In this study, we assess systematically the impact of different initialisation procedures on the predictability of the sea ice in the Southern Ocean. These initialisation strategies are based on three data assimilation methods: the nudging, the particle filter with sequential importance resampling and the nudging proposal particle filter. An Earth system model of intermediate complexity is used to perform hindcast simulations in a perfect model approach. The predictability of the Antarctic sea ice at interannual to multi-decadal timescales is estimated through two aspects: the spread of the hindcast ensemble, indicating the uncertainty of the ensemble, and the correlation between the ensemble mean and the pseudo-observations, used to assess the accuracy of the prediction. Our results show that at decadal timescales more sophisticated data assimilation methods as well as denser pseudo-observations used to initialise the hindcasts decrease the spread of the ensemble. However, our experiments did not clearly demonstrate that one of the initialisation methods systematically provides with a more accurate prediction of the sea ice in the Southern Ocean than the others. Overall, the predictability at interannual timescales is limited to 3 years ahead at most. At multi-decadal timescales, the trends in sea ice extent computed over the time period just after the initialisation are clearly better correlated between the hindcasts and the pseudo-observations if the initialisation takes into account the pseudo-observations. The correlation reaches values larger than 0.5 in winter. This high correlation has likely its origin in the slow evolution of the ocean ensured by its strong thermal inertia, showing the importance of the quality of the initialisation below the sea ice.
Springer ((Germany) Heidelberg)
Climate Dynamics
Scientific Computing

Zunz, V., Goosse, H., & Dubinkina, S. (2015). Impact of the initialisation on the predictability of the Southern Ocean sea ice at interannual to multi-decadal timescales. Climate Dynamics.