1. Results from the Downscaling Needs Assessment Survey April 2011 Sarah Trainor (Sarah.Trainor@alaska.edu) Courtesy of Tony Weyiouanna Sr. & Dave Atkinson Courtesy of SNAP

2. Most of the examples given are potential future decisions (65 %) Examples include: –Determine the focus of research topics and areas to monitor based on uncertainty, vulnerability, and risk –Selection of funding sources –Habitat assessment and conservation efforts –Recommendations to policy makers, government, and wildlife management –Used to inform other models –Develop adaptation strategies and/or mitigate the effects of climate change –Determine the effect of climate change on regional hydrology with respect to seasonal discharge and temperature Decisions that Utilize Downscaled Climate Data

3. Role of SNAP Data in Decision Making Overall 51% indicated that SNAP data is not applicable for the reported example decisions Among the first examples given, fewer than 50% utilized SNAP data (45%)

4. Priorities for Downscale Data Higher resolution Accessing and reducing uncertainty Ease of use (i.e. availability & format) Clear explanation of the limitations, assumptions, and proper use of the data Ability to be coupled with other models

5. Geographic Scope of Climate Data

6. Geographic Scope of Downscale Data Summary Larger spatial areas (> borough) were of more interest Most relevant to least: 1.Other regional geographic unit: LCC regions, NPS boundaries, the Seward Peninsula, etc. 2.River drainage or watershed 3.Statewide 4.Transboundary 5.Borough or census district 6.Village, town or particular location Coastal areas, Western Alaska, and North Slope were areas of interest

7. Time-frame and Decisions Related to Climate Change 11-50 years the most useful timeframe Least useful 50 years

8. Decision & Risk Providing a value of uncertainty is very much needed A majority are comfortable making decisions when: –probability of the future or potential occurrence is > 66% A majority are uncomfortable making decisions when: –probability of the future or potential occurrence between 66% and 33% The potential impact of an even needs to be considered when determining risk “I think there are still decisions that can be made in the face of uncertainty, we just need some additional analyses to determine the consequence and severity of error.”

9. Extreme Events Defined by a combination of frequency, duration, and potential consequences It is difficult to make wildlife management decisions based on extreme events Little is known about the threshold for determining an extreme event and its associated consequences Examples: –Extreme high/low precipitation events on salmon, vegetation, and fire risk –Events that influence survivorship, population size, and movement of ungulates, such as deep snow or icing events –Erosion risk based on extreme storms and surge events

10. Figures Made sense to >90 % of responders Useful when shown together. For example, “This can be used to emphasize the point that there will be less monthly differences in precipitation than in temperature” Error bars are confusing and are not very useful unless better explained Explanation of why past years (2001-2010) use modeled data Improvements in temporal resolution are preferred since spatial already at the community level

11. Figures cont. Made sense to > 90% of responders Very useful for illustrating potential impacts on the ecosystem (i.e. vegetation, wildlife, invasive species, etc.) Useful for predictions and decisions on a more focused area Spatial resolution helpful for modeling wildlife & plant distributions

12. Figures cont. Figure on right made more sense (67 vs. 75%) Need more information, including a better definition of standard deviation and uncertainty Useful for selecting areas to focus research Illustrates confidence in predictions Too course for some projects, but useful for statewide

13. Made sense to most responders (67%), but was not useful (58%) Needs a legend Too course spatially, but temporally too short Most did not know if there would be greater value from improvements in spatial or temporal resolution Figures cont.

14. Figure Summary All the figures made sense to most responders (> 67%) Most were useful Legends could be more detailed to better interpret the figures Disclosure of limitations, caveats, and proper use Improvements in temporal resolution are preferred over spatial resolution Improvements in resolution come at the cost of increased uncertainty Data used to make the figures would be useful for further analysis

15. Thank You Courtesy of National Weather Service; Eagle, AK