Katrina Grantz
CVEN 6833
Hydroclimatology
4 May 2004
Determining Dominant Modes of Variability in Summertime
Precipitation over the Southwestern United States
1. Motivation
The North American Monsoon (also known as the Southwest, Arizona, or
Mexican Monsoon) is the large-scale atmospheric circulation system that drives the
dramatic increase in rainfall experienced in the desert southwest US and northwestern
Mexico during the summer months of July and August. These summer thunderstorms
typically last until mid-September and can account for as much as 50-70 percent of the
annual precipitation in the arid region (Carleton et al., 1990, Douglas et al., 1993;
Higgins et al., 1997; Mitchell et al., 2002; Sheppard et al., 2002). The variability of this
important moisture source is of particular concern for watershed managers, ranchers, and
planners of southwestern North America. Too little summer rainfall has negative
agricultural and environmental impacts, while heavy summer thunderstorms present the
danger of flash floods. Predicting the variability in the strength and location of
monsoonal precipitation is understandably very important for local communities.
2. Background
The North American monsoon has been studied for well over a century—most
extensively in the recent decade. Recent projects, such as the South-West Area Monsoon
Project (SWAMP) and the North American Monsoon Experiment (NAME) project have
contributed and continue to contribute greatly to the general body of knowledge. The
principal motivation behind the studies has been to understand monsoonal variability.
Scientists have focused on the physical mechanisms (both synoptic and topographic) as
well as the spatial and temporal variability of these summer rains.
The seasonal shift in the winds that bring in monsoonal moisture depends
primarily upon the relative location of the subtropical ridge during the summer months.
The subtropical jet is a westerly wind stream located between 20° and 50° latitude. Three
main pressure centers (a weak high pressure center in the four-corners region of the US, a
thermal low pressure area along the Colorado River valley, and the Bermuda high
pressure center off the southeastern coastline of the US) direct the subtropical jet in a
sinuous motion as it crosses the North American land mass. These pressure centers
produce a ridge over the intermountain western US and the trough along the west coast of
the US. The subtropical ridge typically migrates northward during the summer months.
Several studies have shown that a more northward displacement of the subtropical ridge
is associated with a wetter monsoon over the southwestern US. In years when the ridge
stays in a more southerly position, the transport of tropical moisture is inhibited.
(Carleton 1986; Carleton et al., 1990; Adams and Comrie, 1997; Comrie and Glen, 1998;
Ellis and Hawkins 2001; Hawkins et al., 2002) In the summer months, the three pressure
centers act to draw lower and middle atmospheric moisture from the south northward,
producing a pattern of increased lower-atmospheric moisture which stretches from the
western coastline of Mexico northward into the southwestern US.
For several decades scientists have debated whether North American monsoonal
moisture comes from the Gulf of Mexico or from the Gulf of California. Determining the
source of monsoonal moisture is particularly important for prediction purposes. The
general consensus today is that while the Gulf of California and eastern Pacific provide
the majority of total monsoonal moisture, the Gulf of Mexico also introduces an
important component. Many recent studies show that low-level moisture comes
primarily from the Gulf of California ant that upper-level moisture is transported from the
Gulf of Mexico. It is believed that southerly and southwesterly winds advect moisture
northward from the Gulf of California and it is orographically uplifted by the Sierra
Madre Occidental where it mixes with the high-level moisture from the Gulf of Mexico
(Carleton, 1986).
The complex nature of the moisture source and transport mechanism, together
with extremely varied topography in the region, make it extremely difficult to understand
the variability of the North American monsoon. Summer precipitation in the monsoonal
region varies spatially as well as temporally. Regionally, the intensity of the North
American monsoon decreases as one moves northward of the Sierra Madre Occidental.
Not only is the intensity of the monsoon much weaker in Arizona, New Mexico, and
southern Colorado, but the variability of the monsoon is also much larger in these
regions. While northwestern Mexico experiences shower activity almost every day
during the monsoon season, daily precipitation in southwestern US is much less
predictable. Throughout the NA monsoon region, regional-scale variability depends
heavily upon the penetration of moisture into the interior regions (Adams and Comrie,
1997). Yet, due to the nature of thunderstorms, there is also considerable local-scale
variability associated with the NA monsoon. Not surprisingly, McDonald (1956) found
interstation correlations in Arizona to be very small to moderate.
Temporal variability of the North American monsoon ranges from diurnal to
seasonal, to interannual, to interdecadal. On an intra-seasonal scale, particularly the
northern parts of the monsoon region experience wet and dry spells within a monsoon
season. This is likely related to the gulf surge phenomenon described by Hales (1972)
and Brenner (1974), however no study has directly quantified the relationship (Adams
and Comrie, 1997). Carleton (1986, 1987) demonstrated that periods of convective
activity across the southwestern US are associated with passing upper-level troughs in the
westerlies. Also, as stated earlier in this paper, the position of the subtropical ridge
significantly affects convective activity. The position of the ridge can shift within a
monsoon season, bringing more convective activity to the north when it shifts northward
and less convective activity when it shifts southward.
Interannual variability is presumed to result from variability in certain synopticscale patterns as well as variability in the initial conditions of the landmass. Carleton et
al. (1990) suggest that shifts in the subtropical ridge are also responsible for interannual
variability. They argue that the position of the subtropical ridge is related to the phase of
the PNA. A positive (negative) PNA pattern in winter is typically followed by a
northward (southward) displacement of the subtropical jet and a wet (dry) summer
monsoon. While the PNA pattern is related to ENSO, no direct link between ENSO and
the position of the subtropical ridge has been established.
In addition to atmospheric circulation, researchers have studied the link between
monsoonal rainfall and SSTs in the Pacific Ocean and Gulf of California. Evidence
suggests that anomalously cold SSTs in the northern Pacific and anomalously warm SSTs
in the subtropical northern Pacific contribute to a wetter and earlier monsoon season.
(Higgins and Shi, 2000; Mo and Paegle, 2000) Mitchell et al. (2002) determined certain
threshold SST values for the northern Gulf of California that are associated with the
regional onset of the North American monsoon. Gao et al. (2002) suggest that because
the North American monsoon is strongly modulated by SSTs, use of the higher resolution
MODIS data set (rather than the Reynolds SST data) may improve monsoon modeling
efforts. Though the correlation between Pacific SSTs and the NA monsoon is clear, Mo
and Peagle (2000) argue that SSTs in equatorial Pacific alone are not sufficient to explain
monsoon rainfall variability.
Many researchers believe that land surface conditions play an extensive role in the
onset and intensity of the North American monsoon. Within a monsoon season,
increased soil moisture impacts evapotranspiration between storm events, thus enhancing
future storm systems and precipitation. (Xu and Small) On an interseasonal scale,
several studies have demonstrated the inverse relationship between winter precipitation,
particularly snowfall, and subsequent summer precipitation (Gutlzer, 2000; Higgins and
Shi, 2000; Lo and Clark, 2002; McCabe and Clark, 2003). This relationship is thought
result from snowfall acting as an energy sink. Greater amounts of snowfall in winter
require more energy to melt and evaporate the moisture by summer. Larger snow cover
areas also increase the albedo in spring, thus reinforcing the relationship. The resulting
delayed and decreased warming of the North American landmass upsets the land-ocean
heating contrasts necessary for monsoonal circulation patterns, thus delaying and
decreasing the intensity of the North American monsoon. The relationship between
winter snowfall and monsoonal precipitation, however, appears to depend on the relative
strength of the monsoon. McCabe and Clark (2003) suggest that an overall increasing
trend in monsoonal precipitation in New Mexico over the past 70 years is responsible for
the increasing relationship with antecedent winter precipitation. This trend coincides
with a general decrease in monsoonal precipitation in Arizona and decrease in the
correlation with antecedent winter precipitation.
Though not directly related with the North American monsoon, Singhrattna and
Rajagopalan (2003) demonstrate that the strength of the Indian and Thai monsoon could
be related to the position of heating in the tropics. Years that exhibit anamolous heating
in the eastern tropical Pacific tend to produce a stronger monsoon in Thailand and weaker
monsoon in India, and vice-versa for the years in which heating is closer to the date line.
A similar relationship with the North American monsoon is not evident in the literature.
3. Proposed Research
The primary objective of this study is to determine the dominant modes of spatial
and temporal variability associated with summertime precipitation in the southwestern
United States. By gaining this understanding, we hope to eventually develop predictors
for use in a forecasting model. We investigate relationships between the dominant modes
and antecedent and concurrent atmospheric and climatic variables to gain a better
understanding of the state of system. We also explore the proposed spatial shift in the
strength of the monsoon over Arizona and New Mexico and the potential relationship
with the location of heating in the tropics in warm ENSO years. By investigating
correlations and composites, we hope to propose physical explanations for the trends in
the system. Any trends in relationships and must be realized before predictors can be
developed for forecasting.
4. Data
Climate division data of monthly precipitation and temperature for the years
1930-2003 were obtained for Arizona and New Mexico. (www.cpc.ncep.noaa.gov) This
data set was chosen for its relatively long period of record and broad spatial coverage in
the area of interest. Data for years before 1930 were removed due to relative unreliability
in data quality.
Monthly atmospheric data for the period 1948-2002 were obtained from the
NCEP/NCAR reanalysis data set (Kalnay et al., 1996). Global sea level pressures (SLP),
sea surface temperatures (SSTs), 500mb geopotential height (Z500) fields, and vector
winds were analyzed. These data are available from the Climate Diagnostics Center web
page: www.cdc.noaa.gov.
5. Methods
This study focuses on the southwestern US monsoon as defined by the
July/August precipitation in Arizona and New Mexico. General trends in the mean and
variance of monsoonal precipitation are analyzed using a 20 –year moving window.
Results from McCabe and Clark (2003) indicate that trends in the mean and variance
differ between the New Mexico and Arizona and between July and August. We therefore
perform the moving window analysis separately for each state and for each month.
Principal component analysis (PCA) is performed to isolate the dominant modes
of spatial and temporal variability. We first perform the analysis on the entire region
(Arizona and New Mexico) to determine the spatial loadings of the leading principal
components (PCs). PCA is then preformed for each state and each month separately.
Correlations between the leading PCs and antecedent winter and spring precipitation and
temperature are computed. Finally, the leading PCs are correlated with global
atmospheric variables (i.e., SST, SLP, and Z500 heights).
A composite analysis is performed on atmospheric variables in warm ENSO years
pre-1980 and post-1980. Specifically, anomalies of SSTs, outgoing long-wave radiation
(OLR), and vector winds are analyzed.
6. Results
a. Monsoon Season
An analysis of the annual cycle of total precipitation in Arizona and New Mexico
(Figure 1.) demonstrates that the bulk of precipitation falls in July and August for both
states. We thus base all subsequent analysis of the southwestern US monsoon on the
months of July and August. It is recognized that September precipitation may compose a
significant portion of the total monsoonal precipitation. However, because the monsoon
season typically ends sometime in mid-September (Adams and Comrie, 1997), we choose
to restrict our study to months that are affected by the monsoon for their entire duration.
1.5
0.5
Precipitation (in)
Arizona Monthly Average Precipitation
2
4
6
8
10
12
Month
1.5
0.5
Precipitation (in)
New Mexico Monthly Average Precipitation
2
4
6
8
10
12
Month
Figure 1. Total monthly precipitation in Arizona (top) and New Mexico (bottom)
b. Non-stationarity in mean and variance of precipitation
Trends in the 20-yr mean precipitation for Arizona and New Mexico are shown in
figure 2. Overall, monsoonal precipitation in New Mexico exhibits an increasing trend,
especially in August. July mean precipitation appears to have increased in the earlier part
of the record and remained constant in the later part of the record. The plot suggests that
mean precipitation in Arizona has a slightly decreasing trend in the later period for July
with a general decrease and then slight increase in August in the middle part of the
record. These results indicate that the mean monsoon precipitation in New Mexico has
been increasing over the past 70 years, but that precipitation in Arizona has not
experienced as dramatic of a trend over the same period of record. The variance of
summertime precipitation in New Mexico and Arizona (Figure 3) does not exhibit a
significant trend, with the possible exception of a decreasing trend in the later part of the
record in Arizona in August. These results generally corroborate results presented by
McCabe and Clark (2003).
Mean Precipiation over 20-year moving window
Precipitation (in)
3
2.5
New Mexico
July
New Mexico
August
Arizona July
2
1.5
1
Arizona
August
0.5
0
1940
1960
1980
2000
Year (center of 20-yr window)
Figure 2. Mean Precipitation in Arizona and New Mexico over a 20-year moving window
Variance
Variance of Precipitation over 20 yr moving window
1.4
1.2
New Mexico
July
1
0.8
0.6
0.4
New Mexico
August
0.2
0
1940
Arizona
August
Arizona July
1960
1980
2000
Year (center of 20-yr window)
Figure 3. Variance in Precipitation in Arizona and New Mexico over a 20-year moving
window
c. Principal Component Analysis
The dominant modes of variability are determined through a principal component
analysis of the entire region. The first PCs in both July and August capture over 50
percent of the total variance, while the second PC captures roughly 20 percent of the total
variance (figure 4). Correlations between PC1 and the average precipitation for the
region are .89 and .87 for July and August, respectively. The loadings for PC1 (figure 5)
exhibit the same sign and have little variability across the region. This relatively high
correlation with average precipitation and minimal variability suggests that PC1
represents the covariance of monthly precipitation among the climate divisions in
Arizona and New Mexico. The spatial pattern of PC2 (figure 6) shows positive loadings
in New Mexico and negative loadings in Arizona, corroborating results presented by
McCabe and Clark (2003) and Hu and Feng (2002). These results indicate that 20
percent of variance in New Mexico and Arizona monsoonal precipitation is characterized
by an inverse relationship between the two states.
0.4
0.2
0.0
% variance explained
Variance of July PCs
2
4
6
8
10
12
14
PC
Variance of Aug PCs
6
8
10
12
35
37
4
14
31
2
33
Longitude
0.4
0.2
0.0
% variance explained
Spatial Loadings of PC1 (July)
PC
-114
-112
-110
-108
-106
-104
-102
Figure 4. Percent of variance explained for New Mexico/
Arizona joint analysis
Latitude
Spatial Loadings of PC1 (Aug)
Spatial Loadings of PC1 (Aug)
-108
-106
-104
-102
37
35
-114
31
-110
33
35
33
31
33
-112
31
-114
Longitude
35
Longitude
37
Longitude
Spatial Loadings of PC1 (July)
35
33
31
Longitude
37
37
Spatial Loadings of PC1 (July)
Latitude
-112
-110
-108
-106
-104
-102
Latitude
-114 -112 -110 -108 -106 -104 -102
-114 -112 -110 -108 -106 -104 -102
Figure 5. Spatial loadings of PC1 New Mexico/Arizona joint
analysis
Latitude
37
Spatial Loadings of PC1
(Aug)
Latitude
Spatial Loadings of PC2 (Aug)
-108
-106
-104
-102
35
31
-110
33
35
33
Longitude
-112
31
-114
Longitude
37
37
35
33
31
Longitude
Spatial Loadings of PC2 (July)
Latitude
-114 -112 -110 -108 -106 -104 -102
Latitude
-114 -112 -110 -108 -106 -104 -102
Latitude
Figure 6. Spatial loadings of PC2
Because a significant portion of variance (from PC2) is distinctly different for
Arizona and New Mexico, separate principal component analyses were conducted for
each state. The total percent of variance explained by the leading PCs is significantly
higher when the states are analyzed separately. PC1 explains over 70 percent of the
variance in Arizona and over 60 percent of the variance in New Mexico. PC2 explains
roughly 10 percent of the variance in Arizona and 20 percent in New Mexico. (See
figure 7.) The two leading PCs capture over 80 percent of the variance and are
considered to represent signal while the remaining PCs represent noise.
3
4
5
6
7
0.0 0.1 0.2 0.3 0.4 0.5 0.6
8
1
2
3
4
5
6
7
PC
AZ July Variance of PCs
AZ August Variance of PCs
0.4
% variance explained
0.4
0.0
0.2
0.0
8
0.6
PC
0.2
% variance explained
% variance explained
2
0.6
1
% variance explained
NM August Variance of PCs
0.0 0.1 0.2 0.3 0.4 0.5 0.6
NM July Variance of PCs
1
2
3
4
PC
5
6
7
1
2
3
4
5
6
7
PC
Figure 7. Percent of variance explained by PCs
The first PCs correlate very highly with the spatially averaged precipitation of the
corresponding region and month. (Correlation coefficients range between -.99 and -1.)
PC1, therefore, is assumed to represent the covariance of monthly precipitation among
the climate divisions in each state.
To investigate the proposed inverse relationship between North American
monsoonal precipitation and antecedent precipitation, we correlate PC1 with the previous
winter’s and spring’s precipitation in a moving 20-year window (figure 8). The moving
window is used to identify any trends in this relationship. The same analysis is
performed using antecedent air temperature instead of precipitation (figure 9). Based on
a standard t-test, correlations must exceed .43 to be considered statistically significant. In
general, winter shows more statistically significant correlations than spring, and
precipitation more than air temperature. The correlations with winter precipitation in
New Mexico are stronger in the later period, though this is probably due to the increase in
the strength of the monsoon during this period. Correlations for Arizona, conversely,
decreased in the later period. The temperature results indicate almost no statistically
significant correlations. This result is surprising given that air temperature should
fluctuate with winter precipitation. Perhaps the air temperature over the entire western
US would provide a better relationship than the regional temperature.
New Mexico Spring Precipitation
Correlated with Monsoon Precip (PC1)
0.6
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
1920
July
August
1940
1960
1980
Correlation Coeff
Correlation Coeff
New Mexico Winter Precipitation
Correlated with Monsoon Precip (PC1)
0.4
0.2
-0.2
-0.4
-0.6
1920
2000
Year (center of 20-yr window)
1940
1960
1980
2000
Year (center of 20-yr window)
Arizona Winter Precipitation Correlated
with Monsoon Precip (PC1)
Arizona Spring Precipitation Correlated
with Monsoon Precip (PC1)
0.6
0.8
0.6
0.4
0.2
0
-0.2
July
August
-0.4
-0.6
1920
1940
1960
1980
Correlation Coeff
Correlation Coeff
July
August
0
0.4
0.2
July
August
0
-0.2
-0.4
-0.6
1920
2000
Year (center of 20-yr moving
window)
1940
1960
1980
2000
Year (center of 20-yr window)
Figure 8. Correlations between PC1 and antecedent winter (DJF) and spring (MAM)
total precipitation in 20-year moving windows.
New Mexico Spring Temperature
Correlated with Monsoon Precip
0.4
0.6
0.3
0.4
0.2
0.2
July
0.1
August
0
-0.1
Correlation Coeff
Correlation Coeff
New Mexico Winter Temperature
Correlated with Monsoon Precip
1940
1950
1960
1970
1980
1990
2000
July
August
1940
1960
1980
2000
1940
1960
1980
2000
Arizona Spring Temperature Correlated
with Monsoon Precip
Correlation Coeff
Correlation Coeff
Arizona Winter Temperature Correlated
with Monsoon Precip
Year (center of 20-yr window)
August
Year (center of 20-yr window)
Year (center of 20-yr window)
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
1920
July
-0.4
-0.6
-0.8
-1
1920
-0.2
-0.3
1930
0
-0.2
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
1920
July
August
1940
1960
1980
2000
Year (center of 20-yr window)
Figure 8. Correlations between PC1 and antecedent winter (DJF) and spring (MAM) air
temperature in 20-year moving windows.
The driving mechanisms of PC2 are not as straightforward as those for PC1.
Because PC1 represents the basic climatology of the area, it seems reasonable that PC2
represents some larger, slowly varying mechanism. It is hypothesized that factors related
to PC2 may be responsible for the eastward shift in the strength of the monsoon.
Correlations between the Pacific Decadal Oscillation index and PC2, however, are not
statistically significant. Correlations with the Pacific North American (PNA) index are
statistically significant only in the later period of record for both New Mexico and
Arizona in August (but not July). Previous studies (e.g., Carleton 1986; Carleton et al.,
1990; Adams and Comrie, 1997; Comrie and Glen, 1998; Ellis and Hawkins 2001;
Hawkins et al., 2002) have shown that shifts in subtropical jet (related to the PNA
pattern) significantly affect the strength of the North American monsoon. It is thus
recognized that more general variables, such as Z500 height fields, may provide more
information than the PNA index.
d. Correlations with Global Variables
Figures 9, 10, and 11 show the correlations between PC1 and global gridded
anomalies of SLP, SST, and Z500 height fields, respectively. Figures 12, 13, and 14
show the same for PC2. All plots show regions of statistical significance (i.e., greater
than 0.23). Correlation patterns for July are generally stronger than those for August,
indicating that anomalies earlier in the monsoon season may play a larger role in overall
monsoon variability. Patterns are generally similar for PC1 and PC2, though the relative
strengths of the correlations vary. The SLP correlation patterns indicate that variability in
New Mexico is slightly more dominated by SLPs than Arizona, though this could be due
to the relative strength of the monsoon in each state. Correlations with SLPs are slightly
higher for PC1 than for PC2, with strong areas of correlation lying in the mid-Pacific
region. SST correlation patterns indicate that SSTs along the western coast and the
northern Pacific may be related to monsoonal precipitation. Tropical SSTs do not appear
to as strongly correlated with monsoonal precipitation. The Z500 height fields,
particularly with the PC2 for New Mexico, show a striking similarity to the PNA pattern,
though shifted slightly southward. This correlation makes sense, given the relationship
between monsoon strength and the location of the subtropical jet. Given that large-scale
atmospheric circulation patterns generally persist for several months, these correlations
could potentially provide useful predictors to monsoonal precipitation.
Figure 9. Correlations between SLP anomalies and PC1 in Arizona (left) and New
Mexico (right) in July (top) and August (bottom)
Figure 10. Same as figure 9, except with SST
Figure 11. Same as figure 9, except with Z500 height fields
Figure 12. Correlations between SLP anomalies and PC2 in Arizona (left) and New
Mexico (right) in July (top) and August (bottom)
Figure 13. Same as figure 12, except with SST
Figure 14. Same as figure 12, except with Z500 height fields
e. Composite Analysis
The eastward shift in the magnitude of monsoon precipitation is evidenced in the
composite maps of OLR pre-1980 and post-1980 warm ENSO years (figure 15). In the
pre-1980 period negative OLR anomalies in western monsoon region suggest cooler
temperatures and more rain. Conversely, positive OLR anomalies in eastern monsoon
region indicate less rain in the pre-1980 period. In the post-1980 period the negative
OLR anomalies (indicating more precipitation) moved eastward to be centered more
toward NM. The shift in July appears to be more dramatic (in the spatial degree of the
shift) than the August shift (figure 16). Composites of vector winds show that more air,
and hence more moisture, moved into Arizona in the earlier period and into New Mexico
in the later period (figure 17). It is also interesting to note the direction from which the
wind is moving. In the pre-1980 period the wind appears to originate primarily in the
Gulf of Mexico and move into Arizona. In the post-1980 period the wind appears to
originate predominantly from the Gulf of California. These results could add information
to the age-old debate regarding the source of North American monsoonal moisture.
Figure 15. OLR in pre-1980 (left), post-1980 (center), and pre- minus post-1980 (right)
warm ENSO years
Figure 16. OLR in pre- minus post-1980 warm ENSO years in July (left) and August
(right)
Figure 17. Same as figure 15, except for vector winds
7. Summary and Conclusions
Results from this study corroborate the results of McCabe and Clark (2003) that
there is an increasing trend in summer precipitation over new Mexico and a slightly
decreasing trend in summer precipitation over Arizona. No significant correlations were
found with the previous winter/spring air temperature, however, these results may be due
to the relative small region for temperature data. North American monsoonal
precipitation was found to vary inversely with antecedent winter precipitation. The
relationship with antecedent spring precipitation was not as strong.
Correlations with large-scale atmospheric variables indicate that monsoonal
precipitation may be more dominated by large-scale patterns set up early in the monsoon
season. SSTs, SLPs, and Z500 height fields all exhibit statistically significant
correlations with monsoonal precipitation, slightly more so in New Mexico. The Z500
height fields show patterns similar to the PNA pattern The general persistence of these
patterns suggests that these correlations could potentially provide useful predictors to
monsoonal precipitation.
The eastward shift in the strength of the monsoon is evidenced through composite
maps of OLR and vector winds. The dominant source of precipitation may also have
shifted more from the Gulf of Mexico in pre-1980 warm ENSO years to more from the
Gulf of California in post-1980 warm ENSO years. It is unclear whether this shift is
related to the relative position of heating in the tropics.
Clearly, the North American monsoon system is very complex. While several
trends and relationships have been identified in this study, these relationships are not
fully understood. These processes need to be better understood to develop predictors and
advance modeling and forecasting efforts. Suggestions for extension of this study
include using CMAP data to include analysis of NW Mexico (the “epicenter” of the
monsoon). CMAP data is limited, however, only dates back to 1979, trends may not be
as discernable as those in this study. A joint PCA on Arizona and New Mexico together
would provide more insight into the relative loadings between New Mexico and Arizona.
One point correlations between SWE over the entire western US and PC1 could add more
information to the antecedent winter precipitation hypothesis. It is also important to
further explore the relationship between the location of heating in the tropics and the
strength and location of the monsoon. Finally, correlations with variables from previous
months could provide useful predictors for implementation in a forecasting model.
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