Articles | Volume 12, issue 6
https://doi.org/10.5194/tc-12-2005-2018
© Author(s) 2018. 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-12-2005-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2018
Research article |
| 15 Jun 2018
| 15 Jun 2018
Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system
Takuya Nakanowatari
CORRESPONDING AUTHOR
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Jun Inoue
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Kazutoshi Sato
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
present address: Kitami Institute of Technology, Kitami, 090-8507, Japan
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Thormøhlens gate 47, 5006 Bergen, Norway
Jiping Xie
Nansen Environmental and Remote Sensing Center, Thormøhlens gate 47, 5006 Bergen, Norway
Mio Matsueda
Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba,
Ibaraki 305-8577, Japan
Akio Yamagami
Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba,
Ibaraki 305-8577, Japan
Takeshi Sugimura
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Hironori Yabuki
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Natsuhiko Otsuka
Arctic Research Center, Hokkaido University, Kita-21 Nishi-11 Kita-ku, Sapporo, 001-0021, Japan
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Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
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The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet Discuss., https://doi.org/10.5194/sp-2024-20, https://doi.org/10.5194/sp-2024-20, 2024
Revised manuscript accepted for SP
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Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity and currents will evolve over the coming days and weeks, as well as to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet Discuss., https://doi.org/10.5194/sp-2024-24, https://doi.org/10.5194/sp-2024-24, 2024
Revised manuscript accepted for SP
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Forecasts of sea ice are in high demand in the polar regions, they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 days ahead – and an outlook of their upcoming developments.
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-1896, https://doi.org/10.5194/egusphere-2024-1896, 2024
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This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011–2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes on sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
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We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
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Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin
The Cryosphere, 18, 1597–1619, https://doi.org/10.5194/tc-18-1597-2024, https://doi.org/10.5194/tc-18-1597-2024, 2024
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Individual satellite passes instead of daily means of sea ice concentration are used to correct the sea ice model forecast in the Barents Sea. The use of passes provides a significantly larger improvement of the forecasts even after a 7 d period due to the more precise information on temporal and spatial variability contained in the passes. One major advantage of the use of satellite passes is that there is no need to wait for the daily mean availability in order to update the forecast.
Marta Umbert, Eva De Andrés, Maria Sánchez, Carolina Gabarró, Nina Hoareau, Veronica González-Gambau, Aina García-Espriu, Estrella Olmedo, Roshin P. Raj, Jiping Xie, and Rafael Catany
Ocean Sci., 20, 279–291, https://doi.org/10.5194/os-20-279-2024, https://doi.org/10.5194/os-20-279-2024, 2024
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Satellite retrievals of sea surface salinity (SSS) offer insights into freshwater changes in the Arctic Ocean. This study evaluates freshwater content in the Beaufort Gyre using SMOS and reanalysis data, revealing underestimation with reanalysis alone. Incorporating satellite SSS measurements improves freshwater content estimation, especially near ice-melting areas. Adding remotely sensed salinity aids in monitoring Arctic freshwater content and in understanding its impact on global climate.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
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This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, https://doi.org/10.5194/os-19-269-2023, 2023
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Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Fabio Mangini, Léon Chafik, Antonio Bonaduce, Laurent Bertino, and Jan Even Ø. Nilsen
Ocean Sci., 18, 331–359, https://doi.org/10.5194/os-18-331-2022, https://doi.org/10.5194/os-18-331-2022, 2022
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We validate the recent ALES-reprocessed coastal satellite altimetry dataset along the Norwegian coast between 2003 and 2018. We find that coastal altimetry and conventional altimetry products perform similarly along the Norwegian coast. However, the agreement with tide gauges slightly increases in terms of trends when we use the ALES coastal altimetry data. We then use the ALES dataset and hydrographic stations to explore the steric contribution to the Norwegian sea-level anomaly.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
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Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
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Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021, https://doi.org/10.5194/amt-14-4971-2021, 2021
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A cloud particle sensor (CPS) sonde is an observing system to obtain the signals of the phase, size, and the number of cloud particles. Based on the field experiments in the Arctic regions and numerical experiments, we proposed a method to correct the CPS sonde data and found that the CPS sonde system can appropriately observe the liquid cloud if our correction method is applied.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
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Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Takehiko Nose, Takuji Waseda, Tsubasa Kodaira, and Jun Inoue
The Cryosphere, 14, 2029–2052, https://doi.org/10.5194/tc-14-2029-2020, https://doi.org/10.5194/tc-14-2029-2020, 2020
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Accurate wave modelling in and near ice-covered ocean requires true sea ice concentration mapping of the model region. The information derived from satellite instruments has considerable uncertainty depending on retrieval algorithms and sensors. This study shows that the accuracy of satellite-retrieved sea ice concentration estimates is a major error source in wave–ice models. A similar feedback effect of sea ice concentration uncertainty may also apply to modelling lower atmospheric conditions.
Roshin P. Raj, Sourav Chatterjee, Laurent Bertino, Antonio Turiel, and Marcos Portabella
Ocean Sci., 15, 1729–1744, https://doi.org/10.5194/os-15-1729-2019, https://doi.org/10.5194/os-15-1729-2019, 2019
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In this study we investigated the variability of the Arctic Front (AF), an important biologically productive region in the Norwegian Sea, using a suite of satellite data, atmospheric reanalysis and a regional coupled ocean–sea ice data assimilation system. We show evidence of the two-way interaction between the atmosphere and the ocean at the AF. The North Atlantic Oscillation is found to influence the strength of the AF and may have a profound influence on the region's biological productivity.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Annette Samuelsen, and Tsuyoshi Wakamatsu
Ocean Sci., 15, 1191–1206, https://doi.org/10.5194/os-15-1191-2019, https://doi.org/10.5194/os-15-1191-2019, 2019
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Two gridded sea surface salinity (SSS) products have been derived from the European Space Agency’s Soil Moisture and Ocean Salinity mission. The uncertainties of these two products in the Arctic are quantified against two SSS products in the Copernicus Marine Environment Monitoring Services, two climatologies, and other in situ data. The results compared with independent in situ data clearly show a common challenge for the six SSS products to represent central Arctic freshwater masses (<24 psu).
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
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This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
Yugo Kanaya, Kazuyuki Miyazaki, Fumikazu Taketani, Takuma Miyakawa, Hisahiro Takashima, Yuichi Komazaki, Xiaole Pan, Saki Kato, Kengo Sudo, Takashi Sekiya, Jun Inoue, Kazutoshi Sato, and Kazuhiro Oshima
Atmos. Chem. Phys., 19, 7233–7254, https://doi.org/10.5194/acp-19-7233-2019, https://doi.org/10.5194/acp-19-7233-2019, 2019
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Ozone and carbon monoxide levels were uniquely observed (for > 10 000 h) over oceans from 67° S to 75° N. Tropospheric chemistry reanalysis v2 reproduced the observed evolution of pollution plumes from continents but underpredicted and overpredicted ozone levels in the Arctic and in the western Pacific equatorial region, respectively. Processes to explain the gaps are proposed, including halogen-mediated destruction in the low latitudes. Our open data set will complement the TOAR data collection.
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
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We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
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We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
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The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
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Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Kristoffer Aalstad, Sebastian Westermann, Thomas Vikhamar Schuler, Julia Boike, and Laurent Bertino
The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, https://doi.org/10.5194/tc-12-247-2018, 2018
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We demonstrate how snow cover data from satellites can be used to constrain estimates of snow distributions at sites in the Arctic. In this effort, we make use of data assimilation to combine the information contained in the snow cover data with a simple snow model. By comparing our snow distribution estimates to independent observations, we find that this method performs favorably. Being modular, this method could be applied to other areas as a component of a larger reanalysis system.
Yoshimi Kawai, Masaki Katsumata, Kazuhiro Oshima, Masatake E. Hori, and Jun Inoue
Atmos. Meas. Tech., 10, 2485–2498, https://doi.org/10.5194/amt-10-2485-2017, https://doi.org/10.5194/amt-10-2485-2017, 2017
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The model RS92 radiosonde manufactured by Vaisala Ltd. is now being replaced with a successor model, the RS41, and we need to clarify accuracy differences between them for a variety of research. For this purpose, 36 twin-radiosonde flights were performed over the oceans from the Arctic to the tropics. Basically the differences between the RS41 and RS92 were smaller than the nominal combined uncertainties of the RS41; however, we found non-negligible biases in relative humidity and pressure.
Jiping Xie, Laurent Bertino, François Counillon, Knut A. Lisæter, and Pavel Sakov
Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, https://doi.org/10.5194/os-13-123-2017, 2017
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The Arctic Ocean plays an important role in the global climate system, but the concerned interpretation about its changes is severely hampered by the sparseness of the observations of sea ice and ocean. The focus of this study is to provide a quantitative assessment of the performance of the TOPAZ4 reanalysis for ocean and sea ice variables in the pan-Arctic region (north of 63 °N) in order to guide the user through its skills and limitations.
Jiping Xie, François Counillon, Laurent Bertino, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 10, 2745–2761, https://doi.org/10.5194/tc-10-2745-2016, https://doi.org/10.5194/tc-10-2745-2016, 2016
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As a potentially operational daily product, the SMOS-Ice can improve the statements of sea ice thickness and concentration. In this study, focusing on the SMOS-Ice data assimilated into the TOPAZ system, the quantitative evaluation for the impacts and the concerned comparison with the present observation system are valuable to understand the further improvement of the accuracy of operational ocean forecasting system.
Naoya Yokoi, Kohei Matsuno, Mutsuo Ichinomiya, Atsushi Yamaguchi, Shigeto Nishino, Jonaotaro Onodera, Jun Inoue, and Takashi Kikuchi
Biogeosciences, 13, 913–923, https://doi.org/10.5194/bg-13-913-2016, https://doi.org/10.5194/bg-13-913-2016, 2016
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We studied short-term changes in the microplankton community in the Chukchi Sea with regards to responses to the strong wind event (SWE) during autumn (September 2013). It is assumed that atmospheric turbulences, such as SWE, may supply sufficient nutrients to the surface layer that subsequently enhance the small bloom under the weak stratification. After the bloom, the dominant diatom community then shifts from centric-dominated to one where centric/pennate are more equal in abundance.
T. Sueyoshi, K. Saito, S. Miyazaki, J. Mori, T. Ise, H. Arakida, R. Suzuki, A. Sato, Y. Iijima, H. Yabuki, H. Ikawa, T. Ohta, A. Kotani, T. Hajima, H. Sato, T. Yamazaki, and A. Sugimoto
Earth Syst. Sci. Data, 8, 1–14, https://doi.org/10.5194/essd-8-1-2016, https://doi.org/10.5194/essd-8-1-2016, 2016
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This paper describes the construction of a forcing data set for land surface models (LSMs) with eight meteorological variables for the 35-year period from 1979 to 2013. The data set is intended for use in a model intercomparison (MIP) study, called GTMIP. In order to prepare a set of site-fitted forcing data for LSMs with realistic yet continuous entries, four observational sites were selected to construct a blended data set using both global reanalysis and observational data.
S. Miyazaki, K. Saito, J. Mori, T. Yamazaki, T. Ise, H. Arakida, T. Hajima, Y. Iijima, H. Machiya, T. Sueyoshi, H. Yabuki, E. J. Burke, M. Hosaka, K. Ichii, H. Ikawa, A. Ito, A. Kotani, Y. Matsuura, M. Niwano, T. Nitta, R. O'ishi, T. Ohta, H. Park, T. Sasai, A. Sato, H. Sato, A. Sugimoto, R. Suzuki, K. Tanaka, S. Yamaguchi, and K. Yoshimura
Geosci. Model Dev., 8, 2841–2856, https://doi.org/10.5194/gmd-8-2841-2015, https://doi.org/10.5194/gmd-8-2841-2015, 2015
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The paper provides an overall outlook and the Stage 1 experiment (site simulations) protocol of GTMIP, an open model intercomparison project for terrestrial Arctic, conducted as an activity of the Japan-funded Arctic Climate Change Research Project (GRENE-TEA). Models are driven by 34-year data created with the GRENE-TEA observations at four sites in Finland, Siberia and Alaska, and evaluated for physico-ecological key processes: energy budgets, snow, permafrost, phenology, and carbon budget.
K. Matsuno, A. Yamaguchi, S. Nishino, J. Inoue, and T. Kikuchi
Biogeosciences, 12, 4005–4015, https://doi.org/10.5194/bg-12-4005-2015, https://doi.org/10.5194/bg-12-4005-2015, 2015
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We performed high-frequency samplings of zooplankton community and gut pigment of copepods in the Chukchi Sea. Zooplankton showed no changes with a strong wind event and dominant copepods prepared for diapause. Yet, feeding activity of the copepods increased as a result of temporal phytoplankton bloom, enhanced by the wind event. Because of the long generation length of copepods, a smaller effect was detected for their abundance, population, lipid accumulation and gonad maturation.
D. Mignac, C. A. S. Tanajura, A. N. Santana, L. N. Lima, and J. Xie
Ocean Sci., 11, 195–213, https://doi.org/10.5194/os-11-195-2015, https://doi.org/10.5194/os-11-195-2015, 2015
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Edge displacement scores
Toward a method for downscaling sea ice pressure for navigation purposes
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation
Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations
Wave energy attenuation in fields of colliding ice floes – Part 1: Discrete-element modelling of dissipation due to ice–water drag
Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns
Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies
IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area
New insight from CryoSat-2 sea ice thickness for sea ice modelling
Investigating future changes in the volume budget of the Arctic sea ice in a coupled climate model
Saskia Kahl, Carolin Mehlmann, and Dirk Notz
The Cryosphere, 19, 129–141, https://doi.org/10.5194/tc-19-129-2025, https://doi.org/10.5194/tc-19-129-2025, 2025
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Ice mélange, a mixture of sea ice and icebergs, can impact sea-ice–ocean interactions. But climate models do not yet represent it due to computational limits. To address this shortcoming and include ice mélange into climate models, we suggest representing icebergs as particles. We integrate their feedback into mathematical equations used to model the sea-ice motion in climate models. The setup is computationally efficient due to the iceberg particle usage and enables a realistic representation.
Joseph Fogarty, Elie Bou-Zeid, Mitchell Bushuk, and Linette Boisvert
The Cryosphere, 18, 4335–4354, https://doi.org/10.5194/tc-18-4335-2024, https://doi.org/10.5194/tc-18-4335-2024, 2024
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We hypothesize that using a broad set of surface characterization metrics for polar sea ice surfaces will lead to more accurate representations in general circulation models. However, the first step is to identify the minimum set of metrics required. We show via numerical simulations that sea ice surface patterns can play a crucial role in determining boundary layer structures. We then statistically analyze a set of high-resolution sea ice surface images to obtain this minimal set of parameters.
Christopher Riedel and Jeffrey Anderson
The Cryosphere, 18, 2875–2896, https://doi.org/10.5194/tc-18-2875-2024, https://doi.org/10.5194/tc-18-2875-2024, 2024
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Accurate sea ice conditions are crucial for quality sea ice projections, which have been connected to rapid warming over the Arctic. Knowing which observations to assimilate into models will help produce more accurate sea ice conditions. We found that not assimilating sea ice concentration led to more accurate sea ice states. The methods typically used to assimilate observations in our models apply assumptions to variables that are not well suited for sea ice because they are bounded variables.
Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
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Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
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This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
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We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
The Cryosphere, 18, 1215–1239, https://doi.org/10.5194/tc-18-1215-2024, https://doi.org/10.5194/tc-18-1215-2024, 2024
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We present a new atmosphere–ocean–wave–sea ice coupled model to study the influences of ocean waves on Arctic sea ice simulation. Our results show (1) smaller ice-floe size with wave breaking increases ice melt, (2) the responses in the atmosphere and ocean to smaller floe size partially reduce the effect of the enhanced ice melt, (3) the limited oceanic energy is a strong constraint for ice melt enhancement, and (4) ocean waves can indirectly affect sea ice through the atmosphere and the ocean.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
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Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
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We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
Huy Dinh, Dimitrios Giannakis, Joanna Slawinska, and Georg Stadler
The Cryosphere, 17, 3883–3893, https://doi.org/10.5194/tc-17-3883-2023, https://doi.org/10.5194/tc-17-3883-2023, 2023
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We develop a numerical method to simulate the fracture in kilometer-sized chunks of floating ice in the ocean. Our approach uses a mathematical model that balances deformation energy against the energy required for fracture. We study the strength of ice chunks that contain random impurities due to prior damage or refreezing and what types of fractures are likely to occur. Our model shows that crack direction critically depends on the orientation of impurities relative to surrounding forces.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
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A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
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We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
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This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
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Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Nicolas Guillaume Alexandre Mokus and Fabien Montiel
The Cryosphere, 16, 4447–4472, https://doi.org/10.5194/tc-16-4447-2022, https://doi.org/10.5194/tc-16-4447-2022, 2022
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On the fringes of polar oceans, sea ice is easily broken by waves. As small pieces of ice, or floes, are more easily melted by the warming waters than a continuous ice cover, it is important to incorporate these floe sizes in climate models. These models simulate climate evolution at the century scale and are built by combining specialised modules. We study the statistical distribution of floe sizes under the impact of waves to better understand how to connect sea ice modules to wave modules.
Mauricio Arboleda-Zapata, Michael Angelopoulos, Pier Paul Overduin, Guido Grosse, Benjamin M. Jones, and Jens Tronicke
The Cryosphere, 16, 4423–4445, https://doi.org/10.5194/tc-16-4423-2022, https://doi.org/10.5194/tc-16-4423-2022, 2022
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We demonstrate how we can reliably estimate the thawed–frozen permafrost interface with its associated uncertainties in subsea permafrost environments using 2D electrical resistivity tomography (ERT) data. In addition, we show how further analyses considering 1D inversion and sensitivity assessments can help quantify and better understand 2D ERT inversion results. Our results illustrate the capabilities of the ERT method to get insights into the development of the subsea permafrost.
Adam William Bateson, Daniel L. Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, https://doi.org/10.5194/tc-16-2565-2022, 2022
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Numerical models are used to understand the mechanisms that drive the evolution of the Arctic sea ice cover. The sea ice cover is formed of pieces of ice called floes. Several recent studies have proposed variable floe size models to replace the standard model assumption of a fixed floe size. In this study we show the need to include floe fragmentation processes in these variable floe size models and demonstrate that model design can determine the impact of floe size on size ice evolution.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
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In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
Kees Nederhoff, Li Erikson, Anita Engelstad, Peter Bieniek, and Jeremy Kasper
The Cryosphere, 16, 1609–1629, https://doi.org/10.5194/tc-16-1609-2022, https://doi.org/10.5194/tc-16-1609-2022, 2022
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Diminishing sea ice is impacting waves across the Arctic region. Recent work shows the effect of the sea ice on offshore waves; however, effects within the nearshore are less known. This study characterizes the wave climate in the central Beaufort Sea coast of Alaska. We show that the reduction of sea ice correlates strongly with increases in the average and extreme waves. However, found trends deviate from offshore, since part of the increase in energy is dissipated before reaching the shore.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arne Melsom
The Cryosphere, 15, 3785–3796, https://doi.org/10.5194/tc-15-3785-2021, https://doi.org/10.5194/tc-15-3785-2021, 2021
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This study presents new methods to assess how well observations of sea ice expansion are reproduced by results from models. The aim is to provide information about the quality of forecasts for changes in the sea ice extent to operators in or near ice-infested waters. A test using 2 years of model results demonstrates the practical applicability and usefulness of the methods that are presented.
Jean-François Lemieux, L. Bruno Tremblay, and Mathieu Plante
The Cryosphere, 14, 3465–3478, https://doi.org/10.5194/tc-14-3465-2020, https://doi.org/10.5194/tc-14-3465-2020, 2020
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Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Sea ice forecasting systems can predict the evolution of pressure. However, these systems have low spatial resolution (a few km) compared to the dimensions of ships. We study the downscaling of pressure from the km-scale to scales relevant for navigation. We find that the pressure applied on a ship beset in heavy ice conditions can be markedly larger than the pressure predicted by the forecasting system.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
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The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to assume simplified distributions to reasonably translate the simulated sea ice into satellite
language.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
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The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool that translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Nils Hutter and Martin Losch
The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, https://doi.org/10.5194/tc-14-93-2020, 2020
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Sea ice is composed of a multitude of floes that constantly deform due to wind and ocean currents and thereby form leads and pressure ridges. These features are visible in the ice as stripes of open-ocean or high-piled ice. High-resolution sea ice models start to resolve these deformation features. In this paper we present two simulations that agree with satellite data according to a new evaluation metric that detects deformation features and compares their spatial and temporal characteristics.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2887–2900, https://doi.org/10.5194/tc-13-2887-2019, https://doi.org/10.5194/tc-13-2887-2019, 2019
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Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. Close to the ice edge, processes contributing to dissipation include collisions between ice floes and turbulence generated under the ice due to velocity differences between ice and water. This paper analyses details of those processes both theoretically and by means of a numerical model.
Evelyn Jäkel, Johannes Stapf, Manfred Wendisch, Marcel Nicolaus, Wolfgang Dorn, and Annette Rinke
The Cryosphere, 13, 1695–1708, https://doi.org/10.5194/tc-13-1695-2019, https://doi.org/10.5194/tc-13-1695-2019, 2019
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The sea ice surface albedo parameterization of a coupled regional climate model was validated against aircraft measurements performed in May–June 2017 north of Svalbard. The albedo parameterization was run offline from the model using the measured parameters surface temperature and snow depth to calculate the surface albedo and the individual fractions of the ice surface subtypes. An adjustment of the variables and additionally accounting for cloud cover reduced the root-mean-squared error.
Damien Ringeisen, Martin Losch, L. Bruno Tremblay, and Nils Hutter
The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, https://doi.org/10.5194/tc-13-1167-2019, 2019
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We study the creation of fracture in sea ice plastic models. To do this, we compress an ideal piece of ice of 8 km by 25 km. We use two different mathematical expressions defining the resistance of ice. We find that the most common one is unable to model the fracture correctly, while the other gives better results but brings instabilities. The results are often in opposition with ice granular nature (e.g., sand) and call for changes in ice modeling.
Charles Gignac, Monique Bernier, and Karem Chokmani
The Cryosphere, 13, 451–468, https://doi.org/10.5194/tc-13-451-2019, https://doi.org/10.5194/tc-13-451-2019, 2019
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The IcePAC tool is made to estimate the probabilities of specific sea ice conditions based on historical sea ice concentration time series from the EUMETSAT OSI-409 product (12.5 km grid), modelled using the beta distribution and used to build event probability maps, which have been unavailable until now. Compared to the Canadian ice service atlas, IcePAC showed promising results in the Hudson Bay, paving the way for its usage in other regions of the cryosphere to inform stakeholders' decisions.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
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This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Ann Keen and Ed Blockley
The Cryosphere, 12, 2855–2868, https://doi.org/10.5194/tc-12-2855-2018, https://doi.org/10.5194/tc-12-2855-2018, 2018
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As the climate warms during the 21st century, our model shows extra melting at the top and the base of the Arctic sea ice. The reducing ice cover affects the impact these processes have on the sea ice volume budget, where the largest individual change is a reduction in the amount of growth at the base of existing ice. Using different forcing scenarios we show that, for this model, changes in the volume budget depend on the evolving ice area but not on the speed at which the ice area declines.
Cited articles
Barnett, T. P. and Schlesinger, M. E.: Detecting changes in global climate induced by greenhouse gases, J. Geophys. Res., 92, 14772–14780, https://doi.org/10.1029/JD092iD12p14772, 1987.
Bengtsson, L., Hodges, K. I., and Roeckner, E.: Storm Tracks and Climate Change, J. Climate, 19, 3518–3543, https://doi.org/10.1175/JCLI3815.1, 2006.
Blanchard-Wrigglesworth, E. and Bitz, C. M.: Characteristics of Arctic Sea-Ice Thickness Variability in GCMs, J. Climate 27, 8244–8258, 2014.
Bushuk, M., Msadek, R., Winton, M., Vecchi, G. A., Gudgel, R., Rosati, A., and Yang, X.: Skillful regional prediction of Arctic sea ice onseasonal timescales, Geophys. Res. Lett. 44, 4953–4964, https://doi.org/10.1002/2017GL073155, 2017.
Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability and trends, 1979–2006, J. Geophys. Res., 113, C07003, https://doi.org/10.1029/2007JC004558, 2008.
Chen, Z., Liu, J., Song, M., Yang, Q., and Xu, S.: Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System, J. Climate, 30, 8429–8446, 2017.
Collow, T., Wang, W., Kumar, A., and Zhang, J.: Improving Arctic Sea Ice Prediction Using PIOMAS Initial Sea Ice Thickness in a Coupled Ocean–Atmosphere Model, Mon. Weather Rev., 143, 4618–4630, https://doi.org/10.1175/MWR-D-15-0097.1, 2015.
Dee, D. P., Uppala, S. M., Simmons, A. J., et al.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Eguíluz, V. M., Fernández-Gracia, J., Irigoien, X., and Duarte, C. M.: A quantitative assessment of Arctic shipping in 2010–2014, Sci. Rep., 6, 30682, https://doi.org/10.1038/srep30682, 2016.
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003.
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics, J. Geophys. Res., 102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997.
Froude, L. S. R.: TIGGE: Comparison of the prediction of Northern Hemisphere extratropical cyclones by different ensemble prediction systems, Weather Forecast., 25, 819–836, https://doi.org/10.1175/2010WAF2222326.1, 2010.
Grosfeld, K., Treffeisen, R., Asseng, J., Bartsch, A., Bräuer, B., Fritzsch, B., Gerdes, R., Hendricks, S., Hiller, W., Heygster, G., Krumpen, T., Lemke, P., Melsheimer, C., Nicolaus, M., Ricker, R., and Weigelt, M.: Online sea-ice knowledge and data platform, available at: www.meereisportal.de (last access: 26 May 2016), Polarforschung, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research and German Society of Polar Research, 85, 143–155, https://doi.org/10.2312/polfor.2016.011, 2016.
Holland, M. M., Bailey, D. A., and Vavrus, S.: Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3, Clim. Dynam., 36, 1239–1253, https://doi.org/10.1007/s00382-010-0792-4, 2011.
Honda, M., Inoue, J., and Yamane, S.: Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters, Geophys. Res. Lett., 36, L08707, https://doi.org/10.1029/2008GL037079, 2009.
Hunke, E. and Dukowicz, J.: An Elastic–Viscous–Plastic Model for Sea Ice Dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997.
Inoue, J., Hori, M., and Takaya, K.: The role of Barents sea ice in the wintertime cyclone track and emergence of a Warm-Arctic Cold Siberian anomaly, J. Climate, 25, 2561–2568, 2012.
Inoue, J., Yamazaki, A., Ono, J., Dethloff, K., Maturilli, M., Neuber, R., Edwards, P., and Yamaguchi, H.: Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route, Sci. Rep., 5, 16868, https://doi.org/10.1038/srep16868, 2015.
Jung, T. and Matsueda, M.: Verification of global numerical weather forecasting systems in polar regions using TIGGE data, Q. J. Roy. Meteor. Soc., 142, 574–582, https://doi.org/10.1002/qj.2437, 2016.
Kaplan, D. and Glass, L.: Understanding nonlinear dynamics, Springer-Verlag, New York, 420 pp., 1995.
Kara, A., Rochford, P., and Hurlburt, H.: Efficient and Accurate Bulk Parameterizations of Air–Sea Fluxes for Use in General Circulation Models, J. Atmos. Ocean. Tech., 17, 1421–1438, 2000.
Kashiwase, H., Ohshima, K. I., Nihashi, S., and Eicken, H.: Evidence for ice-ocean albedo feedback in the Arctic Ocean shifting to a seasonal ice zone, Sci. Rep., 7, 8170, https://doi.org/10.1038/s41598-017-08467-z, 2017.
Kimura, N., Nishimura, A., Tanaka, Y., and Yamaguchi, H.: Influence of winter sea-ice motion on summer ice cover in the Arctic, Polar Res., 32, 20193, https://doi.org/10.3402/polar.v32i0.20193, 2013.
Large, W. G. and Pond, S.: Open Ocean Momentum Flux Measurements in Moderate to Strong Winds, J. Phys. Oceanogr., 11, 324–336, 1981.
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.
Leppäranta, M.: The Drift of Sea Ice, Springer-Verlang, Germany, 266 pp., 2005.
Lisæter, K. A., Rosanova, J., and Evensen, G.: Assimilation of ice concentration in a coupled ice-ocean model, using the Ensemble Kalman filter, Ocean Dynam., 53, 368–388, https://doi.org/10.1007/s10236-003-0049-4, 2003.
Lindsay, R. W. and Zhang, J.: Arctic Ocean Ice Thickness: Modes of Variability and the Best Locations from Which to Monitor Them, J. Phys. Oceanogr., 36, 496–506, https://doi.org/10.1175/JPO2861.1, 2006.
Lindsay, R. W., Zhang, J., Schweiger, A. J., and Steele, M. A.: Seasonal predictions of ice extent in the Arctic Ocean, J. Geophys. Res., 113, C02023, https://doi.org/10.1029/2007JC004259, 2008.
McPhee, M. G.: Advances in understanding ice-ocean stress during and since AIDJEX, Cold Reg. Sci. Technol., 76, 24–36, 2012.
Melia, N., Haines, K., and Hawkins, E.: Improved Arctic sea ice thickness projections using bias-corrected CMIP5 simulations, The Cryosphere, 9, 2237–2251, https://doi.org/10.5194/tc-9-2237-2015, 2015.
Melia, N., Haines, K., and Hawkins, E.: Sea ice decline and 21st century trans-Arctic shipping routes, Geophys. Res. Lett., 43, 9720–9728, https://doi.org/10.1002/2016GL069315, 2016.
Melia, N., Haines, K., Hawkins, E., and Day, J. J.: Towards seasonal Arctic shipping route predictions, Environ. Res. Lett., 12, 084005, https://doi.org/10.1088/1748-9326/aa7a60, 2017.
Mohammadi-Aragh, M., Goessling, H. F., Losch, M., Hutter, N., and Jung, T.: Predictability of Arctic sea ice on weather time scales, Sci. Rep., 8, 6514, https://doi.org/10.1038/s41598-018-24660-0, 2018.
Mori, M., Watanabe, M., Shiogama, H., Inoue, J., and Kimoto, M.: Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades, Nat. Geosci., 7, 869–873, 2014.
Nishii, K., Nakamura, H., and Orsolini, Y. J.: Arctic summer storm track in CMIP3/5 climate models, Clim. Dynam., 44, 1311, https://doi.org/10.1007/s00382-014-2229-y, 2015.
Ono, J., Inoue, J., Yamazaki, A., Dethloff, K., and Yamaguchi, H.: 2016. The impact of radiosonde data on forecasting sea-ice distribution along the Northern Sea Route during an extremely developed cyclone, J. Adv. Model Earth Syst. 8, 292–303, https://doi.org/10.1002/2015MS000552, 2016.
Orsolini, Y. J. and Sorteberg, A.: Projected changes in Eurasian and Arctic summer cyclones under global warming in the Bergen climate model, Atmospheric and Oceanic Science Letters, 2, 62–67, 2009.
Overland, J. E., Francis, J. A., Hall, R., Hanna, E., Kim, S.-J., and Vihma, T.: The melting Arctic and mid-latitude weather patterns: Are they connected?, J. Climate, 28, 7917–7932, https://doi.org/10.1175/JCLI-D-14-00822.1, 2015.
Park, H.-S. and Stewart, A. L.: An analytical model for wind-driven Arctic summer sea ice drift, The Cryosphere, 10, 227–244, https://doi.org/10.5194/tc-10-227-2016, 2016.
Pastusiak, T.: The Northern sea route as a shipping lane, Springer, Swizerland, 219 pp., 2016.
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runcimen, K., and Nghiem, S. V.: Increasing solar heating of the Arctic Ocean and adjacent seas, 1979–2005: Attribution and the role of ice-albedo feedback, Geophys. Res. Lett., 34, L19505, https://doi.org/10.1029/2007GL031480, 2007.
Perovich, D., Richter-Menge, J., Elder, B., Arbetter, T., Claffey, K., and Polashenski, C.: Observing and understanding climate change: Monitoring the mass balance, motion, and thickness of Arctic sea ice, Cold Regions Research and Engineering Laboratory, http://imb-crrel-dartmouth.org/imb.crrel (last access: 20 June 2017), 2013.
Persson, A.: User guide to ECMWF forecast products ver. 1.2, October 2011, ECMWF, Reading, 121 pp., 2011.
Petoukhov, V. and Semenov, V. A.: A link between reduced Barents-Kara sea ice and cold winter extremes over northern continents, J. Geophys. Res., 115, D21111, https://doi.org/10.1029/2009JD013568, 2010.
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017.
Sakov, P. and Oke, P. R.: A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters, Tellus A, 60, 361–371, 2008.
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012.
Sato, K. and Inoue, J.: Comparison of Arctic sea ice thickness and snow depth estimates from CFSR with in situ observations, Clim. Dynam., 50, 289–301, https://doi.org/10.1007/s00382-017-3607-z, 2018.
Schøyen, H. and Bråthen, S.: The Northern Sea route versus the Suez Canal: cases from bulk shipping, J. Transp. Geogr., 19, 977–983, 2011.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.: Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011.
Schweiger, A. J. and Zhang, J.: Accuracy of short-term sea ice drift forecasts using a coupled ice-ocean model, J. Geophys. Res.-Oceans, 120, 7827–7841, https://doi.org/10.1002/2015JC011273, 2015.
Screen, J. A.: Simulated Atmospheric Response to Regional and Pan-Arctic Sea Ice Loss, J. Climate, 30, 3945–3962, https://doi.org/10.1175/JCLI-D-16-0197.1, 2017.
Semtner, A.: A Model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate, J. Phys. Oceanogr., 6, 379–389, 1976.
Serreze, M. C. and Barrett, A. P.: The summer cyclone maximum over the central Arctic Ocean, J. Climate, 21, 1048–1065, 2008.
Serreze, M. C. and Barry, R. G.: Synoptic activity in the Arctic basin, 1979–85, J. Climate, 1, 1276–1295, 1988.
Simonsen, M., Hackett, B., Bertino, L., Røed, L. P., Waagbø, G. A., Drivdal, M., and Sutherland, G.: PRODUCT USER MANUAL For Arctic Ocean Physical and Bio Analysis and Forecasting Products, EU, Copernicus Marine Service, Issue: 5.5, 56 pp., available at: http://marine.copernicus.eu (last access: 29 December 2016), 2017.
Simmonds, I. and Rudeva, I.: The great Arctic cyclone of August 2012, Geophys. Res. Lett., 39, L23709, https://doi.org/10.1029/2012GL054259, 2012.
Stark, J. D., Ridley, J., Martin, M., and Hines, A.: Sea ice concentration and motion assimilation in a sea ice−ocean model, J. Geophys. Res., 113, C05S91, https://doi.org/10.1029/2007JC004224, 2008.
Steele, M., Ermold, W., and Zhang, J.: Arctic Ocean surface warming trends over the past 100 years, Geophys. Res. Lett., 35, L02614, https://doi.org/10.1029/2007GL031651, 2008.
Stroeve, J., Hamilton, L. C., Bitz, C. M., and Blanchard-Wrigglesworth, E.: Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008–2013, Geophys. Res. Lett., 41, 2411–2418, https://doi.org/10.1002/2014GL059388, 2014.
Tan, X., Su, K., Riska, K., and Moan, T.: A six-degrees-of-freedom numerical model for level ice–ship interaction, Cold Reg. Sci. Technol., 92, 1–16, https://doi.org/10.1016/j.coldregions.2013.03.006, 2013.
Thorndike, A. S. and Colony, R.: Sea ice motion in response to geostrophic winds, J. Geophys. Res., 87, 5845–5852, https://doi.org/10.1029/JC087iC08p05845, 1982.
Wang, W., Chen, M., and Kumar, A.: Seasonal Prediction of Arctic Sea Ice Extent from a Coupled Dynamical Forecast System, Mon. Weather Rev., 141, 1375–1394, https://doi.org/10.1175/MWR-D-12-00057.1, 2013.
Wang, X., Key, J., Kwok, R., and Zhang, J.: Comparison of Arctic Sea ice thickness from satellites, aircraft, and PIOMAS data, Remote Sens., 8, 713, https://doi.org/10.3390/rs8090713, 2016.
Wassmann, P.: Arctic marine ecosystems in an era of rapid climate change, Prog. Oceanogr., 90, 1–17, 2011.
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013, Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, 2017.
Yamagami, A., Matsueda, M., and Tanaka, H. L.: Extreme Arctic cyclone in August 2016, Atmos. Sci. Lett., 18, 307–314, https://doi.org/10.1002/asl.757, 2017.
Yamagami, A., Matsueda, M., and Tanaka, H. L.: Predictability of the 2012 great Arctic cyclone on medium-range timescales, J. Volcanol. Geoth. Res., 15, 13–23, https://doi.org/10.1016/j.polar.2018.01.002, 2018.
Yamamoto-Kawai, M., McLaughlin, F. A., and Carmack, E. C.: Effects of ocean acidification, warming and melting of sea ice on aragonite saturation of the Canada Basin surface water, Geophys. Res. Lett., 38, L03601, https://doi.org/10.1029/2010GL045501, 2011.
Yamazaki, A., Inoue, J., Dethloff, K., Maturilli, M., and König-Langlo, G.: Impact of radiosonde observations on forecasting summertime Arctic cyclone formation, J. Geophys. Res., 120, 3249–3273, https://doi.org/10.1002/2014JD022925, 2015.
Zhang, J. and Rothrock, D. A.: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, 681–697, 2003.
Short summary
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was...
