Articles | Volume 13, issue 2
https://doi.org/10.5194/tc-13-491-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-13-491-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Feb 2019
Research article |
| 08 Feb 2019
| 08 Feb 2019
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system
UiT The Arctic University of Norway, Tromsø, Norway
Rune Graversen
UiT The Arctic University of Norway, Tromsø, Norway
Kai H. Christensen
The Norwegian Meteorological Institute, Oslo, Norway
Philip Rostosky
Institute of Environmental Physics, University of Bremen, Bremen,
Germany
Keguang Wang
The Norwegian Meteorological Institute, Tromsø, Norway
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Total article views: 3,814 (including HTML, PDF, and XML)
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Cited
30 citations as recorded by crossref.
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- Toward Earth system modeling with resolved clouds and ocean submesoscales on heterogeneous many-core HPCs S. Zhang et al. 10.1093/nsr/nwad069
- Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system P. Duarte et al. 10.5194/gmd-15-4373-2022
- Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model M. Durán Moro et al. 10.5194/tc-18-1597-2024
- GNSS-R Sea Ice Thickness Retrieval Based on Ensemble Learning Method Y. Hu et al. 10.1109/TGRS.2025.3558734
- Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard K. Wang et al. 10.3389/fmars.2025.1588769
- Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. 10.1080/16000870.2019.1697166
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Wave measurements from ship mounted sensors in the Arctic marginal ice zone T. Løken et al. 10.1016/j.coldregions.2020.103207
- Overview of the MOSAiC expedition: Snow and sea ice M. Nicolaus et al. 10.1525/elementa.2021.000046
- 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. 10.5194/tc-17-2509-2023
- Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard J. Röhrs et al. 10.5194/gmd-16-5401-2023
- Satellite-Based Data Assimilation System for the Initialization of Arctic Sea Ice Concentration and Thickness Using CICE5 J. Lee & Y. Ham 10.3389/fclim.2022.797733
- 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. 10.5194/tc-17-1735-2023
- Development of Coupled Data Assimilation With the BCC Climate System Model: Highlighting the Role of Sea‐Ice Assimilation for Global Analysis X. Liu et al. 10.1029/2020MS002368
- Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
- Year-round impact of winter sea ice thickness observations on seasonal forecasts B. Balan-Sarojini et al. 10.5194/tc-15-325-2021
- An evaluation of Antarctic sea-ice thickness from the Global Ice-Ocean Modeling and Assimilation System based on in situ and satellite observations S. Liao et al. 10.5194/tc-16-1807-2022
- Forecast uncertainty and ensemble spread in surface currents from a regional ocean model M. Idžanović et al. 10.3389/fmars.2023.1177337
- Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting T. Grigoryev et al. 10.3390/rs14225837
- Modelled dispersal of snow crab (Chionoecetes opilio) larvae and potential settlement areas in the western Barents Sea M. Huserbråten et al. 10.1093/icesjms/fsad062
- Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. 10.1029/2020JC016277
- Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice‐Ocean Data Assimilation System X. Liang et al. 10.1029/2019JC015073
- Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
- Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. 10.34133/olar.0025
- Intercomparison of Arctic sea ice simulation in ROMS-CICE and ROMS-Budgell R. Kumar et al. 10.1016/j.polar.2021.100716
- Implementation of an Adaptive Bias‐Aware Extended Kalman Filter for Sea‐Ice Data Assimilation in the HARMONIE‐AROME Numerical Weather Prediction System Y. Batrak 10.1029/2021MS002533
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system S. Fritzner et al. 10.5194/tc-13-491-2019
29 citations as recorded by crossref.
- Improved Arctic Sea Ice Freeboard Retrieval From Satellite Altimetry Using Optimized Sea Surface Decorrelation Scales J. Landy et al. 10.1029/2021JC017466
- Toward Earth system modeling with resolved clouds and ocean submesoscales on heterogeneous many-core HPCs S. Zhang et al. 10.1093/nsr/nwad069
- Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system P. Duarte et al. 10.5194/gmd-15-4373-2022
- Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model M. Durán Moro et al. 10.5194/tc-18-1597-2024
- GNSS-R Sea Ice Thickness Retrieval Based on Ensemble Learning Method Y. Hu et al. 10.1109/TGRS.2025.3558734
- Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard K. Wang et al. 10.3389/fmars.2025.1588769
- Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. 10.1080/16000870.2019.1697166
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Wave measurements from ship mounted sensors in the Arctic marginal ice zone T. Løken et al. 10.1016/j.coldregions.2020.103207
- Overview of the MOSAiC expedition: Snow and sea ice M. Nicolaus et al. 10.1525/elementa.2021.000046
- 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. 10.5194/tc-17-2509-2023
- Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard J. Röhrs et al. 10.5194/gmd-16-5401-2023
- Satellite-Based Data Assimilation System for the Initialization of Arctic Sea Ice Concentration and Thickness Using CICE5 J. Lee & Y. Ham 10.3389/fclim.2022.797733
- 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. 10.5194/tc-17-1735-2023
- Development of Coupled Data Assimilation With the BCC Climate System Model: Highlighting the Role of Sea‐Ice Assimilation for Global Analysis X. Liu et al. 10.1029/2020MS002368
- Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
- Year-round impact of winter sea ice thickness observations on seasonal forecasts B. Balan-Sarojini et al. 10.5194/tc-15-325-2021
- An evaluation of Antarctic sea-ice thickness from the Global Ice-Ocean Modeling and Assimilation System based on in situ and satellite observations S. Liao et al. 10.5194/tc-16-1807-2022
- Forecast uncertainty and ensemble spread in surface currents from a regional ocean model M. Idžanović et al. 10.3389/fmars.2023.1177337
- Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting T. Grigoryev et al. 10.3390/rs14225837
- Modelled dispersal of snow crab (Chionoecetes opilio) larvae and potential settlement areas in the western Barents Sea M. Huserbråten et al. 10.1093/icesjms/fsad062
- Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. 10.1029/2020JC016277
- Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice‐Ocean Data Assimilation System X. Liang et al. 10.1029/2019JC015073
- Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration J. Lee & Y. Ham 10.1038/s41612-023-00402-6
- Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. 10.34133/olar.0025
- Intercomparison of Arctic sea ice simulation in ROMS-CICE and ROMS-Budgell R. Kumar et al. 10.1016/j.polar.2021.100716
- Implementation of an Adaptive Bias‐Aware Extended Kalman Filter for Sea‐Ice Data Assimilation in the HARMONIE‐AROME Numerical Weather Prediction System Y. Batrak 10.1029/2021MS002533
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
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Short summary
In this work, a coupled ocean and sea-ice ensemble-based assimilation system is used to assess the impact of different observations on the assimilation system. The focus of this study is on sea-ice observations, including the use of satellite observations of sea-ice concentration, sea-ice thickness and snow depth for assimilation. The study showed that assimilation of sea-ice thickness in addition to sea-ice concentration has a large positive impact on the coupled model.
In this work, a coupled ocean and sea-ice ensemble-based assimilation system is used to assess...
