Articles | Volume 15, issue 10
https://doi.org/10.5194/tc-15-4703-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-4703-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2021
Research article |
| 07 Oct 2021
| 07 Oct 2021
Lasting impact of winds on Arctic sea ice through the ocean's memory
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Laboratory for Regional Oceanography and Numerical Modeling,
Pilot National Laboratory for Marine Science and Technology, Qingdao, China
Sergey Danilov
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Department of Mathematics and Logistics, Jacobs University, Bremen, Germany
A. M. Obukhov Institute of Atmospheric Physics Russian Academy of Science, Moscow, Russia
Longjiang Mu
Laboratory for Ocean and Climate Dynamics, Pilot National Laboratory for Marine Science and Technology, Qingdao, China
Dmitry Sidorenko
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Claudia Wekerle
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Related authors
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.
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.
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.
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.
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.
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.
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, https://doi.org/10.5194/gmd-15-5829-2022, 2022
Short summary
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.
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.
Tingfeng Dou, Cunde Xiao, Jiping Liu, Qiang Wang, Shifeng Pan, Jie Su, Xiaojun Yuan, Minghu Ding, Feng Zhang, Kai Xue, Peter A. Bieniek, and Hajo Eicken
The Cryosphere, 15, 883–895, https://doi.org/10.5194/tc-15-883-2021, https://doi.org/10.5194/tc-15-883-2021, 2021
Short summary
Short summary
Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, greatly influencing the ice–albedo feedback. We found that spring ROS events have shifted to earlier dates over the Arctic Ocean in recent decades, which is correlated with sea ice melt onset in the Pacific sector and most Eurasian marginal seas. There has been a clear transition from solid to liquid precipitation, leading to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade since the 1980s.
Claudia Wekerle, Tore Hattermann, Qiang Wang, Laura Crews, Wilken-Jon von Appen, and Sergey Danilov
Ocean Sci., 16, 1225–1246, https://doi.org/10.5194/os-16-1225-2020, https://doi.org/10.5194/os-16-1225-2020, 2020
Short summary
Short summary
The high-resolution ocean models ROMS and FESOM configured for the Fram Strait reveal very energetic ocean conditions there. The two main currents meander strongly and shed circular currents of water, called eddies. Our analysis shows that this region is characterised by small and short-lived eddies (on average around a 5 km radius and 10 d lifetime). Both models agree on eddy properties and show similar patterns of baroclinic and barotropic instability of the West Spitsbergen Current.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
Short summary
This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, 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 Omar Abdelazim 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 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2198, https://doi.org/10.5194/egusphere-2025-2198, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of climate projections. The system 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.
Alejandra Quintanilla-Zurita, Benjamin Rabe, Claudia Wekerle, Torsten Kanzow, Ivan Kuznetsov, Sinhue Torres-Valdes, Enric Pallàs-Sanz, and Ying-Chih Fang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3773, https://doi.org/10.5194/egusphere-2025-3773, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
During a year-long Arctic expedition, we discovered nine underwater eddies beneath the sea ice in the central Arctic Ocean. These hidden structures form within a layered part of the ocean just below the surface and may reshape water layers and transport heat, freshwater, and nutrients. Using drifting ice platforms, we measured their size, depth, and motion to understand how they form.
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.
Fernanda DI Alzira Oliveira Matos, Dmitry Sidorenko, Xiaoxu Shi, Lars Ackermann, Janini Pereira, Gerrit Lohmann, and Christian Stepanek
EGUsphere, https://doi.org/10.5194/egusphere-2025-2326, https://doi.org/10.5194/egusphere-2025-2326, 2025
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) is responsible for about 25 % of the poleward ocean heat transport. Currently, the AMOC strength is mostly calculated in depth space (z-AMOC). However, we argue that, in warmer climates, the AMOC should be calculated in density space (ρ-AMOC). We performed simulations with CO2 forcing of 280 ppmv (PI) and 1120 ppmv of (4xCO2) and find that ρ-AMOC provides more physical and meaningful information about the AMOC in warmer climates.
Torsten Kanzow, Angelika Humbert, Thomas Mölg, Mirko Scheinert, Matthias Braun, Hans Burchard, Francesca Doglioni, Philipp Hochreuther, Martin Horwath, Oliver Huhn, Maria Kappelsberger, Jürgen Kusche, Erik Loebel, Katrina Lutz, Ben Marzeion, Rebecca McPherson, Mahdi Mohammadi-Aragh, Marco Möller, Carolyne Pickler, Markus Reinert, Monika Rhein, Martin Rückamp, Janin Schaffer, Muhammad Shafeeque, Sophie Stolzenberger, Ralph Timmermann, Jenny Turton, Claudia Wekerle, and Ole Zeising
The Cryosphere, 19, 1789–1824, https://doi.org/10.5194/tc-19-1789-2025, https://doi.org/10.5194/tc-19-1789-2025, 2025
Short summary
Short summary
The Greenland Ice Sheet represents the second-largest contributor to global sea-level rise. We quantify atmosphere, ice and ocean processes related to the mass balance of glaciers in northeast Greenland, focusing on Greenland’s largest floating ice tongue, the 79° N Glacier. We find that together, the different in situ and remote sensing observations and model simulations reveal a consistent picture of a coupled atmosphere–ice sheet–ocean system that has entered a phase of major change.
Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-189, https://doi.org/10.5194/gmd-2024-189, 2025
Revised manuscript under review for GMD
Short summary
Short summary
In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
Xiaoyu Wang, Longjiang Mu, and Xianyao Chen
Ocean Sci., 21, 577–586, https://doi.org/10.5194/os-21-577-2025, https://doi.org/10.5194/os-21-577-2025, 2025
Short summary
Short summary
The East Siberian Sea has nearly 80 % of the subsea permafrost worldwide. The cold layer with a temperature around −1.5 ºC above the seafloor prevents heat transporting from above to melt permafrost and release methane from sediments. However, we observed a warming trend at the seafloor caused by wave-induced vertical mixing in the shelf. The intensified mixing can transport enormous heat downward, leading to warming of more than 3 °C at the bottom, putting the subsea permafrost at high risk.
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere 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.
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.
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.
Kacper Nowak, Sergey Danilov, Vasco Müller, and Caili Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1119, https://doi.org/10.5194/egusphere-2024-1119, 2024
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 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.
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.
Cara Nissen, Ralph Timmermann, Mathias van Caspel, and Claudia Wekerle
Ocean Sci., 20, 85–101, https://doi.org/10.5194/os-20-85-2024, https://doi.org/10.5194/os-20-85-2024, 2024
Short summary
Short summary
The southeastern Weddell Sea is important for global ocean circulation due to the cross-shelf-break exchange of Dense Shelf Water and Warm Deep Water, but their exact circulation pathways remain elusive. Using Lagrangian model experiments in an eddy-permitting ocean model, we show how present circulation pathways and transit times of these water masses on the continental shelf are altered by 21st-century climate change, which has implications for local ice-shelf basal melt rates and ecosystems.
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.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
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.
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.
Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
Short summary
Short summary
Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
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.
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, https://doi.org/10.5194/gmd-15-5829-2022, 2022
Short summary
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.
Xiaoxu Shi, Martin Werner, Carolin Krug, Chris M. Brierley, Anni Zhao, Endurance Igbinosa, Pascale Braconnot, Esther Brady, Jian Cao, Roberta D'Agostino, Johann Jungclaus, Xingxing Liu, Bette Otto-Bliesner, Dmitry Sidorenko, Robert Tomas, Evgeny M. Volodin, Hu Yang, Qiong Zhang, Weipeng Zheng, and Gerrit Lohmann
Clim. Past, 18, 1047–1070, https://doi.org/10.5194/cp-18-1047-2022, https://doi.org/10.5194/cp-18-1047-2022, 2022
Short summary
Short summary
Since the orbital parameters of the past are different from today, applying the modern calendar to the past climate can lead to an artificial bias in seasonal cycles. With the use of multiple model outputs, we found that such a bias is non-ignorable and should be corrected to ensure an accurate comparison between modeled results and observational records, as well as between simulated past and modern climates, especially for the Last Interglacial.
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.
Tingfeng Dou, Cunde Xiao, Jiping Liu, Qiang Wang, Shifeng Pan, Jie Su, Xiaojun Yuan, Minghu Ding, Feng Zhang, Kai Xue, Peter A. Bieniek, and Hajo Eicken
The Cryosphere, 15, 883–895, https://doi.org/10.5194/tc-15-883-2021, https://doi.org/10.5194/tc-15-883-2021, 2021
Short summary
Short summary
Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, greatly influencing the ice–albedo feedback. We found that spring ROS events have shifted to earlier dates over the Arctic Ocean in recent decades, which is correlated with sea ice melt onset in the Pacific sector and most Eurasian marginal seas. There has been a clear transition from solid to liquid precipitation, leading to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade since the 1980s.
Xuewei Li, Qinghua Yang, Lejiang Yu, Paul R. Holland, Chao Min, Longjiang Mu, and Dake Chen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-359, https://doi.org/10.5194/tc-2020-359, 2021
Preprint withdrawn
Short summary
Short summary
The Arctic sea ice thickness record minimum is confirmed occurring in autumn 2011. The dynamic and thermodynamic processes leading to the minimum thickness is analyzed based on a daily sea ice thickness reanalysis data covering the melting season. The results demonstrate that the dynamic transport of multiyear ice and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice thickness in 2011.
Chao Min, Qinghua Yang, Longjiang Mu, Frank Kauker, and Robert Ricker
The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, https://doi.org/10.5194/tc-15-169-2021, 2021
Short summary
Short summary
An ensemble of four estimates of the sea-ice volume (SIV) variations in Baffin Bay from 2011 to 2016 is generated from the locally merged satellite observations, three modeled sea ice thickness sources (CMST, NAOSIM, and PIOMAS) and NSIDC ice drift data (V4). Results show that the net increase of the ensemble mean SIV occurs from October to April with the largest SIV increase in December, and the reduction occurs from May to September with the largest SIV decline in July.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
Short summary
Short summary
The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Claudia Wekerle, Tore Hattermann, Qiang Wang, Laura Crews, Wilken-Jon von Appen, and Sergey Danilov
Ocean Sci., 16, 1225–1246, https://doi.org/10.5194/os-16-1225-2020, https://doi.org/10.5194/os-16-1225-2020, 2020
Short summary
Short summary
The high-resolution ocean models ROMS and FESOM configured for the Fram Strait reveal very energetic ocean conditions there. The two main currents meander strongly and shed circular currents of water, called eddies. Our analysis shows that this region is characterised by small and short-lived eddies (on average around a 5 km radius and 10 d lifetime). Both models agree on eddy properties and show similar patterns of baroclinic and barotropic instability of the West Spitsbergen Current.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
Short summary
This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Lars Nerger, Qi Tang, and Longjiang Mu
Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, https://doi.org/10.5194/gmd-13-4305-2020, 2020
Short summary
Short summary
Data assimilation combines observations with numerical models to get an improved estimate of the model state. This work discusses the technical aspects of how a coupled model that simulates the ocean and the atmosphere can be augmented by data assimilation functionality provided in generic form by the open-source software PDAF (Parallel Data Assimilation Framework). A very efficient program is obtained that can be executed on high-performance computers.
Cited articles
Armitage, T. W. K., Bacon, S., Ridout, A. L., Petty, A. A., Wolbach, S., and Tsamados, M.: Arctic Ocean surface geostrophic circulation 2003–2014, The Cryosphere, 11, 1767–1780, https://doi.org/10.5194/tc-11-1767-2017, 2017. a
Bouchat, A. and Tremblay, B.: Using sea-ice deformation fields to constrain the
mechanical strength parameters of geophysical sea ice, J. Geophys.
Res.-Oceans, 122, 5802–5825, 2017. a
Comiso, J. C., Meier, W. N., and Gersten, R.: Variability and trends in the
Arctic Sea ice cover: Results from different techniques, J. Geophys. Res.- Oceans, 122, 6883–6900, 2017. a
Danilov, S., Kivman, G., and Schröter, J.: A finite-element ocean model:
principles and evaluation, Ocean Modell., 6, 125–150, 2004. a
Danilov, S., Wang, Q., Timmermann, R., Iakovlev, N., Sidorenko, D., Kimmritz, M., Jung, T., and Schröter, J.: Finite-Element Sea Ice Model (FESIM), version 2, Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, 2015. a, b
Fetterer, F., Knowles, K., Meier, W., Savoie, M., and Windnage, A. K.: Sea Ice
Index, Version 3, NSIDC: National Snow and Ice Data
Center, Boulder, Colorado USA, 2017, updated daily. a
Hendricks, S. and Ricker, R.: Product User Guide and Algorithm Specification:
AWI CryoSat-2 Sea Ice Thickness (version 2.2), Tech. rep., Alfred Wegener
Institute, Bremerhaven, 2019. a
Heorton, H. D. B. S., Tsamados, M., Cole, S. T., Ferreira, A. M. G.,
Berbellini, A., Fox, M., and Armitage, T. W. K.: Retrieving Sea Ice Drag
Coefficients and Turning Angles From In Situ and Satellite Observations Using
an Inverse Modeling Framework, J. Geophys. Res.-Oceans, 124,
6388–6413, 2019. a, b
Hunke, E. and Dukowicz, J.: An elastic-viscous-plastic model for sea ice
dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997. a
Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020. a
Hutter, N., Zampieri, L., and Losch, M.: Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm, The Cryosphere, 13, 627–645, https://doi.org/10.5194/tc-13-627-2019, 2019. a
Johnson, H. L., Cornish, S. B., Kostov, Y., Beer, E., and Lique, C.: Arctic
Ocean Freshwater Content and Its Decadal Memory of Sea‐Level Pressure,
Geophys. Res. Lett., 45, 4991–5001, 2018. a
Jung, T. and Hilmer, M.: The link between the North Atlantic oscillation and
Arctic Sea ice export through Fram Strait, J. Climate, 14,
3932–3943, 2001. a
Jung, T., Gordon, N. D., Bauer, P., Bromwich, D. H., Chevallier, M., Day,
J. J., Dawson, J., Doblas-Reyes, F., Fairall, C., Goessling, H. F., Holland,
M., Inoue, J., Iversen, T., Klebe, S., Lemke, P., Losch, M., Makshtas, A.,
Mills, B., Nurmi, P., Perovich, D., Reid, P., Renfrew, I. A., Smith, G.,
Svensson, G., Tolstykh, M., and Yang, Q.: Advancing Polar Prediction
Capabilities on Daily to Seasonal Time Scales, B. Am.
Meteorol. Soc., 97, 1631–1647, https://doi.org/10.1175/BAMS-D-14-00246.1,
2016. a
Koldunov, N. V., Serra, N., Köhl, A., Stammer, D., Henry, O., Cazenave, A.,
Prandi, P., Knudsen, P., Andersen, O. B., Gao, Y., and Johannessen, J.:
Multimodel simulations of Arctic Ocean sea surface height variability in
the period 1970–2009, J. Geophys. Res.-Oceans, 119,
8936–8954, 2014. a
Krishfield, R. A., Proshutinsky, A., Tateyama, K., Williams, W. J., Carmack,
E. C., McLaughlin, F. A., and Timmermans, M. L.: Deterioration of perennial sea ice
in the Beaufort Gyre from 2003 to 2012 and its impact on the oceanic
freshwater cycle, J. Geophys. Res.-Oceans, 119, 1271–1305, 2014. a
Krumpen, T., Belter, H. J., Boetius, A., Damm, E., Haas, C., Hendricks, S.,
Nicolaus, M., Nöthig, E.-M., Paul, S., Peeken, I., Ricker, R., and Stein,
R.: Arctic warming interrupts the Transpolar Drift and affects long-range
transport of sea ice and ice-rafted matter, Sci. Rep.-UK, 9, 5459,
https://doi.org/10.1038/s41598-019-41456-y, 2019. a
Krumpen, T., von Albedyll, L., Goessling, H. F., Hendricks, S., Juhls, B., Spreen, G., Willmes, S., Belter, H. J., Dethloff, K., Haas, C., Kaleschke, L., Katlein, C., Tian-Kunze, X., Ricker, R., Rostosky, P., Rückert, J., Singha, S., and Sokolova, J.: MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years, The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, 2021. a
Kwok, R.: Deformation of the Arctic Ocean Sea Ice Cover between November
1996 and April 1997: A Qualitative Survey, in: IUTAM Symposium on Scaling
Laws in Ice Mechanics and Ice Dynamics, edited by: Dempsey, J. P. and Shen,
H. H., Springer Netherlands, Dordrecht, 94, 315–322, 2001. a
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses
and coupled variability (1958–2018), Environ. Res. Lett., 13,
105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Kwok, R. and Morison, J.: Recent changes in Arctic sea ice and ocean
circulation, US CLIVAR Variations (Summer 2017), 15, 1–6, 2017. a
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, 1994. a
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
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen,
R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield,
R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates of Arctic
sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737, 2013. a
Lindsay, R. W., Zhang, J., and Rothrock, D. A.: Sea-ice deformation rates from
satellite measurements and in a model, Atmosphere-Ocean, 41, 35–47, 2003. a
Marshall, J., Scott, J., and Proshutinsky, A.: “Climate response functions” for the Arctic Ocean: a proposed coordinated modelling experiment, Geosci. Model Dev., 10, 2833–2848, https://doi.org/10.5194/gmd-10-2833-2017, 2017. a
McPhee, M. G.: Intensification of Geostrophic Currents in the Canada Basin,
Arctic Ocean, J. Climate, 26, 3130–3138,
https://doi.org/10.1175/JCLI-D-12-00289.1, 2013. a, b, c
McPhee, M. G., Proshutinsky, A., Morison, J. H., Steele, M., and Alkire, M. B.:
Rapid change in freshwater content of the Arctic Ocean, Geophys. Res. Lett., 36, L10602, https://doi.org/10.1029/2009GL037525, 2009. a, b
Meneghello, G., Marshall, J., Campin, J., Doddridge, E., and Timmermans, M.-L.:
The Ice-Ocean Governor: Ice-Ocean Stress Feedback Limits Beaufort Gyre
Spin-Up, Geophys. Res. Lett., 45, 11293–11299, https://doi.org/10.1029/2018GL080171, 2018. a, b
Mohammadi-Aragh, M., Goessling, H. F., Losch, M., Hutter, N., and Jung, T.:
Predictability of Arctic sea ice on weather time scales, Sci.
Rep.-UK, 8, 6514, https://doi.org/10.1038/s41598-018-24660-0, 2018. a
Mohammadi-Aragh, M., Losch, M., and Goessling, H. F.: Comparing Arctic Sea
Ice Model Simulations to Satellite Observations by Multiscale Directional
Analysis of Linear Kinematic Features, Mon. Weather Rev., 148,
3287–3303, 2020. a
Olonscheck, D., Mauritsen, T., and Notz, D.: Arctic sea-ice variability is
primarily driven by atmospheric temperature fluctuations, Nat. Geosci.,
12, 430–434, 2019. a
Parkinson, C. L. and Washington, W. M.: A large-scale numerical model of sea
ice, J. Geophys. Res.-Oceans, 84, 311–337, 1979. a
Petty, A., Hutchings, J., Richter‐Menge, J., and Tschudi, M.: Sea ice
circulation around the Beaufort Gyre: The changing role of wind forcing and
the sea ice state, J. Geophys. Res.-Oceans, 121, 3278–3296, 2016. a
Platov, G., Iakshina, D., and Krupchatnikov, V.: Characteristics of Atmospheric
Circulation Associated with Variability of Sea Ice in the Arctic,
Geosciences, 10, 359, https://doi.org/10.3390/geosciences10090359, 2020. a
Polyakov, I., Bhatt, U., Walsh, J., Abrahamsen, E. P., Pnyushkov, A., and
Wassmann, P.: Recent oceanic changes in the Arctic in the context of
long-term observations, Ecol. Appl., 23, 1745–1764, 2013. a
Polyakov, I., Pnyushkov, A. V., Alkire, M. B., Ashik, I. M., Baumann, T. M.,
Carmack, E. C., Goszczko, I., Guthrie, J., Ivanov, V. V., Kanzow, T.,
Krishfield, R., Kwok, R., Sundfjord, A., Morison, J., Rember, R., and Yulin,
A.: Greater role for Atlantic inflows on sea-ice loss in the Eurasian
Basin of the Arctic Ocean, Science, 356, 285, https://doi.org/10.1126/science.aai8204,
2017. a
Polyakov, I. V., Rippeth, T. P., Fer, I., Baumann, T. M., Carmack, E. C.,
Ivanov, V. V., Janout, M., Padman, L., Pnyushkov, A. V., and Rember, R.:
Intensification of Near-Surface Currents and Shear in the Eastern Arctic
Ocean, Geophys. Res. Lett., 47, e2020GL089469,
https://doi.org/10.1029/2020GL089469, 2020. a, b
Proshutinsky, A., Dukhovskoy, D., Timmermans, M., Krishfield, R., and Bamber,
J.: Arctic circulation regimes, Phil. T. Roy. Soc. A, 373, 20140160, https://doi.org/10.1098/rsta.2014.0160,
2015. a, b
Proshutinsky, A., Krishfield, R., Toole, J. M., Timmermans, M. L., Williams,
W., Zimmermann, S., Yamamoto-Kawai, M., Armitage, T. W. K., Dukhovskoy, D.,
Golubeva, E., Manucharyan, G. E., Platov, G., Watanabe, E., Kikuchi, T.,
Nishino, S., Itoh, M., Kang, S. H., Cho, K. H., Tateyama, K., and Zhao, J.:
Analysis of the Beaufort Gyre Freshwater Content in 2003–2018, J. Geophys.
Res.-Oceans, 124, 9658–9689, 2019. a, b, c, d
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and
deformation rate of Arctic sea ice, 1979–2007, J. Geophys.
Res.-Oceans, 114, C05013, https://doi.org/10.1029/2008JC005066, 2009. a
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., Korosov, A., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, 2019. a
Årthun, M., Eldevik, T., and Smedsrud, L. H.: The Role of Atlantic Heat
Transport in Future Arctic Winter Sea Ice Loss, J. Climate, 32,
3327–3341, 2019. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res.-Oceans,
116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. a
Serreze, M. C. and Meier, W. N.: The Arctic's sea ice cover: trends,
variability, predictability, and comparisons to the Antarctic, Ann. N. Y. Acad.
Sci., 1436, 36–53, https://doi.org/10.1111/nyas.13856, 2019. a, b
Shu, Q., Wang, Q., Song, Z., and Qiao, F.: The poleward enhanced Arctic Ocean
cooling machine in a warming climate, Nat. Commun., 12, 2966,
https://doi.org/10.1038/s41467-021-23321-7, 2021. a
Smagorinsky, J.: General circulation experiments with the primitive equations:
I. The basic experiment, Mon. Weather Rev., 91, 99–164, 1963. a
Spall, M. A.: Dynamics and Thermodynamics of the Mean Transpolar Drift and
Ice Thickness in the Arctic Ocean, J. Climate, 32, 8449–8463,
https://doi.org/10.1175/JCLI-D-19-0252.1, 2019. a
Spreen, G., Kwok, R., and Menemenlis, D.: Trends in Arctic sea ice drift and
role of wind forcing: 1992–2009, Geophys. Res. Lett., 38, L19501, https://doi.org/10.1029/2011GL048970, 2011. a
Spreen, G., de Steur, L., Divine, D., Gerland, S., Hansen, E., and Kwok, R.:
Arctic Sea Ice Volume Export Through Fram Strait From 1992 to 2014, J. Geophys. Res.-Oceans, 125, e2019JC016039,
https://doi.org/10.1029/2019JC016039, 2020. a
Steele, M., Zhang, J., Rothrock, D., and Stern, H.: The force balance of sea
ice in a numerical model of the Arctic Ocean, J. Geophys.
Res.-Oceans, 102, 21061–21079,
https://doi.org/10.1029/97JC01454, 1997. a
Steele, M., Morley, R., and Ermold, W.: PHC: A global ocean hydrography with a
high quality Arctic Ocean, J. Climate, 14, 2079–2087, 2001. a
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland,
M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and
observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676, 2012. a
Thompson, D. W. J. and Wallace, J. M.: The Arctic Oscillation signature in
the wintertime geopotential height and temperature fields, Geophys. Res.
Lett., 25, 1297–1300, 1998. a
Tian, T., Yang, S., Karami, M. P., Massonnet, F., Kruschke, T., and Koenigk, T.: Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3, Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, 2021. a
Timmermans, M. . L., Proshutinsky, A., Krishfield, R. A., Perovich, D. K.,
Richter-Menge, J. A., Stanton, T. P., and Toole, J. M.: Surface freshening
in the Arctic Ocean's Eurasian Basin: An apparent consequence of recent
change in the wind-driven circulation, J. Geophys. Res.-Oceans, 116,
C00D03, https://doi.org/10.1029/2011JC006975, 2011. a
Tsamados, M., Feltham, D., Schroeder, D., Flocco, D., Farrell, S., Kurtz, N.,
Laxon, S., and Bacon, S.: Impact of Variable Atmospheric and Oceanic Form
Drag on Simulations of Arctic Sea Ice, J. Phys. Oceanogr., 44, 1329–1353,
2014. a
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G.,
Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W.,
Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J.,
Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C.,
Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J.,
Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.:
JRA-55 based surface dataset for driving ocean–sea-ice models
(JRA55-do), Ocean Modell., 130, 79–139,
https://doi.org/10.1016/j.ocemod.2018.07.002, 2018. a
Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., Arsouze, T., Bentsen, M., Bernardello, R., Böning, C. W., Bozec, A., Chassignet, E. P., Danilov, S., Dussin, R., Exarchou, E., Fogli, P. G., Fox-Kemper, B., Guo, C., Ilicak, M., Iovino, D., Kim, W. M., Koldunov, N., Lapin, V., Li, Y., Lin, P., Lindsay, K., Liu, H., Long, M. C., Komuro, Y., Marsland, S. J., Masina, S., Nummelin, A., Rieck, J. K., Ruprich-Robert, Y., Scheinert, M., Sicardi, V., Sidorenko, D., Suzuki, T., Tatebe, H., Wang, Q., Yeager, S. G., and Yu, Z.: Evaluation of 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, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, 2020. a
Vihma, T.: Effects of Arctic Sea Ice Decline on Weather and Climate: A
Review, Surv. Geophys., 35, 1175–1214, 2014. a
Wang, J., Zhang, J., Watanabe, E., Ikeda, M., Mizobata, K., Walsh, J. E., Bai,
X., and Wu, B.: Is the Dipole Anomaly a major driver to record lows in
Arctic summer sea ice extent?, Geophys. Res. Lett., 36, L05706,
https://doi.org/10.1029/2008GL036706, 2009. a
Wang, Q.: FESOM data for the study on lasting impact of winds on sea ice through the ocean's memory, Zenodo [data set], https://doi.org/10.5281/zenodo.4509500, 2021b. a
Wang, Q., Danilov, S., and Schröter, J.: Finite Element Ocean circulation
Model based on triangular prismatic elements, with application in studying
the effect of vertical discretization, J. Geophys. Res.-Oceans, 113,
C05015, https://doi.org/10.1029/2007JC004482, 2008. a
Wang, Q., Danilov, S., Sidorenko, D., Timmermann, R., Wekerle, C., Wang, X., Jung, T., and Schröter, J.: The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model, Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, 2014. a, b
Wang, Q., Danilov, S., Jung, T., Kaleschke, L., and Wernecke, A.: Sea ice leads
in the Arctic Ocean: Model assessment, interannual variability and trends,
Geophys. Res. Lett., 43, 7019–7027, 2016a. a
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A.,
Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet,
E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E.,
Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T.,
Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. J. G., Rabe,
B., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H.,
Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the
Arctic Ocean in a suite of interannual CORE-II simulations. Part I: Sea
ice and solid freshwater, Ocean Modell., 99, 110–132, 2016b. a
Wang, Q., Wekerle, C., Danilov, S., Wang, X., and Jung, T.: The Finite-Element Sea ice-Ocean Model (FESOM v1.4) (Version 2), Zenodo [code], https://doi.org/10.5281/zenodo.1116851, 2017. a
Wang, Q., Wekerle, C., Danilov, S., Koldunov, N., Sidorenko, D., Sein, D.,
Rabe, B., and Jung, T.: Arctic sea ice decline significantly contributed to
the unprecedented liquid freshwater accumulation in the Beaufort Gyre of the
Arctic Ocean, Geophys. Res. Lett., 45, 4956–4964, 2018. a
Wang, Q., Wang, X., Wekerle, C., Danilov, S., Jung, T., Koldunov, N., Lind, S.,
Sein, D., Shu, Q., and Sidorenko, D.: Ocean Heat Transport Into the Barents
Sea: Distinct Controls on the Upward Trend and Interannual Variability,
Geophys. Res. Lett., 46, 13180–13190,
https://doi.org/10.1029/2019GL083837, 2019b. a
Wang, Q., Wekerle, C., Wang, X., Danilov, S., Koldunov, N., Sein, D.,
Sidorenko, D., von Appen, W.-J., and Jung, T.: Intensification of the
Atlantic Water Supply to the Arctic Ocean Through Fram Strait Induced
by Arctic Sea Ice Decline, Geophys. Res. Lett., 47,
e2019GL086682, https://doi.org/10.1029/2019GL086682, 2020. a
Wang, Q., Ricker, R., and Mu, L.: Arctic sea ice decline preconditions events
of anomalously low sea ice volume export through Fram Strait in the early
21st century, J. Geophys. Res.-Oceans, 126, e2020JC016607,
https://doi.org/10.1029/2020JC016607, 2021. a, b, c
Wang, S., Wang, Q., Shu, Q., Song, Z., Lohmann, G., Danilov, S., and Qiao, F.:
Nonmonotonic Change of the Arctic Ocean Freshwater Storage Capability in a
Warming Climate, Geophys. Res. Lett., 48, e2020GL090951, https://doi.org/10.1029/2020GL090951,
2021. a
Wekerle, C., Wang, Q., Danilov, S., Schourup-Kristensen, V., von Appen, W.-J.,
and Jung, T.: Atlantic Water in the Nordic Seas: locally eddy-permitting
ocean simulation in a global setup, J. Geophys. Res.-Oceans, 122, 914–940,
2017a. a
Wekerle, C., Wang, Q., von Appen, W.-J., Danilov, S., Schourup-Kristensen, V.,
and Jung, T.: Eddy-Resolving Simulation of the Atlantic Water Circulation
in the Fram Strait With Focus on the Seasonal Cycle, J. Geophys.
Res.-Oceans, 122, 8385–8405, 2017b. a
Wunderling, N., Willeit, M., Donges, J. F., and Winkelmann, R.: Global warming
due to loss of large ice masses and Arctic summer sea ice, Nat.
Commun., 11, 5177, https://doi.org/10.1038/s41467-020-18934-3, 2020. a
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.
Using simulations, we found that changes in ocean freshwater content induced by wind...
