Articles | Volume 16, issue 7
https://doi.org/10.5194/tc-16-2927-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-2927-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jul 2022
Research article |
| 20 Jul 2022
| 20 Jul 2022
Predictability of Arctic sea ice drift in coupled climate models
Simon Felix Reifenberg
CORRESPONDING AUTHOR
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
now at: MARUM – Center for Marine Environmental Science & Institute of Environmental Physics, University of Bremen, Bremen, Germany
Helge Friedrich Goessling
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
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Simon F. Reifenberg, Anna Martin, Matthias Kohl, Sara Bacer, Zaneta Hamryszczak, Ivan Tadic, Lenard Röder, Daniel J. Crowley, Horst Fischer, Katharina Kaiser, Johannes Schneider, Raphael Dörich, John N. Crowley, Laura Tomsche, Andreas Marsing, Christiane Voigt, Andreas Zahn, Christopher Pöhlker, Bruna A. Holanda, Ovid Krüger, Ulrich Pöschl, Mira Pöhlker, Patrick Jöckel, Marcel Dorf, Ulrich Schumann, Jonathan Williams, Birger Bohn, Joachim Curtius, Hardwig Harder, Hans Schlager, Jos Lelieveld, and Andrea Pozzer
Atmos. Chem. Phys., 22, 10901–10917, https://doi.org/10.5194/acp-22-10901-2022, https://doi.org/10.5194/acp-22-10901-2022, 2022
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In this work we use a combination of observational data from an aircraft campaign and model results to investigate the effect of the European lockdown due to COVID-19 in spring 2020. Using model results, we show that the largest relative changes to the atmospheric composition caused by the reduced emissions are located in the upper troposphere around aircraft cruise altitude, while the largest absolute changes are present at the surface.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Alexei Koldunov, Tobias Kölling, Josh Kousal, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Domokos Sármány, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
EGUsphere, https://doi.org/10.5194/egusphere-2024-913, https://doi.org/10.5194/egusphere-2024-913, 2024
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale"), and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Colin Gareth Jones, Fanny Adloff, Ben Booth, Peter Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene Hewitt, Hazel Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm Roberts, Benjamin Sanderson, Roland Séférian, Samuel Somot, Pier-Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco J. Doblas-Reyes, Markus G. Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Froelicher, Neven Fuckar, Matthew Gidden, Helge Goessling, Rune Grand Graversen, Silvio Gualdi, Jose Manuel Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason A. Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Melia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard Wood, Shuting Yang, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2024-453, https://doi.org/10.5194/egusphere-2024-453, 2024
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We propose a number of priority areas for the international climate research community to address over the coming ~6 years (i.e. to 2030). Advances in these areas will increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, as well as deliver significantly improved scientific support to international climate policy, such as the 7th IPCC Assessment Report and the UNFCCC Global Stocktake under the Paris Climate Agreement.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
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We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Simon F. Reifenberg, Anna Martin, Matthias Kohl, Sara Bacer, Zaneta Hamryszczak, Ivan Tadic, Lenard Röder, Daniel J. Crowley, Horst Fischer, Katharina Kaiser, Johannes Schneider, Raphael Dörich, John N. Crowley, Laura Tomsche, Andreas Marsing, Christiane Voigt, Andreas Zahn, Christopher Pöhlker, Bruna A. Holanda, Ovid Krüger, Ulrich Pöschl, Mira Pöhlker, Patrick Jöckel, Marcel Dorf, Ulrich Schumann, Jonathan Williams, Birger Bohn, Joachim Curtius, Hardwig Harder, Hans Schlager, Jos Lelieveld, and Andrea Pozzer
Atmos. Chem. Phys., 22, 10901–10917, https://doi.org/10.5194/acp-22-10901-2022, https://doi.org/10.5194/acp-22-10901-2022, 2022
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In this work we use a combination of observational data from an aircraft campaign and model results to investigate the effect of the European lockdown due to COVID-19 in spring 2020. Using model results, we show that the largest relative changes to the atmospheric composition caused by the reduced emissions are located in the upper troposphere around aircraft cruise altitude, while the largest absolute changes are present at the surface.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
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We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Helge F. Goessling and Sebastian Bathiany
Earth Syst. Dynam., 7, 697–715, https://doi.org/10.5194/esd-7-697-2016, https://doi.org/10.5194/esd-7-697-2016, 2016
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Carbon dioxide, while warming the Earth's surface, cools the atmosphere beyond about 15 km (the middle atmosphere). This cooling is considered a fingerprint of anthropogenic global warming, yet the physical reason behind it remains prone to misconceptions. Here we use a simple radiation model to illustrate the physical essence of stratospheric cooling, and a complex climate model to quantify how strongly different mechanisms contribute.
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.
H. F. Goessling and C. H. Reick
Atmos. Chem. Phys., 13, 5567–5585, https://doi.org/10.5194/acp-13-5567-2013, https://doi.org/10.5194/acp-13-5567-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Sea Ice
Why is summertime Arctic sea ice drift speed projected to decrease?
Impact of atmospheric rivers on Arctic sea ice variations
The impacts of anomalies in atmospheric circulations on Arctic sea ice outflow and sea ice conditions in the Barents and Greenland seas: case study in 2020
A large-scale high-resolution numerical model for sea-ice fragmentation dynamics
Atmospheric highs drive asymmetric sea ice drift during lead opening from Point Barrow
Experimental modelling of the growth of tubular ice brinicles from brine flows under sea ice
Spatial characteristics of frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery
Modelling the evolution of Arctic multiyear sea ice over 2000–2018
A quasi-objective single-buoy approach for understanding Lagrangian coherent structures and sea ice dynamics
Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
A collection of wet beam models for wave–ice interaction
First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data
Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region
Recovering and monitoring the thickness, density, and elastic properties of sea ice from seismic noise recorded in Svalbard
Influences of changing sea ice and snow thicknesses on simulated Arctic winter heat fluxes
Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model
A new state-dependent parameterization for the free drift of sea ice
Arctic sea ice sensitivity to lateral melting representation in a coupled climate model
Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements
A generalized stress correction scheme for the Maxwell elasto-brittle rheology: impact on the fracture angles and deformations
Wave dispersion and dissipation in landfast ice: comparison of observations against models
The influence of snow on sea ice as assessed from simulations of CESM2
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
Surface-based Ku- and Ka-band polarimetric radar for sea ice studies
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations
Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations
Modeling the annual cycle of daily Antarctic sea ice extent
Changes of the Arctic marginal ice zone during the satellite era
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
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology
On the multi-fractal scaling properties of sea ice deformation
Brief communication: Pancake ice floe size distribution during the winter expansion of the Antarctic marginal ice zone
What historical landfast ice observations tell us about projected ice conditions in Arctic archipelagoes and marginal seas under anthropogenic forcing
Interannual sea ice thickness variability in the Bay of Bothnia
Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness
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Reflective properties of melt ponds on sea ice
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.
Jan Åström, Jari Haapala, and Arttu Polojärvi
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-97, https://doi.org/10.5194/tc-2023-97, 2023
Revised manuscript accepted for TC
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The HiDEM code has been developed for fracture and fragmentation of brittle materials, and applied extensively to glacier calving. Here we report on the adaptaion of the code to sea ice dynamics and break up. The code demonstrate capability to simulate sea ice dynamics on the 100 km scale with unprecedented resolution. We argue that codes of this type may become useful for improving sea ice dynamics forecasts.
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.
Sergio Testón-Martínez, Laura M. Barge, Jan Eichler, C. Ignacio Sainz-Díaz, and Julyan H. E. Cartwright
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-100, https://doi.org/10.5194/tc-2023-100, 2023
Revised manuscript accepted for TC
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Brinicles are ice tubular 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 sea water. We have recreated succesfully 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, study the behaviour of the brinicles created when changing the flow of water, as well as their importance for natural brinicles.
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.
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.
M. Jeffrey Mei, Ted Maksym, Blake Weissling, and Hanumant Singh
The Cryosphere, 13, 2915–2934, https://doi.org/10.5194/tc-13-2915-2019, https://doi.org/10.5194/tc-13-2915-2019, 2019
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Sea ice thickness is hard to measure directly, and current datasets are very limited to sporadically conducted drill lines. However, surface elevation is much easier to measure. Converting surface elevation to ice thickness requires making assumptions about snow depth and density, which leads to large errors (and may not generalize to new datasets). A deep learning method is presented that uses the surface morphology as a direct predictor of sea ice thickness, with testing errors of < 20 %.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
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In this article, we look at how the Arctic sea ice cover, as a solid body, behaves on different temporal and spatial scales. We show that the numerical model neXtSIM uses a new approach to simulate the mechanics of sea ice and reproduce the characteristics of how sea ice deforms, as observed by satellite. We discuss the importance of this model performance in the context of simulating climate processes taking place in polar regions, like the exchange of energy between the ocean and atmosphere.
Alberto Alberello, Miguel Onorato, Luke Bennetts, Marcello Vichi, Clare Eayrs, Keith MacHutchon, and Alessandro Toffoli
The Cryosphere, 13, 41–48, https://doi.org/10.5194/tc-13-41-2019, https://doi.org/10.5194/tc-13-41-2019, 2019
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Existing observations do not provide quantitative descriptions of the floe size distribution for pancake ice floes. This is important during the Antarctic winter sea ice expansion, when hundreds of kilometres of ice cover around the Antarctic continent are composed of pancake floes (D = 0.3–3 m). Here, a new set of images from the Antarctic marginal ice zone is used to measure the shape of individual pancakes for the first time and to infer their size distribution.
Frédéric Laliberté, Stephen E. L. Howell, Jean-François Lemieux, Frédéric Dupont, and Ji Lei
The Cryosphere, 12, 3577–3588, https://doi.org/10.5194/tc-12-3577-2018, https://doi.org/10.5194/tc-12-3577-2018, 2018
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Ice that forms over marginal seas often gets anchored and becomes landfast. Landfast ice is fundamental to the local ecosystems, is of economic importance as it leads to hazardous seafaring conditions and is also a choice hunting ground for both the local population and large predators. Using observations and climate simulations, this study shows that, especially in the Canadian Arctic, landfast ice might be more resilient to climate change than is generally thought.
Iina Ronkainen, Jonni Lehtiranta, Mikko Lensu, Eero Rinne, Jari Haapala, and Christian Haas
The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, https://doi.org/10.5194/tc-12-3459-2018, 2018
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We quantify the sea ice thickness variability in the Bay of Bothnia using various observational data sets. For the first time we use helicopter and shipborne electromagnetic soundings to study changes in drift ice of the Bay of Bothnia. Our results show that the interannual variability of ice thickness is larger in the drift ice zone than in the fast ice zone. Furthermore, the mean thickness of heavily ridged ice near the coast can be several times larger than that of fast ice.
Edward W. Blockley and K. Andrew Peterson
The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, https://doi.org/10.5194/tc-12-3419-2018, 2018
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Arctic sea-ice prediction on seasonal time scales is becoming increasingly more relevant to society but the predictive capability of forecasting systems is low. Several studies suggest initialization of sea-ice thickness (SIT) could improve the skill of seasonal prediction systems. Here for the first time we test the impact of SIT initialization in the Met Office's GloSea coupled prediction system using CryoSat-2 data. We show significant improvements to Arctic extent and ice edge location.
Jeff K. Ridley and Edward W. Blockley
The Cryosphere, 12, 3355–3360, https://doi.org/10.5194/tc-12-3355-2018, https://doi.org/10.5194/tc-12-3355-2018, 2018
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The climate change conference held in Paris in 2016 made a commitment to limiting global-mean warming since the pre-industrial era to well below 2 °C and to pursue efforts to limit the warming to 1.5 °C. Since global warming is already at 1 °C, the 1.5 °C can only be achieved at considerable cost. It is thus important to assess the risks associated with the higher target. This paper shows that the decline of Arctic sea ice, and associated impacts, can only be halted with the 1.5 °C target.
Aleksey Malinka, Eleonora Zege, Larysa Istomina, Georg Heygster, Gunnar Spreen, Donald Perovich, and Chris Polashenski
The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, https://doi.org/10.5194/tc-12-1921-2018, 2018
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Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Cited articles
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A.,
Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J.,
Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y.,
Downie, R., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with
probabilistic deep learning, Nat. Commun., 12, 5124,
https://doi.org/10.1038/s41467-021-25257-4, 2021. a
Barber, D. G., Babb, D. G., Ehn, J. K., Chan, W., Matthes, L., Dalman, L. A.,
Campbell, Y., Harasyn, M. L., Firoozy, N., Theriault, N., Lukovich, J. V.,
Zagon, T., Papakyriakou, T., Capelle, D. W., Forest, A., and Gariepy, A.:
Increasing Mobility of High Arctic Sea Ice Increases Marine Hazards Off the
East Coast of Newfoundland, Geophys. Res. Lett., 45, 2370–2379,
https://doi.org/10.1002/2017GL076587, 2018. a
Collins, M.: Climate predictability on interannual to decadal time scales: the
initial value problem, Clim. Dynam., 19, 671–692,
https://doi.org/10.1007/s00382-002-0254-8, 2002. a, b, c
Cruz-García, R., Guemas, V., Chevallier, M., and Massonnet, F.: Seasonal
Arctic sea ice forecasting with probabilistic deep learning, Clim.
Dynam., 53, 427–440, https://doi.org/10.1007/s00382-018-4592-6,
2019. a
Dawson, J., Pizzolato, L., Howell, S. E., Copland, L., and Johnston, M. E.:
Temporal and Spatial Patterns of Ship Traffic in the Canadian Arctic from
1990 to 2015, Arctic, 71, 15–26, 2018. a
Day, J. J., Hawkins, E., and Tietsche, S.: Will Arctic sea ice thickness
initialization improve seasonal forecast skill?, Geophys. Res. Lett., 41,
7566–7575, https://doi.org/10.1002/2014GL061694, 2014. a, b, c
Day, J. J., Tietsche, S., Collins, M., Goessling, H. F., Guemas, V., Guillory, A., Hurlin, W. J., Ishii, M., Keeley, S. P. E., Matei, D., Msadek, R., Sigmond, M., Tatebe, H., and Hawkins, E.: The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1, Geosci. Model Dev., 9, 2255–2270, https://doi.org/10.5194/gmd-9-2255-2016, 2016. a, b, c, d, e
DelSole, T., Yan, X., Dirmeyer, P. A., Fennessy, M., and Altshuler, E.:
Changes in Seasonal Predictability due to Global Warming, J.
Climate, 27, 300–311, https://doi.org/10.1175/JCLI-D-13-00026.1, 2014. a
de Vos, M., Barnes, M., Biddle, L. C., Swart, S., Ramjukadh, C.-L., and Vichi,
M.: Evaluating numerical and free-drift forecasts of sea ice drift during a
Southern Ocean research expedition: An operational perspective, J.
Oper. Oceanogr., 1–17, https://doi.org/10.1080/1755876X.2021.1883293,
2021. a
Donner, L. J., Wyman, B. L., Hemler, R. S., Horowitz, L. W., Ming, Y., Zhao,
M., Golaz, J.-C., Ginoux, P., Lin, S.-J., Schwarzkopf, M. D., Austin, J.,
Alaka, G., Cooke, W. F., Delworth, T. L., Freidenreich, S. M., Gordon, C. T.,
Griffies, S. M., Held, I. M., Hurlin, W. J., Klein, S. A., Knutson, T. R.,
Langenhorst, A. R., Lee, H.-C., Lin, Y., Magi, B. I., Malyshev, S. L., Milly,
P. C. D., Naik, V., Nath, M. J., Pincus, R., Ploshay, J. J., Ramaswamy, V.,
Seman, C. J., Shevliakova, E., Sirutis, J. J., Stern, W. F., Stouffer, R. J.,
Wilson, R. J., Winton, M., Wittenberg, A. T., and Zeng, F.: The Dynamical
Core, Physical Parameterizations, and Basic Simulation Characteristics of the
Atmospheric Component AM3 of the GFDL Global Coupled Model CM3, J.
Climate, 24, 3484–3519, https://doi.org/10.1175/2011JCLI3955.1, 2011. a
Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L., and
Robinson, N.: Do seasonal-to-decadal climate predictions underestimate the
predictability of the real world?, Geophys. Res. Lett., 41,
5620–5628, https://doi.org/10.1002/2014GL061146, 2014. a
Gascard, J.-C., Riemann-Campe, K., Gerdes, R., Schyberg, H., Randriamampianina,
R., Karcher, M., Zhang, J., and Rafizadeh, M.: Future sea ice conditions and
weather forecasts in the Arctic: Implications for Arctic shipping, Ambio,
46, 355–367, https://doi.org/10.1007/s13280-017-0951-5, 2017. a
Goessling, H., Tietsche, S., Day, J., Hawkins, E., and Jung, T.: Predictability
of the Arctic sea ice edge, Geophys. Res. Lett., 43, 1642–1650,
https://doi.org/10.1002/2015GL067232, 2016. a
Goessling, H. F. and Jung, T.: A probabilistic verification score for
contours: Methodology and application to Arctic ice-edge forecasts,
Q. J. Roy. Meteor. Soc., 144, 735–743,
https://doi.org/10.1002/qj.3242, 2018. a
Goessling, H. F. and Reifenberg, S. F.: spheRlab, GitHub [code], https://github.com/FESOM/spheRlab, last access: 15 July 2022. a
Griffies, S. M., Winton, M., Donner, L. J., Horowitz, L. W., Downes, S. M.,
Farneti, R., Gnanadesikan, A., Hurlin, W. J., Lee, H.-C., Liang, Z., Palter,
J. B., Samuels, B. L., Wittenberg, A. T., Wyman, B. L., Yin, J., and Zadeh,
N.: The GFDL CM3 Coupled Climate Model: Characteristics of the Ocean and Sea
Ice Simulations, J. Climate, 24, 3520–3544,
https://doi.org/10.1175/2011JCLI3964.1, 2011. a
Grumbine, R. W.: Virtual Floe Ice Drift Forecast Model Intercomparison,
Weather Forecast., 13, 886–890,
https://doi.org/10.1175/1520-0434(1998)013<0886:VFIDFM>2.0.CO;2, 1998. a
Grumbine, R. W.: Long Range Sea Ice Drift Model Verification, MMAB Tech. Note
315, 2013. a
Hawkins, E., Tietsche, S., Day, J. J., Melia, N., Haines, K., and Keeley, S.:
Aspects of designing and evaluating seasonal-to-interannual Arctic sea-ice
prediction systems, Q. J. Roy. Meteor. Soc.,
142, 672–683, https://doi.org/10.1002/qj.2643, 2016. a
Hebert, D. A., Allard, R. A., Metzger, E. J., Posey, P. G., Preller, R. H.,
Wallcraft, A. J., Phelps, M. W., and Smedstad, O. M.: Short-term sea ice
forecasting: An assessment of ice concentration and ice drift forecasts using
the U.S. Navy's Arctic Cap Nowcast/Forecast System, J. Geophys.
Res.-Oceans, 120, 8327–8345,
https://doi.org/10.1002/2015JC011283, 2015. a
Hoskins, B.: The potential for skill across the range of the seamless
weather-climate prediction problem: a stimulus for our science, Q.
J. Roy. Meteor. Soc., 139, 573–584,
https://doi.org/10.1002/qj.1991, 2013. a
Johns, T. C., Durman, C. F., Banks, H. T., Roberts, M. J., McLaren,
A. J., Ridley, J. K., Senior, C. A., Williams, K. D., Jones, A.,
Rickard, G. J., Cusack, S., Ingram, W. J., Crucifix, M., Sexton,
D. M. H., Joshi, M. M., Dong, B.-W., Spencer, H., Hill, R. S. R.,
Gregory, J. M., Keen, A. B., Pardaens, A. K., Lowe, J. A.,
Bodas-Salcedo, A., Stark, S., and Searl, Y.: The New Hadley Centre
Climate Model (HadGEM1): Evaluation of Coupled Simulations, J.
Climate, 19, 1327, https://doi.org/10.1175/JCLI3712.1, 2006. a
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019. a
Jungclaus, J. H., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D.,
Mikolajewicz, U., Notz, D., and von Storch, J. S.: Characteristics of the
ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean
component of the MPI-Earth system model, J. Adv. Model.
Earth Sy., 5, 422–446, https://doi.org/10.1002/jame.20023, 2013. a
Leppäranta, M.: The Drift of Sea Ice, Springer-Verlag Berlin Heidelberg, UK, 2nd
Edn., https://doi.org/10.1007/978-3-642-04683-4, 2011. a
Lorenz, E. N.: The predictability of a flow which possesses many scales of
motion, Tellus, 21, 289–307, https://doi.org/10.1111/j.2153-3490.1969.tb00444.x,
1969. a
Lorenz, E. N.: Climatic Predictability, in: The Physical Bases of Climate
and Climate Modelling, World Meteorological Organization, GARP Publications Series- No. 16, 132–136,
1975. a
Melia, N., Haines, K., and Hawkins, E.: Sea ice decline and 21st century
trans-Arctic shipping routes, Geophys. Res. Lett., 43, 9720–9728,
https://doi.org/10.1002/2016GL069315, 2016. a
Mudryk, L. R., Dawson, J., Howell, S. E. L., Derksen, C., Zagon, T. A., and
Brady, M.: Impact of 1, 2 and 4 ∘C of global warming on ship
navigation in the Canadian Arctic, Nat. Clim. Change, 11, 673–679,
https://doi.org/10.1038/s41558-021-01087-6, 2021. a
Murphy, A. H. and Winkler, R. L.: Diagnostic verification of probability
forecasts, Int. J. Forecast., 7, 435–455, 1992. a
Nansen, F.: The Oceanography of the North Polar Basin: Norwegian North Polar
Expedition 1893–1896, Scientific Results, 3, 9, 1902. a
Nicolaus, M., Perovich, D. K., Spreen, G., Granskog, M. A., von Albedyll, L.,
Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H. J., Bessonov, V.,
Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B.,
Clemens-Sewall, D., Dadic, R., Damm, E., de Boer, G., Demir, O., Dethloff,
K., Divine, D. V., Fong, A. A., Fons, S., Frey, M. M., Fuchs, N., Gabarró,
C., Gerland, S., Goessling, H. F., Gradinger, R., Haapala, J., Haas, C.,
Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A., Heuzé, C.,
Hoppmann, M., Høyland, K. V., Huntemann, M., Hutchings, J. K., Hwang, B.,
Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein, C.,
Kolabutin, N., Krampe, D., Kristensen, S. S., Krumpen, T., Kurtz, N.,
Lampert, A., Lange, B. A., Lei, R., Light, B., Linhardt, F., Liston, G. E.,
Loose, B., Macfarlane, A. R., Mahmud, M., Matero, I. O., Maus, S.,
Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M., Oppelt,
N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R., Polashenski, C.,
Rabe, B., Raphael, I. A., Regnery, J., Rex, M., Ricker, R., Riemann-Campe,
K., Rinke, A., Rohde, J., Salganik, E., Scharien, R. K., Schiller, M.,
Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M. D., Smith, M. M.,
Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J., Thielke, L.,
Timofeeva, A., Tonboe, R. T., Tavri, A., Tsamados, M., Wagner, D. N.,
Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC
expedition: Snow and sea ice, Elementa, 10, 000046,
https://doi.org/10.1525/elementa.2021.000046, 2022. a
Notz, D., Haumann, F. A., Haak, H., Jungclaus, J. H., and Marotzke, J.: Arctic
sea-ice evolution as modeled by Max Planck Institute for Meteorology's Earth
system model, J. Adv. Model. Earth Sy., 5, 173–194,
https://doi.org/10.1002/jame.20016, 2013. a
Olason, E. and Notz, D.: Drivers of variability in Arctic sea-ice drift speed,
J. Geophys. Res.-Oceans, 119, 5755–5775,
https://doi.org/10.1002/2014JC009897, 2014. a
Palerme, C. and Müller, M.: Calibration of sea ice drift forecasts using random forest algorithms, The Cryosphere, 15, 3989–4004, https://doi.org/10.5194/tc-15-3989-2021, 2021. a
PAME: The Increase in Arctic Shipping 2013–2019: Arctic Shipping Status
Report (ASSR) #1, Protection of the Marine Environment (PAME),
https://pame.is/projects/arctic-marine-shipping/arctic-shipping-status-reports (last access: 15 July 2022),
2020. a
Posey, P. G., Metzger, E. J., Wallcraft, A. J., Preller, R. H., Smedstad, O. M., and Phelps, M. W.: Validation of the 1/12 Arctic Cap Nowcast/Forecast System (ACNFS), NRL Report NRL/MR/7320-10-9287, 2010. a
Rabatel, M., Rampal, P., Carrassi, A., Bertino, L., and Jones, C. K. R. T.: Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic, The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, 2018. a
Rampal, P., Weiss, J., Marsan, D., and Bourgoin, M.: Arctic sea ice velocity
field: General circulation and turbulent-like fluctuations, J.
Geophys. Res.-Oceans, 114, C10014, https://doi.org/10.1029/2008JC005227, 2009. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new Lagrangian sea ice model, The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, 2016. a
Schweiger, A. J. and Zhang, J.: Accuracy of short-term sea ice drift forecasts
using a coupled ice-ocean model, J. Geophys. Res.-Oceans,
120, 7827–7841, https://doi.org/10.1002/2015JC011273, 2015. a
Shaffrey, L. C., Stevens, I., Norton, W. A., Roberts, M. J., Vidale, P. L.,
Harle, J. D., Jrrar, A., Stevens, D. P., Woodage, M. J., Demory, M. E.,
Donners, J., Clark, D. B., Clayton, A., Cole, J. W., Wilson, S. S.,
Connolley, W. M., Davies, T. M., Iwi, A. M., Johns, T. C., King, J. C., New,
A. L., Slingo, J. M., Slingo, A., Steenman-Clark, L., and Martin, G. M.:
U.K. HiGEM: The New U.K. High-Resolution Global Environment Model—Model
Description and Basic Evaluation, J. Climate, 22, 1861–1896,
https://doi.org/10.1175/2008JCLI2508.1, 2009. a
Sidorenko, D., Rackow, T., Jung, T., Semmler, T., Barbi, D., Danilov, S.,
Dethloff, K., Dorn, W., Fieg, K., Goessling, H. F., Handorf, D., Harig, S.,
Hiller, W., Juricke, S., Losch, M., Schröter, J., and Sein, D.
V., and Wang, Q.: Towards multi-resolution global climate modeling with
ECHAM6–FESOM. Part I: model formulation and mean climate, Clim. Dynam.,
44, 757–780, https://doi.org/10.1007/s00382-014-2290-6, 2015. a
SIMIP Community: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47, e2019GL086749,
https://doi.org/10.1029/2019GL086749, 2020. a
Smith, L. C. and Stephenson, S. R.: New Trans-Arctic shipping routes navigable
by midcentury, P. Natl. Acad. Sci. USA, 110,
E1191–E1195, https://doi.org/10.1073/pnas.1214212110, 2013. a
Spreen, G., Kwok, R., and Menemenlis, D.: Trends in Arctic sea ice drift and
role of wind forcing: 1992–2009, Geophys. Res. Lett., 38, L19501,
https://doi.org/10.1029/2011GL048970, 2011. a
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland,
M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and
observations, Geophys. Res. Lett., 39, L16502,
https://doi.org/10.1029/2012GL052676, 2012. a
Thorndike, A. S. and Colony, R.: Sea ice motion in response to geostrophic
winds, J. Geophys. Res.-Oceans, 87, 5845–5852,
https://doi.org/10.1029/JC087iC08p05845, 1982.
a
Tietsche, S., Day, J., Guémas, V., Hurlin, W., Keeley, S., Matei, D., Msadek,
R., Collins, M., and Hawkins, E.: Seasonal to interannual Arctic sea ice
predictability in current global climate models, Geophys. Res.
Lett., 41, 1035–1043, https://doi.org/10.1002/2013GL058755, 2014. a, b
Zampieri, L., Goessling, H. F., and Jung, T.: Bright Prospects for Arctic Sea
Ice Prediction on Subseasonal Time Scales, Geophys. Res. Lett., 45,
9731–9738, https://doi.org/10.1029/2018GL079394, 2018. a
Zhang, J., Lindsay, R., Schweiger, A., and Rigor, I.: Recent changes in the
dynamic properties of declining Arctic sea ice: A model study, Geophys.
Res. Lett., 39, 20503, https://doi.org/10.1029/2012GL053545, 2012. a
Short summary
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.
Using model simulations, we analyze the impact of chaotic error growth on Arctic sea ice drift...
