104 citations as recorded by crossref.

1. Data Synergy between Altimetry and L-Band Passive Microwave Remote Sensing for the Retrieval of Sea Ice Parameters—A Theoretical Study of Methodology S. Xu et al. https://doi.org/10.3390/rs9101079
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3. An evaluation of the E3SMv1 Arctic ocean and sea-ice regionally refined model M. Veneziani et al. https://doi.org/10.5194/gmd-15-3133-2022
4. Using records from submarine, aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness J. Stroeve et al. https://doi.org/10.5194/tc-8-1839-2014
5. A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses J. Stroeve et al. https://doi.org/10.1029/2019JC015900
6. On retrieving sea ice freeboard from ICESat laser altimeter K. Khvorostovsky & P. Rampal https://doi.org/10.5194/tc-10-2329-2016
7. Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model J. Urrego‐Blanco et al. https://doi.org/10.1002/2015JC011558
8. The Copernicus Marine Environment Monitoring Service Ocean State Report K. von Schuckmann et al. https://doi.org/10.1080/1755876X.2016.1273446
9. Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval L. Zhou et al. https://doi.org/10.5194/tc-15-345-2021
10. A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements H. Li et al. https://doi.org/10.1016/j.rse.2021.112840
11. Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses F. Cocetta et al. https://doi.org/10.5194/tc-18-4687-2024
12. Reprint of: When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume F. Diebold et al. https://doi.org/10.1016/j.jeconom.2023.105645
13. Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project M. Chevallier et al. https://doi.org/10.1007/s00382-016-2985-y
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15. Polar Applications of Spaceborne Scatterometers D. Long https://doi.org/10.1109/JSTARS.2016.2629418
16. Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar J. Karvonen et al. https://doi.org/10.5194/tc-16-1821-2022
17. Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice S. Gerland et al. https://doi.org/10.1088/1748-9326/ab09b3
18. Retrieval of sea ice thickness from FY-3E data using Random Forest method H. Li et al. https://doi.org/10.1016/j.asr.2024.03.061
19. Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. https://doi.org/10.5194/os-19-1375-2023
20. The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise S. Kern et al. https://doi.org/10.5194/tc-9-37-2015
21. Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F T. Williams et al. https://doi.org/10.5194/tc-15-3207-2021
22. On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data L. Zhou et al. https://doi.org/10.5194/tc-12-993-2018
23. Evaluating the impact of blowing-snow sea salt aerosol on springtime BrO and O3 in the Arctic J. Huang et al. https://doi.org/10.5194/acp-20-7335-2020
24. Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements F. Garnier et al. https://doi.org/10.5194/tc-15-5483-2021
25. Surface Properties Linked to Retrieval Uncertainty of Satellite Sea-Ice Thickness with Upward-Looking Sonar Measurements K. Khvorostovsky et al. https://doi.org/10.3390/rs12183094
26. Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system S. Fritzner et al. https://doi.org/10.5194/tc-13-491-2019
27. Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness C. Soriot et al. https://doi.org/10.1038/s41598-024-70136-9
28. Impact of model resolution on Arctic sea ice and North Atlantic Ocean heat transport D. Docquier et al. https://doi.org/10.1007/s00382-019-04840-y
29. Recent advances in understanding the Arctic climate system state and change from a sea ice perspective: a review R. Döscher et al. https://doi.org/10.5194/acp-14-13571-2014
30. Estimation of Arctic Sea Ice Thickness Using HY-2B Altimeter Data C. Pang et al. https://doi.org/10.3390/rs16234565
31. Regional distribution and variability of model-simulated Arctic snow on sea ice K. Castro-Morales et al. https://doi.org/10.1016/j.polar.2017.05.003
32. Inter-comparisons of Arctic snow depth products M. Li et al. https://doi.org/10.1080/17538947.2024.2376286
33. Arctic Sea Ice in CMIP6 D. Notz & S. Community https://doi.org/10.1029/2019GL086749
34. Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations R. Lindsay & A. Schweiger https://doi.org/10.5194/tc-9-269-2015
35. Sea ice and snow characteristics from year-long transects at the MOSAiC Central Observatory P. Itkin et al. https://doi.org/10.1525/elementa.2022.00048
36. Improved Arctic sea ice thickness projections using bias-corrected CMIP5 simulations N. Melia et al. https://doi.org/10.5194/tc-9-2237-2015
37. A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data Z. Dong et al. https://doi.org/10.3390/rs14041041
38. Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards I. Lawrence et al. https://doi.org/10.5194/tc-12-3551-2018
39. Arctic sea ice density observation and its impact on sea ice thickness retrieval from CryoSat–2 Q. Ji et al. https://doi.org/10.1016/j.coldregions.2020.103177
40. Sea Ice Enhancements to Polar WRF* K. Hines et al. https://doi.org/10.1175/MWR-D-14-00344.1
41. Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble Z. Labe et al. https://doi.org/10.1175/JCLI-D-17-0436.1
42. When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume F. Diebold et al. https://doi.org/10.2139/ssrn.4299618
43. Novel methods to study sea ice deformation, linear kinematic features and coherent dynamic clusters from imaging remote sensing data P. Itkin https://doi.org/10.5194/tc-19-1135-2025
44. When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume F. Diebold et al. https://doi.org/10.1016/j.jeconom.2023.105479
45. Rise and fall of sea ice production in the Arctic Ocean’s ice factories S. Cornish et al. https://doi.org/10.1038/s41467-022-34785-6
46. Modelling the evolution of Arctic multiyear sea ice over 2000–2018 H. Regan et al. https://doi.org/10.5194/tc-17-1873-2023
47. The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities C. Rousset et al. https://doi.org/10.5194/gmd-8-2991-2015
48. Sea Ice Roughness Overlooked as a Key Source of Uncertainty in CryoSat‐2 Ice Freeboard Retrievals J. Landy et al. https://doi.org/10.1029/2019JC015820
49. The evolution of the Fram Strait sea ice volume export decomposed by age: estimating with parameter-optimized sea ice-ocean model outputs Y. Yang et al. https://doi.org/10.1088/1748-9326/acaf3b
50. Assessment of Satellite Altimetric and Compact Polarimetric SAR Parameters Over Early Spring Snow-Covered Landfast Sea Ice in the Canadian Arctic H. Lam et al. https://doi.org/10.1109/JSTARS.2025.3595821
51. Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic M. Kimmritz et al. https://doi.org/10.1029/2019MS001825
52. Estimation of Arctic Winter Snow Depth, Sea Ice Thickness and Bulk Density, and Ice Freeboard by Combining CryoSat-2, AVHRR, and AMSR Measurements H. Shi et al. https://doi.org/10.1109/TGRS.2023.3265274
53. Estimating parameters in a sea ice model using an ensemble Kalman filter Y. Zhang et al. https://doi.org/10.5194/tc-15-1277-2021
54. Automated Calibration of a Snow‐On‐Sea‐Ice Model A. Cabaj et al. https://doi.org/10.1029/2022EA002655
55. Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013 J. Xie et al. https://doi.org/10.5194/os-13-123-2017
56. A review of interactions between ocean heat transport and Arctic sea ice D. Docquier & T. Koenigk https://doi.org/10.1088/1748-9326/ac30be
57. ESD Reviews: Climate feedbacks in the Earth system and prospects for their evaluation C. Heinze et al. https://doi.org/10.5194/esd-10-379-2019
58. Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and human activity W. Meier et al. https://doi.org/10.1002/2013RG000431
59. Early predictors of seasonal Arctic sea-ice volume loss: the impact of spring and early-summer cloud radiative conditions M. King et al. https://doi.org/10.1017/aog.2020.60
60. Potential for estimation of snow depth on Arctic sea ice from CryoSat-2 and SARAL/AltiKa missions K. Guerreiro et al. https://doi.org/10.1016/j.rse.2016.07.013
61. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system J. Lellouche et al. https://doi.org/10.5194/os-14-1093-2018
62. Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al. https://doi.org/10.1016/j.rse.2025.114607
63. Spatiotemporal dynamics of sea ice volume and their potential drivers in Liaodong Bay, China, using Sentinel-3 data over five ice seasons H. Ling et al. https://doi.org/10.1080/01490419.2025.2522674
64. neXtSIM: a new Lagrangian sea ice model P. Rampal et al. https://doi.org/10.5194/tc-10-1055-2016
65. Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6 D. Docquier et al. https://doi.org/10.5194/tc-11-2829-2017
66. Challenges in identifying Antarctic coastal polynyas in satellite observations and climate model output to support ecological climate change research L. Landrum et al. https://doi.org/10.5194/tc-20-1815-2026
67. Slowdown of global surface air temperature increase and acceleration of ice melting A. Berger et al. https://doi.org/10.1002/2017EF000554
68. Bias correction of Arctic sea ice thickness products based on factor selection and machine learning methods H. Wu et al. https://doi.org/10.1016/j.apor.2024.104069
69. Differences Between ICESat and CryoSat‐2 Sea Ice Thicknesses Over the Arctic: Consequences for Analyzing the Ice Volume Trend J. Kim et al. https://doi.org/10.1029/2020JD033103
70. Convective cloud fields in the Atlantic sector of the Arctic: Satellite and ground-based observations I. Esau & A. Chernokulsky https://doi.org/10.1134/S000143381509008X
71. Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system J. Xie et al. https://doi.org/10.5194/tc-10-2745-2016
72. Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products M. Li et al. https://doi.org/10.1080/01431161.2019.1637961
73. Variability of Arctic sea ice thickness and volume from CryoSat-2 R. Kwok & G. Cunningham https://doi.org/10.1098/rsta.2014.0157
74. Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network A. Braakmann-Folgmann & C. Donlon https://doi.org/10.5194/tc-13-2421-2019
75. Relating the Age of Arctic Sea Ice to its Thickness, as Measured during NASA’s ICESat and IceBridge Campaigns M. Tschudi et al. https://doi.org/10.3390/rs8060457
76. Warm Arctic, Increased Winter Sea Ice Growth? A. Petty et al. https://doi.org/10.1029/2018GL079223
77. Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method F. Xiao et al. https://doi.org/10.3390/s20247011
78. Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson https://doi.org/10.5194/tc-18-2875-2024
79. An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al. https://doi.org/10.1109/JSTARS.2025.3636999
80. Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness I. Glissenaar et al. https://doi.org/10.5194/tc-15-4909-2021
81. Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice S. Xu et al. https://doi.org/10.5194/tc-14-751-2020
82. Change of Arctic sea-ice volume and its relationship with sea-ice extent in CMIP5 simulations M. Song https://doi.org/10.1080/16742834.2015.1126153
83. Linking timescale-dependent Antarctic sea ice kinematic observations to ice thickness T. Tian et al. https://doi.org/10.1016/j.rse.2023.113813
84. An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting N. Kurtz et al. https://doi.org/10.5194/tc-8-1217-2014
85. Investigating snow sinks on level sea ice: A case study in the western Arctic I. Merkouriadi et al. https://doi.org/10.1017/jog.2025.34
86. Spatiotemporal Variation Characteristics and Drivers of Winter Arctic Sea Ice Thickness Under the New Arctic Regime Y. Yin & X. Wang https://doi.org/10.3390/jmse14100888
87. The Arctic Surface Climate in CMIP6: Status and Developments since CMIP5 R. Davy & S. Outten https://doi.org/10.1175/JCLI-D-19-0990.1
88. An assessment of ten ocean reanalyses in the polar regions P. Uotila et al. https://doi.org/10.1007/s00382-018-4242-z
89. Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness I. Sievers et al. https://doi.org/10.5194/tc-18-5985-2024
90. Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements H. Shi et al. https://doi.org/10.5194/tc-14-3761-2020
91. Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover R. Mallett et al. https://doi.org/10.5194/tc-15-2429-2021
92. Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction F. Massonnet https://doi.org/10.1007/s40641-019-00151-w
93. Assessment of radar freeboard, radar penetration rate, and snow depth for potential improvements in Arctic sea ice thickness retrieved from CryoSat-2 Y. Zhou et al. https://doi.org/10.1016/j.coldregions.2024.104408
94. Seasonal Variability of Sea Ice Bulk Density During the MOSAiC Expedition: Results From Synergistic In Situ, Airborne, and Satellite Observations Y. Zhou et al. https://doi.org/10.1109/TGRS.2025.3650353
95. Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years L. Smedsrud et al. https://doi.org/10.5194/tc-11-65-2017
96. Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements A. Jutila et al. https://doi.org/10.5194/tc-16-259-2022
97. Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data M. Li et al. https://doi.org/10.1002/joc.6972
98. Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach L. Edel et al. https://doi.org/10.5194/tc-19-731-2025
99. Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation Q. Yang et al. https://doi.org/10.5194/tc-10-761-2016
100. Arctic sea-ice change tied to its mean state through thermodynamic processes F. Massonnet et al. https://doi.org/10.1038/s41558-018-0204-z
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103. A comparison of viscous-plastic sea ice solvers with and without replacement pressure M. Kimmritz et al. https://doi.org/10.1016/j.ocemod.2017.05.006
104. Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection A. Petty et al. https://doi.org/10.5194/tc-17-127-2023