1140 / 2019-07-31 09:35:38

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

15、冰冻圈科学 > 专题4：海冰变化和过程监测及模拟

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model (FIO-ESM) climate forecast system, here daily satellite-derived Arctic sea ice concentration and sea ice thickness from the Pan-Arctic Ice-Ocean Modeling and Assimilation System are assimilated into this system, using the method of localized error subspace transform ensemble Kalman filter (LESTKF). Five-year (2014-2018) Arctic sea ice assimilation experiments and a 2-month real time forecast in August 2018 were conducted to study the roles of ice data assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent, but there is no much improvement in ice volume and ice thickness simulations. However, all the biases of modeled ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The real time forecast results indicate that ice data assimilation can improve the Arctic sea ice forecast skill significantly in the FIO-ESM climate forecast system. About 1/3 Arctic integrated ice edge error is reduced in this 2-month real time forecast by using the initialization with sea ice data assimilation. Compared with the six real time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) Prediction Project, FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.