Liu, X. and Zhang, L., 2018. Study on optimization of sea ice concentration with adjoint method. In: Wang, D. and Guido-Aldana, P.A. (eds.), Select Proceedings from the 3rd International Conference on Water Resource and Environment (WRE2017). Journal of Coastal Research, Special Issue No. 84, pp. 44–50. Coconut Creek (Florida), ISSN 0749-0208.

Obtaining initial sea ice concentration (SIC) values with high accuracy that are consistent with other models have been a hot topic in both sea ice prediction and sea ice modeling studies. Here, an ocean-sea ice coupled numerical model and its adjoint code have been utilized to carry out numerical experiments to optimize the initial SIC values. In the experiments, the cost function was defined as the difference between the SIC values from the reanalysis dataset and the modeled results. The gradient of cost function, relative to SIC and other model variables, was computed by the adjoint model, and a linear search algorithm was employed to optimize the SIC values by minimizing the cost function. The influences of the weight coefficients of the cost function, the extent of the geographical region, and the seawater temperature and sea ice thickness initial values on the optimization results have been analyzed. The weight coefficients of the cost function had little effect on the SIC distribution pattern but substantial influence on the SIC values. The optimized SIC in the Greenland Sea, Okhotsk Sea, and the Arctic Ocean, with a constant weight coefficient, is better than that with variable weight coefficients. The errors in the initial model fields, other than SIC, may deteriorate the overall result, implying that optimizing multiple model fields simultaneously may improve the optimization effect. Decreasing the size of the geographical region for optimization does not improve the SIC optimization results substantially. Compared to the results from a global cost function, the Barents Sea SIC values from a northern hemispheric cost function are poorly optimized.