Subseasonal-to-Seasonal Arctic Sea Ice Forecast Skill Improvement from Sea Ice Concentration Assimilation

Abstract The current GFDL seasonal prediction system, the Seamless System for Prediction and Earth Research (SPEAR), has shown skillful of Arctic sea ice extent with atmosphere ocean constrained by observations. In this study we present improvements in subseasonal predictions directly assimilating initial conditions from a data assimilation (DA) system that assimilates satellite concentration (SIC) observations are used to produce set reforecast experiments (IceDA) starting first day each month 1992 2017. Our evaluation daily skill concludes SPEAR generally outperforms anomaly persistence forecast at lead times beyond 1 month. We primarily focus our analysis on gridcell-level fields. SIC DA improves forecasts prominently June-, July-, August-, September-initialized reforecasts. evaluate two additional user-oriented metrics: ice-free probability (IFP) date (IFD). IFP is grid cell experiencing given year, IFD which free. A combined demonstrates model can make local melt as early May, modest DA.