Articles | Volume 10, issue 2
https://doi.org/10.5194/tc-10-761-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-761-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Brief communication
| 
06 Apr 2016
Brief communication |
| 06 Apr 2016
Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation
Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing, China
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Martin Losch
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Svetlana N. Losa
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
St. Petersburg Department of P. P. Shirshov Institute of Oceanology, St. Petersburg, Russia
Thomas Jung
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
University of Bremen, Bremen, Germany
Lars Nerger
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Thomas Lavergne
Norwegian Meteorological Institute, Oslo, Norway
Viewed
Total article views: 4,551 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Apr 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,555 | 1,558 | 438 | 4,551 | 162 | 154 |
- HTML: 2,555
- PDF: 1,558
- XML: 438
- Total: 4,551
- BibTeX: 162
- EndNote: 154
Total article views: 3,799 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Apr 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,168 | 1,229 | 402 | 3,799 | 141 | 133 |
- HTML: 2,168
- PDF: 1,229
- XML: 402
- Total: 3,799
- BibTeX: 141
- EndNote: 133
Total article views: 752 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Apr 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 387 | 329 | 36 | 752 | 21 | 21 |
- HTML: 387
- PDF: 329
- XML: 36
- Total: 752
- BibTeX: 21
- EndNote: 21
Cited
17 citations as recorded by crossref.
- 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
- The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors O. Sus et al. 10.5194/amt-11-3373-2018
- Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice S. Kern 10.3390/rs13214421
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. 10.34133/olar.0025
- Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT) S. Losa et al. 10.3389/fmars.2017.00203
- DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations H. Luo et al. 10.1017/jog.2021.57
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- Insights on Sea Ice Data Assimilation from Perfect Model Observing System Simulation Experiments Y. Zhang et al. 10.1175/JCLI-D-17-0904.1
- Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system Q. Shu et al. 10.1007/s13131-021-1768-4
- Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts F. Zhao et al. 10.5194/gmd-17-6867-2024
- Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model Q. Yang et al. 10.1016/j.atmosres.2019.04.021
- Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records T. Lavergne et al. 10.5194/tc-13-49-2019
- Polar climate system modeling in China: Recent progress and future challenges Z. Wang & D. Chen 10.1007/s11430-018-9355-2
- Arctic‐Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice‐Ocean Model with Data Assimilation During the CryoSat‐2 Period L. Mu et al. 10.1029/2018JC014316
- Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts duringXuelong's first trans-Arctic Passage in summer 2017 L. Mu et al. 10.1017/jog.2019.55
- A Spatial Evaluation of Arctic Sea Ice and Regional Limitations in CMIP6 Historical Simulations M. Watts et al. 10.1175/JCLI-D-20-0491.1
17 citations as recorded by crossref.
- 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
- The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors O. Sus et al. 10.5194/amt-11-3373-2018
- Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice S. Kern 10.3390/rs13214421
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations C. Min et al. 10.34133/olar.0025
- Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT) S. Losa et al. 10.3389/fmars.2017.00203
- DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations H. Luo et al. 10.1017/jog.2021.57
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- Insights on Sea Ice Data Assimilation from Perfect Model Observing System Simulation Experiments Y. Zhang et al. 10.1175/JCLI-D-17-0904.1
- Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system Q. Shu et al. 10.1007/s13131-021-1768-4
- Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts F. Zhao et al. 10.5194/gmd-17-6867-2024
- Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model Q. Yang et al. 10.1016/j.atmosres.2019.04.021
- Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records T. Lavergne et al. 10.5194/tc-13-49-2019
- Polar climate system modeling in China: Recent progress and future challenges Z. Wang & D. Chen 10.1007/s11430-018-9355-2
- Arctic‐Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice‐Ocean Model with Data Assimilation During the CryoSat‐2 Period L. Mu et al. 10.1029/2018JC014316
- Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts duringXuelong's first trans-Arctic Passage in summer 2017 L. Mu et al. 10.1017/jog.2019.55
- A Spatial Evaluation of Arctic Sea Ice and Regional Limitations in CMIP6 Historical Simulations M. Watts et al. 10.1175/JCLI-D-20-0491.1
Saved (final revised paper)
Latest update: 18 Nov 2024
Short summary
We assimilate the summer SICCI sea ice concentration data with an ensemble-based Kalman Filter. Comparing with the approach using a constant data uncertainty, the sea ice concentration estimates are further improved when the SICCI-provided uncertainty are taken into account, but the sea ice thickness cannot be improved. We find the data assimilation system cannot give a reasonable ensemble spread of sea ice concentration and thickness if the provided uncertainty are directly used.
We assimilate the summer SICCI sea ice concentration data with an ensemble-based Kalman Filter....
