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NNA Track 2: PredictFest: To Build Capacity for Arctic Stakeholders in Need of Multi-Scale Predictions


Project start
Project end
Type of project
Project theme
Weather, climate & atmosphere
Project topic
Education & Outreach

Project details

Science / project summary

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by supporting planning activities with clear potential to develop novel, leading edge research ideas and approaches to address NNA goals. It integrates aspects of the natural environment and social systems, addresses important societal challenges, and engages with local and Indigenous communities. Significant progress has been made to improve the accuracy both of short-term weather forecasts and longer-term climate, sea-ice, and other environmental forecasts. Nevertheless, there remains a forecasting gap on the scale from two weeks to a few months, especially in the Arctic. This subseasonal-to-seasonal (S2S) scale is important for planning subsistence hunting, commercial fishing, hazard response and risk mitigation, and other activities. This project supports development of improved S2S prediction capacity through a collaborative design process and workshop, PredictFest, that engages Arctic stakeholders to identify needs and applications for such predictions. PredictFest participants will prepare research pitches that articulate stakeholder planning and decision making needs, identify technical and modelling approaches that respond to those needs, identify relevant connections and applications, and set out a plan for continued development. PredictFest provides an opportunity for Arctic residents and scientists to work together to develop applications for seasonal forecasts of sea ice, precipitation, and other environmental factors, which support traditional and commercial uses of marine and coastal regions. In addition, the project fosters a team-centered approach to applied research, education, and outreach and builds relationships between Arctic residents and scientists to support future work. Direct engagement between scientists and other Arctic residents, including Indigenous organizations, community members, business owners, and public decision makers, encourages new thinking about how seasonal forecast information may be used and what steps may be taken to realize stated goals. This grant supports an innovative new approach to capacity building that leverages emergent S2S platforms and science to identify research programs to support the diverse set of Arctic stakeholder planning and decision-making needs. The project contains three phases: (1) stakeholder engagement and development of sub-seasonal to seasonal prediction focus, 2) composition of development teams and implementation of PredictFest, and 3) post-assessment and collaborative refinement of PredictFest outputs. Co-learning and co-production contribute to the rapid identification of arenas where seasonal scale predictions might be most beneficial to decision making in support of natural, built, and social environments. During PredictFest each participating team is comprised of natural and social scientists, stakeholders and community representatives, as well as technical support staff. The topic of focus, Marine Environments and the Near-shore Coastal Interface, generates distinct predictions that can be used to support community subsistence centered on marine mammals, commercial fisheries including fixed gear participants, and hazard response and risk mitigation planning. From a prediction standpoint a number of approaches will be pursued by the teams. These include developing new relationships between variables typically using linear models or tree-based learning, using time-series forecasting techniques models such as Autoregressive Integrated Moving Average (ARIMA), and pattern recognition.