Mapping the extent of
temperature-sensitive
snowcover and the relative
frequency of warm winters in
the western US
Anne Nolin
Department of Geosciences
Oregon State University
MTNCLIM 2006
Acknowledgements
• Chris Daly, Oregon State University
• NASA Cooperative Agreement NNG04GC52A
MTNCLIM 2006
Research Goals
• Map temperature sensitive snowcover in the
Western US
• Quantify the relative frequency of warm winters
(recent and potential) for selected areas
• Consider impacts on
• hydrology
• ski industry
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Focus Areas
Background image: PRISM digital elevation
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Model output
is too coarse
(10 x 12 km)
for watershedscale
hydrology
Data-driven
approach can
provide higher
resolution
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Mote et al., 2005
Mapping temperature sensitive snowcover
Snow classification based on Sturm et al., 1995:
• Used temperature, precipitation, and wind speed to define
snow classes
• 0.5 x 0.5 degree grid resolution
(Data courtesy NSIDC)
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DATA
• PRISM temperature
and precipitation
– Historical monthly
averages for 1971-2000
– 4 km x 4 km
• MODIS Vegetation
Cover Fraction (VCF)
product (proxy for
wind speed)
Jan Tmean
Jan Pre
% treecover
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Precipitation is classified based on a temperature threshold,
Tsnow, above which all precipitation is considered to fall as rain
0oC
0oC
(a)
Colder than
0oC
Colder than
0oC
(b)
Because this threshold temperature is somewhat
arbitrary, we use a range of temperatures in the snow
classification exercise
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Now…
Let’s assume climate warming over the next 40-60
years
•Using the IPCC Climate Model output for the Pacific
Northwest, the models are in good general agreement
that temperatures will continue to warm at the rate of
0.2-0.6oC per decade
•Here, we modify the transition temperature for warm vs.
cold snow by 0.5 degree increments for a total warming
of 2oC
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Decision tree thresholds
• Snow vs. No Snow:
DJF Tmean -2.0 to +2.0oC, in 0.5oC increments
• Warm snow vs. cold snow:
DJF Tmean -2.0 to 0oC, in 0.5oC increments
• High precip vs. low precip:
DJF P ≥ 2mm/day
• Low wind vs. high wind:
Forest cover density ≥ 35%
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Western US Snowcover Classification
Temperature-sensitive snow
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Pacific Northwest Snowcover Classification
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Sensitivity to Rain-Snow Temperature Threshold
15000
14000
% of PNW snow at risk
13000
2
12000
11000
1.5
area of PNW
at-risk snow
10000
9000
1
8000
7000
0.5
(6.5 km3 of water)
6000
5000
-2.5
0
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Rain-Snow Temperature Threshold (oC)
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2
2.5
Percent of PNW Snow Cover At Risk
2.5
Percent of Snow-Covered Area That is “At-Risk”
Pacific Northwest study area………<3%
1. Oregon Cascades…………………..22%
2. Washington Cascades……………..12%
3. Olympic Range.……………………..61%
1
2
3
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Sierra Nevada, CA
Total snow area = 24,128 km2
At-risk snow area = 7872 km2
At-risk snow percent = 32%
2300 - 2700 m elevation
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White Mountains, AZ
Total snow area = 1600 km2
At-risk snow area = 640 km2
At-risk snow percent = 40%
2400 - 2600 m elevation
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What is the relative frequency of warm winters?
First, what is a “warm winter”?
• Winter = DJF
• Warm = When at least one winter month has a mean
temperature above the 0oC
• If Tmean LE 0oC in December and January and February
then it is not a warm winter
Relative Frequency:
•
•
The number of times (N’) an event occurs within a number of N
trials
Thus, the relative frequency of an event is N’/N
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We use monthly DJF Tmean from PRISM data (19712000)
Evaluate relative frequency of DJF Tmean below a
threshold temperature
Shift threshold temperature upwards by increments
of 0.5oC (going from -2oC to 0oC)
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Table 2. List of Pacific Northwest ski areas that are projected to experience a significant
increase in the relative frequency of warm winters for a range of temperature thresholds.
Relative frequency of winters with a mean DJF
temperature exceeding:
Ski Areas by
Base
-2.0oC
-1.5oC
-1.0oC
-1.0oC
0.0oC
Region
Elevation (m)
Oregon Cascades
0.10
0.43
0.30
0.13
0.07
Timberline
1509
0.13
Mt. Hood
0.47
0.40
0.23
0.07
Meadows
1379
0.53
0.73
0.63
0.63
0.30
Mt. Hood Ski Bowl 1082
0.57
0.73
0.67
0.63
0.40
Cooper Spur
1219
0.27
0.67
0.57
0.43
0.07
Hoodoo
1423
0.00
0.33
0.13
0.07
0.00
Mt. Bachelor
1920
0.27
0.67
0.50
0.37
0.03
Willamette Pass
1561
0.33
0.63
0.60
0.50
0.20
Warner Canyon
1606
Mt. Ashland
1935
0.40
0.40
0.27
0.17
0.07
Eastern Oregon and Washington
0.00
0.40
0.30
0.17
0.00
Spout Springs
1478
0.33
0.57
0.53
0.50
0.27
Mount Spokane
1164
0.27
0.53
0.40
0.33
0.03
Bluewood
1385
Washington Cascades
0.03
0.33
0.13
0.03
0.03
Mt. Baker
1082
0.07
0.37
0.27
0.17
0.07
Mission Ridge
1393
0.03
0.47
0.27
0.13
0.00
Crystal Mountain
1341
The Summit at
0.57
0.53
0.43
0.33
0.27
866
Snoqualmie
0.07
0.47
0.30
0.20
0.00
White Pass
1372
0.03
0.37
0.27
0.10
0.03
Stevens Pass
1238
Olympic Range
Hurricane Ridge
1463
0.77
0.63
0.57
0.43
0.33
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Current
In 40-60 yrs
Nolin and Daly, 2006
California Ski Areas
Relative Frequency of Winter Monthly Mean
Temperature
1.2
Future
Present-day
Alpine Meadows
Badger Pass
Bear Valley
Big Bear
Boreal Ridge
Heavenly Valley
Homewood
June Mountain
Mammoth Mountain
Mt. Shasta Ski Park
Squaw Valley
SugarBowl
Tahoe Donner
1
0.8
0.6
0.4
0.2
0
-2
-1.5
-1
Temperature (deg C)
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-0.5
0
Hydrologic Implications
• Temporal centroid of hydrograph will continue to
shift to earlier date (Stewart et al., 2005)
• Snowmelt is a significant contributor to
mountainfront groundwater recharge
– Snowmelt vs. rainfall runoff
– Occurs during season of low evapotranspiration
• How will landscape controls (geology, vegetation)
interact with climate controls to change the spatial
and temporal patterns of streamflow?
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Monthly discharge for the Clear Lake, OR watershed in two historical periods
(1948-1952, 2001-2005) and a predicted future discharge
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from Jefferson et al., submitted to Hydrological Proc.
To summarize:
• Data-driven approach is useful for sensitivity studies
• “At risk” snow represents a proportion of the Oregon and
southern Washington Cascades, Olympic range, CA Sierra
Nevada, and AZ White Mountains
• Relative frequency of warm winters will likely influence
lower elevation ski areas across the Western US
• Hydrologic impacts are already evident
• Mapping efforts such as this can help identify sensitive
areas that need to be integrated into climate measurement
networks
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