An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport
Abstract
:1. Introduction
2. Materials and Methods
2.1. Observational Data
2.2. Model Data
2.2.1. neXtSIM-F
2.2.2. TOPAZ4
2.2.3. CPLDA
2.2.4. ECMWF
2.3. Performance Metrics
3. Results
3.1. Case Study at Mooring Location M3
3.2. Analysis of Correlation and Residuals
3.2.1. Model Analysis Fields
3.2.2. Model Forecast Fields
3.3. Forecast Accuracy
3.4. Analysis Field Variability
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Forecasting system | Seasonal multi-system | ENS-extended | CPLDA (GLO-CPL) | GLO-HR | TOPAZ4 (ARC-MFC TOPAZ) | neXtSIM-F (ARC-MFS neXtSIM-F) | BAL-MFC |
Provider | C3S | ECMWF | CMS (UK Met Office) | CMS (MERCATOR) | CMS (MET Norway) | CMS (NERSC) | CMS (DMI/BSH) 2 |
Forecast range | 6 months (LR) | 7 weeks (ER) | 10 days (SMR) | 10 days (SMR) | 10 days (SMR) | 7 days (SMR) | 6 days (SMR) |
Components | Sea ice, ocean and atmosphere | Sea ice, ocean and atmosphere | Sea ice, ocean and atmosphere | Sea ice and ocean | Sea ice and ocean | Sea ice | Sea ice and ocean |
Spatial coverage | Global | Global | Global | Global | Arctic | Arctic | Baltic |
Update frequency | Monthly | Twice-weekly | Daily | Daily | Daily | Daily | Twice-daily |
Spatial resolution | 1° 1 | 36 km | 1/4° | 1/12° | 12.5 km | 3 km | 2 km |
Model system | CPLDA | Ensemble hindcast (Cycle 46R1) | TOPAZ4 | neXtSIM-F |
Institution | UK Met Office | ECMWF | Met Norway | NERSC |
Atmosphere | Coupled to Unified Model (UM) atmosphere | Coupled to the Integrated Forecast System (IFS) | ECMWF atmospheric forcing | ECMWF atmospheric forcing |
Ocean | Coupled to Nucleus for European Modelling of the Ocean (NEMO) 3.4 | Coupled to NEMO 3.4 | Coupled to Hybrid Coordinate Ocean Model (HYCOM) ocean | TOPAZ ocean forcing |
Ice resolution | ~13 km | ~13 km | 12.5 km | 7.5 km 1 |
Ice assimilation | Sea ice concentration (SIC) from Special Sensor Microwave Imager/Sounder (SSMIS) | Univariate three-dimensional variational data assimilation—first guess at appropriate time (3DVAR-FGAT) of SIC | SSMIS SIC and Soil Moisture and Ocean Salinity (SMOS) satellite derived sea ice thickness (SIT) assimilated using the ensemble Kalman filter | SIC from SSMIS and Advanced Microwave Scanning Radiometer 2 (AMSR2) |
Ice dynamics | Elastic Viscous Plastic (EVP) [25] | Viscous Plastic (VP) [26] | EVP [25] | Brittle Bingham–Maxwell (BBM) [27] |
Thermodynamics | Zero-layer model [28] | Three-layer [28] | Three-layer [28] | Three category model [29] |
Time period of data used | December 2016–July 2019 | March 2016–July 2019 | March 2016–July 2019 | November 2018–July 2019 |
Model parameter h0 | 0.5 m | 0.6 m | 0.1 m | 0.05 m |
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Bilge, T.A.; Fournier, N.; Mignac, D.; Hume-Wright, L.; Bertino, L.; Williams, T.; Tietsche, S. An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport. J. Mar. Sci. Eng. 2022, 10, 265. https://doi.org/10.3390/jmse10020265
Bilge TA, Fournier N, Mignac D, Hume-Wright L, Bertino L, Williams T, Tietsche S. An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport. Journal of Marine Science and Engineering. 2022; 10(2):265. https://doi.org/10.3390/jmse10020265
Chicago/Turabian StyleBilge, Tarkan Aslan, Nicolas Fournier, Davi Mignac, Laura Hume-Wright, Laurent Bertino, Timothy Williams, and Steffen Tietsche. 2022. "An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport" Journal of Marine Science and Engineering 10, no. 2: 265. https://doi.org/10.3390/jmse10020265
APA StyleBilge, T. A., Fournier, N., Mignac, D., Hume-Wright, L., Bertino, L., Williams, T., & Tietsche, S. (2022). An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport. Journal of Marine Science and Engineering, 10(2), 265. https://doi.org/10.3390/jmse10020265