Articles | Volume 14, issue 4
https://doi.org/10.5194/tc-14-1259-2020
© Author(s) 2020. 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-14-1259-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Apr 2020
Research article |
| 20 Apr 2020
| 20 Apr 2020
Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations
Alice K. DuVivier
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, CO, USA
Patricia DeRepentigny
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Institute of Arctic and Alpine Research, University of Colorado,
Boulder, CO, USA
Marika M. Holland
National Center for Atmospheric Research, Boulder, CO, USA
Melinda Webster
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Jennifer E. Kay
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, CO, USA
Donald Perovich
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
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Mark J. Webb, Timothy Andrews, Alejandro Bodas-Salcedo, Sandrine Bony, Christopher S. Bretherton, Robin Chadwick, Hélène Chepfer, Hervé Douville, Peter Good, Jennifer E. Kay, Stephen A. Klein, Roger Marchand, Brian Medeiros, A. Pier Siebesma, Christopher B. Skinner, Bjorn Stevens, George Tselioudis, Yoko Tsushima, and Masahiro Watanabe
Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, https://doi.org/10.5194/gmd-10-359-2017, 2017
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The Cloud Feedback Model Intercomparison Project (CFMIP) aims to improve understanding of cloud-climate feedback mechanisms and evaluation of cloud processes and cloud feedbacks in climate models. CFMIP also aims to improve understanding of circulation, regional-scale precipitation and non-linear changes. CFMIP is contributing to the 6th phase of the Coupled Model Intercomparison Project (CMIP6) by coordinating a hierarchy of targeted experiments with cloud-related model outputs.
Allison H. Baker, Dorit M. Hammerling, Sheri A. Mickelson, Haiying Xu, Martin B. Stolpe, Phillipe Naveau, Ben Sanderson, Imme Ebert-Uphoff, Savini Samarasinghe, Francesco De Simone, Francesco Carbone, Christian N. Gencarelli, John M. Dennis, Jennifer E. Kay, and Peter Lindstrom
Geosci. Model Dev., 9, 4381–4403, https://doi.org/10.5194/gmd-9-4381-2016, https://doi.org/10.5194/gmd-9-4381-2016, 2016
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We apply lossy data compression to output from the Community Earth System Model Large Ensemble Community Project. We challenge climate scientists to examine features of the data relevant to their interests and identify which of the ensemble members have been compressed, and we perform direct comparisons on features critical to climate science. We find that applying lossy data compression to climate model data effectively reduces data volumes with minimal effect on scientific results.
Related subject area
Discipline: Sea ice | Subject: Arctic (e.g. Greenland)
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Variations in Arctic sea ice are related not only to its macroscale properties but also to its microstructure. Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
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Sea ice forecasts are operationally produced using physical-based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique (also called calibration) using machine learning in order to improve the skill of short-term (up to 10 days) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows to reduce the errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
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Sea ice is an important constituent of the global climate system. We here use satellite data to identify regions in the Arctic where the sea ice breaks up in so-called leads (i.e., linear cracks) regularly during winter. This information is important because leads determine, e.g., how much heat is exchanged between the ocean and the atmosphere. We here provide first insights into the reasons for the observed patterns in sea-ice leads and their relation to ocean currents and winds.
Isolde A. Glissenaar, Jack C. Landy, David G. Babb, Geoffrey J. Dawson, and Stephen E. L. Howell
The Cryosphere, 17, 3269–3289, https://doi.org/10.5194/tc-17-3269-2023, https://doi.org/10.5194/tc-17-3269-2023, 2023
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Observations of large-scale ice thickness have unfortunately only been available since 2003, a short record for researching trends and variability. We generated a proxy for sea ice thickness in the Canadian Arctic for 1996–2020. This is the longest available record for large-scale sea ice thickness available to date and the first record reliably covering the channels between the islands in northern Canada. The product shows that sea ice has thinned by 21 cm over the 25-year record in April.
Marion Bocquet, Sara Fleury, Fanny Piras, Eero Rinne, Heidi Sallila, Florent Garnier, and Frédérique Rémy
The Cryosphere, 17, 3013–3039, https://doi.org/10.5194/tc-17-3013-2023, https://doi.org/10.5194/tc-17-3013-2023, 2023
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Sea ice has a large interannual variability, and studying its evolution requires long time series of observations. In this paper, we propose the first method to extend Arctic sea ice thickness time series to the ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each year during the winter period between 1995 and 2021.
Ole Rieke, Marius Årthun, and Jakob Simon Dörr
The Cryosphere, 17, 1445–1456, https://doi.org/10.5194/tc-17-1445-2023, https://doi.org/10.5194/tc-17-1445-2023, 2023
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The Barents Sea is the region of most intense winter sea ice loss, and future projections show a continued decline towards ice-free conditions by the end of this century but with large fluctuations. Here we use climate model simulations to look at the occurrence and drivers of rapid ice change events in the Barents Sea that are much stronger than the average ice loss. A better understanding of these events will contribute to improved sea ice predictions in the Barents Sea.
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023, https://doi.org/10.5194/tc-17-233-2023, 2023
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During 1979–2020, four winter polynyas occurred in December 1986 and February 2011, 2017, and 2018 north of Greenland. Instead of ice melting due to the anomalous warm air intrusion, the extreme wind forcing resulted in greater ice transport offshore. Based on the two ensemble runs, representing a 1980s thicker ice vs. a 2010s thinner ice, a dominant cause of these winter polynyas stems from internal variability of atmospheric forcing rather than from the forced response to a warming climate.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
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We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
John E. Walsh, Hajo Eicken, Kyle Redilla, and Mark Johnson
The Cryosphere, 16, 4617–4635, https://doi.org/10.5194/tc-16-4617-2022, https://doi.org/10.5194/tc-16-4617-2022, 2022
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Indicators for the start and end of annual breakup and freeze-up of sea ice at various coastal locations around the Arctic are developed. Relative to broader offshore areas, some of the coastal indicators show an earlier freeze-up and later breakup, especially at locations where landfast ice is prominent. However, the trends towards earlier breakup and later freeze-up are unmistakable over the post-1979 period in synthesized metrics of the coastal breakup/freeze-up indicators.
Abigail Smith, Alexandra Jahn, Clara Burgard, and Dirk Notz
The Cryosphere, 16, 3235–3248, https://doi.org/10.5194/tc-16-3235-2022, https://doi.org/10.5194/tc-16-3235-2022, 2022
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The timing of Arctic sea ice melt each year is an important metric for assessing how sea ice in climate models compares to satellite observations. Here, we utilize a new tool for creating more direct comparisons between climate model projections and satellite observations of Arctic sea ice, such that the melt onset dates are defined the same way. This tool allows us to identify climate model biases more clearly and gain more information about what the satellites are observing.
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844, https://doi.org/10.5194/tc-16-1821-2022, https://doi.org/10.5194/tc-16-1821-2022, 2022
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We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic utilizing radar altimeter (RA) measurement lines and C-band SAR imagery. The RA data are from CryoSat-2, and SAR imagery is from Sentinel-1. By combining them we get a SIT grid covering the whole test area instead of only narrow measurement lines from RA. This kind of SIT estimation can be extended to cover the whole Arctic (and Antarctic) for operational SIT monitoring.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
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A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Jinlei Chen, Shichang Kang, Wentao Du, Junming Guo, Min Xu, Yulan Zhang, Xinyue Zhong, Wei Zhang, and Jizu Chen
The Cryosphere, 15, 5473–5482, https://doi.org/10.5194/tc-15-5473-2021, https://doi.org/10.5194/tc-15-5473-2021, 2021
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Sea ice is retreating with rapid warming in the Arctic. It will continue and approach the worst predicted pathway released by the IPCC. The irreversible tipping point might show around 2060 when the oldest ice will have completely disappeared. It has a huge impact on human production. Ordinary merchant ships will be able to pass the Northeast Passage and Northwest Passage by the midcentury, and the opening time will advance to the next 10 years for icebreakers with moderate ice strengthening.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
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Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Henrieka Detlef, Brendan Reilly, Anne Jennings, Mads Mørk Jensen, Matt O'Regan, Marianne Glasius, Jesper Olsen, Martin Jakobsson, and Christof Pearce
The Cryosphere, 15, 4357–4380, https://doi.org/10.5194/tc-15-4357-2021, https://doi.org/10.5194/tc-15-4357-2021, 2021
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Here we examine the Nares Strait sea ice dynamics over the last 7000 years and their implications for the late Holocene readvance of the floating part of Petermann Glacier. We propose that the historically observed sea ice dynamics are a relatively recent feature, while most of the mid-Holocene was marked by variable sea ice conditions in Nares Strait. Nonetheless, major advances of the Petermann ice tongue were preceded by a shift towards harsher sea ice conditions in Nares Strait.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
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neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
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.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Guangyu Zuo, Dawei Gui, Qiongqiong Cai, H. Jakob Belter, and Wangxiao Yang
The Cryosphere, 15, 1321–1341, https://doi.org/10.5194/tc-15-1321-2021, https://doi.org/10.5194/tc-15-1321-2021, 2021
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Quantification of ice deformation is useful for understanding of the role of ice dynamics in climate change. Using data of 32 buoys, we characterized spatiotemporal variations in ice kinematics and deformation in the Pacific sector of Arctic Ocean for autumn–winter 2018/19. Sea ice in the south and west has stronger mobility than in the east and north, which weakens from autumn to winter. An enhanced Arctic dipole and weakened Beaufort Gyre in winter lead to an obvious turning of ice drifting.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
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Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
Chao Min, Qinghua Yang, Longjiang Mu, Frank Kauker, and Robert Ricker
The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, https://doi.org/10.5194/tc-15-169-2021, 2021
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An ensemble of four estimates of the sea-ice volume (SIV) variations in Baffin Bay from 2011 to 2016 is generated from the locally merged satellite observations, three modeled sea ice thickness sources (CMST, NAOSIM, and PIOMAS) and NSIDC ice drift data (V4). Results show that the net increase of the ensemble mean SIV occurs from October to April with the largest SIV increase in December, and the reduction occurs from May to September with the largest SIV decline in July.
Mohammed E. Shokr, Zihan Wang, and Tingting Liu
The Cryosphere, 14, 3611–3627, https://doi.org/10.5194/tc-14-3611-2020, https://doi.org/10.5194/tc-14-3611-2020, 2020
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This paper uses sequential daily SAR images covering the Robeson Channel to quantitatively study kinematics of individual ice floes with exploration of wind influence and the evolution of the ice arch at the entry of the channel. Results show that drift of ice floes within the Robeson Channel and the arch are both significantly influenced by wind. The study highlights the advantage of using the high-resolution daily SAR coverage in monitoring sea ice cover in narrow water passages.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, https://doi.org/10.5194/tc-14-3479-2020, 2020
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We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change.
The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
Abigail Smith, Alexandra Jahn, and Muyin Wang
The Cryosphere, 14, 2977–2997, https://doi.org/10.5194/tc-14-2977-2020, https://doi.org/10.5194/tc-14-2977-2020, 2020
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The annual cycle of Arctic sea ice can be used to gain more information about how climate model simulations of sea ice compare to observations. In some models, the September sea ice area agrees with observations for the wrong reasons because biases in the timing of seasonal transitions compensate for other unrealistic sea ice characteristics. This research was done to provide new process-based metrics of Arctic sea ice using satellite observations, the CESM Large Ensemble, and CMIP6 models.
Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, https://doi.org/10.5194/tc-14-2235-2020, 2020
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The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
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In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Sukun Cheng, Justin Stopa, Fabrice Ardhuin, and Hayley H. Shen
The Cryosphere, 14, 2053–2069, https://doi.org/10.5194/tc-14-2053-2020, https://doi.org/10.5194/tc-14-2053-2020, 2020
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Wave states in ice in polar oceans are mostly studied near the ice edge. However, observations in the internal ice field, where ice morphology is very different from the ice edge, are rare. Recently derived wave data from satellite imagery are easier and cheaper than field studies and provide large coverage. This work presents a way of using these data to have a close view of some key features in the wave propagation over hundreds of kilometers and calibrate models for predicting wave decay.
Jutta E. Wollenburg, Morten Iversen, Christian Katlein, Thomas Krumpen, Marcel Nicolaus, Giulia Castellani, Ilka Peeken, and Hauke Flores
The Cryosphere, 14, 1795–1808, https://doi.org/10.5194/tc-14-1795-2020, https://doi.org/10.5194/tc-14-1795-2020, 2020
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Based on an observed omnipresence of gypsum crystals, we concluded that their release from melting sea ice is a general feature in the Arctic Ocean. Individual gypsum crystals sank at more than 7000 m d−1, suggesting that they are an important ballast mineral. Previous observations found gypsum inside phytoplankton aggregates at 2000 m depth, supporting gypsum as an important driver for pelagic-benthic coupling in the ice-covered Arctic Ocean.
Xiaoyong Yu, Annette Rinke, Wolfgang Dorn, Gunnar Spreen, Christof Lüpkes, Hiroshi Sumata, and Vladimir M. Gryanik
The Cryosphere, 14, 1727–1746, https://doi.org/10.5194/tc-14-1727-2020, https://doi.org/10.5194/tc-14-1727-2020, 2020
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This study presents an evaluation of Arctic sea ice drift speed for the period 2003–2014 in a state-of-the-art coupled regional model for the Arctic, called HIRHAM–NAOSIM. In particular, the dependency of the drift speed on the near-surface wind speed and sea ice conditions is presented. Effects of sea ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice are discussed.
Yinghui Liu, Jeffrey R. Key, Xuanji Wang, and Mark Tschudi
The Cryosphere, 14, 1325–1345, https://doi.org/10.5194/tc-14-1325-2020, https://doi.org/10.5194/tc-14-1325-2020, 2020
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This study provides a consistent and accurate multi-decadal product of ice thickness and ice volume from 1984 to 2018 based on satellite-derived ice age. Sea ice volume trends from this dataset are stronger than the trends from other datasets. Changes in sea ice thickness contribute more to overall sea ice volume trends than changes in sea ice area do in all months.
Xiao-Yi Yang, Guihua Wang, and Noel Keenlyside
The Cryosphere, 14, 693–708, https://doi.org/10.5194/tc-14-693-2020, https://doi.org/10.5194/tc-14-693-2020, 2020
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The post-2007 Arctic sea ice cover is characterized by a remarkable increase in annual cycle amplitude, which is attributed to multiyear variability in spring Bering sea ice extent. We demonstrated that changes of NPGO mode, by anomalous wind stress curl and Ekman pumping, trigger subsurface variability in the Bering basin. This accounts for the significant decadal oscillation of spring Bering sea ice after 2007. The study helps us to better understand the recent Arctic climate regime shift.
Adam W. Bateson, Daniel L. Feltham, David Schröder, Lucia Hosekova, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, https://doi.org/10.5194/tc-14-403-2020, 2020
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The Arctic sea ice cover has been observed to be decreasing, particularly in summer. We use numerical models to gain insight into processes controlling its seasonal and decadal evolution. Sea ice is made of pieces of ice called floes. Previous models have set these floes to be the same size, which is not supported by observations. In this study we show that accounting for variable floe size reveals the importance of sea ice regions close to the open ocean in driving seasonal retreat of sea ice.
Alex West, Mat Collins, Ed Blockley, Jeff Ridley, and Alejandro Bodas-Salcedo
The Cryosphere, 13, 2001–2022, https://doi.org/10.5194/tc-13-2001-2019, https://doi.org/10.5194/tc-13-2001-2019, 2019
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This study presents a framework for examining the causes of model errors in Arctic sea ice volume, using HadGEM2-ES as a case study. Simple models are used to estimate how much of the error in energy arriving at the ice surface is due to error in key Arctic climate variables. The method quantifies how each variable affects sea ice volume balance and shows that for HadGEM2-ES an annual mean low bias in ice thickness is likely due to errors in surface melt onset.
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, https://doi.org/10.5194/tc-13-1661-2019, 2019
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A warm bias and higher total precipitation and snowfall were found in ERA5 compared with ERA-Interim (ERA-I) over Arctic sea ice. The warm bias in ERA5 was larger in the cold season when 2 m air temperature was < −25 °C and smaller in the warm season than in ERA-I. Substantial anomalous Arctic rainfall in ERA-I was reduced in ERA5, particularly in summer and autumn. When using ERA5 and ERA-I to force a 1-D sea ice model, the effects on ice growth are very small (cm) during the freezing period.
John E. Walsh, J. Scott Stewart, and Florence Fetterer
The Cryosphere, 13, 1073–1088, https://doi.org/10.5194/tc-13-1073-2019, https://doi.org/10.5194/tc-13-1073-2019, 2019
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Persistence-based statistical forecasts of a Beaufort Sea ice severity index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the trends are removed from the data. This finding is consistent with the notion of a springtime “predictability barrier” that has been found in sea ice forecasts based on more sophisticated methods.
Leandro Ponsoni, François Massonnet, Thierry Fichefet, Matthieu Chevallier, and David Docquier
The Cryosphere, 13, 521–543, https://doi.org/10.5194/tc-13-521-2019, https://doi.org/10.5194/tc-13-521-2019, 2019
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The Arctic is a main component of the Earth's climate system. It is fundamental to understand the behavior of Arctic sea ice coverage over time and in space due to many factors, e.g., shipping lanes, the travel and tourism industry, hunting and fishing activities, mineral resource extraction, and the potential impact on the weather in midlatitude regions. In this work we use observations and results from models to understand how variations in the sea ice thickness change over time and in space.
John R. Mioduszewski, Stephen Vavrus, Muyin Wang, Marika Holland, and Laura Landrum
The Cryosphere, 13, 113–124, https://doi.org/10.5194/tc-13-113-2019, https://doi.org/10.5194/tc-13-113-2019, 2019
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Arctic sea ice is projected to thin substantially in every season by the end of the 21st century with a corresponding increase in its interannual variability as the rate of ice loss peaks. This typically occurs when the mean ice thickness falls between 0.2 and 0.6 m. The high variability in both growth and melt processes is the primary factor resulting in increased ice variability. This study emphasizes the importance of short-term variations in ice cover within the mean downward trend.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96, https://doi.org/10.5194/tc-13-79-2019, https://doi.org/10.5194/tc-13-79-2019, 2019
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The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
Abigail Smith and Alexandra Jahn
The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019, https://doi.org/10.5194/tc-13-1-2019, 2019
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Here we assessed how natural climate variations and different definitions impact the diagnosed and projected Arctic sea ice melt season length using model simulations. Irrespective of the definition or natural variability, the sea ice melt season is projected to lengthen, potentially by as much as 4–5 months by 2100 under the business as usual scenario. We also find that different definitions have a bigger impact on melt onset, while natural variations have a bigger impact on freeze onset.
Yuanyuan Zhang, Xiao Cheng, Jiping Liu, and Fengming Hui
The Cryosphere, 12, 3747–3757, https://doi.org/10.5194/tc-12-3747-2018, https://doi.org/10.5194/tc-12-3747-2018, 2018
Aaron Letterly, Jeffrey Key, and Yinghui Liu
The Cryosphere, 12, 3373–3382, https://doi.org/10.5194/tc-12-3373-2018, https://doi.org/10.5194/tc-12-3373-2018, 2018
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Significant reductions in Arctic sea ice and snow cover on Arctic land have led to increases in absorbed solar energy by the surface. Does one play a more important role in Arctic climate change? Using 34 years of satellite data we found that solar energy absorption increased by 10 % over the ocean, which was 3 times greater than over land. Therefore, the decreasing sea ice cover, not changes in terrestrial snow cover, has been the dominant feedback mechanism over the last few decades.
Thomas Kaminski, Frank Kauker, Leif Toudal Pedersen, Michael Voßbeck, Helmuth Haak, Laura Niederdrenk, Stefan Hendricks, Robert Ricker, Michael Karcher, Hajo Eicken, and Ola Gråbak
The Cryosphere, 12, 2569–2594, https://doi.org/10.5194/tc-12-2569-2018, https://doi.org/10.5194/tc-12-2569-2018, 2018
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We present mathematically rigorous assessments of the observation impact (added value) of remote-sensing products and in terms of the uncertainty reduction in a 4-week forecast of sea ice volume and snow volume for three regions along the Northern Sea Route by a coupled model of the sea-ice–ocean system. We quantify the difference in impact between rawer (freeboard) and higher-level (sea ice thickness) products, and the impact of adding a snow depth product.
Steffen Tietsche, Magdalena Alonso-Balmaseda, Patricia Rosnay, Hao Zuo, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, https://doi.org/10.5194/tc-12-2051-2018, 2018
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We compare Arctic sea-ice thickness from L-band microwave satellite observations and an ocean–sea ice reanalysis. There is good agreement for some regions and times but systematic discrepancy in others. Errors in both the reanalysis and observational products contribute to these discrepancies. Thus, we recommend proceeding with caution when using these observations for model validation or data assimilation. At the same time we emphasise their unique value for improving sea-ice forecast models.
Elena V. Shalina and Stein Sandven
The Cryosphere, 12, 1867–1886, https://doi.org/10.5194/tc-12-1867-2018, https://doi.org/10.5194/tc-12-1867-2018, 2018
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In this paper we analyze snow data from Soviet airborne expeditions, Sever, which operated in late winter 1959-1986, in the Arctic and made snow measurements on the ice around plane landing sites. The snow measurements were made on the multiyear ice in the central Arctic and on the first-year ice in the Eurasian seas in the areas for which snow characteristics are poorly described in the literature. The main goal of this study is to produce an improved data set of snow depth on the sea ice.
Rebecca J. Rolph, Andrew R. Mahoney, John Walsh, and Philip A. Loring
The Cryosphere, 12, 1779–1790, https://doi.org/10.5194/tc-12-1779-2018, https://doi.org/10.5194/tc-12-1779-2018, 2018
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Using thresholds of physical climate variables developed from community observations, together with two large-scale datasets, we have produced local indices directly relevant to the impacts of a reduced sea ice cover on Alaska coastal communities. We demonstrate how community observations can inform use of large-scale datasets to derive these locally relevant indices.
Cited articles
Alexander, M. A.: The Atmospheric Response to Realistic Arctic Sea Ice
Anomalies in an AGCM during Winter, J. Clim., 17, 890–905,
https://doi.org/10.1175/1520-0442(2004)017<0890:TARTRA>2.0.CO;2,
2004.
Barnes, E. A. and Screen, J. A.: The impact of Arctic warming on the
midlatitude jet-stream: Can it? Has it? Will it?, WIRES
Clim. Change, 6, 277–286, https://doi.org/10.1002/wcc.337, 2015.
Barnhart, K. R., Miller, C. R., Overeem, I., and Kay, J. E.: Mapping the
future expansion of Arctic open water, Nat. Clim. Change, 6, 280–285,
https://doi.org/10.1038/nclimate2848, 2015.
Bitz, C. M. and Roe, G. H.: A Mechanism for the High Rate of Sea Ice
Thinning in the Arctic Ocean, J. Clim., 17, 3623–3632,
https://doi.org/10.1175/1520-0442(2004)017<3623:AMFTHR>2.0.CO;2,
2004.
Blanchard-Wrigglesworth, E., Bitz, C. M., and Holland, M. M.: Influence of
initial conditions and climate forcing on predicting Arctic sea ice,
Geophys. Res. Lett., 38, L18503, https://doi.org/10.1029/2011GL048807, 2011.
Bliss, A. C. and Anderson, M. R.: Arctic Sea Ice Melt Onset Timing From
Passive Microwave-Based and Surface Air Temperature-Based Methods, J.
Geophys. Res.-Atmos., 123, 9063–9080, https://doi.org/10.1029/2018JD028676,
2018.
Boisvert, L. N. and Stroeve, J. C.: The Arctic is becoming warmer and wetter
as revealed by the Atmospheric Infrared Sounder, Geophys. Res. Lett.,
42, 4439–4446, https://doi.org/10.1002/2015GL063775, 2015.
Bromwich, D. H., Hines, K. M., and Bai, L.-S.: Development and testing of
Polar Weather Research and Forecasting model: 2. Arctic Ocean, J. Geophys.
Res., 114, D08122, https://doi.org/10.1029/2008JD010300, 2009.
DeRepentigny, P., Tremblay, L. B., Newton, R., and Pfirman, S.: Patterns of
Sea Ice Retreat in the Transition to a Seasonally Ice-Free Arctic, J. Clim.,
29, 6993–7008, https://doi.org/10.1175/JCLI-D-15-0733.1, 2016.
Deser, C. and Kay, J. E.: CESM Large Ensemble Community Project, available at: http://www.cesm.ucar.edu/projects/community-projects/LENS/, last access: 7 April, 2020.
Deser, C., Sun, L., Tomas, R. A., and Screen, J.: Does ocean coupling matter
for the northern extratropical response to projected Arctic sea ice loss?,
Geophys. Res. Lett., 43, 2149–2157, https://doi.org/10.1002/2016GL067792, 2016.
Dethloff, K., Rex, M., and Shupe, M.: Multidisciplinary drifting Observatory
for the Study of Arctic Climate (MOSAiC), Geophsyical Res. Abstr.,
18, EGU2016-3064, 1 pp., 2016.
DuVivier, A. K.: Analysis scripts for MOSAIC manuscript submitted to The Cryosphere, available at: https://github.com/duvivier/MOSAIC_TC_2020, last access: 7 April 2019.
Goosse, H., Kay, J. E., Armour, K. C., Bodas-Salcedo, A., Chepfer, H.,
Docquier, D., Jonko, A., Kushner, P. J., Lecomte, O., Massonnet, F., Park,
H.-S., Pithan, F., Svensson, G., and Vancoppenolle, M.: Quantifying climate
feedbacks in polar regions, Nat. Commun., 9, 1919,
https://doi.org/10.1038/s41467-018-04173-0, 2018.
Hall, A.: The Role of Surface Albedo Feedback in Climate, J. Clim., 17, 1550–1568,
https://doi.org/10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2,
2004.
Holland, M. M., Blanchard-Wrigglesworth, E., Kay, J., and Vavrus, S.:
Initial-value predictability of Antarctic sea ice in the Community Climate
System Model 3, Geophys. Res. Lett., 40, 2121–2124,
https://doi.org/10.1002/grl.50410, 2013.
Hunke, E. C. and Lipscomb, W. H.: CICE: the Los Alamos Sea Ice Model
Documentation and Software Version 4.0, Los Alamos Natl. Lab. Los Alamos NM,
LA-CC-06-012, 76, 2008.
IASC: MOSAiC Implementation Plan, International Arctic Science Committee,
2016.
Intrieri, J. M., Fairall, C. W., Shupe, M. D., Persson, P. O. G., Andreas,
E. L., Guest, P. S., and Moritz, R. E.: An annual cycle of Arctic surface
cloud forcing at SHEBA, J. Geophys. Res., 107, 8039,
https://doi.org/10.1029/2000JC000439, 2002.
Jahn, A., Kay, J. E., Holland, M. M., and Hall, D. M.: How predictable is the
timing of a summer ice-free Arctic?, Geophys. Res. Lett., 43,
9113–9120, https://doi.org/10.1002/2016GL070067, 2016.
Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal
Arctic sea ice loss, J. Geophys. Res., 114, D18204, https://doi.org/10.1029/2009JD011773,
2009.
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) Large Ensemble Project: A Community
Resource for Studying Climate Change in the Presence of Internal Climate
Variability, B. Am. Meteorol. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1, 2015.
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A.,
de Boer, G., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster,
M. J., Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose,
C., Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G.
M., Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M.,
Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C.,
Turner, D. D., Veron, D. E., Salzen, K. von, Walker, G. K., Wang, Z., Wolf,
A. B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model
simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic
Cloud Experiment – I: single-layer cloud, Q. J. Roy. Meteor. Soc., 135,
979–1002, https://doi.org/10.1002/qj.416, 2009.
Krumpen, T.: Across the Arctic: What course will Polarstern follow?,
available at:
https://www.meereisportal.de/en/mosaic/polarstern-drift/, last access: 6 September 2019.
Krumpen, T., Belter, H. J., Boetius, A., Damm, E., Haas, C., Hendricks, S.,
Nicolaus, M., Nöthig, E.-M., Paul, S., Peeken, I., Ricker, R., and Stein,
R.: Arctic warming interrupts the Transpolar Drift and affects long-range
transport of sea ice and ice-rafted matter, Sci. Rep., 9, 5459,
https://doi.org/10.1038/s41598-019-41456-y, 2019.
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage:
losses and coupled variability (1958–2018), Environ. Res. Lett., 13,
105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018.
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi,
D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008,
J. Geophys. Res.-Oceans, 114, C07005, https://doi.org/10.1029/2009JC005312, 2009.
Labe, Z., Magnusdottir, G., and Stern, H.: Variability of Arctic Sea Ice
Thickness Using PIOMAS and the CESM Large Ensemble, J. Clim., 31,
3233–3247, https://doi.org/10.1175/JCLI-D-17-0436.1, 2018.
Light, B., Dickinson, S., Perovich, D. K., and Holland, M. M.: Evolution of
summer Arctic sea ice albedo in CCSM4 simulations: Episodic summer snowfall
and frozen summers, J. Geophys. Res.-Oceans, 120, 284–303,
https://doi.org/10.1002/2014JC010149, 2015.
Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos. Sci., 20,
130–141, 1963.
Maslanik, J., Stroeve, J., Fowler, C., and Emery, W.: Distribution and trends
in Arctic sea ice age through spring 2011, Geophys. Res. Lett., 38,
L13502, https://doi.org/10.1029/2011GL047735, 2011.
Meier, W. N., Fetterer, F., Savoie, M. H., Mallory, S., Duerr, R., and
Stroeve, J. C.: NOAA/NSIDC Climate Data Record of Passive Microwave
Sea Ice Concentration, Version 3 [1988–2016], Natl. Snow Ice Data Cent.
Boulder, CO, USA, https://doi.org/10.7265/N59P2ZTG, 2017.
Morison, J. and Goldberg, D.: A brief study of the force balance between a
small iceberg, the ocean, sea ice, and atmosphere in the Weddell Sea, Cold
Reg. Sci. Technol., 76–77, 69–76, https://doi.org/10.1016/j.coldregions.2011.10.014,
2012.
Morrison, A., Kay, J. E., Frey, W. R., Chepfer, H., and Guzman, R.: Cloud
Response to Arctic Sea Ice Loss and Implications for Future Feedbacks in the
CESM1 Climate Model, J. Geophys. Res.-Atmos., 124, 1003–1020, https://doi.org/10.1029/2018JD029142, 2019.
Nghiem, S. V., Rigor, I. G., Perovich, D. K., Clemente-Colón, P.,
Weatherly, J. W., and Neumann, G.: Rapid reduction of Arctic perennial sea
ice, Geophys. Res. Lett., 34, L19504, https://doi.org/10.1029/2007GL031138, 2007.
Perovich, D.: The Changing Arctic Sea Ice Cover, Oceanography, 24,
162–173, https://doi.org/10.5670/oceanog.2011.68, 2011.
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runciman, K., and
Nghiem, S. V.: Increasing solar heating of the Arctic Ocean and adjacent
seas, 1979–2005: Attribution and role in the ice-albedo feedback, Geophys.
Res. Lett., 34, L19505, https://doi.org/10.1029/2007GL031480, 2007.
Qu, X. and Hall, A.: What Controls the Strength of Snow-Albedo Feedback?, J.
Clim., 20, 3971–3981, https://doi.org/10.1175/JCLI4186.1, 2007.
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and
deformation rate of Arctic sea ice, 1979–2007, J. Geophys. Res., 114,
C05013, https://doi.org/10.1029/2008JC005066, 2009.
Richter-Menge, J. A., Osborne, E., Druckenmiller, M., and Jeffries, M. O. (Eds.): The Arctic, in: State of the Climate in 2018, B. Am. Meteorol.
Soc., 100, S141–S168, 2019.
Serreze, M. C. and Stroeve, J.: Arctic sea ice trends, variability and
implications for seasonal ice forecasting, Philos. T. R. Soc. A, 373, 20140159, https://doi.org/10.1098/rsta.2014.0159, 2015.
Stammerjohn, S., Massom, R., Rind, D., and Martinson, D.: Regions of rapid
sea ice change: An inter-hemispheric seasonal comparison, Geophys. Res.
Lett., 39, L06501, https://doi.org/10.1029/2012GL050874, 2012.
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all
seasons, Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56,
2018.
Swart, N. C., Fyfe, J. C., Hawkins, E., Kay, J. E., and Jahn, A.: Influence
of internal variability on Arctic sea-ice trends, Nat. Clim. Change, 5,
86–89, https://doi.org/10.1038/nclimate2483, 2015.
Tschudi, M. A., Fowler, C., Maslanik, J. A., Stewart, J. S., and Meier, W.
N.: Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version
3 [1988–2016], NASA Natl. Snow Ice Data Cent. Distrib. Act. Arch. Cent.
Boulder, CO, USA, https://doi.org/10.5067/O57VAIT2AYYY, 2016.
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice motion and age products, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-40, in review, 2019.
Uttal, T., Curry, J. A., Mcphee, M. G., Perovich, D. K., Moritz, R. E.,
Maslanik, J. A., Guest, P. S., Stern, H. L., Moore, J. A., Turenne, R.,
Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A.,
Halle, C., Morison, J. H., Wheeler, P. A., Makshtas, A., Welch, H., Shupe,
M. D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Pegau, W.
S., Stanton, T. P., and Grenfeld, T. C.: Surface Heat Budget of the Arctic
Ocean, B. Am. Meteorol. Soc., 83, 255–275,
https://doi.org/10.1175/1520-0477(2002)083< 0255:SHBOTA> 2.3.CO;2,
2002.
Short summary
In autumn 2019, a ship will be frozen into the Arctic sea ice for a year to study system changes. We analyze climate model data from a group of experiments and follow virtual sea ice floes throughout a year. The modeled sea ice conditions along possible tracks are highly variable. Observations that sample a wide range of sea ice conditions and represent the variety and diversity in possible conditions are necessary for improving climate model parameterizations over all types of sea ice.
In autumn 2019, a ship will be frozen into the Arctic sea ice for a year to study system...
