16 Aug 2022
16 Aug 2022
Status: this preprint is open for discussion.

Novel Arctic sea ice data assimilation combining ensemble Kalman filter with a Lagrangian sea ice model

Sukun Cheng1, Yumeng Chen2, Ali Aydoğdu3, Laurent Bertino1, Alberto Carrassi2,4, Pierre Rampal5, and Christopher K. R. T. Jones6 Sukun Cheng et al.
  • 1Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
  • 2Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading RG6 6AH, UK
  • 3Ocean Modelling and Data Assimilation Division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), 40127, Bologna, Italy
  • 4Department of Physics and Astronomy “Augusto Righi", University of Bologna. Bologna, Italy
  • 5Institut de Géophysique de l’Environnement, Université Grenoble Alpes/CNRS/IRD/G-INP, CS 40700, 38 058 Grenoble CEDEX 9, France
  • 6Department of Mathematics, University of North Carolina, Chapel Hill, USA

Abstract. Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve the state estimates and consequent forecasts. We interface the deterministic Ensemble Kalman filter (DEnKF) to the Lagrangian sea ice model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble-DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble DA. As a solution, we perform the DEnKF analysis on a fixed reference mesh, where model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea-ice observations on the model's forecast skills of the Arctic sea ice by comparing against satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvement of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. In contrast, the bivariate improvements between SIC and SIT are unobvious. Our ensemble-DA system based on the stand-alone sea ice model is computationally efficient and demonstrates comparable skills to operational forecasting models that use DA.

Sukun Cheng et al.

Status: open (until 15 Oct 2022)

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Sukun Cheng et al.

Sukun Cheng et al.


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Short summary
We design an ensemble data assimilation system that merges satellite observations in a Lagrangian sea ice model to improve its predictability. We made ensemble runs in Arctic winter 2019–2020 by perturbing air/ocean boundary conditions. Each member has its own mesh moving in time. Significant(moderate) improvements in ice thickness(concentration) imply the system’s effectiveness benefited from data assimilation. We discuss the effect of multivariate assimilation and challenges in the system.