Hidden Climate Variability in
Mountainous Terrain
Christopher Daly and David Conklin
Oregon State University
Introduction
•
Climate varies spatially over complex terrain, responding to
factors such as elevation, aspect, and coastal proximity
•
It is generally assumed that climatic variations in time respond
less strongly to these factors, and are fairly consistent on a
regional basis.
•
The assumption of temporal synchrony of climate is made in
nearly every field study using off-site meteorological data.
•
Is the assumption of climate synchrony really true? Let’s find
out…
Northwest Oregon
1278 m
442 m
Daily Temp Gradient (C/km), VANMET-PRIMET
1995-2000
Tmax
Tmin
Environmental
Lapse Rate
(-6.5C/km)
What causes this lapse rate variability?
Losleben,
Pepin, et al.
(2000)
Atmospheric
Circulation
Analysis
700-mb pressure
heights
Anticyclonic
700-mb Flow
Curvature Types
Zonal
Cyclonic
HJA VANMET-PRIMET Daily Minimum Temperature Gradient
Vs
700-mb Flow Strength and Curvature
1987-2005
4
Flow Strength
L = Low
M = Medium
H = High
Mean Daily Tmin Gradient (C / km)
3
2
1
0
L
M
H
L
M
H
L
M
-1
-2
-3
-4
-5
-6
Anti-cyclonic
Zonal
Cyclonic
H
Comparison of December Tmax Anomalies
VANMET-PRIMET Tmin Difference
(C)
5
VANMET-PRIMET Estimated Tmin Difference
Given A-C Increases
VAN-PRI tmin+5A-C
VAN-PRI tmin+10A-C
4
3
2
1
0
-1
-2
Jan Feb Mar Apr May Jun
Jul
Aug Sep Oct Nov Dec
Month of Year
5
VANMET-PRIMET Estimated Tmax Difference
Given A-C Increases
VAN-PRI tmax+5A-C
VAN-PRI tmax+10A-C
VANMET-PRIMET Tmax
Difference (C)
4
3
2
1
0
-1
-2
Jan Feb Mar Apr May Jun
Jul
Aug Sep Oct Nov Dec
Month of Year
Elevation and Topographic Position as Predictors of
Slope of Regression Function between A-C and December Tmax
Big Implications for Climate Change Projections!
HJA December Tmax Change
+2.5C Regional Change and +10 A-C
Spatio-Temporal Landscape
•
Temperature variations in a mountainous environment can
be highly asynchronous, even among locations less than a
km apart.
•
At topographically exposed locations, temperatures are
highly responsive to changes in upper-atmospheric
circulation patterns, while at sheltered sites, cold air
drainage and pooling dampen responses to those circulation
patterns.
•
The result is a complex temperature landscape composed of
steep gradients in temporal variation, controlled largely by
gradients in elevation and topographic position.
Importance of Fine Scale Factors
•
Other factors not accounted for in our preliminary
analysis, but likely to be important, include slope and
aspect, forest canopy cover, snow cover, and riparian
effects.
•
The same factors that control the spatial patterns of
climate also appear to control the temporal variations
in climate.
•
To get it right, we cannot ignore fine scale processes.
But need supporting data sets at this scale.
Implications for Climate Change
•
If future climate changes are accompanied by changes
in the frequency distribution of upper-air circulation
patterns, actual temperature responses could diverge
widely between very closely-spaced locations.
•
The magnitude of this divergence might equal or
exceed that of the projected temperature change itself.
•
There is ample evidence that cold air drainage occurs
worldwide. Therefore, it is likely to be of global
importance in understanding the implications of climate
change.
Downscaling Issues
• Our simple first efforts to statistically model the
effects were reasonably successful, at least in our
study area.
• The challenge will be developing general statistical
models that are applicable to other mountainous
regions.
• Given that the most explanatory power in our model
was offered by topographic position at the 150-m
scale (and probably finer), the challenge to dynamic
downscaling will be to simulate cold air drainage
and related processes at ultra-fine grid resolutions.
Recommendations
• More Data: Exploit existing and establish new
measurement programs designed to understand the
complexities of climatic asynchrony in mountainous
terrain
• More Nooks and Crannies: Encompass a wide
spectrum of elevations and topographic positions
within small areas
• More Places: A variety of climatic and
physiographic settings to allow results to be
generalized
Recommendations
• Long Time: At least 10 years of data
• No Cheating! Infilling of missing data not allowed
• Make Better Maps: New data should be applied
directly to research and analysis programs that
result in better downscaled data sets
• CIRMOUNT can help: As organizational
umbrella, cheer leader, and more