Oct 17, 2003 MISR
Mountain Influence on
Climate
Alex Hall, Sebastien Conil, Mimi Hughes, Greg Masi
UCLA Atmospheric and Oceanic Sciences
SIMULATING
MOUNTAIN
CLIMATE
Experimental Design
model: MM5
boundary conditions: eta
model reanalysis
resolution: domain 1, 54
km, domain 2, 18 km,
domain 3, 6 km
time period: 1995 to
present.
One can think of this as a
reconstruction of weather
conditions over this time
period consistent with
three constraints: (1) our
best guess of the largescale conditions, (2) the
physics of the MM5
model, and (3) the
prescribed topography,
consistent with model
resolution.
Comparison of “dynamical
downscaling” vs “statistical
interpolation”
DIURNAL
CYCLE
AMPLITUDE
There are large gradients in this quantity, with high elevations having
amplitudes on the order of 6 degrees C, and low elevations having amplitudes
more than twice as large. (Hughes et al. (2005))
Amplitude (m/s) and orientation of August diurnal mountain winds and land/sea
breeze, as measured by the difference between the winds at 3PM and 3AM.
Upslope flow occurs during the day, while downslope flow takes place at night.
increasing potential temperature
WARM
COLD
The S. California atmosphere
is usually highly stratified.
CONVERGING
COLD AIR
During the daytime, upslope
winds bring cold air to the
highest elevations, reducing
daily maximum temperature.
PRECIPITATION
A scatter plot of precipitation vs. elevation shows two distinct clusters
with different slopes and intercepts.
IN
TE
R
IO
R
C
O
A
ST
L
A
It turns out that if we divide up the region into a coastal and an
interior domain, this corresponds to the two clusters in the precip
vs elevation plot.
A scatter plot of precipitation vs. elevation shows two distinct clusters
with different slopes and intercepts.
COASTAL
INTERIOR
If we regress precipitation against elevation, we can calculate slopes
and intercepts for the two clusters. The slope measures the elevation
enhancement effect. The intercept measures the precipitation
occurring at sea level.
Slope and intercept of regression of precipitation vs
elevation by calendar month
coastal
interior
coastal
adjacent open ocean value
interior
Southern California Climatological Precipitation (cm/yr)
e
at
im
ox
pr
ap
ct
re
di
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of
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flo
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Some of the effects of topography on the flow result in non-trivial relationships between
precipitation and elevation. For example, at the coast, we might expect no orographic
enhancement. Yet precipitation is clearly greater than it would be if there were no
topography. (Masi et al. 2005)
Southern California Climatological Precipitation (cm/yr)
e
at
im
ox
pr
ap
ct
re
di
BLO
CKE
DF
LOW
n
io
of
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flo
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en
ev
Precipitation is enhanced in the coastal zone because of blocked flows that develop
parallel to the coast as low-level air masses are forced toward the interior. Once this
blocked flow is in place, the moisture-laden onshore flow must surmount it in addition to
the topography, enhancing precipitation well away from the coastal ranges. This is
another example of how local dynamics profoundly affect the geographical distribution of
a quantity relevant to climate.
The precipitation field
recorded in the PRISM data
set shows a rough
consistency with the
simulation. Not only is the
overall magnitude very similar,
but so is the geographical
distribution.
If anything, the coastal
blocking effect is more
apparent in the PRISM data.
SANTA ANAS
A view of the Santa
Anas from space,
taken by the Multiangle Imaging
SpectroRadiometer
(MISR) on February
9, 2002.
The winds simulated by
the model during the
Santa Ana event of
February 9-12, 2002.
Note the intense flow,
reaching speeds on the
order of 10 meters per
second, being channeled
through mountain
passes.
The fact that the model
simulates this and other
Santa Ana events with
the correct timing
demonstrates that the
conditions for Santa Ana
events are contained
within the boundary
condition information
provided to the model.
Cluster Analysis
To classify the regimes of wind variability in Southern
California, we performed a probabalistic cluster analysis
algorithm (Smyth et al. 1999) on the October to March dailymean winds.
The clustering technique provides an quantitative means of
defining preferred modes of wind variability.
We chose to focus on the wet season because of the
interesting combination of phenomena (Santa Ana events
and precipitation) during this period.
We found that the wind regimes can be well-described in
terms of three clusters, which together account for 82% of
the days.
10 m winds associated with the Santa Ana regime. Speeds greater than 6 m/s are shaded.
The “Santa Ana” regime is characterized by intense offshore flow
through mountain passes. Its average duration is 1.7 days and it
accounts for 13% of the total days in the analysis. The days
belonging to this cluster coincide with extreme dryness.
PNA
These are the primary modes
of pressure variability during
when the entire SiberianPacific-North American sector
is considered.
They are revealed by EOF
analysis of 500 hPa NCEP
reanalysis data.
These correspond with known
modes of variability.
For
example, the top pattern
corresponds to the Pacific -North American pattern (PNA).
probability of occurrence
Here is the chance of occurrence of
the Santa Ana wind regime when the
normalized magnitude of the various
large-scale modes is large, either in
the positive or negative phase.
The positive phase of PC4 is slightly
more likely to be associated with the
Santa Anas than its negative phase or
the positive and negative phases of
the other modes.
However, the overall impression is
that the large-scale modes have very
little predictive power of the local
Santa Ana regime. (Conil et al. 2005)
Therefore, local modes of variability
are related in a non-trivial way to
larger-scale forcing.
This is the sea level pressure pattern associated with the Santa Ana
mode. It consists of a localized high over the Great Basin.
Why is this pattern so important for Southern California?
10 m winds
associated with
the Santa Ana
regime.
6 km resolution
54 km resolution
A Local Perspective on Climate
We show in our examination of
(1) The amplitude of the diurnal cycle
(2) The geographical distribution of precipitation
(3) The Santa Ana mode of wind variability
that spatial and temporal climate variability in this mountainous region
is either generated by in situ by local processes, or related in a nontrivial way to the large-scale forcing.
__________________________
To predict mountain climate change on spatial scales relevant for
humans and ecosystems, we must understand these local processes
and the complex relationships of large-scale forcing to local variability.