Grant: Discovery Grants Program - Individual

The shrinking and thinning of Arctic sea ice, together with an increasing desire to utilize the Arctic's abundant natural resources, and the potential competitiveness of Arctic shipping routes, all provide for increased marine access throughout the Arctic Ocean. With interest in commercial shipping growing, as well as concerns for safe travel on the sea ice by local communities, there is a growing need for reliable sea ice forecasts. Nevertheless, the science of sea ice forecasting lags weather forecasting by several decades. In response, the Sea Ice Prediction Network (SIPN) was formed to bring together sea ice forecasters, data producers and stakeholders to advance sea ice forecasting. SIPN has been highly successful at galvanizing the sea ice forecasting community, yet it remains largely focused on the pan-Arctic scale, rather than tailoring forecasts for specific regions and/or communities. As a new Canada 150 Research Chair at the Centre for Earth Observation Science (CEOS) at UoM, I will continue my work on sea ice forecasting by developing new data products and forecasting models, but with a specific focus on Canadian regions of relevance to local communities. My DG programme is focused around a single overarching research question: How well can we forecast sea ice conditions at 3- to 6-month lead times in the Canadian Arctic? To answer this question requires research into ways we can improve detection of sea ice conditions, and assessing sources of predictability using a variety of observational and modeling platforms. Specifically, the DG will focus on developing improved understanding of mechanisms controlling sea ice variability in Hudson and Baffin Bays, developing novel statistical forecasting models and assessing how well these processes are represented in climate models. Results will be disseminated through SIPN, which currently receives over 24,000 page views each year. A key component will be to assess the needs of local communities and tailor our forecasts of sea ice state variables that are useful (i.e. sea ice thickness, timing of ice retreat and advance). The following interlinked objectives will help to achieve my overarching objective: 1.Sea ice detection - How can we improve detection of the geophysical state of sea ice in Hudson and Baffin Bays through a full annual cycle? 2.Sea ice processes - What are the sources of predictability that influence sea ice variability within these regions? 3.Remote sensing - How can remote sensing and surface-based measurements improve the determination of (1) and (2) over a range of spatial and temporal time-scales? 4.Climate models - How well do existing coupled ocean-sea-ice-atmosphere models predict (1) and (2) at small spatial scales? 5.Improving forecasts - How can we integrate observational time-series data and numerical models to improve seasonal ice forecasts? 6.Benefits to communities - How can we reduce risk and vulnerabilities for safe travel on the sea ice?