Geology Reference
In-Depth Information
Role of Precipitation Phase
Measured Delay Times
2
=0.1
τ
τ
=1.3
τ
=0.1
Alps
2000
4
2
=1.3
τ
Olympics
Patagonia
1
Oregon
Coast Range
2
1000
1
Wasatch
Calif. Coast
Range
ppt
0
0
0
4
0
0
20
40
60
0
20
40
60
8
12
16
A
B
C
Mean Annual Temperature (°C)
Distance across range (km)
Distance across range (km)
Fig. 11.24
Role of precipitation phase.
A. Measured delay times of precipitation in ranges dominated by snow (low mean annual temperatures) and rainfall
(higher temperatures). B, C. Results of coupled models of landscape evolution and orographic precipitation with a
preferred wind direction (from left). Delay times (
t
) reflect the phase of precipitation (solid lines = short delay = rain;
dashed lines = long delay times = snow). B. Mean elevation and precipitation rate profiles across the range.
C. Ridge-valley relief across the range. Modified after Anders
et al.
(2008).
precipitation is explicitly taken into account, they
show that the feedbacks are indeed strong. The
closely coupled system associated with rainfall
(short delay times) generates strong asymmetry
in precipitation, elevation, and ridge-valley relief,
whereas snowfall-dominated ranges (long delay
times) show smaller variation in elevation, pre-
cipitation, and relief (Fig. 11.24B and C).
But, topography is not one-dimensional:
mountain ranges do not wrap around the globe.
Galewsky (2009) has elegantly dealt with the
broad-scale two-dimensionality of mountain
ranges. His models predict that air masses can
dodge being lifted by a range if the range is
short enough to go around, and that a range can
“block” the air mass, causing it to rise well before
the slope of the range would otherwise dictate
(Fig. 11.25). These effects give rise to precipita-
tion patterns that differ significantly from those
predicted in simple one-dimensional models.
In the numerical analysis of the interactions of
air masses with topography, a diagnostic rela-
tionship is
Nh
/
U
, in which
N
is a measure of
atmospheric stability,
h
is the maximum relief of
the range, and
U
is the wind speed prior to
encountering the range (Galewsky, 2009). High
wind speeds and low relief (
Nh
/
U
<< 0) permit
storms to flow directly over mountain ranges
without deflection (Fig. 11.25A). Such conditions
are typical of one-dimensional orographic pre-
cipitation models, which predict precipitation
maxima on the windward slopes of the range
(e.g., Fig. 11.23). In contrast, low wind speeds
and high relief (
Nh
/
U
>> 1) tend to block flow
over a range, deflect moisture around its ends,
create a zone of relatively stagnant air on their
windward flanks, and cause precipitation well
upwind of the range (Fig. 11.25B). Another key
predictive parameter in these models is the
symmetry of the range,
b
, which represents the
ratio of the length to the width of a range. For
conical ranges,
b
= 1, whereas for elongate ranges,
b
>> 1. Conical ranges deflect winds and moisture
much less than do elongate ranges (Fig. 11.25C).
Finally, rotation due to Coriolis effects can
enhance range-parallel flow and introduce
greater asymmetries in precipitation patterns
(Fig. 11.25D). Galewsky's (2009) exploration of
configurations of different individual and paired
ranges provides predictions of wind and mois-
ture patterns that show striking variation depend-
ent on range height, spacing, and sequencing
(high range upwind of a low range, or vice
versa). Such models provide a template for con-
sidering precipitation in growing ranges and in
active orogenic belts where successions of elon-
gate ranges grow above laterally extensive faults.
Finally, we illustrate here perhaps the newest
tool to be employed in addressing the interac-
tion of the atmosphere and topography
(Fig. 11.26 and Plate 11). We are beginning to
utilize weather forecasting models to predict
