Compilation of Key Challenges and Strategies
GAPS IN OUR UNDERSTA NDING
• Overarching Challenges
o Treating Sea Ice as Part of a Global System. - A key to advancing our understanding and predictive capabilities is the treatment of the sea ice cover as an integral part of the complex Arctic system which, in turn, is an integral element of the global system. Adding to this complexity is the broad range of human activities that not only influence the Arctic system, but are also influenced by it.
o Impacts of the Regime Shift of Arctic Sea Ice. - Understanding how the recent regime shift in the Arctic sea ice cover, resulting in a significant reduction in the amount of multiyear ice compared with first-year ice, affects the processes governing the atmosphere-sea ice-ocean system and the models and observations used to study and predict Arctic sea ice dynamics.
o Identifying Diverse and Emerging Stakeholder Requirements. - As the Arctic is being transformed by globalization and climate change, new stakeholders with additional and more sophisticated requirements are emerging. Clearly defining these diverse needs as they relate to seasonal to decadal sea ice prediction is crucial to inform the future directions of modeling, observations, and overall research.
• Challenges in Advancing Predictive Capability
o Competing Approaches to Seasonal Sea Ice Prediction. - Although limitations in the various approaches used to generate seasonal forecasts are generally acknowledged, there is a lack of quantitative information about their relative strengths and weaknesses.
o Observational Requirements for Seasonal Sea Ice Prediction. - 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.
o Projecting Realistic Forcings and Feedbacks for Decadal Sea Ice Predictions. - 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.