Adjoint-Based Simultaneous State and Parameter Estimation in an Arctic Sea Ice-Ocean Model using MITgcm (c63m)

Abstract. Parameters in sea ice-ocean coupled models greatly affect the simulated ocean and sea ice evolution, and are normally tunned to bring the model state close to the observations. Using an adjoint method, spatiotemporally varying parameters of Arctic sea ice-ocean coupled model are optimized simultaneously with the initial condition and the atmospheric forcing by assimilating satellite and in-situ observations. The assimilation results show that the joint state and parameter estimation (SPE) substantially improves the sea ice concentration simulation. Particularly in October when the ocean surface starts to refreeze, SPE reduces the lead closing parameter H_o, which determines the minimum ice thickness formed in the open water, to increase the lateral sea ice growth and facilitate the seasonal rapid sea ice recovery in the Pacific sector. Comparisons with sea ice thickness observations from the moored upward looking sonars and Ice Mass Balance buoys demonstrate that the inclusion of model parameters in the optimization also leads to better sea ice thickness estimation. Overall, the adjoint-based SPE scheme has the potential to better reproduce the Arctic ocean and sea ice state and will be applied to reproduce a new Arctic sea ice-ocean reanalysis.