Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2811-2023
© Author(s) 2023. 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-16-2811-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 May 2023
Development and technical paper |
| 25 May 2023
| 25 May 2023
Enhanced ocean wave modeling by including effect of breaking under both deep- and shallow-water conditions
Yue Xu
Department of Hydraulic Engineering, Tsinghua University, Beijing,
China
Department of Ocean Science and Engineering, Southern University of
Science and Technology, Shenzhen, China
Related authors
No articles found.
Anyifang Zhang and Xiping Yu
Hydrol. Earth Syst. Sci., 29, 2505–2520, https://doi.org/10.5194/hess-29-2505-2025, https://doi.org/10.5194/hess-29-2505-2025, 2025
Short summary
Short summary
The simultaneous or sequential occurrence of different flood processes, including extreme storm surges and heavy precipitation, tends to trigger compound floods, which are often destructive. We develop a land–river–ocean coupled model for the prediction of compound floods in a big river delta region. The coupled model is shown to perform well in simulating the combined effect of heavy precipitation, river flood routing, storm surges, and inundation for events induced by tropical cyclones.
Peida Han and Xiping Yu
Ocean Sci., 18, 1573–1590, https://doi.org/10.5194/os-18-1573-2022, https://doi.org/10.5194/os-18-1573-2022, 2022
Short summary
Short summary
Hurricane Irene generated strong near-inertial currents in ocean waters when passing over the Mid-Atlantic Bight of the US East Coast in late August 2011. It is demonstrated that a combination of valuable field data and detailed model results can be exploited to study the development and decay mechanism of this event. The near-inertial kinetic energy is shown to mainly have been gained from wind power during the hurricane event. Its decay, however, depends on several factors.
Related subject area
Oceanography
GREAT v1.0: Global Real-time Early Assessment of Tsunamis
Using automatic calibration to improve the physics behind complex numerical models: an example from a 3D lake model using Delft3D (v6.02.10) and DYNO-PODS (v1.0)
Improvements to the Met Office's global ocean–sea ice forecasting system including model and data assimilation changes
Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models
A new global high-resolution wave model for the tropical ocean using WAVEWATCH III version 7.14
sedInterFoam 1.0: a three-phase numerical model for sediment transport applications with free surfaces
The Ross Sea and Amundsen Sea Ice–Sea Model (RAISE v1.0): a high-resolution ocean–sea ice–ice shelf coupling model for simulating the Dense Shelf Water and Antarctic Bottom Water in the Ross Sea, Antarctica
Sensitivity of the tropical Atlantic to vertical mixing in two ocean models (ICON-O v2.6.6 and FESOM v2.5)
HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures
A wave-resolving two-dimensional vertical Lagrangian approach to model microplastic transport in nearshore waters based on TrackMPD 3.0
HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
Comparing an idealized deterministic-stochastic model (SUP model, version 1) of the tide-and-wind driven sea surface currents in the Gulf of Trieste to HF Radar observations
Impacts of CICE sea ice model and ERA atmosphere on an Antarctic MetROMS ocean model, MetROMS-UHel-v1.0
DalROMS-NWA12 v1.0, a coupled circulation–ice–biogeochemistry modelling system for the northwest Atlantic Ocean: development and validation
A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature
Evaluating an accelerated forcing approach for improving computational efficiency in coupled ice sheet–ocean modelling
An optimal transformation method for inferring ocean tracer sources and sinks
Data-driven rolling model for global wave height
PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks
Wave forecast investigations on downscaling, source terms, and tides for Aotearoa New Zealand
An Effective Communication Topology for Performance Optimization: A Case Study of the Finite Volume WAve Modeling (FVWAM)
Experimental design for the Marine Ice Sheet–Ocean Model Intercomparison Project – phase 2 (MISOMIP2)
Development of a total variation diminishing (TVD) sea ice transport scheme and its application in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids
Spurious numerical mixing under strong tidal forcing: a case study in the south-east Asian seas using the Symphonie model (v3.1.2)
Modelling the water isotope distribution in the Mediterranean Sea using a high-resolution oceanic model (NEMO-MED12-watiso v1.0): evaluation of model results against in situ observations
LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry
PIBM 1.0: An individual-based model for simulating phytoplankton acclimation, diversity, and evolution in the ocean
Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0
A simple approach to represent precipitation-derived freshwater fluxes into nearshore ocean models: an FVCOM4.1 case study of Quatsino Sound, British Columbia
An optimal transformation method applied to diagnose the ocean carbon budget
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 2: Towards a better representation of total alkalinity when modeling the carbonate system and air–sea CO2 fluxes
Development of a novel storm surge inundation model framework for efficient prediction
Skin sea surface temperature schemes in coupled ocean–atmosphere modelling: the impact of chlorophyll-interactive e-folding depth
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
StraitFlux – precise computations of water strait fluxes on various modeling grids
Comparison of the Coastal and Regional Ocean COmmunity model (CROCO) and NCAR-LES in non-hydrostatic simulations
Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System
CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago
Implementation of additional spectral wave field exchanges in a three-dimensional wave–current coupled WAVEWATCH-III (version 6.07) and CROCO (version 1.2) configuration: assessment of their implications for macro-tidal coastal hydrodynamics
Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system
LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2
New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4
MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries
Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts
Great Lakes wave forecast system on high-resolution unstructured meshes
Impact of increased resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 (OMIP-2)
A high-resolution physical–biogeochemical model for marine resource applications in the northwest Atlantic (MOM6-COBALT-NWA12 v1.0)
A flexible z-layers approach for the accurate representation of free surface flows in a coastal ocean model (SHYFEM v. 7_5_71)
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 1: Evolution of ecosystem composition under limited light and nutrient conditions
Ocean wave tracing v.1: a numerical solver of the wave ray equations for ocean waves on variable currents at arbitrary depths
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Geosci. Model Dev., 18, 3487–3507, https://doi.org/10.5194/gmd-18-3487-2025, https://doi.org/10.5194/gmd-18-3487-2025, 2025
Short summary
Short summary
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, maps risk areas, and estimates tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.
Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard, and Marco Toffolon
Geosci. Model Dev., 18, 3473–3486, https://doi.org/10.5194/gmd-18-3473-2025, https://doi.org/10.5194/gmd-18-3473-2025, 2025
Short summary
Short summary
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.
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
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
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 d forecasts. The new system performance in past conditions, where subsurface 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.
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.
Axelle Gaffet, Xavier Bertin, Damien Sous, Héloïse Michaud, Aron Roland, and Emmanuel Cordier
Geosci. Model Dev., 18, 1929–1946, https://doi.org/10.5194/gmd-18-1929-2025, https://doi.org/10.5194/gmd-18-1929-2025, 2025
Short summary
Short summary
This study presents a new global wave model that improves predictions of sea states in tropical areas by using a high-resolution grid and corrected wind fields. The model is validated globally with satellite data and nearshore using in situ data. The model allows for the first time direct comparisons with in situ data collected at 10–30 m water depth, which is very close to shore due to the steep slope usually surrounding volcanic islands.
Antoine Mathieu, Yeulwoo Kim, Tian-Jian Hsu, Cyrille Bonamy, and Julien Chauchat
Geosci. Model Dev., 18, 1561–1573, https://doi.org/10.5194/gmd-18-1561-2025, https://doi.org/10.5194/gmd-18-1561-2025, 2025
Short summary
Short summary
Most of the tools available to model sediment transport do not account for complex physical mechanisms such as surface-wave-driven processes. In this study, a new model, sedInterFoam, allows us to reproduce numerically complex configurations in order to investigate coastal sediment transport applications dominated by surface waves and to gain insight into the complex physical processes associated with breaking waves and morphodynamics.
Zhaoru Zhang, Chuan Xie, Chuning Wang, Yuanjie Chen, Heng Hu, and Xiaoqiao Wang
Geosci. Model Dev., 18, 1375–1393, https://doi.org/10.5194/gmd-18-1375-2025, https://doi.org/10.5194/gmd-18-1375-2025, 2025
Short summary
Short summary
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.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
Short summary
Short summary
Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
Short summary
Short summary
HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
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
Short summary
Short summary
This study presents a novel modeling approach for understanding microplastic transport in coastal waters. The model accurately replicates experimental data and reveals key transport mechanisms. The findings enhance our knowledge of how microplastics move in nearshore environments, aiding in coastal management and efforts to combat plastic pollution globally.
Greig Oldford, Tereza Jarníková, Villy Christensen, and Michael Dunphy
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
Short summary
Short summary
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.
Sofia Flora, Laura Ursella, and Achim Wirth
EGUsphere, https://doi.org/10.5194/egusphere-2024-3391, https://doi.org/10.5194/egusphere-2024-3391, 2025
Short summary
Short summary
We developed a hierarchy of idealized deterministic-stochastic models to simulate the sea surface currents in the Gulf of Trieste. They include tide-and-wind driven sea surface current components, resolving the slowly varying part of the flow and a stochastic signal, representing the fast-varying small-scale dynamics. The comparison with High Frequency Radar observations shows that the non-Gaussian stochastic model captures the essential dynamics and permits to mimic the observed fat-tailed PDF.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David A. Bailey, and Petteri Uotila
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-213, https://doi.org/10.5194/gmd-2024-213, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-181, https://doi.org/10.5194/gmd-2024-181, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
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
Short summary
Short summary
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.
Rafael Santana, Richard Gorman, Emily Lane, Stuart Moore, Cyprien Bosserelle, Glen Reeve, and Christo Rautenbach
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-110, https://doi.org/10.5194/gmd-2024-110, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This research explores improving wave forecasts in New Zealand, particularly at Banks Peninsula and Baring Head. We used detailed models, finding that forecasts at Baring Head improved significantly due to its strong tidal currents, but changes at Banks Peninsula were minimal. The study demonstrates that local conditions greatly influence the effectiveness of wave prediction models, highlighting the need for tailored approaches in coastal forecasting to enhance accuracy in the predictions.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Iria Sala and Bingzhang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-130, https://doi.org/10.5194/gmd-2024-130, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Phytoplankton, tiny photosynthetic organisms, produce nearly half of Earth's oxygen. To analyze their physiology, diversity, and evolution in the ocean, we developed a model that treats phytoplankton as individual particles. Moreover, our model considers phytoplankton size, temperature, and light traits, and allows for mutations in phytoplankton cells. Thus, our model provides a valuable tool for advancing the study of phytoplankton physiology, diversity, and evolution.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Ardhuin, F., Rogers, E., Babanin, A. V., Filipot, J.-F., Magne, R., Roland,
A., van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., and
Collard, F.: Semiempirical dissipation source functions for ocean waves.
Part I: Definition, calibration, and validation,
J. Phys. Oceanogr., 40, 1917–1941, https://doi.org/10.1175/2010JPO4324.1, 2010.
Babanin, A. and Young, I.: Two-phase behaviour of the spectral dissipation
of wind waves, Proceedings of the 5th International Symposium Ocean Wave
Measurement and Analysis, Madrid, June 2005, 51, 2005.
Babanin, A. V., Banner, M. L., Young, I. R., and Donelan, M. A.:
Wave-follower field measurements of the wind-input spectral function. Part
III: Parameterization of the wind-input enhancement due to wave breaking,
J. Phys. Oceanogr., 37, 2764–2775,
https://doi.org/10.1175/2007JPO3757.1, 2007.
Badulin, S. I., Babanin, A. V., Zakharov, V. E., and Resio, D.: Weakly
turbulent laws of wind-wave growth, J. Fluid Mech., 591,
339–378, https://doi.org/10.1017/S0022112007008282, 2007.
Banner, M. L. and Melville, W. K.: On the separation of air flow over water
waves, J. Fluid Mech., 77, 825–842,
https://doi.org/10.1017/S0022112076002905, 1976.
Battjes, J. A. and Janssen, J. P. F. M.: Energy Loss and Set-Up Due to
Breaking of Random Waves, Coast. Eng., 32, 569–587,
https://doi.org/10.1061/9780872621909.034, 1978.
Berrisford, P., Soci, C., Bell, B., Dahlgren, P., A Horányi, Nicolas, J., Radu R., Villaume S., Bidlot J., and Haimberger L.: The era5 global reanalysis: preliminary extension to 1950, Q. J. Roy. Meteor. Soc., 147, 4186–4227, https://doi.org/10.1002/qj.4174, 2021.
Beyá, J., Álvarez, M., Gallardo, A., Hidalgo, H., and Winckler, P.:
Generation and validation of the Chilean Wave Atlas database,
Ocean Model., 116, 16–32, https://doi.org/10.1016/j.ocemod.2017.06.004, 2017.
Campos, R. M., Alves, J. H. G. M., Guedes Soares, C., Guimaraes, L. G., and
Parente, C. E.: Extreme wind-wave modeling and analysis in the south
Atlantic ocean, Ocean Model., 124, 75–93,
https://doi.org/10.1016/j.ocemod.2018.02.002, 2018.
Cavaleri, L., Alves, J. H. G. M., Ardhuin, F., Babanin, A., Banner, M.,
Belibassakis, K., Benoit, M., Donelan, M., Groeneweg, J., Herbers, T. H. C.,
Hwang, P., Janssen, P. A. E. M., Janssen, T., Lavrenov, I. V., Magne, R.,
Monbaliu, J., Onorato, M., Polnikov, V., Resio, D., Rogers, W. E., Sheremet,
A., McKee Smith, J., Tolman, H. L., van Vledder, G., Wolf, J., and Young,
I.: Wave modelling – The state of the art, Prog. Oceanogr., 75,
603–674, https://doi.org/10.1016/j.pocean.2007.05.005, 2007.
Cavaleri, L., Barbariol, F., and Benetazzo, A.: Wind–wave modeling: Where
we are, where to go, Journal of Marine Science and Engineering, 8, 260,
https://doi.org/10.3390/jmse8040260, 2020.
CERC: Shore protection manual, US Army Coast. Eng. Research Center,
Vols. 1–3, 1977.
Chalikov, D.: The parameterization of the wave boundary layer, J. Phys. Oceanogr., 25, 1333–1349,
https://doi.org/10.1175/1520-0485(1995)025<1333:TPOTWB>2.0.CO;2, 1995.
Chalikov, D. V. and Belevich, M. Y.: One-dimensional theory of the wave
boundary layer, Bound.-Lay. Meteorol., 63, 65–96,
https://doi.org/10.1007/BF00705377, 1993.
Chen, Y. and Yu, X.: Sensitivity of storm wave modeling to wind stress
evaluation methods, J. Adv. Model. Earth Sy., 9,
893–907, https://doi.org/10.1002/2016MS000850, 2017.
Csanady, G. T.: Air-sea interaction: laws and mechanisms, Cambridge
University Press, https://doi.org/10.1017/CBO9781139164672, 2001.
Donelan, M. A.: A nonlinear dissipation function due to wave breaking,
Proceedings of ECMWF Workshop on Ocean Wave Forecasting, Reading, UK, 2–4 July, 2001, 87–94, 2001.
Donelan, M. A. and Pierson, W. J.: Radar scattering and equilibrium ranges
in wind-generated waves with application to scatterometry, J. Geophys. Res.-Oceans, 92, 4971–5029,
https://doi.org/10.1029/JC092iC05p04971, 1987.
Donelan, M. A., Babanin, A. V., Young, I. R., and Banner, M. L.:
Wave-follower field measurements of the wind-input spectral function. Part
II: Parameterization of the wind input, J. Phys. Oceanogr.,
36, 1672–1689, https://doi.org/10.1175/JPO2933.1, 2006.
Earle, M. D., Steele, K. E., and Wang, D. W. C.: Use of advanced directional
wave spectra analysis methods, Ocean Eng., 26, 1421–1434,
https://doi.org/10.1016/S0029-8018(99)00010-4, 1999.
Eldeberky, Y.: Nonlinear transformationations of wave spectra in the
nearshore zone, Unpublished doctoral dissertation, Delft University of
Technology, Delft, The Netherlands, 1996.
Fan, Y. and Rogers, W. E.: Drag coefficient comparisons between observed and
model simulated directional wave spectra under hurricane conditions, Ocean
Model., 102, 1–13, https://doi.org/10.1016/j.ocemod.2016.04.004, 2016.
Fan, Y., Ginis, I., Hara, T., Wright, C. W., and Walsh, E. J.: Numerical
simulations and observations of surface wave fields under an extreme
tropical cyclone, J. Phys. Oceanogr., 39, 2097–2116,
https://doi.org/10.1175/2009JPO4224.1, 2009.
Hasselmann, K.: On the spectral dissipation of ocean waves due to white
capping, Bound.-Lay. Meteorol., 6, 107–127,
https://doi.org/10.1007/BF00232479, 1974.
Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E.,
Enke, K., Ewing, J., Gienapp, A., Hasselmann, D., and Kruseman, P.:
Measurements of wind-wave growth and swell decay during the Joint North Sea
Wave Project (JONSWAP), Ergaenzungsheft zur Deutschen Hydrographischen
Zeitschrift, Reihe A, https://doi.org/10.1093/ije/27.2.335, 1973.
Hwang, P. A.: Temporal and spatial variation of the drag coefficient of a
developing sea under steady wind-forcing, J. Geophys. Res.-Oceans, 110, 1–6, https://doi.org/10.1029/2005JC002912, 2005.
Hwang, P. A. and Wang, D. W.: An empirical investigation of source term
balance of small scale surface waves, Geophys. Res. Lett., 31, 121–141,
https://doi.org/10.1029/2004GL020080, 2004.
Janssen, P. A. E. M.: Wave-induced stress and the drag of air flow over sea
waves, J. Phys. Oceanogr., 19, 745–754,
https://doi.org/10.1175/1520-0485(1989)019<0745:WISATD>2.0.CO;2, 1989.
Janssen, P. A. E. M.: Quasi-linear theory of wind-wave generation applied to
wave forecasting, J. Phys. Oceanogr., 21, 1631–1642,
https://doi.org/10.1175/1520-0485(1991)021<1631:QLTOWW>2.0.CO;2, 1991.
Janssen, P. A. E. M.: The interaction of ocean waves and wind, Cambridge
University Press, https://doi.org/10.1017/CBO9780511525018, 2004.
Jones, I. S. and Toba, Y.: Wind stress over the ocean, Cambridge University
Press, https://doi.org/10.1017/CBO9780511552076, 2001.
Kahma, K. K. and Calkoen, C. J.: Reconciling discrepancies in the observed
growth of wind-generated waves, J. Phys. Oceanogr., 22,
1389–1405, https://doi.org/10.1175/1520-0485(1992)022<1389:RDITOG>2.0.CO;2, 1992.
Kim, T., Lin, L.-H., and Wang, H.: Application of maximum entropy method to
the real sea data, Coast. Eng., 24, 340–355,
https://doi.org/10.1061/9780784400890.027, 1994.
Leckler, F., Ardhuin, F., Filipot, J.-F., and Mironov, A.: Dissipation
source terms and whitecap statistics, Ocean Model., 70, 62–74,
https://doi.org/10.1016/j.ocemod.2013.03.007, 2013.
Liu, Q., Babanin, A., Fan, Y., Zieger, S., Guan, C., and Moon, I.-J.:
Numerical simulations of ocean surface waves under hurricane conditions:
Assessment of existing model performance, Ocean Model., 118, 73–93,
https://doi.org/10.1016/j.ocemod.2017.08.005, 2017.
Longuet-Higgins, M. S.: On wave breaking and the equilibrium spectrum of
wind-generated waves, P. R. Soc. Lond. A, 310, 151–159,
https://doi.org/10.1098/rspa.1969.0069, 1969.
Longuet-Higgins, M. S., Cartwright, D. E., and Smith, N. D: Observations of
the Directional Spectrum of Sea Waves Using The Motion of a Floating Buoy,
Ocean Wave Spectra, Prentice Hall, Englewood Cliffs, N. J., 111–136,
https://doi.org/10.1016/0011-7471(65)91457-9, 1963.
Makin, V. K. and Kudryavtsev, V. N.: Coupled sea surface-atmosphere model:
1. Wind over waves coupling, J. Geophys. Res.-Oceans, 104,
7613–7623, https://doi.org/10.1029/1999JC900006, 1999.
Melville, W. K. and Matusov, P.: Distribution of breaking waves at the ocean
surface, Nature, 417, 58–63, https://doi.org/10.1038/417058a, 2002.
Mentaschi, L., Besio, G., Cassola, F., and Mazzino, A.: Performance
evaluation of Wavewatch III in the Mediterranean Sea, Ocean Model., 90,
82–94, https://doi.org/10.1016/j.ocemod.2015.04.003, 2015.
Miles, J. W.: On the generation of surface waves by shear flows, J. Fluid Mech., 3, 185–204, https://doi.org/10.1017/S0022112057000567,
1957.
Miles, J. W.: A note on the interaction between surface waves and wind profiles, J. Fluid Mech., 22, 823–827, https://doi.org/10.1017/S0022112065001167, 1965.
Moody's Risk Management Solutions: Wind dataset, https://www.rms.com/event-response/hwind (last access: 22 May 2023), 2022.
Moon, I.-J., Ginis, I., and Hara, T.: Impact of the reduced drag coefficient
on ocean wave modeling under hurricane conditions, Mon. Weather Rev.,
136, 1217–1223, https://doi.org/10.1175/2007MWR2131.1, 2008.
Moskowitz, L.: Estimates of the power spectrums for fully developed seas for
wind speeds of 20 to 40 knots, J. Geophys. Res.,
69, 5161–5179, https://doi.org/10.1029/JZ069i024p05161, 1964.
National Centers for Environmental Information (NOAA): ETOPO 2022 15 Arc-Second Global Relief Model, [data set], https://doi.org/10.25921/fd45-gt74, 2022.
National Data Buoy Center (NOAA): Buoy dataset, https://www.ndbc.noaa.gov (last access: 22 May 2023), 2022.
Phillips, O. M.: Spectral and statistical properties of the equilibrium
range in wind-generated gravity waves, J. Fluid Mech., 156,
505–531, https://doi.org/10.1017/S0022112085002221, 1985.
Phillips, O. M., Posner, F. L., and Hansen, J. P.: High range resolution
radar measurements of the speed distribution of breaking events in
wind-generated ocean waves: Surface impulse and wave energy dissipation
rates, J. Phys. Oceanogr., 31, 450–460,
https://doi.org/10.1175/1520-0485(2001)031<0450:HRRRMO>2.0.CO;2, 2001.
Pierson Jr., W. J. and Moskowitz, L.: A proposed spectral form for fully
developed wind seas based on the similarity theory of S. A. Kitaigorodskii,
J. Geophys. Res., 69, 5181–5190,
https://doi.org/10.1029/JZ069i024p05181, 1964.
Polnikov, V. G.: On a description of a wind–wave energy dissipation function, in: The Air–sea Interface. Radio and Acoustic Sensing, Turbulence and Wave Dynamics, edited by: Donelan, M. A., Hui, W. H., and Plant, W. J., Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, 277–282, 1993.
Rogers, W. E., Babanin, A. V., and Wang, D. W.: Observation-consistent input
and whitecapping dissipation in a model for wind-generated surface waves:
Description and simple calculations, J. Atmos. Ocean.
Tech., 29, 1329–1346, https://doi.org/10.1175/JTECH-D-11-00092.1, 2012.
Sanders, J. W: A growth-stage scaling model for the wind-driven sea,
Deutsche Hydrografische Zeitschrift, 29, 136–161,
https://doi.org/10.1007/BF02227029, 1976.
Snyder, R. L., Dobson, F. W., Elliott, J. A., and Long, R. B.: Array
measurements of atmospheric pressure fluctuations above surface gravity
waves, J. Fluid Mech., 102, 1–59,
https://10.1017/S0022112081002528, 1981.
Stewart, R. W.: The wave drag of wind over water, J. Fluid Mech., 10, 189–194, https://10.1017/S0022112061000172, 1961.
Stopa, J. E., Ardhuin, F., Babanin, A., and Zieger, S.: Comparison and
validation of physical wave parameterizations in spectral wave models, Ocean
Model., 103, 2–17, https://doi.org/10.1016/j.ocemod.2015.09.003, 2016.
Tolman, H. L: Validation of WAVEWATCH-III version 1.15, NOAA/NWS/NCEP/MMAB Tech. Rep., 213, 33 pp., 2002.
Tolman, H. L. and Chalikov, D.: Source terms in a third-generation wind wave
model, J. Phys. Oceanogr., 26, 2497–2518,
https://doi.org/10.1175/1520-0485(1996)026<2497:STIATG>2.0.CO;2, 1996.
Wang, D. W., Mitchell, D. A., Teague, W. J., Jarosz, E., and Hulbert, M. S.:
Extreme waves under Hurricane Ivan, Science, 309, 896–896,
https://10.1126/science.1112509, 2005.
WAVEWATCH III R Development Group (WW3DG): User manual and system
documentation of WAVEWATCH III R version 5.16, Technical Note 329,
NOAA/NWS/NCEP/MMAB, College Park, MD, USA, 326 pp. + Appendices, 2016.
Xu, Y. and Yu, X.: Enhanced formulation of wind energy input into waves in
developing sea, Prog. Oceanogr., 186, 102376,
https://doi.org/10.1016/j.pocean.2020.102376, 2020.
Xu, Y. and Yu, X.: Enhanced atmospheric wave boundary layer model for
evaluation of wind stress over waters of finite depth, Prog.
Oceanogr., 198, 102664, https://doi.org/10.1016/j.pocean.2021.102664,
2021.
Xu, Y. and Yu, X.: Enhanced Ocean Wave Modeling by Including Effect of
Breaking under Both Deep- and Shallow-Water Conditions – code files,
Zenodo [code], https://doi.org/10.5281/zenodo.7047221, 2022a.
Xu, Y. and Yu, X.: Enhanced Ocean Wave Modeling by Including Effect of
Breaking under Both Deep- and Shallow-Water Conditions – input files of the
controlled normal condition cases, Zenodo [code],
https://doi.org/10.5281/zenodo.7047234, 2022b.
Xu, Y. and Yu, X.: Enhanced Ocean Wave Modeling by Including Effect of
Breaking under Both Deep- and Shallow-Water Conditions – input files of
hurricane Ivan case, Zenodo [code], https://doi.org/10.5281/zenodo.7047240, 2022c.
Xu, Y. and Yu, X.: Enhanced Ocean Wave Modeling by Including Effect of
Breaking under Both Deep- and Shallow-Water Conditions – input files of
hurricane Katrina case, Zenodo [code], https://doi.org/10.5281/zenodo.7047244,
2022d.
Young, I. R.: Wind generated ocean waves, Elsevier, eBook ISBN 9780080543802, 1999.
Young, I. R. and Verhagen, L. A.: The growth of fetch limited waves in water
of finite depth. Part 1. Total energy and peak frequency, Coast. Eng., 29, 47–78, https://doi.org/10.1016/S0378-3839(96)00006-3, 1996.
Yuan, Y., Tung, C. C., and Huang, N. E.: Statistical characteristics of
breaking waves, in: Wave Dynamics and Radio Probing of the Ocean Surface,
edited by: Phillips, O. M., and Hasselmann, K., Springer US, Boston, MA,
265–272, https://doi.org/10.1007/978-1-4684-8980-4_18, 1986.
Zakharov, V., Resio, D., and Pushkarev, A.: Balanced source terms for wave generation within the Hasselmann equation, Nonlin. Processes Geophys., 24, 581–597, https://doi.org/10.5194/npg-24-581-2017, 2017.
Zakharov, V. E., Resio, D., and Pushkarev, A.: New wind input term
consistent with experimental, theoretical and numerical considerations,
arXiv [preprint], https://doi.org/10.48550/arXiv.1212.1069, 2012.
Zieger, S., Babanin, A. V., Erick Rogers, W., and Young, I. R.:
Observation-based source terms in the third-generation wave model WAVEWATCH,
Ocean Model., 96, 2–25, https://doi.org/10.1016/j.ocemod.2015.07.014,
2015.
Short summary
An accurate description of the wind energy input into ocean waves is crucial to ocean wave modeling, and a physics-based consideration of the effect of wave breaking is absolutely necessary to obtain such an accurate description, particularly under extreme conditions. This study evaluates the performance of a recently improved formula, taking into account not only the effect of breaking but also the effect of airflow separation on the leeside of steep wave crests in a reasonably consistent way.
An accurate description of the wind energy input into ocean waves is crucial to ocean wave...
