Dr. Frank Herr Ocean Battlespace Sensing S&T Department Head Dr. Scott L. Harper Program Officer

1. ONR's Arctic and Global Prediction Program Dr. Frank Herr Ocean Battlespace Sensing S&T Department Head Dr. Scott L. Harper Program Officer Team Lead, 322AGP Dr. Martin O. Jeffries Program Officer Arctic Science Advisor

2. The Changing Arctic • Naval Needs and Key Questions… • Task Force Climate Change “Arctic Roadmap”: • Must have Arctic environmental information and predictions to support investment and policy decisions, and future operations • NORTHCOM: • Must improve ability to observe and predict the Arctic environment • N2N6E Capabilities Based Assessment: Capability Gap in Provision of Environmental Information • Insufficient ability to provide oceanographic information, ice reports, accurate navigation charts, meteorological analysis and forecasts • How little sea ice will there be, and when will the key changes occur? • Need better prediction capability underpinned by basic research • How is the Arctic region as a whole going to be different? • Need research into how the entire Arctic environmental system functions • What does the Navy need to know to operate in the Arctic? • Need sustained observations and improved predictions of the state of the Arctic • How will the changing Arctic affect the rest of the earth, and vice-versa? • Need an Arctic environmental system model integrated within global prediction models • 2

3. Emerging Dynamics of the Marginal Ice Zone: DRI • Department Research Initiative (DRI): FY12-FY16 • Main field experiment in spring-summer-early autumn 2014 in the Beaufort Sea. • Components: • Deployment of acoustic navigation and communication array, under-ice gliders and floats, ice-tethered profilers, ice mass balance buoys, wave buoys on ice and in open water. • Development of the Marginal Ice Zone Modeling and Assimilation System (MIZMAS) and Arctic-Cap Nowcast/Forecast System for improved sea ice prediction. • Objectives: • Collect and analyze a benchmark data set that resolves the key processes controlling the evolution of the “new” MIZ. • Identify key interactions and feedbacks in the ice-ocean-atmosphere system and investigate how these might change with the predicted increased seasonality of the Arctic sea ice cover. • Evaluate the ability of existing models to predict the seasonal evolution of the MIZ. Improve parameterization of key processes with the goal of enhancing seasonal forecast capability. August 1990 August 2012

4. Sea State and Boundary Layer Physics of the Emerging Arctic Ocean: DRI • Department Research Initiative (DRI): FY13-FY17 • Main field experiment in summer-early autumn 2015 in the Beaufort and Chukchi seas. • Proposals are currently under review • Anticipate deployment of moorings with upward-looking acoustic wave profilers, drifting wave and meteorological buoys in open water and in the marginal ice zone, synthetic aperture radar (SAR) remote sensing of wind and wave spectra, and efforts to improve wave models for both open ocean and sea ice cover in the Arctic. • Objectives: • Understand the physics of heat and mass transfer from the ocean to the atmosphere, and the seasonal variability of fluxes during summer ice retreat and autumn ice advance. • Develop a new sea state climatology, identify factors affecting the spatial and temporal variability of sea state, and improve forecasting of waves on the open ocean and in the marginal ice zone in the Arctic. • Develop a climatology of and improve theory of wave attenuation and scattering in the sea ice cover. • Use wave scattering theory directly in integrated Arctic system models, and indirectly to define an ice rheology for use in Arctic system models.

5. High-Resolution Arctic Prediction Assimilating SAR Data Example of daily coverage from COSMO-SkyMed Merging of data from multiple platforms will provide daily coverage of the Arctic at high spatial resolution. • Develop an improved modeling capability for the Arctic for both basic understanding and prediction • Coupling ocean models with ice, wave, and atmospheric models in the Arctic • Data assimilation techniques for the Arctic Ocean • Building tools to help optimize the Arctic observing system • Role of remote sensing and in situ data in constraining Arctic models • Improved models and methods for prediction of sea ice (nowcast to 6+ months) • Algorithm development for data assimilation into predictive models: • Ice concentration and dynamics • Ice ridges • Ice types • Open water waves • Data collections are beginning now, with a focus on the Bering Strait, Beaufort and Chukchi Sea areas in preparation to support the DRI field efforts in 2014 and 2015.

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