Seasonal-to-Decadal Predictions of Arctic Sea Ice: Challenges and Strategies Sponsors: NASA, ONR, Intelligence Community Report available: October 30, 2012 Teaser: Tasking and Approach

2 Committee Membership and Process Jackie Richter-Menge, co-chair Cold Regions Research and Engineering Laboratory John Walsh, co-chair University of Alaska Fairbanks Lawson Brigham University of Alaska Fairbanks Jennifer Francis Rutgers University Marika Holland National Center for Atmospheric Research Son Nghiem Jet Propulsion Laboratory Robert Raye Shell Projects and Technology Fall 2011 Committee formed Spring 2012 Workshop planning May 2012 Community workshop, Boulder, CO – participants included polar researchers, agency representatives, and end users June 2012 Writing meeting Summer/Fall 2012 Report writing and review process

3 Study Context Recent dramatic changes in the thickness and extent of Arctic sea ice cover Implications for a growing community of diverse stakeholders (including local and indigenous populations, natural resource industries, fishing communities, commercial shippers, marine tourism operators, national security organizations, regulatory agencies, and the scientific research community) Promising strategies are emerging to enable continued improvement of sea ice predictions with more effective use of available resources Pancake ice off the coast of Greenland Photo Credit: Andy Mahoney

4 Charge to the Committee What key scientific questions remain in terms of understanding current and future sea ice changes, and how can we improve our understanding of the coupling between oceans, atmosphere, and sea ice (e.g., on what processes should observations be focused)? What systems of monitoring and observations are needed to better understand and predict the connection between changes in Arctic sea ice and its impacts on climate? What aspects of coupled sea ice models do we understand the best and in what ways can models better utilize existing observations and monitoring of sea ice to enhance our understanding of processes and future changes, and improve sea ice prediction?

5 Overarching Questions Posed by Committee Given the significant investments and progress that has been made in observing and modeling the Arctic sea ice cover, why aren’t we further advanced in the ability to predict its condition on seasonal-to-decadal time scales? How can we apply the tools and insights we have developed in a systematic way to more effectively address the questions posed in the Statement of Task?

6 Workshop Goal: Foster dialogue among stakeholders (Arctic indigenous residents, polar scientists, agency representatives, and commercial interests) to: – Explore current major challenges in sea ice prediction – Identify new methods, observations, and technologies that might advance seasonal-to-decadal sea ice predictive capabilities Panel discussions and breakout group sessions: – Stakeholder needs – Observations – Modeling – Challenges and opportunities Stakeholders  a prominent part of the underlying discussion Understanding stakeholders’ needs for seasonal–to-decadal sea ice prediction is crucial to guiding the future directions of modeling, observations, and overall research

7 Two Types of Predictability Seasonal (21 days to 1 year) – Predictability derived from initial conditions – Models capture evolution from initial state (deterministic prediction) – “Adequate knowledge” of initial ice-ocean state is needed – Preliminary model studies indicate that that initial conditions provide some skill for sea ice forecasts (extent, volume) out to a year or two Decadal (4 to 30 years) – Predictability derived from an underlying trend – Source of forecast skill at long forecast ranges (multidecadal and longer), when trend accounts for most of the change from – Trend is driven by external forcing (greenhouse gas increases) Intermediate range (1 to 4 years): the window of little or no predictability at present

8 Two Types of Predictability Seasonal: Sea Ice Outlook Average Sept 2011 & Median of July 2102 Outlooks Actual Minimum Extent Sept 2012: All predictions over estimate record minimum

9 Two Types of Predictability Decadal Adapted from Holland et al., 2011 Model Obs CCSM3: Single Model Run Adapted from Wang and Overland, th IPCC: 23 Global Climate Models Skill is improving, but variation is predictions continues to be wide

10 Report: What to expect Key science questions Gaps in understanding Future strategies – Overarching – Predictive Skill

11 Report Next Steps Free PDF available at on October 31, 2012www.nap.edu Final report printing (November) Community webinar (November 29) AGU presentation (December 3, pm poster)