Sea ice Forecast Verification in the Canadian Global Ice Ocean Prediction System

Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. Here we present the verification of ice forecast skill from a system recently implemented operationally at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10-day forecasts on a 1/4°-resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sea level anomaly and sea surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3DVar method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the partial thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method is found to reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis-based error assessment focusing on the marginal ice zone and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium-range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Smaller biases are found in both hemispheres during melt periods, whereas larger biases are present during the period of rapid ice formation in fall. Ice forecast skill is found to be highly sensitive to the assimilation of sea surface temperature near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.