81 citations as recorded by crossref.

1. The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system N. Williams et al. https://doi.org/10.5194/tc-17-2509-2023
2. Summertime sea-ice prediction in the Weddell Sea improved by sea-ice thickness initialization Y. Morioka et al. https://doi.org/10.1038/s41598-021-91042-4
3. Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system A. Liu et al. https://doi.org/10.1016/j.atmosres.2023.107014
4. Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning Z. He et al. https://doi.org/10.5194/tc-19-3279-2025
5. Toward improved sea ice freeboard observation with SAR altimetry using the physical retracker SAMOSA+ A. Laforge et al. https://doi.org/10.1016/j.asr.2020.02.001
6. Strain response and energy dissipation of floating saline ice under cyclic compressive stress M. Wei et al. https://doi.org/10.5194/tc-14-2849-2020
7. The SARAL/AltiKa mission: A step forward to the future of altimetry J. Verron et al. https://doi.org/10.1016/j.asr.2020.01.030
8. An anatomy of Arctic sea ice forecast biases in the seasonal prediction system with EC-Earth R. Cruz-García et al. https://doi.org/10.1007/s00382-020-05560-4
9. A Spring Barrier for Regional Predictions of Summer Arctic Sea Ice D. Bonan et al. https://doi.org/10.1029/2019GL082947
10. Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting? E. Hunke et al. https://doi.org/10.1007/s40641-020-00162-y
11. Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. https://doi.org/10.1080/01431161.2021.1897183
12. Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM) E. Fiedler et al. https://doi.org/10.5194/tc-16-61-2022
13. Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. https://doi.org/10.5194/os-19-1375-2023
14. Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3 T. Tian et al. https://doi.org/10.5194/gmd-14-4283-2021
15. Assimilating summer sea ice thickness enhances predictions of Arctic sea ice and surrounding atmosphere within two months A. Liu et al. https://doi.org/10.1038/s41612-025-01050-8
16. Enhancing sea ice knowledge through assimilation of sea ice thickness from ENVISAT and CS2SMOS N. Williams et al. https://doi.org/10.5194/tc-20-853-2026
17. Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. https://doi.org/10.1029/2019MS001937
18. Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. https://doi.org/10.1016/j.scib.2018.11.018
19. Evaluating Benefits of Two-Way Ocean–Atmosphere Coupling for Global NWP Forecasts M. Vellinga et al. https://doi.org/10.1175/WAF-D-20-0035.1
20. Year-round impact of winter sea ice thickness observations on seasonal forecasts B. Balan-Sarojini et al. https://doi.org/10.5194/tc-15-325-2021
21. Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets S. Sandven et al. https://doi.org/10.1007/s10712-023-09781-0
22. Sea ice data assimilation in ORAS6 P. Browne et al. https://doi.org/10.5194/tc-20-3299-2026
23. Satellite-Based Data Assimilation System for the Initialization of Arctic Sea Ice Concentration and Thickness Using CICE5 J. Lee & Y. Ham https://doi.org/10.3389/fclim.2022.797733
24. Added value of assimilating springtime Arctic sea ice concentration in summer-fall climate predictions J. Acosta Navarro et al. https://doi.org/10.1088/1748-9326/ac6c9b
25. Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization S. Sun & A. Solomon https://doi.org/10.5194/tc-18-3033-2024
26. Winter Coastal Divergence as a Predictor for the Minimum Sea Ice Extent in the Laptev Sea C. Brunette et al. https://doi.org/10.1175/JCLI-D-18-0169.1
27. Assessment of contemporary satellite sea ice thickness products for Arctic sea ice H. Sallila et al. https://doi.org/10.5194/tc-13-1187-2019
28. Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. https://doi.org/10.34133/olar.0025
29. Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y. Chen et al. https://doi.org/10.5194/tc-18-2381-2024
30. Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model P. Dai et al. https://doi.org/10.1007/s00382-020-05196-4
31. Analyzing the impact of CryoSat-2 ice thickness initialization on seasonal Arctic Sea Ice prediction R. Allard et al. https://doi.org/10.1017/aog.2020.15
32. Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model Y. Wang et al. https://doi.org/10.5194/tc-16-1141-2022
33. A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations W. Gregory et al. https://doi.org/10.5194/tc-15-2857-2021
34. 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
35. Regime-dependence when constraining a sea ice model with observations: lessons from a single-column perspective M. Wieringa & C. Bitz https://doi.org/10.5194/tc-19-6207-2025
36. A Review of Arctic Sea Ice Climate Predictability in Large-Scale Earth System Models M. Holland & E. Hunke https://doi.org/10.5670/oceanog.2022.113
37. Assimilation of radar freeboard and snow altimetry observations in the Arctic and Antarctic with a coupled ocean/sea ice modelling system A. Chenal et al. https://doi.org/10.5194/tc-20-369-2026
38. Improved Arctic Sea Ice Freeboard Retrieval From Satellite Altimetry Using Optimized Sea Surface Decorrelation Scales J. Landy et al. https://doi.org/10.1029/2021JC017466
39. New insight from CryoSat-2 sea ice thickness for sea ice modelling D. Schröder et al. https://doi.org/10.5194/tc-13-125-2019
40. Sea-ice information and forecast needs for industry maritime stakeholders P. Wagner et al. https://doi.org/10.1080/1088937X.2020.1766592
41. Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System L. Mu et al. https://doi.org/10.1029/2022MS003176
42. The Summer North Atlantic Oscillation, Arctic sea ice, and Arctic jet Rossby wave forcing C. Folland et al. https://doi.org/10.1126/sciadv.adk6693
43. From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. https://doi.org/10.3389/fmars.2019.00234
44. Retrieval of thin-ice thickness in arctic marginal seas through a combination of thermal and optical observations from polar orbiting satellites Z. Suo et al. https://doi.org/10.1080/15481603.2025.2567117
45. Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone B. Duncan et al. https://doi.org/10.1029/2019RG000652
46. Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice G. Dawson & J. Landy https://doi.org/10.5194/tc-17-4165-2023
47. Sea ice in the Baltic Sea during 1993/94–2020/21 ice seasons from satellite observations and model reanalysis S. Singh et al. https://doi.org/10.5194/tc-19-4741-2025
48. Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability A. Subramanian et al. https://doi.org/10.3389/fmars.2019.00427
49. A multi-frequency altimetry snow depth product over Arctic sea ice A. Carret et al. https://doi.org/10.1038/s41597-024-04343-4
50. Prediction of Pan-Arctic Sea Ice Using Attention-Based LSTM Neural Networks J. Wei et al. https://doi.org/10.3389/fmars.2022.860403
51. Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model Q. Yang et al. https://doi.org/10.1016/j.atmosres.2019.04.021
52. Seasonal Predictions of Regional and Pan-Antarctic Sea Ice With a Dynamical Forecast System R. Payne et al. https://doi.org/10.1080/07055900.2023.2252387
53. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission M. Kern et al. https://doi.org/10.5194/tc-14-2235-2020
54. Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations C. Yang et al. https://doi.org/10.1029/2019MS001938
55. Data Assimilation of Satellite-Derived Arctic Sea-Ice Thickness During Boreal Summer J. Lee et al. https://doi.org/10.1109/JSTARS.2025.3561257
56. Respective influences of perturbed atmospheric and ocean–sea ice initial conditions on the skill of seasonal Antarctic sea ice predictions: A study with NEMO3.6–LIM3 S. Marchi et al. https://doi.org/10.1016/j.ocemod.2020.101591
57. Advances in Seasonal Predictions of Arctic Sea Ice With NOAA UFS J. Zhu et al. https://doi.org/10.1029/2022GL102392
58. A Mechanism for the Arctic Sea Ice Spring Predictability Barrier M. Bushuk et al. https://doi.org/10.1029/2020GL088335
59. How to get your message across: designing an impactful knowledge transfer plan in a European project S. Pasqualetto et al. https://doi.org/10.5194/gc-5-87-2022
60. 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
61. Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model J. Ono et al. https://doi.org/10.1017/aog.2020.13
62. 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
63. A 10-year record of Arctic summer sea ice freeboard from CryoSat-2 G. Dawson et al. https://doi.org/10.1016/j.rse.2021.112744
64. 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
65. Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations W. Gregory et al. https://doi.org/10.5194/tc-16-1653-2022
66. Alleviation of an Arctic Sea Ice Bias in a Coupled Model Through Modifications in the Subgrid‐Scale Orographic Parameterization G. Gastineau et al. https://doi.org/10.1029/2020MS002111
67. Intercomparison of Arctic sea ice simulation in ROMS-CICE and ROMS-Budgell R. Kumar et al. https://doi.org/10.1016/j.polar.2021.100716
68. Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts duringXuelong's first trans-Arctic Passage in summer 2017 L. Mu et al. https://doi.org/10.1017/jog.2019.55
69. Scalable interpolation of satellite altimetry data with probabilistic machine learning W. Gregory et al. https://doi.org/10.1038/s41467-024-51900-x
70. Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates R. Mallett et al. https://doi.org/10.5194/tc-14-251-2020
71. Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. https://doi.org/10.1029/2023GL105672
72. 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
73. Understanding the Forecast Skill of Rapid Arctic Sea Ice Loss on Subseasonal Time Scales M. McGraw et al. https://doi.org/10.1175/JCLI-D-21-0301.1
74. Predictability of Antarctic Sea Ice Edge on Subseasonal Time Scales L. Zampieri et al. https://doi.org/10.1029/2019GL084096
75. Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season G. Smith et al. https://doi.org/10.3389/fmars.2019.00429
76. Arctic supply chain reliability in Baffin Bay and Greenland J. Taarup-Esbensen & O. Gudmestad https://doi.org/10.1080/1088937X.2022.2032447
77. Effectiveness of wind-constrained sea-ice momentum on formation of sea-ice distribution and upper halocline of Arctic Ocean in climate model J. Ono et al. https://doi.org/10.1088/2752-5295/ad3fdc
78. Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system Q. Shu et al. https://doi.org/10.1007/s13131-021-1768-4
79. Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham https://doi.org/10.1038/s41612-023-00402-6
80. Summer predictions of Arctic sea ice edge in multi-model seasonal re-forecasts L. Batté et al. https://doi.org/10.1007/s00382-020-05273-8
81. Bounded and categorized: targeting data assimilation for sea ice fractional coverage and nonnegative quantities in a single-column multi-category sea ice model M. Wieringa et al. https://doi.org/10.5194/tc-18-5365-2024