Articles | Volume 15, issue 7
https://doi.org/10.5194/tc-15-3207-2021
© Author(s) 2021. 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-15-3207-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jul 2021
Research article |
| 12 Jul 2021
| 12 Jul 2021
Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F
Timothy Williams
CORRESPONDING AUTHOR
Nansen Environmental and Remote Sensing Center, Jahnebakken 3, 5007 Bergen, Norway, and Bjerknes Centre for Climate Research, Bergen, Norway
Anton Korosov
Nansen Environmental and Remote Sensing Center, Jahnebakken 3, 5007 Bergen, Norway, and Bjerknes Centre for Climate Research, Bergen, Norway
Pierre Rampal
CNRS, Institut Géophysique de l'Environnement, Grenoble, France
Nansen Environmental and Remote Sensing Center, Jahnebakken 3, 5007 Bergen, Norway, and Bjerknes Centre for Climate Research, Bergen, Norway
Einar Ólason
Nansen Environmental and Remote Sensing Center, Jahnebakken 3, 5007 Bergen, Norway, and Bjerknes Centre for Climate Research, Bergen, Norway
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- Quantitative comparison of causal inference methods for climate tipping points N. Lohmann et al. https://doi.org/10.5194/npg-33-313-2026
- Tuning parameters of a sea ice model using machine learning A. Korosov et al. https://doi.org/10.5194/gmd-18-885-2025
- Modelling the Arctic wave-affected marginal ice zone: a comparison with ICESat-2 observations G. Boutin et al. https://doi.org/10.1098/rsta.2021.0262
- Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets S. Sandven et al. https://doi.org/10.1007/s10712-023-09781-0
- Modeling Antarctic Sea Ice Variability Using a Brittle Rheology R. Santana et al. https://doi.org/10.1029/2024MS004584
- Machine learning for the physics of climate A. Bracco et al. https://doi.org/10.1038/s42254-024-00776-3
- STEPNet: A Spatial and Temporal Encoding Pipeline to Handle Temporal Heterogeneity in Climate Modeling Using AI: A Use Case of Sea Ice Forecasting S. Wang et al. https://doi.org/10.1109/JSTARS.2025.3532219
- Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard K. Wang et al. https://doi.org/10.3389/fmars.2025.1588769
- An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport T. Bilge et al. https://doi.org/10.3390/jmse10020265
- Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration A. Kvanum et al. https://doi.org/10.5194/tc-19-4149-2025
- Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system A. Liu et al. https://doi.org/10.1016/j.atmosres.2023.107014
- Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting T. Grigoryev et al. https://doi.org/10.3390/rs14225837
- The next generation sea-ice model neXtSIM, version 2 E. Ólason et al. https://doi.org/10.5194/gmd-19-6467-2026
- Did the central Arctic become a sediment-starved basin in the Quaternary? M. O'Regan et al. https://doi.org/10.1016/j.margeo.2026.107729
- A description of existing operational ocean forecasting services around the globe M. Cirano et al. https://doi.org/10.5194/sp-5-opsr-5-2025
- Towards improving short-term sea ice predictability using deformation observations A. Korosov et al. https://doi.org/10.5194/tc-17-4223-2023
- Holocene sea ice and paleoenvironment conditions in the Beaufort Sea (Canadian Arctic) reconstructed with lipid biomarkers M. Santos et al. https://doi.org/10.5194/cp-22-187-2026
- Toward an Arctic Ocean forecast system based on Finite Volume Community Ocean model Y. Zhang et al. https://doi.org/10.1016/j.ocemod.2026.102714
- The MET Norway Ice Service: a comprehensive review of the historical and future evolution, ice chart creation, and end user interaction within METAREA XIX W. Copeland et al. https://doi.org/10.3389/fmars.2024.1400479
- Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework G. Boutin et al. https://doi.org/10.5194/tc-17-617-2023
- Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al. https://doi.org/10.5194/tc-19-5613-2025
- High-resolution regional sea-ice model based on the discrete element method with boundary conditions from a large-scale model for ice drift A. Tsarau et al. https://doi.org/10.1017/aog.2024.26
- Improving short-term sea ice concentration forecasts using deep learning C. Palerme et al. https://doi.org/10.5194/tc-18-2161-2024
- A comparison of an operational wave–ice model product and drifting wave buoy observation in the central Arctic Ocean: investigating the effect of sea-ice forcing in thin ice cover T. Nose et al. https://doi.org/10.33265/polar.v42.8874
- Response of Arctic benthic foraminiferal traits to past environmental changes K. Hansen et al. https://doi.org/10.1038/s41598-023-47603-w
- 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
- 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
Saved (final revised paper)
Latest update: 09 Aug 2026
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of...
