主讲：刘骥平，University at Albany, State University of New York
摘要: We assimilate sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and sea ice thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 in the NCEP climate forecast system using a recently developed localized error subspace transform ensemble kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Comparisons with the assimilated satellite products and independent sea ice thickness data sets show that assimilating sea ice concentration and thickness leads to improved predictive skill of Arctic sea ice. LESTKF-1 improves sea ice forecast initially, but the initial improvement in the ice extent (thickness) gradually diminishes after a few weeks of integration (remains steady through the integration). Large biases in both the ice extent and thickness in CTL are reduced remarkably through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation remarkably reduces systematic bias in the predicted ice thickness compared to with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of the ice extent/concentration due to covariability of thickness and concentration. Thus, the corrected state of sea ice thickness would aid in the forecast procedure. Impacts of the number of ensemble member and extending the integration period to generate estimates of initial model states and uncertainties on sea ice prediction will be also discussed.