Environmental intelligence

Enhance frameworks for environmental intelligence gathering, interpretation, and application toward decision support

To adequately support decision-making in the face of unprecedented change in the Arctic, the United States and its international partners need improved scientific data collection and stewardship, understanding, and environmental predictions. This challenge requires frameworks for generating Environmental Intelligence: integrated environmental knowledge that is timely, reliable and suitable for the decisions at hand.

Developing suitable Environmental Intelligence frameworks requires the integration of two distinct aspects of research. The first concerns the end-to-end integration of research across the linked and iterative steps of problem identification, environmental observing, understanding, prediction, and decision support. For example, safe marine transit through Arctic waters requires engagement with operators to understand the details of their information needs, such as high resolution sea ice forecasts. To produce these forecasts, sparse yet detailed observations of sea ice from drifting ice buoys, community-based observers, and other in situ observations must be synthesized with broad, low- resolution satellite observations. Synthesized observations must then be assimilated into forecast models, which subsequently must be tested and validated through efforts like observational process studies—feeding back into an iterative cycle of improved observing and modeling capabilities.

The second aspect of Environmental Intelligence requires integration of research across the components of the Arctic System, as most decision-making contexts require a holistic view. Building on the example in the previous paragraph, research is needed to inform how gridded estimates of sea ice thickness are interdependent with weather systems and ocean currents. With its emphasis on understanding the interconnected nature of the Arctic, IK presents a model for Arctic System integration.

Interagency collaboration is ideal for making progress on both end-to-end and Arctic System integration, because capacities and mission mandates to provide decision support tend to be distributed across many institutions and independently sponsored work. For example, NOAA and the Department of the Interior (DOI) sponsor many Alaska-based programs directly concerned with research for stakeholder engagement and decision support, such as ACCAP, AOOS and FWS’s LCCs. These agencies and others like NSF, DOE, and NASA also support sustained observing of the Arctic environments; DOE, NSF, NASA, ONR, and NOAA all contribute to models for improved predictions and projections, and many agencies support data centers that contribute comprehensive data stewardship for valuable Arctic data products. The Arctic Domain Awareness Center (ADAC), sponsored by DHS, bridges between research and operations to improve maritime domain awareness in support of the U.S. Coast Guard’s (USGC) mission. IARPC Collaborations will serve as a valuable forum for sharing practices and linking capabilities across IARPC agencies and outside collaborators.

While these efforts in the Arctic provide a solid foundation of knowledge and expertise, the Environmental Intelligence Goal addresses key areas for interagency progress. The sparseness of observational coverage and limited year-round environmental intelligence gathering have hobbled efforts to fully understand the impacts of changing environmental conditions on global processes as well as weather patterns, ecosystems, economic development, and safety. Interagency collaboration can leverage sparse observing assets and propel enhancements through the development of autonomous technologies (Research Objective 9.1). Modeling is a vital tool to advance system integration, to capture feedbacks within the systems, and to extend current understanding into the future. Progress is needed on how Arctic-specific processes and feedbacks are represented in models (Research Objective 9.2). Further, Arctic modeling can benefit from global and regional improvements to things like model resolution, as well as from comparative assessments, including quantified uncertainties among models (Research Objective 9.3). Arctic data stewardship, sharing, and access is evolving from systems where data are discovered in data catalogues and downloaded to the local machines of users, to a system of distributed data nodes with visualization and collaboration platform capabilities made to enable interoperability. Interagency collaboration is needed to understand the connection between these distributed nodes and work toward common visions (Research Objective 9.4) for exchanging and integrating data, in particular across disciplines. Finally, the practices of and frameworks for exchanging knowledge between researchers and stakeholders are in an exciting and dynamic growth period, yet few organizations have the capacity or mandate to adequately address the needs. IARPC Collaborations can serve as a valuable forum for advancing dialog on engagement research, decision support, and science communications (Research Objective 9.5) and feedback critical areas for progress (e.g. specific data needs) to the other Research Objectives in this Goal.

Improvements within and across each of these areas will improve the ability to understand, communicate about, and support decisions in response to the impacts of Arctic change. These efforts, across the scales from community to global at which IARPC agencies engage, support each policy driver of this plan (Well-being, Stewardship, Security, Arctic-Global Systems).

The work focuses on the following objectives

Learn more about the Environmental Intelligence Collaboration Team, and the Observing, Data, and Modeling sub-teams, implementing this section of the plan.

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