Articles | Volume 12, issue 1
https://doi.org/10.5194/gmd-12-441-2019
© Author(s) 2019. 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-12-441-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
25 Jan 2019
Model evaluation paper |
| 25 Jan 2019
| 25 Jan 2019
A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system
Carlos Rocha
CORRESPONDING AUTHOR
Coastal and Regional Oceanography Lab, School of Mathematics and Statistics,
UNSW Sydney, Sydney, NSW, 2052, Australia
Christopher A. Edwards
Department of Ocean Sciences, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95062, USA
Moninya Roughan
Coastal and Regional Oceanography Lab, School of Mathematics and Statistics,
UNSW Sydney, Sydney, NSW, 2052, Australia
School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW,
2052, Australia
Paulina Cetina-Heredia
Coastal and Regional Oceanography Lab, School of Mathematics and Statistics,
UNSW Sydney, Sydney, NSW, 2052, Australia
Colette Kerry
Coastal and Regional Oceanography Lab, School of Mathematics and Statistics,
UNSW Sydney, Sydney, NSW, 2052, Australia
Related authors
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Manh Cuong Tran, Moninya Roughan, and Amandine Schaeffer
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-480, https://doi.org/10.5194/essd-2024-480, 2024
Preprint under review for ESSD
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The East Australian Current (EAC) plays an important role in the marine ecosystem and climate of the region. To understand the EAC regime and the inner shelf dynamics, we implement the variational approach to produce the first multi-year coastal radar dataset (2012–2023) in this region. The validated data allows for a comprehensive investigation of the EAC dynamics. This dataset will be useful for understanding the complex EAC regime and its far-reaching impacts on the shelf environment.
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.
Michael Hemming, Moninya Roughan, and Amandine Schaeffer
Earth Syst. Sci. Data, 16, 887–901, https://doi.org/10.5194/essd-16-887-2024, https://doi.org/10.5194/essd-16-887-2024, 2024
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We present new datasets that are useful for exploring extreme ocean temperature events in Australian coastal waters. These datasets span multiple decades, starting from the 1940s and 1950s, and include observations from the surface to the bottom at four coastal sites. The datasets provide valuable insights into the intensity, frequency and timing of extreme warm and cold temperature events and include event characteristics such as duration, onset and decline rates and their categorisation.
Michael P. Hemming, Moninya Roughan, Neil Malan, and Amandine Schaeffer
Ocean Sci., 19, 1145–1162, https://doi.org/10.5194/os-19-1145-2023, https://doi.org/10.5194/os-19-1145-2023, 2023
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We estimate subsurface linear and non-linear temperature trends at five coastal sites adjacent to the East Australian Current (EAC). We see accelerating trends at both 34.1 and 42.6 °S and place our results in the context of previously reported trends, highlighting that magnitudes are depth-dependent and vary across latitude. Our results indicate the important role of regional dynamics and show the necessity of subsurface data for the improved understanding of regional climate change impacts.
Joao Marcos Azevedo Correia de Souza, Sutara H. Suanda, Phellipe P. Couto, Robert O. Smith, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 211–231, https://doi.org/10.5194/gmd-16-211-2023, https://doi.org/10.5194/gmd-16-211-2023, 2023
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The current paper describes the configuration and evaluation of the Moana Ocean Hindcast, a > 25-year simulation of the ocean state around New Zealand using the Regional Ocean Modeling System v3.9. This is the first open-access, long-term, continuous, realistic ocean simulation for this region and provides information for improving the understanding of the ocean processes that affect the New Zealand exclusive economic zone.
David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
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Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
David E. Gwyther, Colette Kerry, Moninya Roughan, and Shane R. Keating
Geosci. Model Dev., 15, 6541–6565, https://doi.org/10.5194/gmd-15-6541-2022, https://doi.org/10.5194/gmd-15-6541-2022, 2022
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The ocean current flowing along the southeastern coast of Australia is called the East Australian Current (EAC). Using computer simulations, we tested how surface and subsurface observations might improve models of the EAC. Subsurface observations are particularly important for improving simulations, and if made in the correct location and time, can have impact 600 km upstream. The stability of the current affects model estimates could be capitalized upon in future observing strategies.
William J. Crawford, Polly J. Smith, Ralph F. Milliff, Jerome Fiechter, Christopher K. Wikle, Christopher A. Edwards, and Andrew M. Moore
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 171–192, https://doi.org/10.5194/ascmo-2-171-2016, https://doi.org/10.5194/ascmo-2-171-2016, 2016
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We present a method for estimating intrinsic model error in a model of the California Current System. The estimated model error covariance matrix is used in the weak constraint formulation of the Regional Ocean Modeling System, four-dimensional variational data assimilation system, and comparison of the circulation estimates computed in this way show demonstrable improvement to those computed in the strong constraint formulation, where intrinsic model error is not taken into account.
Colette Kerry, Brian Powell, Moninya Roughan, and Peter Oke
Geosci. Model Dev., 9, 3779–3801, https://doi.org/10.5194/gmd-9-3779-2016, https://doi.org/10.5194/gmd-9-3779-2016, 2016
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Ocean circulation drives weather and climate and supports marine ecosystems, so providing accurate predictions is important. The ocean circulation is complex, 3-D and highly variable, and its prediction requires advanced numerical models combined with real-time measurements. Focusing on the dynamic East Austr. Current, we use novel mathematical techniques to combine an ocean model with measurements to better estimate the circulation. This is an important step towards improving ocean forecasts.
Peter R. Oke, Roger Proctor, Uwe Rosebrock, Richard Brinkman, Madeleine L. Cahill, Ian Coghlan, Prasanth Divakaran, Justin Freeman, Charitha Pattiaratchi, Moninya Roughan, Paul A. Sandery, Amandine Schaeffer, and Sarath Wijeratne
Geosci. Model Dev., 9, 3297–3307, https://doi.org/10.5194/gmd-9-3297-2016, https://doi.org/10.5194/gmd-9-3297-2016, 2016
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The Marine Virtual Laboratory (MARVL) is designed to help ocean modellers hit the ground running. Usually, setting up an ocean model involves a handful of technical steps that time and effort. MARVL provides a user-friendly interface that allows users to choose what options they want for their model, including the region, time period, and input data sets. The user then hits "go", and MARVL does the rest – delivering a "take-away bundle" that contains all the files needed to run the model.
Amandine Schaeffer, Moninya Roughan, Emlyn M. Jones, and Dana White
Biogeosciences, 13, 1967–1975, https://doi.org/10.5194/bg-13-1967-2016, https://doi.org/10.5194/bg-13-1967-2016, 2016
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The water properties of the coastal ocean such as temperature, salt, oxygen, or chlorophyll content vary spatially, and estimates need to be made regarding the scales of variability. Here, we use statistical techniques to determine the spatial variability of ocean properties from high-resolution measurements by gliders. We show that biological activity is patchy compared to the distribution of physical characteristics, and that the size and shape of this is determined by coastal ocean processes.
Paulina Cetina-Heredia, Erik van Sebille, Richard Matear, and Moninya Roughan
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-53, https://doi.org/10.5194/bg-2016-53, 2016
Revised manuscript not accepted
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Characterizing phytoplankton growth influences fisheries and climate. We use a lagrangian approach to identify phytoplankton blooms in the Great Australian Bight (GAB), and associate them with nitrate sources. We find that 88 % of the nitrate utilized in blooms is originated between the GAB and the SubAntarctic Front. Large nitrate concentrations are supplied at depth but do not reach the euphotic zone often. As a result, 55 % of blooms utilize nitrate supplied in the top 100 m.
A. M. Waite, V. Rossi, M. Roughan, B. Tilbrook, P. A. Thompson, M. Feng, A. S. J. Wyatt, and E. J. Raes
Biogeosciences, 10, 5691–5702, https://doi.org/10.5194/bg-10-5691-2013, https://doi.org/10.5194/bg-10-5691-2013, 2013
Related subject area
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PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks
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
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
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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
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StraitFlux – precise computations of water strait fluxes on various modeling grids
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Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System
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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
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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
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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
A revised ocean mixed layer model for better simulating the diurnal variation of ocean skin temperature
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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.
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.
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.
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
EGUsphere, https://doi.org/10.5194/egusphere-2024-1372, https://doi.org/10.5194/egusphere-2024-1372, 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 oceans’ 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.
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.
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.
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. Discuss., https://doi.org/10.5194/gmd-2024-23, https://doi.org/10.5194/gmd-2024-23, 2024
Revised manuscript accepted for GMD
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The recently available ERA5 hourly ocean skin temperature (Tint) data is expected to be valuable for various science studies. However, when analyzing the hourly variations of Tint, questions arise about its reliability, the deficiency of which may be related to errors in the ocean mixed layer (OML) model. To address this, we reexamined and corrected significant errors in the OML model. Validation of the simulated SST using the revised OML model against observations demonstrated good agreement.
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.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-244, https://doi.org/10.5194/gmd-2023-244, 2024
Revised manuscript accepted for GMD
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We have introduced an "accelerated forcing" approach to address the discrepancy in timescales between ice sheet and ocean models in coupled modelling, by reducing the ocean model simulation duration. We evaluate the approach's applicability and limitations based on idealized coupled models. Our results suggest that, when used carefully, the approach can be a useful tool in coupled ice sheet-ocean modelling, especially relevant to studies on sea level rise projections.
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.
Jan David Zika and Sohail Taimoor
EGUsphere, https://doi.org/10.5194/egusphere-2023-1220, https://doi.org/10.5194/egusphere-2023-1220, 2023
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We describe a method to relate the fluxes of heat and fresh water at the sea surface, to the resulting distribution of sea water among categories such as warm and salty, cold and salty, etc. 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.
Rafael Santana, Helen Macdonald, Joanne O'Callaghan, Brian Powell, Sarah Wakes, and Sutara H. Suanda
Geosci. Model Dev., 16, 3675–3698, https://doi.org/10.5194/gmd-16-3675-2023, https://doi.org/10.5194/gmd-16-3675-2023, 2023
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We show the importance of assimilating subsurface temperature and velocity data in a model of the East Auckland Current. Assimilation of velocity increased the representation of large oceanic vortexes. Assimilation of temperature is needed to correctly simulate temperatures around 100 m depth, which is the most difficult region to simulate in ocean models. Our simulations showed improved results in comparison to the US Navy global model and highlight the importance of regional models.
David Byrne, Jeff Polton, Enda O'Dea, and Joanne Williams
Geosci. Model Dev., 16, 3749–3764, https://doi.org/10.5194/gmd-16-3749-2023, https://doi.org/10.5194/gmd-16-3749-2023, 2023
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Validation is a crucial step during the development of models for ocean simulation. The purpose of validation is to assess how accurate a model is. It is most commonly done by comparing output from a model to actual observations. In this paper, we introduce and demonstrate usage of the COAsT Python package to standardise the validation process for physical ocean models. We also discuss our five guiding principles for standardised validation.
Katherine Hutchinson, Julie Deshayes, Christian Éthé, Clément Rousset, Casimir de Lavergne, Martin Vancoppenolle, Nicolas C. Jourdain, and Pierre Mathiot
Geosci. Model Dev., 16, 3629–3650, https://doi.org/10.5194/gmd-16-3629-2023, https://doi.org/10.5194/gmd-16-3629-2023, 2023
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Bottom Water constitutes the lower half of the ocean’s overturning system and is primarily formed in the Weddell and Ross Sea in the Antarctic due to interactions between the atmosphere, ocean, sea ice and ice shelves. Here we use a global ocean 1° resolution model with explicit representation of the three large ice shelves important for the formation of the parent waters of Bottom Water. We find doing so reduces salt biases, improves water mass realism and gives realistic ice shelf melt rates.
Daniele Bianchi, Daniel McCoy, and Simon Yang
Geosci. Model Dev., 16, 3581–3609, https://doi.org/10.5194/gmd-16-3581-2023, https://doi.org/10.5194/gmd-16-3581-2023, 2023
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We present NitrOMZ, a new model of the oceanic nitrogen cycle that simulates chemical transformations within oxygen minimum zones (OMZs). We describe the model formulation and its implementation in a one-dimensional representation of the water column before evaluating its ability to reproduce observations in the eastern tropical South Pacific. We conclude by describing the model sensitivity to parameter choices and environmental factors and its application to nitrogen cycling in the ocean.
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
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In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
Pengcheng Wang and Natacha B. Bernier
Geosci. Model Dev., 16, 3335–3354, https://doi.org/10.5194/gmd-16-3335-2023, https://doi.org/10.5194/gmd-16-3335-2023, 2023
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Effects of sea ice are typically neglected in operational flood forecast systems. In this work, we capture these effects via the addition of a parameterized ice–ocean stress. The parameterization takes advantage of forecast fields from an advanced ice–ocean model and features a novel, consistent representation of the tidal relative ice–ocean velocity. The new parameterization leads to improved forecasts of tides and storm surges in polar regions. Associated physical processes are discussed.
Yue Xu and Xiping Yu
Geosci. Model Dev., 16, 2811–2831, https://doi.org/10.5194/gmd-16-2811-2023, https://doi.org/10.5194/gmd-16-2811-2023, 2023
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An accurate description of the wind energy input into ocean waves is crucial to ocean wave modeling, and a physics-based consideration of the effect of wave breaking is absolutely necessary to obtain such an accurate description, particularly under extreme conditions. This study evaluates the performance of a recently improved formula, taking into account not only the effect of breaking but also the effect of airflow separation on the leeside of steep wave crests in a reasonably consistent way.
Yankun Gong, Xueen Chen, Jiexin Xu, Jieshuo Xie, Zhiwu Chen, Yinghui He, and Shuqun Cai
Geosci. Model Dev., 16, 2851–2871, https://doi.org/10.5194/gmd-16-2851-2023, https://doi.org/10.5194/gmd-16-2851-2023, 2023
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Internal solitary waves (ISWs) play crucial roles in mass transport and ocean mixing in the northern South China Sea. Massive numerical investigations have been conducted in this region, but there was no systematic evaluation of a three-dimensional model about precisely simulating ISWs. Here, an ISW forecasting model is employed to evaluate the roles of resolution, tidal forcing and stratification in accurately reproducing wave properties via comparison to field and remote-sensing observations.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
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MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
Y. Joseph Zhang, Tomas Fernandez-Montblanc, William Pringle, Hao-Cheng Yu, Linlin Cui, and Saeed Moghimi
Geosci. Model Dev., 16, 2565–2581, https://doi.org/10.5194/gmd-16-2565-2023, https://doi.org/10.5194/gmd-16-2565-2023, 2023
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Simulating global ocean from deep basins to coastal areas is a daunting task but is important for disaster mitigation efforts. We present a new 3D global ocean model on flexible mesh to study both tidal and nontidal processes and total water prediction. We demonstrate the potential for
seamlesssimulation, on a single mesh, from the global ocean to a few estuaries along the US West Coast. The model can serve as the backbone of a global tide surge and compound flooding forecasting framework.
Qi Shu, Qiang Wang, Chuncheng Guo, Zhenya Song, Shizhu Wang, Yan He, and Fangli Qiao
Geosci. Model Dev., 16, 2539–2563, https://doi.org/10.5194/gmd-16-2539-2023, https://doi.org/10.5194/gmd-16-2539-2023, 2023
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Ocean models are often used for scientific studies on the Arctic Ocean. Here the Arctic Ocean simulations by state-of-the-art global ocean–sea-ice models participating in the Ocean Model Intercomparison Project (OMIP) were evaluated. The simulations on Arctic Ocean hydrography, freshwater content, stratification, sea surface height, and gateway transports were assessed and the common biases were detected. The simulations forced by different atmospheric forcing were also evaluated.
Manuel Aghito, Loris Calgaro, Knut-Frode Dagestad, Christian Ferrarin, Antonio Marcomini, Øyvind Breivik, and Lars Robert Hole
Geosci. Model Dev., 16, 2477–2494, https://doi.org/10.5194/gmd-16-2477-2023, https://doi.org/10.5194/gmd-16-2477-2023, 2023
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The newly developed ChemicalDrift model can simulate the transport and fate of chemicals in the ocean and in coastal regions. The model combines ocean physics, including transport due to currents, turbulence due to surface winds and the sinking of particles to the sea floor, with ocean chemistry, such as the partitioning, the degradation and the evaporation of chemicals. The model will be utilized for risk assessment of ocean and sea-floor contamination from pollutants emitted from shipping.
Cited articles
Alvera-Azcárate, A., Barth, A., Sirjacobs, D., Lenartz, F., and Beckers,
J. M.: Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for
geophysical data analyses, Mediterr. Mar. Sci., 12, 5–11,
https://doi.org/10.12681/mms.64, 2010.
Andersen, V., Nival, P., and Harris, R. P.: Modelling of a planktonic
ecosystem in an enclosed water column, J. Mar. Biol. Assoc. UK., 67,
407–430, 1987.
Anderson, T. R.: Plankton functional type modelling: running before we can
walk?, J. Plankton Res., 27, 1073–1081, https://doi.org/10.1093/plankt/fbi076, 2005.
Armbrecht, L. H., Roughan, M., Rossi, V., Schaeffer, A., Davies, P. L.,
Waite, A. M., and Armand, L. K.: Phytoplankton composition under contrasting
oceanographic conditions: Upwelling and downwelling (Eastern Australia),
Cont. Shelf Res., 75, 54–67, https://doi.org/10.1016/j.csr.2013.11.024, 2013.
Baird, M. E., Timko, P. G., Suthers, I. M., and Middleton, J. H.: Coupled
physical-biological modelling study of the East Australian Current with
idealised wind forcing, Part I: Biological model intercomparison, J. Mar.
Syst., 59, 249–270, 2006a.
Baird, M. E., Timko, P. G., Suthers, I. M., and Middleton, J. H.: Coupled
physical-biological modelling study of the East Australian Current with
idealised wind forcing, Part II: Biological dynamical analysis, J. Mar.
Syst., 59, 271–291, 2006b.
Baird, M. E., Cherukuru, N., Jones, E., Margvelashvili, N., Mongin, M.,
Oubelkheir, K., Ralph, P. J., Rizwi, F., Robson, B. J., Schroeder, T., and
Skerratt, J.: Remote sensing reflectance and true colour produced by a
coupled hydrodynamic, optical, sediment, biogeochemical model of the Great
Barrier Reef, Australia: comparison with satellite data, Environ. Modell.
Soft., 78, 79–96, https://doi.org/10.1016/j.envsoft.2015.11.025, 2016.
Bjornsson, H. and Venegas, S. A.: A Manual for EOF and SVD Analyses of
Climatic Data, Feb. 1997, 52 pp., CCGCR Report No. 97-1, 1997.
Blain, S., Quéguiner, B., Armand, L., Belviso, S., Bombled, B., Bopp, L.,
Bowie, A., Brunet, C., Brussaard, C., Carlotti, F., Christaki, U.,
Corbière, A., Durand, I., Ebersbach, F., Fuda, J., Garcia, N., Gerringa,
L., Griffiths, B., Guigue, C., Guillerm, C., Jacquet, S., Jeandel, C., Laan,
P., Lefèvre, D., Lo Monaco, C., Malits, A., Mosseri, J., Obernosterer,
I., Park, Y., Picheral, M., Pondaven, P., Remenyi, T., Sandroni, V., Sarthou,
G., Savoye, N., Scouarnec, L., Souhaut, M., Thuiller, D., Timmermans, K.,
Trull, T., Uitz, J., van Beek, P., Veldhuis, M., Vincent, D., Viollier, E.,
Vong, L., and Wagener, T.: Effect of natural iron fertilization on carbon
sequestration in the Southern Ocean, Nature, 446, 1070,
https://doi.org/10.1038/nature05700, 2007.
Blanchard, J. L., Jennings, S., Holmes, R., Harle, J., Merino, G., Allen, J.
I., Holt, J., Dulvy, N. K., and Barange, M.: Potential consequences of
climate change for primary production and fish production in large marine
ecosystems, Phil. Trans. R. Soc. B, 367, 2979–2989,
https://doi.org/10.1098/rstb.2012.0231, 2012.
Cetina-Heredia, P., Roughan, M., Van Sebille, E., and Coleman, M. A.:
Long-term trends in the East Australian Current separation latitude and eddy
driven transport, J. Geophys. Res.-Oceans, 119, 4351–4366,
https://doi.org/10.1002/2014JC010071, 2014.
Chapman, D. C.: Numerical treatment of Cross-Shelf Open Boundaries in a
Barotropic Coastal Ocean Model, J. Phys. Oceanogr., 15, 1060–1075, 1985.
Clementson, L. A., Parslow, J. S., Griffiths, F. B., Lyne, V. D., Mackey, D.
J., Harris, G. P., McKenzie, D. C., Bonham, P. I., Rathbone, C. E., and
Rintoul, S.: Controls on phytoplankton production in the Australasian sector
of the subtropical convergence, Deep-Sea Res. Pt. I, 45, 1627–1661, 1998.
Condie, S. and Dunn, J. R.: Seasonal characteristics of the surface mixed
layer in the Australasian region: Implications for primary production regimes
and biogeography, Mar. Freshwater Res., 57, 569–590, https://doi.org/10.1071/MF06009,
2006.
Doney, S. C., Lima, I., Lindsay, K., Moore, J. K., Dutkiewicz, S.,
Friedrichs, M. A. M., and Matear, R. J.: Marine biogeochemical modeling:
Recent advances and future challenges. Oceanography, 14, 93–107,
https://doi.org/10.5670/oceanog.2001.10, 2001.
Everett, J., Baird, M., Oke, P., and Suthers, I.: An avenue of eddies:
Quantifying the biophysical properties of mesoscale eddies in the Tasman Sea,
Geophys. Res. Lett., 39, L16608, https://doi.org/10.1029/2012GL053091, 2012.
Everett, J., Baird, M., Roughan, M., Suthers, I., and Doblin, M.: Relative
impact of seasonal and oceanographic drivers on surface chlorophyll a along a
Western Boundary Current, Prog. Oceanogr., 120, 340–351, 2014.
Everett, J., Macdonald, H., Baird, M., Humphries, J., Roughan, M., and
Suthers, I. M.: Cyclonic entrainment of preconditioned shelf waters into a
frontal eddy, J. Geophys. Res.-Oceans, 120, 677–691,
https://doi.org/10.1002/2014JC010301, 2015.
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., and Young, G.
S.: Bulk parameterization of air-sea fluxes for tropical oceanglobal
atmosphere Coupled-Ocean Atmosphere Response Experiment, J. Geophys. Res.,
101, 3747–3764, 1996.
Fasham, M., Ducklow, H., and McKelvie, S.: A nitrogen-based model of plankton
dynamics in the oceanic mixed layer, J. Mar. Res., 48, 591–639, 1990.
Fasham, M. J. R.: Variations in the seasonal cycle of biological production
in subarctic oceans: A model sensitivity analysis, Deep-Sea Res. Pt. I, 42,
1111–1149, 1995.
Fennel, K., Losch, M., Schroter, J., and Wenzel, M.: Testing a marine
ecosystem model: Sensitivity analysis and parameter optimization, J. Mar.
Syst., 28, 45–63, 2001.
Fennel, K., Wilkin, J., Levin, J., Moisan, J., O'Reilly, J., and Haidvogel,
D.: Nitrogen cycling in the Middle Atlantic Bight: Results from a
three-dimensional model and implications for the North Atlantic nitrogen
budget, Global Biogeochem. Cy., 20, GB3007, https://doi.org/10.1029/2005GB002456, 2006.
Fiechter, J., Edwards, C. A., and Moore, A. M.: Wind, circulation, and
topographic effects on alongshore phytoplankton variability in the California
Current, Geophys. Res. Lett., 45, 3238–3245, https://doi.org/10.1002/2017GL076839, 2018.
Flather, R. A.: A tidal model of the northwest European continental shelf,
Mem. Soc. R. Sci. Liege, 6, 141–164, 1976.
Franks, P. J. S.: Planktonic ecosystem models: perplexing parameterizations
and a failure to fail, J. Plankton Res., 31, 1299–1306,
https://doi.org/10.1093/plankt/fbp069, 2009.
Friedrichs, M. A., Dusenberry, J. A., Anderson, L. A., Armstrong, R. A.,
Chai, F., Christian, J. R., Doney, S. C., Dunne, J., Fujii, M., Hood, R., and
McGillicuddy Jr, D. J.: Assessment of skill and portability in regional
marine biogeochemical models: Role of multiple planktonic groups, J. Geophys.
Res., 112, C08001, https://doi.org/10.1029/2006JC003852, 2007.
Gaube, P., McGillicuddy Jr., D. J., Chelton, D. B., Behrenfeld, M. J., and
Strutton, P. G.: Regional variations in the influence of mesoscale eddies on
near-surface chlorophyll, J. Geophys. Res.-Oceans, 119, 8195–8220,
https://doi.org/10.1002/2014JC010111, 2014.
Geider, R. J., McIntyre, H. L., and Kana, T. M.: Dynamic model of
phytoplankton growth and acclimation: Responses of the balanced growth rate
and the chlorophyll a: Carbon ratio to light, nutrient-limitation and
temperature, Mar. Ecol. Prog. Ser., 148, 187–200, 1997.
Hassler, C., Djajadikarta J., Doblin, M., Everett, J., and Thompson, P.:
Characterisation of water masses and phytoplankton nutrient limitation in the
East Australian Current separation zone during spring 2008, Deep-Sea Res. Pt.
II, 58, 664–677, https://doi.org/10.1016/j.dsr2.2010.06.008, 2011.
Jones, E. M., Baird, M. E., Mongin, M., Parslow, J., Skerratt, J., Lovell,
J., Margvelashvili, N., Matear, R. J., Wild-Allen, K., Robson, B., Rizwi, F.,
Oke, P., King, E., Schroeder, T., Steven, A., and Taylor, J.: Use of
remote-sensing reflectance to constrain a data assimilating marine
biogeochemical model of the Great Barrier Reef, Biogeosciences, 13,
6441–6469, https://doi.org/10.5194/bg-13-6441-2016, 2016.
Kerry, C., Powell, B., Roughan, M., and Oke, P.: Development and evaluation
of a high-resolution reanalysis of the East Australian Current region using
the Regional Ocean Modelling System (ROMS 3.4) and Incremental
Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation,
Geosci. Model Dev., 9, 3779–3801, https://doi.org/10.5194/gmd-9-3779-2016,
2016.
Kishi, M. J., Kashiwai, M., Ware, D. M., Megrey, B. A., Eslinger, D. L.,
Werner, F. E., Noguchi-Aita, M., Azumaya, T., Fujii, M., Hashimoto, S.,
Huang, D., Iizumi, H., Ishida, Y., Kang, S., Kantakov, G. A., Kim, H.-C.,
Komatsu, K., Navrotsky, V. V., Smith, L. S., Tadokoro, K., Tsuda, A.,
Yamamura, O., Yamanaka, Y., Yokouchi, K., Yoshie, N., Zhang, J., Zuenko, Y.
I., and Zvalinsky, V. I.: Nemuro – a lower trophic level model for the North
Pacific marine ecosystem, Ecol. Modell., 202, 12–25, 2007.
Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Woolen, J.,
Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H.,
Jenne, R., and Fiorino, M.: The NCEP/NCAR 50-Year Reanalysis, B. Am.
Meteorol. Soc., 82, 247–268, 2001.
Laiolo, L., McInnes, A. S., Matear, R., and Doblin, M. A.: Key Drivers of
Seasonal Plankton Dynamics in Cyclonic and Anticyclonic Eddies off East
Australia, Front. Mar. Sci., 3, 155, 2016.
Laiolo, L., Matear, R., Baird, M. E., Soja-Woźniak, M., and Doblin, M.
A.: Information content of in situ and remotely sensed chlorophyll-a:
Learning from size-structured phytoplankton model, J. Mar. Syst., 183, 1–12,
https://doi.org/10.1016/j.jmarsys.2018.03.005, 2018.
Leggett, R. W. and Williams, L. R.: A reliability index for models, Ecol.
Modell., 13, 303–312, 1981.
Leonard, C. L., McClain, C. R., Murtugudde, R., Hofmann, E. E., and Harding,
L. W.: An iron-based ecosystem model of the central equatorial Pacific, J.
Geophys. Res., 104, 1325–1341, 1999.
Lima, I. D. and Doney, S. C.: A three-dimensional, multi-nutrient and
size-structured ecosystem model for the North Atlantic, Global Biogeochem.
Cy., 18, GB3019, https://doi.org/10.1029/2003GB002146, 2004.
Loague, K. and Green, R. E.: Statistical and graphical methods for evaluating
solute transport models: overview and application, J. Contam. Hydrol., 7,
51–73, 1991.
Macdonald, H.: Numerical modelling of mesoscale eddies in the Tasman Sea, PhD
Thesis, UNSW Sydney, 2013.
Macdonald, H., Baird, M., and Middleton, J. H.: Effect of wind on continental
shelf carbon fluxes off southeast Australia: A numerical model, J. Geophys.
Res., 114, C05016, https://doi.org/10.1029/2008JC004946, 2009.
Macdonald, H., Roughan, M., Baird, M., and Wilkin, J.: The formation of a
cold-core eddy in the East Australian Current, Cont. Shelf Res., 114, 72–84,
https://doi.org/10.1016/j.csr.2016.01.002, 2016.
Maritorena, S. and Siegel, D. A.: Consistent merging of satellite ocean color
data sets using a bio-optical model, Remote Sens. Environ., 94, 429–440,
https://doi.org/10.1016/j.rse.2004.08.014, 2005.
Matear, R. J. and Jones, E.: Marine Biogeochemical Modelling and Data
Assimilation, in: Operational Oceanography in the 21st Century, edited by:
Schiller, A. and Brassington, G., Springer, Dordrecht, 2011.
Matear, R. J., Chamberlain, M. A., Sun, C., and Feng, M.: Climate change
projection of the Tasman Sea from an Eddy-resolving Ocean Model, J. Geophys.
Res.-Oceans, 118, 2961–2976, https://doi.org/10.1002/jgrc.20202, 2013.
Mattern, J., Song, H., Edwards, C. A., Moore, A. M., and Fiechter, J.: Data
assimilation of physical and chlorophyll a observations in the California
Current System using two biogeochemical models, Ocean Model, 109, 55–71,
https://doi.org/10.1016/j.ocemod.2016.12.002, 2016.
McGillicuddy, D. J., Robinson, A. R., Siegel, A. A., Jannasch, H. W.,
Johnson, R., Dickey, T. D., McNeil, J., Michaels, A. F., and Knap, A. H.:
Influence of mesoscale eddies on new production in the Sargasso Sea, Nature,
394, 263–266, 1998.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for
geophysical fluid problems, Rev. Geophys. Space Ge., 20, 851–875,
1982.
Mongin, M., Matear, R., and Chamberlain, M.: Seasonal and spatial variability
of remotely sensed chlorophyll and physical fields in the SAZ-sense region,
Deep-Sea Res. Pt. II, 58, 2082–2093, https://doi.org/10.1016/j.dsr2.2011.06.002, 2011.
Moore, T., Matear, R., Marra, J., and Clementson, L.: Phytoplankton
variability off the Western Australian Coast: Mesoscale eddies and their role
in cross-shelf exchange, Deep-Sea Res. Pt. II, 54, 943–960, 2007.
Moskilde, E.: Topics in Non-Linear Dynamics: Application to Physics, Biology
and Economic Systems, World Sci., Hackensack, N. J., 1996.
Nash, J. E. and Sutcliffe, J. V.: River flowforecasting through conceptual
models, Part 1 – a discussion of principles, J. Hydrol., 10, 282–290, 1970.
Oke, P., Sakov, P., Cahill, M. L., Dunn, J. R., Fiedler, R., Griffin, D. A.,
Mansbridge, J. V., Ridgway, K. R., and Schiller, A.: Towards a dynamically
balanced eddy-resolving ocean reanalysis: BRAN3, Ocean Modell., 67, 52–70,
2013.
Oke, P. R., Brassington, G. B., Griffin, D. A., and Schiller, A.: The
Bluelink ocean data assimilation system (BODAS), Ocean Modell., 21, 46–70,
2008.
Olson, R. J.: Differential photoinhibition of marine nitrifying bacteria: A
possible mechanism for the formation of the primary nitrite maximum, J. Mar.
Res., 39, 227–238, 1981.
Oschlies, A., and Garcon, V.: An eddy-permitting coupled physical- biological
models of the North Atlantic: 1. Sensitivity to advection numerics and mixed
layer, Global Biogeochem. Cy., 13, 135–160, 1999.
Powell T. P., Lewis, C. V. W., Curchitser, E. N., Haidvogel, D. B., Hermann,
A. J., and Dobbins, E. L.: Results from a three- dimensional, nested
biological-physical model of the California Current System and comparisons
with statistics from satellite imagery, J. Geophys. Res., 111, C07018,
https://doi.org/10.1029/2004JC002506, 2006.
Pritchard, T. R., Lee, R. S., Ajani, P. A., Rendell, P. S., Black, K., and
Koop, K.: Phytoplankton responses to nutrient sources in coastal waters off
southeastern Australia, Aquat. Ecosyst. Health, 6, 105–117, 2003.
Ridgway, K. R. and Dunn, J. R.: Mesoscale structure of the mean East
Australian Current System and its relationship with topography, Prog.
Oceanogr., 56, 189–222, https://doi.org/10.1016/S0079-6611(03)00004-1, 2003.
Rombouts, I., Beaugrand, G., Artigas, L. F., Dauvin, J.-C., Gevaert, F.,
Goberville, E., Kopp, D., Lefebvre, S., Luczak, C., Spilmont, N.,
Travers-Trolet, M., Villanueva, M. C., and Kirby, R. R.: Evaluating marine
ecosystem health: case studies of indicators using direct observations and
modelling methods, Ecol. Indic., 24, 353–365,
https://doi.org/10.1016/j.ecolind.2012.07.001, 2013.
Roughan, M. and Middleton, J.: On the East Australian Current; Variability,
encroachment and upwelling, J. Geophys. Res.-Oceans, 109, C07003,
https://doi.org/10.1029/2003JC001833, 2004.
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling
system (ROMS): a split-explicit, free-surface,
topography-following-coordinate oceanic model, Ocean Modell., 9, 347–404,
2005.
Souza, J., Powell, B. S., Castillo-Trujillo, A. C., and Flament, P.: The
Vorticity Balance of the Ocean Surface in Hawaii from a Regional Reanalysis,
J. Phys. Oceanogr., 45, 424–440, 2014.
Stow, C. A., Jolliff, J., McGillicuddy, D. J., Doney, S. C., Allen, J. I.,
Friedrichs, M. A. M., Rose, K. A., and Wallhead, P.: Skill Assessment for
Coupled Biological/Physical Models of Marine Systems, J. Mar. Syst., 76,
4–15, https://doi.org/10.1016/j.jmarsys.2008.03.011, 2009.
Taylor, A. H.: Characteristic properties of models for the vertical
distribution of phytoplankton under stratification, Ecol. Modell., 40,
175–199, 1988.
Taylor, A. H., Watson, A. J., Ainsworth, M., Robertson, J. E., and Turner, D.
R.: A modelling investigation of the role of phytoplankton in the balance of
carbon at the surface of the North Atlantic, Global Biogeochem. Cy., 5,
151–171, 1991.
Tranter, D. J., Carpenter, D. J., and Leech, G. S.: The coastal enrichment
effects of the East Australian Current eddy field, Deep-Sea Res., 33,
1705–1728, 1986.
Whiteway, T.: Australian Bathymetry and Topography Grid, Scale 1:5000000,
Geoscience Australia, Canberra, 2009.
Wroblewski, J. S.: A model of the spring bloom in the North Atlantic and its
impact on ocean optics, Limnol. Oceanogr., 34, 1563–1571, 1989.
Short summary
Off southeast Australia, the East Australian Current (EAC) moves warm nutrient-poor waters towards the pole. In this region, the EAC and a large number of vortices pinching off it strongly affect phytoplankton’s access to nutrients and light. To study these dynamics, we created a numerical model that is able to solve the ocean conditions and how they modulate the foundation of the region’s ecosystem. We validated model results against available data and this showed that the model performs well.
Off southeast Australia, the East Australian Current (EAC) moves warm nutrient-poor waters...
