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Results from the Downscaling Needs Assessment Survey. April 2011 Sarah Trainor (Sarah.Trainor@alaska.edu). Courtesy of SNAP. Courtesy of Tony Weyiouanna Sr. & Dave Atkinson. Decisions that Utilize Downscaled Climate Data. Most of the examples given are potential future decisions (65 %)
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Results from the Downscaling Needs Assessment Survey April 2011 Sarah Trainor (Sarah.Trainor@alaska.edu) Courtesy of SNAP Courtesy of Tony Weyiouanna Sr. & Dave Atkinson
Decisions that Utilize Downscaled Climate Data • 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
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%)
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
Geographic Scope of Downscale Data Summary • Larger spatial areas (> borough) were of more interest • Most relevant to least: • Other regional geographic unit: LCC regions, NPS boundaries, the Seward Peninsula, etc. • River drainage or watershed • Statewide • Transboundary • Borough or census district • Village, town or particular location • Coastal areas, Western Alaska, and North Slope were areas of interest
Time-frame and Decisions Related to Climate Change • 11-50 years the most useful timeframe • Least useful <1 and >50 years
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.”
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
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
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
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
Figures cont. • 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
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