Articles | Volume 12, issue 2
https://doi.org/10.5194/tc-12-675-2018
© 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-12-675-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2018
Research article |
| 26 Feb 2018
| 26 Feb 2018
Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC
Jun Ono
CORRESPONDING AUTHOR
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Hiroaki Tatebe
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Yoshiki Komuro
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Masato I. Nodzu
Tokyo Metropolitan University, Hachioji, 192-0397, Japan
Masayoshi Ishii
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
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Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
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In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets to track real-world changes over time. To make our work relevant to policymakers, we follow methods from the Intergovernmental Panel on Climate Change (IPCC). Human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the IPCC assessment.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
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The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Hideo Shiogama, Hiroaki Tatebe, Michiya Hayashi, Manabu Abe, Miki Arai, Hiroshi Koyama, Yukiko Imada, Yu Kosaka, Tomoo Ogura, and Masahiro Watanabe
Earth Syst. Dynam., 14, 1107–1124, https://doi.org/10.5194/esd-14-1107-2023, https://doi.org/10.5194/esd-14-1107-2023, 2023
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We produced one of the largest single model initial-condition ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). MIROC6-LE includes historical simulations, eight single forcing historical experiments, five future scenario experiments and three single forcing future experiments with 10- or 50-ensemble members. We describe the experimental design and show initial analyses. This dataset would be useful to a wide range of research communities.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
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This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Rumi Ohgaito, Akitomo Yamamoto, Tomohiro Hajima, Ryouta O'ishi, Manabu Abe, Hiroaki Tatebe, Ayako Abe-Ouchi, and Michio Kawamiya
Geosci. Model Dev., 14, 1195–1217, https://doi.org/10.5194/gmd-14-1195-2021, https://doi.org/10.5194/gmd-14-1195-2021, 2021
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Using the MIROC-ES2L Earth system model, selected time periods of the past were simulated. The ability to simulate the past is also an evaluation of the performance of the model in projecting global warming. Simulations for 21 000, 6000, and 127 000 years ago, and a simulation for 1000 years starting in 850 CE were simulated. The results showed that the model can generally describe past climate change.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
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We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Michio Watanabe, Hiroaki Tatebe, Hiroshi Koyama, Tomohiro Hajima, Masahiro Watanabe, and Michio Kawamiya
Ocean Sci., 16, 1431–1442, https://doi.org/10.5194/os-16-1431-2020, https://doi.org/10.5194/os-16-1431-2020, 2020
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Carbon flux between air and sea is known to fluctuate in response to inherent climate variations. In this study, observed ocean hydrographic data were assimilated into Earth system models, and the carbon flux in the equatorial Pacific was evaluated. Our results suggest that, when observed ocean hydrographic data are assimilated into models for carbon cycle predictions on interannual to decadal timescales, the reproducibility of the internal climate variations in the model itself is important.
Karina von Schuckmann, Lijing Cheng, Matthew D. Palmer, James Hansen, Caterina Tassone, Valentin Aich, Susheel Adusumilli, Hugo Beltrami, Tim Boyer, Francisco José Cuesta-Valero, Damien Desbruyères, Catia Domingues, Almudena García-García, Pierre Gentine, John Gilson, Maximilian Gorfer, Leopold Haimberger, Masayoshi Ishii, Gregory C. Johnson, Rachel Killick, Brian A. King, Gottfried Kirchengast, Nicolas Kolodziejczyk, John Lyman, Ben Marzeion, Michael Mayer, Maeva Monier, Didier Paolo Monselesan, Sarah Purkey, Dean Roemmich, Axel Schweiger, Sonia I. Seneviratne, Andrew Shepherd, Donald A. Slater, Andrea K. Steiner, Fiammetta Straneo, Mary-Louise Timmermans, and Susan E. Wijffels
Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020, https://doi.org/10.5194/essd-12-2013-2020, 2020
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Understanding how much and where the heat is distributed in the Earth system is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to obtain the Earth heat inventory over the period 1960–2018.
Hiroyuki Tsujino, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro Iovino, Who M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, and Zipeng Yu
Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, https://doi.org/10.5194/gmd-13-3643-2020, 2020
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The OMIP-2 framework for global ocean–sea-ice model simulations is assessed by comparing multi-model means from 11 CMIP6-class global ocean–sea-ice models calculated separately for the OMIP-1 and OMIP-2 simulations. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet key improvements in transitioning from OMIP-1 to OMIP-2 are also identified. Thus, the present assessment justifies that future ocean–sea-ice model development and analysis studies use the OMIP-2 framework.
Tomohiro Hajima, Michio Watanabe, Akitomo Yamamoto, Hiroaki Tatebe, Maki A. Noguchi, Manabu Abe, Rumi Ohgaito, Akinori Ito, Dai Yamazaki, Hideki Okajima, Akihiko Ito, Kumiko Takata, Koji Ogochi, Shingo Watanabe, and Michio Kawamiya
Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, https://doi.org/10.5194/gmd-13-2197-2020, 2020
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We developed a new Earth system model (ESM) named MIROC-ES2L. This model is based on a state-of-the-art climate model and includes carbon–nitrogen cycles for the land and multiple biogeochemical cycles for the ocean. The model's performances on reproducing historical climate and biogeochemical changes are confirmed to be reasonable, and the new model is likely to be an
optimisticmodel in projecting future climate change among ESMs in the Coupled Model Intercomparison Project Phase 6.
Hiroaki Tatebe, Tomoo Ogura, Tomoko Nitta, Yoshiki Komuro, Koji Ogochi, Toshihiko Takemura, Kengo Sudo, Miho Sekiguchi, Manabu Abe, Fuyuki Saito, Minoru Chikira, Shingo Watanabe, Masato Mori, Nagio Hirota, Yoshio Kawatani, Takashi Mochizuki, Kei Yoshimura, Kumiko Takata, Ryouta O'ishi, Dai Yamazaki, Tatsuo Suzuki, Masao Kurogi, Takahito Kataoka, Masahiro Watanabe, and Masahide Kimoto
Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, https://doi.org/10.5194/gmd-12-2727-2019, 2019
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For a deeper understanding of a wide range of climate science issues, the latest version of the Japanese climate model, called MIROC6, was developed. The climate model represents observed mean climate and climate variations well, for example tropical precipitation, the midlatitude westerlies, and the East Asian monsoon, which influence human activity all over the world. The improved climate simulations could add reliability to climate predictions under global warming.
Tomoo Ogura, Hideo Shiogama, Masahiro Watanabe, Masakazu Yoshimori, Tokuta Yokohata, James D. Annan, Julia C. Hargreaves, Naoto Ushigami, Kazuya Hirota, Yu Someya, Youichi Kamae, Hiroaki Tatebe, and Masahide Kimoto
Geosci. Model Dev., 10, 4647–4664, https://doi.org/10.5194/gmd-10-4647-2017, https://doi.org/10.5194/gmd-10-4647-2017, 2017
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Present-day climate simulated by coupled ocean atmosphere models exhibits significant biases in top-of-atmosphere radiation and clouds. This study shows that only limited part of the biases can be removed by parameter tuning in a climate model. The results underline the importance of improving parameterizations in climate models based on cloud process studies. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases.
Stephen M. Griffies, Gokhan Danabasoglu, Paul J. Durack, Alistair J. Adcroft, V. Balaji, Claus W. Böning, Eric P. Chassignet, Enrique Curchitser, Julie Deshayes, Helge Drange, Baylor Fox-Kemper, Peter J. Gleckler, Jonathan M. Gregory, Helmuth Haak, Robert W. Hallberg, Patrick Heimbach, Helene T. Hewitt, David M. Holland, Tatiana Ilyina, Johann H. Jungclaus, Yoshiki Komuro, John P. Krasting, William G. Large, Simon J. Marsland, Simona Masina, Trevor J. McDougall, A. J. George Nurser, James C. Orr, Anna Pirani, Fangli Qiao, Ronald J. Stouffer, Karl E. Taylor, Anne Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Maria Valdivieso, Qiang Wang, Michael Winton, and Stephen G. Yeager
Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, https://doi.org/10.5194/gmd-9-3231-2016, 2016
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The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This document defines OMIP and details a protocol both for simulating global ocean/sea-ice models and for analysing their output.
Jonathan J. Day, Steffen Tietsche, Mat Collins, Helge F. Goessling, Virginie Guemas, Anabelle Guillory, William J. Hurlin, Masayoshi Ishii, Sarah P. E. Keeley, Daniela Matei, Rym Msadek, Michael Sigmond, Hiroaki Tatebe, and Ed Hawkins
Geosci. Model Dev., 9, 2255–2270, https://doi.org/10.5194/gmd-9-2255-2016, https://doi.org/10.5194/gmd-9-2255-2016, 2016
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Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable.
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Improving Seasonal Arctic Sea Ice Predictions with the Combination of Machine Learning and Earth System Model
Estimation of duration and its changes in Lagrangian observations relying on ice floes in the Arctic Ocean utilizing sea ice motion product
Seasonal evolution of the sea ice floe size distribution in the Beaufort Sea from 2 decades of MODIS data
Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization
Seasonality and scenario dependence of rapid Arctic sea ice loss events in CMIP6 simulations
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Predictability of Arctic sea ice drift in coupled climate models
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A new state-dependent parameterization for the free drift of sea ice
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Meltwater sources and sinks for multiyear Arctic sea ice in summer
An X-ray micro-tomographic study of the pore space, permeability and percolation threshold of young sea ice
Calibration of sea ice drift forecasts using random forest algorithms
Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions
The flexural strength of bonded ice
Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification
An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models
Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products
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Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations
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An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC)
Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice
Sea ice volume variability and water temperature in the Greenland Sea
Sea ice export through the Fram Strait derived from a combined model and satellite data set
MacKenzie E. Jewell, Jennifer K. Hutchings, and Angela C. Bliss
The Cryosphere, 19, 1413–1430, https://doi.org/10.5194/tc-19-1413-2025, https://doi.org/10.5194/tc-19-1413-2025, 2025
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We quantify the contribution of sea ice drift during coastal-lead events to seasonal ice transport in the Beaufort Sea. Data from GPS tracker buoys deployed as part of the Sea Ice Dynamic Experiment (SIDEx) in spring 2021 show that sea ice in the southern Beaufort Sea became more responsive to wind forcing during fracturing events detected from satellite observations. Nearly all the spring 2021 sea ice transport occurred during these events, highlighting their importance at seasonal timescales.
Astrid Pacini, Michael Steele, and Mary-Beth Schreck
The Cryosphere, 19, 1391–1411, https://doi.org/10.5194/tc-19-1391-2025, https://doi.org/10.5194/tc-19-1391-2025, 2025
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While sea ice concentration data are critically important for climate research, obtaining high-resolution data remains a challenge. Here we present and validate the US National Weather Service Alaska Sea Ice Program (ASIP) ice maps. These maps are shown to be highly accurate when compared to in situ observations and to outperform a passive-microwave-based product, especially at low concentrations. Therefore, ASIP data provide an exciting new tool to study ice conditions in the Pacific Arctic.
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4092, https://doi.org/10.5194/egusphere-2024-4092, 2025
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Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Fanyi Zhang, Ruibo Lei, Meng Qu, Na Li, Ying Chen, and Xiaoping Pang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2723, https://doi.org/10.5194/egusphere-2024-2723, 2024
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We reconstructed sea ice drift trajectories and identified optimal deployment areas for Lagrangian observations. It revealed a preference for ice advection towards the Transpolar Drift region over the Beaufort Gyre, with endpoints influenced by large-scale atmospheric circulation patterns. This study could help the future ice camp/buoy deployment strategies, ensuring the sustainability of crucial Arctic observations in the face of changing environmental conditions.
Ellen M. Buckley, Leela Cañuelas, Mary-Louise Timmermans, and Monica M. Wilhelmus
The Cryosphere, 18, 5031–5043, https://doi.org/10.5194/tc-18-5031-2024, https://doi.org/10.5194/tc-18-5031-2024, 2024
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Arctic sea ice cover evolves seasonally from large plates separated by long, linear leads in the winter to a mosaic of smaller sea ice floes in the summer. Here, we present a new image segmentation algorithm applied to thousands of images and identify over 9 million individual pieces of ice. We observe the characteristics of the floes and how they evolve throughout the summer as the ice breaks up.
Shan Sun and Amy Solomon
The Cryosphere, 18, 3033–3048, https://doi.org/10.5194/tc-18-3033-2024, https://doi.org/10.5194/tc-18-3033-2024, 2024
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The study brings to light the suitability of CICE for seasonal prediction being contingent on several factors, such as initial conditions like sea ice coverage and thickness, as well as atmospheric and oceanic conditions including oceanic currents and sea surface temperature. We show there is potential to improve seasonal forecasting by using a more reliable sea ice thickness initialization. Thus, data assimilation of sea ice thickness is highly relevant for advancing seasonal prediction skills.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
EGUsphere, https://doi.org/10.5194/egusphere-2024-1873, https://doi.org/10.5194/egusphere-2024-1873, 2024
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Our study analyses rapid Arctic sea ice loss events (RILEs), which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the mid-century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
Jan Åström, Fredrik Robertsen, Jari Haapala, Arttu Polojärvi, Rivo Uiboupin, and Ilja Maljutenko
The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, https://doi.org/10.5194/tc-18-2429-2024, 2024
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The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle materials and has been extensively applied to glacier calving. Here, we report on the adaptation of the code to sea-ice dynamics and breakup. The code demonstrates the capability to simulate sea-ice dynamics on a 100 km scale with an unprecedented resolution. We argue that codes of this type may become useful for improving forecasts of sea-ice dynamics.
Sergio Testón-Martínez, Laura M. Barge, Jan Eichler, C. Ignacio Sainz-Díaz, and Julyan H. E. Cartwright
The Cryosphere, 18, 2195–2205, https://doi.org/10.5194/tc-18-2195-2024, https://doi.org/10.5194/tc-18-2195-2024, 2024
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Brinicles are tubular ice structures that grow under the sea ice in cold regions. This happens because the salty water going downwards from the sea ice is colder than the seawater. We have successfully recreated an analogue of these structures in our laboratory. Three methods were used, producing different results. In this paper, we explain how to use these methods and study the behaviour of the brinicles created when changing the flow of water and study the importance for natural brinicles.
Jamie L. Ward and Neil F. Tandon
The Cryosphere, 18, 995–1012, https://doi.org/10.5194/tc-18-995-2024, https://doi.org/10.5194/tc-18-995-2024, 2024
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Over the long term, the speed at which sea ice in the Arctic moves has been increasing during all seasons. However, nearly all climate models project that sea ice motion will decrease during summer. This study aims to understand the mechanisms responsible for these projected decreases in summertime sea ice motion. We find that models produce changes in winds and ocean surface tilt which cause the sea ice to slow down, and it is realistic to expect such changes to also occur in the real world.
Linghan Li, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and Fred Martin Ralph
The Cryosphere, 18, 121–137, https://doi.org/10.5194/tc-18-121-2024, https://doi.org/10.5194/tc-18-121-2024, 2024
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We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice variations using hourly atmospheric ERA5 for 1981–2020 at 0.25° × 0.25° resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that the rate of change in sea ice concentration has significant anticorrelation with moisture, northward wind and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
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Atmospheric circulation anomalies lead to high Arctic sea ice outflow in winter 2020, causing heavy ice conditions in the Barents–Greenland seas, subsequently impeding the sea surface temperature warming. This suggests that the winter–spring Arctic sea ice outflow can be considered a predictor of changes in sea ice and other marine environmental conditions in the Barents–Greenland seas, which could help to improve our understanding of the physical connections between them.
MacKenzie E. Jewell, Jennifer K. Hutchings, and Cathleen A. Geiger
The Cryosphere, 17, 3229–3250, https://doi.org/10.5194/tc-17-3229-2023, https://doi.org/10.5194/tc-17-3229-2023, 2023
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Sea ice repeatedly fractures near a prominent Alaskan headland as winds move ice along the coast, challenging predictions of sea ice drift. We find winds from high-pressure systems drive these fracturing events, and the Alaskan coastal boundary modifies the resultant ice drift. This observational study shows how wind patterns influence sea ice motion near coasts in winter. Identified relations between winds, ice drift, and fracturing provide effective test cases for dynamic sea ice models.
Katarzyna Bradtke and Agnieszka Herman
The Cryosphere, 17, 2073–2094, https://doi.org/10.5194/tc-17-2073-2023, https://doi.org/10.5194/tc-17-2073-2023, 2023
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The frazil streaks are one of the visible signs of complex interactions between the mixed-layer dynamics and the forming sea ice. Using high-resolution visible satellite imagery we characterize their spatial properties, relationship with the meteorological forcing, and role in modifying wind-wave growth in the Terra Nova Bay Polynya. We provide a simple statistical tool for estimating the extent and ice coverage of the region of high ice production under given wind speed and air temperature.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
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Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Nikolas O. Aksamit, Randall K. Scharien, Jennifer K. Hutchings, and Jennifer V. Lukovich
The Cryosphere, 17, 1545–1566, https://doi.org/10.5194/tc-17-1545-2023, https://doi.org/10.5194/tc-17-1545-2023, 2023
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Coherent flow patterns in sea ice have a significant influence on sea ice fracture and refreezing. We can better understand the state of sea ice, and its influence on the atmosphere and ocean, if we understand these structures. By adapting recent developments in chaotic dynamical systems, we are able to approximate ice stretching surrounding individual ice buoys. This illuminates the state of sea ice at much higher resolution and allows us to see previously invisible ice deformation patterns.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
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Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo
The Cryosphere, 17, 1327–1341, https://doi.org/10.5194/tc-17-1327-2023, https://doi.org/10.5194/tc-17-1327-2023, 2023
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In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. It is therefore essential to monitor sea ice properties with fine temporal and spatial resolution. In this paper, we show that icequakes recorded on sea ice can be processed with artificial intelligence to produce accurate maps of sea ice thickness with high temporal and spatial resolutions.
Sasan Tavakoli and Alexander V. Babanin
The Cryosphere, 17, 939–958, https://doi.org/10.5194/tc-17-939-2023, https://doi.org/10.5194/tc-17-939-2023, 2023
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We have tried to develop some new wave–ice interaction models by considering two different types of forces, one of which emerges in the ice and the other of which emerges in the water. We have checked the ability of the models in the reconstruction of wave–ice interaction in a step-wise manner. The accuracy level of the models is acceptable, and it will be interesting to check whether they can be used in wave climate models or not.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, https://doi.org/10.5194/tc-17-105-2023, 2023
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It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott
The Cryosphere, 16, 3753–3773, https://doi.org/10.5194/tc-16-3753-2022, https://doi.org/10.5194/tc-16-3753-2022, 2022
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Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.
Simon Felix Reifenberg and Helge Friedrich Goessling
The Cryosphere, 16, 2927–2946, https://doi.org/10.5194/tc-16-2927-2022, https://doi.org/10.5194/tc-16-2927-2022, 2022
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Using model simulations, we analyze the impact of chaotic error growth on Arctic sea ice drift predictions. Regarding forecast uncertainty, our results suggest that it matters in which season and where ice drift forecasts are initialized and that both factors vary with the model in use. We find ice velocities to be slightly more predictable than near-surface wind, a main driver of ice drift. This is relevant for future developments of ice drift forecasting systems.
Agathe Serripierri, Ludovic Moreau, Pierre Boue, Jérôme Weiss, and Philippe Roux
The Cryosphere, 16, 2527–2543, https://doi.org/10.5194/tc-16-2527-2022, https://doi.org/10.5194/tc-16-2527-2022, 2022
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As a result of global warming, the sea ice is disappearing at a much faster rate than predicted by climate models. To better understand and predict its ongoing decline, we deployed 247 geophones on the fast ice in Van Mijen Fjord in Svalbard, Norway, in March 2019. The analysis of these data provided a precise daily evolution of the sea-ice parameters at this location with high spatial and temporal resolution and accuracy. The results obtained are consistent with the observations made in situ.
Laura L. Landrum and Marika M. Holland
The Cryosphere, 16, 1483–1495, https://doi.org/10.5194/tc-16-1483-2022, https://doi.org/10.5194/tc-16-1483-2022, 2022
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High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
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We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
Charles Brunette, L. Bruno Tremblay, and Robert Newton
The Cryosphere, 16, 533–557, https://doi.org/10.5194/tc-16-533-2022, https://doi.org/10.5194/tc-16-533-2022, 2022
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Sea ice motion is a versatile parameter for monitoring the Arctic climate system. In this contribution, we use data from drifting buoys, winds, and ice thickness to parameterize the motion of sea ice in a free drift regime – i.e., flowing freely in response to the forcing from the winds and ocean currents. We show that including a dependence on sea ice thickness and taking into account a climatology of the surface ocean circulation significantly improves the accuracy of sea ice motion estimates.
Madison M. Smith, Marika Holland, and Bonnie Light
The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, https://doi.org/10.5194/tc-16-419-2022, 2022
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Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas
The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, https://doi.org/10.5194/tc-16-259-2022, 2022
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Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.
Mathieu Plante and L. Bruno Tremblay
The Cryosphere, 15, 5623–5638, https://doi.org/10.5194/tc-15-5623-2021, https://doi.org/10.5194/tc-15-5623-2021, 2021
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We propose a generalized form for the damage parameterization such that super-critical stresses can return to the yield with different final sub-critical stress states. In uniaxial compression simulations, the generalization improves the orientation of sea ice fractures and reduces the growth of numerical errors. Shear and convergence deformations however remain predominant along the fractures, contrary to observations, and this calls for modification of the post-fracture viscosity formulation.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
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We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Don Perovich, Madison Smith, Bonnie Light, and Melinda Webster
The Cryosphere, 15, 4517–4525, https://doi.org/10.5194/tc-15-4517-2021, https://doi.org/10.5194/tc-15-4517-2021, 2021
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During summer, Arctic sea ice melts on its surface and bottom and lateral edges. Some of this fresh meltwater is stored on the ice surface in features called melt ponds. The rest flows into the ocean. The meltwater flowing into the upper ocean affects ice growth and melt, upper ocean properties, and ocean ecosystems. Using field measurements, we found that the summer meltwater was equal to an 80 cm thick layer; 85 % of this meltwater flowed into the ocean and 15 % was stored in melt ponds.
Sönke Maus, Martin Schneebeli, and Andreas Wiegmann
The Cryosphere, 15, 4047–4072, https://doi.org/10.5194/tc-15-4047-2021, https://doi.org/10.5194/tc-15-4047-2021, 2021
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As the hydraulic permeability of sea ice is difficult to measure, observations are sparse. The present work presents numerical simulations of the permeability of young sea ice based on a large set of 3D X-ray tomographic images. It extends the relationship between permeability and porosity available so far down to brine porosities near the percolation threshold of a few per cent. Evaluation of pore scales and 3D connectivity provides novel insight into the percolation behaviour of sea ice.
Cyril Palerme and Malte Müller
The Cryosphere, 15, 3989–4004, https://doi.org/10.5194/tc-15-3989-2021, https://doi.org/10.5194/tc-15-3989-2021, 2021
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Methods have been developed for calibrating sea ice drift forecasts from an operational prediction system using machine learning algorithms. These algorithms use predictors from sea ice concentration observations during the initialization of the forecasts, sea ice and wind forecasts, and some geographical information. Depending on the calibration method, the mean absolute error is reduced between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift.
Dongyang Fu, Bei Liu, Yali Qi, Guo Yu, Haoen Huang, and Lilian Qu
The Cryosphere, 15, 3797–3811, https://doi.org/10.5194/tc-15-3797-2021, https://doi.org/10.5194/tc-15-3797-2021, 2021
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Our results show three main sea ice drift patterns have different multiscale variation characteristics. The oscillation period of the third sea ice transport pattern is longer than the other two, and the ocean environment has a more significant influence on it due to the different regulatory effects of the atmosphere and ocean environment on sea ice drift patterns on various scales. Our research can provide a basis for the study of Arctic sea ice dynamics parameterization in numerical models.
Andrii Murdza, Arttu Polojärvi, Erland M. Schulson, and Carl E. Renshaw
The Cryosphere, 15, 2957–2967, https://doi.org/10.5194/tc-15-2957-2021, https://doi.org/10.5194/tc-15-2957-2021, 2021
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The strength of refrozen floes or piles of ice rubble is an important factor in assessing ice-structure interactions, as well as the integrity of an ice cover itself. The results of this paper provide unique data on the tensile strength of freeze bonds and are the first measurements to be reported. The provided information can lead to a better understanding of the behavior of refrozen ice floes and better estimates of the strength of an ice rubble pile.
H. Jakob Belter, Thomas Krumpen, Luisa von Albedyll, Tatiana A. Alekseeva, Gerit Birnbaum, Sergei V. Frolov, Stefan Hendricks, Andreas Herber, Igor Polyakov, Ian Raphael, Robert Ricker, Sergei S. Serovetnikov, Melinda Webster, and Christian Haas
The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, https://doi.org/10.5194/tc-15-2575-2021, 2021
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Summer sea ice thickness observations based on electromagnetic induction measurements north of Fram Strait show a 20 % reduction in mean and modal ice thickness from 2001–2020. The observed variability is caused by changes in drift speeds and consequential variations in sea ice age and number of freezing-degree days. Increased ocean heat fluxes measured upstream in the source regions of Arctic ice seem to precondition ice thickness, which is potentially still measurable more than a year later.
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
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We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428, https://doi.org/10.5194/tc-14-2409-2020, https://doi.org/10.5194/tc-14-2409-2020, 2020
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The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
H. Jakob Belter, Thomas Krumpen, Stefan Hendricks, Jens Hoelemann, Markus A. Janout, Robert Ricker, and Christian Haas
The Cryosphere, 14, 2189–2203, https://doi.org/10.5194/tc-14-2189-2020, https://doi.org/10.5194/tc-14-2189-2020, 2020
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The validation of satellite sea ice thickness (SIT) climate data records with newly acquired moored sonar SIT data shows that satellite products provide modal rather than mean SIT in the Laptev Sea region. This tendency of satellite-based SIT products to underestimate mean SIT needs to be considered for investigations of sea ice volume transports. Validation of satellite SIT in the first-year-ice-dominated Laptev Sea will support algorithm development for more reliable SIT records in the Arctic.
Mark S. Handcock and Marilyn N. Raphael
The Cryosphere, 14, 2159–2172, https://doi.org/10.5194/tc-14-2159-2020, https://doi.org/10.5194/tc-14-2159-2020, 2020
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Traditional methods of calculating the annual cycle of sea ice extent disguise the variation of amplitude and timing (phase) of the advance and retreat of the ice. We present a multiscale model that explicitly allows them to vary, resulting in a much improved representation of the cycle. We show that phase is the dominant contributor to the variability in the cycle and that the anomalous decay of Antarctic sea ice in 2016 was due largely to a change of phase.
Rebecca J. Rolph, Daniel L. Feltham, and David Schröder
The Cryosphere, 14, 1971–1984, https://doi.org/10.5194/tc-14-1971-2020, https://doi.org/10.5194/tc-14-1971-2020, 2020
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It is well known that the Arctic sea ice extent is declining, and it is often assumed that the marginal ice zone (MIZ), the area of partial sea ice cover, is consequently increasing. However, we find no trend in the MIZ extent during the last 40 years from observations that is consistent with a widening of the MIZ as it moves northward. Differences of MIZ extent between different satellite retrievals are too large to provide a robust basis to verify model simulations of MIZ extent.
Mark A. Tschudi, Walter N. Meier, and J. Scott Stewart
The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, https://doi.org/10.5194/tc-14-1519-2020, 2020
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A new version of a set of data products that contain the velocity of sea ice and the age of this ice has been developed. We provide a history of the product development and discuss the improvements to the algorithms that create these products. We find that changes in sea ice motion and age show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice to a sea ice cover dominated by younger ice, which is more susceptible to summer melt.
Angela Cheng, Barbara Casati, Adrienne Tivy, Tom Zagon, Jean-François Lemieux, and L. Bruno Tremblay
The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, https://doi.org/10.5194/tc-14-1289-2020, 2020
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Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration produced by analysts. The accuracy of manually derived ice concentrations is not well understood. The subsequent uncertainty of ice charts results in downstream uncertainties for ice charts users, such as models and climatology studies, and when used as a verification source for automated sea ice classifiers. This study quantifies the level of accuracy and inter-analyst agreement for ice charts by CIS.
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, https://doi.org/10.5194/tc-14-1083-2020, 2020
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In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.
Shiming Xu, Lu Zhou, and Bin Wang
The Cryosphere, 14, 751–767, https://doi.org/10.5194/tc-14-751-2020, https://doi.org/10.5194/tc-14-751-2020, 2020
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Sea ice thickness parameters are key to polar climate change studies and forecasts. Airborne and satellite measurements provide complementary observational capabilities. The study analyzes the variability in freeboard and snow depth measurements and its changes with scale in Operation IceBridge, CryoVEx, CryoSat-2 and ICESat. Consistency between airborne and satellite data is checked. Analysis calls for process-oriented attribution of variability and covariability features of these parameters.
Valeria Selyuzhenok, Igor Bashmachnikov, Robert Ricker, Anna Vesman, and Leonid Bobylev
The Cryosphere, 14, 477–495, https://doi.org/10.5194/tc-14-477-2020, https://doi.org/10.5194/tc-14-477-2020, 2020
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This study explores a link between the long-term variations in the integral sea ice volume in the Greenland Sea and oceanic processes. We link the changes in the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) regional sea ice volume with the mixed layer, depth and upper-ocean heat content derived using the ARMOR dataset.
Chao Min, Longjiang Mu, Qinghua Yang, Robert Ricker, Qian Shi, Bo Han, Renhao Wu, and Jiping Liu
The Cryosphere, 13, 3209–3224, https://doi.org/10.5194/tc-13-3209-2019, https://doi.org/10.5194/tc-13-3209-2019, 2019
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Sea ice volume export through the Fram Strait has been studied using varied methods, however, mostly in winter months. Here we report sea ice volume estimates that extend over summer seasons. A recent developed sea ice thickness dataset, in which CryoSat-2 and SMOS sea ice thickness together with SSMI/SSMIS sea ice concentration are assimilated, is used and evaluated in the paper. Results show our estimate is more reasonable than that calculated by satellite data only.
Cited articles
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Short summary
Sea ice in the Arctic Ocean has experienced rapid decline since the beginning of satellite observations. To assess the predictability of sea ice extent (SIE) in the Arctic Ocean and to clarify the underlying physical processes, we conducted prediction experiments using an initialized climate model (MIROC5). The present study suggests that subsurface ocean heat content originating from the North Atlantic contributes to the skillful prediction of winter SIE at lead times up to 11 months.
Sea ice in the Arctic Ocean has experienced rapid decline since the beginning of satellite...
