Articles | Volume 14, issue 2
https://doi.org/10.5194/gmd-14-1125-2021
© 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-14-1125-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Feb 2021
Development and technical paper |
| 25 Feb 2021
| 25 Feb 2021
Global storm tide modeling with ADCIRC v55: unstructured mesh design and performance
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA
Damrongsak Wirasaet
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA
Keith J. Roberts
School of Marine and Atmospheric Science, Stony Brook University, Stony Brook, NY, USA
Joannes J. Westerink
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA
Related authors
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-47, https://doi.org/10.5194/wes-2025-47, 2025
Preprint under review for WES
Short summary
Short summary
This study introduces a system that combines weather, ocean, and wave models to better understand their interactions during tropical storms and their impact on offshore structures like wind turbines. Tested using Hurricane Henri (2021), the system improves storm predictions by including how waves and ocean cooling affect storm strength and wind patterns. The results show this approach helps assess risks to offshore infrastructure during severe weather, making it more accurate and reliable.
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
Short summary
Short summary
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.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
Short summary
Short summary
This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Keith J. Roberts, William J. Pringle, and Joannes J. Westerink
Geosci. Model Dev., 12, 1847–1868, https://doi.org/10.5194/gmd-12-1847-2019, https://doi.org/10.5194/gmd-12-1847-2019, 2019
Short summary
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Computer simulations can be used to reproduce the dynamics of the ocean near the coast. These simulations often use a mesh of triangles to represent the domain since they can be orientated and disparately sized in such a way to accurately fit the coastline shape. This paper describes a software package (OceanMesh2D v1.0) that has been developed in order to automatically and objectively design triangular meshes based on geospatial data inputs that represent the coastline and ocean depths.
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-47, https://doi.org/10.5194/wes-2025-47, 2025
Preprint under review for WES
Short summary
Short summary
This study introduces a system that combines weather, ocean, and wave models to better understand their interactions during tropical storms and their impact on offshore structures like wind turbines. Tested using Hurricane Henri (2021), the system improves storm predictions by including how waves and ocean cooling affect storm strength and wind patterns. The results show this approach helps assess risks to offshore infrastructure during severe weather, making it more accurate and reliable.
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
Short summary
Short summary
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.
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
Short summary
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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.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
Short summary
Short summary
This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Keith J. Roberts, William J. Pringle, and Joannes J. Westerink
Geosci. Model Dev., 12, 1847–1868, https://doi.org/10.5194/gmd-12-1847-2019, https://doi.org/10.5194/gmd-12-1847-2019, 2019
Short summary
Short summary
Computer simulations can be used to reproduce the dynamics of the ocean near the coast. These simulations often use a mesh of triangles to represent the domain since they can be orientated and disparately sized in such a way to accurately fit the coastline shape. This paper describes a software package (OceanMesh2D v1.0) that has been developed in order to automatically and objectively design triangular meshes based on geospatial data inputs that represent the coastline and ocean depths.
Related subject area
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A coupled fine-resolution ocean–ice model is developed for the Ross Sea and adjacent regions in Antarctica, a key area for the formation of global ocean bottom water, the Antarctic Bottom Water (AABW), which affects global ocean circulation. The model has a high skill level in simulating sea ice production driving the AABW source water formation and AABW properties when assessed against observations. A model experiment shows a significant impact of ice shelf melting on the AABW characteristics.
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Isabel Jalón-Rojas, Damien Sous, and Vincent Marieu
Geosci. Model Dev., 18, 319–336, https://doi.org/10.5194/gmd-18-319-2025, https://doi.org/10.5194/gmd-18-319-2025, 2025
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Geosci. Model Dev., 18, 211–237, https://doi.org/10.5194/gmd-18-211-2025, https://doi.org/10.5194/gmd-18-211-2025, 2025
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We developed a 3D ocean model called the Hindcast of the Salish Sea (HOTSSea v1) that recreates physical conditions throughout the Salish Sea from 1980 to 2018. It was not clear that acceptable accuracy could be achieved because of computational and data limitations, but the model's predictions agreed well with observations. When we used the model to examine ocean temperature trends in areas that lack observations, it indicated that some seasons and areas are warming faster than others.
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
Geosci. Model Dev., 17, 8697–8733, https://doi.org/10.5194/gmd-17-8697-2024, https://doi.org/10.5194/gmd-17-8697-2024, 2024
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We developed a modelling system of the northwest Atlantic Ocean that simulates the currents, temperature, salinity, and parts of the biochemical cycle of the ocean, as well as sea ice. The system combines advanced, open-source models and can be used to study, for example, the ocean capture of atmospheric carbon dioxide, which is a key process in the global climate. The system produces realistic results, and we use it to investigate the roles of tides and sea ice in the northwest Atlantic Ocean.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
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Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
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We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
Jan D. Zika and Taimoor Sohail
Geosci. Model Dev., 17, 8049–8068, https://doi.org/10.5194/gmd-17-8049-2024, https://doi.org/10.5194/gmd-17-8049-2024, 2024
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We describe a method to relate fluxes of heat and freshwater at the sea surface to the resulting distribution of seawater among categories such as warm and salty or cold and salty. The method exploits the laws that govern how heat and salt change when water mixes. The method will allow the climate community to improve estimates of how much heat the ocean is absorbing and how rainfall and evaporation are changing across the globe.
Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini
Geosci. Model Dev., 17, 7347–7364, https://doi.org/10.5194/gmd-17-7347-2024, https://doi.org/10.5194/gmd-17-7347-2024, 2024
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Monitoring the ocean is essential for studying marine life and human impact. Our new software, PPCon, uses ocean data to predict key factors like nitrate and chlorophyll levels, which are hard to measure directly. By leveraging machine learning, PPCon offers more accurate and efficient predictions.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc’h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-3143, https://doi.org/10.5194/egusphere-2024-3143, 2024
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We describe major improvements of the Met Office's global ocean-sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1-day forecasts. The new system performance in past conditions, where sub-surface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard, and Marco Toffolon
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-118, https://doi.org/10.5194/gmd-2024-118, 2024
Revised manuscript accepted for GMD
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Models simplify reality using assumptions, which can sometimes introduce flaws and affect their accuracy. Properly calibrating model parameters is essential, and although automated tools can speed up this process, they may occasionally produce incorrect values due to inconsistencies in the model. We demonstrate that by carefully applying automated tools, we were able to identify and correct a flaw in a widely used model for lake environments.
Renbo Pang, Fujiang Yu, Yuanyong Gao, Ye Yuan, Liang Yuan, and Zhiyi Gao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2515, https://doi.org/10.5194/egusphere-2024-2515, 2024
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The application of the distributed graph communication topology in earth models has been rarely studied. We tested and compared this topology with the traditional point-to-point communication method using a global wave model. We found that this topology is more efficient. Additionally, using this topology can greatly improve the performance of the wave model and could help improve the performance of other earth models.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
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Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Qian Wang, Yang Zhang, Fei Chai, Y. Joseph Zhang, and Lorenzo Zampieri
Geosci. Model Dev., 17, 7067–7081, https://doi.org/10.5194/gmd-17-7067-2024, https://doi.org/10.5194/gmd-17-7067-2024, 2024
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We coupled an unstructured hydro-model with an advanced column sea ice model to meet the growing demand for increased resolution and complexity in unstructured sea ice models. Additionally, we present a novel tracer transport scheme for the sea ice coupled model and demonstrate that this scheme fulfills the requirements for conservation, accuracy, efficiency, and monotonicity in an idealized test. Our new coupled model also has good performance in realistic tests.
Adrien Garinet, Marine Herrmann, Patrick Marsaleix, and Juliette Pénicaud
Geosci. Model Dev., 17, 6967–6986, https://doi.org/10.5194/gmd-17-6967-2024, https://doi.org/10.5194/gmd-17-6967-2024, 2024
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Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations is made challenging by errors that result in unwanted mixing, compromising simulation realism. Here we illustrate the spurious effect that tides can have on simulations of south-east Asia. Although they play an important role in determining the state of the ocean, they can increase numerical errors and make simulation outputs less realistic. We also provide insights into how to reduce these errors.
Mohamed Ayache, Jean-Claude Dutay, Anne Mouchet, Kazuyo Tachikawa, Camille Risi, and Gilles Ramstein
Geosci. Model Dev., 17, 6627–6655, https://doi.org/10.5194/gmd-17-6627-2024, https://doi.org/10.5194/gmd-17-6627-2024, 2024
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Water isotopes (δ18O, δD) are one of the most widely used proxies in ocean climate research. Previous studies using water isotope observations and modelling have highlighted the importance of understanding spatial and temporal isotopic variability for a quantitative interpretation of these tracers. Here we present the first results of a high-resolution regional dynamical model (at 1/12° horizontal resolution) developed for the Mediterranean Sea, one of the hotspots of ongoing climate change.
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, https://doi.org/10.5194/gmd-17-6415-2024, 2024
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Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-139, https://doi.org/10.5194/gmd-2024-139, 2024
Revised manuscript accepted for GMD
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The GREAT v1.0 software introduces a novel tsunami warning technology for global real-time analysis. It leverages acoustic signals generated by tsunamis, which propagate faster than the tsunami itself, enabling real-time detection and assessment. Integrating various models, the software provides reliable and rapid assessment, mapping risk areas, and estimating tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
Geosci. Model Dev., 17, 6123–6136, https://doi.org/10.5194/gmd-17-6123-2024, https://doi.org/10.5194/gmd-17-6123-2024, 2024
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Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, GPU-accelerated computing is introduced in WAve Modeling Cycle 6 (WAM6). With this effort, global high-resolution wave simulations can now run on GPUs up to tens of times faster than the currently available models can on a CPU node with results that are just as accurate.
Krysten Rutherford, Laura Bianucci, and William Floyd
Geosci. Model Dev., 17, 6083–6104, https://doi.org/10.5194/gmd-17-6083-2024, https://doi.org/10.5194/gmd-17-6083-2024, 2024
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Nearshore ocean models often lack complete information about freshwater fluxes due to numerous ungauged rivers and streams. We tested a simple rain-based hydrological model as inputs into an ocean model of Quatsino Sound, Canada, with the aim of improving the representation of the land–ocean connection in the nearshore model. Through multiple tests, we found that the performance of the ocean model improved when providing 60 % or more of the freshwater inputs from the simple runoff model.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
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The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 17, 5851–5882, https://doi.org/10.5194/gmd-17-5851-2024, https://doi.org/10.5194/gmd-17-5851-2024, 2024
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The carbonate system is typically studied using measurements, but modeling can contribute valuable insights. Using a biogeochemical model, we propose a new representation of total alkalinity, dissolved inorganic carbon, pCO2, and pH in a highly dynamic Mediterranean coastal area, the Bay of Marseille, a useful addition to measurements. Through a detailed analysis of pCO2 and air–sea CO2 fluxes, we show that variations are strongly impacted by the hydrodynamic processes that affect the bay.
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, https://doi.org/10.5194/gmd-17-5497-2024, 2024
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Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
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This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
EGUsphere, https://doi.org/10.5194/egusphere-2024-1414, https://doi.org/10.5194/egusphere-2024-1414, 2024
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The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the HadGEM3 coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the sea floor exerts on ocean currents, and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
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We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
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Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, https://doi.org/10.5194/gmd-17-4095-2024, 2024
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Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
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The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
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In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
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Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
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Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
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The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
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A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
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We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
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Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024, https://doi.org/10.5194/gmd-17-1023-2024, 2024
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This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
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We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Luca Arpaia, Christian Ferrarin, Marco Bajo, and Georg Umgiesser
Geosci. Model Dev., 16, 6899–6919, https://doi.org/10.5194/gmd-16-6899-2023, https://doi.org/10.5194/gmd-16-6899-2023, 2023
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We propose a discrete multilayer shallow water model based on z-layers which, thanks to the insertion and removal of surface layers, can deal with an arbitrarily large tidal oscillation independently of the vertical resolution. The algorithm is based on a two-step procedure used in numerical simulations with moving boundaries (grid movement followed by a grid topology change, that is, the insertion/removal of surface layers), which avoids the appearance of very thin surface layers.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, France Van Wambeke, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 16, 6701–6739, https://doi.org/10.5194/gmd-16-6701-2023, https://doi.org/10.5194/gmd-16-6701-2023, 2023
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While several studies have shown that mixotrophs play a crucial role in the carbon cycle, the impact of environmental forcings on their dynamics remains poorly investigated. Using a biogeochemical model that considers mixotrophs, we study the impact of light and nutrient concentration on the ecosystem composition in a highly dynamic Mediterranean coastal area: the Bay of Marseille. We show that mixotrophs cope better with oligotrophic conditions compared to strict auto- and heterotrophs.
Trygve Halsne, Kai Håkon Christensen, Gaute Hope, and Øyvind Breivik
Geosci. Model Dev., 16, 6515–6530, https://doi.org/10.5194/gmd-16-6515-2023, https://doi.org/10.5194/gmd-16-6515-2023, 2023
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Surface waves that propagate in oceanic or coastal environments get influenced by their surroundings. Changes in the ambient current or the depth profile affect the wave propagation path, and the change in wave direction is called refraction. Some analytical solutions to the governing equations exist under ideal conditions, but for realistic situations, the equations must be solved numerically. Here we present such a numerical solver under an open-source license.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
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Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Doroteaciro Iovino, Pier Giuseppe Fogli, and Simona Masina
Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, https://doi.org/10.5194/gmd-16-6127-2023, 2023
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This paper describes the model performance of three global ocean–sea ice configurations, from non-eddying (1°) to eddy-rich (1/16°) resolutions. Model simulations are obtained following the Ocean Model Intercomparison Project phase 2 (OMIP2) protocol. We compare key global climate variables across the three models and against observations, emphasizing the relative advantages and disadvantages of running forced ocean–sea ice models at higher resolution.
Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, and Kai H. Christensen
Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, https://doi.org/10.5194/gmd-16-5401-2023, 2023
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A model to predict ocean currents, temperature, and sea ice is presented, covering the Barents Sea and northern Norway. To quantify forecast uncertainties, the model calculates ensemble forecasts with 24 realizations of ocean and ice conditions. Observations from satellites, buoys, and ships are ingested by the model. The model forecasts are compared with observations, and we show that the ocean model has skill in predicting sea surface temperatures.
Jin-Song von Storch, Eileen Hertwig, Veit Lüschow, Nils Brüggemann, Helmuth Haak, Peter Korn, and Vikram Singh
Geosci. Model Dev., 16, 5179–5196, https://doi.org/10.5194/gmd-16-5179-2023, https://doi.org/10.5194/gmd-16-5179-2023, 2023
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The new ocean general circulation model ICON-O is developed for running experiments at kilometer scales and beyond. One targeted application is to simulate internal tides crucial for ocean mixing. To ensure their realism, which is difficult to assess, we evaluate the barotropic tides that generate internal tides. We show that ICON-O is able to realistically simulate the major aspects of the observed barotropic tides and discuss the aspects that impact the quality of the simulated tides.
Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael L. Forget, Christian Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, and Shubha Sathyendranath
Geosci. Model Dev., 16, 4639–4657, https://doi.org/10.5194/gmd-16-4639-2023, https://doi.org/10.5194/gmd-16-4639-2023, 2023
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While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Cited articles
Arbic, B. K., Garner, S. T., Hallberg, R. W., and Simmons, H. L.: The accuracy
of surface elevations in forward global barotropic and baroclinic tide
models, Deep-Sea Res. Pt. II, 51,
3069–3101, https://doi.org/10.1016/j.dsr2.2004.09.014, 2004. a
Bouwer, L. M.: Next-generation coastal risk models, Nat. Clim. Change, 8,
7–8, https://doi.org/10.1038/s41558-018-0262-2, 2018. a
Bunya, S., Dietrich, J. C., Westerink, J. J., Ebersole, B. A., Smith, J. M.,
Atkinson, J. H., Jensen, R., Resio, D. T., Luettich, R. A., Dawson, C.,
Cardone, V. J., Cox, A. T., Powell, M. D., Westerink, H. J., and Roberts,
H. J.: A High-Resolution Coupled Riverine Flow, Tide, Wind, Wind Wave, and
Storm Surge Model for Southern Louisiana and Mississippi. Part I: Model
Development and Validation, Mon. Weather Rev., 138, 345–377,
https://doi.org/10.1175/2009MWR2906.1, 2010. a, b, c, d, e, f, g, h
Castro, M. J., Ortega, S., and Parés, C.: Reprint of: Well-balanced
methods for the shallow water equations in spherical coordinates, Comput. Fluids, 169, 129–140, https://doi.org/10.1016/j.compfluid.2018.03.052, 2018. a
Chen, C., Liu, H., and Beardsley, R. C.: An unstructured grid, finite-volume,
three-dimensional, primitive equations ocean model: Application to coastal
ocean and estuaries, J. Atmos. Ocean. Tech., 20,
159–186, https://doi.org/10.1175/1520-0426(2003)020<0159:AUGFVT>2.0.CO;2, 2003. a
Danilov, S.: Ocean modeling on unstructured meshes, Ocean Model., 69,
195–210, https://doi.org/10.1016/j.ocemod.2013.05.005, 2013. a, b
Dietrich, J., Zijlema, M., Westerink, J., Holthuijsen, L., Dawson, C.,
Luettich, R., Jensen, R., Smith, J., Stelling, G., and Stone, G.: Modeling
hurricane waves and storm surge using integrally-coupled, scalable
computations, Coast. Eng., 58, 45–65,
https://doi.org/10.1016/j.coastaleng.2010.08.001, 2011. a, b, c
Dietrich, J. C., Bunya, S., Westerink, J. J., Ebersole, B. A., Smith, J. M.,
Atkinson, J. H., Jensen, R., Resio, D. T., Luettich, R. A., Dawson, C.,
Cardone, V. J., Cox, A. T., Powell, M. D., Westerink, H. J., and Roberts,
H. J.: A High-Resolution Coupled Riverine Flow, Tide, Wind, Wind Wave, and
Storm Surge Model for Southern Louisiana and Mississippi. Part II: Synoptic
Description and Analysis of Hurricanes Katrina and Rita, Mon. Weather Rev., 138, 378–404, https://doi.org/10.1175/2009mwr2907.1, 2009. a
Dresback, K. M., Kolar, R. L., and Luettich, R. A.: On the Form of the
Momentum Equation and Lateral Stress Closure Law in Shallow Water Modeling,
in: Estuarine and Coastal Modeling, American Society of Civil
Engineers, Reston, VA, 399–418, https://doi.org/10.1061/40876(209)23, 2005. a
Dullaart, J. C., Muis, S., Bloemendaal, N., and Aerts, J. C.: Advancing global
storm surge modelling using the new ERA5 climate reanalysis, Clim.
Dynam., 54, 1007–1021, https://doi.org/10.1007/s00382-019-05044-0, 2019. 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
Egbert, G. D. and Erofeeva, S. Y.: TPXO9-Atlas,
available at: https://www.tpxo.net/global/tpxo9-atlas (last access: 23 February 2021), 2019. a
Fortunato, A. B., Freire, P., Bertin, X., Rodrigues, M., Ferreira, J., and
Liberato, M. L.: A numerical study of the February 15, 1941 storm in the
Tagus estuary, Cont. Shelf Res., 144, 50–64,
https://doi.org/10.1016/j.csr.2017.06.023, 2017. a
Fringer, O. B., Dawson, C. N., He, R., Ralston, D. K., and Zhang, Y. J.: The
future of coastal and estuarine modeling: Findings from a workshop, Ocean Model., 143, 101458, https://doi.org/10.1016/j.ocemod.2019.101458,
2019. a, b
Funakoshi, Y., Feyen, J. C., Aikman, F., Tolman, H. L., van der Westhuysen,
A. J., Chawla, A., Rivin, I., and Taylor, A.: Development of Extratropical
Surge and Tide Operational Forecast System (ESTOFS), in: 12th International
Conference on Estuarine and Coastal Modeling, 201–212, 7–9 November 2011, St. Augustine,
Florida, 2011. a
Garratt, J. R.: Review of drag coefficients over oceans and continents, Mon.
Weather Rev., 105, 915–929, 1977. a
Garrett, C. and Kunze, E.: Internal Tide Generation in the Deep Ocean, Annu.
Rev. Fluid Mech., 39, 57–87,
https://doi.org/10.1146/annurev.fluid.39.050905.110227, 2007. a
GEBCO Compilation Group: GEBCO 2019 Grid, National Oceanography Centre,
https://doi.org/10.5285/836f016a-33be-6ddc-e053-6c86abc0788e, 2019. a
Gorman, G. J., Piggott, M. D., Pain, C. C., de Oliveira, C. R., Umpleby, A. P.,
and Goddard, A. J.: Optimisation based bathymetry approximation through
constrained unstructured mesh adaptivity, Ocean Model., 12, 436–452,
https://doi.org/10.1016/j.ocemod.2005.09.004, 2006. a
Greenberg, D. A., Dupont, F., Lyard, F. H., Lynch, D. R., and Werner, F. E.:
Resolution issues in numerical models of oceanic and coastal circulation,
Cont. Shelf Res., 27, 1317–1343, https://doi.org/10.1016/j.csr.2007.01.023,
2007. a
Hervouet, J.-M.: Equations of free surface hydrodynamics, in: Hydrodynamics
of Free Surface Flows: Modelling with the Finite Element Method, chap. 2, John Wiley & Sons, Ltd, 5 April 2007,
5–75, https://doi.org/10.1002/9780470319628.ch2, 2007. a
Herzfeld, M., Engwirda, D., and Rizwi, F.: A coastal unstructured model using
Voronoi meshes and C-grid staggering, Ocean Model., 148, 101599,
https://doi.org/10.1016/j.ocemod.2020.101599, 2020. a
Hoch, K. E., Petersen, M. R., Brus, S. R., Engwirda, D., Roberts, A. F., Rosa,
K. L., and Wolfram, P. J.: MPAS-Ocean Simulation Quality for
Variable-Resolution North American Coastal Meshes, J. Adv.
Model. Earth Syst., 12, e2019MS001848, https://doi.org/10.1029/2019MS001848, 2020. a
Holland, G. J.: An analytical model of the wind and pressure profiles in
hurricanes, Mon. Weather Rev., 108, 1212–1218, 1980. a
Hope, M. E., Westerink, J. J., Kennedy, A. B., Kerr, P. C., Dietrich, J. C.,
Dawson, C., Bender, C. J., Smith, J. M., Jensen, R. E., Zijlema, M.,
Holthuijsen, L. H., Luettich, R. A., Powell, M. D., Cardone, V. J., Cox,
A. T., Pourtaheri, H., Roberts, H. J., Atkinson, J. H., Tanaka, S.,
Westerink, H. J., and Westerink, L. G.: Hindcast and validation of Hurricane
Ike (2008) waves, forerunner, and storm surge, J. Geophys.
Res.-Oceans, 118, 4424–4460, https://doi.org/10.1002/jgrc.20314, 2013. a
Idier, D., Bertin, X., Thompson, P., and Pickering, M. D.: Interactions
Between Mean Sea Level, Tide, Surge, Waves and Flooding: Mechanisms and
Contributions to Sea Level Variations at the Coast, Surv. Geophys.,
40, 1603–1630, https://doi.org/10.1007/s10712-019-09549-5, 2019. a, b
Kennedy, A. B., Gravois, U., Zachry, B. C., Westerink, J. J., Hope, M. E.,
Dietrich, J. C., Powell, M. D., Cox, A. T., Luettich, R. A., and Dean, R. G.:
Origin of the Hurricane Ike forerunner surge, Geophys. Res. Lett.,
38, L08608, https://doi.org/10.1029/2011GL047090, 2011. a
Kolar, R. L., Gray, W. G., Westerink, J. J., and Luettich, R. A.: Shallow
water modeling in spherical coordinates: Equation formulation, numerical
implementation, and application: Modélisation des équations de
saint-venant, en coordonnées sphériques: Formulation, resolution
numérique et application, J. Hydraul. Res., 32, 3–24,
https://doi.org/10.1080/00221689409498786, 1994. a
Lambrechts, J., Comblen, R., Legat, V., Geuzaine, C., and Remacle, J.-F.:
Multiscale mesh generation on the sphere, Ocean Dynam., 58, 461–473,
https://doi.org/10.1007/s10236-008-0148-3, 2008. a, b
Le Bars, Y., Lyard, F., Jeandel, C., and Dardengo, L.: The AMANDES tidal
model for the Amazon estuary and shelf, Ocean Model., 31, 132–149,
https://doi.org/10.1016/j.ocemod.2009.11.001, 2010. a
Le Bars, Y., Vallaeys, V., Deleersnijder, É., Hanert, E., Carrere, L.,
and Channelière, C.: Unstructured-mesh modeling of the Congo
river-to-sea continuum, Ocean Dynam., 66, 589–603,
https://doi.org/10.1007/s10236-016-0939-x, 2016. a
LeBlond, P. H.: Tides and their Interactions with Other Oceanographic
Phenomena in Shallow Water (Review), in: Tidal hydrodynamics, edited by:
Parker, B. B., pp. 357–378, John Wiley & Sons, Ltd., New York, USA, 1991. a
Lefevre, F., Provost, C. L., and Lyard, F. H.: How can we improve a global
ocean tide model at a region scale? A test on the Yellow Sea and the East
China Sea, J. Geophys. Res.-Oceans, 105, 8707–8725,
https://doi.org/10.1029/1999JC900281, 2000. a
Legrand, S., Legat, V., and Deleersnijder, E.: Delaunay mesh generation for an
unstructured-grid ocean general circulation model, Ocean Model., 2,
17–28, https://doi.org/10.1016/s1463-5003(00)00005-6, 2000. a
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M.,
Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K., Paver, C. R., and
Smolyar, I. V.: World Ocean Atlas 2018. Volume 1: Temperature, Tech. rep.,
NOAA Atlas NESDIS 81,
available at: http://www.nodc.noaa.gov/OC5/indprod.html (last access: 23 February 2021), 2019. a
Luettich, R. A. and Westerink, J. J.: Formulation and Numerical Implementation
of the 2D/3D ADCIRC Finite Element Model Version 44.XX, Tech. rep.,
University of North Carolina at Chapel Hill & University of Notre Dame,
2004. a
Lyard, F., Lefevre, F., Letellier, T., and Francis, O.: Modelling the global
ocean tides: modern insights from FES2004, Ocean Dynam., 56, 394–415,
https://doi.org/10.1007/s10236-006-0086-x, 2006. a
Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C., and Ward, P. J.: A
global reanalysis of storm surges and extreme sea levels, Nat.
Commun., 7, 1–11, https://doi.org/10.1038/ncomms11969, 2016. a
Muis, S., Lin, N., Verlaan, M., Winsemius, H. C., Ward, P. J., and Aerts,
J. C.: Spatiotemporal patterns of extreme sea levels along the western
North-Atlantic coasts, Sci. Rep.-UK, 9, 3391,
https://doi.org/10.1038/s41598-019-40157-w, 2019. a, b
Ngodock, H. E., Souopgui, I., Wallcraft, A. J., Richman, J. G., Shriver, J. F.,
and Arbic, B. K.: On improving the accuracy of the M2 barotropic tides
embedded in a high-resolution global ocean circulation model, Ocean Model., 97, 16–26, https://doi.org/10.1016/j.ocemod.2015.10.011, 2016. a, b, c, d
Pringle, W. J.: Global Storm Tide Modeling on Unstructured Meshes with ADCIRC
v55 – Simulation Results and Model Setup, Zenodo, https://doi.org/10.5281/zenodo.3911282,
2020. a, b
Pringle, W. J. and Roberts, K. J.: CHLNDDEV/OceanMesh2D: OceanMesh2D V3.0.0, Zenodo,
https://doi.org/10.5281/zenodo.3721137, 2020. a, b
Pringle, W. J., Wirasaet, D., Suhardjo, A., Meixner, J., Westerink, J. J.,
Kennedy, A. B., and Nong, S.: Finite-Element Barotropic Model for the Indian
and Western Pacific Oceans: Tidal Model-Data Comparisons and Sensitivities,
Ocean Model., 129, 13–38, https://doi.org/10.1016/j.ocemod.2018.07.003,
2018a. a, b, c, d, e, f
Pringle, W. J., Wirasaet, D., and Westerink, J. J.: Modifications to Internal
Tide Conversion Parameterizations and Implementation into Barotropic Ocean
Models, EarthArXiv, p. 9, https://doi.org/10.31223/osf.io/84w53, 2018b. a
Ray, R. D.: Ocean self-attraction and loading in numerical tidal models,
Marine Geodesy, 21, 181–192, https://doi.org/10.1080/01490419809388134, 1998. a
Roberts, H. and Cobell, Z.: 2017 Coastal Master Plan. Attachment C3-25.1:
Storm Surge, Tech. rep., Coastal Protection and Restoration Authority, Baton
Rouge, LA,
available at: http://coastal.la.gov/wp-content/uploads/2017/04/Attachment-C3-25.1_FINAL_04.05.2017.pdf (last access: 23 February 2021),
2017. a
Roeber, V. and Bricker, J. D.: Destructive tsunami-like wave generated by surf
beat over a coral reef during Typhoon Haiyan, Nat. Commun., 6,
https://doi.org/10.1038/ncomms8854, 2015. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-y., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber,
J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar,
A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and
Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B.
Am. Meteorol. Soc., 91, 1015–1058,
https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D.,
Hou, Y. T., Chuang, H. Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez,
M. P., Van Den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.:
The NCEP climate forecast system version 2, J. Climate, 27,
2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Schaffer, J., Timmermann, R., Arndt, J. E., Kristensen, S. S., Mayer, C., Morlighem, M., and Steinhage, D.: A global, high-resolution data set of ice sheet topography, cavity geometry, and ocean bathymetry, Earth Syst. Sci. Data, 8, 543–557, https://doi.org/10.5194/essd-8-543-2016, 2016. a
Stammer, D., Ray, R. D., Andersen, O. B., Arbic, B. K., Bosch, W.,
Carrère, L., Cheng, Y., Chinn, D. S., Dushaw, B. D., Egbert, G. D.,
Erofeeva, S. Y., Fok, H. S., Green, J. A. M., Griffiths, S., King, M. A.,
Lapin, V., Lemoine, F. G., Luthcke, S. B., Lyard, F., Morison, J.,
Müller, M., Padman, L., Richman, J. G., Shriver, J. F., Shum, C. K.,
Taguchi, E., and Yi, Y.: Accuracy assessment of global barotropic ocean tide
models, Rev. Geophys., 52, 243–282, https://doi.org/10.1002/2014RG000450,
2014. a, b, c, d, e
URS Group Inc: Final Coastal and Riverine High Water Mark Collection for
Hurricane Katrina in Mississippi. FEMA-1604-DR-MS, Task Orders 413 and 420,
Tech. rep., Federal Emergency Management Agency, Washington D.C.,
available at: https://www.fema.gov/pdf/hazard/flood/recoverydata/katrina/katrina_ms_hwm_public.pdf (last access: 23 February 2021),
2006a. a, b
URS Group Inc: High Water Mark Collection for Hurricane Katrina in
Louisiana. FEMA-1603-DR-LA, Task Orders 412 and 419, Tech. rep., Federal
Emergency Management Agency, Washington D.C.,
available at: https://www.fema.gov/pdf/hazard/flood/recoverydata/katrina/katrina_la_hwm_public.pdf (last access: 23 February 2021),
2006b. a, b
Verlaan, M., De Kleermaeker, S., and Buckman, L.: GLOSSIS: Global storm
surge forecasting and information system, in: Australasian Coasts & Ports
Conference 2015: 22nd Australasian Coastal and Ocean Engineering Conference
and the 15th Australasian Port and Harbour Conference, pp. 229–234,
Auckland, New Zealand, 2015. a
Vinogradov, S. V., Myers, E., Funakoshi, Y., and Kuang, L.: Development and
Validation of Operational Storm Surge Model Guidance, in: 15th Symposium on
the Coastal Environment at the 97th AMS Annual Meeting, Seattle, Washington,
2017. a
Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M., Jevrejeva, S.,
Jackson, L. P., and Feyen, L.: Global probabilistic projections of extreme
sea levels show intensification of coastal flood hazard, Nat.
Commun., 9, 1–12, https://doi.org/10.1038/s41467-018-04692-w, 2018. a, b
Wang, X., Chao, Y., Shum, C. K., Yi, Y., and Fok, H. S.: Comparison of two
methods to assess ocean tide models, J. Atmos. Ocean. Tech., 29, 1159–1167, https://doi.org/10.1175/JTECH-D-11-00166.1, 2012. a, b
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical,
high-resolution shoreline database, J. Geophys. Res.-Sol.
Ea., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996. a
Westerink, J. J., Luettich, R. A., and Muccino, J. C.: Modelling tides in the
western North Atlantic using unstructured graded grids, Tellus A, 46,
178–199, https://doi.org/10.1034/j.1600-0870.1994.00007.x, 1994. a
Westerink, J. J., Luettich, R. A., Feyen, J. C., Atkinson, J. H., Dawson, C.,
Roberts, H. J., Powell, M. D., Dunion, J. P., Kubatko, E. J., and Pourtaheri,
H.: A Basin- to Channel-Scale Unstructured Grid Hurricane Storm Surge Model
Applied to Southern Louisiana, Mon. Weather Rev., 136, 833–864,
https://doi.org/10.1175/2007MWR1946.1, 2008.
a, b
Zaron, E. D.: Topographic and frictional controls on tides in the Sea of
Okhotsk, Ocean Model., 117, 1–11, https://doi.org/10.1016/j.ocemod.2017.06.011,
2017. a
Zaron, E. D.: Simultaneous Estimation of Ocean Tides and Underwater Topography
in the Weddell Sea, J. Geophys. Res.-Oceans, 124,
3125–3148, https://doi.org/10.1029/2019JC015037, 2019. a
Zhang, Y. J., Ye, F., Stanev, E. V., and Grashorn, S.: Seamless cross-scale
modeling with SCHISM, Ocean Model., 102, 64–81,
https://doi.org/10.1016/j.ocemod.2016.05.002, 2016. a
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A.,
Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K., Paver, C. R.,
and Smolyar, I. V.: World Ocean Atlas 2018. Volume 2: Salinity, Tech. rep.,
NOAA Atlas NESDIS 81,
available at: http://www.nodc.noaa.gov/OC5/indprod.html (last access: 23 February 2021), 2019. a
Short summary
We improve and test a computer model that simulates tides and storm surge over all of Earth's oceans and seas. The model varies mesh resolution (triangular element sizes) freely so that coastal areas, especially storm landfall locations, are well-described. We develop systematic tests of the resolution in order to suggest good mesh design criteria that balance computational efficiency with accuracy for both global astronomical tides and coastal storm tides under extreme weather forcing.
We improve and test a computer model that simulates tides and storm surge over all of Earth's...
