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VOLUME 128
Intraseasonal Modulation of Summer Precipitation over North America
KINGTSE C. MO
Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, Maryland
(Manuscript received 5 May 1999, in final form 19 July 1999)
ABSTRACT
The intraseasonal rainfall variability over North America is examined using singular spectrum analysis (SSA)
and composites of outgoing longwave radiation anomalies (OLRAs), 200-hPa divergence and a gridded rainfall
dataset over the United States. The evolution of the Arizona and New Mexico (AZNM) monsoon based on
composites indicates that rainfall anomalies propagate eastward from the North Pacific through AZNM, the
Great Plains, to the eastern United States. During summer, the wet and dry periods of the AZNM monsoon are
modulated by an oscillatory mode with a period of 22–25 days (22-day mode). This is also the dominant mode
associated with rainfall events over the Great Plains. The influence of the Madden–Julian Oscillation (MJO) on
the AZNM monsoon is secondary. The strongest impact of the MJO is on precipitation over Mexico. SSA
performed on the 200-hPa divergence and OLRAs averaged over Mexico show only one oscillatory mode with
a period of about 36–40 days.
The 22–25-day mode also exists in the vertically integrated moisture fluxes over the Great Plains. During the
wet periods of the AZNM monsoon, more moisture is transported from both the Gulf of Mexico and the Gulf
of California to AZNM. The situation reverses when the oscillation reaches the other phase. The 22-day mode
is linked to tropical convection. When rainfall associated with the 22-day mode travels eastward from AZNM
to the Great Plains, the OLRA composites show westward propagating waves just north of the equator. When
enhanced convection reaches the western Pacific, rainfall diminishes over AZNM. When convection in the
western Pacific is suppressed and enhanced convection is located in the central Pacific, rainfall intensifies over
AZNM.
1. Introduction
During the Northern Hemisphere (NH) summer, the
dominant rainfall regime over the United States is characterized by an inverse relationship between rainfall
anomalies over the Southwest including Arizona and
New Mexico (AZNM) and anomalies over the Great
Plains (Mo et al. 1997; Higgins et al. 1997). This rainfall
dipole pattern is partially explained by the low-level
moisture transport. The low-level jet (LLJ) (Wang and
Paegle 1996; Helfand and Schubert 1995) over the Great
Plains transports moisture from the Gulf of Mexico to
the central United States and moisture flux convergence
downwind from the jet core enhances rainfall. When the
LLJ transports more moisture to the central United
States, less moisture is transported to AZNM. That implies wetness over the Great Plains and dryness over
AZNM. In addition to the moisture supply from the Gulf
of Mexico, rainfall over AZNM is also supported by
moisture sources from the Gulf of California (Adams
Corresponding author address: Kingtse Mo, Climate Prediction
Center, NCEP/NWS/NOAA, 5200 Auth Rd., Camp Springs, MD
20746.
E-mail: [email protected]
and Comrie 1997). Many studies examined the influence
of boundary forcing and soil moisture on summer rainfall regimes and the role played by the LLJ on interannual timescales (Ting and Wang 1997; Lau and Peng
1992; Trenberth and Branstator 1992). However, there
is little emphasis on the intraseasonal rainfall variability.
During winter, the rainfall regimes over the Southwest
are modulated by two modes in the intraseasonal band
(Mo 1999). One mode has a spectral peak near 40 days
and is associated with the Madden–Julian Oscillation
(MJO) (Madden and Julian 1972, 1994). The other oscillatory mode has a period of 22–28 days (22-day
mode). The composites of outgoing longwave radiation
anomalies (OLRAs) associated with this 22-day mode
reveal that cloud bands propagate northward from the
eastern Pacific just north of the ITCZ, through California
to the Pacific Northwest. In the Tropics, OLRAs propagate westward from the central Pacific through the
western Pacific to the Indian Ocean.
During the NH summer, the MJO is known to modulate the Indian monsoon onset and the Asian Mei-yu
development (Lau and Chan 1986). In addition to the
MJO, a 10–20-day mode was found in the westward
propagating waves associated with the Indian monsoon.
Together with the MJO, they determine the monsoon
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ridge positions and regulate the monsoon wet and dry
periods (Krishnamurti et al. 1985).
The present paper examines the impact of intraseasonal oscillations on the summer precipitation regimes
over North America. Singular spectrum analysis (SSA;
Vautard and Ghil 1989; Vautard et al. 1992) is used to
determine the oscillatory modes. The same method was
used by Mo (1999) to study the winter precipitation
regimes in the Southwest. Evidence will show that the
intraseasonal mode with a period of 22–25 days (22day mode) regulates the summer precipitation regime.
The life cycle of the summer 22-day mode is documented and its linkages to the Tropics are discussed.
The datasets and procedures used are described in section 2. Evidence that intraseasonal oscillations modulate
precipitation over AZNM and the Great Plains is presented in section 3. The 22-day mode is examined in
section 4. Conclusions are given in section 5.
2. Data and procedures
The data used in this study were global gridded analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research 40yr reanalysis (Kalnay et al. 1996). The data are on a
2.58 lat 3 2.58 long grid and cover the period from 1
January 1968 to 31 December 1997. Daily averages of
the National Oceanic and Atmospheric (NOAA) satellite
outgoing longwave radiation (OLR) data (Liebmann and
Smith 1996) were used as a proxy for tropical convection. The OLR data cover the period from 1 January
1979 to 31 December 1997.
The seasonal cycle at each grid point is defined as
the grand mean plus the first and second harmonics with
periods of 12 and 6 months, respectively. The difference
between the field and the seasonal cycle is defined as
the anomaly at that grid point. To obtain the intraseasonal signal, data were filtered using the minimum bias
window developed by Papoulis (1973) to retain periods
in the range of 10–90 days.
Over the United States, daily observed precipitation
derived from gridded hourly station data (Higgins et al.
1996) was used to obtain precipitation composites. The
data are on a 2.08 lat 3 2.58 long grid covering the
period from 1963 to 1995. Daily mean climatology was
obtained for the entire period and was smoothed using
a 7-day running average. Daily precipitation anomalies
are defined as departures from the smoothed mean daily
climatology.
SSA was used to determine oscillatory modes in time
series. SSA is basically a statistical technique related to
empirical orthogonal function (EOF) analysis, but in the
time space (Vautard and Ghil 1989; Vautard et al. 1992).
Quasi-periodic signals appear as pairs of degenerate eigenmodes and their corresponding eigenfunctions in the
time domain (T-EOFs) are in quadrature with each other.
This means that the maximum correlation between the
two T-EOFs is higher than 0.9 and the lag at which the
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maximum occurs is about ¼ of the period. The original
time series can be projected onto T-EOFs to obtain principal components in the time domain (T-PCs). The same
procedure was used to study the intraseasonal modulation of the winter precipitation regimes over the western region of the United States (Mo 1999). For details,
readers are referred to Vautard and Ghil (1989). A window length of 61 days was used to highlight oscillations
on intraseasonal timescales. Results are not sensitive to
the particular window length used. The SSA modes are
not pure sines and cosines. The dominant periods of
T-PCs were estimated using a Blackman–Tukey analysis
with a bandwidth of 0.0074.
3. Intraseasonal modulation of precipitation over
the United States
Rainfall does not obey a normal distribution and data
are not long enough to determine a rainfall distribution
function needed to compute the covariance matrix for
SSA. OLRAs may not be a good representation of rainfall in midlatitudes. Therefore, the 200-hPa divergence
field is used in this study. An example is given in Fig.
1. It shows the 200-hPa divergence and rainfall anomalies averaged from 328 to 368N for June–September
1979. Both fields were smoothed by a 7-day running
average. Positive (negative) rainfall anomalies correspond to anomalous divergence (convergence). In June
and July 1979, both fields show stationary and eastwardmoving components. The eastward propagation of
anomalies was stronger and more regular in August and
September. Negative (positive) anomalies over the central United States (808–1008W) are often accompanied
with positive (negative) anomalies over the Southwest
(1008–1108W). The average interval between wet periods (anomalous divergence) over the Southwest is
roughly about 28 days
To examine intraseasonal variability, the AZNM
D200 index was constructed by averaging the 10–90day-filtered 200-hPa divergence over AZNM (107.58–
112.58W, 328–368N). The rainfall and OLRA indices
were constructed the same way. The rainfall index was
smoothed by a 7-day running average, but it was not
10–90-day filtered. To study rainfall events over the
Great Plains, the Great Plains indices for OLRAs, 200hPa divergence, and rainfall anomalies were formed by
averaging the anomalies over the area (858–1058W, 328–
408N).
Positive and negative events were selected according
to the threshold criterion. The standard deviation of the
AZNM D200 index for June–September (JJAS) was
computed. A positive event starts when the index is
above 1.2 standard deviations. That date is defined as
the onset date. The event ends when the index drops
below the threshold. Negative events can be selected
the same way.
Lagged composites of the 10–90-day-filtered 200-hPa
divergence and rainfall anomalies over the United States
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FIG. 1. (a) Time–longitude of cross section (Hovmöller diagram) of daily rainfall anomalies averaged from 328 to 368N for Jun–Sept 1979.
Anomalies are smoothed by a 7-day running average. Contour interval is 1.6 mm day 21 . Zero contours are omitted. Contours 20.8 and 0.8
mm day21 are added. Positive values are shaded. (b) Same as (a) but for daily 200-hPa anomalous divergence. Contour interval is 2 3 1026
s21 .
were computed for positive and negative events for JJAS
from 20 days before to 20 days after onset. The evolution of the 200-hPa divergence difference between
positive and negative events (Fig. 2) shows that a threecell pattern propagates eastward from the North Pacific
through AZNM, the Great Plains, to the eastern United
States. Anomalies over AZNM are negative at day 210.
They move eastward slowly from day 210 to day 26,
but the speed increases after day 26. Positive anomalies
are located over AZNM at day 22 and reach a maximum
at day 2. After day 2, negative anomalies move from
the west coast of California to AZNM, while positive
anomalies move toward the eastern United States. At
day 10, negative anomalies are located over AZNM.
The wet and dry cycle of the AZNM monsoon rainfall
estimated from the 200-hPa divergence composites is
on average about 20–28 days. These findings can be
summed up in a time-longitude cross section (Hovmöl-
ler diagram) of the 200-hPa divergence difference between positive and negative events averaged from 328
to 368N (Fig. 3b). It has both stationary and eastward
moving components. Anomalies propagate from the
Southwest to the eastern United States and there is a
phase reversal between anomalies over AZNM and the
Great Plains.
The above results are confirmed by the rainfall composites keyed to the same AZNM D200 index (Fig. 3a).
There is a good correspondence between positive (negative) rainfall anomalies and the 200-hPa anomalous
divergence (convergence). The resemblance between
the rainfall and 200-hPa divergence differences suggests
that the 200-hPa divergence is a good representation of
the low-frequency rainfall signal.
The above results are reproduced in a longer rainfall
dataset (Higgins et al. 1996) covering the period from
1963 to 1995. For each season, the seasonal mean anom-
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FIG. 2. Map sequence of the 200-hPa divergence composite difference between positive and negative events keyed to the AZNM D200
index for (a) day 210, (b) day 26, (c) day 22, (d) day 2, (e) day 6, and (f ) day 10. Contour interval is 1 3 1026 s21 . Zero contours are
omitted. Contours 20.5 3 1026 s21 and 0.5 3 1026 s21 are added. Areas where positive (negative) values are statistically significant at the
95% level are shaded dark (light).
aly from June to September was removed from rainfall
daily anomalies to concentrate on the intraseasonal
band. The AZNM rainfall index was formed by averaging rainfall anomalies over the same AZNM area. A
positive (negative) event starts when the AZNM rainfall
index is above 85th (below 15th) percentile. Composites
of rainfall and the 10–90-day filtered 200-hPa divergence were computed from 20 days before to 20 days
after the onset for summer. The time–longitude crosssection plots of difference fields (3c and 3d) averaged
from 328 to 368N should be compared with Figs. 3a and
3b. The magnitudes of anomalies are weaker because
the rainfall data are noisier. There are differences among
composites, but key features such as the eastward propagation of rainfall anomalies from AZNM to the Great
Plains and an inverse relationship between anomalies
over the two regions are well reproduced. In each case,
rainfall anomalies over AZNM change sign twice within
30 days. This suggests that rainfall over AZNM and the
Great Plains is modulated by intraseasonal oscillations.
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4. Oscillatory modes
a. SSA results
In this section, SSA was performed on the time series
of the AZNM D200 index to determine the periods of
oscillations. SSA determines EOFs in the time space
(T-EOFs). Then, the time series of the AZNM D200
index was projected on the selected T-EOFs to get the
corresponding T-PCs.
The leading mode has a period of 22–26 days, which
explains 30% of the variance of the AZNM D200 index.
The corresponding T-EOFs are given in Fig. 4a. This
mode is referred to as the 22-day mode. The second
mode explains about 22% of the variance of the AZNM
D200 index. The T-EOFs (Fig. 4b) are not pure sines
and cosines. They have a 40-day component, but the
corresponding T-PCs also show a weak but statistically
significant second peak at about 20 days. The absence
of the SSA mode with a period of 40–48 days indicates
that the MJO signal is weak. The next mode has a period
of 17 days (Fig. 4c). The 17-day mode explains about
16% of the variance. Three modes together explain
about 68% of the variance. SSA was also performed on
the AZNM OLRA index. The first six T-EOFs are reproduced. The first, second, and third pairs explain
about 26%, 21%, and 18% of the variance of the AZNM
OLR index, respectively. SSA results are consistent with
composites (Fig. 2). Both indicate that the dominant
mode is the 22-day mode. The MJO does not play an
important role in regulating the AZNM monsoon.
The time series corresponding to these leading modes
can be reconstructed based on T-PCs and their corresponding T-EOFs. The reconstructed time series for the
22-day mode (crosses) and the time series of the AZNM
D200 index (open circles) are displayed for selected
summers in Fig. 5. The summation of the first three
modes is also given (dark line). Figure 5 should be
compared with the daily AZNM rainfall index (Fig. 6).
The rainfall time series plotted are the 7-day running
means with the seasonal mean removed, but they are
not 10–90-day filtered. These summers are selected because of their strong intraseasonal oscillations.
Notice that the magnitudes of rainfall minima are not
as large as the magnitudes of rainfall maxima (Fig. 6).
This again indicates that rainfall does not follow a normal distribution. Because of different distribution functions, and smoothing used, the rainfall maxima and minima do not always coincide with the maxima and minima of the 200-hPa divergence index. However, there
is a good correspondence between the AZNM D200
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index and rainfall index overall. Anomalous divergence
(convergence) corresponds with a wet (dry) period. For
most years, the 22-day mode dominates the time series.
The first three modes capture the phase of the total index
well. The 22-day mode explains only about 30% of the
variance of the AZNM D200 index, which is not large.
However, for some years, it plays a role in regulating
the wet and dry periods of the AZNM monsoon.
SSA was performed on the D200 index for the Great
Plains. The T-EOFs are the same as those for the AZNM
index (Figs. 4a–c). The leading mode is the 22-day
mode, which explains about 26% of the variance of the
index in the 10–90-day band. This is expected because
of the inverse relationship of rainfall anomalies between
the two regions. SSA was also performed on the index
averaged over the Gulf of Mexico (858–1008W, 258–
308N). The leading mode is the 22-day mode. However,
the index over Mexico (97.58–1058W, 158–258N) shows
only one oscillatory mode and it has a period of about
36–40 days. The composites of the 30–60-day anomalies obtained by Knutson and Weickmann (1987) for
the extended summer (May–October) indicate that
OLRAs in the area of Central America and Mexico are
in phase with OLRAs in the western Pacific, but out of
phase with OLRAs in the central Pacific. Enhanced convection in the central Pacific produces enhanced Walker
circulation with the descending branch located over
Central America and Mexico. This may explain the
strong presence of the MJO over Mexico.
b. Composites
Composites of daily rainfall anomalies over the United States, the 10–90-day filtered 200-hPa divergence,
OLRAs, 200-hPa streamfunction anomalies, and the
vertically integrated moisture fluxes were produced for
JJAS based on the reconstructed time series of the
AZNM D200 index for the 22-day modes. The threshold
is again 1.2 standard deviations of the reconstructed time
series. Composites were obtained from 20 days before
to 20 days after onset. The statistical significance was
assessed by assuming that anomalies obey a normal distribution. There are about 330–350 maps in each composite. Areas where values are statistically significant
at the 95% level are shaded. Because rainfall maps are
only used for verification, no statistical significance is
given.
Figure 7 shows the evolution of the 200-hPa divergence difference keyed to the 22-day mode, which
should be compared to Fig. 2. The composite differences
←
FIG. 3. (a) Time–longitude of cross section (Hovmöller diagram) of daily rainfall composite difference averaged from 328 to 368N between
positive and negative events from 20 days before to 20 days after onset keyed to (a) the AZNM D200 index. Contour interval is 0.5 mm
day21 . Negative anomalies are shaded and zero contours are omitted. Contours 20.3 and 0.3 mm day21 are added. (b) Same as (a) but for
the 200-hPa divergence. Contour interval is 1 3 1026 s21 . Contours 20.5 3 1026 s21 and 0.5 3 1026 s21 are added. (c) Same as (a) but
keyed to the rainfall index for the period 1963–95. (d) Same as (b) but keyed to the rainfall index.
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FIG. 4. (a) T-EOFs 1 and 2, (b) T-EOFs 3 and 4, and (c) T-EOFs 5 and 6 based on SSA
analysis on the AZNM D200 index.
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FIG. 5. Reconstructed time series based on the AZNM 22 day mode (crosses), the sum of the
first three modes (dark line) and the AZNM D200 index (open circles) for the (a) 1979, (b) 1981,
and (c) 1995 summer. The unit is 1 3 1026 s21 .
keyed to the 22-day mode bear strong resemblance to
the differences keyed to the total AZNM D200 index.
The magnitudes of anomalies are comparable. This confirms the dominance of the 22-day mode. Both show
the eastward propagation of anomalies and a phase reversal between anomalies over AZNM and the Great
Plains. Figure 7 shows more regular oscillations and
less stationary components. These differences are due
to the presence of other modes in the total AZNM index.
They contribute to the composites keyed to the total
AZNM index (Fig. 2).
The time–longitude cross-section plots for rainfall
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FIG. 6. The 7-day running mean precipitation averaged over AZNM based on daily precipitation
analysis by Higgins et al. (1996) for the (a) 1979, (b) 1981, and (c) 1995 summer. The unit is
mm day21 .
anomalies and 200-hPa divergence differences are given
in Figs. 8a and 8b, respectively. There is a good correspondence between positive (negative) rainfall anomalies and the anomalous 200-hPa divergence (convergence). In comparison to the corresponding plots keyed
to the total AZNM D200 index (Figs. 3a and 3b), they
show more regular oscillations with a period of 22–25
days. The magnitudes of anomalies are comparable.
They all show the eastward propagation of anomalies,
and a phase reversal between anomalies over AZNM
and the Great Plains.
c. Low-level jet
One important feature associated with precipitation
over the central United States and AZNM is the LLJ
over the Great Plains (Bonner 1968). The LLJ can be
represented by the vertically integrated meridional
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FIG. 7. Same as Fig. 2 but keyed to the 22-day mode.
moisture transport (qv). SSA analysis performed on the
(qv) averaged over the Great Plains indicates that the
leading mode is the 22-day mode.
Figure 8d shows the time–longitude cross section of
the vertical integrated meridional moisture flux (qv) difference averaging from 328 to 368N between positive
and negative events keyed to the 22-day mode. The
comparison with Fig. 8a indicates a very good correspondence between the (qv) and rainfall anomalies. The
negative (qv) anomalies (958–858W), representing the
weakening of the LLJ coincide with less rain (negative
rainfall anomalies) over the Great Plains, while positive
(qv) anomalies coincide with more rain (positive rainfall
anomalies). Similar to the rainfall dipole, there is a di-
pole of (qv) anomalies with centers of action located
over the Gulf of Mexico and over the Gulf of California.
The similar dipole also appears in the convergence of
the moisture flux D(Q) (Fig. 8c).
Figure 9 displays the rainfall difference between positive and negative events and the corresponding D(Q)
and moisture flux (qu; qv) at two opposite phases of the
22-day mode. Overall, rainfall anomalies are consistent
with the composites of the D(Q) differences. When
AZNM is dry (negative rainfall anomalies), there is less
moisture transported into AZNM from the Gulf of Mexico and from the Gulf of California (Figs. 9d and 9f).
When AZNM is wet (Fig. 9b), increased southerlies are
located between a cyclonic–anticyclonic pair (Fig. 9e).
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FIG. 9. (a) Rainfall composite difference between positive and negative events for day 210, keyed to the reconstructed time series based
on the 22-day mode. Contour interval is 0.4 mm day21 . Negative values are shaded. (b) Same as (a) but for day 2, and (c) same as (a) but
for day 12, (d) vertically integrated moisture flux difference (qu; qv) (arrows) and moisture flux divergence difference (contours) between
positive and negative events averaged from days 211 to 29. Contour interval for D(Q) is 0.4 mm day21 . Negative values are shaded. (e)
Same as (d) but for the average from days 1 to 3. Contour interval is 0.5 mm day 21 . (f ) Same as (d) but for the average from days 11 to
13. The unit for the moisture flux is 10 kg (m s)21 .
←
FIG. 8. Time–longitude cross section of the rainfall difference averaged from 328 to 368N between positive and negative events from 20
days before to 20 days after onset keyed to the 22-day mode. Contour interval is 0.5 mm day 21 . Contours 0.3 and 20.3 mm day21 are added.
(b) Same as (b) but for the 200-hPa divergence. Contour interval 1 3 1026 s21 . (c) Same as (a) but for the moisture flux divergence D(Q).
Contour interval 0.5 mm day21 . (d) Same as (a) but for the vertically integrated meridional moisture flux (qv). Contour interval is 10 kg
(m s)21 .
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The LLJ brings more moisture to AZNM. At the same
time, the jet that transports moisture from the Gulf of
California to the AZNM area also strengthens.
d. Tropical linkages
Similar to the winter 22-day mode, the summer 22day mode also has a tropical connection. The OLRA
and corresponding 200-hPa streamfunction differences
keyed to the summer 22-day mode from day 22 to day
6 are given in Fig. 10. From days 22 to 6, anomalous
divergence (positive rain anomalies) moves from
AZNM to the central United States (Figs. 7c–e). In the
tropical Indian–Pacific sector, OLRA composites show
a three-cell pattern propagating westward. Enhanced
convection (negative OLRAs) centered near 1508E at
day 22, moves to 1308E at day 6, while positive OLRAs
located in the western Pacific at day 22 move to the
Indian Ocean at day 6. The 200-hPa streamfunction
composites also show westward-propagating waves.
The wave trains extend from the area of enhanced convection through the North Pacific, the Gulf of Alaska,
to North America (Figs. 10d and 10e).
At day 22, OLRAs along the west coast show another
three-cell pattern with positive anomalies over the Pacific Northwest and central America and negative anomalies over the Southwest (Fig. 10a). The corresponding
200-hPa streamfunction difference (Fig. 10d) over
North America is consistent with OLRAs. At day 2,
rainfall over AZNM reaches a maximum (Fig. 9b). In
addition to the three-cell OLRA pattern in the Indian–
Pacific sector, negative OLRAs are found in the eastern
Pacific with positive OLRAs located over Central America. The corresponding 200-hPa streamfunction difference shows negative anomalies over the Southwest and
positive anomalies located over the Pacific Northwest
and the central United States (Fig. 10e). This is consistent with the composite difference of moisture fluxes.
Figure 9e shows increasing southerlies located between
the cyclonic–anticyclonic dipole. At day 6, rainfall
(anomalous 200-hPa divergence) moves from AZNM
into the central United States. The 200-hPa streamfunction anomaly pattern (Fig. 10f) is similar to the
composite keyed to wet events over the central United
States (Mo et al. 1997).
The westward propagation of the OLRAs and the corresponding westward moving 200-hPa streamfunction
anomalies over the Pacific–North American sector are
also features of the winter 22-day mode. These anomalies are statistically significant at the 95% level. However, the magnitudes of anomalies are weaker in comparison with the composites keyed to the winter 22-day
mode. The westward propagation is more apparent in
the time–longitude cross section centered just north of
the equator (108–208N). OLRAs propagate westward
from the central Pacific through the western Pacific to
the Indian Ocean (Fig. 11) and the propagation completes a cycle in about 22–25 days. During winter, OL-
VOLUME 128
RAs propagate northward from the eastern Pacific
through California to the Pacific Northwest. For summer
composites, there is a three-cell pattern over the west
coast of North America but there is little evidence of
northward propagation.
5. Conclusions
Over the United States, the summer rainfall regime
is dominated by a dipole pattern with centers of action
located over AZNM and the Great Plains. The evolution
of the AZNM monsoon based on composites indicates
that rainfall anomalies propagate eastward from the
North Pacific through AZNM, the Great Plains, to the
eastern United States. Anomalies over AZNM change
signs twice within 30 days indicating an intraseasonal
modulation of rainfall.
The intraseasonal variability is examined further using singular spectrum analysis and composites of OLRAs, 200-hPa divergence and a gridded rainfall dataset.
SSA performed on the AZNM index shows that the
dominant mode is the 22-day mode. The second mode
has an MJO component, but it also has a second spectral
peak at 20 days. The third mode is the 17-day mode.
Three modes together explain about 68% of the variance
in the intraseasonal band. The wet and dry periods of
the AZNM monsoon are modulated by the 22-day mode.
This is also the dominant mode for rainfall variability
over the Great Plains. The influence of the MJO is secondary.
The 22–25-day mode is found in the vertically integrated moisture fluxes over the Great Plains. When
AZNM is wet, more moisture is transported from both
the Gulf of Mexico and the Gulf of California to AZNM.
The situation reverses when the oscillation reaches the
other phase. The largest impact of the MJO is on precipitation over Mexico. SSA performed on the 200-hPa
divergence and OLRAs averaged over Mexico shows
only one oscillatory mode of about 36–40 days.
The 22-day mode is linked to tropical convection.
When rainfall associated with the 22-day mode travels
eastward from AZNM to the Great Plains, the OLRAs
composites show westward propagating waves just
north of the equator. When enhanced convection reaches
the western Pacific, rainfall diminishes in AZNM. When
convection in the western Pacific is suppressed and enhanced convection is located in the central Pacific, rainfall intensifies in AZNM.
During the summer season, intraseasonal oscillations
in the Tropics are as strong as their counterparts in winter. The 22-day mode, the MJO and the 17-day mode
are also leading oscillatory modes in the Tropics (Ghil
and Mo 1991). For both seasons, the MJO is the dominant mode in the Tropics. The second mode is the 22day mode. In comparison to the winter 22-day mode,
the magnitudes of OLRAs in the Tropics associated with
the summer 22-day mode are weaker. The ratio of the
10–30-day OLR variance to the 10–90-day OLR vari-
MO
FIG. 10. (a) OLRA composite difference between positive and negative events keyed to reconstructed time series based on the 22-day mode for day 22. Contour interval is 4 W m22 . Zero
contours are omitted. Areas where positive (negative) values are statistically significant at the 95% level are shaded dark (light). (b) Same as (a) but for day 2, (c) same as (a) but for day
6, (d) same as (a) but for 200-hPa streamfunction difference. Contour interval is 1 3 106 m 2 s21 . (e) Same as (d) but for day 2, and (f ) same as (d) but for day 6.
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FIG. 11. Time–longitude cross section for OLRAs composite difference between positive and
negative events averaged from 108 to 208N from 20 days before to 20 days after onset keyed to
the 22-day mode. Contour interval is 4 W m22 . Zero contours are omitted. Areas where positive
(negative) values are statistically significant at the 95% level are shaded dark (light).
ance is only about 35%–50% in the western and the
central Pacific for summer. The ratio increases to 50%–
60% for the areas in the eastern Pacific, and the Gulf
of Mexico and North America. This may explain the
weaker magnitudes of OLR anomalies in the Tropics
(Fig. 10).
The summer 22-day mode has some features similar
to the winter mode. Both 22-day modes show that OLRAs propagate westward in the Tropics just above the
equator (108–208N). Both modes have impact on precipitation anomalies over the United States. During winter, the impact is felt over the western region of the
United States and during summer it modulates the
AZNM monsoon and rainfall activities over the Great
Plains.
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