Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6177-2021
© Author(s) 2021. 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-14-6177-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Oct 2021
Model description paper |
| 15 Oct 2021
| 15 Oct 2021
S2P3-R v2.0: computationally efficient modelling of shelf seas on regional to global scales
College of Life and Environmental Sciences, University of Exeter,
Exeter, UK
Jennifer K. McWhorter
College of Life and Environmental Sciences, University of Exeter,
Exeter, UK
School of Biological Sciences, the University of Queensland, Brisbane,
Queensland, Australia
Beatriz Arellano Nava
College of Life and Environmental Sciences, University of Exeter,
Exeter, UK
Robert Marsh
University of Southampton, National Oceanography Centre, Southampton,
UK
William Skirving
Coral Reef Watch, National Oceanic and Atmospheric Administration,
College Park, MD, USA
ReefSense Pty Ltd, Cranbrook, Queensland, Australia
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The third climatological estimation of sea surface dimethyl sulfide (DMS) concentrations based on in situ measurements was created (DMS-Rev3). The update includes a much larger input dataset and includes improvements in the data unification, filtering, and smoothing algorithm. The DMS-Rev3 climatology provides more realistic monthly estimates of DMS, and shows significant regional differences compared to past climatologies.
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L. Kwiatkowski, A. Yool, J. I. Allen, T. R. Anderson, R. Barciela, E. T. Buitenhuis, M. Butenschön, C. Enright, P. R. Halloran, C. Le Quéré, L. de Mora, M.-F. Racault, B. Sinha, I. J. Totterdell, and P. M. Cox
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L. Bopp, L. Resplandy, J. C. Orr, S. C. Doney, J. P. Dunne, M. Gehlen, P. Halloran, C. Heinze, T. Ilyina, R. Séférian, J. Tjiputra, and M. Vichi
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Revised manuscript not accepted
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Roland Séférian, Marion Gehlen, Laurent Bopp, Laure Resplandy, James C. Orr, Olivier Marti, John P. Dunne, James R. Christian, Scott C. Doney, Tatiana Ilyina, Keith Lindsay, Paul R. Halloran, Christoph Heinze, Joachim Segschneider, Jerry Tjiputra, Olivier Aumont, and Anastasia Romanou
Geosci. Model Dev., 9, 1827–1851, https://doi.org/10.5194/gmd-9-1827-2016, https://doi.org/10.5194/gmd-9-1827-2016, 2016
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R. Marsh, A. E. Hickman, and J. Sharples
Geosci. Model Dev., 8, 3163–3178, https://doi.org/10.5194/gmd-8-3163-2015, https://doi.org/10.5194/gmd-8-3163-2015, 2015
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P. R. Halloran, B. B. B. Booth, C. D. Jones, F. H. Lambert, D. J. McNeall, I. J. Totterdell, and C. Völker
Biogeosciences, 12, 4497–4508, https://doi.org/10.5194/bg-12-4497-2015, https://doi.org/10.5194/bg-12-4497-2015, 2015
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R. Marsh, V. O. Ivchenko, N. Skliris, S. Alderson, G. R. Bigg, G. Madec, A. T. Blaker, Y. Aksenov, B. Sinha, A. C. Coward, J. Le Sommer, N. Merino, and V. B. Zalesny
Geosci. Model Dev., 8, 1547–1562, https://doi.org/10.5194/gmd-8-1547-2015, https://doi.org/10.5194/gmd-8-1547-2015, 2015
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Calved icebergs account for around 50% of total freshwater input to the ocean from the Greenland and Antarctic ice sheets. As they melt, icebergs interact with the ocean. We have developed and tested interactive icebergs in a state-of-the-art global ocean model, showing how sea ice, temperatures, and currents are disturbed by iceberg melting. With this new model capability, we are better prepared to predict how future increases in iceberg numbers might influence the oceans and climate.
L. Kwiatkowski, A. Yool, J. I. Allen, T. R. Anderson, R. Barciela, E. T. Buitenhuis, M. Butenschön, C. Enright, P. R. Halloran, C. Le Quéré, L. de Mora, M.-F. Racault, B. Sinha, I. J. Totterdell, and P. M. Cox
Biogeosciences, 11, 7291–7304, https://doi.org/10.5194/bg-11-7291-2014, https://doi.org/10.5194/bg-11-7291-2014, 2014
J. H. T. Williams, I. J. Totterdell, P. R. Halloran, and P. J. Valdes
Geosci. Model Dev., 7, 1419–1431, https://doi.org/10.5194/gmd-7-1419-2014, https://doi.org/10.5194/gmd-7-1419-2014, 2014
L. Bopp, L. Resplandy, J. C. Orr, S. C. Doney, J. P. Dunne, M. Gehlen, P. Halloran, C. Heinze, T. Ilyina, R. Séférian, J. Tjiputra, and M. Vichi
Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, https://doi.org/10.5194/bg-10-6225-2013, 2013
O. D. Andrews, N. L. Bindoff, P. R. Halloran, T. Ilyina, and C. Le Quéré
Biogeosciences, 10, 1799–1813, https://doi.org/10.5194/bg-10-1799-2013, https://doi.org/10.5194/bg-10-1799-2013, 2013
F. Joos, R. Roth, J. S. Fuglestvedt, G. P. Peters, I. G. Enting, W. von Bloh, V. Brovkin, E. J. Burke, M. Eby, N. R. Edwards, T. Friedrich, T. L. Frölicher, P. R. Halloran, P. B. Holden, C. Jones, T. Kleinen, F. T. Mackenzie, K. Matsumoto, M. Meinshausen, G.-K. Plattner, A. Reisinger, J. Segschneider, G. Shaffer, M. Steinacher, K. Strassmann, K. Tanaka, A. Timmermann, and A. J. Weaver
Atmos. Chem. Phys., 13, 2793–2825, https://doi.org/10.5194/acp-13-2793-2013, https://doi.org/10.5194/acp-13-2793-2013, 2013
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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.
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. Discuss., https://doi.org/10.5194/gmd-2023-183, https://doi.org/10.5194/gmd-2023-183, 2023
Revised manuscript accepted for GMD
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In order to improve the 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 were found to be quite realistic.
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.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Francis Auclair, Peter P. Sullivan, and Paul S. Hall
EGUsphere, https://doi.org/10.5194/egusphere-2023-1657, https://doi.org/10.5194/egusphere-2023-1657, 2023
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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 similarly accurate, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
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.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
EGUsphere, https://doi.org/10.5194/egusphere-2023-715, https://doi.org/10.5194/egusphere-2023-715, 2023
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Here a new method of modeling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean circulation 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.
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.
Yue Xu and Xiping Yu
Geosci. Model Dev., 16, 2811–2831, https://doi.org/10.5194/gmd-16-2811-2023, https://doi.org/10.5194/gmd-16-2811-2023, 2023
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An accurate description of the wind energy input into ocean waves is crucial to ocean wave modeling, and a physics-based consideration of the effect of wave breaking is absolutely necessary to obtain such an accurate description, particularly under extreme conditions. This study evaluates the performance of a recently improved formula, taking into account not only the effect of breaking but also the effect of airflow separation on the leeside of steep wave crests in a reasonably consistent way.
Yankun Gong, Xueen Chen, Jiexin Xu, Jieshuo Xie, Zhiwu Chen, Yinghui He, and Shuqun Cai
Geosci. Model Dev., 16, 2851–2871, https://doi.org/10.5194/gmd-16-2851-2023, https://doi.org/10.5194/gmd-16-2851-2023, 2023
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Internal solitary waves (ISWs) play crucial roles in mass transport and ocean mixing in the northern South China Sea. Massive numerical investigations have been conducted in this region, but there was no systematic evaluation of a three-dimensional model about precisely simulating ISWs. Here, an ISW forecasting model is employed to evaluate the roles of resolution, tidal forcing and stratification in accurately reproducing wave properties via comparison to field and remote-sensing observations.
Jilian Xiong and Parker MacCready
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-45, https://doi.org/10.5194/gmd-2023-45, 2023
Revised manuscript accepted for GMD
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A new offline particle tracking model, Tracker, was introduced to work with the Regional Ocean Modeling System (ROMS). Its performance was compared with four other particle tracking models and passive dye, which span a representative range of tracking algorithms. All tracking models perform similarly, especially for the first day. The comparison of multiple tracking codes in the same circulation model establishes confidence in all, and allows comparison of performance and ease of use.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
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MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
Y. Joseph Zhang, Tomas Fernandez-Montblanc, William Pringle, Hao-Cheng Yu, Linlin Cui, and Saeed Moghimi
Geosci. Model Dev., 16, 2565–2581, https://doi.org/10.5194/gmd-16-2565-2023, https://doi.org/10.5194/gmd-16-2565-2023, 2023
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Simulating global ocean from deep basins to coastal areas is a daunting task but is important for disaster mitigation efforts. We present a new 3D global ocean model on flexible mesh to study both tidal and nontidal processes and total water prediction. We demonstrate the potential for
seamlesssimulation, on a single mesh, from the global ocean to a few estuaries along the US West Coast. The model can serve as the backbone of a global tide surge and compound flooding forecasting framework.
Qi Shu, Qiang Wang, Chuncheng Guo, Zhenya Song, Shizhu Wang, Yan He, and Fangli Qiao
Geosci. Model Dev., 16, 2539–2563, https://doi.org/10.5194/gmd-16-2539-2023, https://doi.org/10.5194/gmd-16-2539-2023, 2023
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Ocean models are often used for scientific studies on the Arctic Ocean. Here the Arctic Ocean simulations by state-of-the-art global ocean–sea-ice models participating in the Ocean Model Intercomparison Project (OMIP) were evaluated. The simulations on Arctic Ocean hydrography, freshwater content, stratification, sea surface height, and gateway transports were assessed and the common biases were detected. The simulations forced by different atmospheric forcing were also evaluated.
Manuel Aghito, Loris Calgaro, Knut-Frode Dagestad, Christian Ferrarin, Antonio Marcomini, Øyvind Breivik, and Lars Robert Hole
Geosci. Model Dev., 16, 2477–2494, https://doi.org/10.5194/gmd-16-2477-2023, https://doi.org/10.5194/gmd-16-2477-2023, 2023
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The newly developed ChemicalDrift model can simulate the transport and fate of chemicals in the ocean and in coastal regions. The model combines ocean physics, including transport due to currents, turbulence due to surface winds and the sinking of particles to the sea floor, with ocean chemistry, such as the partitioning, the degradation and the evaporation of chemicals. The model will be utilized for risk assessment of ocean and sea-floor contamination from pollutants emitted from shipping.
Nieves G. Valiente, Andrew Saulter, Breogan Gomez, Christopher Bunney, Jian-Guo Li, Tamzin Palmer, and Christine Pequignet
Geosci. Model Dev., 16, 2515–2538, https://doi.org/10.5194/gmd-16-2515-2023, https://doi.org/10.5194/gmd-16-2515-2023, 2023
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We document the Met Office operational global and regional wave models which provide wave forecasts up to 7 d ahead. Our models present coarser resolution offshore to higher resolution near the coastline. The increased resolution led to replication of the extremes but to some overestimation during modal conditions. If currents are included, wave directions and long period swells near the coast are significantly improved. New developments focus on the optimisation of the models with resolution.
Maxime Beauchamp, Quentin Febvre, Hugo Georgenthum, and Ronan Fablet
Geosci. Model Dev., 16, 2119–2147, https://doi.org/10.5194/gmd-16-2119-2023, https://doi.org/10.5194/gmd-16-2119-2023, 2023
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4DVarNet is a learning-based method based on traditional data assimilation (DA). This new class of algorithms can be used to provide efficient reconstructions of a dynamical system based on single observations. We provide a 4DVarNet application to sea surface height reconstructions based on nadir and future Surface Water and Ocean and Topography data. It outperforms other methods, from optimal interpolation to sophisticated DA algorithms. This work is part of on-going AI Chair Oceanix projects.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev., 16, 1755–1777, https://doi.org/10.5194/gmd-16-1755-2023, https://doi.org/10.5194/gmd-16-1755-2023, 2023
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A new global surface-wave–tide–circulation coupled ocean model (FIO-COM32) with a resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are shown to be important contributors to the significant improvements in FIO-COM32 simulations. It is time to merge these separated model components (surface waves, tidal currents and ocean circulation) and start a new generation of ocean model development.
Jeff Polton, James Harle, Jason Holt, Anna Katavouta, Dale Partridge, Jenny Jardine, Sarah Wakelin, Julia Rulent, Anthony Wise, Katherine Hutchinson, David Byrne, Diego Bruciaferri, Enda O'Dea, Michela De Dominicis, Pierre Mathiot, Andrew Coward, Andrew Yool, Julien Palmiéri, Gennadi Lessin, Claudia Gabriela Mayorga-Adame, Valérie Le Guennec, Alex Arnold, and Clément Rousset
Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, https://doi.org/10.5194/gmd-16-1481-2023, 2023
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The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community.
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, https://doi.org/10.5194/gmd-16-1297-2023, 2023
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Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding and erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this paper we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tide–ice interactions.
Sébastien Petton, Valérie Garnier, Matthieu Caillaud, Laurent Debreu, and Franck Dumas
Geosci. Model Dev., 16, 1191–1211, https://doi.org/10.5194/gmd-16-1191-2023, https://doi.org/10.5194/gmd-16-1191-2023, 2023
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The nesting AGRIF library is implemented in the MARS3D hydrodynamic model, a semi-implicit, free-surface numerical model which uses a time scheme as an alternating-direction implicit (ADI) algorithm. Two applications at the regional and coastal scale are introduced. We compare the two-nesting approach to the classic offline one-way approach, based on an in situ dataset. This method is an efficient means to significantly improve the physical hydrodynamics and unravel ecological challenges.
Noam S. Vogt-Vincent and Helen L. Johnson
Geosci. Model Dev., 16, 1163–1178, https://doi.org/10.5194/gmd-16-1163-2023, https://doi.org/10.5194/gmd-16-1163-2023, 2023
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Ocean currents transport things over large distances across the ocean surface. Predicting this transport is key for tackling many environmental problems, such as marine plastic pollution and coral reef resilience. However, doing this requires a good understanding ocean currents, which is currently lacking. Here, we present and validate state-of-the-art simulations for surface currents in the southwestern Indian Ocean, which will support future marine dispersal studies across this region.
Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
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Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, https://doi.org/10.5194/gmd-16-271-2023, 2023
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We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
Joao Marcos Azevedo Correia de Souza, Sutara H. Suanda, Phellipe P. Couto, Robert O. Smith, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 211–231, https://doi.org/10.5194/gmd-16-211-2023, https://doi.org/10.5194/gmd-16-211-2023, 2023
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The current paper describes the configuration and evaluation of the Moana Ocean Hindcast, a > 25-year simulation of the ocean state around New Zealand using the Regional Ocean Modeling System v3.9. This is the first open-access, long-term, continuous, realistic ocean simulation for this region and provides information for improving the understanding of the ocean processes that affect the New Zealand exclusive economic zone.
Hao Huang, Pengyang Song, Shi Qiu, Jiaqi Guo, and Xueen Chen
Geosci. Model Dev., 16, 109–133, https://doi.org/10.5194/gmd-16-109-2023, https://doi.org/10.5194/gmd-16-109-2023, 2023
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The Oceanic Regional Circulation and Tide Model (ORCTM) is developed to reproduce internal solitary wave dynamics. The three-dimensional nonlinear momentum equations are involved with the nonhydrostatic pressure obtained via solving the Poisson equation. The validation experimental results agree with the internal wave theories and observations, demonstrating that the ORCTM can successfully describe the life cycle of nonlinear internal solitary waves under different oceanic environments.
David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
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Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
Shun Ohishi, Takemasa Miyoshi, and Misako Kachi
Geosci. Model Dev., 15, 9057–9073, https://doi.org/10.5194/gmd-15-9057-2022, https://doi.org/10.5194/gmd-15-9057-2022, 2022
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An adaptive observation error inflation (AOEI) method was proposed for atmospheric data assimilation to mitigate erroneous analysis updates caused by large observation-minus-forecast differences for satellite brightness temperature around clear- and cloudy-sky boundaries. This study implemented the AOEI with an ocean data assimilation system, leading to an improvement of analysis accuracy and dynamical balance around the frontal regions with large meridional temperature differences.
Diego Bruciaferri, Marina Tonani, Isabella Ascione, Fahad Al Senafi, Enda O'Dea, Helene T. Hewitt, and Andrew Saulter
Geosci. Model Dev., 15, 8705–8730, https://doi.org/10.5194/gmd-15-8705-2022, https://doi.org/10.5194/gmd-15-8705-2022, 2022
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More accurate predictions of the Gulf's ocean dynamics are needed. We investigate the impact on the predictive skills of a numerical shelf sea model of the Gulf after changing a few key aspects. Increasing the lateral and vertical resolution and optimising the vertical coordinate system to best represent the leading physical processes at stake significantly improve the accuracy of the simulated dynamics. Additional work may be needed to get real benefit from using a more realistic bathymetry.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Shun Ohishi, Tsutomu Hihara, Hidenori Aiki, Joji Ishizaka, Yasumasa Miyazawa, Misako Kachi, and Takemasa Miyoshi
Geosci. Model Dev., 15, 8395–8410, https://doi.org/10.5194/gmd-15-8395-2022, https://doi.org/10.5194/gmd-15-8395-2022, 2022
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We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.
Hector S. Torres, Patrice Klein, Jinbo Wang, Alexander Wineteer, Bo Qiu, Andrew F. Thompson, Lionel Renault, Ernesto Rodriguez, Dimitris Menemenlis, Andrea Molod, Christopher N. Hill, Ehud Strobach, Hong Zhang, Mar Flexas, and Dragana Perkovic-Martin
Geosci. Model Dev., 15, 8041–8058, https://doi.org/10.5194/gmd-15-8041-2022, https://doi.org/10.5194/gmd-15-8041-2022, 2022
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Wind work at the air-sea interface is the scalar product of winds and currents and is the transfer of kinetic energy between the ocean and the atmosphere. Using a new global coupled ocean-atmosphere simulation performed at kilometer resolution, we show that all scales of winds and currents impact the ocean dynamics at spatial and temporal scales. The consequential interplay of surface winds and currents in the numerical simulation motivates the need for a winds and currents satellite mission.
Zhanpeng Zhuang, Quanan Zheng, Yongzeng Yang, Zhenya Song, Yeli Yuan, Chaojie Zhou, Xinhua Zhao, Ting Zhang, and Jing Xie
Geosci. Model Dev., 15, 7221–7241, https://doi.org/10.5194/gmd-15-7221-2022, https://doi.org/10.5194/gmd-15-7221-2022, 2022
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We evaluate the impacts of surface waves and internal tides on the upper-ocean mixing in the Indian Ocean. The surface-wave-generated turbulent mixing is dominant if depth is < 30 m, while the internal-tide-induced mixing is larger than surface waves in the ocean interior from 40
to 130 m. The simulated thermal structure, mixed layer depth and surface current are all improved when the mixing schemes are jointly incorporated into the ocean model because of the strengthened vertical mixing.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, NOAA Tech. Memo., NESDIS NGDC-24,
https://doi.org/10.1594/PANGAEA.769615, 2009.
Australian Institute of Marine Science (AIMS): NRSYON: Northern Australia Automated Marine Weather and Oceanographic Stations, Sites: [Yongala], https://doi.org/10.25845/5c09bf93f315d, 2020.
Bahamondes Dominguez, A. A., Hickman, A. E., Marsh, R., and Moore, C. M.: Constraining the response of phytoplankton to zooplankton grazing and photo-acclimation in a temperate shelf sea with a 1-D model – towards S2P3 v8.0, Geosci. Model Dev., 13, 4019–4040, https://doi.org/10.5194/gmd-13-4019-2020, 2020.
Barnes, M. K., Tilstone, G. H., Suggett, D. J., Widdicombe, C. E., Bruun,
J., Martinez-Vicente, V., and Smyth, T. J.: Temporal variability in total,
micro- and nano-phytoplankton primary production at a coastal site in the
Western English Channel, Prog. Oceanogr., 137 (Part B), 470–483,
https://doi.org/10.1016/j.pocean.2015.04.017, 2015.
Beaman, R.: Project 3DGBR: a high-resolution depth model for the Great
Barrier Reef and Coral Sea, MTSRF Final Report Project 2.5i.1a, Reef and Rainforest Research Centre MTSRF Final Report Marine and Tropical Sciences Research Facility, James Cook University, available at: https://www.deepreef.org/images/stories/publications/reports/Project3DGBRFinal_RRRC2010.pdf (last access: 1 July 2021), 2010.
Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T., and
Bellouin, N.: Erratum: Aerosols implicated as a prime driver of
twentieth-century North Atlantic climate variability, Nature, 484,
228–232, https://doi.org/10.1038/nature11138, 2012.
Bowen, B. W., Gaither, M. R., DiBattista, J. D., Iacchei, M., Andrews, K.
R., Grant, W. S., Toonen, R. J., and Briggs, J. C.: Comparative
phylogeography of the ocean planet, P. Natl. Acad. Sci. USA, 113, 7962–7969,
https://doi.org/10.1073/pnas.1602404113, 2016.
Canuto, V. M., Howard, A., Cheng, Y., and Dubovikov, M. S.: Ocean
turbulence. Part I: One-point closure model-momentum and heat vertical
diffusivities, J. Phys. Oceanogr., 31, 1413–1426,
https://doi.org/10.1175/1520-0485(2001)031<1413:OTPIOP>2.0.CO;2, 2001.
Capuzzo, E., Lynam, C. P., Barry, J., Stephens, D., Forster, R. M.,
Greenwood, N., McQuatters-Gollop, A., Silva, T., van Leeuwen, S. M., and
Engelhard, G. H.: A decline in primary production in the North Sea over 25
years, associated with reductions in zooplankton abundance and fish stock
recruitment, Glob. Chang. Biol., 24, e352–e364, https://doi.org/10.1111/gcb.13916, 2018.
Chen, T., Rossow, W. B., and Zhang, Y.: Radiative effects of cloud-type
variations, J. Climate, 13, 264–286, https://doi.org/10.1175/1520-0442(2000)013<0264:REOCTV>2.0.CO;2, 2000.
Chiswell, S. M.: Annual cycles and spring blooms in phytoplankton: Don't
abandon Sverdrup completely, Mar. Ecol. Prog. Ser., 443, 39–50,
https://doi.org/10.3354/meps09453, 2011.
Chiswell, S. M., Calil, P. H. R., and Boyd, P. W.: Spring blooms and annual
cycles of phytoplankton: A unified perspective, J. Plankton Res., 37, 500–508,
https://doi.org/10.1093/plankt/fbv021, 2015.
Darecki, M. and Stramski, D.: An evaluation of MODIS and SeaWiFS bio-optical
algorithms in the Baltic Sea, Remote Sens. Environ., 89, 326–350,
https://doi.org/10.1016/j.rse.2003.10.012, 2004.
Doney, S. C.: The Growing Human Footprint on Coastal and Open-Ocean Biogeochemistry, Science, 328, 1512–1516, https://doi.org/10.1126/science.1185198, 2010.
Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M., and
Hoegh-Gulberg, O.: Global assessment of coral bleaching and required rates
of adaptation under climate change, Glob. Chang. Biol., 11, 2251–2265,
https://doi.org/10.1111/j.1365-2486.2005.01073.x, 2005.
Dooley, H. D.: Hypotheses concerning the circulation of the northern North
Sea, ICES J. Mar. Sci., 36, 54–61, https://doi.org/10.1093/icesjms/36.1.54, 1974.
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic
ocean tides, J. Atmos. Ocean. Technol., 19, 183–204,
https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002.
Findlay, H. S., Yool, A., Nodale, M., and Pitchford, J. W.: Modelling of
autumn plankton bloom dynamics, J. Plankton Res., 28, 209–220,
https://doi.org/10.1093/plankt/fbi114, 2006.
Furnas, M. J. and Mitchell, A. W.: Phytoplankton dynamics in the central
Great Barrier Reef-I. Seasonal changes in biomass and community structure
and their relation to intrusive activity, Cont. Shelf Res., 6, 363–384,
https://doi.org/10.1016/0278-4343(86)90078-6, 1986.
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res. Ocean., 118, 6704–6716,
https://doi.org/10.1002/2013JC009067, 2013.
Graham, J. A., O'Dea, E., Holt, J., Polton, J., Hewitt, H. T., Furner, R., Guihou, K., Brereton, A., Arnold, A., Wakelin, S., Castillo Sanchez, J. M., and Mayorga Adame, C. G.: AMM15: a new high-resolution NEMO configuration for operational simulation of the European north-west shelf, Geosci. Model Dev., 11, 681–696, https://doi.org/10.5194/gmd-11-681-2018, 2018.
Halloran, P.: PaulHalloran/S2P3Rv2.0: S2P3-R v2.0: computationally efficient modelling of shelf seas on regional to global scales SUBMISSION (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.4147559, 2020a.
Halloran, P.: S2P3Rv2.0 bias data, Zenodo [data set], https://doi.org/10.5281/zenodo.4018815, 2020b.
Haywood, J. and Boucher, O.: Estimates of the direct and indirect radiative
forcing due to tropospheric aerosols: A review, Rev. Geophys., 38, 513–543,
https://doi.org/10.1029/1999RG000078, 2000.
Hersbach, H., De Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C.,
Abdalla, S., Balmaseda, A., Balsamo, G., Bechtold, P., Berrisford, P.,
Bidlot, J., De Boisséson, E., Bonavita, M., Browne, P., Buizza, R.,
Dahlgren, P., Dee, D., Dragani, R., Diamantakis, M., Flemming, J., Forbes,
R., Geer, A., Haiden, T., Hólm, E., Haimberger, L., Hogan, R.,
Horányi, A., Janisková, M., Laloyaux, P., Lopez, P.,
Muñoz-Sabater, J., Peubey, C., Radu, R., Richardson, D., Thépaut,
J.-N., Vitart, F., Yang, X., Zsótér, E., and Zuo, H.: Operational
global reanalysis: progress, future directions and synergies with NWP
including updates on the ERA5 production status, ERA Rep. Ser., Document Number 27,
https://doi.org/10.21957/tkic6g3wm, 2018.
Hersbach, H., Bell, B., Berrisford, P., Horányi, A., Sabater, J. M.,
Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.:
Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF Newsl., Newsletter number 159, pp. 17–24,
https://doi.org/10.21957/vf291hehd7, 2019.
Holt, J., Harle, J., Proctor, R., Michel, S., Ashworth, M., Batstone, C.,
Allen, I., Holmes, R., Smyth, T., Haines, K., Bretherton, D., and Smith, G.:
Modelling the global coastal ocean, Philos. Trans. R. Soc. A, 367, 939–951, https://doi.org/10.1098/rsta.2008.0210, 2009.
Holt, J., Butenschön, M., Wakelin, S. L., Artioli, Y., and Allen, J. I.: Oceanic controls on the primary production of the northwest European continental shelf: model experiments under recent past conditions and a potential future scenario, Biogeosciences, 9, 97–117, https://doi.org/10.5194/bg-9-97-2012, 2012.
Integrated Marine Observing System (IMOS): GBRHIS: Heron Island South Shelf Mooring component of the GBR Mooring Array, available at: https://apps.aims.gov.au/metadata/view/9a19eaa5-6069-4ed6-b004-5f7590664881, (last access: 21 September 2021), 2009a.
Integrated Marine Observing System (IMOS): GBRLSH: Lizard Island Shelf Mooring component of the GBR Mooring Array, available at: https://apps.aims.gov.au/metadata/view/ee39900f-141e-43a6-8261-0164267c8f95 (last access: 21 September 2021), 2009b.
Integrated Marine Observing System (IMOS): GBROTE: One Tree Island Shelf Mooring component of the GBR Mooring Array, available at: https://apps.aims.gov.au/metadata/view/05c9319d-ebde-4ba5-8c25-08ea82cbe77f (last access: 21 September 2021), 2009c.
Integrated Marine Observing System (IMOS): GBRPPS: Palm Passage Shelf Mooring component of the GBR Mooring Array, available at: https://apps.aims.gov.au/metadata/view/11c307bd-89bf-4616-b3df-52645ca56b6e (last access: 21 September 2021), 2009d.
Integrated Marine Observing System (IMOS): IMOS – ANMN National Reference Station (NRS) Ningaloo Mooring (NRSNIN), available at: https://apps.aims.gov.au/metadata/view/a581c961-8632-497f-bc5e-2002957577ec (last access: 21 September 2021), 2017.
Integrated Marine Observing System (IMOS): Facility for the Automated
Intelligent Monitoring of Marine Systems – FAIMMS, available at: https://apps.aims.gov.au/metadata/view/d63dc150-0d02-11dd-bbbb-00008a07204e, last access: 1 July 2021.
Kwiatkowski, L., Halloran, P. R., Mumby, P. J., and Stephenson, D. B.: What
spatial scales are believable for climate model projections of sea surface
temperature?, Clim. Dynam., 43, 1483–1496, https://doi.org/10.1007/s00382-013-1967-6,
2014.
Lenhart, H. J., Radach, G., and Ruardij, P.: The effects of river input on
the ecosystem dynamics in the continental coastal zone of the North Sea
using ERSEM, J. Sea Res., 38, 249–274, https://doi.org/10.1016/S1385-1101(97)00049-X,
1997.
Levitus, S.: Climatological Atlas of the World Ocean, EOS, 64, 962–963, https://doi.org/10.1029/EO064i049p00962-02, 1983.
Marsh, R., Hickman, A. E., and Sharples, J.: S2P3-R (v1.0): a framework for efficient regional modelling of physical and biological structures and processes in shelf seas, Geosci. Model Dev., 8, 3163–3178, https://doi.org/10.5194/gmd-8-3163-2015, 2015.
Marsh, R., Haigh, I. D., Cunningham, S. A., Inall, M. E., Porter, M., and Moat, B. I.: Large-scale forcing of the European Slope Current and associated inflows to the North Sea, Ocean Sci., 13, 315–335, https://doi.org/10.5194/os-13-315-2017, 2017.
Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K.,
Fiedler, E., Good, S. A., Mittaz, J., Rayner, N. A., Berry, D., Eastwood,
S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., and Donlon, C.:
Satellite-based time-series of sea-surface temperature since 1981 for
climate applications, Sci. data, 6, 223, https://doi.org/10.1038/s41597-019-0236-x,
2019.
Mora, C., Wei, C.-L., Rollo, A., Amaro, T., Baco, A. R., Billett, D., Bopp,
L., Chen, Q., Collier, M., Danovaro, R., Gooday, A. J., Grupe, B. M.,
Halloran, P. R., Ingels, J., Jones, D. O. B., Levin, L. A., Nakano, H.,
Norling, K., Ramirez-Llodra, E., Rex, M., Ruhl, H. A., Smith, C. R.,
Sweetman, A. K., Thurber, A. R., Tjiputra, J. F., Usseglio, P., Watling, L.,
Wu, T., and Yasuhara, M.: Biotic and Human Vulnerability to Projected
Changes in Ocean Biogeochemistry over the 21st Century, PLoS Biol., 11, e1001682,
https://doi.org/10.1371/journal.pbio.1001682, 2013.
Sathyendranath, S., Brewin, R. J. W., Brockmann, C., Brotas, V., Calton, B.,
Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon,
C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T.,
Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin,
F., Moore, T. S., Müller, D., Regner, P., Roy, S., Steele, C. J.,
Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A.,
Zühlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin,
R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G.,
Muller-Karger, F. E., Sosik, H. M., Voss, K. J., Werdell, J., and Platt, T.:
An ocean-colour time series for use in climate studies: The experience of
the ocean-colour climate change initiative (OC-CCI), Sensors, 19, 4285,
https://doi.org/10.3390/s19194285, 2019.
Sathyendranath, S., Jackson, T., Brockmann, C., Brotas, V., Calton, B.,
Chuprin, A., Clements, O., Cipollini, P., Danne, O., Dingle, J., Donlon, C.,
Grant, M., Groom, S., Krasemann, H., Lavender, S., Mazeran, C., Mélin,
F., Moore, T. S., Müller, D., Regner, P., Steinmetz, F., Steele, C.,
Swinton, J., Valente, A., Zühlke, M., Feldman, G., Franz, B., Frouin,
R., Werdell, J., and Platt, T.: ESA Ocean Colour Climate Change Initiative
(Ocean_Colour_cci): Global chlorophyll-a data
products gridded on a sinusoidal projection, Version 4.2, Cent. Environ.
Data Anal., Centre for Environmental Data Analysis, available at:
https://catalogue.ceda.ac.uk/uuid/99348189bd33459cbd597a58c30d8d10 (last access: 1 August 2021),
2020.
Sharples, J.: Potential impacts of the spring-neap tidal cycle on shelf sea
primary production, J. Plankton Res., 12, S12–S28, https://doi.org/10.1093/plankt/fbm088,
2008.
Sharples, J., Ross, O. N., Scott, B. E., Greenstreet, S. P. R., and Fraser,
H.: Inter-annual variability in the timing of stratification and the spring
bloom in the North-western North Sea, Cont. Shelf Res., 26, 733–751,
https://doi.org/10.1016/j.csr.2006.01.011, 2006.
Sheehan, P. M. F., Berx, B., Gallego, A., Hall, R. A., Heywood, K. J., and
Queste, B. Y.: Weekly variability of hydrography and transport of
northwestern inflows into the northern North Sea, J. Mar. Syst., 204, 103288,
https://doi.org/10.1016/j.jmarsys.2019.103288, 2020.
Simpson, J. H. and Sharples, J.: Introduction to the Physical and Biological
Oceanography of Shelf Seas, Cambridge University Press, https://doi.org/10.1017/cbo9781139034098, 2012.
Sivyer: Cefas SmartBuoy Monitoring Network, Cefas [data set],
https://doi.org/10.14466/CefasDataHub.10, 2016.
Skirving, W., Heron, M., and Heron, S.: The hydrodynamics of a bleaching
event: Implications for management and monitoring, Coral Reefs and Climate Change: Science and Management, 61,
available at: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/61CE09 (last access: 1 July 2021), 2011.
Smith, S. D. and Banke, E. G.: Variation of the sea surface drag coefficient
with wind speed, Q. J. Roy. Meteor. Soc., 101, 665–673,
https://doi.org/10.1002/qj.49710142920, 1975.
Smyth, T., Atkinson, A., Widdicombe, S., Frost, M., Allen, I., Fishwick, J.,
Queiros, A., Sims, D., and Barange, M.: The Western Channel Observatory, 137, 335–341,
https://doi.org/10.1016/j.pocean.2015.05.020, 2015.
Song, H., Ji, R., Stock, C., Kearney, K., and Wang, Z.: Interannual
variability in phytoplankton blooms and plankton productivity over the Nova
Scotian Shelf and in the Gulf of Maine, Mar. Ecol. Prog. Ser., 426, 105–118,
https://doi.org/10.3354/meps09002, 2011.
Steven, A. D. L., Baird, M. E., Brinkman, R., Car, N. J., Cox, S. J.,
Herzfeld, M., Hodge, J., Jones, E., King, E., Margvelashvili, N., Robillot,
C., Robson, B., Schroeder, T., Skerratt, J., Tickell, S., Tuteja, N.,
Wild-Allen, K., and Yu, J.: eReefs: An operational information system for
managing the Great Barrier Reef, J. Oper. Oceanogr., 12, S12–S28,
https://doi.org/10.1080/1755876X.2019.1650589, 2019.
Sverdrup, H. U.: On conditions for the vernal blooming of phytoplankton,
ICES J. Mar. Sci., 18, 287–295, https://doi.org/10.1093/icesjms/18.3.287, 1953.
Tinker, J. P. and Howes, E. L.: The impacts of climate change on temperature (air and sea), relevant to the coastal and marine environment around the UK, MCCIP Science Review 2020, available at: http://marine.gov.scot/sma/content/impacts-climate-change-temperature-air-and-sea-relevant-coastal-and-marine-environment (last access: 1 July 2021), 2020.
van der Molen, J., Ruardij, P., and Greenwood, N.: A 3D SPM model for
biogeochemical modelling, with application to the northwest European
continental shelf, J. Sea Res., 127, 63–81,
https://doi.org/10.1016/j.seares.2016.12.003, 2017.
Van Hooidonk, R., Maynard, J., Tamelander, J., Gove, J., Ahmadia, G.,
Raymundo, L., Williams, G., Heron, S. F., and Planes, S.: Local-scale
projections of coral reef futures and implications of the Paris Agreement,
Sci. Rep., 6, 39666, https://doi.org/10.1038/srep39666, 2016.
Wafar, M. V. M., Le Corre, P., and Birrien, J. L.: Nutrients and primary
production in permanently well-mixed temperate coastal waters, Estuar.
Coast. Shelf Sci., 17, 431–446, https://doi.org/10.1016/0272-7714(83)90128-2, 1983.
Short summary
This paper describes the latest version of a simple model for simulating coastal oceanography in response to changes in weather and climate. The latest revision of this model makes scientific improvements but focuses on improvements that allow the model to be run simply at large scales and for long periods of time to explore the implications of (for example) future climate change along large areas of coastline.
This paper describes the latest version of a simple model for simulating coastal oceanography in...
