Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations

摘要

To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short‐term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat‐2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System. Plain Language Summary We have developed a coupled atmosphere‐sea ice‐ocean model configured for the Arctic to enhance our capability to predict Arctic sea ice and climate. It is well known that the accuracy of model initial condition strongly influences Arctic sea ice predictions with dynamic models at short‐term periods. A data assimilation system is combined with the new coupled model to assimilate satellite sea ice observations to improve initial sea ice conditions. We perform Arctic sea ice predictions using the new modeling system for the summers of 2017 and 2018. Predictions show good predictive skills compared with the observations. Our prediction experiments also suggest that the use of appropriate uncertainty in observed sea ice thickness can improve the predicted sea ice spatial pattern. Our new modeling system initialized by the seasonal forecasts of the National Centers for Environmental Prediction Climate Forecast System and our data assimilation procedures perform much better in predicting Arctic sea ice cover as well as sea ice conditions in the Arctic shipping routes than the National Centers for Environmental Prediction Climate Forecast System.