Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-157-2023
© 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-16-157-2023
© 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
| 
04 Jan 2023
Development and technical paper |
| 04 Jan 2023
| 04 Jan 2023
How does 4DVar data assimilation affect the vertical representation of mesoscale eddies? A case study with observing system simulation experiments (OSSEs) using ROMS v3.9
Coastal and Regional Oceanography Lab, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
Shane R. Keating
School of Mathematics and Statistics, UNSW Sydney, Sydney, NSW, Australia
Colette Kerry
Coastal and Regional Oceanography Lab, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
Moninya Roughan
Coastal and Regional Oceanography Lab, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
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The Cryosphere, 19, 3355–3380, https://doi.org/10.5194/tc-19-3355-2025, https://doi.org/10.5194/tc-19-3355-2025, 2025
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The terrain of the seafloor has important controls on the access of warm water below floating ice shelves around Antarctica. Here, we present an open-source method to infer what the seafloor looks like around the Antarctic continent and within these ice shelf cavities, using measurements of the Earth's gravitational field. We present an improved seafloor map for the Vincennes Bay region in East Antarctica and assess its impact on ice melt rates.
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ISOMIP+ compares 12 ocean models that simulate ice-ocean interactions in a common, idealised, static ice shelf cavity setup, aiming to assess and understand inter-model variability. Models simulate similar basal melt rate patterns, ocean profiles and circulation but differ in ice-ocean boundary layer properties and spatial distributions of melting. Ice-ocean boundary layer representation is a key area for future work, as are realistic-domain ice sheet-ocean model intercomparisons.
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Melting beneath Antarctica’s floating ice shelves is key to future sea-level rise. We compare several different ocean simulations with satellite measurements, and provide the first multi-model average estimate of melting and refreezing driven by both ocean temperature and currents beneath ice shelves. The multi-model average can provide a useful tool for better understanding the role of ice shelf melting in present-day and future ice-sheet changes and informing coastal adaptation efforts.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
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We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
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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.
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.
Chen Zhao, Rupert Gladstone, Benjamin Keith Galton-Fenzi, David Gwyther, and Tore Hattermann
Geosci. Model Dev., 15, 5421–5439, https://doi.org/10.5194/gmd-15-5421-2022, https://doi.org/10.5194/gmd-15-5421-2022, 2022
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We use a coupled ice–ocean model to explore an oscillation feature found in several contributing models to MISOMIP1. The oscillation is closely related to the discretized grounding line retreat and likely strengthened by the buoyancy–melt feedback and/or melt–geometry feedback near the grounding line, and frequent ice–ocean coupling. Our model choices have a non-trivial impact on mean melt and ocean circulation strength, which might be interesting for the coupled ice–ocean community.
Ole Richter, David E. Gwyther, Matt A. King, and Benjamin K. Galton-Fenzi
The Cryosphere, 16, 1409–1429, https://doi.org/10.5194/tc-16-1409-2022, https://doi.org/10.5194/tc-16-1409-2022, 2022
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Tidal currents may play an important role in Antarctic ice sheet retreat by changing the rate at which the ocean melts glaciers. Here, using a computational ocean model, we derive the first estimate of present-day tidal melting that covers all of Antarctica. Our results suggest that large-scale ocean models aiming to accurately predict ice melt rates will need to account for the effects of tides. The inclusion of tide-induced friction at the ice–ocean interface should be prioritized.
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, https://doi.org/10.5194/gmd-15-617-2022, 2022
Short summary
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Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Lawrence A. Bird, Vitaliy Ogarko, Laurent Ailleres, Lachlan Grose, Jérémie Giraud, Felicity S. McCormack, David E. Gwyther, Jason L. Roberts, Richard S. Jones, and Andrew N. Mackintosh
The Cryosphere, 19, 3355–3380, https://doi.org/10.5194/tc-19-3355-2025, https://doi.org/10.5194/tc-19-3355-2025, 2025
Short summary
Short summary
The terrain of the seafloor has important controls on the access of warm water below floating ice shelves around Antarctica. Here, we present an open-source method to infer what the seafloor looks like around the Antarctic continent and within these ice shelf cavities, using measurements of the Earth's gravitational field. We present an improved seafloor map for the Vincennes Bay region in East Antarctica and assess its impact on ice melt rates.
Claire K. Yung, Xylar S. Asay-Davis, Alistair Adcroft, Christopher Y. S. Bull, Jan De Rydt, Michael S. Dinniman, Benjamin K. Galton-Fenzi, Daniel Goldberg, David E. Gwyther, Robert Hallberg, Matthew Harrison, Tore Hattermann, David M. Holland, Denise Holland, Paul R. Holland, James R. Jordan, Nicolas C. Jourdain, Kazuya Kusahara, Gustavo Marques, Pierre Mathiot, Dimitris Menemenlis, Adele K. Morrison, Yoshihiro Nakayama, Olga Sergienko, Robin S. Smith, Alon Stern, Ralph Timmermann, and Qin Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2025-1942, https://doi.org/10.5194/egusphere-2025-1942, 2025
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ISOMIP+ compares 12 ocean models that simulate ice-ocean interactions in a common, idealised, static ice shelf cavity setup, aiming to assess and understand inter-model variability. Models simulate similar basal melt rate patterns, ocean profiles and circulation but differ in ice-ocean boundary layer properties and spatial distributions of melting. Ice-ocean boundary layer representation is a key area for future work, as are realistic-domain ice sheet-ocean model intercomparisons.
Manh Cuong Tran, Moninya Roughan, and Amandine Schaeffer
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Benjamin Keith Galton-Fenzi, Richard Porter-Smith, Sue Cook, Eva Cougnon, David E. Gwyther, Wilma G. C. Huneke, Madelaine G. Rosevear, Xylar Asay-Davis, Fabio Boeira Dias, Michael S. Dinniman, David Holland, Kazuya Kusahara, Kaitlin A. Naughten, Keith W. Nicholls, Charles Pelletier, Ole Richter, Helene L. Seroussi, and Ralph Timmermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-4047, https://doi.org/10.5194/egusphere-2024-4047, 2025
Short summary
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Melting beneath Antarctica’s floating ice shelves is key to future sea-level rise. We compare several different ocean simulations with satellite measurements, and provide the first multi-model average estimate of melting and refreezing driven by both ocean temperature and currents beneath ice shelves. The multi-model average can provide a useful tool for better understanding the role of ice shelf melting in present-day and future ice-sheet changes and informing coastal adaptation efforts.
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.
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.
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
Short summary
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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
Short summary
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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
Short summary
Short summary
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, 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
Short summary
Short summary
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.
Chen Zhao, Rupert Gladstone, Benjamin Keith Galton-Fenzi, David Gwyther, and Tore Hattermann
Geosci. Model Dev., 15, 5421–5439, https://doi.org/10.5194/gmd-15-5421-2022, https://doi.org/10.5194/gmd-15-5421-2022, 2022
Short summary
Short summary
We use a coupled ice–ocean model to explore an oscillation feature found in several contributing models to MISOMIP1. The oscillation is closely related to the discretized grounding line retreat and likely strengthened by the buoyancy–melt feedback and/or melt–geometry feedback near the grounding line, and frequent ice–ocean coupling. Our model choices have a non-trivial impact on mean melt and ocean circulation strength, which might be interesting for the coupled ice–ocean community.
Ole Richter, David E. Gwyther, Matt A. King, and Benjamin K. Galton-Fenzi
The Cryosphere, 16, 1409–1429, https://doi.org/10.5194/tc-16-1409-2022, https://doi.org/10.5194/tc-16-1409-2022, 2022
Short summary
Short summary
Tidal currents may play an important role in Antarctic ice sheet retreat by changing the rate at which the ocean melts glaciers. Here, using a computational ocean model, we derive the first estimate of present-day tidal melting that covers all of Antarctica. Our results suggest that large-scale ocean models aiming to accurately predict ice melt rates will need to account for the effects of tides. The inclusion of tide-induced friction at the ice–ocean interface should be prioritized.
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, https://doi.org/10.5194/gmd-15-617-2022, 2022
Short summary
Short summary
Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Cited articles
Abernathey, R. and Haller, G.: Transport by Lagrangian Vortices in the Eastern
Pacific, J. Phys. Oceanogr., 48, 667–685,
https://doi.org/10.1175/jpo-d-17-0102.1, 2018. a
Ballabrera-Poy, J., Hackert, E., Murtugudde, R., and Busalacchi, A. J.: An
Observing System Simulation Experiment for an Optimal Moored Instrument Array
in the Tropical Indian Ocean, J. Climate, 20, 3284–3299,
https://doi.org/10.1175/jcli4149.1, 2007. a
Bannister, R. N.: A review of forecast error covariance statistics in
atmospheric variational data assimilation. I: Characteristics and
measurements of forecast error covariances, Q. J. Roy.
Meteor. Soc., 134, 1951–1970, https://doi.org/10.1002/qj.339,
2008a. a, b
Bannister, R. N.: A review of forecast error covariance statistics in
atmospheric variational data assimilation. II: Modelling the forecast error
covariance statistics, Q. J. Roy. Meteor. Soc.,
134, 1971–1996, https://doi.org/10.1002/qj.340, 2008b. a
Bonavita, M., Raynaud, L., and Isaksen, L.: Estimating background-error
variances with the ECMWF Ensemble of Data Assimilations system: some
effects of ensemble size and day-to-day variability, Q. J.
Roy. Meteor. Soc., 137, 423–434, https://doi.org/10.1002/qj.756, 2011. a, b
Brassington, G., Pugh, T., Spillman, C., Schulz, E., Beggs, H., Schiller, A.,
and Oke, P.: BLUElink> Development of Operational Oceanography and Servicing
in Australia, J. Res. Pract. Inf. Tech., 39, 151–164,
2007. a
Brink, K.: Cross-Shelf Exchange, Annu. Rev. Mar. Sci., 8, 59–78,
https://doi.org/10.1146/annurev-marine-010814-015717, 2016. a
Chamberlain, M. A., Oke, P. R., Fiedler, R. A. S., Beggs, H. M., Brassington, G. B., and Divakaran, P.: Next generation of Bluelink ocean reanalysis with multiscale data assimilation: BRAN2020, Earth Syst. Sci. Data, 13, 5663–5688, https://doi.org/10.5194/essd-13-5663-2021, 2021. a
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of
nonlinear mesoscale eddies, Progr. Oceanogr., 91, 167–216,
https://doi.org/10.1016/j.pocean.2011.01.002, 2011. a
Copernicus Marine Service (CMEMS): Global ocean along-track L3 sea surface heights reprocessed (1993–ongoing) tailored for data assimilation (SEALEVEL_GLO_PHY_L3_MY_008_062), https://doi.org/10.48670/moi-00146,
2022. a
Denes, M. C., Froyland, G., and Keating, S. R.: Persistence and material
coherence of a mesoscale ocean eddy, Phys. Rev. Fl., 7, 034501,
https://doi.org/10.1103/physrevfluids.7.034501, 2022. a
de Paula, T. P., Lima, J. A. M., Tanajura, C. A. S., Andrioni, M., Martins,
R. P., and Arruda, W. Z.: The impact of ocean data assimilation on the
simulation of mesoscale eddies at São Paulo plateau (Brazil) using the
regional ocean modeling system, Ocean Model., 167, 101889,
https://doi.org/10.1016/j.ocemod.2021.101889, 2021. a
Dong, C., McWilliams, J. C., Liu, Y., and Chen, D.: Global heat and salt
transports by eddy movement, Nat. Commun., 5, 3294,
https://doi.org/10.1038/ncomms4294, 2014. a
Elzahaby, Y., Schaeffer, A., Roughan, M., and Delaux, S.: Oceanic Circulation
Drives the Deepest and Longest Marine Heatwaves in the East Australian
Current System, Geophys. Res. Lett., 48, e2021GL094785,
https://doi.org/10.1029/2021gl094785, 2021. a
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., and Young, G. S.:
Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere
Coupled-Ocean Atmosphere Response Experiment, J. Geophys.
Res.-Oceans, 101, 3747–3764, https://doi.org/10.1029/95jc03205, 1996. a
Fiedler, P. C.: Comparison of objective descriptions of the thermocline,
Limnol. Oceanogr.-Meth., 8, 313–325,
https://doi.org/10.4319/lom.2010.8.313, 2010. a, b, c
Gasparin, F., Guinehut, S., Mao, C., Mirouze, I., Rémy, E., King, R. R.,
Hamon, M., Reid, R., Storto, A., Traon, P.-Y. L., Martin, M. J., and Masina,
S.: Requirements for an Integrated in situ Atlantic Ocean Observing System
From Coordinated Observing System Simulation Experiments, Front.
Mar. Sci., 6, https://doi.org/10.3389/fmars.2019.00083, 2019. a
Gill, A., Green, J., and Simmons, A.: Energy partition in the large-scale ocean
circulation and the production of mid-ocean eddies, Deep-Sea Res., 21, 499–528, https://doi.org/10.1016/0011-7471(74)90010-2,
1974. a
Gwyther, D. E., Kerry, C., Roughan, M., and Keating, S. R.: Observing system simulation experiments reveal that subsurface temperature observations improve estimates of circulation and heat content in a dynamic western boundary current, Geosci. Model Dev., 15, 6541–6565, https://doi.org/10.5194/gmd-15-6541-2022, 2022a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Gwyther, D. E., Kerry, C., Roughan, M., and Keating, S.: A high-resolution, 1-year, suite of 4D-Var Observing System Simulation Experiments of the East Australian Current System using the Regional Ocean Modeling System (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.6804480, 2022b. a
Gwyther, D. E., Kerry, C., Roughan, M., and Keating, S.: A high-resolution, 1-year, suite of 4D-Var Observing System Simulation Experiments of the East Australian Current System using the Regional Ocean Modeling System, UNSW Sydney [data set], https://doi.org/10.26190/unsworks/24146, 2022c. a
Halliwell, G. R., Srinivasan, A., Kourafalou, V., Yang, H., Willey, D.,
Hénaff, M. L., and Atlas, R.: Rigorous Evaluation of a Fraternal Twin
Ocean OSSE System for the Open Gulf of Mexico, J. Atmos.
Ocean. Tech., 31, 105–130, https://doi.org/10.1175/jtech-d-13-00011.1, 2014. a, b
Halliwell, G. R., Kourafalou, V., Hénaff, M. L., Shay, L. K., and Atlas,
R.: OSSE impact analysis of airborne ocean surveys for improving
upper-ocean dynamical and thermodynamical forecasts in the Gulf of Mexico,
Prog. Oceanogr., 130, 32–46, https://doi.org/10.1016/j.pocean.2014.09.004,
2015. a
Halliwell, G. R., Mehari, M. F., Hénaff, M. L., Kourafalou, V. H.,
Androulidakis, I. S., Kang, H. S., and Atlas, R.: North Atlantic Ocean
OSSE system: Evaluation of operational ocean observing system components
and supplemental seasonal observations for potentially improving tropical
cyclone prediction in coupled systems, J. Oper. Oceanogr.,
10, 154–175, https://doi.org/10.1080/1755876x.2017.1322770, 2017. a
Kang, D. and Curchitser, E. N.: Energetics of Eddy–Mean Flow
Interactions in the Gulf Stream Region, J. Phys. Oceanogr., 45,
1103–1120, https://doi.org/10.1175/jpo-d-14-0200.1, 2015. a
Keating, S.: shane-keating/normal-modes: Initial release (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.6999169, 2022. a
Kelly, S. M.: The Vertical Mode Decomposition of Surface and Internal Tides in
the Presence of a Free Surface and Arbitrary Topography, J. Phys.
Oceanogr., 46, 3777–3788, https://doi.org/10.1175/jpo-d-16-0131.1, 2016. a
Kerry, C. and Roughan, M.: Downstream Evolution of the East Australian Current
System: Mean Flow, Seasonal, and Intra-annual Variability, J.
Geophys. Res.-Oceans, 125, e2019JC015227, https://doi.org/10.1029/2019jc015227, 2020. a, b
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. a, b, c, d, e, f, g, h, i
Kerry, C., Roughan, M., Powell, B., and Oke, P: A high-resolution reanalysis of the East Australian Current System assimilating an unprecedented observational data set using 4D-Var data assimilation over a two-year period (2012–2013), Version 2017, UNSW Sydney [code and data set], https://doi.org/10.26190/5ebe1f389dd87, 2020. a
Kerry, C., Roughan, M., and Powell, B.: Observation Impact in a Regional
Reanalysis of the East Australian Current System, J. Geophys.
Res.-Oceans, 123, 7511–7528, https://doi.org/10.1029/2017jc013685, 2018. a, b
Klocker, A. and Abernathey, R.: Global Patterns of Mesoscale Eddy Properties
and Diffusivities, J. Phys. Oceanogr., 44, 1030–1046,
https://doi.org/10.1175/jpo-d-13-0159.1, 2014. a
Lee, J. C. K. and Huang, X.-Y.: Background error statistics in the Tropics:
Structures and impact in a convective-scale numerical weather prediction
system, Q. J. Roy. Meteor. Soc., 146,
2154–2173, https://doi.org/10.1002/qj.3785, 2020. a, b
Li, B., de Queiroz, A. R., DeCarolis, J. F., Bane, J., He, R., Keeler, A. G.,
and Neary, V. S.: The economics of electricity generation from Gulf Stream
currents, Energy, 134, 649–658, https://doi.org/10.1016/j.energy.2017.06.048, 2017. a
Li, J., Kerry, C., and Roughan, M.: A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 2.0), UNSW Sydney [code and data set], https://doi.org/10.26190/TT1Q-NP46, 2021. a
Li, J., Roughan, M., and Kerry, C.: Variability and Drivers of Ocean
Temperature Extremes in a Warming Western Boundary Current, J.
Climate, 35, 1097–1111, https://doi.org/10.1175/JCLI-D-21-0622.1,
2022a. a, b
Li, J., Roughan, M., Kerry, C., and Rao, S.: Impact of Mesoscale Circulation on
the Structure of River Plumes During Large Rainfall Events Inshore of the
East Australian Current, Front. Mar. Sci., 9,
https://doi.org/10.3389/fmars.2022.815348, 2022b. a
Li, Z., McWilliams, J. C., Ide, K., and Farrara, J. D.: A Multiscale
Variational Data Assimilation Scheme: Formulation and Illustration, Mon.
Weather Rev., 143, 3804–3822, https://doi.org/10.1175/mwr-d-14-00384.1, 2015. a, b
Lorenc, A. C. and Jardak, M.: A comparison of hybrid variational data
assimilation methods for global NWP, Q. J. Roy.
Meteor. Soc., 144, 2748–2760, https://doi.org/10.1002/qj.3401, 2018. a
Malan, N., Archer, M., Roughan, M., Cetina-Heredia, P., Hemming, M., Rocha, C.,
Schaeffer, A., Suthers, I., and Queiroz, E.: Eddy-Driven Cross-Shelf
Transport in the East Australian Current Separation Zone, J.
Geophys. Res.-Oceans, 125, e2019JC015613, https://doi.org/10.1029/2019jc015613, 2020. a
Mata, M. M., Wijffels, S. E., Church, J. A., and Tomczak, M.: Eddy shedding
and energy conversions in the East Australian Current, J.
Geophys. Res., 111, C09034, https://doi.org/10.1029/2006jc003592, 2006. a
McGillicuddy, D. J., Robinson, A. R., Siegel, D. 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, https://doi.org/10.1038/28367, 1998. a
Melet, A., Verron, J., and Brankart, J.-M.: Potential outcomes of glider data
assimilation in the Solomon Sea: Control of the water mass properties and
parameter estimation, J. Marine Syst., 94, 232–246,
https://doi.org/10.1016/j.jmarsys.2011.12.003, 2012. a
Michel, Y. and Auligné, T.: Inhomogeneous Background Error Modeling and
Estimation over Antarctica, Mon. Weather Rev., 138, 2229–2252,
https://doi.org/10.1175/2009mwr3139.1, 2010. a
Moore, A. M., Arango, H. G., Broquet, G., Powell, B. S., Weaver, A. T., and
Zavala-Garay, J.: The Regional Ocean Modeling System (ROMS) 4-dimensional
variational data assimilation systems: Part I – System overview and
formulation, Prog. Oceanogr., 91, 34–49,
https://doi.org/10.1016/j.pocean.2011.05.004, 2011. a
Moore, A. M., Martin, M. J., Akella, S., Arango, H. G., Balmaseda, M., Bertino,
L., Ciavatta, S., Cornuelle, B., Cummings, J., Frolov, S., Lermusiaux, P.,
Oddo, P., Oke, P. R., Storto, A., Teruzzi, A., Vidard, A., and Weaver, A. T.:
Synthesis of Ocean Observations Using Data Assimilation for Operational,
Real-Time and Reanalysis Systems: A More Complete Picture of the State of the
Ocean, Front. Mar. Sci., 6, https://doi.org/10.3389/fmars.2019.00090, 2019. a
Oke, P. R. and Griffin, D. A.: The cold-core eddy and strong upwelling off the
coast of New South Wales in early 2007, Deep-Sea Res. Pt. II, 58, 574–591, https://doi.org/10.1016/j.dsr2.2010.06.006,
2011. a
Oke, P. R. and Schiller, A.: A Model-Based Assessment and Design of a Tropical
Indian Ocean Mooring Array, J. Climate, 20, 3269–3283,
https://doi.org/10.1175/jcli4170.1, 2007. a
Oke, P. R., Brassington, G. B., Griffin, D. A., and Schiller, A.: The Bluelink
ocean data assimilation system (BODAS), Ocean Model., 21, 46–70,
https://doi.org/10.1016/j.ocemod.2007.11.002, 2008. a
Penny, S. G., Behringer, D. W., Carton, J. A., and Kalnay, E.: A Hybrid Global
Ocean Data Assimilation System at NCEP, Mon. Weather Rev., 143,
4660–4677, https://doi.org/10.1175/mwr-d-14-00376.1, 2015. a
Pilo, G. S., Oke, P. R., Coleman, R., Rykova, T., and Ridgway, K.: Impact of
data assimilation on vertical velocities in an eddy resolving ocean model,
Ocean Model., 131, 71–85, https://doi.org/10.1016/j.ocemod.2018.09.003, 2018. a
Puri, K., Dietachmayer, G., Steinle, P., Dix, M., Rikus, L., Logan, L.,
Naughton, M., Tingwell, C., Xiao, Y., Barras, V., Bermous, I., Bowen, R., Deschamps, L., Franklin, C., Fraser, J., Glowacki, T., Harris, B., Lee, J., Le, T., Roff, G., Sulaiman, A., Sims, H., Sun, X., Sun, Z., Zhu, H., Chattopadhyay, M., and Engel, C.: Implementation of
the initial ACCESS numerical weather prediction system, Austr.
Meteorol. Ocean. J., 63, 265–284, 2013. a
Research Technology Services, UNSW Sydney:
Katana computational cluster, https://doi.org/10.26190/669x-a286, 2022. a
Rocha, C., Edwards, C. A., Roughan, M., Cetina-Heredia, P., and Kerry, C.: A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system, Geosci. Model Dev., 12, 441–456, https://doi.org/10.5194/gmd-12-441-2019, 2019. a
Roughan, M., Keating, S. R., Schaeffer, A., Heredia, P. C., Rocha, C., Griffin,
D., Robertson, R., and Suthers, I. M.: A tale of two eddies: The biophysical
characteristics of two contrasting cyclonic eddies in the East Australian
Current System, J. Geophys. Res.-Oceans, 122, 2494–2518,
https://doi.org/10.1002/2016jc012241, 2017. a
Schiller, A., Wijffels, S. E., and Meyers, G. A.: Design Requirements for an
Argo Float Array in the Indian Ocean Inferred from Observing System
Simulation Experiments, J. Atmos. Ocean. Tech., 21,
1598–1620, https://doi.org/10.1175/1520-0426(2004)021<1598:drfaaf>2.0.co;2, 2004. a
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system
(ROMS): a split-explicit, free-surface, topography-following-coordinate
oceanic model, Ocean Model., 9, 347–404,
https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Siripatana, A., Kerry, C., Roughan, M., Souza, J. M. A. C., and Keating, S.:
Assessing the Impact of Nontraditional Ocean Observations for Prediction of
the East Australian Current, J. Geophys. Res.-Oceans, 125, e2020JC016580,
https://doi.org/10.1029/2020jc016580, 2020. a, b, c, d
Smith, K. S.: The geography of linear baroclinic instability in
Earth′s oceans, J. Mar. Res., 65, 655–683,
https://doi.org/10.1357/002224007783649484, 2007. a, b
Smith, K. S. and Vallis, G. K.: The Scales and Equilibration of Midocean
Eddies: Freely Evolving Flow, J. Phys. Oceanogr., 31, 554–571,
https://doi.org/10.1175/1520-0485(2001)031<0554:tsaeom>2.0.co;2, 2001. a
Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., Rennie, S., Franklin, C., Dharssi, I., and Zhu, H.: BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049–2068, https://doi.org/10.5194/gmd-12-2049-2019, 2019. a
Wang, Y., Beron-Vera, F. J., and Olascoaga, M. J.: The life cycle of a coherent
Lagrangian Agulhas ring, J. Geophys. Res.-Oceans, 121,
3944–3954, https://doi.org/10.1002/2015jc011620, 2016. a
Weaver, A. and Courtier, P.: Correlation modelling on the sphere using a
generalized diffusion equation, Q. J. Roy. Meteor.
Soc., 127, 1815–1846, https://doi.org/10.1002/qj.49712757518, 2001. a, b
Whiteway, T.: Australian bathymetry and topography grid, June 2009, Record 2009/021, Geoscience Australia [data set], Canberra,
https://doi.org/10.4225/25/53D99B6581B9A, 2009. a
Wong, A. P. S., Wijffels, S. E., Riser, S. C., Pouliquen, S., Hosoda, S.,
Roemmich, D., Gilson, J., Johnson, G. C., Martini, K., Murphy, D. J.,
Scanderbeg, M., Bhaskar, T. V. S. U., Buck, J. J. H., Merceur, F., Carval,
T., Maze, G., Cabanes, C., André, X., Poffa, N., Yashayaev, I., Barker,
P. M., Guinehut, S., Belbéoch, M., Ignaszewski, M., Baringer, M. O.,
Schmid, C., Lyman, J. M., McTaggart, K. E., Purkey, S. G., Zilberman, N.,
Alkire, M. B., Swift, D., Owens, W. B., Jayne, S. R., Hersh, C., Robbins, P.,
West-Mack, D., Bahr, F., Yoshida, S., Sutton, P. J. H., Cancouët, R.,
Coatanoan, C., Dobbler, D., Juan, A. G., Gourrion, J., Kolodziejczyk, N.,
Bernard, V., Bourlès, B., Claustre, H., D'Ortenzio,
F., Reste, S. L., Traon, P.-Y. L., Rannou, J.-P., Saout-Grit, C., Speich, S.,
Thierry, V., Verbrugge, N., Angel-Benavides, I. M., Klein, B., Notarstefano,
G., Poulain, P.-M., Vélez-Belchí, P., Suga, T., Ando, K.,
Iwasaska, N., Kobayashi, T., Masuda, S., Oka, E., Sato, K., Nakamura, T.,
Sato, K., Takatsuki, Y., Yoshida, T., Cowley, R., Lovell, J. L., Oke, P. R.,
van Wijk, E. M., Carse, F., Donnelly, M., Gould, W. J., Gowers, K., King,
B. A., Loch, S. G., Mowat, M., Turton, J., Rao, E. P. R., Ravichandran, M.,
Freeland, H. J., Gaboury, I., Gilbert, D., Greenan, B. J. W., Ouellet, M.,
Ross, T., Tran, A., Dong, M., Liu, Z., Xu, J., Kang, K., Jo, H., Kim, S.-D.,
and Park, H.-M.: Argo Data 1999–2019: Two Million
Temperature-Salinity Profiles and Subsurface Velocity Observations From a
Global Array of Profiling Floats, Front. Mar. Sci., 7,
https://doi.org/10.3389/fmars.2020.00700, 2020. a
Wunsch, C.: The Vertical Partition of Oceanic Horizontal Kinetic Energy,
J. Phys. Oceanogr., 27, 1770–1794,
https://doi.org/10.1175/1520-0485(1997)027<1770:tvpooh>2.0.co;2, 1997. a
Zavala-Garay, J., Wilkin, J. L., and Arango, H. G.: Predictability of
Mesoscale Variability in the East Australian Current Given Strong-Constraint
Data Assimilation, J. Phys. Oceanogr., 42, 1402–1420,
https://doi.org/10.1175/jpo-d-11-0168.1, 2012. a, b
Zhang, W. G., Wilkin, J. L., and Arango, H. G.: Towards an integrated
observation and modeling system in the New York Bight using variational
methods. Part I: 4DVAR data assimilation, Ocean Model., 35, 119–133,
https://doi.org/10.1016/j.ocemod.2010.08.003, 2010.
a, b
Zhang, Z., Wang, W., and Qiu, B.: Oceanic mass transport by mesoscale eddies,
Science, 345, 322–324, https://doi.org/10.1126/science.1252418, 2014. a
Short summary
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.
Ocean eddies are important for weather, climate, biology, navigation, and search and rescue....
