Articles | Volume 12, issue 3
https://doi.org/10.5194/tc-12-935-2018
© 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-12-935-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2018
Research article |
| 16 Mar 2018
| 16 Mar 2018
Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic
Matthias Rabatel
CORRESPONDING AUTHOR
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Pierre Rampal
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Alberto Carrassi
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Christopher K. R. T. Jones
Department of Mathematics, University of North Carolina, Chapel
Hill, USA
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Cited
21 citations as recorded by crossref.
- On the Feedback of Ice–Ocean Stress Coupling from Geostrophic Currents in an Anticyclonic Wind Regime over the Beaufort Gyre Q. Wang et al. 10.1175/JPO-D-18-0185.1
- Challenges and Prospects in Ocean Circulation Models B. Fox-Kemper et al. 10.3389/fmars.2019.00065
- Probabilistic modelling of surface drift prediction in marine disasters based on the NN–GA and ARMA model K. Zhu et al. 10.1016/j.oceaneng.2023.114804
- On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model F. Massonnet et al. 10.5194/gmd-12-3745-2019
- Predictability of Arctic sea ice drift in coupled climate models S. Reifenberg & H. Goessling 10.5194/tc-16-2927-2022
- Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion S. Cheng et al. 10.3390/oceans1040022
- Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology T. Finn et al. 10.5194/tc-17-2965-2023
- Calibration of sea ice drift forecasts using random forest algorithms C. Palerme & M. Müller 10.5194/tc-15-3989-2021
- Brief communication: Full-field deformation measurement for uniaxial compression of sea ice using the digital image correlation method A. Wang et al. 10.5194/tc-13-1487-2019
- 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. 10.3389/fmars.2019.00429
- Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F T. Williams et al. 10.5194/tc-15-3207-2021
- Estimating a mean transport velocity in the marginal ice zone using ice–ocean prediction systems G. Sutherland et al. 10.5194/tc-16-2103-2022
- Quantitative assessment of two oil-in-ice surface drift algorithms V. de Aguiar et al. 10.1016/j.marpolbul.2022.113393
- Calculation of the destruction of ice structures by the grid-characteristic method on structured grids A. Favorskaya & I. Petrov 10.1016/j.procs.2021.09.151
- Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting? E. Hunke et al. 10.1007/s40641-020-00162-y
- Data assimilation using adaptive, non-conservative, moving mesh models A. Aydoğdu et al. 10.5194/npg-26-175-2019
- Towards improving short-term sea ice predictability using deformation observations A. Korosov et al. 10.5194/tc-17-4223-2023
- Copernicus Marine Service Ocean State Report, Issue 5 K. von Schuckmann et al. 10.1080/1755876X.2021.1946240
- 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
- Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model S. Yan et al. 10.3390/app13179969
- Predictability of Arctic sea ice on weather time scales M. Mohammadi-Aragh et al. 10.1038/s41598-018-24660-0
20 citations as recorded by crossref.
- On the Feedback of Ice–Ocean Stress Coupling from Geostrophic Currents in an Anticyclonic Wind Regime over the Beaufort Gyre Q. Wang et al. 10.1175/JPO-D-18-0185.1
- Challenges and Prospects in Ocean Circulation Models B. Fox-Kemper et al. 10.3389/fmars.2019.00065
- Probabilistic modelling of surface drift prediction in marine disasters based on the NN–GA and ARMA model K. Zhu et al. 10.1016/j.oceaneng.2023.114804
- On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model F. Massonnet et al. 10.5194/gmd-12-3745-2019
- Predictability of Arctic sea ice drift in coupled climate models S. Reifenberg & H. Goessling 10.5194/tc-16-2927-2022
- Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion S. Cheng et al. 10.3390/oceans1040022
- Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology T. Finn et al. 10.5194/tc-17-2965-2023
- Calibration of sea ice drift forecasts using random forest algorithms C. Palerme & M. Müller 10.5194/tc-15-3989-2021
- Brief communication: Full-field deformation measurement for uniaxial compression of sea ice using the digital image correlation method A. Wang et al. 10.5194/tc-13-1487-2019
- 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. 10.3389/fmars.2019.00429
- Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F T. Williams et al. 10.5194/tc-15-3207-2021
- Estimating a mean transport velocity in the marginal ice zone using ice–ocean prediction systems G. Sutherland et al. 10.5194/tc-16-2103-2022
- Quantitative assessment of two oil-in-ice surface drift algorithms V. de Aguiar et al. 10.1016/j.marpolbul.2022.113393
- Calculation of the destruction of ice structures by the grid-characteristic method on structured grids A. Favorskaya & I. Petrov 10.1016/j.procs.2021.09.151
- Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting? E. Hunke et al. 10.1007/s40641-020-00162-y
- Data assimilation using adaptive, non-conservative, moving mesh models A. Aydoğdu et al. 10.5194/npg-26-175-2019
- Towards improving short-term sea ice predictability using deformation observations A. Korosov et al. 10.5194/tc-17-4223-2023
- Copernicus Marine Service Ocean State Report, Issue 5 K. von Schuckmann et al. 10.1080/1755876X.2021.1946240
- 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
- Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model S. Yan et al. 10.3390/app13179969
1 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 18 Apr 2024
Short summary
Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Large deviations still exist between sea ice forecasts and observations because of both missing...
