Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8705-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-8705-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
01 Dec 2022
Model description paper |
| 01 Dec 2022
| 01 Dec 2022
GULF18, a high-resolution NEMO-based tidal ocean model of the Arabian/Persian Gulf
Diego Bruciaferri
CORRESPONDING AUTHOR
Met Office, Exeter, EX1 3PB, UK
Marina Tonani
Met Office, Exeter, EX1 3PB, UK
now at: Mercator Ocean International, Toulouse, France
Isabella Ascione
Met Office, Exeter, EX1 3PB, UK
Fahad Al Senafi
Department of Marine Science, Kuwait University, Safat, Kuwait
Enda O'Dea
Met Office, Exeter, EX1 3PB, UK
Helene T. Hewitt
Met Office, Exeter, EX1 3PB, UK
Andrew Saulter
Met Office, Exeter, EX1 3PB, UK
Related authors
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
Short summary
Short summary
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.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
Marina Tonani, Eric Chassignet, Mauro Cirano, Yasumasa Miyazawa, and Begoña Pérez Gómez
State Planet, 5-opsr, 3, https://doi.org/10.5194/sp-5-opsr-3-2025, https://doi.org/10.5194/sp-5-opsr-3-2025, 2025
Short summary
Short summary
This article provides an overview of the main characteristics of ocean forecast systems covering a limited region of the ocean. Their main components are described, as well as the spatial and temporal scales they resolve. The oceanic variables that these systems are able to predict are also explained. An overview of the main forecasting systems currently in operation is also provided.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
Short summary
Short summary
The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
John Patrick Dunne, Helene T. Hewitt, Julie Arblaster, Frédéric Bonou, Olivier Boucher, Tereza Cavazos, Paul J. Durack, Birgit Hassler, Martin Juckes, Tomoki Miyakawa, Matthew Mizielinski, Vaishali Naik, Zebedee Nicholls, Eleanor O’Rourke, Robert Pincus, Benjamin M. Sanderson, Isla R. Simpson, and Karl E. Taylor
EGUsphere, https://doi.org/10.5194/egusphere-2024-3874, https://doi.org/10.5194/egusphere-2024-3874, 2024
Short summary
Short summary
This manuscript provides the motivation and experimental design for the seventh phase of the Coupled Model Intercomparison Project (CMIP7) to coordinate community based efforts to answer key and timely climate science questions and facilitate delivery of relevant multi-model simulations for: prediction and projection, characterization, attribution and process understanding; vulnerability, impacts and adaptations analysis; national and international climate assessments; and society at large.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Juan Manuel Castillo, Huw W. Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev., 15, 4193–4223, https://doi.org/10.5194/gmd-15-4193-2022, https://doi.org/10.5194/gmd-15-4193-2022, 2022
Short summary
Short summary
A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land, and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air–sea coupling is included.
Maialen Iturbide, José M. Gutiérrez, Lincoln M. Alves, Joaquín Bedia, Ruth Cerezo-Mota, Ezequiel Cimadevilla, Antonio S. Cofiño, Alejandro Di Luca, Sergio Henrique Faria, Irina V. Gorodetskaya, Mathias Hauser, Sixto Herrera, Kevin Hennessy, Helene T. Hewitt, Richard G. Jones, Svitlana Krakovska, Rodrigo Manzanas, Daniel Martínez-Castro, Gemma T. Narisma, Intan S. Nurhati, Izidine Pinto, Sonia I. Seneviratne, Bart van den Hurk, and Carolina S. Vera
Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, https://doi.org/10.5194/essd-12-2959-2020, 2020
Short summary
Short summary
We present an update of the IPCC WGI reference regions used in AR5 for the synthesis of climate change information. This revision was guided by the basic principles of climatic consistency and model representativeness (in particular for the new CMIP6 simulations). We also present a new dataset of monthly CMIP5 and CMIP6 spatially aggregated information using the new reference regions and describe a worked example of how to use this dataset to inform regional climate change studies.
Cited articles
Al Azhar, M., Temimi, M., Zhao, J., and Ghedira, H.: Modeling of circulation
in the Arabian Gulf and the Sea of Oman: Skill assessment and seasonal
thermohaline structure, J. Geophys. Res.-Oceans, 121,
1700–1720, https://doi.org/10.1002/2015JC011038, 2016. a
Al Senafi, F. and Anis, A.: Internal Waves on the Continental Shelf of the
Northwestern Arabian Gulf, Front. Mar. Sci., 6, 805,
https://doi.org/10.3389/fmars.2019.00805, 2020a. a
Al Senafi, F. and Anis, A.: Wind-driven flow dynamics off the Northwestern
Arabian Gulf Coast, Estuar. Coast. Shelf Sci., 233, 106511,
https://doi.org/10.1016/j.ecss.2019.106511, 2020b. a, b
Al Senafi, F., Anis, A., and Menezes, V.: Surface Heat Fluxes over the
Northern Arabian Gulf and the Northern Red Sea: Evaluation of ECMWF-ERA5 and
NASA-MERRA2 Reanalyses, Atmosphere, 10, 504, https://doi.org/10.3390/atmos10090504,
2019. a
Al Shehhi, M. R., Gherboudj, I., and Ghedira, H.: An overview of historical
harmful algae blooms outbreaks in the Arabian Seas,
Mar. Pollut. Bull., 86, 314–324, https://doi.org/10.1016/j.marpolbul.2014.06.048, 2014. a
Amemou, H., Koné, V., Aman, A., and Lett, C.: Assessment of a Lagrangian
model using trajectories of oceanographic drifters and fishing devices in the
Tropical Atlantic Ocean, Prog. Oceanogr., 188, 102426,
https://doi.org/10.1016/j.pocean.2020.102426, 2020. a, b
Arakawa, A. and Lamb, V. R.: A Potential Enstrophy and Energy Conserving
Scheme for the Shallow Water Equations, Mon. Weather Rev., 109, 18–36,
https://doi.org/10.1175/1520-0493(1981)109<0018:APEAEC>2.0.CO;2, 1981. a
Barron, C. N., Smedstad, L. F., Dastugue, J. M., and Smedstad, O. M.:
Evaluation of ocean models using observed and simulated drifter
trajectories: Impact of sea surface height on synthetic profiles for data
assimilation, J. Geophys. Res., 112, C07019,
https://doi.org/10.1029/2006JC003982, 2007. a, b
Berntsen, J.: Internal pressure errors in sigma coordinate ocean models,
J. Atmos. Ocean. Tech., 21, 1403–1413,
https://doi.org/10.1016/j.ocemod.2005.05.001, 2002. a
Breivik, Ø., Allen, A. A., Maisondieu, C., and Olagnon, M.: Advances in
search and rescue at sea, Ocean Dynam., 63, 83–88,
https://doi.org/10.1007/s10236-012-0581-1, 2013. a
Bruciaferri, D.: GULF18, a high-resolution NEMO-based tidal ocean model of the Arabian/Persian Gulf, Zenodo, https://doi.org/10.5281/zenodo.6865886, 2022a. a
Bruciaferri, D.: GULF18, a high-resolution NEMO-based tidal ocean model of the Arabian/Persian Gulf, Zenodo [data set], https://doi.org/10.5281/zenodo.6862364, 2022b. a, b
Bruciaferri, D., Shapiro, G., Stanichny, S., Zatsepin, A., Ezer, T., Wobus, F.,
Francis, X., and Hilton, D.: The development of a 3D computational mesh to
improve the representation of dynamic processes: The Black Sea test case,
Ocean Model., 146, 101534, https://doi.org/10.1016/j.ocemod.2019.101534, 2020. a, b, c, d, e, f
Bruciaferri, D., Tonani, M., Lewis, H. W., Siddorn, J. R., Saulter, A.,
Castillo, J. M., Valiente, N. G., Conley, D., Sykes, P., Ascione, I., and
McConnell, N.: The impact of ocean‐wave coupling on the upper ocean
circulation during storm events, J. Geophys. Res.-Oceans, 126, e2021JC017343,
https://doi.org/10.1029/2021JC017343, 2021. a, b, c
Carniel, S., Warner, J. C., Chiggiato, J., and Sclavo, M.: Investigating the
impact of surface wave breaking on modeling the trajectories of drifters in
the northern Adriatic Sea during a wind-storm event, Ocean Model., 30,
225–239, https://doi.org/10.1016/j.ocemod.2009.07.001, 2009. a, b
CMEMS: Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis – SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001, European Copernicus Marine Environment Monitoring Service [data set], https://doi.org/10.48670/moi-00165, 2022a. a
CMEMS: Global Ocean- In-Situ Near-Real-Time Observations -INSITU_GLO_NRT_OBSERVATIONS_013_030, European Copernicus Marine Environment Monitoring Service [data set], https://doi.org/10.48670/moi-00036, 2022b. a
Chelton, D. B., DeSzoeke, R. A., Schlax, M. G., El Naggar, K., and Siwertz,
N.: Geographical Variability of the First Baroclinic Rossby Radius of
Deformation, J. Phys. Oceanogr., 28, 433–460,
https://doi.org/10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2, 1998. a
Crocker, R., Maksymczuk, J., Mittermaier, M., Tonani, M., and Pequignet, C.: An approach to the verification of high-resolution ocean models using spatial methods, Ocean Sci., 16, 831–845, https://doi.org/10.5194/os-16-831-2020, 2020. a
Dagestad, K.-F. and Röhrs, J.: Prediction of ocean surface trajectories
using satellite derived vs. modeled ocean currents,
Remote Sens. Environ., 223, 130–142, https://doi.org/10.1016/j.rse.2019.01.001, 2019. a, b, c
Dagestad, K.-F., Röhrs, J., Breivik, Ø., and Ådlandsvik, B.: OpenDrift v1.0: a generic framework for trajectory modelling, Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, 2018. a
De Dominicis, M., Bruciaferri, D., Gerin, R., Pinardi, N., Poulain, P. M.,
Garreau, P., Zodiatis, G., Perivoliotis, L., Fazioli, L., Sorgente, R.,
Manganiello, C., and Garreaue, P.: A multi-model assessment of the impact of
currents, waves and wind in modelling surface drifters and oil spill,
Deep-Sea Res. Pt. II,
21–38, https://doi.org/10.1016/j.dsr2.2016.04.002, 2016. a, b, c
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a, b, c
Dukhovskoy, D. S., Morey, S. L., Martin, P. J., O'Brien, J. J., and Cooper, C.:
Application of a vanishing, quasi-sigma, vertical coordinate for simulation
of high-speed, deep currents over the Sigsbee Escarpment in the Gulf of
Mexico, Ocean Model., 28, 250–265, https://doi.org/10.1016/j.ocemod.2009.02.009,
2009. a, b
Egbert, G. D. and Erofeeva, S. Y.: Efficient Inverse Modeling of Barotropic
Ocean Tides, J. Atmos. Ocean. Tech., 19, 183–204,
https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002. a
Essa, S., Harahsheh, H., Shiobara, M., and Nishidai, T.: Chapter 3 Operational
remote sensing for the detection and monitoring of oil pollution in the
arabian gulf: Case studies from the United Arab emirates, Developments in Earth and Environmental Sciences, 3, 31–48,
https://doi.org/10.1016/S1571-9197(05)80027-8, 2005. a
Ezer, T. and Mellor, G. L.: Sensitivity studies with the North Atlantic sigma
coordinate Princeton Ocean Model, Dynam. Atmos. Oceans, 32,
185–208, 2000. a
Flather, R. A.: A tidal model of the northwest European continental shelf,
Memoires de la Societe Royale des Sciences de Liege, 10, 141–164, 1976. a
Fox-Kemper, B. and Bachman, S.: Principles and advances in subgrid modelling
for eddy-rich simulations, CLIVAR Exchanges: Special Issue: High Resolution
Ocean Climate Modelling, 19, 2014. a
Gherboudj, I. and Ghedira, H.: Spatiotemporal assessment of dust loading over
the United Arab Emirates, Int. J. Climatol., 34,
3321–3335, https://doi.org/10.1002/joc.3909, 2014. a
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, 2018a. a, b
Graham, J. A., Rosser, J. P., O'Dea, E., and Hewitt, H. T.: Resolving Shelf
Break Exchange Around the European Northwest Shelf, Geophys. Res.
Lett., 45, 12,386–12,395, https://doi.org/10.1029/2018GL079399, 2018b. a
Griffiths, S. D.: Kelvin wave propagation along straight boundaries in C-grid
finite-difference models, J. Computat. Phys., 255, 639–659,
https://doi.org/10.1016/j.jcp.2013.08.040, 2013. a
Haidvogel, D. and Beckmann, A.: Numerical Ocean Circulation Modeling,
Imperial College Press, https://doi.org/10.2277/0521781825, 1999. a
Hallberg, R.: Using a resolution function to regulate parameterizations of
oceanic mesoscale eddy effects, Ocean Model., 72, 92–103,
https://doi.org/10.1016/j.ocemod.2013.08.007, 2013. a, b, c
Hofmeister, R., Burchard, H., and Beckers, J. M.: Non-uniform adaptive
vertical grids for 3D numerical ocean models, Ocean Model., 33, 70–86,
https://doi.org/10.1016/j.ocemod.2009.12.003, 2010. a
Hordoir, R., Axell, L., Höglund, A., Dieterich, C., Fransner, F., Gröger, M., Liu, Y., Pemberton, P., Schimanke, S., Andersson, H., Ljungemyr, P., Nygren, P., Falahat, S., Nord, A., Jönsson, A., Lake, I., Döös, K., Hieronymus, M., Dietze, H., Löptien, U., Kuznetsov, I., Westerlund, A., Tuomi, L., and Haapala, J.: Nemo-Nordic 1.0: a NEMO-based ocean model for the Baltic and North seas – research and operational applications, Geosci. Model Dev., 12, 363–386, https://doi.org/10.5194/gmd-12-363-2019, 2019. a
Johns, W., Jacobs, G., Kindle, J., Murray, S., and Carron, M.: Arabian
marginal seas and gulfs: Report of a work-shop held at Stennis Space Center,
Miss., 11–13 May, 1999, Tech. rep., University of Miami RSMAS, https://www2.whoi.edu/site/bower-lab/wp-content/uploads/sites/12/2018/03/TechRpt_ArabianMarginal.pdf (last access: 23 November 2022), 1999. a
Klingbeil, K. and Burchard, H.: Implementation of a direct nonhydrostatic
pressure gradient discretisation into a layered ocean model, Ocean
Model., 65, 64–77, https://doi.org/10.1016/j.ocemod.2013.02.002, 2013. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air–sea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009. a, b
Lazure, P. and Dumas, F.: An external–internal mode coupling for a 3D
hydrodynamical model for applications at regional scale (MARS), Adv.
Water Resour., 31, 233–250, https://doi.org/10.1016/j.advwatres.2007.06.010, 2008. a
Levier, B., Treguier, A. M., Madec, G., and Garnier, V.: Free surface and
variable volume in the NEMO code, Tech. rep., IFREMER, Brest, France,
MESRSEA IP report WP09-CNRS-STR03-1, Zenodo, https://doi.org/10.5281/zenodo.3244182, 2007. a
Li, D., Anis, A., and Al Senafi, F.: Neap-spring variability of tidal
dynamics in the Northern Arabian Gulf, Cont. Shelf Res., 197,
104086, https://doi.org/10.1016/j.csr.2020.104086, 2020. a, b
Liu, Y. and Weisberg, R. H.: Evaluation of trajectory modeling in different
dynamic regions using normalized cumulative Lagrangian separation, J. Geophys. Res., 116, C09013, https://doi.org/10.1029/2010JC006837, 2011. a
Lorenz, M., Klingbeil, K., and Burchard, H.: Numerical Study of the Exchange
Flow of the Persian Gulf Using an Extended Total Exchange Flow Analysis
Framework, J. Geophys. Res.-Oceans, 125, e2019JC015527,
https://doi.org/10.1029/2019JC015527, 2020. a
Lyard, F. H., Allain, D. J., Cancet, M., Carrère, L., and Picot, N.: FES2014 global ocean tide atlas: design and performance, Ocean Sci., 17, 615–649, https://doi.org/10.5194/os-17-615-2021, 2021. a
Madec, G. and NEMO-team: NEMO ocean engine, Note du Pôle de
modélisation, Institut Pierre-Simon Laplace (IPSL),
Zenodo, https://doi.org/10.5281/zenodo.1475234, 2012. a, b
Madec, G. and NEMO-team: NEMO ocean engine, Note du Pôle de
modélisation, Institut Pierre-Simon Laplace (IPSL), 357 pp.,
Zenodo, https://doi.org/10.5281/zenodo.3248739, 2016. a, b, c
Madec, G. and NEMO-team: NEMO ocean engine, Scientific Notes of Climate
Modelling Center, Institut Pierre-Simon Laplace (IPSL),
Zenodo, https://doi.org/10.5281/zenodo.1464816, 2019. a, b, c
Mahmood, S., Lewis, H., Arnold, A., Castillo, J., Sanchez, C., and Harris, C.:
The impact of time‐varying sea surface temperature on UK regional
atmosphere forecasts, Meteorol. Appl., 28, e1983,
https://doi.org/10.1002/met.1983, 2021. a
Martinho, A. S. and Batteen, M. L.: On reducing the slope parameter in
terrain-following numerical ocean models, Ocean Model., 13, 166–175,
https://doi.org/10.1016/j.ocemod.2006.01.003, 2006. a, b, c
Martinsen, E. A. and Engedahl, H.: Implementation and testing of a lateral
boundary scheme as an open boundary condition in a barotropic ocean model,
Coast. Eng., 11, 603–627, https://doi.org/10.1016/0378-3839(87)90028-7, 1987. a
Mashayekh Poul, H., Backhaus, J., and Huebner, U.: A description of the
tides and effect of Qeshm canal on that in the Persian Gulf using
two-dimensional numerical model, Arab. J. Geosci., 9, 148,
https://doi.org/10.1007/s12517-015-2259-8, 2016. a, b, c
Matsuyama, M., Kitade, Y., Senjyu, T., Koike, Y., and Ishimaru, T.: Vertical
structure of a current and density front in the Strait of Hormuz, in:
Offshore Environment ROPME Sea Area After War-Related Oil Spill, edited by:
Otsuki, A., Abdulraheem, M. Y., and Reynolds, R. M., 23–24, Terra
Scientific (TERRAPUB), Tokyo, http://lib.ugent.be/catalog/ebk01:1000000000419189 (last access: 23 November 2022), 1998. a
Mellor, G. L., Oey, L. Y., and Ezer, T.: Sigma coordinate pressure gradient
errors and the seamount Problem, J. Atmos. Ocean. Tech., 15, 1122–1131,
https://doi.org/10.1175/1520-0426(1998)015<1122:SCPGEA>2.0.CO;2, 1998. a, b
Minobe, S., Kuwano-Yoshida, A., Komori, N., Xie, S.-P., and Small, R. J.:
Influence of the Gulf Stream on the troposphere, Nature, 452, 206–209,
https://doi.org/10.1038/nature06690, 2008. a
Moreton, S. M., Ferreira, D., Roberts, M. J., and Hewitt, H. T.: Evaluating
surface eddy properties in coupled climate simulations with “eddy-present”
and “eddy-rich” ocean resolution, Ocean Model., 147, 101567,
https://doi.org/10.1016/j.ocemod.2020.101567, 2020. a
O'Dea, E., Furner, R., Wakelin, S., Siddorn, J., While, J., Sykes, P., King, R., Holt, J., and Hewitt, H.: The CO5 configuration of the 7 km Atlantic Margin Model: large-scale biases and sensitivity to forcing, physics options and vertical resolution, Geosci. Model Dev., 10, 2947–2969, https://doi.org/10.5194/gmd-10-2947-2017, 2017. a, b
O'Dea, E., Bell, M. J., Coward, A., and Holt, J.: Implementation and
assessment of a flux limiter based wetting and drying scheme in NEMO, Ocean
Model., 155, 101708, https://doi.org/10.1016/j.ocemod.2020.101708, 2020. a, b
O'Dea, E. J., Arnold, A. K., Edwards, K. P., Furner, R., Hyder, P., Martin,
M. J., Siddorn, J. R., Storkey, D., While, J., Holt, J. T., and Liu, H.: An
operational ocean forecast system incorporating NEMO and SST data
assimilation for the tidally driven European North-West shelf, J.
Oper. Oceanogr., 5, 3–17, https://doi.org/10.1080/1755876X.2012.11020128,
2012. a, b, c
Paquin, J.-P., Lu, Y., Taylor, S., Blanken, H., Marcotte, G., Hu, X., Zhai, L.,
Higginson, S., Nudds, S., Chanut, J., Smith, G. C., Bernier, N., and Dupont,
F.: High-resolution modelling of a coastal harbour in the presence of strong
tides and significant river runoff, Ocean Dynam., 70, 365–385,
https://doi.org/10.1007/s10236-019-01334-7, 2020. a
Pous, S., Lazure, P., and Carton, X.: A model of the general circulation in
the Persian Gulf and in the Strait of Hormuz: Intraseasonal to interannual
variability, Cont. Shelf Res., 94, 55–70,
https://doi.org/10.1016/j.csr.2014.12.008, 2015. a
Proctor, R., Flather, R. A., and Elliott, A. J.: Modelling tides and surface
drift in the Arabian Gulf–application to the Gulf oil spill, Cont.
Shelf Res., 14, 531–545, https://doi.org/10.1016/0278-4343(94)90102-3, 1994. a, b
Reynolds, M. R.: Physical oceanography of the Gulf, Strait of Hormuz, and the
Gulf of Oman–Results from the Mt Mitchell expedition, Mar. Pollut. Bull., 27, 35–59, https://doi.org/10.1016/0025-326X(93)90007-7, 1993. a, b
Richlen, M. L., Morton, S. L., Jamali, E. A., Rajan, A., and Anderson, D. M.:
The catastrophic 2008–2009 red tide in the Arabian gulf region, with
observations on the identification and phylogeny of the fish-killing
dinoflagellate Cochlodinium polykrikoides, Harmful Algae, 9, 163–172,
https://doi.org/10.1016/j.hal.2009.08.013, 2010. a
Roos, P. C. and Schuttelaars, H. M.: Influence of topography on tide
propagation and amplification in semi-enclosed basins, Ocean Dynam., 61,
21–38, https://doi.org/10.1007/s10236-010-0340-0, 2011. a
Sein, D. V., Koldunov, N. V., Danilov, S., Sidorenko, D., Wekerle, C., Cabos,
W., Rackow, T., Scholz, P., Semmler, T., Wang, Q., and Jung, T.: The
Relative Influence of Atmospheric and Oceanic Model Resolution on the
Circulation of the North Atlantic Ocean in a Coupled Climate Model, J. Adv. Model. Earth Sy., 10, 2026–2041,
https://doi.org/10.1029/2018MS001327, 2018. a
Shapiro, G., Wobus, F., Solovyev, V., Francis, X., Hyder, P., Chen, F., and
Asif, M.: Cascading of high salinity bottom waters from the Arabian/Persian
Gulf to the northern Arabian Sea, in: EGU General Assembly Conference
Abstracts, Vienna, Austria,
23–28 April 2017,
19, p. 7366, EGU2017-7366, 2017. a, b
Shapiro, G., Luneva, M., Pickering, J., and Storkey, D.: The effect of various vertical discretization schemes and horizontal diffusion parameterization on the performance of a 3-D ocean model: the Black Sea case study, Ocean Sci., 9, 377–390, https://doi.org/10.5194/os-9-377-2013, 2013. a, b
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. a
Simpson, J. H. and Sharples, J.: Introduction to the Physical and Biological
oceanography of Shelf Seas, Cambridge and New York, Cambridge University
Press, https://doi.org/10.1017/CBO9781139034098, 2012. a
Song, Y. and Haidvogel, D.: A Semi-implicit Ocean Circulation Model Using a
Generalized Topography-Following Coordinate System, J. Computat.
Phys., 115, 228–244, https://doi.org/10.1006/jcph.1994.1189, 1994. a, b, c
Storkey, D., Blockley, E. W., Furner, R., Guiavarc'h, C., Lea, D., Martin,
M. J., Barciela, R. M., Hines, A., Hyder, P., and Siddorn, J. R.:
Forecasting the ocean state using NEMO:The new FOAM system, J.
Oper. Oceanogr., 3, 3–15, https://doi.org/10.1080/1755876X.2010.11020109,
2010. a
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae, J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, 2018. a
Tonani, M., Sykes, P., King, R. R., McConnell, N., Péquignet, A.-C., O'Dea, E., Graham, J. A., Polton, J., and Siddorn, J.: The impact of a new high-resolution ocean model on the Met Office North-West European Shelf forecasting system, Ocean Sci., 15, 1133–1158, https://doi.org/10.5194/os-15-1133-2019, 2019. a, b
Umlauf, L. and Burchard, H.: Second-order turbulence closure models for
geophysical boundary layers. A review of recent work, Cont. Shelf
Res., 25, 795–827, https://doi.org/10.1016/j.csr.2004.08.004, 2005. a
UNESCO: Algorithms for computation of fundamental property of sea water,
Techn. Paper in Mar. Sci, 44, https://doi.org/10.25607/OBP-1450, 1983. a
Vasou, P., Vervatis, V., Krokos, G., Hoteit, I., and Sofianos, S.: Variability
of water exchanges through the Strait of Hormuz, Ocean Dynam., 70,
1053–1065, https://doi.org/10.1007/s10236-020-01384-2, 2020. a
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a, b
Wehde, H., Schuckmann, K. V., Pouliquen, S., Grouazel, A., Bartolome, T.,
Tintore, J., De Alfonso Alonso-Munoyerro, M., Carval, T., Racapé, V.,
and the Instac Team: Global Ocean- In-Situ Near-Real-Time Observations, Tech.
rep., CMEMS, https://doi.org/10.13155/75807, 2021. a
Yankovsky, E., Zanna, L., and Smith, K. S.: Influences of Mesoscale Ocean
Eddies on Flow Vertical Structure in a Resolution‐Based Model Hierarchy,
J. Adv. Model. Earth Sy., 14, e2022MS003203, https://doi.org/10.1029/2022MS003203,
2022. a
Zhao, J. and Ghedira, H.: Monitoring red tide with satellite imagery and
numerical models: A case study in the Arabian Gulf, Mar. Pollut. Bull., 79, 305–313, https://doi.org/10.1016/j.marpolbul.2013.10.057, 2014. a
Zhao, J., Temimi, M., Ghedira, H., and Hu, C.: Exploring the potential of
optical remote sensing for oil spill detection in shallow coastal waters-a
case study in the Arabian Gulf, Opt. Express, 22, 13755,
https://doi.org/10.1364/OE.22.013755, 2014.
a
Zhao, J., Temimi, M., Al Azhar, M., and Ghedira, H.: Satellite-Based
Tracking of Oil Pollution in the Arabian Gulf and the Sea of Oman, Can.
J. Remote Sens., 41, 113–125, https://doi.org/10.1080/07038992.2015.1042543,
2015. a
Short summary
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.
More accurate predictions of the Gulf's ocean dynamics are needed. We investigate the impact on...
Special issue