Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5829-2022
© Author(s) 2022. 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-15-5829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
27 Jul 2022
Methods for assessment of models |
| 27 Jul 2022
| 27 Jul 2022
Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models
Takaya Uchida
CORRESPONDING AUTHOR
Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, France
Julien Le Sommer
Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, France
Charles Stern
Lamont-Doherty Earth Observatory, Columbia University in the City of New York, New York City, USA
Ryan P. Abernathey
Lamont-Doherty Earth Observatory, Columbia University in the City of New York, New York City, USA
Chris Holdgraf
2i2c.org, Portland, Oregon, USA
Aurélie Albert
Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, France
Laurent Brodeau
Ocean Next, Grenoble, France
Datlas, Grenoble, France
Eric P. Chassignet
Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA
Xiaobiao Xu
Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA
Jonathan Gula
Univ. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
Institut Universitaire de France (IUF), Paris, France
Guillaume Roullet
Univ. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
Nikolay Koldunov
Alfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
Sergey Danilov
Alfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
Qiang Wang
Alfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
Dimitris Menemenlis
Jet Propulsion Laboratory, National Aeronautics and Space Administration (NASA), Palisades, California, USA
Clément Bricaud
Mercator Ocean International, Toulouse, France
Brian K. Arbic
Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, Michigan, USA
Jay F. Shriver
Oceanography Division, US Naval Research Laboratory, Hancock, Mississippi, USA
Fangli Qiao
First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
Bin Xiao
First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
Arne Biastoch
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany
Department of Ocean Circulation and Climate Dynamics, Kiel University, Kiel, Germany
René Schubert
Univ. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany
Baylor Fox-Kemper
Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, Rhode Island, USA
William K. Dewar
Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, France
Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida, USA
Alan Wallcraft
Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA
Related authors
No articles found.
Matteo Nurisso, Jost von Hardenberg, Marco Cadau, Silvia Caprioli, Paolo Ghinassi, Supriyo Ghosh, Nikolay Koldunov, Natalia Nazarova, Maqsood Mubarak Rajput, Emanuele Tovazzi, and Paolo Davini
Geosci. Model Dev., 19, 7725–7740, https://doi.org/10.5194/gmd-19-7725-2026, https://doi.org/10.5194/gmd-19-7725-2026, 2026
Short summary
Short summary
We present a new open-source software called AQUA designed to handle and evaluate the vast amounts of data produced by high-resolution climate simulations. As climate models become more detailed, their output can become too large and complex for traditional analysis methods. We introduce a new framework for data access, processing, and comparison that is fast, reliable, and easy to automate. Our results show that it can efficiently manage very large datasets and support real-time quality checks.
Dante C. Napolitano, Jonathan Gula, Solange Coadou-Chaventon, Sabrina Speich, Cesar B. Rocha, James C. McWilliams, Dongxiao Zhang, and Xavier Carton
Ocean Sci., 22, 2425–2447, https://doi.org/10.5194/os-22-2425-2026, https://doi.org/10.5194/os-22-2425-2026, 2026
Short summary
Short summary
The Amazon River plume interaction with the North Brazil Current creates sharp temperature-salinity differences called fronts that are important for modulating air-sea interactions and biogeochemical cycles. This study uses data from robotic sailboats and a numerical model to provide a comprehensive picture of these fronts associated with the local dynamics. We characterize three distinct frontal regimes and the temperature-salinity relations linked to the Amazon discharge and current strength.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O'Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Oluwayemi Garuba, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
Geosci. Model Dev., 19, 6043–6078, https://doi.org/10.5194/gmd-19-6043-2026, https://doi.org/10.5194/gmd-19-6043-2026, 2026
Short summary
Short summary
The ocean and sea ice are central to Earth's climate system, influencing global heat and carbon cycles, weather patterns, and sea level rise. Here the earth system model variables needed for studies of the ocean and sea ice are prioritized and requested. These requested variables were developed through an international, community-based process.
Tobias Schulzki, Franziska U. Schwarzkopf, and Arne Biastoch
Ocean Sci., 22, 2027–2058, https://doi.org/10.5194/os-22-2027-2026, https://doi.org/10.5194/os-22-2027-2026, 2026
Short summary
Short summary
Marine heatwaves are periods of unusually warm ocean temperatures that can affect ecosystems and weather. In the North Sea, they have become more frequent over recent decades, but their peak intensity has decreased in many areas. Our results show that marine heatwaves depend on both short-term weather and longer seasonal to decadal variability. The local atmospheric conditions play a key role in the German Bight.
Rémi Laxenaire, Eric P. Chassignet, Xiaobiao Xu, Alan J. Wallcraft, Luna Hiron, Brian K. Arbic, Maarten C. Buijsman, Miguel Solano, and Shane Elipot
Geosci. Model Dev., 19, 5571–5599, https://doi.org/10.5194/gmd-19-5571-2026, https://doi.org/10.5194/gmd-19-5571-2026, 2026
Short summary
Short summary
Surface kinetic energy reflects the distribution of ocean circulation across temporal and spatial scales, shaping energy transfer and mixing in the upper ocean. In a series of North and Equatorial Atlantic numerical simulations, we show that the surface kinetic energy is sensitive to the model's choices in grid and seafloor resolution and to the tides and wind variability, offering guidance for the configuration of future ocean simulations.
Gokhan Danabasoglu, Frederic S. Castruccio, Burcu Boza, Alice M. Barthel, Arne Biastoch, Adam Blaker, Alexandra Bozec, Diego Bruciaferri, Frank O. Bryan, Eric P. Chassignet, Yao Fu, Ian Grooms, Catherine Guiavarc'h, Hakase Hayashida, Andrew McC. Hogg, Ryan M. Holmes, Doroteaciro Iovino, Andrew E. Kiss, M. Susan Lozier, Gustavo Marques, Alex Megann, Franziska U. Schwarzkopf, Dave Storkey, Luke van Roekel, Jon Wolfe, Xiaobiao Xu, and Rong Zhang
Geosci. Model Dev., 19, 5071–5117, https://doi.org/10.5194/gmd-19-5071-2026, https://doi.org/10.5194/gmd-19-5071-2026, 2026
Short summary
Short summary
A comparison of simulated and observed overturning transports across the Overturning in the Subpolar North Atlantic Program sections is presented. Eighteen ocean simulations participate in the study with resolutions ranging from coarse to eddy-resolving. The simulated transports are in general agreement with observations, with better agreement at high resolution. Analyzing overturning circulations in both depth and density space together provides a more complete picture.
Yonglin Huang, Annalisa Bracco, Kurt Polzin, and Jonathan Gula
EGUsphere, https://doi.org/10.5194/egusphere-2026-2869, https://doi.org/10.5194/egusphere-2026-2869, 2026
Short summary
Short summary
We compared four high-resolution simulations with observations from the Brazil Basin to test how models represent deep-ocean mixing above rough seafloor. By tracking virtual particles, we found that the simulations reproduce strong mixing near the bottom, and higher resolution produces enhanced mixing in the ocean interior. Our results indicate that smoothed seafloor details weaken wave breaking and leave too much energy in eddies, making them more intense than they likely are in the real ocean.
Brian Henn, Christopher S. Bretherton, Nikolay Koldunov, Christian Lessig, Maria J. Molina, Troy Arcomano, Oliver Watt-Meyer, Guillaume Couairon, Renu Singh, Robert Brunstein, Yana Hasson, Antonia Jost, Noah Brenowitz, Peter Manshausen, Nathaniel Cresswell-Clay, Dale Durran, Kyle Joseph Chen Hall, Janni Yuval, Dmitrii Kochkov, Stephan Hoyer, and Ignacio Lopez-Gomez
EGUsphere, https://doi.org/10.48550/arXiv.2605.06944, https://doi.org/10.48550/arXiv.2605.06944, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
AIMIP (AI Model Intercomparison Project) is a community effort to rigorously evaluate AI weather and climate models, which simulate Earth's climate with extraordinary efficiency compared to traditional systems. Phase 1 is an atmosphere-only standardized experiment, showing that AI models are competitive on average historical patterns but may struggle with long-term warming trends and generalizing to unseen scenarios. The AIMIP Phase 1 dataset is publicly available for open model evaluation.
Rohit Ghosh, Suvarchal Kumar Cheedela, Sebastian Beyer, Nikolay Koldunov, Stella Berzina, Audrey Delpech, Svetlana Loza, Chathurika Wikramage, Stephy Libera, Matthias Aengenheyster, Amal John, Armelle Remedio, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Fabian Wachsmann, and Thomas Jung
EGUsphere, https://doi.org/10.5194/egusphere-2026-1289, https://doi.org/10.5194/egusphere-2026-1289, 2026
Short summary
Short summary
Understanding climate change requires detailed computer simulations of the Earth system. Here we present one of the first century-long global simulations in which both the atmosphere and ocean are represented at kilometre-scale resolution. Such simulations are extremely computationally demanding, but we demonstrate their feasibility and show that they can capture key climate patterns while representing important small-scale ocean processes.
Erwan Le Roux, Pierre Tandeo, Carlos Granero Belinchon, Melika Baklouti, Julien Le Sommer, Florence Sevault, Samuel Somot, Antoine Doury, and Mahmoud Al Najar
EGUsphere, https://doi.org/10.5194/egusphere-2026-1991, https://doi.org/10.5194/egusphere-2026-1991, 2026
Short summary
Short summary
Projected impacts of climate change are assessed with impact models driven by climate projections. However, impact models can have a high computational cost. Here, we design statistical emulators of impact models based on automatic equation discovery. To our knowledge, this approach is a promising innovation that could pave the way to develop simple and interpretable emulators for various climate impact studies, but also to validate existing knowledge and to understand novel links.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Costanza Anerdi, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo-Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Kesari Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
Geosci. Model Dev., 19, 2821–2848, https://doi.org/10.5194/gmd-19-2821-2026, https://doi.org/10.5194/gmd-19-2821-2026, 2026
Short summary
Short summary
The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of global climate projections. It produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
Anthony Chen, He Wang, Brian Arbic, and Robert Krasny
EGUsphere, https://doi.org/10.48550/arXiv.2602.01416, https://doi.org/10.48550/arXiv.2602.01416, 2026
Short summary
Short summary
Self Attraction and Loading (SAL) is an important force that affects many oceanic motions, including tides. Computing SAL is challenging and ocean models neglected to include the impacts of SAL for a long time. Recent work has proposed a method for incorporating the effects of SAL, but the method has several limitations that limit the accuracy. This work proposes an alternative method. Tests of this new method in an ocean model indicate that it reduces the amount of error in the modeled tides.
Alexis Barge, Julien Le Sommer, Andrea Storto, and Sophie Valcke
EGUsphere, https://doi.org/10.5194/egusphere-2026-854, https://doi.org/10.5194/egusphere-2026-854, 2026
Short summary
Short summary
Scientists use programs, called Earth System Models, to study and predict climate. These models are based on physical theories but can be completed with AI tools. However, combining these tools with traditional models is difficult due to their different nature. Our research introduces a new method that connects these AI tools with existing climate models. We tested this method by integrating it with an ocean model. This work should help scientists explore new ways of making climate predictions.
Peter W. Thorne, John M. Nicklas, John J. Kennedy, Bruce Calvert, Baylor Fox-Kemper, Mark T. Richardson, Adrian Simmons, Ed Hawkins, Robert Rhode, Kathryn Cowtan, Nerilie J. Abram, Axel Andersson, Simon Noone, Phillipe Marbaix, Nathan Lenssen, Dirk Olonscheck, Tristram Walsh, Stephen Outten, Ingo Bethke, Bjorn H. Samset, Chris Smith, Anna Pirani, Jan Fuglestvedt, Lavanya Rajamani, Richard A. Betts, Elizabeth C. Kent, Blair Trewin, Colin Morice, Tim Osborn, Samantha N. Burgess, Oliver Geden, Andrew Parnell, Piers M. Forster, Chris Hewitt, Zeke Hausfather, Valerie Masson-Delmotte, Jochem Marotzke, Nathan Gillett, Sonia I. Seneviratne, Gavin A. Schmidt, Duo Chan, Stefan Brönnimann, Andy Reisinger, Matthew Menne, Maisa Rojas Corradi, Christopher Kadow, Peter Huybers, David B. Stephenson, Emily Wallis, Joeri Rogelj, Andrew Schurer, Karen McKinnon, Panmao Zhai, Fatima Driouech, Wilfran Moufouma Okia, Saeed Vazifehkhah, Sophie Szopa, Christopher J. Merchant, Shoji Hirahara, Masayoshi Ishii, Francois A. Engelbrecht, Qingxiang Li, June-Yi Lee, Alex J. Cannon, Christophe Cassou, Karina von Schuckmann, Amir H. Delju, and Ellie Murtagh
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-825, https://doi.org/10.5194/essd-2025-825, 2026
Preprint under review for ESSD
Short summary
Short summary
We reassess the basis for determining the present level of long-term global warming. Unbiased estimates of both realised warming and anthropogenic warming are possible that approximate a 20-year retrospective mean. Our resulting estimates of 1.40 [1.23–1.58] °C (realised) and 1.34 [1.18–1.50] °C (anthropogenic) as at end of 2024 highlight the urgency of immediate, far-reaching and sustained climate mitigation actions if we are to meet the long term temperature goal of the Paris Agreement.
Vadim Bertrand, Julien Le Sommer, Victor Vianna Zaia De Almeida, Adeline Samson, and Emmanuel Cosme
Ocean Sci., 22, 241–255, https://doi.org/10.5194/os-22-241-2026, https://doi.org/10.5194/os-22-241-2026, 2026
Short summary
Short summary
Understanding ocean surface currents is essential for navigation and environmental monitoring. Traditionally, currents are estimated from satellite sea surface height using a simplified balance. We developed a new method that goes beyond this approximation, providing more accurate currents estimates. Using a model simulation, satellite observations, and drifters data, we show that these corrections become increasingly important at finer spatial scales, especially in energetic regions.
Yannick Wölker, Willi Rath, Matthias Renz, and Arne Biastoch
Ocean Sci., 21, 3541–3562, https://doi.org/10.5194/os-21-3541-2025, https://doi.org/10.5194/os-21-3541-2025, 2025
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) is a large current system that helps regulating Earth's climate. Monitoring the AMOC relies on fixed instruments anchored to the seafloor. This study explores, in a high-resolution model, whether data from Argo floats, autonomous drifters collecting hydrographic profiles, can be used to monitor the AMOC cost-effectively with the help of Machine Learning. Results suggest that Argo floats can extend AMOC monitoring beyond current fixed arrays.
Peter Van Katwyk, Baylor Fox-Kemper, Sophie Nowicki, Hélène Seroussi, and Karianne J. Bergen
EGUsphere, https://doi.org/10.5194/egusphere-2025-4914, https://doi.org/10.5194/egusphere-2025-4914, 2025
Short summary
Short summary
We developed ISEFlow, a new climate emulator model that predicts how melting ice sheets in Greenland and Antarctica will contribute to future sea levels. Unlike past tools, it uses advanced machine learning to capture complex ice processes, distinguish between different greenhouse gas scenarios, and provide clearer estimates of uncertainty. This makes sea level projections more accurate and reliable, helping scientists and policymakers better plan for climate risks.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
Geosci. Model Dev., 18, 7735–7761, https://doi.org/10.5194/gmd-18-7735-2025, https://doi.org/10.5194/gmd-18-7735-2025, 2025
Short summary
Short summary
The Next Generation of Earth Modeling Systems project (nextGEMS) developed two Earth system models that use horizontal grid spacing of 10 km and finer, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS simulated the Earth System climate over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Tobias Schulzki, Franziska U. Schwarzkopf, and Arne Biastoch
Ocean Sci., 21, 2481–2504, https://doi.org/10.5194/os-21-2481-2025, https://doi.org/10.5194/os-21-2481-2025, 2025
Short summary
Short summary
Exceptionally high ocean temperatures can cause long-lasting damage to marine ecosystems. Most existing knowledge about such temperature extremes is focused on near-surface waters, yet ecosystems also thrive at greater depths. In this study, we present a comprehensive analysis of temperature extremes across the entire Atlantic Ocean, from the surface to the seafloor. Our findings underscore the importance of the ocean circulation in driving extreme temperature events.
Kacper Nowak, Sergey Danilov, Vasco Müller, and Caili Liu
Geosci. Model Dev., 18, 6541–6551, https://doi.org/10.5194/gmd-18-6541-2025, https://doi.org/10.5194/gmd-18-6541-2025, 2025
Short summary
Short summary
A new method called coarse-graining scale analysis is gaining traction as an alternative to Fourier analysis. However, it requires data to be on a regular grid. To address this, we present a high-performance Python package of the coarse-graining technique using discrete Laplacians. This method can handle any mesh type and is ideal for processing output directly from unstructured-mesh models. Computation is split into preparation and solving phases, with GPU acceleration ensuring fast processing.
Théo Picard, Chelsey A. Baker, Jonathan Gula, Ronan Fablet, Laurent Mémery, and Richard Lampitt
Biogeosciences, 22, 4309–4331, https://doi.org/10.5194/bg-22-4309-2025, https://doi.org/10.5194/bg-22-4309-2025, 2025
Short summary
Short summary
Ocean sediment traps measure the sequestrated sinking organic carbon. While sinking, the particles are affected by local currents, which presents a challenge with regard to linking the deep flux with the surface. We present a machine learning tool that predicts the source location of the sinking particles based on satellite data. The predictions demonstrate a stronger correlation between surface and deep carbon fluxes, allowing a more comprehensive understanding of the deep carbon sequestration drivers.
Xiaole Li, Zhenya Song, Xiongbo Zheng, Zhanpeng Zhuang, Fangli Qiao, Haibin Zhou, and Mingze Ji
EGUsphere, https://doi.org/10.5194/egusphere-2025-2636, https://doi.org/10.5194/egusphere-2025-2636, 2025
Preprint archived
Short summary
Short summary
Based on the variable-limit integration method, this study developed a novel numerical approach for the thermohaline equations in ocean models. This method significantly enhances the simulation accuracy of temperature and salinity, improves model stability, and better simulates seawater overflow dynamics across steep ridges. The variable-limit integral method designed herein for thermohaline equations can be readily applied to other ocean numerical models.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian L. E. Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Dae-Won Kim, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana N. Loza, Wonsun Park, Woncheol Roh, Dmitry V. Sein, Sahil Sharma, Dmitry Sidorenko, Jun-Hyeok Son, Malte F. Stuecker, Qiang Wang, Gyuseok Yi, Martina Zapponini, Thomas Jung, and Axel Timmermann
Earth Syst. Dynam., 16, 1103–1134, https://doi.org/10.5194/esd-16-1103-2025, https://doi.org/10.5194/esd-16-1103-2025, 2025
Short summary
Short summary
Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere and 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability, and extreme events. The 10-year-long high-resolution simulations for the 2000s, 2030s, 2060s, and 2090s were initialized from a coarser-resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.
Peter Van Katwyk, Baylor Fox-Kemper, Sophie Nowicki, Hélène Seroussi, and Karianne J. Bergen
EGUsphere, https://doi.org/10.5194/egusphere-2025-870, https://doi.org/10.5194/egusphere-2025-870, 2025
Preprint archived
Short summary
Short summary
We present ISEFlow, a machine learning emulator that predicts how the melting of the Antarctic and Greenland ice sheets will contribute to sea level. ISEFlow is fast and accurate, allowing scientists to explore different climate scenarios with greater confidence. ISEFlow distinguishes between high and low emissions scenarios, helping us understand how today’s choices impact future sea levels. ISEFlow supports more reliable climate predictions and helps scientists study the future of ice sheets.
Léo C. Aroucha, Joke F. Lübbecke, Peter Brandt, Franziska U. Schwarzkopf, and Arne Biastoch
Ocean Sci., 21, 661–678, https://doi.org/10.5194/os-21-661-2025, https://doi.org/10.5194/os-21-661-2025, 2025
Short summary
Short summary
The west African coastal region sustains highly productive fisheries and marine ecosystems influenced by sea surface temperature. We use oceanic models to show that the freshwater input from land to ocean strengthens a surface northward (southward) coastal current north (south) of the Congo River mouth, promoting a transfer of cooler (warmer) waters to north (south) of the Congo discharge location. We highlight the significant impact of river discharge on ocean temperatures and circulation.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
Short summary
Short summary
Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Michel Tchilibou, Loren Carrere, Florent Lyard, Clément Ubelmann, Gérald Dibarboure, Edward D. Zaron, and Brian K. Arbic
Ocean Sci., 21, 325–342, https://doi.org/10.5194/os-21-325-2025, https://doi.org/10.5194/os-21-325-2025, 2025
Short summary
Short summary
Sea level observations along the swaths of the new SWOT (Surface Water and Ocean Topography) mission were used to characterize internal tides at three semidiurnal frequencies off the Amazon shelf in the tropical Atlantic during the SWOT calibration/validation period. The atlases were derived using harmonic analysis and principal component analysis. The SWOT-derived internal tide atlas outperforms the reference atlas previously used to correct SWOT observations.
Hendrik Großelindemann, Frederic S. Castruccio, Gokhan Danabasoglu, and Arne Biastoch
Ocean Sci., 21, 93–112, https://doi.org/10.5194/os-21-93-2025, https://doi.org/10.5194/os-21-93-2025, 2025
Short summary
Short summary
This study investigates the Agulhas Leakage and examines its role in the global ocean circulation. It utilises a high-resolution Earth system model and a preindustrial climate to look at the response of the Agulhas Leakage to the wind field and the Atlantic Meridional Overturning Circulation (AMOC) and its evolution under climate change. The Agulhas Leakage could influence the stability of the AMOC, whose possible collapse would impact the climate in the Northern Hemisphere.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
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
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Théo Picard, Jonathan Gula, Ronan Fablet, Jeremy Collin, and Laurent Mémery
Ocean Sci., 20, 1149–1165, https://doi.org/10.5194/os-20-1149-2024, https://doi.org/10.5194/os-20-1149-2024, 2024
Short summary
Short summary
The biological carbon pump plays a key role in the climate system. Plankton absorb and transform CO2 into organic carbon, forming particles that sink to the ocean floor. Sediment traps catch these particles and measure the carbon stored in the abyss. However, the particles' surface origin is unknown as ocean currents alter their paths. Here, we train an AI model to predict the origin of these particles. This new tool enables a better link between deep-ocean observations and satellite images.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, https://doi.org/10.5194/gmd-17-4095-2024, 2024
Short summary
Short summary
Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Kristin Burmeister, Franziska U. Schwarzkopf, Willi Rath, Arne Biastoch, Peter Brandt, Joke F. Lübbecke, and Mark Inall
Ocean Sci., 20, 307–339, https://doi.org/10.5194/os-20-307-2024, https://doi.org/10.5194/os-20-307-2024, 2024
Short summary
Short summary
We apply two different forcing products to a high-resolution ocean model to investigate their impact on the simulated upper-current field in the tropical Atlantic. Where possible, we compare the simulated results to long-term observations. We find large discrepancies between the two simulations regarding the wind and current fields. We propose that long-term observations, once they have reached a critical length, need to be used to test the quality of wind-driven simulations.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
Short summary
Short summary
We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Arne Bendinger, Sophie Cravatte, Lionel Gourdeau, Laurent Brodeau, Aurélie Albert, Michel Tchilibou, Florent Lyard, and Clément Vic
Ocean Sci., 19, 1315–1338, https://doi.org/10.5194/os-19-1315-2023, https://doi.org/10.5194/os-19-1315-2023, 2023
Short summary
Short summary
New Caledonia is a hot spot of internal-tide generation due to complex bathymetry. Regional modeling quantifies the coherent internal tide and shows that most energy is converted in shallow waters and on very steep slopes. The region is a challenge for observability of balanced dynamics due to strong internal-tide sea surface height (SSH) signatures at similar wavelengths. Correcting the SSH for the coherent internal tide may increase the observability of balanced motion to < 100 km.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Gaspard Geoffroy, Jonas Nycander, Maarten C. Buijsman, Jay F. Shriver, and Brian K. Arbic
Ocean Sci., 19, 811–835, https://doi.org/10.5194/os-19-811-2023, https://doi.org/10.5194/os-19-811-2023, 2023
Short summary
Short summary
The ocean state is sensitive to the mixing originating from internal tides (ITs). To date, our knowledge of the magnitude and spatial distribution of this mixing mostly relies on uncertain modeling. Here, we use novel observations from autonomous floats to validate the spatial variability in the semidiurnal IT in a realistic ocean simulation. The numerical simulation is found to correctly reproduce the main spatial patterns of the observed tidal energy but to be biased low at the global scale.
Qi Shu, Qiang Wang, Chuncheng Guo, Zhenya Song, Shizhu Wang, Yan He, and Fangli Qiao
Geosci. Model Dev., 16, 2539–2563, https://doi.org/10.5194/gmd-16-2539-2023, https://doi.org/10.5194/gmd-16-2539-2023, 2023
Short summary
Short summary
Ocean models are often used for scientific studies on the Arctic Ocean. Here the Arctic Ocean simulations by state-of-the-art global ocean–sea-ice models participating in the Ocean Model Intercomparison Project (OMIP) were evaluated. The simulations on Arctic Ocean hydrography, freshwater content, stratification, sea surface height, and gateway transports were assessed and the common biases were detected. The simulations forced by different atmospheric forcing were also evaluated.
Tongya Liu and Ryan Abernathey
Earth Syst. Sci. Data, 15, 1765–1778, https://doi.org/10.5194/essd-15-1765-2023, https://doi.org/10.5194/essd-15-1765-2023, 2023
Short summary
Short summary
Nearly all existing datasets of mesoscale eddies are based on the Eulerian method because of its operational simplicity. Using satellite observations and a Lagrangian method, we present a global Lagrangian eddy dataset (GLED v1.0). We conduct the statistical comparison between two types of eddies and the dataset validation. Our dataset offers relief from dilemma that the Eulerian eddy dataset is nearly the only option for studying mesoscale eddies.
Shanice T. Bailey, C. Spencer Jones, Ryan P. Abernathey, Arnold L. Gordon, and Xiaojun Yuan
Ocean Sci., 19, 381–402, https://doi.org/10.5194/os-19-381-2023, https://doi.org/10.5194/os-19-381-2023, 2023
Short summary
Short summary
This study explores the variability of water mass transformation within the Weddell Gyre (WG). The WG is the largest source of Antarctic Bottom Water (AABW). Changes to our climate can modify the mechanisms that transform waters to become AABW. In this study, we computed water mass transformation volume budgets by using three ocean models and a mathematical framework developed by Walin. Out of the three models, we found one to be most useful in studying the interannual variability of AABW.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev., 16, 1755–1777, https://doi.org/10.5194/gmd-16-1755-2023, https://doi.org/10.5194/gmd-16-1755-2023, 2023
Short summary
Short summary
A new global surface-wave–tide–circulation coupled ocean model (FIO-COM32) with a resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are shown to be important contributors to the significant improvements in FIO-COM32 simulations. It is time to merge these separated model components (surface waves, tidal currents and ocean circulation) and start a new generation of ocean model development.
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, https://doi.org/10.5194/gmd-16-1297-2023, 2023
Short summary
Short summary
Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding and erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this paper we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tide–ice interactions.
Torge Martin and Arne Biastoch
Ocean Sci., 19, 141–167, https://doi.org/10.5194/os-19-141-2023, https://doi.org/10.5194/os-19-141-2023, 2023
Short summary
Short summary
How is the ocean affected by continued Greenland Ice Sheet mass loss? We show in a systematic set of model experiments that atmospheric feedback needs to be accounted for as the large-scale ocean circulation is more than twice as sensitive to the meltwater otherwise. Coastal winds, boundary currents, and ocean eddies play a key role in redistributing the meltwater. Eddy paramterization helps the coarse simulation to perform better in the Labrador Sea but not in the North Atlantic Current region.
Stephanie Leroux, Jean-Michel Brankart, Aurélie Albert, Laurent Brodeau, Jean-Marc Molines, Quentin Jamet, Julien Le Sommer, Thierry Penduff, and Pierre Brasseur
Ocean Sci., 18, 1619–1644, https://doi.org/10.5194/os-18-1619-2022, https://doi.org/10.5194/os-18-1619-2022, 2022
Short summary
Short summary
The goal of the study is to evaluate the predictability of the ocean circulation
at a kilometric scale, in order to anticipate the requirements of the future operational forecasting systems. For that purpose, ensemble experiments have been performed with a regional model for the Western Mediterranean (at 1/60° horizontal resolution). From these ensemble experiments, we show that it is possible to compute targeted predictability scores, which depend on initial and model uncertainties.
Sergei Kirillov, Igor Dmitrenko, David G. Babb, Jens K. Ehn, Nikolay Koldunov, Søren Rysgaard, David Jensen, and David G. Barber
Ocean Sci., 18, 1535–1557, https://doi.org/10.5194/os-18-1535-2022, https://doi.org/10.5194/os-18-1535-2022, 2022
Short summary
Short summary
The sea ice bridge usually forms during winter in Nares Strait and prevents ice drifting south. However, this bridge has recently become unstable, and in this study we investigate the role of oceanic heat flux in this decline. Using satellite data, we identify areas where sea ice is relatively thin and further attribute those areas to the heat fluxes from the warm subsurface water masses. We also discuss the potential role of such an impact on ice bridge instability and earlier ice break up.
Alan D. Fox, Patricia Handmann, Christina Schmidt, Neil Fraser, Siren Rühs, Alejandra Sanchez-Franks, Torge Martin, Marilena Oltmanns, Clare Johnson, Willi Rath, N. Penny Holliday, Arne Biastoch, Stuart A. Cunningham, and Igor Yashayaev
Ocean Sci., 18, 1507–1533, https://doi.org/10.5194/os-18-1507-2022, https://doi.org/10.5194/os-18-1507-2022, 2022
Short summary
Short summary
Observations of the eastern subpolar North Atlantic in the 2010s show exceptional freshening and cooling of the upper ocean, peaking in 2016 with the lowest salinities recorded for 120 years. Using results from a high-resolution ocean model, supported by observations, we propose that the leading cause is reduced surface cooling over the preceding decade in the Labrador Sea, leading to increased outflow of less dense water and so to freshening and cooling of the eastern subpolar North Atlantic.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
Short summary
Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Jörg Fröhle, Patricia V. K. Handmann, and Arne Biastoch
Ocean Sci., 18, 1431–1450, https://doi.org/10.5194/os-18-1431-2022, https://doi.org/10.5194/os-18-1431-2022, 2022
Short summary
Short summary
Three deep-water masses pass the southern exit of the Labrador Sea. Usually they are defined by explicit density intervals linked to the formation region. We evaluate this relation in an ocean model by backtracking the paths the water follows for 40 years: 48 % densify without contact to the atmosphere, 24 % densify in contact with the atmosphere, and 19 % are from the Nordic Seas. All three contribute to a similar density range at 53° N with weak specific formation location characteristics.
Giulia Bonino, Doroteaciro Iovino, Laurent Brodeau, and Simona Masina
Geosci. Model Dev., 15, 6873–6889, https://doi.org/10.5194/gmd-15-6873-2022, https://doi.org/10.5194/gmd-15-6873-2022, 2022
Short summary
Short summary
The sea surface temperature (SST) is highly influenced by the transfer of energy driven by turbulent air–sea fluxes (TASFs). In the NEMO ocean general circulation model, TASFs are computed by means of bulk formulas. Bulk formulas require the choice of a given bulk parameterization, which influences the magnitudes of the TASFs. Our results show that parameterization-related SST differences are primarily sensitive to the wind stress differences across parameterizations.
Gustavo M. Marques, Nora Loose, Elizabeth Yankovsky, Jacob M. Steinberg, Chiung-Yin Chang, Neeraja Bhamidipati, Alistair Adcroft, Baylor Fox-Kemper, Stephen M. Griffies, Robert W. Hallberg, Malte F. Jansen, Hemant Khatri, and Laure Zanna
Geosci. Model Dev., 15, 6567–6579, https://doi.org/10.5194/gmd-15-6567-2022, https://doi.org/10.5194/gmd-15-6567-2022, 2022
Short summary
Short summary
We present an idealized ocean model configuration and a set of simulations performed using varying horizontal grid spacing. While the model domain is idealized, it resembles important geometric features of the Atlantic and Southern oceans. The simulations described here serve as a framework to effectively study mesoscale eddy dynamics, to investigate the effect of mesoscale eddies on the large-scale dynamics, and to test and evaluate eddy parameterizations.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
Short summary
Short summary
We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Yuejin Ye, Zhenya Song, Shengchang Zhou, Yao Liu, Qi Shu, Bingzhuo Wang, Weiguo Liu, Fangli Qiao, and Lanning Wang
Geosci. Model Dev., 15, 5739–5756, https://doi.org/10.5194/gmd-15-5739-2022, https://doi.org/10.5194/gmd-15-5739-2022, 2022
Short summary
Short summary
The swNEMO_v4.0 is developed with ultrahigh scalability through the concepts of hardware–software co-design based on the characteristics of the new Sunway supercomputer and NEMO4. Three breakthroughs, including an adaptive four-level parallelization design, many-core optimization and mixed-precision optimization, are designed. The simulations achieve 71.48 %, 83.40 % and 99.29 % parallel efficiency with resolutions of 2 km, 1 km and 500 m using 27 988 480 cores, respectively.
Jens Zinke, Takaaki K. Watanabe, Siren Rühs, Miriam Pfeiffer, Stefan Grab, Dieter Garbe-Schönberg, and Arne Biastoch
Clim. Past, 18, 1453–1474, https://doi.org/10.5194/cp-18-1453-2022, https://doi.org/10.5194/cp-18-1453-2022, 2022
Short summary
Short summary
Salinity is an important and integrative measure of changes to the water cycle steered by changes to the balance between rainfall and evaporation and by vertical and horizontal movements of water parcels by ocean currents. However, salinity measurements in our oceans are extremely sparse. To fill this gap, we have developed a 334-year coral record of seawater oxygen isotopes that reflects salinity changes in the globally important Agulhas Current system and reveals its main oceanic drivers.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
Short summary
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Ehud Strobach, Andrea Molod, Donifan Barahona, Atanas Trayanov, Dimitris Menemenlis, and Gael Forget
Geosci. Model Dev., 15, 2309–2324, https://doi.org/10.5194/gmd-15-2309-2022, https://doi.org/10.5194/gmd-15-2309-2022, 2022
Short summary
Short summary
The Green's functions methodology offers a systematic, easy-to-implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. Herein, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-52, https://doi.org/10.5194/gmd-2022-52, 2022
Revised manuscript not accepted
Short summary
Short summary
A new global surface wave-tide-circulation coupled ocean model FIO-COM32 with resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are proved to be important contributors to the significant improvements of FIO-COM32 simulations. It should be the time to merge these separated model components (surface wave, tidal current and ocean circulation) for new generation ocean model development.
Ioana Ivanciu, Katja Matthes, Arne Biastoch, Sebastian Wahl, and Jan Harlaß
Weather Clim. Dynam., 3, 139–171, https://doi.org/10.5194/wcd-3-139-2022, https://doi.org/10.5194/wcd-3-139-2022, 2022
Short summary
Short summary
Greenhouse gas concentrations continue to increase, while the Antarctic ozone hole is expected to recover during the twenty-first century. We separate the effects of ozone recovery and of greenhouse gases on the Southern Hemisphere atmospheric and oceanic circulation, and we find that ozone recovery is generally reducing the impact of greenhouse gases, with the exception of certain regions of the stratosphere during spring, where the two effects reinforce each other.
Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, Qiang Wang, Nikolay Koldunov, Dmitry Sein, and Thomas Jung
Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, https://doi.org/10.5194/gmd-15-335-2022, 2022
Short summary
Short summary
Structured-mesh ocean models are still the most mature in terms of functionality due to their long development history. However, unstructured-mesh ocean models have acquired new features and caught up in their functionality. This paper continues the work by Scholz et al. (2019) of documenting the features available in FESOM2.0. It focuses on the following two aspects: (i) partial bottom cells and embedded sea ice and (ii) dealing with mixing parameterisations enabled by using the CVMix package.
Vera Fofonova, Tuomas Kärnä, Knut Klingbeil, Alexey Androsov, Ivan Kuznetsov, Dmitry Sidorenko, Sergey Danilov, Hans Burchard, and Karen Helen Wiltshire
Geosci. Model Dev., 14, 6945–6975, https://doi.org/10.5194/gmd-14-6945-2021, https://doi.org/10.5194/gmd-14-6945-2021, 2021
Short summary
Short summary
We present a test case of river plume spreading to evaluate coastal ocean models. Our test case reveals the level of numerical mixing (due to parameterizations used and numerical treatment of processes in the model) and the ability of models to reproduce complex dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of applied model architecture or numerical grid than it does on the type of advection scheme.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
Short summary
Short summary
Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Arne Biastoch, Franziska U. Schwarzkopf, Klaus Getzlaff, Siren Rühs, Torge Martin, Markus Scheinert, Tobias Schulzki, Patricia Handmann, Rebecca Hummels, and Claus W. Böning
Ocean Sci., 17, 1177–1211, https://doi.org/10.5194/os-17-1177-2021, https://doi.org/10.5194/os-17-1177-2021, 2021
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) quantifies the impact of the ocean on climate and climate change. Here we show that a high-resolution ocean model is able to realistically simulate ocean currents. While the mean representation of the AMOC depends on choices made for the model and on the atmospheric forcing, the temporal variability is quite robust. Comparing the ocean model with ocean observations, we able to identify that the AMOC has declined over the past two decades.
Cited articles
Abernathey, R. P.: Petabytes of Ocean Data, Part I: NASA ECCO Data
Portal,
https://medium.com/pangeo/petabytes-of-ocean-data-part-1-nasa-ecco-data-portal-81e3c5e077be (last access: 8 July 2022),
2019. a, b
Abernathey, R. P.:
fastjmd95: Numba implementation of Jackett &
McDougall (1995) ocean equation of state, Zenodo [code], https://doi.org/10.5281/zenodo.4498376,
2020. a
Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone,
T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie,
T. A., Robinson, N. H., and Signell, R. P.: Cloud-Native Repositories for Big Scientific Data, Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/MCSE.2021.3059437,
2021a. a, b
Abernathey, R. P., Busecke, J., Smith, T., et al.:
xgcm: General Circulation Model Postprocessing with
xarray, Zenodo [code], https://doi.org/10.5281/zenodo.3634752, 2021b. a, b
Abernathey, R. P., Dougie, S., Nicholas, T., Bourbeau, J., Joseph, G., Yunyi,
Y., Bailey, S., Bell, R., and Spring, A.:
xhistogram: Fast, flexible,
label-aware histograms for numpy and xarray, Zenodo [code], https://doi.org/10.5281/zenodo.5757149,
2021c. a
Abernathey, R. P., Dussin, R., Smith, T., et al.:
xmitgcm: Read
MITgcm mds binary files into xarray, Zenodo [code], https://doi.org/10.5281/zenodo.596253,
2021d. a
Ajayi, A., Le Sommer, J., Chassignet, E., Molines, J.-M., Xu, X., Albert, A.,
and Dewar, W. K.: Diagnosing cross-scale kinetic energy exchanges from two
submesoscale permitting ocean models, J. Adv. Model. Earth
Sy., 13, 6, https://doi.org/10.1029/2019MS001923, 2021. a, b
Arbic, B. K., Garner, S. T., Hallberg, R. W., and Simmons, H. L.: The accuracy
of surface elevations in forward global barotropic and baroclinic tide
models, Deep-Sea Res. Pt. II, 51,
3069–3101, https://doi.org/10.1016/j.dsr2.2004.09.014, 2004. a
Arbic, B. K., Polzin, K. L., Scott, R. B., Richman, J. G., and Shriver, J. F.:
On eddy viscosity, energy cascades, and the horizontal resolution of gridded
satellite altimeter products, J. Phys. Oceanogr., 43, 283–300,
https://doi.org/10.1175/JPO-D-11-0240.1, 2013. a
Arbic, B. K., Alford, M., Ansong, J., Buijsman, M., Ciotti, R., Farrar, J.,
Hallberg, R., Henze, C., Hill, C., Luecke, C., Menemenlis, D., Metzger, E.,
Müeller, M., Nelson, A., Nelson, B., Ngodock, H., Ponte, R., Richman,
J., Savage, A., and Zhao, Z.: A primer on global internal tide and internal
gravity wave continuum modeling in HYCOM and MITgcm, in: New Frontiers in
Operational Oceanography, edited by: Chassignet, E., Pascual, A., Tintoré,
J., and Verron, J., GODAE Ocean View, 307–392,
https://doi.org/10.17125/gov2018.ch13, 2018. a, b, c
Bachman, S. D., Fox-Kemper, B., Taylor, J. R., and Thomas, L. N.:
Parameterization of Frontal Symmetric Instabilities. I: Theory for Resolved
Fronts, Ocean Model., 109, 72–95, https://doi.org/10.1016/j.ocemod.2016.12.003,
2017. a
Barham, W., Bachman, S., and Grooms, I.: Some effects of horizontal
discretization on linear baroclinic and symmetric instabilities, Ocean Model., 125, 106–116, https://doi.org/10.1016/j.ocemod.2018.03.004, 2018. a
Barkan, R., Srinivasan, K., Yang, L., McWilliams, J. C., Gula, J., and Vic, C.:
Oceanic mesoscale eddy depletion catalyzed by internal waves, Geophys. Res. Lett., 48, e2021GL094376, https://doi.org/10.1029/2021GL094376, 2021. a, b
Beg, M., Taka, J., Kluyver, T., Konovalov, A., Ragan-Kelley, M., Thiéry,
N. M., and Fangohr, H.: Using Jupyter for reproducible scientific workflows,
Comput. Sci. Eng., 23, 36–46,
https://doi.org/10.1109/MCSE.2021.3052101, 2021. a
Bleck, R.: An oceanic general circulation model framed in hybrid
isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88,
https://doi.org/10.1016/S1463-5003(01)00012-9, 2002. a
Boccaletti, G., Ferrari, R., and Fox-Kemper, B.: Mixed layer instabilities and
restratification, J. Phys. Oceanogr., 37, 2228–2250,
https://doi.org/10.1175/JPO3101.1, 2007. a, b, c
Bodner, A. S. and Fox-Kemper, B.: A breakdown in potential vorticity estimation
delineates the submesoscale-to-turbulence boundary in large eddy simulations,
J. Adv. Model. Earth Sy., 12, e2020MS002049,
https://doi.org/10.1029/2020MS002049, 2020. a
Brannigan, L., Marshall, D. P., Naveira Garabato, A. C., Nurser, A. J. G., and
Kaiser, J.: Submesoscale Instabilities in Mesoscale Eddies, J. Phys. Oceanogr., 47, 3061–3085, https://doi.org/10.1175/jpo-d-16-0178.1, 2017. a
Brodeau, L., Albert, A., and Le Sommer, J.: NEMO-eNATL60 description and
assessment repository, Zenodo [data set], https://doi.org/10.5281/zenodo.4032732, 2020. a, b, c
Brydon, D., Sun, S., and Bleck, R.: A new approximation of the equation of
state for seawater, suitable for numerical ocean models, J. Geophys. Res.-Oceans, 104, 1537–1540, https://doi.org/10.1029/1998JC900059,
1999. a
Buckingham, C. E., Lucas, N., Belcher, S., Rippeth, T., Grant, A., Le Sommer,
J., Ajayi, A. O., and Garabato, A. C. N.: The contribution of surface and
submesoscale processes, J. Adv. Model. Earth Sy., 11, 12,
https://doi.org/10.1029/2019MS001801, 2019. a
Callies, J. and Ferrari, R.: Note on the Rate of Restratification in the
Baroclinic Spindown of Fronts, J. Phys. Oceanogr., 48,
1543–1553, https://doi.org/10.1175/jpo-d-17-0175.1, 2018. a
Calvert, D., Nurser, G., Bell, M. J., and Fox-Kemper, B.: The impact of a
parameterisation of submesoscale mixed layer eddies on mixed layer depths in
the NEMO ocean model, Ocean Model., 154, 101678,
https://doi.org/10.1016/j.ocemod.2020.101678, 2020. a, b
Cao, H., Fox-Kemper, B., and Jing, Z.: Submesoscale Eddies in the Upper Ocean
of the Kuroshio Extension from High-Resolution Simulation: Energy Budget,
J. Phys. Oceanogr., 51, 2181–2201,
https://doi.org/10.1175/JPO-D-20-0267.1, 2021. a
Chassignet, E. P. and Xu, X.: On the Importance of High-Resolution in
Large-Scale Ocean Models, Adv. Atmos. Sci., 38, 1–14,
https://doi.org/10.1007/s00376-021-0385-7, 2021. a, b, c, d
Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings,
J. A., Halliwell, G. R., Bleck, R., Baraille, R., Wallcraft, A. J., Lozano,
C., Tolman, H. L., Srinivasan, A., Hankin, S., Cornillon, P., Weisberg, R., Barth, A., He, R., Werner, F., and Wilkin, J.: US GODAE: Global ocean prediction with the HYbrid
Coordinate Ocean Model (HYCOM), Oceanography, 22, 64–75, 2009. a
Chassignet, E. P., Yeager, S. G., Fox-Kemper, B., Bozec, A., Castruccio, F., Danabasoglu, G., Horvat, C., Kim, W. M., Koldunov, N., Li, Y., Lin, P., Liu, H., Sein, D. V., Sidorenko, D., Wang, Q., and Xu, X.: Impact of horizontal resolution on global ocean–sea ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, 2020. a, b
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of
nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216,
https://doi.org/10.1016/j.pocean.2011.01.002, 2011. a
Danilov, S., Sidorenko, D., Wang, Q., and Jung, T.: The Finite-volumE Sea ice–Ocean Model (FESOM2), Geosci. Model Dev., 10, 765–789, https://doi.org/10.5194/gmd-10-765-2017, 2017. a
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., and Iudicone,
D.: Mixed layer depth over the global ocean: An examination of profile data
and a profile-based climatology, J. Geophys. Res.-Oceans,
109, C12003, https://doi.org/10.1029/2004JC002378, 2004. a, b
Dong, J., Fox-Kemper, B., Zhang, H., and Dong, C.: The scale of submesoscale
baroclinic instability globally, J. Phys. Oceanogr., 50,
2649–2667, https://doi.org/10.1175/JPO-D-20-0043.1, 2020. a, b
Dong, J., Fox-Kemper, B., Zhu, J., and Dong, C.: Application of symmetric
instability parameterization in the Coastal and Regional Ocean Community
Model (CROCO), J. Adv. Model. Earth Sy., 13,
e2020MS002302, https://doi.org/10.1029/2020MS002302, 2021. a
Ducousso, N., Le Sommer, J., Molines, J.-M., and Bell, M.: Impact of the
“Symmetric Instability of the Computational Kind” at mesoscale-and
submesoscale-permitting resolutions, Ocean Model., 120, 18–26,
https://doi.org/10.1016/j.ocemod.2017.10.006, 2017. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fangohr, H., Beg, M., Bergemann, M., Bondar, V., Brockhauser, S., Carinan, C.,
Costa, R., Fortmann, C., Marsa, D. F., Giovanetti, G., Göries, D.,
Hauf, S.,
Hickin, D. G.,
Jarosiewicz, T.,
Kamil, E.,
Karnevskiy, M.,
Kirienko, Y.,
Klimovskaia, A.,
Kluyver, T. A.,
Kuster, M.,
Le Guyader, L.,
Madsen, A.,
Maia, L. G.,
Mamchyk, D.,
Mercadier, L.,
Michelat, T.,
Möller, J.,
Mohacsi, I.,
Parenti, A.,
Reiser, M.,
Rosca, R.
Rueck, D. B .,
Rüter, T.,
Santos, H.,
Schaffer, R.,
Scherz A.,
Scholz, M.,
Silenzi, A.,
Spirzewski, M.,
Sztuk-Dambietz, J.,
Szuba, J.,
Trojanowski, S.,
Wrona, K.
Yaroslavtsev, A. A.,
Zhu, J.,
Rod, T. H.,
Selknaes, J. R.,
Taylor, J. W.,
Copenhagen, D. A.,
Campbell, A.,
Götz, J., and
Kieffer, J.: Data
exploration and analysis with jupyter notebooks, in: 17th Biennial
International Conference on Accelerator and Large Experimental Physics
Control Systems, TALK-2020-009, https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPR02,
2019. a
Fernandes, F.:
python-seawater: Python re-write of the CSIRO seawater
toolbox SEAWATER-3.3 for calculating the properties of sea water, Zenodo [code],
https://doi.org/10.5281/zenodo.11395, 2014. a
Firing, E., Fernandes, F., Barna, A., and Abernathey, R. P.:
GSW-python:
Python implementation of the Thermodynamic Equation of Seawater 2010
(TEOS-10), Zenodo [code], https://doi.org/10.5281/zenodo.5214122, 2021. a
Flexas, M. M., Thompson, A. F., Torres, H. S., Klein, P., Farrar, J. T., Zhang,
H., and Menemenlis, D.: Global estimates of the energy transfer from the wind
to the ocean, with emphasis on near-inertial oscillations, J. Geophys. Res.-Oceans, 124, 5723–5746, https://doi.org/10.1029/2018JC014453,
2019. a
Fox-Kemper, B. and Ferrari, R.: Parameterization of mixed layer eddies. Part
II: Prognosis and impact, J. Phys. Oceanogr., 38, 1166–1179,
https://doi.org/10.1175/2007JPO3788.1, 2008. a, b
Fox-Kemper, B., Danabasoglu, G., Ferrari, R., Griffies, S., Hallberg, R.,
Holland, M., Maltrud, M., Peacock, S., and Samuels, B.: Parameterization of
mixed layer eddies. Part III: Implementation and impact in global ocean
climate simulations, Ocean Model., 39, 61–78,
https://doi.org/10.1016/j.ocemod.2010.09.002, 2011. a, b, c, d
Garrett, C. and Munk, W.: Space-time scales of internal waves: A progress
report, J. Geophys. Res., 80, 291–297,
https://doi.org/10.1029/JC080i003p00291, 1975. a
Gomez-Navarro, L., Fablet, R., Mason, E., Pascual, A., Mourre, B., Cosme, E.,
and Le Sommer, J.: SWOT spatial scales in the western Mediterranean sea
derived from pseudo-observations and an Ad Hoc filtering, Remote Sens., 10,
599, https://doi.org/10.3390/rs10040599, 2018. a
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A.,
Fukimori, I., Gomez, N., Kopp, R. E., Landerer, F., Cozannet, G. L., et al.:
Concepts and terminology for sea level: Mean, variability and change, both
local and global, Surv. Geophys., 40, 1251–1289,
https://doi.org/10.1007/s10712-019-09525-z, 2019. a
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016. a, b, c
Grooms, I., Loose, N., Abernathey, R., Steinberg, J., Bachman, S. D., Marques,
G., Guillaumin, A. P., and Yankovsky, E.: Diffusion-based smoothers for
spatial filtering of gridded geophysical data, J. Adv.
Model. Earth Sy., 13, e2021MS002552, https://doi.org/10.1029/2021MS002552, 2021. a, b
Gula, J., Theetten, S., Cambon, G., and Roullet, G.: Description of the
GIGATL simulations, Zenodo [data set], https://doi.org/10.5281/zenodo.4948523, 2021. a, b, c
Hallberg, R.: Using a resolution function to regulate parameterizations of
oceanic mesoscale eddy effects, Ocean Model., 72, 92–103,
https://doi.org/10.1016/j.ocemod.2013.08.007, 2013. a
Hamlington, P. E., Van Roekel, L. P., Fox-Kemper, B., Julien, K., and Chini,
G. P.: Langmuir–submesoscale interactions: Descriptive analysis of
multiscale frontal spindown simulations, J. Phys. Oceanogr.,
44, 2249–2272, https://doi.org/10.1175/JPO-D-13-0139.1, 2014. a
Holte, J., Talley, L. D., Gilson, J., and Roemmich, D.: An Argo mixed layer
climatology and database, Geophys. Res. Lett., 44, 5618–5626,
https://doi.org/10.1002/2017GL073426, 2017. a
Huang, C. J., Qiao, F., and Dai, D.: Evaluating CMIP5 simulations of mixed
layer depth during summer, J. Geophys. Res.-Oceans, 119,
2568–2582, https://doi.org/10.1002/2013JC009535, 2014. a
Jackett, D. R. and McDougall, T. J.: Minimal adjustment of hydrographic
profiles to achieve static stability, J. Atmos. Ocean.
Tech., 12, 381–389,
https://doi.org/10.1175/1520-0426(1995)012<0381:MAOHPT>2.0.CO;2, 1995. a
Jing, Z., Wang, S., Wu, L., Chang, P., Zhang, Q., Sun, B., Ma, X., Qiu, B.,
Small, J., Jin, F.-F., Chen, Z., Gan, B., Yang, Y.,Yang, H., and Wan, X.: Maintenance of mid-latitude oceanic fronts by
mesoscale eddies, Sci. Adv., 6, eaba7880,
https://doi.org/10.1126/sciadv.aba7880, 2020. a
Jing, Z., Fox-Kemper, B., Cao, H., Zheng, R., and Du, Y.: Submesoscale fronts
and their dynamical processes associated with symmetric instability in the
northwest Pacific subtropical Ocean, J. Phys. Oceanogr., 51,
83–100, https://doi.org/10.1175/JPO-D-20-0076.1, 2021. a
Johnson, L., Lee, C. M., and D’Asaro, E. A.: Global estimates of lateral
springtime restratification, J. Phys. Oceanogr., 46,
1555–1573, https://doi.org/10.1175/JPO-D-15-0163.1, 2016. a, b
Khatri, H., Griffies, S. M., Uchida, T., Wang, H., and Menemenlis, D.: Role of
mixed-layer instabilities in the seasonal evolution of eddy kinetic energy
spectra in a global submesoscale permitting simulation, Geophys. Res.
Lett., 48, e2021GL094777, https://doi.org/10.1029/2021GL094777, 2021. a, b
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A
review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94rg01872, 1994. a
Le Corre, M., Gula, J., and Tréguier, A.-M.: Barotropic vorticity balance of the North Atlantic subpolar gyre in an eddy-resolving model, Ocean Sci., 16, 451–468, https://doi.org/10.5194/os-16-451-2020, 2020. a
Le Guillou, F., Lahaye, N., Ubelmann, C., Metref, S., Cosme, E., Ponte, A.,
Le Sommer, J., Blayo, E., and Vidard, A.: Joint estimation of balanced
motions and internal tides from future wide-swath altimetry, J.
Adv. Model. Earth Sy., 13, e2021MS002613,
https://doi.org/10.1029/2021MS002613, 2021. a
Lévy, M., Franks, P. J., and Smith, K. S.: The role of submesoscale
currents in structuring marine ecosystems, Nat. Commun., 9, 1–16,
https://doi.org/10.1038/s41467-018-07059-3, 2018. a
Li, J., Dong, J., Yang, Q., and Zhang, X.: Spatial-temporal variability of
submesoscale currents in the South China Sea, J. Oceanol.
Limnol., 37, 474–485, https://doi.org/10.1007/s00343-019-8077-1, 2019. a, b
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri,
D., Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Christian, E.,
Simona, F.,
Tim, G.,
James, H.,
Doroteaciro, I.
Dan, L.,
Claire, L.,
Tomas, L.,
Nicolas, M.,
Sébastien, M.,
Silvia, M.,
Julien, P.,
Clément, R.,
Dave, S.,
Andrea, S., and
Martin, V.:
NEMO ocean engine, Zenodo [code], https://doi.org/10.5281/zenodo.3878122, 2019. a
Mahadevan, A., D'asaro, E., Lee, C., and Perry, M. J.: Eddy-driven
stratification initiates North Atlantic spring phytoplankton blooms, Science,
337, 54–58, https://doi.org/10.1126/science.1218740, 2012. a
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A
finite-volume, incompressible Navier Stokes model for studies of the ocean on
parallel computers, J. Geophys. Res.-Oceans, 102, 5753–5766,
https://doi.org/10.1029/96JC02775, 1997. a
McWilliams, J. C.: Submesoscale currents in the ocean, Proceedings of the Royal
Society A: Mathematical, Phys. Eng. Sci., 472, 20160117,
https://doi.org/10.1098/rspa.2016.0117, 2016. a, b
Menemenlis, D., Campin, J.-M., Heimbach, P., Hill, C., Lee, T., Nguyen, A.,
Schodlok, M., and Zhang, H.: ECCO2: High resolution global ocean and sea ice
data synthesis, Mercator Ocean Quarterly Newsletter, 31, 13–21, 2008. a
Mensa, J. A., Garraffo, Z., Griffa, A., Özgökmen, T. M., Haza, A., and
Veneziani, M.: Seasonality of the submesoscale dynamics in the Gulf Stream
region, Ocean Dynam., 63, 923–941, https://doi.org/10.1007/s10236-013-0633-1, 2013. a, b, c
Met Office: Cartopy: a cartographic python library with a Matplotlib
interface, Exeter, Devon, https://scitools.org.uk/cartopy (last access: 8 July 2022),
2010–2015. a
Metref, S., Cosme, E., Le Guillou, F., Le Sommer, J., Brankart, J.-M., and
Verron, J.: Wide-swath altimetric satellite data assimilation with
correlated-error reduction, Front. Marine Sci., 6, 822,
https://doi.org/10.3389/fmars.2019.00822, 2020. a
Miles, A., Kirkham, J., Durant, M., et al.:
zarr: A format for the
storage of chunked, compressed, N-dimensional arrays, Zenodo [code],
https://doi.org/10.5281/zenodo.3773450, 2020. a
Morrow, R., Fu, L.-L., Ardhuin, F., Benkiran, M., Chapron, B., Cosme, E.,
d'Ovidio, F., Farrar, J. T., Gille, S. T., Lapeyre, G., Traon, P.-Y. L.,
Pascual, A., Ponte, A., Qiu, B., Rascle, N., Ubelmann, C., Wang, J., and
Zaron, E. D.: Global Observations of Fine-Scale Ocean Surface Topography With
the Surface Water and Ocean Topography (SWOT) Mission, Front. Marine
Sci., 6, 232, https://doi.org/10.3389/fmars.2019.00232, 2019. a
Richards, K. J., Whitt, D. B., Brett, G., Bryan, F. O., Feloy, K., and Long,
M. C.: The impact of climate change on ocean submesoscale activity, J. Geophys. Res.-Oceans, 126, e2020JC016750,
https://doi.org/10.1029/2020JC016750, 2021. a, b
Rocha, C. B., Chereskin, T. K., Gille, S. T., and Menemenlis, D.: Mesoscale to
submesoscale wavenumber spectra in Drake Passage, J. Phys. Oceanogr., 46, 601–620, https://doi.org/10.1175/JPO-D-15-0087.1, 2016. a, b, c
Savage, A. C., Arbic, B. K., Alford, M. H., Ansong, J. K., Farrar, J. T.,
Menemenlis, D., O'Rourke, A. K., Richman, J. G., Shriver, J. F., Voet, G.,
et al.: Spectral decomposition of internal gravity wave sea surface height in
global models, J. Geophys. Res.-Oceans, 122, 7803–7821,
https://doi.org/10.1002/2017JC013009, 2017a. a, b, c, d, e
Savage, A. C., Arbic, B. K., Richman, J. G., Shriver, J. F., Alford, M. H.,
Buijsman, M. C., Thomas Farrar, J., Sharma, H., Voet, G., Wallcraft, A. J., and Zamudio, L.: Frequency content of sea surface height variability from internal
gravity waves to mesoscale eddies, J. Geophys. Res.-Oceans,
122, 2519–2538, https://doi.org/10.1002/2016JC012331, 2017b. a, b, c
Schaffer, J., Timmermann, R., Arndt, J. E., Kristensen, S. S., Mayer, C., Morlighem, M., and Steinhage, D.: A global, high-resolution data set of ice sheet topography, cavity geometry, and ocean bathymetry, Earth Syst. Sci. Data, 8, 543–557, https://doi.org/10.5194/essd-8-543-2016, 2016. a
Schubert, R., Schwarzkopf, F. U., Baschek, B., and Biastoch, A.: Submesoscale
Impacts on Mesoscale Agulhas Dynamics, J. Adv. Model. Earth
Sy., 11, 18, https://doi.org/10.1029/2019MS001724, 2019. a
Schubert, R., Gula, J., Greatbatch, R. J., Baschek, B., and Biastoch, A.: The
Submesoscale Kinetic Energy Cascade: Mesoscale Absorption of Submesoscale
Mixed-Layer Eddies and Frontal Downscale Fluxes, J. Phys. Oceanogr., https://doi.org/10.1175/JPO-D-19-0311.1, 2020. a
Shchepetkin, A. and McWilliams, J. C.: The Regional Oceanic Modeling System
(ROMS): A split-explicit, free-surface, topography-following- coordinate
ocean model, Ocean Model., 9, 347–404,
https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Smith, W. H. and Sandwell, D. T.: Global sea floor topography from satellite
altimetry and ship depth soundings, Science, 277, 1956–1962,
https://doi.org/10.1126/science.277.5334.1956, 1997. a
Soufflet, Y., Marchesiello, P., Lemarié, F., Jouanno, J., Capet, X.,
Debreu, L., and Benshila, R.: On effective resolution in ocean models, Ocean Model., 98, 36–50, https://doi.org/10.1016/j.ocemod.2015.12.004, 2016. a
Souza, A. N., Wagner, G., Ramadhan, A., Allen, B., Churavy, V., Schloss, J.,
Campin, J., Hill, C., Edelman, A., Marshall, J., Flierl, G., and Ferrari, R.: Uncertainty
Quantification of Ocean Parameterizations: Application to the
K-Profile-Parameterization for Penetrative Convection, J. Adv.
Model. Earth Sy., 12, e2020MS002108, https://doi.org/10.1029/2020MS002108,
2020. a
Stern, C., Uchida, T., and Abernathey, R. P.: pangeo-data/swot_adac_ogcms: v0.0.0 (v0.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.6762536, 2022b. a, b, c
Stewart, A. L., Klocker, A., and Menemenlis, D.: Circum-Antarctic shoreward
heat transport derived from an eddy-and tide-resolving simulation,
Geophys. Res. Lett., 45, 834–845, https://doi.org/10.1002/2017GL075677, 2018.
a
Su, Z., Wang, J., Klein, P., Thompson, A. F., and Menemenlis, D.: Ocean
submesoscales as a key component of the global heat budget, Nat.
Commun., 9, 1–8, https://doi.org/10.1038/s41467-018-02983-w, 2018. a, b
Torres, H. S., Klein, P., Menemenlis, D., Qiu, B., Su, Z., Wang, J., Chen, S.,
and Fu, L.-L.: Partitioning ocean motions into balanced motions and internal
gravity waves: A modeling study in anticipation of future space missions,
J. Geophys. Res.-Oceans, 123, 8084–8105,
https://doi.org/10.1029/2018JC014438, 2018. a, b
Uchida, T., Abernathey, R. P., and Smith, S. K.: Seasonality of eddy kinetic
energy in an eddy permitting global climate model, Ocean Model., 118,
41–58, https://doi.org/10.1016/j.ocemod.2017.08.006, 2017. a, b
Uchida, T., Balwada, D., Abernathey, R. P., McKinley, G. A., Smith, S. K.,
and Lévy, M.: The contribution of submesoscale over mesoscale eddy iron
transport in the open Southern Ocean, J. Adv. Model. Earth
Sy., 11, 3934–3958, https://doi.org/10.1029/2019MS001805, 2019. a, b, c
Uchida, T., Rokem, A., Squire, D., et al.:
xrft: Fourier
transforms for xarray data, Zenodo [code], https://doi.org/10.5281/zenodo.1402635, 2021. a
Xiao, B., Qiao, F., Shu, Q., Yin, X., Wang, G., and Wang, S.: The development and validation of a global
Yang, P., Jing, Z., Sun, B., Wu, L., Qiu, B., Chang, P., and Ramachandran, S.:
On the upper-ocean vertical eddy heat transport in the Kuroshio extension.
Part I: Variability and dynamics, J. Phys. Oceanogr., 51,
229–246, https://doi.org/10.1175/JPO-D-20-0068.1, 2021. a, b, c, d
Yu, X., Ponte, A. L., Lahaye, N., Caspar-Cohen, Z., and Menemenlis, D.:
Geostrophy Assessment and Momentum Balance of the Global Oceans in a Tide-and
Eddy-Resolving Model, J. Geophys. Res.-Oceans, 126,
e2021JC017422, https://doi.org/10.1029/2021JC017422, 2021. a, b, c
Short summary
Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and...
