Articles | Volume 12, issue 3
The Cryosphere, 12, 993–1012, 2018
https://doi.org/10.5194/tc-12-993-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Cryosphere, 12, 993–1012, 2018
https://doi.org/10.5194/tc-12-993-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article 22 Mar 2018
Research article | 22 Mar 2018
On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data
Lu Zhou et al.
Related authors
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
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For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Shiming Xu, Jialiang Ma, Lu Zhou, Yan Zhang, Jiping Liu, and Bin Wang
Geosci. Model Dev., 14, 603–628, https://doi.org/10.5194/gmd-14-603-2021, https://doi.org/10.5194/gmd-14-603-2021, 2021
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A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version 1.2.1). The resolution range includes 0.45, 0.15, and 0.05°. Based on atmospherically forced sea ice experiments, the model simulates reasonable sea ice kinematics and scaling properties. Landfast ice thickness can also be systematically shifted due to non-convergent solutions to an
elastic–viscous–plastic (EVP) model. This work is a framework for multi-scale modeling of the ocean and sea ice with CESM.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
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Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
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.
Mengzhen Qi, Yan Liu, Jiping Liu, Xiao Cheng, Yijing Lin, Qiyang Feng, Qiang Shen, and Zhitong Yu
Earth Syst. Sci. Data, 13, 4583–4601, https://doi.org/10.5194/essd-13-4583-2021, https://doi.org/10.5194/essd-13-4583-2021, 2021
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A total of 1975 annual calving events larger than 1 km2 were detected on the Antarctic ice shelves from August 2005 to August 2020. The average annual calved area was measured as 3549.1 km2, and the average calving rate was measured as 770.3 Gt yr-1. Iceberg calving is most prevalent in West Antarctica, followed by the Antarctic Peninsula and Wilkes Land in East Antarctica. This annual iceberg calving dataset provides consistent and precise calving observations with the longest time coverage.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, https://doi.org/10.5194/gmd-14-4891-2021, 2021
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The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
Short summary
Short summary
For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-220, https://doi.org/10.5194/gmd-2021-220, 2021
Preprint under review for GMD
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We present an improved coupled modeling system for Arctic sea ice and climate prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also show extended predictive skill of Arctic sea ice after the summer season. This provides added values of this prediction system for decision-making.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chaoyuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-134, https://doi.org/10.5194/tc-2021-134, 2021
Revised manuscript under review for TC
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The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Antarctic Zhongshan Station. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modelling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Chao Sun, Li Liu, Ruizhe Li, Xinzhu Yu, Hao Yu, Biao Zhao, Guansuo Wang, Juanjuan Liu, Fangli Qiao, and Bin Wang
Geosci. Model Dev., 14, 2635–2657, https://doi.org/10.5194/gmd-14-2635-2021, https://doi.org/10.5194/gmd-14-2635-2021, 2021
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Data assimilation (DA) provides better initial states of model runs by combining observations and models. This work focuses on the technical challenges in developing a coupled ensemble-based DA system and proposes a new DA framework DAFCC1 based on C-Coupler2. DAFCC1 enables users to conveniently integrate a DA method into a model with automatic and efficient data exchanges. A sample DA system that combines GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.
Shiming Xu, Jialiang Ma, Lu Zhou, Yan Zhang, Jiping Liu, and Bin Wang
Geosci. Model Dev., 14, 603–628, https://doi.org/10.5194/gmd-14-603-2021, https://doi.org/10.5194/gmd-14-603-2021, 2021
Short summary
Short summary
A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version 1.2.1). The resolution range includes 0.45, 0.15, and 0.05°. Based on atmospherically forced sea ice experiments, the model simulates reasonable sea ice kinematics and scaling properties. Landfast ice thickness can also be systematically shifted due to non-convergent solutions to an
elastic–viscous–plastic (EVP) model. This work is a framework for multi-scale modeling of the ocean and sea ice with CESM.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
Short summary
Short summary
Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Hao Yu, Li Liu, Chao Sun, Ruizhe Li, Xinzhu Yu, Cheng Zhang, Zhiyuan Zhang, and Bin Wang
Geosci. Model Dev., 13, 6253–6263, https://doi.org/10.5194/gmd-13-6253-2020, https://doi.org/10.5194/gmd-13-6253-2020, 2020
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Routing network generation is a major step for initializing the data transfer functionality for model coupling. The distributed implementation for routing network generation (DiRong1.0) proposed in this paper can significantly improve the global implementation of routing network generation used in some existing coupling software, because it does not introduce any gather–broadcast communications and achieves much lower complexity in terms of time, memory, and communication.
Shaoqing Zhang, Haohuan Fu, Lixin Wu, Yuxuan Li, Hong Wang, Yunhui Zeng, Xiaohui Duan, Wubing Wan, Li Wang, Yuan Zhuang, Hongsong Meng, Kai Xu, Ping Xu, Lin Gan, Zhao Liu, Sihai Wu, Yuhu Chen, Haining Yu, Shupeng Shi, Lanning Wang, Shiming Xu, Wei Xue, Weiguo Liu, Qiang Guo, Jie Zhang, Guanghui Zhu, Yang Tu, Jim Edwards, Allison Baker, Jianlin Yong, Man Yuan, Yangyang Yu, Qiuying Zhang, Zedong Liu, Mingkui Li, Dongning Jia, Guangwen Yang, Zhiqiang Wei, Jingshan Pan, Ping Chang, Gokhan Danabasoglu, Stephen Yeager, Nan Rosenbloom, and Ying Guo
Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020, https://doi.org/10.5194/gmd-13-4809-2020, 2020
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Science advancement and societal needs require Earth system modelling with higher resolutions that demand tremendous computing power. We successfully scale the 10 km ocean and 25 km atmosphere high-resolution Earth system model to a new leading-edge heterogeneous supercomputer using state-of-the-art optimizing methods, promising the solution of high spatial resolution and time-varying frequency. Corresponding technical breakthroughs are of significance in modelling and HPC design communities.
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.
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.
Yifan Ding, Xiao Cheng, Jiping Liu, Fengming Hui, and Zhenzhan Wang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-208, https://doi.org/10.5194/tc-2019-208, 2019
Preprint withdrawn
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This study develops a new melt pond fraction (MPF) data set over sea ice on Arctic-wide scale, using a method of ensemble-based deep neural network. Based on the new dataset, we analyze the spatial-temporal variations of MPF on different ice types and the prediction of MPF to the Arctic sea ice extent in recent years. The new dataset may help improve the prediction of the Arctic sea ice minimum by assimilating the MPF in models.
Haoyu Yang, Li Liu, Cheng Zhang, Ruizhe Li, Chao Sun, Xinzhu Yu, Hao Yu, Zhiyuan Zhang, and Bin Wang
Geosci. Model Dev., 12, 3311–3328, https://doi.org/10.5194/gmd-12-3311-2019, https://doi.org/10.5194/gmd-12-3311-2019, 2019
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PatCC1 demonstrates that triangulation can achieve parallel efficiency, commonality, and parallel consistency (exactly the same result under different parallel settings) at the same time.
Tingfeng Dou, Cunde Xiao, Jiping Liu, Wei Han, Zhiheng Du, Andrew R. Mahoney, Joshua Jones, and Hajo Eicken
The Cryosphere, 13, 1233–1246, https://doi.org/10.5194/tc-13-1233-2019, https://doi.org/10.5194/tc-13-1233-2019, 2019
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The variability and potential trends of rain-on-snow events over Arctic sea ice and their role in sea-ice losses are poorly understood. This study demonstrates that rain-on-snow events are a critical factor in initiating the onset of surface melt over Arctic sea ice, and onset of spring rainfall over sea ice has shifted to earlier dates since the 1970s, which may have profound impacts on ice melt through feedbacks involving earlier onset of surface melt.
Yuanyuan Zhang, Xiao Cheng, Jiping Liu, and Fengming Hui
The Cryosphere, 12, 3747–3757, https://doi.org/10.5194/tc-12-3747-2018, https://doi.org/10.5194/tc-12-3747-2018, 2018
Li Liu, Cheng Zhang, Ruizhe Li, Bin Wang, and Guangwen Yang
Geosci. Model Dev., 11, 3557–3586, https://doi.org/10.5194/gmd-11-3557-2018, https://doi.org/10.5194/gmd-11-3557-2018, 2018
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C-Coupler2 is a new version of C-Coupler with a series of new enhancements. It is ready for use to develop various coupled or nested models. It has passed a number of test cases involving model coupling and nesting, and with various MPI process layouts between component models, and has already been used in several real coupled models. Its source code can be downloaded from https://github.com/C-Coupler-Group/c-coupler-lib.
Guangliang Fu, Hai Xiang Lin, Arnold Heemink, Sha Lu, Arjo Segers, Nils van Velzen, Tongchao Lu, and Shiming Xu
Geosci. Model Dev., 10, 1751–1766, https://doi.org/10.5194/gmd-10-1751-2017, https://doi.org/10.5194/gmd-10-1751-2017, 2017
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We propose a mask-state algorithm (MS) which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. It will reduce the computational cost in the analysis step for plume assimilation applications. Ensemble-based DA with the mask-state algorithm is generic and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change of the full model.
Chao-Yuan Yang, Jiping Liu, Yongyun Hu, Radley M. Horton, Liqi Chen, and Xiao Cheng
The Cryosphere, 10, 2429–2452, https://doi.org/10.5194/tc-10-2429-2016, https://doi.org/10.5194/tc-10-2429-2016, 2016
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The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales.
S. Xu, B. Wang, and J. Liu
Geosci. Model Dev., 8, 3471–3485, https://doi.org/10.5194/gmd-8-3471-2015, https://doi.org/10.5194/gmd-8-3471-2015, 2015
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This article applies Schwarz-Christoffel (SC) conformal mappings for single-connected and multiple-connected regions to the generation of general orthogonal grids for OGCMs, to achieve 1) the enlarged lat-lon proportion, 2) the removal of landmass and easier load balancing, 3) better spatial resolution on continental boundaries, and 4) alignment of grid lines to large-scale coastlines. The generated grids could be readily utilized by the majority of OGCMs that support general orthogonal grids.
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Winter sea ice export from the Laptev Sea preconditions the local summer sea ice cover and fast ice decay
Sea ice assimilation into a coupled ocean–sea ice model using its adjoint
Modelling radiative transfer through ponded first-year Arctic sea ice with a plane-parallel model
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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.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-174, https://doi.org/10.5194/tc-2021-174, 2021
Revised manuscript accepted for TC
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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 Discuss., https://doi.org/10.5194/tc-2021-114, https://doi.org/10.5194/tc-2021-114, 2021
Revised manuscript accepted for TC
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During summer, Arctic sea ice melts on its surface, 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.
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.
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.
Jun Ono, Hiroaki Tatebe, Yoshiki Komuro, Masato I. Nodzu, and Masayoshi Ishii
The Cryosphere, 12, 675–683, https://doi.org/10.5194/tc-12-675-2018, https://doi.org/10.5194/tc-12-675-2018, 2018
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Sea ice in the Arctic Ocean has experienced rapid decline since the beginning of satellite observations. To assess the predictability of sea ice extent (SIE) in the Arctic Ocean and to clarify the underlying physical processes, we conducted prediction experiments using an initialized climate model (MIROC5). The present study suggests that subsurface ocean heat content originating from the North Atlantic contributes to the skillful prediction of winter SIE at lead times up to 11 months.
Agnieszka Herman, Karl-Ulrich Evers, and Nils Reimer
The Cryosphere, 12, 685–699, https://doi.org/10.5194/tc-12-685-2018, https://doi.org/10.5194/tc-12-685-2018, 2018
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In regions close to the ice edge, sea ice is composed of many separate ice floes of different sizes and shapes. Strong fragmentation is caused mainly by ice breaking by waves coming from the open ocean. At present, this process, although recognized as important for many other physical processes, is not well understood. In this study we present results of a laboratory study of ice breaking by waves, and we provide interpretation of those results that may guide analysis of other similar datasets.
Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, and Walter N. Meier
The Cryosphere, 12, 433–452, https://doi.org/10.5194/tc-12-433-2018, https://doi.org/10.5194/tc-12-433-2018, 2018
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There was significant scientific and media attention surrounding Arctic sea ice in 2016, due primarily to the record-warm air temperatures and low sea ice conditions observed at the start of the year. Here we quantify and assess the record-low monthly sea ice cover in winter, spring and fall, and the lack of record-low sea ice conditions in summer. We explore the primary drivers of these monthly sea ice states and explore the implications for improved summer sea ice forecasting.
Lettie A. Roach, Samuel M. Dean, and James A. Renwick
The Cryosphere, 12, 365–383, https://doi.org/10.5194/tc-12-365-2018, https://doi.org/10.5194/tc-12-365-2018, 2018
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This paper evaluates Antarctic sea ice simulated by global climate models against satellite observations. We find biases in high-concentration and low-concentration sea ice that are consistent across the population of 40 models, in spite of the differences in physics between different models. Targeted model experiments show that biases in low-concentration sea ice can be significantly reduced by enhanced lateral melt, a result that may be valuable for sea ice model development.
David W. Rees Jones and Andrew J. Wells
The Cryosphere, 12, 25–38, https://doi.org/10.5194/tc-12-25-2018, https://doi.org/10.5194/tc-12-25-2018, 2018
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Frazil or granular ice grows rapidly from turbulent water cooled beneath its freezing temperature. We analyse numerical models of a population of ice crystals to provide insight into the treatment of frazil ice in large-scale models and hence in the environment. We determine critical conditions for explosively rapid frazil growth. We show that frazil-ice processes impact whether a plume of ice shelf water beneath an Antarctic ice shelf intrudes at depth or reaches the end of the shelf.
Jamie G. L. Rae, Alexander D. Todd, Edward W. Blockley, and Jeff K. Ridley
The Cryosphere, 11, 3023–3034, https://doi.org/10.5194/tc-11-3023-2017, https://doi.org/10.5194/tc-11-3023-2017, 2017
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Several studies have highlighted links between Arctic summer storms and September sea ice extent in observations. Here we use model and reanalysis data to investigate the sensitivity of such links to the analytical methods used, in order to determine their robustness. The links were found to depend on the resolution of the model and dataset, the method used to identify storms and the time period used in the analysis. We therefore recommend caution when interpreting the results of such studies.
Amelia A. Marks, Maxim L. Lamare, and Martin D. King
The Cryosphere, 11, 2867–2881, https://doi.org/10.5194/tc-11-2867-2017, https://doi.org/10.5194/tc-11-2867-2017, 2017
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Arctic sea ice extent is declining rapidly. Prediction of sea ice trends relies on sea ice models that need to be evaluated with real data. A realistic sea ice environment is created in a laboratory by the Royal Holloway sea ice simulator and is used to show a sea ice model can replicate measured properties of sea ice, e.g. reflectance. Black carbon, a component of soot found in atmospheric pollution, is also experimentally shown to reduce the sea ice reflectance, which could exacerbate melting.
David Docquier, François Massonnet, Antoine Barthélemy, Neil F. Tandon, Olivier Lecomte, and Thierry Fichefet
The Cryosphere, 11, 2829–2846, https://doi.org/10.5194/tc-11-2829-2017, https://doi.org/10.5194/tc-11-2829-2017, 2017
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Our study provides a new way to evaluate the performance of a climate model regarding the interplay between sea ice motion, area and thickness in the Arctic against different observation datasets. We show that the NEMO-LIM model is good in that respect and that the relationships between the different sea ice variables are complex. The metrics we developed can be used in the framework of the Coupled Model Intercomparison Project 6 (CMIP6), which will feed the next IPCC report.
Agnieszka Herman
The Cryosphere, 11, 2711–2725, https://doi.org/10.5194/tc-11-2711-2017, https://doi.org/10.5194/tc-11-2711-2017, 2017
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It is often assumed that ocean waves break sea ice into floes with sizes depending on wavelength. The results of this modeling study (in agreement with some earlier observations and models) suggest that this is not the case; instead the sizes of ice floes produced by wave breaking depend only on ice thickness and mechanical properties. This may have important consequences for predicting sea ice response to oceanic and atmospheric forcing in regions where sea ice is influenced by waves.
Ron Kwok, Nathan T. Kurtz, Ludovic Brucker, Alvaro Ivanoff, Thomas Newman, Sinead L. Farrell, Joshua King, Stephen Howell, Melinda A. Webster, John Paden, Carl Leuschen, Joseph A. MacGregor, Jacqueline Richter-Menge, Jeremy Harbeck, and Mark Tschudi
The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, https://doi.org/10.5194/tc-11-2571-2017, 2017
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Since 2009, the ultra-wideband snow radar on Operation IceBridge has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Existing snow depth retrieval algorithms differ in the way the air–snow and snow–ice interfaces are detected and localized in the radar returns and in how the system limitations are addressed. Here, we assess five retrieval algorithms by comparisons with field measurements, ground-based campaigns, and analyzed fields of snow depth.
Polona Itkin and Thomas Krumpen
The Cryosphere, 11, 2383–2391, https://doi.org/10.5194/tc-11-2383-2017, https://doi.org/10.5194/tc-11-2383-2017, 2017
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By means of airborne sea ice thickness surveys, remote sensing data and results from a numerical model, we show that winter ice dynamic in the Laptev Sea has a preconditioning effect on local summer ice extent in addition to atmospheric processes acting on the ice cover between May and September. We conclude that the observed tendency towards an increased ice export further accelerates pack ice retreat in summer and fast ice decay.
Nikolay V. Koldunov, Armin Köhl, Nuno Serra, and Detlef Stammer
The Cryosphere, 11, 2265–2281, https://doi.org/10.5194/tc-11-2265-2017, https://doi.org/10.5194/tc-11-2265-2017, 2017
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The paper describes one of the first attempts to use the so-called adjoint data assimilation method to bring Arctic Ocean model simulations closer to observation, especially in terms of the sea ice. It is shown that after assimilation the model bias in simulating the Arctic sea ice is considerably reduced. There is also additional improvement in the sea ice thickens representation that is not assimilated directly.
Torbjørn Taskjelle, Stephen R. Hudson, Mats A. Granskog, and Børge Hamre
The Cryosphere, 11, 2137–2148, https://doi.org/10.5194/tc-11-2137-2017, https://doi.org/10.5194/tc-11-2137-2017, 2017
Christian Katlein, Stefan Hendricks, and Jeffrey Key
The Cryosphere, 11, 2111–2116, https://doi.org/10.5194/tc-11-2111-2017, https://doi.org/10.5194/tc-11-2111-2017, 2017
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In the public debate, increasing sea ice extent in the Antarctic is often highlighted as counter-indicative of global warming. Here we show that the slight increases in Antarctic sea ice extent are not able to counter Arctic losses. Using bipolar satellite observations, we demonstrate that even in the Antarctic polar ocean solar shortwave energy absorption is increasing in accordance with strongly increasing shortwave energy absorption in the Arctic Ocean rather than compensating Arctic losses.
Timothy D. Williams, Pierre Rampal, and Sylvain Bouillon
The Cryosphere, 11, 2117–2135, https://doi.org/10.5194/tc-11-2117-2017, https://doi.org/10.5194/tc-11-2117-2017, 2017
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As the Arctic sea ice extent drops, more ship traffic seeks to take advantage of this, and a need for better wave and sea ice forecasts arises. One aspect of this is the location of the sea ice edge. The waves here can be quite large, but they die away as they travel into the ice. This causes momentum to be transferred from the waves to the ice, causing ice drift. However, our study found that the effect of the wind drag had more impact on the ice edge position than the waves.
Véronique Dansereau, Jérôme Weiss, Pierre Saramito, Philippe Lattes, and Edmond Coche
The Cryosphere, 11, 2033–2058, https://doi.org/10.5194/tc-11-2033-2017, https://doi.org/10.5194/tc-11-2033-2017, 2017
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A new mechanical framework is used to model the drift of sea ice in a narrow channel between Greenland and Ellesmere Island. It is able to reproduce its main features : curved cracks, ice “bridges” that stop the flow of ice for several months of the year and some thick, strongly localized ridged ice. The simulations suggest that a mechanical weakening of the sea ice cover can shorten the lifespan of ice bridges and result in an increased export of ice through the narrow channels of the Arctic.
Kevin Guerreiro, Sara Fleury, Elena Zakharova, Alexei Kouraev, Frédérique Rémy, and Philippe Maisongrande
The Cryosphere, 11, 2059–2073, https://doi.org/10.5194/tc-11-2059-2017, https://doi.org/10.5194/tc-11-2059-2017, 2017
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We analyse CryoSat-2 and Envisat freeboard height discrepancy over Arctic sea ice and we study the potential role of ice roughness.
Based on our results, we build a CryoSat-2-like version of Envisat freeboard height. The improved Envisat freeboard is converted to sea ice draught and compared to in situ mooring observations to demonstrate the potential of our methodology to produce accurate ice thickness estimates over the 2002–2012 period.
Stefan Muckenhuber and Stein Sandven
The Cryosphere, 11, 1835–1850, https://doi.org/10.5194/tc-11-1835-2017, https://doi.org/10.5194/tc-11-1835-2017, 2017
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Sea ice drift has a strong impact on sea ice distribution on different temporal and spatial scales. An open-source sea ice drift algorithm for Sentinel-1 satellite imagery is introduced based on the combination of feature tracking and pattern matching. The algorithm is designed to utilise the respective advantages of the two approaches and allows drift calculation at user-defined locations.
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Short summary
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.
This work proposes a new data synergy method for the retrieval of sea ice thickness and snow...
