Articles | Volume 9, issue 1
https://doi.org/10.5194/os-9-19-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-9-19-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
15 Jan 2013
Research article |
| 15 Jan 2013
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects
S. Tietsche
Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
International Max Planck Research School on Earth System Modelling, Hamburg, Germany
now at: NCAS-Climate, University of Reading, Reading, UK
Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
J. H. Jungclaus
Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
J. Marotzke
Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
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Cited
52 citations as recorded by crossref.
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- Southern Ocean sea ice concentration budgets of five ocean-sea ice reanalyses Y. Nie et al. https://doi.org/10.1007/s00382-022-06260-x
- Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea ice concentration F. Bunzel et al. https://doi.org/10.1002/2015GL066928
- Seasonal predictability of European summer climate re-assessed N. Neddermann et al. https://doi.org/10.1007/s00382-019-04678-4
- Evaluation of Summertime Passive Microwave and Reanalysis Sea‐Ice Concentration in the Central Arctic K. Song & P. Minnett https://doi.org/10.1029/2023EA003214
- The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis L. Jean-Michel et al. https://doi.org/10.3389/feart.2021.698876
- Long-Term Variability of Surface Albedo and Its Correlation with Climatic Variables over Antarctica M. Seo et al. https://doi.org/10.3390/rs8120981
- 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
- Assimilating SMOS sea ice thickness into a coupled ice‐ocean model using a local SEIK filter Q. Yang et al. https://doi.org/10.1002/2014JC009963
- Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al. https://doi.org/10.5194/tc-19-5613-2025
- 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
- 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
- Thin Arctic sea ice in L-band observations and an ocean reanalysis S. Tietsche et al. https://doi.org/10.5194/tc-12-2051-2018
- Barents-Kara sea ice and European winters in EC-Earth I. Ringgaard et al. https://doi.org/10.1007/s00382-020-05174-w
- 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
- Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020 S. Cheng et al. https://doi.org/10.5194/tc-17-1735-2023
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- Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model K. Wang et al. https://doi.org/10.5194/tc-17-4487-2023
- The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model J. Baehr et al. https://doi.org/10.1007/s00382-014-2399-7
- 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
- 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
- Predictability of large interannual Arctic sea-ice anomalies S. Tietsche et al. https://doi.org/10.1007/s00382-013-1698-8
- Analyzing the Characteristics of Sea Ice Initial Conditions for a Global Ocean and Sea Ice Prediction System, the NEMO-CICE/NEMOVAR over the Arctic Region J. Ahn & S. Lee https://doi.org/10.5467/JKESS.2015.36.1.82
- Multivariate deep learning approach to forecast of an ice-free Arctic summer under the SSP2-4.5 and SSP5-8.5 scenarios M. Bilgili https://doi.org/10.1016/j.polar.2025.101308
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. https://doi.org/10.1016/j.scib.2018.11.018
- An Improved Estimate of the Coupled Arctic Energy Budget M. Mayer et al. https://doi.org/10.1175/JCLI-D-19-0233.1
- Assimilating summer sea-ice concentration into a coupled ice–ocean model using a LSEIK filter Q. Yang et al. https://doi.org/10.3189/2015AoG69A740
- Arctic sea ice in the global eddy-permitting ocean reanalysis ORAP5 S. Tietsche et al. https://doi.org/10.1007/s00382-015-2673-3
- Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter X. Liang et al. https://doi.org/10.1175/JTECH-D-16-0166.1
- Sea ice assimilation into a coupled ocean–sea ice model using its adjoint N. Koldunov et al. https://doi.org/10.5194/tc-11-2265-2017
- 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
- Optimized sea ice simulation in MITgcm-ECCO2 forced by ERA5 E. Gavilan Pascual-Ahuir & Z. Wang https://doi.org/10.1016/j.ocemod.2023.102183
- Estimation of summer pan-Arctic ice draft from satellite passive microwave observations J. Kim et al. https://doi.org/10.1016/j.rse.2023.113662
- Atmospheric and Oceanic Contributions to Irreducible Forecast Uncertainty of Arctic Surface Climate S. Tietsche et al. https://doi.org/10.1175/JCLI-D-15-0421.1
- The role of atmospheric uncertainty in Arctic summer sea ice data assimilation and prediction Q. Yang et al. https://doi.org/10.1002/qj.2523
- 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
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- Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model M. Kimmritz et al. https://doi.org/10.1080/16000870.2018.1435945
- Impacts of Sea Ice Thickness Initialization on Seasonal Arctic Sea Ice Predictions A. Dirkson et al. https://doi.org/10.1175/JCLI-D-16-0437.1
- Advancing Short‐Term Forecasts of Ice Conditions in the Beaufort Sea M. Yaremchuk et al. https://doi.org/10.1029/2018JC014581
- C-GLORSv5: an improved multipurpose global ocean eddy-permitting physical reanalysis A. Storto & S. Masina https://doi.org/10.5194/essd-8-679-2016
- Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System Z. Chen et al. https://doi.org/10.1175/JCLI-D-17-0093.1
- Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data J. Rusin et al. https://doi.org/10.1017/aog.2024.6
- 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
- Design and implementation of a Newtonian relaxation scheme in the NOAA GFDL Sea Ice Model (SIS2) D. Dukhovskoy et al. https://doi.org/10.5194/gmd-19-7169-2026
- The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation C. Burgard et al. https://doi.org/10.5194/tc-14-2387-2020
- Improved Seasonal Prediction of European Summer Temperatures With New Five‐Layer Soil‐Hydrology Scheme F. Bunzel et al. https://doi.org/10.1002/2017GL076204
- The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment H. Zuo et al. https://doi.org/10.5194/os-15-779-2019
- Relaxation Method for Assimilation of Sea Ice Concentration Data in the NEMO—LIM3 Multicategory Sea Ice Model B. Strukov et al. https://doi.org/10.3103/S1068373920020053
- CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization X. Liu et al. https://doi.org/10.3390/jmse9090920
- DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations H. Luo et al. https://doi.org/10.1017/jog.2021.57
- Potential of coupled data assimilation studies in cryosphere K. SUZUKI https://doi.org/10.5331/seppyo.80.2_185
52 citations as recorded by crossref.
- 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
- Southern Ocean sea ice concentration budgets of five ocean-sea ice reanalyses Y. Nie et al. https://doi.org/10.1007/s00382-022-06260-x
- Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea ice concentration F. Bunzel et al. https://doi.org/10.1002/2015GL066928
- Seasonal predictability of European summer climate re-assessed N. Neddermann et al. https://doi.org/10.1007/s00382-019-04678-4
- Evaluation of Summertime Passive Microwave and Reanalysis Sea‐Ice Concentration in the Central Arctic K. Song & P. Minnett https://doi.org/10.1029/2023EA003214
- The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis L. Jean-Michel et al. https://doi.org/10.3389/feart.2021.698876
- Long-Term Variability of Surface Albedo and Its Correlation with Climatic Variables over Antarctica M. Seo et al. https://doi.org/10.3390/rs8120981
- 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
- Assimilating SMOS sea ice thickness into a coupled ice‐ocean model using a local SEIK filter Q. Yang et al. https://doi.org/10.1002/2014JC009963
- Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al. https://doi.org/10.5194/tc-19-5613-2025
- 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
- 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
- Thin Arctic sea ice in L-band observations and an ocean reanalysis S. Tietsche et al. https://doi.org/10.5194/tc-12-2051-2018
- Barents-Kara sea ice and European winters in EC-Earth I. Ringgaard et al. https://doi.org/10.1007/s00382-020-05174-w
- 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
- Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020 S. Cheng et al. https://doi.org/10.5194/tc-17-1735-2023
- Seasonal Predictability over Europe Arising from El Niño and Stratospheric Variability in the MPI-ESM Seasonal Prediction System D. Domeisen et al. https://doi.org/10.1175/JCLI-D-14-00207.1
- Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model K. Wang et al. https://doi.org/10.5194/tc-17-4487-2023
- The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model J. Baehr et al. https://doi.org/10.1007/s00382-014-2399-7
- 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
- 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
- Predictability of large interannual Arctic sea-ice anomalies S. Tietsche et al. https://doi.org/10.1007/s00382-013-1698-8
- Analyzing the Characteristics of Sea Ice Initial Conditions for a Global Ocean and Sea Ice Prediction System, the NEMO-CICE/NEMOVAR over the Arctic Region J. Ahn & S. Lee https://doi.org/10.5467/JKESS.2015.36.1.82
- Multivariate deep learning approach to forecast of an ice-free Arctic summer under the SSP2-4.5 and SSP5-8.5 scenarios M. Bilgili https://doi.org/10.1016/j.polar.2025.101308
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. https://doi.org/10.1016/j.scib.2018.11.018
- An Improved Estimate of the Coupled Arctic Energy Budget M. Mayer et al. https://doi.org/10.1175/JCLI-D-19-0233.1
- Assimilating summer sea-ice concentration into a coupled ice–ocean model using a LSEIK filter Q. Yang et al. https://doi.org/10.3189/2015AoG69A740
- Arctic sea ice in the global eddy-permitting ocean reanalysis ORAP5 S. Tietsche et al. https://doi.org/10.1007/s00382-015-2673-3
- Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter X. Liang et al. https://doi.org/10.1175/JTECH-D-16-0166.1
- Sea ice assimilation into a coupled ocean–sea ice model using its adjoint N. Koldunov et al. https://doi.org/10.5194/tc-11-2265-2017
- 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
- Optimized sea ice simulation in MITgcm-ECCO2 forced by ERA5 E. Gavilan Pascual-Ahuir & Z. Wang https://doi.org/10.1016/j.ocemod.2023.102183
- Estimation of summer pan-Arctic ice draft from satellite passive microwave observations J. Kim et al. https://doi.org/10.1016/j.rse.2023.113662
- Atmospheric and Oceanic Contributions to Irreducible Forecast Uncertainty of Arctic Surface Climate S. Tietsche et al. https://doi.org/10.1175/JCLI-D-15-0421.1
- The role of atmospheric uncertainty in Arctic summer sea ice data assimilation and prediction Q. Yang et al. https://doi.org/10.1002/qj.2523
- 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
- 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
- Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model M. Kimmritz et al. https://doi.org/10.1080/16000870.2018.1435945
- Impacts of Sea Ice Thickness Initialization on Seasonal Arctic Sea Ice Predictions A. Dirkson et al. https://doi.org/10.1175/JCLI-D-16-0437.1
- Advancing Short‐Term Forecasts of Ice Conditions in the Beaufort Sea M. Yaremchuk et al. https://doi.org/10.1029/2018JC014581
- C-GLORSv5: an improved multipurpose global ocean eddy-permitting physical reanalysis A. Storto & S. Masina https://doi.org/10.5194/essd-8-679-2016
- Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System Z. Chen et al. https://doi.org/10.1175/JCLI-D-17-0093.1
- Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data J. Rusin et al. https://doi.org/10.1017/aog.2024.6
- 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
- Design and implementation of a Newtonian relaxation scheme in the NOAA GFDL Sea Ice Model (SIS2) D. Dukhovskoy et al. https://doi.org/10.5194/gmd-19-7169-2026
- The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation C. Burgard et al. https://doi.org/10.5194/tc-14-2387-2020
- Improved Seasonal Prediction of European Summer Temperatures With New Five‐Layer Soil‐Hydrology Scheme F. Bunzel et al. https://doi.org/10.1002/2017GL076204
- The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment H. Zuo et al. https://doi.org/10.5194/os-15-779-2019
- Relaxation Method for Assimilation of Sea Ice Concentration Data in the NEMO—LIM3 Multicategory Sea Ice Model B. Strukov et al. https://doi.org/10.3103/S1068373920020053
- CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization X. Liu et al. https://doi.org/10.3390/jmse9090920
- DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations H. Luo et al. https://doi.org/10.1017/jog.2021.57
- Potential of coupled data assimilation studies in cryosphere K. SUZUKI https://doi.org/10.5331/seppyo.80.2_185
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