Articles | Volume 17, issue 4
https://doi.org/10.5194/tc-17-1735-2023
https://doi.org/10.5194/tc-17-1735-2023
Research article
 | 
25 Apr 2023
Research article |  | 25 Apr 2023

Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020

Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones

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AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (31 Jan 2023) by Masashi Niwano
AR by Sukun Cheng on behalf of the Authors (08 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (10 Feb 2023) by Masashi Niwano
RR by Francois Massonnet (10 Feb 2023)
ED: Publish as is (14 Mar 2023) by Masashi Niwano
AR by Sukun Cheng on behalf of the Authors (23 Mar 2023)  Manuscript 
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Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.