Articles | Volume 12, issue 6
https://doi.org/10.5194/tc-12-2005-2018
© Author(s) 2018. 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-12-2005-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2018
Research article |
| 15 Jun 2018
| 15 Jun 2018
Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system
Takuya Nakanowatari
CORRESPONDING AUTHOR
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Jun Inoue
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Kazutoshi Sato
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
present address: Kitami Institute of Technology, Kitami, 090-8507, Japan
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Thormøhlens gate 47, 5006 Bergen, Norway
Jiping Xie
Nansen Environmental and Remote Sensing Center, Thormøhlens gate 47, 5006 Bergen, Norway
Mio Matsueda
Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba,
Ibaraki 305-8577, Japan
Akio Yamagami
Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba,
Ibaraki 305-8577, Japan
Takeshi Sugimura
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Hironori Yabuki
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, 190-8518, Japan
Natsuhiko Otsuka
Arctic Research Center, Hokkaido University, Kita-21 Nishi-11 Kita-ku, Sapporo, 001-0021, Japan
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Cited
14 citations as recorded by crossref.
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- The Challenge of Arctic Sea Ice Thickness Prediction by ECMWF on Subseasonal Time Scales Y. Xiu et al. 10.1029/2021GL097476
- Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
- Medium range sea ice prediction in support of Japanese research vessel MIRAI’s expedition cruise in 2018 L. De Silva et al. 10.1080/1088937X.2019.1707317
- Review of forecast skills for weather and sea ice in supporting Arctic navigation J. Inoue 10.1016/j.polar.2020.100523
- Sea-ice information and forecast needs for industry maritime stakeholders P. Wagner et al. 10.1080/1088937X.2020.1766592
- An Research on the design and optimization of shipping routes in the Arctic X. Li 10.1088/1742-6596/1848/1/012138
- Statistical characteristics of Arctic forecast busts and their relationship to Arctic weather patterns in summer A. Yamagami & M. Matsueda 10.1002/asl.1038
- Intensity of Level Ice Simulated with the CICE Model for Oil-Gas Exploitation in the Southern Kara Sea, Arctic C. Duan et al. 10.1007/s11802-022-4914-5
- Ensemble forecast experiments of summertime sea ice in the Arctic Ocean using the TOPAZ4 ice-ocean data assimilation system T. Nakanowatari et al. 10.1016/j.envres.2022.112769
- Skill of medium-range reforecast for summertime extraordinary Arctic Cyclones in 1986–2016 A. Yamagami et al. 10.1016/j.polar.2019.02.003
- Information retrieval for Northern Sea Route (NSR) navigation: A statistical approach using the AIS and TOPAZ4 data T. Koyama et al. 10.1016/j.polar.2020.100626
- Spatial and temporal variations of recent shipping along the Northern Sea Route X. Li et al. 10.1016/j.polar.2020.100569
- Influence of sea ice on ship routes and speed along the Arctic Northeast Passage Y. Shu et al. 10.1016/j.ocecoaman.2024.107320
14 citations as recorded by crossref.
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- The Challenge of Arctic Sea Ice Thickness Prediction by ECMWF on Subseasonal Time Scales Y. Xiu et al. 10.1029/2021GL097476
- Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. 10.1080/01431161.2021.1897183
- Medium range sea ice prediction in support of Japanese research vessel MIRAI’s expedition cruise in 2018 L. De Silva et al. 10.1080/1088937X.2019.1707317
- Review of forecast skills for weather and sea ice in supporting Arctic navigation J. Inoue 10.1016/j.polar.2020.100523
- Sea-ice information and forecast needs for industry maritime stakeholders P. Wagner et al. 10.1080/1088937X.2020.1766592
- An Research on the design and optimization of shipping routes in the Arctic X. Li 10.1088/1742-6596/1848/1/012138
- Statistical characteristics of Arctic forecast busts and their relationship to Arctic weather patterns in summer A. Yamagami & M. Matsueda 10.1002/asl.1038
- Intensity of Level Ice Simulated with the CICE Model for Oil-Gas Exploitation in the Southern Kara Sea, Arctic C. Duan et al. 10.1007/s11802-022-4914-5
- Ensemble forecast experiments of summertime sea ice in the Arctic Ocean using the TOPAZ4 ice-ocean data assimilation system T. Nakanowatari et al. 10.1016/j.envres.2022.112769
- Skill of medium-range reforecast for summertime extraordinary Arctic Cyclones in 1986–2016 A. Yamagami et al. 10.1016/j.polar.2019.02.003
- Information retrieval for Northern Sea Route (NSR) navigation: A statistical approach using the AIS and TOPAZ4 data T. Koyama et al. 10.1016/j.polar.2020.100626
- Spatial and temporal variations of recent shipping along the Northern Sea Route X. Li et al. 10.1016/j.polar.2020.100569
- Influence of sea ice on ship routes and speed along the Arctic Northeast Passage Y. Shu et al. 10.1016/j.ocecoaman.2024.107320
Latest update: 23 Nov 2024
Short summary
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was...
