Articles | Volume 16, issue 18
https://doi.org/10.5194/gmd-16-5401-2023
© 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-16-5401-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Yvonne Gusdal
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Edel S. U. Rikardsen
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Marina Durán Moro
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Jostein Brændshøi
Norwegian Defence Research Establishment, Instituttveien 20, 2007 Kjeller, Norway
Nils Melsom Kristensen
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Sindre Fritzner
Department of Physics and Technology, UiT The Arctic University of Norway, P.O. Box 6050 Langnes, 9037 Tromsø, Norway
Keguang Wang
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Ann Kristin Sperrevik
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Martina Idžanović
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Thomas Lavergne
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Jens Boldingh Debernard
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Kai H. Christensen
Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway
Department of Geosciences, University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
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Lagrangian coherent structures (LCS) describe material transport in ocean flow by describing transport barriers and accumulation regions. Noting that circulation fields from models are prone to uncertainties, we discuss the implications for LCS analysis. LCSs add value to forecasting when these are certain and long-lived. Averaging LCS reveals where these are more certain and long-lived, often influenced by bottom topography. Large scale LCSs show a higher degree of certainty and longevity.
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Simulations of hypothetical oil spills are presented to investigate how the vertical mixing of oil affects transport towards various directions. It is shown that the horizontal transport of oil greatly varies for different oil types and weather conditions. These differences are a consequence of the entrainment of oil from the surface into the ocean. While oil spills often get entrained into the water by waves, we show that submerged oil typically resurfaces after a few hours or days.
Lars R. Hole, Knut-Frode Dagestad, Johannes Röhrs, Cecilie Wettre, Vassiliki H. Kourafalou, Ioannis Androulidakis, Matthieu Le Hénaff, Heesook Kang, and Oscar Garcia-Pineda
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-130, https://doi.org/10.5194/os-2018-130, 2018
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This study shows how the Mississippi river influenced the spreading of oil in the Gulf of Mexico after the DeepWater Horizon disaster. High resolution numerical models for ocean and atmosphere circulation are used to force an oil drift model. The circulation is totally different when river input is removed in the ocean model. The study also showcase the importance of the choice of oil droplet size distribution. Model output is compared with satellite observation of surface oil.
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Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, https://doi.org/10.5194/gmd-11-1405-2018, 2018
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We have developed a computer code with ability to predict how various substances and objects drift in the ocean. This may be used to, e.g. predict the drift of oil to aid cleanup operations, the drift of man-over-board or lifeboats to aid search and rescue operations, or the drift of fish eggs and larvae to understand and manage fish stocks. This new code merges all such applications into one software tool, allowing to optimise and channel any available resources and developments.
A. K. Sperrevik, K. H. Christensen, and J. Röhrs
Ocean Sci., 11, 237–249, https://doi.org/10.5194/os-11-237-2015, https://doi.org/10.5194/os-11-237-2015, 2015
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EGUsphere, https://doi.org/10.48550/arXiv.2401.07619, https://doi.org/10.48550/arXiv.2401.07619, 2024
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We observe strongly modulated waves-in-ice significant wave height using buoys deployed East of Svalbard. We show that these observations likely cannot be explained by wave-current interaction or tide-induced modulation alone. We also demonstrate a strong correlation between the waves height modulation, and the rate of sea ice convergence. Therefore, our data suggest that the rate of sea ice convergence and divergence may modulate wave in ice energy dissipation.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
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The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
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The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
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Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-1171, https://doi.org/10.5194/egusphere-2024-1171, 2024
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Thomas Lavergne and Emily Down
Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, https://doi.org/10.5194/essd-15-5807-2023, 2023
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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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The Cryosphere, 17, 4487–4510, https://doi.org/10.5194/tc-17-4487-2023, https://doi.org/10.5194/tc-17-4487-2023, 2023
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A simple, efficient. and accurate data assimilation method, local analytical optimal nudging (LAON), is introduced to assimilate high-resolution sea ice concentration in a pan-Arctic high-resolution coupled ocean and sea ice model. The method provides a new vision by nudging the model evolution to the optimal estimate forwardly, continuously, and smoothly. This method is applicable to the general nudging theory and applications in physics, Earth science, psychology, and behavior sciences.
Silje Christine Iversen, Ann Kristin Sperrevik, and Olivier Goux
Ocean Sci., 19, 729–744, https://doi.org/10.5194/os-19-729-2023, https://doi.org/10.5194/os-19-729-2023, 2023
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We present two methods to refine the assimilation of satellite sea surface temperatures (SSTs) into a regional ocean model. First, we correct the SSTs for biases and show that this correction reduces the model SST errors. Then, we implement a special observation operator that handles the spatial resolution mismatch between coarse passive microwave SSTs and the high-resolution model. We find that excluding this operator spatially smooths the modeled SST, whereas its inclusion prevents this.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Pedro Duarte, Jostein Brændshøi, Dmitry Shcherbin, Pauline Barras, Jon Albretsen, Yvonne Gusdal, Nicholas Szapiro, Andreas Martinsen, Annette Samuelsen, Keguang Wang, and Jens Boldingh Debernard
Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, https://doi.org/10.5194/gmd-15-4373-2022, 2022
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Sea ice models are often implemented for very large domains beyond the regions of sea ice formation, such as the whole Arctic or all of Antarctica. In this study, we implement changes in the Los Alamos Sea Ice Model, allowing it to be implemented for relatively small regions within the Arctic or Antarctica and yet considering the presence and influence of sea ice outside the represented areas. Such regional implementations are important when spatially detailed results are required.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
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High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
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Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
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We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Øyvind Seland, Mats Bentsen, Dirk Olivié, Thomas Toniazzo, Ada Gjermundsen, Lise Seland Graff, Jens Boldingh Debernard, Alok Kumar Gupta, Yan-Chun He, Alf Kirkevåg, Jörg Schwinger, Jerry Tjiputra, Kjetil Schanke Aas, Ingo Bethke, Yuanchao Fan, Jan Griesfeller, Alf Grini, Chuncheng Guo, Mehmet Ilicak, Inger Helene Hafsahl Karset, Oskar Landgren, Johan Liakka, Kine Onsum Moseid, Aleksi Nummelin, Clemens Spensberger, Hui Tang, Zhongshi Zhang, Christoph Heinze, Trond Iversen, and Michael Schulz
Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, https://doi.org/10.5194/gmd-13-6165-2020, 2020
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The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. The temperature and precipitation patterns has improved compared to NorESM1. The model reaches present-day warming levels to within 0.2 °C of observed temperature but with a delayed warming during the late 20th century. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period of 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.1, and 3.9 K.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
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Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
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A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, https://doi.org/10.5194/tc-13-1661-2019, 2019
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A warm bias and higher total precipitation and snowfall were found in ERA5 compared with ERA-Interim (ERA-I) over Arctic sea ice. The warm bias in ERA5 was larger in the cold season when 2 m air temperature was < −25 °C and smaller in the warm season than in ERA-I. Substantial anomalous Arctic rainfall in ERA-I was reduced in ERA5, particularly in summer and autumn. When using ERA5 and ERA-I to force a 1-D sea ice model, the effects on ice growth are very small (cm) during the freezing period.
Sindre Fritzner, Rune Graversen, Kai H. Christensen, Philip Rostosky, and Keguang Wang
The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, https://doi.org/10.5194/tc-13-491-2019, 2019
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In this work, a coupled ocean and sea-ice ensemble-based assimilation system is used to assess the impact of different observations on the assimilation system. The focus of this study is on sea-ice observations, including the use of satellite observations of sea-ice concentration, sea-ice thickness and snow depth for assimilation. The study showed that assimilation of sea-ice thickness in addition to sea-ice concentration has a large positive impact on the coupled model.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
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The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Johannes Röhrs, Knut-Frode Dagestad, Helene Asbjørnsen, Tor Nordam, Jørgen Skancke, Cathleen E. Jones, and Camilla Brekke
Ocean Sci., 14, 1581–1601, https://doi.org/10.5194/os-14-1581-2018, https://doi.org/10.5194/os-14-1581-2018, 2018
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Simulations of hypothetical oil spills are presented to investigate how the vertical mixing of oil affects transport towards various directions. It is shown that the horizontal transport of oil greatly varies for different oil types and weather conditions. These differences are a consequence of the entrainment of oil from the surface into the ocean. While oil spills often get entrained into the water by waves, we show that submerged oil typically resurfaces after a few hours or days.
Lars R. Hole, Knut-Frode Dagestad, Johannes Röhrs, Cecilie Wettre, Vassiliki H. Kourafalou, Ioannis Androulidakis, Matthieu Le Hénaff, Heesook Kang, and Oscar Garcia-Pineda
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-130, https://doi.org/10.5194/os-2018-130, 2018
Revised manuscript not accepted
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This study shows how the Mississippi river influenced the spreading of oil in the Gulf of Mexico after the DeepWater Horizon disaster. High resolution numerical models for ocean and atmosphere circulation are used to force an oil drift model. The circulation is totally different when river input is removed in the ocean model. The study also showcase the importance of the choice of oil droplet size distribution. Model output is compared with satellite observation of surface oil.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
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A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Knut-Frode Dagestad, Johannes Röhrs, Øyvind Breivik, and Bjørn Ådlandsvik
Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, https://doi.org/10.5194/gmd-11-1405-2018, 2018
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We have developed a computer code with ability to predict how various substances and objects drift in the ocean. This may be used to, e.g. predict the drift of oil to aid cleanup operations, the drift of man-over-board or lifeboats to aid search and rescue operations, or the drift of fish eggs and larvae to understand and manage fish stocks. This new code merges all such applications into one software tool, allowing to optimise and channel any available resources and developments.
Petri Räisänen, Risto Makkonen, Alf Kirkevåg, and Jens B. Debernard
The Cryosphere, 11, 2919–2942, https://doi.org/10.5194/tc-11-2919-2017, https://doi.org/10.5194/tc-11-2919-2017, 2017
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While snow grains are non-spherical, spheres are often assumed in radiation calculations. Here, we replace spherical snow grains with non-spherical snow grains in a climate model. This leads to a somewhat higher snow albedo (by 0.02–0.03), increased snow and sea ice cover, and a distinctly colder climate (by over 1 K in the global mean). It also impacts the radiative effects of aerosols in snow. Overall, this work highlights the important role of snow albedo parameterization for climate models.
Kai Håkon Christensen, Ana Carrasco, Jean-Raymond Bidlot, and Øyvind Breivik
Ocean Sci., 13, 589–597, https://doi.org/10.5194/os-13-589-2017, https://doi.org/10.5194/os-13-589-2017, 2017
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In this note we investigate when and where we would expect the bottom to influence the dynamics of surface waves. In deep water, where the presence of the bottom is not felt by the waves, modelers can use a simpler description of wave-mean flow interactions; hence, the results are relevant for coupled wave-ocean modeling systems. The most pronounced influence is on the Northwest Shelf during winter, and can sometimes be significant even far from the coast.
A. K. Sperrevik, K. H. Christensen, and J. Röhrs
Ocean Sci., 11, 237–249, https://doi.org/10.5194/os-11-237-2015, https://doi.org/10.5194/os-11-237-2015, 2015
T. G. Bell, W. De Bruyn, S. D. Miller, B. Ward, K. H. Christensen, and E. S. Saltzman
Atmos. Chem. Phys., 13, 11073–11087, https://doi.org/10.5194/acp-13-11073-2013, https://doi.org/10.5194/acp-13-11073-2013, 2013
G. Sutherland, B. Ward, and K. H. Christensen
Ocean Sci., 9, 597–608, https://doi.org/10.5194/os-9-597-2013, https://doi.org/10.5194/os-9-597-2013, 2013
Related subject area
Oceanography
DalROMS-NWA12 v1.0, a coupled circulation–ice–biogeochemistry modelling system for the northwest Atlantic Ocean: development and validation
A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature
Evaluating an accelerated forcing approach for improving computational efficiency in coupled ice sheet–ocean modelling
An optimal transformation method for inferring ocean tracer sources and sinks
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
HIDRA3: a robust deep-learning model for multi-point ensemble sea level forecasting
Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0
A simple approach to represent precipitation-derived freshwater fluxes into nearshore ocean models: an FVCOM4.1 case study of Quatsino Sound, British Columbia
An optimal transformation method applied to diagnose the ocean carbon budget
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 2: Towards a better representation of total alkalinity when modeling the carbonate system and air–sea CO2 fluxes
Development of a novel storm surge inundation model framework for efficient prediction
Skin sea surface temperature schemes in coupled ocean–atmosphere modelling: the impact of chlorophyll-interactive e-folding depth
A wave-resolving 2DV Lagrangian approach to model microplastic transport in the nearshore
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
StraitFlux – precise computations of water strait fluxes on various modeling grids
Comparison of the Coastal and Regional Ocean COmmunity model (CROCO) and NCAR-LES in non-hydrostatic simulations
HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System
CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago
Implementation of additional spectral wave field exchanges in a three-dimensional wave–current coupled WAVEWATCH-III (version 6.07) and CROCO (version 1.2) configuration: assessment of their implications for macro-tidal coastal hydrodynamics
Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system
LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2
New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4
MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries
Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts
Great Lakes wave forecast system on high-resolution unstructured meshes
Impact of increased resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 (OMIP-2)
A high-resolution physical–biogeochemical model for marine resource applications in the northwest Atlantic (MOM6-COBALT-NWA12 v1.0)
A flexible z-layers approach for the accurate representation of free surface flows in a coastal ocean model (SHYFEM v. 7_5_71)
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 1: Evolution of ecosystem composition under limited light and nutrient conditions
Ocean wave tracing v.1: a numerical solver of the wave ray equations for ocean waves on variable currents at arbitrary depths
Design and evaluation of an efficient high-precision ocean surface wave model with a multiscale grid system (MSG_Wav1.0)
Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)
Open-ocean tides simulated by ICON-O, version icon-2.6.6
Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll
Data assimilation sensitivity experiments in the East Auckland Current system using 4D-Var
Using the COAsT Python package to develop a standardised validation workflow for ocean physics models
Improving Antarctic Bottom Water precursors in NEMO for climate applications
Formulation, optimization, and sensitivity of NitrOMZv1.0, a biogeochemical model of the nitrogen cycle in oceanic oxygen minimum zones
Waves in SKRIPS: WAVEWATCH III coupling implementation and a case study of Tropical Cyclone Mekunu
Adding sea ice effects to a global operational model (NEMO v3.6) for forecasting total water level: approach and impact
Enhanced ocean wave modeling by including effect of breaking under both deep- and shallow-water conditions
An internal solitary wave forecasting model in the northern South China Sea (ISWFM-NSCS)
The 3D biogeochemical marine mercury cycling model MERCY v2.0 – linking atmospheric Hg to methylmercury in fish
Global seamless tidal simulation using a 3D unstructured-grid model (SCHISM v5.10.0)
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
Geosci. Model Dev., 17, 8697–8733, https://doi.org/10.5194/gmd-17-8697-2024, https://doi.org/10.5194/gmd-17-8697-2024, 2024
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We developed a modelling system of the northwest Atlantic Ocean that simulates the currents, temperature, salinity, and parts of the biochemical cycle of the ocean, as well as sea ice. The system combines advanced, open-source models and can be used to study, for example, the ocean capture of atmospheric carbon dioxide, which is a key process in the global climate. The system produces realistic results, and we use it to investigate the roles of tides and sea ice in the northwest Atlantic Ocean.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
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Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
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We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
Jan D. Zika and Taimoor Sohail
Geosci. Model Dev., 17, 8049–8068, https://doi.org/10.5194/gmd-17-8049-2024, https://doi.org/10.5194/gmd-17-8049-2024, 2024
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We describe a method to relate fluxes of heat and freshwater at the sea surface to the resulting distribution of seawater among categories such as warm and salty or cold and salty. The method exploits the laws that govern how heat and salt change when water mixes. The method will allow the climate community to improve estimates of how much heat the ocean is absorbing and how rainfall and evaporation are changing across the globe.
Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini
Geosci. Model Dev., 17, 7347–7364, https://doi.org/10.5194/gmd-17-7347-2024, https://doi.org/10.5194/gmd-17-7347-2024, 2024
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Monitoring the ocean is essential for studying marine life and human impact. Our new software, PPCon, uses ocean data to predict key factors like nitrate and chlorophyll levels, which are hard to measure directly. By leveraging machine learning, PPCon offers more accurate and efficient predictions.
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.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2068, https://doi.org/10.5194/egusphere-2024-2068, 2024
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HIDRA3 is a novel deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms the current state-of-the-art models by achieving up to 15 % lower mean absolute error, proving effective for coastal flood forecasting in diverse conditions.
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
Geosci. Model Dev., 17, 6123–6136, https://doi.org/10.5194/gmd-17-6123-2024, https://doi.org/10.5194/gmd-17-6123-2024, 2024
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Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, GPU-accelerated computing is introduced in WAve Modeling Cycle 6 (WAM6). With this effort, global high-resolution wave simulations can now run on GPUs up to tens of times faster than the currently available models can on a CPU node with results that are just as accurate.
Krysten Rutherford, Laura Bianucci, and William Floyd
Geosci. Model Dev., 17, 6083–6104, https://doi.org/10.5194/gmd-17-6083-2024, https://doi.org/10.5194/gmd-17-6083-2024, 2024
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Nearshore ocean models often lack complete information about freshwater fluxes due to numerous ungauged rivers and streams. We tested a simple rain-based hydrological model as inputs into an ocean model of Quatsino Sound, Canada, with the aim of improving the representation of the land–ocean connection in the nearshore model. Through multiple tests, we found that the performance of the ocean model improved when providing 60 % or more of the freshwater inputs from the simple runoff model.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
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The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 17, 5851–5882, https://doi.org/10.5194/gmd-17-5851-2024, https://doi.org/10.5194/gmd-17-5851-2024, 2024
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The carbonate system is typically studied using measurements, but modeling can contribute valuable insights. Using a biogeochemical model, we propose a new representation of total alkalinity, dissolved inorganic carbon, pCO2, and pH in a highly dynamic Mediterranean coastal area, the Bay of Marseille, a useful addition to measurements. Through a detailed analysis of pCO2 and air–sea CO2 fluxes, we show that variations are strongly impacted by the hydrodynamic processes that affect the bay.
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, https://doi.org/10.5194/gmd-17-5497-2024, 2024
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Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
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This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Isabel Jalón-Rojas, Damien Sous, and Vincent Marieu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-100, https://doi.org/10.5194/gmd-2024-100, 2024
Revised manuscript accepted for GMD
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This study presents a novel modeling approach for understanding microplastic transport in coastal waters. The model accurately replicates experimental data and reveals key transport mechanisms. The findings enhance our knowledge of how microplastics move in nearshore environments, aiding in coastal management and efforts to combat plastic pollution globally.
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.
Greig Oldford, Tereza Jarníková, Villy Christensen, and Michael Dunphy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-58, https://doi.org/10.5194/gmd-2024-58, 2024
Revised manuscript accepted for GMD
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We developed a physical ocean model called the Hindcast of the Salish Sea (HOTSSea) that recreates conditions throughout the Salish Sea from 1980 to 2018, filling in the gaps in patchy measurements. The model predicts physical ocean properties with sufficient accuracy to be useful for a variety of applications. The model corroborates observed ocean temperature trends and was used to examine areas with few observations. Results indicate that some seasons and areas are warming faster than others.
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
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The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
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In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
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Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
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Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
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The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
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A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
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We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
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Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024, https://doi.org/10.5194/gmd-17-1023-2024, 2024
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This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
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We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Luca Arpaia, Christian Ferrarin, Marco Bajo, and Georg Umgiesser
Geosci. Model Dev., 16, 6899–6919, https://doi.org/10.5194/gmd-16-6899-2023, https://doi.org/10.5194/gmd-16-6899-2023, 2023
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We propose a discrete multilayer shallow water model based on z-layers which, thanks to the insertion and removal of surface layers, can deal with an arbitrarily large tidal oscillation independently of the vertical resolution. The algorithm is based on a two-step procedure used in numerical simulations with moving boundaries (grid movement followed by a grid topology change, that is, the insertion/removal of surface layers), which avoids the appearance of very thin surface layers.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, France Van Wambeke, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 16, 6701–6739, https://doi.org/10.5194/gmd-16-6701-2023, https://doi.org/10.5194/gmd-16-6701-2023, 2023
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While several studies have shown that mixotrophs play a crucial role in the carbon cycle, the impact of environmental forcings on their dynamics remains poorly investigated. Using a biogeochemical model that considers mixotrophs, we study the impact of light and nutrient concentration on the ecosystem composition in a highly dynamic Mediterranean coastal area: the Bay of Marseille. We show that mixotrophs cope better with oligotrophic conditions compared to strict auto- and heterotrophs.
Trygve Halsne, Kai Håkon Christensen, Gaute Hope, and Øyvind Breivik
Geosci. Model Dev., 16, 6515–6530, https://doi.org/10.5194/gmd-16-6515-2023, https://doi.org/10.5194/gmd-16-6515-2023, 2023
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Surface waves that propagate in oceanic or coastal environments get influenced by their surroundings. Changes in the ambient current or the depth profile affect the wave propagation path, and the change in wave direction is called refraction. Some analytical solutions to the governing equations exist under ideal conditions, but for realistic situations, the equations must be solved numerically. Here we present such a numerical solver under an open-source license.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
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Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Doroteaciro Iovino, Pier Giuseppe Fogli, and Simona Masina
Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, https://doi.org/10.5194/gmd-16-6127-2023, 2023
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This paper describes the model performance of three global ocean–sea ice configurations, from non-eddying (1°) to eddy-rich (1/16°) resolutions. Model simulations are obtained following the Ocean Model Intercomparison Project phase 2 (OMIP2) protocol. We compare key global climate variables across the three models and against observations, emphasizing the relative advantages and disadvantages of running forced ocean–sea ice models at higher resolution.
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.
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.
Cited articles
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation
for ensemble filters, Tellus A, 61, 72–83,
https://doi.org/10.1111/j.1600-0870.2008.00361.x, 2009. a, b
Anderson, J. L.: A Quantile-Conserving Ensemble Filter Framework.
Part I: Updating an Observed Variable, Mon. Weather Rev., 150,
1061–1074, https://doi.org/10.1175/MWR-D-21-0229.1, 2022. a
Asbjørnsen, H., Årthun, M., Skagseth, O., and Eldevik, T.: Mechanisms
Underlying Recent Arctic Atlantification, Geophys. Res. Lett., 47,
e2020GL088036, https://doi.org/10.1029/2020GL088036, 2020. a
Batrak, Y. and Müller, M.: On the warm bias in atmospheric reanalyses induced
by the missing snow over Arctic sea-ice, Nat. Commun., 10, 4170,
https://doi.org/10.1038/s41467-019-11975-3, 2019. a
Bishop, C. H.: The GIGG-EnKF: ensemble Kalman filtering for highly skewed
non-negative uncertainty distributions, Q. J. Roy. Meteor. Soc., 142,
1395–1412, https://doi.org/10.1002/qj.2742, 2016. a, b
Breivik, O., Mogensen, K., Bidlot, J.-R., Balmaseda, M. A., and Janssen, P. A.
E. M.: Surface wave effects in the NEMO ocean model: Forced and coupled
experiments, J. Geophys. Res.-Oceans, 120, 2973–2992,
https://doi.org/10.1002/2014JC010565, 2015. a
Bröcker, J. and Smith, L. A.: Increasing the Reliability of Reliability
Diagrams, Weather Forecast., 22, 651–661, https://doi.org/10.1175/WAF993.1, 2007. a, b
Burgers, G., Leeuwen, P. J. V., and Evensen, G.: Analysis Scheme in the
Ensemble Kalman Filter, Mon. Weather Rev., 126, 1719–1724,
https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a
Canuto, V. M., Howard, A., Cheng, Y., and Dubovikov, M. S.: Ocean Turbulence.
Part I: One-Point Closure Model – Momentum and Heat
Vertical Diffusivities, J. Phys. Oceanogr., 31, 1413–1426,
https://doi.org/10.1175/1520-0485(2001)031<1413:OTPIOP>2.0.CO;2, 2001. a
Chan, M.-Y., Chen, X., and Anderson, J. L.: The Potential Benefits of
Handling Mixture Statistics via a Bi-Gaussian EnKF: Tests
With All-Sky Satellite Infrared Radiances, J. Adv. Model. Earth
Sy., 15, e2022MS003357, https://doi.org/10.1029/2022MS003357, 2023. a, b
Chang, H.-L., Yang, S.-C., Yuan, H., Lin, P.-L., and Liou, Y.-C.: Analysis of
the Relative Operating Characteristic and Economic Value Using
the LAPS Ensemble Prediction System in Taiwan, Mon. Weather Rev.,
143, 1833–1848, https://doi.org/10.1175/MWR-D-14-00189.1, 2015. a
Cipollone, A., Banerjee, D. S., Iovino, D., Aydogdu, A., and Masina, S.: Bivariate sea-ice assimilation for global ocean Analysis/Reanalysis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-254, 2023. a
Copernicus Marine Service: Global Ocean – In-Situ Near-Real-Time Observations, https://doi.org/10.48670/moi-00036, 2023. a
Craig, P. D. and Banner, M. L.: Modeling Wave-Enhanced Turbulence in the
Ocean Surface Layer, J. Phys. Oceanogr., 24, 2546–2559,
https://doi.org/10.1175/1520-0485(1994)024<2546:MWETIT>2.0.CO;2, 1994. a
Dagestad, K.-F. and Röhrs, J.: Prediction of ocean surface trajectories using
satellite derived vs. modeled ocean currents, Remote Sens. Environ., 223,
130–142, https://doi.org/10.1016/j.rse.2019.01.001, 2019. a
Dagestad, K.-F., Röhrs, J., Breivik, Ø., and Ådlandsvik, B.: OpenDrift v1.0: a generic framework for trajectory modelling, Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, 2018. a
de Aguiar, V., Röhrs, J., Johansson, A. M., and Eltoft, T.: Assessing ocean
ensemble drift predictions by comparison with observed oil slicks, Front.
Mar. Sci., 10, https://doi.org/10.3389/fmars.2023.1122192, 2023. a
Debernard, J., Kristensen, N. M., Maartensson, S., Wang, K., Hedstrom, K., Brændshøi, J., and Szapiro, N.: metno/metroms: Version 0.4.1 (v0.4.1), Zenodo [code], https://doi.org/10.5281/zenodo.5067164, 2021. a, b
Dinessen, F. and Hackett, B.: Product user manual for regional high resolution
sea ice charts Svalbard region (version 2.3), Tech. rep., Copernicus,
https://www.yumpu.com/en/document/view/45590964/product-user-manual-for-regional-high-myocean (last access: 15 September 2023),
2011. a
Duarte, P., Brændshøi, J., Shcherbin, D., Barras, P., Albretsen, J., Gusdal, Y., Szapiro, N., Martinsen, A., Samuelsen, A., Wang, K., and Debernard, J. B.: Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system, Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, 2022. a, b
Durán Moro, M., Sperrevik, A. K., Lavergne, T., Bertino, L., Gusdal, Y., Iversen, S. C., and Rusin, J.: Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean-sea ice model, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2023-115, in review, 2023. a
ECMWF: EcFlow scheduling software, GitHub [code], https://github.com/ecmwf/ecflow, last access: 29 January 2023. a
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic
ocean tides, J. Atmos. Ocean. Tech., 19, 183–204,
https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002. a
El Gharamti, M.: Enhanced Adaptive Inflation Algorithm for Ensemble
Filters, Mon. Weather Rev., 146, 623–640, https://doi.org/10.1175/MWR-D-17-0187.1,
2018. a, b
Evensen, G.: Inverse methods and data assimilation in nonlinear ocean models,
Physica D, 77, 108–129,
https://doi.org/10.1016/0167-2789(94)90130-9, 1994. a, b
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to
the treatment of ice thermodynamics and dynamics, J. Geophys. Res.-Oceans,
102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997. a
Fritzner, S., Graversen, R., and Christensen, K. H.: Assessment of
High-Resolution Dynamical and Machine Learning Models for
Prediction of Sea Ice Concentration in a Regional Application, J.
Geophys. Res.-Oceans, 125, e2020JC016277, https://doi.org/10.1029/2020JC016277,
2020. a
Fritzner, S. M., Graversen, R. G., Wang, K., and Christensen, K. H.: Comparison
between a multi-variate nudging method and the ensemble Kalman filter for
sea-ice data assimilation, J. Glaciol., 64, 387–396,
https://doi.org/10.1017/jog.2018.33, 2018. a
Fritzner, S., Graversen, R., Christensen, K. H., Rostosky, P., and Wang, K.: Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system, The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, 2019. a
Furevik, B. R., Schyberg, H., Noer, G., Tveter, F., and Röhrs, J.: ASAR and
ASCAT in Polar Low Situations, J. Atmos. Ocean Tech., 32, 783–792,
https://doi.org/10.1175/JTECH-D-14-00154.1, 2015. a
Hallerstig, M., Magnusson, L., Kolstad, E. W., and Mayer, S.: How grid-spacing
and convection representation affected the wind speed forecasts of four polar
lows, Q. J. Roy. Meteor. Soc., 147, 150–165, https://doi.org/10.1002/qj.3911, 2021. a
Hamill, T. M.: Interpretation of Rank Histograms for Verifying Ensemble
Forecasts, Mon. Weather Rev., 129, 550–560,
https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2, 2001. a
Heorton, H. D. B. S., Feltham, D. L., and Tsamados, M.: Stress and deformation
characteristics of sea ice in a high-resolution, anisotropic sea ice model,
Philos. T. Roy. Soc. A, 376, 20170349, https://doi.org/10.1098/rsta.2017.0349, 2018. a
Hibler III, W. D.: Modeling a Variable Thickness Sea Ice Cover, Mon.
Weather Rev., 108, 1943–1973,
https://doi.org/10.1175/1520-0493(1980)108<1943:MAVTSI>2.0.CO;2, 1980. a
Houtekamer, P. L. and Mitchell, H. L.: Data Assimilation Using an
Ensemble Kalman Filter Technique, Mon. Weather Rev., 126, 796–811,
https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998. a
Houtekamer, P. L. and Zhang, F.: Review of the Ensemble Kalman Filter for
Atmospheric Data Assimilation, Mon. Weather Rev., 144, 4489–4532,
https://doi.org/10.1175/MWR-D-15-0440.1, 2016. a
Hunke, E. C. and Dukowicz, J. K.: An Elastic–Viscous–Plastic Model
for Sea Ice Dynamics, J. Phys. Oceanogr., 27, 1849–1867,
https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997. a
Idžanović, M., Rikardsen, E. S. U., and Röhrs, J.: Forecast uncertainty and
ensemble spread in surface currents from a regional ocean model, Front. Mar.
Sci., 10, 1177337,
https://doi.org/10.3389/fmars.2023.1177337,
2023. a
Ingvaldsen, R. B., Assmann, K. M., Primicerio, R., Fossheim, M., Polyakov,
I. V., and Dolgov, A. V.: Physical manifestations and ecological implications
of Arctic Atlantification, Nat. Rev. Earth. Environ., 2, 874–889,
https://doi.org/10.1038/s43017-021-00228-x, 2021. a
Iversen, S. C., Sperrevik, A. K., and Goux, O.: Improving sea surface temperature in a regional ocean model through refined sea surface temperature assimilation, Ocean Sci., 19, 729–744, https://doi.org/10.5194/os-19-729-2023, 2023. a
Jacobs, G., D'Addezio, J., Ngodock, H., and Souopgui, I.: Observation and
model resolution implications to ocean prediction, Ocean Model., 159, 101760,
https://doi.org/10.1016/j.ocemod.2021.101760, 2021. a
Janssen, P.: Ocean wave effects on the daily cycle in SST, J. Geophys. Res.,
117, C00J32, https://doi.org/10.1029/2012JC007943, 2012. a
Kusahara, K., Williams, G. D., Massom, R., Reid, P., and Hasumi, H.: Roles of
wind stress and thermodynamic forcing in recent trends in Antarctic sea ice
and Southern Ocean SST: An ocean-sea ice model study, Global Planet.
Change, 158, 103–118, https://doi.org/10.1016/j.gloplacha.2017.09.012, 2017. a
Larson, J., Jacob, R., and Ong, E.: The model coupling toolkit: A new
fortran90 toolkit for building multiphysics parallel coupled models, The
Int. J. High Perform. C., 19,
277–292, https://doi.org/10.1177/1094342005056115, 2005. a, b
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A.: Sea
ice motion from low-resolution satellite sensors: An alternative method and
its validation in the Arctic, J. Geophys. Res.-Oceans, 115, C10032,
https://doi.org/10.1029/2009JC005958, 2010. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a
Lind, S., Ingvaldsen, R. B., and Furevik, T.: Arctic warming hotspot in the
northern Barents Sea linked to declining sea-ice import, Nat. Clim.
Change, 8, 634–639, https://doi.org/10.1038/s41558-018-0205-y, 2018. a
Lipscomb, W. H. and Hunke, E. C.: Modeling Sea Ice Transport Using
Incremental Remapping, Mon. Weather Rev., 132, 1341–1354,
https://doi.org/10.1175/1520-0493(2004)132<1341:MSITUI>2.0.CO;2, 2004. a
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.: Ridging,
strength, and stability in high-resolution sea ice models, J. Geophys. Res.-Oceans, 112, C03S91, https://doi.org/10.1029/2005JC003355, 2007. a
Lisæter, K. A., Rosanova, J., and Evensen, G.: Assimilation of ice
concentration in a coupled ice–ocean model, using the Ensemble Kalman
filter, Ocean Dynam., 53, 368–388, https://doi.org/10.1007/s10236-003-0049-4, 2003. a
Mile, M., Azad, R., and Marseille, G.-J.: Assimilation of Aeolus
Rayleigh-Clear Winds Using a Footprint Operator in
AROME-Arctic Mesoscale Model, Geophys. Res. Lett., 49,
e2021GL097615, https://doi.org/10.1029/2021GL097615, 2022. a
Mittermaier, M. P.: The Potential Impact of Using Persistence as a
Reference Forecast on Perceived Forecast Skill, Weather Forecast.,
23, 1022–1031, https://doi.org/10.1175/2008WAF2007037.1, 2008. 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
Müller, M., Batrak, Y., Kristiansen, J., Køltzow, M. A. O., Noer, G., and
Korosov, A.: Characteristics of a Convective-Scale Weather
Forecasting System for the European Arctic, Mon. Weather Rev., 145,
4771–4787, https://doi.org/10.1175/MWR-D-17-0194.1, 2017. a, b, c
Naughten, K. A., Galton-Fenzi, B. K., Meissner, K. J., England, M. H.,
Brassington, G. B., Colberg, F., Hattermann, T., and Debernard, J. B.:
Spurious sea ice formation caused by oscillatory ocean tracer advection
schemes, Ocean Model., 116, 108–117, https://doi.org/10.1016/j.ocemod.2017.06.010,
2017. a
Naughten, K. A., Meissner, K. J., Galton-Fenzi, B. K., England, M. H., Timmermann, R., Hellmer, H. H., Hattermann, T., and Debernard, J. B.: Intercomparison of Antarctic ice-shelf, ocean, and sea-ice interactions simulated by MetROMS-iceshelf and FESOM 1.4, Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, 2018. a
Noer, G., Saetra, O., Lien, T., and Gusdal, Y.: A climatological study of polar
lows in the Nordic Seas, Q. J. Roy. Meteor. Soc., 137, 1762–1772,
https://doi.org/10.1002/qj.846, 2011. a
Norwegian Meteorological Institute: Barents-2.5 ocean and ice forecast archive, Norwegian Meteorological Institute [data set], https://thredds.met.no/thredds/fou-hi/barents_eps.html, last access: 15 September 2023a. a
Norwegian Meteorological Institute: OSI SAF Sea ice concentration, Norwegian Meteorological Institute [data set], https://thredds.met.no/thredds/osisaf/osisaf_seaiceconc.html, last access: 15 September 2023b. a
Norwegian Meteorological Institute: Ice charts from the Norwegian Ice Service, Norwegian Meteorological Institute [data set], https://cryo.met.no/en/latest-ice-chart, last access: 15 September 2023c. a
Norwegian Meteorological Institute: High-Frequency radar radial current estimates, Norwegian Meteorological Institute [data set], https://thredds.met.no/thredds/catalog/remotesensinghfradar/catalog.html, last access: 15 September 2023d. a
EUMETSAF Data Services: OSI SAF Global Low Resolution Sea Ice Drift, OSI-405-c, EUMETSAT Ocean and Sea Ice Satellite Application Facility [data set], https://doi.org/10.15770/EUM_SAF_OSI_NRT_2007, last access: 15 September 2023. a
Price, J. F., Weller, R. A., and Pinkel, R.: Diurnal cycling: Observations
and models of the upper ocean response to diurnal heating, cooling, and wind
mixing, J. Geophys. Res.-Oceans, 91, 8411–8427,
https://doi.org/10.1029/JC091iC07p08411, 1986. a
Rothrock, D. A.: The energetics of the plastic deformation of pack ice by
ridging, J. Geophys. Res., 80, 4514–4519, https://doi.org/10.1029/JC080i033p04514,
1975. a, b
Röhrs, J.: Configuration setup for Barents-2.5 Ocean and Ice forecast model. (2.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7607191, 2023. a, b, c
Röhrs, J. and Christensen, K. H.: Drift in the uppermost part of the ocean,
Geophys. Res. Lett., 42, 1–8, https://doi.org/10.1002/2015GL066733, 2015. a
Röhrs, J., Christensen, K. H., Vikebø, F. B., Sundby, S., Saetra, O., and
Broström, G.: Wave-induced transport and vertical mixing of pelagic eggs and
larvae, Limnol. Oceanogr., 59(4), 1213–1227,
https://doi.org/10.4319/lo.2014.59.4.1213, 2014. a
Röhrs, J., Sutherland, G., Jeans, G., Bedington, M., Sperrevik, A. K.,
Dagestad, K.-F., Gusdal, Y., Mauritzen, C., Dale, A., and LaCasce, J. H.:
Surface currents in operational oceanography: Key applications, mechanisms,
and methods, J. Oper. Oceanogr., 16, 60–88,
https://doi.org/10.1080/1755876X.2021.1903221, 2023. a
Rusin, J., Lavergne, T., Doulgeries, A. P., and Scott, K. A.: Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data, Ann. Glaciol., submitted, 2023. a
Saetra, O., Hersbach, H., Bidlot, J.-R., and Richardson, D. S.: Effects of
Observation Errors on the Statistics for Ensemble Spread and
Reliability, Mon. Weather Rev., 132, 1487–1501,
https://doi.org/10.1175/1520-0493(2004)132<1487:EOOEOT>2.0.CO;2, 2004. a, b
Sakov, P.: EnKF-C v.2.9.9 data assimilation framework, GitHub [code], https://github.com/sakov/EnKF-C.git, commit 7eea4d8, last access: 8 July 2021. a
Sakov, P. and Oke, P. R.: Implications of the Form of the Ensemble
Transformation in the Ensemble Square Root Filters, Mon. Weather
Rev., 136, 1042–1053, https://doi.org/10.1175/2007MWR2021.1, 2008b. a
Samuelsen, E. M.: Ship-icing prediction methods applied in operational weather
forecasting, Q. J. Roy. Meteor. Soc., 144, 13–33, https://doi.org/10.1002/qj.3174,
2018. a
Schweiger, A. J. and Zhang, J.: Accuracy of short-term sea ice drift forecasts
using a coupled ice-ocean model, J. Geophys. Res.-Oceans, 120, 7827–7841,
https://doi.org/10.1002/2015JC011273, 2015. a
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89 GHz channels, J. Geophys. Res., 113, C02S03, https://doi.org/10.1029/2005JC003384, 2008. a
Strand, K. O., Sundby, S., Albretsen, J., and Vikebø, F. B.: The Northeast
Greenland Shelf as a Potential Habitat for the Northeast Arctic
Cod, Front. Mar. Sci., 4, 304, https://doi.org/10.3389/fmars.2017.00304, 2017.
a, b
Strand, K. O., Huserbråten, M., Dagestad, K.-F., Mauritzen, C., Grøsvik,
B. E., Nogueira, L. A., Melsom, A., and Röhrs, J.: Potential sources of
marine plastic from survey beaches in the Arctic and Northeast
Atlantic, Sci. Total Environ., 790, 148009,
https://doi.org/10.1016/j.scitotenv.2021.148009, 2021. a
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness
distribution of sea ice, J. Geophys. Res., 80, 4501–4513,
https://doi.org/10.1029/JC080i033p04501, 1975. a
Turner, A. K., Hunke, E. C., and Bitz, C. M.: Two modes of sea-ice gravity
drainage: A parameterization for large-scale modeling, J. Geophys. Res.-Oceans, 118, 2279–2294, https://doi.org/10.1002/jgrc.20171, 2013. a
Umlauf, L. and Burchard, H.: Second-order turbulence closure models for
geophysical boundary layers. A review of recent work, Cont. Shelf Res., 25,
795–827, https://doi.org/10.1016/j.csr.2004.08.004, 2005. a
van Leeuwen, P. J.: A consistent interpretation of the stochastic version of
the Ensemble Kalman Filter, Q. J. Roy. Meteor. Soc., 146, 2815–2825,
https://doi.org/10.1002/qj.3819, 2020. a
Warner, J. C., Sherwood, C. R., Arango, H. G., and Signell, R. P.: Performance
of four turbulence closure models implemented using a generic length scale
method, Ocean Model., 8, 81–113, https://doi.org/10.1016/j.ocemod.2003.12.003, 2005. a, b, c
Whitaker, J. S. and Hamill, T. M.: Ensemble Data Assimilation without
Perturbed Observations, Mon. Weather Rev., 130, 1913–1924,
https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2, 2002. a
WMO: Sea-Ice Information Services in the World. Edition 2017.,
Report, World Meteorological Organization, JCOMM Expert Team on Sea Ice
(ETSI),
https://repository.oceanbestpractices.org/handle/11329/394 (last access: 15 September 2023),
2017. a
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013, Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, 2017. a
Zeng, X. and Beljaars, A.: A prognostic scheme of sea surface skin temperature
for modeling and data assimilation, Geophys. Res. Lett., 32, L14605,
https://doi.org/10.1029/2005GL023030, 2005. a
Short summary
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.
A model to predict ocean currents, temperature, and sea ice is presented, covering the Barents...