Improved Arctic sea ice forecasting by combining ensemble Kalman filter with a Lagrangian sea ice model
- 1Nansen Environmental and Remote Sensing Center, 5006 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, University of Bologna. Bologna, Italy
- 5Institut de G\'eophysique de l’Environnement, Universit\'e Grenoble Alpes/CNRS/IRD/G-INP, CS 40700, 38 058 Grenoble CEDEX 9, France
- 6Department of Mathematics, University of North Carolina, Chapel Hill, USA
Advanced data assimilation methods can improve the forecast of Arctic sea ice, which has been widely used in climate modeling systems to merge observations into simulations. We apply the deterministic Ensemble Kalman filter (DEnKF) to a Lagrangian sea ice model, neXtSIM for sea ice forecast. neXtSIM is computationally solved on a time-dependent evolving mesh, causing a key challenge for applying the EnKF since the mesh grid number and positions are generally different in each ensemble member. The DEnKF analysis is performed on a fixed reference mesh, where model variables are interpolated between the reference mesh and the individual ensemble meshes before and after the assimilation. An ensemble-DA forecasting system for Arctic sea ice forecast based on neXtSIM is built by assimilating the OSI-SAF sea ice concentration (SIC) and the CS2SMOS sea ice thickness (SIT). The ensemble is generated by perturbing atmospheric and oceanic forcing online throughout the forecast. We evaluate the impact of sea-ice assimilation on the Arctic winter sea-ice forecast skills against the satellite observations and a free run during the 2019-2020 Arctic winter. Significant improvements in modeled SIT indicate the importance of assimilating CS2SMOS thickness. While the improvement of SIC and ice extend are clearly observed only in the case with daily assimilating OSI-SAF SIC, which avoids the constraint of daily loaded ocean variables. We found that assimilating a special observation gives the best forecast skill of the relevant variables. With a proper assimilation strategy, neXtSIM as a stand-alone sea ice model could perform computationally efficiently and maintain good forecast skills compared with coupled models.
How to cite: Cheng, S., Chen, Y., Aydogdu, A., Bertino, L., Carrassi, A., Rampal, P., and K. R. T. Jones, C.: Improved Arctic sea ice forecasting by combining ensemble Kalman filter with a Lagrangian sea ice model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1113, https://doi.org/10.5194/egusphere-egu22-1113, 2022.