in the atmosphere (e.g. Byrkjedal et al., 2008; Girard and Bekcic, 2005) and ocean (e.g., Fieg-Rudiger et al., 2010) Higher resolution information may also be necessary to meet certain stakeholder needs. However simply increasing model resolution is not a panacea, because enhanced computational and storage costs need to be considered in light of the relevant benefits for sea ice prediction. Moreover, model parameterizations that have been developed for coarse resolutions may not be ideal for considerably finer spatial-scales (e.g., Lipscomb et al., 2007; Girard et al., 2009) and may need to be revisited, requiring further model developments. Nevertheless, with computational resources increasing and likely benefits in terms of simulation quality, increased resolution in global and regional models together with regionally refined and adaptive model grids need to be explored in the context of benefits for sea ice predictive capability.


Key Strategy: Improved Information and Data Management

Given the vast amounts of disparate data on Arctic sea ice and the numerous stakeholders who use these data, there is a need for a coordinated and centralized information hub for Arctic datasets that facilitates timely access to observational and modeling results and encourages sustained communication among stakeholders.

No single organization or agency has adequate resources to systematically undertake the task of robust field observations, data synthesis, and environmental modeling. Collaborative efforts and data sharing are therefore essential. Moreover, data continuity is a fundamental imperative so that long-term Arctic sea ice trends can be ascertained and provided to stakeholders for reliable planning. Sharing data also enables researchers to communicate and collaborate more effectively.

Given the vast amounts of disparate data on Arctic sea ice, background information, model results, observational date, etc. can be difficult to find for the numerous stakeholders who use these data, particularly for new users. The committee acknowledges that a more centralized framework could improve information management (Parsons et al., 2011). Although there are numerous data repositories for climate-relevant data, they tend to be scattered and inconsistently cross-linked. Rich measurement datasets are often reduced to their basic parameters with a loss of important information. Also, data transfer and data transformation at data centers add additional layers of complexity and data latency.

In the committee’s opinion, the main purpose of a centralized information hub is to serve as a primary launching pad for searches aimed at gaining access to this wide array of information. The intention is not to recreate existing and diverse resources, but to facilitate the ease of their retrieval. A central information hub would unify the various databases, providing a seamless and consistent system for information and data

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