Articles | Volume 15, issue 8
https://doi.org/10.5194/tc-15-3797-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-3797-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Aug 2021
Research article |
| 17 Aug 2021
| 17 Aug 2021
Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions
Dongyang Fu
School of Electronics and
Information Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China
Guangdong Provincial Engineering and Technology Research Center of Marine Remote Sensing and Information Technology, Zhanjiang, Guangdong, China
Bei Liu
CORRESPONDING AUTHOR
School of Electronics and
Information Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China
Yali Qi
School of Electronics and
Information Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China
Guangdong Provincial Engineering and Technology Research Center of Marine Remote Sensing and Information Technology, Zhanjiang, Guangdong, China
Guo Yu
School of Electronics and
Information Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China
Guangdong Provincial Engineering and Technology Research Center of Marine Remote Sensing and Information Technology, Zhanjiang, Guangdong, China
Haoen Huang
School of Electronics and
Information Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China
Lilian Qu
School of Electronics and
Information Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China
Related subject area
Discipline: Sea ice | Subject: Sea Ice
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
Analysis of micro-seismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region
Predictability of Arctic sea ice drift in coupled climate models
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
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
Brief communication: Solar radiation management not as effective as CO2 mitigation for Arctic sea ice loss in hitting the 1.5 and 2 °C COP climate targets
Reflective properties of melt ponds on sea ice
The color of melt ponds on Arctic sea ice
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-212, https://doi.org/10.5194/tc-2022-212, 2022
Revised manuscript accepted for TC
Short summary
Short summary
In the perspective of 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.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
EGUsphere, https://doi.org/10.5194/egusphere-2022-1122, https://doi.org/10.5194/egusphere-2022-1122, 2022
Short summary
Short summary
Information on the sea ice surface topography are important for studies of sea ice, and ship navigation through the ice. The satellite called "ICESat-2" senses the sea ice surface with six laser beams. To proof the accuracy of those measurements, we have carried out a helicopter flight at the same time and along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner, showing that ICESat-2 can see even bumps of only few meters in the sea ice cover.
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
Short summary
Short summary
Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.
Simon Felix Reifenberg and Helge Friedrich Goessling
The Cryosphere, 16, 2927–2946, https://doi.org/10.5194/tc-16-2927-2022, https://doi.org/10.5194/tc-16-2927-2022, 2022
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Peng Lu, Matti Leppäranta, Bin Cheng, Zhijun Li, Larysa Istomina, and Georg Heygster
The Cryosphere, 12, 1331–1345, https://doi.org/10.5194/tc-12-1331-2018, https://doi.org/10.5194/tc-12-1331-2018, 2018
Short summary
Short summary
It is the first time that the color of melt ponds on Arctic sea ice was quantitatively and thoroughly investigated. We answer the question of why the color of melt ponds can change and what the physical and optical reasons are that lead to such changes. More importantly, melt-pond color was provided as potential data in determining ice thickness, especially under the summer conditions when other methods such as remote sensing are unavailable.
Cited articles
Bader, J., Mesquita, M. D. S., Hodges, K. I., Keenlyside, N., Østerhus, S., and
Miles, M.: A review on Northern Hemisphere sea-ice, storminess and the North
Atlantic Oscillation: Observations and projected changes, Atmos.
Res., 101, 809–834, 2011. a
Bi, H., Sun, K., Zhou, X., Huang, H., and Xu, X.: Arctic Sea Ice Area Export
Through the Fram Strait Estimated From Satellite-Based Data: 1988–2012, IEEE
J. Sel. Top. Appl.,
9, 1–14, https://doi.org/10.1109/JSTARS.2016.2584539, 2016. a
Bi, H., Wang, Y., Xu, X., Liang, Y., Huang, J., Liu, Y., Fu, M., and Huang, H.:
Satellite-observed sea ice area flux through Baffin Bay: 1988–2015, The
Cryosphere Discuss., 1–28, https://doi.org/10.5194/tc-2018-136, 2018. a
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M.,
and Girard, F.: Towards a coupled model to investigate wave-sea ice interactions
in the Arctic marginal ice zone, The Cryosphere, 14, 709–735,
https://doi.org/10.5194/tc-14-709-2020, 2020. a
Cavalieri, D. J., Parkinson, C., Gloersen, P., and Zwally, H.: Sea ice
concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave
data,Version 1, Boulder, Colorado USA, NASA DAAC at the National Snow and Ice
Data Center [data set], 25, 1091, https://doi.org/10.5067/8GQ8LZQVL0VL, 1996. a
Cheng, X. and Xu, G.: The integration of JERS-1 and ERS SAR in differential
interferometry for measurement of complex glacier motion, J.
Glaciol., 52, 80–88, https://doi.org/10.3189/172756506781828881, 2006. a
Deser, C. and Teng, H.: Evolution of Arctic sea ice concentration trends and
the role of atmospheric circulation forcing, 1979–2007, Geophys. Res.
Lett., 35, L02504, https://doi.org/10.1029/2007GL032023, 2008. a
Guarino, M. V., Sime, L., Schreder, D., Malmierca-Vallet, I., Rosenblum, E.,
Ringer, M., Ridley, J., Feltham, D., Bitz, C., Steig, E., Wolff, E., Stroeve,
J., and Sellar, A.: Sea-ice-free Arctic during the Last Interglacial supports
fast future loss, Nat. Clim. Change, 10, 1–5,
https://doi.org/10.1038/s41558-020-0865-2, 2020. a
Hakkinen, S., Proshutinsky, A., and Ashik, I.: Sea ice drift in the Arctic
since the 1950s, Geophys. Res. Lett., 35, 116–122, 2008. a
Howell, S. E. L., Komarov, A. S., Dabboor, M., Montpetit, B., Brady, M.,
Scharien, R. K., Mahmud, M. S., Nandan, V., Geldsetzer, T., and Yackel,
J. J.: Comparing L-and C-band synthetic aperture radar estimates of sea ice
motion over different ice regimes, Remote Sens. Environ., 204,
380–391, 2018. a, b
Huang, N. and Shen, S.: Hilbert-Huang Transform and Its Applications [M],
Interdiscip. Math. Sci., 5, 107–127, https://doi.org/10.1142/5862, 2005. a
Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung,
C.-C., and Liu, H.: The empirical mode decomposition and the Hilbert spectrum
for nonlinear and non-stationary time series analysis, Proc.
Roy. Soc. Lond. Ser. A, 454, 903–995, https://doi.org/10.1098/rspa.1998.0193, 1998. a
Huang, N. E. and Wu, Z.: A review on Hilbert-Huang transform: Method and its
applications to geophysical studies, Rev. Geophys., 46, RG2006, https://doi.org/10.1029/2007RG000228, 2008. a
Iida, T. and Saitoh, S. I.: Temporal and spatial variability of chlorophyll
concentrations in the Bering Sea using empirical orthogonal function (EOF)
analysis of remote sensing data, Deep-Sea Res. Pt. II, 54, 2657–2671, 2007. a
Kållberg, P., Simmons, A., Uppala, S., and Fuentes, M.: The ERA-40 archive [data set], p. 31, available at: https://www.ecmwf.int/node/10595 (last access: 29 April 2020), 2004.
Kaur, S., Ehn, J. K., and Barber, D. G.: Pan-arctic winter drift speeds and
changing patterns of sea ice motion: 1979–2015, Polar Record, 54, 1–9, 2019. a
Kawaguchi, Y., Hutchings, J. K., Kikuchi, T., Morison, J. H., and Krishfield,
R. A.: Anomalous sea-ice reduction in the Eurasian Basin of the Arctic Ocean
during summer 2010, Polar Ence, 6, 39–53, 2012. a
Kwok, R.: Outflow of Arctic Ocean Sea Ice into the Greenland and Barents Seas:
1979–2007, J. Clim., 22, 2438–2457, https://doi.org/10.1175/2008JCLI2819.1,
2009. a
Kwok, R. and Cunningham, G.: Deformation of the Arctic Ocean ice cover after
the 2007 record minimum in summer ice extent, Cold Reg. Sci.
Technol., 76, 17–23, https://doi.org/10.1016/j.coldregions.2011.04.003, 2012. a
Kwok, R., Spreen, G., and Pang, S.: Arctic sea ice circulation and drift speed:
Decadal trends and ocean currents, J. Geophys. Res.-Atmos., 118, 2408–2425, 2013. a
Lannuzel, D., Moreau, S., Meiners, K., Fripiat, F., Wongpan, P., Vancoppenolle,
M., Miller, L., Stefels, J., van Leeuwe, M., Flores, H., and Tedesco, L.: The
future of Arctic sea-ice biogeochemistry and ice-associated ecosystems,
Natu. Clim. Change, 10, 983–992, 2020. a
Leppäranta, M.: The drift of sea ice, Springer Science & Business Media, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-04683-4,
2011. a
Lindsay, R. W., Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Arctic Sea
Ice Retreat in 2007 Follows Thinning Trend, J. Clim., 22, 165–176,
2009. a
Martin, T., Steele, M., and Zhang, J.: Seasonality and long-term trend of
Arctic Ocean surface stress in a model, J. Geophys. Res.-Ocean., 119, 1723–1738, 2014. a
Nghiem, S., Rigor, I., Perovich, D., Clemente-Colón, P., Weatherly, J., and
Neumann, G.: Rapid reduction of Arctic perennial sea ice, Geophys.
Res. Lett., 34, L19504, https://doi.org/10.1029/2007GL031138, 2007. a
Olason, E. and Notz, D.: Drivers of variability in Arctic sea-ice drift speed,
J. Geophys. Res.-Ocean., 119, 5755–5775,
https://doi.org/10.1002/2014JC009897, 2014. a
Olonscheck, D., Mauritsen, T., and Notz, D.: Arctic sea-ice variability is
primarily driven by atmospheric temperature fluctuations, Nat. Geosci.,
12, 430–434, https://doi.org/10.1038/s41561-019-0363-1, 2019. a
Rabe, B., Karcher, M., Kauker, F., Schauer, U., Toole, J. M., Krishfield,
R. A., Pisarev, S., Kikuchi, T., and Su, J.: Arctic Ocean basin liquid
freshwater storage trend 1992–2012, Geophys. Res. Lett., 41, 8213, https://doi.org/10.1002/2013GL058121,
2014. a
Screen, J., Simmonds, I., and Keay, K.: Dramatic interannual changes of
perennial Arctic sea ice linked to abnormal summer storm activity, J.
Geophys. Res., 116, D15105, https://doi.org/10.1029/2011JD015847, 2011. a, b
Serreze, M. C., Mclaren, A. S., and Barry, R. G.: Seasonal variations of sea
ice motion in the transpolar drift stream, Geophys. Res. Lett., 16,
811–814, 2013. a
Smedsrud, L. H., Sirevaag, A., Kloster, K., Sorteberg, A., and Sandven, S.:
Recent wind driven high sea ice area export in the Fram Strait contributes to
Arctic sea ice decline, The Cryosphere, 5, 821–829,
https://doi.org/10.5194/tc-5-821-2011, 2011. a
Sumata, H., Kwok, R., Gerdes, R., Kauker, F., and Karcher, M.: Uncertainty
assessment of Arctic summer ice drift for data assimilation, in: REKLIM
workshop, 2015. a
Thomas, D. N.: Sea ice thickness distribution, chap. 2, John Wiley and Sons, Ltd, 42–64, https://doi.org/10.1002/9781118778371.ch2, 2016. a
Thorndike, A. S. and Colony, R.: Sea ice motion in response to geostrophic
winds, J. Geophys. Res.-Ocean., 87, 5845–5852, 1982. a
Tschudi, M., Meier, W., Stewart, J., Fowler, C., and Maslanik, J.: Polar
Pathfinder daily 25 km EASE-Grid sea ice motion vectors, version 4, dataset
0116, NASA NationalSnow and Ice Data Center Distributed Active Archive
Center, Boulder, CO, USA, https://doi.org/10.5067/INAWUWO7QH7B, 2019. a
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice
motion and age products at the National Snow and Ice Data Center (NSIDC), The
Cryosphere [data set], 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, 2020. a
Van Angelen, J., Van den Broeke, M., and Kwok, R.: The Greenland Sea Jet: A
mechanism for wind-driven sea ice export through Fram Strait, Geophys.
Res. Lett., 38, L12805, https://doi.org/10.1029/2011GL047837, 2011.
a, b
Vantrepotte, V. and Melin, F.: Inter-annual variations in the SeaWiFS global
chlorophyll a concentration (1997–2007), Deep-Sea Res. Pt. I, 58, 429–441, 2011. a
Wu, Z. and Huang, N. E.: A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method, Proc. Math. Phys. Eng. Sci., 460, 1597–1611, 2004.
Yu, X., Rinke, A., Dorn, W., Spreen, G., Lüpkes, C., Sumata, H., and Gryanik, V. M.: Evaluation of Arctic sea ice drift and its dependency on near-surface wind and sea ice conditions in the coupled regional climate model HIRHAM–NAOSIM, The Cryosphere, 14, 1727–1746, https://doi.org/10.5194/tc-14-1727-2020, 2020. a, b
Zamani, B., Krumpen, T., Smedsrud, L. H., and Gerdes, R.: Fram Strait sea ice
export affected by thinning: comparing high-resolution simulations and
observations, Clim. Dynam., 53, 3257–3270,
https://doi.org/10.1007/s00382-019-04699-z, 2019. a
Zhang, J., Rothrock, D., and Steele, M.: Recent Changes in Arctic Sea Ice: The
Interplay between Ice Dynamics and Thermodynamics, J. Clim., 13,
3099–3114, https://doi.org/10.1175/1520-0442(2000)013<3099:RCIASI>2.0.CO;2, 2000. a
Short summary
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.
Our results show three main sea ice drift patterns have different multiscale variation...
