Transcript What decisions do you make or expect to make in the future that

Results from the Downscaling Needs
Assessment Survey
April 2011
Sarah Trainor ([email protected])
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
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•
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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 Climate Data
18
# of responses
16
14
12
1
3
3
0
3
10
8
6
5
6
0
6
7
5
2
8
6
3
4
2
6
4
1
4
5
Village/ Town
Borough/ Census
Never
3
2
1
0
1
River drainage/
Watershed
Occastionally
2
01
Other: LCC
regions, NPS,
Seward Peninsula,
etc.
Sometimes
Often
1
2
3
Statewide
Transboundary
Most often
Geographic Scope of Downscale Data Summary
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•
Larger spatial areas (> borough) were of more interest
Most relevant to least:
1.
2.
3.
4.
5.
6.
•
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
16
14
# of responses
12
10
1
0
2
1
2
1
3
4
3
3
6
2
5
8
4
5
6
4
5
8
2
4
3
2
3
1
2
2
1
2
1
1
1
4
3
1
6
6
50 - 75 years
More than 75
years
3
0
Next 12 months
1 - 5 years
Never
6 - 10 years
Occastionally
11 - 20 years
Sometimes
21 - 50 years
Often
• 11-50 years the most useful timeframe
• Least useful <1 and >50 years
Most often
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
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•
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•
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.
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•
•
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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
Thank You
Courtesy of National Weather Service; Eagle, AK