Mapping Temperature across Complex Terrain

Jessica Lundquist1, Nick Pepin2, Phil Mote31Assistant Professor, Civil and Environmental Engineering, University of Washington

2 Lecturer, Department of Geography, University of Portsmouth3Washington State Climatologist, Climate Impacts Group, U. Washington

• World overview: temperatures at higher elevations

• Implications for snowmelt and the rain-snow line

• Temperature inversions

• Mapping cold-air pools

• Temperature Toolbox webpage

Talk outline

Trends from 1084 stations ranging in elevation from 500-4700 m across the globe (Pepin and Lundquist, in press, and Pepin and Seidel, 2005)

Tropical Stations Extratropical Stations

elevation

Tem

pera

ture

tren

d (o C

per d

ecad

e)

mean

0

-0.5

-1

0.5

1

0

-0.5

-1

0.5

1

elevation

• 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.

What accounts for huge scatter?

Streamflow simulations depend on knowing high elevation

temperatures.

0

10

20

30

40

50

60

70

80

10 11 12 1 2 3 4 5 6 7 8 9

Month

Stre

amflo

w (C

MS)

OBSSIM

winter spring

Snohomish River in Western Washington, courtesy of Alan Hamlet

Streamflow simulations depend on knowing high elevation

temperatures.

0

10

20

30

40

50

60

70

80

10 11 12 1 2 3 4 5 6 7 8 9

Month

Stre

amflo

w (C

MS)

OBSSIM

winter spring

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

+15+15°°CC Sea level00°°CC

elev

atio

nDepends on lapse rate:

Average decrease = 6.5°C per km

But could range from 3 to 9.8 °C per km

A biased valley temperature sensor could also misrepresent the elevation where snow melts.

+15+15°°CC Sea level00°°CC

elev

atio

n

Temperature inversions and cold- air 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.

iButton Location

Loch Vale, Rocky Mtn NP

Primary Mode of Variability is

cold-air pooling:

Explains 72% of the Variance

Positive Weight

Negative Weight

Mapping likely cold-air pools using a digital elevation map (DEM).

Flat slope

Local depression ConcaveLund

quis

t et a

l. 20

08 (s

ubm

itted

to J

GR)

Impo

rtan

t fac

tors

iden

tifie

d in

fore

stry

lite

ratu

re.

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

Topographic Amplification Factor• Represents how much more a valley

cools at night compared to a flat plain

From

mou

ntai

n m

eteo

rolo

gy li

tera

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,W

hite

man

, 199

0; M

cKee

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eal 1

989

Energy lost to space = Volume x ΔT

W

H

(TAF)

Topographic Amplification Factor• Represents how much more a valley

cools at night compared to a flat plain

Whi

tem

an, 1

990;

McK

ee a

nd O

’Nea

l 198

9

Smaller enclosed volume = cools more = bigger TAF

W

H

Topographic Amplification Factor

• Areas that cool more have local higher pressure.

• Winds flow from high to low pressure.

• Places where TAF decreases down- valley drain, and where TAF

increases, cold air pools

Whi

tem

an, 1

990;

McK

ee a

nd O

’Nea

l 198

9

W

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.

Valley Fog, Guipuzcoa, Basque Country, Spain from wallpaperme.com

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.

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:

Dave Clow (Colorado USGS), Mark Losleben (Colorado Mountain Research Station(MRS)),

Kurt Chowanski (MRS), Todd Ackerman(MRS), Jen Kelley, Hollings Scholarship Program

Caitlin Rochford

CIRES Innovative Research Fellowship

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