An understanding of Arctic sea ice is important because the Arctic plays a role in influencing not only the global climate system, but also the global economic system through changes in marine access and natural resource development (Box S-1). Recent dramatic changes in the thickness and extent of the Arctic sea ice cover, which can be linked to the warming climate, are well documented. These changes affect a growing community of diverse stakeholders, including local populations (e.g., indigenous populations), natural resource industries, fishing communities, commercial shippers, marine tourism operators, national security organizations, regulatory agencies, and the scientific research community. Accompanying this growing interest is an urgent demand to increase the pace and scope of the advancements in predictive capabilities. Added pressure comes with the reality of the limited resources (e.g., funding and infrastructure) available to enable continued improvement of Arctic sea ice prediction and the likelihood that, in the face of continued warming, the Arctic will remain a dynamic environment well into the 21st century.
As tasked by the National Aeronautics and Space Administration, the Office of Naval Research, and the intelligence community, the committee convened a
KEY SCIENCE QUESTIONS
• What are the implications of the recent dramatic shifts in the Arctic from predominantly multiyear ice to first-year ice, and how will the associated complexities of this regime shift affect sea ice variability and predictability?
• In a rapidly changing Arctic regime, how will forcings and couplings between the various components of the ocean, atmosphere, cryosphere, seafloor, and land systems modify or influence the processes governing the characteristics of the sea ice cover?
• What are the impacts of extreme events and feedback mechanisms on Arctic sea ice evolution and our ability to predict it?
• How will changing Arctic sea ice characteristics and dynamics affect stakeholders on a variety of timescales, including prediction requirements?
workshop with the goal of exploring current major challenges in sea ice prediction and identifying new methods, observations, and technologies that might advance seasonal to decadal sea ice predictive capabilities through improved understanding of the Arctic system. The content of this report is largely informed by the discussions held during the workshop and is augmented by the committee’s deliberations.
A key theme resonating throughout the report is the importance of a coordinated and integrative approach to advance sea ice prediction. In fact, fundamental to the success of the workshop was a purposeful approach taken to foster a dialogue between polar researchers, agency representatives, and end users. The committee concludes that there is a need for this dialogue to be sustained well beyond the confines of the workshop format. A committed and deliberately integrative approach, founded on a sustained and coordinated conversation among the user, modeling, and observation communities, is necessary to:
• Identify and address key gaps in our fundamental understanding of the Arctic environment and its connection to the global climate system;
• Balance high-priority stakeholder needs against realistic predictive capabilities;
• Foster coordinated support of this work within the private and public sectors;
• Provide guidance in allocation of resources to support the most promising avenues in addressing the most pressing needs.
In this spirit, there are several key overarching challenges, not unique to the topic of sea ice prediction, that hinder advancements in our predictive capabilities:
• Treating the Arctic sea ice cover not in isolation, but as an integral part of the complex Arctic system which, in turn, is an integral element of the global system;
• Understanding how the recent regime shift in the Arctic sea ice cover from predominantly multiyear to first-year ice affects the processes governing the atmosphere-sea ice-ocean system, the power of statistical prediction methods, the validity of current numerical models and their parameterizations, and observational requirements, including instrument design; and
• Clearly defining the needs of the growing number of stakeholders, many with additional and more sophisticated requirements, and balancing these needs against realistic predictive capabilities.
The detailed needs of the diverse stakeholder community are reflected in an equally diverse set of temporal and spatial
requirements. Likewise, many of the needs and challenges associated with sea ice prediction depend on the timescales of interest. At shorter timescales (seasonal to interannual), predictive capability is thought to reside primarily in an adequate knowledge of the initial ice-ocean state, although admittedly little information exists on what constitutes an “adequate knowledge.” Challenges on the seasonal timescales include:
• Understanding the relative strengths and weaknesses of the different existing approaches used to generate seasonal ice forecasts (statistical algorithms, coupled ice-ocean models driven by prescribed atmospheric forcing, and coupled atmosphere-ocean-ice models);
• Establishing specific key observational data requirements necessary for defining the initial ice-ocean state for seasonal sea ice predictions; and
• Providing access to observational data at fast turnaround times.
At longer (decadal and greater) timescales, the role of trends in external forcings (e.g., increasing greenhouse gases) and of factors that control the forcings is likely to provide some predictive potential because they account for increasingly large fractions of the change from present sea ice conditions. A critical point of uncertainty remains regarding the timescale at which a transition occurs between these two regimes, and there is likely to be an intermediate timescale for which the potential predictability is low. The primary challenge at these longer timescales is to improve the ability to simulate realistic forcings by the atmosphere and ocean using coupled climate models at decadal timescales, and to identify the model variable and/or processes that contribute to unrealistic simulations.
In light of these challenges and while recognizing that there are limitations in current modeling and observational techniques, the committee offers possible strategies to significantly enhance our understanding and predictions of the Arctic sea ice cover over seasonal to decadal timescales:
• A systematic evaluation of the existing seasonal prediction capabilities to establish baseline expectations for predictive power and to set the stage for advances in predictive capability;
• A highly coordinated and integrated process-based study of seasonal sea ice focused on understanding the impact of the increasing predominance of younger, first-year ice on sea ice predictions and offering an opportunity to identify, develop, and test instruments and observational platforms;
• Inform research investments related to observational needs (e.g., observation types, locations, and coverage) in support of sea ice modeling and prediction by conducting an organized set of model sensitivity studies.
• Enhance the capabilities of numerical models through a coordinated experiment with multiple models to (a)
identify which variables and processes are critical to simulating a realistic ice cover, (b) determine the sources of climate model drift, and (c) guide decisions regarding high-priority model development needs and the expansion of models to include additional capabilities and variables of interest to stakeholders; and
• Create a centralized information hub that facilitates the timely access to observational and modeling results and encourages sustained communication among stakeholders.
These strategies are offered as guidance toward facilitating a transformative change in (1) our understanding of Arctic sea ice predictability on seasonal to decadal timescales and (2) our collective ability to realize and effectively communicate useful predictive power. The rate of advancement in sea ice predictions will likely be determined by the extent to which the broad user, modeling, and observational communities can achieve a sustained, integrative approach to refining and implementing these and other strategies.