Seasonal sea ice prediction capability depends on adequate knowledge of initial ice-ocean conditions, even though the specific requirements associated with “adequate knowledge” have yet to be established. This challenge is compounded by the need for a fast turnaround in acquiring and accessing observational data.


There has been a dearth of experiments performed to systematically evaluate the sensitivities of sea ice predictions to the type, quality, density, and frequency of observations of the Arctic atmosphere, ocean, and sea ice. These sensitivities arise from the specification of the initial state of the sea ice and its drivers in numerical models.

During the forecast period, atmospheric variations, which are largely unpredictable beyond a week or two, are expected to have a significant influence on sea ice and will limit its seasonal predictability. Instead, sea ice predictability on these timescales resides in the initial ice and ocean state. Factors believed to be among the most important for predicting sea ice behavior on the seasonal scale include accurate knowledge of (1) sea ice conditions at the start of the season (particularly the ice thickness distribution and the partitioning between seasonal and multiyear ice) and (2) upper ocean conditions, such as ocean mixed-layer heat content.. However, the relationship between prediction skill and the uncertainty in each of these factors is poorly known. The importance of initial values of other variables—such as snow on sea ice, ocean temperature and salinity profiles below the ice, and ocean current distributions—is poorly understood, but may be considerable. The accuracy requirements for bathymetry, which constrains ocean currents and controls the distribution of warm and cold water masses, are largely unknown (Jakobsson et al., 2012).

There is little information on what observational quality, spatial density, location, and accuracy are required for different variables to realize useful predictive power. Compounding the problem is the fact that observations of the atmosphere, sea ice, ocean, and seafloor are made from vastly different sensors, including in situ, airborne, and satellite instruments, along with a variety of methods. Each approach brings with it a specific set of characteristics, which often go undocumented.

There has been little if any effort to establish and broadly apply measurement protocols and standard definitions. For instance, even for one of the most fundamental parameters such as air temperature, there is no existing national or international protocol for predeployment calibration, maintenance of measurement stability during deployment, post-deployment calibration, and cross calibration among different sensors and different algorithms (e.g., different algorithms to measure ice surface temperature from different satellite sensors).

Adequately acquiring and making observations available within several days of the beginning date of the model simulation are necessary if the observations are to be useful for initializing operational seasonal model predictions. Ice extent is available within a day of satellite data acquisitions from multiple satellite sensors (passive microwave, active microwave, and



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