Improving sea ice thickness estimates by assimilating <scp>CryoSat</scp>‐2 and <scp>SMOS</scp> sea ice thickness data simultaneously

Abstract

The impact of assimilating weekly CryoSat ‐2 sea ice thickness data together with daily SMOS sea ice thickness and daily SSMIS sea ice concentration data on the sea ice fields of a coupled sea ice–ocean model of the Arctic Ocean is investigated. The sea‐ice model is based on the Massachusetts Institute of Technology general circulation model ( MITgcm ) and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman ( LSEIK ) filter coded in the Parallel Data Assimilation Framework ( PDAF ). A period of three months from 1 November 2011 to 30 January 2012 is selected to assess the skill of the assimilation system in the cold season. Compared to the unassimilated solution and a solution where only sea ice concentration is assimilated, the model–data misfits are substantially reduced in areas of both thick and thin ice. The sea ice thickness estimates agree significantly better with in situ observations in the central Arctic Ocean than the sea ice thickness obtained from assimilating SMOS data alone, while the sea ice concentration shows very small improvements. The sea ice fields obtained by the joint assimilation of SMOS and CryoSat ‐2 data also have lower errors in thickness and concentration than those obtained from directly assimilating a statistically merged SMOS and CryoSat ‐2 sea ice thickness product. These lower errors suggest that model dynamics play a significant role in data blending.