Articles | Volume 8, issue 6
https://doi.org/10.5194/tc-8-2147-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-8-2147-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
25 Nov 2014
Research article |
| 25 Nov 2014
First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 1: In situ observations
Department of Geography, University of Victoria, Victoria, British Columbia, Canada
J. Landy
Centre for Earth Observation Science, Faculty of Environment Earth and Resources, University of Manitoba, Winnipeg, Manitoba, Canada
D. G. Barber
Centre for Earth Observation Science, Faculty of Environment Earth and Resources, University of Manitoba, Winnipeg, Manitoba, Canada
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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.
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
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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.
Lu Zhou, Shiming Xu, Jiping Liu, and Bin Wang
The Cryosphere, 12, 993–1012, https://doi.org/10.5194/tc-12-993-2018, https://doi.org/10.5194/tc-12-993-2018, 2018
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This work proposes a new data synergy method for the retrieval of sea ice thickness and snow depth by using colocating L-band passive remote sensing and active laser altimetry. Physical models are adopted for the retrieval, including L-band radiation model and buoyancy relationship. Covariability of snow depth and total freeboard is further utilized to mitigate resolution differences and improve retrievability. The method can be applied to future campaigns including ICESat-2 and WCOM.
Ross M. Lieblappen, Deip D. Kumar, Scott D. Pauls, and Rachel W. Obbard
The Cryosphere, 12, 1013–1026, https://doi.org/10.5194/tc-12-1013-2018, https://doi.org/10.5194/tc-12-1013-2018, 2018
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We imaged first-year sea ice using micro-computed tomography to visualize, capture, and quantify the 3-D complex structure of salt water channels weaving through sea ice. From these data, we then built a mathematical network to better understand the pathways transporting heat, gases, and salts between the ocean and the atmosphere. Powered with this structural knowledge, we can create new modeled brine channels for a given sea ice depth and temperature that accurately mimic field conditions.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
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Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Friedrich Richter, Matthias Drusch, Lars Kaleschke, Nina Maaß, Xiangshan Tian-Kunze, and Susanne Mecklenburg
The Cryosphere, 12, 921–933, https://doi.org/10.5194/tc-12-921-2018, https://doi.org/10.5194/tc-12-921-2018, 2018
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L-band (1.4 GHz) brightness temperatures from ESA's Soil Moisture and Ocean Salinity SMOS mission have been used to derive thin sea ice thickness. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems reducing the data latency and providing a more consistent first guess. We studied the forward (observation) operator that translates geophysical sea ice parameters from the ECMWF Ocean ReAnalysis Pilot 5 (ORAP5) into brightness temperatures.
Cited articles
Andreas, E. L., Persson, P. O. G., Jordan, R. E., Horst, T. W., Guest, P. S., Grachev, A. A., and Fairall, C. W.: Parameterizing the turbulent surface fluxes over summer sea ice, 8th Conf. on Polar Meteor. And Oceanog., 9–13 January, San Diego, CA, 2005.
Baghdadi, N., Paillou, P., Grandjean, G., Dubois, P., and Davidson, M.: Relationship between profile length and roughness variables for natural surfaces, Int. J. Remote Sens., 21, 3375–3381, https://doi.org/10.1080/014311600750019994, 2000.
Baghdadi, N., Gherboudj, I., Zribi, M., Sahebi, M., Bonn, F., and King, C.: Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements, Int. J. Remote Sens., 25, 593–3623, https://doi.org/10.1080/01431160310001654392, 2004.
Barber, D. G., Papakyriakou, T. N., LeDrew, E. F., and Shokr, M. E.: An examination of the relation between the spring period evolution of the scattering coefficient (σ°) and radiative fluxes over landfast sea-ice, Int. J. Remote Sensing, 16, 3343–3363, https://doi.org/10.1080/01431169508954634, 1995.
Barber, D. G. and Yackel, J. J.: The physical, radiative and microwave scattering characteristics of melt ponds on Arctic landfast sea ice , Int. J. Remote Sens., 20, 2069–2090, https://doi.org/10.1080/014311699212353, 1999.
Belchansky, G. I., Douglas, D. C., Eremeev, V. A., and Platonov, N. G.: Variations in the Arctic's multiyear sea ice cover: A neural network analysis of SMMR-SSM/I data, 1979–2004, Geophys. Res. Lett., 32, L09605, https://doi.org/10.1029/2005GL022395, 2005.
Bock, E. J. and Tetsu H.: Optical Measurements of Capillary-Gravity Wave Spectra Using a Scanning Laser Slope Gauge, J. Atmos. Oceanic Technol., 12, 395–403, https://doi.org/10.1175/1520-0426(1995)012<0395:OMOCGW>2.0.CO;2, 1995.
Brekke, C., Holt, B., Jones, C., and Skrunes, S.: Towards oil slick monitoring in the Arctic environment, Proc. POLinSAR 2013, Frascati, Italy, 8 pp., 28 January–1 February, 2013.
Church, E. L.: Fractal surface finish, Appl. Opt., 278, 1518–1526, 1988.
Davidson, M. W. J., Le Toan, T., Mattia, F., Satalino, G., Manninen, T., and Borgeaud, M.: On the characterization of agricultural soil roughness for radar remote sensing studies, IEEE Trans. Geosci. Remote Sens., 38, 630–640, https://doi.org/10.1109/36.841993, 2000.
De Abreu, R., Yackel, J. J., Barber, D. G., and Arkett, M.: Operational satellite sensing of Arctic first-year sea ice melt, Can. J. Remote Sens., 27, 487–501, 2001.
Donelan, M. A. and Pierson Jr., W. J.: Radar scattering and equilibrium ranges in wind-generated waves with application to scatterometry, J. Geophys. Res., 92, 4971–5029, https://doi.org/10.1029/JC092iC05p04971, 1987.
Drinkwater, M. R. and Crocker, G. B.:, Modeling changes in the dielectric and scattering properties of young snow covered sea ice at GHz frequencies, J. Glaciol., 34, 274–282, 1988.
Drinkwater, M. R.: LIMEX '87 Ice Surface Characteristics: Implications for C-Band SAR Backscatter Signatures, IEEE T. Geosci. Remote, 27, 501–513, https://doi.org/10.1109/TGRS.1989.35933, 1989.
Dierking, W., Pettersson, M. I., and Askne, J.: Multifrequency scatterometer measurements of Baltic Sea ice during EMAC-95, Int. J. Remote Sens., 20, 349–372, https://doi.org/10.1080/014311699213488, 1999.
Eicken, H., Krouse, H. R., Kadko, D., and Perovich, D. K.: Tracer studies of pathways and rates of meltwater transport through Arctic summer sea ice, J. Geophys. Res., 107, SHE 22-1–SHE 22-20, https://doi.org/10.1029/2000JC000583, 2002.
Eicken, H., Grenfell, T. C., Perovich, D. K., Richter-Menge, J. A., and Frey, K.: Hydraulic controls on summer Arctic pack ice albedo, J. Geophys. Res., 109, C08007, https://doi.org/10.1029/2003JC001989, 2004.
ESA: Sentinel-1: ESA's radar observatory mission for GMES operational services, ESA SP-1322/1, 2012.
Fung, A. K.: Microwave Scattering and Emission Models and Their Applications, Artech House, Inc., Norwood, Ma, 1994.
Geldsetzer, T. and Yackel, J. J.: Sea ice type and open water discrimination using dual co-polarized C-band SAR, Can. J. Remote Sens., 35, 73–84, https://doi.org/10.5589/m08-075, 2009.
Geldsetzer, T., Mead, J. B., Yackel, J. J., Scharien, R. K., and Howell, S. E. L.: Surface-based polarimetric C-band scatterometer for field measurements of sea ice, IEEE Trans. Geosci. Remote Sens., 45, 3405–3416, https://doi.org/10.1109/TGRS.2007.907043, 2007.
Hallikainen, M. T. and Winebrenner, D. P.: The physical basis for sea ice remote sensing, in: Microwave Remote Sensing of Sea Ice, Geophysical Monograph 68, edited by: Carsey, F., 29–46, AGU, Washington, D.C, 1992.
Hajnsek, I., Pottier, E., and Cloude, S. R.: Inversion of surface parameters from polarimetric SAR, IEEE Trans. Geosci. Remote Sens., 41, 727–744, https://doi.org/10.1109/TGRS.2003.810702, 2003.
Hanesiak, J. M., Yackel, J. J., and Barber, D. G.: Effect of melt ponds on first-year sea ice ablation-integration of RADARSAT-1 and thermodynamic modelling, Can. J. Remote Sens., 27, 433–442, 2001a.
Hanesiak, J. M., Barber, D. G., De Abreu, R. A., and Yackel, J. J.: Local and regional albedo observations of arctic first-year sea ice during melt ponding, J. Geophys. Res., 106, 1005–1016, https://doi.org/10.1029/1999JC000068, 2001b.
Heygster, G., Alexandrov, V., Dybkjær, G., von Hoyningen-Huene, W., Girard-Ardhuin, F., Katsev, I. L., Kokhanovsky, A., Lavergne, T., Malinka, A. V., Melsheimer, C., Toudal Pedersen, L., Prikhach, A. S., Saldo, R., Tonboe, R., Wiebe, H., and Zege, E. P.: Remote sensing of sea ice: advances during the DAMOCLES project, The Cryosphere, 6, 1411–1434, https://doi.org/10.5194/tc-6-1411-2012, 2012.
Holland, M. M., Bailey, D. A., Briegleb, B. P., Light, B., and Hunke, E.: Improved sea ice shortwave radiation physics in ccsm4: the impact of melt ponds and aerosols on arctic sea ice, J. Climate, 25, 1413–1430, https://doi.org/10.1175/JCLI-D-11-00078.1, 2012.
Howell, S. E. L., Tivy, A., Yackel, J. J., and Scharien, R. K.: Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago, J. Geophys. Res., 111, C07025, https://doi.org/10.1029/2005JC003193, 2006.
IGOS: Integrated Global Observing Strategy Cryosphere Theme Report – For the Monitoring of our Environment from Space and from Earth, WMO/TD-No. 1405, World Meteorological Organization, Geneva, 100 pp., 2007.
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi, D.: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008, J. Geophys. Res., 114, C07005, https://doi.org/10.1029/2009JC005312, 2009.
Landy, J. C., Isleifson, D., Komarov, A. S., and Barber, D. G.: Parameterization of centimetre-scale sea ice surface roughness using terrestrial LiDAR, IEEE T. Geosci. Remote, 53, 1271–1286, https://doi.org/10.1109/TGRS.2014.2336833, 2014.
Livingstone, C. E., Singh, K. P., and Gray, L.: Seasonal and regional variations of active/passive microwave signatures of sea ice, Geosci. Remote Sens., IEEE Trans., GE-25, 159–173, https://doi.org/10.1109/TGRS.1987.289815, 1987.
Manninen, A. T.: Surface roughness of Baltic sea ice, J. Geophys. Res., 102, 1119–1139, https://doi.org/10.1029/96JC02991, 1997.
Markus, T., Cavalieri, D. J., Tschudi, M. A., and Ivanoff, A.: Comparison of aerial video and Landsat 7 data over ponded sea ice, Remote Sens. Environ., 86, 458–469, https://doi.org/10.1016/S0034-4257(03)00124-X, 2003.
Mätzler, C., Aebisher, H., and Schanda, E.: Microwave dielectric properties of surface snow, IEEE J. Ocean. Eng., 9, 366–371, https://doi.org/10.1109/JOE.1984.1145644, 1984.
Morassutti, M. P. and Ledrew, E. F.: Albedo and depth of melt ponds on sea ice, Int. J. Climatol., 16, 817–838, https://doi.org/10.1002/(SICI)1097-0088(199607)16:7<817::AID-JOC44>3.0.CO;2-5, 1996.
Nakamura, K., Wakabayashi, H., Uto, S., Ushio, S., and Nishio, F.: Observation of sea-ice thickness using ENVISAT data from Lützow-Holm Bay, East Antarctica, IEEE Geosci. Remote Sens. Lett., 6, 277–281, https://doi.org/10.1109/LGRS.2008.2011061, 2009.
Nghiem, S. V. and Bertoia, C.: Study of multi-polarization C-band backscatter signatures for Arctic sea ice mapping with future satellite SAR, Can. J. Remote Sens., 27, 387–402, 2001.
Perovich, D. K., Grenfell, T. C., Richter-Menge, J. A., Light, B., Tucker III, W. B., and Eicken, H.: Thin and thinner: Sea ice mass balance measurements during SHEBA, J. Geophys. Res., 108, 8050, https://doi.org/10.1029/2001JC001079, 2003.
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runciman, K., and Nghiem, S. V.: Increasing solar heating of the Arctic Ocean and adjacent seas, 1979–2005: Attribution and role in the ice-albedo feedback, Geophys. Res. Lett., 34, L19505, https://doi.org/10.1029/2007GL031480, 2007.
Plant, W. J., Keller, W. C., Hesany, V., Hara, T., Bock, E., and Donelan, M. A.: Bound waves and Bragg scattering in a wind-wave tank, J. Geophys. Res., 104, 3243–3263, https://doi.org/10.1029/1998JC900061, 1999.
Polashenski, C., Perovich, D., and Courville, Z.: The mechanisms of sea ice melt pond formation and evolution, J. Geophys. Res., 117, C01001, https://doi.org/10.1029/2011JC007231, 2012.
Rösel, A. and Kaleschke, L.: Exceptional melt pond occurrence in the years 2007 and 2011 on the Arctic sea ice revealed from MODIS satellite data, J. Geophys. Res., 117, C05018, https://doi.org/10.1029/2011JC007869, 2012.
Rösel, A., Kaleschke, L., and Birnbaum, G.: Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network, The Cryosphere, 6, 431–446, https://doi.org/10.5194/tc-6-431-2012, 2012.
Rösel, A. and Kaleschke, L.: Comparison of different retrieval techniques for melt ponds on Arctic sea ice from Landsat and MODIS satellite data, Ann. Glaciol., 52, 185–191, 2011.
Scharien, R. K. and Yackel, J. J.: Analysis of surface roughness and morphology of first-year sea ice melt ponds: Implications for microwave scattering, IEEE Trans. Geosci. Remote Sens., 43, 2927–2939, https://doi.org/10.1109/TGRS.2005.857896, 2005.
Scharien, R. K., Yackel, J. J., Granskog, M. A., and Else, B. G. T.: Coincident high resolution optical-SAR image analysis for surface albedo estimation of first-year sea ice during summer melt, Remote Sens. Environ., 111, 160–171, https://doi.org/10.1016/j.rse.2006.10.025, 2007.
Scharien, R. K., Geldsetzer, T., Barber, D. G., Yackel, J. J., and Langlois, A.: Physical, dielectric, and C band microwave scattering properties of first-year sea ice during advanced melt, J. Geophys. Res., 115, C12026, https://doi.org/10.1029/2010JC006257, 2010.
Scharien, R. K., Yackel, J. J., Barber, D. G., Asplin, M., Gupta, M., and Isleifson, D.: Geophysical controls on C band polarimetric backscatter from melt pond covered Arctic first-year sea ice: Assessment using high-resolution scatterometry, J. Geophys. Res., 117, C00G18, https://doi.org/10.1029/2011JC007353, 2012.
Scharien, R. K., Hochheim, K., Landy, J., and Barber, D. G.: First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2, The Cryosphere, 8, 2163–2176, https://doi.org/10.5194/tc-8-2163-2014, 2014.
Scheuchl, B., Flett, D., Caves, C., and Cumming, I.: Potential of RADARSAT-2 for operational sea ice monitoring, Can. J. Remote Sens., 30, 448–461, 2004.
Thomsen, B. B., Nghiem, S. V., and Kwok, R.: Polarimetric C-band SAR observations of sea ice in the Greenland Sea, Proc. Int. Geoscience and Remote Sensing Symp., Seattle, WA, 2502–2504, https://doi.org/10.1109/IGARSS.1998.702259, 1998.
Tschudi, M. A., Maslanik, J. A., and Perovich, D. K.: Derivation of melt pond coverage on Arctic sea ice using MODIS observations, Remote Sens. Environ., 112, 2605–2614, https://doi.org/10.1016/j.rse.2007.12.009, 2008.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing: Active and Passive. From Theory to Applications, Vol. III, Artech House, Inc., Norwood, Massachusetts, 1986.
Untersteiner, N.: Arctic summer time: The short summer of 2004 [Web essay], available at: http://www.arctic.noaa.gov/essay_untersteiner3.html, 2004.
Vachon, P. W. and Wolfe, J.: C-Band cross-polarization wind speed retrieval, Geosci. Remote Sens. Lett., 8, 451–455, https://doi.org/10.1109/LGRS.2010.2085417, 2011.
Wu, T. D., Chen, K. S., Shi, J., and Fung, A. K.: A transition model for the reflection coefficient in surface scattering, IEEE Trans. Geosci. Remote Sens., 39, 2040–2049, https://doi.org/10.1109/36.951094, 2001.
Yackel, J. J. and Barber, D. G.: Melt ponds on sea ice in the Canadian Archipelago: 2. On the use of RADARSAT-1 synthetic aperture radar for geophysical inversion, J. Geophys. Res., 105, 22061–22070, https://doi.org/10.1029/2000JC900076, 2000.
Yackel, J. J., Barber, D. G., Papakyriakou, T. N., and Breneman, C.: First-year sea ice spring melt transitions in the Canadian Arctic Archipelago from time-series synthetic aperture radar data, 1992–2002, Hydrol. Process., 21, 253–265, https://doi.org/10.1002/hyp.6240, 2007.
Zabel, I., Jezek, K., Gogineni, S., and Kanagaratnam, P.: Search for proxy indicators of young sea ice thickness, J. Geophys. Res., 101, 6697–6709, https://doi.org/10.1029/95JC02957, 1996.
Zege, E., Katsev, I., Malinka, A., Prikhach, A., and Heygster, G.: New approach for radiative transfer in sea ice and its application for sea ice satellite remote sensing, Radiation Processes in the Atmosphere and Ocean (IRS2012), AIP Conf. Proc. 1531, 43–46, https://doi.org/10.1063/1.4804703, 2012.
Zhang, B., Perrie, W., and He, Y.: Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model, J. Geophys. Res., 116, C08008, https://doi.org/10.1029/2010JC006522, 2011.
Zribi, M., Baghdadi, N., and Guerin, C.: Analysis of Surface Roughness Heterogeneity and Scattering Behavior for Radar Measurements, Geosci. Remote Sens., IEEE Trans., 44, 2438–2444, https://doi.org/10.1109/TGRS.2006.873742, 2006.
