Mapping Temperature across
Complex Terrain
Jessica Lundquist1, Nick Pepin2, Phil Mote3
1Assistant
Professor, Civil and Environmental Engineering, University of Washington
Department of Geography, University of Portsmouth
3Washington State Climatologist, Climate Impacts Group, U. Washington
2 Lecturer,
Talk outline
• World overview: temperatures at higher elevations
• Implications for snowmelt and the rain-snow line
• Temperature inversions
• Mapping cold-air pools
• Temperature Toolbox webpage
Trends from 1084 stations ranging in elevation
from 500-4700 m across the globe (Pepin and
Lundquist, in press, and Pepin and Seidel, 2005)
1
Temperature trend (oC per decade)
1
0.5
mean
0.5
0
0
-0.5
-0.5
-1 Tropical Stations
elevation
Extratropical Stations
-1
elevation
What accounts for huge scatter?
• Measurements are sparse at higher elevations.
• Measurements may be unrepresentative of
surrounding topography.
•. This can have huge implications for modeling
ecology, snowmelt, and the rain-snow line.
Streamflow simulations depend
on knowing high elevation
temperatures.
80
Streamflow (CMS)
70
OBS
SIM
60
50
40
30
20
10
winter
spring
0
10 11 12
1
2
3
4
Month
5
6
7
8
9
Snohomish River
in Western
Washington,
courtesy of Alan
Hamlet
Streamflow simulations depend
on knowing high elevation
temperatures.
80
Streamflow (CMS)
70
OBS
SIM
60
50
40
30
20
10
winter
spring
0
10 11 12
1
2
3
4
Month
5
6
7
8
9
Modeled
mountain temp
is too cold = too
much snow falls
= underestimates
winter rain
runoff &
overestimates
spring snowmelt
Area contributing to runoff
(through rain or melting snow)
depends on how temperature
decreases with altitude
Depends on lapse
rate:
elevation
Average decrease =
6.5°C per km
But could range from
3 to 9.8 °C per km
0°C
+15°C Sea level
elevation
A biased valley temperature
sensor could also misrepresent
the elevation where snow melts.
0°C
+15°C Sea level
Temperature inversions and coldair pools are common in mountain
valleys, which is where many
temperature sensors are located.
Inversions occur during high pressure,
when large-scale winds are weak, and
local topography controls mountain
weather.
alpine sites
valley sites
From Lundquist and Cayan, 2007
Inversions are common
in valleys near Mt.
Rainier, making the
standard lapse rate often
wrong.
Fortunately, we know how mountain winds
and cold air pools work and can map them
with a DEM (Example, Loch Vale, Rocky
Mountain National Park, Colorado)
1) Density-driven drainage
2) Pressure-gradient-driven drainage
1) At night, longwave radiation cools air
adjacent to the surface.
2) Cold air is denser than warm air, and flows
down hill and down valley.
3) Therefore, cold air can collect in flat valley
bottoms and local depressions.
Loch Vale,
Rocky Mtn NP
iButton
Location
Primary Mode
of Variability is
cold-air
pooling:
Explains 72%
of the Variance
Positive
Weight
Negative
Weight
Flat slope
Local depression
Concave
Important factors identified in forestry literature.
Lundquist et al. 2008 (submitted to JGR)
Mapping likely cold-air pools using
a digital elevation map (DEM).
This method identifies flat valley
bottoms (white), but only one of two
identified is cold-air pool.
Andrew’s
Meadow = flat
bottom without
cold-air pooling
Loch Vale =
correctly
identified
cold-air pool
From mountain meteorology literature,
Whiteman, 1990; McKee and O’Neal 1989
Topographic Amplification Factor (TAF)
•
W
Represents how much more a valley
cools at night compared to a flat plain
H
Energy lost to space = Volume x ΔT
Whiteman, 1990; McKee and O’Neal 1989
Topographic Amplification Factor
•
W
Represents how much more a valley
cools at night compared to a flat plain
H
Smaller enclosed volume = cools more = bigger TAF
Whiteman, 1990; McKee and O’Neal 1989
Topographic Amplification Factor
W
•
Areas that cool more have local higher
pressure.
•
Winds flow from high to low pressure.
•
Places where TAF decreases downvalley drain, and where TAF
increases, cold air pools
TAF decreases through Andrew’s
Meadow, so it drains.
Main Loch Vale: TAF increases,
cold air pools.
Applied mapping algorithm to study
areas in the Rocky Mountains, Pyrenees,
and Sierra Nevada, and can predict areas
of CAP with over 80% accuracy.
Lundquist et al. 2008, submitted to JGR
Using CAP-mapping to interpolate station
temperature data results in an average of 1oC
improvement over standard interpolation techniques.
http://faculty.washington.edu/jdlund/TemperatureToolbox/
Find: Links to all the papers discussed here.
Directions for deploying temperature sensors in trees.
Code for CAP-mapping algorithm and empirical orthogonal
function (EOF) based method of identifying modes of
temperature variability.
Valley Fog, Guipuzcoa, Basque Country, Spain from wallpaperme.com
Conclusions
1) Mountains poorly sampled: samples may not
represent surrounding topography
2) Temperature patterns strongly influenced by
large-scale weather patterns and by local
topography
3) GIS-based mapping can help improve how we
model temperature variations across complex
terrain
The Temperature Sensors:
Dallas Semiconductor Maxim iButton DS-1922L
- 17.35 mm diameter
- 5.89 mm thickness
- temperature range:
-35°C to +85°C
- records temperature at user-defined rate:
8192 8-bit readings (0.5°C resolution) or
4096 16-bit readings (0.0625°C resolution)
at intervals ranging from:
1s to 273hr
- 512 bytes for application info
- 64 bytes for calibration data
0.5°C resolution+
Sample once per hour=
11 months of data
Tuolumne i-buttons
Different radiation
shielding from trees
The Pacific
Northwest has
taller trees
than most of
the Rockies or
the Sierra
Jeremy Littel
Biology Professor Janneke Hille Ris Lambers
Eset
Alemu
Rocky Mountain Field Study:
Caitlin Rochford
CIRES Innovative Research Fellowship
Dave Clow (Colorado USGS), Mark Losleben
(Colorado Mountain Research Station(MRS)),
Kurt Chowanski (MRS), Todd Ackerman(MRS),
Jen Kelley, Hollings Scholarship Program
Acknowledgements
Yosemite Field Study: Brian Huggett (NPS),
Dan Cayan (SIO), Mike Dettinger (USGS),
Heidi Roop (Mt. Holyoke), Jim Roche (NPS),
Frank Gehrke (CA DWR), Kelly Redmond
(WRCC), Canon Research Fellowship, NSF
RoadNET program, CIRES Postdoctoral
Fellowship