Articles | Volume 11, issue 5
https://doi.org/10.5194/gmd-11-1849-2018
© Author(s) 2018. 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-11-1849-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 May 2018
Model description paper |
| 14 May 2018
| 14 May 2018
Cohesive and mixed sediment in the Regional Ocean Modeling System (ROMS v3.6) implemented in the Coupled Ocean–Atmosphere–Wave–Sediment Transport Modeling System (COAWST r1234)
Christopher R. Sherwood
CORRESPONDING AUTHOR
U.S. Geological Survey, 384 Woods Hole Road, Woods Hole, MA 02543-1598, USA
Alfredo L. Aretxabaleta
U.S. Geological Survey, 384 Woods Hole Road, Woods Hole, MA 02543-1598, USA
Courtney K. Harris
Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA 23062, USA
J. Paul Rinehimer
Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA 23062, USA
currently at: WEST Consultants, Bellevue, WA 98055, USA
Romaric Verney
IFREMER, Plouzane, France
Bénédicte Ferré
U.S. Geological Survey, 384 Woods Hole Road, Woods Hole, MA 02543-1598, USA
currently at: CAGE-Centre for Arctic Gas Hydrate, Environment, and
Climate; Department of Geosciences, UiT The Arctic University of Norway, 9037 Tromsø, Norway
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We describe a new model of coastal barrier ecogeomorphic change that operates over spatiotemporal scales congruous with effective management practices, incorporates key ecogeomorphic feedbacks, and provides probabilistic projections. The model skillfully captures important barrier dynamics through robust data integration and calibration of relatively simple model parameterizations, and can be used to understand and predict when, where, and how barriers evolve to inform decision-making processes.
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We describe a new model of coastal barrier ecogeomorphic change that operates over spatiotemporal scales congruous with effective management practices, incorporates key ecogeomorphic feedbacks, and provides probabilistic projections. The model skillfully captures important barrier dynamics through robust data integration and calibration of relatively simple model parameterizations, and can be used to understand and predict when, where, and how barriers evolve to inform decision-making processes.
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Water levels in bays are affected by open-ocean changes and wind. Tides are more dampened in the bays than storm surges and sea level rise. We compare observed and modeled levels with ocean conditions and combine them with analytical models. We consider the local setup, caused by wind along the bay. Expansion using the ADCIRC tidal database will allow coverage of other bay systems on the United States East Coast. Spatial estimates of water level can inform decisions about bay flooding hazards.
Neil K. Ganju, Jeremy M. Testa, Steven E. Suttles, and Alfredo L. Aretxabaleta
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-335, https://doi.org/10.5194/bg-2018-335, 2018
Revised manuscript not accepted
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Estuaries are productive ecosystems that provide habitat for flora and fauna. We measured changes in light and oxygen, along with variables such as tides and waves, to understand how productivity in the estuary changed over daily and seasonal time periods. We found large differences in productivity between channels and seagrass beds, as well as a link between light climate and productivity. This study will help us understand how estuaries will respond to future changes in conditions.
Tarandeep S. Kalra, Alfredo Aretxabaleta, Pranay Seshadri, Neil K. Ganju, and Alexis Beudin
Geosci. Model Dev., 10, 4511–4523, https://doi.org/10.5194/gmd-10-4511-2017, https://doi.org/10.5194/gmd-10-4511-2017, 2017
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The paper details the sensitivity of vegetation properties that are input to a 3-D submerged aquatic vegetation model within a coupled hydrodynamics and wave model. It describes a novel strategy to perform sensitivity analysis efficiently by using a combination of the Effective Quadratures method and Sobol' indices. This method reduces the number of simulations to understand the sensitivity patterns and also quantifies the amount of sensitivity.
Julia M. Moriarty, Courtney K. Harris, Katja Fennel, Marjorie A. M. Friedrichs, Kehui Xu, and Christophe Rabouille
Biogeosciences, 14, 1919–1946, https://doi.org/10.5194/bg-14-1919-2017, https://doi.org/10.5194/bg-14-1919-2017, 2017
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In coastal aquatic environments, resuspension of sediment and organic material from the seabed into the overlying water can impact biogeochemistry. Here, we used a novel modeling approach to quantify this impact for the Rhône River delta. In the model, resuspension increased oxygen consumption during individual resuspension events, and when results were averaged over 2 months. This implies that observations and models that only represent calm conditions may underestimate net oxygen consumption.
M. Grifoll, A. L. Aretxabaleta, J. L. Pelegrí, and M. Espino
Ocean Sci., 12, 137–151, https://doi.org/10.5194/os-12-137-2016, https://doi.org/10.5194/os-12-137-2016, 2016
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We investigate the rapidly changing equilibrium between the momentum sources and sinks during the passage of a single two-peak storm over the Catalan inner shelf (NW Mediterranean Sea). At 24m water depth, a primary momentum balance between acceleration, pressure gradient and frictional forces (surface and bottom) is established. The frictional adjustment timescale was around 10h, consistent with the e-folding time obtained from bottom drag parameterizations.
A. L. Aretxabaleta, K. W. Smith, and J. Ballabrera-Poy
Ocean Sci. Discuss., https://doi.org/10.5194/osd-12-983-2015, https://doi.org/10.5194/osd-12-983-2015, 2015
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We estimate global surface salinity means and trends using historical (1950-2014) monthly fields and recent SMOS satellite data. We separate the regimes by fitting a Gaussian Mixture Model with a non-subjective method. There are three separate regimes: A (1950-1990) with small trends; B (1990-2009) with enhanced trends; and C (2009-2014) with significantly larger trends. The trend acceleration could be related to an enhanced hydrological cycle or to changes in sampling methodology.
N. K. Ganju, J. L. Miselis, and A. L. Aretxabaleta
Biogeosciences, 11, 7193–7205, https://doi.org/10.5194/bg-11-7193-2014, https://doi.org/10.5194/bg-11-7193-2014, 2014
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Light availability to seagrass is an important factor in their success. We deployed instrumentation to measure light in Barnegat Bay, New Jersey, and found lower availability in the southern bay due to high turbidity (suspended sediment), while the northern bay has higher availability. In the northern bay, dissolved organic material and chlorophyll are most responsible for blocking light to the seagrass canopy. We also found that boat wakes do not have a large effect on sediment resuspension.
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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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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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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.
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.
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
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In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
Pengcheng Wang and Natacha B. Bernier
Geosci. Model Dev., 16, 3335–3354, https://doi.org/10.5194/gmd-16-3335-2023, https://doi.org/10.5194/gmd-16-3335-2023, 2023
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Effects of sea ice are typically neglected in operational flood forecast systems. In this work, we capture these effects via the addition of a parameterized ice–ocean stress. The parameterization takes advantage of forecast fields from an advanced ice–ocean model and features a novel, consistent representation of the tidal relative ice–ocean velocity. The new parameterization leads to improved forecasts of tides and storm surges in polar regions. Associated physical processes are discussed.
Cited articles
Amoudry, L. O. and Souza, A. J.: Deterministic coastal morphological and sediment transport modeling: a review and discussion, Rev. Geophys., 49, RG2002, https://doi.org/10.1029/2010RG000341, 2011.
Ariathurai, R. and Arulanandan, K.: Erosion Rates of Cohesive Soils, Journal of Hydraulic Division, ASCE, 104, 279–283, 1978.
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions: 1. Model description and validation, J. Geophys. Res., 104, 7649–7666, https://doi.org/10.1029/98JC02622, 1999.
Boudreau, B. P.: Is burial velocity a master parameter for bioturbation?, Geochim. Cosmochim. Ac., 58, 1243–1250, 1994.
Boudreau, B. P.: Diagenetic Models and Their Implementation, Springer-Verlag, Berlin, 414 pp., 1997.
Beudin, A., Kalra, T. S., Ganju, N. K., and Warner, J. C.: Development of a Coupled Wave-Flow-Vegetation Interaction Model, Comput. Geosci., 100, 76–86, https://doi.org/10.1016/j.cageo.2016.12.010, 2017.
Birchler, J. J., Harris, C. K., Kniskern, T. A., and Sherwood, C.R .: Numerical model of geochronological tracers for deposition and reworking applied to the Mississippi subaqueous delta, J. Coast. Res., SI 85: 1–5, https://doi.org/10.2112/SI85-001.1, 2018.
Burchard, H. and Baumert, H.: The formation of estuary turbidity maxima due to density effects in the salt wedge. A hydrodynamic process study, J. Phys. Oceanogr., 20, 309–321, 1998.
Butman B., Aretxabaleta, A. L., Dickhudt, P. J., Dalyander, P. S., Sherwood, C. R., Anderson, D. M., Keafer, B. A., and Signell, R. P.: Investigating the importance of sediment resuspension in Alexandrium fundyense cyst population dynamics in the Gulf of Maine, Deep-Sea Res. Pt. II, 103, 74–95, https://doi.org/10.1016/j.dsr2.2013.10.011, 2014.
Caldwell, R. L. and Edmonds, D. A.: The effects of sediment properties on deltaic processes and morphologies: A numerical modeling study, J. Geophys. Res.-Earth, 119, 961–982, https://doi.org/10.1002/2013JF002965, 2014.
Cartwright, G. M., Friedrichs, C. T., and Smith, J. S.: A test of the ADV-based Reynolds-flux method for in situ estimation of sediment settling velocity in a muddy estuary, Geo-Mar. Lett., 33, 477–484, https://doi.org/10.1007/s00367-013-0340-4, 2013.
de Boer, P. L.: Mechanical effects of micro-organisms on intertidal bedform migration, Sedimentology, 28, 129–132, https://doi.org/10.1111/j.1365-3091.1981.tb01670.x, 1981.
de Deckere, E. M. G. T., Tolhurst, T. J., and de Brouwer, J. F. C.: Destabilization of Cohesive Intertidal Sediments by Infauna, Estuar. Coast. Shelf S., 53, 665–669, https://doi.org/10.1006/ecss.2001.0811, 2001.
del Barrio, P., Ganju, N. K., Aretxabaleta, A. L., Hayn, M., García, A., and Howarth, R. W.: Modeling Future Scenarios of Light Attenuation and Potential Seagrass Success in a Eutrophic Estuary, Estuar. Coast. Shelf S., 149, 13–23, https://doi.org/10.1016/j.ecss.2014.07.005, 2014.
Dickhudt, P. J.: Controls on erodibility in a partially mixed estuary: York River, Virginia, MS thesis, College of William and Mary, Gloucester Point, VA, 2008.
Dickhudt, P. J., Friedrichs, C. T., Schaffner, L. C., and Sanford, L. P.: Spatial and temporal variation in cohesive sediment erodibility in the York River estuary, eastern USA: A biologically influenced equilibrium modified by seasonal deposition, Mar. Geol., 267, 128–140, https://doi.org/10.1016/j.margeo.2009.09.009, 2009.
Dickhudt, P. J., Friedrichs, C. T., and Sanford, L. P.: Mud matrix solids fraction and bed erodibility in the York River estuary, USA, and other muddy environments, Cont. Shelf Res., 31, S3–S13, https://doi.org/10.1016/j.csr.2010.02.008, 2011.
DiToro, D. M.: Sediment Flux Modeling, Wiley-Interscience, New York, 624 pp., 2001.
Ditschke, D. and Markofsky, M.: A time-dependent flocculation model, in: Sediment and Ecohydraulics – INTERCOH 2005, Proceedings in Marine Science, edited by: Kusuda, T., Yamanishi, H., Spearman, J., and Gailani, J. Z., Elsevier, Amsterdam, 9, 241–253, https://doi.org/10.1016/S1568-2692(08)80019-8, 2008.
Droppo, I. G., Leppard, G. G., Liss, S. N., and Milligan, T. G.: Opportunities, needs, and strategic direction for research on flocculation in natural and engineered systems, in: Flocculation in Natural and Engineered Environmental Systems, edited by: Droppo, I. G., Leppard, G. G., Liss, S. N., and Milligan, T. G., CRC Press, London, 407–421, 2005.
Dyer, K. R.: Coastal and Estuarine Sediment Dynamics, John Wiley and Sons, Chichester, 1986.
Edmonds, D. A. and Slingerland, R. L.: Significant effect of sediment cohesion on delta morphology, Nat. Geosci., 3, 105–109, https://doi.org/10.1038/ngeo730, 2010.
Einstein H. A. and Krone R. B.: Experiments to determine modes of cohesive sediment transport in salt water, J. Geophys. Res., 67, 1451–1461, https://doi.org/10.1029/JZ067i004p01451, 1962.
Eisma, D.: Flocculation and de-flocculation of suspended matter in estuaries, Neth. J. Sea Res., 20, 183–199, 1986.
Fall, K. A., Harris, C. K., Friedrichs, C. T., Rinehimer, J. P., and Sherwood, C. R.: Model behavior and sensitivity in an application of the cohesive bed component of the Community Sediment Transport Modeling System for the York River Estuary, VA, USA, J. Mar. Sci. Eng., 2, 413–436, https://doi.org/10.3390/jmse2020413, 2014.
Fasham, M. J. R., Ducklow, H. W., and McKelvie S. M.: A nitrogen-based model of plankton dynamics in the oceanic mixed layer, J. Mar. Res., 48, 591–639, 1990.
Fennel, K., Wilkin, J., Levin, J., Moisan, J., and O'Reilly, J.: Nitrogen cycling in the Middle Atlantic Bight: Results from a three-dimensional model and implications for the North Atlantic nitrogen budget, Global Biogeochem. Cy., 20, GB3007, https://doi.org/10.1029/2005GB002456, 2006.
Harris, C. K. and Wiberg, P. L.: Approaches to quantifying long-term continental shelf sediment transport with an example from the northern California STRESS mid-shelf site, Cont. Shelf Res., 17, 1389–1418, 1997.
Harris, C. K. and Wiberg, P. L.: A two-dimensional, time-dependent model of suspended sediment transport and bed reworking for continental shelves, Comput. Geosci., 27, 675–690, 2001.
Hill, P. S. and Nowell, A. R. M.: Comparison of two models of aggregation in continental-shelf bottom boundary layers, J. Geophys. Res., 100, 22749–22763, 1995.
Hirano, M.: River bed degradation with armouring, in: Proceedings, Japan Society of Civil Engineers, Japan, 195, 55-65, https://doi.org/10.2208/jscej1969.1971.195_55, 1971.
Hsu T.-J., Jenkins, J. T., and Liu, P. L.-F.: On two-phase sediment transport: Dilute flow, J. Geophys. Res., 108, 3057, https://doi.org/10.1029/2001JC001276, 2003.
Huettel, M., Ziebis, W., and Forster, S.: Flow-induced uptake of particulate matter in permeable sediments, Limnol. Oceanogr., 41, 309–322, https://doi.org/10.4319/lo.1996.41.2.0309, 1999.
HydroQual, Inc.: A Primer for ECOMSED Version 1.4 Users Manual, HydroQual, Inc., Mahwah, NJ, 2004.
Jacobs, W., Le Hir, P., Van Kesteren, W., and Cann, P.: Erosion threshold of sand – mud mixtures, Cont. Shelf Res., 31, S14–S25, https://doi.org/10.1016/j.csr.2010.05.012, 2011.
Keyvani, A. and Strom, K.: Influence of Cycles of High and Low Turbulent Shear on the Growth Rate and Equilibrium Size of Mud Flocs, Mar. Geol., 354, 1–14, https://doi.org/10.1016/j.margeo.2014.04.010, 2014.
Khelifa, A. and Hill, P. S.: Models for effective density and settling velocity of flocs, J. Hydraul. Res., 44, 390–401, 2006.
Kirby, R.: High Concentration Suspension (Fluid Mud) Layers in Estuaries, in: Physical Processes in Estuaries, edited by: Dronkers, J. and van Leussen, W., Springer, Berlin, Heidelberg, 463–487, https://doi.org/10.1007/978-3-642-73691-9_23, 1988.
Knoch, D. and Malcherek, A.: A numerical model for simulation of fluid mud with different rheological behaviors, Ocean Dynam., 61, 245–256, https://doi.org/10.1007/s10236-010-0327-x, 2011.
Kranenburg, C.: The fractal structure of cohesive sediment aggregates, Estuar. Coast. Shelf Sci., 39, 451–460, 1994.
Krone, R. B.: Flume studies of the transport of sediment in estuarial shoaling processes, Final Report, Hydraulic Engineering Laboratory and Sanitary Engineering Research Laboratory, Univ. of California, Berkeley, 1962.
Le Hir, P., Bassoullet, P., and Jestin, H.: Application of the continuous modeling concept to simulate high-concentration suspended sediment in a macrotidal estuary, in: Proceedings in Marine Science, Coastal and Estuarine Fine Sediment Processes, edited by: McAnally, W. H. and Mehta, A. J., Elsevier, 229–247, https://doi.org/10.1016/S1568-2692(00)80124-2, 2000.
Le Hir, P., Cayocca, F., and Waeles, B.: Dynamics of sand and mud mixtures: A multiprocess-based modelling strategy, Cont. Shelf Res., 3, S135–S149, 2011.
Lecroart, P., Maire, O., Schmidt, S., Grémare, A., Anschutz, P., and Meysman F. J. R.: Bioturbation, short-lived radioisotopes, and the tracer-dependence of biodiffusion coefficients, Geochim. Cosmochim. Ac., 74, 21, 6049–6063, https://doi.org/10.1016/j.gca.2010.06.010, 2010.
Letter, J. V.: Significance of probabilistic parameterization in cohesive sediment bed exchange, PhD thesis, Univ. of Florida, Gainesville, 2009.
Letter, J. V. and Mehta, A. J.: A heuristic examination of cohesive sediment bed exchange in turbulent flows, Coast. Eng., 58, 779–789, 2011.
Li, Q. W., Benson M. Harlan, M., Robichaux, P., Sha, X., Xu, K., and Straub, K. M.: Influence of sediment cohesion on deltaic morphodynamics and stratigraphy over basin-filling time scales, J. Geophys. Res.-Earth, 122, 1808–1826, https://doi.org/10.1002/2017JF004216, 2017.
Lick, W., Huang, H., and Jepsen, R.: Flocculation of fine-grained sediment due to differential settling, J. Geophys. Res., 98, 10279–10288, 1993.
Lumborg, U.: Modelling the deposition, erosion, and flux of cohesive sediment through Oresund, J. Mar. Syst., 56, 179–193, 2005.
Lumborg, U. and Pejrup, M.: Modelling of cohesive sediment transport in a tidal lagoon – an annual budget, Mar. Geol., 218, 1–16, 2005.
Lumborg, U. and Windelin, A.: Hydrography and cohesive sediment modelling: application to the Romo Dyb tidal area, J. Mar. Syst., 38, 287–303, 2003.
Maa, J. P.-Y., Sanford, L. P., and Schoellhamer, D. H. (Eds): Estuarine and Coastal Fine Sediment Dynamics: INTERCOH 2003, vol. 8, Proceedings in Marine Science, Elsevier, Amsterdam, 2007.
MacCready, P. and Geyer, W. R.: Estuarine salt flux through an isohaline surface, J. Geophys. Res., 106, 11629–11637, 2001.
MacDonald, I., Vincent, C. E., Thorne, P. D., and Moate, B. D.: Acoustic scattering from a suspension of flocculated sediments, J. Geophys. Res., 118, 2581–2594, https://doi.org/10.1002/jgrc.20197, 2013.
Maerz, J., Verney, R., Wirtz, K., and Feudel, U.: Modeling flocculation processes: Intercomparison of a size class-based model and a distribution-based model, Cont. Shelf Res., 31, S84–S93, https://doi.org/10.1016/j.csr.2010.05.011, 2011.
Malarkey, J., Baas, J. H., Hope, J. A., Aspden, R. J., Parsons, D. R., Peakall, J., Paterson, D. M., Schindler, R. J., Ye, L., Lichtman, I. D., Bass, S. J., Davies, A. G., Manning, A. J., and Thorne, P. D.: The pervasive role of biological cohesion in bedform development, Nat. Commun., 6, 6257, https://doi.org/10.1038/ncomms7257, 2015.
Manning, A. J. and Dyer, K. R.: Mass settling flux of fine sediments in Northern European estuaries: measurements and predictions, Mar. Geol., 245, 107–122, 2007.
Manning, A. J., Baugh, J. V., Spearman, J. R., and Whitehouse, R. J. S.: Flocculation settling characteristics of mud: sand mixtures, Ocean Dynam., 60, 237–253, https://doi.org/10.1007/s10236-009-0251-0, 2010.
Manning, A. J., Baugh, J. V., Spearman, J. R., Pidduck, E. L., and Whitehouse, R. J. S.: The settling dynamics of flocculating mud-sand mixtures: Part 1 – Empirical algorithm development, Ocean Dynam., 61, 311–350, https://doi.org/10.1007/s10236-011-0394-7, 2011.
McCave, I. N.: Size spectra and aggregation of suspended particles in the deep ocean, Deep-Sea Res., 31, 329–352, 1984.
McCave, I. N. and Swift, S. A.: A physical model for the deposition of fine-grained sediments in the deep sea, GSA Bulletin, 87, 541–546, 1976.
Mehta, A. J.: Characterization of cohesive sediment properties and transport processes in estuaries, in: Lecture Notes on Coastal and Estuarine Studies, edited by: Mehta, A. J., Springer, Berlin, 14, 290–325, 1986.
Mehta, A. J.: Understanding fluid mud in a dynamic environment, Geo-Mar. Lett., 11, 113–118, https://doi.org/10.1007/BF02430995, 1991.
Mehta, A. J.: An Introduction to the Hydraulics of Fine Sediment Transport, World Scientific, 1039 pp, 2014.
Mehta, A. J., Manning, A. J., and Khare, Y. P.: A note on the Krone deposition equation and significance of floc aggregation, Mar. Geol., 354, 34–39, https://doi.org/10.1016/j.margeo.2014.04.002, 2014.
Mengual, B., Hir, P.L., Cayocca, F., and Garlan, T.: Modelling Fine Sediment Dynamics: Towards a Common Erosion Law for Fine Sand, Mud and Mixtures, Water, 9, 564, https://doi.org/10.3390/w9080564, 2017.
Merkel, U. H. and Kopmann, R.: A continuous vertical grain sorting model for Telemac & Sisyphe, in: River Flow 2012, edited by: Munoz, R. M., Taylor & Francis, London, 2012.
Michalakes, J., Chen, S., Dudhia, J., Hart, L., Klemp, J., Middlecoff, J., and Skamarock, W.: Development of a next-generation regional weather research and forecast model, in: Developments in Teracomputing, World Scientific, 269–276, https://doi.org/10.1142/9789812799685_0024, 2001.
Mietta, F., Chassagne, C., Manning, A. J., and Winterwerp, J. C.: Influence of shear rate, organic matter content, pH and salinity on mud flocculation, Ocean Dynam., 59, 751–763, https://doi.org/10.1007/s10236-009-0231-4, 2009.
Mitchener, H. and Torfs, H.: Erosion of mud/sand mixtures, Coast. Eng., 29, 1–25, 1996.
Moriarty, J. M., Harris, C. K., Fennel, K., Friedrichs, M. A. M., Xu, K., and Rabouille, C.: The roles of resuspension, diffusion and biogeochemical processes on oxygen dynamics offshore of the Rhône River, France: a numerical modeling study, Biogeosciences, 14, 1919–1946, https://doi.org/10.5194/bg-14-1919-2017, 2017.
Nguyen, H.-H. and Chua, L. H. C.: Simplified physically based model for estimating effective floc density, J. Hydraul. Eng., 137, 843–846, 2011.
Panagiotopoulos, I., Voulgaris, G., and Collins, M. B.: The influence of clay on the threshold of movement in fine sandy beds, Coast. Eng., 32, 19–43, 1997.
Parsons, D. R., Schindler, R. J., Hope, J. A., Malarkey, J., Baas, J. H., Peakall, J., Manning, A. J., Ye, L., Simmons, S., Paterson D. M., Aspden, R. J., Bass, S, J., Davies, A. G., Lichtman, I. D., and Thorne, P. D.: The role of biophysical cohesion on subaqueous bed form size, Geophys. Res. Lett., 43, 1566–1573, https://doi.org/10.1002/2016GL067667, 2016.
Paterson, D. M., Crawford, R. M., and Little, C.: Subaerial exposure and changes in the stability of intertidal estuarine sediments, Estuar. Coast. Shelf S., 30, 541–556, https://doi.org/10.1016/0272-7714(90)90091-5, 1990.
Pilditch, C. A., Widdows, J., Kuhn, N. J., Pope, N. D., and Brinsley, M. D.: Effects of low tide rainfall on the erodibility of intertidal cohesive sediments, Cont. Shelf Res., 28, 1854–1865. https://doi.org/10.1016/j.csr.2008.05.001, 2008.
Ralston, D. K., Geyer, W. R., and Warner, J. C.: Bathymetric controls on sediment transport in the Hudson River estuary: Lateral asymmetry and frontal trapping, J. Geophys. Res., 117, C10013, https://doi.org/10.1029/2012JC008124, 2012.
Rinehimer, J. P., Harris, C. K., Sherwood, C. R., and Sanford, L. P.: Sediment consolidation in a muddy, tidally-dominated environment: Model behavior and sensitivity, in: 10th International Conference on Estuarine and Coastal Modeling, 5–7 November 2007, Newport, Rhode Island, USA, 819–838, https://doi.org/10.1061/40990(324)44, 2008.
Sanford, L. P.: Modeling a dynamically varying mixed sediment bed with erosion, deposition, bioturbation, consolidation, and armoring, Comput. Geosci., 34, 1263–1283, 2008.
Sanford, L. P. and Maa, J. P. Y.: A unified erosion formulation for fine sediments, Mar. Geol., 179, 9–23, 2001.
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.
Sherwood, C. R., Drake, D. E., Wiberg, P. L., and Wheatcroft, R. A.: Prediction of the fate of p,p'-DDE in sediment on the Palos Verdes shelf, California, USA, Cont. Shelf Res., 22, 1025–1058, 2002.
Slade, W. H., Boss, E. S., and Russo, C.: Effects of particle aggregation and disaggregation on their inherent optical properties, Opt. Express, 19, 7945–7959, 2011.
Smoluchowski, M.: Versuch einer mathematischen theorie des koagulations-kinetik kolloid losungren, Zeitschrift fur Physikalische Chemie, 92, 129–168, 1917.
Soulsby, R. L., Manning, A. J., Spearman, J., and Whitehouse, R. J. S.: Settling Velocity and Mass Settling Flux of Flocculated Estuarine Sediments, Mar. Geol., 339, 1–12, https://doi.org/10.1016/j.margeo.2013.04.006, 2013.
Spearman, J. and Manning, A. J.: On the significance of mud transport algorithms for the modelling of intertidal flats, Chapter 28, in: Proceedings in Marine Science, Sediment and Ecohydraulics, edited by: Kusuda, T., Yamanishi, H., Spearman, J., and Gailani, J. Z., Elsevier, https://doi.org/10.1016/S1568-2692(08)80030-7, 411–430, 2008.
Spearman, J. R., Manning, A. J., and Whitehouse, R. J. S.: The settling dynamics of flocculating mud-sand mixtures: Part 2 – Numerical modelling, Ocean Dyn., 61, 351–370, 2011.
Swift, D. J. P., Stull, J. K., Niedoroda, A. W., Reed, C. W., Wong, G. T. F., and Foyle, B. A.: Estimates of the Biodiffusion Coefficient, DB, from Composition of the Benthic Infaunal Community, Report prepared for the Los Angeles County Sanitation Districts, Contribution No. 5 of the Sediment Dynamics Laboratory, Old Dominion University, Norfolk, Virginia, 1994.
Swift, D. J. P., Stull, J. K., Niedoroda, A. W., Reed, C. W., and Wong, G. T. F.: Contaminant dispersal on the Palos Verdes continental margin: II. Estimates of biodiffusion coefficient, Db, from composition of the benthic infaunal community, Sci. Total Environ., 179, 91–107, 1996.
Tassi, P. and Villaret, C.: SISYPHE v6.3 User's Manual, EDF, Laboratoire National d'Hydrulique et Environnement, Chatou, France, 73 pp., 2014.
Thorne, P. D., MacDonald, I. T., and Vincent, C. E.: Modelling acoustic scattering by suspended flocculating sediments, Cont. Shelf Res., 88, 81–91, https://doi.org/10.1016/j.csr.2014.07.003, 2014.
Tolman, H. L. and the WAVEWATCH III Development Group: User manual and system documentation of WAVEWATCH III version 4.18, Technical Note, Environmental Modeling Center, National Centers for Environmental Prediction, National Weather Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce, College Park, MD, 2014.
van der Wegen, M., Dastgheib, A., Jaffe, B. E., and Roelvink, D.: Bed composition generation for morphodynamic modeling: case study of San Pablo Bay in California, USA, Ocean Dynam., 61, 173–186, https://doi.org/10.1007/s10236-010-0314-2, 2011.
van Ledden, M., van Kesteren, W. G. M., and Winterwerp, J. C.: A conceptual framework for the erosion behaviour of sand – mud mixtures, Cont. Shelf Res., 24, 1–11, https://doi.org/10.1016/j.csr.2003.09.002, 2004.
van Leussen, W.: Estuarine macroflocs and their role in fine-grained sediment transport, PhD thesis, University of Utrecht, Utrecht, The Netherlands, 1994.
van Leussen, W.: Aggregation of Particles, Settling Velocity of Mud Flocs A Review, in: Physical Processes in Estuaries, Springer, Berlin, Heidelberg, 347–403, https://doi.org/10.1007/978-3-642-73691-9_19, 1988.
Verney, R., Lafite, R., Brun-Cottan, J. C., and Le Hir, P.: Behaviour of a floc population during a tidal cycle: Laboratory experiments and numerical modeling, Cont. Shelf Res., 31, S64–S83, https://doi.org/10.1016/j.csr.2010.02.005, 2011.
Villaret, C., Hervouet, J.-M., Kopmann, R., Merkel, U., and Davies, A. G.: Morphodynamic modeling using the Telemac finite-element system, Comput. Geosci., 53, 105–113, https://doi.org/10.1016/j.cageo.2011.10.004, 2011.
Warner, J. C., Sherwood, C. R., Signell, R. P., Harris, C. K., and Arango, H. G.: Development of a three-dimensional, regional, coupled wave, current, and sediment-transport model, Comput. Geosci., 34, 1284–1306, 2008.
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.
Wheatcroft, R. A. and Martin, W. R.: Spatial variation in short-term (234Th) sediment bioturbation intensity along an organic-carbon gradient, J. Mar. Res., 54, 763–792, 1996.
Whitehouse, R. J. S., Soulsby, R. L., Roberts, W., and Mitchener, H.: Dynamics of Marine Muds, Thomas Telford, London, 2000.
Wiberg, P. L., Drake, D. E., and Cacchione, D. A.: Sediment resuspension and bed armoring during high bottom stress events on the northern California inner continental shelf: measurements and predictions, Cont. Shelf Res., 14, 1191–1219, 1994.
Winterwerp, J. C.: A simple model for turbulence induced flocculation of cohesive sediment, J. Hydraulic Res., 36, 309–326, https://doi.org/10.1080/00221689809498621, 1998.
Winterwerp, J. C.: On the Dynamics of High-Concentrated Mud Suspensions, Technical University of Delft, Delft, The Netherlands, 1999.
Winterwerp, J. C.: On the flocculation and settling velocity of estuarine mud, Cont. Shelf Res., 22, 1339–1360, 2002.
Winterwerp, J. C. and Kranenburg, C. (Eds.): Fine Sediment Dynamics in the Marine Environment, Proceed. Marine Sci., Vol. 5, Elsevier, Amsterdam, 2002.
Winterwerp, J. C. and van Kesteren, W. G. M.: Introduction to the Physics of Cohesive Sediment in the Marine Environment, Elsevier, Amsterdam, 2004.
Winterwerp, J. C., Bale, A. J., Christie, M. C., Dyer, K. R., Jones, S., Lintern, D. G., Manning, A. J., Roberts, W., and Kranenburg, C.: Flocculation and settling velocity of fine sediment, Proceed. Marine Sci., 5, 25–40, 2002.
Winterwerp, J. C., Manning, A. J., Martens, C., de Mulder, T., and Vanlede J.: A heuristic formula for turbulence-induced flocculation of cohesive sediment, Estuar. Coast. Shelf S., 68, 195–207, 2006.
Winterwerp, J. C., Maa, J. P.-Y., Sanford, L. P., and Schoellhamer, D. H.: On the sedimentation rate of cohesive sediment, Proceed. Marine Sci., 8, 209–226, 2007.
Xu, F., Wang, D.-P., and Riemer, N.: Modeling flocculation processes of fine-grained particles using a size-resolved method: Comparison with published laboratory experiments, Cont. Shelf Res., 28, 2668–2677, https://doi.org/10.1016/j.csr.2008.09.001, 2008.
Xu, F., Wang, D.-P., and Riemer, N.: An idealized model study of flocculation on sediment trapping in an estuary turbidity maximum, Cont. Shelf Res., 30, 1314–1323, https://doi.org/10.1016/j.csr.2010.04.014, 2010.
Short summary
Cohesive sediment (mud) is ubiquitous in the world's coastal regions, but its behavior is complicated and often oversimplified by computer models. This paper describes extensions to a widely used open-source coastal ocean model that allow users to simulate important components of cohesive sediment transport.
Cohesive sediment (mud) is ubiquitous in the world's coastal regions, but its behavior is...
