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Sea Ice Data Assimilation

Arctic sea ice is a crucial component of the climate system

The radiation balance and the dynamics of the atmosphere and ocean have strong interactions with sea ice. Observations show that the extent of Arctic Sea ice has been declining over the last few decades. This could have a significant impact on the climate, ecosystems and human activities and highlights the need for improved sea ice prediction.

Sea ice dynamics are complex and its inhospitable environment makes taking in-situ observations near impossible. Data assimilation (DA) allows for the use of satellite observations to correct model fields and parameters, both observed and unobserved. In cooperation with scientists across the world, scientists at NCEO are developing novel DA methodologies and working on improving state and parameter estimations using the ensemble Kalman filters (EnKF) to improve sea ice predictions.

Past and ongoing projects include:

Exploring subgrid-scale sea ice thickness in the CICE sea ice model in collaboration with Centre for Polar Observation and Modelling.
Exploring DA strategies for sea ice observations in the Lagrangian grid sea ice model neXtSIM in collaboration with Nansen Environmental and Remote Sensing Center in Norway.
Investigating estimating sea ice state and parameters using the EnKF and the neXt generation Sea Ice Model (neXtSIM_DG), a novel sea ice model under development as part of the Scale-Aware Sea Ice Project (SASIP).
Exploiting the properties of the numerical methods used by neXtSIM-DG for the benefit of DA.
Use machine learning to develop better melt pond parametrisations (Driscoll et al., 2023) and help the EnKF with the non-Gaussian errors encountered in sea ice models.

NCEO also has expertise in running the neXtSIM sea ice model developed by the Nansen Environmental and Remote Sensing Center in Norway. Compared to other sea ice models, neXtSIM uses a novel sea ice rheology that better represents the physical properties of sea ice. The sea ice model is used operationally as part of the Copernicus marine services and is under continuous development.

References

Cheng, S., Chen, Y., Aydoğdu, A., Bertino, L., Carrassi, A., Rampal, P., and Jones, C. K. R. T.: Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020, The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, 2023.
Chen, Y., Smith, P., Carrassi, A., Pasmans, I., Bertino, L., Bocquet, M., Finn, T. S., Rampal, P., and Dansereau, V.: Multivariate state and parameter estimation with data assimilation on sea-ice models using a Maxwell-Elasto-Brittle rheology, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1809, 2023.
Driscoll, S., Carrassi, A., Brajard, J., Bertino, L., Bocquet, M., & Olason, E. (2023). Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks. arXiv preprint arXiv:2304.05407.
Pasmans, I., Chen, Y., Carrassi, A., & Jones, C. K. (2023). Tailoring data assimilation to discontinuous Galerkin models. arXiv preprint arXiv:2305.02950.
Williams, N., Byrne, N., Feltham, D., Van Leeuwen, P. J., Bannister, R., Schroeder, D., Ridout, A., and Nerger, L.: The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system, The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, 2023.