson, J. J., Okubo, A., Robbins, A. S., and Richards, F. A.: A model for nitrite and nitrate distributions in oceanic oxygen minimum zones, Deep-Sea Res., 29, 1113–1140, 1982.
Arndt, S. and Regnier, P.: A model for the benthic-pelagic coupling of silica in estuarine ecosystems: sensitivity analysis and system scale simulation, Biogeosciences, 4, 331–352, https://doi.org/10.5194/bg-4-331-2007, 2007.
Bektursunova, R. and L'Heureux, I.: A reaction-transport model of periodic precipitation of pyrite in anoxic marine sediments, Chem. Geol., 287, 158–170, https://doi.org/10.1016/j.chemgeo.2011.06.004, 2011.
Berner, R. A.: Principles of Chemical Sedimentology, McGraw-Hill Inc., 1971.
Berner, R. A. Early Diagenesis: A Theoretical Approach, Princeton Series in Geochemistry, 1, 241 pp., 1980.
Blackford, J. C., Allen, J. I., and Gilbert, F. J.: Ecosystem dynamics at six contrasting sites: A generic modelling study, J. Marine Syst., 52, 191–215, https://doi.org/10.1016/j.jmarsys.2004.02.004, 2004.
Blackwelder, P., Hood, T., Alvarez-Zarikian, C., Nelsen, T. A., and McKee, B.: Benthic foraminifera from the necop study area impacted by the Mississippi River plume and seasonal hypoxia, Quarternary Int., 31, 19–36, https://doi.org/10.1016/1040-6182(95)00018-E, 1996.
Boudreau, B. P.: A method-of-lines code for carbon and nutrient diagenesis in aquatic sediments, Comput. Geosci., 22, 479–496, https://doi.org/10.1016/0098-3004(95)00115-8, 1996.
Boudreau, B. P.: Diagenetic models and their implementation, Modelling transport and reactions in aquatic sediments, https://doi.org/10.1007/978-3-642-60421-8, 1997.
Boudreau, B. P. and Jorgensen, B. B.: The Benthic Boundary Layer: Transport Processes and Biogeochemistry, edited by: Boudreau, B. P. and Jorgensen, B. B., Oxford University Press, 1st Edn., 2001.
Brezonik, P. L. and Arnold, W. A.: Water Chemistry An Introduction to the Chemistry of Natural and Engineered Aquatic Systems, Oxford University Press, New York, 1st Edn., 2011.
Brigolin, D., Lovato, T., Rubino, A., and Pastres, R.: Coupling early-diagenesis and pelagic biogeochemical models for estimating the seasonal variability of N and P fluxes at the sediment-water interface: Application to the northwestern Adriatic coastal zone, J. Marine Syst., 87, 239–255, https://doi.org/10.1016/j.jmarsys.2011.04.006, 2011.
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical models, Environ. Modell. Softw., 61, 249–265, https://doi.org/10.1016/j.envsoft.2014.04.002, 2014.
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 (3–4 Spec. Iss.), 180–211, https://doi.org/10.1016/j.jmarsys.2005.04.011, 2006.
Butenschön, M., Zavatarelli, M., and Vichi, M.: Sensitivity of a marine coupled physical biogeochemical model to time resolution, integration scheme and time splitting method, Ocean Model., 52–53, 36–53, https://doi.org/10.1016/j.ocemod.2012.04.008, 2012.
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.
Canfield, D. E., Thamdrup, B., and Kristensen, E.: Aquatic geomicrobiology, in: Advances in Marine Biology, edited by: Southward, A. J., Tyler, P. A., Young, C. M., and Fuiman, L. A., Elsevier Academic Press, Amsterdam, vol. 48, 640 pp., 2005.
Cerco, C. F., Noel, M. R., and Kim, S.-C.: Three-dimensional Management Model for Lake Washington, Part II: Eutrophication Modeling and Skill Assessment, Lake Reserv. Manage., 22, 115–131, https://doi.org/10.1080/07438140609353889, 2006.
Cooper, D. C. and Morse, J. W.: The Chemistry of Offatts Bayou, Texas: A Seasonally Highly Sulfidic Basin, Estuaries, 19, 595, https://doi.org/10.2307/1352520, 1996.
Couture, R. M., Shafei, B., Van Cappellen, P., Tessier, A., and Gobeil, C.: Non-steady state modeling of arsenic diagenesis in lake sediments, Environ. Sci. Technol., 44, 197–203, https://doi.org/10.1021/es902077q, 2010.
Dade, W. B., Hogg, A. J., and Boudreau, B. P.: Physics of Flow Above the Sediment-Water Interface, in: The Benthic Boundary Layer, edited by: Boudreau, B. P. and Jorgensen, B. B., Oxford University Press, New York, 4–43, 2001.
Davison, W.: Iron and manganese in lakes, Earth-Sci. Rev., 34, 119–163, 1993.
Debolskaya, E. I., Yakushev, E. V., and Kuznetsov, I. S.: Analysis of the hydrophysical structure of the Sea of Azov in the period of the bottom anoxia development, J. Marine Syst., 70, 300–307, https://doi.org/10.1016/j.jmarsys.2007.02.027, 2008.
Diaz, R. J. and Rosenberg, R.: Spreading dead zones and consequences for marine ecosystems, Science, 321, 926–929, https://doi.org/10.1126/science.1156401, 2008.
Dickson, A. G.: The development of the alkalinity concept in marine chemistry, Mar. Chem., 40, 49–63, https://doi.org/10.1016/0304-4203(92)90047-E, 1992.
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.
Glud, R. N.: Oxygen dynamics of marine sediments, Mar. Biol. Res., 4, 243–289, https://doi.org/10.1080/17451000801888726, 2008.
Gregoire, M. and Lacroix, G.: Study of the oxygen budget of the Black Sea waters using a 3D coupled hydrodynamical – biogeochemical model, J. Marine Syst., 31, 175–202, https://doi.org/10.1016/S0924-7963(01)00052-5, 2001.
He, Y., Stanev, E. V., Yakushev, E., and Staneva, J.: Black Sea biogeochemistry: Response to decadal atmospheric variability during 1960–2000 inferred from numerical modeling, Mar. Environ. Res., 77, 90–102, https://doi.org/10.1016/j.marenvres.2012.02.007, 2012.
Holtappels, M. and Lorke, A.: Estimating turbulent diffusion in a benthic boundary layer, Limnol. Oceanogr.-Meth, 9, 29–41, https://doi.org/10.4319/lom.2011.9.29, 2011.
Hunter, K. S., Wang, Y., and Van Cappellen, P.: Kinetic modeling of microbially-driven redox chemistry of subsurface environments: coupling transport, microbial metabolism and geochemistry, J. Hydrol., 209, 53–80, https://doi.org/10.1016/S0022-1694(98)00157-7, 1998.
Jensen, D. L., Boddum, J. K., Tjell, J. C., and Christensen, T. H.: The solubility of rhodochrosite (MnCO3) and siderite (FeCO3) in anaerobic aquatic environments, Appl. Geochem., 17, 503–511, https://doi.org/10.1016/S0883-2927(01)00118-4, 2002.
Jorgensen, B., Bang, M., and Blackburn, T.: Anaerobic mineralization in marine sediments from the Baltic Sea-North Sea transition, Mar. Ecol. Prog. Ser., 59, 39–54, https://doi.org/10.3354/meps059039, 1990.
Jourabchi, P., Van Cappellen, P., and Regnier, P.: Quantitative interpretation of pH distributions in aquatic sediments: A reaction-transport modeling approach, Am. J. Sci., 305, 919–956, https://doi.org/10.2475/ajs.305.9.919, 2005.
Jourabchi, P., Meile, C., Pasion, L. R., and Van Cappellen, P.: Quantitative interpretation of pore water O2 and pH distributions in deep-sea sediments, Geochim. Cosmochim. Ac., 72, 1350–1364, https://doi.org/10.1016/j.gca.2007.12.012, 2008.
Kamyshny, A., Yakushev, E., Jost, G., and Podymov, O.: Chemical Structure of Pelagic Redox Interfaces, in: The Handbook of Environmental Chemistry, edited by: Yakushev, E., Springer Berlin Heidelberg, 2013.
Katsev, S., Sundby, B., and Mucci, A.: Modeling vertical excursions of the redox boundary in sediments: Application to deep basins of the Arctic Ocean, Limnol. Oceanogr., 51, 1581–1593, https://doi.org/10.4319/lo.2006.51.4.1581, 2006.
Katsev, S., Chaillou, G., Sundby, B., and Mucci, A.: Effects of progressive oxygen depletion on sediment diagenesis and fluxes: A model for the lower St. Lawrence River Estuary, Limnol. Oceanogr., 52, 2555–2568, https://doi.org/10.4319/lo.2007.52.6.2555, 2007.
Konovalov, S. K., Murray, J. W., Luther, G. W., and Tebo, B. M.: Processes controlling the redox budget for the oxic/anoxic water column of the Black Sea, Deep. Res. Pt. II., 53, 1817–1841, https://doi.org/10.1016/j.dsr2.2006.03.013, 2006.
Lancelot, C., Spitz, Y., Gypens, N., Ruddick, K., Becquevort, S., Rousseau, V., Lacroix, G., and Billen, G.: Modelling diatom and Phaeocystis blooms and nutrient cycles in the Southern Bight of the North Sea: The MIRO model, Mar. Ecol. Prog. Ser., 289, 63–78, https://doi.org/10.3354/meps289063, 2005.
Lee, J. Y., Tett, P., Jones, K., Jones, S., Luyten, P., Smith, C., and Wild-Allen, K.: The PROWQM physical-biological model with benthicpelagic coupling applied to the northern North Sea, J. Sea Res., 48, 287–331, https://doi.org/10.1016/S1385-1101(02)00182-X, 2002.
Lewis, E. and Wallace, D.: Program developed for CO2 system calculations, 4735, 1–21, 1998.
Lopes, F., Viollier, E., Thiam, A., Michard, G., Abril, G., Groleau, A., Prévot, F., Carrias, J.-F., Albéric, P., and Jézéquel, D.: Biogeochemical modelling of anaerobic vs. aerobic methane oxidation in a meromictic crater lake (Lake Pavin, France), Appl. Geochem., 26, 1919–1932, https://doi.org/10.1016/j.apgeochem.2011.06.021, 2011.
Luff, R. and Moll, A.: Seasonal dynamics of the North Sea sediments using a three-dimensional coupled sediment-water model system, Cont. Shelf Res., 24, 1099–1127, https://doi.org/10.1016/j.csr.2004.03.010, 2004.
Luff, R., Haeckel, M. and Wallmann, K.: Robust and fast FORTRAN and MATLAB libraries to calculate pH distributions in marine systems, Comput. Geosci., 27, 157–169, https://doi.org/10.1016/S0098-3004(00)00097-2, 2001.
McCarthy, M. J., McNeal, K. S., Morse, J. W., and Gardner, W. S.: Bottom-water hypoxia effects on sediment-water interface nitrogen transformations in a seasonally hypoxic, shallow bay (Corpus Christi Bay, TX, USA), Estuar. Coasts, 31, 521–531, https://doi.org/10.1007/s12237-008-9041-z, 2008.
Meile, C., Koretsky, C. M., and Van Cappellen, P.: Quantifying bioirrigation in aquatic sedimnets: An inverse modeling approach, Limnol. Oceanogr., 1, 164–177, 2001.
Meire, L., Soetaert, K. E. R., and Meysman, F. J. R.: Impact of global change on coastal oxygen dynamics and risk of hypoxia, Biogeosciences, 10, 2633–2653, https://doi.org/10.5194/bg-10-2633-2013, 2013.
Meysman, F. J. R., Boudreau, B. P., and Middelburg, J. J.: Modeling reactive transport in sediments subject to bioturbation and compaction, Geochim. Cosmochim. Ac., 69, 3601–3617, https://doi.org/10.1016/j.gca.2005.01.004, 2005.
Millero, F. J.: Thermodynamics of the carbon dioxide system in the oceans, Geochim. Cosmochim. Ac., 59, 661–677, https://doi.org/10.1016/0016-7037(94)00354-O, 1995.
Mobley, C. D. and Boss, E. S.: Improved irradiances for use in ocean heating, primary production, and photo-oxidation calculations, Appl. Optics, 51, 6549–6560, https://doi.org/10.1364/AO.51.006549, 2012.
Morgan, J. J.: Manganese and its role in biological processes, in: Metal ions in biological systems, edited by: Sigel, A. and Sigel, H. Marcel Dekker, Inc., Basel, New York, 31, 1–30, 2000.
Morse, J. W. and Eldridge, P. M.: A non-steady state diagenetic model for changes in sediment biogeochemistry in response to seasonally hypoxic/anoxic conditions in the “dead zone” of the Louisiana shelf, Mar. Chem., 106, 239–255, https://doi.org/10.1016/j.marchem.2006.02.003, 2007.
Munhoven, G.: Mathematics of the total alkalinity–pH equation – pathway to robust and universal solution algorithms: the SolveSAPHE package v1.0.1, Geosci. Model Dev., 6, 1367–1388, https://doi.org/10.5194/gmd-6-1367-2013, 2013.
Nightingale, P. D., Malin, G., Law, C. S., Watson, A. J., Liss, P. S., Liddicoat, M. I., Boutin, J., and Upstill-Goddard, R. C.: In situ evaluation of air-sea gas exchange parameterizations using novel conservative and volatile tracers, Global Biogeochem. Cy., 14, 373–387, https://doi.org/10.1029/1999GB900091, 2000.
Pakhomova, S. V., Hall, P. O. J., Kononets, M. Y., Rozanov, A. G., Tengberg, A., and Vershinin, A. V.: Fluxes of iron and manganese across the sediment-water interface under various redox conditions, Mar. Chem., 107, 319–331, https://doi.org/10.1016/j.marchem.2007.06.001, 2007.
Paraska, D. W., Hipsey, M. R., and Salmon, S. U.: Sediment diagenesis models: Review of approaches, challenges and opportunities, Environ. Model. Softw., 61, 297–325, https://doi.org/10.1016/j.envsoft.2014.05.011, 2014.
Pavlidou, A., Kontoyiannis, H., Anagnostou, C., Siokou–Frangou, I., Pagou, K., Krasakopoulou, E., Assimakopoulou, G., Zervoudaki, S., Zeri, C., Chatzianestis, J., and Psyllidou-Giouranovits, R.: Biogeochemical Characteristics in the Elefsis Bay (Aegean Sea, Eastern Mediterranean) in Relation to Anoxia and Climate Changes, in: The Handbook of Environmental Chemistry, edited by: Yakushev, E., 161–201, Springer Berlin Heidelberg, 2013.
Pearson, T. H. and Rosenberg, R.: Macrobenthic succession in relation to organic enrichment and pollution of the marine environment, Oceanogr. Mar. Biol. Ann. Rev., 16, 229–311, https://doi.org/10.1111/j.1540-5834.2012.00707.x, 1978.
Popova, E. E. and Srokosz, M. A.: Modelling the ecosystem dynamics at the Iceland-Faeroes Front: Biophysical interactions, J. Marine Syst., 77, 182–196, https://doi.org/10.1016/j.jmarsys.2008.12.005, 2009.
Queirós, A. M., Norling, K., Amaro, T., Nunes, J., Cummings, D., Yakushev, E., Sorensen, K., Harris, C., Woodward, M., Danovaro, R., Rastelli, E., Alve, E., Vittor, C. De, Karuza, A., Cibic, T., Monti, M., Ingrosso, G., Fornasaro, D., Beaubien, S. E., Guilini, K., Vanreu, A., Bigalke, N., and Widdicombe, S.: Potential impact of CCS leakage on marine communities, Technical Report, Deliverable 4.1: Potential impact of CCS leakage on marine communitiesWP4; lead beneficiary: Plymouth Marine Laboratory, 2014.
Rabalais, N. N., Turner, R. E., and Scavia, D.: Beyond Science into Policy: Gulf of Mexico Hypoxia and the Mississippi River, Bioscience, 52, 129, https://doi.org/10.1641/0006-3568(2002)052[0129:BSIPGO]2.0.CO;2, 2002.
Raymond, P. A., Zappa, C. J., Butman, D., Bott, T. L., Potter, J., Mulholland, P., Laursen, a. E., McDowell, W. H., and Newbold, D.: Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers, Limnol. Oceanogr. Fluids Environ., 2, 41–53, https://doi.org/10.1215/21573689-1597669, 2012.
Reed, D. C., Slomp, C. P., and Gustafsson, B. G.: Sedimentary phosphorus dynamics and the evolution of bottom-water hypoxia: A coupled benthic-pelagic model of a coastal system, Limnol. Oceanogr., 56, 1075–1092, https://doi.org/10.4319/lo.2011.56.3.1075, 2011.
Richards, F.: Anoxic basins and fjords, in: Chemical Oceanography, edited by: Riley, J. and Skirrow, G., Academic Press, London, 611–645, 1965.
Richardson, K. and Jørgensen, B.: Eutrophication: Definition, history and effects, in: Eutrophication in Coastal Marine Ecosystems, https://doi.org/10.1029/CE052p0001, 1996.
Rickard, D. and Luther, G. W.: Kinetics of pyrite formation by the H2S oxidation of iron (II) monosulfide in aqueous solutions between 25 and 125°C: The mechanism, Geochim. Cosmochim. Ac., 61, 135–147, https://doi.org/10.1016/S0016-7037(96)00322-5, 1997.
Roden, E. E. and Tuttle, J. H.: Sulfide release from estuarine sediments underlying anoxic bottom water, Limnol. Oceanogr., 37, 725–738, https://doi.org/10.4319/lo.1992.37.4.0725, 1992.
Roy, R. N., Roy, L. N., Vogel, K. M., Porter-Moore, C., Pearson, T., Good, C. E., Millero, F. J., and Campbell, D. M.: The dissociation constants of carbonic acid in seawater at salinities 5 to 45 and temperatures 0 to 45°C, Mar. Chem., 44, 249–267, https://doi.org/10.1016/0304-4203(93)90207-5, 1993.
Rutgers Van Der Loeff, M. M. and Boudreau, B. P.: The effect of resuspension on chemical exchanges at the sediment-water interface in the deep sea – A modelling and natural radiotracer approach, J. Marine Syst., 11, 305–342, https://doi.org/10.1016/S0924-7963(96)00128-5, 1997.
Savchuk, O. and Wulff, F.: Biogeochemical Transformations of Nitrogen and Phosphorus in the Environment, Coupling Hydrodynamic and Biogeochemical Processes in Models for the Baltic Proper, 2, 79 pp., 1996.
Savchuk, O. P.: Nutrient biogeochemical cycles in the Gulf of Riga: Scaling up field studies with a mathematical model, J. Marine Syst., 32, 253–280, https://doi.org/10.1016/S0924-7963(02)00039-8, 2002.
Schippers, A. and Jorgensen, B. B.: Biogeochemistry of pyrite and iron sulfide oxidation in marine sediments, Geochim. Cosmochim. Ac., 66, 85–92, https://doi.org/10.1016/S0016-7037(01)00745-1, 2002.
Schlüter, M., Sauter, E., Hansen, H. P., and Suess, E.: Seasonal variations of bioirrigation in coastal sediments: Modelling of field data, Geochim. Cosmochim. Ac., 64, 821–834, https://doi.org/10.1016/S0016-7037(99)00375-0, 2000.
Schneider, B., Nausch, G., Kubsch, H., and Petersohn, I.: Accumulation of total CO2 during stagnation in the Baltic Sea deep water and its relationship to nutrient and oxygen concentrations, Mar. Chem., 77, 277–291, https://doi.org/10.1016/S0304-4203(02)00008-7, 2002.
Sell, K. S. and Morse, J. W.: Dissolved Fe2+ and ∑ H2S Behavior in Sediments Seasonally Overlain by Hypoxic-to-anoxic Waters as Determined by CSV Microelectrodes, Aquat. Geochem., 12, 179–198, https://doi.org/10.1007/s10498-005-4574-2, 2006.
Sen Gupta, B. K., Turner, R. E., and Rabalais, N. N.: Seasonal oxygen depletion in continental-shelf waters of Louisiana: Historical record of benthic foraminifers, Geology, 24, 227–230, https://doi.org/10.1130/0091-7613(1996)024<0227:SODICS>2.3.CO;2, 1996.
Soetaert, K. and Middelburg, J. J.: Modeling eutrophication and oligotrophication of shallow-water marine systems: The importance of sediments under stratified and well-mixed conditions, Hydrobiologia, 629, 239–254, https://doi.org/10.1007/s10750-009-9777-x, 2009.
Soetaert, K., Herman, P. M. J., and Middelburg, J. J.: A model of early diagenetic processes from the shelf to abyssal depths, Geochim. Cosmochim. Ac., 60, 1019–1040, https://doi.org/10.1016/0016-7037(96)00013-0, 1996.
Soetaert, K., Middelburg, J. J., Herman, P. M. J., and Buis, K.: On the coupling of benthic and pelagic biogeochemical models, Earth-Science On the coupling of benthic and pelagic biogeochemical models, April 2016, 173–201, https://doi.org/10.1016/S0012-8252(00)00004-0, 2000.
Soetaert, K., Herman, P. M. J., Middelburg, J. J., Heip, C., Smith, C. L., Tett, P., and Wild-allen, K.: Numerical modelling of the shelf break ecosystem?: reproducing benthic and pelagic measurements, Deep Sea Res. Pt. II, 48, 3141–3177, 2001.
Soetaert, K., Hofmann, A. F., Middelburg, J. J., Meysman, F. J. R., and Greenwood, J.: The effect of biogeochemical processes on pH (Reprinted from Marine Chemistry, vol 105, pg 30–51, 2007), Mar. Chem., 106, 380–401, https://doi.org/10.1016/j.marchem.2007.06.008, 2007.
Sohma, A., Sekiguchi, Y., Kuwae, T., and Nakamura, Y.: A benthic-pelagic coupled ecosystem model to estimate the hypoxic estuary including tidal flat-Model description and validation of seasonal/daily dynamics, Ecol. Modell., 215, 10–39, https://doi.org/10.1016/j.ecolmodel.2008.02.027, 2008.
Stanev, E. V., He, Y., Staneva, J., and Yakushev, E.: Mixing in the Black Sea detected from the temporal and spatial variability of oxygen and sulfide – Argo float observations and numerical modelling, Biogeosciences, 11, 5707–5732, https://doi.org/10.5194/bg-11-5707-2014, 2014.
Tebo, B. M.: Manganese(II) oxidation in the suboxic zone of the Black Sea, Deep Sea Res. Pt., 38, S883–S905, https://doi.org/10.1016/S0198-0149(10)80015-9, 1991.
Tebo, B. M., Ghiorse, W. C., van Waasbergen, L. G., Siering, P. L., and Caspi, R.: Bacterially mediated mineral formation: Insights into manganese(II) oxidation from molecular genetic and biochemical studies, in: Geomicrobiology: Interactions Between Microbes and Minerals. The Mineralogical Society of America, Washington, D.C., 225–266, 1997.
Thorpe, S. A.: The Turbulent Ocean, Cambridge University Press, 2005.
UNESCO, Progress on oceanographic tables and standards 1983–1986: Work and recommendations of the UNESCO/SCOR/ICES/IAPSO Joint Panel. chap. 7.5: Oxygen solubility. UNESCO Technical Papers in Marine Science No. 50, 11 pp., 1986.
Umlauf, L., Burchard, H. and Bolding, K.: General ocean turbulence model, Source code documentation, Balt. Sea Res. Inst. Warn. Tech. Rep, 63, p. 346, 2005.
Van Cappellen, P. and Wang, Y. F.: Cycling of iron and manganese in surface sediments: A general theory for the coupled transport and reaction of carbon, oxygen, nitrogen, sulfur, iron, and manganese, Am. J. Sci., 296, 197–243, https://doi.org/10.2475/ajs.296.3.197, 1996.
Volkov, I. I.: Geokhimiya Sery v Osadkakh Okeana (Geochemistry of Sulfur in Ocean Sediments), Nauka, Moscow, 1984.
Wanninkhof, R.: Relationship between wind speed and gas exchange over the ocean revisited, Limnol. Oceanogr., 12, 351–362, https://doi.org/10.4319/lom.2014.12.351, 2014.
Wersin, P.: The Fe(II)-CO2-H2O system in anoxic natural waters: equilibria and surface chemistry, Swiss Fed. Inst. Technol., Dissertation ETH No.9230, 1990.
Wijsman, J. W. M., Herman, P. M. J., Middelburg, J. J., and Soetaert, K.: A Model for Early Diagenetic Processes in Sediments of the Continental Shelf of the Black Sea, Estuar. Coast. Shelf S., 54, 403–421, https://doi.org/10.1006/ecss.2000.0655, 2002.
Wolf-Gladrow, D. A., Zeebe, R. E., Klaas, C., Körtzinger, A., and Dickson, A. G.: Total alkalinity: The explicit conservative expression and its application to biogeochemical processes, Mar. Chem., 106, 287–300, https://doi.org/10.1016/j.marchem.2007.01.006, 2007.
Wollast, R.: Rate and mechanism of dissolution of carbonates in the system CaCO3-MgCO3, Aquatic chemical kinetics: reaction rates of processes in natural waters, Chemistry, in: Aquatic Chemical Kinetics: Reaction Rates of Processes in Natural Waters, edited by: Stumm, W., 431–445, 1990.
Yakushev, E.: RedOx Layer Model: A Tool for Analysis of the Water Column Oxic/Anoxic Interface Processes, in: Chemical Structure of Pelagic Redox Interfaces: Observation and Modeling, edited by: Yakushev, E. V. Springer-Verlag Berlin Heidelberg, 203–234, 2013.
Yakushev, E., Pakhomova, S., Sørenson, K., and Skei, J.: Importance of the different manganese species in the formation of water column redox zones: Observations and modeling, Mar. Chem., 117, 59–70, https://doi.org/10.1016/j.marchem.2009.09.007, 2009.
Yakushev, E. V and Neretin, L. N.: One-dimensional modeling of nitrogen and sulfur cycles in the aphotic zones of the Black and Arabian Seas, Global Biogeochem. Cy., 11, 401–414, https://doi.org/10.1029/97GB00782, 1997.
Yakushev, E. V., Pollehne, F., Jost, G., Kuznetsov, I., Schneider, B., and Umlauf, L.: Redox Layer Model (ROLM): A Tool for Analysis of the Water Column Oxic/anoxic Interface Processes, Warnemunde, 2006.
Yakushev, E. V., Pollehne, F., Jost, G., Kuznetsov, I., Schneider, B., and Umlauf, L.: Analysis of the water column oxic/anoxic interface in the Black and Baltic seas with a numerical model, Mar. Chem., 107, 388–410, https://doi.org/10.1016/j.marchem.2007.06.003, 2007.
Yakushev, E. V., Chasovnikov, V. K., Murray, J. W., Pakhomova, S. V., Podymov, O. I., and Stunzhas, P. A.: Vertical hydrochemical structure of the black sea, Handb. Environ. Chem. Vol. 5 Water Pollut., 52007, 277–307, https://doi.org/10.1007/698_5_088, 2008.
Yakushev, E. V., Kuznetsov, I. S., Podymov, O. I., Burchard, H., Neumann, T., and Pollehne, F.: Modeling the influence of oxygenated inflows on the biogeochemical structure of the Gotland Sea, central Baltic Sea: Changes in the distribution of manganese, Comput. Geosci., 37, 398–409, https://doi.org/10.1016/j.cageo.2011.01.001, 2011.
Yakushev, E. V., Debolskaya, E. I., Kuznetsov, I. S., and Staalstrøm, A.: Modelling of the Meromictic Fjord Hunnbunn (Norway) with an Oxygen Depletion Model (OxyDep), edited by: Yakushev, E. V., Handb. Environ. Chem., July 2011, https://doi.org/10.1007/698_2011_110, 2013a.
Yakushev, E. V., Sorensen, K., and Sørensen, K.: On seasonal changes of the carbonate system in the Barents Sea: observations and modeling, Mar. Biol. Res., 9, 822–830, https://doi.org/10.1080/17451000.2013.775454, 2013b.
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, 2015.
Zeebe, R. E. and Wolf-Gladrow, D.: CO2 in Seawater, Equilibrium, Kinetics, Isotopes, Elsevier, 2001.
Short summary
This paper presents a new benthic–pelagic biogeochemical model (BROM) that combines a relatively simple pelagic ecosystem model with a detailed biogeochemical model of the coupled cycles of N, P, Si, C, O, S, Mn, Fe in the water column, benthic boundary layer, and sediments, with a focus on oxygen and redox state. BROM should be of interest for the study of a range of environmental applications in addition to hypoxia, such as benthic nutrient recycling, redox biogeochemistry, and eutrophication.
This paper presents a new benthic–pelagic biogeochemical model (BROM) that combines a...
