We have investigated the effects of assimilating sea ice concentration (SIC) data on a simulation of Arctic Ocean climate using an atmosphere-ocean-sea ice coupled model. Our results show that the normal overestimation of summertime SIC in the East Siberian Sea and the Beaufort Sea in simulations without sea-ice data input can be greatly reduced by assimilating seaice data and that this improvement is also evident in a following hindcast experiment for 3−4 years after the initialization of the assimilation. In the hindcast experiment, enhanced heat storage in both sea ice and in the ocean surface layer plays a central role in improving the accuracy of the sea ice distribution, particularly in summer. Our detailed investigation suggests that the ice-albedo feedback and the feedback associated with the atmospheric pressure pattern generated by the improved estimation of SIC work more effectively to retain the heat signal after initialization for a coupled atmosphere-ocean-sea ice system prediction. In addition, comparison with field observations confirms that the model fails to produce a realistic feedback loop, which is (presumably) due to inadequacies in both the ice-cloud feedback model and the feedback via the Beaufort Gyre circulation. Further development of coupled models is thus required to better define Arctic Ocean climate processes and to improve the accuracy of their predictions.
IntroductionThe Arctic Ocean climate system is largely characterized by the presence of sea ice. Recently, satellite and in-situ observations clearly identify a retreat of sea ice in the Arctic Ocean (e.g., Maslanik et al. 2007). Previous studies have so far reported the importance of (a) ice-albedo feedback (Curry et al. 1995), (b) the sea level pressure pattern, and in particular the dipolar anomaly (e.g., Watanabe et al. 2006), (c) ice-cloud feedback (Ikeda et al. 2003), and (d) the warmer Pacific Summer Water (Shimada et al. 2006). These four factors can enhance the impact of global warming. Such studies underline the importance of the positive feedback loop for sea ice reduction via both the atmosphere and the ocean that is required to advance our understanding of the mechanisms responsible for the variation in the Arctic Ocean climate. One promising approach to the investigation of coupled processes in this area of sea ice research makes use of sophisticated numerical models. However, current state-of-the-art simulation models are not yet sufficiently advanced to be able to reproduce highlatitude processes adequately.Data assimilation methods have recently focused on obtaining an optimal synthesis of observations and models for better descriptions of realistic physical processes. Their application successfully reduced the errors inherent in ocean-sea ice coupled simulations of the Arctic Ocean (e.g., Lindsay and Zhang 2006). Therefore, the assimilation of observational sea-ice data represents a promising means of improving the atmosphere-ocean-sea ice coupled simulations. Moreover, since the data assimilation appr...