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 sealevel 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.

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

A global deterministic wave prediction system (GDWPS) is used to improve regional forecasts of waves off the Canadian coastline and help support the practice of safe marine activities in Canadian waters. The wave model has a grid spacing of 1 /48 with spectral resolution of 36 frequency bins and 36 directional bins. The wave model is driven with hourly 10-m winds generated by the operational global atmospheric prediction system. Ice conditions are updated every three hours using the ice concentration forecasts generated by the Global IceOcean Prediction System. Wave forecasts are evaluated over two periods from 15 , as well as over select cases during the fall of 2012. The global system is shown to improve wave forecast skill over regions where forecasts were previously produced using limited-area models only. The usefulness of a global expansion is demonstrated for large swell events affecting the northeast Pacific. The first validation of a Canadian operational wave forecast system in the Arctic is presented. Improvements in the representation of forecast wave fields associated with tropical cyclones are also demonstrated. Finally, the GDWPS is shown to result in gains of at least 12 h of lead time.

Abstract. A fully coupled atmosphere–ocean–ice model has been used to produce global weather forecasts at Environment and Climate Change Canada (ECCC) since November 2017. Currently, the system relies on four uncoupled data assimilation (DA) components for initializing the fully coupled global atmosphere–ocean–ice forecast model: atmosphere, ocean, sea ice and sea surface temperature (SST). The goal of the present study is to implement a weakly coupled data assimilation (WCDA) between the atmosphere and ocean components and evaluate its performance against uncoupled DA. The WCDA system uses coupled atmosphere–ocean–ice short-term forecasts as background states for the atmospheric and the ocean DA components that independently compute atmospheric and ocean analyses. This system leads to better agreement between the coupled atmosphere–ocean analyses and the coupled atmosphere–ocean–ice forecasts than between the uncoupled analyses and the coupled forecasts. The use of WCDA improves the atmospheric forecast score near the surface, but a slight increase in the atmospheric temperature bias is observed. A small positive impact from using the short-term SST forecast on the satellite radiance observation-minus-forecast statistics is noted. Ocean temperature and salinity forecasts are also improved near the surface. The next steps toward stronger DA coupling are highlighted.

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