Probabilistic forecasts of sea ice trajectories in the arctic: impact of uncertainties in surface wind and ice cohesion

Cheng, S. ORCID: https://orcid.org/0000-0002-7304-2442, Aydoğdu, A. ORCID: https://orcid.org/0000-0002-1557-7502, Rampal, P., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Bertino, L. (2020) Probabilistic forecasts of sea ice trajectories in the arctic: impact of uncertainties in surface wind and ice cohesion. Oceans, 1 (4). pp. 326-342. ISSN 2673-1924 doi: 10.3390/oceans1040022

Abstract/Summary

We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/94945
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	MDPI
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