multispectral sensors). Recent efforts undertaken by the National Aeronautics and Space Administration (NASA) IceBridge project (Kurtz et al., 2012) and the Sea Ice Outlook,3 for example, have demonstrated the ability to deliver observations of sea ice thickness and snow depth within a period of weeks from airborne platforms. This achievement was the result of a coordinated effort backed by dedicated resources (i.e., measurement platform, instruments, personnel, and funding resources).
However, near-real-time access remains problematic for many other types of data, such as ocean temperatures, salinities, and ice draft collected from seafloor moorings. Moreover, ocean currents, the distribution of warm and cold waters, and ocean mixing are controlled, guided, or restricted by seafloor characteristics. About 89 percent of the Arctic Ocean still needs to be surveyed to determine where interpolation or extrapolation may result in unrealistic bathymetric features (e.g., false seamounts) or where important structures (e.g., canyons) have been missed (Jakobsson et al., 2012). In general, prioritizing data acquisition with sufficient temporal and spatial coverage and timely accessibility to observations is critical if they are to play an effective role in seasonal, operational sea ice prediction.
Coupled climate models calculate their own atmospheric and oceanic variables from the basic laws of physics, providing the thermodynamic and dynamic drivers of sea ice evolution. Considering the number of variables and interactions that affect the ice cover, it is a tremendous achievement that the models simulate the observed ice behavior as well as they do. Any errors in the variables, processes, or feedbacks involved in these calculations, however, will be propagated and integrated by the ice over time and result in an unrealistic representation. A key challenge is simulating realistic atmospheric and oceanic conditions, which in turn depend on assumptions about future trends in carbon dioxide emissions, aerosol loading, changing surface characteristics, etc. Related to this challenge is the difficulty in determining which processes in a particular model are responsible for unrealistic aspects of sea ice simulations, especially systematic biases such as those responsible for the large model spread in Figure 1.3.
A key challenge in coupled climate models is the capability to realistically simulate atmospheric and oceanic conditions of relevance to sea ice variability, including the identification of model processes that contribute to unrealistic forcings and feedbacks.
Phenomena such as El Niño-Southern Oscillation (ENSO), the Arctic Oscillation (AO), the Pacific Decadal Oscillation, and the Arctic Dipole are known to affect the ice cover through their influence on ice transport, storm tracks, and heat transport (e.g., Rigor et al., 2002; Wang et al., 2009; Ogi et al., 2010). What drives these