Abstract
Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently 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 3D‐Var 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 thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may 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. Using the contingency table approach, GIOPS forecasts show a significant open‐water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.
Highlights
As numerical weather prediction (NWP) systems become further refined, the interactions across the air–ice–ocean interface are becoming increasingly important (e.g. Smith et al, 2013b)
Perhaps the most significant study is that of Van Woert et al (2004), who provide a detailed evaluation of the Polar Ice Prediction System (PIPS) using an analysis-based verification method that focuses on changes in the simulated and analysed sea ice concentration
Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society
Summary
As numerical weather prediction (NWP) systems become further refined, the interactions across the air–ice–ocean interface are becoming increasingly important (e.g. Smith et al, 2013b). Perhaps the most significant study is that of Van Woert et al (2004), who provide a detailed evaluation of the Polar Ice Prediction System (PIPS) using an analysis-based verification method that focuses on changes in the simulated and analysed sea ice concentration. Using this method, PIPS was shown to have only marginally significant skill, with correct forecasts only 25% of the time and with little or no skill in winter (November to January).
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